From 8bd36eea9ae25fa1e5eccde926f67c6c30b5579b Mon Sep 17 00:00:00 2001 From: Richard Murray Date: Sat, 28 Dec 2024 13:57:26 -0800 Subject: [PATCH 01/93] Update split_tf and combine_tf docstrings + combine_tf kwargs --- control/bdalg.py | 2 ++ control/xferfcn.py | 1 + 2 files changed, 3 insertions(+) diff --git a/control/bdalg.py b/control/bdalg.py index f066b72b5..37879cb58 100644 --- a/control/bdalg.py +++ b/control/bdalg.py @@ -580,6 +580,7 @@ def combine_tf(tf_array, **kwargs): [array([1.]), array([1.]), array([1.])], [array([1.]), array([1.]), array([1, 0])]], name='G', outputs=3, inputs=3) + """ # Find common timebase or raise error dt_list = [] @@ -681,6 +682,7 @@ def split_tf(transfer_function): array([1, 1]), name='G', outputs=1, inputs=1)]], dtype=object) + """ tf_split_lst = [] for i_out in range(transfer_function.noutputs): diff --git a/control/xferfcn.py b/control/xferfcn.py index 1ebd32f05..eaaff0fc1 100644 --- a/control/xferfcn.py +++ b/control/xferfcn.py @@ -1483,6 +1483,7 @@ def _convert_to_transfer_function( returned. If sys is a scalar, then the number of inputs and outputs can be specified manually, as in: + >>> from control.xferfcn import _convert_to_transfer_function >>> sys = _convert_to_transfer_function(3.) # Assumes inputs = outputs = 1 >>> sys = _convert_to_transfer_function(1., inputs=3, outputs=2) From 210ab452fffba6e0a3b8de1663c13b42080ab724 Mon Sep 17 00:00:00 2001 From: Richard Murray Date: Sat, 28 Dec 2024 13:57:26 -0800 Subject: [PATCH 02/93] improved docstring checking + first round of fixes --- control/config.py | 2 +- control/flatsys/flatsys.py | 45 ++++++++--- control/iosys.py | 6 ++ control/lti.py | 2 +- control/nlsys.py | 34 ++------ control/optimal.py | 12 +++ control/statesp.py | 19 ++--- control/tests/docstrings_test.py | 133 ++++++++++++++++++------------- control/xferfcn.py | 10 --- 9 files changed, 146 insertions(+), 117 deletions(-) diff --git a/control/config.py b/control/config.py index 721871ed3..07f7910f8 100644 --- a/control/config.py +++ b/control/config.py @@ -381,7 +381,7 @@ def use_legacy_defaults(version): # exception is raised. # def _process_legacy_keyword(kwargs, oldkey, newkey, newval): - if kwargs.get(oldkey) is not None: + if oldkey in kwargs: warnings.warn( f"keyword '{oldkey}' is deprecated; use '{newkey}'", FutureWarning) diff --git a/control/flatsys/flatsys.py b/control/flatsys/flatsys.py index d00c2b311..0c2e14bdc 100644 --- a/control/flatsys/flatsys.py +++ b/control/flatsys/flatsys.py @@ -9,6 +9,7 @@ from .poly import PolyFamily from .systraj import SystemTrajectory from ..exception import ControlArgument +from ..config import _process_legacy_keyword from ..nlsys import NonlinearIOSystem from ..timeresp import _check_convert_array @@ -315,7 +316,7 @@ def point_to_point( Parameters ---------- - flatsys : FlatSystem object + sys : FlatSystem object Description of the differentially flat system. This object must define a function `flatsys.forward()` that takes the system state and produceds the flag of flat outputs and a system `flatsys.reverse()` @@ -364,6 +365,20 @@ def point_to_point( The constraints are applied at each time point along the trajectory. + initial_guess : 2D array_like, optional + Initial guess for the trajectory coefficients (not implemented). + + params : dict, optional + Parameter values for the system. Passed to the evaluation + functions for the system as default values, overriding internal + defaults. + + minimize_method : str, optional + Set the method used by :func:`scipy.optimize.minimize`. + + minimize_options : str, optional + Set the options keyword used by :func:`scipy.optimize.minimize`. + minimize_kwargs : str, optional Pass additional keywords to :func:`scipy.optimize.minimize`. @@ -399,9 +414,8 @@ def point_to_point( T0 = timepts[0] if len(timepts) > 1 else T0 # Process keyword arguments - if trajectory_constraints is None: - # Backwards compatibility - trajectory_constraints = kwargs.pop('constraints', None) + trajectory_constraints = _process_legacy_keyword( + kwargs, 'constraints', 'trajectory_constraints', trajectory_constraints) minimize_kwargs = {} minimize_kwargs['method'] = kwargs.pop('minimize_method', None) @@ -632,7 +646,7 @@ def solve_flat_ocp( Parameters ---------- - flatsys : FlatSystem object + sys : FlatSystem object Description of the differentially flat system. This object must define a function `flatsys.forward()` that takes the system state and produceds the flag of flat outputs and a system `flatsys.reverse()` @@ -684,6 +698,17 @@ def solve_flat_ocp( initial_guess : 2D array_like, optional Initial guess for the optimal trajectory of the flat outputs. + params : dict, optional + Parameter values for the system. Passed to the evaluation + functions for the system as default values, overriding internal + defaults. + + minimize_method : str, optional + Set the method used by :func:`scipy.optimize.minimize`. + + minimize_options : str, optional + Set the options keyword used by :func:`scipy.optimize.minimize`. + minimize_kwargs : str, optional Pass additional keywords to :func:`scipy.optimize.minimize`. @@ -724,12 +749,10 @@ def solve_flat_ocp( T0 = timepts[0] if len(timepts) > 1 else 0 # Process keyword arguments - if trajectory_constraints is None: - # Backwards compatibility - trajectory_constraints = kwargs.pop('constraints', None) - if trajectory_cost is None: - # Compatibility with point_to_point - trajectory_cost = kwargs.pop('cost', None) + trajectory_constraints = _process_legacy_keyword( + kwargs, 'constraints', 'trajectory_constraints', trajectory_constraints) + trajectory_cost = _process_legacy_keyword( + kwargs, 'cost', 'trajectory_cost', trajectory_cost) minimize_kwargs = {} minimize_kwargs['method'] = kwargs.pop('minimize_method', None) diff --git a/control/iosys.py b/control/iosys.py index 293657319..a72f89791 100644 --- a/control/iosys.py +++ b/control/iosys.py @@ -558,6 +558,12 @@ def update_names(self, **kwargs): Description of output signals; defaults to `y[i]`. states : int, list of str, int, or None, optional Description of system states; defaults to `x[i]`. + input_prefix : string, optional + Set the prefix for input signals. Default = 'u'. + output_prefix : string, optional + Set the prefix for output signals. Default = 'y'. + state_prefix : string, optional + Set the prefix for state signals. Default = 'x'. """ self.name = kwargs.pop('name', self.name) diff --git a/control/lti.py b/control/lti.py index b5f634169..89754e9f7 100644 --- a/control/lti.py +++ b/control/lti.py @@ -153,7 +153,7 @@ def bandwidth(self, dbdrop=-3): Parameters ---------- - dpdrop : float, optional + dbdrop : float, optional A strictly negative scalar in dB (default = -3) defines the amount of gain drop for deciding bandwidth. diff --git a/control/nlsys.py b/control/nlsys.py index 39d61c4ee..7760490cd 100644 --- a/control/nlsys.py +++ b/control/nlsys.py @@ -462,30 +462,7 @@ def output(self, t, x, u, params=None): t, np.asarray(x).reshape(-1), np.asarray(u).reshape(-1)) def feedback(self, other=1, sign=-1, params=None): - """Feedback interconnection between two input/output systems - - Parameters - ---------- - sys1: InputOutputSystem - The primary process. - sys2: InputOutputSystem - The feedback process (often a feedback controller). - sign: scalar, optional - The sign of feedback. `sign` = -1 indicates negative feedback, - and `sign` = 1 indicates positive feedback. `sign` is an optional - argument; it assumes a value of -1 if not specified. - - Returns - ------- - out: InputOutputSystem - - Raises - ------ - ValueError - if the inputs, outputs, or timebases of the systems are - incompatible. - - """ + """Feedback interconnection between two input/output systems.""" # Convert sys2 to an I/O system if needed other = _convert_static_iosystem(other) @@ -1045,11 +1022,10 @@ def set_output_map(self, output_map): self.noutputs = output_map.shape[0] def unused_signals(self): - """Find unused subsystem inputs and outputs + """Find unused subsystem inputs and outputs. Returns ------- - unused_inputs : dict A mapping from tuple of indices (isys, isig) to string '{sys}.{sig}', for all unused subsystem inputs. @@ -1083,6 +1059,7 @@ def unused_signals(self): return ({inputs[i][:2]: inputs[i][2] for i in unused_sysinp}, {outputs[i][:2]: outputs[i][2] for i in unused_sysout}) + def connection_table(self, show_names=False, column_width=32): """Print table of connections inside an interconnected system model. @@ -1183,6 +1160,8 @@ def _find_outputs_by_basename(self, basename): for sig, isig in sys.output_index.items() if sig == (basename)} + # TODO: change `warning` to `print_warning` or `warn_unsed`? + # TODO: change to internal function? (not sure users need to see this) def check_unused_signals( self, ignore_inputs=None, ignore_outputs=None, warning=True): """Check for unused subsystem inputs and outputs @@ -1207,6 +1186,9 @@ def check_unused_signals( If the 'sig' form is used, all subsystem outputs with that name are considered ignored. + warning : bool, optional + If `True`, print a warning listing any unused signals. + Returns ------- dropped_inputs: list of tuples diff --git a/control/optimal.py b/control/optimal.py index 10384fce0..3411c9e9d 100644 --- a/control/optimal.py +++ b/control/optimal.py @@ -748,6 +748,7 @@ def _simulate_states(self, x0, inputs): # # Compute the optimal trajectory from the current state + # TODO: update docstring to refer to primary function (?) def compute_trajectory( self, x, squeeze=None, transpose=None, return_states=True, initial_guess=None, print_summary=True, **kwargs): @@ -757,6 +758,14 @@ def compute_trajectory( ---------- x : array-like or number, optional Initial state for the system. + initial_guess : (tuple of) 1D or 2D array_like + Initial states and/or inputs to use as a guess for the optimal + trajectory. For shooting methods, an array of inputs for each + time point should be specified. For collocation methods, the + initial guess is either the input vector or a tuple consisting + guesses for the state and the input. Guess should either be a + 2D vector of shape (ninputs, ntimepts) or a 1D input of shape + (ninputs,) that will be broadcast by extension of the time axis. return_states : bool, optional If True (default), return the values of the state at each time. squeeze : bool, optional @@ -768,6 +777,9 @@ def compute_trajectory( If True, assume that 2D input arrays are transposed from the standard format. Used to convert MATLAB-style inputs to our format. + print_summary : bool, optional + If `True` (default), print a short summary of the computation. + Returns ------- diff --git a/control/statesp.py b/control/statesp.py index a5b5f9fd2..c6696138e 100644 --- a/control/statesp.py +++ b/control/statesp.py @@ -863,20 +863,11 @@ def horner(self, x, warn_infinite=True): Expects inputs and outputs to be formatted correctly. Use ``sys(x)`` for a more user-friendly interface. - Parameters - ---------- - x : complex array_like or complex - Complex frequencies - - Returns - ------- - output : (self.noutputs, self.ninputs, len(x)) complex ndarray - Frequency response - Notes ----- - Attempts to use Laub's method from Slycot library, with a - fall-back to python code. + Attempts to use Laub's method from Slycot library, with a fall-back + to Python code. + """ # Make sure the argument is a 1D array of complex numbers x_arr = np.atleast_1d(x).astype(complex, copy=False) @@ -1390,8 +1381,9 @@ def dcgain(self, warn_infinite=False): """ return self._dcgain(warn_infinite) + # TODO: decide if we need this function (already in NonlinearIOSystem def dynamics(self, t, x, u=None, params=None): - """Compute the dynamics of the system + """Compute the dynamics of the system. Given input `u` and state `x`, returns the dynamics of the state-space system. If the system is continuous, returns the time derivative dx/dt @@ -1439,6 +1431,7 @@ def dynamics(self, t, x, u=None, params=None): return (self.A @ x).reshape((-1,)) \ + (self.B @ u).reshape((-1,)) # return as row vector + # TODO: decide if we need this function (already in NonlinearIOSystem def output(self, t, x, u=None, params=None): """Compute the output of the system diff --git a/control/tests/docstrings_test.py b/control/tests/docstrings_test.py index 44963cd7c..e9ce0411d 100644 --- a/control/tests/docstrings_test.py +++ b/control/tests/docstrings_test.py @@ -12,6 +12,7 @@ import pytest import re +import numpydoc.docscrape as npd import control import control.flatsys @@ -54,7 +55,21 @@ control.bode_plot: ['sharex', 'sharey', 'margin_info'], # deprecated control.eigensys_realization: ['arg'], # quasi-positional control.find_operating_point: ['method'], # internal use - control.zpk: ['args'] # 'dt' (manual) + control.zpk: ['args'], # 'dt' (manual) + control.StateSpace.dynamics: ['params'], # not allowed + control.StateSpace.output: ['params'], # not allowed + control.flatsys.point_to_point: [ + 'method', 'options', # minimize_kwargs + ], + control.flatsys.solve_flat_ocp: [ + 'method', 'options', # minimize_kwargs + ], + control.optimal.OptimalControlProblem.compute_trajectory: [ + 'return_x', # legacy + ], + control.optimal.OptimalEstimationProblem.create_mhe_iosystem: [ + 'inputs', 'outputs', 'states', # doc'd elsewhere + ], } # Decide on the level of verbosity (use -rP when running pytest) @@ -68,19 +83,27 @@ def test_parameter_docs(module, prefix): checked = set() # Keep track of functions we have checked # Look through every object in the package - if verbose > 1: - print(f"Checking module {module}") + _info(f"Checking module {module}", 0) for name, obj in inspect.getmembers(module): + _info(f"Checking object {name}", 4) + + # Parse the docstring using numpydoc + doc = None if obj is None else npd.FunctionDoc(obj) + # Skip anything that is outside of this module - if inspect.getmodule(obj) is not None and ( - not inspect.getmodule(obj).__name__.startswith('control') - or prefix != "" and inspect.getmodule(obj) != module): + if inspect.getmodule(obj) is not None and \ + not inspect.getmodule(obj).__name__.startswith('control'): # Skip anything that isn't part of the control package continue + # Skip non-top-level functions without parameter lists + if prefix != "" and inspect.getmodule(obj) != module and \ + doc is not None and doc["Parameters"] == []: + _info(f"skipping {prefix}.{name}", 2) + continue + if inspect.isclass(obj): - if verbose > 1: - print(f" Checking class {name}") + _info(f"Checking class {name}", 1) # Check member functions within the class test_parameter_docs(obj, prefix + name + '.') @@ -94,11 +117,9 @@ def test_parameter_docs(module, prefix): checked.add(obj) # Get the docstring (skip w/ warning if there isn't one) - if verbose > 1: - print(f" Checking function {name}") + _info(f"Checking function {name}", 2) if obj.__doc__ is None: - warnings.warn( - f"{module.__name__}.{name} is missing docstring") + _warn(f"{module.__name__}.{name} is missing docstring", 2) continue else: docstring = inspect.getdoc(obj) @@ -106,22 +127,16 @@ def test_parameter_docs(module, prefix): # Skip deprecated functions if ".. deprecated::" in docstring: - if verbose > 1: - print(" [deprecated]") + _info(" [deprecated]", 2) continue elif re.search(name + r"(\(\))? is deprecated", docstring) or \ "function is deprecated" in docstring: - if verbose > 1: - print(" [deprecated, but not numpydoc compliant]") - elif verbose: - print(f" {name} deprecation is not numpydoc compliant") - warnings.warn(f"{name} deprecated, but not numpydoc compliant") + _info(" [deprecated, but not numpydoc compliant]", 2) + _warn(f"{name} deprecated, but not numpydoc compliant", 0) continue elif re.search(name + r"(\(\))? is deprecated", source): - if verbose: - print(f" {name} is deprecated, but not documented") - warnings.warn(f"{name} deprecated, but not documented") + _warn(f"{name} is deprecated, but not documented", 1) continue # Get the signature for the function @@ -142,7 +157,7 @@ def test_parameter_docs(module, prefix): hash = hashlib.md5( (docstring + source).encode('utf-8')).hexdigest() if function_docstring_hash[obj] != hash: - pytest.fail( + _fail( f"source/docstring for {name}() modified; " f"recheck docstring and update hash to " f"{hash=}") @@ -150,17 +165,14 @@ def test_parameter_docs(module, prefix): # Too complicated to check if f"*{argname}" not in docstring: - if verbose: - print(f" {name} has positional arguments; " - "check manually") - warnings.warn( + _warn( f"{name} {argname} has positional arguments; " "docstring not checked") continue # Check for keyword arguments (then look at code for parsing) elif par.kind == inspect.Parameter.VAR_KEYWORD: - # See if we documented the keyward argumnt directly + # See if we documented the keyward argument directly # if f"**{argname} :" in docstring: # continue @@ -169,46 +181,45 @@ def test_parameter_docs(module, prefix): for _, kwargname in re.findall( argname + r"(\[|\.pop\(|\.get\()'([\w]+)'", source): - if verbose > 2: - print(" Found direct keyword argument", - kwargname) + _info(f"Found direct keyword argument {kwargname}", 2) kwargnames.add(kwargname) # Look for kwargs accessed via _get_param for kwargname in re.findall( r"_get_param\(\s*'\w*',\s*'([\w]+)',\s*" + argname, source): - if verbose > 2: - print(" Found config keyword argument", - {kwargname}) + _info(f"Found config keyword argument {kwargname}", 2) kwargnames.add(kwargname) # Look for kwargs accessed via _process_legacy_keyword for kwargname in re.findall( r"_process_legacy_keyword\([\s]*" + argname + r",[\s]*'[\w]+',[\s]*'([\w]+)'", source): - if verbose > 2: - print(" Found legacy keyword argument", - {kwargname}) + _info(f"Found legacy keyword argument {kwargname}", 2) kwargnames.add(kwargname) for kwargname in kwargnames: if obj in keyword_skiplist and \ kwargname in keyword_skiplist[obj]: continue - if verbose > 3: - print(f" Checking keyword argument {kwargname}") + _info(f"Checking keyword argument {kwargname}", 3) _check_parameter_docs( name, kwargname, inspect.getdoc(obj), prefix=prefix) # Make sure this argument is documented properly in docstring else: - if verbose > 3: - print(f" Checking argument {argname}") + _info(f" Checking argument {argname}", 3) _check_parameter_docs( name, argname, docstring, prefix=prefix) + # Look at the return values + for val in doc["Returns"]: + if val.name == '' and \ + (match := re.search("([\w]+):", val.type)) is not None: + retname = match.group(1) + _warn(f"{obj.__name__} '{retname}' docstring missing space") + @pytest.mark.parametrize("module, prefix", [ (control, ""), (control.flatsys, "flatsys."), @@ -240,8 +251,7 @@ def test_deprecated_functions(module, prefix): # Get the docstring (skip w/ warning if there isn't one) if obj.__doc__ is None: - warnings.warn( - f"{module.__name__}.{name} is missing docstring") + _warn(f"{module.__name__}.{name} is missing docstring") continue else: docstring = inspect.getdoc(obj) @@ -251,12 +261,11 @@ def test_deprecated_functions(module, prefix): if ".. deprecated::" in docstring: # Make sure a FutureWarning is issued if not re.search("FutureWarning", source): - pytest.fail( - f"{name} deprecated but does not issue FutureWarning") + _fail(f"{name} deprecated but does not issue FutureWarning") else: if re.search(name + r"(\(\))? is deprecated", docstring) or \ re.search(name + r"(\(\))? is deprecated", source): - pytest.fail( + _fail( f"{name} deprecated but w/ non-standard docs/warnings") assert name != 'ss2io' @@ -492,10 +501,10 @@ def _check_parameter_docs( funcname = prefix + funcname # Find the "Parameters" section of docstring, where we start searching - if not (match := re.search( - r"\nParameters\n----", docstring)): + # TODO: rewrite to use numpydoc + if not (match := re.search(r"\nParameters\n----", docstring)): if fail_if_missing: - pytest.fail(f"{funcname} docstring missing Parameters section") + _fail(f"{funcname} docstring missing Parameters section") else: return False else: @@ -520,18 +529,16 @@ def _check_parameter_docs( "\n" + r"((\w+|\.{3}), )*" + argname + r"(, (\w+|\.{3}))*:", docstring): # Found the string, but not in numpydoc form - if verbose: - print(f" {funcname}: {argname} docstring missing space") - warnings.warn(f"{funcname} '{argname}' docstring missing space") + _warn(f"{funcname}: {argname} docstring missing space") elif not (match := re.search( "\n" + r"((\w+|\.{3}), )*" + argname + r"(, (\w+|\.{3}))* :", docstring)): - if verbose: - print(f" {funcname}: {argname} not documented") if fail_if_missing: + _fail(f"{funcname} '{argname}' not documented") pytest.fail(f"{funcname} '{argname}' not documented") else: + _info(f"{funcname} '{argname}' not documented") return False # Make sure there isn't another instance @@ -539,6 +546,22 @@ def _check_parameter_docs( "\n" + r"((\w+|\.{3}), )*" + argname + r"(, (\w+|\.{3}))*[ ]*:", docstring[match.end():]) if second_match: - pytest.fail(f"{funcname} '{argname}' documented twice") + _fail(f"{funcname} '{argname}' documented twice") return True + + +# Utility function to warn with verbose output +def _info(str, level=0): + if verbose > level: + print(" " * level + str) + +def _warn(str, level=0): + if verbose > level: + print(" " * level + str) + warnings.warn(str) + +def _fail(str, level=0): + if verbose > level: + print(" " * level + str) + pytest.fail(str) diff --git a/control/xferfcn.py b/control/xferfcn.py index eaaff0fc1..d66535953 100644 --- a/control/xferfcn.py +++ b/control/xferfcn.py @@ -378,16 +378,6 @@ def horner(self, x, warn_infinite=True): Expects inputs and outputs to be formatted correctly. Use ``sys(x)`` for a more user-friendly interface. - Parameters - ---------- - x : complex array_like or complex scalar - Complex frequencies - - Returns - ------- - output : (self.noutputs, self.ninputs, len(x)) complex ndarray - Frequency response - """ # Make sure the argument is a 1D array of complex numbers x_arr = np.atleast_1d(x).astype(complex, copy=False) From f2285e48474525dda55541b1f35fd4c209b1b769 Mon Sep 17 00:00:00 2001 From: Richard Murray Date: Sat, 21 Dec 2024 10:14:20 -0800 Subject: [PATCH 03/93] add standalone docstrings_test mode + fix docstrings --- control/flatsys/flatsys.py | 2 +- control/lti.py | 2 +- control/nichols.py | 8 +- control/nlsys.py | 4 +- control/optimal.py | 6 +- control/phaseplot.py | 4 +- control/pzmap.py | 2 +- control/robust.py | 2 +- control/tests/docstrings_test.py | 324 ++++++++++++++++++------------- 9 files changed, 206 insertions(+), 148 deletions(-) diff --git a/control/flatsys/flatsys.py b/control/flatsys/flatsys.py index 0c2e14bdc..8b2b653f3 100644 --- a/control/flatsys/flatsys.py +++ b/control/flatsys/flatsys.py @@ -235,7 +235,7 @@ def flatsys(*args, updfcn=None, outfcn=None, **kwargs): Returns ------- - sys: :class:`FlatSystem` + sys : :class:`FlatSystem` Flat system. Other Parameters diff --git a/control/lti.py b/control/lti.py index 89754e9f7..d4e7c68ef 100644 --- a/control/lti.py +++ b/control/lti.py @@ -277,7 +277,7 @@ def zeros(sys): Returns ------- - zeros: ndarray + zeros : ndarray Array that contains the system's zeros. See Also diff --git a/control/nichols.py b/control/nichols.py index 933bf7507..919f71d9e 100644 --- a/control/nichols.py +++ b/control/nichols.py @@ -208,14 +208,14 @@ def nichols_grid(cl_mags=None, cl_phases=None, line_style='dotted', ax=None, Returns ------- - cl_mag_lines: list of `matplotlib.line.Line2D` + cl_mag_lines : list of `matplotlib.line.Line2D` The constant closed-loop gain contours - cl_phase_lines: list of `matplotlib.line.Line2D` + cl_phase_lines : list of `matplotlib.line.Line2D` The constant closed-loop phase contours - cl_mag_labels: list of `matplotlib.text.Text` + cl_mag_labels : list of `matplotlib.text.Text` mcontour labels; each entry corresponds to the respective entry in ``cl_mag_lines`` - cl_phase_labels: list of `matplotlib.text.Text` + cl_phase_labels : list of `matplotlib.text.Text` ncontour labels; each entry corresponds to the respective entry in ``cl_phase_lines`` """ diff --git a/control/nlsys.py b/control/nlsys.py index 7760490cd..3985d7132 100644 --- a/control/nlsys.py +++ b/control/nlsys.py @@ -1191,11 +1191,11 @@ def check_unused_signals( Returns ------- - dropped_inputs: list of tuples + dropped_inputs : list of tuples A list of the dropped input signals, with each element of the list in the form of (isys, isig). - dropped_outputs: list of tuples + dropped_outputs : list of tuples A list of the dropped output signals, with each element of the list in the form of (osys, osig). diff --git a/control/optimal.py b/control/optimal.py index 3411c9e9d..e57029093 100644 --- a/control/optimal.py +++ b/control/optimal.py @@ -785,7 +785,7 @@ def compute_trajectory( ------- res : OptimalControlResult Bundle object with the results of the optimal control problem. - res.success: bool + res.success : bool Boolean flag indicating whether the optimization was successful. res.time : array Time values of the input. @@ -830,7 +830,7 @@ def compute_mpc(self, x, squeeze=None): Parameters ---------- - x: array-like or number, optional + x : array-like or number, optional Initial state for the system. squeeze : bool, optional If True and if the system has a single output, return the system @@ -1706,7 +1706,7 @@ def compute_estimate( Estimated disturbance inputs for the system trajectory. res.states : array Time evolution of the estimated state vector. - res.outputs: array + res.outputs : array Estimated measurement noise for the system trajectory. """ diff --git a/control/phaseplot.py b/control/phaseplot.py index f5621253e..10794e962 100644 --- a/control/phaseplot.py +++ b/control/phaseplot.py @@ -747,7 +747,7 @@ def meshgrid(xvals, yvals): Returns ------- - grid: 2D array + grid : 2D array Array of points with shape (n * m, 2) defining the mesh """ @@ -777,7 +777,7 @@ def circlegrid(centers, radius, num): Returns ------- - grid: 2D array + grid : 2D array Array of points with shape (p * num, 2) defining the circles. """ diff --git a/control/pzmap.py b/control/pzmap.py index b62e0dcf0..546e39cbc 100644 --- a/control/pzmap.py +++ b/control/pzmap.py @@ -225,7 +225,7 @@ def pole_zero_plot( See :class:`ControlPlot` for more detailed information. - poles, zeros: list of arrays + poles, zeros : list of arrays (legacy) If the `plot` keyword is given, the system poles and zeros are returned. diff --git a/control/robust.py b/control/robust.py index 3e44c1bb3..356b45072 100644 --- a/control/robust.py +++ b/control/robust.py @@ -396,7 +396,7 @@ def mixsyn(g, w1=None, w2=None, w3=None): then cl is the system from w->z with `u = -k*y`. - info: tuple + info : tuple gamma: scalar H-infinity norm of cl. rcond: array diff --git a/control/tests/docstrings_test.py b/control/tests/docstrings_test.py index e9ce0411d..5589dad85 100644 --- a/control/tests/docstrings_test.py +++ b/control/tests/docstrings_test.py @@ -6,8 +6,16 @@ # is an explicitly listed argument, as well as attempt to find keyword # arguments that are extracted using kwargs.pop(), config._get_param(), or # config.use_legacy_defaults. +# +# This module can also be run in standalone mode: +# +# python docstrings_test.py [verbose] +# +# where 'verbose' is an integer indicating what level of verbosity is +# desired (0 = only warnings/errors, 10 = everything). import inspect +import sys import warnings import pytest @@ -29,7 +37,7 @@ # Checksums to use for checking whether a docstring has changed function_docstring_hash = { - control.append: '1bddbac0fe932755c85e9fb0bfb97d88', + control.append: 'fad0bd0766754c3524e0ea27b06bf74a', control.describing_function_plot: '95f894706b1d3eeb3b854934596af09f', control.dlqe: 'e1e9479310e4e5a6f50f5459fb3d2dfb', control.dlqr: '56d7f3a452bc8d7a7256a52d9d1dcb37', @@ -72,13 +80,14 @@ ], } -# Decide on the level of verbosity (use -rP when running pytest) -verbose = 1 +# Set global variables +verbose = 0 # Level of verbosity (use -rP when running pytest) +standalone = False # Controls how failures are treated -@pytest.mark.parametrize("module, prefix", [ +module_list = [ (control, ""), (control.flatsys, "flatsys."), - (control.optimal, "optimal."), (control.phaseplot, "phaseplot.") -]) + (control.optimal, "optimal."), (control.phaseplot, "phaseplot.")] +@pytest.mark.parametrize("module, prefix", module_list) def test_parameter_docs(module, prefix): checked = set() # Keep track of functions we have checked @@ -88,137 +97,145 @@ def test_parameter_docs(module, prefix): _info(f"Checking object {name}", 4) # Parse the docstring using numpydoc - doc = None if obj is None else npd.FunctionDoc(obj) + with warnings.catch_warnings(): + warnings.simplefilter('ignore') # debug via sphinx, not here + doc = None if obj is None else npd.FunctionDoc(obj) # Skip anything that is outside of this module if inspect.getmodule(obj) is not None and \ not inspect.getmodule(obj).__name__.startswith('control'): # Skip anything that isn't part of the control package + _info(f"member '{name}' is outside `control` module", 5) continue - # Skip non-top-level functions without parameter lists - if prefix != "" and inspect.getmodule(obj) != module and \ - doc is not None and doc["Parameters"] == []: - _info(f"skipping {prefix}.{name}", 2) - continue - + # If this is a class, recurse through methods + # TODO: check top level documenation here (__init__, attributes?) if inspect.isclass(obj): _info(f"Checking class {name}", 1) # Check member functions within the class test_parameter_docs(obj, prefix + name + '.') - if inspect.isfunction(obj): - # Skip anything that is inherited, hidden, deprecated, or checked - if inspect.isclass(module) and name not in module.__dict__ \ - or name.startswith('_') or obj in function_skiplist or \ - obj in checked: + # Skip anything that is inherited, hidden, deprecated, or checked + if not inspect.isfunction(obj) or \ + inspect.isclass(module) and name not in module.__dict__ \ + or name.startswith('_') or obj in function_skiplist \ + or obj in checked: + _info(f"skipping {prefix}.{name}", 6) continue - else: - checked.add(obj) - # Get the docstring (skip w/ warning if there isn't one) - _info(f"Checking function {name}", 2) - if obj.__doc__ is None: - _warn(f"{module.__name__}.{name} is missing docstring", 2) - continue - else: - docstring = inspect.getdoc(obj) - source = inspect.getsource(obj) + # Skip non-top-level functions without parameter lists + if prefix != "" and inspect.getmodule(obj) != module and \ + doc is not None and doc["Parameters"] == []: + _info(f"skipping {prefix}{name} (not top-level, no params)", 2) + continue - # Skip deprecated functions - if ".. deprecated::" in docstring: - _info(" [deprecated]", 2) - continue - elif re.search(name + r"(\(\))? is deprecated", docstring) or \ - "function is deprecated" in docstring: - _info(" [deprecated, but not numpydoc compliant]", 2) - _warn(f"{name} deprecated, but not numpydoc compliant", 0) - continue + # Add this to the list of functions we have checked + checked.add(obj) - elif re.search(name + r"(\(\))? is deprecated", source): - _warn(f"{name} is deprecated, but not documented", 1) - continue + # Get the docstring (skip w/ warning if there isn't one) + _info(f"Checking function {name}", 2) + if obj.__doc__ is None: + _warn(f"{module.__name__}.{name} is missing docstring", 2) + continue + else: + # TODO: use doc from above + docstring = inspect.getdoc(obj) + source = inspect.getsource(obj) - # Get the signature for the function - sig = inspect.signature(obj) + # Skip deprecated functions + if ".. deprecated::" in docstring: + _info(" [deprecated]", 2) + continue + elif re.search(name + r"(\(\))? is deprecated", docstring) or \ + "function is deprecated" in docstring: + _info(" [deprecated, but not numpydoc compliant]", 2) + _warn(f"{name} deprecated, but not numpydoc compliant", 0) + continue - # Go through each parameter and make sure it is in the docstring - for argname, par in sig.parameters.items(): + elif re.search(name + r"(\(\))? is deprecated", source): + _warn(f"{name} is deprecated, but not documented", 1) + continue - # Look for arguments that we can skip - if argname == 'self' or argname[0] == '_' or \ - obj in keyword_skiplist and argname in keyword_skiplist[obj]: - continue + # Get the signature for the function + sig = inspect.signature(obj) - # Check for positional arguments - if par.kind == inspect.Parameter.VAR_POSITIONAL: - if obj in function_docstring_hash: - import hashlib - hash = hashlib.md5( - (docstring + source).encode('utf-8')).hexdigest() - if function_docstring_hash[obj] != hash: - _fail( - f"source/docstring for {name}() modified; " - f"recheck docstring and update hash to " - f"{hash=}") - continue + # Go through each parameter and make sure it is in the docstring + for argname, par in sig.parameters.items(): + # Look for arguments that we can skip + if argname == 'self' or argname[0] == '_' or \ + obj in keyword_skiplist and argname in keyword_skiplist[obj]: + continue + + # Check for positional arguments (*arg) + if par.kind == inspect.Parameter.VAR_POSITIONAL: + if obj in function_docstring_hash: + import hashlib + hash = hashlib.md5( + (docstring + source).encode('utf-8')).hexdigest() + if function_docstring_hash[obj] != hash: + _fail( + f"source/docstring for {name}() modified; " + f"recheck docstring and update hash to " + f"{hash=}") + continue - # Too complicated to check - if f"*{argname}" not in docstring: - _warn( - f"{name} {argname} has positional arguments; " - "docstring not checked") + # Too complicated to check + if f"*{argname}" not in docstring: + _warn( + f"{name} {argname} has positional arguments; " + "docstring not checked") continue - # Check for keyword arguments (then look at code for parsing) - elif par.kind == inspect.Parameter.VAR_KEYWORD: - # See if we documented the keyward argument directly - # if f"**{argname} :" in docstring: - # continue - - # Look for direct kwargs argument access - kwargnames = set() - for _, kwargname in re.findall( - argname + r"(\[|\.pop\(|\.get\()'([\w]+)'", - source): - _info(f"Found direct keyword argument {kwargname}", 2) - kwargnames.add(kwargname) - - # Look for kwargs accessed via _get_param - for kwargname in re.findall( - r"_get_param\(\s*'\w*',\s*'([\w]+)',\s*" + argname, - source): - _info(f"Found config keyword argument {kwargname}", 2) - kwargnames.add(kwargname) - - # Look for kwargs accessed via _process_legacy_keyword - for kwargname in re.findall( - r"_process_legacy_keyword\([\s]*" + argname + - r",[\s]*'[\w]+',[\s]*'([\w]+)'", source): - _info(f"Found legacy keyword argument {kwargname}", 2) - kwargnames.add(kwargname) - - for kwargname in kwargnames: - if obj in keyword_skiplist and \ - kwargname in keyword_skiplist[obj]: - continue - _info(f"Checking keyword argument {kwargname}", 3) - _check_parameter_docs( - name, kwargname, inspect.getdoc(obj), - prefix=prefix) - - # Make sure this argument is documented properly in docstring - else: - _info(f" Checking argument {argname}", 3) + # Check for keyword arguments (then look at code for parsing) + elif par.kind == inspect.Parameter.VAR_KEYWORD: + # See if we documented the keyward argument directly + # if f"**{argname} :" in docstring: + # continue + + # Look for direct kwargs argument access + kwargnames = set() + for _, kwargname in re.findall( + argname + r"(\[|\.pop\(|\.get\()'([\w]+)'", source): + _info(f"Found direct keyword argument {kwargname}", 2) + kwargnames.add(kwargname) + + # Look for kwargs accessed via _get_param + for kwargname in re.findall( + r"_get_param\(\s*'\w*',\s*'([\w]+)',\s*" + argname, + source): + _info(f"Found config keyword argument {kwargname}", 2) + kwargnames.add(kwargname) + + # Look for kwargs accessed via _process_legacy_keyword + for kwargname in re.findall( + r"_process_legacy_keyword\([\s]*" + argname + + r",[\s]*'[\w]+',[\s]*'([\w]+)'", source): + _info(f"Found legacy keyword argument {kwargname}", 2) + kwargnames.add(kwargname) + + for kwargname in kwargnames: + if obj in keyword_skiplist and \ + kwargname in keyword_skiplist[obj]: + continue + _info(f"Checking keyword argument {kwargname}", 3) _check_parameter_docs( + name, kwargname, inspect.getdoc(obj), + prefix=prefix) + + # Make sure this argument is documented properly in docstring + else: + _info(f"Checking argument {argname}", 3) + _check_parameter_docs( name, argname, docstring, prefix=prefix) - # Look at the return values - for val in doc["Returns"]: - if val.name == '' and \ - (match := re.search("([\w]+):", val.type)) is not None: - retname = match.group(1) - _warn(f"{obj.__name__} '{retname}' docstring missing space") + # Look at the return values + for val in doc["Returns"]: + if val.name == '' and \ + (match := re.search(r"([\w]+):", val.type)) is not None: + retname = match.group(1) + _warn( + f"{obj} return value '{retname}' " + "docstring missing space") @pytest.mark.parametrize("module, prefix", [ @@ -267,7 +284,6 @@ def test_deprecated_functions(module, prefix): re.search(name + r"(\(\))? is deprecated", source): _fail( f"{name} deprecated but w/ non-standard docs/warnings") - assert name != 'ss2io' # # Tests for I/O system classes @@ -374,14 +390,14 @@ def test_iosys_primary_classes(cls, fcn, args): r"created.*(with|by|using).*the[\s]*" f":func:`~control\\..*{fcn.__name__}`" r"[\s]factory[\s]function", docstring, re.DOTALL) is None: - pytest.fail( + _fail( f"{cls.__name__} does not reference factory function " f"{fcn.__name__}") # Make sure we don't reference parameters from the factory function for argname in factory_args[fcn]: if re.search(f"[\\s]+{argname}(, .*)*[\\s]*:", docstring) is not None: - pytest.fail( + _fail( f"{cls.__name__} references factory function parameter " f"'{argname}'") @@ -420,14 +436,15 @@ def test_iosys_attribute_lists(cls, ignore_future_warning): # Couldn't find in main documentation; look in parent classes for parent in cls.__mro__: if parent == object: - pytest.fail( + _fail( f"{cls.__name__} attribute '{name}' not documented") + break if _check_parameter_docs( parent.__name__, name, inspect.getdoc(parent), fail_if_missing=False): if name not in iosys_parent_attributes + factory_args[fcn]: - warnings.warn( + _warn( f"{cls.__name__} attribute '{name}' only documented " f"in parent class {parent.__name__}") break @@ -445,15 +462,14 @@ def test_iosys_container_classes(cls): continue # Look through all classes in hierarchy - if verbose: - print(f"{name=}") + _info(f"{name=}", 1) for parent in cls.__mro__: if parent == object: - pytest.fail( + _fail( f"{cls.__name__} attribute '{name}' not documented") + break - if verbose: - print(f" {parent=}") + _info(f" {parent=}", 2) if _check_parameter_docs( parent.__name__, name, inspect.getdoc(parent), fail_if_missing=False): @@ -466,12 +482,13 @@ def test_iosys_intermediate_classes(cls): # Make sure there is not a parameters section if re.search(r"\nParameters\n----", docstring) is not None: - pytest.fail(f"intermediate {cls} docstring contains Parameters section") + _fail(f"intermediate {cls} docstring contains Parameters section") + return # Make sure we reference the factory function fcn = class_factory_function[cls] if re.search(f":func:`~control.{fcn.__name__}`", docstring) is None: - pytest.fail( + _fail( f"{cls.__name__} does not reference factory function " f"{fcn.__name__}") @@ -491,7 +508,7 @@ def test_iosys_factory_functions(fcn): if argname in std_factory_args: continue if re.search(f"[\\s]+{argname}(, .*)*[\\s]*:", docstring) is not None: - pytest.fail( + _fail( f"{fcn.__name__} references class attribute '{argname}'") @@ -505,6 +522,7 @@ def _check_parameter_docs( if not (match := re.search(r"\nParameters\n----", docstring)): if fail_if_missing: _fail(f"{funcname} docstring missing Parameters section") + return False # for standalone mode else: return False else: @@ -536,9 +554,9 @@ def _check_parameter_docs( docstring)): if fail_if_missing: _fail(f"{funcname} '{argname}' not documented") - pytest.fail(f"{funcname} '{argname}' not documented") + return False # for standalone mode else: - _info(f"{funcname} '{argname}' not documented") + _info(f"{funcname} '{argname}' not documented (OK)", 6) return False # Make sure there isn't another instance @@ -547,21 +565,61 @@ def _check_parameter_docs( docstring[match.end():]) if second_match: _fail(f"{funcname} '{argname}' documented twice") + return False # for standalone mode return True # Utility function to warn with verbose output -def _info(str, level=0): +def _info(str, level): if verbose > level: print(" " * level + str) -def _warn(str, level=0): +def _warn(str, level=-1): if verbose > level: - print(" " * level + str) - warnings.warn(str) + print("WARN: " + " " * level + str) + if not standalone: + warnings.warn(str, stacklevel=2) -def _fail(str, level=0): +def _fail(str, level=-1): if verbose > level: - print(" " * level + str) - pytest.fail(str) + print("FAIL: " + " " * level + str) + if not standalone: + pytest.fail(str) + + +if __name__ == "__main__": + verbose = 0 if len(sys.argv) == 1 else int(sys.argv[1]) + standalone = True + + for module, prefix in module_list: + print(f"--- test_parameter_docs(): {module.__name__} ----") + test_parameter_docs(module, prefix) + + for module, prefix in module_list: + print(f"--- test_deprecated_functions(): {module.__name__} ----") + test_deprecated_functions + + for cls, fcn, args in [ + (cls, class_factory_function[cls], class_args[cls]) + for cls in class_args.keys()]: + print(f"--- test_iosys_primary_classes(): {cls.__name__} ----") + test_iosys_primary_classes(cls, fcn, args) + + for cls in class_args.keys(): + print(f"--- test_iosys_attribute_lists(): {cls.__name__} ----") + with warnings.catch_warnings(): + warnings.simplefilter('ignore', FutureWarning) + test_iosys_attribute_lists(cls, None) + + for cls in [ct.InputOutputSystem, ct.LTI]: + print(f"--- test_iosys_container_classes(): {cls.__name__} ----") + test_iosys_container_classes(cls) + + for cls in [ct.InterconnectedSystem, ct.LinearICSystem]: + print(f"--- test_iosys_intermediate_classes(): {cls.__name__} ----") + test_iosys_intermediate_classes(cls) + + for fcn in factory_args.keys(): + print(f"--- test_iosys_factory_functions(): {fcn.__name__} ----") + test_iosys_factory_functions(fcn) From 8463f682bcc93ffb6ecc2d1fb47f9cfc9c3f76a8 Mon Sep 17 00:00:00 2001 From: Richard Murray Date: Sat, 21 Dec 2024 10:51:40 -0800 Subject: [PATCH 04/93] fix flatsys "legacy" keywords + enhance legacy keyword processing --- control/config.py | 9 +++++---- control/flatsys/flatsys.py | 5 ++++- control/tests/flatsys_test.py | 18 ++++++++++-------- 3 files changed, 19 insertions(+), 13 deletions(-) diff --git a/control/config.py b/control/config.py index 07f7910f8..26caccbbf 100644 --- a/control/config.py +++ b/control/config.py @@ -380,11 +380,12 @@ def use_legacy_defaults(version): # warning. If both the old and new keyword are present, a ControlArgument # exception is raised. # -def _process_legacy_keyword(kwargs, oldkey, newkey, newval): +def _process_legacy_keyword(kwargs, oldkey, newkey, newval, warn_oldkey=True): if oldkey in kwargs: - warnings.warn( - f"keyword '{oldkey}' is deprecated; use '{newkey}'", - FutureWarning) + if warn_oldkey: + warnings.warn( + f"keyword '{oldkey}' is deprecated; use '{newkey}'", + FutureWarning, stacklevel=3) if newval is not None: raise ControlArgument( f"duplicate keywords '{oldkey}' and '{newkey}'") diff --git a/control/flatsys/flatsys.py b/control/flatsys/flatsys.py index 8b2b653f3..dd516f50d 100644 --- a/control/flatsys/flatsys.py +++ b/control/flatsys/flatsys.py @@ -415,7 +415,10 @@ def point_to_point( # Process keyword arguments trajectory_constraints = _process_legacy_keyword( - kwargs, 'constraints', 'trajectory_constraints', trajectory_constraints) + kwargs, 'constraints', 'trajectory_constraints', + trajectory_constraints, warn_oldkey=False) + cost = _process_legacy_keyword( + kwargs, 'trajectory_cost', 'cost', cost, warn_oldkey=False) minimize_kwargs = {} minimize_kwargs['method'] = kwargs.pop('minimize_method', None) diff --git a/control/tests/flatsys_test.py b/control/tests/flatsys_test.py index df30d7090..772dea7b0 100644 --- a/control/tests/flatsys_test.py +++ b/control/tests/flatsys_test.py @@ -200,7 +200,8 @@ def test_kinematic_car_ocp( 'ignore', message="unable to solve", category=UserWarning) traj_ocp = fs.solve_flat_ocp( vehicle_flat, timepts, x0, u0, - cost=traj_cost, constraints=input_constraints, + trajectory_cost=traj_cost, + trajectory_constraints=input_constraints, terminal_cost=terminal_cost, basis=basis, initial_guess=initial_guess, minimize_kwargs={'method': method}, @@ -445,8 +446,8 @@ def test_flat_solve_ocp(self, basis): (sp.optimize.NonlinearConstraint, lambda x, u: x, lb, ub)] traj_nlconst = fs.solve_flat_ocp( flat_sys, timepts, x0, u0, - cost=trajectory_cost, terminal_cost=terminal_cost, - constraints=nl_constraints, basis=basis, + trajectory_cost=trajectory_cost, terminal_cost=terminal_cost, + trajectory_constraints=nl_constraints, basis=basis, ) x_nlconst, u_nlconst = traj_nlconst.eval(timepts) np.testing.assert_almost_equal(x_const, x_nlconst) @@ -685,7 +686,7 @@ def test_solve_flat_ocp_errors(self): # Try to optimize with insufficient degrees of freedom with pytest.raises(ValueError, match="basis set is too small"): traj = fs.solve_flat_ocp( - flat_sys, timepts, x0, u0, cost=cost_fcn, + flat_sys, timepts, x0, u0, trajectory_cost=cost_fcn, basis=fs.PolyFamily(2)) # Solve with the errors in the various input arguments @@ -700,20 +701,21 @@ def test_solve_flat_ocp_errors(self): with pytest.raises(TypeError, match="must be a list"): traj = fs.solve_flat_ocp( flat_sys, timepts, x0, u0, cost_fcn, - constraints=np.eye(2), basis=fs.PolyFamily(8)) + trajectory_constraints=np.eye(2), basis=fs.PolyFamily(8)) # Unknown constraint type with pytest.raises(TypeError, match="unknown constraint type"): traj = fs.solve_flat_ocp( flat_sys, timepts, x0, u0, cost_fcn, - constraints=[(None, 0, 0, 0)], basis=fs.PolyFamily(8)) + trajectory_constraints=[(None, 0, 0, 0)], + basis=fs.PolyFamily(8)) # Method arguments, parameters traj_method = fs.solve_flat_ocp( - flat_sys, timepts, x0, u0, cost=cost_fcn, + flat_sys, timepts, x0, u0, trajectory_cost=cost_fcn, basis=fs.PolyFamily(6), minimize_method='slsqp') traj_kwarg = fs.solve_flat_ocp( - flat_sys, timepts, x0, u0, cost=cost_fcn, + flat_sys, timepts, x0, u0, trajectory_cost=cost_fcn, basis=fs.PolyFamily(6), minimize_kwargs={'method': 'slsqp'}) np.testing.assert_allclose( traj_method.eval(timepts)[0], traj_kwarg.eval(timepts)[0], From f00c89d3f7bc0d7441e48189097f0d583979a702 Mon Sep 17 00:00:00 2001 From: Richard Murray Date: Sat, 21 Dec 2024 13:22:20 -0800 Subject: [PATCH 05/93] add unit test for the unit test --- control/tests/docstrings_test.py | 57 ++++++++++++++++++++++++++++++++ 1 file changed, 57 insertions(+) diff --git a/control/tests/docstrings_test.py b/control/tests/docstrings_test.py index 5589dad85..20983661e 100644 --- a/control/tests/docstrings_test.py +++ b/control/tests/docstrings_test.py @@ -587,6 +587,63 @@ def _fail(str, level=-1): if not standalone: pytest.fail(str) +# +# Test function for the unit test +# +class simple_class: + def simple_function(arg1, arg2, opt1=None, **kwargs): + """Simple function for testing.""" + kwargs['test'] = None + +Failed = pytest.fail.Exception + +doc_header = simple_class.simple_function.__doc__ +doc_parameters = "\nParameters\n----------\n" +doc_arg1 = "\narg1 : int\nArgument 1.\n" +doc_arg2 = "\narg2 : int\nArgument 2.\n" +doc_arg2_nospace = "\narg2: int\nArgument 2.\n" +doc_arg3 = "\narg3 : int\nNon-existent argument 1.\n" +doc_opt1 = "\nopt1 : int\nKeyword argument 1.\n" +doc_test = "\ntest : int\nInternal keyword argument 1.\n" +doc_returns = "\nReturns\n-------\n" +doc_ret = "\nout : int\n" +doc_ret_nospace = "\nout: int\n" + +@pytest.mark.parametrize("docstring, exception, match", [ + (None, UserWarning, "missing docstring"), + (doc_header + doc_parameters + doc_arg1 + doc_arg2 + doc_opt1 + + doc_test + doc_returns + doc_ret, None, ""), + (doc_header + doc_parameters + doc_arg1 + doc_arg2 + doc_opt1 + doc_test, + None, ""), # no return section (OK) + (doc_header + doc_parameters + doc_arg1 + doc_arg2_nospace + doc_opt1 + + doc_test + doc_returns + doc_ret, UserWarning, "missing space"), + (doc_header + doc_parameters + doc_arg1 + doc_opt1 + + doc_test + doc_returns + doc_ret, Failed, "'arg2' not documented"), + (doc_header + doc_parameters + doc_arg1 + doc_arg2 + doc_arg2 + doc_opt1 + + doc_test + doc_returns + doc_ret, Failed, "'arg2' documented twice"), + (doc_header + doc_parameters + doc_arg1 + doc_arg2 + doc_opt1 + + doc_returns + doc_ret, Failed, "'test' not documented"), + (doc_header + doc_parameters + doc_arg1 + doc_arg2_nospace + doc_opt1 + + doc_test + doc_returns + doc_ret_nospace, UserWarning, "missing space"), + (doc_header + doc_arg1 + doc_arg2_nospace + doc_opt1 + doc_test + + doc_returns + doc_ret_nospace, Failed, "missing Parameters section"), + (doc_header, None, ""), + (doc_header + "\n.. deprecated::", None, ""), + (doc_header + "\n simple_function() is deprecated", + UserWarning, "deprecated, but not numpydoc compliant"), +]) +def test_check_parameter_docs(docstring, exception, match): + simple_class.simple_function.__doc__ = docstring + if exception is None: + # Pass prefix to allow empty parameters to work + assert test_parameter_docs(simple_class, "test") is None + elif exception in [UserWarning]: + with pytest.warns(exception, match=match): + test_parameter_docs(simple_class, "") is None + elif exception in [Failed]: + with pytest.raises(exception, match=match): + test_parameter_docs(simple_class, "") is None + if __name__ == "__main__": verbose = 0 if len(sys.argv) == 1 else int(sys.argv[1]) From edc453fa3fdd685b7e3d6d11868a56d1f5e49f7e Mon Sep 17 00:00:00 2001 From: Richard Murray Date: Sat, 21 Dec 2024 15:43:46 -0800 Subject: [PATCH 06/93] add numpydoc to test environment --- .github/conda-env/test-env.yml | 1 + pyproject.toml | 2 +- 2 files changed, 2 insertions(+), 1 deletion(-) diff --git a/.github/conda-env/test-env.yml b/.github/conda-env/test-env.yml index 6731443ab..b0e6c3cea 100644 --- a/.github/conda-env/test-env.yml +++ b/.github/conda-env/test-env.yml @@ -9,3 +9,4 @@ dependencies: - numpy - matplotlib - scipy + - numpydoc diff --git a/pyproject.toml b/pyproject.toml index 46d41fbf5..a81fc117c 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -40,7 +40,7 @@ dynamic = ["version"] packages = ["control"] [project.optional-dependencies] -test = ["pytest", "pytest-timeout", "ruff"] +test = ["pytest", "pytest-timeout", "ruff", "numpydoc"] slycot = [ "slycot>=0.4.0" ] cvxopt = [ "cvxopt>=1.2.0" ] From d366472fca6b69570519e74e2208e5785e992178 Mon Sep 17 00:00:00 2001 From: Richard Murray Date: Sat, 21 Dec 2024 16:19:56 -0800 Subject: [PATCH 07/93] refine use of numpydoc parser --- control/tests/docstrings_test.py | 26 +++++++++++++++++++------- 1 file changed, 19 insertions(+), 7 deletions(-) diff --git a/control/tests/docstrings_test.py b/control/tests/docstrings_test.py index 20983661e..9aaa669f0 100644 --- a/control/tests/docstrings_test.py +++ b/control/tests/docstrings_test.py @@ -137,17 +137,19 @@ def test_parameter_docs(module, prefix): if obj.__doc__ is None: _warn(f"{module.__name__}.{name} is missing docstring", 2) continue + elif doc is None: + _fail(f"{module.__name__}.{name} docstring not parseable", 2) else: - # TODO: use doc from above docstring = inspect.getdoc(obj) source = inspect.getsource(obj) # Skip deprecated functions - if ".. deprecated::" in docstring: + doc_extended = "\n".join(doc["Extended Summary"]) + if ".. deprecated::" in doc_extended: _info(" [deprecated]", 2) continue - elif re.search(name + r"(\(\))? is deprecated", docstring) or \ - "function is deprecated" in docstring: + elif re.search(name + r"(\(\))? is deprecated", doc_extended) or \ + "function is deprecated" in doc_extended: _info(" [deprecated, but not numpydoc compliant]", 2) _warn(f"{name} deprecated, but not numpydoc compliant", 0) continue @@ -258,6 +260,11 @@ def test_deprecated_functions(module, prefix): # Check member functions within the class test_deprecated_functions(obj, prefix + name + '.') + # Parse the docstring using numpydoc + with warnings.catch_warnings(): + warnings.simplefilter('ignore') # debug via sphinx, not here + doc = None if obj is None else npd.FunctionDoc(obj) + if inspect.isfunction(obj): # Skip anything that is inherited, hidden, or checked if inspect.isclass(module) and name not in module.__dict__ \ @@ -275,7 +282,8 @@ def test_deprecated_functions(module, prefix): source = inspect.getsource(obj) # Look for functions marked as deprecated in doc string - if ".. deprecated::" in docstring: + doc_extended = "\n".join(doc["Extended Summary"]) + if ".. deprecated::" in doc_extended: # Make sure a FutureWarning is issued if not re.search("FutureWarning", source): _fail(f"{name} deprecated but does not issue FutureWarning") @@ -380,6 +388,9 @@ def test_deprecated_functions(module, prefix): for cls in class_args.keys()]) def test_iosys_primary_classes(cls, fcn, args): docstring = inspect.getdoc(cls) + with warnings.catch_warnings(): + warnings.simplefilter('ignore') # debug via sphinx, not here + doc = npd.FunctionDoc(cls) # Make sure the typical arguments are there for argname in args + std_class_attributes + class_attributes[cls]: @@ -389,7 +400,8 @@ def test_iosys_primary_classes(cls, fcn, args): if re.search( r"created.*(with|by|using).*the[\s]*" f":func:`~control\\..*{fcn.__name__}`" - r"[\s]factory[\s]function", docstring, re.DOTALL) is None: + r"[\s]factory[\s]function", "\n".join(doc["Extended Summary"]), + re.DOTALL) is None: _fail( f"{cls.__name__} does not reference factory function " f"{fcn.__name__}") @@ -629,7 +641,7 @@ def simple_function(arg1, arg2, opt1=None, **kwargs): doc_returns + doc_ret_nospace, Failed, "missing Parameters section"), (doc_header, None, ""), (doc_header + "\n.. deprecated::", None, ""), - (doc_header + "\n simple_function() is deprecated", + (doc_header + "\n\n simple_function() is deprecated", UserWarning, "deprecated, but not numpydoc compliant"), ]) def test_check_parameter_docs(docstring, exception, match): From 555ba16bb0bdaf2799485dce4d2af0523d01b3b4 Mon Sep 17 00:00:00 2001 From: Richard Murray Date: Sun, 22 Dec 2024 16:14:29 -0800 Subject: [PATCH 08/93] add numpydoc style testing + corrections --- control/bdalg.py | 12 +-- control/canonical.py | 42 +++++------ control/config.py | 6 +- control/ctrlplot.py | 2 +- control/ctrlutil.py | 14 ++-- control/delay.py | 41 +--------- control/descfcn.py | 18 ++--- control/dtime.py | 12 +-- control/exception.py | 13 ++-- control/flatsys/basis.py | 2 +- control/flatsys/flatsys.py | 2 +- control/flatsys/systraj.py | 4 +- control/frdata.py | 2 +- control/freqplot.py | 36 ++++----- control/iosys.py | 16 ++-- control/lti.py | 53 +++++++------ control/margins.py | 68 +++++++++-------- control/mateqn.py | 39 +++++----- control/modelsimp.py | 18 ++--- control/nichols.py | 16 ++-- control/nlsys.py | 33 ++++---- control/optimal.py | 42 +++++------ control/passivity.py | 29 +++---- control/phaseplot.py | 4 +- control/robust.py | 16 ++-- control/sisotool.py | 4 +- control/statefbk.py | 69 ++++++++--------- control/statesp.py | 69 +++++++++-------- control/stochsys.py | 34 +++++---- control/tests/docstrings_test.py | 126 +++++++++++++++++++++++++------ control/timeplot.py | 2 +- control/timeresp.py | 16 ++-- control/xferfcn.py | 26 +++---- 33 files changed, 473 insertions(+), 413 deletions(-) diff --git a/control/bdalg.py b/control/bdalg.py index 37879cb58..a040ef5fb 100644 --- a/control/bdalg.py +++ b/control/bdalg.py @@ -72,7 +72,7 @@ def series(sys1, *sysn, **kwargs): r"""series(sys1, sys2, [..., sysn]) - Return the series connection (`sysn` \* ...\ \*) `sys2` \* `sys1`. + Return series connection (`sysn` \* ...\ \*) `sys2` \* `sys1`. Parameters ---------- @@ -144,7 +144,7 @@ def series(sys1, *sysn, **kwargs): def parallel(sys1, *sysn, **kwargs): r"""parallel(sys1, sys2, [..., sysn]) - Return the parallel connection `sys1` + `sys2` (+ ...\ + `sysn`). + Return parallel connection `sys1` + `sys2` (+ ...\ + `sysn`). Parameters ---------- @@ -440,9 +440,9 @@ def connect(sys, Q, inputv, outputv): values mean the feedback is negative. A zero value is ignored. Inputs and outputs are indexed starting at 1 to communicate sign information. inputv : 1D array - list of final external inputs, indexed starting at 1 + List of final external inputs, indexed starting at 1. outputv : 1D array - list of final external outputs, indexed starting at 1 + List of final external outputs, indexed starting at 1. Returns ------- @@ -513,7 +513,7 @@ def connect(sys, Q, inputv, outputv): return Ytrim * sys * Utrim def combine_tf(tf_array, **kwargs): - """Combine array-like of transfer functions into MIMO transfer function. + """Combine array of transfer functions into MIMO transfer function. Parameters ---------- @@ -640,7 +640,7 @@ def combine_tf(tf_array, **kwargs): def split_tf(transfer_function): - """Split MIMO transfer function into NumPy array of SISO transfer functions. + """Split MIMO transfer function into SISO transfer functions. System and signal names for the array of SISO transfer functions are copied from the MIMO system. diff --git a/control/canonical.py b/control/canonical.py index 67d3127a9..6462fb782 100644 --- a/control/canonical.py +++ b/control/canonical.py @@ -23,7 +23,7 @@ def canonical_form(xsys, form='reachable'): Parameters ---------- xsys : StateSpace object - System to be transformed, with state 'x' + System to be transformed, with state 'x'. form : str Canonical form for transformation. Chosen from: * 'reachable' - reachable canonical form @@ -33,9 +33,9 @@ def canonical_form(xsys, form='reachable'): Returns ------- zsys : StateSpace object - System in desired canonical form, with state 'z' + System in desired canonical form, with state 'z'. T : (M, M) real ndarray - Coordinate transformation matrix, z = T * x + Coordinate transformation matrix, z = T * x. Examples -------- @@ -75,14 +75,14 @@ def reachable_form(xsys): Parameters ---------- xsys : StateSpace object - System to be transformed, with state `x` + System to be transformed, with state `x`. Returns ------- zsys : StateSpace object - System in reachable canonical form, with state `z` + System in reachable canonical form, with state `z`. T : (M, M) real ndarray - Coordinate transformation: z = T * x + Coordinate transformation: z = T * x. Examples -------- @@ -138,14 +138,14 @@ def observable_form(xsys): Parameters ---------- xsys : StateSpace object - System to be transformed, with state `x` + System to be transformed, with state `x`. Returns ------- zsys : StateSpace object - System in observable canonical form, with state `z` + System in observable canonical form, with state `z`. T : (M, M) real ndarray - Coordinate transformation: z = T * x + Coordinate transformation: z = T * x. Examples -------- @@ -190,7 +190,7 @@ def observable_form(xsys): def similarity_transform(xsys, T, timescale=1, inverse=False): - """Perform a similarity transformation, with option time rescaling. + """Similarity transformation, with option time rescaling. Transform a linear state space system to a new state space representation z = T x, or x = T z, where T is an invertible matrix. @@ -198,11 +198,11 @@ def similarity_transform(xsys, T, timescale=1, inverse=False): Parameters ---------- xsys : StateSpace object - System to transform + System to transform. T : (M, M) array_like The matrix `T` defines the new set of coordinates z = T x. timescale : float, optional - If present, also rescale the time unit to tau = timescale * t + If present, also rescale the time unit to tau = timescale * t. inverse : bool, optional If False (default), transform so z = T x. If True, transform so x = T z. @@ -210,7 +210,7 @@ def similarity_transform(xsys, T, timescale=1, inverse=False): Returns ------- zsys : StateSpace object - System in transformed coordinates, with state 'z' + System in transformed coordinates, with state 'z'. Examples @@ -397,20 +397,20 @@ def bdschur(a, condmax=None, sort=None): Parameters ---------- a : (M, M) array_like - Real matrix to decompose + Real matrix to decompose. condmax : None or float, optional - If None (default), use 1/sqrt(eps), which is approximately 1e8 + If None (default), use 1/sqrt(eps), which is approximately 1e8. sort : {None, 'continuous', 'discrete'} Block sorting; see below. Returns ------- amodal : (M, M) real ndarray - Block-diagonal Schur decomposition of `a` + Block-diagonal Schur decomposition of `a`. tmodal : (M, M) real ndarray - Similarity transform relating `a` and `amodal` + Similarity transform relating `a` and `amodal`. blksizes : (N,) int ndarray - Array of Schur block sizes + Array of Schur block sizes. Notes ----- @@ -486,7 +486,7 @@ def modal_form(xsys, condmax=None, sort=False): Parameters ---------- xsys : StateSpace object - System to be transformed, with state `x` + System to be transformed, with state `x`. condmax : None or float, optional An upper bound on individual transformations. If None, use `bdschur` default. @@ -497,9 +497,9 @@ def modal_form(xsys, condmax=None, sort=False): Returns ------- zsys : StateSpace object - System in modal canonical form, with state `z` + System in modal canonical form, with state `z`. T : (M, M) ndarray - Coordinate transformation: z = T * x + Coordinate transformation: z = T * x. Examples -------- diff --git a/control/config.py b/control/config.py index 26caccbbf..e1d50884e 100644 --- a/control/config.py +++ b/control/config.py @@ -265,9 +265,11 @@ def use_matlab_defaults(): # Set defaults to match FBS (Astrom and Murray) def use_fbs_defaults(): - """Use `Feedback Systems `_ (FBS) compatible settings. + """Use Feedback Systems (FBS) compatible settings. + + The following conventions fomr `Feedback Systems `_ + are used: - The following conventions are used: * Bode plots plot gain in powers of ten, phase in degrees, frequency in rad/sec, no grid * Nyquist plots use dashed lines for mirror image of Nyquist curve diff --git a/control/ctrlplot.py b/control/ctrlplot.py index bb57f19fd..310a2d9c4 100644 --- a/control/ctrlplot.py +++ b/control/ctrlplot.py @@ -281,7 +281,7 @@ def pole_zero_subplots( Returns ------- ax_array : array - 2D array of axes + 2D array of axes. """ from .grid import nogrid, sgrid, zgrid diff --git a/control/ctrlutil.py b/control/ctrlutil.py index c16b8c35a..ce51e00ef 100644 --- a/control/ctrlutil.py +++ b/control/ctrlutil.py @@ -55,14 +55,14 @@ def unwrap(angle, period=2*math.pi): Parameters ---------- angle : array_like - Array of angles to be unwrapped + Array of angles to be unwrapped. period : float, optional - Period (defaults to `2*pi`) + Period (defaults to `2*pi`). Returns ------- angle_out : array_like - Output array, with jumps of period/2 eliminated + Output array, with jumps of period/2 eliminated. Examples -------- @@ -106,12 +106,12 @@ def db2mag(db): Parameters ---------- db : float or ndarray - input value or array of values, given in decibels + Input value or array of values, given in decibels. Returns ------- mag : float or ndarray - corresponding magnitudes + Corresponding magnitudes. Examples -------- @@ -134,12 +134,12 @@ def mag2db(mag): Parameters ---------- mag : float or ndarray - input magnitude or array of magnitudes + Input magnitude or array of magnitudes. Returns ------- db : float or ndarray - corresponding values in decibels + Corresponding values in decibels. Examples -------- diff --git a/control/delay.py b/control/delay.py index 9a05675b0..5828ded2e 100644 --- a/control/delay.py +++ b/control/delay.py @@ -5,43 +5,6 @@ # Author: Sawyer Fuller # Date: 26 Aug 2010 # -# This file contains functions for implementing time delays (currently -# only the pade() function). -# -# Copyright (c) 2010 by California Institute of Technology -# All rights reserved. -# -# Redistribution and use in source and binary forms, with or without -# modification, are permitted provided that the following conditions -# are met: -# -# 1. Redistributions of source code must retain the above copyright -# notice, this list of conditions and the following disclaimer. -# -# 2. Redistributions in binary form must reproduce the above copyright -# notice, this list of conditions and the following disclaimer in the -# documentation and/or other materials provided with the distribution. -# -# 3. Neither the name of the California Institute of Technology nor -# the names of its contributors may be used to endorse or promote -# products derived from this software without specific prior -# written permission. -# -# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS -# "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT -# LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS -# FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL CALTECH -# OR THE CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, -# SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT -# LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF -# USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND -# ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, -# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT -# OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF -# SUCH DAMAGE. -# -# $Id$ - __all__ = ['pade'] @@ -54,9 +17,9 @@ def pade(T, n=1, numdeg=None): Parameters ---------- T : number - time delay + Time. delay n : positive integer - degree of denominator of approximation + Degree of denominator of approximation. numdeg : integer, or None (the default) If numdeg is `None`, numerator degree equals denominator degree. If numdeg >= 0, specifies degree of numerator. diff --git a/control/descfcn.py b/control/descfcn.py index e17582c3f..09f0480a4 100644 --- a/control/descfcn.py +++ b/control/descfcn.py @@ -78,7 +78,7 @@ def _f(self, x): def describing_function( F, A, num_points=100, zero_check=True, try_method=True): - """Numerically compute the describing function of a nonlinear function. + """Numerically compute describing function of a nonlinear function. The describing function of a nonlinearity is given by magnitude and phase of the first harmonic of the function when evaluated along a sinusoidal @@ -285,8 +285,8 @@ def describing_function_response( Parameters ---------- H : LTI system - Linear time-invariant (LTI) system (state space, transfer function, or - FRD) + Linear time-invariant (LTI) system (state space, transfer function, + or FRD). F : static nonlinear function A static nonlinearity, either a scalar function or a single-input, single-output, static input/output system. @@ -391,7 +391,7 @@ def describing_function_plot( *sysdata, point_label="%5.2g @ %-5.2g", label=None, **kwargs): """describing_function_plot(data, *args, **kwargs) - Plot a Nyquist plot with a describing function for a nonlinear system. + Nyquist plot with describing function for a nonlinear system. This function generates a Nyquist plot for a closed loop system consisting of a linear system with a static nonlinear function in the @@ -413,8 +413,8 @@ def describing_function_plot( A describing function response data object created by :func:`~control.describing_function_response`. H : LTI system - Linear time-invariant (LTI) system (state space, transfer function, or - FRD) + Linear time-invariant (LTI) system (state space, transfer function, + or FRD). F : static nonlinear function A static nonlinearity, either a scalar function or a single-input, single-output, static input/output system. @@ -427,7 +427,7 @@ def describing_function_plot( refine : bool, optional If True (default), refine the location of the intersection of the Nyquist curve for the linear system and the describing function to - determine the intersection point + determine the intersection point. label : str or array_like of str, optional If present, replace automatically generated label with the given label. point_label : str, optional @@ -552,7 +552,7 @@ def _find_intersection(L1a, L1b, L2a, L2b): # Saturation nonlinearity class saturation_nonlinearity(DescribingFunctionNonlinearity): - """Create saturation nonlinearity for use in describing function analysis. + """Saturation nonlinearity for describing function analysis. This class creates a nonlinear function representing a saturation with given upper and lower bounds, including the describing function for the @@ -614,7 +614,7 @@ def describing_function(self, A): # Relay with hysteresis (FBS2e, Example 10.12) class relay_hysteresis_nonlinearity(DescribingFunctionNonlinearity): - """Relay w/ hysteresis nonlinearity for describing function analysis. + """Relay w/ hysteresis for describing function analysis. This class creates a nonlinear function representing a a relay with symmetric upper and lower bounds of magnitude `b` and a hysteretic region diff --git a/control/dtime.py b/control/dtime.py index 6d1545fc0..aada8c574 100644 --- a/control/dtime.py +++ b/control/dtime.py @@ -59,11 +59,11 @@ def sample_system(sysc, Ts, method='zoh', alpha=None, prewarp_frequency=None, Parameters ---------- sysc : LTI (:class:`StateSpace` or :class:`TransferFunction`) - Continuous time system to be converted + Continuous time system to be converted. Ts : float > 0 - Sampling period + Sampling period. method : string - Method to use for conversion, e.g. 'bilinear', 'zoh' (default) + Method to use for conversion, e.g. 'bilinear', 'zoh' (default). alpha : float within [0, 1] The generalized bilinear transformation weighting parameter, which should only be specified with method="gbt", and is ignored @@ -71,12 +71,12 @@ def sample_system(sysc, Ts, method='zoh', alpha=None, prewarp_frequency=None, prewarp_frequency : float within [0, infinity) The frequency [rad/s] at which to match with the input continuous- time system's magnitude and phase (only valid for method='bilinear', - 'tustin', or 'gbt' with alpha=0.5) + 'tustin', or 'gbt' with alpha=0.5). Returns ------- - sysd : LTI of the same class (:class:`StateSpace` or :class:`TransferFunction`) - Discrete time system, with sampling rate Ts + sysd : LTI of the same class (StateSpace or TransferFunction) + Discrete time system, with sampling rate Ts. Other Parameters ---------------- diff --git a/control/exception.py b/control/exception.py index feaf1f0ae..2e5f8b117 100644 --- a/control/exception.py +++ b/control/exception.py @@ -40,28 +40,27 @@ # $Id$ class ControlSlycot(ImportError): - """Exception for Slycot import. Used when we can't import a function - from the slycot package""" + """Slycot import failed.""" pass class ControlDimension(ValueError): - """Raised when dimensions of system objects are not correct""" + """Raised when dimensions of system objects are not correct.""" pass class ControlArgument(TypeError): - """Raised when arguments to a function are not correct""" + """Raised when arguments to a function are not correct.""" pass class ControlIndexError(IndexError): - """Raised when arguments to an indexed object are not correct""" + """Raised when arguments to an indexed object are not correct.""" pass class ControlMIMONotImplemented(NotImplementedError): - """Function is not currently implemented for MIMO systems""" + """Function is not currently implemented for MIMO systems.""" pass class ControlNotImplemented(NotImplementedError): - """Functionality is not yet implemented""" + """Functionality is not yet implemented.""" pass # Utility function to see if slycot is installed diff --git a/control/flatsys/basis.py b/control/flatsys/basis.py index 04abce88a..cd0e0194c 100644 --- a/control/flatsys/basis.py +++ b/control/flatsys/basis.py @@ -41,7 +41,7 @@ # Basis family class (for use as a base class) class BasisFamily: - """Base class for implementing basis functions for flat systems. + """Base class for basis functions for flat systems. A BasisFamily object is used to construct trajectories for a flat system. The class must implement a single function that computes the jth diff --git a/control/flatsys/flatsys.py b/control/flatsys/flatsys.py index dd516f50d..855f8e3ee 100644 --- a/control/flatsys/flatsys.py +++ b/control/flatsys/flatsys.py @@ -231,7 +231,7 @@ def flatsys(*args, updfcn=None, outfcn=None, **kwargs): defaults. name : string, optional - System name (used for specifying signals) + System name (used for specifying signals). Returns ------- diff --git a/control/flatsys/systraj.py b/control/flatsys/systraj.py index 0fbd4e982..902c07ecb 100644 --- a/control/flatsys/systraj.py +++ b/control/flatsys/systraj.py @@ -75,7 +75,7 @@ def __init__(self, sys, basis, coeffs=[], flaglen=[], params=None): # Evaluate the trajectory over a list of time points def eval(self, tlist): - """Return the state and input for a trajectory at a list of times. + """Compute state and input for a trajectory at a list of times. Evaluate the trajectory at a list of time points, returning the state and input vectors for the trajectory: @@ -121,7 +121,7 @@ def eval(self, tlist): # Return the system trajectory as a TimeResponseData object def response(self, tlist, transpose=False, return_x=False, squeeze=None): - """Return the trajectory of a system as a TimeResponseData object + """Compute trajectory of a system as a TimeResponseData object. Evaluate the trajectory at a list of time points, returning the state and input vectors for the trajectory: diff --git a/control/frdata.py b/control/frdata.py index 195d73bfb..f64e9f46b 100644 --- a/control/frdata.py +++ b/control/frdata.py @@ -618,7 +618,7 @@ def eval(self, omega, squeeze=None): Parameters ---------- omega : float or 1D array_like - Frequencies in radians per second + Frequencies in radians per second. squeeze : bool, optional If squeeze=True, remove single-dimensional entries from the shape of the output even if the system is not SISO. If squeeze=False, diff --git a/control/freqplot.py b/control/freqplot.py index db06da0c1..0b714a363 100644 --- a/control/freqplot.py +++ b/control/freqplot.py @@ -223,28 +223,28 @@ def bode_plot( the specified value. Default value is `False` and can be set using config.defaults['freqplot.wrap_phase']. - The default values for Bode plot configuration parameters can be reset - using the `config.defaults` dictionary, with module name 'bode'. - See Also -------- frequency_response Notes ----- - 1. Starting with python-control version 0.10, `bode_plot` returns a - :class:`ControlPlot` object instead of magnitude, phase, and - frequency. To recover the old behavior, call `bode_plot` with - `plot=True`, which will force the legacy values (mag, phase, omega) - to be returned (with a warning). To obtain just the frequency - response of a system (or list of systems) without plotting, use the - :func:`~control.frequency_response` command. - - 2. If a discrete time model is given, the frequency response is plotted - along the upper branch of the unit circle, using the mapping ``z = - exp(1j * omega * dt)`` where `omega` ranges from 0 to `pi/dt` and `dt` - is the discrete timebase. If timebase not specified (``dt=True``), - `dt` is set to 1. + Starting with python-control version 0.10, `bode_plot` returns a + :class:`ControlPlot` object instead of magnitude, phase, and + frequency. To recover the old behavior, call `bode_plot` with + `plot=True`, which will force the legacy values (mag, phase, omega) to + be returned (with a warning). To obtain just the frequency response of + a system (or list of systems) without plotting, use the + :func:`~control.frequency_response` command. + + If a discrete time model is given, the frequency response is plotted + along the upper branch of the unit circle, using the mapping ``z = + exp(1j * omega * dt)`` where `omega` ranges from 0 to `pi/dt` and `dt` + is the discrete timebase. If timebase not specified (``dt=True``), + `dt` is set to 1. + + The default values for Bode plot configuration parameters can be reset + using the `config.defaults` dictionary, with module name 'bode'. Examples -------- @@ -2093,7 +2093,7 @@ def _compute_curve_offset(resp, mask, max_offset): # def gangof4_response( P, C, omega=None, omega_limits=None, omega_num=None, Hz=False): - """Compute the response of the "Gang of 4" transfer functions for a system. + """Compute response of "Gang of 4" transfer functions. Generates a 2x2 frequency response for the "Gang of 4" sensitivity functions [T, PS; CS, S]. @@ -2172,7 +2172,7 @@ def gangof4_response( def gangof4_plot( *args, omega=None, omega_limits=None, omega_num=None, Hz=False, **kwargs): - """Plot the response of the "Gang of 4" transfer functions for a system. + """Plot response of "Gang of 4" transfer functions. Plots a 2x2 frequency response for the "Gang of 4" sensitivity functions [T, PS; CS, S]. Can be called in one of two ways: diff --git a/control/iosys.py b/control/iosys.py index a72f89791..cedcc4ff6 100644 --- a/control/iosys.py +++ b/control/iosys.py @@ -37,6 +37,7 @@ # Named signal class class NamedSignal(np.ndarray): + """Named signal class.""" def __new__(cls, input_array, signal_labels=None, trace_labels=None): # See https://numpy.org/doc/stable/user/basics.subclassing.html obj = np.asarray(input_array).view(cls) # Cast to our class type @@ -598,7 +599,7 @@ def isctime(self, strict=False): Parameters ---------- sys : Named I/O system - System to be checked + System to be checked. strict : bool, optional If strict is True, make sure that timebase is not None. Default is False. @@ -610,7 +611,7 @@ def isctime(self, strict=False): def isdtime(self, strict=False): """ - Check to see if a system is a discrete-time system + Check to see if a system is a discrete-time system. Parameters ---------- @@ -643,9 +644,9 @@ def issiso(sys, strict=False): Parameters ---------- sys : I/O or LTI system - System to be checked + System to be checked. strict : bool (default = False) - If strict is True, do not treat scalars as SISO + If strict is True, do not treat scalars as SISO. """ if isinstance(sys, (int, float, complex, np.number)) and not strict: return True @@ -694,12 +695,13 @@ def timebase(sys, strict=True): def common_timebase(dt1, dt2): """ - Find the common timebase when interconnecting systems + Find the common timebase when interconnecting systems. Parameters ---------- - dt1, dt2 : number or system with a 'dt' attribute (e.g. TransferFunction - or StateSpace system) + dt1, dt2 : InputOutputSystem or float + Number or system with a 'dt' attribute (e.g. TransferFunction + or StateSpace system). Returns ------- diff --git a/control/lti.py b/control/lti.py index d4e7c68ef..c5440b5fd 100644 --- a/control/lti.py +++ b/control/lti.py @@ -20,7 +20,7 @@ class LTI(InputOutputSystem): - """LTI is a parent class to linear time-invariant (LTI) system objects. + """Parent class for linear time-invariant (LTI) system objects. LTI is the parent to the StateSpace and TransferFunction child classes. It contains the number of inputs and outputs, and the timebase (dt) for the @@ -39,16 +39,16 @@ def __init__(self, inputs=1, outputs=1, states=None, name=None, **kwargs): name=name, inputs=inputs, outputs=outputs, states=states, **kwargs) def damp(self): - '''Natural frequency, damping ratio of system poles + '''Natural frequency, damping ratio of system poles. Returns ------- wn : array - Natural frequency for each system pole + Natural frequency for each system pole. zeta : array - Damping ratio for each system pole + Damping ratio for each system pole. poles : array - System pole locations + System pole locations. ''' poles = self.poles() @@ -61,8 +61,7 @@ def damp(self): return wn, zeta, poles def frequency_response(self, omega=None, squeeze=None): - """Evaluate the linear time-invariant system at an array of angular - frequencies. + """Evaluate LTI system response at an array of frequencies. For continuous time systems, computes the frequency response as @@ -146,7 +145,7 @@ def _dcgain(self, warn_infinite): return zeroresp def bandwidth(self, dbdrop=-3): - """Evaluate the bandwidth of the LTI system for a given dB drop. + """Evaluate bandwidth of an LTI system for a given dB drop. Evaluate the first frequency that the response magnitude is lower than DC gain by dbdrop dB. @@ -162,7 +161,7 @@ def bandwidth(self, dbdrop=-3): bandwidth : ndarray The first frequency (rad/time-unit) where the gain drops below dbdrop of the dc gain of the system, or nan if the system has - infinite dc gain, inf if the gain does not drop for all frequency + infinite dc gain, inf if the gain does not drop for all frequency. Raises ------ @@ -248,7 +247,7 @@ def poles(sys): Parameters ---------- sys : StateSpace or TransferFunction - Linear system + Linear system. Returns ------- @@ -273,7 +272,7 @@ def zeros(sys): Parameters ---------- sys : StateSpace or TransferFunction - Linear system + Linear system. Returns ------- @@ -292,24 +291,23 @@ def zeros(sys): def damp(sys, doprint=True): - """ - Compute natural frequencies, damping ratios, and poles of a system. + """Compute system's natural frequencies, damping ratios, and poles. Parameters ---------- sys : LTI (StateSpace or TransferFunction) - A linear system object + A linear system object. doprint : bool (optional) - if True, print table with values + If True, print table with values. Returns ------- wn : array - Natural frequency for each system pole + Natural frequency for each system pole. zeta : array - Damping ratio for each system pole + Damping ratio for each system pole. poles : array - System pole locations + System pole locations. See Also -------- @@ -353,7 +351,7 @@ def damp(sys, doprint=True): def evalfr(sys, x, squeeze=None): - """Evaluate the transfer function of an LTI system for complex frequency x. + """Evaluate transfer function of LTI system at complex frequency. Returns the complex frequency response `sys(x)` where `x` is `s` for continuous-time systems and `z` for discrete-time systems, with @@ -368,9 +366,9 @@ def evalfr(sys, x, squeeze=None): Parameters ---------- sys : StateSpace or TransferFunction - Linear system + Linear system. x : complex scalar or 1D array_like - Complex frequency(s) + Complex frequency(s). squeeze : bool, optional (default=True) If squeeze=True, remove single-dimensional entries from the shape of the output even if the system is not SISO. If squeeze=False, keep all @@ -412,11 +410,12 @@ def evalfr(sys, x, squeeze=None): def frequency_response( sysdata, omega=None, omega_limits=None, omega_num=None, Hz=None, squeeze=None): - """Frequency response of an LTI system at multiple angular frequencies. + """Frequency response of an LTI system. - In general the system may be multiple input, multiple output (MIMO), where - `m = sys.ninputs` number of inputs and `p = sys.noutputs` number of - outputs. + Computes the frequency response of an LTI system at a list of + frequencies. In general the system may be multiple input, multiple + output (MIMO), where `m = sys.ninputs` number of inputs and `p = + sys.noutputs` number of outputs. Parameters ---------- @@ -588,7 +587,7 @@ def dcgain(sys): def bandwidth(sys, dbdrop=-3): - """Return the first freqency where the gain drop by dbdrop of the system. + """Find first freqency where the gain drop by dbdrop of the system. Parameters ---------- @@ -603,7 +602,7 @@ def bandwidth(sys, dbdrop=-3): bandwidth : ndarray The first frequency (rad/time-unit) where the gain drops below dbdrop of the dc gain of the system, or nan if the system has infinite dc - gain, inf if the gain does not drop for all frequency + gain, inf if the gain does not drop for all frequency. Raises ------ diff --git a/control/margins.py b/control/margins.py index 80edc12c9..95e86a764 100644 --- a/control/margins.py +++ b/control/margins.py @@ -252,18 +252,16 @@ def _likely_numerical_inaccuracy(sys): # systems +# TODO: consider handling sysdata similar to margin (via *sysdata?) def stability_margins(sysdata, returnall=False, epsw=0.0, method='best'): - """Calculate stability margins and associated crossover frequencies. + """Stability margins and associated crossover frequencies. Parameters ---------- - sysdata : LTI system or (mag, phase, omega) sequence - sys : LTI system - Linear SISO system representing the loop transfer function - mag, phase, omega : sequence of array_like - Arrays of magnitudes (absolute values, not dB), phases (degrees), - and corresponding frequencies. Crossover frequencies returned are - in the same units as those in `omega` (e.g., rad/sec or Hz). + sysdata : LTI system or 3-tuple of array_like + Linear SISO system representing the loop transfer function. + Alternatively, a three tuple of the form (mag, phase, omega) + providing the frequency response can be passed. returnall : bool, optional If true, return all margins found. If False (default), return only the minimum stability margins. For frequency data or FRD systems, only @@ -284,11 +282,11 @@ def stability_margins(sysdata, returnall=False, epsw=0.0, method='best'): Returns ------- gm : float or array_like - Gain margin + Gain margin. pm : float or array_like - Phase margin + Phase margin. sm : float or array_like - Stability margin, the minimum distance from the Nyquist plot to -1 + Stability margin, the minimum distance from the Nyquist plot to -1. wpc : float or array_like Phase crossover frequency (where phase crosses -180 degrees), which is associated with the gain margin. @@ -296,14 +294,16 @@ def stability_margins(sysdata, returnall=False, epsw=0.0, method='best'): Gain crossover frequency (where gain crosses 1), which is associated with the phase margin. wms : float or array_like - Stability margin frequency (where Nyquist plot is closest to -1) - - Note that the gain margin is determined by the gain of the loop - transfer function at the phase crossover frequency(s), the phase - margin is determined by the phase of the loop transfer function at - the gain crossover frequency(s), and the stability margin is - determined by the frequency of maximum sensitivity (given by the - magnitude of 1/(1+L)). + Stability margin frequency (where Nyquist plot is closest to -1). + + Notes + ----- + The gain margin is determined by the gain of the loop transfer function + at the phase crossover frequency(s), the phase margin is determined by + the phase of the loop transfer function at the gain crossover + frequency(s), and the stability margin is determined by the frequency + of maximum sensitivity (given by the magnitude of 1/(1+L)). + """ # TODO: FRD method for cont-time systems doesn't work try: @@ -463,20 +463,20 @@ def _dstab(w): # Contributed by Steffen Waldherr def phase_crossover_frequencies(sys): - """Compute frequencies and gains at intersections with real axis - in Nyquist plot. + """Compute Nyquist plot real-axis crossover frequencies and gains. Parameters ---------- - sys : SISO LTI system + sys : LTI + SISO LTI system. Returns ------- omega : ndarray 1d array of (non-negative) frequencies where Nyquist plot - intersects the real axis + intersects the real axis. gains : ndarray - 1d array of corresponding gains + 1d array of corresponding gains. Examples -------- @@ -509,23 +509,25 @@ def phase_crossover_frequencies(sys): def margin(*args): """margin(sysdata) - Calculate gain and phase margins and associated crossover frequencies. + Gain and phase margins and associated crossover frequencies. + + Can be called as ``margin(sys)`` where ``sys`` is a SISO LTI sytem or + ``margin(mag, phase, omega)``. Parameters ---------- - sysdata : LTI system or (mag, phase, omega) sequence - sys : StateSpace or TransferFunction - Linear SISO system representing the loop transfer function - mag, phase, omega : sequence of array_like - Input magnitude, phase (in deg.), and frequencies (rad/sec) from - bode frequency response data + sys : StateSpace or TransferFunction + Linear SISO system representing the loop transfer function. + mag, phase, omega : sequence of array_like + Input magnitude, phase (in deg.), and frequencies (rad/sec) from + bode frequency response data. Returns ------- gm : float - Gain margin + Gain margin. pm : float - Phase margin (in degrees) + Phase margin (in degrees). wcg : float or array_like Crossover frequency associated with gain margin (phase crossover frequency), where phase crosses below -180 degrees. diff --git a/control/mateqn.py b/control/mateqn.py index 9100f567c..c38b0ff3e 100644 --- a/control/mateqn.py +++ b/control/mateqn.py @@ -113,11 +113,11 @@ def lyap(A, Q, C=None, E=None, method=None): Parameters ---------- A, Q : 2D array_like - Input matrices for the Lyapunov or Sylvestor equation + Input matrices for the Lyapunov or Sylvestor equation. C : 2D array_like, optional - If present, solve the Sylvester equation + If present, solve the Sylvester equation. E : 2D array_like, optional - If present, solve the generalized Lyapunov equation + If present, solve the generalized Lyapunov equation. method : str, optional Set the method used for computing the result. Current methods are 'slycot' and 'scipy'. If set to None (default), try 'slycot' first @@ -126,7 +126,7 @@ def lyap(A, Q, C=None, E=None, method=None): Returns ------- X : 2D array - Solution to the Lyapunov or Sylvester equation + Solution to the Lyapunov or Sylvester equation. """ # Decide what method to use @@ -239,11 +239,11 @@ def dlyap(A, Q, C=None, E=None, method=None): Parameters ---------- A, Q : 2D array_like - Input matrices for the Lyapunov or Sylvestor equation + Input matrices for the Lyapunov or Sylvestor equation. C : 2D array_like, optional - If present, solve the Sylvester equation + If present, solve the Sylvester equation. E : 2D array_like, optional - If present, solve the generalized Lyapunov equation + If present, solve the generalized Lyapunov equation. method : str, optional Set the method used for computing the result. Current methods are 'slycot' and 'scipy'. If set to None (default), try 'slycot' first @@ -252,7 +252,7 @@ def dlyap(A, Q, C=None, E=None, method=None): Returns ------- X : 2D array (or matrix) - Solution to the Lyapunov or Sylvester equation + Solution to the Lyapunov or Sylvester equation. """ # Decide what method to use @@ -367,9 +367,9 @@ def care(A, B, Q, R=None, S=None, E=None, stabilizing=True, method=None, Parameters ---------- A, B, Q : 2D array_like - Input matrices for the Riccati equation + Input matrices for the Riccati equation. R, S, E : 2D array_like, optional - Input matrices for generalized Riccati equation + Input matrices for generalized Riccati equation. method : str, optional Set the method used for computing the result. Current methods are 'slycot' and 'scipy'. If set to None (default), try 'slycot' first @@ -381,11 +381,11 @@ def care(A, B, Q, R=None, S=None, E=None, stabilizing=True, method=None, Returns ------- X : 2D array (or matrix) - Solution to the Ricatti equation + Solution to the Ricatti equation. L : 1D array - Closed loop eigenvalues + Closed loop eigenvalues. G : 2D array (or matrix) - Gain matrix + Gain matrix. """ # Decide what method to use @@ -497,8 +497,7 @@ def care(A, B, Q, R=None, S=None, E=None, stabilizing=True, method=None, def dare(A, B, Q, R, S=None, E=None, stabilizing=True, method=None, _As="A", _Bs="B", _Qs="Q", _Rs="R", _Ss="S", _Es="E"): - """Solves the discrete-time algebraic Riccati - equation. + """Solves the discrete-time algebraic Riccati equation. X, L, G = dare(A, B, Q, R) solves @@ -524,9 +523,9 @@ def dare(A, B, Q, R, S=None, E=None, stabilizing=True, method=None, Parameters ---------- A, B, Q : 2D arrays - Input matrices for the Riccati equation + Input matrices for the Riccati equation. R, S, E : 2D arrays, optional - Input matrices for generalized Riccati equation + Input matrices for generalized Riccati equation. method : str, optional Set the method used for computing the result. Current methods are 'slycot' and 'scipy'. If set to None (default), try 'slycot' first @@ -538,11 +537,11 @@ def dare(A, B, Q, R, S=None, E=None, stabilizing=True, method=None, Returns ------- X : 2D array (or matrix) - Solution to the Ricatti equation + Solution to the Ricatti equation. L : 1D array - Closed loop eigenvalues + Closed loop eigenvalues. G : 2D array (or matrix) - Gain matrix + Gain matrix. """ # Decide what method to use diff --git a/control/modelsimp.py b/control/modelsimp.py index 968003051..ecb15008b 100644 --- a/control/modelsimp.py +++ b/control/modelsimp.py @@ -33,12 +33,12 @@ def hankel_singular_values(sys): Parameters ---------- sys : StateSpace - A state space system + State space system. Returns ------- H : array - A list of Hankel singular values + List of Hankel singular values. See Also -------- @@ -175,7 +175,7 @@ def _expand_key(key): elim = np.atleast_1d(_expand_key(elim)) keep = np.atleast_1d(_expand_key(keep)) - + if len(elim) > 0 and len(keep) > 0: raise ValueError( "can't provide both 'keep' and 'elim' for same variables") @@ -212,7 +212,7 @@ def _expand_key(key): if sys.isdtime(strict=True): raise NotImplementedError( "'matchdc' not (yet) supported for discrete time systems") - + # if matchdc, residualize # Check if the matrix A22 is invertible if np.linalg.matrix_rank(A22) != len(elim_states): @@ -424,7 +424,7 @@ def _block_hankel(Y, m, n): def eigensys_realization(arg, r, m=None, n=None, dt=True, transpose=False): r"""eigensys_realization(YY, r) - Calculate ERA model of order `r` based on impulse-response data `YY`. + Calculate ERA model based on impulse-response data. This function computes a discrete time system @@ -433,7 +433,7 @@ def eigensys_realization(arg, r, m=None, n=None, dt=True, transpose=False): x[k+1] &= A x[k] + B u[k] \\\\ y[k] &= C x[k] + D u[k] - for a given impulse-response data (see [1]_). + of order `r` for a given impulse-response data (see [1]_). The function can be called with 2 arguments: @@ -533,10 +533,10 @@ def eigensys_realization(arg, r, m=None, n=None, dt=True, transpose=False): def markov(*args, m=None, transpose=False, dt=None, truncate=False): """markov(Y, U, [, m]) - Calculate the first `m` Markov parameters [D CB CAB ...] from data. + Calculate Markov parameters [D CB CAB ...] from data. - This function computes the Markov parameters for a discrete time - system + This function computes the the first `m` Markov parameters [D CB CAB + ...] for a discrete time system. .. math:: diff --git a/control/nichols.py b/control/nichols.py index 919f71d9e..1006c0c6e 100644 --- a/control/nichols.py +++ b/control/nichols.py @@ -46,7 +46,7 @@ def nichols_plot( List of LTI systems or :class:`FrequencyResponseData` objects. A single system or frequency response can also be passed. omega : array_like - Range of frequencies (list or bounds) in rad/sec + Range of frequencies (list or bounds) in rad/sec. *fmt : :func:`matplotlib.pyplot.plot` format string, optional Passed to `matplotlib` as the format string for all lines in the plot. The `omega` parameter must be present (use omega=None if needed). @@ -200,7 +200,7 @@ def nichols_grid(cl_mags=None, cl_phases=None, line_style='dotted', ax=None, Nichols chart. Must be in the range -360 < cl_phases < 0 line_style : string, optional :doc:`Matplotlib linestyle \ - ` + `. ax : matplotlib.axes.Axes, optional Axes to add grid to. If ``None``, use ``matplotlib.pyplot.gca()``. label_cl_phases : bool, optional @@ -209,15 +209,15 @@ def nichols_grid(cl_mags=None, cl_phases=None, line_style='dotted', ax=None, Returns ------- cl_mag_lines : list of `matplotlib.line.Line2D` - The constant closed-loop gain contours + The constant closed-loop gain contours. cl_phase_lines : list of `matplotlib.line.Line2D` - The constant closed-loop phase contours + The constant closed-loop phase contours. cl_mag_labels : list of `matplotlib.text.Text` - mcontour labels; each entry corresponds to the respective entry - in ``cl_mag_lines`` + Magnitude contour labels; each entry corresponds to the respective entry. + in ``cl_mag_lines``. cl_phase_labels : list of `matplotlib.text.Text` - ncontour labels; each entry corresponds to the respective entry - in ``cl_phase_lines`` + Phase contour labels; each entry corresponds to the respective entry + in ``cl_phase_lines``. """ if ax is None: ax = plt.gca() diff --git a/control/nlsys.py b/control/nlsys.py index 3985d7132..08898686c 100644 --- a/control/nlsys.py +++ b/control/nlsys.py @@ -374,7 +374,7 @@ def _rhs(self, t, x, u): self.updfcn(t, x, u, self._current_params)).reshape(-1) def dynamics(self, t, x, u, params=None): - """Compute the dynamics of a differential or difference equation. + """Dynamics of a differential or difference equation. Given time `t`, input `u` and state `x`, returns the value of the right hand side of the dynamical system. If the system is continuous, @@ -395,13 +395,13 @@ def dynamics(self, t, x, u, params=None): Parameters ---------- t : float - the time at which to evaluate + Time at which to evaluate. x : array_like - current state + Current state. u : array_like - input + Current input. params : dict, optional - system parameter values + System parameter values. Returns ------- @@ -433,7 +433,7 @@ def _out(self, t, x, u): self.outfcn(t, x, u, self._current_params)).reshape(-1) def output(self, t, x, u, params=None): - """Compute the output of the system + """Compute the output of the system. Given time `t`, input `u` and state `x`, returns the output of the system: @@ -445,13 +445,13 @@ def output(self, t, x, u, params=None): Parameters ---------- t : float - the time at which to evaluate + The time at which to evaluate. x : array_like - current state + Current state. u : array_like - input + Current input. params : dict, optional - system parameter values + System parameter values. Returns ------- @@ -1061,7 +1061,7 @@ def unused_signals(self): def connection_table(self, show_names=False, column_width=32): - """Print table of connections inside an interconnected system model. + """Table of connections inside an interconnected system. Intended primarily for :class:`InterconnectedSystem`'s that have been connected implicitly using signal names. @@ -1164,7 +1164,7 @@ def _find_outputs_by_basename(self, basename): # TODO: change to internal function? (not sure users need to see this) def check_unused_signals( self, ignore_inputs=None, ignore_outputs=None, warning=True): - """Check for unused subsystem inputs and outputs + """Check for unused subsystem inputs and outputs. Check to see if there are any unused signals and return a list of unused input and output signal descriptions. If `warning` is True @@ -1759,7 +1759,7 @@ def ivp_rhs(t, x): class OperatingPoint(): - """A class for representing the operating point of a nonlinear I/O system. + """Class for representing operating point of nonlinear I/O system. The ``OperatingPoint`` class stores the operating point of a nonlinear system, consisting of the state and input vectors for the system. The @@ -2230,7 +2230,7 @@ def interconnect( Parameters ---------- syslist : list of InputOutputSystems - The list of input/output systems to be connected + The list of input/output systems to be connected. connections : list of connections, optional Description of the internal connections between the subsystems: @@ -2855,7 +2855,7 @@ def _convert_static_iosystem(sys): outputs=sys.shape[0], inputs=sys.shape[1], dt=None) def connection_table(sys, show_names=False, column_width=32): - """Print table of connections inside an interconnected system model. + """Print table of connections inside interconnected system. Intended primarily for :class:`InterconnectedSystems` that have been connected implicitly using signal names. @@ -2863,7 +2863,7 @@ def connection_table(sys, show_names=False, column_width=32): Parameters ---------- sys : :class:`InterconnectedSystem` - Interconnected system object + Interconnected system object. show_names : bool, optional Instead of printing out the system number, print out the name of each system. Default is False because system name is not usually @@ -2872,7 +2872,6 @@ def connection_table(sys, show_names=False, column_width=32): column_width : int, optional Character width of printed columns. - Examples -------- >>> P = ct.ss(1,1,1,0, inputs='u', outputs='y', name='P') diff --git a/control/optimal.py b/control/optimal.py index e57029093..923c6440c 100644 --- a/control/optimal.py +++ b/control/optimal.py @@ -752,7 +752,7 @@ def _simulate_states(self, x0, inputs): def compute_trajectory( self, x, squeeze=None, transpose=None, return_states=True, initial_guess=None, print_summary=True, **kwargs): - """Compute the optimal input at state x + """Compute the optimal input at state x. Parameters ---------- @@ -823,7 +823,7 @@ def compute_trajectory( # Compute the current input to apply from the current state (MPC style) def compute_mpc(self, x, squeeze=None): - """Compute the optimal input at state x + """Compute the optimal input at state x. This function calls the :meth:`compute_trajectory` method and returns the input at the first time point. @@ -1148,7 +1148,7 @@ def solve_ocp( def create_mpc_iosystem( sys, timepts, cost, constraints=None, terminal_cost=None, terminal_constraints=None, log=False, **kwargs): - """Create a model predictive I/O control system + """Create a model predictive I/O control system. This function creates an input/output system that implements a model predictive control for a system given the time points, cost function and @@ -1672,7 +1672,7 @@ def _print_statistics(self, reset=True): def compute_estimate( self, Y, U, X0=None, initial_guess=None, squeeze=None, print_summary=True): - """Compute the optimal input at state x + """Compute the optimal input at state x. Parameters ---------- @@ -1760,7 +1760,7 @@ def compute_estimate( def create_mhe_iosystem( self, estimate_labels=None, measurement_labels=None, control_labels=None, inputs=None, outputs=None, **kwargs): - """Create an I/O system implementing an MPC controller + """Create an I/O system implementing an MPC controller. This function creates an input/output system that implements a moving horizon estimator for a an optimal estimation problem. The @@ -1959,7 +1959,7 @@ def solve_oep( trajectory_constraints=None, initial_guess=None, squeeze=None, print_summary=True, **kwargs): - """Compute the solution to a moving horizon estimation problem + """Compute the solution to a moving horizon estimation problem. Parameters ---------- @@ -2043,7 +2043,7 @@ def solve_oep( # the `_cost_function` evaluates the cost at each point in the trajectory. # def quadratic_cost(sys, Q, R, x0=0, u0=0): - """Create quadratic cost function + """Create quadratic cost function. Returns a quadratic cost function that can be used for an optimal control problem. The cost function is of the form @@ -2096,7 +2096,7 @@ def quadratic_cost(sys, Q, R, x0=0, u0=0): def gaussian_likelihood_cost(sys, Rv, Rw=None): - """Create cost function for Gaussian likelihoods + """Create cost function for Gaussian likelihoods. Returns a quadratic cost function that can be used for an optimal estimation problem. The cost function is of the form @@ -2158,7 +2158,7 @@ def gaussian_likelihood_cost(sys, Rv, Rw=None): # keep things consistent with the terminology in scipy.optimize. # def state_poly_constraint(sys, A, b): - """Create state constraint from polytope + """Create state constraint from polytope. Creates a linear constraint on the system state of the form A x <= b that can be used as an optimal control constraint (trajectory or terminal). @@ -2168,9 +2168,9 @@ def state_poly_constraint(sys, A, b): sys : InputOutputSystem I/O system for which the constraint is being defined. A : 2D array - Constraint matrix + Constraint matrix. b : 1D array - Upper bound for the constraint + Upper bound for the constraint. Returns ------- @@ -2193,7 +2193,7 @@ def state_poly_constraint(sys, A, b): def state_range_constraint(sys, lb, ub): - """Create state constraint from range + """Create state constraint from range. Creates a linear constraint on the system state that bounds the range of the individual states to be between `lb` and `ub`. The upper and lower @@ -2230,7 +2230,7 @@ def state_range_constraint(sys, lb, ub): # Create a constraint polytope on the system input def input_poly_constraint(sys, A, b): - """Create input constraint from polytope + """Create input constraint from polytope. Creates a linear constraint on the system input of the form A u <= b that can be used as an optimal control constraint (trajectory or terminal). @@ -2240,9 +2240,9 @@ def input_poly_constraint(sys, A, b): sys : InputOutputSystem I/O system for which the constraint is being defined. A : 2D array - Constraint matrix + Constraint matrix. b : 1D array - Upper bound for the constraint + Upper bound for the constraint. Returns ------- @@ -2266,7 +2266,7 @@ def input_poly_constraint(sys, A, b): def input_range_constraint(sys, lb, ub): - """Create input constraint from polytope + """Create input constraint from polytope. Creates a linear constraint on the system input that bounds the range of the individual states to be between `lb` and `ub`. The upper and lower @@ -2315,7 +2315,7 @@ def input_range_constraint(sys, lb, ub): # [A @ sys.C, np.zeros((A.shape[0], sys.ninputs))]) # def output_poly_constraint(sys, A, b): - """Create output constraint from polytope + """Create output constraint from polytope. Creates a linear constraint on the system output of the form A y <= b that can be used as an optimal control constraint (trajectory or terminal). @@ -2325,9 +2325,9 @@ def output_poly_constraint(sys, A, b): sys : InputOutputSystem I/O system for which the constraint is being defined. A : 2D array - Constraint matrix + Constraint matrix. b : 1D array - Upper bound for the constraint + Upper bound for the constraint. Returns ------- @@ -2354,7 +2354,7 @@ def _evaluate_output_poly_constraint(x, u): def output_range_constraint(sys, lb, ub): - """Create output constraint from range + """Create output constraint from range. Creates a linear constraint on the system output that bounds the range of the individual states to be between `lb` and `ub`. The upper and lower @@ -2395,7 +2395,7 @@ def _evaluate_output_range_constraint(x, u): # def disturbance_range_constraint(sys, lb, ub): - """Create constraint for bounded disturbances + """Create constraint for bounded disturbances. This function computes a constraint that puts a bound on the size of input disturbances. The output of this function can be passed as a diff --git a/control/passivity.py b/control/passivity.py index 0f4104186..a2d5b5bf0 100644 --- a/control/passivity.py +++ b/control/passivity.py @@ -43,16 +43,16 @@ def solve_passivity_LMI(sys, rho=None, nu=None): Parameters ---------- sys : LTI - System to be checked + System to be checked. rho : float or None - Output feedback passivity index + Output feedback passivity index. nu : float or None - Input feedforward passivity index + Input feedforward passivity index. Returns ------- solution : ndarray - The LMI solution + The LMI solution. """ if cvx is None: raise ModuleNotFoundError("cvxopt required for passivity module") @@ -190,12 +190,12 @@ def get_output_fb_index(sys): Parameters ---------- sys : LTI - System to be checked + System to be checked. Returns ------- float - The OFP index + The OFP index. """ sol = solve_passivity_LMI(sys, nu=0.0) if sol is None: @@ -205,11 +205,11 @@ def get_output_fb_index(sys): def get_input_ff_index(sys): - """Return the input feedforward passivity (IFP) index for the system. + """Input feedforward passivity (IFP) index for a system. - The IFP is the largest gain that can be placed in negative parallel - interconnection with a system such that the new interconnected system is - passive. + The input feedforward passivity (IFP) is the largest gain that can be + placed in negative parallel interconnection with a system such that the + new interconnected system is passive. Parameters ---------- @@ -219,7 +219,8 @@ def get_input_ff_index(sys): Returns ------- float - The IFP index + The IFP index. + """ sol = solve_passivity_LMI(sys, rho=0.0) if sol is None: @@ -261,11 +262,11 @@ def ispassive(sys, ofp_index=0, ifp_index=0): Parameters ---------- sys : LTI - System to be checked + System to be checked. ofp_index : float - Output feedback passivity index + Output feedback passivity index. ifp_index : float - Input feedforward passivity index + Input feedforward passivity index. Returns ------- diff --git a/control/phaseplot.py b/control/phaseplot.py index 10794e962..e8127d644 100644 --- a/control/phaseplot.py +++ b/control/phaseplot.py @@ -748,7 +748,7 @@ def meshgrid(xvals, yvals): Returns ------- grid : 2D array - Array of points with shape (n * m, 2) defining the mesh + Array of points with shape (n * m, 2) defining the mesh. """ xvals, yvals = np.meshgrid(xvals, yvals) @@ -1273,7 +1273,7 @@ def phase_plot(odefun, X=None, Y=None, scale=1, X0=None, T=None, # Utility function for generating initial conditions around a box def box_grid(xlimp, ylimp): - """box_grid generate list of points on edge of box + """Generate list of points on edge of box. .. deprecated:: 0.10.0 Use :func:`phaseplot.boxgrid` instead. diff --git a/control/robust.py b/control/robust.py index 356b45072..6dc32bda0 100644 --- a/control/robust.py +++ b/control/robust.py @@ -397,19 +397,21 @@ def mixsyn(g, w1=None, w2=None, w3=None): then cl is the system from w->z with `u = -k*y`. info : tuple - gamma: scalar - H-infinity norm of cl. - rcond: array - Estimates of reciprocal condition numbers computed during - synthesis. See hinfsyn for details. + Two-tuple `(gamma, rcond)` containing additional information: - If a weighting w is scalar, it will be replaced by I*w, where I is - ny-by-ny for w1 and w3, and nu-by-nu for w2. + * gamma (scalar): H-infinity norm of cl. + * rcond (array): Estimates of reciprocal condition numbers computed + during synthesis. See hinfsyn for details. See Also -------- hinfsyn, augw + Notes + ----- + If a weighting `w` is scalar, it will be replaced by `I*w`, where `I` is + ny-by-ny for `w1` and `w3`, and nu-by-nu for `w2`. + """ nmeas = g.noutputs ncon = g.ninputs diff --git a/control/sisotool.py b/control/sisotool.py index f34b210c6..a2c31cc86 100644 --- a/control/sisotool.py +++ b/control/sisotool.py @@ -27,7 +27,7 @@ def sisotool(sys, initial_gain=None, xlim_rlocus=None, ylim_rlocus=None, plotstr_rlocus='C0', rlocus_grid=False, omega=None, dB=None, Hz=None, deg=None, omega_limits=None, omega_num=None, margins_bode=True, tvect=None, kvect=None): - """Sisotool style collection of plots inspired by MATLAB's sisotool. + """Collection of plots inspired by MATLAB's sisotool. The left two plots contain the bode magnitude and phase diagrams. The top right plot is a clickable root locus plot, clicking on the @@ -58,7 +58,7 @@ def sisotool(sys, initial_gain=None, xlim_rlocus=None, ylim_rlocus=None, Control of x-axis range, normally with tuple (see :doc:`matplotlib:api/axes_api`). ylim_rlocus : tuple or list, optional - control of y-axis range + Control of y-axis range. plotstr_rlocus : :func:`matplotlib.pyplot.plot` format string, optional Plotting style for the root locus plot(color, linestyle, etc). rlocus_grid : boolean (default = False) diff --git a/control/statefbk.py b/control/statefbk.py index 5d635196a..7d7aa7f5b 100644 --- a/control/statefbk.py +++ b/control/statefbk.py @@ -86,16 +86,16 @@ def place(A, B, p): Parameters ---------- A : 2D array_like - Dynamics matrix + Dynamics matrix. B : 2D array_like - Input matrix + Input matrix. p : 1D array_like - Desired eigenvalue locations + Desired eigenvalue locations. Returns ------- K : 2D array (or matrix) - Gain such that A - B K has eigenvalues given in p + Gain such that A - B K has eigenvalues given in p. Notes ----- @@ -142,16 +142,17 @@ def place(A, B, p): def place_varga(A, B, p, dtime=False, alpha=None): """Place closed loop eigenvalues. + K = place_varga(A, B, p, dtime=False, alpha=None) Parameters ---------- A : 2D array_like - Dynamics matrix + Dynamics matrix. B : 2D array_like - Input matrix + Input matrix. p : 1D array_like - Desired eigenvalue locations + Desired eigenvalue locations. dtime : bool, optional False for continuous time pole placement or True for discrete time. The default is dtime=False. @@ -250,15 +251,15 @@ def acker(A, B, poles): Parameters ---------- A, B : 2D array_like - State and input matrix of the system + State and input matrix of the system. poles : 1D array_like - Desired eigenvalue locations + Desired eigenvalue locations. Returns ------- K : 2D array (or matrix) - Gains such that A - B K has given eigenvalues - + Gains such that A - B K has given eigenvalues. + See Also -------- place, place_varga @@ -311,13 +312,13 @@ def lqr(*args, **kwargs): Parameters ---------- A, B : 2D array_like - Dynamics and input matrices + Dynamics and input matrices. sys : LTI StateSpace system - Linear system + Linear system. Q, R : 2D array - State and input weight matrices + State and input weight matrices. N : 2D array, optional - Cross weight matrix + Cross weight matrix. integral_action : ndarray, optional If this keyword is specified, the controller includes integral action in addition to state feedback. The value of the @@ -334,11 +335,11 @@ def lqr(*args, **kwargs): Returns ------- K : 2D array (or matrix) - State feedback gains + State feedback gains. S : 2D array (or matrix) - Solution to Riccati equation + Solution to Riccati equation. E : 1D array - Eigenvalues of the closed loop system + Eigenvalues of the closed loop system. See Also -------- @@ -458,13 +459,13 @@ def dlqr(*args, **kwargs): Parameters ---------- A, B : 2D array - Dynamics and input matrices + Dynamics and input matrices. dsys : LTI :class:`StateSpace` - Discrete-time linear system + Discrete-time linear system. Q, R : 2D array - State and input weight matrices + State and input weight matrices. N : 2D array, optional - Cross weight matrix + Cross weight matrix. integral_action : ndarray, optional If this keyword is specified, the controller includes integral action in addition to state feedback. The value of the @@ -481,11 +482,11 @@ def dlqr(*args, **kwargs): Returns ------- K : 2D array (or matrix) - State feedback gains + State feedback gains. S : 2D array (or matrix) - Solution to Riccati equation + Solution to Riccati equation. E : 1D array - Eigenvalues of the closed loop system + Eigenvalues of the closed loop system. See Also -------- @@ -1070,14 +1071,14 @@ def ctrb(A, B, t=None): Parameters ---------- A, B : array_like or string - Dynamics and input matrix of the system + Dynamics and input matrix of the system. t : None or integer - maximum time horizon of the controllability matrix, max = A.shape[0] + Maximum time horizon of the controllability matrix, max = A.shape[0]. Returns ------- C : 2D array (or matrix) - Controllability matrix + Controllability matrix. Examples -------- @@ -1113,14 +1114,14 @@ def obsv(A, C, t=None): Parameters ---------- A, C : array_like or string - Dynamics and output matrix of the system + Dynamics and output matrix of the system. t : None or integer - maximum time horizon of the controllability matrix, max = A.shape[0] + Maximum time horizon of the controllability matrix, max = A.shape[0]. Returns ------- O : 2D array (or matrix) - Observability matrix + Observability matrix. Examples -------- @@ -1156,16 +1157,16 @@ def gram(sys, type): Parameters ---------- sys : StateSpace - System description + System description. type : String Type of desired computation. `type` is either 'c' (controllability) or 'o' (observability). To compute the Cholesky factors of Gramians - use 'cf' (controllability) or 'of' (observability) + use 'cf' (controllability) or 'of' (observability). Returns ------- gram : 2D array (or matrix) - Gramian of system + Gramian of system. Raises ------ diff --git a/control/statesp.py b/control/statesp.py index c6696138e..665078d72 100644 --- a/control/statesp.py +++ b/control/statesp.py @@ -799,21 +799,23 @@ def __call__(self, x, squeeze=None, warn_infinite=True): return _process_frequency_response(self, x, out, squeeze=squeeze) def slycot_laub(self, x): - """Evaluate system's transfer function at complex frequency - using Laub's method from Slycot. + """Laub's method to evaluate response at complex frequency. - Expects inputs and outputs to be formatted correctly. Use ``sys(x)`` - for a more user-friendly interface. + Evaluate transfer function at complex frequency using Laub's + method from Slycot. Expects inputs and outputs to be + formatted correctly. Use ``sys(x)`` for a more user-friendly + interface. Parameters ---------- x : complex array_like or complex - Complex frequency + Complex frequency. Returns ------- output : (number_outputs, number_inputs, len(x)) complex ndarray - Frequency response + Frequency response. + """ from slycot import tb05ad @@ -1056,7 +1058,7 @@ def lft(self, other, nu=-1, ny=-1): Parameters ---------- other : LTI - The lower LTI system + The lower LTI system. ny : int, optional Dimension of (plant) measurement output. nu : int, optional @@ -1184,9 +1186,9 @@ def returnScipySignalLTI(self, strict=True): Returns ------- out : list of list of :class:`scipy.signal.StateSpace` - continuous time (inheriting from :class:`scipy.signal.lti`) + Continuous time (inheriting from :class:`scipy.signal.lti`) or discrete time (inheriting from :class:`scipy.signal.dlti`) - SISO objects + SISO objects. """ if strict and self.dt is None: raise ValueError("with strict=True, dt cannot be None") @@ -1260,7 +1262,7 @@ def __getitem__(self, key): def sample(self, Ts, method='zoh', alpha=None, prewarp_frequency=None, name=None, copy_names=True, **kwargs): - """Convert a continuous time system to discrete time + """Convert a continuous time system to discrete time. Creates a discrete-time system from a continuous-time system by sampling. Multiple methods of conversion are supported. @@ -1268,7 +1270,7 @@ def sample(self, Ts, method='zoh', alpha=None, prewarp_frequency=None, Parameters ---------- Ts : float - Sampling period + Sampling period. method : {"gbt", "bilinear", "euler", "backward_diff", "zoh"} Which method to use: @@ -1281,7 +1283,7 @@ def sample(self, Ts, method='zoh', alpha=None, prewarp_frequency=None, alpha : float within [0, 1] The generalized bilinear transformation weighting parameter, which should only be specified with method="gbt", and is ignored - otherwise + otherwise. prewarp_frequency : float within [0, infinity) The frequency [rad/s] at which to match with the input continuous- time system's magnitude and phase (the gain=1 crossover frequency, @@ -1302,7 +1304,7 @@ def sample(self, Ts, method='zoh', alpha=None, prewarp_frequency=None, Returns ------- sysd : StateSpace - Discrete-time system, with sampling rate Ts + Discrete-time system, with sampling rate Ts. Other Parameters ---------------- @@ -1348,7 +1350,7 @@ def sample(self, Ts, method='zoh', alpha=None, prewarp_frequency=None, return StateSpace(sysd, **kwargs) def dcgain(self, warn_infinite=False): - """Return the zero-frequency gain + """Return the zero-frequency gain. The zero-frequency gain of a continuous-time state-space system is given by: @@ -1405,11 +1407,11 @@ def dynamics(self, t, x, u=None, params=None): Parameters ---------- t : float (ignored) - time + Time. x : array_like - current state + Current state. u : array_like (optional) - input, zero if omitted + Input, zero if omitted. Returns ------- @@ -1433,7 +1435,7 @@ def dynamics(self, t, x, u=None, params=None): # TODO: decide if we need this function (already in NonlinearIOSystem def output(self, t, x, u=None, params=None): - """Compute the output of the system + """Compute the output of the system. Given input `u` and state `x`, returns the output `y` of the state-space system: @@ -1452,11 +1454,11 @@ def output(self, t, x, u=None, params=None): Parameters ---------- t : float (ignored) - time + Time. x : array_like - current state + Current state. u : array_like (optional) - input (zero if omitted) + Input (zero if omitted). Returns ------- @@ -1835,16 +1837,16 @@ def tf2ss(*args, **kwargs): Parameters ---------- sys : LTI (StateSpace or TransferFunction) - A linear system + A linear system. num : array_like, or list of list of array_like - Polynomial coefficients of the numerator + Polynomial coefficients of the numerator. den : array_like, or list of list of array_like - Polynomial coefficients of the denominator + Polynomial coefficients of the denominator. Returns ------- out : StateSpace - New linear system in state space form + New linear system in state space form. Other Parameters ---------------- @@ -1913,33 +1915,34 @@ def ssdata(sys): Parameters ---------- sys : LTI (StateSpace, or TransferFunction) - LTI system whose data will be returned + LTI system whose data will be returned. Returns ------- - (A, B, C, D): list of matrices - State space data for the system + A, B, C, D : list of matrices + State space data for the system. """ ss = _convert_to_statespace(sys) return ss.A, ss.B, ss.C, ss.D +# TODO: combine with sysnorm? def linfnorm(sys, tol=1e-10): - """L-infinity norm of a linear system + """L-infinity norm of a linear system. Parameters ---------- sys : LTI (StateSpace or TransferFunction) - system to evalute L-infinity norm of + System to evalute L-infinity norm of. tol : real scalar - tolerance on norm estimate + Tolerance on norm estimate. Returns ------- gpeak : non-negative scalar - L-infinity norm + L-infinity norm. fpeak : non-negative scalar - Frequency, in rad/s, at which gpeak occurs + Frequency, in rad/s, at which gpeak occurs. For stable systems, the L-infinity and H-infinity norms are equal; for unstable systems, the H-infinity norm is infinite, while the diff --git a/control/stochsys.py b/control/stochsys.py index 1dca7c448..9230333c9 100644 --- a/control/stochsys.py +++ b/control/stochsys.py @@ -39,8 +39,9 @@ def lqe(*args, **kwargs): r"""lqe(A, G, C, QN, RN, [, NN]) - Linear quadratic estimator design (Kalman filter) for continuous-time - systems. Given the system + Continuous-time linear quadratic estimator (Kalman filter). + + Given the continuous time system .. math:: @@ -73,12 +74,12 @@ def lqe(*args, **kwargs): Parameters ---------- A, G, C : 2D array_like - Dynamics, process noise (disturbance), and output matrices + Dynamics, process noise (disturbance), and output matrices. sys : LTI (StateSpace or TransferFunction) Linear I/O system, with the process noise input taken as the system input. QN, RN : 2D array_like - Process and sensor noise covariance matrices + Process and sensor noise covariance matrices. NN : 2D array, optional Cross covariance matrix. Not currently implemented. method : str, optional @@ -89,16 +90,16 @@ def lqe(*args, **kwargs): Returns ------- L : 2D array - Kalman estimator gain + Kalman estimator gain. P : 2D array - Solution to Riccati equation + Solution to Riccati equation. .. math:: A P + P A^T - (P C^T + G N) R^{-1} (C P + N^T G^T) + G Q G^T = 0 E : 1D array - Eigenvalues of estimator poles eig(A - L C) + Eigenvalues of estimator poles eig(A - L C). Notes ----- @@ -188,8 +189,9 @@ def lqe(*args, **kwargs): def dlqe(*args, **kwargs): r"""dlqe(A, G, C, QN, RN, [, N]) - Linear quadratic estimator design (Kalman filter) for discrete-time - systems. Given the system + Discrete-time linear quadratic estimator (Kalman filter). + + Given the system .. math:: @@ -212,11 +214,11 @@ def dlqe(*args, **kwargs): Parameters ---------- A, G : 2D array_like - Dynamics and noise input matrices + Dynamics and noise input matrices. QN, RN : 2D array_like - Process and sensor noise covariance matrices + Process and sensor noise covariance matrices. NN : 2D array, optional - Cross covariance matrix (not yet supported) + Cross covariance matrix (not yet supported). method : str, optional Set the method used for computing the result. Current methods are 'slycot' and 'scipy'. If set to None (default), try 'slycot' first @@ -225,16 +227,16 @@ def dlqe(*args, **kwargs): Returns ------- L : 2D array - Kalman estimator gain + Kalman estimator gain. P : 2D array - Solution to Riccati equation + Solution to Riccati equation. .. math:: A P + P A^T - (P C^T + G N) R^{-1} (C P + N^T G^T) + G Q G^T = 0 E : 1D array - Eigenvalues of estimator poles eig(A - L C) + Eigenvalues of estimator poles eig(A - L C). Examples -------- @@ -500,7 +502,7 @@ def create_estimator_iosystem( else: # Generate labels corresponding to measured values from C measurement_labels = _process_labels( - measurement_labels, 'measurement', + measurement_labels, 'measurement', [f'y[{i}]' for i in range(C.shape[0])]) control_labels = _process_labels( control_labels, 'control', diff --git a/control/tests/docstrings_test.py b/control/tests/docstrings_test.py index 9aaa669f0..2cff47a38 100644 --- a/control/tests/docstrings_test.py +++ b/control/tests/docstrings_test.py @@ -30,6 +30,7 @@ control.ControlPlot.reshape, # needed for legacy interface control.phase_plot, # legacy function control.drss, # documention in rss + control.flatsys.flatsys, # tested separately below control.frd, # tested separately below control.ss, # tested separately below control.tf, # tested separately below @@ -83,6 +84,7 @@ # Set global variables verbose = 0 # Level of verbosity (use -rP when running pytest) standalone = False # Controls how failures are treated +max_summary_len = 64 # Maximum length of a summary line module_list = [ (control, ""), (control.flatsys, "flatsys."), @@ -94,7 +96,11 @@ def test_parameter_docs(module, prefix): # Look through every object in the package _info(f"Checking module {module}", 0) for name, obj in inspect.getmembers(module): - _info(f"Checking object {name}", 4) + if getattr(obj, '__module__', None): + objname = ".".join([obj.__module__.removeprefix("control."), name]) + else: + objname = name + _info(f"Checking object {objname}", 4) # Parse the docstring using numpydoc with warnings.catch_warnings(): @@ -105,13 +111,14 @@ def test_parameter_docs(module, prefix): if inspect.getmodule(obj) is not None and \ not inspect.getmodule(obj).__name__.startswith('control'): # Skip anything that isn't part of the control package - _info(f"member '{name}' is outside `control` module", 5) + _info(f"member '{objname}' is outside `control` module", 5) continue # If this is a class, recurse through methods # TODO: check top level documenation here (__init__, attributes?) if inspect.isclass(obj): _info(f"Checking class {name}", 1) + _check_numpydoc_style(obj, doc) # Check member functions within the class test_parameter_docs(obj, prefix + name + '.') @@ -125,20 +132,23 @@ def test_parameter_docs(module, prefix): # Skip non-top-level functions without parameter lists if prefix != "" and inspect.getmodule(obj) != module and \ - doc is not None and doc["Parameters"] == []: + doc is not None and doc["Parameters"] == [] and \ + doc["Returns"] == [] and doc["Yields"] == []: _info(f"skipping {prefix}{name} (not top-level, no params)", 2) continue + _check_numpydoc_style(obj, doc) + # Add this to the list of functions we have checked checked.add(obj) # Get the docstring (skip w/ warning if there isn't one) _info(f"Checking function {name}", 2) if obj.__doc__ is None: - _warn(f"{module.__name__}.{name} is missing docstring", 2) + _warn(f"{objname} is missing docstring", 2) continue elif doc is None: - _fail(f"{module.__name__}.{name} docstring not parseable", 2) + _fail(f"{objname} docstring not parseable", 2) else: docstring = inspect.getdoc(obj) source = inspect.getsource(obj) @@ -151,11 +161,11 @@ def test_parameter_docs(module, prefix): elif re.search(name + r"(\(\))? is deprecated", doc_extended) or \ "function is deprecated" in doc_extended: _info(" [deprecated, but not numpydoc compliant]", 2) - _warn(f"{name} deprecated, but not numpydoc compliant", 0) + _warn(f"{objname} deprecated, but not numpydoc compliant", 0) continue elif re.search(name + r"(\(\))? is deprecated", source): - _warn(f"{name} is deprecated, but not documented", 1) + _warn(f"{objname} is deprecated, but not documented", 1) continue # Get the signature for the function @@ -176,7 +186,7 @@ def test_parameter_docs(module, prefix): (docstring + source).encode('utf-8')).hexdigest() if function_docstring_hash[obj] != hash: _fail( - f"source/docstring for {name}() modified; " + f"source/docstring for {objname}() modified; " f"recheck docstring and update hash to " f"{hash=}") continue @@ -184,7 +194,7 @@ def test_parameter_docs(module, prefix): # Too complicated to check if f"*{argname}" not in docstring: _warn( - f"{name} {argname} has positional arguments; " + f"{objname} {argname} has positional arguments; " "docstring not checked") continue @@ -228,7 +238,7 @@ def test_parameter_docs(module, prefix): else: _info(f"Checking argument {argname}", 3) _check_parameter_docs( - name, argname, docstring, prefix=prefix) + objname, argname, docstring, prefix=prefix) # Look at the return values for val in doc["Returns"]: @@ -275,7 +285,7 @@ def test_deprecated_functions(module, prefix): # Get the docstring (skip w/ warning if there isn't one) if obj.__doc__ is None: - _warn(f"{module.__name__}.{name} is missing docstring") + _warn(f"{objname} is missing docstring") continue else: docstring = inspect.getdoc(obj) @@ -286,12 +296,14 @@ def test_deprecated_functions(module, prefix): if ".. deprecated::" in doc_extended: # Make sure a FutureWarning is issued if not re.search("FutureWarning", source): - _fail(f"{name} deprecated but does not issue FutureWarning") + _fail(f"{objname} deprecated but does not issue " + "FutureWarning") else: if re.search(name + r"(\(\))? is deprecated", docstring) or \ re.search(name + r"(\(\))? is deprecated", source): _fail( - f"{name} deprecated but w/ non-standard docs/warnings") + f"{objname} deprecated but with non-standard " + "docs/warnings") # # Tests for I/O system classes @@ -391,6 +403,7 @@ def test_iosys_primary_classes(cls, fcn, args): with warnings.catch_warnings(): warnings.simplefilter('ignore') # debug via sphinx, not here doc = npd.FunctionDoc(cls) + _check_numpydoc_style(cls, doc) # Make sure the typical arguments are there for argname in args + std_class_attributes + class_attributes[cls]: @@ -436,6 +449,11 @@ def test_iosys_attribute_lists(cls, ignore_future_warning): docstring = inspect.getdoc(cls) for name, obj in inspect.getmembers(sys): + if getattr(obj, '__module__', None): + objname = ".".join([obj.__module__.removeprefix("control."), name]) + else: + objname = name + if name.startswith('_') or inspect.ismethod(obj) or name in ignore_args: # Skip hidden variables; class methods are checked elsewhere continue @@ -468,6 +486,11 @@ def test_iosys_container_classes(cls): sys = cls(states=2, outputs=1, inputs=1) docstring = inspect.getdoc(cls) + with warnings.catch_warnings(): + warnings.simplefilter('ignore') # debug via sphinx, not here + doc = npd.FunctionDoc(cls) + _check_numpydoc_style(cls, doc) + for name, obj in inspect.getmembers(sys): if name.startswith('_') or inspect.ismethod(obj): # Skip hidden variables; class methods are checked elsewhere @@ -491,13 +514,19 @@ def test_iosys_container_classes(cls): @pytest.mark.parametrize("cls", [ct.InterconnectedSystem, ct.LinearICSystem]) def test_iosys_intermediate_classes(cls): docstring = inspect.getdoc(cls) + with warnings.catch_warnings(): + warnings.simplefilter('ignore') # debug via sphinx, not here + doc = npd.FunctionDoc(cls) + _check_numpydoc_style(cls, doc) # Make sure there is not a parameters section + # TODO: replace with numpdoc check if re.search(r"\nParameters\n----", docstring) is not None: _fail(f"intermediate {cls} docstring contains Parameters section") return # Make sure we reference the factory function + # TODO: check extended summary fcn = class_factory_function[cls] if re.search(f":func:`~control.{fcn.__name__}`", docstring) is None: _fail( @@ -508,6 +537,11 @@ def test_iosys_intermediate_classes(cls): @pytest.mark.parametrize("fcn", factory_args.keys()) def test_iosys_factory_functions(fcn): docstring = inspect.getdoc(fcn) + with warnings.catch_warnings(): + warnings.simplefilter('ignore') # debug via sphinx, not here + doc = npd.FunctionDoc(fcn) + _check_numpydoc_style(fcn, doc) + cls = list(class_factory_function.keys())[ list(class_factory_function.values()).index(fcn)] @@ -582,6 +616,56 @@ def _check_parameter_docs( return True +# Utility function to check numpydoc style consistency +def _check_numpydoc_style(obj, doc): + name = ".".join([obj.__module__.removeprefix("control."), obj.__name__]) + + # Standard checks for all objects + summary = "".join(doc["Summary"]) + if len(doc["Summary"]) > 1: + _warn(f"{name} summary is more than one line") + if summary and summary[-1] != '.' and re.match(":$", summary) is None: + _warn(f"{name} summary doesn't end in period") + if summary[0:1].islower(): + _warn(f"{name} summary starts with lower case letter") + if len(summary) > max_summary_len: + _warn(f"{name} summary is longer than {max_summary_len} characters") + + if inspect.isclass(obj): + # Specialized checks for classes + pass + elif inspect.isfunction(obj): + # Specialized checks for functions + def _check_param(param, empty_ok=False, noname_ok=False): + param_desc = "\n".join(param.desc) + param_name = f"{name} '{param.name}'" + + # Make sure we have a name and description + if param.name == "" and not noname_ok: + _fail(f"{param_name} has improperly formatted parameter") + return + elif param_desc == "": + if not empty_ok: + _warn(f"{param_name} isn't documented") + return + + # Description should end in a period (colon also allowed) + if re.search(r"\.$|\.[\s]|:$", param_desc, re.MULTILINE) is None: + _warn(f"{param_name} description doesn't contain period") + + if param_desc[0:1].islower(): + _warn(f"{param_name} description starts with lower case letter") + + for param in doc["Parameters"] + doc["Other Parameters"]: + _check_param(param) + for param in doc["Returns"]: + _check_param(param, empty_ok=True, noname_ok=True) + for param in doc["Yields"]: + _check_param(param, empty_ok=True, noname_ok=True) + else: + raise TypeError("unknown object type for {obj}") + + # Utility function to warn with verbose output def _info(str, level): if verbose > level: @@ -660,35 +744,35 @@ def test_check_parameter_docs(docstring, exception, match): if __name__ == "__main__": verbose = 0 if len(sys.argv) == 1 else int(sys.argv[1]) standalone = True - + for module, prefix in module_list: - print(f"--- test_parameter_docs(): {module.__name__} ----") + _info(f"--- test_parameter_docs(): {module.__name__} ----", 0) test_parameter_docs(module, prefix) for module, prefix in module_list: - print(f"--- test_deprecated_functions(): {module.__name__} ----") + _info(f"--- test_deprecated_functions(): {module.__name__} ----", 0) test_deprecated_functions for cls, fcn, args in [ (cls, class_factory_function[cls], class_args[cls]) for cls in class_args.keys()]: - print(f"--- test_iosys_primary_classes(): {cls.__name__} ----") + _info(f"--- test_iosys_primary_classes(): {cls.__name__} ----", 0) test_iosys_primary_classes(cls, fcn, args) for cls in class_args.keys(): - print(f"--- test_iosys_attribute_lists(): {cls.__name__} ----") + _info(f"--- test_iosys_attribute_lists(): {cls.__name__} ----", 0) with warnings.catch_warnings(): warnings.simplefilter('ignore', FutureWarning) test_iosys_attribute_lists(cls, None) for cls in [ct.InputOutputSystem, ct.LTI]: - print(f"--- test_iosys_container_classes(): {cls.__name__} ----") + _info(f"--- test_iosys_container_classes(): {cls.__name__} ----", 0) test_iosys_container_classes(cls) for cls in [ct.InterconnectedSystem, ct.LinearICSystem]: - print(f"--- test_iosys_intermediate_classes(): {cls.__name__} ----") + _info(f"--- test_iosys_intermediate_classes(): {cls.__name__} ----", 0) test_iosys_intermediate_classes(cls) for fcn in factory_args.keys(): - print(f"--- test_iosys_factory_functions(): {fcn.__name__} ----") + _info(f"--- test_iosys_factory_functions(): {fcn.__name__} ----", 0) test_iosys_factory_functions(fcn) diff --git a/control/timeplot.py b/control/timeplot.py index 6fde8c67a..ac7ceefb8 100644 --- a/control/timeplot.py +++ b/control/timeplot.py @@ -671,7 +671,7 @@ def _make_line_label(signal_index, signal_labels, trace_index): def combine_time_responses(response_list, trace_labels=None, title=None): - """Combine multiple individual time responses into a multi-trace response. + """Combine individual time responses into multi-trace response. This function combines multiple instances of :class:`TimeResponseData` into a multi-trace :class:`TimeResponseData` object. diff --git a/control/timeresp.py b/control/timeresp.py index e62812634..916ddc5a8 100644 --- a/control/timeresp.py +++ b/control/timeresp.py @@ -768,7 +768,9 @@ def plot(self, *args, **kwargs): # class TimeResponseList(list): - """This class consist of a list of :class:`TimeResponseData` objects. + """List of TimeResponseData objects with plotting capability. + + This class consists of a list of :class:`TimeResponseData` objects. It is a subclass of the Python `list` class, with a `plot` method that plots the individual :class:`TimeResponseData` objects. @@ -1534,7 +1536,7 @@ def step_response( def step_info(sysdata, T=None, T_num=None, yfinal=None, params=None, SettlingTimeThreshold=0.02, RiseTimeLimits=(0.1, 0.9)): """ - Step response characteristics (Rise time, Settling Time, Peak and others). + Step response characteristics (rise time, settling time, etc). Parameters ---------- @@ -1557,9 +1559,9 @@ def step_info(sysdata, T=None, T_num=None, yfinal=None, params=None, params : dict, optional If system is a nonlinear I/O system, set parameter values. SettlingTimeThreshold : float, optional - Defines the error to compute settling time (default = 0.02) + Defines the error to compute settling time (default = 0.02). RiseTimeLimits : tuple (lower_threshold, upper_theshold) - Defines the lower and upper threshold for RiseTime computation + Defines the lower and upper threshold for RiseTime computation. Returns ------- @@ -1777,11 +1779,11 @@ def initial_response( I/O system(s) for which initial response is computed. sys : StateSpace or TransferFunction - LTI system to simulate + LTI system to simulate. T : array_like or float, optional Time vector, or simulation time duration if a number (time vector is - autocomputed if not given; see :func:`step_response` for more detail) + autocomputed if not given; see :func:`step_response` for more detail). X0 : array_like or float, optional Initial condition (default = 0). Numbers are converted to constant @@ -1898,7 +1900,7 @@ def impulse_response( T : array_like or float, optional Time vector, or simulation time duration if a scalar (time vector is - autocomputed if not given; see :func:`step_response` for more detail) + autocomputed if not given; see :func:`step_response` for more detail). input : int, optional Only compute the impulse response for the listed input. If not diff --git a/control/xferfcn.py b/control/xferfcn.py index d66535953..c96941796 100644 --- a/control/xferfcn.py +++ b/control/xferfcn.py @@ -948,9 +948,9 @@ def returnScipySignalLTI(self, strict=True): Returns ------- out : list of list of :class:`scipy.signal.TransferFunction` - continuous time (inheriting from :class:`scipy.signal.lti`) + Continuous time (inheriting from :class:`scipy.signal.lti`) or discrete time (inheriting from :class:`scipy.signal.dlti`) - SISO objects + SISO objects. """ if strict and self.dt is None: raise ValueError("with strict=True, dt cannot be None") @@ -1146,7 +1146,7 @@ def sample(self, Ts, method='zoh', alpha=None, prewarp_frequency=None, Parameters ---------- Ts : float - Sampling period + Sampling period. method : {"gbt", "bilinear", "euler", "backward_diff", "zoh", "matched"} Method to use for sampling: @@ -1180,7 +1180,7 @@ def sample(self, Ts, method='zoh', alpha=None, prewarp_frequency=None, Returns ------- sysd : TransferFunction system - Discrete-time system, with sample period Ts + Discrete-time system, with sample period Ts. Other Parameters ---------------- @@ -1820,22 +1820,22 @@ def ss2tf(*args, **kwargs): Parameters ---------- sys : StateSpace - A linear system + A linear system. A : array_like or string - System matrix + System matrix. B : array_like or string - Control matrix + Control matrix. C : array_like or string - Output matrix + Output matrix. D : array_like or string - Feedthrough matrix + Feedthrough matrix. **kwargs : keyword arguments - Additional arguments passed to :func:`tf` (e.g., signal names) + Additional arguments passed to :func:`tf` (e.g., signal names). Returns ------- out : TransferFunction - New linear system in transfer function form + New linear system in transfer function form. Other Parameters ---------------- @@ -1906,12 +1906,12 @@ def tfdata(sys): Parameters ---------- sys : LTI (StateSpace, or TransferFunction) - LTI system whose data will be returned + LTI system whose data will be returned. Returns ------- (num, den): numerator and denominator arrays - Transfer function coefficients (SISO only) + Transfer function coefficients (SISO only). """ tf = _convert_to_transfer_function(sys) From b360c2a53e4040c93200d7a898b160aca556cebc Mon Sep 17 00:00:00 2001 From: Richard Murray Date: Sun, 2 Feb 2025 20:27:07 -0800 Subject: [PATCH 09/93] isort -m2 on touched files --- control/canonical.py | 14 ++++++-------- control/ctrlutil.py | 8 +++++--- control/exception.py | 6 +++--- control/flatsys/bezier.py | 2 ++ control/flatsys/bspline.py | 4 +++- control/flatsys/flatsys.py | 12 +++++++----- control/flatsys/linflat.py | 4 +++- control/flatsys/poly.py | 2 ++ control/flatsys/systraj.py | 2 ++ control/frdata.py | 6 ++---- control/lti.py | 13 +++++++------ control/margins.py | 8 ++++---- control/mateqn.py | 6 +++--- control/nlsys.py | 6 +++--- control/optimal.py | 12 ++++++------ control/passivity.py | 1 + control/robust.py | 6 ++++-- control/statesp.py | 17 ++++++++--------- control/stochsys.py | 12 ++++++------ control/sysnorm.py | 3 ++- control/tests/docstrings_test.py | 4 ++-- control/timeplot.py | 2 +- control/xferfcn.py | 10 ++++------ 23 files changed, 86 insertions(+), 74 deletions(-) diff --git a/control/canonical.py b/control/canonical.py index 6462fb782..fee7ab52b 100644 --- a/control/canonical.py +++ b/control/canonical.py @@ -1,17 +1,15 @@ # canonical.py - functions for converting systems to canonical forms # RMM, 10 Nov 2012 +import numpy as np +from numpy import poly, transpose, zeros_like +from numpy.linalg import matrix_rank, solve +from scipy.linalg import schur + from .exception import ControlNotImplemented, ControlSlycot from .iosys import issiso -from .statesp import StateSpace, _convert_to_statespace from .statefbk import ctrb, obsv - -import numpy as np - -from numpy import zeros_like, poly, transpose -from numpy.linalg import solve, matrix_rank - -from scipy.linalg import schur +from .statesp import StateSpace, _convert_to_statespace __all__ = ['canonical_form', 'reachable_form', 'observable_form', 'modal_form', 'similarity_transform', 'bdschur'] diff --git a/control/ctrlutil.py b/control/ctrlutil.py index ce51e00ef..c9e1c471b 100644 --- a/control/ctrlutil.py +++ b/control/ctrlutil.py @@ -40,12 +40,14 @@ # # $Id$ -# Packages that we need access to -from . import lti -import numpy as np import math import warnings +import numpy as np + +# Packages that we need access to +from . import lti + __all__ = ['unwrap', 'issys', 'db2mag', 'mag2db'] # Utility function to unwrap an angle measurement diff --git a/control/exception.py b/control/exception.py index 2e5f8b117..95dfc3ef7 100644 --- a/control/exception.py +++ b/control/exception.py @@ -70,7 +70,7 @@ def slycot_check(): global slycot_installed if slycot_installed is None: try: - import slycot # noqa: F401 + import slycot # noqa: F401 slycot_installed = True except: slycot_installed = False @@ -84,7 +84,7 @@ def pandas_check(): global pandas_installed if pandas_installed is None: try: - import pandas # noqa: F401 + import pandas # noqa: F401 pandas_installed = True except: pandas_installed = False @@ -97,7 +97,7 @@ def cvxopt_check(): global cvxopt_installed if cvxopt_installed is None: try: - import cvxopt # noqa: F401 + import cvxopt # noqa: F401 cvxopt_installed = True except: cvxopt_installed = False diff --git a/control/flatsys/bezier.py b/control/flatsys/bezier.py index fcf6201e9..b3de70dff 100644 --- a/control/flatsys/bezier.py +++ b/control/flatsys/bezier.py @@ -40,8 +40,10 @@ import numpy as np from scipy.special import binom, factorial + from .basis import BasisFamily + class BezierFamily(BasisFamily): r"""Bezier curve basis functions. diff --git a/control/flatsys/bspline.py b/control/flatsys/bspline.py index d42cb4074..f580834bf 100644 --- a/control/flatsys/bspline.py +++ b/control/flatsys/bspline.py @@ -7,9 +7,11 @@ # import numpy as np -from .basis import BasisFamily from scipy.interpolate import BSpline +from .basis import BasisFamily + + class BSplineFamily(BasisFamily): """B-spline basis functions. diff --git a/control/flatsys/flatsys.py b/control/flatsys/flatsys.py index 855f8e3ee..86cfd997d 100644 --- a/control/flatsys/flatsys.py +++ b/control/flatsys/flatsys.py @@ -2,16 +2,18 @@ # RMM, 10 Nov 2012 import itertools +import warnings + import numpy as np import scipy as sp import scipy.optimize -import warnings -from .poly import PolyFamily -from .systraj import SystemTrajectory -from ..exception import ControlArgument + from ..config import _process_legacy_keyword +from ..exception import ControlArgument from ..nlsys import NonlinearIOSystem from ..timeresp import _check_convert_array +from .poly import PolyFamily +from .systraj import SystemTrajectory # Flat system class (for use as a base class) @@ -245,8 +247,8 @@ def flatsys(*args, updfcn=None, outfcn=None, **kwargs): 'u', 'y', 'x'. """ - from .linflat import LinearFlatSystem from ..statesp import StateSpace + from .linflat import LinearFlatSystem if len(args) == 1 and isinstance(args[0], StateSpace): # We were passed a linear system, so call linflat diff --git a/control/flatsys/linflat.py b/control/flatsys/linflat.py index e03df514d..6996b8a92 100644 --- a/control/flatsys/linflat.py +++ b/control/flatsys/linflat.py @@ -36,9 +36,11 @@ # SUCH DAMAGE. import numpy as np + import control -from .flatsys import FlatSystem + from ..statesp import StateSpace +from .flatsys import FlatSystem class LinearFlatSystem(FlatSystem, StateSpace): diff --git a/control/flatsys/poly.py b/control/flatsys/poly.py index f315091aa..a24bf6fde 100644 --- a/control/flatsys/poly.py +++ b/control/flatsys/poly.py @@ -39,8 +39,10 @@ import numpy as np from scipy.special import factorial + from .basis import BasisFamily + class PolyFamily(BasisFamily): r"""Polynomial basis functions. diff --git a/control/flatsys/systraj.py b/control/flatsys/systraj.py index 902c07ecb..21d493557 100644 --- a/control/flatsys/systraj.py +++ b/control/flatsys/systraj.py @@ -37,8 +37,10 @@ # SUCH DAMAGE. import numpy as np + from ..timeresp import TimeResponseData + class SystemTrajectory: """Class representing a trajectory for a flat system. diff --git a/control/frdata.py b/control/frdata.py index f64e9f46b..baa2513dc 100644 --- a/control/frdata.py +++ b/control/frdata.py @@ -15,12 +15,10 @@ from warnings import warn import numpy as np -from numpy import absolute, array, empty, eye, imag, linalg, ones, \ - real, sort +from numpy import absolute, array, empty, eye, imag, linalg, ones, real, sort from scipy.interpolate import splev, splprep -from . import config -from . import bdalg +from . import bdalg, config from .exception import pandas_check from .iosys import InputOutputSystem, NamedSignal, _extended_system_name, \ _process_iosys_keywords, _process_subsys_index, common_timebase diff --git a/control/lti.py b/control/lti.py index c5440b5fd..9c08bdfbe 100644 --- a/control/lti.py +++ b/control/lti.py @@ -4,16 +4,17 @@ and TransferFunction. It is designed for use in the python-control library. """ -import numpy as np import math - -# todo: override built-in abs -from numpy import real, abs +from typing import Callable from warnings import warn + +import numpy as np +from numpy import abs, real + +import control + from . import config from .iosys import InputOutputSystem -import control -from typing import Callable __all__ = ['poles', 'zeros', 'damp', 'evalfr', 'frequency_response', 'freqresp', 'dcgain', 'bandwidth', 'LTI'] diff --git a/control/margins.py b/control/margins.py index 95e86a764..370bc3300 100644 --- a/control/margins.py +++ b/control/margins.py @@ -49,13 +49,13 @@ import math from warnings import warn + import numpy as np import scipy as sp -from . import xferfcn -from .iosys import issiso -from . import frdata -from . import freqplot + +from . import frdata, freqplot, xferfcn from .exception import ControlMIMONotImplemented +from .iosys import issiso __all__ = ['stability_margins', 'phase_crossover_frequencies', 'margin'] diff --git a/control/mateqn.py b/control/mateqn.py index c38b0ff3e..f16b5aa9c 100644 --- a/control/mateqn.py +++ b/control/mateqn.py @@ -36,13 +36,13 @@ # SUCH DAMAGE. import warnings -import numpy as np -from numpy import eye, finfo, inexact +import numpy as np import scipy as sp +from numpy import eye, finfo, inexact from scipy.linalg import eigvals, solve -from .exception import ControlSlycot, ControlArgument, ControlDimension, \ +from .exception import ControlArgument, ControlDimension, ControlSlycot, \ slycot_check # Make sure we have access to the right slycot routines diff --git a/control/nlsys.py b/control/nlsys.py index 08898686c..a1471ec9c 100644 --- a/control/nlsys.py +++ b/control/nlsys.py @@ -26,8 +26,8 @@ from . import config from .iosys import InputOutputSystem, _parse_spec, _process_iosys_keywords, \ common_timebase, iosys_repr, isctime, isdtime -from .timeresp import _check_convert_array, _process_time_response, \ - TimeResponseData, TimeResponseList +from .timeresp import TimeResponseData, TimeResponseList, \ + _check_convert_array, _process_time_response __all__ = ['NonlinearIOSystem', 'InterconnectedSystem', 'nlsys', 'input_output_response', 'find_eqpt', 'linearize', @@ -1372,8 +1372,8 @@ def nlsys(updfcn, outfcn=None, **kwargs): ... kincar, timepts, [10, 0.05 * np.sin(timepts)]) """ - from .statesp import StateSpace from .iosys import _extended_system_name + from .statesp import StateSpace if isinstance(updfcn, StateSpace): sys_ss = updfcn diff --git a/control/optimal.py b/control/optimal.py index 923c6440c..ae64e346a 100644 --- a/control/optimal.py +++ b/control/optimal.py @@ -14,18 +14,18 @@ """ +import logging +import time +import warnings + import numpy as np import scipy as sp import scipy.optimize as opt + import control as ct -import warnings -import logging -import time from . import config -from .iosys import _process_labels, \ - _process_control_disturbance_indices - +from .iosys import _process_control_disturbance_indices, _process_labels # Define module default parameter values _optimal_trajectory_methods = {'shooting', 'collocation'} diff --git a/control/passivity.py b/control/passivity.py index a2d5b5bf0..3f2b2ea22 100644 --- a/control/passivity.py +++ b/control/passivity.py @@ -6,6 +6,7 @@ """ import numpy as np + from control import statesp from control.exception import ControlArgument, ControlDimension diff --git a/control/robust.py b/control/robust.py index 6dc32bda0..d2fff7c0a 100644 --- a/control/robust.py +++ b/control/robust.py @@ -39,9 +39,11 @@ # # $Id$ +import warnings + # External packages and modules import numpy as np -import warnings + from .exception import ControlSlycot from .statesp import StateSpace @@ -286,7 +288,7 @@ def augw(g, w1=None, w2=None, w3=None): h2syn, hinfsyn, mixsyn """ - from . import append, ss, connect + from . import append, connect, ss if w1 is None and w2 is None and w3 is None: raise ValueError("At least one weighting must not be None") diff --git a/control/statesp.py b/control/statesp.py index 665078d72..4f3964757 100644 --- a/control/statesp.py +++ b/control/statesp.py @@ -22,26 +22,25 @@ import scipy as sp import scipy.linalg # array needed in eval() call -from numpy import array # noqa: F401 -from numpy import any, asarray, concatenate, cos, delete, empty, \ - exp, eye, isinf, pad, sin, squeeze, zeros +from numpy import array # noqa: F401 +from numpy import any, asarray, concatenate, cos, delete, empty, exp, eye, \ + isinf, pad, sin, squeeze, zeros from numpy.linalg import LinAlgError, eigvals, matrix_rank, solve from numpy.random import rand, randn from scipy.signal import StateSpace as signalStateSpace from scipy.signal import cont2discrete -from . import config -from . import bdalg +import control + +from . import bdalg, config from .exception import ControlDimension, ControlMIMONotImplemented, \ ControlSlycot, slycot_check from .frdata import FrequencyResponseData -from .iosys import InputOutputSystem, NamedSignal, \ - _process_iosys_keywords, _process_signal_list, _process_subsys_index, \ - common_timebase, issiso +from .iosys import InputOutputSystem, NamedSignal, _process_iosys_keywords, \ + _process_signal_list, _process_subsys_index, common_timebase, issiso from .lti import LTI, _process_frequency_response from .mateqn import _check_shape from .nlsys import InterconnectedSystem, NonlinearIOSystem -import control try: from slycot import ab13dd diff --git a/control/stochsys.py b/control/stochsys.py index 9230333c9..28dcf2490 100644 --- a/control/stochsys.py +++ b/control/stochsys.py @@ -17,19 +17,19 @@ __email__ = "murray@cds.caltech.edu" import warnings +from math import sqrt import numpy as np import scipy as sp -from math import sqrt +from .config import _process_legacy_keyword +from .exception import ControlArgument, ControlNotImplemented +from .iosys import _process_control_disturbance_indices, _process_labels, \ + isctime, isdtime from .lti import LTI -from .iosys import (isctime, isdtime, _process_labels, - _process_control_disturbance_indices) +from .mateqn import _check_shape, care, dare from .nlsys import NonlinearIOSystem -from .mateqn import care, dare, _check_shape from .statesp import StateSpace -from .exception import ControlArgument, ControlNotImplemented -from .config import _process_legacy_keyword __all__ = ['lqe', 'dlqe', 'create_estimator_iosystem', 'white_noise', 'correlation'] diff --git a/control/sysnorm.py b/control/sysnorm.py index 680bb4b15..0bf92bcfe 100644 --- a/control/sysnorm.py +++ b/control/sysnorm.py @@ -11,9 +11,10 @@ Author: Henrik Sandberg """ +import warnings + import numpy as np import numpy.linalg as la -import warnings import control as ct diff --git a/control/tests/docstrings_test.py b/control/tests/docstrings_test.py index 2cff47a38..4e63abd1e 100644 --- a/control/tests/docstrings_test.py +++ b/control/tests/docstrings_test.py @@ -15,12 +15,12 @@ # desired (0 = only warnings/errors, 10 = everything). import inspect +import re import sys import warnings -import pytest -import re import numpydoc.docscrape as npd +import pytest import control import control.flatsys diff --git a/control/timeplot.py b/control/timeplot.py index ac7ceefb8..25417dcf3 100644 --- a/control/timeplot.py +++ b/control/timeplot.py @@ -15,7 +15,7 @@ import numpy as np from . import config -from .ctrlplot import ControlPlot, _make_legend_labels,\ +from .ctrlplot import ControlPlot, _make_legend_labels, \ _process_legend_keywords, _update_plot_title __all__ = ['time_response_plot', 'combine_time_responses'] diff --git a/control/xferfcn.py b/control/xferfcn.py index c96941796..902b2209b 100644 --- a/control/xferfcn.py +++ b/control/xferfcn.py @@ -23,15 +23,13 @@ import numpy as np import scipy as sp # float64 needed in eval() call -from numpy import float64 # noqa: F401 -from numpy import array, delete, empty, exp, finfo, ndarray, \ - nonzero, ones, poly, polyadd, polymul, polyval, real, roots, sqrt, \ - where, zeros +from numpy import float64 # noqa: F401 +from numpy import array, delete, empty, exp, finfo, ndarray, nonzero, ones, \ + poly, polyadd, polymul, polyval, real, roots, sqrt, where, zeros from scipy.signal import TransferFunction as signalTransferFunction from scipy.signal import cont2discrete, tf2zpk, zpk2tf -from . import config -from . import bdalg +from . import bdalg, config from .exception import ControlMIMONotImplemented from .frdata import FrequencyResponseData from .iosys import InputOutputSystem, NamedSignal, _process_iosys_keywords, \ From 884e86516c864ac33a6a007f83301e24e18c5662 Mon Sep 17 00:00:00 2001 From: Richard Murray Date: Sun, 2 Feb 2025 19:04:52 -0800 Subject: [PATCH 10/93] use numpydoc to capture function signature for var_positional args --- control/bdalg.py | 14 ++-- control/freqplot.py | 4 +- control/margins.py | 6 +- control/stochsys.py | 4 +- control/tests/docstrings_test.py | 112 +++++++++++++++++++------------ 5 files changed, 84 insertions(+), 56 deletions(-) diff --git a/control/bdalg.py b/control/bdalg.py index a040ef5fb..365a414e6 100644 --- a/control/bdalg.py +++ b/control/bdalg.py @@ -69,8 +69,8 @@ 'combine_tf', 'split_tf'] -def series(sys1, *sysn, **kwargs): - r"""series(sys1, sys2, [..., sysn]) +def series(*sys, **kwargs): + r"""series(sys1, sys2[, ..., sysn]) Return series connection (`sysn` \* ...\ \*) `sys2` \* `sys1`. @@ -136,13 +136,13 @@ def series(sys1, *sysn, **kwargs): (2, 1, 5) """ - sys = reduce(lambda x, y: y * x, sysn, sys1) + sys = reduce(lambda x, y: y * x, sys[1:], sys[0]) sys.update_names(**kwargs) return sys -def parallel(sys1, *sysn, **kwargs): - r"""parallel(sys1, sys2, [..., sysn]) +def parallel(*sys, **kwargs): + r"""parallel(sys1, sys2[, ..., sysn]) Return parallel connection `sys1` + `sys2` (+ ...\ + `sysn`). @@ -206,7 +206,7 @@ def parallel(sys1, *sysn, **kwargs): (3, 4, 7) """ - sys = reduce(lambda x, y: x + y, sysn, sys1) + sys = reduce(lambda x, y: x + y, sys[1:], sys[0]) sys.update_names(**kwargs) return sys @@ -354,7 +354,7 @@ def feedback(sys1, sys2=1, sign=-1, **kwargs): return sys def append(*sys, **kwargs): - """append(sys1, sys2, [..., sysn]) + """append(sys1, sys2[, ..., sysn]) Group LTI models by appending their inputs and outputs. diff --git a/control/freqplot.py b/control/freqplot.py index 0b714a363..5512ab8a4 100644 --- a/control/freqplot.py +++ b/control/freqplot.py @@ -2172,7 +2172,9 @@ def gangof4_response( def gangof4_plot( *args, omega=None, omega_limits=None, omega_num=None, Hz=False, **kwargs): - """Plot response of "Gang of 4" transfer functions. + """gangof4_plot(response) | gangof4_plot(P, C, omega) + + Plot response of "Gang of 4" transfer functions. Plots a 2x2 frequency response for the "Gang of 4" sensitivity functions [T, PS; CS, S]. Can be called in one of two ways: diff --git a/control/margins.py b/control/margins.py index 370bc3300..528191002 100644 --- a/control/margins.py +++ b/control/margins.py @@ -507,11 +507,13 @@ def phase_crossover_frequencies(sys): def margin(*args): - """margin(sysdata) + """ + margin(sys) \ + margin(mag, phase, omega) Gain and phase margins and associated crossover frequencies. - Can be called as ``margin(sys)`` where ``sys`` is a SISO LTI sytem or + Can be called as ``margin(sys)`` where ``sys`` is a SISO LTI system or ``margin(mag, phase, omega)``. Parameters diff --git a/control/stochsys.py b/control/stochsys.py index 28dcf2490..dd726f4a8 100644 --- a/control/stochsys.py +++ b/control/stochsys.py @@ -213,8 +213,8 @@ def dlqe(*args, **kwargs): Parameters ---------- - A, G : 2D array_like - Dynamics and noise input matrices. + A, G, C : 2D array_like + Dynamics, process noise (disturbance), and output matrices. QN, RN : 2D array_like Process and sensor noise covariance matrices. NN : 2D array, optional diff --git a/control/tests/docstrings_test.py b/control/tests/docstrings_test.py index 4e63abd1e..790c7b070 100644 --- a/control/tests/docstrings_test.py +++ b/control/tests/docstrings_test.py @@ -36,23 +36,6 @@ control.tf, # tested separately below ] -# Checksums to use for checking whether a docstring has changed -function_docstring_hash = { - control.append: 'fad0bd0766754c3524e0ea27b06bf74a', - control.describing_function_plot: '95f894706b1d3eeb3b854934596af09f', - control.dlqe: 'e1e9479310e4e5a6f50f5459fb3d2dfb', - control.dlqr: '56d7f3a452bc8d7a7256a52d9d1dcb37', - control.lqe: '0447235d11b685b9dfaf485dd01fdb9a', - control.lqr: 'a3e0a85f781fc9c0f69a4b7da4f0bd22', - control.margin: 'f02b3034f5f1d44ce26f916cc3e51600', - control.parallel: 'bfc470aef75dbb923f9c6fb8bf3c9b43', - control.series: '9aede1459667738f05cf4fc46603a4f6', - control.ss2tf: 'e779b8d70205bc1218cc2a4556a66e4b', - control.tf2ss: '086a3692659b7321c2af126f79f4bc11', - control.markov: 'a4199c54cb50f07c0163d3790739eafe', - control.gangof4: 'f9673ae4c6d26c202060ed4b9ef54800', -} - # List of keywords that we can skip testing (special cases) keyword_skiplist = { control.input_output_response: ['method'], @@ -171,6 +154,9 @@ def test_parameter_docs(module, prefix): # Get the signature for the function sig = inspect.signature(obj) + # If first argument is *args, try to use docstring instead + sig = _replace_var_positional_with_docstring(sig, doc) + # Go through each parameter and make sure it is in the docstring for argname, par in sig.parameters.items(): # Look for arguments that we can skip @@ -180,22 +166,11 @@ def test_parameter_docs(module, prefix): # Check for positional arguments (*arg) if par.kind == inspect.Parameter.VAR_POSITIONAL: - if obj in function_docstring_hash: - import hashlib - hash = hashlib.md5( - (docstring + source).encode('utf-8')).hexdigest() - if function_docstring_hash[obj] != hash: - _fail( - f"source/docstring for {objname}() modified; " - f"recheck docstring and update hash to " - f"{hash=}") - continue - - # Too complicated to check if f"*{argname}" not in docstring: - _warn( - f"{objname} {argname} has positional arguments; " - "docstring not checked") + _fail( + f"{objname} has undocumented, unbound positional " + f"argument '{argname}'; " + "use docstring signature instead") continue # Check for keyword arguments (then look at code for parsing) @@ -621,7 +596,7 @@ def _check_numpydoc_style(obj, doc): name = ".".join([obj.__module__.removeprefix("control."), obj.__name__]) # Standard checks for all objects - summary = "".join(doc["Summary"]) + summary = "\n".join(doc["Summary"]) if len(doc["Summary"]) > 1: _warn(f"{name} summary is more than one line") if summary and summary[-1] != '.' and re.match(":$", summary) is None: @@ -666,6 +641,55 @@ def _check_param(param, empty_ok=False, noname_ok=False): raise TypeError("unknown object type for {obj}") +# Utility function to replace positional signature with docstring signature +def _replace_var_positional_with_docstring(sig, doc): + # If no documentation is available, there is nothing we can do... + if doc is None: + return sig + + # Check to see if the first argument is positional + parameter_items = iter(sig.parameters.items()) + try: + argname, par = next(parameter_items) + if par.kind != inspect.Parameter.VAR_POSITIONAL or \ + (signature := doc["Signature"]) == '': + return sig + except StopIteration: + return sig + + # Try parsing the docstring signature + arg_list = [] + while (1): + if (match_fcn := re.match( + r"^([\s]*\|[\s]*)*[\w]+\(", signature)) is None: + break + arg_idx = match_fcn.span(0)[1] + while (1): + match_arg = re.match( + r"[\s]*([\w]+)(,|,\[|\[,|\)|\]\))(,[\s]*|[\s]*[.]{3},[\s]*)*", + signature[arg_idx:]) + if match_arg is None: + break + else: + arg_idx += match_arg.span(0)[1] + arg_list.append(match_arg.group(1)) + signature = signature[arg_idx:] + if arg_list == []: + return sig + + # Create the new parameter list + parameter_list = [ + inspect.Parameter(arg, inspect.Parameter.POSITIONAL_ONLY) + for arg in arg_list] + + # Add any remaining parameters that were in the original signature + for argname, par in parameter_items: + if argname not in arg_list: + parameter_list.append(par) + + # Return the new signature + return sig.replace(parameters=parameter_list) + # Utility function to warn with verbose output def _info(str, level): if verbose > level: @@ -693,17 +717,17 @@ def simple_function(arg1, arg2, opt1=None, **kwargs): Failed = pytest.fail.Exception -doc_header = simple_class.simple_function.__doc__ +doc_header = simple_class.simple_function.__doc__ + "\n" doc_parameters = "\nParameters\n----------\n" -doc_arg1 = "\narg1 : int\nArgument 1.\n" -doc_arg2 = "\narg2 : int\nArgument 2.\n" -doc_arg2_nospace = "\narg2: int\nArgument 2.\n" -doc_arg3 = "\narg3 : int\nNon-existent argument 1.\n" -doc_opt1 = "\nopt1 : int\nKeyword argument 1.\n" -doc_test = "\ntest : int\nInternal keyword argument 1.\n" +doc_arg1 = "arg1 : int\n Argument 1.\n" +doc_arg2 = "arg2 : int\n Argument 2.\n" +doc_arg2_nospace = "arg2: int\n Argument 2.\n" +doc_arg3 = "arg3 : int\n Non-existent argument 1.\n" +doc_opt1 = "opt1 : int\n Keyword argument 1.\n" +doc_test = "test : int\n Internal keyword argument 1.\n" doc_returns = "\nReturns\n-------\n" -doc_ret = "\nout : int\n" -doc_ret_nospace = "\nout: int\n" +doc_ret = "out : int\n" +doc_ret_nospace = "out: int\n" @pytest.mark.parametrize("docstring, exception, match", [ (None, UserWarning, "missing docstring"), @@ -721,8 +745,8 @@ def simple_function(arg1, arg2, opt1=None, **kwargs): doc_returns + doc_ret, Failed, "'test' not documented"), (doc_header + doc_parameters + doc_arg1 + doc_arg2_nospace + doc_opt1 + doc_test + doc_returns + doc_ret_nospace, UserWarning, "missing space"), - (doc_header + doc_arg1 + doc_arg2_nospace + doc_opt1 + doc_test + - doc_returns + doc_ret_nospace, Failed, "missing Parameters section"), + (doc_header + doc_returns + doc_ret_nospace, + Failed, "missing Parameters section"), (doc_header, None, ""), (doc_header + "\n.. deprecated::", None, ""), (doc_header + "\n\n simple_function() is deprecated", From 724adc0808ebb3b65df1de2bb47bd66d8a1d919c Mon Sep 17 00:00:00 2001 From: Richard Murray Date: Sun, 29 Dec 2024 16:51:05 -0800 Subject: [PATCH 11/93] Add matlab docstring testing + fix section label capitalization --- control/matlab/timeresp.py | 107 ++++++++++++++++--------------- control/matlab/wrappers.py | 90 ++++++++++++++++---------- control/nlsys.py | 2 +- control/phaseplot.py | 12 ++-- control/statesp.py | 2 +- control/sysnorm.py | 2 +- control/tests/docstrings_test.py | 6 +- control/timeresp.py | 2 +- 8 files changed, 128 insertions(+), 95 deletions(-) diff --git a/control/matlab/timeresp.py b/control/matlab/timeresp.py index fe8bfbd71..348e625ea 100644 --- a/control/matlab/timeresp.py +++ b/control/matlab/timeresp.py @@ -17,25 +17,26 @@ def step(sys, T=None, input=0, output=None, return_x=False): Parameters ---------- - sys: StateSpace, or TransferFunction - LTI system to simulate - T: array-like or number, optional + sys : StateSpace, or TransferFunction + LTI system to simulate. + T : array-like or number, optional Time vector, or simulation time duration if a number (time vector is - autocomputed if not given) - input: int + autocomputed if not given). + input : int Index of the input that will be used in this simulation. - output: int + output : int If given, index of the output that is returned by this simulation. + return_x : bool, optional + If True, return the state vector in addition to outputs. Returns ------- - yout: array - Response of the system - T: array - Time values of the output - xout: array (if selected) - Individual response of each x variable - + yout : array + Response of the system. + T : array + Time values of the output. + xout : array (if selected) + Individual response of each x variable. See Also -------- @@ -59,7 +60,8 @@ def step(sys, T=None, input=0, output=None, return_x=False): def stepinfo(sysdata, T=None, yfinal=None, SettlingTimeThreshold=0.02, RiseTimeLimits=(0.1, 0.9)): - """Step response characteristics (Rise time, Settling Time, Peak and others) + """ + Step response characteristics (rise time, settling time, etc). Parameters ---------- @@ -76,9 +78,9 @@ def stepinfo(sysdata, T=None, yfinal=None, SettlingTimeThreshold=0.02, used for a given time series of response data. Scalar for SISO, (noutputs, ninputs) array_like for MIMO systems. SettlingTimeThreshold : float, optional - Defines the error to compute settling time (default = 0.02) + Defines the error to compute settling time (default = 0.02). RiseTimeLimits : tuple (lower_threshold, upper_theshold) - Defines the lower and upper threshold for RiseTime computation + Defines the lower and upper threshold for RiseTime computation. Returns ------- @@ -109,7 +111,6 @@ def stepinfo(sysdata, T=None, yfinal=None, SettlingTimeThreshold=0.02, To get the step response characteristics from the j-th input to the i-th output, access ``S[i][j]`` - See Also -------- step, lsim, initial, impulse @@ -142,24 +143,26 @@ def impulse(sys, T=None, input=0, output=None, return_x=False): Parameters ---------- - sys: StateSpace, TransferFunction - LTI system to simulate - T: array-like or number, optional + sys : StateSpace, TransferFunction + LTI system to simulate. + T : array-like or number, optional Time vector, or simulation time duration if a number (time vector is - autocomputed if not given) - input: int + autocomputed if not given). + input : int Index of the input that will be used in this simulation. - output: int + output : int Index of the output that will be used in this simulation. + return_x : bool, optional + If True, return the state vector in addition to outputs. Returns ------- - yout: array - Response of the system - T: array - Time values of the output - xout: array (if selected) - Individual response of each x variable + yout : array + Response of the system. + T : array + Time values of the output. + xout : array (if selected) + Individual response of each x variable. See Also -------- @@ -189,27 +192,29 @@ def initial(sys, T=None, X0=0., input=None, output=None, return_x=False): Parameters ---------- - sys: StateSpace, or TransferFunction - LTI system to simulate - T: array-like or number, optional + sys : StateSpace, or TransferFunction + LTI system to simulate. + T : array-like or number, optional Time vector, or simulation time duration if a number (time vector is - autocomputed if not given) - X0: array-like object or number, optional - Initial condition (default = 0) - input: int + autocomputed if not given). + X0 : array-like object or number, optional + Initial condition (default = 0). + input : int This input is ignored, but present for compatibility with step and impulse. - output: int + output : int If given, index of the output that is returned by this simulation. + return_x : bool, optional + If True, return the state vector in addition to outputs. Returns ------- - yout: array - Response of the system - T: array - Time values of the output - xout: array (if selected) - Individual response of each x variable + yout : array + Response of the system. + T : array + Time values of the output. + xout : array (if selected) + Individual response of each x variable. See Also -------- @@ -240,25 +245,25 @@ def lsim(sys, U=0., T=None, X0=0.): Parameters ---------- - sys: LTI (StateSpace, or TransferFunction) - LTI system to simulate - U: array-like or number, optional + sys : LTI (StateSpace, or TransferFunction) + LTI system to simulate. + U : array-like or number, optional Input array giving input at each time `T` (default = 0). If `U` is ``None`` or ``0``, a special algorithm is used. This special algorithm is faster than the general algorithm, which is used otherwise. - T: array-like, optional for discrete LTI `sys` + T : array-like, optional for discrete LTI `sys` Time steps at which the input is defined; values must be evenly spaced. - X0: array-like or number, optional + X0 : array-like or number, optional Initial condition (default = 0). Returns ------- - yout: array + yout : array Response of the system. - T: array + T : array Time values of the output. - xout: array + xout : array Time evolution of the state vector. See Also diff --git a/control/matlab/wrappers.py b/control/matlab/wrappers.py index 153342096..0042b5d09 100644 --- a/control/matlab/wrappers.py +++ b/control/matlab/wrappers.py @@ -15,7 +15,7 @@ __all__ = ['bode', 'nyquist', 'ngrid', 'rlocus', 'pzmap', 'dcgain', 'connect'] def bode(*args, **kwargs): - """bode(syslist[, omega, dB, Hz, deg, ...]) + """bode(sys[, omega, dB, Hz, deg, ...]) Bode plot of the frequency response. @@ -28,23 +28,22 @@ def bode(*args, **kwargs): a list of systems can be entered, or several systems can be specified (i.e. several parameters). The sys arguments may also be interspersed with format strings. A frequency argument (array_like) - may also be added, some examples:: - - >>> bode(sys, w) # one system, freq vector # doctest: +SKIP - >>> bode(sys1, sys2, ..., sysN) # several systems # doctest: +SKIP - >>> bode(sys1, sys2, ..., sysN, w) # doctest: +SKIP - >>> bode(sys1, 'plotstyle1', ..., sysN, 'plotstyleN') # + plot formats # doctest: +SKIP - - omega: freq_range - Range of frequencies in rad/s + may also be added (see Examples). + omega : array + Range of frequencies in rad/s. dB : boolean - If True, plot result in dB + If True, plot result in dB. Hz : boolean - If True, plot frequency in Hz (omega must be provided in rad/sec) + If True, plot frequency in Hz (omega must be provided in rad/sec). deg : boolean - If True, return phase in degrees (else radians) + If True, return phase in degrees (else radians). plot : boolean - If True, plot magnitude and phase + If True, plot magnitude and phase. + + Returns + ------- + mag, phase, omega : array + Magnitude, phase, and frequencies represented in the Bode plot. Examples -------- @@ -61,6 +60,12 @@ def bode(*args, **kwargs): * >>> bode(sys1, sys2, ..., sysN) # doctest: +SKIP * >>> bode(sys1, sys2, ..., sysN, w) # doctest: +SKIP * >>> bode(sys1, 'plotstyle1', ..., sysN, 'plotstyleN') # doctest: +SKIP + + >>> bode(sys, w) # one system, freq vector # doctest: +SKIP + >>> bode(sys1, sys2, ..., sysN) # several systems # doctest: +SKIP + >>> bode(sys1, sys2, ..., sysN, w) # doctest: +SKIP + >>> bode(sys1, 'plotstyle1', ..., sysN, 'plotstyleN') # doctest: +SKIP + """ from ..freqplot import bode_plot @@ -99,19 +104,25 @@ def nyquist(*args, plot=True, **kwargs): Parameters ---------- - sys1, ..., sysn : list of LTI + syslist : list of LTI List of linear input/output systems (single system is OK). omega : array_like Set of frequencies to be evaluated, in rad/sec. + omega_limits : array_like of two values + Set limits for plotted frequency range. If Hz=True the limits are + in Hz otherwise in rad/s. Specifying ``omega`` as a list of two + elements is equivalent to providing ``omega_limits``. + plot : bool + If `False`, do not generate a plot. Returns ------- real : ndarray (or list of ndarray if len(syslist) > 1)) - real part of the frequency response array + Real part of the frequency response array. imag : ndarray (or list of ndarray if len(syslist) > 1)) - imaginary part of the frequency response array + Imaginary part of the frequency response array. omega : ndarray (or list of ndarray if len(syslist) > 1)) - frequencies in rad/s + Frequencies in rad/s. """ from ..freqplot import nyquist_response, nyquist_plot @@ -217,6 +228,8 @@ def rlocus(*args, **kwargs): ylim : tuple or list, optional Set limits of y axis, normally with tuple (see :doc:`matplotlib:api/axes_api`). + plot : bool + If `False`, do not generate a plot. Returns ------- @@ -257,19 +270,19 @@ def pzmap(*args, **kwargs): Parameters ---------- - sys: LTI (StateSpace or TransferFunction) + sys : LTI (StateSpace or TransferFunction) Linear system for which poles and zeros are computed. - plot: bool, optional + plot : bool, optional If ``True`` a graph is generated with Matplotlib, otherwise the poles and zeros are only computed and returned. - grid: boolean (default = False) + grid : boolean (default = False) If True plot omega-damping grid. Returns ------- - poles: array + poles : array The system's poles. - zeros: array + zeros : array The system's zeros. Notes @@ -302,26 +315,36 @@ def ngrid(): def dcgain(*args): - '''Compute the gain of the system in steady state. + '''dcgain(sys) \ + dcgain(num, den) \ + dcgain(Z, P, k) \ + dcgain(A, B, C, D) + + Compute the gain of the system in steady state. The function takes either 1, 2, 3, or 4 parameters: + * dcgain(sys) + * dcgain(num, den) + * dcgain(Z, P, k) + * dcgain(A, B, C, D) + Parameters ---------- - A, B, C, D: array-like + A, B, C, D : array-like A linear system in state space form. - Z, P, k: array-like, array-like, number + Z, P, k : array-like, array-like, number A linear system in zero, pole, gain form. - num, den: array-like + num, den : array-like A linear system in transfer function form. - sys: LTI (StateSpace or TransferFunction) + sys : LTI (StateSpace or TransferFunction) A linear system object. Returns ------- - gain: ndarray + gain : ndarray The gain of each output versus each input: - :math:`y = gain \\cdot u` + :math:`y = gain \\cdot u`. Notes ----- @@ -354,8 +377,9 @@ def dcgain(*args): from ..bdalg import connect as ct_connect def connect(*args): + """connect(sys, Q, inputv, outputv) - """Index-based interconnection of an LTI system. + Index-based interconnection of an LTI system. The system `sys` is a system typically constructed with `append`, with multiple inputs and outputs. The inputs and outputs are connected @@ -378,9 +402,9 @@ def connect(*args): values mean the feedback is negative. A zero value is ignored. Inputs and outputs are indexed starting at 1 to communicate sign information. inputv : 1D array - list of final external inputs, indexed starting at 1 + List of final external inputs, indexed starting at 1. outputv : 1D array - list of final external outputs, indexed starting at 1 + List of final external outputs, indexed starting at 1. Returns ------- diff --git a/control/nlsys.py b/control/nlsys.py index a1471ec9c..6a51e57d1 100644 --- a/control/nlsys.py +++ b/control/nlsys.py @@ -1467,7 +1467,7 @@ def input_output_response( system signals are named, these names will be used as labels for the time response. - Other parameters + Other Parameters ---------------- ignore_errors : bool, optional If ``False`` (default), errors during computation of the trajectory diff --git a/control/phaseplot.py b/control/phaseplot.py index e8127d644..59d340182 100644 --- a/control/phaseplot.py +++ b/control/phaseplot.py @@ -118,7 +118,7 @@ def phase_plane_plot( See :class:`ControlPlot` for more detailed information. - Other parameters + Other Parameters ---------------- dir : str, optional Direction to draw streamlines: 'forward' to flow forward in time @@ -280,7 +280,7 @@ def vectorfield( ------- out : Quiver - Other parameters + Other Parameters ---------------- rcParams : dict Override the default parameters used for generating plots. @@ -381,7 +381,7 @@ def streamlines( ------- out : list of Line2D objects - Other parameters + Other Parameters ---------------- rcParams : dict Override the default parameters used for generating plots. @@ -493,7 +493,7 @@ def equilpoints( ------- out : list of Line2D objects - Other parameters + Other Parameters ---------------- rcParams : dict Override the default parameters used for generating plots. @@ -585,7 +585,7 @@ def separatrices( ------- out : list of Line2D objects - Other parameters + Other Parameters ---------------- rcParams : dict Override the default parameters used for generating plots. @@ -1066,7 +1066,7 @@ def phase_plot(odefun, X=None, Y=None, scale=1, X0=None, T=None, params: tuple, optional List of parameters to pass to vector field: `func(x, t, *params)` - See also + See Also -------- box_grid : construct box-shaped grid of initial conditions diff --git a/control/statesp.py b/control/statesp.py index 4f3964757..9326a5420 100644 --- a/control/statesp.py +++ b/control/statesp.py @@ -1948,7 +1948,7 @@ def linfnorm(sys, tol=1e-10): L-infinity norm is finite if the system has no poles on the imaginary axis. - See also + See Also -------- slycot.ab13dd : the Slycot routine linfnorm that does the calculation """ diff --git a/control/sysnorm.py b/control/sysnorm.py index 0bf92bcfe..67183c29f 100644 --- a/control/sysnorm.py +++ b/control/sysnorm.py @@ -25,7 +25,7 @@ def _h2norm_slycot(sys, print_warning=True): """H2 norm of a linear system. For internal use. Requires Slycot. - See also + See Also -------- ``slycot.ab13bd`` : the Slycot routine that does the calculation https://github.com/python-control/Slycot/issues/199 : Post on issue with ``ab13bf`` diff --git a/control/tests/docstrings_test.py b/control/tests/docstrings_test.py index 790c7b070..9ca25df7b 100644 --- a/control/tests/docstrings_test.py +++ b/control/tests/docstrings_test.py @@ -24,6 +24,7 @@ import control import control.flatsys +import control.matlab # List of functions that we can skip testing (special cases) function_skiplist = [ @@ -71,7 +72,8 @@ module_list = [ (control, ""), (control.flatsys, "flatsys."), - (control.optimal, "optimal."), (control.phaseplot, "phaseplot.")] + (control.optimal, "optimal."), (control.phaseplot, "phaseplot."), + (control.matlab, "matlab.")] @pytest.mark.parametrize("module, prefix", module_list) def test_parameter_docs(module, prefix): checked = set() # Keep track of functions we have checked @@ -560,6 +562,8 @@ def _check_parameter_docs( if match_other and match_returns: docstring = docstring[start:match_returns.start()] + \ docstring[match_other.start():] + elif match_returns: + docstring = docstring[start:match_returns.start()] else: docstring = docstring[start:] diff --git a/control/timeresp.py b/control/timeresp.py index 916ddc5a8..1342390e2 100644 --- a/control/timeresp.py +++ b/control/timeresp.py @@ -303,7 +303,7 @@ def __init__( the system is SISO. The default value can be set using config.defaults['control.squeeze_time_response']. - Other parameters + Other Parameters ---------------- input_labels, output_labels, state_labels: array of str, optional Optional labels for the inputs, outputs, and states, given as a From aad3c75c3072d9cdc4a7b61af201bf16fd167698 Mon Sep 17 00:00:00 2001 From: Richard Murray Date: Sun, 29 Dec 2024 17:03:11 -0800 Subject: [PATCH 12/93] add check for empty section + fix correlation() docstring --- control/stochsys.py | 4 ++++ control/tests/docstrings_test.py | 14 ++++++++++---- 2 files changed, 14 insertions(+), 4 deletions(-) diff --git a/control/stochsys.py b/control/stochsys.py index dd726f4a8..80cf72661 100644 --- a/control/stochsys.py +++ b/control/stochsys.py @@ -683,6 +683,10 @@ def correlation(T, X, Y=None, squeeze=True): Returns ------- + tau : array + Array of time offsets. + Rtau : array + Correlation for each offset tau. """ T = np.atleast_1d(T) diff --git a/control/tests/docstrings_test.py b/control/tests/docstrings_test.py index 9ca25df7b..9ccd82e36 100644 --- a/control/tests/docstrings_test.py +++ b/control/tests/docstrings_test.py @@ -615,10 +615,14 @@ def _check_numpydoc_style(obj, doc): pass elif inspect.isfunction(obj): # Specialized checks for functions - def _check_param(param, empty_ok=False, noname_ok=False): + def _check_param(param, empty_ok=False, noname_ok=False, section="??"): param_desc = "\n".join(param.desc) param_name = f"{name} '{param.name}'" + # Check for empty section + if param.name == "" and param.type == '': + _fail(f"Empty {section} section in {name}") + # Make sure we have a name and description if param.name == "" and not noname_ok: _fail(f"{param_name} has improperly formatted parameter") @@ -636,11 +640,13 @@ def _check_param(param, empty_ok=False, noname_ok=False): _warn(f"{param_name} description starts with lower case letter") for param in doc["Parameters"] + doc["Other Parameters"]: - _check_param(param) + _check_param(param, section="Parameters") for param in doc["Returns"]: - _check_param(param, empty_ok=True, noname_ok=True) + _check_param( + param, empty_ok=True, noname_ok=True, section="Returns") for param in doc["Yields"]: - _check_param(param, empty_ok=True, noname_ok=True) + _check_param( + param, empty_ok=True, noname_ok=True, section="Yields") else: raise TypeError("unknown object type for {obj}") From d1ae6d2134b4c627c3c2803dad0d50bd5ce86f66 Mon Sep 17 00:00:00 2001 From: Richard Murray Date: Sat, 21 Dec 2024 00:02:11 -0800 Subject: [PATCH 13/93] improved docstring checking + first round of fixes --- control/nlsys.py | 1 - control/tests/docstrings_test.py | 29 ++++++++++++++++++++++------- 2 files changed, 22 insertions(+), 8 deletions(-) diff --git a/control/nlsys.py b/control/nlsys.py index 6a51e57d1..76e65e8dd 100644 --- a/control/nlsys.py +++ b/control/nlsys.py @@ -1059,7 +1059,6 @@ def unused_signals(self): return ({inputs[i][:2]: inputs[i][2] for i in unused_sysinp}, {outputs[i][:2]: outputs[i][2] for i in unused_sysout}) - def connection_table(self, show_names=False, column_width=32): """Table of connections inside an interconnected system. diff --git a/control/tests/docstrings_test.py b/control/tests/docstrings_test.py index 9ccd82e36..ab009fd17 100644 --- a/control/tests/docstrings_test.py +++ b/control/tests/docstrings_test.py @@ -92,6 +92,9 @@ def test_parameter_docs(module, prefix): warnings.simplefilter('ignore') # debug via sphinx, not here doc = None if obj is None else npd.FunctionDoc(obj) + # Parse the docstring using numpydoc + doc = None if obj is None else npd.FunctionDoc(obj) + # Skip anything that is outside of this module if inspect.getmodule(obj) is not None and \ not inspect.getmodule(obj).__name__.startswith('control'): @@ -99,6 +102,12 @@ def test_parameter_docs(module, prefix): _info(f"member '{objname}' is outside `control` module", 5) continue + # Skip non-top-level functions without parameter lists + if prefix != "" and inspect.getmodule(obj) != module and \ + doc is not None and doc["Parameters"] == []: + _info(f"skipping {prefix}.{name}", 2) + continue + # If this is a class, recurse through methods # TODO: check top level documenation here (__init__, attributes?) if inspect.isclass(obj): @@ -106,14 +115,15 @@ def test_parameter_docs(module, prefix): _check_numpydoc_style(obj, doc) # Check member functions within the class test_parameter_docs(obj, prefix + name + '.') + continue # Skip anything that is inherited, hidden, deprecated, or checked if not inspect.isfunction(obj) or \ inspect.isclass(module) and name not in module.__dict__ \ or name.startswith('_') or obj in function_skiplist \ or obj in checked: - _info(f"skipping {prefix}.{name}", 6) - continue + _info(f"skipping {prefix}.{name}", 6) + continue # Skip non-top-level functions without parameter lists if prefix != "" and inspect.getmodule(obj) != module and \ @@ -226,6 +236,13 @@ def test_parameter_docs(module, prefix): f"{obj} return value '{retname}' " "docstring missing space") + # Look at the return values + for val in doc["Returns"]: + if val.name == '' and \ + (match := re.search("([\w]+):", val.type)) is not None: + retname = match.group(1) + _warn(f"{obj.__name__} '{retname}' docstring missing space") + @pytest.mark.parametrize("module, prefix", [ (control, ""), (control.flatsys, "flatsys."), @@ -262,7 +279,7 @@ def test_deprecated_functions(module, prefix): # Get the docstring (skip w/ warning if there isn't one) if obj.__doc__ is None: - _warn(f"{objname} is missing docstring") + _warn(f"{module.__name__}.{name} is missing docstring") continue else: docstring = inspect.getdoc(obj) @@ -273,14 +290,12 @@ def test_deprecated_functions(module, prefix): if ".. deprecated::" in doc_extended: # Make sure a FutureWarning is issued if not re.search("FutureWarning", source): - _fail(f"{objname} deprecated but does not issue " - "FutureWarning") + _fail(f"{name} deprecated but does not issue FutureWarning") else: if re.search(name + r"(\(\))? is deprecated", docstring) or \ re.search(name + r"(\(\))? is deprecated", source): _fail( - f"{objname} deprecated but with non-standard " - "docs/warnings") + f"{name} deprecated but w/ non-standard docs/warnings") # # Tests for I/O system classes From e31ce000310e6c8c317269292968622d80fe9b12 Mon Sep 17 00:00:00 2001 From: Richard Murray Date: Sat, 21 Dec 2024 10:14:20 -0800 Subject: [PATCH 14/93] add standalone docstrings_test mode + fix docstrings --- control/robust.py | 2 +- control/tests/docstrings_test.py | 22 ++++++++++++++-------- 2 files changed, 15 insertions(+), 9 deletions(-) diff --git a/control/robust.py b/control/robust.py index d2fff7c0a..77aca5bd2 100644 --- a/control/robust.py +++ b/control/robust.py @@ -400,7 +400,7 @@ def mixsyn(g, w1=None, w2=None, w3=None): info : tuple Two-tuple `(gamma, rcond)` containing additional information: - + * gamma (scalar): H-infinity norm of cl. * rcond (array): Estimates of reciprocal condition numbers computed during synthesis. See hinfsyn for details. diff --git a/control/tests/docstrings_test.py b/control/tests/docstrings_test.py index ab009fd17..d84346444 100644 --- a/control/tests/docstrings_test.py +++ b/control/tests/docstrings_test.py @@ -16,6 +16,7 @@ import inspect import re + import sys import warnings @@ -74,6 +75,7 @@ (control, ""), (control.flatsys, "flatsys."), (control.optimal, "optimal."), (control.phaseplot, "phaseplot."), (control.matlab, "matlab.")] + @pytest.mark.parametrize("module, prefix", module_list) def test_parameter_docs(module, prefix): checked = set() # Keep track of functions we have checked @@ -93,13 +95,15 @@ def test_parameter_docs(module, prefix): doc = None if obj is None else npd.FunctionDoc(obj) # Parse the docstring using numpydoc - doc = None if obj is None else npd.FunctionDoc(obj) + with warnings.catch_warnings(): + warnings.simplefilter('ignore') # debug via sphinx, not here + doc = None if obj is None else npd.FunctionDoc(obj) # Skip anything that is outside of this module if inspect.getmodule(obj) is not None and \ not inspect.getmodule(obj).__name__.startswith('control'): # Skip anything that isn't part of the control package - _info(f"member '{objname}' is outside `control` module", 5) + _info(f"member '{name}' is outside `control` module", 5) continue # Skip non-top-level functions without parameter lists @@ -236,12 +240,14 @@ def test_parameter_docs(module, prefix): f"{obj} return value '{retname}' " "docstring missing space") - # Look at the return values - for val in doc["Returns"]: - if val.name == '' and \ - (match := re.search("([\w]+):", val.type)) is not None: - retname = match.group(1) - _warn(f"{obj.__name__} '{retname}' docstring missing space") + # Look at the return values + for val in doc["Returns"]: + if val.name == '' and \ + (match := re.search(r"([\w]+):", val.type)) is not None: + retname = match.group(1) + _warn( + f"{obj} return value '{retname}' " + "docstring missing space") @pytest.mark.parametrize("module, prefix", [ From 624439a2bf7042fa62fb217e6cadb6ab33c3ce3d Mon Sep 17 00:00:00 2001 From: Richard Murray Date: Sun, 22 Dec 2024 16:14:29 -0800 Subject: [PATCH 15/93] add numpydoc style testing + corrections --- control/freqplot.py | 3 ++- control/robust.py | 2 +- control/tests/docstrings_test.py | 11 +++++++---- 3 files changed, 10 insertions(+), 6 deletions(-) diff --git a/control/freqplot.py b/control/freqplot.py index 5512ab8a4..d44edad28 100644 --- a/control/freqplot.py +++ b/control/freqplot.py @@ -2172,7 +2172,8 @@ def gangof4_response( def gangof4_plot( *args, omega=None, omega_limits=None, omega_num=None, Hz=False, **kwargs): - """gangof4_plot(response) | gangof4_plot(P, C, omega) + """gangof4_plot(response) \ + gangof4_plot(P, C, omega) Plot response of "Gang of 4" transfer functions. diff --git a/control/robust.py b/control/robust.py index 77aca5bd2..d2fff7c0a 100644 --- a/control/robust.py +++ b/control/robust.py @@ -400,7 +400,7 @@ def mixsyn(g, w1=None, w2=None, w3=None): info : tuple Two-tuple `(gamma, rcond)` containing additional information: - + * gamma (scalar): H-infinity norm of cl. * rcond (array): Estimates of reciprocal condition numbers computed during synthesis. See hinfsyn for details. diff --git a/control/tests/docstrings_test.py b/control/tests/docstrings_test.py index d84346444..e28c16ea0 100644 --- a/control/tests/docstrings_test.py +++ b/control/tests/docstrings_test.py @@ -103,7 +103,7 @@ def test_parameter_docs(module, prefix): if inspect.getmodule(obj) is not None and \ not inspect.getmodule(obj).__name__.startswith('control'): # Skip anything that isn't part of the control package - _info(f"member '{name}' is outside `control` module", 5) + _info(f"member '{objname}' is outside `control` module", 5) continue # Skip non-top-level functions without parameter lists @@ -285,7 +285,7 @@ def test_deprecated_functions(module, prefix): # Get the docstring (skip w/ warning if there isn't one) if obj.__doc__ is None: - _warn(f"{module.__name__}.{name} is missing docstring") + _warn(f"{objname} is missing docstring") continue else: docstring = inspect.getdoc(obj) @@ -296,12 +296,14 @@ def test_deprecated_functions(module, prefix): if ".. deprecated::" in doc_extended: # Make sure a FutureWarning is issued if not re.search("FutureWarning", source): - _fail(f"{name} deprecated but does not issue FutureWarning") + _fail(f"{objname} deprecated but does not issue " + "FutureWarning") else: if re.search(name + r"(\(\))? is deprecated", docstring) or \ re.search(name + r"(\(\))? is deprecated", source): _fail( - f"{name} deprecated but w/ non-standard docs/warnings") + f"{objname} deprecated but with non-standard " + "docs/warnings") # # Tests for I/O system classes @@ -721,6 +723,7 @@ def _replace_var_positional_with_docstring(sig, doc): # Return the new signature return sig.replace(parameters=parameter_list) + # Utility function to warn with verbose output def _info(str, level): if verbose > level: From dd078ae104655da342e5cf8894249b096d985168 Mon Sep 17 00:00:00 2001 From: Richard Murray Date: Tue, 24 Dec 2024 07:22:41 -0800 Subject: [PATCH 16/93] add unit test for sphinx documentation --- doc/test_sphinxdocs.py | 138 +++++++++++++++++++++++++++++++++++++++++ 1 file changed, 138 insertions(+) create mode 100644 doc/test_sphinxdocs.py diff --git a/doc/test_sphinxdocs.py b/doc/test_sphinxdocs.py new file mode 100644 index 000000000..74c853cda --- /dev/null +++ b/doc/test_sphinxdocs.py @@ -0,0 +1,138 @@ +# test_sphinxdocs.py - pytest checks for user guide +# RMM, 23 Dec 2024 +# +# This set of tests is used to make sure that all primary functions are +# referenced in the documentation. + +import inspect +import os +import sys +import warnings +from importlib import resources + +import pytest +import numpydoc.docscrape as npd + +import control +import control.flatsys + +# Location of the documentation and files to check +sphinx_dir = str(resources.files('control')) + '/../doc/generated/' + +# Functions that should not be referenced +legacy_functions = [ + 'balred', # balanced_reduction + 'bode', # bode_plot + 'c2d', # sample_system + 'era', # eigensys_realization + 'evalfr', # use __call__() + 'find_eqpt', # find_operating_point + 'gangof4', # gangof4_plot + 'hsvd', # hankel_singular_values + 'minreal', # minimal_realization + 'modred', # model_reduction + 'nichols', # nichols_plot + 'nyquist', # nyquist_plot + 'pzmap', # pole_zero_plot + 'rlocus', # root_locus_plot + 'rlocus', # root_locus_plot + 'root_locus', # root_locus_plot +] + +# Functons that we can skip +object_skiplist = [ + control.NamedSignal, # users don't need to know about this + control.common_timebase, # mainly internal use + control.cvxopt_check, # mainly internal use + control.pandas_check, # mainly internal use + control.slycot_check, # mainly internal use +] + +# Global list of objects we have checked +checked = set() + +# Decide on the level of verbosity (use -rP when running pytest) +verbose = 0 +standalone = False + +control_module_list = [ + control, control.flatsys, control.optimal, control.phaseplot +] +@pytest.mark.parametrize("module", control_module_list) +def test_sphinx_functions(module): + + # Look through every object in the package + _info(f"Checking module {module}", 1) + + for name, obj in inspect.getmembers(module): + objname = ".".join([module.__name__, name]) + + # Skip anything that is outside of this module + if inspect.getmodule(obj) is not None and \ + not inspect.getmodule(obj).__name__.startswith('control'): + # Skip anything that isn't part of the control package + continue + + elif inspect.isclass(obj) and issubclass(obj, Exception): + continue + + elif inspect.isclass(obj) or inspect.isfunction(obj): + # Skip anything that is inherited, hidden, deprecated, or checked + if inspect.isclass(module) and name not in module.__dict__ \ + or name.startswith('_') or obj in checked: + continue + else: + checked.add(obj) + + # Get the relevant information about this object + exists = os.path.exists(sphinx_dir + objname + ".rst") + deprecated = _check_deprecated(obj) + skip = obj in object_skiplist + referenced = f" {objname} referenced in sphinx docs" + legacy = name in legacy_functions + + _info(f" Checking {objname}", 2) + match exists, skip, deprecated, legacy: + case True, True, _, _: + _info(f"skipped object" + referenced, -1) + case True, _, True, _: + _warn(f"deprecated object" + referenced) + case True, _, _, True: + _warn(f"legacy object" + referenced) + case False, False, False, False: + _fail(f"{objname} not referenced in sphinx docs") + + +def _check_deprecated(obj): + with warnings.catch_warnings(): + warnings.simplefilter('ignore') # debug via sphinx, not here + doc = npd.FunctionDoc(obj) + + doc_extended = "" if doc is None else "\n".join(doc["Extended Summary"]) + return ".. deprecated::" in doc_extended + + +# Utility function to warn with verbose output +def _info(str, level): + if verbose > level: + print(("INFO: " if level < 0 else " " * level) + str) + +def _warn(str, level=-1): + if verbose > level: + print("WARN: " + " " * level + str) + if not standalone: + warnings.warn(str, stacklevel=2) + +def _fail(str, level=-1): + if verbose > level: + print("FAIL: " + " " * level + str) + if not standalone: + pytest.fail(str) + + +if __name__ == "__main__": + verbose = 0 if len(sys.argv) == 1 else int(sys.argv[1]) + standalone = True + + for module in control_module_list: + test_sphinx_functions(module) From d6058076c8188c0422a4981da7e903e446680ee8 Mon Sep 17 00:00:00 2001 From: Richard Murray Date: Tue, 24 Dec 2024 07:23:17 -0800 Subject: [PATCH 17/93] update sphinxdocs and docstrings to fix missing function refs --- control/optimal.py | 9 +++++++-- control/statesp.py | 4 ++++ control/tests/statesp_test.py | 11 +++++++---- doc/control.rst | 4 ++++ doc/optimal.rst | 1 + doc/plotting.rst | 2 ++ 6 files changed, 25 insertions(+), 6 deletions(-) diff --git a/control/optimal.py b/control/optimal.py index ae64e346a..03e87953a 100644 --- a/control/optimal.py +++ b/control/optimal.py @@ -1953,13 +1953,18 @@ def __init__( self.outputs = response.outputs -# Compute the moving horizon estimate for a nonlinear system +# Compute the finite horizon estimate for a nonlinear system def solve_oep( sys, timepts, Y, U, trajectory_cost, X0=None, trajectory_constraints=None, initial_guess=None, squeeze=None, print_summary=True, **kwargs): - """Compute the solution to a moving horizon estimation problem. + """Compute the solution to a finite horizon estimation problem. + + This function computes the maximum likelihood estimate of a system + state given the input and output over a fixed horizon. The likelihood + is evaluated according to a cost function whose value is minimized + to compute the maximum likelhood estimate. Parameters ---------- diff --git a/control/statesp.py b/control/statesp.py index 9326a5420..f03a4eb59 100644 --- a/control/statesp.py +++ b/control/statesp.py @@ -1929,6 +1929,9 @@ def ssdata(sys): def linfnorm(sys, tol=1e-10): """L-infinity norm of a linear system. + .. deprecated:: 0.10.2 + This functionality is now available in :func:`sysnorm`. + Parameters ---------- sys : LTI (StateSpace or TransferFunction) @@ -1952,6 +1955,7 @@ def linfnorm(sys, tol=1e-10): -------- slycot.ab13dd : the Slycot routine linfnorm that does the calculation """ + warn("linfnorm() is deprecated; use sysnorm(sys, p='inf')", FutureWarning) if ab13dd is None: raise ControlSlycot("Can't find slycot module 'ab13dd'") diff --git a/control/tests/statesp_test.py b/control/tests/statesp_test.py index 542edc654..54f347d69 100644 --- a/control/tests/statesp_test.py +++ b/control/tests/statesp_test.py @@ -19,11 +19,11 @@ from control.config import defaults from control.dtime import sample_system from control.lti import evalfr -from control.statesp import (StateSpace, _convert_to_statespace, _rss_generate, - _statesp_defaults, drss, linfnorm, rss, ss, tf2ss) -from control.tests.conftest import (assert_tf_close_coeff, editsdefaults, - slycotonly) +from control.statesp import StateSpace, _convert_to_statespace, tf2ss, \ + _statesp_defaults, _rss_generate, linfnorm, ss, rss, drss from control.xferfcn import TransferFunction, ss2tf +from control.tests.conftest import assert_tf_close_coeff, editsdefaults, \ + slycotonly class TestStateSpace: @@ -1515,6 +1515,7 @@ def dt_siso(self, request): return ct.c2d(systype(*sysargs), dt), refgpeak, reffpeak @slycotonly + @pytest.mark.usefixtures('ignore_future_warning') def test_linfnorm_ct_siso(self, ct_siso): sys, refgpeak, reffpeak = ct_siso gpeak, fpeak = linfnorm(sys) @@ -1522,6 +1523,7 @@ def test_linfnorm_ct_siso(self, ct_siso): np.testing.assert_allclose(fpeak, reffpeak) @slycotonly + @pytest.mark.usefixtures('ignore_future_warning') def test_linfnorm_dt_siso(self, dt_siso): sys, refgpeak, reffpeak = dt_siso gpeak, fpeak = linfnorm(sys) @@ -1530,6 +1532,7 @@ def test_linfnorm_dt_siso(self, dt_siso): np.testing.assert_allclose(fpeak, reffpeak) @slycotonly + @pytest.mark.usefixtures('ignore_future_warning') def test_linfnorm_ct_mimo(self, ct_siso): siso, refgpeak, reffpeak = ct_siso sys = ct.append(siso, siso) diff --git a/doc/control.rst b/doc/control.rst index 766e593d8..fdb173ea9 100644 --- a/doc/control.rst +++ b/doc/control.rst @@ -30,12 +30,14 @@ System interconnections :toctree: generated/ append + combine_tf connect feedback interconnect negate parallel series + split_tf connection_table combine_tf split_tf @@ -81,6 +83,7 @@ Control system analysis .. autosummary:: :toctree: generated/ + bandwidth dcgain describing_function frequency_response @@ -88,6 +91,7 @@ Control system analysis get_output_fb_index ispassive margin + solve_passivity_LMI stability_margins step_info phase_crossover_frequencies diff --git a/doc/optimal.rst b/doc/optimal.rst index bc37607e1..5d748f544 100644 --- a/doc/optimal.rst +++ b/doc/optimal.rst @@ -492,5 +492,6 @@ The following classes and functions are defined in the ~control.optimal.output_range_constraint ~control.optimal.quadratic_cost ~control.optimal.solve_ocp + ~control.optimal.solve_oep ~control.optimal.state_poly_constraint ~control.optimal.state_range_constraint diff --git a/doc/plotting.rst b/doc/plotting.rst index cc292bdbe..8b47392cc 100644 --- a/doc/plotting.rst +++ b/doc/plotting.rst @@ -673,7 +673,9 @@ Plotting functions ~control.nichols_plot ~control.nyquist_plot ~control.phase_plane_plot + phaseplot.circlegrid phaseplot.equilpoints + phaseplot.meshgrid phaseplot.separatrices phaseplot.streamlines phaseplot.vectorfield From 810e91067859ecbf77dc33095f9a09a0a01e6b68 Mon Sep 17 00:00:00 2001 From: Richard Murray Date: Tue, 24 Dec 2024 07:30:34 -0800 Subject: [PATCH 18/93] remove references to deprecated and legacy functions --- doc/control.rst | 4 +--- doc/test_sphinxdocs.py | 1 + 2 files changed, 2 insertions(+), 3 deletions(-) diff --git a/doc/control.rst b/doc/control.rst index fdb173ea9..7bee58eb1 100644 --- a/doc/control.rst +++ b/doc/control.rst @@ -31,7 +31,6 @@ System interconnections append combine_tf - connect feedback interconnect negate @@ -75,7 +74,7 @@ Time domain simulation impulse_response initial_response input_output_response - phase_plot + phase_plane_plot step_response Control system analysis @@ -182,7 +181,6 @@ Utility functions and conversions isctime isdtime issiso - issys mag2db modal_form norm diff --git a/doc/test_sphinxdocs.py b/doc/test_sphinxdocs.py index 74c853cda..955533c0d 100644 --- a/doc/test_sphinxdocs.py +++ b/doc/test_sphinxdocs.py @@ -21,6 +21,7 @@ # Functions that should not be referenced legacy_functions = [ + 'FRD', # FrequencyResponseData (or frd) 'balred', # balanced_reduction 'bode', # bode_plot 'c2d', # sample_system From 6070ca782a48addeb353189ccc33560e4995a557 Mon Sep 17 00:00:00 2001 From: Richard Murray Date: Tue, 24 Dec 2024 09:13:12 -0800 Subject: [PATCH 19/93] add pytest to CI workflow --- .github/workflows/doctest.yml | 7 +++++++ 1 file changed, 7 insertions(+) diff --git a/.github/workflows/doctest.yml b/.github/workflows/doctest.yml index 4c8559ce0..9006c3687 100644 --- a/.github/workflows/doctest.yml +++ b/.github/workflows/doctest.yml @@ -36,6 +36,13 @@ jobs: make html make doctest + - name: Run pytest + shell: bash -l {0} + working-directory: doc + run: | + make html + PYTHONPATH=../ pytest + - name: Archive results uses: actions/upload-artifact@v4 with: From 3153dfa044372f54fba31b604b2915b629b05e26 Mon Sep 17 00:00:00 2001 From: Richard Murray Date: Tue, 24 Dec 2024 07:45:12 -0800 Subject: [PATCH 20/93] fix docstring example errors (in newly included functions) --- control/lti.py | 4 ++-- control/xferfcn.py | 8 ++++---- 2 files changed, 6 insertions(+), 6 deletions(-) diff --git a/control/lti.py b/control/lti.py index 9c08bdfbe..4e0127c60 100644 --- a/control/lti.py +++ b/control/lti.py @@ -612,8 +612,8 @@ def bandwidth(sys, dbdrop=-3): ValueError if 'dbdrop' is not a negative scalar - Example - ------- + Examples + -------- >>> G = ct.tf([1], [1, 1]) >>> ct.bandwidth(G) np.float64(0.9976283451102316) diff --git a/control/xferfcn.py b/control/xferfcn.py index 902b2209b..98edf54b7 100644 --- a/control/xferfcn.py +++ b/control/xferfcn.py @@ -1288,8 +1288,8 @@ def _isstatic(self): #: The ``s`` constant can be used to create continuous time transfer #: functions using algebraic expressions. #: - #: Example - #: ------- + #: Examples + #: -------- #: >>> s = TransferFunction.s # doctest: +SKIP #: >>> G = (s + 1)/(s**2 + 2*s + 1) # doctest: +SKIP #: @@ -1301,8 +1301,8 @@ def _isstatic(self): #: The ``z`` constant can be used to create discrete time transfer #: functions using algebraic expressions. #: - #: Example - #: ------- + #: Examples + #: -------- #: >>> z = TransferFunction.z # doctest: +SKIP #: >>> G = 2 * z / (4 * z**3 + 3*z - 1) # doctest: +SKIP #: From 3692ef4ee7e753db7485711ad170b58f54555212 Mon Sep 17 00:00:00 2001 From: Richard Murray Date: Thu, 26 Dec 2024 08:05:46 -0800 Subject: [PATCH 21/93] update Makefile and conf.py for current version of sphinx --- doc/Makefile | 2 +- doc/conf.py | 5 ++--- 2 files changed, 3 insertions(+), 4 deletions(-) diff --git a/doc/Makefile b/doc/Makefile index f1a54c3cc..f2c2b891c 100644 --- a/doc/Makefile +++ b/doc/Makefile @@ -38,5 +38,5 @@ ctrlplot-servomech.png: ../control/tests/ctrlplot_test.py # Catch-all target: route all unknown targets to Sphinx using the new # "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS). -html pdf clean doctest: Makefile $(FIGS) +html latexpdf clean doctest: Makefile $(FIGS) @$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) diff --git a/doc/conf.py b/doc/conf.py index 75981d630..b116c0393 100644 --- a/doc/conf.py +++ b/doc/conf.py @@ -23,7 +23,6 @@ try: import sphinx_rtd_theme html_theme = 'sphinx_rtd_theme' - html_theme_path = [sphinx_rtd_theme.get_html_theme_path()] except ImportError: html_theme = 'default' @@ -49,13 +48,13 @@ # If your documentation needs a minimal Sphinx version, state it here. # -needs_sphinx = '3.1' +needs_sphinx = '3.4' # Add any Sphinx extension module names here, as strings. They can be # extensions coming with Sphinx (named 'sphinx.ext.*') or your custom # ones. extensions = [ - 'sphinx.ext.autodoc', 'sphinx.ext.todo', 'sphinx.ext.napoleon', + 'sphinx.ext.autodoc', 'sphinx.ext.todo', 'sphinx.ext.intersphinx', 'sphinx.ext.imgmath', 'sphinx.ext.autosummary', 'nbsphinx', 'numpydoc', 'sphinx.ext.linkcode', 'sphinx.ext.doctest' From f089d3a88585ec6dd7650236b0cdc12020ee24fd Mon Sep 17 00:00:00 2001 From: Richard Murray Date: Thu, 26 Dec 2024 08:08:22 -0800 Subject: [PATCH 22/93] add sphinx_copybutton to user guide --- .github/conda-env/doctest-env.yml | 1 + doc/conf.py | 7 +++---- doc/requirements.txt | 1 + 3 files changed, 5 insertions(+), 4 deletions(-) diff --git a/.github/conda-env/doctest-env.yml b/.github/conda-env/doctest-env.yml index ab7965b7b..a03b64ae7 100644 --- a/.github/conda-env/doctest-env.yml +++ b/.github/conda-env/doctest-env.yml @@ -13,3 +13,4 @@ dependencies: - nbsphinx - docutils - numpydoc + - sphinx-copybutton diff --git a/doc/conf.py b/doc/conf.py index b116c0393..0887e5a4d 100644 --- a/doc/conf.py +++ b/doc/conf.py @@ -54,10 +54,9 @@ # extensions coming with Sphinx (named 'sphinx.ext.*') or your custom # ones. extensions = [ - 'sphinx.ext.autodoc', 'sphinx.ext.todo', - 'sphinx.ext.intersphinx', 'sphinx.ext.imgmath', - 'sphinx.ext.autosummary', 'nbsphinx', 'numpydoc', - 'sphinx.ext.linkcode', 'sphinx.ext.doctest' + 'sphinx.ext.autodoc', 'sphinx.ext.todo', 'sphinx.ext.intersphinx', + 'sphinx.ext.imgmath', 'sphinx.ext.autosummary', 'nbsphinx', 'numpydoc', + 'sphinx.ext.linkcode', 'sphinx.ext.doctest', 'sphinx_copybutton' ] # scan documents for autosummary directives and generate stub pages for each. diff --git a/doc/requirements.txt b/doc/requirements.txt index 123dcc0a2..5fdf9113d 100644 --- a/doc/requirements.txt +++ b/doc/requirements.txt @@ -3,6 +3,7 @@ numpy scipy matplotlib sphinx_rtd_theme +sphinx-copybutton numpydoc ipykernel nbsphinx From d874bcabe3e806b97a635805ee45763f3e90619e Mon Sep 17 00:00:00 2001 From: Richard Murray Date: Thu, 26 Dec 2024 17:05:42 -0800 Subject: [PATCH 23/93] move examples and figures to subdirectories --- doc/Makefile | 25 ++------------- doc/cruise-control.py | 1 - doc/cruise.ipynb | 1 - doc/describing_functions.ipynb | 1 - doc/era_msd.py | 1 - doc/examples/cruise-control.py | 1 + doc/{ => examples}/cruise-control.rst | 0 doc/examples/cruise.ipynb | 1 + doc/examples/describing_functions.ipynb | 1 + doc/examples/era_msd.py | 1 + doc/{ => examples}/era_msd.rst | 0 doc/examples/interconnect_tutorial.ipynb | 1 + doc/examples/kincar-flatsys.py | 1 + doc/{ => examples}/kincar-flatsys.rst | 0 doc/examples/kincar-fusion.ipynb | 1 + doc/examples/markov.py | 1 + doc/{ => examples}/markov.rst | 0 doc/examples/mhe-pvtol.ipynb | 1 + doc/examples/mpc_aircraft.ipynb | 1 + doc/examples/mrac_siso_lyapunov.py | 1 + doc/{ => examples}/mrac_siso_lyapunov.rst | 0 doc/examples/mrac_siso_mit.py | 1 + doc/{ => examples}/mrac_siso_mit.rst | 0 doc/examples/phase_plane_plots.py | 1 + doc/{ => examples}/phase_plane_plots.rst | 0 doc/examples/pvtol-lqr-nested.ipynb | 1 + doc/examples/pvtol-lqr.py | 1 + doc/{ => examples}/pvtol-lqr.rst | 0 doc/examples/pvtol-nested.py | 1 + doc/{ => examples}/pvtol-nested.rst | 0 doc/examples/pvtol-outputfbk.ipynb | 1 + doc/examples/pvtol.py | 1 + doc/examples/robust_mimo.py | 1 + doc/{ => examples}/robust_mimo.rst | 0 doc/examples/robust_siso.py | 1 + doc/{ => examples}/robust_siso.rst | 0 doc/examples/rss-balred.py | 1 + doc/{ => examples}/rss-balred.rst | 0 doc/examples/scherer_etal_ex7_H2_h2syn.py | 1 + .../scherer_etal_ex7_H2_h2syn.rst | 0 doc/examples/scherer_etal_ex7_Hinf_hinfsyn.py | 1 + .../scherer_etal_ex7_Hinf_hinfsyn.rst | 0 doc/examples/secord-matlab.py | 1 + doc/{ => examples}/secord-matlab.rst | 0 .../simulating_discrete_nonlinear.ipynb | 1 + doc/examples/steering-gainsched.py | 1 + doc/{ => examples}/steering-gainsched.rst | 0 doc/examples/steering-optimal.py | 1 + doc/{ => examples}/steering-optimal.rst | 0 doc/examples/steering.ipynb | 1 + doc/examples/stochresp.ipynb | 1 + doc/figures/Makefile | 30 ++++++++++++++++++ doc/{ => figures}/classes.fig | 0 doc/{ => figures}/classes.pdf | Bin .../ctrlplot-pole_zero_subplots.png | Bin doc/{ => figures}/ctrlplot-servomech.png | Bin doc/{ => figures}/freqplot-gangof4.png | Bin .../freqplot-mimo_bode-default.png | Bin .../freqplot-mimo_bode-magonly.png | Bin .../freqplot-mimo_svplot-default.png | Bin doc/{ => figures}/freqplot-nyquist-custom.png | Bin .../freqplot-nyquist-default.png | Bin .../freqplot-siso_bode-default.png | Bin .../freqplot-siso_bode-omega.png | Bin .../freqplot-siso_nichols-default.png | Bin doc/{ => figures}/mpc-overview.png | Bin .../phaseplot-dampedosc-default.png | Bin .../phaseplot-invpend-meshgrid.png | Bin .../phaseplot-oscillator-helpers.png | Bin .../pzmap-siso_ctime-default.png | Bin .../rlocus-siso_ctime-clicked.png | Bin .../rlocus-siso_ctime-default.png | Bin .../rlocus-siso_dtime-default.png | Bin .../rlocus-siso_multiple-nogrid.png | Bin doc/{ => figures}/steering-optimal.png | Bin .../timeplot-mimo_ioresp-mt_tr.png | Bin .../timeplot-mimo_ioresp-ov_lm.png | Bin .../timeplot-mimo_step-default.png | Bin .../timeplot-mimo_step-linestyle.png | Bin .../timeplot-mimo_step-pi_cs.png | Bin doc/interconnect_tutorial.ipynb | 1 - doc/kincar-flatsys.py | 1 - doc/kincar-fusion.ipynb | 1 - doc/markov.py | 1 - doc/mhe-pvtol.ipynb | 1 - doc/mpc_aircraft.ipynb | 1 - doc/mrac_siso_lyapunov.py | 1 - doc/mrac_siso_mit.py | 1 - doc/phase_plane_plots.py | 1 - doc/pvtol-lqr-nested.ipynb | 1 - doc/pvtol-lqr.py | 1 - doc/pvtol-nested.py | 1 - doc/pvtol-outputfbk.ipynb | 1 - doc/pvtol.py | 1 - doc/robust_mimo.py | 1 - doc/robust_siso.py | 1 - doc/rss-balred.py | 1 - doc/scherer_etal_ex7_H2_h2syn.py | 1 - doc/scherer_etal_ex7_Hinf_hinfsyn.py | 1 - doc/secord-matlab.py | 1 - doc/simulating_discrete_nonlinear.ipynb | 1 - doc/steering-gainsched.py | 1 - doc/steering-optimal.py | 1 - doc/steering.ipynb | 1 - doc/stochresp.ipynb | 1 - 105 files changed, 61 insertions(+), 52 deletions(-) delete mode 120000 doc/cruise-control.py delete mode 120000 doc/cruise.ipynb delete mode 120000 doc/describing_functions.ipynb delete mode 120000 doc/era_msd.py create mode 120000 doc/examples/cruise-control.py rename doc/{ => examples}/cruise-control.rst (100%) create mode 120000 doc/examples/cruise.ipynb create mode 120000 doc/examples/describing_functions.ipynb create mode 120000 doc/examples/era_msd.py rename doc/{ => examples}/era_msd.rst (100%) create mode 120000 doc/examples/interconnect_tutorial.ipynb create mode 120000 doc/examples/kincar-flatsys.py rename doc/{ => examples}/kincar-flatsys.rst (100%) create mode 120000 doc/examples/kincar-fusion.ipynb create mode 120000 doc/examples/markov.py rename doc/{ => examples}/markov.rst (100%) create mode 120000 doc/examples/mhe-pvtol.ipynb create mode 120000 doc/examples/mpc_aircraft.ipynb create mode 120000 doc/examples/mrac_siso_lyapunov.py rename doc/{ => examples}/mrac_siso_lyapunov.rst (100%) create mode 120000 doc/examples/mrac_siso_mit.py rename doc/{ => examples}/mrac_siso_mit.rst (100%) create mode 120000 doc/examples/phase_plane_plots.py rename doc/{ => examples}/phase_plane_plots.rst (100%) create mode 120000 doc/examples/pvtol-lqr-nested.ipynb create mode 120000 doc/examples/pvtol-lqr.py rename doc/{ => examples}/pvtol-lqr.rst (100%) create mode 120000 doc/examples/pvtol-nested.py rename doc/{ => examples}/pvtol-nested.rst (100%) create mode 120000 doc/examples/pvtol-outputfbk.ipynb create mode 120000 doc/examples/pvtol.py create mode 120000 doc/examples/robust_mimo.py rename doc/{ => examples}/robust_mimo.rst (100%) create mode 120000 doc/examples/robust_siso.py rename doc/{ => examples}/robust_siso.rst (100%) create mode 120000 doc/examples/rss-balred.py rename doc/{ => examples}/rss-balred.rst (100%) create mode 120000 doc/examples/scherer_etal_ex7_H2_h2syn.py rename doc/{ => examples}/scherer_etal_ex7_H2_h2syn.rst (100%) create mode 120000 doc/examples/scherer_etal_ex7_Hinf_hinfsyn.py rename doc/{ => examples}/scherer_etal_ex7_Hinf_hinfsyn.rst (100%) create mode 120000 doc/examples/secord-matlab.py rename doc/{ => examples}/secord-matlab.rst (100%) create mode 120000 doc/examples/simulating_discrete_nonlinear.ipynb create mode 120000 doc/examples/steering-gainsched.py rename doc/{ => examples}/steering-gainsched.rst (100%) create mode 120000 doc/examples/steering-optimal.py rename doc/{ => examples}/steering-optimal.rst (100%) create mode 120000 doc/examples/steering.ipynb create mode 120000 doc/examples/stochresp.ipynb create mode 100644 doc/figures/Makefile rename doc/{ => figures}/classes.fig (100%) rename doc/{ => figures}/classes.pdf (100%) rename doc/{ => figures}/ctrlplot-pole_zero_subplots.png (100%) rename doc/{ => figures}/ctrlplot-servomech.png (100%) rename doc/{ => figures}/freqplot-gangof4.png (100%) rename doc/{ => figures}/freqplot-mimo_bode-default.png (100%) rename doc/{ => figures}/freqplot-mimo_bode-magonly.png (100%) rename doc/{ => figures}/freqplot-mimo_svplot-default.png (100%) rename doc/{ => figures}/freqplot-nyquist-custom.png (100%) rename doc/{ => figures}/freqplot-nyquist-default.png (100%) rename doc/{ => figures}/freqplot-siso_bode-default.png (100%) rename doc/{ => figures}/freqplot-siso_bode-omega.png (100%) rename doc/{ => figures}/freqplot-siso_nichols-default.png (100%) rename doc/{ => figures}/mpc-overview.png (100%) rename doc/{ => figures}/phaseplot-dampedosc-default.png (100%) rename doc/{ => figures}/phaseplot-invpend-meshgrid.png (100%) rename doc/{ => figures}/phaseplot-oscillator-helpers.png (100%) rename doc/{ => figures}/pzmap-siso_ctime-default.png (100%) rename doc/{ => figures}/rlocus-siso_ctime-clicked.png (100%) rename doc/{ => figures}/rlocus-siso_ctime-default.png (100%) rename doc/{ => figures}/rlocus-siso_dtime-default.png (100%) rename doc/{ => figures}/rlocus-siso_multiple-nogrid.png (100%) rename doc/{ => figures}/steering-optimal.png (100%) rename doc/{ => figures}/timeplot-mimo_ioresp-mt_tr.png (100%) rename doc/{ => figures}/timeplot-mimo_ioresp-ov_lm.png (100%) rename doc/{ => figures}/timeplot-mimo_step-default.png (100%) rename doc/{ => figures}/timeplot-mimo_step-linestyle.png (100%) rename doc/{ => figures}/timeplot-mimo_step-pi_cs.png (100%) delete mode 120000 doc/interconnect_tutorial.ipynb delete mode 120000 doc/kincar-flatsys.py delete mode 120000 doc/kincar-fusion.ipynb delete mode 120000 doc/markov.py delete mode 120000 doc/mhe-pvtol.ipynb delete mode 120000 doc/mpc_aircraft.ipynb delete mode 120000 doc/mrac_siso_lyapunov.py delete mode 120000 doc/mrac_siso_mit.py delete mode 120000 doc/phase_plane_plots.py delete mode 120000 doc/pvtol-lqr-nested.ipynb delete mode 120000 doc/pvtol-lqr.py delete mode 120000 doc/pvtol-nested.py delete mode 120000 doc/pvtol-outputfbk.ipynb delete mode 120000 doc/pvtol.py delete mode 120000 doc/robust_mimo.py delete mode 120000 doc/robust_siso.py delete mode 120000 doc/rss-balred.py delete mode 120000 doc/scherer_etal_ex7_H2_h2syn.py delete mode 120000 doc/scherer_etal_ex7_Hinf_hinfsyn.py delete mode 120000 doc/secord-matlab.py delete mode 120000 doc/simulating_discrete_nonlinear.ipynb delete mode 120000 doc/steering-gainsched.py delete mode 120000 doc/steering-optimal.py delete mode 120000 doc/steering.ipynb delete mode 120000 doc/stochresp.ipynb diff --git a/doc/Makefile b/doc/Makefile index f2c2b891c..227f63fbd 100644 --- a/doc/Makefile +++ b/doc/Makefile @@ -14,29 +14,8 @@ help: .PHONY: help Makefile -# Rules to create figures -FIGS = classes.pdf timeplot-mimo_step-default.png \ - freqplot-siso_bode-default.png rlocus-siso_ctime-default.png \ - phaseplot-dampedosc-default.png ctrlplot-servomech.png -classes.pdf: classes.fig - fig2dev -Lpdf $< $@ - -timeplot-mimo_step-default.png: ../control/tests/timeplot_test.py - PYTHONPATH=.. python $< - -freqplot-siso_bode-default.png: ../control/tests/freqplot_test.py - PYTHONPATH=.. python $< - -rlocus-siso_ctime-default.png: ../control/tests/rlocus_test.py - PYTHONPATH=.. python $< - -phaseplot-dampedosc-default.png: ../control/tests/phaseplot_test.py - PYTHONPATH=.. python $< - -ctrlplot-servomech.png: ../control/tests/ctrlplot_test.py - PYTHONPATH=.. python $< - # Catch-all target: route all unknown targets to Sphinx using the new # "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS). -html latexpdf clean doctest: Makefile $(FIGS) +html latexpdf clean doctest: Makefile + make -C figures @$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) diff --git a/doc/cruise-control.py b/doc/cruise-control.py deleted file mode 120000 index cfa1c8195..000000000 --- a/doc/cruise-control.py +++ /dev/null @@ -1 +0,0 @@ -../examples/cruise-control.py \ No newline at end of file diff --git a/doc/cruise.ipynb b/doc/cruise.ipynb deleted file mode 120000 index f712e2d5f..000000000 --- a/doc/cruise.ipynb +++ /dev/null @@ -1 +0,0 @@ -../examples/cruise.ipynb \ No newline at end of file diff --git a/doc/describing_functions.ipynb b/doc/describing_functions.ipynb deleted file mode 120000 index 14bcb69a4..000000000 --- a/doc/describing_functions.ipynb +++ /dev/null @@ -1 +0,0 @@ -../examples/describing_functions.ipynb \ No newline at end of file diff --git a/doc/era_msd.py b/doc/era_msd.py deleted file mode 120000 index 0cf6a5282..000000000 --- a/doc/era_msd.py +++ /dev/null @@ -1 +0,0 @@ -../examples/era_msd.py \ No newline at end of file diff --git a/doc/examples/cruise-control.py b/doc/examples/cruise-control.py new file mode 120000 index 000000000..b232fda38 --- /dev/null +++ b/doc/examples/cruise-control.py @@ -0,0 +1 @@ +../../examples/cruise-control.py \ No newline at end of file diff --git a/doc/cruise-control.rst b/doc/examples/cruise-control.rst similarity index 100% rename from doc/cruise-control.rst rename to doc/examples/cruise-control.rst diff --git a/doc/examples/cruise.ipynb b/doc/examples/cruise.ipynb new file mode 120000 index 000000000..4e737aa10 --- /dev/null +++ b/doc/examples/cruise.ipynb @@ -0,0 +1 @@ +../../examples/cruise.ipynb \ No newline at end of file diff --git a/doc/examples/describing_functions.ipynb b/doc/examples/describing_functions.ipynb new file mode 120000 index 000000000..b45877fc1 --- /dev/null +++ b/doc/examples/describing_functions.ipynb @@ -0,0 +1 @@ +../../examples/describing_functions.ipynb \ No newline at end of file diff --git a/doc/examples/era_msd.py b/doc/examples/era_msd.py new file mode 120000 index 000000000..40783be13 --- /dev/null +++ b/doc/examples/era_msd.py @@ -0,0 +1 @@ +../../examples/era_msd.py \ No newline at end of file diff --git a/doc/era_msd.rst b/doc/examples/era_msd.rst similarity index 100% rename from doc/era_msd.rst rename to doc/examples/era_msd.rst diff --git a/doc/examples/interconnect_tutorial.ipynb b/doc/examples/interconnect_tutorial.ipynb new file mode 120000 index 000000000..69b840e70 --- /dev/null +++ b/doc/examples/interconnect_tutorial.ipynb @@ -0,0 +1 @@ +../../examples/interconnect_tutorial.ipynb \ No newline at end of file diff --git a/doc/examples/kincar-flatsys.py b/doc/examples/kincar-flatsys.py new file mode 120000 index 000000000..287ee0065 --- /dev/null +++ b/doc/examples/kincar-flatsys.py @@ -0,0 +1 @@ +../../examples/kincar-flatsys.py \ No newline at end of file diff --git a/doc/kincar-flatsys.rst b/doc/examples/kincar-flatsys.rst similarity index 100% rename from doc/kincar-flatsys.rst rename to doc/examples/kincar-flatsys.rst diff --git a/doc/examples/kincar-fusion.ipynb b/doc/examples/kincar-fusion.ipynb new file mode 120000 index 000000000..5e6002937 --- /dev/null +++ b/doc/examples/kincar-fusion.ipynb @@ -0,0 +1 @@ +../../examples/kincar-fusion.ipynb \ No newline at end of file diff --git a/doc/examples/markov.py b/doc/examples/markov.py new file mode 120000 index 000000000..39015d0c9 --- /dev/null +++ b/doc/examples/markov.py @@ -0,0 +1 @@ +../../examples/markov.py \ No newline at end of file diff --git a/doc/markov.rst b/doc/examples/markov.rst similarity index 100% rename from doc/markov.rst rename to doc/examples/markov.rst diff --git a/doc/examples/mhe-pvtol.ipynb b/doc/examples/mhe-pvtol.ipynb new file mode 120000 index 000000000..fcbf4577b --- /dev/null +++ b/doc/examples/mhe-pvtol.ipynb @@ -0,0 +1 @@ +../../examples/mhe-pvtol.ipynb \ No newline at end of file diff --git a/doc/examples/mpc_aircraft.ipynb b/doc/examples/mpc_aircraft.ipynb new file mode 120000 index 000000000..f5664d841 --- /dev/null +++ b/doc/examples/mpc_aircraft.ipynb @@ -0,0 +1 @@ +../../examples/mpc_aircraft.ipynb \ No newline at end of file diff --git a/doc/examples/mrac_siso_lyapunov.py b/doc/examples/mrac_siso_lyapunov.py new file mode 120000 index 000000000..6dea0c1bb --- /dev/null +++ b/doc/examples/mrac_siso_lyapunov.py @@ -0,0 +1 @@ +../../examples/mrac_siso_lyapunov.py \ No newline at end of file diff --git a/doc/mrac_siso_lyapunov.rst b/doc/examples/mrac_siso_lyapunov.rst similarity index 100% rename from doc/mrac_siso_lyapunov.rst rename to doc/examples/mrac_siso_lyapunov.rst diff --git a/doc/examples/mrac_siso_mit.py b/doc/examples/mrac_siso_mit.py new file mode 120000 index 000000000..1ab820a72 --- /dev/null +++ b/doc/examples/mrac_siso_mit.py @@ -0,0 +1 @@ +../../examples/mrac_siso_mit.py \ No newline at end of file diff --git a/doc/mrac_siso_mit.rst b/doc/examples/mrac_siso_mit.rst similarity index 100% rename from doc/mrac_siso_mit.rst rename to doc/examples/mrac_siso_mit.rst diff --git a/doc/examples/phase_plane_plots.py b/doc/examples/phase_plane_plots.py new file mode 120000 index 000000000..65ee1dacd --- /dev/null +++ b/doc/examples/phase_plane_plots.py @@ -0,0 +1 @@ +../../examples/phase_plane_plots.py \ No newline at end of file diff --git a/doc/phase_plane_plots.rst b/doc/examples/phase_plane_plots.rst similarity index 100% rename from doc/phase_plane_plots.rst rename to doc/examples/phase_plane_plots.rst diff --git a/doc/examples/pvtol-lqr-nested.ipynb b/doc/examples/pvtol-lqr-nested.ipynb new file mode 120000 index 000000000..879d6b73d --- /dev/null +++ b/doc/examples/pvtol-lqr-nested.ipynb @@ -0,0 +1 @@ +../../examples/pvtol-lqr-nested.ipynb \ No newline at end of file diff --git a/doc/examples/pvtol-lqr.py b/doc/examples/pvtol-lqr.py new file mode 120000 index 000000000..45af4dec9 --- /dev/null +++ b/doc/examples/pvtol-lqr.py @@ -0,0 +1 @@ +../../examples/pvtol-lqr.py \ No newline at end of file diff --git a/doc/pvtol-lqr.rst b/doc/examples/pvtol-lqr.rst similarity index 100% rename from doc/pvtol-lqr.rst rename to doc/examples/pvtol-lqr.rst diff --git a/doc/examples/pvtol-nested.py b/doc/examples/pvtol-nested.py new file mode 120000 index 000000000..8037992d3 --- /dev/null +++ b/doc/examples/pvtol-nested.py @@ -0,0 +1 @@ +../../examples/pvtol-nested.py \ No newline at end of file diff --git a/doc/pvtol-nested.rst b/doc/examples/pvtol-nested.rst similarity index 100% rename from doc/pvtol-nested.rst rename to doc/examples/pvtol-nested.rst diff --git a/doc/examples/pvtol-outputfbk.ipynb b/doc/examples/pvtol-outputfbk.ipynb new file mode 120000 index 000000000..22f1b3622 --- /dev/null +++ b/doc/examples/pvtol-outputfbk.ipynb @@ -0,0 +1 @@ +../../examples/pvtol-outputfbk.ipynb \ No newline at end of file diff --git a/doc/examples/pvtol.py b/doc/examples/pvtol.py new file mode 120000 index 000000000..c36bee0cf --- /dev/null +++ b/doc/examples/pvtol.py @@ -0,0 +1 @@ +../../examples/pvtol.py \ No newline at end of file diff --git a/doc/examples/robust_mimo.py b/doc/examples/robust_mimo.py new file mode 120000 index 000000000..2075f6463 --- /dev/null +++ b/doc/examples/robust_mimo.py @@ -0,0 +1 @@ +../../examples/robust_mimo.py \ No newline at end of file diff --git a/doc/robust_mimo.rst b/doc/examples/robust_mimo.rst similarity index 100% rename from doc/robust_mimo.rst rename to doc/examples/robust_mimo.rst diff --git a/doc/examples/robust_siso.py b/doc/examples/robust_siso.py new file mode 120000 index 000000000..05b0eeab8 --- /dev/null +++ b/doc/examples/robust_siso.py @@ -0,0 +1 @@ +../../examples/robust_siso.py \ No newline at end of file diff --git a/doc/robust_siso.rst b/doc/examples/robust_siso.rst similarity index 100% rename from doc/robust_siso.rst rename to doc/examples/robust_siso.rst diff --git a/doc/examples/rss-balred.py b/doc/examples/rss-balred.py new file mode 120000 index 000000000..7c5d94c71 --- /dev/null +++ b/doc/examples/rss-balred.py @@ -0,0 +1 @@ +../../examples/rss-balred.py \ No newline at end of file diff --git a/doc/rss-balred.rst b/doc/examples/rss-balred.rst similarity index 100% rename from doc/rss-balred.rst rename to doc/examples/rss-balred.rst diff --git a/doc/examples/scherer_etal_ex7_H2_h2syn.py b/doc/examples/scherer_etal_ex7_H2_h2syn.py new file mode 120000 index 000000000..8459ba382 --- /dev/null +++ b/doc/examples/scherer_etal_ex7_H2_h2syn.py @@ -0,0 +1 @@ +../../examples/scherer_etal_ex7_H2_h2syn.py \ No newline at end of file diff --git a/doc/scherer_etal_ex7_H2_h2syn.rst b/doc/examples/scherer_etal_ex7_H2_h2syn.rst similarity index 100% rename from doc/scherer_etal_ex7_H2_h2syn.rst rename to doc/examples/scherer_etal_ex7_H2_h2syn.rst diff --git a/doc/examples/scherer_etal_ex7_Hinf_hinfsyn.py b/doc/examples/scherer_etal_ex7_Hinf_hinfsyn.py new file mode 120000 index 000000000..b96545990 --- /dev/null +++ b/doc/examples/scherer_etal_ex7_Hinf_hinfsyn.py @@ -0,0 +1 @@ +../../examples/scherer_etal_ex7_Hinf_hinfsyn.py \ No newline at end of file diff --git a/doc/scherer_etal_ex7_Hinf_hinfsyn.rst b/doc/examples/scherer_etal_ex7_Hinf_hinfsyn.rst similarity index 100% rename from doc/scherer_etal_ex7_Hinf_hinfsyn.rst rename to doc/examples/scherer_etal_ex7_Hinf_hinfsyn.rst diff --git a/doc/examples/secord-matlab.py b/doc/examples/secord-matlab.py new file mode 120000 index 000000000..4ddd3f3f3 --- /dev/null +++ b/doc/examples/secord-matlab.py @@ -0,0 +1 @@ +../../examples/secord-matlab.py \ No newline at end of file diff --git a/doc/secord-matlab.rst b/doc/examples/secord-matlab.rst similarity index 100% rename from doc/secord-matlab.rst rename to doc/examples/secord-matlab.rst diff --git a/doc/examples/simulating_discrete_nonlinear.ipynb b/doc/examples/simulating_discrete_nonlinear.ipynb new file mode 120000 index 000000000..4bc577d4b --- /dev/null +++ b/doc/examples/simulating_discrete_nonlinear.ipynb @@ -0,0 +1 @@ +../../examples/simulating_discrete_nonlinear.ipynb \ No newline at end of file diff --git a/doc/examples/steering-gainsched.py b/doc/examples/steering-gainsched.py new file mode 120000 index 000000000..3eabc17c4 --- /dev/null +++ b/doc/examples/steering-gainsched.py @@ -0,0 +1 @@ +../../examples/steering-gainsched.py \ No newline at end of file diff --git a/doc/steering-gainsched.rst b/doc/examples/steering-gainsched.rst similarity index 100% rename from doc/steering-gainsched.rst rename to doc/examples/steering-gainsched.rst diff --git a/doc/examples/steering-optimal.py b/doc/examples/steering-optimal.py new file mode 120000 index 000000000..c351e70e7 --- /dev/null +++ b/doc/examples/steering-optimal.py @@ -0,0 +1 @@ +../../examples/steering-optimal.py \ No newline at end of file diff --git a/doc/steering-optimal.rst b/doc/examples/steering-optimal.rst similarity index 100% rename from doc/steering-optimal.rst rename to doc/examples/steering-optimal.rst diff --git a/doc/examples/steering.ipynb b/doc/examples/steering.ipynb new file mode 120000 index 000000000..051713e10 --- /dev/null +++ b/doc/examples/steering.ipynb @@ -0,0 +1 @@ +../../examples/steering.ipynb \ No newline at end of file diff --git a/doc/examples/stochresp.ipynb b/doc/examples/stochresp.ipynb new file mode 120000 index 000000000..56db315a2 --- /dev/null +++ b/doc/examples/stochresp.ipynb @@ -0,0 +1 @@ +../../examples/stochresp.ipynb \ No newline at end of file diff --git a/doc/figures/Makefile b/doc/figures/Makefile new file mode 100644 index 000000000..913cc0258 --- /dev/null +++ b/doc/figures/Makefile @@ -0,0 +1,30 @@ +# Makefile- rules to create figures +# RMM, 26 Dec 2024 + +# List of figures that need to be created (first figure generated is OK) +FIGS = classes.pdf timeplot-mimo_step-default.png \ + freqplot-siso_bode-default.png rlocus-siso_ctime-default.png \ + phaseplot-dampedosc-default.png ctrlplot-servomech.png + +# Location of the control package +SRCDIR = ../.. + +all: $(FIGS) + +classes.pdf: classes.fig + fig2dev -Lpdf $< $@ + +timeplot-mimo_step-default.png: $(SRCDIR)/control/tests/timeplot_test.py + PYTHONPATH=$(SRCDIR) python $< + +freqplot-siso_bode-default.png: $(SRCDIR)/control/tests/freqplot_test.py + PYTHONPATH=$(SRCDIR) python $< + +rlocus-siso_ctime-default.png: $(SRCDIR)/control/tests/rlocus_test.py + PYTHONPATH=$(SRCDIR) python $< + +phaseplot-dampedosc-default.png: $(SRCDIR)/control/tests/phaseplot_test.py + PYTHONPATH=$(SRCDIR) python $< + +ctrlplot-servomech.png: $(SRCDIR)/control/tests/ctrlplot_test.py + PYTHONPATH=$(SRCDIR) python $< diff --git a/doc/classes.fig b/doc/figures/classes.fig similarity index 100% rename from doc/classes.fig rename to doc/figures/classes.fig diff --git a/doc/classes.pdf b/doc/figures/classes.pdf similarity index 100% rename from doc/classes.pdf rename to doc/figures/classes.pdf diff --git a/doc/ctrlplot-pole_zero_subplots.png b/doc/figures/ctrlplot-pole_zero_subplots.png similarity index 100% rename from doc/ctrlplot-pole_zero_subplots.png rename to doc/figures/ctrlplot-pole_zero_subplots.png diff --git a/doc/ctrlplot-servomech.png b/doc/figures/ctrlplot-servomech.png similarity index 100% rename from doc/ctrlplot-servomech.png rename to doc/figures/ctrlplot-servomech.png diff --git a/doc/freqplot-gangof4.png b/doc/figures/freqplot-gangof4.png similarity index 100% rename from doc/freqplot-gangof4.png rename to doc/figures/freqplot-gangof4.png diff --git 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-../examples/steering-gainsched.py \ No newline at end of file diff --git a/doc/steering-optimal.py b/doc/steering-optimal.py deleted file mode 120000 index 506033ec1..000000000 --- a/doc/steering-optimal.py +++ /dev/null @@ -1 +0,0 @@ -../examples/steering-optimal.py \ No newline at end of file diff --git a/doc/steering.ipynb b/doc/steering.ipynb deleted file mode 120000 index a7f083b90..000000000 --- a/doc/steering.ipynb +++ /dev/null @@ -1 +0,0 @@ -../examples/steering.ipynb \ No newline at end of file diff --git a/doc/stochresp.ipynb b/doc/stochresp.ipynb deleted file mode 120000 index 36190a54c..000000000 --- a/doc/stochresp.ipynb +++ /dev/null @@ -1 +0,0 @@ -../examples/stochresp.ipynb \ No newline at end of file From 3128e0b6fb7ec67f644371e0934a385b4ce629ed Mon Sep 17 00:00:00 2001 From: Richard Murray Date: Thu, 26 Dec 2024 20:17:15 -0800 Subject: [PATCH 24/93] move plotting -> response and conventions -> {linear, timeplot, config} --- doc/{conventions.rst => config.rst} | 0 doc/linear.rst | 373 ++++++++++++++++++++++++++++ doc/{plotting.rst => response.rst} | 0 doc/timeresp.rst | 373 ++++++++++++++++++++++++++++ 4 files changed, 746 insertions(+) rename doc/{conventions.rst => config.rst} (100%) create mode 100644 doc/linear.rst rename doc/{plotting.rst => response.rst} (100%) create mode 100644 doc/timeresp.rst diff --git a/doc/conventions.rst b/doc/config.rst similarity index 100% rename from doc/conventions.rst rename to doc/config.rst diff --git a/doc/linear.rst b/doc/linear.rst new file mode 100644 index 000000000..d2394e040 --- /dev/null +++ b/doc/linear.rst @@ -0,0 +1,373 @@ +.. _conventions-ref: + +.. currentmodule:: control + +******************* +Library conventions +******************* + +The python-control library uses a set of standard conventions for the +way that different types of standard information used by the library. +Throughout this manual, we assume the `control` package has been +imported as `ct`. + +LTI system representation +========================= + +Linear time invariant (LTI) systems are represented in python-control in +state space, transfer function, or frequency response data (FRD) form. Most +functions in the toolbox will operate on any of these data types, and +functions for converting between compatible types are provided. + +State space systems +------------------- +The :class:`StateSpace` class is used to represent state-space realizations +of linear time-invariant (LTI) systems: + +.. math:: + + \frac{dx}{dt} &= A x + B u \\ + y &= C x + D u + +where u is the input, y is the output, and x is the state. + +To create a state space system, use the :func:`ss` function:: + + sys = ct.ss(A, B, C, D) + +State space systems can be manipulated using standard arithmetic operations +as well as the :func:`feedback`, :func:`parallel`, and :func:`series` +function. A full list of functions can be found in :ref:`function-ref`. + +Transfer functions +------------------ +The :class:`TransferFunction` class is used to represent input/output +transfer functions + +.. math:: + + G(s) = \frac{\text{num}(s)}{\text{den}(s)} + = \frac{a_0 s^m + a_1 s^{m-1} + \cdots + a_m} + {b_0 s^n + b_1 s^{n-1} + \cdots + b_n}, + +where n is generally greater than or equal to m (for a proper transfer +function). + +To create a transfer function, use the :func:`tf` function:: + + sys = ct.tf(num, den) + +Transfer functions can be manipulated using standard arithmetic operations +as well as the :func:`feedback`, :func:`parallel`, and :func:`series` +function. A full list of functions can be found in :ref:`function-ref`. + +Frequency response data (FRD) systems +------------------------------------- +The :class:`FrequencyResponseData` (FRD) class is used to represent systems in +frequency response data form. + +The main data members are `omega` and `fresp`, where `omega` is a 1D array +with the frequency points of the response, and `fresp` is a 3D array, with +the first dimension corresponding to the output index of the system, the +second dimension corresponding to the input index, and the 3rd dimension +corresponding to the frequency points in omega. + +FRD systems can be created with the :func:`~control.frd` factory function. +Frequency response data systems have a somewhat more limited set of +functions that are available, although all of the standard algebraic +manipulations can be performed. + +The FRD class is also used as the return type for the +:func:`frequency_response` function (and the equivalent method for the +:class:`StateSpace` and :class:`TransferFunction` classes). This +object can be assigned to a tuple using:: + + mag, phase, omega = response + +where `mag` is the magnitude (absolute value, not dB or log10) of the +system frequency response, `phase` is the wrapped phase in radians of +the system frequency response, and `omega` is the (sorted) frequencies +at which the response was evaluated. If the system is SISO and the +`squeeze` argument to :func:`frequency_response` is not True, +`magnitude` and `phase` are 1D, indexed by frequency. If the system +is not SISO or `squeeze` is False, the array is 3D, indexed by the +output, input, and frequency. If `squeeze` is True then +single-dimensional axes are removed. The processing of the `squeeze` +keyword can be changed by calling the response function with a new +argument:: + + mag, phase, omega = response(squeeze=False) + +Frequency response objects are also available as named properties of the +``response`` object: ``response.magnitude``, ``response.phase``, and +``response.response`` (for the complex response). For MIMO systems, these +elements of the frequency response can be accessed using the names of the +inputs and outputs:: + + response.magnitude['y[0]', 'u[1]'] + +where the signal names are based on the system that generated the frequency +response. + +Note: The ``fresp`` data member is stored as a NumPy array and cannot be +accessed with signal names. Use ``response.response`` to access the +complex frequency response using signal names. + +Discrete time systems +--------------------- +A discrete time system is created by specifying a nonzero 'timebase', dt. +The timebase argument can be given when a system is constructed: + +* `dt = 0`: continuous time system (default) +* `dt > 0`: discrete time system with sampling period 'dt' +* `dt = True`: discrete time with unspecified sampling period +* `dt = None`: no timebase specified + +Only the :class:`StateSpace`, :class:`TransferFunction`, and +:class:`InputOutputSystem` classes allow explicit representation of +discrete time systems. + +Systems must have compatible timebases in order to be combined. A discrete +time system with unspecified sampling time (`dt = True`) can be combined with +a system having a specified sampling time; the result will be a discrete time +system with the sample time of the latter system. Similarly, a system with +timebase `None` can be combined with a system having a specified timebase; the +result will have the timebase of the latter system. For continuous time +systems, the :func:`sample_system` function or the :meth:`StateSpace.sample` +and :meth:`TransferFunction.sample` methods can be used to create a discrete +time system from a continuous time system. See +:ref:`utility-and-conversions`. The default value of `dt` can be changed by +changing the value of `control.config.defaults['control.default_dt']`. + +Conversion between representations +---------------------------------- +LTI systems can be converted between representations either by calling the +constructor for the desired data type using the original system as the sole +argument or using the explicit conversion functions :func:`ss2tf` and +:func:`tf2ss`. + +Subsystems +---------- +Subsets of input/output pairs for LTI systems can be obtained by indexing +the system using either numerical indices (including slices) or signal +names:: + + subsys = sys[[0, 2], 0:2] + subsys = sys[['y[0]', 'y[2]'], ['u[0]', 'u[1]']] + +Signal names for an indexed subsystem are preserved from the original +system and the subsystem name is set according to the values of +``control.config.defaults['iosys.indexed_system_name_prefix']`` and +``control.config.defaults['iosys.indexed_system_name_suffix']``. The default +subsystem name is the original system name with '$indexed' appended. + +Simulating LTI systems +====================== + +A number of functions are available for computing the output (and +state) response of an LTI systems: + +.. autosummary:: + :toctree: generated/ + + initial_response + step_response + impulse_response + forced_response + +Each of these functions returns a :class:`TimeResponseData` object +that contains the data for the time response (described in more detail +in the next section). + +The :func:`forced_response` system is the most general and allows by +the zero initial state response to be simulated as well as the +response from a non-zero initial condition. + +For linear time invariant (LTI) systems, the :func:`impulse_response`, +:func:`initial_response`, and :func:`step_response` functions will +automatically compute the time vector based on the poles and zeros of +the system. If a list of systems is passed, a common time vector will be +computed and a list of responses will be returned in the form of a +:class:`TimeResponseList` object. The :func:`forced_response` function can +also take a list of systems, to which a single common input is applied. +The :class:`TimeResponseList` object has a `plot()` method that will plot +each of the responses in turn, using a sequence of different colors with +appropriate titles and legends. + +In addition the :func:`input_output_response` function, which handles +simulation of nonlinear systems and interconnected systems, can be +used. For an LTI system, results are generally more accurate using +the LTI simulation functions above. The :func:`input_output_response` +function is described in more detail in the :ref:`iosys-module` section. + +.. currentmodule:: control +.. _time-series-convention: + +Time series data +---------------- +A variety of functions in the library return time series data: sequences of +values that change over time. A common set of conventions is used for +returning such data: columns represent different points in time, rows are +different components (e.g., inputs, outputs or states). For return +arguments, an array of times is given as the first returned argument, +followed by one or more arrays of variable values. This convention is used +throughout the library, for example in the functions +:func:`forced_response`, :func:`step_response`, :func:`impulse_response`, +and :func:`initial_response`. + +.. note:: + The convention used by python-control is different from the convention + used in the `scipy.signal + `_ library. In + Scipy's convention the meaning of rows and columns is interchanged. + Thus, all 2D values must be transposed when they are used with functions + from `scipy.signal`_. + +The time vector is a 1D array with shape (n, ):: + + T = [t1, t2, t3, ..., tn ] + +Input, state, and output all follow the same convention. Columns are different +points in time, rows are different components:: + + U = [[u1(t1), u1(t2), u1(t3), ..., u1(tn)] + [u2(t1), u2(t2), u2(t3), ..., u2(tn)] + ... + ... + [ui(t1), ui(t2), ui(t3), ..., ui(tn)]] + +(and similarly for `X`, `Y`). So, `U[:, 2]` is the system's input at the +third point in time; and `U[1]` or `U[1, :]` is the sequence of values for +the system's second input. + +When there is only one row, a 1D object is accepted or returned, which adds +convenience for SISO systems: + +The initial conditions are either 1D, or 2D with shape (j, 1):: + + X0 = [[x1] + [x2] + ... + ... + [xj]] + +Functions that return time responses (e.g., :func:`forced_response`, +:func:`impulse_response`, :func:`input_output_response`, +:func:`initial_response`, and :func:`step_response`) return a +:class:`TimeResponseData` object that contains the data for the time +response. These data can be accessed via the +:attr:`~TimeResponseData.time`, :attr:`~TimeResponseData.outputs`, +:attr:`~TimeResponseData.states` and :attr:`~TimeResponseData.inputs` +properties:: + + sys = ct.rss(4, 1, 1) + response = ct.step_response(sys) + plt.plot(response.time, response.outputs) + +The dimensions of the response properties depend on the function being +called and whether the system is SISO or MIMO. In addition, some time +response function can return multiple "traces" (input/output pairs), +such as the :func:`step_response` function applied to a MIMO system, +which will compute the step response for each input/output pair. See +:class:`TimeResponseData` for more details. + +The input, output, and state elements of the response can be accessed using +signal names in place of integer offsets:: + + plt.plot(response. time, response.states['x[1]'] + +For multi-trace systems generated by :func:`step_response` and +:func:`impulse_response`, the input name used to generate the trace can be +used to access the appropriate input output pair:: + + plt.plot(response.time, response.outputs['y[0]', 'u[1]']) + +The time response functions can also be assigned to a tuple, which extracts +the time and output (and optionally the state, if the `return_x` keyword is +used). This allows simple commands for plotting:: + + t, y = ct.step_response(sys) + plot(t, y) + +The output of a MIMO LTI system can be plotted like this:: + + t, y = ct.forced_response(sys, t, u) + plot(t, y[0], label='y_0') + plot(t, y[1], label='y_1') + +The convention also works well with the state space form of linear +systems. If `D` is the feedthrough matrix (2D array) of a linear system, +and `U` is its input (array), then the feedthrough part of the system's +response, can be computed like this:: + + ft = D @ U + +Finally, the `to_pandas()` function can be used to create a pandas dataframe:: + + df = response.to_pandas() + +The column labels for the data frame are `time` and the labels for the input, +output, and state signals (`u[i]`, `y[i]`, and `x[i]` by default, but these +can be changed using the `inputs`, `outputs`, and `states` keywords when +constructing the system, as described in :func:`ss`, :func:`tf`, and other +system creation function. Note that when exporting to pandas, "rows" in the +data frame correspond to time and "cols" (DataSeries) correspond to signals. + +.. currentmodule:: control +.. _package-configuration-parameters: + +Package configuration parameters +================================ + +The python-control library can be customized to allow for different default +values for selected parameters. This includes the ability to set the style +for various types of plots and establishing the underlying representation for +state space matrices. + +To set the default value of a configuration variable, set the appropriate +element of the `control.config.defaults` dictionary:: + + ct.config.defaults['module.parameter'] = value + +The :func:`~control.set_defaults` function can also be used to +set multiple configuration parameters at the same time:: + + ct.set_defaults('module', param1=val1, param2=val2, ...] + +Finally, there are also functions available set collections of variables based +on standard configurations. + +Selected variables that can be configured, along with their default values: + + * freqplot.dB (False): Bode plot magnitude plotted in dB (otherwise powers + of 10) + + * freqplot.deg (True): Bode plot phase plotted in degrees (otherwise radians) + + * freqplot.Hz (False): Bode plot frequency plotted in Hertz (otherwise + rad/sec) + + * freqplot.grid (True): Include grids for magnitude and phase plots + + * freqplot.number_of_samples (1000): Number of frequency points in Bode plots + + * freqplot.feature_periphery_decade (1.0): How many decades to include in + the frequency range on both sides of features (poles, zeros). + + * statesp.default_dt and xferfcn.default_dt (None): set the default value + of dt when constructing new LTI systems + + * statesp.remove_useless_states (True): remove states that have no effect + on the input-output dynamics of the system + +Additional parameter variables are documented in individual functions + +Functions that can be used to set standard configurations: + +.. autosummary:: + :toctree: generated/ + + reset_defaults + use_fbs_defaults + use_matlab_defaults + use_legacy_defaults diff --git a/doc/plotting.rst b/doc/response.rst similarity index 100% rename from doc/plotting.rst rename to doc/response.rst diff --git a/doc/timeresp.rst b/doc/timeresp.rst new file mode 100644 index 000000000..d2394e040 --- /dev/null +++ b/doc/timeresp.rst @@ -0,0 +1,373 @@ +.. _conventions-ref: + +.. currentmodule:: control + +******************* +Library conventions +******************* + +The python-control library uses a set of standard conventions for the +way that different types of standard information used by the library. +Throughout this manual, we assume the `control` package has been +imported as `ct`. + +LTI system representation +========================= + +Linear time invariant (LTI) systems are represented in python-control in +state space, transfer function, or frequency response data (FRD) form. Most +functions in the toolbox will operate on any of these data types, and +functions for converting between compatible types are provided. + +State space systems +------------------- +The :class:`StateSpace` class is used to represent state-space realizations +of linear time-invariant (LTI) systems: + +.. math:: + + \frac{dx}{dt} &= A x + B u \\ + y &= C x + D u + +where u is the input, y is the output, and x is the state. + +To create a state space system, use the :func:`ss` function:: + + sys = ct.ss(A, B, C, D) + +State space systems can be manipulated using standard arithmetic operations +as well as the :func:`feedback`, :func:`parallel`, and :func:`series` +function. A full list of functions can be found in :ref:`function-ref`. + +Transfer functions +------------------ +The :class:`TransferFunction` class is used to represent input/output +transfer functions + +.. math:: + + G(s) = \frac{\text{num}(s)}{\text{den}(s)} + = \frac{a_0 s^m + a_1 s^{m-1} + \cdots + a_m} + {b_0 s^n + b_1 s^{n-1} + \cdots + b_n}, + +where n is generally greater than or equal to m (for a proper transfer +function). + +To create a transfer function, use the :func:`tf` function:: + + sys = ct.tf(num, den) + +Transfer functions can be manipulated using standard arithmetic operations +as well as the :func:`feedback`, :func:`parallel`, and :func:`series` +function. A full list of functions can be found in :ref:`function-ref`. + +Frequency response data (FRD) systems +------------------------------------- +The :class:`FrequencyResponseData` (FRD) class is used to represent systems in +frequency response data form. + +The main data members are `omega` and `fresp`, where `omega` is a 1D array +with the frequency points of the response, and `fresp` is a 3D array, with +the first dimension corresponding to the output index of the system, the +second dimension corresponding to the input index, and the 3rd dimension +corresponding to the frequency points in omega. + +FRD systems can be created with the :func:`~control.frd` factory function. +Frequency response data systems have a somewhat more limited set of +functions that are available, although all of the standard algebraic +manipulations can be performed. + +The FRD class is also used as the return type for the +:func:`frequency_response` function (and the equivalent method for the +:class:`StateSpace` and :class:`TransferFunction` classes). This +object can be assigned to a tuple using:: + + mag, phase, omega = response + +where `mag` is the magnitude (absolute value, not dB or log10) of the +system frequency response, `phase` is the wrapped phase in radians of +the system frequency response, and `omega` is the (sorted) frequencies +at which the response was evaluated. If the system is SISO and the +`squeeze` argument to :func:`frequency_response` is not True, +`magnitude` and `phase` are 1D, indexed by frequency. If the system +is not SISO or `squeeze` is False, the array is 3D, indexed by the +output, input, and frequency. If `squeeze` is True then +single-dimensional axes are removed. The processing of the `squeeze` +keyword can be changed by calling the response function with a new +argument:: + + mag, phase, omega = response(squeeze=False) + +Frequency response objects are also available as named properties of the +``response`` object: ``response.magnitude``, ``response.phase``, and +``response.response`` (for the complex response). For MIMO systems, these +elements of the frequency response can be accessed using the names of the +inputs and outputs:: + + response.magnitude['y[0]', 'u[1]'] + +where the signal names are based on the system that generated the frequency +response. + +Note: The ``fresp`` data member is stored as a NumPy array and cannot be +accessed with signal names. Use ``response.response`` to access the +complex frequency response using signal names. + +Discrete time systems +--------------------- +A discrete time system is created by specifying a nonzero 'timebase', dt. +The timebase argument can be given when a system is constructed: + +* `dt = 0`: continuous time system (default) +* `dt > 0`: discrete time system with sampling period 'dt' +* `dt = True`: discrete time with unspecified sampling period +* `dt = None`: no timebase specified + +Only the :class:`StateSpace`, :class:`TransferFunction`, and +:class:`InputOutputSystem` classes allow explicit representation of +discrete time systems. + +Systems must have compatible timebases in order to be combined. A discrete +time system with unspecified sampling time (`dt = True`) can be combined with +a system having a specified sampling time; the result will be a discrete time +system with the sample time of the latter system. Similarly, a system with +timebase `None` can be combined with a system having a specified timebase; the +result will have the timebase of the latter system. For continuous time +systems, the :func:`sample_system` function or the :meth:`StateSpace.sample` +and :meth:`TransferFunction.sample` methods can be used to create a discrete +time system from a continuous time system. See +:ref:`utility-and-conversions`. The default value of `dt` can be changed by +changing the value of `control.config.defaults['control.default_dt']`. + +Conversion between representations +---------------------------------- +LTI systems can be converted between representations either by calling the +constructor for the desired data type using the original system as the sole +argument or using the explicit conversion functions :func:`ss2tf` and +:func:`tf2ss`. + +Subsystems +---------- +Subsets of input/output pairs for LTI systems can be obtained by indexing +the system using either numerical indices (including slices) or signal +names:: + + subsys = sys[[0, 2], 0:2] + subsys = sys[['y[0]', 'y[2]'], ['u[0]', 'u[1]']] + +Signal names for an indexed subsystem are preserved from the original +system and the subsystem name is set according to the values of +``control.config.defaults['iosys.indexed_system_name_prefix']`` and +``control.config.defaults['iosys.indexed_system_name_suffix']``. The default +subsystem name is the original system name with '$indexed' appended. + +Simulating LTI systems +====================== + +A number of functions are available for computing the output (and +state) response of an LTI systems: + +.. autosummary:: + :toctree: generated/ + + initial_response + step_response + impulse_response + forced_response + +Each of these functions returns a :class:`TimeResponseData` object +that contains the data for the time response (described in more detail +in the next section). + +The :func:`forced_response` system is the most general and allows by +the zero initial state response to be simulated as well as the +response from a non-zero initial condition. + +For linear time invariant (LTI) systems, the :func:`impulse_response`, +:func:`initial_response`, and :func:`step_response` functions will +automatically compute the time vector based on the poles and zeros of +the system. If a list of systems is passed, a common time vector will be +computed and a list of responses will be returned in the form of a +:class:`TimeResponseList` object. The :func:`forced_response` function can +also take a list of systems, to which a single common input is applied. +The :class:`TimeResponseList` object has a `plot()` method that will plot +each of the responses in turn, using a sequence of different colors with +appropriate titles and legends. + +In addition the :func:`input_output_response` function, which handles +simulation of nonlinear systems and interconnected systems, can be +used. For an LTI system, results are generally more accurate using +the LTI simulation functions above. The :func:`input_output_response` +function is described in more detail in the :ref:`iosys-module` section. + +.. currentmodule:: control +.. _time-series-convention: + +Time series data +---------------- +A variety of functions in the library return time series data: sequences of +values that change over time. A common set of conventions is used for +returning such data: columns represent different points in time, rows are +different components (e.g., inputs, outputs or states). For return +arguments, an array of times is given as the first returned argument, +followed by one or more arrays of variable values. This convention is used +throughout the library, for example in the functions +:func:`forced_response`, :func:`step_response`, :func:`impulse_response`, +and :func:`initial_response`. + +.. note:: + The convention used by python-control is different from the convention + used in the `scipy.signal + `_ library. In + Scipy's convention the meaning of rows and columns is interchanged. + Thus, all 2D values must be transposed when they are used with functions + from `scipy.signal`_. + +The time vector is a 1D array with shape (n, ):: + + T = [t1, t2, t3, ..., tn ] + +Input, state, and output all follow the same convention. Columns are different +points in time, rows are different components:: + + U = [[u1(t1), u1(t2), u1(t3), ..., u1(tn)] + [u2(t1), u2(t2), u2(t3), ..., u2(tn)] + ... + ... + [ui(t1), ui(t2), ui(t3), ..., ui(tn)]] + +(and similarly for `X`, `Y`). So, `U[:, 2]` is the system's input at the +third point in time; and `U[1]` or `U[1, :]` is the sequence of values for +the system's second input. + +When there is only one row, a 1D object is accepted or returned, which adds +convenience for SISO systems: + +The initial conditions are either 1D, or 2D with shape (j, 1):: + + X0 = [[x1] + [x2] + ... + ... + [xj]] + +Functions that return time responses (e.g., :func:`forced_response`, +:func:`impulse_response`, :func:`input_output_response`, +:func:`initial_response`, and :func:`step_response`) return a +:class:`TimeResponseData` object that contains the data for the time +response. These data can be accessed via the +:attr:`~TimeResponseData.time`, :attr:`~TimeResponseData.outputs`, +:attr:`~TimeResponseData.states` and :attr:`~TimeResponseData.inputs` +properties:: + + sys = ct.rss(4, 1, 1) + response = ct.step_response(sys) + plt.plot(response.time, response.outputs) + +The dimensions of the response properties depend on the function being +called and whether the system is SISO or MIMO. In addition, some time +response function can return multiple "traces" (input/output pairs), +such as the :func:`step_response` function applied to a MIMO system, +which will compute the step response for each input/output pair. See +:class:`TimeResponseData` for more details. + +The input, output, and state elements of the response can be accessed using +signal names in place of integer offsets:: + + plt.plot(response. time, response.states['x[1]'] + +For multi-trace systems generated by :func:`step_response` and +:func:`impulse_response`, the input name used to generate the trace can be +used to access the appropriate input output pair:: + + plt.plot(response.time, response.outputs['y[0]', 'u[1]']) + +The time response functions can also be assigned to a tuple, which extracts +the time and output (and optionally the state, if the `return_x` keyword is +used). This allows simple commands for plotting:: + + t, y = ct.step_response(sys) + plot(t, y) + +The output of a MIMO LTI system can be plotted like this:: + + t, y = ct.forced_response(sys, t, u) + plot(t, y[0], label='y_0') + plot(t, y[1], label='y_1') + +The convention also works well with the state space form of linear +systems. If `D` is the feedthrough matrix (2D array) of a linear system, +and `U` is its input (array), then the feedthrough part of the system's +response, can be computed like this:: + + ft = D @ U + +Finally, the `to_pandas()` function can be used to create a pandas dataframe:: + + df = response.to_pandas() + +The column labels for the data frame are `time` and the labels for the input, +output, and state signals (`u[i]`, `y[i]`, and `x[i]` by default, but these +can be changed using the `inputs`, `outputs`, and `states` keywords when +constructing the system, as described in :func:`ss`, :func:`tf`, and other +system creation function. Note that when exporting to pandas, "rows" in the +data frame correspond to time and "cols" (DataSeries) correspond to signals. + +.. currentmodule:: control +.. _package-configuration-parameters: + +Package configuration parameters +================================ + +The python-control library can be customized to allow for different default +values for selected parameters. This includes the ability to set the style +for various types of plots and establishing the underlying representation for +state space matrices. + +To set the default value of a configuration variable, set the appropriate +element of the `control.config.defaults` dictionary:: + + ct.config.defaults['module.parameter'] = value + +The :func:`~control.set_defaults` function can also be used to +set multiple configuration parameters at the same time:: + + ct.set_defaults('module', param1=val1, param2=val2, ...] + +Finally, there are also functions available set collections of variables based +on standard configurations. + +Selected variables that can be configured, along with their default values: + + * freqplot.dB (False): Bode plot magnitude plotted in dB (otherwise powers + of 10) + + * freqplot.deg (True): Bode plot phase plotted in degrees (otherwise radians) + + * freqplot.Hz (False): Bode plot frequency plotted in Hertz (otherwise + rad/sec) + + * freqplot.grid (True): Include grids for magnitude and phase plots + + * freqplot.number_of_samples (1000): Number of frequency points in Bode plots + + * freqplot.feature_periphery_decade (1.0): How many decades to include in + the frequency range on both sides of features (poles, zeros). + + * statesp.default_dt and xferfcn.default_dt (None): set the default value + of dt when constructing new LTI systems + + * statesp.remove_useless_states (True): remove states that have no effect + on the input-output dynamics of the system + +Additional parameter variables are documented in individual functions + +Functions that can be used to set standard configurations: + +.. autosummary:: + :toctree: generated/ + + reset_defaults + use_fbs_defaults + use_matlab_defaults + use_legacy_defaults From 5308888567f6b71851cdd9b87a29d938b9dd2388 Mon Sep 17 00:00:00 2001 From: Richard Murray Date: Fri, 27 Dec 2024 09:39:39 -0800 Subject: [PATCH 25/93] initial refactoring of user guide and reference manual --- doc/Makefile | 9 +- doc/classes.rst | 2 +- doc/conf.py | 5 +- doc/config.rst | 324 +---- doc/descfcn.rst | 10 +- doc/examples.rst | 57 +- doc/examples/python-control_tutorial.ipynb | 1 + doc/figures/Makefile | 3 + doc/flatsys.rst | 2 - doc/freqresp.rst | 9 + doc/{control.rst => functions.rst} | 157 ++- doc/index.rst | 79 +- doc/intro.rst | 57 +- doc/iosys.rst | 2 - doc/linear.rst | 304 +---- doc/matlab.rst | 4 + doc/nlsys.rst | 11 + doc/nonlinear.rst | 14 + doc/optimal.rst | 21 +- doc/pzmap.rst | 11 + doc/response.rst | 53 +- doc/statesp.rst | 23 + doc/stochastic.rst | 3 + doc/timeresp.rst | 236 +--- doc/xferfcn.rst | 25 + examples/python-control_tutorial.ipynb | 1242 ++++++++++++++++++++ 26 files changed, 1680 insertions(+), 984 deletions(-) create mode 120000 doc/examples/python-control_tutorial.ipynb create mode 100644 doc/freqresp.rst rename doc/{control.rst => functions.rst} (69%) create mode 100644 doc/nlsys.rst create mode 100644 doc/nonlinear.rst create mode 100644 doc/pzmap.rst create mode 100644 doc/statesp.rst create mode 100644 doc/stochastic.rst create mode 100644 doc/xferfcn.rst create mode 100644 examples/python-control_tutorial.ipynb diff --git a/doc/Makefile b/doc/Makefile index 227f63fbd..ff8e16ca7 100644 --- a/doc/Makefile +++ b/doc/Makefile @@ -16,6 +16,13 @@ help: # Catch-all target: route all unknown targets to Sphinx using the new # "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS). -html latexpdf clean doctest: Makefile +html latexpdf doctest: Makefile make -C figures @$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) + +clean: + @$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) + +distclean: clean + make -C figures clean + /bin/rm -rf generated diff --git a/doc/classes.rst b/doc/classes.rst index 3bf8492ee..d9a1bbc1f 100644 --- a/doc/classes.rst +++ b/doc/classes.rst @@ -27,7 +27,7 @@ The following figure illustrates the relationship between the classes and some of the functions that can be used to convert objects from one class to another: -.. image:: classes.pdf +.. image:: figures/classes.pdf :width: 800 Additional classes diff --git a/doc/conf.py b/doc/conf.py index 0887e5a4d..ff5da5436 100644 --- a/doc/conf.py +++ b/doc/conf.py @@ -246,8 +246,9 @@ def linkcode_resolve(domain, info): # (source start file, target name, title, # author, documentclass [howto, manual, or own class]). latex_documents = [ - (master_doc, 'PythonControlLibrary.tex', u'Python Control Library Documentation', - u'RMM', 'manual'), + (master_doc, 'python-control.tex', + u'Python Control Systems Library User Guide', + u'Python Control Developers', 'manual'), ] diff --git a/doc/config.rst b/doc/config.rst index d2394e040..15337b056 100644 --- a/doc/config.rst +++ b/doc/config.rst @@ -1,318 +1,3 @@ -.. _conventions-ref: - -.. currentmodule:: control - -******************* -Library conventions -******************* - -The python-control library uses a set of standard conventions for the -way that different types of standard information used by the library. -Throughout this manual, we assume the `control` package has been -imported as `ct`. - -LTI system representation -========================= - -Linear time invariant (LTI) systems are represented in python-control in -state space, transfer function, or frequency response data (FRD) form. Most -functions in the toolbox will operate on any of these data types, and -functions for converting between compatible types are provided. - -State space systems -------------------- -The :class:`StateSpace` class is used to represent state-space realizations -of linear time-invariant (LTI) systems: - -.. math:: - - \frac{dx}{dt} &= A x + B u \\ - y &= C x + D u - -where u is the input, y is the output, and x is the state. - -To create a state space system, use the :func:`ss` function:: - - sys = ct.ss(A, B, C, D) - -State space systems can be manipulated using standard arithmetic operations -as well as the :func:`feedback`, :func:`parallel`, and :func:`series` -function. A full list of functions can be found in :ref:`function-ref`. - -Transfer functions ------------------- -The :class:`TransferFunction` class is used to represent input/output -transfer functions - -.. math:: - - G(s) = \frac{\text{num}(s)}{\text{den}(s)} - = \frac{a_0 s^m + a_1 s^{m-1} + \cdots + a_m} - {b_0 s^n + b_1 s^{n-1} + \cdots + b_n}, - -where n is generally greater than or equal to m (for a proper transfer -function). - -To create a transfer function, use the :func:`tf` function:: - - sys = ct.tf(num, den) - -Transfer functions can be manipulated using standard arithmetic operations -as well as the :func:`feedback`, :func:`parallel`, and :func:`series` -function. A full list of functions can be found in :ref:`function-ref`. - -Frequency response data (FRD) systems -------------------------------------- -The :class:`FrequencyResponseData` (FRD) class is used to represent systems in -frequency response data form. - -The main data members are `omega` and `fresp`, where `omega` is a 1D array -with the frequency points of the response, and `fresp` is a 3D array, with -the first dimension corresponding to the output index of the system, the -second dimension corresponding to the input index, and the 3rd dimension -corresponding to the frequency points in omega. - -FRD systems can be created with the :func:`~control.frd` factory function. -Frequency response data systems have a somewhat more limited set of -functions that are available, although all of the standard algebraic -manipulations can be performed. - -The FRD class is also used as the return type for the -:func:`frequency_response` function (and the equivalent method for the -:class:`StateSpace` and :class:`TransferFunction` classes). This -object can be assigned to a tuple using:: - - mag, phase, omega = response - -where `mag` is the magnitude (absolute value, not dB or log10) of the -system frequency response, `phase` is the wrapped phase in radians of -the system frequency response, and `omega` is the (sorted) frequencies -at which the response was evaluated. If the system is SISO and the -`squeeze` argument to :func:`frequency_response` is not True, -`magnitude` and `phase` are 1D, indexed by frequency. If the system -is not SISO or `squeeze` is False, the array is 3D, indexed by the -output, input, and frequency. If `squeeze` is True then -single-dimensional axes are removed. The processing of the `squeeze` -keyword can be changed by calling the response function with a new -argument:: - - mag, phase, omega = response(squeeze=False) - -Frequency response objects are also available as named properties of the -``response`` object: ``response.magnitude``, ``response.phase``, and -``response.response`` (for the complex response). For MIMO systems, these -elements of the frequency response can be accessed using the names of the -inputs and outputs:: - - response.magnitude['y[0]', 'u[1]'] - -where the signal names are based on the system that generated the frequency -response. - -Note: The ``fresp`` data member is stored as a NumPy array and cannot be -accessed with signal names. Use ``response.response`` to access the -complex frequency response using signal names. - -Discrete time systems ---------------------- -A discrete time system is created by specifying a nonzero 'timebase', dt. -The timebase argument can be given when a system is constructed: - -* `dt = 0`: continuous time system (default) -* `dt > 0`: discrete time system with sampling period 'dt' -* `dt = True`: discrete time with unspecified sampling period -* `dt = None`: no timebase specified - -Only the :class:`StateSpace`, :class:`TransferFunction`, and -:class:`InputOutputSystem` classes allow explicit representation of -discrete time systems. - -Systems must have compatible timebases in order to be combined. A discrete -time system with unspecified sampling time (`dt = True`) can be combined with -a system having a specified sampling time; the result will be a discrete time -system with the sample time of the latter system. Similarly, a system with -timebase `None` can be combined with a system having a specified timebase; the -result will have the timebase of the latter system. For continuous time -systems, the :func:`sample_system` function or the :meth:`StateSpace.sample` -and :meth:`TransferFunction.sample` methods can be used to create a discrete -time system from a continuous time system. See -:ref:`utility-and-conversions`. The default value of `dt` can be changed by -changing the value of `control.config.defaults['control.default_dt']`. - -Conversion between representations ----------------------------------- -LTI systems can be converted between representations either by calling the -constructor for the desired data type using the original system as the sole -argument or using the explicit conversion functions :func:`ss2tf` and -:func:`tf2ss`. - -Subsystems ----------- -Subsets of input/output pairs for LTI systems can be obtained by indexing -the system using either numerical indices (including slices) or signal -names:: - - subsys = sys[[0, 2], 0:2] - subsys = sys[['y[0]', 'y[2]'], ['u[0]', 'u[1]']] - -Signal names for an indexed subsystem are preserved from the original -system and the subsystem name is set according to the values of -``control.config.defaults['iosys.indexed_system_name_prefix']`` and -``control.config.defaults['iosys.indexed_system_name_suffix']``. The default -subsystem name is the original system name with '$indexed' appended. - -Simulating LTI systems -====================== - -A number of functions are available for computing the output (and -state) response of an LTI systems: - -.. autosummary:: - :toctree: generated/ - - initial_response - step_response - impulse_response - forced_response - -Each of these functions returns a :class:`TimeResponseData` object -that contains the data for the time response (described in more detail -in the next section). - -The :func:`forced_response` system is the most general and allows by -the zero initial state response to be simulated as well as the -response from a non-zero initial condition. - -For linear time invariant (LTI) systems, the :func:`impulse_response`, -:func:`initial_response`, and :func:`step_response` functions will -automatically compute the time vector based on the poles and zeros of -the system. If a list of systems is passed, a common time vector will be -computed and a list of responses will be returned in the form of a -:class:`TimeResponseList` object. The :func:`forced_response` function can -also take a list of systems, to which a single common input is applied. -The :class:`TimeResponseList` object has a `plot()` method that will plot -each of the responses in turn, using a sequence of different colors with -appropriate titles and legends. - -In addition the :func:`input_output_response` function, which handles -simulation of nonlinear systems and interconnected systems, can be -used. For an LTI system, results are generally more accurate using -the LTI simulation functions above. The :func:`input_output_response` -function is described in more detail in the :ref:`iosys-module` section. - -.. currentmodule:: control -.. _time-series-convention: - -Time series data ----------------- -A variety of functions in the library return time series data: sequences of -values that change over time. A common set of conventions is used for -returning such data: columns represent different points in time, rows are -different components (e.g., inputs, outputs or states). For return -arguments, an array of times is given as the first returned argument, -followed by one or more arrays of variable values. This convention is used -throughout the library, for example in the functions -:func:`forced_response`, :func:`step_response`, :func:`impulse_response`, -and :func:`initial_response`. - -.. note:: - The convention used by python-control is different from the convention - used in the `scipy.signal - `_ library. In - Scipy's convention the meaning of rows and columns is interchanged. - Thus, all 2D values must be transposed when they are used with functions - from `scipy.signal`_. - -The time vector is a 1D array with shape (n, ):: - - T = [t1, t2, t3, ..., tn ] - -Input, state, and output all follow the same convention. Columns are different -points in time, rows are different components:: - - U = [[u1(t1), u1(t2), u1(t3), ..., u1(tn)] - [u2(t1), u2(t2), u2(t3), ..., u2(tn)] - ... - ... - [ui(t1), ui(t2), ui(t3), ..., ui(tn)]] - -(and similarly for `X`, `Y`). So, `U[:, 2]` is the system's input at the -third point in time; and `U[1]` or `U[1, :]` is the sequence of values for -the system's second input. - -When there is only one row, a 1D object is accepted or returned, which adds -convenience for SISO systems: - -The initial conditions are either 1D, or 2D with shape (j, 1):: - - X0 = [[x1] - [x2] - ... - ... - [xj]] - -Functions that return time responses (e.g., :func:`forced_response`, -:func:`impulse_response`, :func:`input_output_response`, -:func:`initial_response`, and :func:`step_response`) return a -:class:`TimeResponseData` object that contains the data for the time -response. These data can be accessed via the -:attr:`~TimeResponseData.time`, :attr:`~TimeResponseData.outputs`, -:attr:`~TimeResponseData.states` and :attr:`~TimeResponseData.inputs` -properties:: - - sys = ct.rss(4, 1, 1) - response = ct.step_response(sys) - plt.plot(response.time, response.outputs) - -The dimensions of the response properties depend on the function being -called and whether the system is SISO or MIMO. In addition, some time -response function can return multiple "traces" (input/output pairs), -such as the :func:`step_response` function applied to a MIMO system, -which will compute the step response for each input/output pair. See -:class:`TimeResponseData` for more details. - -The input, output, and state elements of the response can be accessed using -signal names in place of integer offsets:: - - plt.plot(response. time, response.states['x[1]'] - -For multi-trace systems generated by :func:`step_response` and -:func:`impulse_response`, the input name used to generate the trace can be -used to access the appropriate input output pair:: - - plt.plot(response.time, response.outputs['y[0]', 'u[1]']) - -The time response functions can also be assigned to a tuple, which extracts -the time and output (and optionally the state, if the `return_x` keyword is -used). This allows simple commands for plotting:: - - t, y = ct.step_response(sys) - plot(t, y) - -The output of a MIMO LTI system can be plotted like this:: - - t, y = ct.forced_response(sys, t, u) - plot(t, y[0], label='y_0') - plot(t, y[1], label='y_1') - -The convention also works well with the state space form of linear -systems. If `D` is the feedthrough matrix (2D array) of a linear system, -and `U` is its input (array), then the feedthrough part of the system's -response, can be computed like this:: - - ft = D @ U - -Finally, the `to_pandas()` function can be used to create a pandas dataframe:: - - df = response.to_pandas() - -The column labels for the data frame are `time` and the labels for the input, -output, and state signals (`u[i]`, `y[i]`, and `x[i]` by default, but these -can be changed using the `inputs`, `outputs`, and `states` keywords when -constructing the system, as described in :func:`ss`, :func:`tf`, and other -system creation function. Note that when exporting to pandas, "rows" in the -data frame correspond to time and "cols" (DataSeries) correspond to signals. - .. currentmodule:: control .. _package-configuration-parameters: @@ -365,9 +50,8 @@ Additional parameter variables are documented in individual functions Functions that can be used to set standard configurations: .. autosummary:: - :toctree: generated/ - reset_defaults - use_fbs_defaults - use_matlab_defaults - use_legacy_defaults + reset_defaults + use_fbs_defaults + use_matlab_defaults + use_legacy_defaults diff --git a/doc/descfcn.rst b/doc/descfcn.rst index 1e4a2f3fd..45d5b668a 100644 --- a/doc/descfcn.rst +++ b/doc/descfcn.rst @@ -1,8 +1,7 @@ .. _descfcn-module: -******************** Describing functions -******************** +==================== For nonlinear systems consisting of a feedback connection between a linear system and a static nonlinearity, it is possible to obtain a @@ -28,7 +27,7 @@ Describing function analysis is a simple method, but it is approximate because it assumes that higher harmonics can be neglected. Module usage -============ +------------ The function :func:`~control.describing_function` can be used to compute the describing function of a nonlinear function:: @@ -57,7 +56,7 @@ calls the :func:`~control.describing_function_plot` function. Pre-defined nonlinearities -========================== +-------------------------- To facilitate the use of common describing functions, the following nonlinearity constructors are predefined: @@ -81,9 +80,8 @@ These functions use the analytical description of the describing function. Module classes and functions -============================ +---------------------------- .. autosummary:: - :toctree: generated/ :template: custom-class-template.rst ~control.DescribingFunctionNonlinearity diff --git a/doc/examples.rst b/doc/examples.rst index 5c253e0a9..65ac68695 100644 --- a/doc/examples.rst +++ b/doc/examples.rst @@ -19,24 +19,25 @@ other sources. .. toctree:: :maxdepth: 1 + :glob: - secord-matlab - pvtol-nested - pvtol-lqr - rss-balred - phase_plane_plots - robust_siso - robust_mimo - scherer_etal_ex7_H2_h2syn - scherer_etal_ex7_Hinf_hinfsyn - cruise-control - steering-gainsched - steering-optimal - kincar-flatsys - mrac_siso_mit - mrac_siso_lyapunov - markov - era_msd + examples/secord-matlab + examples/pvtol-nested + examples/pvtol-lqr + examples/rss-balred + examples/phase_plane_plots + examples/robust_siso + examples/robust_mimo + examples/scherer_etal_ex7_H2_h2syn + examples/scherer_etal_ex7_Hinf_hinfsyn + examples/cruise-control + examples/steering-gainsched + examples/steering-optimal + examples/kincar-flatsys + examples/mrac_siso_mit + examples/mrac_siso_lyapunov + examples/markov + examples/era_msd Jupyter notebooks ================= @@ -51,17 +52,17 @@ online sources. .. toctree:: :maxdepth: 1 - cruise - describing_functions - interconnect_tutorial - kincar-fusion - mhe-pvtol - mpc_aircraft - pvtol-lqr-nested - pvtol-outputfbk - simulating_discrete_nonlinear - steering - stochresp + examples/cruise + examples/describing_functions + examples/interconnect_tutorial + examples/kincar-fusion + examples/mhe-pvtol + examples/mpc_aircraft + examples/pvtol-lqr-nested + examples/pvtol-outputfbk + examples/simulating_discrete_nonlinear + examples/steering + examples/stochresp Google Colab Notebooks ====================== diff --git a/doc/examples/python-control_tutorial.ipynb b/doc/examples/python-control_tutorial.ipynb new file mode 120000 index 000000000..98e828daf --- /dev/null +++ b/doc/examples/python-control_tutorial.ipynb @@ -0,0 +1 @@ +../../examples/python-control_tutorial.ipynb \ No newline at end of file diff --git a/doc/figures/Makefile b/doc/figures/Makefile index 913cc0258..7f96202f1 100644 --- a/doc/figures/Makefile +++ b/doc/figures/Makefile @@ -11,6 +11,9 @@ SRCDIR = ../.. all: $(FIGS) +clean: + /bin/rm -f $(FIGS) + classes.pdf: classes.fig fig2dev -Lpdf $< $@ diff --git a/doc/flatsys.rst b/doc/flatsys.rst index 2ed873b23..de7fcdc5e 100644 --- a/doc/flatsys.rst +++ b/doc/flatsys.rst @@ -312,7 +312,6 @@ Module classes and functions ============================ .. autosummary:: - :toctree: generated/ :template: custom-class-template.rst ~control.flatsys.BasisFamily @@ -324,7 +323,6 @@ Module classes and functions ~control.flatsys.SystemTrajectory .. autosummary:: - :toctree: generated/ ~control.flatsys.flatsys ~control.flatsys.point_to_point diff --git a/doc/freqresp.rst b/doc/freqresp.rst new file mode 100644 index 000000000..c072715f0 --- /dev/null +++ b/doc/freqresp.rst @@ -0,0 +1,9 @@ +Frequency responses +=================== + +Bode plots +---------- + +Nyquist plots +------------- + diff --git a/doc/control.rst b/doc/functions.rst similarity index 69% rename from doc/control.rst rename to doc/functions.rst index 7bee58eb1..ebb3cd257 100644 --- a/doc/control.rst +++ b/doc/functions.rst @@ -12,58 +12,53 @@ Function reference System creation =============== + +The control toolbox makes use of "factory functions" to create input/output +systems of different types (classes): + .. autosummary:: :toctree: generated/ ss tf frd + nlsys zpk rss drss - nlsys +Systems can also be created by transforming existing systems: + +.. autosummary:: + :toctree: generated/ + + canonical_form + modal_form + observable_form + reachable_form + similarity_transform + pade + ss2tf + tf2ss + tfdata System interconnections ======================= .. autosummary:: :toctree: generated/ - append - combine_tf + series + parallel + negate feedback interconnect - negate - parallel - series + append + combine_tf split_tf connection_table combine_tf split_tf - -Frequency domain plotting -========================= - -.. autosummary:: - :toctree: generated/ - - bode_plot - describing_function_plot - nyquist_plot - gangof4_plot - nichols_plot - nichols_grid - -Note: For plotting commands that create multiple axes on the same plot, the -individual axes can be retrieved using the axes label (retrieved using the -`get_label` method for the matplotliib axes object). The following labels -are currently defined: - -* Bode plots: `control-bode-magnitude`, `control-bode-phase` -* Gang of 4 plots: `control-gangof4-s`, `control-gangof4-cs`, - `control-gangof4-ps`, `control-gangof4-t` - Time domain simulation ====================== @@ -76,6 +71,21 @@ Time domain simulation input_output_response phase_plane_plot step_response + time_response_plot + +Frequency response +================== + +.. autosummary:: + :toctree: generated/ + + bode_plot + describing_function_plot + frequency_response + nyquist_plot + gangof4_plot + nichols_plot + nichols_grid Control system analysis ======================= @@ -83,13 +93,14 @@ Control system analysis :toctree: generated/ bandwidth + damp dcgain describing_function - frequency_response get_input_ff_index get_output_fb_index ispassive margin + norm solve_passivity_LMI stability_margins step_info @@ -102,19 +113,6 @@ Control system analysis StateSpace.__call__ TransferFunction.__call__ -Matrix computations -=================== -.. autosummary:: - :toctree: generated/ - - care - ctrb - dare - dlyap - lyap - obsv - gram - Control system synthesis ======================== .. autosummary:: @@ -122,6 +120,7 @@ Control system synthesis acker create_statefbk_iosystem + create_estimator_iosystem dlqr h2syn hinfsyn @@ -131,8 +130,8 @@ Control system synthesis place_varga rootlocus_pid_designer -Model simplification tools -========================== +System ID and model reduction +============================= .. autosummary:: :toctree: generated/ @@ -166,38 +165,76 @@ Stochastic system support lqe white_noise +Optimal control +=============== +.. autosummary:: + :toctree: generated/ + + optimal.create_mpc_iosystem + optimal.disturbance_range_constraint + optimal.gaussian_likelihood_cost + optimal.input_poly_constraint + optimal.input_range_constraint + optimal.output_poly_constraint + optimal.output_range_constraint + optimal.quadratic_cost + optimal.solve_ocp + optimal.solve_oep + optimal.state_poly_constraint + optimal.state_range_constraint + + +Describing functions +==================== +.. autosummary:: + :toctree: generated/ + + friction_backlash_nonlinearity + relay_hysteresis_nonlinearity + saturation_nonlinearity + +Differentially flat systems +=========================== +.. autosummary:: + :toctree: generated/ + + flatsys.flatsys + flatsys.point_to_point + flatsys.solve_flat_ocp + +Matrix computations +=================== +.. autosummary:: + :toctree: generated/ + + care + ctrb + dare + dlyap + lyap + obsv + gram + .. _utility-and-conversions: -Utility functions and conversions -================================= +Utility functions +================= .. autosummary:: :toctree: generated/ augw bdschur - canonical_form - damp db2mag isctime isdtime issiso mag2db - modal_form - norm - observable_form - pade - reachable_form reset_defaults sample_system set_defaults - similarity_transform - ss2tf ssdata - tf2ss - tfdata timebase unwrap use_fbs_defaults + use_legacy_defaults use_matlab_defaults - - diff --git a/doc/index.rst b/doc/index.rst index ec556e7ce..dc3e05097 100644 --- a/doc/index.rst +++ b/doc/index.rst @@ -7,36 +7,69 @@ implements basic operations for analysis and design of feedback control systems. .. rubric:: Features -- Linear input/output systems in state-space and frequency domain +- Linear input/output systems in state space and frequency domain - Nonlinear input/output system modeling, simulation, and analysis - Block diagram algebra: serial, parallel, and feedback interconnections -- Time response: initial, step, impulse -- Frequency response: Bode and Nyquist plots -- Control analysis: stability, reachability, observability, stability margins -- Control design: eigenvalue placement, LQR, H2, Hinf -- Model reduction: balanced realizations, Hankel singular values -- Estimator design: linear quadratic estimator (Kalman filter) +- Time response: initial, step, impulse, and forced response +- Frequency response: Bode, Nyquist, and Nichols plots +- Control analysis: stability, reachability, observability, stability + margins, phase plane plots, root locus plots +- Control design: eigenvalue placement, LQR, H2, Hinf, and MPC/RHC +- Trajectory generation: optimal control and differential flatness +- Model reduction: balanced realizations and Hankel singular values +- Estimator design: linear quadratic estimator (Kalman filter), MLE, and MHE + +.. rubric:: Links: + +- GitHub repository: https://github.com/python-control/python-control +- Issue tracker: https://github.com/python-control/python-control/issues +- Mailing list: http://sourceforge.net/p/python-control/mailman/ -.. rubric:: Documentation +.. rubric:: How to cite -.. toctree:: - :maxdepth: 2 +An `article `_ +about the library is available on IEEE Explore. If the Python Control +Systems Library helped you in your research, please cite:: + + @inproceedings{python-control2021, + title={The Python Control Systems Library (python-control)}, + author={Fuller, Sawyer and Greiner, Ben and Moore, Jason and + Murray, Richard and van Paassen, Ren{\'e} and Yorke, Rory}, + booktitle={60th IEEE Conference on Decision and Control (CDC)}, + pages={4875--4881}, + year={2021}, + organization={IEEE} + } +or the GitHub site: https://github.com/python-control/python-control. + +.. toctree:: + :caption: User Guide + :maxdepth: 1 + :numbered: 2 + intro - conventions - control - classes - plotting - matlab - flatsys - iosys - descfcn - optimal + Tutorial + Linear systems + I/O response and plotting + Nonlinear systems + Interconnected I/O systems + Stochastic systems examples + genindex -* :ref:`genindex` +.. toctree:: + :caption: Reference Manual + :maxdepth: 1 + + functions + classes + config + matlab -.. rubric:: Development +*********** +Development +*********** You can check out the latest version of the source code with the command:: @@ -62,7 +95,3 @@ Please see the `Developer's Wiki`_ for detailed instructions. .. _Developer's Wiki: https://github.com/python-control/python-control/wiki -.. rubric:: Links - -- Issue tracker: https://github.com/python-control/python-control/issues -- Mailing list: http://sourceforge.net/p/python-control/mailman/ diff --git a/doc/intro.rst b/doc/intro.rst index a45bb030e..dac2a9280 100644 --- a/doc/intro.rst +++ b/doc/intro.rst @@ -2,13 +2,13 @@ Introduction ************ -Welcome to the Python Control Systems Toolbox (python-control) User's -Manual. This manual contains information on using the python-control +Welcome to the Python Control Systems Library (python-control) User +Guide. This guide contains information on using the python-control package, including documentation for all functions in the package and examples illustrating their use. -Overview of the toolbox -======================= +Package overview +================ The python-control package is a set of python classes and functions that implement common operations for the analysis and design of feedback control @@ -18,21 +18,7 @@ to work through the examples in the textbook `Feedback Systems available that provides many of the common functions corresponding to commands available in the MATLAB Control Systems Toolbox. -Some differences from MATLAB -============================ -The python-control package makes use of `NumPy `_ and -`SciPy `_. A list of general differences between -NumPy and MATLAB can be found `here -`_. - -In terms of the python-control package more specifically, here are -some things to keep in mind: - -* You must include commas in vectors. So [1 2 3] must be [1, 2, 3]. -* Functions that return multiple arguments use tuples. -* You cannot use braces for collections; use tuples instead. -* Time series data have time as the final index (see - :ref:`time-series-convention`). +.. todo:: Add information from :module:`control`? Installation ============ @@ -85,14 +71,33 @@ To install in your home directory, use:: pip install . -Getting started -=============== +The python-control package can also be used with `Google Colab +`_ by including the following lines to import the +control package:: -There are two different ways to use the package. For the default interface -described in :ref:`function-ref`, simply import the control package as follows:: + try: + import control as ct + print("python-control", ct.__version__) + except ImportError: + !pip install control + import control as ct - >>> import control as ct +Note that Google colab does not currently support Slycot, so some +functionality may not be available. -If you want to have a MATLAB-like environment, use the :ref:`matlab-module`:: +Some differences from MATLAB +============================ +The python-control package makes use of `NumPy `_ and +`SciPy `_. A list of general differences between +NumPy and MATLAB can be found `here +`_. - >>> from control.matlab import * +In terms of the python-control package more specifically, here are +some things to keep in mind: + +* You must include commas in vectors. So [1 2 3] must be [1, 2, 3]. +* Functions that return multiple values use objects (with elements for + each return value) or tuples. +* You cannot use braces for collections; use tuples instead. +* Time series data have time as the final index (see + :ref:`time-series-convention`). diff --git a/doc/iosys.rst b/doc/iosys.rst index 0554683f3..00ff8efbf 100644 --- a/doc/iosys.rst +++ b/doc/iosys.rst @@ -562,7 +562,6 @@ Module classes and functions ============================ .. autosummary:: - :toctree: generated/ :template: custom-class-template.rst ~control.InputOutputSystem @@ -572,7 +571,6 @@ Module classes and functions ~control.OperatingPoint .. autosummary:: - :toctree: generated/ ~control.find_operating_point ~control.interconnect diff --git a/doc/linear.rst b/doc/linear.rst index d2394e040..be52b2079 100644 --- a/doc/linear.rst +++ b/doc/linear.rst @@ -1,24 +1,17 @@ -.. _conventions-ref: +.. module:: control -.. currentmodule:: control - -******************* -Library conventions -******************* - -The python-control library uses a set of standard conventions for the -way that different types of standard information used by the library. -Throughout this manual, we assume the `control` package has been -imported as `ct`. - -LTI system representation -========================= +******************************************** +Linear System Modeling, Analysis, and Design +******************************************** Linear time invariant (LTI) systems are represented in python-control in state space, transfer function, or frequency response data (FRD) form. Most functions in the toolbox will operate on any of these data types, and functions for converting between compatible types are provided. +Creating LTI systems +==================== + State space systems ------------------- The :class:`StateSpace` class is used to represent state-space realizations @@ -39,6 +32,18 @@ State space systems can be manipulated using standard arithmetic operations as well as the :func:`feedback`, :func:`parallel`, and :func:`series` function. A full list of functions can be found in :ref:`function-ref`. +Systems, inputs, outputs, and states can be given labels to allow more +customized access to system information:: + + sys = ct.ss( + A, B, C, D, name='sys', + states=['x1', 'x2'], inputs=['u'], outputs=['y']) + +State space can be manipulated using standard arithmetic operations as +well as the :func:`feedback`, :func:`parallel`, and :func:`series` +function. A full list of functions can be found in +:ref:`function-ref`. + Transfer functions ------------------ The :class:`TransferFunction` class is used to represent input/output @@ -57,6 +62,11 @@ To create a transfer function, use the :func:`tf` function:: sys = ct.tf(num, den) +The system name as well as input and output labels can be specified in +the same way as state space systems:: + + sys = ct.tf(A, B, C, D, name='sys', inputs=['u'], outputs=['y']) + Transfer functions can be manipulated using standard arithmetic operations as well as the :func:`feedback`, :func:`parallel`, and :func:`series` function. A full list of functions can be found in :ref:`function-ref`. @@ -72,7 +82,7 @@ the first dimension corresponding to the output index of the system, the second dimension corresponding to the input index, and the 3rd dimension corresponding to the frequency points in omega. -FRD systems can be created with the :func:`~control.frd` factory function. +FRD systems can be created with the :func:`frd` factory function. Frequency response data systems have a somewhat more limited set of functions that are available, although all of the standard algebraic manipulations can be performed. @@ -113,8 +123,33 @@ Note: The ``fresp`` data member is stored as a NumPy array and cannot be accessed with signal names. Use ``response.response`` to access the complex frequency response using signal names. +Multi-input, multi-output (MIMO) systems +---------------------------------------- + +.. todo:: Add information on building MIMO systems + +Subsets of input/output pairs for LTI systems can be obtained by indexing +the system using either numerical indices (including slices) or signal +names:: + + subsys = sys[[0, 2], 0:2] + subsys = sys[['y[0]', 'y[2]'], ['u[0]', 'u[1]']] + +Signal names for an indexed subsystem are preserved from the original +system and the subsystem name is set according to the values of +``config.defaults['iosys.indexed_system_name_prefix']`` and +``config.defaults['iosys.indexed_system_name_suffix']``. The default +subsystem name is the original system name with '$indexed' appended. + +.. include:: statesp.rst + +.. include:: xferfcn.rst + Discrete time systems ---------------------- +===================== + +.. todo:: add anchor for time base (?) + A discrete time system is created by specifying a nonzero 'timebase', dt. The timebase argument can be given when a system is constructed: @@ -123,10 +158,6 @@ The timebase argument can be given when a system is constructed: * `dt = True`: discrete time with unspecified sampling period * `dt = None`: no timebase specified -Only the :class:`StateSpace`, :class:`TransferFunction`, and -:class:`InputOutputSystem` classes allow explicit representation of -discrete time systems. - Systems must have compatible timebases in order to be combined. A discrete time system with unspecified sampling time (`dt = True`) can be combined with a system having a specified sampling time; the result will be a discrete time @@ -137,7 +168,11 @@ systems, the :func:`sample_system` function or the :meth:`StateSpace.sample` and :meth:`TransferFunction.sample` methods can be used to create a discrete time system from a continuous time system. See :ref:`utility-and-conversions`. The default value of `dt` can be changed by -changing the value of `control.config.defaults['control.default_dt']`. +changing the value of `config.defaults['control.default_dt']`. + + +Model conversion and reduction +=============================== Conversion between representations ---------------------------------- @@ -146,228 +181,7 @@ constructor for the desired data type using the original system as the sole argument or using the explicit conversion functions :func:`ss2tf` and :func:`tf2ss`. -Subsystems ----------- -Subsets of input/output pairs for LTI systems can be obtained by indexing -the system using either numerical indices (including slices) or signal -names:: - - subsys = sys[[0, 2], 0:2] - subsys = sys[['y[0]', 'y[2]'], ['u[0]', 'u[1]']] - -Signal names for an indexed subsystem are preserved from the original -system and the subsystem name is set according to the values of -``control.config.defaults['iosys.indexed_system_name_prefix']`` and -``control.config.defaults['iosys.indexed_system_name_suffix']``. The default -subsystem name is the original system name with '$indexed' appended. - -Simulating LTI systems -====================== - -A number of functions are available for computing the output (and -state) response of an LTI systems: - -.. autosummary:: - :toctree: generated/ - - initial_response - step_response - impulse_response - forced_response - -Each of these functions returns a :class:`TimeResponseData` object -that contains the data for the time response (described in more detail -in the next section). - -The :func:`forced_response` system is the most general and allows by -the zero initial state response to be simulated as well as the -response from a non-zero initial condition. - -For linear time invariant (LTI) systems, the :func:`impulse_response`, -:func:`initial_response`, and :func:`step_response` functions will -automatically compute the time vector based on the poles and zeros of -the system. If a list of systems is passed, a common time vector will be -computed and a list of responses will be returned in the form of a -:class:`TimeResponseList` object. The :func:`forced_response` function can -also take a list of systems, to which a single common input is applied. -The :class:`TimeResponseList` object has a `plot()` method that will plot -each of the responses in turn, using a sequence of different colors with -appropriate titles and legends. - -In addition the :func:`input_output_response` function, which handles -simulation of nonlinear systems and interconnected systems, can be -used. For an LTI system, results are generally more accurate using -the LTI simulation functions above. The :func:`input_output_response` -function is described in more detail in the :ref:`iosys-module` section. - -.. currentmodule:: control -.. _time-series-convention: - -Time series data ----------------- -A variety of functions in the library return time series data: sequences of -values that change over time. A common set of conventions is used for -returning such data: columns represent different points in time, rows are -different components (e.g., inputs, outputs or states). For return -arguments, an array of times is given as the first returned argument, -followed by one or more arrays of variable values. This convention is used -throughout the library, for example in the functions -:func:`forced_response`, :func:`step_response`, :func:`impulse_response`, -and :func:`initial_response`. - -.. note:: - The convention used by python-control is different from the convention - used in the `scipy.signal - `_ library. In - Scipy's convention the meaning of rows and columns is interchanged. - Thus, all 2D values must be transposed when they are used with functions - from `scipy.signal`_. - -The time vector is a 1D array with shape (n, ):: - - T = [t1, t2, t3, ..., tn ] - -Input, state, and output all follow the same convention. Columns are different -points in time, rows are different components:: - - U = [[u1(t1), u1(t2), u1(t3), ..., u1(tn)] - [u2(t1), u2(t2), u2(t3), ..., u2(tn)] - ... - ... - [ui(t1), ui(t2), ui(t3), ..., ui(tn)]] - -(and similarly for `X`, `Y`). So, `U[:, 2]` is the system's input at the -third point in time; and `U[1]` or `U[1, :]` is the sequence of values for -the system's second input. - -When there is only one row, a 1D object is accepted or returned, which adds -convenience for SISO systems: - -The initial conditions are either 1D, or 2D with shape (j, 1):: - - X0 = [[x1] - [x2] - ... - ... - [xj]] - -Functions that return time responses (e.g., :func:`forced_response`, -:func:`impulse_response`, :func:`input_output_response`, -:func:`initial_response`, and :func:`step_response`) return a -:class:`TimeResponseData` object that contains the data for the time -response. These data can be accessed via the -:attr:`~TimeResponseData.time`, :attr:`~TimeResponseData.outputs`, -:attr:`~TimeResponseData.states` and :attr:`~TimeResponseData.inputs` -properties:: - - sys = ct.rss(4, 1, 1) - response = ct.step_response(sys) - plt.plot(response.time, response.outputs) - -The dimensions of the response properties depend on the function being -called and whether the system is SISO or MIMO. In addition, some time -response function can return multiple "traces" (input/output pairs), -such as the :func:`step_response` function applied to a MIMO system, -which will compute the step response for each input/output pair. See -:class:`TimeResponseData` for more details. - -The input, output, and state elements of the response can be accessed using -signal names in place of integer offsets:: - - plt.plot(response. time, response.states['x[1]'] - -For multi-trace systems generated by :func:`step_response` and -:func:`impulse_response`, the input name used to generate the trace can be -used to access the appropriate input output pair:: - - plt.plot(response.time, response.outputs['y[0]', 'u[1]']) - -The time response functions can also be assigned to a tuple, which extracts -the time and output (and optionally the state, if the `return_x` keyword is -used). This allows simple commands for plotting:: - - t, y = ct.step_response(sys) - plot(t, y) - -The output of a MIMO LTI system can be plotted like this:: - - t, y = ct.forced_response(sys, t, u) - plot(t, y[0], label='y_0') - plot(t, y[1], label='y_1') - -The convention also works well with the state space form of linear -systems. If `D` is the feedthrough matrix (2D array) of a linear system, -and `U` is its input (array), then the feedthrough part of the system's -response, can be computed like this:: - - ft = D @ U - -Finally, the `to_pandas()` function can be used to create a pandas dataframe:: - - df = response.to_pandas() - -The column labels for the data frame are `time` and the labels for the input, -output, and state signals (`u[i]`, `y[i]`, and `x[i]` by default, but these -can be changed using the `inputs`, `outputs`, and `states` keywords when -constructing the system, as described in :func:`ss`, :func:`tf`, and other -system creation function. Note that when exporting to pandas, "rows" in the -data frame correspond to time and "cols" (DataSeries) correspond to signals. - -.. currentmodule:: control -.. _package-configuration-parameters: - -Package configuration parameters -================================ - -The python-control library can be customized to allow for different default -values for selected parameters. This includes the ability to set the style -for various types of plots and establishing the underlying representation for -state space matrices. - -To set the default value of a configuration variable, set the appropriate -element of the `control.config.defaults` dictionary:: - - ct.config.defaults['module.parameter'] = value - -The :func:`~control.set_defaults` function can also be used to -set multiple configuration parameters at the same time:: - - ct.set_defaults('module', param1=val1, param2=val2, ...] - -Finally, there are also functions available set collections of variables based -on standard configurations. - -Selected variables that can be configured, along with their default values: - - * freqplot.dB (False): Bode plot magnitude plotted in dB (otherwise powers - of 10) - - * freqplot.deg (True): Bode plot phase plotted in degrees (otherwise radians) - - * freqplot.Hz (False): Bode plot frequency plotted in Hertz (otherwise - rad/sec) - - * freqplot.grid (True): Include grids for magnitude and phase plots - - * freqplot.number_of_samples (1000): Number of frequency points in Bode plots - - * freqplot.feature_periphery_decade (1.0): How many decades to include in - the frequency range on both sides of features (poles, zeros). - - * statesp.default_dt and xferfcn.default_dt (None): set the default value - of dt when constructing new LTI systems - - * statesp.remove_useless_states (True): remove states that have no effect - on the input-output dynamics of the system - -Additional parameter variables are documented in individual functions - -Functions that can be used to set standard configurations: - -.. autosummary:: - :toctree: generated/ +Model reduction +--------------- - reset_defaults - use_fbs_defaults - use_matlab_defaults - use_legacy_defaults +.. automodule:: modelsimp diff --git a/doc/matlab.rst b/doc/matlab.rst index eac1d157a..9dd129768 100644 --- a/doc/matlab.rst +++ b/doc/matlab.rst @@ -9,6 +9,10 @@ :no-inherited-members: :no-special-members: +.. warning:: This module is not closely maintained and some functionality + in main control package may not be be available via the MATLAB + compatibility module. + Creating linear models ====================== .. autosummary:: diff --git a/doc/nlsys.rst b/doc/nlsys.rst new file mode 100644 index 000000000..cc44b6c9f --- /dev/null +++ b/doc/nlsys.rst @@ -0,0 +1,11 @@ +Nonlinear system models +======================= + +Creating nonlinear models +------------------------- + +Operating points and linearization +---------------------------------- + +Simulations and plotting +------------------------ diff --git a/doc/nonlinear.rst b/doc/nonlinear.rst new file mode 100644 index 000000000..a5ae87723 --- /dev/null +++ b/doc/nonlinear.rst @@ -0,0 +1,14 @@ +*********************************************** +Nonlinear System Modeling, Analysis, and Design +*********************************************** + +.. todo:: Figure out how to get these sections to display in a good + way without making the chapter too long. + +.. toctree:: + :maxdepth: 1 + + nlsys + phaseplot + optimal + descfcn diff --git a/doc/optimal.rst b/doc/optimal.rst index 5d748f544..d65c1a61f 100644 --- a/doc/optimal.rst +++ b/doc/optimal.rst @@ -1,8 +1,7 @@ .. _optimal-module: -************************** Optimization-based control -************************** +========================== .. automodule:: control.optimal :no-members: @@ -10,7 +9,7 @@ Optimization-based control :no-special-members: Optimal control problem setup -============================= +----------------------------- Consider the *optimal control problem*: @@ -80,7 +79,7 @@ trajectory is applied to the system for a fraction of the horizon length. This process is then repeated, resulting in a sampled data feedback law. This approach is illustrated in the following figure: -.. image:: mpc-overview.png +.. image:: figures/mpc-overview.png Every :math:`\Delta T` seconds, an optimal control problem is solved over a :math:`T` second horizon, starting from the current state. The first @@ -100,7 +99,7 @@ conditions (see, for example, the FBS2e supplement on `Optimization-Based Control `_. Optimal estimation problem setup -================================ +-------------------------------- Consider a nonlinear system with discrete time dynamics of the form @@ -203,7 +202,7 @@ implement an estimator by repeatedly solving the optimization of a window of length :math:`N` backwards in time. Module usage -============ +------------ The optimization-based control module provides a means of computing optimal trajectories for nonlinear systems and implementing @@ -315,7 +314,7 @@ problems: ~control.optimal.disturbance_range_constraint Example -======= +------- Consider the vehicle steering example described in FBS2e. The dynamics of the system can be defined as a nonlinear input/output system using the @@ -411,7 +410,7 @@ Plotting the results:: yields -.. image:: steering-optimal.png +.. image:: figures/steering-optimal.png An example showing the use of the optimal estimation problem and moving @@ -419,7 +418,7 @@ horizon estimation (MHE) is given in the :doc:`mhe-pvtol Jupyter notebook `. Optimization Tips -================= +----------------- The python-control optimization module makes use of the SciPy optimization toolbox and it can sometimes be tricky to get the optimization to converge. @@ -466,13 +465,12 @@ formulations. Module classes and functions -============================ +---------------------------- The following classes and functions are defined in the ``optimal`` module: .. autosummary:: - :toctree: generated/ :template: custom-class-template.rst ~control.optimal.OptimalControlProblem @@ -481,7 +479,6 @@ The following classes and functions are defined in the ~control.optimal.OptimalEstimationResult .. autosummary:: - :toctree: generated/ ~control.optimal.create_mpc_iosystem ~control.optimal.disturbance_range_constraint diff --git a/doc/pzmap.rst b/doc/pzmap.rst new file mode 100644 index 000000000..554c7fab1 --- /dev/null +++ b/doc/pzmap.rst @@ -0,0 +1,11 @@ +Pole/zero maps +============== + +Pole/zero plots +--------------- + +Root locus maps +--------------- + +Nichols charts +-------------- diff --git a/doc/response.rst b/doc/response.rst index 8b47392cc..8298d5e23 100644 --- a/doc/response.rst +++ b/doc/response.rst @@ -68,7 +68,7 @@ response for a two-input, two-output can be plotted using the commands:: which produces the following plot: -.. image:: timeplot-mimo_step-default.png +.. image:: figures/timeplot-mimo_step-default.png The :class:`~control.TimeResponseData` object can also be used to access the data from the simulation:: @@ -104,7 +104,7 @@ following plot:: title="Step response for 2x2 MIMO system " + "[plot_inputs, overlay_signals]") -.. image:: timeplot-mimo_step-pi_cs.png +.. image:: figures/timeplot-mimo_step-pi_cs.png Input/output response plots created with either the :func:`~control.forced_response` or the @@ -123,7 +123,7 @@ keyword:: title="I/O response for 2x2 MIMO system " + "[plot_inputs='overlay', legend_map]") -.. image:: timeplot-mimo_ioresp-ov_lm.png +.. image:: figures/timeplot-mimo_ioresp-ov_lm.png Another option that is available is to use the `transpose` keyword so that instead of plotting the outputs on the top and inputs on the bottom, the @@ -142,7 +142,7 @@ following figure:: title="I/O responses for 2x2 MIMO system, multiple traces " "[transpose]") -.. image:: timeplot-mimo_ioresp-mt_tr.png +.. image:: figures/timeplot-mimo_ioresp-mt_tr.png This figure also illustrates the ability to create "multi-trace" plots using the :func:`~control.combine_time_responses` function. The line @@ -160,7 +160,7 @@ and styles for various signals and traces:: input_props=[{'color': c} for c in ['red', 'green']], trace_props=[{'linestyle': s} for s in ['-', '--']]) -.. image:: timeplot-mimo_step-linestyle.png +.. image:: figures/timeplot-mimo_step-linestyle.png Frequency response data ======================= @@ -180,7 +180,7 @@ be generated using the :func:`~control.bode_plot` function:: ct.bode_plot(response, initial_phase=0) -.. image:: freqplot-siso_bode-default.png +.. image:: figures/freqplot-siso_bode-default.png Computing the response for multiple systems at the same time yields a common frequency range that covers the features of all listed systems. @@ -193,7 +193,7 @@ Bode plots can also be created directly using the [[[1, 0.6, 1], [1, 1, 1]], [[1, 0.4, 1], [1, 2, 1]]], name="sys_mimo") ct.frequency_response(sys_mimo).plot() -.. image:: freqplot-mimo_bode-default.png +.. image:: figures/freqplot-mimo_bode-default.png A variety of options are available for customizing Bode plots, for example allowing the display of the phase to be turned off or @@ -202,7 +202,7 @@ overlaying the inputs or outputs:: ct.frequency_response(sys_mimo).plot( plot_phase=False, overlay_inputs=True, overlay_outputs=True) -.. image:: freqplot-mimo_bode-magonly.png +.. image:: figures/freqplot-mimo_bode-magonly.png The :func:`~control.singular_values_response` function can be used to generate Bode plots that show the singular values of a transfer @@ -210,7 +210,7 @@ function:: ct.singular_values_response(sys_mimo).plot() -.. image:: freqplot-mimo_svplot-default.png +.. image:: figures/freqplot-mimo_svplot-default.png Different types of plots can also be specified for a given frequency response. For example, to plot the frequency response using a a Nichols @@ -218,7 +218,7 @@ plot, use `plot_type='nichols'`:: response.plot(plot_type='nichols') -.. image:: freqplot-siso_nichols-default.png +.. image:: figures/freqplot-siso_nichols-default.png Another response function that can be used to generate Bode plots is the :func:`~control.gangof4_response` function, which computes the four primary @@ -229,7 +229,7 @@ sensitivity functions for a feedback control system in standard form:: response = rect.gangof4_response(proc, ctrl) ct.bode_plot(response) # or response.plot() -.. image:: freqplot-gangof4.png +.. image:: figures/freqplot-gangof4.png Nyquist analysis can be done using the :func:`~control.nyquist_response` function, which evaluates an LTI system along the Nyquist contour, and @@ -238,7 +238,7 @@ the :func:`~control.nyquist_plot` function, which generates a Nyquist plot:: sys = ct.tf([1, 0.2], [1, 1, 3, 1, 1], name='sys') nyquist_plot(sys) -.. image:: freqplot-nyquist-default.png +.. image:: figures/freqplot-nyquist-default.png The :func:`~control.nyquist_response` function can be used to compute the number of encirclements of the -1 point and can return the Nyquist @@ -260,7 +260,7 @@ the computation of the Nyquist curve and the way the data are plotted:: arrows=[0, 0.15, 0.3, 0.6, 0.7, 0.925], label='sys') print("Encirclements =", nyqresp.count) -.. image:: freqplot-nyquist-custom.png +.. image:: figures/freqplot-nyquist-custom.png All frequency domain plotting functions will automatically compute the range of frequencies to plot based on the poles and zeros of the frequency @@ -272,7 +272,7 @@ array of frequencies as a second argument (after the list of systems):: omega = np.logspace(-2, 2, 500) ct.frequency_response([sys1, sys2], omega).plot(initial_phase=0) -.. image:: freqplot-siso_bode-omega.png +.. image:: figures/freqplot-siso_bode-omega.png Alternatively, frequency ranges can be specified by passing a list of the form ``[wmin, wmax]``, where ``wmin`` and ``wmax`` are the minimum and @@ -309,14 +309,14 @@ zeros and can be used to generate a pole/zero plot:: response = ct.pole_zero_map(sys) ct.pole_zero_plot(response) -.. image:: pzmap-siso_ctime-default.png +.. image:: figures/pzmap-siso_ctime-default.png A root locus plot shows the location of the closed loop poles of a system as a function of the loop gain:: ct.root_locus_map(sys).plot() -.. image:: rlocus-siso_ctime-default.png +.. image:: figures/rlocus-siso_ctime-default.png The grid in the left hand plane shows lines of constant damping ratio as well as arcs corresponding to the frequency of the complex pole. The grid @@ -329,7 +329,7 @@ root locus diagram will mark the pole locations on all branches of the diagram and display the gain and damping ratio for the clicked point below the plot title: -.. image:: rlocus-siso_ctime-clicked.png +.. image:: figures/rlocus-siso_ctime-clicked.png Root locus diagrams are also supported for discrete time systems, in which case the grid is show inside the unit circle:: @@ -337,7 +337,7 @@ case the grid is show inside the unit circle:: sysd = sys.sample(0.1) ct.root_locus_plot(sysd) -.. image:: rlocus-siso_dtime-default.png +.. image:: figures/rlocus-siso_dtime-default.png Lists of systems can also be given, in which case the root locus diagram for each system is plotted in different colors:: @@ -346,7 +346,7 @@ for each system is plotted in different colors:: sys2 = ct.tf([1, 0.2], [1, 1, 3, 1, 1], name='sys2') ct.root_locus_plot([sys1, sys2], grid=False) -.. image:: rlocus-siso_multiple-nogrid.png +.. image:: figures/rlocus-siso_multiple-nogrid.png Phase plane plots @@ -367,7 +367,7 @@ The default method for generating a phase plane plot is to provide a T = 8 ct.phase_plane_plot(sys, axis_limits, T) -.. image:: phaseplot-dampedosc-default.png +.. image:: figures/phaseplot-dampedosc-default.png By default, the plot includes streamlines generated from starting points on limits of the plot, with arrows showing the flow of the @@ -392,7 +392,7 @@ an inverted pendulum system, which is created using a mesh grid:: plt.xlabel(r"$\theta$ [rad]") plt.ylabel(r"$\dot\theta$ [rad/sec]") -.. image:: phaseplot-invpend-meshgrid.png +.. image:: figures/phaseplot-invpend-meshgrid.png This figure shows several features of more complex phase plane plots: multiple equilibrium points are shown, with saddle points showing @@ -423,11 +423,12 @@ are part of the :mod:`~control.phaseplot` (pp) module:: oscillator, np.array([[1, 0]]), 2*pi, arrows=6, color='b') plt.gca().set_aspect('equal') -.. image:: phaseplot-oscillator-helpers.png +.. image:: figures/phaseplot-oscillator-helpers.png The following helper functions are available: .. autosummary:: + phaseplot.equilpoints phaseplot.separatrices phaseplot.streamlines @@ -611,7 +612,7 @@ features:: fig.suptitle("Loop analysis for servomechanism control design") plt.tight_layout() -.. image:: ctrlplot-servomech.png +.. image:: figures/ctrlplot-servomech.png As this example illustrates, python-control plotting functions and Matplotlib plotting functions can generally be intermixed. One type of @@ -629,7 +630,7 @@ example:: cplt = ct.root_locus_plot([sys1, sys2], ax=ax_array[1, 0]) cplt.set_plot_title("Root locus plots (w/ specified axes)") -.. image:: ctrlplot-pole_zero_subplots.png +.. image:: figures/ctrlplot-pole_zero_subplots.png Alternatively, turning off the omega-damping grid (using ``grid=False`` or ``grid='empty'``) allows use of Matplotlib layout commands. @@ -647,7 +648,6 @@ number of encirclements for a Nyquist plot) as well as plotting (via the ``plot`` method). .. autosummary:: - :toctree: generated/ ~control.describing_function_response ~control.frequency_response @@ -666,7 +666,6 @@ Plotting functions ------------------ .. autosummary:: - :toctree: generated/ ~control.bode_plot ~control.describing_function_plot @@ -692,7 +691,6 @@ carry out other operations in creating control plots. .. autosummary:: - :toctree: generated/ phaseplot.boxgrid ~control.combine_time_responses @@ -706,7 +704,6 @@ Response and plotting classes The following classes are used in generating response data. .. autosummary:: - :toctree: generated/ ~control.ControlPlot ~control.DescribingFunctionResponse diff --git a/doc/statesp.rst b/doc/statesp.rst new file mode 100644 index 000000000..bcaf1f514 --- /dev/null +++ b/doc/statesp.rst @@ -0,0 +1,23 @@ +.. module: control + +State space analysis and design +=============================== + +State space properties +---------------------- + +State feedback design +--------------------- + +Canonical forms +--------------- + +State estimation +---------------- + +Passive systems +--------------- +.. automodule:: passive + :no-members: + :no-inherited-members: + :no-special-members: diff --git a/doc/stochastic.rst b/doc/stochastic.rst new file mode 100644 index 000000000..f9a8dbb51 --- /dev/null +++ b/doc/stochastic.rst @@ -0,0 +1,3 @@ +****************** +Stochastic Systems +****************** diff --git a/doc/timeresp.rst b/doc/timeresp.rst index d2394e040..29dd08483 100644 --- a/doc/timeresp.rst +++ b/doc/timeresp.rst @@ -1,179 +1,18 @@ -.. _conventions-ref: +Time responses +============== -.. currentmodule:: control - -******************* -Library conventions -******************* - -The python-control library uses a set of standard conventions for the -way that different types of standard information used by the library. -Throughout this manual, we assume the `control` package has been -imported as `ct`. - -LTI system representation -========================= - -Linear time invariant (LTI) systems are represented in python-control in -state space, transfer function, or frequency response data (FRD) form. Most -functions in the toolbox will operate on any of these data types, and -functions for converting between compatible types are provided. - -State space systems -------------------- -The :class:`StateSpace` class is used to represent state-space realizations -of linear time-invariant (LTI) systems: - -.. math:: - - \frac{dx}{dt} &= A x + B u \\ - y &= C x + D u - -where u is the input, y is the output, and x is the state. - -To create a state space system, use the :func:`ss` function:: - - sys = ct.ss(A, B, C, D) - -State space systems can be manipulated using standard arithmetic operations -as well as the :func:`feedback`, :func:`parallel`, and :func:`series` -function. A full list of functions can be found in :ref:`function-ref`. - -Transfer functions ------------------- -The :class:`TransferFunction` class is used to represent input/output -transfer functions - -.. math:: - - G(s) = \frac{\text{num}(s)}{\text{den}(s)} - = \frac{a_0 s^m + a_1 s^{m-1} + \cdots + a_m} - {b_0 s^n + b_1 s^{n-1} + \cdots + b_n}, - -where n is generally greater than or equal to m (for a proper transfer -function). - -To create a transfer function, use the :func:`tf` function:: - - sys = ct.tf(num, den) - -Transfer functions can be manipulated using standard arithmetic operations -as well as the :func:`feedback`, :func:`parallel`, and :func:`series` -function. A full list of functions can be found in :ref:`function-ref`. - -Frequency response data (FRD) systems -------------------------------------- -The :class:`FrequencyResponseData` (FRD) class is used to represent systems in -frequency response data form. - -The main data members are `omega` and `fresp`, where `omega` is a 1D array -with the frequency points of the response, and `fresp` is a 3D array, with -the first dimension corresponding to the output index of the system, the -second dimension corresponding to the input index, and the 3rd dimension -corresponding to the frequency points in omega. - -FRD systems can be created with the :func:`~control.frd` factory function. -Frequency response data systems have a somewhat more limited set of -functions that are available, although all of the standard algebraic -manipulations can be performed. - -The FRD class is also used as the return type for the -:func:`frequency_response` function (and the equivalent method for the -:class:`StateSpace` and :class:`TransferFunction` classes). This -object can be assigned to a tuple using:: - - mag, phase, omega = response - -where `mag` is the magnitude (absolute value, not dB or log10) of the -system frequency response, `phase` is the wrapped phase in radians of -the system frequency response, and `omega` is the (sorted) frequencies -at which the response was evaluated. If the system is SISO and the -`squeeze` argument to :func:`frequency_response` is not True, -`magnitude` and `phase` are 1D, indexed by frequency. If the system -is not SISO or `squeeze` is False, the array is 3D, indexed by the -output, input, and frequency. If `squeeze` is True then -single-dimensional axes are removed. The processing of the `squeeze` -keyword can be changed by calling the response function with a new -argument:: - - mag, phase, omega = response(squeeze=False) - -Frequency response objects are also available as named properties of the -``response`` object: ``response.magnitude``, ``response.phase``, and -``response.response`` (for the complex response). For MIMO systems, these -elements of the frequency response can be accessed using the names of the -inputs and outputs:: - - response.magnitude['y[0]', 'u[1]'] - -where the signal names are based on the system that generated the frequency -response. - -Note: The ``fresp`` data member is stored as a NumPy array and cannot be -accessed with signal names. Use ``response.response`` to access the -complex frequency response using signal names. - -Discrete time systems ---------------------- -A discrete time system is created by specifying a nonzero 'timebase', dt. -The timebase argument can be given when a system is constructed: - -* `dt = 0`: continuous time system (default) -* `dt > 0`: discrete time system with sampling period 'dt' -* `dt = True`: discrete time with unspecified sampling period -* `dt = None`: no timebase specified - -Only the :class:`StateSpace`, :class:`TransferFunction`, and -:class:`InputOutputSystem` classes allow explicit representation of -discrete time systems. - -Systems must have compatible timebases in order to be combined. A discrete -time system with unspecified sampling time (`dt = True`) can be combined with -a system having a specified sampling time; the result will be a discrete time -system with the sample time of the latter system. Similarly, a system with -timebase `None` can be combined with a system having a specified timebase; the -result will have the timebase of the latter system. For continuous time -systems, the :func:`sample_system` function or the :meth:`StateSpace.sample` -and :meth:`TransferFunction.sample` methods can be used to create a discrete -time system from a continuous time system. See -:ref:`utility-and-conversions`. The default value of `dt` can be changed by -changing the value of `control.config.defaults['control.default_dt']`. - -Conversion between representations ----------------------------------- -LTI systems can be converted between representations either by calling the -constructor for the desired data type using the original system as the sole -argument or using the explicit conversion functions :func:`ss2tf` and -:func:`tf2ss`. - -Subsystems ----------- -Subsets of input/output pairs for LTI systems can be obtained by indexing -the system using either numerical indices (including slices) or signal -names:: - - subsys = sys[[0, 2], 0:2] - subsys = sys[['y[0]', 'y[2]'], ['u[0]', 'u[1]']] - -Signal names for an indexed subsystem are preserved from the original -system and the subsystem name is set according to the values of -``control.config.defaults['iosys.indexed_system_name_prefix']`` and -``control.config.defaults['iosys.indexed_system_name_suffix']``. The default -subsystem name is the original system name with '$indexed' appended. - -Simulating LTI systems -====================== +LTI response functions +---------------------- A number of functions are available for computing the output (and state) response of an LTI systems: .. autosummary:: - :toctree: generated/ - initial_response - step_response - impulse_response - forced_response + initial_response + step_response + impulse_response + forced_response Each of these functions returns a :class:`TimeResponseData` object that contains the data for the time response (described in more detail @@ -313,61 +152,6 @@ constructing the system, as described in :func:`ss`, :func:`tf`, and other system creation function. Note that when exporting to pandas, "rows" in the data frame correspond to time and "cols" (DataSeries) correspond to signals. -.. currentmodule:: control -.. _package-configuration-parameters: - -Package configuration parameters -================================ - -The python-control library can be customized to allow for different default -values for selected parameters. This includes the ability to set the style -for various types of plots and establishing the underlying representation for -state space matrices. - -To set the default value of a configuration variable, set the appropriate -element of the `control.config.defaults` dictionary:: - - ct.config.defaults['module.parameter'] = value - -The :func:`~control.set_defaults` function can also be used to -set multiple configuration parameters at the same time:: - - ct.set_defaults('module', param1=val1, param2=val2, ...] - -Finally, there are also functions available set collections of variables based -on standard configurations. - -Selected variables that can be configured, along with their default values: - - * freqplot.dB (False): Bode plot magnitude plotted in dB (otherwise powers - of 10) - - * freqplot.deg (True): Bode plot phase plotted in degrees (otherwise radians) - - * freqplot.Hz (False): Bode plot frequency plotted in Hertz (otherwise - rad/sec) - - * freqplot.grid (True): Include grids for magnitude and phase plots - - * freqplot.number_of_samples (1000): Number of frequency points in Bode plots - - * freqplot.feature_periphery_decade (1.0): How many decades to include in - the frequency range on both sides of features (poles, zeros). - - * statesp.default_dt and xferfcn.default_dt (None): set the default value - of dt when constructing new LTI systems - - * statesp.remove_useless_states (True): remove states that have no effect - on the input-output dynamics of the system - -Additional parameter variables are documented in individual functions - -Functions that can be used to set standard configurations: - -.. autosummary:: - :toctree: generated/ +Input/output response +--------------------- - reset_defaults - use_fbs_defaults - use_matlab_defaults - use_legacy_defaults diff --git a/doc/xferfcn.rst b/doc/xferfcn.rst new file mode 100644 index 000000000..fcd678500 --- /dev/null +++ b/doc/xferfcn.rst @@ -0,0 +1,25 @@ +Frequency domain analysis and design +==================================== + +Transfer function properties +---------------------------- + +Frequency domain methods +------------------------ + +Input/output norms +------------------ + +Stability margins +----------------- + +Frequency domain synthesis +-------------------------- + +Transfer functions from data +---------------------------- + +.. todo:: rename to match FBS2e termincology + +Systems with time delays +------------------------ diff --git a/examples/python-control_tutorial.ipynb b/examples/python-control_tutorial.ipynb new file mode 100644 index 000000000..b256b1483 --- /dev/null +++ b/examples/python-control_tutorial.ipynb @@ -0,0 +1,1242 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "numerous-rochester", + "metadata": {}, + "source": [ + "# Introduction to the Python Control Systems Library (python-control)\n", + "\n", + "Richard M. Murray, 13 Nov 2021 (updated 26 Dec 2024)\n", + "\n", + "This Jupyter notebook contains an introduction to the basic operations in the Python Control Systems Library (python-control), a Python package for control system design. The tutorial consists of two examples:\n", + "\n", + "* Example 1: Open loop analysis of a coupled mass spring system\n", + "* Example 2: Trajectory tracking for a kinematic car model" + ] + }, + { + "cell_type": "markdown", + "id": "9531972e-c4b8-4a87-87d8-d83a01d4271f", + "metadata": {}, + "source": [ + "## Initialization\n", + "\n", + "The python-control package can be installed using `pip` or from conda-forge. The code below will import the control toolbox either from your local installation or via pip. We use the prefix `ct` to access control toolbox commands:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "macro-vietnamese", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "python-control 0.10.1.dev324+g2fd3802a.d20241218\n" + ] + } + ], + "source": [ + "# Import the packages needed for the examples included in this notebook\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# Import the python-control package\n", + "try:\n", + " import control as ct\n", + " print(\"python-control\", ct.__version__)\n", + "except ImportError:\n", + " !pip install control\n", + " import control as ct" + ] + }, + { + "cell_type": "markdown", + "id": "distinct-communist", + "metadata": {}, + "source": [ + "### Installation notes\n", + "\n", + "If you get an error importing the `control` package, it may be that it is not in your current Python path. You can fix this by setting the PYTHONPATH environment variable to include the directory where the python-control package is located. If you are invoking Jupyter from the command line, try using a command of the form\n", + "\n", + " PYTHONPATH=/path/to/control jupyter notebook\n", + " \n", + "If you are using [Google Colab](https://colab.research.google.com), use the following command at the top of the notebook to install the `control` package:\n", + "\n", + " !pip install control\n", + "\n", + "(The import code above automatically runs this command if needed.)\n", + " \n", + "For the examples below, you will need version 0.10.0 or higher of the python-control toolbox. You can find the version number using the command\n", + "\n", + " print(ct.__version__)" + ] + }, + { + "cell_type": "markdown", + "id": "5dad04d8", + "metadata": {}, + "source": [ + "### More information on Python, NumPy, python-control\n", + "\n", + "* [Python tutorial](https://docs.python.org/3/tutorial/)\n", + "* [NumPy tutorial](https://numpy.org/doc/stable/user/quickstart.html)\n", + "* [NumPy for MATLAB users](https://numpy.org/doc/stable/user/numpy-for-matlab-users.html)\n", + "* [Python Control Systems Library (python-control) documentation](https://python-control.readthedocs.io/en/latest/)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "1c619183", + "metadata": { + "id": "qMVGK15gNQw2" + }, + "source": [ + "## Example 1: Open loop analysis of a coupled mass spring system\n", + "\n", + "Consider the spring mass system below:\n", + "\n", + "
\n", + "\n", + "We wish to analyze the time and frequency response of this system using a variety of python-control functions for linear systems analysis.\n", + "\n", + "### System dynamics\n", + "\n", + "The dynamics of the system can be written as\n", + "\n", + "$$\n", + "\\begin{aligned}\n", + " m \\ddot{q}_1 &= -2 k q_1 - c \\dot{q}_1 + k q_2, \\\\\n", + " m \\ddot{q}_2 &= k q_1 - 2 k q_2 - c \\dot{q}_2 + ku\n", + "\\end{aligned}\n", + "$$\n", + "\n", + "or in state space form:\n", + "\n", + "$$\n", + "\\begin{aligned}\n", + " \\dfrac{dx}{dt} &= \\begin{bmatrix}\n", + " 0 & 0 & 1 & 0 \\\\\n", + " 0 & 0 & 0 & 1 \\\\[0.5ex]\n", + " -\\dfrac{2k}{m} & \\dfrac{k}{m} & -\\dfrac{c}{m} & 0 \\\\[0.5ex]\n", + " \\dfrac{k}{m} & -\\dfrac{2k}{m} & 0 & -\\dfrac{c}{m}\n", + " \\end{bmatrix} x\n", + " + \\begin{bmatrix}\n", + " 0 \\\\ 0 \\\\[0.5ex] 0 \\\\[1ex] \\dfrac{k}{m}\n", + " \\end{bmatrix} u.\n", + "\\end{aligned}\n", + "$$\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "9f86a07f", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + ": coupled spring mass\n", + "Inputs (1): ['u[0]']\n", + "Outputs (2): ['q1', 'q2']\n", + "States (4): ['x[0]', 'x[1]', 'x[2]', 'x[3]']\n", + "\n", + "A = [[ 0. 0. 1. 0. ]\n", + " [ 0. 0. 0. 1. ]\n", + " [-4. 2. -0.1 0. ]\n", + " [ 2. -4. 0. -0.1]]\n", + "\n", + "B = [[0.]\n", + " [0.]\n", + " [0.]\n", + " [2.]]\n", + "\n", + "C = [[1. 0. 0. 0.]\n", + " [0. 1. 0. 0.]]\n", + "\n", + "D = [[0.]\n", + " [0.]]\n", + "\n" + ] + } + ], + "source": [ + "# Define the parameters for the system\n", + "m, c, k = 1, 0.1, 2\n", + "# Create a linear system\n", + "A = np.array([\n", + " [0, 0, 1, 0],\n", + " [0, 0, 0, 1],\n", + " [-2*k/m, k/m, -c/m, 0],\n", + " [k/m, -2*k/m, 0, -c/m]\n", + "])\n", + "B = np.array([[0], [0], [0], [k/m]])\n", + "C = np.array([[1, 0, 0, 0], [0, 1, 0, 0]])\n", + "D = 0\n", + "\n", + "sys = ct.ss(A, B, C, D, outputs=['q1', 'q2'], name=\"coupled spring mass\")\n", + "print(sys)" + ] + }, + { + "cell_type": "markdown", + "id": "1941fba0", + "metadata": { + "id": "YmH87LEXWo1U" + }, + "source": [ + "### Initial response\n", + "\n", + "The `initial_response` function can be used to compute the response of the system with no input, but starting from a given initial condition. This function returns a response object, which can be used for plotting." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "195a3289", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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pdPjpT3+atILY0/cGAEwmE37wgx/gL3/5CxwOB26//XZ897vf7dVYnFTcdNNNWLhwIe666y786Ec/wvr165VduH0Z+RLPqFGj8Oabb2LevHkQBAG//e1vI6rqyf7crF69GosXL8ZZZ52FoqIirF69Gk1NTSn/A4EoGituRP1Ap9Phb3/7G5566imUlZXFDTMajQaLFi3CunXrMGnSJPzf//0f/vznPx+S158yZQq+/PJLVFVV4eSTT8b06dPx29/+FqWlpQCkERRvvvkmTjvtNIwfPx5PPvkkXnnlFUycOFG5xjXXXIPu7m4cd9xxuPXWW/GTn/wEN954o/L+5557DsceeywuuOACnHDCCRBFER988EGv559ZLBZs374dl156KcaMGYMbb7wRt912G2666SYAwNlnn43//e9/+PTTTzFz5kzMmjULCxcuxLBhw1L6Wtx+++34+c9/jp///OeYPHkyPvroI2W8RKreeOMNzJw5E9///vcxYcIE/PKXv1Sqqscccwxee+01LFq0CJMmTcI999yD+++/H9dee63y8X/9619RUVGBOXPm4IorrsCdd94Ji8US8zp//OMf8dBDD2Hq1Kn46quv8M4776CgoCDuPfX0vQak71VFRQVOOeUUXHLJJbjxxhtRVFSU8PPs6XsDSCHnkksuwXnnnYezzjoLkyZNigiph8rw4cPx+uuv480338SUKVPwxBNP4Ne//jUAwGg0HtLXevjhh5Gbm4vZs2dj3rx5OPvss3HMMcco70/258Zut2Pp0qU477zzMGbMGPzmN7/BX//6V5x77rmH9B7p6COIvV0cQERHrblz52LatGk8mqifJTpxI9Pce++9ePvttwfsKLbf//73ePLJJ1FbWzsgr0/Un9gqJSKiQeXxxx/HzJkzkZ+fj+XLl+PPf/4zbrvttoG+LaJ+weBGRESDSlVVFR544AG0trZi6NCh+PnPf4677757oG+LqF+wVUpEREQ0SHBzAhEREdEgweBGRERENEgwuBERERENEgxuRERERIMEgxsRERHRIMHgRkRERDRIMLgRERERDRIMbkRERESDBIMbERER0SDB4EZEREQ0SDC4EREREQ0SDG5EREREgwSDGxEREdEgweBGRERENEgwuBERERENEgxuRERERIMEgxsRERHRIMHgRkRERDRI6Ab6BjJFMBhEXV0dbDYbBEEY6NshIiKio4goinA6nSgrK4NGk7iuxuAWUldXh4qKioG+DSIiIjqK1dbWory8POH7GdxCbDYbAOkLZrfbB/huiIiI6GjicDhQUVGh5JFEGNxC5Pao3W5ncCMiIqIB0dNyLW5OICIiIhokGNyIiIiIBomMD24LFizAhAkToNFosGjRooTP6+7uxlVXXQWbzYahQ4filVde6ce7JCIiIjr8Mj64jR49Go8++iiOO+64pM9bsGABWltbceDAASxatAi33HILdu7c2U932TuiKCIQFAf6NoiIiGiQyvjgdtVVV+HMM8+EyWRK+rwXX3wRCxYsgN1ux+zZs3HhhRcmrdB5PB44HI6I/w6nn7+2AVPu/QRf7mw8rK9DRERER66MD2690dbWhoaGBkyePFl5bOrUqdiyZUvCj3nwwQeRnZ2t/He4Z7j5g0E4PX5sq3ce1tchIiKiI9cREdxcLhe0Wi0sFovymN1uh8vlSvgxd999Nzo6OpT/amtrD+s9ji+VRoxsrT+8lT0iIiI6ch0Rc9ysVisCgQC6urqU8OZwOGC1WhN+jNFohNFo7K9bxIiCLADA/rbufntNIiIiOrIcERW33NxclJSUYNOmTcpjGzZswMSJEwfwriJlm/UAAGe3b4DvhIiIiAarjA9uPp8PbrcbwWAw4tfRrrrqKvzud7+D0+nEqlWr8O677+Lyyy8fgDuOzx4Kbg43gxsRERGlJ+OD2w033ACz2YyvvvoK11xzDcxmM5YuXYqXXnopoqJ2//33Izs7G6Wlpbjsssvw+OOPY+zYsQN455GU4NbthyhyJAgRERGlThCZIgBIa+Kys7PR0dFxWM4qdXn8mLTgYwDA9t+dA5Nee8hfg4iIiAan3uaQjK+4HSmyDFpoQufGdnCdGxEREaWBwa2fCIKALKO0ibfT4x/guyEiIqLBiMGtH5lD7dFuX2CA74SIiIgGIwa3fmQ2SMHNzeBGREREaWBw60dKxc0bO86EiIiIqCcMbv3IxFYpERER9QGDWz/iGjciIiLqCwa3fqSscfMyuBEREVHqGNz6EStuRERE1BcMbv2Ia9yIiIioLxjc+pHZIH25u9kqJSIiojQwuPUjg1aquHkDHAdCREREqWNw60cGnfTl9voZ3IiIiCh1DG79iMGNiIiI+oLBrR8ZGdyIiIioDxjc+pFBGwpuXONGREREaWBw60dslRIREVFfMLj1Izm4eRjciIiIKA0Mbv2IrVIiIiLqCwa3fhRulXIALxEREaWOwa0fcY0bERER9QWDWz9SghtbpURERJQGBrd+pKxxY8WNiIiI0sDg1o/YKiUiIqK+YHDrR6y4ERERUV8wuPWjQ7HGbfmuZry6Zh/8XCdHRER01NEN9A0cTfo6gNfjD+CHz6+B1x+E1x/E1SdUHsK7IyIiokzHils/6murdF9Ll/Kx1U2dh+y+iIiIaHBgcOtHRlWrVBTFlD9+T3M4rB1o7z5k90VERESDA4NbP5JbpaII+IOpB7eDDrfy6wNtDG5ERERHGwa3fiQHNyC9dmlbl0/5dUe3L8kziYiI6EjE4NaP5DVuQLrBzav8msGNiIjo6MPg1o90Wg00gvTrdEaCtKsqbi6PnyNBiIiIjjIMbv2sL6cnqCtuAOB0+w/JPREREdHgwODWz+R2aTqz3FxRQc3hZruUiIjoaMLg1s8MOi2A9CpuXd5AxNusuBERER1dGNz6mbEPx165fZHBrTvqbSIiIjqyZXxwa2pqwvnnnw+LxYKxY8di8eLFcZ937bXXwmg0wmq1wmq1YuLEif18p73TlzVu0RW3bi+DGxER0dEk44PbrbfeirKyMjQ3N+Ohhx7CZZddhra2trjPve++++ByueByubBly5Z+vtPekde4pbMjtMsrtUZzLHoArLgREREdbTI6uLlcLrzzzju4//77YbFYMH/+fEyaNAnvvffeQN9a2nRaaR5Ieq1S6WPyLIbQ2wxuRERER5OMDm5VVVXIzs5GaWmp8tjUqVMTVtP+/Oc/Iz8/H7Nnz8bSpUuTXtvj8cDhcET81x/0oYqbL5DakVf+QFAJe3lZUnBjq5SIiOjoktHBzeVywW63Rzxmt9vhcrlinnvHHXdg165dqK+vx6233op58+ahtrY24bUffPBBZGdnK/9VVFQc8vuPx6AEt9Qqbl2q6poc3KLXvBEREdGRLaODm9VqjamEORwOWK3WmOdOnz4dubm5MBgMuPLKK3HCCSfg008/TXjtu+++Gx0dHcp/yULeoaTXSa3SVIObOxTSNAKQbe7fNW4tLk/K90tERESHXkYHt9GjR6OjowMNDQ3KYxs2bOjVjlGNJvmnZjQaYbfbI/7rD+m2SuXqmsWgg8UgzYLrjzVue5o7ccKDn+OSx1ektROWiIiIDp2MDm5WqxUXXnghFixYgO7ubrz77rvYvHkz5s2bF/PcN954A52dnfD7/Xj11VexbNkynHbaaQNw18np022VhoKb2aCFKRTc+mON2+fbG+ENBLHpQAfW7Y2/m5eIiIj6R0YHNwB4/PHHUVtbi/z8fNx555147bXXkJubi5deeimi8vbwww+jrKwMBQUFWLhwId566y1UVlYO3I0nkO4aN7ktatZrYdZrIx47nDYf6FB+vaspdm0hERER9R/dQN9ATwoLC/HBBx/EPH7llVfiyiuvVN5etmxZf95W2pRxICm2HbuVVmn/Brf6jm7l17sZ3IiIiAZUxlfcjjTpr3GThu+a9FqY+3GNW6PDE/6105PkmURERHS4Mbj1s3TXuMnVNYtBC5O+/9a4NTjcyq+bGNyIiIgGFINbPzNo0xsHMhCtUrcvEDErrrkPwa2uvRtXPLMKD3+681DcGhER0VGJwa2f9XUciClic8LhHc/hdPsj3u5LxW3hpzuxoroFjy6uQqfH3/MHEBERUQwGt36m1/W9VarMcTvMrVKn2xf5tsePQDC1wCnbedCp/Hp9bXtfbouIiOioxeDWz9Je46YawCvPcevyHd7KlVxxK7AaVY/5Ej09qerG8I7U2tauvt0YERHRUYrBrZ+lu8Ytbqv0sFfcpOCWl6VXXtPRnXpY7PT40am619o2BjciIqJ0MLj1M12o4ub1p9ZyVLdKjaF2q6eHNW5NTo8yRiQdcnXNZtIr56M60qi4Nbsi18bVtbsTPJOIiIiSYXDrZ+m3SqUAZjFoYZCDW5JrbK1zYNaDi3HqX5bAleZmALniZjPpYDdLs5o7uvse3Nq6vGndDxER0dGOwa2fHYpWqVEntS29/iBEMX7l7osdjQgERRx0eLB+X3ta9+r0yMFND7spVHFLI7hF70Zt62RwIyIiSgeDWz87FAN45YqbdJ34wW1LXfiM0Y0H2lO8S0m4VapTWqXpVNyaXFJQK7AaAABtXeltcCAiIjraMbj1s3TnuKkH8BpVwc2bIADWd4TXkdW2dsd9Tk8iW6V9WOMWqriNLrIBYKuUiIgoXQxu/SzdOW7qVqlBqwpuCQ6rV58xqj4oPhVyxc2u2pyQXsUtFNyKraHr+lP+/ImIiIjBrd+lu8bN7QvPcdNoBOg00nU8/tiRIKIoRqwrq2s/BBU3k7Q5IZ1xIK2hVumIgiwI0m2jne1SIiKilDG49TO5VepN88gr+dQEeZ1bvIqb0+OPaKGme1SVvBvVagy3StOpuDk90sfkWAxK5a6d7VIiIqKUMbj1M3mOmy9BizMReR6bSd+L4BZ1xmh7tw/+NFqTXarTGuRdpemcnKCu3OVauEGBiIgoXQxu/UyfdqtUer5ccVOG8MYJbq5QULKbdBAEQBRjg1IwKKLFlbwSJ2+IMBu0sIVapenMhAsHNz1yLVIAbOVIECIiopQxuPUzQxrjQPyBoNL6jGmVxrmOS9WazDHHD0o/fH4Njn3gM7z97YGEryuvqzPrtbCGglt0Na831GNFckIVt45uBjciIqJUMbj1s3TGgXT5whsQlFaptudWqc2kQ16WFJRaOsPVtUanG1/ubAIA/HddbcLX7VYFN5vSKk2/4mY16pBllCt36Z2z+sWORhz/h8/w1JfVaX08ERHRYMbg1s/SGcDrDrUsNUK4RWpQnZ4QTb2pID/LCCCy4vat6iSFbfXOhK+rBDeDBlajXHFLbW2a1x9U2rl2k165Tmeax3D97n9bcdDhwYMfbk/7KC8iIqLBisGtnxl0qa9xU28SEELzNAy9WOOmrripg9ue5k7l162d3oQH0XerZsfZVWvcEh2zFY866FlNuj6tlWvt9GJ3U/je19a0pnwNIiKiwYzBrZ+l1SpVBSiZMUmrVF1xywsdM9XiCge3vS1dEc8/0BY75y0YFJVQqF7jFhTD99Mbcps0y6CFViMgy5D+WrlNBzoi3t7V6Er5Gul4fd1+/Pilddjf1tXzk4mIiA4j3UDfwNEmPMet9xU39TmlMqNevk5siFLWlJl0yLPEVtyiA0h9hxuji20Rj7lVg33NBi3Meil4BYIiXB6/slatJy7VQfXyPQHptUp3N0UGtWpV9e1wqW3twp3/3QAACARFPHX1jMP+mkRERIkcsoqb3+/Hddddd6gud8TSqcaB9LblqIzlUFXcerM5wWrUx22VRg/kjXd2aLeqqmbSaSEIgtLmTGWdm3y2qRzYbMb0W6Vym7TIJq3b648K2Bc7GpVff7mzSdlpS0RENBAOWXALBAJ44YUXDtXljlhy4BJFqYLTG/IaNLOq4pZsAK88DsRm0iE3zq7Sgw7pAPrRRdLZoeo2qkyu8hl1GmhCx2uFNyj0PnSpd7gCCO8qTaNVeiB0dNeMylwAPZ8I8dzyPbj8qZXYUteR9HnJbKt3KL92+4IRa+yIiIj6W0qt0vPOOy/h+wJxWnYUS686IN4XEKHTJnlySLxWadLNCUl2lXr8AWUY77hSO6oaXXGH4Soz3FSvKbU7u9MMbpGt0nQqbvKZq1PLc/DBpgY0JglujQ437ntvKwDgF//diA/uODnl1wOA6sbIoLbzoBMTyuxpXevjLQ1wuv249JghyiYTIiKiVKQU3JYuXYr/9//+H4YMGRLzPq/Xi88+++yQ3diRKiK4BYMwo+fklrRVGmetXLw5bnI4k6tUBq0GIwqyAAAtcYJbtze8MUGWTpvTpRq+C4SrdukEt/oOqVI4pTwHgPQ5ef1BJcSqrdzdovx6a70DzS4PCqzGlF9zd7Mr9JrZ2Li/AzsOJh6fkszq3S246cV1AKRzWq8/eURa1yEioqNbSsHt2GOPxYgRI/C9730v5n1utxs33XTTIbuxI5V85BXQ+/NKu7yx1S+l4uZLXHHLMuqQbw2fDRoMijjokIJboc2IHEvi80fVw3dl6axxU0KksW/BrcvrVw64n1Bmh04jwB8U0dLpQWm2Oeb5G2oj26Mb97fjtHHFKb1mR7cPzaE28tkTS7Bxfweq0gxur64NDzpetKaWwY2IiNKS0hq3Bx54AKNHj477PqPRiC+++OKQ3NSRTBAE1XmlvdyckKRVGq/i1u0NP18+1D0QFNHR7UOTU6paFduNSUOU/JrqESTpHHvl9ESucVNapSmucatrl+7bZtQh26xXqmeNjvjt0n2tkRsX1temvs6tpjm8GWJ6RQ6A9HayiqKI5bualbd3NbrQGPo+EBERpSKlitvJJ0vrhF577bWEz1G/77vf/W6at3Vk02s18AUCvR7CG69VakxycoK87s2k18Kg08Bm0sHp9qOl06tU3IpsJlUFLU5wi1PlS/b8RMLnlIbWuIXCojcQhMcfUD6PntR3SOvbSrJN0v3bjWhwuBOuc5N3nJ4+rgiLtzdi4/72Xt9z9GsOyTWjIs8CQJp5FwiK0Gp6v0atvsONgw4PtBoBJXYTDrR3Y3u9E0U2U8r3RERER7e05rg99thjWLlyJUpKSlBeXo79+/ejoaEBs2fPVhZdC4LA4JaAtM4t0OtZbuFWafjblWxXqbyxwBQKRflZBjjdfrR2epVKT7HdqISpeNUvd5xWqdUYen4Kbc6YXaWqINjpSSW4SfddmiO1RQvlilucypUoiqgNVdzOnliCxdsbsbMh9RanHHJL7CaUZpug0wjwBoI46HCjLCe2PZvIzlB7dURBFkYXW3GgvRs7DzoxZ0xhyvdERERHt7SC2/jx43HZZZfhtttuUx577LHHsHnzZjzxxBOH7OaOVHptasdexR3A25vgFhrSm5dlQE1LF1o7PeGKm92Ucqu0L2vc5NfSaTUw67Xo9gXgcvuVzRM9qQ+1SstCFTf549q7Yu+lvcuHzlDYnTtOCkd1HW443D7YQ2G1N+SxKcV2E3RaDYbkmrG3pQv7WrtSCm5ye3VUkRVjim34YFMDdqQRJImIiNKa47Zo0SLccsstEY/ddNNNeOWVVw7JTR3plGOv/L0dwBua49bLXaVuX7hVCgB5oZEgLZ1epbVYZDMqQcwRb3NCklZpahW3yFYpkN5IELltKW9EkDdWyBsW1GpDbdIimxFFNhNK7FLYqzqY2hFZDaHgVmSXvn4VuVK7tLY1tcG/1aETH0YWWjE2dEJFurtTASlQ9nb4cIvLk9YpFWrNLg+eWbob62vb+3QdIiLqu7SC27Bhw2KG7b744ouoqKg4JDd1pEv12Kuku0r9kfPzRFFUjquSj8XKl0eCuLxoCAWgYrspIkBFn+IQ3lUa/i2Szho3OZzJh9QD6e0srVNapVIIy7HIFbfYUSbyRga5Kja6WBo0HL0j1O0LJD0JQd74UBxaiyavc0s5uIXOVB1RmIVRoaHHe5o6e31yhtqK6mac/KcvMOdPX+CTLQ1Jn/v00moc+8BnOPUvS1K+Z5kvEMQVz6zC7z/Yhu88sQKbD6Q/zJiIiPoureD27LPP4t5778WYMWNw+umnY8yYMViwYAH+9a9/Her7OyLJrVJ/nOC2rd6Bz7YejPihnnRXaVSr1BsIQv5QpeImHzTf6VUOlB+Sa4YttGZNFKG0FmXJ1rilFNxU56aGryMHt963XOtDw3fLQhW3bLN0L/FapfLMOnnnabwq15qaVhz7u09x+l+/VFqi0eTH5Q0RQ0PBLXrHak92h3anjiy0KuHP6fErg5BT8ccPt8PrDyIoAn/4YFvC8NfW6cVfPtkJAGh0evDwZztTfi0A+HTrQewMVSr9QRFPLKlO6zoAEAyKqO/oRrCXJ4YQEVGstILbzJkzUV1djWeffRY33ngjnn32WVRXV2PmzJmH+v6OSEqrNGocSFunFxf8fRmu//dafLYtfEZm/F2l8U9OcKvmusmbE+SD5mtaOpWAVpZthkmvgS60OzJ6g4L8mqa4u0pTWOPmiVzjpv61yxNb7drX0oUzFn6J619YG1ENq4+puIWCW5xWaWvoeC+50jgmFNzUrdIH/rcVnd4ADrR344UVNXHvXWmVhs5GlYNbbSj89oY0gkW6nxGFWTDptUrrdm9LaqNF9jR3YuP+cMWrpqULmw844j73k60NEaH+fxvqezwiLJ6vqqQxJscNzwMAfLrtYNz2dE9cHj++8+QKnPDg57j86ZURZ+ESEVHvpX1WqV6vx5w5c3D55Zdjzpw50Ot7v+j7aCdXy6I3J2zY366cX7qsqkl5PFmrNLri5gmFHY0QruwV2KQAsyG0RikvywCzQTo4PtwujfxhHG8Ab6otzmBQVJ6rXuOW7LzSv39ehV2NLny27SDe/vYAAGkNnnyd0lD1K8csfU4dcapW8tBcudIot0rlitvuJhc2qALQZ9sOxlzD7QsolcUiu9wqlap9qVTcdofWt6l38Q7NT165E0WpMhVdTXt3fR0A4JQxhThtXBEAYPWelpiPB8Jz6348dySmlmfDGwji/Y11vb5v2erQCRTXnzQco4us8PqD+HRr7NerJ39bXIVv9rUDANbUtOGppelX7oiIjmaH7JB56r1Ea9y2q3YabqsP/9qttEpV40ASXEO9MUEezSIv6Jdbc2U54flh4Q0KURW3OMFN3pHZ21Zply+gtG1tqlZpssrdUlVglQOVvD4rP8ugfA3CFbfYNW5yq1SuuI0OVdyanB60d3mxZIf0GuNKpMd3HnTFVJHUR4PJ6/PkiluT09PritGeUJt0RIFVeWxY6Dp7W2KDWyAo4voX1oYqU6uUYC6KIt7ZIAXZC6eWKRWwr/e0xn1deS3apCHZuGiadETdexvre3XPsoMON3Y3d0IQgOOH5+OcSSUAgC+2N/bwkZGcbh9eXr0PADB/WhkA4KXV+3q9qzpdu5tc+O6TKzHnT1/g1TX70r5OfUc3fv3WJtz1+kZlowkR0UDJ+ODW1NSE888/HxaLBWPHjsXixYvjPq+7uxtXXXUVbDYbhg4dmtE7XOX2ZPQProaO8FqrA+3hdlxXvLNKE1Tc5I0J6jEeQ6JGV5SpjohSZrNFhbF4h8zL1bkub0CpDCYjX1OnEZTWrvSa0nWidzu2qQYEA1IoCQbDM9nk9WFA8jVuLXKrNFRxsxp1ytdg50GXEg4vOWaIMl5kV2PkxoVmV/hoMDkAZ5v1Sujs7a5OObgNL8xSHhuWnzi4fbylAYtDwejrPa145qvdAIAtdQ7sbuqEUafBWROLcXwouK2paY2pzHn9QWXcyOQh2Th/SikEAVi3t63X9w0Aq0LVtolldmRb9EqVb+nOprhjaBJ5Y91+uDx+jCzMwp++MxWFNiOanB4s3hY/AHZ5/Xj2q9145LOdaIxaf+hw+/CTV77FjAc+xe2vfJtwx2ynx48fvbAWX9e0Yl9rF+56Y1Na4a3R4cbFj63AS6v34dW1tbj0iRVpH3t20OHGiytr8OY3+/u805eIjl4ZH9xuvfVWlJWVobm5GQ899BAuu+wytLW1xTxvwYIFaG1txYEDB5RxJTt3prcg+3BL1CpVD5Ot7+hW3t8ljwPpRas0PHw3/K2Vh8fK1DPIEu0UVda4xWmVApFBr6PLh7U1rTGLzuX2q9WkU8KP/DYQXv8m2xNa81VgNcJi0MLh9mN7gxO1rVKIHaoKbnLFzeMPxuwMbZFbpVnhQ+XHhqprG2rbsbJaCiRzxhRiZGiX567GyEqKXHErsIbnzAmCkPIGhd1KxS0c3IbmS7+Ot8btzW/2AwjPq3tiSTXau7xK2/j08UWwmfTKea1tXT5lx61s50EnvIEgss16lOeaUWw3KUHv/VDVzRcI4tdvbcLU+z7BTxd9G7M7GQgHt1nD8wEAU8tzUGA1wOnxY01N/EpfNFEU8e9VewEA186uhEGnwcXTpQrg/+K0bn2BIK5/YS0eeH8bHvmsCmc/slSpKnZ0+XD1s6vx3oY6NLu8eHdDHX7+2oa4GzT+s2ov9jR3ojTbhO8fNxQAsODdLSmHrvve24oGhxvDC7IwodSO9i4ffvbahrgbi5JZVtWMsx9Zit++swU/e20D5v19WcKdvqIo4quqJjz2xS68u6FO+fOv5nD7elxruPOgEze/uA4n/vFzXPHMqpQrpbJGpxt//WQHfrroWzy+ZJfyj5p0eP3BuLvYiaj30hrA219cLhfeeecd1NTUwGKxYP78+Vi4cCHee+89XHPNNRHPffHFF/H222/Dbrdj9uzZuPDCC7Fo0SLcc889A3T3iSWa46Y+dzMoSv9CH5JjVipu8QbwJtqcoA5cOq0Go4qsSit2QpldeV+i6le8VqlBp4FRp4HHH4TT40O2RQ9RFPGD577G+tp23HbqKNx59ljl+Y6oUxNkicLintCg2tFFVmg1Apbtasa6fW1KSFIHN6tRB61GQCAoor3Lh5Ls8H1Gt0oBaZ3b59sb8dTSanj8QQzJMWNssQ0jC634qqo55gxSeZ1coc0Y8XhFrgVb6hwxwa2j24eGDrcSEKM/p+Gq4Ka0SqOuEQyKWLVbCilPXzMDd/53A7Y3OPGPz3fh9VCgu2R6OQDpyLMxxTZsrXdg84GOiKpquE1qVwLzvKllWLW7Fe9trMNNp4zEQx9ux0uh9uXb6+tQnmuJ+N4BUALuCSOl4KbRCDh1bBH+u24/Fm9rxImjCpTnBoIiPtnSALc/gPMmlyonYqza3YrdTZ3IMmhx8THSvV8wpRRPL92Nxdsa0eX1RywBWLSmFiuqW2AxaFGWY8auRheu/udq3HbqKLy/qR7bG5zItejx0zPG4Pfvb8NHWxqwZEcTTg1VAwEpHDy3vAYA8H9njsF3jilHbWsXlu1qxk9e+RZv33oiTHotfIEgPtzcgE3723HCyHycOrYo4h8YX+xoxPub6qHVCPjHFdNRaDXijIVfYtOBDjzz1R7cMndkxNdrS10HPtvaCKNeg+OH52FqeQ4EAfjnsj34wwfbEBSl3wcujx+7mztx/Qtr8eaPZytrPgFp/ehtL3+jtPMB6ff6ZTPKcfzwPGyrd+KLHY3KJpVjhubgnnkTMS10lq5s+a5m3PjvtcpmpAPt3VhR3YIzxhfjgfmTlJ3SDrcPm/d3wBcUMbIwC+W54T9jHn8Azy2vwT8+3xWxrvXJJdX47QUT8J1jyyO+Xg0dbny5sxEHHR7YTToMybWgxG6Cy+PH13tasWxXE77d1w5/UESRzYhzJpXg6lnDlKUMss0HOvDa2los29WMQFBUQvO4UjtsJh26vQG0dHrR6vLCbNBgRIEVU8qzlbWoAFDX3o33NtThw80NqDrohEYQMKrYijmjCzF7ZD6mlOfAoNOgpdODZqcX7V1eiAByLQbkZulhN+nhcPuwp7kTWw44sLmuA7saXdBrNRhbbMOMylwcPzwfFXlmCIIg7Zh2uFHd6EJtW5dyLJ5Zr4VZr4VGI6DL64fLIw0e7/b6UWQ3YXRoKHdu6O8qp9uHqkYXdh10oaalE25fEFqNtC7YatTBYtAhy6hFlkEHEUAgGAQgwGzQwmKQXktaJiP9/DjocKOhw4MmpwcWgxZF9tBsy2zpyENfIAivPwi3T/oHsC8QhCBI/0gVIP1/tzcAl8cPl1sabG4xaFFoMyIvy4BciwHZZj08/iBcbj+cHh+cbj9cbj9ESN0WrUYI/79WgEYQoNNolLe1oY6MUSf9mWzv8qGj24subwAefxD+QBAa5RrShjqdVvW2VoBBq1FeIyhKfx/5g0F4fEG4/QG4fdLnqdVIP3+l/wTotRroQh8bFEUERekfTmLoGtJjIgJBwBsIwGLQ4eyJJRhoGR3cqqqqkJ2djdLSUuWxqVOnYsuWLRHPa2trQ0NDAyZPnhzxvK+//jrhtT0eDzyecFByOOLvzjsc5E0D0evTov8l2+T0oMBqhD9UyVIHIIM2/lmlcvXJqApcADClPFsJbscMzVUel8Ngpzc6uEnXNUddx2bSw+PyKKFrS51DGcz6/Ioa/N+ZY5RzPJVRIMbIjSu2BJsTakIVqMqCLBRaDVi2qxnr97UrXxd1cBMEATlmPVo6vWjv9io/iERRDAc3VbXs+OF5eOrL3UogO3NCMQRBUCpu1QkrbpHBLd7Ggj3NnbjoH8vgcPvx47kj8ctzxin3orRKC2JbpU1OT0Rw2d3cCZfHD5Neg3ElNvz0jDG4+T/r8OyyPQCklrc6oEwaYsfWege2HOiI+Mtkc10ouJVlK4+dO6kUC97Zgs0HHPi/V9fjrW/D6+Xe3VCHF1bU4MZTRijrGOs7ulHT0gWNAMwMVesAqeL333X7sXj7Qfz2gvEQBCk83/7Kt3h/k1TNe+rL3fj3dcehyG7Cf1ZL1bb504co/0iYPCQbQ/Ms2Nfahc+3N+KCKdK6t0BQxD9DreFfnD0W35s5FLe9/A0Wb2/EXz/dGfp+GPDS9bMwtsSGA+3deHrpbvz54x2YO7ZQCRHvb6pDg8ONQpsRF00rg0YjYOHlU3Heo19he4MT1z2/BtMqcvD6uv3KQOpnvtqD+dPK8NB3psCo06LbG8A972wGAPxwdiUmhr6W98ybiDv/uwGPfLYT504qQWVBFgJBEU8s2YWHP6uKWEJQYDUix6JXqrmXHDMEf7h4Mtq6vLjwH8ux46ATd/53Ax6/8hgIgvSD/YfPfY01NW0w6jQ4Y3wxNh3owL7WLjy3vEYJo2rf7GvHJY8vx+2nj8ZPThsNrUbA/zbW4WevboA3EMTxw/Nw++mj8eXOJjy3fA8+23YQS6uaMLU8G62dXuxu7oS6+DW22IZTxhbCqNPgjXX7lWru1PJsnDmhGB9sasDWegd+8fpGvPHNflxx/DA4un14d0NdwvWW8TQ6Pfj3yr3498q9OHFUPuZPG4JmlxfvbajD1vrIv4v3tnRFBNlESuwmTCyzo7XLi/W17Ygu6n27rx3f7mvHo4uren2f8ayvbcera2sBSL8fzQYtDjo8KS0fiGY36aDRCHGXflDmGFmYxeDWE5fLBbvdHvGY3W5He3t7zPO0Wi0sFkvE81yuxAuJH3zwQdx3332H9H57S664Rbdb5NZHlkGLTm8ATU4PKvLC4SZLVZmQh+vGbk6IPO5Kduupo7CiugVzxhQqQ2CBcMWtK3qOW5xWKSD9BdPs8ij/Ale3zFweP/a2dGJEoVV5GwgHNeU1E5ycEA45FuUev61tU1rGcmiSZVtCwU31l52j268EXfVxWrNHFiA/y4CWUKi7NFT9GRlaexa96Fy9xk0tPIQ3vAbx4U93KtXFp5fuxg9PHI5CmxEHHR50+wLQaoSI9Xk5FgPsJh0cbj/2tXZhXIn0e3zj/nYAUuDSaTU4e2IxTh9XpKx5+9W54yIOt580JBuvrd2PzXWRP+g2hUaETBoSDm55WQZcNWsYnl9Ro4S2G+eMwK/OGYdt9Q5UNbrw+tr9uO6k4QCA1aHK36Qh2RHHhJ00uhB6rYC9LV3Y3dyJkYVWLFqzT6lMmfVabG9w4vvPrMJvLpiAD0Jh7srjhynXEAQB508pxRNLqvH+xnoluH227SBqWrpgN+nw3RkVMBu0ePLqY/GvZXuweHsjhudn4WdnjUFxqLJyyykj8dKqvdha78CnWw/irIklEEURTy+Vgu61syuVyl+RzYS/fW86fvj8GqyobsGKUDWx0GbEMUNzsHhbI95eX4eDDg+euuZYPPThdtS2dqM024T/O3OMcu+XHjMEb397AMt2NeOWl77B3eeOw5NfVivXO3l0AbIMOizb1YxmlwfNLg9Meg3uOmccrp1dCUEQUJptxpNXHYPvPb0KH25uwMOf7sSPTh6BG/69Fmtq2mAz6fDS9cdjSnkOgkERS6ua8OY3B7CvtQtDcsw4ZWwh5o4tBETg9x9swzvr6/DIZ1VYsqMJZTkmfLBJGsx87qQSPHz5NJj0Wpw4qgCXHlOO//fWJqzb24Y1NeHlJuW5ZmQZdKhqdGLHQWfEvMMimxF3nTMOF08fAo1GwM2njMQzX+3Bo4t3YtXuVqVCLDtmaA7GFNvQ0e3D/rZuHHS4YTZoMaHUjjljpGpXgdWItXvb8PLqvfh060Es39WC5bvCu6MNWmkd5/xpQ2Az6bDzoBNb652oOuiE2x+ASadFXpYBeVkGdHkD2N4g/f5tcLiVET6ANMLmomllOH54PgAR3+xtx5c7m7CmplUJ7BpBWlKRa9FDEKRqfXuXD/6gCJ1GQGmOCZPKsjFpSDbGFtvgDQSxYX871uxpxcb9Hco/BAGpulRZkIXKfItSPer2BeDxBeELBpFlkKpmWUYdTHoNDrR3o+qgCwfauyM2hxXZjBhTbMOIwixkGXUIhHbnd3mkil2nx48uXwAaAaFKkbS0xe0LoMsbQLcvgGBQRKHdiBK7dHJMoc2ILm8ABx1uNDo9OOhww+Xxw6jTwKDVwKTXwqDTwKDTQBQBESKCQUCENITdatLDZtTBYtCiyxtAo9OtfK0cbh9MOi2yjDrYTDpYTdLnqQn9o84fDCIQFJX//Kr/DwZF+OTKmC8AvVaDHIseORYDsow6GLRSdU6qeqk/VrqmLyD92ueXrhMMitBo5KqeAKNeC5NOA6NeC4NWuo4vEIQvEIQ/IMIXFOHzBxEURaXKqNEAAoTQdQCtIF3PoNPErBcfKBkd3KxWa0wlzOFwwGq1xjwvEAigq6tLCW/xnqd2991342c/+1nEdfvr5AdDnDluoigqf3hHFVmxYX8HGp0epSqVZZDK7dHXiN2cEGqVRh3ePiw/C8vuOi3mXuRqT3SI6o6zOQFQrU8L7QjdGbVmaFu9Uwlu8nOs0a1SZZBv5L8u5Vlt5bkWTKuQqoK7VS3M8SWRIT4nzgYFeWOC1aiLOMDepNfiL9+dij99tAPzp5VhcrkUauQq3oH2buUPPZC44laRK/3BldcndXr8+Giz9IPSoNXAGwjitbW1uPXUUdjd7FJeQw7rsmH5Wdh0oAN7W9TBTaqUTSnPASAFnCeuOhafbj2IIbnmmHaYXAXapDrNwBcIYlt9bHADgDvPHouqRidW727F946rwK/OGQeNRsDVJwzDPe9swWtra/HDE6VwIbdJZ43Ij7iG1ajDrBH5+KqqGYu3HUSOWY8/fbQDAPDr88bjjPHF+N7TK1Hd1IkfPrcGgFTVU7fnAeD8yVJw+3x7I1weP6xGHZ4NVduunDVMaR/qtRrcdMpI3HRKZFsSAHKzDPjB7Eo8vqQajy6uwpkTirG0qhnb6h0w67W48vihEc+fPaoAb/34RLywogbdvgBOG1eE8yaXwqDTYFlVM256cS1W7m7BjN99pvyD6MFLJke0MgVBwB8vnYz5jy3HtnoHrvmXVNU367W4/6KJSvvQ6w9iTU0rmpwezB6ZH9HGA4Bjh+XhnnkT8du3N+Nvn+/C3z7fBUD6R86/rztO+T2g0QiYO7YIc8cWIZ5Hvzcdp4wpxG/e3oz1te1YLxWC8KOThuP/nTc+IuiPLbHh9ZtPwLZ6J3Y1uZBr0WNciV35x0l7l1cJNj6/iGOH5eLCaWUxyy5umTsS86aW4l/LavBtbRvMei1OHl2I+dPLlB3sPTllTCFOGVOI/W1d+M+qfVhb04q8LANOGl2AeVPKlNYhABwf9Xswnk6PH1vqHNhW70C2WY+Zw/NifsiOKrLhuzMrlL9rvf4gci166KL+bIqiiC5vACa9NuLrJztvstQB6vYGUN3kgtsXQLFdaj9G/znvjU6PH3WhzWiFNqNyKgxRIhkd3EaPHo2Ojg40NDSgpEQqT27YsAHXX399xPNyc3NRUlKCTZs24fjjj1eeN3HixITXNhqNMBqNCd9/OMUbB+Ly+JU2y6giWzi4yQNso8JPj5sT9L37CyTLKP2l3NWLNW5A7Po0OVjJ681qVAvunQnWuCXanNCkOkc1L8uAcSU2pb07LN+CbEtky1X+C65DNRKkJU6bVHbq2CKcGvUDsMQubdzwBUQcdLqVHzyJKm7qzQmiKGLZrmZ4A0EMy7fg1lNH4Zevb8R7G+pw66mjlIG/6o0JynXyLVIbTLWzdEOo4jalPBy4DDoNzp9SGv3hAIDxpTZoBOnr1uhwo8huwq5GF7z+IGxGnbKWTmY16vDS9bMghv51KbtwahkeeH8btjc4saXOgUlDsrEytDHhhDg/NE8fV4SvqprxyZaD2FrnQEe3DxNK7bjmhGHQaTV45cZZuOqfq1Hb2o1xJTbcM29CzDUmltkxvCALe5o7sXjbQQzLz8KamjbotQKunV0Z9/ON5/qTR+D5FTXYUufAx1sa8OSXUvj7/nFD4/4AnFBmx0PfmRLz+EmjC/DqTSfguufXoNHpgVYj4Lfnj48bmMpzLXjlhlm4550t2FrvwMzKXNx93niMLAz/Q9Gg00SsAYzn6lnDEAgE8aePd6DLG8CIwiw8cvk0JbT11iXHlOP4Efl4d30dOj1+nD6+CNNVyyHUBEHAhDJ7TJAGpD9PF00booyPSaY81xL3+5qq8lwLfnXuuD5fJ8uow3HD85QxOckIgqDsSk/0/ixjzz8azQZtzD+O0pFl1MWs8yNKJqODm9VqxYUXXogFCxbgkUcewaefforNmzdj3rx5Mc+96qqr8Lvf/Q6vvPIKtmzZgnfffRerV68egLvumV4XOw5EbpMadBpl0GuTOrgZEwS3QDDiB7HHF7/FmYj8F1TMkVfe+AFQvg85lMlB7aRRBfhyZxP2q04VSHTv1gRr3KLD0jmTSpTgFh24gAQVN2VHae/+1arTalCaY0Jta3eoNRb62icIbkNyzRAEKdi2dHqxZEejcn9nTSjG/9MI2N7gRE1zpxLEJsb5yz28QUH6+vkCQWwNtTzVwS0Zi0GHkYVWVDW6sLmuA6fZTcrGhAll9ogKrZo6tAHSD+yzJ5bgvQ11eG1tLUx6Lfa1dkGvFSLWt8nOnFiCB97fhrV727B2bxs0AvD7iycplYth+VlY/LO52N/WhWH5WXGrFoIg4PzJpfjHF7vwv431yp+FeVPLlFZob+RlGXDNCZV48stq3PyfbwBI1ekb54zo9TVkk4Zk44s752JDbTvKcy0xrXm10cU2vHLjrJRfI9q1Jw7HZTMq0OLyYkiuOe7XqjeG5JhjNksQ0ZEp48eBPP7446itrUV+fj7uvPNOvPbaa8jNzcVLL70UUVG7//77lY0Ml112GR5//HGMHTs2yZUHjk4TOw7E0S2FmGyzXgkLTU636qzPyH8hGlTjPtQ7S+PtKk0mS96cEFX9kufBRbdKbaohvJ0evzJ37aRQdUE9JyzeOaVAeJCvuj3b6fEr6+zk9uQPZw/HscNyMbIwCzedEvuDODvOsVfxdpT2pCI39vD45gStUqNOi1LlyKoufLFdWjQ9d2whciwGZQfmR1salNbntIo4wS1qltvOg054/EHYTDpU5sdW6BKR/8UvH32lHrybiu/OkNb8vf3tAWVkyAkjC2JCNyCFBPW6r7vOGRdT4THoNBhRaE0aROZNlda2fbr1IJbsaIJeK+Anp41O6b4B4PbTR+GYoTkAAEEA/nDJZGWzSqqyjDrMHlWQNLQdallGHYbmW9IObUR0dMnoihsAFBYW4oMPPoh5/Morr8SVV16pvG02m/HSSy/1562lLTzHLbzGTb2Qv8gm/dCJrLhFBij1QFtvIKgEtVRbpfIaN3XFTVq8Kd1bolZpR7dPqbblWvRK60U9OFiuytmjQqd6kK8/EIROq1HapBaDVqkCZlv0eOOW2QnvXT72qr0r3Cpt60qt4gbIwa1FqRZ2ef3K1yO64gYA5XkW1HW48cGmejQ43DDpNcpasLMnluCrqma8trZW2WwRr/U1NE8KZ/Lu1PD6tuyElbJ4JpbZ8da3B5TAJm9UmJxicJs9sgBDcsw40N6tHEh/5oTihM+/9dRROGN8MTQC0m7zjC2x4fIZFcoOvdtOHR2x+7a3LAYdXr5hFr7c2YSheRaML41tAxIRHSkyvuJ2JFLGgagqZfI4DotRqxxq3pisVapaBKu+jlwpM+pSbJWqql/qgbbRlbtwNdCDmmYpdAwvyFIWAte1h8/Y7KlVKr2u9FqJWpPJ5GZJgbCtM7bilptKcAu1pmtD1cJmp3QNk16jVCTV5DEb/wyN6Th1bJHydTprQjEEQVr7J4rSrtXoqh0QrrgdaOuGPxBUdpSmur5JDmhb6hzwq9qtk4akFl60GgGXzwxvzsk265XjqRIZW2Lr89qcP1wyGc9eMwMvX388bj99VNrXMem1OHtiCUMbER3xGNwGgDKAV9Uq7VaG7OoiwpFD3pkZNQtNEIS4O0vlVqkxxc0JkcEtGHqNyMoeAKUa2Oh0R8xdK80xQRCkj5U3CDiTrM+Tr+sMna4gtyYL44ScROTF522qipsc3PJS2JkVHvEhBbcml7S7VX3cldqJoyIX7Mu7zADpQPozxocrVZceWx73NUvsJhh0GviDIura3dgQOhR+SoqVMnWl86tdzej2BZBt1kecjdpbN50yAt+dUY7pQ3PwxJXHKG3xw0mrEXDGhGLMHlUQ92tNRESRMr5VeiQKz3ELt0rl4GQxaJUKjT8oKu276J2ZgBSqvIFg1Bo3+cir3lXc5Fapeo6b+hrRP0yLVKFS3lE6PD8LRp0WhVYjGp0e1Le7UWA1JhwHIn8+HpdXaaemVXGzxG5OSKfiVh4a8SF/rZtCFbd4lTJAOiqrIs+M2tZuVOSZY1qKv58/CYAUzq47cXjca2g0Aipyzahu6sSOg05lrMqUqJEfPbGZ9MruzH+FKoAzK/NSarfKjDot/vSdqSl/HBER9R8GtwFgiFNxk4NTlkEHg06DXIsebV0+5cikLGNsEDPoNIAnfsWtt5sT4h15lWiGGwAU2cNtXHNoTpk8t6002yQFt45uTC7PVjYnxAudVqMOzS6v0k5NNDctGWWNW3fsGrd0NifI58MqITLBvei1Grxywyy8t6E+Zs4VIFXdnrlmRo+vOyw/C9VNnXhtbS38QRGl2SbljNJUTCyzY09zJ76qagYAzBrR80gEIiIanNgqHQDxjrySg5scluSWpDzENbpVCsSf5Savcev95oTYI6/iHVQvk++rtdOLHaFRHSNCpw/IO/nk6eVym9cW597lNpwc7uTglkrFTT5ovq3Lp6yrS6fiVmgzwqjTICgC9e1u1Ic2WCTbmViea8Etc0f2aZL2pFCb89OtBwFIO3PTaReeMqYw4u3TxyfeVEBERIMbg9sA0OviVdzCJyQA4cqWPG4jelcpoD5oPtzmTHWOm1xxc/uCygBgeb2dKU7FLdeiV4JnlzcAQQifwynPQKvvcEMURbSFWpjyJoJ4ryuHu0QDb5ORw5nXH1SqhG1prHETBEFpl9a2dSkt04rcwzsSYnbUgNaTRicf2JrIvKllGBc63P7yGRVp7cwkIqLBga3SAaDXxI4DCVfcpG9JdJsuXgUpbsVNaZX2suKmCoSdXj/sJn3CUxMAKeQUWo3K4dNl2WYlJCoVtw43urwB5b7ys2LDWPR5pU1pbE7IMmih10qnHrR1+aARBGWMRyoVN0CqoFU3daK2tUuZRSeHucPluMo8jCjIwu7mThRYDUnHbyRj0mvx3k9OQnWTC2M5gZ2I6IjG4DYA4p2cEF1xK7RHBph44UcObp5A+psTDFoNdBoB/qCILk8AdpNeNQsu/jWG5WcpwU19YH1pKLjVd3QrLUuTXhN3rZy87i26VVqQQsVNEATkWAxocnrQ1umFvB5fpxFgj7OuLhl5JMj+tm6l4lZ+mCtuGo2A5344E4vW1OKiaWXKRpF06LUa5cxTIiI6crFVOgD0ccZ4yPPMLMb4FbeCOGdvyrPaPL54a9x6F9zU5/LJ69zkql28ihsA5YB2AJhZGZ6YL7dKGzrcPY7lsBnDFTdRFNEcOqoqlVYpELmzVL2+LdW1YnJbdFejC42hEHm4K26AFILvOmccQxcREfUKg9sAiDfHrUuZ4yavcYtcGB9vt6UhzmH1qc5xA2KPveruoeJ2fmhumUGrwbmqGWbhipu7x00CcqvU0e2Do9uvfA7xAmoy8s7Sti6vMog3lfVtMnld2Behs0ctBq2y+YGIiChTsFU6AOTA5Q+q17iF57gBkRU3s16LbHMvd5WmuDkBCFf55KpftzfxOBAAmFqRg5dvOB5mvRYjC8OtUnlDhccfxO7QcU+Jjp7KDYWr1i6fMvDWbtL1+sQHWY5ScfNC/mrG2wzRE3nivjwTb3SRlQNhiYgo4zC4DYB4rdIu1ckJQGSbrizHFHegqiHOrlJlc0IKASj62Cul3RpnHIhs9sjYHZBGnRYFVgOaXV7l6KVEwU2uILZ2epSds8X21GeY5SqnJ/gg5+BUzimVleeakZ9lUE59mJjiCQZERET9ga3SASCP00i2OaE814ySUJAZl+D8RWOcipsnxUPm1a+prHHroeKWjLyzdGu9FNxyE7Qt5XDV4vLiYGjuWzrBLUc+r7TLi5bQSJFEr5mMIAgR89CiZ6MRERFlAlbcBkB4jlu8cSBSWBIEAX+4ZBI+396In585Nu51kg/gTaFVGnXsVbJxID0psZux+YAD23ob3Dq9ysDedIKbvJ6trdOr7FAtTeP0AQD4yemjsb62HeNL7ThtXFFa1yAiIjqcGNwGQHiOW5wjr1QHsp82rhinjUs82ys8gFe6TiAoKmEwleBmNcbfnGBMI7hFh6bSnPghKtwq9aKhQw5uqe0oBcIVvvoOtxJkS7LT2w06vCALn985N62PJSIi6g8MbgMg3hw3OTSlUuWSF/LLFTd5YwKQWqs0enNCT+NAkok+JirRSA15A0EgKCoHrCc7YiqRstCRU3Ud3crXI92KGxERUaZjcBsA0ZsTAkFRqZqpK249UVqlgTjBLZXNCaH2rLzOLtwqTX0J5Jioyf2Jjo0y6rSwmXRwuv3KRgb5HNRUyMGtocMNXaiSyeBGRERHKm5OGAAGbeQaty7VAe+WFDYEyNeRNyS4Q+HPoNXE3YWaSJYx8vgpeXNCKu1W2YSyyI0UyYbY5ofWuTlCa9PSqbgV24zQCNLXUg6c6VyHiIhoMGBwGwB6ZY6bFLTkuWkaIbxurTeMCSpuqQzfBYCsqM0J8gaHdHaVDskxY0roZIULp5YlnYUWfUpCWYL1cMnotJqITQ3ZZn2fjo4iIiLKZPwJNwDC40BEiKKoHIyeZdClNPTVELU5IZ3hu0DsHLfuPlTcAODxK4/BFzuaMH9aWdLnDcvPwpqaNgDSEVipHDCvVpZjRn1og4N85igREdGRiBW3AaBXVdV8ATG8MSHFCldscAsN30214maMnOPW3YfNCYB0OPvVs4bBZkp+goF8zBQAjOjDSQWjVQfdT0gw846IiOhIwOA2AOS1aYC0s1Rem5XKxgQgdlepMnw3xWOj5NaivKvUk2blLlXTh+Yovz5+eF7a1zl+RPhjjxue35dbIiIiymhslQ4AnWrjgNcfTGsUCBA7gDed4buAquLmid5VeniD23GVeZg+NAeNDg+uOn5Y2tc5b3IpPtvaCKfHj3lTS3v+ACIiokGKwW0A6LQaaDUCAkER3kBQWVMmB6jeij6rNO1WaYKTE1K9Tqp0Wg3e+vGJfb6OUafFY1cecwjuiIiIKLOxVTpA5APc3b6Asjkh1d2Q0WeVpr85IXKNW1eoZWpJsXVLREREhxeD2wCRw5XbF1TmuKUyww2IN4BX+n9jimvc1LtKPf6Acj0rgxsREVFGYXAbIEZVxa0r3YqbMoA3uuKW2rdVfl1fQER7l095nMGNiIgoszC4DRC54ubxB9HlSa/iJg/aVSpu6W5OUL3uQYc0D82s10KbwukLREREdPgxuA0Qo9IqVVXcUt2coI0+ZD69zQk6rUapAB50eAAAVhOrbURERJmGwW2AyOEqYnOCPrWwFD2AN905bkB4nZtccWOblIiIKPMwuA0QOVy5/UF0hzYnpDoO5FDtKgXCbdpGBjciIqKMxeA2QOJV3NI98qqvrVIgHNSUVimDGxERUcZhcBsg8sgOj+rkhFTDknociCiKaW9OAMIVt4NOqeKW6vFbREREdPgxuA0QuSrmUVXcstIcwAuEdqemWbkD1GvcpIqbjZsTiIiIMg6D2wAxqXaVyhW3lHeVqoKbNxBU1rilOlYECIdGrnEjIiLKXAxuA0R9ckLarVKtKripK24p7k4FwqGxpdMLgK1SIiKiTMTgNkCM6s0JHnlXaWphSRCEiJEg3X1olUaHRrZKiYiIMg+D2wAxKuNA0l/jJl0nHAC7+9AqjT5uKyuNaxAREdHhldHBbc2aNZg6dSosFgtOOeUU7N27N+FzKysrYbFYYLVaYbVacfPNN/fjnaZO3pzg6PYjEBQBpD7HDQiHtG5vIFxxS2NXaV6WPuLt3CxDytcgIiKiwytjg5vH48Ell1yCO+64A62trZg1axauvvrqpB/z+eefw+VyweVy4cknn+ynO02PPIC3pdOjPJbqIfPqj+n2BdAVGuSbTqu0wGpM+jYRERENvIxdyLRkyRJYrVZcd911AIB77rkHhYWF2Lt3L4YNGzbAd9d38uaEFpe0GSDdQ93l6lqXN6AM4E2n4sbgRkRElPkytuK2detWTJ48WXk7KysLI0eOxNatWxN+zPz581FcXIyLL744aVsVkCp6Docj4r/+JLdK+7qLU26VOt0+eAPBiMdSERvc2ColIiLKNBkb3FwuF+x2e8RjdrsdLpcr7vNffvll1NTUoKqqCkOHDsX8+fMhimLC6z/44IPIzs5W/quoqDik998TueLWGgpu1jTWtwHhtqh8HfW1U1GRZ454O9fC4EZERJRpBiy4nXXWWTCZTHH/e+CBB2C1WmOqYA6HA1arNe71Zs+eDZPJBLvdjoULF6Kqqgp79uxJ+Pp33303Ojo6lP9qa2sP6efXE3k3aHhjQt8qbnLLVSNEnqjQWzZTeHNCXpYBmjTatkRERHR4Ddgat08++STp+z/++GM8/fTTytudnZ2orq7GhAkTery2IAgQhOTBw2g0wmgcuHVc0RsI0hkFAoQ3J8ibHMx6bY+feyI3nzIST35ZjRvnjEjr44mIiOjwythW6dy5c+FyufD888/D4/HggQcewIwZM+JuTNi3bx9WrlwJn8+Hzs5O/OIXv8CwYcNQWVnZ/zfeS3ZT5PiNdEaBALGtUnOaARAAfnXuOGz/3Tm4+ZSRaV+DiIiIDp+MDW5GoxFvvvkmFi5ciJycHCxfvhwvvvii8v6bb75ZmdXmdDpx4403IicnB5WVldi1axfeeecdaDQZ++nFnFSQdqs0eneqoW+fczrr44iIiKh/ZOw4EACYOXMmNm7cGPd96jltEydOxKZNm/rrtg6J6COlciz6BM9MTlnjFqq4WdI4p5SIiIgGh8wtSR3hbFGt0nR3ccqtUblVauJRVUREREcsBrcBYtBpInZ/5qQZ3CxRa9wsbHUSEREdsRjcBpC66pabZqs0endqOsN3iYiIaHBgcBtAJdnhcSR5aR7qHh3U7Ob0AiARERFlPga3AVRiD59WMCw/K61rRAe36E0PREREdORgcBtA6pBVnmtO8szEzFG7SKPnwxEREdGRg8FtAN166iiUZptw/pRS6LXpfSuiB/ey4kZERHTk4k/5ATSqyIoVvzqtT9eIHiPCNW5ERERHLga3AZbuuaKy6E0NbJUSEREdudgqHeQsBi0MqnlwRXZjkmcTERHRYMbgNsgJghAxA67IxuBGRER0pGJwOwJYDOGOd5HNNIB3QkRERIcTg9sRQH1AffRJCkRERHTkYHA7Avz6vPHIseix8LtTB/pWiIiI6DDirtIjwIzKPKy/56yBvg0iIiI6zFhxIyIiIhokGNyIiIiIBgkGNyIiIqJBgsGNiIiIaJBgcCMiIiIaJLirNEQURQCAw+EY4DshIiKio42cP+Q8kgiDW4jT6QQAVFRUDPCdEBER0dHK6XQiOzs74fsFsadod5QIBoOoq6uDzWaDIAiH/PoOhwMVFRWora2F3W4/5Ncf7Pj1SY5fn+T49UmOX5/k+PVJjl+fxA7l10YURTidTpSVlUGjSbySjRW3EI1Gg/Ly8sP+Ona7nb/xk+DXJzl+fZLj1yc5fn2S49cnOX59EjtUX5tklTYZNycQERERDRIMbkRERESDBINbPzEajViwYAGMRuNA30pG4tcnOX59kuPXJzl+fZLj1yc5fn0SG4ivDTcnEBEREQ0SrLgRERERDRIMbkRERESDBIMbERER0SDB4EZEREQ0SDC4EREREQ0SDG5EREREgwSDGxEREdEgweBGRERENEgwuBERERENEgxuRERERIMEgxsRERHRIMHgRkRERDRIMLgRERERDRIMbkRERESDhG6gbyBTBINB1NXVwWazQRCEgb4dIiIiOoqIogin04mysjJoNInragxuIXV1daioqBjo2yAiIqKjWG1tLcrLyxO+n8EtxGazAZC+YHa7fYDvhoiIiI4mDocDFRUVSh5JhMEtRG6P2u12BjciIiIaED0t1+LmBCIiIqJBgsGNiIiIaJBgcCMiIiIaJBjcBoGqg0443b6Bvg0iIiIaYAxuGW5FdTPOfHgpfvn6xoG+FSIiIhpgDG4Z7u+LdwEAPtzcAFEUB/huiIiIaCAxuGU49a7g/W3dA3cjRERENOAY3DKc0+1Xft3S6R3AOyEiIqKBxuCW4VpVYa2ti8GNiIjoaMbgluHaVWGtjRU3IiKioxqDWwbz+APo9AaUt9u6OBKEiIjoaMbglsFcqvVtQGT1LVWfbGnAOY8sxcrqlr7eFhEREQ0QBrcM1qWqtsV7OxWPfFaF7Q1OfP+ZVX29LSIiIhogDG4ZrNMbWXHrS3DbWu/o6+0QERHRAGNwy2Cdnsig1h0V5HorEIwc3Mvjs4iIiAYnBrcM1h1VYetMs+LW0R0Z1Oo73GnfExEREQ0cBrcMFt0qjQ5yvRU9/41jRYiIiAYnBrcM1hUV3KKDXG9F70Z1uNO7DhEREQ0sBrcMJq9xyzJoAfSh4tYZ2Sp1dHONGxER0WDE4JbBPP4gACDPagCQ/q7S6DVuDm5OICIiGpQY3DKYLyAFt2yzHkBs67S3XJ7Ij4sOckRERDQ4MLhlMF+o4pZj7lvFLXptnKOba9yIiIgGIwa3DBZbcQsgGDWTrTc6oypu3T4GNyIiosEo44NbU1MTzj//fFgsFowdOxaLFy+O+7xrr70WRqMRVqsVVqsVEydO7Oc7PfQ8cnCz6JXH3P7Uq27yJgeDTvp2p7vJgYiIiAZWxge3W2+9FWVlZWhubsZDDz2Eyy67DG1tbXGfe99998HlcsHlcmHLli39fKeHns8vVddsJp3yWDrtUrniVmg1pn0NIiIiGngZHdxcLhfeeecd3H///bBYLJg/fz4mTZqE9957r8/X9ng8cDgcEf9lGrlVatRpYdZLI0G6PGkEt9AatwKbFNy6fQxuREREg1FGB7eqqipkZ2ejtLRUeWzq1KkJq2l//vOfkZ+fj9mzZ2Pp0qVJr/3ggw8iOztb+a+iouKQ3vuhIAc3g1ZAljEU3NJYnya3SguypE0ObJUSERENThkd3FwuF+x2e8RjdrsdLpcr5rl33HEHdu3ahfr6etx6662YN28eamtrE1777rvvRkdHh/JfsucOFG8ouOm1GphDQ3ijD57vDbnClh+aB8eKGxER0eCU0cHNarXGtDAdDgesVmvMc6dPn47c3FwYDAZceeWVOOGEE/Dpp58mvLbRaITdbo/4L9P4AtIaN4NOo7RK3WmELnmQb46FFTciIqLBLKOD2+jRo9HR0YGGhgblsQ0bNvRqx6hGk9GfWq/Ic9z0Wg1MfQluoY/JCe1OZcWNiIhocMrodGO1WnHhhRdiwYIF6O7uxrvvvovNmzdj3rx5Mc9944030NnZCb/fj1dffRXLli3DaaedNgB33TvvrD+AV77el/Q54TVu6uAWTPm15IpbrqVvg3yJiIhoYGV0cAOAxx9/HLW1tcjPz8edd96J1157Dbm5uXjppZciKm8PP/wwysrKUFBQgIULF+Ktt95CZWXlwN14Em2dXtyxaD3ufnMTqg46Ez5PWeOmEw5JxS2XFTciIqJBTdfzUwZWYWEhPvjgg5jHr7zySlx55ZXK28uWLevP2+qTNTWtyq+X7WrG6GJb3Of5VJsTTPLw3D6sccsOHZ3l9QcRCIrQaoSUr0VEREQDJ+Mrbkeifa1dyq8bHO6Ez/P6Y3eV9mVzQm5W+AQGVt2IiIgGHwa3AdDo9Ci/bnZ6Ez5P2VWq1cCkk4KbHMJSIYe9bLMeQqjI1uXleaVERESDDYPbAGhUVdmaXJ6Ez4toleqlb1WqFTd/IAh/6GB6k+oEBrc39QBIREREA4vBbQA0u8JVtpYkwS08gDe8OSHVGWzyNQDAqA/Pg0vnBAYA2FbvwE8XfYt9LV09P5mIiIgOqYzfnHAkcrh9yq+d7sQBSqm46VTjQPypBTePanyIUaeV1sp1pj+E9/y/fYWgCPiCIh674pi0rkFERETpYcVtALhUYc3lSRLc/Ko1bmnOcZODnl4rQKsRlIpbOsHN6fYh1HXF59saU/54IiIi6hsGtwHg9PQyuMkDeHXpr3GTK27G0OYGS2h3ajpDeKubOpVfewNB+ANcJ0dERNSfGNwGgFPVKvX6g/AkaH96A30/8krehWoMzYEz6tPfnaoeYxIIihG7Y4mIiOjwY3DrZ75AMKbd2emJH8Z8qs0J5jRbpXIoVIJb6P8ThcVkmqKCWn1Hd8rXICIiovQxuPWzTlVrVK+Vhqq5EmxQkAfwGvowDkQOenLFztiHeXCNzshhwS2uxDPoiIiI6NBjcOtn8i5Ss16rHEEVb51bICgqGwH0Wo3S4kx5V2no+YZQpS3dAAjEVtzau3wJnklERESHA8eB9DM5pFlNunCIihPGfKqF/3pd+OSEVHeDKpsTDkHFra0zssLW1sWKGxERUX9ixa2fyRU3m1GnhKh41S/14Fy9VlCdVZrqGrfIzQl9qbg5Qvc+JMcMAGjrp4rb88v34N53t6S1Lo+IiOhIwopbP3N5pLBjNekQFKVeqCdOGPOpKmJ6TXiNW6rhRX7+oVjj5uiW7n1ongUH2rvR3g8Vt037O3Dve1sBABPL7LhsRsVhf00iIqJMxYpbP5MrblajTnVwfLxWqRTq9FoBGo2QdqvU7YseBxIKgClW7oDwiQ9DcqWKmzPJDLpDZdXuFuXXy3c1H/bXIyIiymSsuPUzZY2bUYcuQQph8dqf6gPmAYRbpSlWyqLHgcgBMNVNDgDg6JbuvSzbBCByh+zhsrvZpfxaPQCYiIjoaMSKWz+TR3/YTHolTCVb4yYHNzlwBYJixMaFnoTXuIVapWlW3HyBILpD91kaWuPWH8HtoCO8k3V3kwtiqL1MRER0NGJw62fK5gSTLulpCNEVNzlwJXp+Ih5ljpscABPvZO3NfQNAiV2quLkSDA4+lA46wrPjOr2BfmnPEhERZSoGt36mbpUalXEg8TYnyAfMS0N6jToNBOmXSuWrN9xKq1SuuIXW1aVYcZM3JtiMOtjNUoe9p4qbPxBMa/eqmrriBsTOkuutjm4fLnpsOW7891oEgqzaERHR4MTg1s+UzQmm8DiQeCHKG5ACjz5UIROE8AaFVEJXeI5b3468kjcm2M16ZBl7Dm7+QBBXPLsax/9hMWpVZ5ymwhcIoqVTCmrZZj2A9IPbf1btxYbadnyy9SA+396Y1jWIiIgGGoNbP1PGgRiTD+D1+uVdpeFvUToz2GI2J6RdcQu3eLMMUnCLd+KDbOXuFny9pxUd3T68tHpfSq8la3Z5IIqATiNgbLENQPrB7es9rcqv19S0JnkmERFR5mJw62dy2El1jRsA1fNT35wQnuPWx4qbKVxx8/iD8CfYKLGlzqH8ekNte0qvJWsMtUmLbEYU2Y0A0g9uW+vD97OlriOtaxAREQ00Brd+5lJvTtAlDmJycJPXuAHh8JXSGjdfZMXNmOQ1k5HXuNnNOmQZtcrjnQk2KFQdDI/x2NbgiPucnsghrdBmRKFNCm6NaQS3To8/IvDtbUmvdUtERDTQGNz6WXgArz48miPJWaXxK26ptEojx4GkewKDuuJm1GmhDwVKlzd+u7TJFQ5K7V2+pG3VRJye8Lo6ObilU3GLDmp17d3wpnFyBBER0UBjcOtnTo/65ITEM9W8oZMTDLq+rnGLOjkhzSOv5DVu9tAmgZ42KDRHBay69u6UXg8IVyetRh0KrXLFzZ3sQ+KSR4pMKLVDrxUQFKX1c0RERIMNg1s/c/V2jps/TsVNl/rpCR5f5Fml6R4yH664SYGtpw0K0cHoQBrBTR1yC0LBrS2N81FbOqWPKbAZlQCY7lo5IiKigcTg1o/8qtMHpF2liY+fit8qDYWuFM4rdR+yilu4bSnfPxC/4hYMikpYmlhmB5Bexc2pOmUiN8sAAGjr9KV8nZZQiCzIMqCgDy1XIiKigcbg1o/UC/mlOW5y9SvJ5gRdeHNC+LzSVE5OCG1OkOe4KWvcUgxuoRBlN8mtUule4gW3jm6fMuR2Snk2gD62Sk065Fmk4NbamX7FLd9qCFfc2ColIqJBiMGtH8ntRpNeA71WE56pFm+OWyDOHDdd6psTvNGbE9I881S9qxRQr3GLvRe5TZpt1mNYfhYA4EBbGsFNHp1i1CE3SwqM3b4AulOoOKrvJ99q7NMmByIiooHG4NaPwsddSSFEOfIq3uaEOGvcjH2Y46a0SlVnnqZSdVPvKgUAS6j61xUnRMrVrAKrAaXZ0rmm0UdX9Yb6XFerUafsZE11nVuLK1RxyzIckuDGg+6JiGigMLj1I3UQAZKP94i3xs3chzlu0QN4E71uItG7Si2hzQnx1tspQclqRJEtFNyidoN2ef1Y9PU+NDoS7xJ1hsKi1aSDIAjITbNdKh+bVXAIKm4batsx8/eLcdFjy1MeqZKuQFDEgfZuBkYiImJw608dUQv8e7XGLWIAb9/HgQiCoIwY6UvFTQ6CXXGCm1O1Hq44dOJBY1TF7ddvbcav3tyEq//5dcJA4lLtKgWAPHmDQroVt0Owxu2PH25Hs8uDDbXteG9DfVrXSNUdi77FiX/8HPe+u6VfXo+IiDIXg1s/koNbthLckq1xO1RHXkVuTpBeV54f17sA6AsElYAmr3GTW6Xxqn9dXjlwaVFklypuLo9fCWJuXwDvrD8AANhx0Inqps64r6s+HgwIB7dUKm6iKEZUAPPlsSJpbHLo6Pbha9U5p1/ubEr6/C+2N+K8R7/Ci6v2pvxash0NTvxvoxQQX1i5F3tb4n+tiIjo6MDg1o+UBf6hICIHqHhT/H3yIfNxBvD2NnBJGxDE0GuFj6lKNQDKFTQgXP1S2rZxTk6QA1eWUVqbJn+M3BbdfKADQVWRLdFZpupxIACUkSCpBDenx6+E4PwsA/JCmxxa05gHt2l/h7JbFgC2Jjnz1OMP4P9eW4+t9Q789u3Nac2xA4DPtzdGvL14W2OCZxIR0dGAwa0fRVfc5JalNxCMaReGW6Xpr3FTV/JM8SpuvVyjJQfOLIMWutD9yKNJ4rVKO1XBDYByQLy8QWGn6hxTANjdHPm2TH1yAgBlJEgq1TK52ibPzZPXyXV0++BPYVctAGwPnbk6rSIHgHSUVqKduZv2d6C9Kzxz7n8b6lJ6LdnmA1I4lH/PfJsg5PbGB5vqcfebG1HbyrNaiYgGKwa3fhQT3EIhSBQBfzBBcNPF21Xay+CmqqgZ+tByVda3he4bSN4q7QyFOfl0heLQBgX5uKpdjZFBraY5Nkh4/AGlUmYNVSiVilsK1bIWZRSI9LHZZj0EQfqat3enNsx3a70U3E4dWwSLQQt/UMS+BCFofVTAWr2nNe7zerIpFNyuOH6o9Pb+9iTPTmzd3jb8+KVv8MrXtbjpxXXc6EBENEgxuPUjR4KKGxDbLg2vcVNvTkgtcMmbD3QaQamUAelU3CKH7wLqVmmyipv0nGKl4iYFt+omKbidPLog4nE1dXtWDoB5Fun1Uzk9oVk1CgQAdFqN8vVPdZ3b9nonAGB8qQ0jCqX5dNWN8auFcnA7d1IJAGDNntaINmtvdHT5lGD4/ZlScKtp6UJHV+qnR/x7ZY3y6631jj5V7oiIaOAwuPWj6MpVsuDmizuAN7SrtJeBS9mYoIv8Nht72FUaXY0J37dOeSyVVmmxPXKWm1xxmzUiH0D8HZ7qNqlWI4XXdNa4yaNA5E0JANI6hcEXCCr3Pb7UjpGFVgBIuLFia51UnfvecUNhM+rg9PixLVSx661dTVJQLMs2YWi+BUPzLACAzUnW1sUjiiKW72oBAOXM12VVzSldg4iIMgODWz+KbpXqNAKEUEHNG7VWKt4h83JY6u3JAXJlTq7UyZLNj3tiSTVG/fpD/G1xlfKYM2oUCBCe4xa3VRo6TSG8xk0Obm50ef3KQv0TRoaCW5yZatGjQID0xoG0RFXcgHAATOU6u5s64Q0EYTXqUJ5rRnmuGQBQ3xG76UAURewPfY4jCrIwNbQmbkOKbc69LVK1TT59YlyJDQCwvcGZ8nWaXR4YtBrceupIAP0X3Dq6fUrw7wuvP5hyxZKI6EjE4NaPooObIAgJd5Z642xOCB+R1dtWaWoVN1EU8chnOxEIilj46U7lB2X08F2gh1ZpaKdpliGyVdrQ4cbuUIUqL8uAscVSEOnyBmLOPHWohu/K0hnAG73GLfI68QPF8l3NeHppdcTnJm9MGFdigyAIKAmF0YaO2DZvS6cXXn8QggCUZJuUzQzr97X3+r4BdXCzKK8NADtTDG5r97YBkM6NnTu2SLqX2vaUjjxTq2vvxopdzT1+/Ieb6jHzgc8w44HP8MGm9Gfe/WfVXkxa8DFm/3ExtqRYbSQiOtIwuPWj6OAGhINZdIhSTk5QHTKf6KzShg533MPeleG7URU3ZX5c9HUc7oj7qGp0Rty3zRTbKo1XcXNFtUrLc6Xgsa+1S2k3jiq0IsuoUzY5RFfdXFGnTACRFbfeLq5vlg+Yz1K1SkMjQeJV3LbWOXD1P1fjDx9sx4J3NyuPbwutbxtXKoWnYlUVMVp9u/RYodUIvVYTDm5R68pEUUy6s1Ve3zY0FNzGltgBANsPphjcQrPnjq3MxbA8C6xGHbyBoBKiU7Fqdwvm/mUJrnh2NW7491oEE1TB3L4A7n5rE7yBILz+IO56fSPa0xjBsq+lC/e9twXeQBAHHR7c+d+NaVfegkER2xsch6QCSEQ0UBjc+okoisp4iIjgFgpRMRW3OK3SeCcn7Gvpwpw/fYHT//plzA9GeVdpdMVNmQcX9Zp7miN/kO8J/WCXA45cqQLCFbd4a9y6Qq1Suc05PNTqa3R68O0+qfozulhaI1Zki3+SQbxWqfz6voCovL8ncStuSdbK/XddrTJj7o1vDijPkdenjS+VwlNJ6AzWhjjBTW4Fl+VI7VS5VbqryaW0nZtdHlz4j+WYdv+neDfBqJCa0LDdYXnS129sqOJWddCZMDDFsyYU3GYOy4NGI2B8KHwmWnPX7PLg0c+q8Nra2ojX8QeC+H9vblJ+by7Z0YTF2+PPlftwcz3au3woyzZhdJEVTo8fz361p9f3LHvsi13wBURMKLXDbtJhW70jreqdy+PHd55cgXMe+QonPvg5Vla3pHwNIqJMkPHBrampCeeffz4sFgvGjh2LxYsXx31ed3c3rrrqKthsNgwdOhSvvPJKP99pcm1dPiUoyXPNANUQ3ug1bklOTlBXuT7Z2gBvIIgGhztmkr8c8BJW3KKCW/RYjt3N0cEtzjiQOAN45eqf/Jxsi16pln285SCAcNsv0dmh0acmAFKVTw6MvW2XymvcCuJsToi3q/QbVTszEBTx2VbpfsOt0lBwC1XcmpyemKqZvO6tLMekfI5DcswQRWm+GwA89OF2bDrQAZfHj3ve2aycNqG2L6pVWplvgUGnQZc3gNq23s1ia+30Khsojh2WCyAcPrfGCW7+QBBXPbsaD3+2E798fSMe+mi78r7X1+3H7uZO5GUZlPEkr3y9L+7rvvWtFEYvnzkUPztzDADgxVV7436eibh9AbwfCmkL5k3AtbMrAQCvrqnt9TVkf/pou/K9dXr8+L9X1yshOp5gsPf/OEiktrUL17+wFic99Dl+9cZGpXKdqrZOL/62uAq/eXsTlu/iphKio13GB7dbb70VZWVlaG5uxkMPPYTLLrsMbW1tMc9bsGABWltbceDAASxatAi33HILdu7cOQB3HF9dqApTaDNGnGJgSLDGTd5Vmmj+mtwq3FIX/uGr/jUQe05p+DrxzzyNbvvJrTR5/EauaoG/eo6bum0piqKyxk1dLasMhQ+5QiW3/eRAFR3cnO7YawCpH3vVIrdK41XcoiuU/gC2hb6Glx1bDkCqHB10uHHQ4YFGCFe98q1GaDUCgmJ45IisPrTurTTbrDw2bWgOAGmAbqPTjbdDR34BQHuXT9n1KXN5/Mq9y61SnVaD0UVSpbK3GxTWhda3jSqyKp/3hFBwi1dx+2hLQ8S1n1q6G0t2NKLbG8CjoQ0rP547EtefNBwAsGRHY0wgcfsCWL1b+nzOnVyCsyaWYGieBR3dPrzxzYGI53Z6/AnnEn6+vREujx9DcsyYWZmHy2ZUAACWVzenNET46z2t+PdK6dixf/5gBirzLWhwuBNWAJdVNeOkhz7HpAUf48J/LFOqxIC0UefBD7bhtL8uwRXPrFLa0NFaO7343tOr8Nm2g9jf1o1Fa2px6RMr4rbWk9nT3IlzHl2KhZ/uxH9W7cOVz67GXz7ekfIcPlEUsXjbQfx00bf48Uvr8Pq6/Ulbzl5/ENvqHaht7Up75p8oiti0vwMvrd6Ld9YfSKtVLmvr9GLT/o64a0qJjja6np8ycFwuF9555x3U1NTAYrFg/vz5WLhwId577z1cc801Ec998cUX8fbbb8Nut2P27Nm48MILsWjRItxzzz0DdPeR5NllZaEWm0wOZrHBLXYAr/r0A48/CJNeq7TTgHCFJvycBJsTEmxykNuVo4qs2NXowp7QiQbxWqWmUHALiuF7AaRQKf88yFKFruEF1ohq1phQqzRREIs+7kqWm6XHgfbuiPVpgaCI9zfVY2yxTQlW8uPy8yLWuCWouG2tc8AbCCIvy4Ab5ozAf9ftx/LqFny5Q6pkji+1K0FSqxFQZDOivsONBodbaZ0C4VZpqeqx6RU5eH9jPVZUN8PrD8IXEHHssFxMKLXjxVV78VVVE86cUKw8Xz6TNNeij9jNO7bYhi11DuxocOLsiSXK4x5/AGtr2jC62IoiW/h15WAxszJXeUypuNU5IIoiBCG8jvI/oXNVbz99NBzdPjy/ogZ3vbERJ44qQH2HG0NyzLhq1jCY9Frl98mKXc04d3Kpco1v9rbB4w+i0GbE6CIrBEHAD0+sxH3vbcVzy/bgyuOGQhCAf3y+C48uroJBp8ED8yfhkmPKI74f8nm286aWQaMRUJFnwYmj8rF8Vwv+u26/UsmTraxuwYb97ZhSno0TRuRDEAS4fQH86o2NAIDLZ1Tg9PHF6PYFcNvL3+LFVXtxy9yREbuut9Y58KMX1ih/Njbu78ClT6zAj04ajpJsM576shqNoX9k7G7qxNq9q/H8D2di9siCiHt54P2tONDejcp8C247bTT+8vEO7Gp04fKnVuKlG2ZhSKiNvvOgE5sPdKDQZsSsEfkRFfZGpxvX/Gs1Djo8GFGQhUlDsvHuhjr844td8AWD+NU54yK+d7WtXfh4SwM6un0YU2zDzMo8FNuNWLm7BX/+eAe+Vf35+2BTA97+9gD+ccV05Kj+XIuiiFfX1OLBD7crgbw024SzJ5ZgdmgXeFWjC1vrHXC6/RiWZ8FF08pw7LDciHvZ19KFX7+9CV+pdi8bdBrMn1aGG+eMxKjQP0ACQRF17d3Y39YNm0mHYfkW5c+81x/Ekh2NWLSmFkt2NCp/r0woteN7x1Xg4ulDIv5+cLh9WL27FftauxAIBmE26JBl0CrDsmtbu7GvtQv727pg1GkxpTwbJ48uwJTyHGXkkCiK2FLnwBfbG7GtwYFubwAFViPKcy2oLLCgxG5CICiiyxuAy+OHPyiiyGbEsHwLynMtynWanB6srWnF6j2tqG5ywR8QUZJtwqgiK8aX2jC+1A67SQ9/QITL60dn6Cxnnz8Is0ELk14LnUbAQYcH+9u6UNvWjYaObmg1GhRYDRhXYsfYEhvKc80w6bXwB4Jo7fSi0elBo9ONZpcXgaAIAYBGEADpfxBF6e8Kf1BEXpYBBVYjimxGFNlMsBi1cHT70OTyoL7Djfp2N9q6vDDqNDAbtLAadbCb9MgKjWjSagRoBOn6giD9najXaqDXaOBw+9De5UNrlxftXV64PH5Y9FpYTXrYTTpkm/Wwm/XQawX4AiL8ARG+YBD+gLTu1xcUEQgGlff55fcFgxBF6extg1YLg04Do04DQ+i/YFCE2xeE2xeAxx+ECOk3jYDw7035t6kQ9TYgRLwvKEq/P/3BIDSCAJNei2yzHscNz8NAy+jgVlVVhezsbJSWhn8oTJ06FVu2bIl4XltbGxoaGjB58uSI53399dcJr+3xeODxhKs8DkdqM7ZS9Umo5XZ8aHaZLHzsVWTVIdkh84C0fs2k10aEtfqof82HK27RrdL4FbfG0Jy144fnYVejCzWha8vhJ09dcVPdS7c3oNybur1kVj1nSnk23vhmPwBgZGGW8sNCDoPR/xp3eUK7SmMqblIAU+8I/fvnVXjksyqY9Vp8+rM5ymYIaROD9AdT3eZNVHGTK4zjSmwYXWTFsHwL9rZ04S+f7AAAzKyM/ANbbDdJwa2jGwitYwOA+lBwk384A8BZE0rwwPvbsHxXi1Jd++GJlTBoNaHgFtkCk7+vQ0PrA2VyMN2hqooFgiKu+efXWL2nFTaTDm/eMhujQzt25fVtxw7Li7iGRpCqkY1Oj7LRwuXxY22NVF36zjHlKLQZsbSqCbubOvFmqFJ2z7wJyvf65NEF2NXowtKqyOC2vFr6XE4aVaD8ML9sRgUWfroTu5s78cjiKtQ0dypr+/zeAH7x+kYMzbNgRuhr7HD78EUoMF84tUy59ndnVEjBbW0t7jh9tPKD8qGPtuOJJdXK8+aOLcTvLpqEx5dUY3dzJ4psRvy/88cDAM6ZWIIhOWYcaO/G6+v246pZw5Sv4y/f2ACPP4g5YwrxwEWTsPDTHXh7fR2eUVXnKvMt+Mlpo/HBpnos3t6IOxatx4d3nKxUj1fsasab3xyAIAAPXz4N04fm4vjhefj+M6tQ09KF7zyxAhdNG4Jlu5qw+UD4752heRb88pyxOH9yKdq7fLjmn1+jtrUbw/ItePWmE1BoM2JmZS5++84WPPXlbgDAr84Zh05vAP/4fBf+tWxPzJIL+QcjIP3D78rjh8Fq1OHppbuxbFczLnl8BZ774UwMy89Ci8uDX725CZ+G/q6ymXRw+wKo73Dj+RU1eH5FDeJ5cdVeTKvIwS1zR2JimR3vrK/D3z+vgtsXhEGrwexR+WjocGN7gxOvrd2P19buR0WeGTqNBgfaumPuuTTbhGK7CXuaOyOquUU2I1o6vdha78A972zBQx9ux9mTSmAxaLHpgAOb9rcjlX0rn207iIWf7kS2WY/ZI/Oh02qweneLEsxTZdBqUJZjQpc3kPY10mHUaXo9aYD6ZnhBFr64c+5A30ZmBzeXywW73R7xmN1uR3t7e8zztFotLBZLxPNcrvhT7QHgwQcfxH333XdI7zeZ8hwzCqwGXDClNOLxxK3S2JMT9FoNdBoB/qCIbl8AloBWaacBwMGoNoIczNSVOulteVdp5Gs2hypuxw7LxUur96G10wun26e0SnNU4Uen1cCg1cAbCKLbF4Bcz+lSjQLRaML3fvr4Ijzw/lb4AiIumBL+QSxfsy3qNABnnF2lgPr0BOnzDgRFvLRaWmfV7Qvgv2v34/9ClRh5fVuuxRBxcoSyOzVqHMj+NilwVeRaIAgCzplYgqeW7lb+Ej4+6l9aiUaCKK1SVXAbmm/B9KE5StVjSI4Z50wsUYKu/ENK3riyN9QKHJZngdpYZZZb+Af+K1/vU47Ucrr9+PXbm/HqjbPQ6Q0oR2apK24mvRYjC61K5UQObquqW+APihiWb1Has89eMwO3L/oWNc1duGXuyIgq35zRhXhueQ2W7myKqNwtCwXTE0eFq1BWow6/PHssfvvOFmVGoE4j4P6LJmHV7ha8u6EO9763Be/eehI0GgGfbjkIrz+oVChkZ08sQY5Fj/oON77c2YjTxhXjjXX7ldB2woh8rNvbhiU7mnDyn74AIAX3h74zJTw/UavBj04ajvv/txXPfLUb3z9uKLQaAa98vQ+bDzhgN+nwl8umoMhmwiPfm45zJpXi1TX74PYFceaEYlw5ayiMOi3Om1yKix5bhp0HXfjZaxvw/LUz4fEH8eu3pd3IV88ahulDpa97RZ4F/735BFzxzGrsae7Ek19K96vXCphekYuqRif2tXbhtpe/xeOl1Wh2edDo9KDIZsS/rztOWQt69QmVEAHcEwpva/a0Ym9Ll/L3wHHD8zCqyIr1+9qxvcEBX0CExaDFJccMwe2njVZmKp49sQTXv7AGu5s7ccHfl+HUsUX4qqoJbV0+6LUC7jxrLK4/eQR8gSCW72rGB5saUNXohChKP7wmlNmRl2XA13ta8d6GOqyvbcdNL66L+L06a0QeHrxkCoYXZEEURXyzrx1PflmNz7YdRG1reP6hHHhcHj+aXV6p2hP6M1RgNeLSY4bg8pkVGFFoRVunF++sP4B/r9ob8Q8K2YiCLEwckg29RpDGDHn9ygaqilwzhuZnoSLXjE6PH1/XtGJZVTM6un34cHODcg2zXosTRxVg1og8WI06NDk9qG3rQk2zNA9RqxFgMWiVylNDhxt7W7vg9QeVf+wKAjCmyIbjhudhcnk2DFoNDrR3Y+dBJ7bWObC7uVNpVRu0GmQZpesZtFII6/YF4A1VrYfkmFGRZ0ZZthkipGU32+odqGp0ocsbUEKbRpCWcBRajSiwGWHQChBFQIRUSRQhVZJMei00goDWTi+aXB40OtxwqE6qybHoUZptRmm2CXlZBvgCQXR5A3C6fXB5/HC5/QiIIoJB6bpBEQiKIgJBEd5AEL5AEDaTHrkWPXItBuRaDLCadOj2BuBw++B0++Fw++Do9sEfFKHTaKDXCtBpBeg1Gui0AnTK/0un/ujlx0KzTz3+IDx+ace61x9UjkjUhipjRr0WRp0G8o8guduv5HrlbVF5v/w+eWmAIMivLyAQquSVRnXMBkpGBzer1RpTCXM4HLBarTHPCwQC6OrqUsJbvOep3X333fjZz34Wcd2KiopDePdRr3feePzynHFQZRkAScaB+GNPTgCkP3Su0Lqglqi1VU0uT8QP0J4qbtFHXsljEspyzMi16NHW5cPOg07lX8Tqipt0L1JwU+8sjR4FIivPteDFHx2PTfs7cM3sYcrjiYbqRh8wL4uullU1OiPWxy3f1awKbqEdpVH3LbdKXR4/PP6A8vXZH1rwLw/XveL4ofjX8j3wBUTkZxlw6riiiOuEd5aGX98fCCrrmKLb4r88exyue34NgqKIP1wyGTqtBjkWg1L92VrnUIYSR89wk8mbI2pauuD2BWDUafBCqBLyo5OG46XVe/H1nlYs3taIoCjCF5CC2LCoyt34UrsU3OocODU0223ZrnClTDai0Ir//eRkxHP8iDzlB9Ke5k6MKLSio9unnKd64qjI6vKVxw/D7uZO/GfVXowosOJ38yfhuOF5OGtiMT7f3ojNBxx4e/0BXHJMuVKNmzelLKIFZ9Jrcekx5fjnsj14dPEuZJsNuPutTQCA208bhZ+dNRa7Gl34f29uwtc1rTDrtbj3wgnK5yj73nEV+NvnVdjb0oUPNtVjzuhC/DVUWf3ZmWMi2s3nTCrBOZNKEM1s0OLv3z8GFz22DEt3NuF3729Fs8uLPc2dKLGbcOfZYyOeX5ptxls/no1Xvq7FvtZOTCjLxvmTS5GXZUCnx4+nl+7Gk19WK5tGKvLMeO7amTHfu2tOqAQghTd5+UFlvgW/OX8CTh9fpHy93L4Aml2emHW1ADChzI63bz0RN7y4Dhtq25Wv99hiGxZePhUTy7IBAFqNFqePL8bp44sRz3dnVOCuc8bhX8v34PV1+9Hi8mBsiR03nDwcF08fotyLIAg4dlgunrlmBto6vdje4IRGkP6uKcsxK5XTji4fdjU50ejwoDTHjMlDspX3AdKf/2tPHI4fzK7EiuoWrN7TCn8giNHFVhw/PF/Zyd0b1544HP5AEBsPdODrPa3QCNKfi5mVeTFDy3sit3zr2rulfxgVWWP+7lLzBaTWn1YjRCyHSYU8qcDl8cNs0CLXYoj4WqXC7Qug2xuAzaSL+EcuZaaMDm6jR49GR0cHGhoaUFIi/cW5YcMGXH/99RHPy83NRUlJCTZt2oTjjz9eed7EiRMTXttoNMJoNCZ8/+EQ7w9VTxW36D/UJr0GLo907JV656XT7UcgtBNOXvehjAPRx1/jFn3mqfpM0qF5FrR1dWBDbYdyH+aov8wsBh0cbn/EoNroUxPUZo3IV465ksmt0ujg5vTEX+MWvT5tQ2g2WrHdiIMODzbXdSAQlP5CbI6zMUG6pvQv5UBQ+ouv2C4HN6kKUJ4n/eU/LD8LT1x5LF5ftx83zBke85d5vFluB50eBEWpkqLeyQpIJ0Wsuvt06HWCcvIEAEwss+NAeze21HUowW1fq9S2HRpVcSu2G5Ft1qOj24fqJhe8/iCqGl0w6TW444zR0Gs1ePLLavz1052YWi798D1lTCGiTSiz490NdRE7S5dWSa1J+QzZnlgMOsyozMWK6hYs3dmEEYVWrN7dgqAoVT7UmzMAQKMRsGDeRPz2/AkQBCg/1AusRtx66ig89NF2/PnjHRhekKXskL5wWlnM6940ZwReXVOLDbXtuPSJFQCA08cV4adnSIF9VJEVr940C3uaO5FvNUaM31Hf+7WzK/HIZ1V44P2tGFVkRVuXD6OLrLhy1rCY5ycytsSG+y+chF++sRHPLa+RPk8B+Ot3p0asTZTlWAy4Ze7ImMezjDr835lj8P3jhuKrqiZkGXU4dWyRMi8x2jUnVGLGsDysqWnFkBwz5o4tjPmBa9JrlWUD8RTZTXjzltn4bNtB7GxwYnSxDWeML0r5B3ehzYi7zhmHu84ZF7NmMp7cLIPy+zxatkUf0dZPRBAEnDiqIKKqmw6dVoNjhubimKG5PT85CW1oDWZFXuKvt5peq0GK2TCGIAjIzTJEbBpLl0mvTTms0sBJOVovW7YMDz/8MD755JOY9/34xz8+JDcls1qtuPDCC7FgwQJ0d3fj3XffxebNmzFv3ryY51511VX43e9+B6fTiVWrVuHdd9/F5Zdffkjv53AwJBgHEm+OGxC5s1RubZbnWpTrtKtajj2fnBBZcZPHI9hMOuUvIPmYpjyLIeYv5HhDeJVTE4y9+0sgN0HbUtlVakpQcQuFsvWhYHnRtCEw6TVw+4LKho1mp1xxiwxQGo2grHlTb4rY3y5VuSpUP+zOmFCMJ68+Nu4Pk5Js6brq4CavbyvJNkW0imXZFn1EaAOASUOkgKXeFRx93JVMEIRwu7TeidfWSqMxzptUCrtJj5vmjIDNKM07WxQam3HWhNhqkbKzNPSade3d2N3UCY0AnDCy9z8MTx4thcKloTV6K0Lz0WaPiv+DGZC+/tG/l354YiWG5JhR3+HGxY9LYezcSSUYXpAV8/FFdhN+N3+i8g+h2SPz8ej3p0d8vQVBwIhCa9zQJrtxzgiMKMjCQYcHy3e1wKDV4I+XTon5M9eT786swF8um4ohOWYML8jCP38wM+1AUZJtwmUzKnDe5NKEoU02ocyOH8yuxBkTitOukmg1As6eWIKfnD4a50wq6XO1pafQRkR9l9Kf0qeeegrf+c53sG7dOtx222047bTT0Noa3g7/n//855Df4OOPP47a2lrk5+fjzjvvxGuvvYbc3Fy89NJLERW1+++/X9nIcNlll+Hxxx/H2LFjk1w5MySquHnjrHEDVLPcvAFlDEWB1YCc0A8o9WLeRGeVxjvyyuMPr5Wwm/VKpeeb0CiE6DYpoB7CG14foRwwb+hdMTdXWePW280JkRW6jaFgOb0iJ3yyQOiUg4PO0AkGttjKam5U5c4fCKIudOJBsiqFmlxxUw/hrYszCqQnE8uk+5aPc/L6g8r4mMr82HuZEgp6n207iHfWSy2u786U2vy5WQbcOGeE8tzJQ7KV3YBq8s7SPS2d6PT4lbNLp5TnJA070eaMkQLKyuoWePwBpd16YgrhD5B+j/7t+9OVPw82oy6m1ah28fRyLP3lqXjrx7Pxnx8dn7QtlYjFoMNzP5yJ08YVYfrQHDzzgxnKrLtUfefYciz/1Wn44s65MS11IqJDKaW/7f785z/j888/x4QJExAMBvHrX/8aJ554Ij755BNUVFSkPe8nmcLCQnzwwQcxj1955ZW48sorlbfNZjNeeumlQ/76h5uxp3EgMRW30I5Qf0CpuBVYjWgwu9Ho9EQEt0QVt3iHzDtVi1OtRp0S3ORFxPHWjsiz3NTX6Uywxi0ReXdplzcAt0+1O9Utt22jKm6q80q9/iCqDkobUCYNycaEUhs21LZjW70D508pVTZrlMRZUJoXtVauweFGIChCrxWU0xx6orRKVZsT5MAVvb4tGXk90a5GF7q9ATQ43AiKUjCOFzpPG1eEZ5ftURZUjyqyRmyc+PGpo2DQadDgcOOmOSPjVv4KbUaUZZtQ1+HGN/valNZkb9uksvEldhRYjWh2efDEkmrsanRJOwlTDG6AtCnmfz85CSurW3Dq2CJlg0QiQ3LMETt30zEsPwv/unZmn65BRNSfUgpujY2NGDduHABAo9HgwQcfxLBhw3DSSSfh/fffZ5k8DfEqboGgqGxrj2mVqs4ZbVGCm0HZnRkZ3HranBB+TfXAW61GiFlbNSQnNojIrZyuXq5xi8cetd6sJFsLURQTtkrVc9+qm1zwBoKwGXUozzUrh9bvCJ3leTC0aUDe/RnvOnLFTV7fNiTHHDfoxCNftzM008lq1Cmt0lQWSRfbjSiwGtDs8mJbgwOO0PdwaJ4l7p+pWSPylRlqgLTmS/08rUbATafErqOKNntUAV5ftx+LtzViyQ7p6KozEixCT0SjEfCdY8vx5JfVeOQzabfomROKkW3pfdVObUyxDWOKbT0/kYjoKJVSq3TkyJFYu3ZtxGM333wz/vSnP+H000+PmItGvRNvjZtP9evozQlmg3qNW/g4p+w4rdLEJyfEVsrksCBXuKIX2Q7JjQ0i8c4rlStu1l6ucRMEIaZd6vEH4Q8l1+gWWHHouLC2Lp/Sxh1faocgCMoP/ColuLlDHxMb3MJr5aTPW9mY0Ms2KSCFU1vo/uSRIHVxRoH0RBCE8Dq3Ax1KIKssiH8vGo2Ap68+FvOnleGeCybgO8eWx31eT+Tq2vMratDpDaA024QpoQ0Nqbhq1tCIyvDVJ/R+cT8REaUmpeD205/+FBs2bIh5/PLLL8eLL76IE0888ZDd2NEi3skJ6hAXXXGTq2fdvshWabZZHmSrXuMmz3HrfcVN3sU5JMccsdZJXhOlFq9V6vLK55T2vpibE7XeTL4XQYhdK5dt1iuz3T4JnXs6IbRGbFToNIZ9rdKojAYluMW2G/OidrNGjwLpreLsyJ2lyjmlKc77mRRql2460IHNodlr8mPxjCi04pHvTcd1Jw1Pu9I9d0xRxE7h+arxDakoz7Xg8SuPwWnjivCHiyfH7BwmIqJDJ6VW6Q9+8AMAwGuvvRb3/TfffHPE+7773e/24daODoY4IcrnVwe36M0J4VMP5IpbvtWQUsUt3iHz8gw3u1n6LaHRCJhakYOlobVP8UJEvFZpV4qtUkAdoqR7kHe3Wg26mLalIAioyLVga71DWZclD2gttBqRY9GjvcuHDbXtyn3FW+MWvTtVXsuXcnCzG7Gr0aUEN3mDQyqbEwAola61NW3KsSuT0qh+pSLbosevzh2HB97fipGFVtyk2tSQqjMmFOOMCam1WYmIKHVpzXF77LHHsHLlSpSUlKC8vBz79+9HQ0MDZs+eHTFwkcGtZ/FbpfLw3dixCXIFqtPjj6q4xQluoUpY9By3eIfMh0eBhKtsvz5vPPa3deHyGRVxZwWZ9dK99KVVCoRPT5A3Csjz6aLXt8nGFFsj5o/J1UBBEDCmyIava1qVmWQ2ky5u9S8vK7I9K1fcejuHSabeWer2BZQgmOqi+Vkj86HTCNjdHD57dvKQwxvcAOAHsytxyTFDYDHo0h7eSURE/Set4DZ+/HhcdtlluO2225THHnvsMWzevBlPPPHEIbu5o0G8zQm+OOeUyuSKWEe3TwkJhTajanNCeKxGws0JcQ6ZDw/fDf+WGFtiw+c/n5vw3uO2Sj2pt0rljQLtoc8n0akJsklDsvF2aAyGzaiLaOOOKrbi65pW5bzF6E0WMvXuVEC9xi21wFWi2lkqhz+rUad8n3rLbtLj2GG5ytFVIwqyYgb4Hi7RQ46JiChzpTVtcdGiRbjlllsiHrvpppvwyiuvHJKbOpokW+MWN7iFfsjubelSzrrLy4rfKk10VqlRFRaDoWvEq7j1JNwqDY8SkatvqczVyolulfZQcVMfv3PmhOKIr9OYImmd287QmJCRhfGPPVPvKvUHgsp6uFQ2JwDhilt9h1sZmptoN2hPLp4+RPn1pWluOCAioiNbWhW3YcOG4YUXXsB1112nPPbiiy8e1rM+j1TGOBW3RKcmAOFD1+WdhwVWA/RajRKUXKp5bN4EFTf1ZgVvIAiTRqscMpxKpSjertJEZ5UmE72rNHqjRLThBVm4/6KJWLe3DXedOy7ifdOijq6RTxmIfc3wHLf6DmmGm0GrQWGKVS65ore3pQv75IPhe5g/lsh3Z1TAFxTR6vLihpPTX29GRERHrrSC27PPPotLL70Uf/zjH1FRUYHa2lq43W688cYbh/r+jnjJxoEYtLFVG3uosiavhZIPw5YDnVytApJtTgi/7fEFYdJrlc0J6VTc4g7g7eG4HrXcqNMQXPK9JAl/15xQqRy2rTaxzA6LQauEyRkJJuHLg23dvqAyVmRYvqXXM9xkIwqlI5n2tHSipjn++aK9pdEIuDqFczKJiOjok1ZwmzlzJqqrq7Fy5UrU19ejtLQUJ5xwAvR6rpVJlSHOuaHKGjdd4lapTB51IbcV1ScgKCcnRLVK9VqNMvTW7Q8gG/qIA+Z7yxJvV6k39V2l0cdPKZsT0jjGSK/V4AezK/HEkmqMKrImPMLIpNeixG5Cg8ONL7ZLw2fjnYvZk/JcCwxaDbz+IFbuls7p7GniPxERUbrSCm4AoNfrMWfOnEN5L0clg1YKP5GtUnlXaWxwy82KDm5SxU0OXOpWqXxWaXSrVHpMgy5vAJ7Qc9QHzPeW3CrtjrM5IaVxIMoOT+ke5HV6qdyL2p1njcVJowowtsSW9NDsygILGhxufB4KbiMSrIdLRqsRMCzfgqpGl7KurjI/9QBIRETUG2ltTqBDJ9mu0uhzSgGg0Bo5k6woFNzk6lS3LwBfIAhRFJVAZYnTtpTbpe5QVc6prHFLvVXaHaqyiaKoOqs0lXEgka1SObjlpHlsklYj4MRRBT3uypQDlry+b0QaFTcgdgOEfGg8ERHRocbgNsDiDuBN0iotijoFILpVCkjrzHwBUdl1Gn1ygvoxueLmSKPiFt0q9QbCR1Wl0yp1uv3wBYJKcMtOIUSmY1hUZUxer5YqdTt2RGGWEkSJiIgONQa3AaaMA+nl5gSTXhsRropDmxP0Wo0y9sPp9ke0L81xgpsxam2dUnFLqVUarvIB4QPmgdijqpLJNuuV0wLau3xKcEul+peOUUXhSplBq1HOC03V2RNLlO/jxdOG9PBsIiKi9KW9xo0ODXnjQOQct8Rr3ABpQfy20MkB6nEXNpMebp8HTrdf+VidRog5qB5QHzQvtVXlNW6pbE6IbpXKbVKTXpPSFH6tRkC2WTqqqq3Li47QRonDXXE7YWR+xK/jVSZ7Y2i+Bc//cCaqmzvxvZkciUNERIcPK24DLN4AXl+SOW4AcMzQHOXX6kn/8vgMp9unVMHiVduAyIpbpzeAUIczpXEgFtUAXlEU0elNfzdonmpnqaOfWqVWow7//MEMXDStDPdfNLFP15o9qgBXzxqW8HtGRER0KLDiNsCMcea4JTs5AQDuOGM09FoNLpxWFjGhX26hujx+2EJVMFOCeWryTlO3L6gEJb1WiDllIRm5QhUUpXvuTGNHqSxHNYS3PbRJoT/Wip0+vjjiJAYiIqJMxuA2wJLuKtXFbzcW2Uy498LYCpF6lluORQpRCStu+nDFTX1SQSpHNal3q3Z7A8oat1TOKZXJGxSaXF50hkLn4a64ERERDTbs6wyweMEt2ZFXydiMUtBxevzo9krXiDcKBAhX3Dz+oLKjNJWNCfL96UMbKLq8AaXiZk1hFIhMPj1hb+j0gXTuh4iI6EjH4DbA5DVu/qCoHPju62FzQiJyxc2l2lWaaMG93BJ1+wJpHTAfvk54CG86w3dl8nmlNaGD2q1GXdLhuUREREcj/mQcYOodn/LaNl8Pa9wSsao2J3R5e2iVqipuzjQOmJdZVDtLleOu0mmVhipuNS1SxY1tUiIiolgMbgNMHdzkIbxycIs+HL4ndtXmBHeSUxOA8Bo3ty+gbE6QW62pkNezRVbc0miVhta47WqUjo3Ky+IQWyIiomhcRDTA1MdayWvbwrtKe79RAIjcnNDdw65Siz5cKXNo06+4ya1S9Rq3vrRKZUW25MdVERERHY0Y3AaYIAgwaDXwBoLhVmmSQ+aTkdeoOd1+dPUwx015rscPCJGPpSLcKvWHg1sfdpXK5DNYiYiIKIzBLQMYdKHg5j90a9zc3uStUnV1TpbKqQkys2pzQl/WyuVGtUZZcSMiIorF4JYBDDoN4EFMcIt3VFUy6gG8PZ2cIJ+y4HL7EBSlCl86gcusOmjekcaxWbLoNW1D8ywpX4OIiOhIx80JGSD62KvwHLfU1rjZVFU0eYdnonEg6ufKJxVEtyt7Q72r1KEa5Juq/CxDRHgbqToAnoiIiCQMbhlAGcIbCIT+P80BvKHAFFFx66FV6vL40d4VOhvU0odWqTe8OzWdyp0gCChRrWsbzeBGREQUg8EtAxiUA98P4Rq33m5OcIeDW04as9OUVqkv8uisdNx74URkGbS46ZQRae1MJSIiOtLxp2MGiG6VyicnGFKuuOmUj292Se1Pa4IApA55gdCJDX1plXZ5/GkfnSU7bngeNt17NjSa1FrERERERwsGtwwQfV6pUnFLcMh8IlkGHQQBEEWgrr0bQDjMRZPDlUO1qzQnjVapXF3r6PYpA3jtfTj1gKGNiIgoMbZKM0B4jVvk5gSDNrUTCDQaAdbQDLVwcIsfoqxRgU4Q0tsNKh9NVdfhRmhzasKwSERERH3D4JYBjIkqbinuKgXC1a5Q9zPhRgGzXgutqrqVbdanVe2Sw96BNikoGnUa5RxUIiIiOrQY3DJAojVu+hTnuAFAblZk1SxRFU0QhIj1b+lsTADCwfBAqMLXlzYpERERJcfglgGiW6XKAN4UNycAsRsMkrUt1e/LSWNjAhBulfbm9YiIiKhvGNwyQPTmhHTnuAHSIFuZRkg+mkMd8qJPLuit6OCWzjo5IiIi6h0GtwwgV9Y8fTw5AQDyssJnfBZYjRHr2KKVZIcH3g7JMaf8WvJrJHubiIiIDh0GtwyQcBxIGhW3UlUYK+zhoPYy1XPLc9MLbllGHbJUpzMU2RnciIiIDhcGtwwQu8ZNjHg8FRWqw9mLeghuQ/OzlF8PL8hK8szkClSv09NrEhERUfoY3DJATMXNn37FbagquJVkJ6+inT2xGIB0isJJowtSfi1ZseqMUfV5o0RERHRocQtgBjBq429OSKfiNrbEhiyDFp3egBLMEinPteC9206CSa+BxZD+b4WxxTZ8vadVeX0iIiI6PBjcMoAxdBD8oRjAq9UI+Oinc7DpQAdOGVPY4/Mnl2en/BrRZlTm4sVVe2HWazGuxN7n6xEREVF8DG4ZQBnAGwjCHwgqpx7oNel1sivyLBFr3Q63C6aUwWLQYWieBWYDT00gIiI6XDJ6jduaNWswdepUWCwWnHLKKdi7d2/C51ZWVsJiscBqtcJqteLmm2/uxzvtG/UaN7lNCgBGfUZ/exRajYAzJxSzTUpERHSYZWwy8Hg8uOSSS3DHHXegtbUVs2bNwtVXX530Yz7//HO4XC64XC48+eST/XSnfScHN48/oLRLgfROTiAiIqIjV8a2SpcsWQKr1YrrrrsOAHDPPfegsLAQe/fuxbBhw/p8fY/HA4/Ho7ztcDj6fM10qQfwykN4tRoBOgY3IiIiUsnYZLB161ZMnjxZeTsrKwsjR47E1q1bE37M/PnzUVxcjIsvvjhpWxUAHnzwQWRnZyv/VVRUHLJ7T1VEq9Sf/jmlREREdGTL2HTgcrlgt0fuULTb7XC5XHGf//LLL6OmpgZVVVUYOnQo5s+fD1EUE17/7rvvRkdHh/JfbW3tIb3/VKgH8MoVt3RGgRAREdGRbcBapWeddRaWLl0a932/+c1vYLVaY9qXDocDVqs17sfMnj0bAGAymbBw4UJkZ2djz549GDFiRNznG41GGI2ZMeVfXXHz+AMRjxERERHJBiy4ffLJJ0nf//HHH+Ppp59W3u7s7ER1dTUmTJjQ47UFQYAgpD4DbaCoB/DKrVIjgxsRERFFydh0MHfuXLhcLjz//PPweDx44IEHMGPGjLgbE/bt24eVK1fC5/Ohs7MTv/jFLzBs2DBUVlb2/42nQd0q9bJVSkRERAlkbDowGo148803sXDhQuTk5GD58uV48cUXlffffPPNyqw2p9OJG2+8ETk5OaisrMSuXbvwzjvvQJPmANv+Fm+OGzcnEBERUbSMHQcCADNnzsTGjRvjvk89p23ixInYtGlTf93WIRexxs0XapXqeQIBERERRWJZJwMYtLEVNyMrbkRERBSF6SADKCcncI0bERERJcF0kAE4DoSIiIh6g+kgAxi14fVsnR4puHEcCBEREUVjOsgA6uqay+OPeYyIiIgIYHDLCOqQ5nT7pMe4OYGIiIiiMB1kAK1GgFYjnfQgV9yMen5riIiIKBLTQYaQK2xOd6hVquUcNyIiIorE4JYh5HapEty4xo2IiIiiMB1kCDmocXMCERERJcJ0kCHkVqkrVHHjOBAiIiKKxnSQIYxKq9QX8TYRERGRjOkgQyhr3NgqJSIiogSYDjJE9Bo3VtyIiIgoGtNBhpDXuIli6G0GNyIiIorCdJAhogfumnSc40ZERESRGNwyRPQRV2YDgxsRERFFYnDLENGtUYtBN0B3QkRERJmKwS1DGKJaoxZW3IiIiCgKg1uGiG6VMrgRERFRNAa3DMFWKREREfWEwS1DRFfYuDmBiIiIojG4ZQi7SR/xNlulREREFI3BLUPYTOHWqEGrgV7Lbw0RERFFYjrIEHZzuOKWbdEneSYREREdrRjcMoS64pZnMQzgnRAREVGmYnDLEOo1bjmsuBEREVEcDG4ZotAWrrLlsuJGREREcTC4ZYiKPIvya0EYwBshIiKijMXgliGMqiOv5owpHMA7ISIiokzF8fwZ5NUbZ2HTgQ58b2bFQN8KERERZSAGtwxy/Ih8HD8if6Bvg4iIiDIUW6VERP+/vfuPibr+4wD+PEA54H5AyHHID9ul3cnJwD+MzUZSa2VzsGLW3C2zWGZrzfGP1Kxlc2H8YT/mllKLETpn1KQysqyZw+ZotGhOhaSgQwroB7/uB3faca/vH8jn6+Hu8vvN4/x4z8d2G7zf9/Hz+jznjtc+n8/7PkREKsHGjYiIiEgl2LgRERERqQQbNyIiIiKVYONGREREpBJcVXqZiAAAXC5XjCshIiKieDPbf8z2I+GwcbvM7XYDAPLz+R1qREREFBtutxtGozHsvEb+qbWLE8FgEENDQ9Dr9dBE4ZlTLpcL+fn5GBwchMFguO7/vtoxn8iYT2TMJzLmExnziYz5hHc9sxERuN1uLF68GAkJ4e9k4xm3yxISEpCXlxf1/RgMBv7Hj4D5RMZ8ImM+kTGfyJhPZMwnvOuVTaQzbbO4OIGIiIhIJdi4EREREakEG7d5kpycjB07diA5OTnWpdyQmE9kzCcy5hMZ84mM+UTGfMKLRTZcnEBERESkEjzjRkRERKQSbNyIiIiIVIKNGxEREZFKsHEjIiIiUgk2bvPgzz//xLp165Camgqr1Yrjx4/HuqSYunjxIp544gnk5eXBaDSivLwcZ86cUebr6+uRlZWFW265BbW1tf/43LabVUdHBxISElBfX6+MMZsZ9fX1yM/Ph16vR0lJCSYmJpTxeM+nq6sLq1evhsFggMViQVNTkzIXj/ns2LEDhYWFSEhIwPvvvx8yFymP7777DsXFxUhNTcWaNWswMDAw36XPi3D5vPfeeygpKYFer4fFYkFDQ0PIdvGez6xAIICioiLYbLaQ8ajmIxR1Dz/8sDz55JPi9Xrlo48+koyMDBkbG4t1WTHj8Xhk586dMjg4KIFAQF577TWxWCwiIvLZZ59JQUGB9PX1ydDQkCxfvlwaGxtjXPH8m56eltLSUrnjjjvk1VdfFRFmM2vPnj1y1113idPplGAwKGfOnBGfz8d8LrPb7fLKK6/I9PS0fP/996LT6eT8+fNxm8+BAwfkyy+/lNLSUjl06JAyHikPv98veXl50tjYKD6fT2pra6WsrCxWhxBV4fJpaGiQjo4O+fvvv+Xs2bNiMpmkvb1dRJjPld544w258847xWq1KmPRzoeNW5S53W5ZuHChDA0NKWNlZWXS3Nwcw6puLBcvXhSNRiN//fWXbNiwQerr65W5xsZGufvuu2NYXWzs27dPtm7dKps2bVIaN2YjEggExGw2S29v71VzzGeGTqeT/v5+5fdVq1bJkSNH4j6fNWvWhPzhjZTHF198ITabTZnzeDySkpIiTqdz/gqeZ3PzmcvhcMju3btFhPnMGhkZkeXLl0tbW1tI4xbtfHipNMp++uknGI1G5OTkKGPFxcU4d+5cDKu6sXR0dCA7OxuZmZno7u5GUVGRMhePWY2NjeHNN9/Eyy+/HDLObIBff/0VPp8PH374IbKzs2G1WpVLOMxnxrPPPosDBw4gEAigs7MTg4ODKC0tZT5zRMpj7lxaWhpuu+02dHd3z3udN4Lp6Wl0dnbCbrcDYD6znnvuOWzfvh1paWkh49HOhw+ZjzKPx3PVg2cNBoNyT068m5ycxJYtW1BXVwfg6rwMBgM8Hk+syouJ7du3o6amBhkZGSHjzAb47bffMDk5ib6+PjidTvT39+Pee++F1WplPpetXbsWjz32GHbu3AkAeOedd2AymZjPHJHyCPe5Ha95vfjii8jNzcX9998PgPkAMyccent70dTUhPb29pC5aOfDxi3KdDodXC5XyJjL5YJOp4tRRTcOv9+PBx98EOvWrUN1dTWAq/OKt6x++OEHdHZ24q233rpqLt6zAYCUlBQAMzcMp6SkwG63Y+PGjTh69CjzATA6OoqKigo0NzejsrISPT09WLt2Lex2O/OZI1Ie/Nz+r4aGBrS2tuLUqVPQaDQAmE8wGMTWrVuxd+9eJZMrRTsfXiqNsmXLlmFychIjIyPK2OnTp5VTzvEqEAhgw4YNWLx4MXbv3q2MFxYWhqwwjbes2tvb0dvbi9zcXJjNZrS0tKCurg6bN2+O+2wA4Pbbb8fChQtDxuTySkDmA/T398NoNOKhhx5CYmIiVqxYgfLycpw8eZL5zBEpj7lzXq8XfX19KCwsnPc6Y2n28+fYsWNYtGiRMh7v+bhcLnR1daGiogJmsxlVVVX4+eefYTabMTU1Ff18rsudchTR+vXr5amnnpKpqSn55JNP4n5VqYjI448/Lvfdd59cunQpZLytrU2WLFki/f39Mjw8LHa7PS5Wvs3yer0yPDysvB555BF54YUXZHx8PO6zmeVwOGTz5s3i9/vlxx9/lJycHPn666+Zj4hMTEyI0WiUI0eOSDAYlJ6eHsnJyZHPP/88bvO5dOmS+Hw+KSsrk/3794vP55Pp6emIecyuCmxqahK/3y/PP//8TbtqMlw+x44dk6ysLDl9+vRV28R7PoFAIORz+vDhw7J06VIZHh6WYDAY9XzYuM2DP/74Qx544AFJSUmRZcuWyVdffRXrkmLK6XQKANFqtZKWlqa8Tp48KSIiu3btkszMTElPT5dt27ZJMBiMccWxc+WqUhFmIyIyPj4uVVVVotPpZMmSJbJ3715ljvnMrGgrLi4WnU4n+fn5UldXp8zFYz6bNm0SACGvEydOiEjkPDo7O6WoqEi0Wq2UlZXdtCsmw+VTXl4uSUlJIZ/RW7ZsUbaL93yudOLEiZBVpSLRzUcjEgffwEhERER0E+A9bkREREQqwcaNiIiISCXYuBERERGpBBs3IiIiIpVg40ZERESkEmzciIiIiFSCjRsRERGRSrBxIyIiIlIJNm5ERAAuXLgQ8jzGaHA6ndBoNNDpdPj4448jvvfw4cPQ6XTQaDQhzzomovjGJycQUdzQ6XTKz16vF6mpqdBoNACA7u5uFBQURHX/TqcTNpsNfr//mrfRaDQYHh6G2WyOYmVEpBZJsS6AiGi+eDwe5WetVotz587h1ltvjV1BRET/I14qJSLCzNkwrVar/K7RaLBv3z4UFBRg0aJFaGlpQVtbGywWC0wmE1paWpT3jo2NweFwwGQywWKxoLm5+Zr3++2332LlypXQ6/Uwm814/fXXr+txEdHNhWfciIjCOHXqFHp7e/Hpp5/i6aefRmVlJc6ePYvjx4+juroa69evR2JiIjZu3IgVK1ZgcHAQv/zyC+655x6UlJSguLj4H/dRU1ODbdu2weFwYHx8HE6nM/oHRkSqxTNuRERh1NbWQqvVoqqqChMTE3jmmWeQmpqKiooKuN1uDA0NYWRkBN988w127dqF5ORk2Gw2OBwOtLa2XtM+FixYgPPnz2NsbAwZGRlYuXJllI+KiNSMjRsRURgmkwkAkJiYiAULFiArK0uZ02q18Hq9uHDhArxeLzIzM5Geno709HS8/fbb+P33369pH++++y56enqwdOlSrF69Gh0dHVE5FiK6OfBSKRHRv5Cbm4v09HSMjo7+X9tbrVZ88MEHCAQCaGhowKOPPoq+vr7rXCUR3Sx4xo2I6F/Izc3FqlWr8NJLL2FqagqBQABdXV3o7u6+pu0PHjyI0dFRJCUlQa/XIzExMcoVE5GasXEjIvqXDh48iIGBAWXFaU1NDXw+3zVte/ToUVitVuj1euzZswdNTU1RrpaI1IxfwEtENE8GBgZgs9mQnJyM/fv3o7KyMux7W1tbUV1dDb/fj4GBAWRnZ89jpUR0o2LjRkRERKQSvFRKREREpBJs3IiIiIhUgo0bERERkUqwcSMiIiJSCTZuRERERCrBxo2IiIhIJdi4EREREakEGzciIiIilWDjRkRERKQSbNyIiIiIVOI/hlYrp2etBGAAAAAASUVORK5CYII=", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "response = ct.initial_response(sys, X0=[1, 0, 0, 0])\n", + "cplt = response.plot()" + ] + }, + { + "cell_type": "markdown", + "id": "3e48c1df", + "metadata": { + "id": "Y4aAxYvZRBnD" + }, + "source": [ + "If you want to play around with the way the data are plotted, you can also use the response object to get direct access to the states and outputs." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "705cac47", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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Ypp6woGIYhmEYhqknnEPFMAzDMOc5tbW1qK6uNnsYAScsLAxWqzUgz8WCimEYhmHOUyRJwunTp1FQUGD2UEwjISEBaWlpfq8TyYKKYRiGYc5ThJhKSUlBdHT0BVV8WpIklJWVIScnBwCQnp7u1+djQcUwDMMw5yG1tbUOMdWkSROzh2MKUVFRAICcnBykpKT4NfzHSekMwzAMcx4icqaio6NNHom5iNfv7xwyFlQMwzAMcx5zIYX51AjU62dBxTAMwzAMU08alKBau3YtrrzySjRt2hQWiwXffPON1//5+eef0bt3b0RGRqJNmzZ499133bZZsGABMjMzERERgczMTCxatMgPo2cYhmEY5nylQQmq0tJSdO/eHW+++aau7Q8fPowrrrgCQ4YMwbZt2/DYY4/h/vvvx4IFCxzbbNiwAddffz0mT56MHTt2YPLkyZg4cSI2bdrkr5fBMAzDMMx5hkWSJMnsQdQFi8WCRYsW4eqrr9bc5h//+AcWL16MPXv2ONbddddd2LFjBzZs2AAAuP7661FUVIQffvjBsc3ll1+Oxo0bY968ebrGUlRUhPj4eBQWFnJzZIZhGCYoqKiowOHDh9G6dWtERkaaPRyfmTdvHm6//XYcPHgQzZo1AwBMnToVv/32G9atW4f4+Hhd+/H0Phh5/m5QDpWvbNiwAaNGjXJaN3r0aGzevNmR7a+1zfr16zX3W1lZiaKiIqc/hmEYhmGMY9KkSejQoQNmz54NAJg1axaWL1+OH374QbeYCiTndR2q06dPIzU11WldamoqampqcPbsWaSnp2tuc/r0ac39zp49G7NmzXJbf+gQ0KOHIUNnGIZhGMORJKCsLPDPGx0N+DrZzmKx4LnnnsN1112Hpk2b4rXXXsO6descbtU111yDNWvWYMSIEfj666/9MGrfOK8FFeA+XVJEOJXr1bbxNM1yxowZmD59uuN+UVERMjIysGYNCyqGYRgmeCkrA2JiAv+8JSVAo0a+/9+4ceOQmZmJWbNmYcWKFejcubPjsfvvvx933HEHPv74YwNHWnfO65BfWlqam9OUk5OD0NBQR9VYrW1cXSslERERiIuLc/oDgNWrDX4BDMMwDHMBs3z5cuzduxe1tbVu5+Xhw4cjNjbWpJG5c147VAMHDsR3333ntG7FihXo06cPwsLCHNusXLkSDz30kNM2gwYN8vn51q2r33gZhmEYxp9ER5NbZMbz+srWrVsxYcIE/Oc//8EXX3yBJ554Al999ZXxgzOIBiWoSkpKcODAAcf9w4cPY/v27UhMTESLFi0wY8YMnDx5Ep988gkAmtH35ptvYvr06Zg2bRo2bNiADz74wGn23gMPPIBLLrkEL7zwAv7yl7/g22+/xapVq/DLL7/4PL78fLJTL/Aq/wzDMEyQYrHULfQWaI4cOYKxY8fi0UcfxeTJk5GZmYm+fftiy5Yt6N27t9nDU6VBhfw2b96Mnj17omfPngCA6dOno2fPnnjyyScBANnZ2Th27Jhj+9atW2Pp0qVYs2YNevTogWeeeQavv/46xo8f79hm0KBB+OKLL/DRRx+hW7dumDt3LubPn4/+/fvXaYxnztTjBTIMwzDMBc65c+cwZswYXHXVVXjssccAAL1798aVV16Jxx9/3OTRadNg61AFE6KOBVCIDRviMGCA2SNiGIZhLnQaeh0qPaxZswZvvvmmx1l+gapD1aBCfg0BdqgYhmEYxv+MHj0aW7duRWlpKZo3b45Fixahb9++po2HBZXBeChfxTAMwzCMQSxfvtzsITjRoHKoGgLsUDEMwzDMhQcLKoNhh4phGIZhLjxYUBkMO1QMwzAMc+HBgspg2KFiGIZhmAsPFlQGww4VwzAMw1x4sKAyGBZUDMMwDHPhwYLKYEpKgOpqs0fBMAzDMEwgYUHlB4qKzB4BwzAMwzCBhAWVgURF0bKw0NxxMAzDMAwTWFhQGUhsLC3ZoWIYhmGYCwsWVAYi+iqyQ8UwDMMwFxYsqAwkPp6W7FAxDMMwzIUFCyoDESE/dqgYhmEYpn7MmzcPkZGROHnypGPd1KlT0a1bNxQG4Yk21OwBnE9wyI9hGIYJaiQJKCsL/PNGRwMWi0//MmnSJPzrX//C7Nmz8eabb2LWrFlYvnw5Nm7ciHgREgoiWFAZCCelMwzDMEFNWRkQExP45y0pARo18ulfLBYLnnvuOVx33XVo2rQpXnvtNaxbtw7NmjXD8ePHMXnyZOTk5CA0NBRPPPEEJkyY4KfB64MFlYGwQ8UwDMMwxjFu3DhkZmZi1qxZWLFiBTp37gwACA0NxZw5c9CjRw/k5OSgV69euOKKK9DIR9FmJCyoDEQ4kCyoGIZhmKAkOprcIjOetw4sX74ce/fuRW1tLVJTUx3r09PTkZ6eDgBISUlBYmIizp07x4LqfIFDfgzDMExQY7H4HHozi61bt2LChAn4z3/+gy+++AJPPPEEvvrqK7ftNm/eDJvNhoyMDBNGKcOCykA45McwDMMw9efIkSMYO3YsHn30UUyePBmZmZno27cvtmzZgt69ezu2y8vLwy233IL333/fxNESXDbBQNihYhiGYZj6ce7cOYwZMwZXXXUVHnvsMQBA7969ceWVV+Lxxx93bFdZWYlrrrkGM2bMwKBBg8wargN2qAyEHSqGYRiGqR+JiYnYs2eP2/pvv/3WcVuSJNx222249NJLMXny5EAOTxN2qAyEk9IZhmEYxv/8+uuvmD9/Pr755hv06NEDPXr0wB9//GHqmNihMhAO+TEMwzCM/7n44oths9nMHoYT7FAZiDLkJ0nmjoVhGIZhmMDBgspAxExUmw2oqDB3LAzDMAzDBA4WVAaiLO1RWmreOBiGYRiGCSwsqAzEagUiIug2CyqGYRiGuXBgQWUwwqViQcUwDMMEA9IFntQbqNfPgspgWFAxDMMwwUBYWBgAoKyszOSRmIt4/eL98BdcNsFgWFAxDMMwwYDVakVCQgJycnIAANHR0bBYLCaPKnBIkoSysjLk5OQgISEBVqvVr8/HgspgWFAxDMMwwUJaWhoAOETVhUhCQoLjffAnLKgMhgUVwzAMEyxYLBakp6cjJSUF1dXVZg8n4ISFhfndmRI0OEH19ttv46WXXkJ2djY6d+6MOXPmYMiQIarb3nbbbfj444/d1mdmZuLPP/8EAMydOxe333672zbl5eWIjIz0eXwsqBiGYZhgw2q1BkxYXKg0qKT0+fPn48EHH8Tjjz+Obdu2YciQIRgzZgyOHTumuv1rr72G7Oxsx9/x48eRmJiICRMmOG0XFxfntF12dnadxBTAgophGIZhLkQalKB65ZVXMGXKFEydOhWdOnXCnDlzkJGRgXfeeUd1+/j4eKSlpTn+Nm/ejPz8fDdHymKxOG1Xn1hrTAwtWVAxDMMwzIVDgxFUVVVV2LJlC0aNGuW0ftSoUVi/fr2ufXzwwQcYOXIkWrZs6bS+pKQELVu2RPPmzTFu3Dhs27bN434qKytRVFTk9Cdgh4phGIZhLjwajKA6e/YsamtrkZqa6rQ+NTUVp0+f9vr/2dnZ+OGHHzB16lSn9R07dsTcuXOxePFizJs3D5GRkRg8eDD279+vua/Zs2cjPj7e8ZeRkeF4jAUVwzAMw1x4NBhBJXCtoSFJkq66GnPnzkVCQgKuvvpqp/UDBgzAzTffjO7du2PIkCH48ssv0b59e7zxxhua+5oxYwYKCwsdf8ePH3c8xoKKYRiGYS48Gswsv6SkJFitVjc3Kicnx821ckWSJHz44YeYPHkywsPDPW4bEhKCvn37enSoIiIiECGa9rnAgophGIZhLjwajEMVHh6O3r17Y+XKlU7rV65ciUGDBnn8359//hkHDhzAlClTvD6PJEnYvn070tPT6zROFlQMwzAMc+HRYBwqAJg+fTomT56MPn36YODAgfjvf/+LY8eO4a677gJAobiTJ0/ik08+cfq/Dz74AP3790eXLl3c9jlr1iwMGDAA7dq1Q1FREV5//XVs374db731Vp3GyIKKYRiGYS48GpSguv7665GXl4enn34a2dnZ6NKlC5YuXeqYtZedne1Wk6qwsBALFizAa6+9prrPgoIC3HnnnTh9+jTi4+PRs2dPrF27Fv369avTGFlQMQzDMMyFh0WSJMnsQTR0ioqKEB8fj8LCQmzYEIfLLwd69AC8VF9gGIZhGMZElOfvuLi4eu2rweRQNRSCzqE6cAAYOxZYu9bskTAMwzDMeUuDCvk1BESl9JISc8fhYMIEYPt24JdfgMJCs0fDMAzDMOcl7FAZTFA5VNnZJKYAoKgI0FEAlWEYhmEY32FBZTBKQWV6dtr8+c73v/3WnHEwDMMwzHkOCyqDiY6mZW0tUF1t7liQlUVLUUl+9WrzxsIwDMMw5zEsqAwmKkq+XV5u3jgAAEeP0nLMGFoeOWLaUBiGYRjmfIYFlcGEh8uGkOmCSgiooUOd7zMMwzAMYygsqAzGYpFdqooKEwciSbJDJQTVmTNBoPIYhmEY5vyDBZUfEILKVO1y9ixQVka3u3eX6zm4VJJnGIZhGKb+sKDyA5GRtDRVUAl3Kj2dBmRvz+NYzzAMwzCMYbCg8gNB4VCJfKlWrZyXKnlUVVXA9OnAU08Bp04FYGwMwzAMc57BldL9QFAIKuFECWfKg0P11VfAq6/S7bVruboCwzAMw/gKO1R+ICgE1ZkztExPp6UHQbVxo3z7t9+ohhbDMAzDMPphQeUHgmKWX14eLZs0oWVaGi1zctw23bRJvl1WBhw65Oex+cqnn5IwfOONICg/zzAMwzDusKDyA0HhULkKquRkWubmOm1WUSG3+0tKouWOHf4fnk+8/jr1Ibz/fuDLL80eDcMwDMO4wYLKDwTFLL9z52jpKqjOnnXabPt2apGTlARcdRWt27kzMEPURWmprPgAYMUK04bCMAzDMFqwoPIDQeVQJSbSUthPublOYTPR7q97d/oDgsyhWr8eqKmR7wfV4BiGYRiGYEHlB4JKULk6VJWV5PrYOXGCli1aAJ070+19+wI0Rj2sWUPLQYNo+eefzgKLYRiGYYIAFlR+wHRBJUnuIb/oaDkWqcijEoKqeXOgaVO6ffp0gMaphz/+oOWNNwKNGlHS1/795o6JYRiGYVxgQeUHTBdUJSWUGAXIgspiUU1MVwoqMRGwoMDkGYpKRKucNm2Arl3pNof9GIZhmCCDBZUfML1sgnCnIiLkwQByHpUiMV0pqBISgPBwui/KWJmOEFQtWgBdutDt3bvNGw/DMAzDqMCCyg+YPstPmT9lscjrvThUFovsUgVF2K+4GMjPp9sZGSSqAO6PwzAMwwQdLKj8gOkhP9eEdIFL6YSKCtmsat6clkElqI4fp2V8PBAXJyd5saBiGIZhggwWVH7AdEElQn6iZIJAWToBwMmTdDcqCmjcmG4HlaBShvsAFlQMwzBM0MKCyg+YLqh0OlSu4T4gyASVcKhYUDEMwzBBDgsqPxA0gsrVoRICyy6ohC4ROgUIMkGl5VDl5sqzGBmGYRgmCGBB5QdMn+VXWEhLEccTiPsFBQDk/KmUFHmToBRUGRm0bNIECAuj20ExQIZhGIYhWFD5AdNn+RUX0zI21nm9hqASqVVAkAkqUbshPZ2WISHyADnsxzAMwwQRLKj8gOkhv6IiWsbFOa9PSKClXVCppVoJtyonx2+j04+a4uM8KoZhGCYIYUHlB4JGULk6VC6CSk2vuJhY5iIGKJLpARZUDMMwTFDCgsoPmC6oRMhPy6EqLARqa1UFlVJz2Wx+HKMe1AYown/Z2YEfD8MwDMNowILKD5guqLQcqvh4p208OVQ2G7UENI3ycqC0lG4rByhui3glwzAMwwQBLKj8gFJQSZIJA9ByqJS9/QoKHIJKmUMVGSn38xNdX0xBCKbQUOfXIQbLgophGIYJIlhQ+QExy0+STCqXpOVQAQ4LSsovcGgSpQFkscgulamCSvQbTEpy7kcoamuJavAMwzAMEwSwoPIDwgQCTAr7aTlUgCNJquJ0gaNOllJQAUGSmK6WkA6wQ8UwDMMEJQ1OUL399tto3bo1IiMj0bt3b6xbt05z2zVr1sBisbj97d2712m7BQsWIDMzExEREcjMzMSiRYvqNcbwcNlUCbigqqykP8CjoCo+XgCAooCNGqluYq5DpZbgBbBDxTAMwwQlDUpQzZ8/Hw8++CAef/xxbNu2DUOGDMGYMWNwTFTU1iArKwvZ2dmOv3bt2jke27BhA66//npMnjwZO3bswOTJkzFx4kRs2rSpzuO0WExMTBfuFADExLg/bldLZacKALhH1IAgc6hcBRU7VAzDMEwQ0qAE1SuvvIIpU6Zg6tSp6NSpE+bMmYOMjAy88847Hv8vJSUFaWlpjj+r1ep4bM6cObjsssswY8YMdOzYETNmzMCIESMwZ86ceo3VdEEVHU0J3a6IkF822U+u/ZMVmwSnQyUGXFoqO3EMwzAMYzINRlBVVVVhy5YtGDVqlNP6UaNGYf369R7/t2fPnkhPT8eIESOwevVqp8c2bNjgts/Ro0d73GdlZSWKioqc/lwxTVB5SkgHHGqp+mwBAHVBFRRJ6VqCKj6eWtAAHPZjGIZhgoYGI6jOnj2L2tpapKamOq1PTU3FaY3Gc+np6fjvf/+LBQsWYOHChejQoQNGjBiBtWvXOrY5ffq0T/sEgNmzZyM+Pt7xlyGa9yowrZ+fp4R0QJ7ld64AgJySpLJJcIb8QkLkAXLYj2EYhgkSVGJCwY3FJeFHkiS3dYIOHTqgQ4cOjvsDBw7E8ePH8e9//xuXXHJJnfYJADNmzMD06dMd94uKitxElXCoxEy6gKHToUJhgdNdtU1MdajEk6spvsREElPsUDEMwzBBQoNxqJKSkmC1Wt2co5ycHDeHyRMDBgzA/v37HffT0tJ83mdERATi4uKc/lwxPYdKy6GyqyVrcYHyrhNB4VCJJ1dWdxdwYjrDMAwTZDQYQRUeHo7evXtj5cqVTutXrlyJQYMG6d7Ptm3bkC76wYFcK9d9rlixwqd9qhG0OVR2oRVaStsFrUMlBJXaAIVrxYKKYRiGCRIaVMhv+vTpmDx5Mvr06YOBAwfiv//9L44dO4a77roLAIXiTp48iU8++QQAzeBr1aoVOnfujKqqKnz22WdYsGABFixY4NjnAw88gEsuuQQvvPAC/vKXv+Dbb7/FqlWr8Msvv9RrrKYLKi2Hyi60wirIyRJulJKgSEovLKSlmqASDlWQhPw++QTIygIee8y9phfDMAxzYdCgBNX111+PvLw8PP3008jOzkaXLl2wdOlStGzZEgCQnZ3tVJOqqqoKDz/8ME6ePImoqCh07twZS5YswRVXXOHYZtCgQfjiiy/wz3/+E0888QTatm2L+fPno3///vUaq+khPy8OVXiltkMltJjK5MXAIEmeHaogCvmdOwdMnUothpYuBTZupGKpDMMwzIVFgxJUAHDPPffgnnvuUX1s7ty5TvcfeeQRPPLII173ed111+G6664zYngOTJ/l50VQRVVrCyrxr8oaoQGlogKoqqLbagMUeVXCxTKRhQvlfo3btwPr1gEjR5o6JIZhGMYEGkwOVUNDdZbfmjV0BvYnpaW01Io92QVVdC2pJU+CqqSEzKKAI9ypkBD1au9BJKjmzXO+X89IMcMwDNNAYUHlJ9xCfnl5wPDhwPjxZGP4C2+Cyq6WoqRyhKLaY8jPZgPKyowfoleEUIqPd++LI9YrtzOJ6mpAlDT7299oyYKKYRjmwoQFlZ9wE1Sffio/OGuW/55Yp6ACgFgUqwqqRo1kHWNK2M9T/hQQNILq8GGgpoa6/Nx6K63buFEOATIMwzAXDiyo/ISboHr/ffnBH38EjhzxzxN7E1RhYZDsg4tDkeosP4tFjrQFpaByFCc1V1BlZdGyfXugc2caVmkpsHu3qcNiGIZhTIAFlZ9wElT5+cCff9KKZs1ouWePf57Ym6ACYGtELlViaLFjnK6Ympjuqaincn2QCKoOHSjdSxTlP3DAvDExDMMw5sCCyk84zfITZ95mzYB+/ei2olq7oegQVDXRlCTVNKZINUUJCBJBFeQhP6WgAoC2bWl58KA542EYhmHMgwWVn3ByqPbupTsdOwLt2tFtEwVVVSQJqrRo7UJTpgoqT0U9AVlQFRcDtbUBGZIa+/bRsn17WrKgYhiGuXBhQeUnnMomKAWVOPv6S1CJaXkeBFVlOKmllChttdQgHCrAxGJZ7FAxDMMwMiyo/IRXh0rYG0ajw6EqDyWHKik8SB0qb4IqIkIuR25SB+eKCuDMGbrdujUthaDiHCqGYZgLDxZUfkJTUAmH6uhRuRq4kegQVGV2QdUkLMgFlVZSOmD6TL/Tp2kZESH3ahaC6vhx/3y0/iQnh6p5fPyx2SNhGIZpmLCg8hNCUFWV1cgxoA4dgNRUqklgsxlfOqG6Wi6C5EFQlYSQoEoI8S6oTOnnpyzsqYXJiemnTtEyLU2u2ZWWRjWpND/asjL/ze6sByUlQN++wMyZwG23AYcOmT0ihmGYhgcLKj8hZvk1KjlD1R+tVqBpUzr7pqXRgyJmZBTCnQI8CqpikFqKDwnSHCrxpKJkuxomC6rsbFo2bSqvs1iAFi3o9okTKv/Qpw+QmQmsXBmQMepl5UpA0VMc771n3lgYhmEaKiyo/IRwqBJKT9KN9HQSVQCQkkLLnBxjn1QIqtBQIDxcc7NCiYRKrBSkIT9hizUAQZWe7rxeCCzhYDmYNk12p55/3q9j85UffqClKJH20UfksjEMwzD6YUHlJ4SgSiy3CypxtgJkQZWba+yT6sifAoACGwmVGFuQCypFmxw3glRQiY/ZSVCVlzu7UmvWANu3+3F0+pEkWVC9/TblhJ05Q211GIZhGP2woPITQlAlVXoQVP5yqLwIqvwaEirRtdpqSZhDHPJTxyeHasMGylJv2hS48kpat2aNv4eoi0OHKDwZHg6MHAl06ULrd+wwd1wMwzANDRZUfkIIqpRqFUGVnExLkwTV2WoSKlHVDdihMnmWn1oOlfK+k6BavZqWw4cDvXvT7SBxqEQUsmNHSqjv3p3uB8nwGIZhGgyhZg/gfEUIqqYIPocqt4IEVURlEAqqykp5pqInh8pUxeejQ/Xzz7QcPhxo0oRuB4kFJARVp0607NGDlkEyPIZhmAYDCyo/IWb5NfMkqEzKoTpjF1RhlUE4y09ZpyEmRns7kwWVEExeBZUkAbt20e0+fWSRuHs3CcewML+P1RNKhwpgh4phGKaucMjPT4SH0zR6VUFlcsjvTCmJkdAybYdK7EJZiSEgCEHVqJE8K1INEwVVTQ1w9izdFhUwBEpBJUmgDfPz6cvQrh3QqhWJqqoqueCriYghCIeqWzdaHjtmalcfhmGYBgcLKj9hsZBL5dGhMkFQSRKQXUouibW0yH7Wd8c0QaUnIR0wVVDl58tvm4jgCYRjVVlJ2zka/rVoQUlKFousWoRzZRKS5B7yS0iQK78fPWrKsBiGYRokLKj8SJOIEsSihO4oY0NCUOXlAbW1xj2hDkFVUQHk1ZBYsdTU2Ls3uyN2UVamqbnqzeuvU87OfffZW/QA+hLSlY+bIKjy8miZkEAlv5RERMgi6+RJyBaQ6KAMyD1qTFYsOTnU5UeYZ4JWrWhpdCF/hmGY8xkWVH6kaRjlSNkiIp3zgZo0obOYJMlnZyPQIaiKioBSKB7XECTR0bSsrfVPX7rqauCppyj5+a23gC++cBlPEDtU4iNzdacEqam0zMmB7FApBZUop64sT24CQjA1ayZPogCAli2dH2cYhmG8w4LKjwhBVZ2QIjd8A8jWEHEVI8N+OgWVhBAUwXOzPuUu/BH2++UXuQcyACxZohgg4N2hEgI1CAWVSJHLzYUsqETWNxA0gko8vRiOgB0qhmEY32FB5UfSrSSWKuOT3R/0Rx2lsjJaCntJBaFXSu0NkrUEVViYPAFN7NZIvvuOlp0703LFCrsTpqftDGCqQyUS0nUJqgMH6I4yphYkgur4cVpmZDivF4KKc6gYhmH0w4LKj6SEkENVGetBUCltmvoiEpF0CKqyUO+l0MVu/OFQ/fgjLZ98kkJkxcXAr7/C95BfSYn/krw08MmhEvUTmjeXN2gggoodKoZhGP2woPIjKRI5VOWxKe4P+kNQCStJmRDjgjDEKsI8h/wA/830s9mA/fvpdq9ewJAhdHvbNvielG6zKTLaA4MQVElJ6o8LQVVwqkx+w5WTEoSCKSw0rdI7IAsqDvkxDMPUHxZUfiQJ5FCVRqs4VP7oReeDQ1UZ4TnkBzjP9DOS7GwaqtVKCdAi7Pfnn9DvUDVqJOelBTjsp9ehqj1hL6ceFSV/3gCNXfyzUDUmoOVQiaT0s2dNKJuhxeLFwAMP2FU3wzBM8MGCyo80qSVBVRIVoJCfDodK6KeqKPNCfsKdat2a8rScBJVeh8piMS0xXa+gspxW9KdRTkoAZFvIxEQlEXF0FVTx8bKYFi12TOXTT4G//IXqbNx4o7GlRhiGYQyCBZUfSayhkF9xVIBCfj44VLVR5oX8RJ72RRfRUgiq3bsBSW9SOmBaYrrepPSwsxoN/wA5p+rkSWMHp5OqKuDMGbrtGvID5CEHhaB65x359t69wFdfmTcWhmEYDVhQ+ZH4anKoCiMCFPLTMctPPJ0tRn/Iz9+Cql07cqqKi4GKXLs48uZQKbcJUocqusCekC760SgRxaqEqgkwojVORIR6LljQCKr9+4ENG4CQEODuu2nd+++bOyaGYRgVWFD5kfgqElQFocEX8pNizcuhEiE/UUkgLAxo355ul+faK8s3AEHlLSk9rtSDQ+VU/TPwCKHUtKl7NBIIIkH1zTe0HDkSuPdeur1+vX+qzTIMw9QDFlT+QpIQW04ny3OhwRfyc4TUTMihOniQlqIDCyALqtpCu6BSVpbXwoQcKmVxey2HSqxPgwdBJdoPmeRQnT5NS9fmzoKgEVRbttBy+HBqONikCX3Pt241d1wMwzAusKDyF6WlCK+lPnnnrAEO+elwqKwJ5uVQidJMymTo1q1paSmxiyM9gsoEh6q0FKipoduNG6tvExZGjzVF8Ib8vAkqsd50QSWEU69eFPa7+GK6v3ateWNiGIZRgQWVv8ilcF85IlFUq9IKxmSHKjTRnJBfTY2c1K08mQtBFVrhg0NlgqASH1dYmMe3GcnJQLonh8rkkF+DcKiKiuT4cM+etLzkElr++qs5Y2IYhtGgwQmqt99+G61bt0ZkZCR69+6NdevWaW67cOFCXHbZZUhOTkZcXBwGDhyI5cuXO20zd+5cWCwWt7+Kior6DdR+osxBCsorVJJUTC7sGdbEnJBfbi6FzUJCnENmophkRHUdcqhKSowboBfEx5WQoJ57JEhOVjhUngRVkDpUQSGotm+nZUaGnJjWqxct//jDlCExDMNo0aAE1fz58/Hggw/i8ccfx7Zt2zBkyBCMGTMGxzRaeKxduxaXXXYZli5dii1btmD48OG48sorsc2lOGBcXByys7Od/iIjI+s3WLtDlYtkqGozo0N+1dVyfR4dDlVEkjkhP3EiT0mhwp6C1q2BENQiymYXhUHuUAk9rEXTxAokIt9+RyXkJ3Ko8vNNSbAWn4PQda4EhaASv1PhTgFyjY3Dh4Oo6ijDMEwDE1SvvPIKpkyZgqlTp6JTp06YM2cOMjIy8I6yTo2COXPm4JFHHkHfvn3Rrl07PP/882jXrh2+E5157VgsFqSlpTn91Ru7Q6UpqMQZuaIC6hv4iDIupyOHKjLFnJCfljPSqhXQCIoTZJAKqny7RvImqNo2ohdaExqhnmzVuDEQGkq37eI7kOh1qPLyTJxQJ8J9nTrJ65KTZbdqz57Aj6k+7N4N/P3v8utiGOa8osEIqqqqKmzZsgWjRo1yWj9q1CisX79e1z5sNhuKi4uRmJjotL6kpAQtW7ZE8+bNMW7cODcHy5XKykoUFRU5/blhP0nmIEVdLylDWka4VEL1hIQA4eGqm0iSrJ+i08wJ+WmdyBs1AlomUuhOCgkB9DiELoKqshJ4803glluA334zasTO6HWoWoVTuK8wWqVKOkCfU6Bm+hUXU89DBeIptQRVkyam6j1CbToo4FJav4Gwaxc1rfz3v4HBg6lAKcMw5xUNRlCdPXsWtbW1SHWJUaSmpuK0OEt74eWXX0ZpaSkmTpzoWNexY0fMnTsXixcvxrx58xAZGYnBgwdjv4eryNmzZyM+Pt7xl+HauwNwCvmp9u61WuXSBUYIKvEkUVGayT0VFRQZBICYdIUYcTnZCvwR8vN0Iu/QjARVdWSs5wQlgYugev114K9/pU4lomSR0egO+VkoVpYXppI/JQiEoPr0U3J0Ro50fPiS5N2hsljkHDcxiSDgnE+C6vHHgXPn6HZuLvD00+aOh2EYw2kwgkpgcTnRSpLktk6NefPmYebMmZg/fz5SUuS6UAMGDMDNN9+M7t27Y8iQIfjyyy/Rvn17vPHGG5r7mjFjBgoLCx1/x9Ua3HoL+QHG5lHpqJKuNNIapdvFnCRpKqZAhvwAoG0qCarKMB3hPsBNUH3yifzQ5s3Azp11HaU2egVVqkSC6kyIB0Hl78T0bdvIrqusBFavBp56CgB93SornYeghoismeJQ1dYCR47QbS1BtXu302qbDfjsM+CXX/w/PJ84dw744Qe6/eGHtPz224BOpmAYxv80GEGVlJQEq9Xq5kbl5OS4uVauzJ8/H1OmTMGXX36JkSNHetw2JCQEffv29ehQRUREIC4uzunPDW8hP0DOEzLiwOpDyYTYWCAkOlKO6WjkUfkz5Kf2kbVoTMKoPMR3QfXHHxRVCQ8HRoyg1R99VM/BqqBXUCVVUsjvhE0lId2xkb3UuqgUajRfful8/623gKoqx2cQH+85siqGZ4pDdfw4OWphYXLfQ4GoAit6GNl5/nlg8mSKrD32WIDGqYevv6bX0r07cNttJBDLykhUMQxz3tBgBFV4eDh69+6NlStXOq1fuXIlBg0apPl/8+bNw2233Yb//e9/GDt2rNfnkSQJ27dvR7raVHdf8DbLDzA2qdqHop5xcaCYjpfn9+csPzWHqlk8Ccti6BRUikrpixfTzTFjgKlT6bbO1Dqf0Cuo4svIoTpS5eF7JGJq/hJU339Py88+IwVbVASsXetwnBRGrSqmOlQi3Ne6tfN0UEB2rA4fdsxs3bXLYcABAF56ybSKFO4Id2riRPrdXX893V+2zLwxMQxjOA1GUAHA9OnT8f777+PDDz/Enj178NBDD+HYsWO46667AFAo7pZbbnFsP2/ePNxyyy14+eWXMWDAAJw+fRqnT59GoSLENmvWLCxfvhyHDh3C9u3bMWXKFGzfvt2xzzqjrEOllkMFGFtHyYeQn4g0OnK4NBwqf+ZQqTlUaTH0PhTU6qhBBTgJQpGEPnw40KMH3d61SzM9rM7oFVTRRSSoDpalQ5I0NhKTI/whqI4coTfAaiWVOW4crV+82OE4afUiFJjqUGnlTwFUlyo0lKYf2svuf/45fdbjxgH9+lEB2U8/DeB4tZAkau4MAEOH0nLIEFpu2mTOmBiG8QsNSlBdf/31mDNnDp5++mn06NEDa9euxdKlS9GyZUsAQHZ2tlNNqv/85z+oqanBvffei/T0dMffAw884NimoKAAd955Jzp16oRRo0bh5MmTWLt2Lfr161f3gUqSPofKyF50yqR0DYSOdEQodQoqI3OohHZIVunGkxJNgiq/yseQX0kJNm+mm336ABddRKGssjLg0KF6DtgFvYIqIo9O9Mdrm2qnyAmHSiQrG4moJN6vHwm3K6+k+z/8oFtQmepQHT1KS1FCX4nVKleCPXgQkkRRNQC4+WbZoQwKQXXkCF1FhIXJRUnFsWX/fv+5kwzDBJxQswfgK/fccw/uuece1cfmzp3rdH/NmjVe9/fqq6/i1VdfNWBkCkpKHLWlcpGM8ECG/HQ4VA5B5eX5lTlUkqRv4p0nJEnWDi6VK2hdOI3jbGUMamvdIz1u2McvlZTgVLGEkBALevQg86JzZ+qru3MnCSyj0CuoQk6TQ5WNdOTmamzvz5Dfrl20FHadcEUOHEDJsXMAEoPboTpxgpau+VOCtm0ph+rQIfyROAwHDpCIHjuWri3uvJM++4IC75+VXxHuVI8e8sVOYiLQoQOQlUUu1RVXmDY8hmGMo0E5VA0G+yW9LTIKpWgU2KR0PzhUNps8K6w+FBfLxdzVal3GWew5VFKMvgrddkFlkSQ0QikyM+Uxd+tGS6Nn+ukSVFVVDhVyCk21HZ5ACCoxIy4x0aEsG+3+HYD+kJ8pDtXJk7TUElRt2tDy4EGI66bhw+knlZwsRwr9VY9MNxs30nLgQOf1AwY4Px4E7N8P6LgGZRhGAxZU/sB+BpKaJAOwoLwc6nk0JjlUenOolLsyIuwn3KmoKHXdF1ImktJjoVaJwo2oKCqQCSAWxejdW36oa1daGt3yTZegsmfeV1vCkIcm2nrJn4JK1Gjq0kVe17cvPe0hfYJKhPyC1qECgIMHHZMPBg+WHxZ6RRhEpiEUvfLLCQRdT8JPP6WvyvDhwF13GZ97yDAXAiyo/IE9IV1SJAqptu8wMindB4fKIai8CLqwMPoDjElM9xTuA+B4H0oQ4zifekQxUzEWxcjMlB8SYT6RimMEkqRTUNnttfyINAAWbUHiL0FVUkIz4ADZoQIcgqrZKd8cqoALKknyLqiEQ3X4sEM0KU0gcdt0A0gIW+XnAMDxZQ2C9jl5eRQiFceo//wHWLjQ3DExTEOEBZU/sDtUllR5Xrpq2M/IpHQdDpWvIT/A2Jl+XgWV/X0oQYw+hwpwElSiPBEAtGhBS42+2XWipES+cneIUjXsgqoohmpQaQoS8UZUVBib+S9O0qmpzqrJLqja5G8BoD4xQInSodKcqegPiorkL1yzZurb2D/g2qPHcewYGZXKeSTitpisYAo5OfTmWSzO/QgB+f6BA8bE0+vBe+/RV7BnT+DRR2md0WmlDHMhwILKH9gFVUhqsiOR22M/PyPLJvjiUPkgqIwM+elxqPQKKsmLoMrNhXbZCh8RujckxKNudUzlL4+nGlSaBlRsrFxc1UiXat8+Wnbs6LzeHv5LqT6JOBTqdqhqaowp5q8b4U41bqz9Rts/YOuZbISjEl27OvfTFgbQ2bMmts4ReWxt2ri/jqZN6fdXW2tqs2RJIkcKAB54ALj/fnKl168HduwwbVgM0yAxRFAVFRXhm2++wZ4gsK+DAnvIz5Kc7KhErXpSNzKHSkfIzy2HSsfzG1ktXa+g0p1DBaAmgs6icSh2KlmUkCCfYHW7XV5QzpL0OOPR7lBVNSFBpXlCVzbMM7J0gmjZIkoLCBIS6EQOoCP2ehVUERHyVySgienewn0AqT37j6sZTjpSkgSNGsmiOivLD2PUg1a4D6DPXqg+lxY6gWTfPvq6REQAEyYA6elUtgyQ68IyDKOPOgmqiRMn4s033wQAlJeXo0+fPpg4cSK6deuGBQsWGDrABomiFLUQVB5DfmblUAVbyM/XHCoApSF0xm+TXIyICHm9xWJ82M+t7IQWdkFVm+Yl5Af4J49KJI7Z67MpsXWgUFMmdnsVVIBJeVR6BJXiA87AccesTiXCoNu71+DxuTB/PpVrePllufk4AM+CCpAFlYlNnn/8kZaDBskXT0JQcSF3hvGNOgmqtWvXYoi9rs2iRYsgSRIKCgrw+uuv49lnnzV0gA0SIagUDpXHkJ8RDpV4goYc8qtDDlWhjd7D1snuotRoQSU+Jq+Cyh7yszbz4lAB/hVUrg4VgLLWdBLvYtntOQ/MjinFPUXJBK38KUFGBgCgBY6ZJqh+/536By5dCjz8MPDMM4oHtUKvgg4daGliyE8IKtH/EgAuv5yWGzbIkzAYhvFOnQRVYWEhEu1nxWXLlmH8+PGIjo7G2LFjPTYVvmCwh/yQnOzQN35PSq9LHSodgs4shyo72+VqX4O8KnoNorGyEn85VOJt08TuUIW3IofKo1byR/sZDw5VQToJqm5hu0XFCY+Y4lCJho9e+mlWp9MHbKageuAB5+/piy8qqvOL5s3t2qn/s6L0gxlIEvDzz3RbKahatSKtV1sLrF1rytDqzKlTVIlCz7GDYYymToIqIyMDGzZsQGlpKZYtW4ZRo0YBAPLz8xHpqX39hYJKyM9jDpWRIT8P739dHCozcqgqQmMhSQ6jxyNnyuk9bBYXOEGlN+QX1UaHQyUqnBplBUiS/IJVBFVOEgmqTpK+vB1TalF56qCt3CycPuBO0cdUw5diIp2/UjuPHiUXx2Kh7+qll9KEvffeA/0ehc0apILq6FHS8cquOAJR06shtRv85RegfXsq6tuzp386OjGMJ+okqB588EHcdNNNaN68OdLT0zFs2DAAFArsKioqXqhIkpNDpSvkV1JS/3npXkJ+NptsRAV7DlVMGjl3evKoThXRe5gS5S6o7BEh3flY3tAV8qupcXz+cR1IUJ0756FQoihoZZSgEtMaLRb5DVBwLIYEVfPqI7o+VFOqpQtBpdZBW8HhWhJUHaLUFbOyFpmo0G8kon/g0KFkpol+6vPmAdJBu00VHy+HdV0RgiovL8DTKImtW2nZpQsQHu78WBAWcvdIXh41xhZf6T//pNpaDBNI6iSo7rnnHmzYsAEffvghfv31V4TYYwdt2rThHKqSErmujDdBJUJ+Nlv95/Z7Cfkpo3pugkpHyM/vOVQ1NY7X0DiD3hdveVS1tcDxAhJUTcLdX4M4H585U6fhuqEr5HfmDIljqxWN25O9Y7N50EtGO1Qi3Jee7n6WBHCqKgk5sNtOOqa/mRLyEx+YF4dqdwkJquZQ/6Kkp5P7UlMjp2UZyTff0HLCBFqOG0c/6aNHgazv7akP7dppTwmNjZUtQKO7eOtg2zZaurpTgCyofvvNP2LUaN56izRp167AunVUjWTBAlk0MkwgqHPZhD59+mDs2LE4efIkampqAABjx47FYGX/hwsRcTKIjgZiYjznUEVHywfb+uZReQn5iQvg8HDFJkIZlJXRWUdjiIAxDlV+Pi1Vq4wrniCxhT5Bdfw4UFBL2zaCe9jUX4LKo0Ml4pRpaQiLCHGIV01BIt4M8ebUFxHuE/FOF3Jzgd3QP10/4EnpkqQ75Lcll15jk5Kjqg6v1Sq/DaKShFFUVVFCOgBcdhkto6KAq66i24dW2vOnvHXmNjHsJ8SGmqDKzCRxWFLi8jWZPx/o3p0S1ExvlEhUVACvv063H38cuPhiYPx4uv/ee+aNi7nwqJOgKisrw5QpUxAdHY3OnTvjmP0gfv/99+Nf//qXoQNscLicDDzmUIWEyBZQffOovIT83BLSAWerRUPQGRnyc8vhUiJef2go0lpS/QNvgmrfPqpZBQAhJdoOVW6uMVfZugSV6OpsT6gW0R6vgsooh0o8v8YMubNnfRNUbg5VeTlZMa1bA598Us/BqlBSItuhXkJ+vx6lsgrhlSWaIbPWrWlptKDasYOMaEXPaQDA6NG0rPhD4VB5wkRBJRyqnj3dH7Na5faDYjusWQPcdBP1J8zKovoKosWRiaxcSSG/Zs1kITVtGi0//9y4wr71pqqKevp8951GLzKmoVMnQTVjxgzs2LEDa9ascUpCHzlyJObPn2/Y4BokGoJK1aECjCud4CXk51bUE3C2q7wIqvqG/GpqZFHmUVDFxCCjBbl23nKf9u2jGYFO/68gOZkMQJvNmEl0unKohKCxF9D0GjLzl6DSmCHnq6Byc6juuANYsoQUyq23UiawkYjfT0yMc+lzF8rLgb3HopEL+xusMfNAVI4wWlCJZO3+/Z0jeiNH0jI21x7CU1abVUM8HuCQ37lz8ldFK+1VzJzcuRP0I7rrLroyGTqUHKpz54AXXgjIeD0h+g5ee63ceGD4cBJYxcVBMlOxqIiszPHjycYcOjSIlB5jFHUSVN988w3efPNNXHzxxbAojiaZmZk4aNKMlaDBJf/Dq6AyygLSGfJzEzNC0GkkphsV8lPuXlVQCbUSE+PIpfbFoVIThKGhskNkRNhPVw6VCPnZBY0QVJqCzugcKh2Cag/s0990FJR0EoR79wJffEErRDhOxFqMQmdC+r59FOU7afU8lVMIKqONFJGsLXKNBE2bUrisFY7QCmGRaSFmYhrZdFIHopRERoa2bnUSVEuWkCsVHw8sXiz3q/nkk8BNp8vJcbOaa2poOIDsTgFk/ot6WsuXB2Z4HnnySWdlt3Ej9flhzivqJKhyc3ORkpLitr60tNRJYF2QuDhUHnOoAOMsIJ0hPzcx42Wmn1F6Tzx/VBQlCrshHKbYWJ8ElSeHCjA2j8qnkJ+vDpVROVRe8o+cHKpDhzx8MQnhUBUVAbXv/JfuXHUVsGIF3V64UF99C73ozJ8SpRAK4/UJKqMdKpF/ZO837cSIYbVoCe3iqk6IJC8xmSBAiPdPq+YoIDtXf/wB4LXX6M7//R/9AIYMoVyq8nJZZPuLkycpLpmaSo6eSF4D3Tx3jkKvrum7QlCZXvH90CHg7bfp9vLlwE8/0e0PP5RrlTHnBXUSVH379sWSJUsc94WIeu+99zBw4EBjRtZQ8SWHCjDGApIkryG/+gqq+uo9j/lTgFPIT3QcOXNGnjCpxv79nh0qwFhB5VPIry45VPUtnaHy/K6cPQucRhpqYhMojOOlEG98POXTWGADPrXnTP3f/9HZtk8fcgxEuW0j0CmohMNSnWYXJBrq2x8OVVWV/LaphctGds5GOKpRg1Dv1d6VDpURn79OhKAStbrU6NyZwpnS6dOQVq+mlaI2hMUC3HAD3V661H8Dra0FbrwR2L6d7h89Srlb9h+UGNawYXK4TzByJDlVe/b4Z5anbt55hyqNjhwJjBpF8cixY+n3d6HnHJ9n1ElQzZ49G48//jjuvvtu1NTU4LXXXsNll12GuXPn4rnnnjN6jA0Ll5CF7pBffRRLdbVc6MhLyM9NDHjJ4TIq5OdVUClCfoq+t5rmR1UVuQ56HSpRGqw+1Cfk51VQVVcbk1PhQVBJkhiHBTXt9eVRhYSQKOyOHbDm59GLtxfyhb3+HNatq/+4BUL5egn5CUEQ2sazQyUMoJMnPdQC85EDByjUFBurrpcGpJJ6O4oWKC6zet6ZuHooLTXOpdSBEKSeBFVMDBlC12IhLDYb0K+fcwhTNP376SevTmedWbCAQmWxsVRFtWtXip//4x8AZEE1fLj7vyYkkIkGAOvX+2d4XqmtBf73P7p9zz3y+kcfpeUXXxgz44cJCuokqAYNGoRff/0VZWVlaNu2LVasWIHU1FRs2LABvcXUkAsVX5PSjVAsyp37kpQOBDzkp8ehsljk84xW2O/QITpB2qI9F0cNlpCfZg5VTAxZQED986hqa+XscRWHp6xM/qpYu+hPTG/SBBiGNXRnyBDZCrD38zQ061coX5WUAiVCEMR28RwyS0sjM6WmxrjSD+Ity8xULzGVUnYEAHAYrb0XxoyKkl9rAPOo9IT8AMqjug72CqYTJzo/2LUrKcrycrmHjdG88w4tH3yQEtbefZfuf/wxKvcfc8yJUBNUADV9BkiLmcLq1XSR1bgxcMUV8vrBg4E2bejA+u23Jg2uDkgSsGsXhfyNKE54nlHnOlRdu3bFxx9/jF27dmH37t347LPPuEo64JaUHpAcKqWzERGhukmDCfnZ7R9veVSi72zqRXZBpVEcVZyrAhLyq62Vn0hvyM9iMW6mX04OvQ8hIXLykwIhKCIigNBuvpVOGA4VK+Dii2mZlWWMBagcpMr4BbW1ck3StD6evyhhYfJ3wKhUL/GWabo79vjiEbTSNwlShP0ClEcl3F1A7s+sRe/2xRgCuwN59dXOD1osslvpD0GVlUWlGkJC5DoIgwZRj5/aWuQ98zYqKuj7mZmpvguRgWKaQyXE0vjxzsdmiwW4+Wa6LRysYKeqimb2du1K9UE6dXLKZ2PqKKisVityVA6geXl5sFq9WNznMypFCQOSQ6Wc4acxKcDrLD8/16HyxaEC5HOMVjKxyGHJ6OC5OKpRDlVNjSwqNQWVUtDYz+K6Ko0blZgu3LHUVNn1UiDGkJQEWDr7UDohsRaXwO5CKQVVYqKsKjZvruuonVH0wdTiyBHKrYuIANL6KWJ6GsXGRFjOqDwa4e5oncTFl/YwWusTVEY3nfTC0aP0NY2O9pqqhuFYjTDU4HhEW/USEMIC8kePGjF9b9Qo5zZK9tlxjRe+j1BUu5WuUBve1q0mVSkQUwzHjnV/TDh+q1Y1jLDf9OnAp5/S7YgI+r5ec02A2ygEN3USVJJG8mRlZSXCVdpdXDDk58ttzu0nhIDkUHlJSAfq7lAJved3h0qRQwWQGw5ol+cRgqp9B4s871slj8qo1ilKraaZQ6UiaLyG/ADjHCodM/wAu/kj1MC+ffJ3VoMelu1IQCEqIuOBHj2cHxSutI4SDLpQ9MHUQoT7OnQArM3SyIaqrZXffxf8Jaj0OFQbN3p9ewMuqMRvqk0bbSEi6HScBMGS6tHqzRT82aNGJLu7ipFx44CUFESV5mEEfkS/ftq7aNWKvkrV1RSpCigHD9KBKjSUXDVXMjMpJ62y0mliR0kJsGgRmX6BbPtz5gzNiBSHESd++YX6+wDUcyk3l36AJ08CjzwSuEF6oLISePhh+sw7dQLmzg3oPA8APgqq119/Ha+//josFgvef/99x/3XX38dr776Ku6991509BaUP58RR2xFVnVAc6g8CCrN/B8fBFV9vpy+OlTeBJUI+bVrJ/+PmkOlS9DoQLw9ERGqLfIIlYRw8fznznk4OBpVi0qnoEpKAiWpxcSQ9eZl6navAgr3HWh6ibvz1aULLY0SVDpCfuKpOnUCuYEi4U5DkBgpqCRJLmquWQTd7lDlxbZGWZk8QU0TEwWVN+I3kqBaahut/jXp1ImuMEpLjVUsRUVy0ViR/C6wWoHrrgMAXI/5HgWVxSJXgndUfA8UK1fScvBgdVvbYiFxCADffw+AdGmrVlSkdNgw4JJL6t9EQw+vv05fwzFj6Of04osux/vHHqPlHXcAf/kLfeYffUTrPvnE9Ir5NTWUovbyy+TA7t0L3H574CdR+iSoXn31Vbz66quQJAnvvvuu4/6rr76Kd999F2VlZXhXJA1eiIg8DhGvQoBzqOriUOmc5Vdbq+NK2wO+5lCJg71WnVjhULVrJ/+P2pFH5DDVV1DpKpkgknTsCemA3AjaY4NkoxwqL2LESVBZLLJL5SXs1+k0CaodjVUyfzt3pqURJ9OqKvk98BDyE4JKPLU3QSI+DiNyqHJz6WtmsWiUmKqpcRwHUvvTBl7Dfio5VCLPW+M6p17oFlQHD8Jy8CBqLKFYg2FU4NMVqxUORWNk2G/NGnov27VTDTUWX3E9AOAaLELfbh5qq0A2Vb0KW6MRkzXU3CmBcN9++AEHD0gYNYqOVcnJdOxdv56qRvjTaVm4EHjgAfr5JSTQsf4f/1DU7P31V5rJGxYGPP20/I8DB1I4trYW+Pe//TdAHTz3HE02jYkhZ+rxx2n9Y4/JujYQ+CSoDh8+jMOHD2Po0KHYsWOH4/7hw4eRlZWF5cuXo3///v4aa/AjBJWiMW3Ac6g0qG/ID6if5vPVoRLH0BMn3GtRlZXJbWnat4dHh0oIqsJCzf7P7lRXu/Xa0lUyQcWhCgtD4Bok+yKoAH2CqqYGzY9QUvKGiGHujwuHavfu+tclEAMMCZGVqApugkrk1wTAoRJipHlzjfkfx4/TCSYiApmXklP4669eduoiCFevJhE4bBhFVEURUaPQLajsxVsPpg5CMeK0Tcg+fWhppGIRWeRDh6o+vDH0YpxEUySgEE22rPC4K+FQBVRQSZJcTkTMhlXjkkvoi3TiBGbdtA+FhRRFPXSIBEJ4OLX+81e199JS4N576fYDD9Ah6Pnn6f4jj9iLus6ZQytuvdW9TsjDD9Pyf//zX+kMLxw/Lo/53XdpmM8+C9x9N6277z7P9QyNpE45VKtXr0ZjEaZgZMRZXkVQGeFQrVhBlZk7dnSJsOgI+dVVUIWFyVEevwoqIYbs70dyMt2UJPfJTyL00LixXTB5cKgaN5bzRHR1yDhzhj6/2Fjg7393rK5LY2SB17CjGQ4VICcBeRJUW7civKIY59AYm6u7uz/eti2dEMrLfbb93fSvGH+TJiSqVLDZ5BwmvQ6VkYJKOKaaLfrELIqWLXHxJfQafvnFi8Mgxp+djSNZlRg/Xv4qHDsGXH99/dxhV3QLKvtZPLcXdXzWNCFFsacdO+o/OIEQVCKr3IXfNofgK0ygO176xwqHaseOAOYkHT1K54PQUGr4qEVUlKPEe+xvqxAdTQ2dY2Lo3+67jzabMcM/LtUbb1CmQOvWFOYDqETWlVfSNeVTd+dAEjMV//pX9x1ceild0BQUmFb+4dlnaazDhpGbJ5g9m9JZ9+0DPvggMGOpc9mEEydO4O2338ajjz6K6dOnO/1dsAhBpQj5GZlD9c9/0mSqrCy5EwQAryE/SfIgaMQKsYELFosxiem+hvwsFu08Kqf8KcCjQ2W1ynpFV9jvxRfpCFNVRTa2vQtuXUN+gCxgNCsLGJVD5Q+Hyl458WcMRW6eyuHCapXn3otscS/YbHIHk2HDFDM5dc7wKyujK3eHqAmgoPIqRoSobN0affqQ1jxzRjt0DYA+EPtv99WHTyI/n6Jo2dn0Vhw4APz3v/UfO0DHAl2CqqrK0SIl9Aovgkoolp07jameWlUlT8fXElS/AfNBYT98+61Hd6RdOzqGlZUFsNOLiPP27u1s86sgjbwMAHAZVuJvf3P+XB57jL4a27cbP5GypkYO682cKeeGWiwktCIigNa/fgpLdTVdyYvmjkqsVrKEAFPKP+TmUogPoGikcpJFfDydMwHgpZeMvSjRok6C6scff0SHDh3w9ttv4+WXX8bq1avx0Ucf4cMPP8T2gAeqgwhxQFc4VELjaIb8dDpUZ886z0xfsEDxBfES8isvl6/M3ASBDnckIIJKKBZFTE2cMF27ozhm+LWH8/9oZG96rQUlOHdOLiQoPhf7ZVt9HCqhDzQLSxpZh0r5hC6I53cIKmHx7N3rFuJ0sGYNLTBMW5CKD0prBoELzz0nC4SffwZuu81+9a1jhp9wZjt2VLQa0ZlDde5c/afOC2GkKUaEOmzVChERcq8/j3lUFovjNez8nuzY//yH5hY8+SRt8vrrdXAoysqAr74CfvjBIXTOnZO/yx7bDG7YQL/J5GS0+AvFzA4c0NAt7drRsae01Ity1Mn27fREiYmKH7mMJJGg2ogBqEzJoN+96C2pgtUqa4GAnZ6E+tEQhEo2NBoJgEpUPHCvc15CkyZydYX33jN0hPjhBzpkJSUBkyY5P9ayJYUAb8LnAADp9ju0d2SfIICVKwNe7POjj+jQ1bevemR1yhQ6HB45QudMJ6qqDLcs6ySoZsyYgb/97W/YtWsXIiMjsWDBAhw/fhxDhw7FhAkTDB1gg6IuIT+damXVKjqQZGbSF+TcOcVMW52NkUNCVDrLB1hQaQoSFUElIlKuuRviStlRlNCDQwX4kJi+Zg2dcTt1cjhT+PZboLCwzjlUgKwPvAqqQOdQtWpF7lhVlbr9UF3tyANZjeEoLNS4yvNBUFVV0dUvQOkX0dEkqubP9z5+QCV/CpB/bxrFPRs3ln+HGpUVdCNeombIT+FQAXLtU6+J6fbX0ALHcN11sulzyy009n37fIyonTlDB4uJE2n607BhQFmZY/xNm3rMEJCTdi67DOnNQtC4MWkyVRMyNFQun2FE2E9cOWoUmDp5kkxkq9WCkAnjaeVXX3ncZcAT04XD5mkKop0ZX/ZEHhIRjyI0Oexez23qVFp++aWxtbQ+/JCWt96qPnP50Wuy0AvbUI1QrIi7TntH3bqRAisvN7avpxckiS48ADlfypWoKLn9pMPlXb2awqwREXTusNc1M4I6Cao9e/bgVrvNFxoaivLycsTExODpp5/GCy+8YNjgGhxi2npdcqi8hPzETIUrrpBn2jpaqOlsjBwXp3J8Eifz4mLNrG1NQVVRoTvbzyWip72BQvE5dbtXIA6KItnUm0Olu3SCyB4eOpTO2O3b0xXM6tXeQ341NfLZWkzjtyMMI82Qn1k5VBYLhSQA9cKcmzcDpaWQmjTBn6Dkc9U8NG9TMhV8+y0NMz2dchzsLdlIZOkI+akKKpGUfu6c6nfAYjEu7KcwoNQRgsq+gV5BVZlGaQItcAwPPiivj42VO5Z4SRWSkSSaM370KP14w8LoYPF//+dz/hRGj4bFoqM6hrCAVKcC+ojIwu/VS/Xh336jZdeuQNgN9hP94sUej0VCUBldOmHPHuChh4A773RESOmqQRykhEWpwdq1wNpfrVhjsc8EVJmSNngwnVJKSz0acT5RXEwOFUCiXY3GK+gLtxKX4bFXkrQdUosFuOoqui2KsQaA9evpAic2lvIMtZgyhcyE1auBnMdeBUaMkHP0KiqAjz82bEx1ElSNGjVCpf3L27RpUxxUHEjPXuhVU5W9uWCcQyWOU4MHy0LCcTHoJeTnMdymXOlLcc+iIoq7NGnitdiHJKkaUM6obCCO0bt2yeGOsjL5KtkhqIxyqISgsieJ4jLKbcDKld5DfmfOkPgKDXUTBF4FlRE5VKWl8vdARVDZbPLrdwgqQJ6htWWL+z7t4T7L0KFo3IQOFao/bx8cqnnzaHn77fRW3XknhWTWrwfOZekP+TkJqrg4+Xus4VIZIahqa+U0OcU1kzNCUNkVi4j4ZGV57iW4NY922LPxUbcokQj56D5XrV1LZ8uICHJaV66kN/mzz2BbvlI5PHVycmRRY28t47U6hsjHEzMG6oNQPY4fuDNCUPXrB5q637QpHY9WrdLcpZtDdfYsJVl360ZtVOowlXLxYtJ8c+ZQOG7ECNqlbccfJKoSE70qVzE7reJiCvupvQaLhQqSA1Tw0wh++IH050UXyReuTkiS48e6KHwStm4FlizxsEMhqL77zrgu5K7jcVF0n31Gy/HjPaepifpa9+AtpMyeTvuZMoV+qz/95L1dgA/USVANGDAAv9pPPmPHjsXf/vY3PPfcc7jjjjswQFTOvVDp2tXJBlLmUKkqfB1J6ZLkXHdJTKpxXAx6CflpNkYG6OpVjEHjhK4qqD74gK6AS0tpCooHd6K8XP6NqQqqyko5lqTYoH17Gl5RkZwe88cftK+UFEVkzUstLV2CqqxMFhXCVhCCasUK74JKhHubNnUrfin0gV8dKmWjPre4LolqkS6gKqjUHKrVcv8+j++hcvaAh0Qfm01u+XbllbRMS5Nvn9rh2WGrrVWZ4SfQmUdVH0GVnS1rZtHSyImKCllx2UN+jRvL7o5W+QRJAhZvo/H3STvu5iKPGEHL3bt1tkx8+21a3nYbPfnQoY7pYkO++RsssHk+zwuXpEcPx8lGvAZNQSXi8/UVVMrwsxeHql8/kPUw3h72+/przd127UqbnjkDnNl6kv75zTfpgLJiBdUq8KHJ99Gj5OxUVNDbe/vtdNh/803g2yfs4b4+fTyWot+8mYxAqxW45Bn7sWbDBlWX9dprabl4sTFpPwsX0nL8eI0h/vEHXblGRKDp3X8BQInrmj/vSy6hg+OZM8b29ysrA/72N/rBRUTQ5/Ttt6iuphAoANx0k/fdPN5/FV4HhfZqHnsSeP99cpGHDze0JkWdBNUrr7ziqDc1c+ZMXHbZZZg/fz5atmyJDwI1PzFYEWrHjtI0Us371ZGUfu6c7DK1aSNfURw/bk+78SHkp4qXE7rbEGtrFVXf7HiYMqs8Poh9OaEUQgoxEBYmH6dF2E958eo4EHhoPQPoFFQ7d1LYLi1NnqU5bBg9yYEDjjOZpsMmnBGXcB/gY8ivrld3ynCfyhFSOEuxsS71k0RIYudO58+hokJWAMOHe27h07Ilna3KyzX6VhB//knf5UaN5EgjANxwg/0pj3sO+R0+TMOKjFS58Nc5068+xT3FR6yimQkRD4yJkb90kJNltQoMrl0LbMym8TerdR9/UpLs1tpNQ21yc+WzpTKx5Mkngfh4NM//A9dioWdBpQj3CbyG/MQPdd8+Hwq+qbB7t1xhUiWuWlsra39HepJIiv7mG81QQHQ0XaCFoxJRE8bSl6lNGwr3jB5NF3Q33KCztgqd40XNqJUrKR9J5CSdW24XFF7CfcKduvFGIGNoGxLh1dWqwm7wYLogzs9XN5N9oaZG/oj/8heNjb74gpZXXIH7Ho9HdDQ9r+gG5EZ4uFzR3qjyCYWF9MJfeYW+19XV5LhefTVyrpyC4nNVSE52bi+qyuHDGDDnelhhw1zciq+7zHR+3OPsDN+ok6Bq06YNutl/4dHR0Xj77bexc+dOLFy4EC0VJQP8wdtvv43WrVsjMjISvXv3xjpHIpE6P//8M3r37o3IyEi0adNGtZL7ggULkJmZiYiICGRmZmJRfXxVD4JK9bcu7J+KCs1LDzHVt3lz0kwJCfI5f+dO1C/kB3gVVG4O1a5ddPKIiQFEztw332js3LnElGp5ISGEIiMVU7cI8XaKHHExecbp4lWnQ+UxGp2VRcvMTFmQxMc7Mt/TTtJRzKtDVR9BZbPVvc+ElxlyyhJPTrRoQSeWmhrZPgLICi8rIyWSmelZlIaFyYLGg1MpxMCgQfQvgjFjSOTFVXh+DcoZfm6CJgClE4SgUvbpdUKZkK4QtaIY9uLF6lf4774LHAON33rimOpG4qQhTENNli6lz7JHD+djUWIiTdsC8E88izatNayGmho5uUYhqIQjePiwxle0RQs6UFRX657tqYrIY+jeXfXCICuLfubR0YpeioMH0++uoMBjXLRbN+BJPI24QzvoO/bTT2Qzff01qa1Tp6h3iRf+/FOeMfbee/J3+bbbSMP2BQmqc221BdWuXXL47tFH7StHaof9rFa54LqHyKYufvuN3qrGjTVy5hXhPkyahORkuR6WR5dKWM3i+wN6r954g77jPpWps9lI4G7fTgfQb74hsf6PfwAhIWi2/EN8hysx6cpS9YsbQVERcNVVsJw7hxNN++IuvIsPPvTSwLIe1FlQ5akcWQsKCtBGT4OoOjJ//nw8+OCDePzxx7Ft2zYMGTIEY8aMwTGNg+jhw4dxxRVXYMiQIdi2bRsee+wx3H///VigmD+5YcMGXH/99Zg8eTJ27NiByZMnY+LEidgkzuC+4tI8NjxcPi6oCiqlZaMxhUMIqosuktc5hf10zvLzKqg0alG5RSU3bKDlgAHyfNtff9UUNCr55s54SLAS4Y5ly+g3Jq6QRDTOacdeHCqPF59CUDmmDtqxh8Ra5NBlsVdBpXK2Ffrg7FkNAyoqSraNXEStzUYOutcZlk6dj93xmO9tz5NxslDEVeZVVwEWi/cm00Lha+QwAXJitmvx69hY+jyT4TnkJ9xJtXI4QSWoXI6BI0bQz/zECfdUnZwcOjmfgF2Il5WpflGHDaOll+tHOdFFnNwUVN11P4oRgx7YgU6HNBJi1qyhDzkpyWkeelKSHOZULVsWEkJKV3MDnQjVrJrYIx96+vZVXHtZraRmANkmUuHyxN/wKOz5nu++K39nY2Lkqpavv+412VJorvHjZedO8MozpegMeg3/935fzfCcaI03frycfqbM2VRD6K36tlJZtoyWo0ZpOK0bN8oXzPbvkZiRu3mzB5dq1Cg62W3fjoI92bjhBnp/7r+fhGb79sATT+gs//HxxyTMoqLoCf/yF8p3+de/ULt4CUoRjdFYgafXj9D+vGpq6Py0axeQng7p64WoRCRWrVLUvjOYOgmqI0eOoFblm1JZWYmTRrV0V+GVV17BlClTMHXqVHTq1Alz5sxBRkYG3hG1g1x499130aJFC8yZMwedOnXC1KlTcccdd+Dfir5Dc+bMwWWXXYYZM2agY8eOmDFjBkaMGIE5oty+r7gc7S0WL+1nIiNlxaWRRyXyp5SCSpz3DxyA15CfOD5rdvMQSkuvQyVsooED6USWluZcwtqFuiSkC8RF8ubNdK7IzSVRI9KcnP5PQ9CJnG+PVQm0BJU9NtW2YLPn1+DBoRL6oLbWwxg0XMJ77qGryPR0L1emqhnnMh4nAIoD+fLldLSrraXkUsARE/AaNhUqw4Og8jTxafyVVWiMArqjEfJzyp1xxUvpBCNyqDxoZkI4M/b8KUFkpPw9dp2p95//kKnTs3+ErFhURKEotr1njwcTs7pajuWIqcAKjpU2wdu4BwDQ+O1n1c9sIjHl2mvd3GIhHjQn8hmRRyXyp9yS5AjloceJ22+n5YoV6rUdystx3fe3wgoblibcKCclCa66ii6GS0rkSpEqlJTIb5FyNqYgcs82WGHDKUtTfL2+KZ55xn2bVavo52W1Uk02B5deSueCXbtUQ+fiZ7p+ff06lalEdJ0RBTqvucZxTklOllvUzJqlIYqSkx0XoC+NXI4vviCdPWoUmYg1NVTV3G6UalNYSH1vAKrWqcwPALCu0eUYgR9xzpKIhL2bSPiLH6fAZqMBC1G2eDEyBjZ3XKCLvs5G45OgWrx4MRbbLdXly5c77i9evBiLFi3CM888g1YGxiOVVFVVYcuWLRglrqbtjBo1CuvFFEgXNmzY4Lb96NGjsXnzZlTbk6C1ttHap0cuvlj1jOtxpp+OUuRqDpWYWOUkqDRCfl4Fla8hP6VDBXjNWPUqqDxYWGlp8mQfMZFk9GjnkJE3h0pXzrcXh6pTqU6HSkVQhYfLY/Aa9lMorm++keusFBXZZxBppVgJpaPxIXuMCF56KTlkWVnkNC5cSBnYTZo4rBGvDpV43a4HNjulpfKFgUtUHABw5UDacS1CcKTI/TWIYo6AhqASboNGXEGZQ+V2MpAkSlKdOdNjk2efQn4uTJ5Myw8/lI8DxcVym7QHHoBHly09nd5im83DhLTff6cvSlKSPNlAwaFDwMv4G8otUbBs2uSu0EtL5XpOYmqhAmULF1W8CKraWsrD0fiKEOL9d7V+7IhDj5ugatMGuPpq+izVSvc88QRiT+zFKaTj9pI33OupWSxyztn772vaKAsW0NvUrp08GdgJe0J2bS+6anj6aeec5/x84A57jcy773Y53DRpIucyqNRzuugi+opUVXlwKmtr6cEvvlAVloWFcg6ak8svqKmRVb+yjwtkl+r334Hvv1d/+jM9LwcAdD21DM2aUarG8uXkTn/wgVyFXczQU+X11+lA07Gjqvr6+mtgEwbglavX0Q97zx46Fy1cSBcV+/fTyeK//6Un/OQTx+9hyhTax0cf+acNkU+C6uqrr8bVV18Ni8WCW2+91XH/6quvxqRJk7By5Uq8rCMGXRfOnj2L2tpapLpMr0lNTcVpjUTY06dPq25fU1PjKO+gtY3WPgFy4oqKipz+AABPPaW6fX3bz4hedkqtKgTVwYPwGvIT52hDBFV+vtz7RVw2i6tJjYzV+oT8APlkJBAHJAf1dahqa2XV6iqoevQAQkKQVnsK6TilLag8JKUDdZvpJ9oL3XknCbm9ez2kqglB5ZYkRXgM+SUkyG/yk0/SZSRAV3j2UGR9Hao//qBzVGqq+gy5JjYaYB6a4JvF7oelw4fpucPDNUJ+Isx2/Lhq9VExI7Sy0uV7IEnUB2faNLr07tVLTthzoa4hP4AMoxYt6DUIA+Rf/6KLnfbt7frFS9hSOHuak6hETPWSS1STFQ8dAnKRghWt7qQVzzzjLBw+/ZS+fxddpJrpq1tQqYT8du8mId2nD71/d9yhMkmnqEh+k1UcqsJCedeqk8lnzJBfh3LW6tKljjjdg1H/RU5NouMQ5sSkSXSw27tXs8+LMG9uvVVjdpz9w8m4pi+mTKG39+qrKddqwwYSMceP0/F79myV//cQ17NYZBGk6lbv3Usf0iWXUP5Rp07AzTc7HRfXriVR3q6dxqHqxx/pYJGUJOdb2ElJkXOp7ryTJvQpWbsWuPlzSkwfY12BDb/UOun6O+6gkB9A+3H9fwA01ldeodszZ7pcOdOhWsy5GDQ1ky4AO3Qg63n8eDpAtG9P4YywMPrAxKQFkOnWuDF9BvXNRVPDJ0Fls9lgs9nQokUL5OTkOO7bbDZUVlYiKysL41SsZiOxuHyLJUlyW+dte9f1vu5z9uzZiI+Pd/xliCOsRlXc+jZIFvUilS3ihFt1+DAgldUz5OeLoBKiKSNDPst6EVT1CfkB9hYI9qmxTz0FXH65ywZeHCohqEpLNSp9Hz1KZ9qICPcCQzExkOwnit7Yoi6oqqvlWJJGgSIhIlQPIspB2j+DkyflHPHHHqPQH0CNU1XRKag0SzxNn05H7NWrKabTqJF89ET9HSplrrGnAeYgxXHAVCI0To8eLrMUBWlp9EOrrVUVJJGR8vffqVr6ihVyTw+RVD1pkmp8vj4OVWgoFYAEqOf2gw/K5duee86ey+JFUInDi3Dq3BC2hVM8XEZEJLcM/zudeNatkx2p8nI5Oeivf1UVZMpaTqpOqUgG2rvXaYOcHJp4oDw8fPQRaVgnxAZNm8q/BwW//UYCpU0bjQuDfv2owmNtLQmJ48dJIIg8z3vvxaledH5SDVvGxckFn9z6lNBPUxTv1GwIItRu3754801KQaqoIAEyaBA5dElJlJCueoGpTExXcck09db+/fS579pFKRyiZMPnn5MDbb/oF+MXCe5uCOto4kQ3MQPQ9VZmJkUkR4ygj7q8nEylUaOA1aV9URTaGPG1+cg47a78n3ySInjKqJ7b8xcUkChSCCHBL7/Q7zchwa73WrYk8Txjhnxws1rpJLF1q1tPnchI+drxzTc13oN64JOg2rRpE3744QccPnwYSfYj7CeffILWrVsjJSUFd955p6Pgp9EkJSXBarW6OUc5OTluDpMgLS1NdfvQ0FA0sZ94tLbR2idArXcKCwsdf8c95I0AOvr5eXGoxPCU9ccyMuj7XlUFVBUGUFAJG9kxxQZeq/7pdqg0NggJoYvOkyfposUNIcSU9awUKJPxVV+i0llQydK09aTLrD7YrK75jh6lg3hUlFvbGYFYrdn6xCXk99VXdDwdNIiOGWJ68+rVGla1F0HltU1ep05kjcfE0BF/yRKnjevrUAlB5TJnw22AuUjGL7+4O3kiAqIaZgE8d9O2Iz4DR+kESZKnWE2fTg80a0YZq2LauJ2qKvl3qCqo8vPlL5dG2sNf/0rpHiUl5D7abOR0OM4bXgSVuNpXDfnZbHKZCy+Cqkm3ZvLrvuceUip//zu5tOnpcj6SCx06kJgtLtZI6m3blpRjaamTsH7oIXpJ7drR57pkCf2mP/kEzuJZZ7jPY6nDN9+k15CVRe/nyJE04OHDgZdfduS6a+aBCUH1zTdugmbJEoqIZWaqthik74Bwuvv0QWQk6bIXX6ShxMTQ7n/9VTPnnj67yEg62KmE7IRptHOn4jdSUUEhrrw8Uit795Kw+/VX+uFu3kxfNJvNs6DKy5MFtkb59EaN6DNr2pT0b6dOdH544AE6/I77Syii/2K30RSz/QRWq1wm7dNPXa7BJUl+8N57VY/FIhp5zTWKC6uYGKpBkZ1NfwUF9Nwa3yORC7ZkiTGtJ5X4JKieeuop7FR8E//44w9MmTIFI0eOxKOPPorvvvsOs1V9zPoTHh6O3r17Y6WLNF+5ciUGaTSgHDhwoNv2K1asQJ8+fRBmV99a22jtEwAiIiIQFxfn9OeJ+jhUxcWyzlKeq61W+bjtEFR1zaHyJSld5EeoCaqTJ1WrrevOofLQKM9icXbonFAKMRWXymqVc59Uw37iBKZR8qOssyyoVDWfsp+HhrPpdjJ3xWWmpWgxIWoW9ukj16FRbZ9Rn5Cf4OabaYBHjrhNxfNaekI4VGfOqLYAEaEazROJfYA1jZOp0KVi9rskyTOT3NxJJV5a4Ijvj+MzWL+e7JZGjcgGjI8n1QNQoofihCpyr8LDNUSpEOWpqRrF1uh7uGgRGX/9+tE5wNFfDPCaWC/cvYMHVa699u6lL0d0tKZqdWo78/jjdPLNy6PQ/Vtv0YPvvqv5OwwLk89RqqIuLIxUE+A4TmzaRFEXi4Vm4icnUysd0XLob39ThP7E2dXX/CklSUkkJEQic2goJc4sWQJERHjvkDN6NJ2pDx50u0AU30mhudwQYca2bR0H27Aw0qpHj9JxcOFCDTEmiIyUBbFKTCo5Wf4NOaqcvPgiff7p6ZTcJK68Bw6k1x0eDnzzDUoff97xusWsUSc++oh+u716eexB2KED/XSUNaxatCAttHAhEDrW/iMVP1oX+vWj45okuWTJ/PILvefR0aqCrqZGrt2q2mrGaqXXrnnlTrRvT8cRSdJVJcMnfBJUO3bswAhFXPWLL75A//798d5772H69Ol4/fXX8aWYAuEHpk+fjvfffx8ffvgh9uzZg4ceegjHjh3DXfbuhzNmzMAtig/irrvuwtGjRzF9+nTs2bMHH374IT744AM8/PDDjm0eeOABrFixAi+88AL27t2LF154AatWrcKDalM46kh9cqjEVXFMjPv3RORR1ZR6zqEy1KESgkpMkQboRCR2LhK+FNQ35OeVsDD5ckUjj8pj/2EhqDTCdYUXkaDqZ/kd1hCVZFVxAvdQMkS3Q1VQgJoaOR1GpLKEhsq3VadNiw+5riE/QWysqiAQIb+CAo26jUlJ8megohq1cv5dB5jYgRSfsr3W7t2k1SMj1TvKO/DSAkcIKsdnIJKZJkyQ37epU+mJtm1zShZSpsipamYP4T4lTZqQVtu0iaIUTk1pvThUycl0vpAkFTNYKJyePVVDNZLk8jUND6cvklCosbGUjC1mfmggdIpm2NElj0pcX0+e7DxZ65//pNdy5AglKwPwOMPPZpPDvh4FFUCfwe+/0/fwzBl6XfZjoxBUrv1BHcTEyGVEFAmLtbXy707UFXNDvCleCnp6xUM9KsClJtnBg3KF0FdfdW+j0r+/w/WJfuFJjMFSdO2qcmFVWSnPkLj7bo8V3gG69vzmGzqeHj9On+Pdd9sjxeI79fvvmldgs2bRUyxYoLhAFO7UTTfJx0MFq1fL6V2aIUudCIP2gw/q399TiU+CKj8/3ykU9vPPP+NyxSVj3759vYa/6sP111+POXPm4Omnn0aPHj2wdu1aLF261FFMNDs726kmVevWrbF06VKsWbMGPXr0wDPPPIPXX38d48VlP4BBgwbhiy++wEcffYRu3bph7ty5mD9/vqMSvBHUx6ESB3+1dkPi/OEph6q2VtZJKmkJzg9oFGryGvIDZHdHRVDVNyldFzrzqFQ1o/jOaCTHnG3WHTWwIlnKVc8REidw8YGo4Iug2rGD3pL4eOcEbHFVqZov62GWnyTpCPl5QexWkjREqcUiu1Qux4CiIvnCQFNQ2QfYbnAyrFYSlMKwEFelQ4dqXjMQvoT8Kirk+e/2Ru8ASPGIY5qiwK/X/CmNkgk+IQTVqVMayX4eehArBZUK587J5rFjiI0bU2ikuJhOfGIKlAdEuE2zTJ9ipt/hw7Kr4yheaSc6mkwygAyW2lp4DPllZdH3LipKY1KCKxYLfeAuvwexa0eXCTWuvpqWCkH1+++0fUKCB72kKyapAyHoVq1SPZ6J48DqnyQq8lRZSSJMZWYmAPpc774bFknCZ7gZ43upzIR9/31SFs2bu88C8kBCgspFRno62amSpNnNuXNnOb3pySdBBwiRtyYSRl0Q4b7x41WvGXxi6FDK3a+q0kgjqSM+CarU1FQctl+JVVVVYevWrRiouFwoLi52hNL8xT333IMjR46gsrISW7ZswSWXXOJ4bO7cuVizZo3T9kOHDsXWrVtRWVmJw4cPO9wsJddddx327t2Lqqoq7NmzB9e61iipJ7pzqDwIKrXUHKFhQiq1Q35KAaEpqLwkyIjh1ZaUy8kTPgiq+pRN0E19Zvp5caiKqqOwC/YjsVrfByMcKkXYVVj5F1/snEYgZlS7hVvKy+Uvl4pDVVQkn5/rKqhCQ2XN5zWPykV0CncqLc1D2Qm7QxXXNsVRk/LFF0n3KFvTeUS8/yKPxQUnh2rtWnpj0tPpyKpEpRNtfRLSdZOcTC6fJGleNmsKKnGZr9H/TnxF09NVGsnGxLhYZdqI68zNmzWcSnFc2LsXb71FL2XUKPfDBUCzvpo0oUPK0k/OyjM2HJUuZUQO3cCB9TuZxsfLqRKasxWvvJKslq1bHcczEb267DK38lyEJMmCykO6iC569KBZR2VlqtN6hw4lAdMx6xuawRgWRrljnlylV1/Fjsh+SEQ+Hlh3nXM05NQpsgwBsk1VZ334iLgoUcmjEsycSW/z998Dx2d9QAepgQNVQ9YVFXK+nWq4rw78+9/0lhkZVPNJUF1++eV49NFHsW7dOsyYMQPR0dEYovDgd+7cibYertIvVHQ7VB5CfmoOlTgwWKu1Q37CdIqN9XAgUgoqlZkl4gCcXHiAHk9IcD8z10dQBcCh8hjV9CaoioDNsGcEqzURFmcrIxyqwkLHcdk1t1gcZ06ckEN4AGSFExqqqljEto0aee7K7g3deVQuDpWYou4xd0RYaCkpjvyaTz6RE5mbN5fzyTQRT7Bvn+o0NCeHSpR7vuIK9xlt48aRkv3jD4dQCohDZbHIT6AR9lMVVJLk3ORSBR1fUV107EhfsbIyjUm9duUk7d7jCOXdf7/6vqKj5dJPy16276xVK9ULK2F0uJQMrBPiLVLNRQTo2CZ+fI66i3RXsxjmvn10sI2M9DCVVScWizytWaVgU2Ii0L9LKV6DvUbTI494sH6JY2ciMK7ia+QiCQmHttL3Pjubxjx+PB0Y+/Sh6YhGIOKiS5Zouq3t25MZZkUNIubaC+6JjHEXFi6ki+Hmzd2vf+pK377O7S6NwCdB9eyzz8JqtWLo0KF477338N577yFccWXz4YcfuhXJZHzIoaqjQxVeqx3y85o/BchnyqoqVVEnhpdaojgqu14NCTFiVsivrg6VJNVPUNlsuhwq4Y6cO6eas+2k+IQJ5hpaiI2VNYPTyUAZ7lO5Sq1vuE8g8qh8nennNX8KcBJUAwbIrr8wu555Rocz0aYNOS3l5aqCxCkpXSmoXElMlBN17NaIV0GlSzXqwEselVJQOa59Dh+myQzh4aruDiB/RZXFgetCSIicryzy/Jywf8iWvLOwFpxF27Zyz1w17r2Xhi0JdaaSP1VdLfcwVC1G6SOaTq8SYZMuWYL8fDk9SlNQiULQffvqdvs8cvPNtNSo/P5sxDNogeM4G9NS7mPjgZUrgRPIwGNdviNFvHYtfZlTUymHICGBEhdV7bc6MGgQJWrl53tsQPnEE8A1Id8ipeI4qhOSVEslAHKB46lTNdrl1JGXX65/ypsSnwRVcnIy1q1bh/z8fOTn5+Mal+kOX331FZ7SKG55IVOfpHRvgioEtYhAlfMTKdAlqGJi5LOVytlSDC+9zMNlrtkhP505VG6C6uxZ+mAsFrmctgvFxS6CSuniHTpEn1tEhMezVWKifJxVrRlrF1S15woc0SO16I3qycCIGX460O1QuYT8hNbwKKhcsuZffZVCfhdfTBfpXsN9AJ0MXGaZKRGCKvrkfqrbExYmJwC7IrJe7YLKY9uZigo5FO7FKfCKF0HVsSO9zIICxdss1HWXLponc6McKkBOilYp5g00agTJfizohD247z6Nhuh20tKoIHcX2POnVKaB/vIL/ayTkjyU3fAB4VB5FFTCYVm9Gqu/L4HNRlpVo26vLKjqG+4TXHQRTRCQJIpNKdm6FcO30fS0x2Pf0GU7i4T6ptcOoDe0Xz9KXBN1IH7+WVOM1wmrVQ6diyRIFdq2BZ5PngMA+CLhLkjh7uHGzZtJ/1mtutL8fCIyUi7WagQ+CSpBfHw8rCoyMTEx0cmxYgivOVQektI9hfxSUoD4CIXdUVeHymLxmEclfq/Nq+omqILBodIM+YkTV2qqZu5AURHwB7qiKiSC3h9ljo5IxOjSxePVncUif4aqYT/7AG3naIBt26rnvKlWq65vDSqd1NWh8noyr6yUG3PbVV94OE03X7dOjn7oQsw+VRFU4v0fUW3P6xgyRDupS8xm/uknQJI8O1QH7KHw+Pj6q1YvgioiQn6JjrCfl3AfYKygEi7RTz+p51Hlp9AAu4Xv1SWEH3pIFlS5qe4J6SIZ+S9/8SzO9CIuSvbu9dATr2NHCt9WVeHkx6QcPZbsMCp/SomofPnRR/JMlHPngJtuQkhtDb7Gdfhv9pVaXxUHNpssfi+7DCRaN26ki8Fdu+hPV6a/j4gk+a++0j75bd6Mdmd+QRXC8MiRe5Rpiw5EP8Qbb/QgaOtBfX+ySgz4ejLeqI9DJdwAtZOhxQK0z1B8UevqUAEez5ZieK1qdQiq06fdXmgw5FBpOlRC3Wi4UwAJqipE4FiaPdahbKTltQS4jMcGvXZBZS0phAU2136gDkRyrwijATCuZIIXvDpUGknpXidBigEqM9/riod+cqJa+lgsoRVq4T5B//50gZKTg8qtfzqGqCqoxIfRvr3X6eZeEYJKtXIm4ZZH5SUhHTBWUPXqRR+Tsi+ckg0F9Blc02GPro+zW1cJPUJJUM3d7Cyoqqtlg8Ol6HWdSU+X+7mLht1uWCyOBtOJG6hxnWa4r6BATiir7ww/JYMH09WEzUahsFdflcuTN22K9/u8C0BzIp2D7dvpNxsbK08qgMVCgrFz5/p/Z7UYNoy+zwUF2j2z7P21dnedhNNIx1//6lzUd9kySmOzWHRFNk2HBVUAqE/ZBHHyEnrHlYuakaCyWUNVHRLx/xrnWhkdDlUbeDgzKusQuVgwARFUdc2hEhagh8r4Yrr58Vb2bMi1a+UHxRFZh6DymG9sn+UXItkQgxJNQSXciawsRd61l8bIRoX8vDpU4vIxJ8eRKFZYKOs9zXxtpeKrrwXhpUFv27RSDMMauuNJUEVEOIpeFS2iy/voaI2ZsrpimjoReXgaTZ4BFUHlpWRCWZlcGswIQWW1ypFSV0fh5Eng+4P0GfSLU/8M3MjORlxNPmpgxbNfdXAyOL/5hr5vyckaxSjriMgD02jZR9jDfsPLliA6StJOhhY7uegiY+0OgIqWiV5106fT8SYxEVi+HAPH0TFb2XxZDVHOatiw+pcb8ImQELnq/jvvuD9+/LjDfuzwzgPo2JG+p9deS8eMrCw5xHf//c6lD4MVFlQBQETi6uJQeYnmoFUa7bTKql6gR8xE9qAXnJ9A5WwZFUUzMVrhCK1QS74WdV8AN0HlNeRnZNkEX2f5iTdILaZqR2i0nA72Ga1KQeWDQyVmZaqaD5GRjvyXBBRoCqrWrUk3l5UpnK4Ahfy8OlRNmshXD/bBCV2QnOzh4zVqgIBcaOiPP1Rn+l0e/hMiUIXipFbej9Ai7Lea+nVoFvXUlXWvE6E6jxzRaJgnC6odO0AXBKdP08A0wjbCIUxI0OFU6+TGG2n52WfOrZDefhvYZSNBFXdCp6Cy1586GdUORVWRjlmeNpvcp/vuu43LlwbkOQceBdXQoagKb4SmyMbU3lu1GlHILX+MDPcJGjemjPj77yeB/3//R6KqSxeHY7ZqlUYJCzsif8qIhH6fmTZN7hupPG4CVKqhuhoYNgxRg3tjwQI6jP/6K51KMjNJYHXqJNcuDXZYUAUA8UP0NYeqokLWWFoOVcsU2mlliLqgUkye8owHQRUSArSLOI4w1EAKD9cOj6kIqqoqubWEqgFVXS1PezMi5OdHh6qoyyA6qh8+TLZ7VhbZTWFhHvNXBB7SzACLBbb4BABAPAo1ozdhYXLuuyPsV9/GyDrx6lCpFPfUVZ5J95dUB5mZdAVQVCQ7RwqGldHsvn0XXeE91GFPTI/ftgZW1GjP8BN9dYy4hG7enL5jVVWafYqEbtq3D6jatE1+bo2WN8pwn1HRnSuuoN/UqVOyQ5KdTcW298DuEh496iFJSYFdUMUO7IKQEGpR8+yzlEK0cycdFh54wJhxC3QJqshIbIolFXJzzLfa24mYm0u7JsOIi6PQ2Nq11BrI/kXs25d+8gUFGjMuQY+JunaaIUt/0qwZFRwDKClSlFD46Sdq5gcAL70EgH66v/xCx7eqKhLUgwcDa9bUr9xLIGFBFQDqmkMlTlxWq3ODXyUtkklQlUvql09GOFQA0CmcjspVzVprz1tVEVRKw0jVoVAKoPo4VCK52FdBpcOhEoIqKiVWPir973/At/aD7PDhHipWygiHSlVQASiPSAAAdG5aoF2EFbIR4phNHSyz/AC3mX5O/eO0MFJQhYbKuUS/u3S7lyT0PE2C6vdkrf4hCnr2BBISEF5ehN7Yoi6oamvlCt9GJPaGhsp5VB5a6CQm0lOfXeE53AcYmz8liIiQC8w/8gidAO+9l64J2w1IgiS+LCqi1g37+5d4SRc8/TSteuIJuc/aSy8Z56wJ+vShw9iJE+rNDwAKV793joqfdT+gMVPt7Fn5e+Yxa914rFa5uoNaMjcgl4HSbOgcCB5/nE5gv/1GDttXX1FCnCRRHQTR9Rv0E8rKorTAPXvI2DI6iupPWFAFgLrmUCnzn7SuLJsm0k5La/3nUAFAh1A6Kpc381DIRkVQCX0TEaERvxeKKzy8fvVbhLul0pwZcO497BRJ0eFQOaV4KWMdX3xBt5VdQj3g0aECUAgaZM82BR73o8yjAhA8s/wA2b10Cfl5dKiMstAEorCMq6DauRONi46hHJFYGzLM+36sVkfY7zKsVBdU+/eT9RwdbZxi8ZJHpYzuVf0W2Bl+Sp54gr5yf/5Jv+9Fi+g3/tprgMVLLpsTiqbIM2aQkEpMpGucl1+mc7DRNGokz5h1aa7hYNUq4FvpSlRZwhF+YI/sRCpZuZKEQbduHrq3+w9RmWDhQtWazI5uLgY3//CN5s2ptQ1AMxYnTqTffI8ejqR0JSEh9FDHjv7Ll/cXLKgCQF1zqMSJSyvcBwDpCeRQFddEqcbRfXaoNOyHi0ClAkpTPRyVPQgqvyakA7JDpCGohONjs7mYWD44VHFxIPGUkEAnu23byFHwUVDl56sPM6c6AQDQuVmBx/24OVQeZvlJkvGz/M6d00zvcRNUAXeoAFlQuTacs59dluFyHM3VGUOwJ56Mwgp1QSUmJXTtalzFQaE+NRwqQBZUMfsDO8NPSWIi8OGHsrEcGkrNZvv1g1uTZE1sNllQde6MkBDKvT57lsKJ06cbO2YlIrFetdk4qGtKEeKxr5XdlRYhKiXCGgqwOyW47DI6dJ44IYf2BGfPkkMF6Ogy4G+uuw747jv6XqSmUm5VQ4rl6YQFVQCoaw6Vnhl6iVG00zJEuaVclJfL4sGroBKqTUNQtbbRUbko2TdBFZAaVIBXQRUZKX8OTmE/H3Ko4uJAn9X8+eSmWSzAf//rseSCkthYOXSh5lKdLKG4brukAo/78cWhMqKPn0Ds3mbTaOED1M2hMlpQDR5My82bne00u6BagPFa6Unu2Ds/DMQGtGqiEk4WkxKMqDgp8NLkGaA5EPEoQFLhIa/Pb1SVdDWuuoq+h19/TXn0jr66okDnH3943oHIswoPdxqgxeJ/d0Ikaa9a5e7uVFfLEf3aybfRjY8+cm6jUlAgd382qqaDj0RFyab5f//r/NjHH1MotlcvY7+edWbcOBLYp0/TYLXyWBowLKgCQH1zqDw5VCFVtNMKRLqdpMV5KjxcR4qPEBTCsXGhRTUdlfMT/eRQ1Sd/CvAqqACVmX7l5fL2Omb5Od7DUaPInfr9d3lasE6ES+U60y8vDzhZSgNsEVcATwiH6vhxoKTI5rHYmPgO1LePH0DfI/E5OvUSVKIQVDabLKg8OlRGJXkJWrYkC8dmky/Rt20Ddu+GFBaG7zEO2dnqIRI3WrfGwZCLEIYatM9e4/64D2UzdCOspP37NTfp1g3oAftzt2ypmWRUUyN/1/zVZrVpU3JAnK4rNLs4uyDyzzp1MnYanw4GD6Zj86lT7n0Jf/qJri1TUoDOj15Jx8czZ5yTlb76iibUdO5sqmIR7fcWLJBLslRVyU3F/REyZdRhQRUAfMqhUhzlddWQstte5YjSFFSpqTqu9oQYOn3aPZ4jSWhWSYLqbIJvOVTCofLadsbPDhWgkpguxGN4uObVkiTJu3QaYmYmNGsbeEAkhrq259q6FSiw51BFVRV63Ediouw2HdyqSApT+aIYnZ4kNI+y+J4TCkF1+jSdb6xWDz3wlDszapCAHIYVNoM9V8N2zXUoRAIqK1UmKKhQVAQst5GVkb7TpYJiba1cIdvIhmBCMWdlaaq+zEygl4XCfRWdtcN9hw+TqIqKCnCKj3CojhyRq+CroQyZBpjISDnsJ9IhBaIdyYQJQGhUmKxKnnyS3tCqKuBf/6J1t95qarJPr140wbCqCnj0UVr35ptkcKakyA4W439YUAUA3TlUNptcYwD6HCqloHLNYRV6QdeFv3Boamrcw35nziCqthQ2WJAT3Up7H0JQ5eY6CqN4NaD8EfLTOAlpCqq0NM0DYmmpvDsdE/m8Inq/igtzwZYtsqDSjqfJiHPusW32L0mjRqqtc3RENH3Ci5EpC6rsbBw6QEKvRQsv5oPRIT9Aztb99lsK0c6bBwCwTn/AYeboCfsdPw6sAIX9wte4CKqdO0ksxMYa61C0a0ffx4ICTSswOhq4JJYE1Ykk7YR0kcLUqZMxbVt0k5goz/h0/bIrEZ3A63BxYgSiB/Hnn8vXJWfOyALL0fbob3+jA3FWFvXKeeQRUiypqVQky2ReeYW+MvPmUTV0Uctr9uz6m/+MflhQBQCvOVTKWIwi7OeLQ1WBSLeUC6VD5ZWwMNkhcG02Z0/COI4MlFSr97sDQP9vtZICsZ9xA5aULv6/tlbzjXYL+fmQP2W1qrZK9BlRd9I1xPDbb74JKpFHlb3L8ww/IRp0pnl5xaugEuK0pgbZO+gL6DHcV1oq5w4aKah69iRRVVtL+S1VVZQ007+/w6lR7anowrFjwGoMRw2sVAJAGasVWcAXX2xsuCoqSi6d4NRjyJleoJIJO63eBZWRfW91oyfsJ6q8e0iq9ydXXUWHjiNHKAkdIHenqoqEiaOTTFycXMfhzTfl2WnPPRcUiqVXL9kw++03up6dNElnU3HGMFhQBQCvIb/QULlkgCIxXZdDZd9pOaLcUi6EXtB9ntKodC4E1UG0VeuOIxMSIj+Z/YwbkCrpADk0wmXSW4vKxxl+Rrj6QlDt3i1fEUsS1Vupi0N1br8+QWVUuMeroAoLc2yUv4sS03WVTIiIMP7ENGeO/ORt2jjiOOK90ONQHTtGM712N7FXybc7XQDk+faaPUnqgTLsp0Z5OZqXUNx4TZG2GDFVUAnXTogmV3Jz5UbaOgrj+oOoKDmaN306sHo18OKLdP/hh11+87fcQi1UkpMpReD11+XeKEHAI49QyuD06cB775HrFlBXkmFBFQi8CipAzqNSOFQi19hTkUdlyO/AAeeHRE6VSIT2italu11QHcBFngUV4HbGDZhDFRLitRaVeB99cahE+odRE1LatKHvQ3m5nLC9dy+5keXhCS4D1Eacb4uPem6MHHCHSvFkZftJUOkumWB0HkqLFvTm7t1LuTr2KxNx3aBXUAHAzu630I25c0kBnztHnVsB/5Sg9iaodu5EiK0WZ5CCn/ela+7GVEGlVb5CIIRW+/b1//3Xg3/+k75++/ZRcfyqKiqYqVpq4K676Dubnw/89a8BH6s3rriCjLSpU1lMmQG/5QFAmUOlObNIhP0UikWcV/UIqgpE4swZZ3NGnLBFhW6vaJ1p9DpUgHmCCvCamC5CfnV1qIzAapVL9IgZ96LFVUYXu2rzQVBVZXtujBxwhwpwCKraYz44VP4qhxweTm+W4vvla8gPAM4Ou45+o/v2Ua+V//2PMu67d/fPDC+3YmMu/PYbAOB39MWfuy2qUW6bTa6raYqg6t+fln/+qd5jU/R9UVTKNoP4eKp+IKo2jB5N9bU86vuGVnGSCQgsqAKAcKgkySnn3BmV0gm6BJXd9rLYVZuoOQPI6R4+CyrXM43d+qqLoNJdh8qIcI/O4p4OQWWCQwXIeRk//khL0Q2+Q/8EuqFDUIkmyXHV9kQ7jbiwmYIqNMdHhypA+BryA4C0drHy/PQxY2R3YsoU/5xcRf6RmAXnil2M7I7pj9paqgrhyoEDdL0VGelF1PqL9HRKTLfZ5ORzJevW0fLiiwM7LhX69yc37+RJMh49plkwjAYsqAKAsku53vYzkiSf+IWzoor90rRREgkqEfaz2eSQn+6DqZeQn18cKqPKJgC6BZVDr5jgUAF0PgaApUtJsH3/Pd2/5KoEulFY6LVIUlgY1RVqgiDLoQIcgiqumASVx/pH/iiZ4AWt6wY1hKBq0QLArFnyPwM0ZdNfWb/C9TpxQn2mnz2MVtqFXCC7YeXE5s3yrgJc4klGuFSuYb/qatmhGjIksGPSICzMlO4xzHkEC6oAIIpqAzpm+omcqHK5KK8eQRWbSoJK9CLNziY3zGr1IX9GLeRXVOSYbuhXh8qMkJ9JDtWll9J34sgR4JlnSGR36gR0udg+wJoat6r5anTo4FlQlZfLr9XoHCrNOlSKJ2uGk4iL89LY1t8hPxX0OlS1tXLj3BYtQN+v1auBO+6gJJvly/2X+xMbS+UTAHf7KTfXcZETO6IfAM+CytSI2qBBtPzpJ+f127eTG9+4sUnxSIYxHhZUAcBi0ZGYLhKt7CdS4aJYrV6iYXZBldSc/l90ehDhPq81gJSI7HVl/QW7QiuPTUYx4vyXQ2VEyE9nUrqZOVQAmZH2nruOmdg33wxYGkXLH5bOPCpPgkoIhuho48YvPt7SUrfC/jIKQdWmjZeImLCJjCqUpQPldYMnI/D0adK2VqvCmOrQgRrWLV5snErVQsx8cxVU69fTsmNHdB+aAECuL6pECCoja476jEjY//ln56tJEe8ePJizp5nzBv4mBwiVnHOPG4jzaUKClxOS/SDV7CISVOLY63P+FCCX8c7JkQdgryFT1LKrcnja+CqojFQsOh2qggKQGhD2WYAdKgB49VXZuenTB7jvPtAH7VYsSxsnQaWS9KEM9xmV5hMTI2t/b8U9haDyiBBUylCanxHvR1WVhxY6kMN9zZsb1/fYJ0RtJtf8I5F0d+mlGDCANPiRI87XQbW18iQ6Ux2qzEz6PlRUyLMvAGDhQlqOG2fOuBjGD7CgChAuBpQ7LoJKV/4U4BBULTrIIb+SErkNmE+CKjZWPrGJ6dp2QVXappvn8QuEOLGH07yG/IxULL4kpQs1EBXlMWzjD4cKIDG0di3wwgvU7d6x/3jfZvp5cqhEDp3Hti8+YrHoKDtgF1QJKETH5iqzu5SYIKjCw+WnE6JJDaf8KTMQsxd+/tm5HZQQVCNHIjbWfZIDQGKqtJS+V2LCoClYLMDll9Nt0Qbo2DHqg2mxAFdfbdrQGMZoWFAFCJcUKe0NVBwqj9h3mJAehaZNKYSxY4ccAvC5o4M4+opkLPvc/sqO3ZXD00YIqrw8oKbGu0NlgqCqrAQqjyrypzzYN/5yqADKaX7kEZfP2EeHKgmU31bRyF1QibIZRs/wUrTrUycuDuVWmmTRpbHWRnZMEFSALJJc+18qMV1QDRxI8eGcHLna+IkTVEohJAQYNgwAFYAHZJ0FyFW/R440yV1TMmkSLT/9lH5QH39M9y++OKChXobxNyyoAkR9Qn4eEQotKsoRIfj9d1lQ+Twj2bUxq11Q1XQmQaWZNyNISqKDvSQBubmeHSqbTRY/ARBUMTFyukbpYe/5U8pdGe1QaSI+cE8NZe0kNSpHNOjzP5AfOEElWrSJhG03LBactpLqahftQVCVl8uvM8CCSqQLBrWgCg8Hhg+n2yvsfQS//JKW/fo5rhBEg9/ly+XDgRBUwhwylREjaNZFSQnFtv/9b1p/113mjothDIYFVYAQIT9fHSqPNaiUO4yKclj/Tz9NuUtxcXKrE90oBdWJExQfs1phyeykHJ42Vqsjn6f21BnH9qoOlbLzsBGCyosYCQmRN6k8oq9rsD8dKlV8cKgs5yjcV41Q7DnprvjMcqgkCThSQ6orw6KluiC7U5GRAXyDCSGSgjrkBwCjqDEzvvqK3tgPPqD7t97q2GTAABKIhYXAggX0uYhZf6JEh6lYLMCjj9Ltzz6jq5SePWXnimHOE1hQBQhfHSpfc6gQFYXJk+H0vwMH1sHuF113d+yQYwjduyOqcaRyeJ6xi5SKo3LWsqqgEmolNNSYzsM6xIgQqNUngtyhckxF9IC92WMemiBrn3vY0iyH6swZ4IiNVEiT8uPaO1KG+wJcebpBOFQAMHEiCc7Nm4HJk6n6ZFQUcMMNjk1CQuSWcm+8QeWybDZqMSg+K9O55RZg9mw6IF18MVWa59l9zHkGf6MDRF0dKl8EVYsWwNix8kNCYPnEoEEUati/H3j+eVp31VVOw/NSc9IhqCqPk2ixWqn3rRtK+8eIE6oOQSU2kbKD1KFyq+3gAYWgcu1QUl0t9531l6DScqgOHgSOgVRI6EkPFpBJ+VOAd4dKkpxLj5hGaqocGvv8c1redZfbF/KOO+gY89tv1BgXAGbODNwwdfHoo+RKr1snX7gxzHkEC6oAEYgcKoDqGk2YAHzzDXDTTXUYaHy8XDtGlF3/y18cw7PZPLTPEdhdnxq7CxQbq6GXjFYrPjhUltwgdahELQXRGdsTCkElcpYFx47RZxUZ6fUl+owI+Wk5VIcOyYLKY0xNCCoTylN7c6jOnZO/Rl5LP/ibJ56Qf5OjR9PUUBeaNQO++EI2faZNk9OvggrVKyuGOT8wqyHBBYdXh8plA12CymajKWuK/+/QQc5brTMTJgDffUe327YFundHdI38cFmZl+Oi3fWxZcuCShUTBVVYnneHqrZWLvsQMEHli0Nlr2CfhybYvZtK/YgCssrG2EZH04RDdeoUfQVdIzc+CyoTHCohqPLyKN/Q9Tsqyo40ayZf65hGYiI1mMvPpy+iRhz/qquoD7FoS8QwTGBhhypA1DWHSk9jZADG5CAJJk6ksMJ991GjOYsFYWFyEW9fi3sGpAYVIAuq4mIqce1hk8hC7w6VKPkABLdDVRrRBLW1dDIViIr5nToZPD7QWxYSQm+xWgsaN0GlFSMWhaxMEFTx8XItVGHEKhGCSnR/CQoaN/aaFNmxI4sphjELFlQBwtfCnrocKqXdZaSgiogA3nmHMlwVuQ5eRaHALqhCzgbYoVLux2MtKgmNivX38YuICGCkog6CKjSNlIGyQ4m92gW6dzdycERoqKxD1QyogweB47BXEy0p0XYMxT8LuyjACLEkxJMSIbIuuihw42EYpmHDgipA+KWwp9hZWFhAqvf5KqjCzgXYoQoLo0KIgOZJPCEBiEEJwmvKncaqhpElsnRTh6T0mJZUg0opqLZvp2WPHsYNTYlwQew9eh1IEk1EK0c0ahLsFpBW2E8kMJmU9S06LakJqqB0qBiGCWoajKDKz8/H5MmTER8fj/j4eEyePBkFHnJlqqur8Y9//ANdu3ZFo0aN0LRpU9xyyy045dIvY9iwYbBYLE5/k/xQH8UvSekuCen+xldBFVEQYIcK8JpH1bgxkAa7O9WokcemzGJ4AQv3AXVyqFI6kaD69VdaXVVFogbwj0MFyELDNVyWk0Na0GIBQlp5KEdus8nTEE0SVHocKhZUDMPopcEIqhtvvBHbt2/HsmXLsGzZMmzfvh2TPdQFKCsrw9atW/HEE09g69atWLhwIfbt24errrrKbdtp06YhOzvb8fef//zH8PH7UjbBZtNZ2DPIBVVkcS5CUOtdUBmpWHQIqlT4NsPPFIeqvNw5R04Ne1J6+4EkqHbsIEGzZw+VTYiP9180TYTCXMWIEHJt2gAhbVrRHZEhr+TMGRpkSIgps/wAWSyJLksCSZLXcciPYRi9NIhZfnv27MGyZcuwceNG9O/fHwDw3nvvYeDAgcjKykIHle6f8fHxWLlypdO6N954A/369cOxY8fQQnFVHB0djTSj55a74ItDVVIi90JtkA5VcjJgsSBEsqEJ8hATk6K+nT8UixdBlZCgcKh01qAKqEMVF0ciw2Yjq8dTwrbdoUpo2wTdu5Og+vFHue9zr17+q5cphIarQ7VnDy0zMz1sBMhhwGbN5NkOAUYr5HfsGH32oaHyNgzDMN5oEA7Vhg0bEB8f7xBTADBgwADEx8dj/fr1uvdTWFgIi8WCBBeV8vnnnyMpKQmdO3fGww8/jGLl9C4VKisrUVRU5PTnDV8cKqEFwsPlafCqBKugCg0FmpBrkoozQRfyC2qHKiREdqm8hf3sggpJSY4GuUuWUD0iALjmGv8MEdDWSsKhysyEdqIVEBRlyMVrOHsWyM2V14uE/sxMLpvEMIx+GoSgOn36NFJS3F2OlJQUnD59Wtc+Kioq8Oijj+LGG29EnMJyuOmmmzBv3jysWbMGTzzxBBYsWIBrr73W475mz57tyOWKj49HRkaG1+fX7VBVVqIgrxYAaQOPDkOwCirA4f6k4kzgktIB33KovAgqUxwqQF9iek2N/BqbNIH4yn7+ObBpE31vrrvOf0MUYiQ317l1oijd0KmTYiNPgsqkGX4Apc8JB2rrVnm9P2dIMgxz/mKqoJo5c6ZbQrjr3+bNmwEAFhVlIUmS6npXqqurMWnSJNhsNrz99ttOj02bNg0jR45Ely5dMGnSJHz99ddYtWoVtiqPsC7MmDEDhYWFjr/jxz30K7Oju2wCgMIzlDvjS2PkQFBXQaXpUAlBEOCQn3CoapM9h/xMcagAfYnpSrHVuDEGDgSuvlpedfnl/i3vFBsrR0yzsmhZWwts2UK3u3eH7FAdPkwPKjF5hp+gd29ainEDLKgYhqkbpuZQ3XfffV5n1LVq1Qo7d+7EGZEYoiA3NxepXvJgqqurMXHiRBw+fBg//fSTkzulRq9evRAWFob9+/ejV69eqttEREQgwsdYgNeyCYrYXklOGYBGPred8TeGCyohGOzhQUPwIYeqPC4N2nP8gtyhEuG+hARHDtJrr1HZp8xM4Mkn/TtEAOjZkwp4//470K8f5U8VF9PkyS5dAFiaU9y6qopm9LVqJf+ziBUa3WjQR3r3BubNY0HFMEz9MVVQJSUlIUmUK/bAwIEDUVhYiN9++w39+vUDAGzatAmFhYUYNGiQ5v8JMbV//36sXr0aTXScuP/8809UV1cj3eDLe68OlcVCG5WXozSXNjofBFUaTmuH/ISgEo6MEYg3TUOMhIUB6SFnABtQHJ3qUVAFtUNln+GnFKMtWgAu8zD8Sv/+JKg2bQLuvRfYuJHW9+snNJ6VBFNWFoX9lIJKdHM2uUmuq0N16hRpPYuFBCPDMIxeGkQOVadOnXD55Zdj2rRp2LhxIzZu3Ihp06Zh3LhxTjP8OnbsiEWLFgEAampqcN1112Hz5s34/PPPUVtbi9OnT+P06dOosnf3PXjwIJ5++mls3rwZR44cwdKlSzFhwgT07NkTgwcPNvQ1eHWoFBuVnQ1OQSVqZhriUFVWUud5wFhBpUOMpFvIoSqIDNIcKvEahAulhiIh3SzEHJFNm2gpBNWAAYqNRG0CMf0PoO/tkSN022RBJWZCHj1KaV3Ll9P6vn2NNU4Zhjn/aRCCCqCZeF27dsWoUaMwatQodOvWDZ9++qnTNllZWSi0nwVPnDiBxYsX48SJE+jRowfS09Mdf2JmYHh4OH788UeMHj0aHTp0wP33349Ro0Zh1apVsBpceVyXu2PfqDwvOAWVeA1CB3nEm6ASDlJIiLEWkBAYwsFxRZKQbKPw8VlrkOZQJSfTUus1ALKgMvGsbzeLsW8fDWfVKrrvJKi6daPlzp3yuv37qdhT48byazWJuDjgkkvo9pdfkuMGUA4awzCMLzSIOlQAkJiYiM8++8zjNpKiCWurVq2c7quRkZGBn3/+2ZDxeUNZNkGSNGbv2RVLZT4JqvMlKV1Si6sJB6lxYxJVRuFNUBUWIkKqBADkWIKwDhUgvwblXH5XVEJ+gaZJEzKY9u4F7rmHXJ74eDhKOACQE5FEYhLgHO7zV6EsH7j+euDnn4FXXpEbYrOgYhjGVxqMQ9XQEWKktpYKRHvaqKoguB0qQ0J+/sifArwLKvvkhkLEIa/M8/sW1A6VmKThZVKGv7n7blp++SUtb7jB5esoBNUff8gz/cS0QJPDfYLx4ynnKzubkvp79ZLdN4ZhGL2woAoQypOMt+Ke1YUNX1DZUig/KQU5iG1kc9/AX4JKODZFRerK1V637DTSvPYfDmqHKieHliYLqilT5OFGRsoCy8FFFzkmWzhm9onwX5AIqpQU4IMPaPxRUcDHHwek1zjDMOcZLKgCRHi4HNnSzEGyK5aaooYvqEobUSHWUNQitlolQdxfgiohQX6j1ZK6s7MB6BNUDcKhUil4G0gaNQK+/x548UVg+3Y5ZcqB1Qp07Uq3t22jePfatXR/4MBADtUjt9xCefJ799pLPjAMw/gIC6oAYbHor5ZuK2n4gqq4Igx5ILEUXexeQ8xvgspqlfepJkhOnQIAnEQzrVJVAGgSYiWlWpnnUOXlyU0dXQkShwqg2X5//zug0lKTEKVNli+ncF9ODtlBQRZXS001vc4owzANGBZUAUSUHfDmUNnKSCg15KT04mLgDOhkb8lREVTCPTJaUAGe86jsguoUmnp0qJTtGTULk/oLMX7RIFmNIHGodDFuHC2XLAFWr6bbAwZwozyGYc4rWFAFEFHgUlNQCWFU2vAdqqIiWVBBpcq93xwqwLOgOnkSAAkqTw6VeCw21oR8mrAwOc6olkdls8nrg8Ch8sqQIWTz5ebSdEAAGDrU3DExDMMYDAuqACIcqpISjQ3sisVa1fDLJigdKo+Cyh/T/g1wqMRj/tB7uvCUR3XunDxjzuQ6TroID3duNBgVBdx+u2nDYRiG8QcsqAKIV4fKrliiQYrFazL0+SCoTAr5nUQzj4XIlWWyTMHTTD/xfiYmkpvVEJgzRy6t/u9/Ay1bmjochmEYo2kwhT3PB/Q6VNEoQ3Q0Xdh7JIgrpXsN+Qk14w/FIlwvV8UkSU4hPz2CKigdKpGQ3hDypwSNGwO//EJT6S66yOzRMAzDGA47VAFEb1J6NMr06YwACyrhsFVVeShOaserQ+XPwpRa7k5hoeM9O4WmKCzUfh2mh/z0OFQNIX9KSWgoiymGYc5bWFAFEF9Cfl4T0gHTBBXg3aXyKKhsNnldmucGxXVCCA17EU8H9nCf1LgxKi30nmn1UDY95CfeF9fXADSsGX4MwzAXCCyoAogvIb9gFFTh4WQyAB5egx2PIb+8PEqqtlj8IwqaN6elPbznwH7f0rSp4/3VCvuZHvJr2pSWdhHohFgntmEYhmFMhwVVAGnoIT9Adqm8CSonhyonh/KXBPZq5UhK8k9SdbNmtDxxwnn90aO0zMhwqp2phgj5meZQeRJUQiiK18kwDMOYDguqAOJVjNgFVRTKvTtUkmSqoNIT8suB3X2qqoJT0ScRxvJHuA+QhUZJiXOFzsOHadm6tWbeuiCoHSohFIUTxzAMw5gOC6oA4tWhsgsjXSG/6mq5LUkABZXXsKWdoiKgEpGoiEqgFcKVUt5OTzd8fABokOINVLpUKoJKq11eUAkqpbsHsKBiGIYJQlhQBRBfktJ1F/UEgjbkBwBlSfbmaCLcBvjfoQJkl0qZR6UQVEEf8hPvTXW18yAliQUVwzBMEMKCKoAYmpQuBJXFoqNglXH4EvIDgMr01nRDiBkgMIJKLTG9IYX8wsPlWlTKsN+5c3LXZk5KZxiGCRpYUAUQX5LSferjZ7EYMTxd6HWoROpSdYaKoBIhv0A4VMLNKSuTZxt6CflJUhAIKkA9j0q8nuRkbi7MMAwTRLCgCiB6k9J9CvkFMNwH6M+hEg6V1MqDQ+WvHCrAPeR35Agt4+KAxo09hvzKyymPHjAx5Ad4FlQc7mMYhgkqWFAFEL0OVQSq0Di2xvPOTBJUvob8rG1VBJUQBf4UVBkZtBRC6tAhWrZuDVgsHkN+wp0KDXUuZhpwxPujDFuK2yyoGIZhggoWVAFEb1I6ADSOLNfYyI7JgsqTQyVJcsgvvIOLoKqslG+3a+efQQJA58603LWLljt20DIzE4Dnzi5iXVJSQKOp7ogGwkoxKm4LwcgwDMMEBSyoAojXcFlEBGygM3jjiDLPOwvikF95uaKiQ6dWdCM/n3rpHTpED8bE+Neh6tKFlidOkOW0dSvd790bgJy+pdZmMGh6D7dvT8t9++R1WVm07NAh8ONhGIZhNGFBFUCUIT/X0kIAUFFpQRnIpUoID05BpSfkJ8J9ANAoNUZWJvv2OQsCf9o/cXEU3gOAP/6QBVWvXgDkdn+FhUBFhfO/Bk3vYTVBtXcvLVlQMQzDBBUsqAKIECO1tfLMdyWFhXAIqhhrww35iXBfbCwQEgKgZ09asXmzLKiEWPAn3brRcvVqOZfKPpb4eHmSnKtLFTQOlQiJ5uaSw1dTAxw4QOs6djRvXAzDMIwbLKgCiHCoAHWHJz8fKAcJpJCK4HSo9IT8hEMVG2tf0bcvLX//PbAhKyGo3n2Xlm3bOiqoWyxy2E9MOhQEjUMVGyuHRffvJ1FYXU2fOedQMQzDBBUsqAJIaKjsiqgJkoIC2aFCWXAKKj0OVdAIqiFDaCkU0mWXOT0sBJOroAoahwqQnbz9++VwX7t2duuPYRiGCRb4qBxghMhQ5hkJnASVt7oEQZxDJUJ+cXH2FUJQ7doFrF9Pt+25TH5l5Ejn53nsMaeHg96hAmRBtWcPsHs33eb8KYZhmKCDBVWAESJDiA4l+flAKbwVq7LTkEJ+6elAq1byBgMGBCaHymIBPvkE6NQJePVVtzCZ1ky/oHKo+vWj5apV9AcA/fubNx6GYRhGlVCzB3ChER9Py8JC98cKCoCEIBdUvialO3j5ZWD8eLo9ZYpfxqZK586ys+OCVsgvqByqMWNouWmTvG7cOHPGwjAMw2jCDlWA8SaoHA5VkOdQ6Smb4Aj5AcC11wJz5gCTJwM33eSv4fmEWshPkoLMoWrWDOjRQ77ftm1g3D2GYRjGJ9ihCjDeQn4NJYequJjEh1opKbeQn+CBB/w6Nl9RC/mJ6gRAkAgqgMTo9u10e/x4k8u3MwzDMGqwoAowuh2qIBVUQhDW1FAtrchI921UQ35BiFqrPOFWJSTIMzJN55FHKHRpsQCXX272aBiGYRgVWFAFGMMElQgJKvr/BQJls+CiInVBpRryC0JEnvzx41TeKSxMrv8p2ugFBRER5FIxDMMwQQvnUAUYw2b5iccDLKhCQmTnSe01AB5CfkFGWhoJQpsNOHaM1onew6JrDcMwDMPoocEIqvz8fEyePBnx8fGIj4/H5MmTUVBQ4PF/brvtNlgsFqe/AQMGOG1TWVmJv/71r0hKSkKjRo1w1VVX4cSJE357HYY7VMry6wHCkyhUrg92QWWxyMJJCCmxVFZ5YBiGYRhvNBhBdeONN2L79u1YtmwZli1bhu3bt2Py5Mle/+/yyy9Hdna242/p0qVOjz/44INYtGgRvvjiC/zyyy8oKSnBuHHjUFtb65fX4UlQnTsX/A4V4F1QNZSQH+AuqETIjx0qhmEYxhcaRA7Vnj17sGzZMmzcuBH97UUN33vvPQwcOBBZWVno4KFydEREBNLEdC4XCgsL8cEHH+DTTz/FyJEjAQCfffYZMjIysGrVKowePdrw1+JJjJw9e344VA0l5AfIwunQIVpyyI9hGIapCw3CodqwYQPi4+MdYgoABgwYgPj4eKwXrUw0WLNmDVJSUtC+fXtMmzYNOaLIEIAtW7aguroao0aNcqxr2rQpunTp4nW/dUXLoaqqIoGiu2xCEDtUDSXkBwBt2tDSNeTHgophGIbxhQbhUJ0+fRopKkWBUlJScNq1zLWCMWPGYMKECWjZsiUOHz6MJ554Apdeeim2bNmCiIgInD59GuHh4WjcuLHT/6Wmpnrcb2VlJSorKx33i7SUhQpagiovj5bllkaAhPPCoWpIIb9Dh+gzyc+n+5xDxTAMw/iCqQ7VzJkz3ZLGXf82b94MALCoFDOUJEl1veD666/H2LFj0aVLF1x55ZX44YcfsG/fPixZssTjuLztd/bs2Y7k+Pj4eGS49IjzhJYYyc2lpTVOZ6V0IbiCTFBJUsNyqES0eNcuYMcOup2S4lwegmEYhmG8YapDdd9992HSpEket2nVqhV27tyJM64dbAHk5uYi1YeGa+np6WjZsiX2798PAEhLS0NVVRXy8/OdXKqcnBwMGjRIcz8zZszA9OnTHfeLiop0iyoth+rsWVqGN24EFMKzQyVJQRvyKyuTK427GH9BSceOQJMm5BC+9BKtGzzY3DExDMMwDQ9TBVVSUhKSkpK8bjdw4EAUFhbit99+Q79+/QAAmzZtQmFhoUfh40peXh6OHz+OdHuJ7N69eyMsLAwrV67ExIkTAQDZ2dnYtWsXXnzxRc39REREIKKOZbSFoKqqAioq5MKYQlBFJDYCjsCzoKqsJFEFBJ1DJUJmVqspQ/OZkBDgkkuARYuA77+ndcOHmzsmhmEYpuHRIJLSO3XqhMsvvxzTpk3Dxo0bsXHjRkybNg3jxo1zmuHXsWNHLFq0CABQUlKChx9+GBs2bMCRI0ewZs0aXHnllUhKSsI111wDAIiPj8eUKVPwt7/9DT/++CO2bduGm2++GV27dnXM+jMaZRhMKUiEoIpO1jHLT/lYkDlUQlA1btxwWs4NHep8/9JLzRkHwzAM03BpEEnpAPD555/j/vvvd8zIu+qqq/Dmm286bZOVlYVCeyzNarXijz/+wCeffIKCggKkp6dj+PDhmD9/PmIVqubVV19FaGgoJk6ciPLycowYMQJz586F1Wr1y+sQlcaLi6mQp8i1F4KqUbJdIFVUALW1ZPW4IvKrwsOB0MB/hHoFVUNBWR2jWTMgM9O8sTAMwzANkwYjqBITE/HZZ5953EYSYTAAUVFRWL58udf9RkZG4o033sAbb7xR7zHqJSmJBNXZs0D79rROCKq4dEWcrKxMPbPbxPwpwLOgEsXrG5Kg6tgR2LQJ2LwZuPjihuOsMQzDMMFDgxFU5xMpKVTvSMzsA2RBFZ8WRWd0kXiuJqhMLJkA6HOoEhICNhxD6NeP/hiGYRimLjSIHKrzjeRkWipqjDoEVVKyRXaetPKogtihaoghP4ZhGIapLyyoTEDkTSkdKnE7KQmy86QlqILYoWqIIT+GYRiGqS8sqExAzaEShdlTUuBdUJlY1BOQBZVag+eGGvJjGIZhmPrAgsoEhEMlBFV1NSDqljZvDlkoaVVLNznkJ8RSRQX9KeGQH8MwDHMhwoLKBIRDJcJ82dmUgx4WZn9MCKqSEvUdmBzyi4+XqzmcO+f8GIf8GIZhmAsRFlQm4OpQnThBy2bNqE6VY2aflqAy2aGyWGTB5Cqo2KFiGIZhLkRYUJmAa1K6UlABkDvzFher78Bkhwqg/ncA9cBTwjlUDMMwzIUICyoTUIb8bDbg5Em637y5fQPhUGkJKpMdKgBITKQlO1QMwzAMw4LKFISgqqmhnCPhULkJqiDNoQK0HSrOoWIYhmEuRFhQmUBEBCV2AzS7z01QeQv5BalDVV4uaz3xOMMwDMNcCLCgMok2bWi5b58c8nPkUOkN+QWZQyWS7MPDZcHIMAzDMBcCLKhMIjOTln/+CRw8SLdbtLA/6C3kJ4SWcLJMQM2hEoIqNZUbDDMMwzAXFiyoTKJzZ1quXElV0q1WoGtX+4PeQn5ivVrj5ACh5lCJ4qRiFiPDMAzDXCiwoDIJ4VCtWUPLLl0UKVF6HSoTBZUnh4oFFcMwDHOhwYLKJIRDJejbV3HHWw5VEAgqTzlUqamBHw/DMAzDmAkLKpNo3RqIjJTv9+uneLABhPzUHCoO+TEMwzAXKiyoTMJqBW67jW6HhgLDhikebAAhP6VDJUl0m0N+DMMwzIUKCyoTeecdYO9eYMsWoF07xQOeQn61tXKxJxMFlShOWlkJFBbSbQ75MQzDMBcqoWYP4EKnQweVlSLkV1JCvWlCFLpX1KACTBVU0dEU9jt3Djh+nHr3cciPYRiGuVBhhyoYUQol4UYJhGtltTonYZlARgYtjx+nJYf8GIZhmAsVFlTBSFSU7Eq5hv2U+VMmV89UCqraWmr2DLCgYhiGYS48WFAFIxaLc9hPSRAkpAuUguroUYpORkQAaWnmjothGIZhAg0LqmBFKzE9SAXV/v10u21b55QvhmEYhrkQ4FNfsNJABZXTbEWGYRiGuUBgQRWsaBX3ZEHFMAzDMEEHC6pgJT6elkVFzuuDUFCdOAHs20e3WVAxDMMwFyIsqIKVxo1pmZ/vvD7IBFWjRkBFBbBsGa1jQcUwDMNciLCgClYSEmhZUOC8PogEVVgYMGKE8zoWVAzDMMyFCAuqYKUBCCoAGDNGvt27N9CsmXljYRiGYRizYEEVrDRAQfXSS6bXGmUYhmEYU+BefsFKAxFULVsC774L1NQAw4ebPRqGYRiGMQcWVMGKVlK6uC8EVxDwf/9n9ggYhmEYxlw45BesaDlU587RMjExkKNhGIZhGMYDLKiCFS1BJRwqFlQMwzAMEzQ0GEGVn5+PyZMnIz4+HvHx8Zg8eTIKXMWGCxaLRfXvpZdecmwzbNgwt8cnTZrk51ejA3aoGIZhGKbB0GByqG688UacOHECy+wVJO+8805MnjwZ3333neb/ZGdnO93/4YcfMGXKFIwfP95p/bRp0/D000877kdFRRk48joicqgKCgBJoulztbVAYSGtZ0HFMAzDMEFDgxBUe/bswbJly7Bx40b0798fAPDee+9h4MCByMrKQocOHVT/Ly0tzen+t99+i+HDh6NNmzZO66Ojo922NR3hUNXWAiUlNKtP6VYFUVI6wzAMw1zoNIiQ34YNGxAfH+8QUwAwYMAAxMfHY/369br2cebMGSxZsgRTpkxxe+zzzz9HUlISOnfujIcffhjFrg2JXaisrERRUZHTn+FERgLh4XRbCCkR7ouNpTLlDMMwDMMEBQ3CoTp9+jRSUlLc1qekpOD06dO69vHxxx8jNjYW1157rdP6m266Ca1bt0ZaWhp27dqFGTNmYMeOHVi5cqXmvmbPno1Zs2b59iJ8xWIhFyonhwRVRgbnTzEMwzBMkGKqQzVz5kzNxHHxt3nzZgCUYO6KJEmq69X48MMPcdNNNyEyMtJp/bRp0zBy5Eh06dIFkyZNwtdff41Vq1Zh69atmvuaMWMGCgsLHX/Hjx/34VX7gGtiOgsqhmEYhglKTHWo7rvvPq8z6lq1aoWdO3fizJkzbo/l5uYiNTXV6/OsW7cOWVlZmD9/vtdte/XqhbCwMOzfvx+9evVS3SYiIgIRERFe91VvXIt7iqVYzzAMwzBMUGCqoEpKSkJSUpLX7QYOHIjCwkL89ttv6NevHwBg06ZNKCwsxKBBg7z+/wcffIDevXuje/fuXrf9888/UV1djfT0dO8vwN+I9yYnh5bsUDEMwzBMUNIgktI7deqEyy+/HNOmTcPGjRuxceNGTJs2DePGjXOa4dexY0csWrTI6X+Liorw1VdfYerUqW77PXjwIJ5++mls3rwZR44cwdKlSzFhwgT07NkTgwcP9vvr8ooQdSJPjAUVwzAMwwQlDUJQATQTr2vXrhg1ahRGjRqFbt264dNPP3XaJisrC4WiTpOdL774ApIk4YYbbnDbZ3h4OH788UeMHj0aHTp0wP33349Ro0Zh1apVsFqtfn09uhClHEQ9La6SzjAMwzBBSYOY5QcAiYmJ+OyzzzxuI0mS27o777wTd955p+r2GRkZ+Pnnnw0Zn18QDpUQVMKh4hwqhmEYhgkqGoxDdUEiHCoR8svNpWWTJuaMh2EYhmEYVVhQBTOuDtXRo7Rs0cKc8TAMwzAMowoLqmBGmUMlScCxY3S/ZUvzxsQwDMMwjBssqIIZIagqK4HDh6mnH0BV0xmGYRiGCRpYUAUzUVFAfDzd3rSJlikptJ5hGIZhmKCBBVWwI/KoNm6kJedPMQzDMEzQwYIq2GnWjJaivAPnTzEMwzBM0MGCKtjp3ZuWO3bQkh0qhmEYhgk6WFAFO64tcNihYhiGYZiggwVVsDNwoPP9oUPNGQfDMAzDMJqwoAp2kpPl223aAD16mDYUhmEYhmHUYUHVEHjrLaBjR+C778weCcMwDMMwKlgktY7CjE8UFRUhPj4ehYWFiIuLM3s4DMMwDMPowMjzNztUDMMwDMMw9YQFFcMwDMMwTD1hQcUwDMMwDFNPWFAxDMMwDMPUExZUDMMwDMMw9YQFFcMwDMMwTD1hQcUwDMMwDFNPWFAxDMMwDMPUExZUDMMwDMMw9YQFFcMwDMMwTD1hQcUwDMMwDFNPWFAxDMMwDMPUExZUDMMwDMMw9YQFFcMwDMMwTD0JNXsA5wOSJAEAioqKTB4JwzAMwzB6EedtcR6vDyyoDCAvLw8AkJGRYfJIGIZhGIbxlby8PMTHx9drHyyoDCAxMfH/27vbmKbuNgzg1+GlyCxURKEwNu18YU4ri7g53ItzOCaZBkJMlPhBwyemLjSby6YmoyYLkCUjzrmXLG7qkiWYbLDsyxKaaVudkQCW2KAhbKJlWscWXyAwJMj9fPDx7OnQPMhpz6n0+iUnof//6Tk3vVLP7Tk9FAAQCAQ0B0La9Pf347HHHkNvby9SU1ONLiemMYvowjyiB7OIHjdv3sTjjz+uHse1YEMVBnFxdz6KZrFY+OaIEqmpqcwiSjCL6MI8ogeziB53j+OathGGOoiIiIhiGhsqIiIiIo3YUIVBUlISqqurkZSUZHQpMY9ZRA9mEV2YR/RgFtEjnFkoEo57BYmIiIhiGM9QEREREWnEhoqIiIhIIzZURERERBqxoSIiIiLSiA2VRp999hlsNhumTZuG/Px8nDhxwuiSYoLX68X69euRnZ0NRVHwww8/hMyLCJxOJ7Kzs5GcnIyXX34ZnZ2dxhQ7hdXW1uKZZ55BSkoKMjIyUFpaiq6urpB1mIV+Pv/8cyxdulT9g5EFBQX46aef1HlmYZza2looigKHw6GOMQ99OJ1OKIoSslitVnU+XDmwodLg6NGjcDgc2LNnD3w+H1588UUUFxcjEAgYXdqUNzg4iLy8PBw4cOCe8x9++CHq6+tx4MABtLa2wmq14tVXX8XAwIDOlU5tHo8H27dvx+nTp+FyuTA6OoqioiIMDg6q6zAL/eTk5KCurg5tbW1oa2vDK6+8gpKSEvXgwCyM0draii+//BJLly4NGWce+lm8eDGCwaC6+P1+dS5sOQhN2rPPPiuVlZUhY08++aS89957BlUUmwBIU1OT+nhsbEysVqvU1dWpY8PDw2KxWOSLL74woMLY0dfXJwDE4/GICLOIBmlpaXLw4EFmYZCBgQFZsGCBuFwuWbVqlVRVVYkI3xt6qq6ulry8vHvOhTMHnqGapJGREbS3t6OoqChkvKioCKdOnTKoKgKAnp4eXL16NSSbpKQkrFq1itlE2M2bNwH884XhzMI4t2/fRkNDAwYHB1FQUMAsDLJ9+3a8/vrrWLNmTcg489BXd3c3srOzYbPZsGnTJly4cAFAeHPglyNP0l9//YXbt28jMzMzZDwzMxNXr141qCoCoL7+98rm0qVLRpQUE0QEb731Fl544QUsWbIEALMwgt/vR0FBAYaHh2E2m9HU1ISnnnpKPTgwC/00NDTgzJkzaG1tHTfH94Z+VqxYgW+++QYLFy7EH3/8gQ8++AArV65EZ2dnWHNgQ6WRoighj0Vk3BgZg9noa8eOHTh79ixOnjw5bo5Z6Cc3NxcdHR24ceMGvv/+e2zZsgUej0edZxb66O3tRVVVFZqbmzFt2rT7rsc8Iq+4uFj92W63o6CgAPPmzcORI0fw3HPPAQhPDrzkN0mzZs1CfHz8uLNRfX194zpd0tfduzeYjX7efPNN/Pjjjzh+/DhycnLUcWahP5PJhPnz52P58uWora1FXl4ePv74Y2ahs/b2dvT19SE/Px8JCQlISEiAx+PB/v37kZCQoL7mzEN/06dPh91uR3d3d1jfF2yoJslkMiE/Px8ulytk3OVyYeXKlQZVRQBgs9lgtVpDshkZGYHH42E2YSYi2LFjBxobG3Hs2DHYbLaQeWZhPBHBrVu3mIXOCgsL4ff70dHRoS7Lly/H5s2b0dHRgSeeeIJ5GOTWrVs4f/48srKywvu+mMQH5um/GhoaJDExUb766is5d+6cOBwOmT59uly8eNHo0qa8gYEB8fl84vP5BIDU19eLz+eTS5cuiYhIXV2dWCwWaWxsFL/fL+Xl5ZKVlSX9/f0GVz61vPHGG2KxWMTtdkswGFSXoaEhdR1moZ9du3aJ1+uVnp4eOXv2rOzevVvi4uKkublZRJiF0f73Lj8R5qGXt99+W9xut1y4cEFOnz4t69atk5SUFPVYHa4c2FBp9Omnn8qcOXPEZDLJsmXL1NvFKbKOHz8uAMYtW7ZsEZE7t8JWV1eL1WqVpKQkeemll8Tv9xtb9BR0rwwAyKFDh9R1mIV+Kioq1H+PZs+eLYWFhWozJcIsjPbvhop56GPjxo2SlZUliYmJkp2dLWVlZdLZ2anOhysHRUQkDGfQiIiIiGIWP0NFREREpBEbKiIiIiKN2FARERERacSGioiIiEgjNlREREREGrGhIiIiItKIDRURERGRRmyoiGhKcjqdePrpp3Xfr9vthqIoUBQFpaWlE3qO0+lUn7Nv376I1kdEkcGGiogeOnebj/stW7duxc6dO/Hzzz8bVmNXVxcOHz48oXV37tyJYDAY8sXSRPRwSTC6ACKiBxUMBtWfjx49ivfffx9dXV3qWHJyMsxmM8xmsxHlAQAyMjIwY8aMCa17t9b4+PjIFkVEEcMzVET00LFarepisVigKMq4sX9f8tu6dStKS0tRU1ODzMxMzJgxA3v37sXo6CjeeecdzJw5Ezk5Ofj6669D9nX58mVs3LgRaWlpSE9PR0lJCS5evPjANX/33Xew2+1ITk5Geno61qxZg8HBQY2vBBFFCzZURBQzjh07hitXrsDr9aK+vh5OpxPr1q1DWloaWlpaUFlZicrKSvT29gIAhoaGsHr1apjNZni9Xpw8eRJmsxlr167FyMjIhPcbDAZRXl6OiooKnD9/Hm63G2VlZeBXqRJNHWyoiChmzJw5E/v370dubi4qKiqQm5uLoaEh7N69GwsWLMCuXbtgMpnwyy+/AAAaGhoQFxeHgwcPwm63Y9GiRTh06BACgQDcbveE9xsMBjE6OoqysjLMnTsXdrsd27ZtM/SSJBGFFz9DRUQxY/HixYiL++f/kZmZmViyZIn6OD4+Hunp6ejr6wMAtLe349dff0VKSkrIdoaHh/Hbb79NeL95eXkoLCyE3W7Ha6+9hqKiImzYsAFpaWkafyMiihZsqIgoZiQmJoY8VhTlnmNjY2MAgLGxMeTn5+Pbb78dt63Zs2dPeL/x8fFwuVw4deoUmpub8cknn2DPnj1oaWmBzWabxG9CRNGGl/yIiO5j2bJl6O7uRkZGBubPnx+yWCyWB9qWoih4/vnnsXfvXvh8PphMJjQ1NUWociLSGxsqIqL72Lx5M2bNmoWSkhKcOHECPT098Hg8qKqqwu+//z7h7bS0tKCmpgZtbW0IBAJobGzEn3/+iUWLFkWweiLSEy/5ERHdxyOPPAKv14t3330XZWVlGBgYwKOPPorCwkKkpqZOeDupqanwer3Yt28f+vv7MWfOHHz00UcoLi6OYPVEpCdFeN8uEVHYuN1urF69GtevX5/wH/a8a+7cuXA4HHA4HBGpjYgih5f8iIgiICcnB+Xl5RNat6amBmazGYFAIMJVEVGk8AwVEVEY/f3337h8+TKAO18pY7Va/+9zrl27hmvXrgG4c/fgg37gnYiMx4aKiIiISCNe8iMiIiLSiA0VERERkUZsqIiIiIg0YkNFREREpBEbKiIiIiKN2FARERERacSGioiIiEgjNlREREREGrGhIiIiItLoP+Uz5E8/u/bTAAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the outputs of the system on the same graph, in different colors\n", + "t = response.time\n", + "x = response.states\n", + "plt.plot(t, x[0], 'b', t, x[1], 'r')\n", + "plt.legend(['$x_1$', '$x_2$'])\n", + "plt.xlim(0, 50)\n", + "plt.ylabel('States')\n", + "plt.xlabel('Time [s]')\n", + "plt.title(\"Initial response from $x_1 = 1$, $x_2 = 0$\");" + ] + }, + { + "cell_type": "markdown", + "id": "b136ca77", + "metadata": { + "id": "Cou0QVnkTou9" + }, + "source": [ + "There are also lots of options available in `initial_response` and `.plot()` for tuning the plots that you get." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "3d127338", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "for X0 in [[1, 0, 0, 0], [0, 2, 0, 0], [1, 2, 0, 0], [0, 0, 1, 0], [0, 0, 2, 0]]:\n", + " response = ct.initial_response(sys, T=20, X0=X0)\n", + " response.plot(label=f\"{X0=}\")" + ] + }, + { + "cell_type": "markdown", + "id": "c09ccc24", + "metadata": { + "id": "b3VFPUBKT4bh" + }, + "source": [ + "### Step response\n", + "\n", + "Similar to `initial_response`, you can also generate a step response for a linear system using the `step_response` function, which returns a time response object:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "91364e84", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "cplt = ct.step_response(sys).plot()" + ] + }, + { + "cell_type": "markdown", + "id": "3bd1f5be", + "metadata": { + "id": "iHZR1Q3IcrFT" + }, + "source": [ + "We can analyze the properties of the step response using the `stepinfo` command:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "00fe1ab8", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Input 0, output 0 rise time = 0.6153902252990775 seconds\n", + "\n" + ] + }, + { + "data": { + "text/plain": [ + "[[{'RiseTime': np.float64(0.6153902252990775),\n", + " 'SettlingTime': np.float64(89.02645259326653),\n", + " 'SettlingMin': NamedSignal(-0.13272846),\n", + " 'SettlingMax': NamedSignal(0.90059949),\n", + " 'Overshoot': NamedSignal(170.17984629),\n", + " 'Undershoot': np.float64(39.81853696610825),\n", + " 'Peak': np.float64(0.9005994876222034),\n", + " 'PeakTime': np.float64(2.3589958636464634),\n", + " 'SteadyStateValue': np.float64(0.33333333333333337)}],\n", + " [{'RiseTime': np.float64(0.6153902252990775),\n", + " 'SettlingTime': np.float64(73.6416969607896),\n", + " 'SettlingMin': NamedSignal(0.22760198),\n", + " 'SettlingMax': NamedSignal(1.13389338),\n", + " 'Overshoot': NamedSignal(70.08400657),\n", + " 'Undershoot': 0,\n", + " 'Peak': np.float64(1.13389337710215),\n", + " 'PeakTime': np.float64(6.564162403190159),\n", + " 'SteadyStateValue': np.float64(0.6666666666666665)}]]" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "step_info = ct.step_info(sys)\n", + "print(\"Input 0, output 0 rise time = \",\n", + " step_info[0][0]['RiseTime'], \"seconds\\n\")\n", + "step_info" + ] + }, + { + "cell_type": "markdown", + "id": "4c43d03c", + "metadata": { + "id": "F8KxXwqHWFab" + }, + "source": [ + "Note that by default the inputs are not included in the step response plot (since they are a bit boring), but you can change that:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "9e0eaa51", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "stepresp = ct.step_response(sys)\n", + "cplt = stepresp.plot(plot_inputs=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "03cdf207", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the inputs on top of the outputs\n", + "cplt = stepresp.plot(plot_inputs='overlay')" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "5cc0e76c", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "stepresp.time.shape=(1348,)\n", + "stepresp.inputs.shape=(1, 1, 1348)\n", + "stepresp.states.shape=(4, 1, 1348)\n", + "stepresp.outputs.shape=(2, 1, 1348)\n" + ] + } + ], + "source": [ + "# Look at the \"shape\" of the step response\n", + "print(f\"{stepresp.time.shape=}\")\n", + "print(f\"{stepresp.inputs.shape=}\")\n", + "print(f\"{stepresp.states.shape=}\")\n", + "print(f\"{stepresp.outputs.shape=}\")" + ] + }, + { + "cell_type": "markdown", + "id": "beecce7f", + "metadata": { + "id": "FDfZkyk1ly0T" + }, + "source": [ + "### Forced response\n", + "\n", + "To compute the response to an input, using the convolution equation, we can use the `forced_response` function:" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "33d8291a", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "T = np.linspace(0, 50, 500)\n", + "U1 = np.cos(T)\n", + "U2 = np.sin(3 * T)\n", + "\n", + "resp1 = ct.forced_response(sys, T, U1)\n", + "resp2 = ct.forced_response(sys, T, U2)\n", + "resp3 = ct.forced_response(sys, T, U1 + U2)\n", + "\n", + "# Plot the individual responses\n", + "resp1.sysname = 'U1'; resp1.plot(color='b')\n", + "resp2.sysname = 'U2'; resp2.plot(color='g')\n", + "resp3.sysname = 'U1 + U2'; resp3.plot(color='r');" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "10a05cb1", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Show that the system response is linear\n", + "cplt = resp3.plot()\n", + "cplt.axes[0, 0].plot(resp1.time, resp1.outputs[0] + resp2.outputs[0], 'k--')\n", + "cplt.axes[1, 0].plot(resp1.time, resp1.outputs[1] + resp2.outputs[1], 'k--')\n", + "cplt.axes[2, 0].plot(resp1.time, resp1.inputs[0] + resp2.inputs[0], 'k--');" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "c03f2556", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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QGTNmcO7cOYKCgjh48GCOxPDLL79k9erVzJo1i1u3buHr68vGjRv54osvivRYTJ06le+//56NGzdy9+5dpk+fjo+Pj+a+FFanTp3o2LEjQ4YM4dChQwQGBrJv3z7NDNuPP/6YI0eO8PXXX+Pn58eqVatYtGgRn3zyieYcXbt2ZdGiRVy5cgVvb2/eeuutXLvuf/vtN7Zv386dO3d45513iI2NZdy4cbnGVdBzXatWLXr16sWECRO4cOECly9fZvz48fm2QBb03AAYGhryxhtvcO3aNU6dOsX777/PK6+8oumGLSmTJk3izp07TJs2DT8/PzZt2qSZRfw8JW1y4+HhwbZt2zStniNHjtS05kH+75sLFy7w7bff4u3tTXBwMNu2bSMyMrLIPyAkqdDKflifJJVf+c2KFUKI48ePixYtWgh9fX1RtWpVMW3aNJGRkaHZ3qlTJ/HBBx88c9y1a9dEjx49hLGxsTAzMxMdOnQQAQEBmu3r1q0TjRs3Fvr6+sLKykp07NhRbNu2TbP93LlzolGjRkJfX180btxYbN26tVCTJ3IbBH/x4kXRvXt3YWpqKkxMTETDhg01kwhOnTolOnXqJKysrISRkZFo2LCh2LhxY477N3nyZPHWW28Jc3NzYWVlJaZPn55jMkVMTIx4/fXXhYWFhTAyMhI9e/bUzDQVQj15wsLCIkdM/57cEBERIQYOHCgcHByEvr6+cHV1FV9++aVmgooQ6pmx2TM4zc3NRcuWLXPMzP2v3CZPZGVlidmzZwsnJyehp6cnGjVqpBmYL8Q/Exbye4yzRUdHi7FjxwobGxthaGgo6tevL7y8vDTbt2zZIurWrSv09PREtWrVxA8//JDj+NDQUNGjRw9hYmIiPD09xd69e3OdPLF+/XrRqlUroa+vL+rUqSOOHDmiOcd/J08Ikf9zLYQQ4eHhok+fPsLAwEBUq1ZNrF69Os/XjRAFPzczZ84UjRo1EosXLxaOjo7C0NBQDB48WMTExGjOkdd7LLfJE/lNKBFCiJ07dwoPDw9hYGAgOnfurJlUlJKSkmv8uZ03t+f5v49lYGCg6NKlizAyMhIuLi5i0aJFOd7r+b1vbt++LXr27Cns7OyEgYGBqFmzpmbShSSVBoUQzzGAR5KkF0rnzp1p3LixXPqpjD148AB3d3euXr1K48aNtR1OnmbNmsWOHTu0ttTdnDlzWLp0KSEhIVq5viSVB3JWrCRJklQhLV68mBYtWmBjY8OZM2f44YcfePfdd7UdliRplUzsJEmSpArJ39+fb775hpiYGKpVq8bHH3/MjBkztB2WJGmV7IqVJEmSJEmqJOSsWEmSJEmSpEpCJnaSJEmSJEmVhEzsJEmSJEmSKgmZ2EmSJEmSJFUSMrGTJEmSJEmqJGRiJ0mSJEmSVEnIxE6SJEmSJKmSkImdJEmSJElSJSETO0mSJEmSpEpCJnaSJEmSJEmVhEzsJEmSJEmSKgmZ2EmSJEmSJFUSMrGTJEmSJEmqJGRiJ0mSJEmSVEnIxE6SJEmSJKmSkImdJEmSJElSJSETO0mSJEmSpEpCJnaSJEmSJEmVhK62AygtKpWKsLAwzMzMUCgU2g5HkiRJkiSpWIQQJCQk4OjoiFKZf5tcpU3swsLCcHFx0XYYkiRJkiRJJSIkJARnZ+d89ynXiV1aWhpvvfUWhw4dIiEhgSZNmrBw4UIaNGhQ4LFmZmaA+kEwNzcv7VAlSZIkSZJKRXx8PC4uLprcJj/lOrHLzMykevXqnD9/HgcHB3755RcGDhxIQEBAgcdmd7+am5vLxE6SJEmSpAqvMEPLFEIIUQaxlIj09HQMDQ2JjIzExsYm333j4+OxsLAgLi5OJnaSJEmSJFVYRclpKtSs2HPnzmFvb59rUpeWlkZ8fHyOv7KyevVqAgMDy+x6kiRJkiRJuakwiV1cXByTJk1izpw5uW6fO3cuFhYWmr+ymjixefNm3njjDTp37kxiYmKZXFOSJEmSJCk3FSKxS01NZeDAgfTp04dx48blus+MGTOIi4vT/IWEhJRJbO3bt8fFxYXg4GC2b99eJteUJEmSJEnKTblP7DIzMxk+fDiOjo78+OOPee5nYGCgmShRlhMmHBwcGD9+PABr164tk2tKkiRJkiTlptwndhMmTCAlJYWVK1eW20LDI0eOBODw4cM8fvxYy9FIkiRJkvSiKteJXVBQECtXruTkyZNYWVlhamqKqakpp06d0nZoOXh4eNCgQQNUKhWnT5/WdjiSJEmSJL2gynVi5+rqihCClJQUEhMTNX8dOnTQdmjPaNu2LaCeuStJkiRJkqQN5Tqxq0jatGkDwO3bt7UciSRJkiRJL6pyvfJERTJgwADu3btH9erVtR2KJEmSJEkvKJnYlRBLS0ssLS21HYYkSZIkSS8w2RUrSZIkSZJUScjErgTt27ePoUOHMn/+fG2HIkmSJEnSC0gmdiUoODiYrVu3cvjwYW2HImlJfHw8KpVK22FIkiRJLyiZ2JWg+vXrA3Dr1i0tRyJpy6JFi+jRowdJSUnaDkWSJEl6AcnErgTVq1cPgJCQEOLi4rQcjVTWhBA0aNCAI0eO8Oabb2o7HEmSJOkFJBO7EmRpaYmTkxMg69m9SLKysgBQKBRYW1sDsGnTJgICArQZliRJkvQCkoldCfP09ASQX+ovkLfeeouOHTty4sQJ2rVrx8svv4wQgoULF2o7NEmSJOkFIxO7ElajRg0A7t27p+VIii44OFjbIVQ4aWlpbN68mVOnTiGEAODdd98FYP369XIihSRJklSmZGJXwmrUqIGBgQHJycnaDqVILl26RPXq1ZkwYQIqlYpbt26xffv25z7v4/hUniSnl0CE5dPBgweJi4vD0dFRs4Zxt27dMDMzIzIyksuXL2s5QkmSJOlFIhO7EjZlyhSSk5OZN2+etkMpkl9//ZWsrCzS0tLw9vamWbNm/O9//9O0QhVVUlomb/x1kZbfHqHFnMP8esS/2Ocqzw4cOADAwIED0dHRAUBPT49u3boB6tqG0otJCEF4eLi2w5Ak6QUjE7sSZmBggFJZsR5WlUrF/v37ARg/fjw1a9YE1GVbfHx8inw+IQTvrL/CCb9IADKyBD8d8mOnT1iJxVxeHD16FECTyGXr3bs3rq6umJiYaCMsScuEEEybNo1GjRpVyGEZkiRVXBUrA5FKhbe3N1FRUZibm9OmTRssLS3p378/AOvWrSvy+byuh3P8biSGekq2vt2G919STyiZvfsWcSkZJRq7NkVERODr64tCoaBTp045to0dO5bAwEA+/vhjLUUnlbW0tDRWr13P4Vvh7PN5wOEjR4iMjGTGjBnaDk2SpBeITOxKwaRJk2jWrBk3b97UdiiFkt1d2L17d/T09AAYNmxYjm2FlZGl4vv9dwCY3NmDZq7WvNfVgxp2JsQmZ7D9ysMSjFy7jh07BkCTJk00ZU6y6erqolAotBGWpCU/LlzCG6+Pol+f3kzeeBvr3h+iUCjYsmUL169f13Z4UhkRQrDl8kMGLzxGv6/Ws+fyfW2HJL1gZGJXCq5du8aVK1e4e/eutkMplHPnzgHw0ksvaW7r2rUrCoWC27dvExoaWuhzHbvzmIexKdia6jOhQ3UA9HSUjGnrBsC6C8GVZqydra0tAwcOZODAgcSlZHDwVgRB0TlXnMjKyiImJkZLEUplJS0ji2+//xEA8zpt0dNRcC/TmuotugKwbNkybYYnlaFfj9zjk83X2Pvjh3jNHEW/dg358pcV2g5LeoHIxO45eV0P41rIkxy3ZZc8qQi17IQQXLlyBYBmzZppbrexsdH8vyhr327yDgFgSFNnjPR1NLfHXtlHVuBF/B8n4vOfx6ui6t69O9u3b6fz8LdoM/cIE9dcpsuPx1l4xB+AjRs3YmVlJVeheAF8tnQryVGhKPUNOf3HbLZPboeOUkGCa0cANm/eTGZmppajlErbSb9IFhz2A+CdmfPR1TdApCXx9UfjOXr6gpajk14UMrF7DolpmXy+/SYDfjvD+FXepGaoVyCoSLXssrKymD59OqNGjaJBgwY5tnXv3h2AI0eOFOpcj+NTOXZXPWFiWHMXze2rV6/mg3cnY/ZY3R112PdRSYReLtyPTOTttVdITs/CUE+JSsD8Q34cu/MYJycnEhISuHBBfqBXZnHJGaxYtRqAjj364ulsR30nC0a3ccXQtREGppZERkZqJtpIlVNaZhZf7lQPv3mjjSvzx3UjPi6Oqg07gFAx8o0xsq6lVCZkYvccUtKz6FbHHh2lgsO+j/hun3psmYeHB1AxWux0dXWZMmUKa9euxcjIKMe2N954g927d7NgwYJCnWvLlYdkqQTNXa3wqGIKQFRUFJMnTwagtpsDAIdvP67wxZCjoqIICgriG6/bJKZl0tLNmmsze2i6nL/cdZP6DRqiUCgIDw8nMjJSuwFrgRCCm6Fx7L4Wht+jhErTBf9ff50KIM73NADT352guX1cO3d0dHXRq9EagF27dmklPqlsbPJ+iK/3aXSDLzKlu3rCmJGhAX/+vhSFvjGP7t9h/ZYd2g1SeiHIxO452JkZMP+VRvw5ujkAK88+4GZoXIXqis1PrVq16Nu3LzY2NgXuK4Rgs7d6YsQrLf5prfvll19ISkqiSZMmLF+4AKVCcGbF17i7u3Pjxo1Si720bdiwATc3Nzb+8Ck6SgXfDWmAga4O03rVxtpEn5CYFM4EJWleC9euXdNyxGXrUXwqo/+6SN+Fp3lvw1V6LDjJxDWXK9WsaID0TBXLth5AlRyHiak5Xbt20WxzsTbmpTr2mDbqweAPvmbatGlajFQqTRlZKpYc9Sf26HICNnzFqj//GVPZp1VdanQYAMA338/XVohlIiMjg1WrVjF8+HDeeOMNTpw4oe2QXkgysSsBXWpXoV8jRwCWnw7UfJkHBweTlpamzdAKdOHCBW7duvXccV4MjCEwKgkTfR36NFC3zKWmprJo0SIAvvjiC6xMDWjuaoNIS0alUrF69ernjl9bsscl6lk58kpzF6rbqVsojfR1eL21KwB/nQmkUaNGAMWqB1hRhcel8Mqyc5zyj0JPR0F9J3P0dBQcuv2I15df0AxZqAwO+z7i0V31a6Fv35c1s8qzDWzshIFDTaKc2uHs7KyNEKUysO9mBAHXzpMRFYSZmRmvv/56ju1TPnwfFEr8r3sTHh6hpShLV2hoKB06dGDMmDFs3LiR1atXExiVVGlb6sszmdiVkIlPZ4DuvhYGRhZYW1tTs2ZNoqKitBxZ/iZMmED9+vXzHP9z8+ZNvvjiC5YuXZrveTY+nTTRr5EjJga6AHh5efHkyRNcXFwYOHAgAK2rW2NST92qsX79erKyKuaX/KXL6i9zffvqvNnePce2Ua2roVTA5aBY3GrWBSp+i50QolDL5KVmZDFpzWWCopNxsTbiwIcd8XqvA9snt8PKWI/rD+OYufNWGURcNjZcDMa89TCmLN3F/7744pntnWvZYainJCg6mdvh8VqIUCoLa849IPH6QQBGjx6NlZVVju1jerTAdeQ3OL27lvAMA22EWKoiIiLo0qULFy5cQM/IFIs2r2Ld8x1mXRK8uuw84XEp2g7xhSITuxLSwNmCZq5WZKoEXtfVY6ru3LmDk5NTsc53MzSOkX+c58udN3kUn1rC0aqpVCr8/dUzOLNXm/iva9euMWfOHFauXJnneeJTM9h7Q7100r+7YdeuXQvAqFGjNKtxtK5ug1H15ugYmhIWFsbp06dL4q6UqfT0dO74+gLQtmUzzXjCbFXMDGnlru6+TjZTt9JUtMTOy8uLhw//qTm4dOlSmjRpQlBQUL7Hfb//DtcfxmFlrMf68a01LZn1nSz4bWRTFAr1j4AL96NLNf6yEBKTzCn/KBQKBR8Oe4l69eo9s4+JgS6datqRGR/JF9/M07RgVzaRkZG8/fbbtGvXjqbNmtOqWz/6TfmRTzf5cOBWRKVutfENj+e8bxDJ/uqyUbnNgjfS16F/n14oDYzZd6NyLTOXlpbGoEGD8Pf3x8DSHrvRP2PbeTRt+wzHUE/JxQcxDPxxD3sPHdN2qC8MmdiVoAGN1d2xu6+HP9eyYjdD4xi29BxnA6JZfS6I0csvkplV8rOpQkJCSE1NRU9PDzc3N83t//4Qbtu2LaDuekxJyf1X1y6fMFIzVHhWMaWJi6XmHEZGRpiamjJq1CjNvk1drTAwMMCwRgsAzVJmFcmtW7fIysxAaWjK2F4tc92nbyN1d7Rfhi19+/ZlyJAhZRnic4mIiGD48OF06tSJx48fk5GRwYIFC/Dz86NHjx55TgS5GBjDyrMPAPjp1ca4WBvn2N7Ww5YRLasBMGv3bVSqiv1l//cl9QSgDp62z9zXf+tcqwoZ0SHsWPItP/30U1mFV6bMzMxYtWoVZ8+e5eqVy1w84oXXgqn88uFw3ly0l9eWXyA2KV3bYZaKNeeDSLp1DLIyadq0KU2aNMl1v971qwLqbtvKNDt2/vz5nD9/Hl0jU2yGfUUtzxrs/7Aju99rz4EPO2KTFo73/DcZOuwVEhMTtR3uC0EmdiWod30HlAq4FvKE4OiCu61yI4Tga6/bpGRk4WRphG5WKnfCYthwseRnkfr5qesteXh4oKOjQ2pGFl/tvk2dL/fT/vujbL38EDc3NxwcHMjIyODSpUu5nie7dt2rLVw0qy0oFAo2bNhAdHQ09evX1+xrqKdDQ2cLDN2bAnDw4MESv1+lbfthdSujYdUa9KhbNdd9sm+/l2zA8vWbmTlzZpnF97xmz55NUlIStra22Nraoqenx5EjR3BxccHPz4+XX36ZhISEHMekpGfx6ZZrCAGvNnehS60quZ770561MDPQxTc8ngO3Ku5Yo8wsFZu9HxJzaAnBW77l8uXLee7b3sMWA8fagILAwEDCwyt+i83x48f58MMPNT8CDQ0NGTp5BnYDPsVuyP+o1nk4BsYmpIffJWL1Rxw7683IPy9Uuskz8anq1XQSr6k/x8aPH5/nvp1q2ZHme4xLP0/iizk/llWIpe7DDz+kfvdXserzMU6u1dkwobWmF8PVxoTVUwaiY2BMSlwU46fO0m6wLwiZ2JUgOzMDWrqrl5b6ecVGWrRowbhx44p0jjP3orkQGIO+rpIP6qUT9MtIgn8axpdfzy3x7ozsxC67G/abPbf560wgqRkqHsam8PHma/x5KlDTanf27NlnznErLI7rD+PQ01EwqMmz3c76+vrP3NbYxRIjt8aAuiXw8ePHJXWXysS+E+cBqF2vQY4izP9mZ2ZAfSdzAE75le9xlv8WHR3N8uXLAfjhhx80Lc8uLi4cPHgQGxsbvL29GTx4MOnp/7TA/HDgLg+ik6lqbsjnfetobs/MzNRMNAGwNNZnbDs3ABYevVdhu+iO3nnMo/gUUu6c5uT+XfmOP3SxNsbVwQY9O/WkmtzeRxVFQkICkyZNokuXLvzyyy/s3bsXgLXngzip2xTj2h2Z+Now/A6txf+OL02aNMFQKTDXycQ3PJ4pG30qfEvtv23xfkhi/BMMjQwxNDRkxIgRee5rrK+Ls2EG6RH32OW1pwyjLF0rLoSR0PR1LGu2YunrzbA3N8yxvZ6LLa+/Nx2ALSuXEhEVq40wXygysSthnZ+2VFwLicXb25urV68W6fjslrlB9ayY+u4kMjPSQZXJk6RUfMMTCji6aLKXPKtVqxa3wuJYd0F97e+HNGBSJ/VkkLn7fHGoqZ7ZeebMmWfOsepp11vPelWxMVUPCv732L3cNHSxRMfECvdOQ/j1119zTf7Kq/RMFdHWDTBvOZjXhw7Md9/ONdWvhWN3HxMeHl4hlpjbsGEDGRkZNGnShI4dO+bYVrt2bfbu3YuJiQmHDx9m9OjRqFQqzt+PZsXZQADmDm6AuaEeKSkp/Pjjjzg7O9OpU6ccXU91dcLRSY7mdng8l4Mq5of835dCSH90n8zkOExNTWndunW++7erYYuBs3oiTUVN7G7evEnDhg35/fffAXjrrbdo3bo1O31C+d/TwryTO9fg6wH1MdDVwcXFhSNHjnD61Em2zBqLvq6SI3ces+5C/uM0KwqVSrD63AN0jMz5bdMB/P39sbS0zPeYAX1fBuDO1fN5Dm2pCIQQbN26lUO3wvjxoPpz7asB9WhazSrX/Rf/7x2MbJ3JSk3ivVmVq+RLbFI60Ynlq/qFTOxKWOdadgAEpJoAEBgYWOhj45IzOHRbvSqD0v8EwcHBuLu78+qPO7Fo8wr7S7jrKntlDA8PD5aeuI8Q0LehA6+2qMb0XrV5tbkLKgGHoy0B9RfSv1tYohPT2OkTBqApzAtw+fJlatasSYsWLXJtkWnsrD6fTrtxTHx7coEfhuXJ8buPUTk2oFb/t3lv9OB8981+LezeuhFHR0fefvvtsgjxuWRPkhkzZkyu21u2bMm2bdvQ09Nj48aN7PTay9SnXbDDW7jQwtmYBQsWUKNGDaZOncqjR48wNDQkOlo9WeLJkye8OXoUj9d+QnpkEKvOVbwv+fC4FI7ffUzqAx9Ava7yf8uc/FdbD1sMnNQtmRUxsTt69Cjt27fnwYMHuLu7c+zYMZYsWcLVR5lM2aR+/ke3cWVqz1qa4RgAVlZWNG7cmPpOFnzWuzaq9BTm7b9bahPCytIJv0geRCdjZqjL4CZOhSpnM7xnW3TMbMnKSOfA4Yq7EsnKlSsZOnQog/r1QaVS8Vrragx/On42NwZ6uoye+A4AO9f9SUJy+UqEnsfCo/foMO8Y6y+Un6L7MrErYbXszahqbojK1BaAuLg4YmML1yqx50Y46Vkqalc148T+nYB6/MKwzurxaIdvl+xSXO+++y7ffvstTVq04tBtddI4saO6pU6hUDB7QD1q2puSbO6Cjp4BKpWKsLAwzfFLjgeQlqmi4dMZwZr7sUfdzeDq6prjQz6bi7URVsZ6ZGSJEm+FLG3br4YC0L+RIzrKZ+/bvzVyscRYX4cMc3UX9bVr18p112NISAiXL19GqVTm26XUo0cP1q9fz5QpUziV4kxITAp2OincWTuLatWqMWXKFMLDw3F1deWvv/4iLCwMOzt1khsfH4+1tTWJsZE83vQlu8/d4nEF+5LfdOkhKgH6EeoC2z169CjwmLY1bDSJ3eXLlytUa82RI0fo1asXcXFxdOjQAW9vbzp37sz+m+FMXneFLJVgcBMnZvWrl+v7PZtd3B0e/TGBx3cuMmtXxS95s+LsAzJiQulf20JT4qkgNexMsa3TCoA1m3eWZnilxtfXl3fffRcAPZeGtHS34cu+z84I/68fP3sfXRMLMp48YvpPf5R2mGXicUIq6y4EkZyehbOVUcEHlBGZ2JUwhUJB51p2KPUMMbVSJ3f3798v1LHZyVVbexXnzp1DoVAwbNgw2nvaIjLTuXr+JD63fEss1pdffpkZM2YQlGVNaoaK6rYmNHCy0Gw31NPh51ebYKBvQNU3F/PT7sua8i33IxNZfV7d2vJxj5y/0rMTuz59+uR6XYVCQaOns2cPnLnCihUrKsRsqbjkDPadvkJK0DW6uuc9CzKbno6S5m7W6NlUQ6mjQ0xMTI7EuLw5f149drBVq1aaRCwvQ4YMwbnXJLZdDUVHqeDTrq7s3LGd2NhYPD09+eOPP7h79y5jx47N0ZpVrVo1Tpw4Qe3atclKjCZi5zzWnn9QmnerRGVkqdh4KRhVWjIxAeq1jwuT2NmaGlC/lgc6JlaohODOnTulHWqJiYyMRKFQMHjwYA4ePIi1tTUbLwUzed0V0rNUvNygKvOGNkRZwA+dfXv3kJ74hOj9C9lz+T5H71TcNaPvPU7kpF8k0fsXMn9sF3bs2FGo4xQKBe06vwTAiaOHSzHC0pGSksLQYcNITk7G0LUxDV5+nSWvNUNft+BUwtTEmD6vjgGFkr2nLleKsZbfbz5NYlQYTapZ0sHTVtvhaMjErhRkd8Epze2BwnXHJqdnciZA3V2Vfl89+7Rjx444ODhgb25IytElRPz9BQt+K/lfOgefJpT9Gzs+84u7rqM5n/aqha6FPV/v8WXDxWBCYpJ5a+1l0jNVdPC0peO/XtARERF4e3sD0Lt37zyv2ehpd+yc90Yxbtw4TVJRnu25EU7s9SM8/vtzlv/0daGOaV3dGoWuHhZV1d0U5XkZtWHDhhEWFsaSJUvy3S86MY1Pt1znlyPqcZQz+9WlW1MP5s+fz/Hjx/H19WX8+PEYGOReiNXa2podO3ZgaGRMWvANFv62hKwK8iG/90Y4YXGp6EXcJDMzAw8PDzw9PQt1bDsPO6oMn8OHq8/kWRKjPBo+fDinT59m/fr1pKqUTNtynWlbb6B62v2+cERTdHUK/ir5/vvvqV69OlnxkcQe+4uvdt8mLbNiFihfeNSfjNgw0kJukp6WRvPmzQt97MiBfUChJDo0sEhDdbRNCMG4iW9x+9YtlMaWeLwyndVvtsHWtPAFlxfN+ZyaH6xENBnKkTsVa9LcfwVGJrJ07ueE/fEWNZ9459taXdYK134sFUk7D1t0lQqyTNQJXmFa7E77R5GeqcLZyogJw1/BtYol9vb2mu2NWrXj8NVDnDhWMuMywsPDuXz5MjVqeHD+fgwAHWvm3krzZnt3/B8lstE7hBnb/klMstfKza21rkWLFlStmnspEFDPjAUwcqpNQlQEly5dolu3bs97t0rV9qsPyXikXv+3WbNmhTqmTXV1oWJhVQ1CA7l+/Tq9evUqtRifl4ODAw4ODiSmZfLX6UAO+z4i7El2V6k6+XqSnEHm00Tsiz51GN3GDYApU6YU+jq1atVizpxv+XjKhwQdWM6eS5Pp36pWSd6VEqdSCZYcVz//3eo5cKV5czp06FDo41tXt+Yv22pcDSteKSRtcq/TkEUnHrD6XBBxKRkoFPBeV08+6uZZ6C80ExMT/vrrLzp37kzitf341mrHijPVeKtTjVKOvmTdiYhn17UwEi7vBqBXr15FWi6uexN3TDxbIXT0CHoch7t7wceUB598OYe/164GhRK3odNY815P3GxNinQO56p2jOnRnGUn7vPHqft0r2tf8EHlkBCCsV8sIPneRZQ6urwxsLu2Q8pBttiVAjNDPZq5WqFr44y9ixuGhoYFHnPEV/3rpVsde1xdXZkwYQL9+/fXbO/RtTMAIfduk5Hx/LWgTpw4Qb9+/Xh97JvEpWRgaqBLw391w/6bQqHg6wF1qXL5d8KWvUlWchzNXa3YMKE1Vcxy3jcvLy8A+vXrl+/1Gzqrr5Vupf5Uy68OWHkQEpPMpQexpD9N7Jo2bVqo4+o7WWCirwPW6lIX169fL7UYS0rokxT6/nqKnw75cf1hHFGJaU//0olKTCdTJWjobMHGia0Z/3QpveL44P13sXevjUhLYvac70rwHpSOHT6h3IlIwNRAl++njOPSpUv8+GPh65G1dLdGoYCAyCQiE8r34PGIiAjat2+P1/5DfLfvDu2/P8rCo/eIS8mgpr0pf09ozZTuNYvcStGpUyfN+Kzo/b/y816fCjWRQqUSfL79JpkpiaTcVHelfvTRR0U6h7mhHr0/+hG7/p8ShnVphFmi0jKzmL3pPAt+mAuAZ//JHJn/nubHeVGNaeuGrlLBGe/rHPfJu3pCefb9un2cXPEtAO9M+ZS6detqOaKcZItdKelcqwoX2g6n67j3eX9Mi3z3VamEplm6W53cf8H0aNWQafrGqNKTuX37tmZx+eJ68OABAPqW6uu1cLPKtztFX0+XxFB/Mp48Yn5nY14Z3PaZfVJTUzl06BAAffv2zff6NqYGuFgbkVLVAyj/id32q6FkJsaQlRSLUqmkYcOGhTpOT0dJC3dr9t5WJ3bltSv2999/Z+vWrYwa/QYrwh15EJ2Mk6URH3TzpJ6jeY6JIqYGujhbFTzGsCA6Ojp8/e33fPLLWuJq9iE+NQNzw/xnl2pLXHIG8/aryzq808UDS2N1iZ6irDBjaaxPLXszzq3/mXZeX3DQa3uOFV/Kk++//54zZ87wyvj3sBv1IwqFgobOFrzdqQY96lUtcOJQfubOncuePXsIDAwk9OCffN+0Bj+92rjkgi9FS04EcDkolpRLW8lMS6Fhw4bF6mno4GnHpQexnL4XychWec8m1bYHUUm8u+EKN0PjqTrye5yeXOfQmvlYmhS/RJWDhRFWt7cQsHMFn0aO4+KO5SUYccmJi4vDz8+PFi1a5Ljtu8UrmDf7c0RGKrWatmX+nFnaCzIPssWulGSPszsbEEVqRv7jSK49fEJUYhpmBrokB11jyZIlzwyuruVgjoG9unXkxNncV4AoiuyxHWmG6q7C1k+7DPOjWV7sYu7j4XR0dNiwYQNTpkyhcePGBZ6voZMlBvbqbpgHDx5oSmKUNxlZKtZfCNa01tWpUwdj48InNq2r26Bv70H9HiP45JNPSivM53Lw4EEOHjzI6oOXCIxKoqq5IVvebsMrzV2o52hB7armmr+SSOqyjX+1Hy2GvUOmrhF7r5fPFRmyVIKpW64REZ+Km40xbW1SiY+PL9a5Wle3IfXhTfxv+XDu3LkSjrRkPH78mMVLlgJg1m4UdRzM+XN0c3a+047eDRyeK6kDMDU15a+//gJAZGay9XJwuahnmJSUxKJFixg2bBiTJk3i4MGDmlnsQqhr1v1w4C4ZTyKI994FwNdff12ssVXtPW0RQnDk7GVCHoaW6P0oKQdvRdB34WluhsZjZazHnx8N4dK2358rqcs2sk9XAC4f3ELQI+0/9/+WmZnJ9OnTcXR0pF27dpoanNGJabTs0ovvPvsIVVoyVWs14cwhrwJLHWmDTOxKSe2q6rInqRkqLgbG5Ltvdjdsx5p2bNywnsmTJ7Nu3boc+xjq6WDnph6DdOL8xeeOL3sx9ye6lgCFalZv164dACdPnsx1u56eHv369WP+/PmF+rCr72SB0tAUC3sXoPy22h289YiI+FT0YtTJcGG7YbO1rm6DrpkNijZvMHLUa6UR4nMRQnD6tHqZNF+Ver3j74Y0wMGi9KfvKxQKhjRVj0/aeuVhuVtDMyIulbfWXubg7Ufo6Sj4dUQTJo4fh62trWY8aVG0crd+urwY5XbC0Cdf/UB6Wir6Dp5MGjGQ3e+1p1td+xIdHN65c2du3rzJ5Jk/olDqMGvXLa3PkszKymLlypVs2bKF33//nZ49e9KgeWs+WbqTwUvO8uVOdYmWTnap6CgVdOnSpcAhJ3lp6GRB/MGF+C2exPe/5j9ZSRv23wznrTWXeLDlO9yzHrL3gw683MChxM7//rgRGFtXRZUSz9TvF5fYeZ9XZmYmgwYN4vvvvyc5ORlTSxsm/H6c9t8fpdk3hwlTWaBrYU+b4R/gd+UsNta5F2TWNpnYlRKFQkFHT1si1n5K7xa1CA7Ou3jhYV/1tP9udatoZpTmNsuqZt0GAFz38Xnu+B4+fAhAoq4lCgXUy2N83b91764eIHr+/Pk8F4IviuzSKnr25bc7VgjB4uPqQs7WqepSJUVN7Oo7mmNqoEt8qnpZpeLwCXnCugtBhMSU/MD7oKAgHj16hFJHF117DzrWtNOsoFIWBjVxIi34OrvnTOB/X2tnrJ1KJdhzPYyPN11j1J/n6b/oNJ1/OEab745w6GlSt3BEE0zTY7h48SJZWVlFfh0AtPhXYnf6bPlrsQuJSmDDanVr2ssjxjN7QH30CjHjtTjq1avH1J61MTPQ5XpIDGu1vCKFubk5u732MPStqTi2HYBCz4BbVy4yf/JgDv46nYxgH2b0rs26ryZz/vx5Vq5cWexkV1dHSf2G6pnRBw4cKsm78dxuhsbxwd8+RB9fRdLt41xbPgNTZWaJXkNHR4fhYyYAsHv9cpLSyscawnPmzMHLyws9A0McB8/AbOwfHHmQwsNYdd3Jdm9MZ9Mxb86sX4CZccFj57VFJnalqEvtKmQlxZIcH5vntPaHscnciUhAqYCWzqbcuqX+VZhbYte5S1ese75DkyGTnzu2kJAQAHTNbKhhZ4ppIQpsOjs706RJE4QQmjUis/n4+PDxxx9r4i+M7LVUdRr0ZsPmbbz55ptFuAdFc/XqVZYuXcrhw4eLVCR4/80IboXFY6Kvw2/z57J06dJ8y7jkRldHSQs3K1Rpyazevr9IXXBCCGbtukWX1z9idOf6tHpnAXtvlGyXZfaPCT07NxS6+rzbxaNEz1+QqhaGuBskkfbwNr8t+pXMzJL9EslNQkICH374ISkpKTyKT2XwkrO8OuoNli/8gTP3orn+MI4H0ckIoR5/uuWttvSq78DatWsBeOmll3BwKHoLhq2pATUbqBPC6z4+pKaWr4kDk+YsIzMhBgMza9Z8/X6pl3CwMzNgQktbHm38go8/m0lAZNnXs8yejJacnsm0vUFcsuiEXocJVJu0DIcmXUGoSL57mlecEpjUqYa6DmejRlSr9nxj4wb2VX+OBNy6Um7qeKZmZPHu+ivEPbhJ/MVtgHr8rZmZWYlf67sZH6Cjb0Tqo0Cmzf+rxM9fVL6+vnz11VcAmHd/Bz3PdjR0tmRqz1qsH9+KazN7sP+jzgxq4lyuSpvkSlRScXFxAhBxcXFai+FJcrowcmsiAPHDr4tz3WfF6fvCdZqXGLbkrDh79qwARNWqVYVKpXpm38O3I4TrNC/R/afjzxVX9mMDCJePNouP/r5a6GNnzpwpADFw4MAct0+ePFkAYsSIEUWKpf33R4TrNC9x2j+ySMcVxdy5c4VCodDc5yFDh4rTfo/F9isPxfmAKJGemZXrcU+S00Xrbw8L12le4scDd54rhmUn7gnrHurH6OWXXy70cavPPRBW3SZpYrcdOEPU/d8+EfA44bni+bdp06YJQJg26iV6/3yyxM5bFJsvBAilsYUAxI4dO0v9em+99ZYAxPBRr4vOPxwTVV+fr3mMJ0z7Rhy+HSEu3I8Wj+JSNMdkZmaK6tWrC0CsWbOm2NeesfWaUJpYCkCcOXOmJO5OifB+ECOMarYRgBg3+cMyu+6qVas1j33zN7/J8/1YGlJTU0X16tXFpEmTxOuL1Z9Ftb/YJ1adDRQJqRlCCCF8fHzE1KlTxZEjR0r02oGRCULHwl4AYsuOXSV67uKau9dXuEzZKgxtnQUgxo4dW6rXGzjmHQEIo6rVRUJKWqleqyBDhgxRx+LRSrhP9xJrzj3I9XtYW4qS05RYi11mZibjxo0rqdNpREZG0qdPH4yNjalVqxZHjhwp8WuUFgsjPRyruQFw9ELuZS723lAXB+5e1z5HN2xuvwhq2qt/NT2ISn6ugq66urqsXbuWVsM/QKlvVKhu2GyDBw/G1dU1R/2ulJQUNmzYAMDYsWOLFEt2d+yN0LgiHVdYu3btYsaMGQghaNCgAZbWNtw0bsyo5Rf5cKMPr/5+nhZzDvP9/juEx/2zzFNaZhYf/n2V8Dj1gPm3Oz9fra3W1W3Qs3MDCl/yJDoxjTlbzvPk5GoApk2bTqeXupOUnsXPh0uuTMClS+rJOPoOngxtVvh6XCWpbxNXrBqqZxfOX1S6Y47Onz/P0qXqyQEJLu0IjEqiet3GfDzjSwCW//AlSf4XaOluTRXzf7pbtm3bxv3797GysmLQoEHFvn7rGrblcpzdL0f8Ma7REpe6zfhgUtHex89j9OjXGTvhLQC8V87mrR/WFXBEyVm7di33799n07adnLgfj76OkrXjWzK6jZumF6NRo0bMmzePrl27lui13WxNsaul7plZu2VXiZ67OEJikll++j5xp9eRGvUQR0dHfvrpp1K95pLvZ6FjYEJ64hPmbT5VqtfKT2pqKpExT0ChxLLj63z2ch1ea537kpgVQYkldllZWaxataqkTqfxzjvv4OjoSFRUFN9//z3Dhg0r9Nqr5UHLxvUB8Ln17BJCEXGpXApST6zo09Ah3/F1AI6WRojoYKKv7Gff0dPFjsnY2JhRo0Zh0nwgADXtTQt9bMOGDQkICMhRjPa3334jNjYWV1fXIn/41XNUJ3b7Dx5m5syZXLlypUjH5ycxMZFJkyYB6uK5s1ftxeLNP0l1aIytqT5tqttga6rPk+QMlhwPoP33x5i42pu5+3zp++tpjt2NxEBXyU+vNubUsSP8/vvv+PsXL6Gq52iBtbM6OXz48CFRUVEFHrPw6D3CTv6NSE+hWbNmfPvtHGYNaooqPYV1f/7GroMnihXLf+kaGqPQN8bI0ZP+jR1L5JxFZainw8BX1RNLTh89VKpLr82ZMweAbv1f4aZwRk9Hwe+jm/HDnFmMHz8elUrFiBEjcoz5zMzM5JtvvgHg/fffx8SkaIVZ/621uzUGjrXQMbMhvZzMFbkZGsdJv0gsGvfg9MmThS7nU1J+X7yQ1p17QFYGK2e9zbwNZTPubOHChQAYNO6HQkeP6b1r08y17GrLte+s/rw8dbxkCs8/jwWH/EiJiSDxsjrJXLZsGZaWlqV6zapVbJmzdC2OE5axxT+jVMYQF4ahoSH135yH0+SVdGnTjDfbV5Cq0XkpSlNg79698/zr0aOHUCqVxW1lzFVCQoLQ19cXYWFhmts6dOggVq1a9cy+qampIi4uTvMXEhKi9a5YIYRYu2mbAISenZsIikrKse2PkwHCdZqXGLJY3R1Tt25dAYg9e/bkeT7ndgPVXZ7j332uuNIzs0SNGXuE6zQvERqbXOzznD17VlhZWQlArFixosjHn7j7WLhO8xJ2jboIQHz77bfFjuW/5s9Xd6/VqFFDXPAL19zfTzdfEynpmeLJkyciPSNTHLgZLl5ddla4TvPK8ddw1gFNF/HAgerHfd68ecWOZ9yKi0LXylEAYv/+/fnuG5uUJjw/3SYU+sYCEHv37tVsq9llqABEs+6Dix3Lv/1xMkBU+3SXGL5Uu92CF+5HCwMn9Xtg1ldfl8o1bt26JQChUChEq2mrn+lmT09PFz179lR3e9vaCm9vbyGEENeuXRPm5ubC3NxcREVFPXccHb87JKp9ulsc8Y147nOVhI82XhWu07zEBxuuaC2G5ORk4V63qQCEjpmN2Hryaqle7+rVq+pr6eoJ5/c3iB4/nRCZWWXb9eZ18a4A9TCR4OCQMr32v4XEJAn36V7Cot0IAYhu3bqV2bVVKpUYtlT9+Tvi93Miowy74rPdCo0TrtO8hNt0L3E7TLs5Q15KrSv25MmTtG/fnldfffWZv6FDh5ZIovlv/v7+WFhY5Bik3KhRo1wH6M+dOxcLCwvNn4uLS4nHUxwtGtUDIPNJOOsv/jPrS6USrD2v/v+AJk4AnD17lqNHj2rKiuTGxV09sP3O3bvFjsnb25vVG7eR+uQRRno6VDUv3uye9evX07ZtW2JjY2ncuDGvv/56kc+R3RWbYeUGUKItdmlpaRgbG/Px1E+ZtvMOmSpB7/pVmTu4AevXrKJGjRrs27uHHvWq8vfENuz7oAOf9KjJ6DaufNm3Lic/7UI7D3W9qTNnzgD/1PIrjjY1bNCvql5XtKAZwBsvhRBz4wQiPZkaNWrQs2dPzbYxI18F4NqZQ6Snpxc7nmwHbz1CoVDSs37JlTMojhZuVlRr2weAJb//WaRJLoW1bNkyAFp27kkE1lib6DOx4z+rZ+jp6bFp0yaaNWtGVFQU/fr1IyMjg4YNG3Lt2jU2b96MjU3BNR8L0sajCgqFQrOcnzbFJKWz45wv8Zd3079m8Vsin5eRkRGXTh7E2qk6WQnRvP7aawQ8Tii162X3MJl4tkLHyIypPWs9d42+ourWpAZVuo6hyitfE5FR+DVXS9qac0GoBPQe/R7btm1j3rx5ZXZthULBvCENMdRTsv/v5TTvNpC0tLJbmeXu3bv8sls9HOXlBg7UcTAvs2uXmqJkjB07dhQbNmzIdVtKSopQKBRFOV2BTp48KWrUqJHjts8++0xMnjz5mX3La4tdenq6cHKtLgzdm4q607eJxKcDcrMnQtSfuV9zW2GM/eYPdWuCs3uxY3rzzTcFICzajxIv/1K8wfLx8fGiY8eOQkdHR3Tu3Fk8evSo2PG0nXtEVHn1GwEId/fi36/cREVFiV8O3BKu07xEyzmHxJOkdCHEPxMGOnXqVOA5/Pz8BCD09fVFSkpKgfvn5cbDJ8KqyzgBiAH/mXzybxmZWaLt3CPCrFk/oVAoxHfffZdje3xSqtAxNheAWLJuW7HjEUKIyLhk4T5d3UL58DlabkvK3J1XhZFHK9Fm7BciI6Pw74vCSEtLE7a2tgIQLd+aJ1yneYmFR/xy3TcuLk70799f/PTTTyUaQ7YdVx8K12leosf8Y+LJkyelco3C+v1EgGZiT6tWrbQaixBC+AUECuvqDYTj+CWi2/zjmkkMJSkrK0s4Oqpbz+0G/090/fGYyCrj1rpsk9ddFq7TvMScPbe1cv3E1AzRYOZ+4TrNSxy+rb0W5CU7TwoUSgEIp5oNxdlzF8rkut169hYolMKm9wfiWkhsmVyzOEqtxe6bb77B09Mz120GBgYcO3asuPllrkxNTZ+p8B4fH4+p6bNjwgwMDDA3N8/xVx7o6ekRfP8erSfPJ0no88sRf1IzspizxxeAES2rYVKIUiPZmtRTD7qOiSh+MVdNqRNzO6rbFX583b+ZmZlx4sQJ0tLSOHbsGFWqFL/uWQMnC/SfLi0WGBhYomModY3M+f2M+v5O61UbC2N1lfD33nsPXV1dTpw4UWDr2dmzZwH12MfCrPubl7oO5ti51wHg/EXvPPc7dPsRoU9S8BjwHn4BgUycODHHdjNjA2q1egmA1Ru3FTsegP6DhxKydAJWUddxsiz9gsQFGdmuJlWG/I8I+9ZEJZVs2ZPY2Fg6d+6Mg7MLEWa10NdVMqJl7iUrzM3N2blzJx9++GGJxpCtg6cdyXdOcfiLAbw+puQnnRXF9quhJPurJ3EMHjxYq7EAeFZ34/bVCzi7e+D/OJFPt1wr8dbbS5cuERYWho6BMUbuTRjbzh1lGbfWZevXUN1Svud6eKm0Uhdk65WHxD55QjVzXbqUYf3K/3qrfwfen7sMhYEJoX7XadumFQ1btmPHzl2lVrg8JiaGo4cPgVDRuHkLGjpblsp1ylqRErsOHTrQrFkzNm3a9Mzf5s2befToUY7bnpenpydxcXFERERobrt27Rr16tV77nOXJaVSwfTe6oTs95P3afXtEe5HJVHFzIB3OqsTmm+++aZQdeCa1asJCiWqzAzCw4tXzyy7OLGOmS017J6v60VHR+e5jgdo4GyBjqEp5nbqLunn7Y598uQJ166pvwzWXQwiMS2T2lXNGNjYSbOPk5MTw4cPByhw5tepU+rZWvl1kReGUqmgV6c2WHV5k/4f/5DnfivOPgBgREsXPNxdsbJ6trr5y73UXbM3L519rphu37hG5pNwmlTPfY3islbNxlhd80/ADp+SXWrJ3t6ezZs3M+rH7SiUOvRt6ICNaf7dX6U1K87aRJ86Hu6oUuI5euRwmdTuy43/owRuPoggNVg9U7u4KymUNHtzIxaPaoaejoJt+47ywQ8lW+fM0dGRSR9Nw6RpX4yMDBmgpUlDoF5XXOfJQ25s+ZnvfltRptcWQrDyzAPizmzA54eRrFu3tkyv/1+/TBvPaq8TWDfqBgolNy6dZdDAATi7e+J95WqJX2/Pnj2osjLRs3VlYr+OJX5+bSnWrNjffvuN1157jU8++YSff/6ZTz75hNdee43FixezZMkSlixZoikn8DxMTU3p378/M2fOJCUlhV27dnHz5s1y8+FTFD3rVWV0C/Uvs7iUDIz0dJj/SiNNC9Lq1av56aefCA3N/8vMs6oFOubqdWjv+N8rViz/FCe2pUYxW+xKUv2n4+wMHNRJ7vMmdlu3bqVx48b0HzCAlWceADCxY/VnfpF/9NFHAGzatEnzmPyXEIIDBw4A6qK0z6tPsxqYtxzE7Ywquf46vxUWx4X70ZASx+ut3fI8z7hh6rFoCeH38Q3MPfaCxMXFEfdY/Xob9NLzJa0laUhTZzLjo/jpp59KvBxISnoW+26rZyS/0ly743AHdu+E0tCMpIR4Lly4UOD+6enpfPDBB3h4eNCwYUMWL1783C08O3xCSX3gA1mZeHh4ULt27ec6X0lq5mrFoCrRPFo/ncVff8rVgJKbKe3i4oJtx1FYdRxN7/oOmBlqb71PQz0dHONukXB5N7/99luZXvvSg1juhUaSdOMQCU9isLOzK9Pr5+a1bs24f24vM9cexa7dMJQGJjyKiuGdHQ+4HFSyY1LXbdkBgHmt1rzcULtjjEtSsRK7OnXq8NNPPxEcHMzZs2cJDg5mwYIF1KlTh2PHjnHs2DGOHi2Z6duLFy8mJCQEGxsbPvnkEzZt2pRrC0Z5duDAAezt7Tm16GO2vt2GrwfW59gnnengqX4TPXnyRFNGo1mzZvmey9ZUH0OrqgBcvln0CRTx8fEkJKgHJOuY2VL9OVvsSoJmAoWlK1D4Om952bZN3T1p4lSLxwlpVDU3pG/DZ3+RN23alM6dO5OZmcmvv/6a67mCgoKIiIjA0NCQjh2f/xdde09bjPR0eBibwpXgZ7ucV5x5QPqjAIIXvs7bY0bk+cVdx90ZU4fqgIKN+4tX/+nAKfWaw7pmNnRvWrarTeTn5YYOJJ7fiP+uxXy/YFGJnDMgIIB79+5xyPcRiWmZOFsZ0dKt7Mpa5KZPYycM3dXLSm3bUXAds+QMuHDrPgEBAdy4cYN33nmHjz/+uNjXF0Kw0yeM5HvqpLJfv37lrm7X7IlDMbNzJCsxhuGTp5XYerJZKqGpITqoiVMBe5e+zz+aBAoloXeucOXG7TK77mbvEJJuHUWVlkzNmjXp0aNHmV07PxZGeswa2YnAI+tZsOMMNUd/w6N0fV5ddp6DtyIKPkEhZGRkcOLIYQC6dO9VqNWXKopiJXZ///03b7/9do7bJk2apClSW5Ls7OzYu3cvycnJ+Pn50a1btxK/Rmmzs7Pj8ePH3Lhxg6bVrHi9tStVLf4Zq5XdQuXu7l7gjDuFQkGjAeOp8uo3uDYqeitLdsuU0tAUpb4h1W2132JnbaKPk6URJvVfYv3+s6xcubLY54qPj+fwYfWbNcRcvbbu2HZu6Ovm/lKfOnUqoF42Jzn52RpKbm5uREVFcejQIYyMnn8MmrG+Lt09zUm8cYT3P/ksx7bQJyns9Akl6eZRhFChr6+f7xft2M9/wvmDDSTbFW9owt4T6i90O9eaGOo9f5d6STE31KNrX/VYr317dpXIDLn58+fj6enJV7NnAzCgsaPWxlRlq2lvRo2m6h8LGzZtzrf17fjdx3T68ThhDUZjO2A6lh1HA7BgwQK2bNlSrOtfDoolJDqR1Pvq8Z7lsSfEyMiIhT8vAMD/6Gb+OvT83XEnTpxg3tJVRMXEYmmsR5sazz/L+Xn1bFEX29otAHj/82/K5JpJaZl4XQ8l/vJuQD3uWKksX6uMmhjo8n6vRnj/PIle9aqSqRKMm7uK9XtPPve5T58+Q2pSPEojc8YO6l4C0ZYfxXoWXV1dnylGvGbNmnJTYqS8qVOnDgqFgujoaB4/fvzM9uzK/wW11mVr3rodRm6Nickq+kD+f4+vc7I0wki/fHyh13cyR9fMhkQj++cat3fokLoEiItbdR5ijYm+DsPzGCAP0KtXL6ZNm8bp06cxNjbOdR8LCwvat29f7Jj+q3dta6L3LuDslt/xD/qne+m3Y/dIz8gk3U/dAldQ+Zgh3duiY2jKaf+oYnXJXXo6ZqVe/bItRlsY747oi46pDWlJCezy2vNc5xJCsGeP+hwR+urult5aLu2SbfTwwSh0DQgPDtR8DvzX/FXbGbfyEnEpGdR0qcLokcNx6jwSizbqsjeTJ08mLq7oK7dsvxpKRnQIqtTEEn+Nl6TRw4fgVqcRIjONL7/+lvTM5xtI//PPP/PZO2NJuOxFtzr26OloP5lRKBRM+Vj9I/Ps3i3cuveg1K+590Y4MXe9yYwJxdzcnDfeeKPUr1lcJga6LBrZhJppdwld/xkTJ04gOiGl4APzseLvrQCYebaga52qJRFmuVGsV/Sff/7JrFmzqFmzJi+99BI1a9Zk5syZ/PWX9hfyLY+MjIyoUUO96sDNmzef2Z4967J169aFOp+7rbr7NDCq6AtHN2zYkHe/+gWLtsPLRTdstpJaWmzv3r0A2NZtg0KhoH9jRyyM8h4/o1Qq+e6772jQoMEz27IXBy9pvVrUwtzZExB88nTx68tBsfx9MZjUwCukJcRiZ2eXo3Zdblq6WaOvoyQsLpX7UUlFikEIQZCfusunU+vC/aAoSx1rVsG2UWcAFv6x8rnOdevWLYKDg9E3METp1AAXayPqOZaPWfOjOtTGpGYbABYs/vOZ7d8tXsknYwYTsW0O/Rras+f99sx/pRGbJrXBqesodK2diYyM1KygUFjpmSr23AhH386N3edvs2fPHvT0tDfOLD8KhYJff/gWgIhLe1lzsvhdlenp6ZplKQ2rN6NXvfLzhf7pmEFYutdHZGUw4LWJpT5DdrP3Q+KfrjIxbtw4zMzMSvV6z0tXR8niD4aha2hMUqgfr38697nO59jhFWxe/pDug18rVz0WJaFYiV2LFi0ICAjgzz//ZOLEifz5558EBATQokWLko6v0shOHP47MUD8q/htYWdd2huqSLx+iP0b/ihyHA4ODjg0645J7fblYuJEtuwJFMcP7eO1115jzZo1RT6HEIJ9+/YBEGmpLisyrIgD5P+91Nf06dNp0qQJXl5eRY4lPwqFgmFPy0oc3rebT7dcY/K6y6gEWIWpJwsMHz68wC9aI30djH13E7F+Oqt3FW0N5YDIJJT2nhg4eNK/HE2cyKaro+SVV9Wzls8eO0hiYtF/xGTLbq2rWrsZSj1DXq7vUG7GkjlYGNFzyEhMm/RB0XhAjm37z17l8ynvAODh4cmCV5tioKv+AqrraM7cYc2wbD8S0zodadE5/x8B/3XSL5InyRnYmRnQq5nnc8/4Lm19X+6Ng6sHIj2FOQuWkJFVvFa7c+fOkZCQgNLYAksXT9p72pZwpMWno6NkyaJFoFAScOEQE75flWNN8LS0NM6dO8f69evZtWsX0dHRxb7W/chEzl73I/X+FRQKBe+++25J3IVS5+HmwrtPh7AcXLeYK/cfFes8QghOPczAtEE33hzSqyRDLBeK3Qatp6dHx44defXVV+nYsWO5/bVXXmS3xp07dy7H7REREejr62NoaEjTpk0Lda4qhhC97xdu7FhSrDIJ9yPVX5LPW+qkJGUndsH+t1m3bp1mJmpRXLt2jfDwcAwMjVA61MOjiilNXCwLdWxycjITJ07Ew8ODgIAA/P39WbRoET4+PqWSBHwwfpT6uoFX2XD6Do/i03DSTSTgojpBK2y3SFbEXdJCbnLoaNHGnJy/H411t0kMmrWahvXrFi34MvLxiN7oWjmQlZ7GivWbi32e7MQu3aERAL3ql59WGoA5k4dj32syZyNg59MSL5f8HjJk6Cuo0pKxqdGQM5t/R/c/XYb9GjrQZ+AQbPp/yt6HRRv4vd0nFCEE/Ro6lvlqC8WhUCiY/slH6FtUIT5Lj+1Xi1cKZ//+/QAYuTela52q5a6lZvjLnRg84SNMG/fiUKwtnX88xitfr8atcTtMLSxp27Yto0aNYsCAAVStWpV33nnnmVqvhbHJ+yG65rYM+nYjv/32m6ZHqSL47vMpmNrYk5UQzXuz8y9VlRefkCeEPknBRF+HzrW0PxO4pGl/cMELok0bdXfLuXPncjSxOzg4EBoayv3799HX1y/UuZrVdQcdPVCp8L13v0hxeHl5ceHEYbJS4stVi52tqQEOFoYYVC1+yZPatWtz4MABag96D4WuHq80dy50UmZoaMiNGzeIi4ujadOmtGrVivT0dHr27MnLL79c5FgKUr9+ffVC61kZ2D04wth2bjRPvkhmZiZdu3Yt9HjLju3VS5z5Xr9cpBmD5++rf+23rq79geN5cbU1oXa7XqCjx/4Lzw5hKIzY2FjNUAcdt2Y4WhjSuJDJflmpVdWMtzurv1g/3HCZun3epH27diSH30PPxIJT+3diZvxsvT2FQsEnPWoBsOtaGHciCvcFH5ecwaFbj0i44sW22WP5+++/S+7OlKK3Jo7n520nMW3YnaXHA4o1QzY7sTN0b1quumH/bcvSH/jr92VYmegTEpPCidsPCbp2lsy0VJTGlhi41EfPphqZmZksXryYtWuLVnsuI0vFlsvqsdaT+nd4ZiJkeWdgYMBHUz4B4KLXem6FPinyOd7/6GPiL26njZN+uUvuS4JM7MpI8+bN6dixI6+99toz63sqFIoc6+EWxNLYAAMrdUHZi9fvFCmOqZ9+ys0Vn5H+6D41qpSfxA6erkBh/3Qt3Dt3SEoq2rgxQ0ND3Bq14Um1jugqFQxq4lzoY5VKJdu2baNWrVrEx8cTGxuLq6srixcvLpUWO4VCweeffw7AvaN/81pdQ76b8w2LFi3i66+/LvR5+ndTz6pMCLmDXyHX1RRCcOp6ACIrg9bVtVvyoyAzpn6My3tria7xMpnF6H47cOAAWVlZWDtXR9fCnp71q5abbth/e/8lT4Y0rsqjHd/hu/cv0qOCMTS35uCB/dTxcMvzuPpOFvRp4EDao0AGjRz7TI9AbnZfDyM9SwX3z3Ht8sUcBeDLM319fUa3q4GZgS73o5I44RdZpOPDw8Px8fEBwLxGM7rU1t4qC/lRKBQMa1GN09O6svS1Zkwb0YMRH85i5oq9/L7/MnOXb6XZx39RZfgcrNsNx7NT0VYLOXrnMY9j47A1NaBrOX0MCjL1vUnoG5mQGfOQ6b8ULbGNi4vjzM41xB5bThvn4q8kVK6V8HJm5UZR1lXTlszMzGKvh1mlTksBiPdn/VjoY1QqlTAxMRWAqPH2H0Kl0s7aiHlZcvyecJ3mJYyt7AQgTp8+XeRzzNlzW7hO8xLjV10qVgxxcXFi1apVYtGiRaW+hmdmZqZo3ry56NKli0hNTS3WOeLi4gQKhQDEL7svFuoYv4h4YVSjhUCpK/5aubpY1y0rqRmZovHsA8J1mpc44lv0dSwTExPF5q3bhMuwz4XrNC9xMTC6FKIsGSqVSvy2drvoNvQN8c6n/xNhYWGFOs7/UYIwbdhDAOLlgUML3H/gb6eF0zurheLp6yYoKOh5Qy9TM7ddFbYDpotB83YW6bhNmzap13yu6inGrSjce6W8SkjNEONXXRKu07xEzc/3istBMSIrK6tQn+mjl50UOqbWolHnPiIqKqoMoi0doydMFkpjS2Hbf6rwi4gv9HHzFq9Qvw6snURKemYpRliySm2tWKlknThxAjs7O95///0iH2vvpC7h4XcvoNDHxMfHk5SkHl9X08Ot3LVcNHdVF57Wq6LulipoDdd/O3DgAFOmfMyaneoadsOaFb617t/Mzc0ZPXo077zzDhYWFsU6R2Hp6Ohw6NAhtm3bhoFB/ktb5cXc3Jyq1dSP196jpwt1zJl7UaQ/fgCqTDyquxXrumXFQFeHwU3Vz+Wf+wv/eshmYmKCTb12KKu3wc7MgGbVym9xc4VCweRRAzm0eSWLvv+q0K34HlVM6T9cPSZz/+4duZZUyhYQmcjV4Cek+quHhLRq1Ypq1fIuB1QeXVr5FVE7v+Pojg34PypcKzXAsGHD6Pi/v7Hu8TY9y9k4y6IyNdBlyaimvFS7CmmZKib8eZLeffoxd27+M0VDYpLZs3UDWYkxRAXcKPXPuNL0y7w5jF24B5M6nVh6ovBDktZsVNd9rN+2a6XshgXZFVvmkpKS2LhxI/Hx8axdu5YnT55oVoIoClc3NwCCHgQV+ph/ihObUcup/MwGy1bfyQJ9HSVUUXfHFqZbKdv69etZsOAnwnyOY2OiX267Wf7L0tISS0vL5zpHi5YtAfC5fKlQ446OXLtPVoK6G6thw/JXw+6/Bta3JXzVR2yY0o9rdwOLfPye6+o1lXvWs9d6UeLS8vX4/ug7eKLKyuTbBYvz3G+zt3pslX6wetWRoUOHlkl8JemNUerZ0onXD7H8VOGXVbz3OJGgdFNMnGrRvU75WBv5eejqKPl1RBM8qpgS7HOGg/v38r///Y8TJ07keczvx/2Iu6BemWf6p1PR1a24qy1YWlryTjd19YNd10IJjyu4rl1Kahq3L6gfnzEjXinV+LRJJnZlrFOnTgwfPpxp06ZpSnpMnDixyOep7alOfh6HF36d0OzETsfcluq25WdGbDZDPR0aOFtg4FQHpY5OocfYqVQqTZkToxrNGdDYqVwUHS0rPTu3R8fYnJQMFf6P8y8Lkpml4vRFdcuXk4trhfjF3sCtCqZG+iBUzPrl2Vpvefn555/57Isv2HVCfX/75bKsXGVR3c6UTgNeA2D5n3+QlZX1zD5JaZlsuBhMVnIcj/zUxamHDBlSpnGWhAEDBmBhZU1WYjRrt+zkSXJ6wQfxT4Lf3tMWK5PCTVQr70yettzZNuqKSf2uqFQqRo4cSWTks+MPY5LS+WPpYjLjHmFlY8u4ceO0EHHJalLNihauFsT7XWTZEd8C91/8926yUhPRNbFgwtCilQiqSF6cb79y4s033wRg6dKlZGZm0rNnT82M2aLo2a0LVV79BpfB0wp9THZip2tmW+4mTmRr6W6NoXM93l5+kl27Cl4/E9Qrd0RGRqI0MMbAqS7DmhevG7aimjhhPMN/OYBVx9c1s13zciM0jrgQdStH86ZNyiK8EjF0mHqVhUO7t5OW+WzS8l9CCBYtWsTcOXOIDb1HVXNDWmh5bdjStmDG2ygNTUmMCuO3NVuf2b7JO4S4lAyMQi+jUqlo2rQp7u7uWoj0+RgYGDBujLrrOebKATZcLPjH7dy5c/nmgzdICbxKnwblY9WRkuJpb8acQfWx7v42ejbOhIWFMWLEiGcKrH/z9wken1gHwLzv5ua50k5Fc2/VdB5vmc0fq9YRn5p/Ufk1fz/thm3zEob6lbdEm0zsythbb73FmDFjAGjUqFGRK8Zna1rLDSO3xsQqLUnNKPiLDiA4OBhQLydWnlad+Lf2HrYodHS5FJpc6Mrr2atNGLo1ob6LNXUcyseqAmVFT0+PNjXUXetn7kXlu+/ZgGjSH6u7Mxs3blTqsZWUz98bBwolSQ99+XNPwV30vr6+BAQEoNTVw8i9KX0bOlTabths9arZ0eSlgQDM+fGXHMVt41IyWHhUndAP79mO/v37M3z4cG2EWSImTJgAQMq9iyw/cLnAGdPr/t5M1O1zKJJj6VFOy5w8j8FNnRnVvia2A6aj1DfkyJEjjB49WlOBwdvvIYv/9w4iPZl6TZpXita6bK8O7AtA1CUvNlwIznO/5PRMgiITQEeP0cMr3hCEopCJXRlTKBT89ddf3L9/n6tXr+Lp6Vms81gZ62FmqB4fERzz7OL1uXl50CvY9P0Y0wZdcbMpn4ldM1cr9HWVPIpPIyAysVDLemUXoDWq0YKhxZw0UdG193ia2PlF5FuV/8y9KDIisxO7xmURWomo5uxEnebq1REWLFpaYNKf3dpr5NoIpb4R/RpV3m7Yf/vt62nomlqTZunKgkN3AXXr5Tdet4lJSsejiin/e3MgO3fu5JNPPtFytMVXp04d2rRtC0JFwNk9HLiV9woEQUFB3Lp+FVDwUvfu+S4xWJHN7FePRg0bYtt/GgqlDn///TdffPEFT5LTmbrxClmpieibmOO1bTNKZeX56h83bhw6unqkh/uxcNPBPNcS3nsjAovuk2kzczuTXytaiZiKpvI8uxWIQqHA3d39uWalKhQKDMOu8uTUOo6d9S7UMUorJ0zrdcGzfrNyOxvIUE+H5q5WZEQ/pFfXTgUmH+Hh4ZrZs2Ye6vF1L6Irh3cQtmQMwXsWcy3kSa77pGZk4R0Ui0ndzvQZMKTQK52UFzOmvAdA4JndnLoTnu++GzZsAMDAozXVrI1p6Fz+xxKWhFZN6vP3satYth/FomMBzNh2nY83XWPz5YcoFDC7fz3N+NPyNiu+qCY9HZucHvmAv87kPalm61Z1t7SBSz2GdqhfJrFpg6GeDktGNcWpYTvshnyJjpEZCS5tGfjbGQKTdKg9Zi579x3A7enEu8qiSpUqDB48CIAHp7axI5dVSYQQLD+tfo281qFWsasQVBQysavAoi7vI+7sBk6dLlyZi/uR6skI5bUbNlvnWnbomFpx3/c6t2/f1nQh5yYwMBBT6yroO3jycqs6WFeSQdFFZWpqQkZ8FOnhfpzMo3DrufvRpGeqqNltBLu3b65wZS5GDB2EmY09qpR4Pp3/Z56tdjdu3OD69esodfUwrt2e/o0cK3wSUxRDmlfjva7qyVUbLoaw7ekX3dtNTNnw8yzCw/NPiiuKYcOGcezsJRwHTeNyUCzXHz7Jdb+V69Qra1jV60D3upWvG/bf3GxN2DSpNbVbdMDxrRXsC9XjQXQy9uYGbP50EC91aK3tEEvFhx98AEDS7eN8t+08yek5l9o8ciuMazduYqSnw8iWFetzrzhkYleBObm4AnA/sOASEEII/l79F8n3LuJqWb6Tn+51q6I0MEHfQd1Nffjw4Tz3rdmwGVUn/YXd4P8xrl3FGwheUlq1agVA+uNA9vnkXgLnwE31CgPd6lapkImOrq4uc7/7DofBnxNu1YhzAblPFFm9ejUABu7N0DUy5dUWLmUZZrnwcY9aTHJ7gu6xn+lVz461b7bg5Oof+O2335g0aZK2wysRxsbGdG7TnL5PZzv/dfrZz0E/Pz9uXLkECiWvDBuCqUHFLe9RWB5VzDjwYUe+e7UFY9q6MatfXQ5+2InaVSvv2OO2bdvSslUryMok4Ngmfjnir9mWmaViyrxlhC+fDId/xNK4fH//lQSZ2FVgNZ7OaAsNybtFK1tsbCy7F88mcutXuNuU79lQ7rYm1LQ3xdBNPWtz9+7dee679nwQGSpoVqc6TatZllGE5Y+Liwv29lVBqLhx3Yf7kTnLnmSpBIduPyIt7C71TVMKPTGlvHln/BgmvjEChY4uPx3yy/V+mJqaYmBsimmD7nStVQUX6/L9ei8NMTExzP30bQIuHubeqs+Y/+kk9uzZg4GBAd988422wytRY9u5kZkQzfazt7gbkbMm6Lxf1DX9jKo3Y/LLLbURnlYY6ukwslU1ZvWvx5h27lgYV85xhf/2+WefAeoft0uPB7D3RjhCCObsuc2dfeofe4O6tdNmiGVGJnYVWL1a6u6WmIiHBe6rKU5sbEFt5/K78Hu2XvWqYlxTvcD9vn37ci3i7Ovnz5oz6pU33mz/fGMWKzqFQkHr1k9b7cLusvdGzu62cwHRRCel8+Tgb4zo3qrQpWTKo7c718BAV8nFexG5Dpqf8MGnOL+9EqMazXmz/YvZimttbc3q1asxMDDgyJEj7NixA4Bly5ZViKLURXHW628i/phAzKl1zNx1U1OkWwjBfewxcKxNx36vUquqmZYjlUpTv379OHHiBB/PX6FexWXdFVp9e4SFv/9FRlQQRiamTJ3yobbDLBMysavAmjeoBUBydHieM4GyBQQ+AJ7WsLMrnzXs/m1oMxf07NzQtXIkLS0NLy+vHNuFEHTr+TI+P4zENC6Q3hV8iaCSkN0dmxZ6h03eD3OUu1h3IQhVeippkQ8AaNGihTZCLBH25obUjj5N6JKxfPDjymcK1M7bf4dMXUPaeNjR1qP8rbBSVgYOHMjp06eZOHEiI0eO5MyZM7zxxhvaDqvE1atXj6yMdBKvHeTkeW++P3AHlUqw4swD7ps1xOmN+SycPkHbYUqlTKFQ0LFjR2b2r8foNq5kJcUS5HOG2ENLAfjisxlYWZXfJQVLUuUfcFCJNa1XEwBVSjy+IY9o5J534c3rd9Vr6RlY2mFrWv7HGFSzMaaDpx27a3cg/txG/vzzT0aMGKHZvv/QUcIe3EOhZ8A7gzq+UCtN5KVjx44ApD28RVB0Ikd8H9GjXlXCnqRw8PYj0sP9ECoVTk5OODpW7PIfzroJqFLiCdj6A2/UrcvfH77M999+g7CvxY4gc5QKmNartrbD1LrmzZvTvHlzbYdRqjp06MDQoUPZsmULUXsWsMS8CmtO3iVZqLsfp/eqjad95R1fJuWkp6Pki9412Tl7DNevqismtG/fnk8//VTLkZUd+W1YgVlaWqJnrO5eOH/tbr77+vqrBxbb2lecGYJj27lh1rgXxm6NGDNxsuZ2IQRvfaRecaNq0x682bXyljAoihYtWtCmTRva9BqEyEjnhwN3SUnP4qvdt8lSCWzi1QOKO3furN1AS8AP33+Hew1PshJj8Jo9mqqejZg9ezZfTR5F+qP7vN25Bk2qvRi/ziX49ddfsbGxIeNxIGF/TOLOj8NJuLCZyZ2rM77Di9kd/yK7fPkykRFhGBgY8MYbb7B79+4KvS5uUb0497SSGvDpr5wNTSfDLP9lcgKD1BMsnJwqTgHfrrWr0LK+J1fM53AqzZ5RKoFSqWDa978RfPsy6Ojxy7yv0deVv08A9PX1OXv2LNGJafT65RT+jxOpP+sAWSqBrlKBYaR6LcUuXbpoOdLnZ2xszJFDB+jSvSdBAf7EB8eBQol1j8mM7d+ZKd1raTtEqQw5ODhw5MgRevbsyaNH6nGXTrHXmNzepcL8kJVKTps2bQgLC0MI8UI+/zKxq+DatWvLpcP+BMem5rtfeJi6llUN94pTw0ehUDCrfz2GLjnHwduPmLT2MhdXzObGqQMAtB88jmEdK86yWGXFxtSAX15tzNiVl0jLVKGjVPBxF1fe/0FdyLpr165ajrBkuLu7c+vaVdZv2srRK3dwa9KRwV1b0tzV6oX8MH/RNWrUiHv37nHkyBGMjY156aWXKtUKC1LRvaifAzKxq+CqP50IkV18OC/ufd4ipbovXTp1LIuwSkxDZ0vmDm7A1C3X2H3gCI+OqydRVG/RlcNri7fObmWXmppKtO859r7dihuPM6jnaM7t80fJzMzEzc2tQi78nhcTExMmjB3NhLHajkQqD0xNTRkwYIC2w5AkrZKJXUX3JJQnp9dx4YYVvN02111UKsFjA0dM61WlQ5M6ZRzg8xvSzJlaVc34dYeCW+IDurdtxhdvjXihxkwURceOHbl06RLr1q1j5MiRAFTr1YudO3fmWjZGkiRJqjzkN2MFlxETRtyZDaTY1yA6MQ0b02fXwAuOSSY1Q4WBrhJXm/K9nFhe6jtZ8Ps7L8M7L2s7lHKvR48eXLp0iTVr1mgSOwMDA/r376/lyCRJkqTSJgcgVHC1PGsAkBn36Jmq69mOXrxGwhUvbBID0FG+mGMOXiRjxoxBqVSyf/9+vL29SUtL03ZIkiRJUhmRiV0Flz1eSpWaiLdfSK77HDpyjJhDSwk/saEsQ5O0xMPDg1GjRgHqosVmZmbMnTuXzMzMAo6UJEmSKjqZ2FVwJiYmWNjaA3Dp2q1c9/ELUBcnrubqVlZhSVr21VdfUbVqVVQqFRkZGRw4cAAdHR1thyVJkiSVMpnYVQKu7uo1Y2/53sl1e2jwAwDq1PQsq5AkLXNzc8Pb25uJEycyf/589uzZ88JO/ZckSXqRyMkTlUC9OrW4fukMwYH3yVKJHOPo4lMziH2krmHXqpFcYulF4uTkxLJly7QdhiRJklSGZItdJdC0YV0AUqIecu9xYo5t10OekBmjTuya1q94pU4kSZIkSSo82WJXCbz+2mscS3bhZrwBV4JjqVXVTLPt1PUAVKmJoFBQs2ZNLUYpSZIkSVJpky12lYC9vT1dWjZCoaPLlaDYHNvOeF8DwM7BBSMjI22EJ0mSJElSGZEtdpVEU1dLAC4H/5PYqVSCUF1Hqr72A591rzzLSEmSJEmSlDvZYldJ3Du7n6jdP3LL+yyPE1IBuB4aR2yGEtvqDXhr1CAtRyhJkiRJUmmTiV0lcf70CZJuHyct+CYHbkYAcPTOYwA61LRFT0c+1ZIkSZJU2clv+0qicePGAKQ/vs+eG+EIIThwM5yYI38g7h4jOTlZuwFKkiRJklTqZGJXSfw7sbsYGMOvR+5x8+49Erx3svy7z2RxWkmSJEl6AZTbxO7u3bv07dsXW1tb7OzseO2114iNjS34wBdUo0aNAMiKjyQjKY4Fh/1ID1OvRNG4cWM5I1aSJEmSXgDlNrGLi4vjlVdeISAggAcPHpCens4nn3yi7bDKLQsLCxo2bAiAwSP1mrHGT9RrxLZp00ZrcUmSJEmSVHbKbWLXsmVLRo8ejYWFBSYmJkyYMIGLFy9qO6xyrWfPngC01H/IoY86Ypv0AJCJnSRJkiS9KMptYvdfZ8+epV69enluT0tLIz4+Psffi6ZHjx7o6OiQkZqMiAvnypUrKJVKOnbsqO3QJEmSJEkqAxWiQLGPjw+//vorJ0+ezHOfuXPnMnv27DKMqvzp1KkT0dHRWFhY8PHHHwPQu3dvHB0dtRyZJEmSJEllQWstdj169MDQ0DDXv2+++UazX2BgIP369WP58uX5ttjNmDGDuLg4zV9ISEhZ3I1yRU9PDwsLCwC6d+8OwIQJE7QZkiRJkiRJZUhrLXYHDx4scJ+IiAi6d+/O//73PwYOHJjvvgYGBhgYGJRQdBVfz549mTt3Ln379tV2KJIkSZIklZFy2xUbFxdHz549GT16NBMnTtR2OBWOQqFg+vTp2g5DkiRJkqQyVG4nT+zYsYPr168zb948TE1NNX+SJEmSJElS7hRCCKHtIEpDfHw8FhYWxMXFYW5uru1wJEmSJEmSiqUoOU257Yp9Xtn56otY9kSSJEmSpMojO5cpTFtcpU3sEhISAHBxcdFyJJIkSZIkSc8vISFBU/0iL5W2K1alUhEWFoaZmRkKhaLUrhMfH4+LiwshISGyy7eckc9N+SSfl/JLPjfll3xuyqeyel6EECQkJODo6IhSmf/0iErbYqdUKnF2di6z65mbm8s3Wzkln5vyST4v5Zd8bsov+dyUT2XxvBTUUpet3M6KlSRJkiRJkopGJnaSJEmSJEmVhEzsnpOBgQEzZ86Uq16UQ/K5KZ/k81J+yeem/JLPTflUHp+XSjt5QpIkSZIk6UUjW+wkSZIkSZIqCZnYSZIkSZIkVRIysZMkSZIkSaokZGInSZIkSZJUScjETpIkSZIkqZKQiZ0kSZIkSVIlIRM7SZIkSZKkSkImdpIkSZIkSZWETOwkSZIkSZIqCZnYSZIkSZIkVRIysZMkSZIkSaokZGInSZIkSZJUScjETpIkSZIkqZKQiZ0kSZIkSVIlIRM7SZIkSZKkSkJX2wGUFpVKRVhYGGZmZigUCm2HI0mSJEmSVCxCCBISEnB0dESpzL9NrtImdmFhYbi4uGg7DEmSJEmSpBIREhKCs7NzvvtU2sTOzMwMUD8I5ubmWo5GkiRJkiSpeOLj43FxcdHkNvmptIlddverubm5TOwkSZIkSarwCjO0TE6ekCRJkiRJqiRkYicVWUJqBoOnzqdZt4EsXbFG2+FIkiRJkvSUTOykIlGpBIOm/sT2Hz/h1l1//ryrS2pGlrbDkiRJkiQJmdhJRXTodgQn/14MgJ6NC5HCjOWnA7UclSRJkiRJIBO7MiWE4PTp0/z++++kpqZqO5xi+W3tNjKigjE0MWPprz+hUCj449R9MrJU2g5NkiRJkl545TqxS0tLY+zYsTg7O2NhYUHnzp25ceOGtsMqtpkzZ9KhQwcmTZrElClTtB1OkSWlZXL6+GEAXu4/iFfb1cY4IYR7W35g0pTPtBydJEmSJEnlOrHLzMykevXqnD9/npiYGPr378/AgQO1HVaxREZG8uOPP2r+v2zZMq5du6bFiIrulH8kiQFXAXhlwMvo6iipa5ZK0o3DbN2wGiGEliOUJEmSpBdbuU7sTExM+N///oezszM6Ojq8++67BAYGEh0d/cy+aWlpxMfH5/grTxISEnjppZdo3rw5Q4YMQaVSsXz5cm2HVSTHrviRERUECgUvvfQSAO+8NhiUOsRHhhNwX461kyRJkiRtKteJ3X+dO3cOe3t7bGxsntk2d+5cLCwsNH/lbTmx6tWrs3v3bk6ePMmIESMAOHLkiJajKprL90Ixrt2BVp17YmtrC0CHOs4YOdYE4O9d+7UZniRJkiS98CpMYhcXF8ekSZOYM2dOrttnzJhBXFyc5i8kJKSMIywcIyMjunTpgpmZGa6urqSlpWk7pEJJy8wiOMsKuwHT+HvTFs3tujpKPBq2BGDvwaPaCk+SJEmSJCrIkmKpqakMHDiQPn36MG7cuFz3MTAwwMDAoIwjK5x79+6hr69PtWrVALC2tiYmJgZd3Qrx8ANwMzSe9CwV1ib6uNoY59jWtUsnbuxdxY3L57UUnSRJkiRJUAFa7DIzMxk+fDiOjo45Jh9UJF9//TWurq7MmzdPc1tFSuoAboXFkfEkgoaOZs+sVTe8j3q8XWJkKI8jo7QRniRJkiRJVIDEbsKECaSkpLBy5cpCLX5bHp07dw6ARo0aPbOtotSzu3InkLBl49n4Yc9nuo9b1HJB38oBXWtnzt64p6UIJUmSJEkq181GQUFBrFy5EkNDQ6ysrDS379u3jw4dOmgxssJ78uQJ/v7+ADRv3pzDtx+x4WIwbewy+eadESQnJ/Po0aNyn7ReueoDgI1tlWe6vHWUCobM3cTZwDjijapqITpJkiRJkqCct9i5uroihCAlJYXExETNX0VJ6gAuX74MgLu7O94RGUxc482RO4/56mgYwcHBREZG8vjxYy1HmT8hBAF+dwGoW69ervu0rFEFgMtBsWUWl6Rdhw8fpmfPnvTo0YOzZ89qO5xSJ4Rg8tw/afTKR3z45yG5RvILKjExkSmfTGXjqZs8jq8YPS7Si6Vct9hVBpcuXQKgefMWzNnji+ppDV+lniEG1o6kRody8+ZN7O3ttRhl/iIT04h/FARA0wZ1c92nmau6RdX7QUyZxSVpz9GjR+nTpw/p6ekYGxtjZmam7ZBK3Qc/rGDJZxMAuLFzGTFxa1g1ZXC5b22XStbrb77Fjk3rqBJkgkODtiwe1ZS2NWy1HZZUxoQQ3Lp1CyMjI2rUqKHtcHIo1y12lYG3tzcAFi61CI5JxsJID58vu1PFzACFtbrW3s2bN7UZYoH8HyWSERMGQL26dXLdx8NKl4h1n3J+1gACI2RyV5llZWUxadJbpKenY1ezKaM+/4XqNXN/XVQWkQlp7Im2xtC9KQAiPYVNC77kpF/5bm2XStbhI0fZsWmd5v9PkjN4f8NVEtMytRiVVNaysrLo378/DRo0wMPDg4ULF2o7pBxkYlfKspcNC1aouypHtKyGpbE+o1q5omerLn9y584drcVXGPejksiMeQhAzZo1c92nqo0l4kk4qrQkdhw5V5bhSWXs77//5t49f5RG5hj2mcHBeAcmrPYmLTOr0i4rt8k7hCyFHt0//InAwED0DY1JC73N179v0nZoUhmaOe8XAGyavYzPn9NwMkzH3+t3Rn34pZYjk8rSrFmz8fLyAkDH1Jpg8/qoVOXns08mdqVs5syZfPDRFO6p1E31g5s6AdCplh26FuqJBvfv39dafIUREBpJVqK6FS6vxE6hUODsoW61OXb2YpnFVhaioqJYvnw5vr6+2g6lXJjz488AWLYYwMSudTHR1+HMvUiGvT+LmjVrEhYWpt0AS5gQgr8vBQMwul0N3NzceHXkawCc27+Ve48TtRmeVEbi4+O5cEy9us648eNxsDCinWEo8Re2sG/9HySnZWg5QqksxMfHM+/H+QDY9P0YxwnL2OybzLwDd7Uc2T9kYlfKXnvtNXq/OZUsfTNcrI3wrGIKQAMnCyzt1UnenXvlO7ELiU7EvPVQ2vQanGN28n81atwEgBvXfMoostL38OFD2rRpw/jx46lbty5Lliwp8Wukp6eX+DlLy8OHD/H1UQ8vePet8XzRty7zX2kMKDi0eyv37t1jwYIFWo2xpO04eo6rq78hM+AcfRo4APDeW+MBSPE7x9ZzftoMTyojK9ZvJisjHV1rZ95/tScAs95/Ex0DEzLio/h53W4tRyiVhT/+WkV6ajK61s78+dWH/Pp6G5wsjXitdTVth6YhE7sycNRXPQ7npdr2moHWOkoF7ZrWw8ClPs51mmkzvAJFpeti1WkMM3/8Ld/9XmqvXlosPPBOpZkx+Pbbk7l375/afO9/+JGmfE1J2LZtG3Xq1CExsWK0+tyPTcey81gsmvRmysA2APSsZ0/X2lUwazkUgOXLl5OcnKzNMEvUqo3bSbp1DJ17pzDS1wHUpYvqNW+LcZ2O7L1Svn+YSSVj4449AHi07IqzlXr1HWNjI5p16gHAijXrtRabNjxJTuf8/WiS01+s8YULl/4OQI2OgxjQ2ImBTZw48nEnzWuiPJCJXSk6deoUx44d4/Rt9YzSTrXscmzv3LQuVUd+R51hn2gjvEJ7GJsCgLOVUb77de/QGoD0xw/wCYou9bhK2+3bt/Hy2g0ocHxzCYaujclMT+PND6cX+5xCCPr37893331HQkICU6ZM4f79++zYsaPE4i5Nh+8nY9FqCKOnzsHCSA9Qd8N/2qs2RjWao2thT2xsLH///beWIy05506o10Du9NJLmtsUCgWnTxyjat+PCEozIjAqSVvhaUVWVhZTp3+OWw0PmjRpysqVK7UdUqkLfKD+HO/VvVuO299983X1du9jRCW8GOVPtl15SOOPVtDn7S9p/91RTvu/GCsOJSUl8eiRuqHmvQlvaBpqDPV0tBnWM2RiV4q++uorunbtyp1zh1EooGm1nN2Y9RzNAbgdFqeN8AolOT2TiNBgMhOicLQwzHdfDw8PdPUNEZnpHDjrUzYBliIDU0tsO4zAtGF3Fkzqw6QpM9B3rEWYSU3SMovXInnz5k12797NzJkzSUhIYOzYsQCsWbOmJEMvFUIIDt1+BMCgp2NFs9VxMKdHfUdMm7wMwMKFCyvFRIqoJ/E8vncdgDdfHZhjm6WxPq2qWwNw/O6LMztWCEG3wa/x4/ffEnQ/AB+fq4wdO5bPPvtM26GVmswsFTavfovT2yt4bUDPHNuG9e+NUs+ArMQYVu45paUIy87N0DjemvEtQb+/Rcq9i8QkZ/D2uss8eAF+3MSmK6gycTnOk35nVOcG2g4nTzKxK0U3btwAQN/OlZpVzDQtHNnqPk3sQqMTCIoony1cobEpxBxcTOjiMWzfuC7ffZVKJfVadsCoenNuhFT8kidefkmYtB1F+3FfMKy5M9+9PYQm7/xGZvX2bLn8sFjn3LRJPYuyd+/eODo68tpr6kH4hw8fLveFqg+evcK9cwfQSXlCm+o2z2x/t4sHpg27o9DVx8fHR7OUXkW25cBJUGWiZ2ZDpxYNn9nevro1aRH32H/hthai047vfl/P8V1/g0KJVZc3sWjzKgBr1m0gJqbiv+9zcyM0jsS0TKyrONDYPWfPi6GhIbUatwJg8w4vbYRXpqYs2kLkYXV35NfvjqJpNUsSUjP5fMeNSvFjLj/H7jxGoVDQunE9LI31tR1OnmRiV0oiIyN59EjduqFn60ozt2cnHZgZ6qE6v4bg+YOZ+c3csg6xUB7GppAZp0443N3dC9x/+dq/qTJsFg+ETYV+kwsh2HIlBIC3OlVHoVBgqKfD+A7qx2DTpZBinXPz5s0ADB2qHo9Wo0YNGjRogEql4vTp0yUUfelY/Ncaonb/QNb5Vbl2PTRysaRLoxoY1+kEwKJFi8o6xBJ38MQZAJxrPZvUAez+ZQYRqz7k2N7txW7FrUjSMrP44aefAWg1YDRBe5fQ440PsO0/jZqTFmFmYanV+ErLhUB1wtqqug06ymcLUg/s9zJKI3PuP44nJb3yvg5uhsZxcu0CUGXRb9AQpnz4Ab8Mb4KuKgOv5T+xwuuktkMsNUIIjt5Rfxd2rlVFy9HkTyZ2pSS76LCJrSNKfUOau+Y+m9Shig0guBvwoOyCK4KQmCSy4tUvZldX1wL3r+dogb6OkqjEdIJjKu4A+p+Xr+fu+SMYkk6Puv+sfzuwiRMiKYbTezbhc79oZT0CAgK4e/cuenp69OvXT3N7u3btADhz5kzJBF9KLp5VJ57tO3TMc5/3unpg3qwvpnU6MnzMhLIKrdT4PC0w3qRZi1y3d+ukfu4SAn3wflD5l9PbfzMC077Tce45gc1L5mFmqMeiEU1wbtaVgHhYfyFY2yGWim8mj+DRpi9xJPeelf998j5tvtyKScuhnPSPLOPoys53q3aT9vAWSh1dliz8BYVCgYu1MTa3NhJ/fjOffvwRKpVK22GWirt+/qx5rxeRu+bR0bN8rzQiE7tSkt0Ni7V6CnSzPBK7mtXVyVJYaGiZxFVUd+6HIDLTUSiVODs7F7i/oZ4O9Z3MyUqOq9Drxi748Qcit3+LU+w1zUxIAFtTA+K2fknM/oUsWLG5SOc8fvw4AK1bt8bCwkJze9u2bQHK9XqrGRmZPL6v7m4c+nK3PPdr4WZNh9YtsOn/KVfTyvev2oIIIYiKVn+R9+jUNtd9Xno6oSIt5BbHfCtX/b7crDsfjFLPkE8+mYpLVXWXZBVzQ6b0qAXAgkN3+fW3pfz666/aDLNEJScnE3rnMqmBV2ha3THXfYyMjOhRT73t4K1HZRlemUnPVLFn40oAeg0YipPTP+Nsl/7wFQodPaL9r7JgReUs2r19/zGykmIRCZHUdbQo+AAtkoldKclO7HRtXLE1NaCade5Toet6qLv2YiLDyyy2ovALCATAytYeff2CxxQkJiZy8PMBPFw4irN3ijcOTdtSUlJ4eO8WAKMG9npme8eX1OUNjhw8UKTzZid2nTt3Zte1MF5Zdo6xKy5Sr0kLrKyscHR0LLfd14fOXkGVnoJS34je7fMvz/NeV08ANlwMJjIhrSzCKxWhT1KwHvY1bh/+zYj+PXLdp0GDBphZWiEyUvE6Ur5bXJ/X/chELj6IQUep4NUWLjm2jWjhgmcVU8JvXeCDd99m6tSp//y4reCOnb0IKhU6JlZ0bV47z/161LNHCMF+7ztkZlW+VqvjvqHE3VH/+Jz24Ts5tjWp60mrfqMA+ObLz8nKqnzd0YeOqyfGuNVtkmt3fHkiE7tSkv2hpmfnSnNXqzwXCm9eT714cHLM43L5pf7gwQMAHF0KV3zR1NQUAz1dAE5fvFpaYZWqPUdPIbIy0TG1Zkjnps9sf32ouhs1wvcSj+JSCn1eS0tLbG1tsazRmPc3XOViYAzH7kYy/eAjHoY/YuvWreV2Qfldh08AYONWBwN9vXz3bedhQ2MXSxIiguk7YlyFKeXyX3fCEwCo5eqAuUnupX6USiWdOqrHFN69ep7HlbjcxVc/LyN89UdUCT9L1f/MkNfVUfJ5nzoYujfF2LMl6enpjB49mtTUiv947DuqTtitXGtjZpj3a18n5gHhS8bg9+cULgZWvkkkuy/ew8ijJTbO1Wnf7tkW7GU/fo3S0JQnYfeZ+8tSLURYum5cvQRA69ZttBxJwWRiV0oWLVpEuzdmYOBcN89uWIDmddWtG6r0FO49LH9jM8JD1ZME3F3dCn1Mo4bqgeb37t4mIbXiLbOzec9hAKrWbIyF0bOtlH26d0Wpq0dWQhQbDhd++bRFixYRGBLKuiD16iN1HcwxNdDF73EiXtcjSib4UnLhgvp+1m3YpMB9FQoFH3WvSZLvCS7uWc/Xc+eVdnilwjc8HlCXcslPrx7qrunUoOuc8it8Pa/Tp0+zatWq4gdYxg7v20N6uD9ViM91e+daVehY0w7rHu9haGqBj48PY8aMqfCtN94+6vW+a9Sul+9+NT09yEqOIzM2jPWHL5VFaGVGCMGVSIFdv6lsPHgGpfLZ1KFhDWfaDFGPq/3um9mVqkh5amoqUSHqQvV9u3XQcjQFk4ldKWnWrBnJNbqga2ab64zYbFYWZugamQFw4WbJrWhQEpLSMsmy9cC85WAGDexX8AFPNWvSCHhaqDjkSSlFV3rOPx3r1rJV7r/MjI2Nca+nTnC2ee0v0rm3Xw0jOjmDatbGbJvclvdf8gDgz9P3EUKU2+XF7vuqa7l1aNu6UPt3qmnHgOGjQanDlYvnOHvRuzTDKxW/f/8FEeunkxWcf8tzly5dAEgL9eXo7cKNlY2Ojmb48OEsXryYtDR1d7UQoly22gOExSQQdus8AJNeH5rnfp/3qYOemRXmfT9FR1eXjRs30q9fP4KCgsoq1BIX6HcH+OcHa14sLCxo2Ey9+o7Xnj3lalH45xUUnUzokxT0dBS0qZH3xIFfv5qOjoU9SbGRzJpTMX/Q5eb0xSugUqE0tuCl5nW0HU6BZGJXSu5HJRGTlI6+rpL6BQy0rN6iKyb1uxKeWL5at0KfpGBYrQGuvScy9vVRhT6u4dMPwPTIBxVuAoVKpSLs6fi6Pvn8MuvZTd1Kc+38KTIKMZ7m8ePHZGWp+OvMAwDebO+OoZ4Or7aohrG+DtfOnaCqozN9+vR5/jtRwlIzsrDo+yl2Az/j1f49Cz7gqR/f6IJ1PfVjOOqDLytc623gjUukhdzEwSz/ruc6deowacpnVBk2k3MBsYX6Qv/hhx8IDQ0lLi6OzMxM9u/fT7t27Th27FhJhV+ilm/Zh0hPQd/Mml6d2+e5X+2q5rzW2hUj10bUHPEFhoaG7Nu3j8GDB+fYb9u2bZqEtjwTQhD9MACA9i2fHZbxX8MGqX8AP7p9nmsPn5RmaGVq/9UAMqIf0sTFEmN93Tz3a1q9Cq1feQfj2h1IcWmV534nTpwgM7PiLEW277j6x76Fs2euvTjljUzsSoGXlxfzfv6NjNhwGjlboK+b/8M8ZvpcbPtMIVG/fE2hfhirbkov6hp42YldRuQDvB9UrLEmF2/dIzPpCSiUDO7WLs/9Rg7uC0Bi0A28A/MvLi2EoHHjxjg4OeF35w6mBroMbaaeYWxhpEcHT1uUhqY8jgjTlMkpT+5GJKC0qIpL087Uq17wzOhsdmYG/DLnCwAenN9Px+kr2XcjvEK0ZDxJSCbpsbp0R8+OLfPdV6FQ8Ov3X2Pj0YSY1Cxuh+feVZktPT2dFStWADD2gxnEZSjZs2cP586d46effiqZO1DC9h1QD0+o26xtrt1w/za1Zy0cLAxJdmxOzxl/0qXrS/TooZ58EpWYxrc7rjBkyBDMq7oxbM46QspxWaRHUbHoVqmOjokVL7VqVOD+/fuqPxfSgm+w+8qDUo6u7Gzaup2wP9/i1ooZBe77/advYzdgGodCIToxZ/Kenp7Ohx9+SOfOnVm+cg17rodz8FYEQQ/DiYgov8NRYjJ0MXCqg2fD5toOpVBkYlcKli5dyu/fTiflvjfNXK0L3N/NxgSg3NV9C4lJJvXhbaxEQpG6iGrXro2uri6q1EQu3fQnqwJ8kWcLTjXEccIy2rz1HVbmpnnu16plC17+cB6O45dwvIB1Ev39/QkPDycmOhodiyr0qGePicE/v3q71KqCnq16ckpERARRUeVr3cWbT5e8q+toXuTJHa/1e4kefQcCgjs7l/D2uiv0/PkkB2+V3w9xgEPnroAqCx1DE5rU8Sxwf31dpaaL6oRf/mNld+7cyePHj/k/e2cdJ1W9/vH3xHZ3B9v00t2hgoSgqIDYKBbW9RrXn3ntvhaKigoGKBKKAtLdLLDAdnd3TP3+OGfWBTZmlt2ZYTnv12tfL51zzpxnmDPnPN8nPo+1kzufproy8o3tOMYKo9i2bt1KdXX15X+ATubMUaGBYOqU1qVu9DjZWvHpgoFYK+WcrHXFbtYL+E64naU/nWDkG9v5dMspFI7uNJbns/aV+7j2hZUkF1Z19UfoEIUNcnznv0H/f/+Mv3vr9wM9vXv3xsvXH526kdUbNrd536xXafh2TyILnvuIZ978mJoayxzJpdPpOHVUSMMPim3fuR0Z7kHfABfqVVq+2ZfGli1bUKvVxMXFMWbMGD788EMAXvhxNw/+cJxF/11BZM9ezLt1gcWWIthEjcF34dvc/bBlz3XXIzl2XUBcnFBsa+0T1qowcXOC3e3RaVSkZFuW/lFCeg4Fq57ix8evN6r2y8bGhgULF+IxbDY1jZorqs7uRFY5Vu4BzBBX3q2hVCpZfPt8FPYu7Epo+0GuT6/ZBcYgt7JhRr8LtbDGRXsht7ZD6eIDwLlz5y7jE3Q+P/2wiooDq/FUdez6/OzDd7GysqI+/QRknySpsJrF3x/jX2viLFYWYs/hkwC4BYQZ5MxqtVoc849T+vcXbD/TtszPZ58vA8C2zxRkCsHB/yFRg19QCI2Njfz999+XZ3wnczY9n6osoc7sjhvb/l3oGRDsxpeLBuNsqyQ+t5L3t6ew/mQujWotg3pH8t2mffQdNBRtQw3Jv77DEz+fsMgF4Pl8sTPa18mg/WUyGTOvF8opMuP2cSCl5Wh+YWU91762gXvnTOWH15byxtMPExQWSVpaWucY3olkldZRkSo8025oQ8NSj0wm44Hx4ei0Gl5+6mGuueYa7OzsiI2N5fDhw9g5uuA193lsh9xIsLs9nt7eqOtr2bNzO39vt8xShNM5wuK2T4Bl69fpkRy7Tqa0tJTsbOHGbu3Vg4EGOHaHNq8l850bOPbdKxaVpkpMTgXA1dMHGxsbo45d8c033Lr0/1A6ezUNjr8S0E8PaKuTWc/YSC/kMuHmn1veuuyJ3rFTBPTBxc6KUREXptz9XOwI93JA6SY4fElJltVEc3DzWsp3f4cmv2N2hYWF8dRTT/Hss8+y9737eWB8OAq5jDXHsnnql1MWuUo/eVpIiYdGRBu0v0wm4+cPX6Lq2AYOHNhPdUPL9UPJycns2L4NkBE2agaHn5vEHSNDkclkKEOFNM/GjRs75TN0FrvPpGMfORz3Hn3oGRVu8HHjorzY+vg4/nVNNHMGBHD/uHDWPTiKdQ+MZP6YGDZvXIezswuNeUns/WMNvxwzfkxfV3M2R7gfGOrYASyYfytDZ9yGfc9xrDx0adNIRa2KeZ/tYf+nT6IqzsDa3gm5jQNlhXncOH+Rxf0ethw9i7osD2QyJo4fZ9Ax1/bxZWJPX6z8e6G0c0StVqNQKAgeNAn32z7EPmIYd44KZdsT49j+8ny8Bgqp+qdeerMrP0qHKCirIqtIuA56+0mO3VWJPlqndPEhItALd4f2Cy3DA4WRVeqqUgotSNA1Xexk8wsIamfPlpnSS4hAbT1r2Wk3PeW1jRxc+TYVh34hwqX9KI29Uodd/HoKfvoPm0+1PEpJp9M1CRPbBvfj2t6+LdZcDgh2s0jHTqPRUpYp2DNpdNu1Zm3x6quv8t///pdAbw+eujaGzxcOQimXsfZEDr8et7ypK6lJCQD06d22xIUemUzG5EkTAahJP8XeVtLzv/21HeQKbHsM5OP7rsHbyZbHp0ZhZ6WgwU9Ic1laA0WmygGvG57liY+Nnyjg42zLgxMieO/mWJ6+LobYINemCKifnx8vvPB/AFQeWcfy3SkW59T8b+nN5Hx+N4oiw3+TEyZM4NsvPsY2sCd/ncknseCfNHOjWsv9K49xcsPXqApTcffwJD7uOPe+9zMyGwcaPKMtTh7m9y3bAfALi7lgYk5byGQy3ryxH70nzMb/wZX437sM/4d/QDb5MZw8fPjwllhemNEbK4Ucb2dbnn7iUQBO7t1KSrpljaX7atUast67ieo/3sLFvu1GKktBcuw6mZMnTwJgZWAaFiAwUBjNoqkps6g6u7xs4QfWo0doh44f6GeDOu8cKUU1nM9vu6DcEtgel0L1iT8o37kCT+eWBWmbY21tTfa+ddRnnGTNppYfxufPn6egoACZ0hob/xiu7+/X4n6xQa5YWaBjdzg+GU1dJcjkTB7ZOYXDGo2G/p6C1h3AK79blt6hTqejFhsUju6MHBJr8HFNsieZp9gQ17KzesY+loAl3zBj8dNNkVtnWytmxfpj4x8DMhlpaWnk5VnOJJpjRkSxjeXee+/F2dkZdWk2cQd3cSC17UYkU6LRaCjLSUVdUUC/SMME2vXE+DpzbW9ftDp4aWM8Gq0OjVbH02tPcSC1BAdPfxydnPns00+IiIjghQUT6fHgCqp730BcjmXVGx47KHSEDhvRejd0S3g72fLL/SOY0scfG48AlLYOTIj2YuPDo5gVG3DBvo/cOBGn4J6g0/HWspWdZntncPDoCUCHh3vnX/9dheTYdTJN9XVePQy+Efr7Cw90TU05aUWW8aOuaVBTKY45i4kMM/r42tpaQv19yPnuX2hqylh9xPLHi/25SxDhdfEOwNm5bVFaEFalY8YLD/PDe3fRoL50pa2PvtgExODp4sCIMI8W32tAsCtW3qHYB/elb9++Hf0Inc7WvYcAcPINwcG+fWe3PRITExkxYgSzZ89m8ZgehHs5UFGn4htRBsYSyKuox3nqw4Q+/D23zTWspgxg4kQhYteQl8iWuAwqai90VnckFLIrsQg7Fw/euue6C7bdNDgQuY099gExjB8/gbIyy5AJKqqoIT4hAZ1Ox8Dgzn+wOTk58eabb3Ltfc9j4x/NWguK3h47fV6Yk620YfwgwyK3enQ6HRNdiynf8il74jO59YuD3LzsAGuP5yCXwco3/01aagrz5s0DhHKMWUMFTcsNJy3n36CiTkVBoqDjOOu69uvrLsbb2Zbltw/h1IvXcPbla/nmzqFEeF+a1lbIZYybKtQmbtiw/vKM7mTOxQtlGT0NjN5bApJj18mcjBOEXK29ezC0R8sP8Yvx9vZGJpeDTsv5NMtwgLLL6tBUFgIQFW68Y2dvb09MjDBXsSE3kbUnsqlXWVaK4WKOHBduYFG9+hh8zJzpgq5bVdoJDqVeKu0ydOhQBl9/Gw69JnBtH1+UipZ/ctE+TrhHDMTr1tdZsOTxDljfNRw6chyAwPDOEeW0s7Pj3LlzHDhwgC+/WMbSyULU7qu9aRZzfegnToR7OWJr1bpm18WEhoYSGhoKWg2VaadY06xmTKXR8vwqYSzbnaN6EOrpcMGxsUFuuNpb4Tn/Ld765hd69ep1+R+kE/h5005yli2m5PuleDkZV2drKPfffz//96+lKOyc+ftcgUG6kKZg235BVNvBJxhne+M+u06n46WnH6fixCaqj67ncHopRzPKsJJp+WT+QCbEeOPpeWGt7fX9/dBp1Py8cTM7du7qtM9xORzPLMN1/J0EjruZ66dO7PD7ONoosbVStLnP4/csBCD//DGLaiTMTRPKMoYNal/H0FKQHLtO5rXla/C+6SVCeg8k1MMw/TeFQoGTq+AEJqZZRn1Bdlkt6grhxxUaGtqh9xg+XJhSYF2STHmtilWHLOOztUSDWkNaotCNOnxQ+2Oz9EwWhYob85LYdCzlku39YgfSOHghjv2mcP1F3bDNUSrkTQXa+k48S+B8vDDzuHcnRRGDgoJ4/fXXAXj66afp76bB38WWijoVmy1EAuWsKO8S42d4wbyeadME2ZLac7v5fFcqdY2Cs/rGzzvZ98rNlK55nvvGhFxynEIuY1yUFzKZjB3nCy/D+s7lr207AfALDO3S8wzt4Y6HgzXltSqLmbN6+LiQffELjTL6WLlczosvvghA/bF1XOddySjdWepWPogsJ67FY0ZHeKGK30LCV0/y1LPPd9juzuR4Rhn24UO46YGnL3FEO5sJw/rT95an8Lv9fU4WWEateXlFJbUlQuZq6ugrQ8MOJMeu04kraMQubBDj+4Ubpfnl5SM0UKRnWkYYPrusDqfBs+k37bYORw/0jp1LldDC/+mOZCrqLKeWqjlnciqoLxC6gMeNMPwHHBwcTEBIGOi0rN205RLJhu3nC6ioU+HtZMPQHm1rGkb7CI5EfGYxdXWtd9makoJMwVkdMWRQp73nkiVLGDFiBFVVVTz+2KPcNFhozvn5iGV0RX73yTtkf3I7OXvWGH3swoVC1EFemU9RVT1PrDnJqkMZvPvhx4COMB9nPJxaXvCNj/YCYG9yMZWVlWi15o9cnRT1y4aOuHToe2dSVFiAW9rfVBxaazFd9OfOChNoonp27P43d+5cpkyZQl1dLZ8/MZ8f3nqKnOwsfv/99xb3t1bKmTJF6A49ceQglZXmr0vWqwQMNkCPtTNYePudWHuHsTvJMmott+4VZv4qnTyINbLO0pxIjl0nszdZ6Ia7WNKiPSZOuQaH3hMo09l2hVlGk11Wi1P/qdy45GkCAw2fNtCcESOEWavp508R6mZLSU0jz/522uI63wAOJRehKhYiiv37ty/C2Zxp10wBIP/cUfYl/9MNuXHjRt5b8Qs6rYa5gwJRyNt29KN8nSha/yZPz+jPzz//bOQn6HzqGjV4LfoAvzv/x5zrJnXa+yoUCr744gvkcjlr164lQiZE6g6kllBYVd9p5+koGckJaKpL8HE2/rc4fPhw4uLi2LB1J0qFnE2n83l65V4qT2wC4L/P/avVY4f18ECn0/HXSwtwcXEhNTW1w5+hM2hUqclLOAnA7Os6noYzhMTERLZ98xaVh35hb6JlRCxz0xIBGDyg7RmxrSGXy/nll1+YMkW4P9jY2PCvf/2rSaC3JaaPHojSzQ+NWsX27ds7dN7OQqXRsv3Xb6lLPUZf38uvrzWEcVHC4mZ3YpFF6BruPCik490Dw9u9f1sSkmPXidyz5CH2/vARmooCRkca59i99fp/8bz+CWpdejSlb8xJdpkQMQp06/gPumfPnri4uFBbW8vdPXUo5TL+OJXHSxvPWsSPtjk7j59Dp1FjY2dPWJhxNYXTrrsWKxs7ZEprfj76T9Tp8SefYuu7j1Bzbjc3D25fMibaR9CzAixCqDSpsAqdXIlfj2hCfTt3xd6nTx8WLVoEwCfvvEb/QBd0OsweralXaSjPFRyqsQbMBr0YmUxGv379GB/tzSfzB9DDw56avd+iUzUwePDgplRtS/i72uHvaodMKdRz6TvszcWmPUfR1lcjs7Jh5oTWx+t1BiNGjMDJyQltXSXxp+MorDSvg6/RaFEE9MLaL5pJIzoerXZ2dmbLli0UFhZSUFDAW2+9hULReq3ZiHAPbENiAfh7x84On7czOHw+i8Ityyhc8wLuStNkWgYEu6I6t52k1W9w4Gy6Sc7ZFioHbxz6TKLfyAnmNsUoJMeuk6iurua7r5dTeegXenvb4uloXLGti50VTuKYKf2M1o5SVVVFYmLiZb1HUmo69dnncNB2fMyNXC5n/PjxAGTHH+HV2UJTwor96dz+9WGKLESzT6vVkVhnT/Djv/LDHzvanYV5MdOmTWNPXAJu4+9k0+k8EguqOHnyJMmJ50GhZPI10y4plm+JKF9HlK6C9l9S8qX1eqbGWNV9Y3n++eeRy+Xk5uYyLkw4x+Z48zp28VklqEpzARg52LjI7cXEODZw+MXrKTu5Fblczttvv91uecbAEDesvXsA5nfs1m7aCoBPeF9sbLp28LmVlRWTJglR4fr0E+xLMe9YvezyOlwmP0DIXe8zop9hItVt4eXlZZAGXJC7PQExQo3vtp27L/u8l8OaTX8DOpx9gggIaL0+uDOxViqoPvQLNfE7+PWvnSY5Z1uoffvgOf0xbrv7fnObYhSSY9dJ/PXXX6gaG1C6+nHzlOFGHy+TyfB3VqKprSDrMh27t956i169ejUVqRcVFdHQYJwTdW7fZgpW/Ytv333xsmxZvHgx//vf/7j55pu5ZWgwH94Si52Vgr3JxUz7aA/7zXwDB0gsrKK8VoWDvS0zxhpfIGttbc2w6CCu7e2LTgf//vUUix9/FgD7yBE8N9uwFb+Xow0u3oK+07nEZKPt6Gy++PAdijd9gH151ziZYWFhnDhxgri4OGYOFiYaHEgpprax5akNpmD7oTjQaVHa2ne4BEHP/fffT22t8Ft+5513mhY5bTEo2A0r0bHTSyeZiwP79gIwcHjXRuv06OVi6rPiOZhi3gYK/aImwsux1U72rmLMaEEvLjH+tFnnx+7aJTiWvQcOM+l5o/rEArDv4CGTnrcl9B3yPf3al7+yJCTHrpP4ZpWgyu4QPYJpbXQ/tsYff/zBlicnU/jLS2SWdNyxa2xs5KOPPkKj0RAdHc33339PdHQ0f/zxh8HvUd2gpqpY6ASKiujRYVtAiGY99NBDTenNWbEBbHhoFJHejhRVNbBw+SH+OGVeMVa9TMmgEDesLuMm/uQ10ehyTvHn64s5suNPAG69byl9Aw1Xa4+MEBycjHTzp2JP7NlCzem/cdF23VD6fv36IZPJCPdyIMDVDpVG11SwbQ4OHDsJgH9olFHNTy3xzTffsGrVKs6cOcNjjz1m0DEDgl2bInbmdOx0Oh2KXpNwHnID826YaZJzjholOJANuec5lm7eBd+Rc2no1Cpiuiha3RajB8SgcPJEq1Fz+PBhk59fT/Ip4dyTJ5o2DTlmhOBIJp6JM2s9dnpeMRnJ59FpNWa5Di4Hi3fsioqKmD59Ovb29kRHR7Nt2zZzm3QJhYWFbP1T6HSaOm1Gh4qu9a3kmuoyMks73hF54MABKisr8fLyYvbs2Zw8eZKysjLWrl1r8HvklNWhFjXsoiMMnw1pKJE+Tqx/aBSzYv3R6uCxn08Sl1Xe6ecxlMNppRT++jKpa98jP7/jkhsJR3aRufJZGrLPAtB/8hw+f/gGo96jf09BWqGsuLAp2mMOGhsbKcsRInUTRnZ8lJih1NXVEW0nOJDmTMOdPSOIkcb0unwxUj8/P+bPn09vI4RNY3ydsfEOBSArK8tsnZHZZXXUe/XGe8o9zLvWsPmgl0u/fv2wd3BA11DD2bNnzdpBv/y1f5P5/o2Un/rb5OfuH+SGxzUPEn3fx03OrqlJzC6iOlso55k/6xqTnnvm1LEAVGWfJ6PEfBHLVWt/J+/rhyj76d842V4Zo8T0WLxj9+CDD+Lv709xcTFvvvkmN910k8WosoOwsr3zoX+haqjD2i+S5++a3aH3+Wf6RCkZxR2PkGzevBmAqVOnIpfLmTt3LiB0aBqajhU07ATHrqMads0pLy9n+fLlvPnmPwOe7a2VvDcvlim9fGjUaHlyTVyLkxu6Gp1Ox774NOqSD7N/02rs7Q3THmyJa6+9lg8++IBhw4bx3HPPceyv1UancfpHBCKzFmxIT0/vsC2Xy/5jp9CpVchs7Jk41HDB5o6wY8cO/Pz82PmZkL4+kGIeqQOtVkelwgWbwF6MG9218h6tYWetICLQB4Wj0KwSHx9vFjv0WnJ9Alyws25bWLazUCqVjBg+HJlcgao0x6yLvfz0RNBqGNgr0uTn7unnhHPUUOpdQymoNk9Zwk+/bwOdFjt3X2IiO39x3xbDBg9CJlegrSnn76PnTHru5uw7dAyAgDDjdQzNjdGO3d69e3n//ffZsmXLJdseeOCBTjFKT3V1NevXr+fll1/G3t6e2bNn06dPHzZu3HjJvg0NDVRWVl7w19XkVdQx/rH/sWnNdwDcsuQp+ga6dui9fHyEonm0GtJyOh41+uuvvwC45hphlTV8+HD8/PyorKxkz549Br1HVunlixM3JyEhgXvvvZdXXnnlgiiUQi7jrbn98HS0IamwmhVmGCuVUlRDnqgs3iMszKBRYq2hUChYunQpBw8e5NVXX22z+601YvyccYgZjdfAa7CyMt8qcfMeob7FJSACe5uutaNfv37U1taSlhCPqiSbMzkVl4zjMgUZpbVY972GkNvf4alHzFcs3dvfBfuYMYycPg8nJ/OkgL7/4Ufq0k7Q30QyF3q++uorlny9G4eY0RzPNM8CPiu/iIYy4f43bZzx9dKXi41SQYyvcB86lV1h8vMDbN8tPCsi+w0x+bnt7OzwCREc6q279pn8/HrOnhFKIfr1jzWbDR3FKMdu2bJl3HjjjRw7doyHHnqIiRMnUlr6T5HrypWdO7w3KSkJFxcX/Pz+GZzev3//Flexr7/+Oi4uLk1/QUHty0tcLp6ONtR69sQhehTjFz3Bl8/c1eH3sra2xs1DmD6RmZ3TodqCqqqqpk46/UQEuVze1G22e7dhXVaJmXnoGoV0cHDw5YsyDh06lLCwMGpqavj2228v2ObmYM1T1wpdZ8t2p1LTYNoV6qG0ElSiMPGA2FiTnrsloryd8LjuEeynPIxPUKjZ7Dh8VBglFhLZ9aOtPDw8mq5Rm+zDaHVwMM30UbuzucJiMMbXyeQF883p5e+M+6R7iZ3/b/r06dpoaUvodDr+XP4mhaufx7Ey3aTnDgkJYViUkL04nllu0nPr2bRLWNRYu3oTFuhjFhv6BjhTdfIvXn760QuesabCdsg8/O76mPseMc94w76xgtRQ/PnLU3e4HPJShWjhuOGmd24vF6PuXm+//Tbbt29n5cqVnD9/nmHDhjFq1CiysgTtrs4udKyurr4kguLs7Ex19aWpymeeeYaKioqmP71NXYmVQs6Htw4g6eBWdnz7DtbKy3sYBPoLHZFVpUWUdyBiERcnFJsGBARc4AyPGTMGwOCIXUKyULjv7OZxWalJPTKZrKl4/O2330aluvCzzRkQQKiHPaU1jfxk4ukDh9NKaSwUHLtYC3DsXOyt8BVrNBPNOFosQRwl1t9E/yY33XQTALXnhRW6OdKxJ9Py0DbW08vfvB1wvcQOvHO55qmvOxIXT2NlCSismD/D+MHvl8vAYDcATmSWoTWD3uUeUZTWJ/TyZU46Sv8gVyoPruHwn6s5ceKESc9dUt3A+YJqrL1CuWnyCJOeW88rL79M0NKfaOw9g0a16SewpGbl0lhRDMi4foJ5/g0uB6M8kcLCwqbB7nK5nNdff52lS5cyevRozpw5c9ldZBfj6Oh4SUq1srISR0fHS/a1sbHB2dn5gj9TMDLcEz/XzklX+PsLzpimurRDkieRkZF89dVX/Oc//7ngdb1jd/DgQYPq7CrkDrhPuZ8F9z1qtA2tcdddd+Hl5UVaWhqvvvrqBduUCjn3jBG6ZlcezDDZzVyn03EgpYTGQsGRtQTHDiDSxxGdWsXRc50jM5Kfn09hoXFq/lW1QsR27AjTrFZnz56NQqGgID0BVWmOWWRw/li7hqz3b2LvVy+b/NzN0TuWKQVlHDpm2oc6wPe/Co1griG98PcwvZP754/LKfjuMQpO7SalqOs6slvj1KlTAET1vPwGmo7SL9AVa98IAI4ePWrSc+8XF1Uxvk54ORmnx9pZDO0dhru7G41qLefzTb/A2bh9PwC2Hv4E+3qY/PyXi1GOXXh4+CUX2f33389bb73FpEmTjNZKa4/IyEgqKiou6FSMi4szqsvsSmLKlCkEDbsWpYsPWR3ojPXx8eGuu+7i/vsvrA+KiYlh4sSJLF682KBOy2KNPU4Dr+eRpY8YbUNr2Nvb89FHHwHwyiuv8PLL/zw8q6qqyNu/lsYzW0g4e/qCsVxdSXxuJQVl1ahKhCihpTh2stwzZL47h5ceWHhZ76PT6Xj++efx8/PDx8eHu+++26D5o9UNatxufoOgpT8xY7xpaoyap2NrE/aRWFBNcbVpBaxTEs8BOkIDfE163ovxdLTB01ZH5ns3MXzwQEpKTBu9/PMPwbEbMMo8avvpaanU5yVRn3nGLHV2mclCCm7ooAEmP7eeSG9H7P2FOrM9B46Y9NxvvvYKRRvfJlhtvrnlMpmM/mK9+kkzNNHsFv/N/cN7mvzcnYFRjt2jjz7aorbSzTffzPfff9/prdmOjo7MnDmTF154gbq6OjZs2MCZM2eYMWNGp57HUnjiiSe44dHXsA3pd9nTJ5ojk8nYtm0bH374IW5ubm3uW1WvakoDB3RSJFLPLbfcwiOPPCLU8Pz5Z9PrCoWCp554nLw/PiLvm0eYP/tazp4926nnbomdCYWoq0tw8vDBw8PjsgVpO4s+UT0AHcV52ZdV3vDm/764IDr69ddf8/zzz7d73HlRlNPf2xNvl8tPxRuKPh2rSRFWy6bUsyuubqBCHCU2pgOjxDqbviHeKJwECSRTdsZWV1eTKuqXzb/ROKmezmLkSKEjuSEvgRMmrrNTabRYRY/Fvuc4pnXxGLW2UCrkxPQRJp8cO37MZOfVarUc/XsDtWd3EWhj3rFuVSf+IP+Hp1nzy68mP7dtaCwuYxYyYdpsk5+7M1Aas/Ptt98OwOrVq1vcfv/991+wbd68eZdhmsCnn37K7bff3vTgXb16dbvOyZWMfjarsalYlUrFihUriI6OpkefQWSX1dMnwNlo/Z2c8jrqMuJwdrRHrhkPdG5H5AcffMCgQYMuiBzZ29uzePFi4s4lcWj/HrLPHWfo0KH8+uuvTd29XcGOhCKsXH35bON+bujr1emlBB1laF+htkfdUEtxcTFeXl5Gv8fWMzk8/38vAOAyaj49oyI4+M3LvP/++zz++ON4eLSeXojPFTrxTF1rdsMNN1BUVES6Y08258CR9FKu7WOa6Nmp7HJURRkADB4Ya5JztkUvf2esPYOpqyjgzJkzjB071iTnXfnLBnQaFUpXX26eap7aomHDBIHaxoIUjqcZV0JwuaQW1eAwcCY+I5SMjO36xqG2GDlsCHv/B/lZ6VRUVBg0kuxy+fXvfTSU5iJTWvPAwjldfr620JZm05B1hrhjphdpLrT2x3XkLSy4+cprnAAjHTs9n3zyCQcOHMDX15fAwECys7PJz89n5MiRTQ9HmUzWKY6dl5cXmzZtuuz3uVLwdVCgqS4jq9S4h3lqaiqLFy/Gzt6B8CfXUNWgxd3BmjX3jyDcS6hJrK2tJT4+niFDWr9Ys0vrKN38CYVluRxZEGvQGCRjkMlkTcPfm7Ns2TI0Wh0Dn/6JhJ/foCbzFLNnz2bLli1NNYKdSX5FfVOaZ0K0N/b2ppV1aIveQZ4oHD3QVJdwPinZaMeuok7F0o9Wo64uxcrRDZ9RN5Ens2LmvU/x3r8Xt+nUATxzxyxK63V4/OeNy/kYRuPh4cEzzzzD+pM5bP7pJEfTTdcNuPtEItq6SmQyOT17mj/90svPBSvPEOpSjlx2xC69uIYdCYW42VtzbR9fbK1al+FZvVGQSwofMtFsoqzh4eG4u3tQWlpC/OlT1DSMx8GmQ48qozmbJyxqevo5IZebd6E3JCYEhbM3mspCTpw40en34pZY9u2PAIT0H4mnW9c7km0xafQw1v/wNYVp56ioVeFib5rrsbpB3VTb2dvMjVQdpUNtnD179uS9994jMzOT/fv3k5mZyfvvv0/Pnj3ZsWMHO3bsYPv27Z1ta7fn0KFDLBoTRd7KJ42O2CUkCFpsclc/qhqEaFhpTSOP/nQStUZLVVUVLi4uDB06tM32+YyiyiYNu/Bw0wpTKuQyZo/qi8+8lwgfPJ76+noWLVp0SRdtZ/DH6Tx0OhgY7Ip/J6ecLxdPR2ts3IVI1aE44wU6v9ufTq17FGNeXMf+Xdt58xbBkT/vPR4ncRZtaxQVFZGXdJqGrDMMiLp8qZuOMCRUEOc9k1tpMvmbXWInpG9wKHZ25r8eevk7Y+Up/PufFqdhdIT1J3O45oPdvLTxLI/+fJLpH+0htY2GBIfx9+K76H3uW9K5mqTGIJPJGDFCqO2sy0ngTI7ptNx2HDiOqjSHGG8Hk52zNXr7O2MjNlCcPdv1Qr0ajZb9WwSN2Nk3mCcN35yxI8XIbX4KJ7NMt8hbu+0gVef24q2sw7sDU6QsgQ45dj/99BNLliy54LX77ruPH3/8sVOMulrRixRrqkvJLq01qr7q/PnzAOhcAvBysmHzo2NxsbPidE4FW84W4OTkRI8ewgzKI0daL8Y9k5gGWg1KK2sCAtp2ArqC6f38kSmssJn6KLNmz2bdunVdItT7+6lcdFoNO16Yy+TJkykuNu9syubIZDI8/AQdxrizxuk4abQ6fjycCcBj0/sxeGAss2L9iQ1ypUGt5cvdQh1ZUVFRi8dv+ksQHrfy7sHIPmEd/QgdRqfTsXPTb1T/+Q6NNZUmKZzWaHXEnxR0+wYPtozUS7C7PU5+wu/1zJkzHaq1TC6s4qlfTlGdn4FfYw7uDtakFNWwcPkhcssvbc5KK67hdE4l9gFR3DZl8GV/hstBn45tyE0gLrvcZOf9+X+vkvvlfeQfMXy2dlcR4e2I99R7CXzkR6bNuzTL0dn87/vfqCvORm5tz1P33dbl52uPXr16obCyRtdYy7ZDp0123u9WrqJ4/RvU7PnOZOfsbDrk2IWEhFwiNPv999+bRBS4O+PrK9YTaVTUVVdSVGV4V6A+YmflHsCi4SFE+zqxcLiw4tc/6IcOFWZ+tjVY+nyi4Eh4BwQjl5tepHVwiBveTjbUaJQ88vrn9O/fv9PPcT6/khOZ5WhLsykvLuDQoUMWV7cZFBIKQFJKqlHH7UwoJKe8Djd7K67rI8jnyGQylk4SOuxWHUhlzo03ERgYSEZGxiXH/7JBeKC5RQ4mxMN0jRN6ZDIZb7zxBiWndlKXcoQjJkjHns+vROYdjmv/KdxsooH37aGQy+jdqxfI5JSVllJQUGD0e/xn3Rlqy0soX/t/aPYuZ/OjYwj3ciAzK5N+Q0dfIP5aVlbGZ5uEqOXoCE88Hc0jc6Fn+PDhuHn7obB3MVlXZE19I8WpQnT0hmsmmuScbWGlkNM3OgKFnRPxnaBpWNeooaq+9ezH+6JqwcDJM/HzNP/90MrKiqAIQV5t7yHT1dmdOioIVI8abb7mmculQ0/u5cuX8+KLLxIVFcWkSZOIiorihRde4Ouvv+5s+64qbG1tmxwMY7Xs4k4LXaQ2noHMHyY4dLcMCUYmgz1JxWSV1jbV1rXl2KWlCY5EaJhp07B65HIZ0/oKDslfZ/6RuTly5Ahbt27tlHN8f0BwaMLJBQSHtyPjv7qSIUOH4dBnEk4hfY06btPpfEq3fk7Bd4+xedPvTa+Pj/YizMuBWrWMxMx8Ghsbeffddy84VqvVsmenUEIxeNQ4szWTzJo1C4C6pIMmcez2J5dgFz6EWUtfZcGC+V1+PkPpG+KJ0+CZTLvrcZRK42rMTmaVczC1lNItH1NbVkRVVRV2MjXf3jWUqr8/pyTpOIOGDGXNmjVUV1cz96abeefua6g6sYm7Rvfook9kOJMnT+b3/adwn7yYuCzTpGI37DiEtrEOuY09U0ebN2Kpp7e/UOemb2jqCHWNGp5cE0f/l7fQ5//+5Jr3d7EhLveCKPCBlBJqA4ehcPLk5aefuGy7O4tBAwcBcPZUXIcVAvLy8vjyyy9JTW1/kVxRXUtxmlDTeuP0KR06nyXQIcduyJAhpKSksHz5chYvXszy5ctJSUlpsyhfwjD8/YVxOpqaMqO07BIShYjdoH698RBX20Hu9gzvIRTKbzlbcEHErqUfiUaroyBLEOvtFW364dd6pvQSUtLbzhWi0erYsWMHI0eO5NZbbyU7O/uy3ruoqoG1xwV9JidxXJI+7WNJzJh2HZ7TH0MRPc7gY9QaLdvOF1CfEUdeyoVyMTKZjAXDQgCwGyx0uy1fvvyClOyOHTuoKC5AZuPA9Mnmi1g0OXZpxzmWWohK07XK87uThH+DsZHGdx93JTG+zrhPvAe/sbfi6elp1LHL96TSkJdIbdIhFAoFv/76K46OjgS62fPj919j4xdFQ3UF8+YJ82h3bNsKMujdfwBjI407V1cgk8noF+iKTCZ06hdWGS+90dDQwI4dO9i1a5dB+o0btu4EwC+yr9GOdFfR29+ZigOref/xRR0SKm5Ua7njm8OsWPEtaZ/cRebbs/j7P7NYsOhOJv17OUfSSjieWcaTa+Jw6DWOf321mevGWIZTCzBx9DDk9i7UqXXktFA+0B5rfv2V4JBQFi9ezEu/nWi3XnPZTxvRqRuxcvJg8vDYDlptfjqca7OysmLs2LHcfPPNjB071qwDy7sT+lFgmupSg7XsSktLqa4QOjxnjBl0wbbJopP099kCYmNjUSqVFBYWtjhyLb+ynvpCUfIh1rhIUWcytIc7zrZKSmoaOZFZxsiRI+nXrx8lJSUsWLAAjUbT4ff+ZEcydSoN/YNcSTt7EhDSPpZGhLfQyZxaXIPGwEkcRzPKKC4sQF2ajUwmu0Qi48aBgdhaySl0iqRnv1jq6ur43//+17T9m29WAODQcyzDo80n0jto0CACAgLQqeopSzreNMO1K6hXadh9+ASNBSmMjnDvsvN0hBhfJwCjlfcr6lRsOVtAxQFBemrhwoX06vWPdMe0Yb35as3vuIy8GZmVsAhUuvoSMO8FPnnkJouR/XG0URLh5YC2vtroqF1KSgq9e/dm4sSJjB8/nr59+5Kent7mMQf3C+PsBgwe2lGTO53e/s40ZJ8l/9wRDh48aPTxy3alcCAhh+oja1GXCxkQbW0F1ae2sOPtxYzoF8WEW+4np7yOUA97np5uvvt+Syy+526ue30jbuNuNzolf/TYcW69dQFqVSPWvpHsLLRhzmf72XQ6r9Vj1vy2HoDewyeYpRSps7hyLe+mNDl2NaUGR+x0Shv8bn0Nj+mPM33ghWmUyT29AUETrBEl/fr1A1pOx2YU1+Ay6lYiZj3EpInmi9hYKeRMiBHs3nq2ABsbG3766SccHR3ZvXu3QSK7LXEur5JVhwTH9d7BHk0yEiNGWN4swEA3e6xkGmoKs0jKNqyxY2dCEQ1ZQo1QbGzsJXWDLvZWzOzvj0wmo8fEBQB8+OGHTQ+8/qMmYRsSi/ega+njbz6pA5lM1hS1q0061KXp2P0pxZTs/4W8FUv5/pN3uuw8HSHG1xmdVkNaciIbxaYWQ9gcn09teTH1KcJv/N///vcl+ywYFclPyz5g8PPrCVjyNbGPf8u3/7mLvoHmlbhozqZNm9j7wmyK1r1BnBEP9YqKCiZOnEhKSgoODg44ODhw9uxZFi1a1Go6r6qugcxTgjD2rbOnd4b5nUJPv386Y/cfMi5il1Nex/92JCO3tuPL1b/z0UcfkZuby7Zt25h783wUVtZoqkupOb2NidEe/LJkpMkkRQxFqVQSG+wKYNQ1oNPpuOWu+9CoGrAPH8wb365nUk8fGtValny8niWPP33JMVqtltP7hVKUuRZSa9tRLCPeLNHEuHHjyCqrI94mmIzSGoOOic+vxTq4H+HudgRfVPAe4uFAhLcjyYXV7E0q5sEHH6SyspKBAy9V108vqcU2sBejoscRERHRKZ+no0zp5cP6k7lsPVfAM9N6EhkZybJly1iwYAGvv/46ERER3HXXXQa/X71KwxOr41BpdEzt5UNdptBl1bdv3w4JAHc1CrmMvG+WUlOQzsbh7sTcdWO7xxxIKaYhV0jJtzYF5rbhoaw+mk2ibQyDhwzj6JFDzJgxg61btyILG4HPLV5c288Pa6V513yzZs3i008/pS75EIdTi5tmCbeHWq02Ko229ngO9RnCbFBTiQAbiou9FW6NRcQtX8KtPztRVVFhUDRtY1wuNfE70Wm1jBgxolVdvmv7+DKllw+55XX4u9qhMLNu28UEBARQX12BrDGBo+nFQLRBxz333HNkZmbi6R+E9/y3qKhtxPPsJl744PVW//2++nUL2roqlHZO3DTN/I0TemytFIRE9aZ8Pxw5dtyoY7/ak0ajWsuwHu4sHNcb2fg+gBA8mDhxIuXln3D6TDyOTk7E9utrMZHai4kNcmPlwUxOZBg+Wm/lmt9IOXUUmdKadz74hCVTe6LV6rj9s22sev8JPm+sZeiA/tx5261Nx/y89RANlcXIlDY8MH92F3wS0yFF7CyMu+++m3c+/gL7iKFklBiWitULuQ4JaTmVNEasmdmbXMxdd93Fo48+SljYpQ/KjBLBkQxxN3035MWMi/LCSiEjtaimSSxy/vz5PPfccwAsXrz4grFkbaHV6vjXL6c4m1eJm70Vr97QBxcXF6ZOncr06ZazOr8YT19Bbubk2YR2962oU3E6p4LG/GSAVutd+wa60D/IFZVOxszH3sDNzQ21Wo2npyfbzgsq/5PEaKk5GT9+PE7OLiicvTkYn2ZQ4fSZM2fo1asXe/fubXotNTWVPXv2tLh/Vb2KP/YeR1NVhJW1daePROwMYvv0BLmSmqoqg+pLq+pVHEgpoTE/CfhnWlBrKOQygtztLc6pA+jduzf29g7oGus4cuKMQbWWJSUlfPXVVwDIx95PjcIJpZMHDLuNe386yx+nWk7DZci88brxBabd8y+LKysaJC7C05LOGzyPvaJWxRff/UDViU3cPTKwRafN1dWVMaNHMaB/P4t16gDObF1N9ie3s2PVxwbX2/73XaHEJGLcHO6fLtwL5XIZH981jsDRgkbf/fffT0bmPyVJOwqsCLj/K+Y++RbuLk6d/ClMi+TYWSA9PAVxzLyKeuoa268n+23deqpPbSXYumXh0X8cu5a1y/TsP3iY6vgdODa0vZ8pcLK1YniY0Pix9ew/Ug+vvPIKCxcuRKPRsGVL++kprVbHv389xca4XJRyGZ8uGIS3ky1Tp05l8+bNvP766132GS6XkFAhrZ6YnNLuvodSS9BoNKgLhX0HD269AHqh2DX9Z7qGQ0eP8f3333O+oIZzeZUo5DLGR5vfsbO2tuZ8YiKhd71PpVyIOLdFZWUlM2fOJCkpiaeffhqdTodWq+WOO+5g+vTpHDt26bzNnw5nUX5OqKsaM3o09vbmX9BcTO8gd6zchYaqMwYIFe9NKkat1THs7pdJS0tj/nzL6fI1FqVSyZAhwnVckXmWc3nt1xp6eHjw07ajeEx/HLuQ/vxnek8OPzeJGf390ergkR+P8/RrH1xQY1xZr2JzQhn24UN45lHzCTO3xvD+0chtHdGoVQZPIdl4MpuC7Sso3fIp57av7WILuxZvZzs01SXU5iaRWFDV7v6ZOXkkHBUWc88+/uAFTquLnRW/f/Mhtv5RNNZWMva6WdTU1HA8s4y/zxWgdPLktUfv7LLPYiokx84CsZNrsGsUmiHaS8eqNFqO//kjJX9+SGN2yz/6YT08UMplZJXWkVlSS2JiIt99990lEYBjf6+j5Pd3Ob655VnApmaq2PjR3LGTyWR89dVXfPLJJ5fIdVyM3qlbcywbuQzevzmWEeFtj9OyJHpGCenw7MxL9eYuZn9KCdrGOqKGjKNPnz5ER7eetprR3x8fZxtyK+rZka1l8ODBfLVX6Ia+vp8f7g7WnfMBLhN/H++mKRR7k9uuM3zggQdIS0sjJCSE9evXI5PJSM4vp6xOQ1VVFTNmzCQv759oTUWdik93JlNzdhcAt9xyS9d9kMsgxtcZK0+hm9kQx267GHWdEONNaGgoTk5XduRB39jUkHOeI+ll7e7fqNby7t5CHPtMZNGIEO4ZE4a3ky0f3BzLjP7+FO9YwZvPPcatt93eFAX+9Vg2tY0aonwcGdbDshpoAPoEuGLtI8hPnThxwqBjvvhxLerSHOwcnY0qWbFEBg8WGgIbC1I4kdn+NXAoV4XPLf8leMod3HbtpVH4fsEevPvpcmTW9mSePYFPSCQ3PPQiWh3MivUn0ufK/s2A5NhZHKmpqdjb25P0yb3odDrSi9t27OJzK2ksEx5Yw/u3PLTawUbJwGChkH5PchGLFy/m9ttvv2AGb02DmpI04cExeezozvgol42+o/d4ZtkFYs3W1tY88MADTV1LDQ0NfP311xek63Q6HU+v/cep+/CWAczoL0Q+Tpw4QW5urgk/SccY1Edwzsrys9tNQexPKUZh68g7n33D6dOn29Tls7VS8MRU4b3f35rEIz+eYP1JQQLmbgvQMGvO6EhPNLUV/L7rUKv7fP/996xatQqFQsEPP/yAjaMLj68+ydSPDlAx+lGsPILIy8tl0Lhr2X0ul5Siau7//hgFGUmoCtOwsrJi7ty5JvxUhtPTr9losdNtO3ZarY4dCUXoNGomWkA6vTMYOXIkAPXZ8RxJa7uJpqamhnUncsgsrcXLyYanro1p2qaQy3jnpn6MuHYOMqUN+3btYMnSJ0jKKuShW6+nbOc3zIp2tMiUZC9/Z6x9wpHbOlFc1n7UMr+inmNbfwNg/oKFV7xz369fP+QKJdq6SvaeON/u/j8eycI2qA9PPfNcqyUGD8waw3/+9z0yGwdqSvLI3PgR8rjfeGV2n8423yxIjp2F0TRWrLEeXWMdacVt19kdTC5AXSmkTiMiWhcVHq1PxyYVM2HCBAD+/vvvpu2nMwpoLBAEHKdMGNPxD9CJ+LnY0SfAGZ0Otp9vWXlfp9Nx9913c/fdd7No0aKmGpT3tyay+uilTh0ItRUBAQH88ssvJvkcHWVwX+HBpCrPb9PBL6pqILFASFWOCDMsIjl3YCDjo72oU2nYEJeLVge3DAmiX6DrZdvdmTQkHyT70zv487NXWnRuk5OTeeABIX32wgsvMHDIMO765ghrj+cgl8GgSH9i7/ovchsH8pJOMe3m25n4zk4OpJagShLSsDNnzsTd3fIiNSCUZehHix2Pa3us0pncCgpLK8j5eCGvPXoHVVXtp60snbFjxyKXy1GX5rDrxLlWpX90Oh2jRo3ivvmzUJXmsHhMGI42FzbR2CgV/PivuUTPfRSAZf97n6hgH2qzztJ4fjc3DLTMyUkudlb0mXEPgY/8wOjZ7Y/6+mlPPLXJwkLo4SWLu9q8LsfW1pbQCGEheuBQ6ws8gLO5lRzPLEcplzFvcNvf58uL57D76GlueOgF7vnPu+xf+Q7OtpZVX9lRJMfOwnBwcGhaYWmqS9uN2O06fg50WqysbZqkUlpiVITg2O1PKWHipMkAbNu2rUkTbvOO/aDTYufqZVGj4ab2EvTUfm+l6FkmkzFu3DgUCgUrV65k6tSprNh+mo+2C00Eb8zpd4FTFxcXx+HDh1EoFIwebRmRydYIDxcaXLR1lRxNyml1v/0pQpqyh20drgbKFSjkMj5fOIjbR4QQG+TKfWPDeNUCV6s3XTcRGTrqss/y5erfL9jW2NjI/Pnzqa6uZuzYsTz77LP83/ozHE4vxclGyU+LR/DbA6M49OZCPvziW2QyOTWn/6Zy/08MDnbmr+8/Zv369bz66qtm+nTto5DL6NNH+F6SEs61qeG4/Xwh9Rkn0dRXczY+HkdHR1OZ2WW4urqy6Pbb8Rp9C1UNWk61Mjd23759xMXFUZF+Fjd396bpOxfj5mDNri9epP+8x/7R8LN3YtVPP+Pva7lRzv49vJHJZJxuR2AX4KtvvweNmpCo3l0yktEcjBsj3KvT4o9T3aBudb8lTz5L6dbPGOpeh5dT+2PxRvfpwdr/vciXrzyOr4drZ5lrdiTHzgJpmj5RXUpqcetF4zqdjqOnhY7JoJDQNtMI/QNdcLJVUlGnwj4gGmdnZ0pLSzl+XGih3yeKc4b2GmBR6YiZolO2L7m4VfX5e++9lz///BNnZ2d2797N4puuQ1WWy5Lx4cwbcqGTqhfknTt37j+zeS0UZ2dnBl4zD5dRt3KmjUHoB1JK0GlU7H1tAe7u7ganmW2tFLw0qw/rHhzFM9N6olRY3u3A39+PfhNmA/DaC/+5YIKASqUiJiYGNzc3Vq5cyd6UUlYfzUYmg2W3DWKoWC8lk8l4aNFc3ntPqMm8NlDLLw+MYVCIOzNnziQmJuaS81oSI/r3wmXUfGYu/W+bExR2nC+kLlnQrpsxY4ZF/Y4vh2++/pq59z2J0smDPUkt11p+/PHHADj0Gse9k/vjYNO65I23sy3Hf3yXvw+f4aMffqekIJ85107oEts7C30k/URmWZvOfVpxDYl7hAXQ/ffebQrTTMLUSeMBaMiK51QrenZl1XXs27CKquN/EOts/KSS7oTl3cklLhApTiyoblXqIbW4htJ8oQEiJqpt3TmlQt6UpjuYXs6kSZMAWL1aaJQ4svMvAEaMtow0rJ5QTwdig1zR6mBjXOuK4VOmTGHrjl3YuPqgKs2h5Id/MdLhwofA+fPn+f777wF4+OGHu9TuzuLpV9/BdfQCkipal/vYl1JMY1EGalUjQJuR2yuRZ/7zf8isbMlJOs3TTz/d5Nw4ODjw3XffcerUKVy9fHnmV0GP7o6RoYyMuHQs1qOPPsqvv/5KdHSUSe2/XPqHuOM6ej7qkGGtSnEUVtZzMquculRBxPb66683pYldzhhx3Jtekqc5ubm5/PLrrwB4D5vJ7SND2n0/uVzGxNgIHr51Os6OltcNfTGDQ9yoPLqBFQ9P57XXXmt1vzUHkpE7uKKwtuHeOxeZ0MKuZcyYMfiE9cI2pD87Ey+9BgD+u+wnNDXlWDm48ugdN5nYQstCcuwsEP2DWVtTRkWdisKqlrWLjqaXoi4XnJ2I8Nbr6/To6+z2JBVxxx13APDNN9+QkJhERU4KyJUsvn1hJ3yCzmXOQEHP7YdDGWjbGK/1a6oczwVv4xAgzMG8+eZ51NUJ0zsqKiq4/fbbaWxs5LrrrrNIzbKW0K/Uz+RUtFhflFVaS1ZpHZoCIfU8ePDgbhOp0TNrZC/8pj0EwNtvv01kZCS1tf/UngYGBvLmX+fJragnyN2Of13TekfwnDlzePHFF7va5E6lv3gNxOdWUq9qOVqz7XwhqsJUNNWlODg4MG6c4TOGrwRGhjpSl7ifIydOkXmRvueyZcvQqNXYBPbintkTcbW3jK7uzqRvoAtKpYLGikL+3LqtxX10Oh1/JZbhc9NLfL3lOB4eV44CQHsEBATw5dqtuE24ix3nL5Xj0ul0rFr5HQDjpt2AtXX3uwaMQXLsLBC9Y+egEYqfE/JbLoI+kl6G0+BZ3Pv619x7773tvu9oMYpxPKOcCVOuISgoCCcnJwrq5QQ8tJKQW15gSEz7q11Tc8OAABxtlKQU1bQqe7H+ZA4/H83CysmdDX9uZd68efzwww/Y2dkB8Mgjj3D48GGcnJz4/PPPrxjnJ9jVGkVVARV5GaQWXZqW19fXOVRlAm3r112p2FopuGX+Qtwm34eVrT2pqak888wzTdsPp5Wy8qDw+d+c0w976+41UCfEwx5XeQPlCYf4eMVPLe7z99kC6lKEaN3kyZOxsWm/vuhK4qWnn6Dwt9eojtvMxlP/lBo0Njby8WefA+A2ZAZ3j7Gsru7OwkapYMBwYTLKkYMHLljY6InPrSS1qAYbpZwbhkWa2sQuZ2yUFwq5jKTCarJKL/z8e86kk39aECZ//rEl5jDPopAcOwtk5MiR3HHHHcT0jQVoVZTxaHopSkd3br1helOBdVv08HQgwNWORo2WIxnlvP7662zatImMGgUKW0fGTJyC3AIV6J1srbhxUCAAH29PviQ1nVxYzTNrhY7BhyZEMLFvMD///DPjx49v2mfEiBEEBgayc+dOgoNbLqy2RFZ+/x2pn95N2fblnMgsv2T7rkRh9aoqaHvixJXOguHBOA+aQfCDK9ix7xCvvPIKIIyKe1pMwd4yJKjFFOyVjkwmI6Axk6JfXuL911+5ZHtto5q9ycVNadhp06aZ2sQu54YbhGkBNWd38sP+5KYO6Z9++onSokIUju7cu/AWvJ1szWlmlzJhWH8UTl6oVY0XTFfRs3zjbtSVhUzq6Y1TN+nubI6LnRUD/O2oSz3WJM+k54UPvgSNGs/gSMaO6J73QGOQHDsLZO7cuXzzzTdMmyVoa7UUsSusrCe9pBaZjCaNuvaQyWRN+la/x+WxYMECevbsyb5kYQafoe9jDhaPDcPWSs7h9FI2nc5ver2mQc2Dq45T26hhRJgHj05uuX7qtttuIykpqcUZuZaMfvSbqjy/KTqnp1GtZU9iMTp1IwXpiUD3jNgBDAhypW+AC2qlPVsL7HB2dgbg3S0JpBbX4OVkwzPTWp6J2h2YNFL4XvMzU5rKC/TsTSqmXqXBL3Yc48aN47rrrjOHiV3KtGnTCAoORltbQcK+v/j1mFBbrA4dice0x/CZdDePTO2+3z/AuChvbEOFLteLxylqtTq+/eBVcj67G+vkHeYwr8tRqVRs/s8cCte8wPL1O5tKU9KKqtm38QcA7rrryp8a0RlIjp0FE+MryJ7E514qSnkgtQRtQy3KE2v47edVBs3SBJg9QKhX+ys+n9pGNVX1KnYkCMWoU3v7dJLlnY+/qx2LxUHwT/96iuOZZWSV1rLo68MkFFTh6WjDh7fGtipI6eDggK3tlbea79FDSC1pKgvZk1h0wfd8NL2UqgY19lVZqNVqvLwsS6qmM5HJZDw3XXhw/3A4k2W7Unjrr/N8uUeYmPHKrD642HW/KIWe60b0ReHojlajZvfefRds+/tcATKZjNsXP8TOnTu75TWgVCp56MEHASjf9wMvrjnEM2tP8eaWJBz7TuK//1qCp2P3Sj9fzOBQNzx6CbXBq378+YLu2D8OxlOeeBTQcfeNljv/+nKwsrJi/BihuS91/x/8fioXnU7HqxtOYRcxHEdPP55ZKqVhQXLsLJa6ujrcNOUAnM+vpOYi7Z4DKSWoynJJ3vwtzzzzjME1YwODXQl2t6e2UcOvx3P4+1wBDWotYZ4O9PJz7uyP0ak8ODGCoT3cqWpQM+fT/Yx5awfHMspwtlXy9R2Du2UaJigoCIVCIUTlCvKbhIgBNscLkctRfcJ49tlnue+++66Y2sGOMDzMgwXDgtHp4PU/z/PpTmEu7qOTI7m2j2VL11wuMb7OuIXHArDyt7+aXm9Ua9l2TliYTelluQuzzmDJkiWEhYejqSwi4Z2bWbFhBw1qLVN7+bBoRKi5zetyrBRyrr3uWuS2jhQV5LFz586mbS++8Q7otATGDKBv7+4buXzgAcFxqz79N8+tPsoDq46zLakM7wmLOHDiLK6uruY10EKQHDsLpKqqCnt7e4bH9sLXHrQ6iLtIx2x/SglqcZSYPl1nCDKZjDtHhQLw/LozPPZzHADX9/e3eKfARqngy9sGMyvWH72pw8Pc+XXJSIubmNBZWFlZNUVg1BX5bBGduXqVhnUnhSLy264Zxn//+9+murPuzCuz+vDk1Ch6eDoQ6e3Ie/P6s3RS9ysUvxiZTMaIUUK0YueuXU2vbz1bQHFlDZzfRoSLuawzDU5OTvyyZo04JUQHh77nuWk9+WzhIIusDe4KpscG4zRgOr6jbiQgULgvxKdmcuIvoanmmWeeNqd5Xc4111xDRGQkuoYaMrd8xe8HzwHw5NRo+nTTZ0BHkBw7C8TR0RF7e0FbKdJJiNQdz/hn+HFmSS2ZpbVoK4WHvDGOHcBtw0OIbjboOMjdjrtHXRndZC72Vnx4ywBOPj+VuP+byk+LR3SLoc1tof9+1WV5/HQkC41Wx5azBVTUqfBzsW3qdr4akMtlPDQxkh1Pjmfr4+OYMzDQ4hckncWiOULtXE5CHDlFwtzUVYcyqEs9Tsb69xk2ZLDBJRlXKgMGDCAjI4M//viD3Rt+5N6xYa2WX3RHruntQ/h1d2Mz+g7SVU7U19cz68b56FQNuAbHsOS27q3fJpfLeefttwGoOraR7E9u47EBViwea9wzsLsjOXYWiEwma5I8CbQRFLSPpP/j2P0VL0TqXNXlgPGOnVIh5+s7h3Dz4CCG9XDny0WDcTFwFJWl4GJvdcXZ3FH69u0LgKwknZzyOn4+ksXbm4Vh2LN6e7Bl818UFV2q7STRvbhh/BDsPAPRaVT832c/s/18AftTSqg9txMQmq6uBifX0dGRadOmERnZ/SO1F2OjVDRN03nl93O89v0mUk7sR6a04YNPPrsqvv9Zs2bxxhtvoFAoAKhOPnJVfG5j6F6CT90If39/UlJS8FbUAY4cSC2hok6Fi51VU1eobb3QJRlugDjxxQS42vHmjf0602SJLmLGjBk4OTlR7BLFn8Xw7G+CtIufiy0DHcq5ZvI0/P39yclpfZ6sxJWPQiHn/978gP8dKGZbbSDbVhxFXVlEfdJBABYsWGBmCyVMwZLx4fxxKo+Mkhre/uK/KJw8uXbxs9x+/Xhzm2Yy/v3vf/Pwww9TV1fXrYSYOwvJsbNQmsZC1ZYR5dODxIJqNsfnMzjEjZNZ5chkUF0sPMiNjdhJXFlMmjSJSZMmodZoWbLqOFvPFuBqb8W78/pzaIMwIq276tdJXMhTd8whx+4kG+OE+krFqfVo1CrGjx9/xUn5SHQMZ1srlt02iKd+OcW52U9yw/AYXrnh6luk29vbN5UsSVyI5NhZKHrHLj8/n5nX+PPOlkRWHszgz9NCGnZcuDsrs7IAybG7WlAq5Hy+cBCpRdWEejpgpZDz9u7dAAwfPtzM1kmYArlcxke3xDK9ry8rP/+AVfvWAVxxY9IkLo+efs5sfHg0Op1OSkNKXIJUY2eh6B27vLw85g4KxNFGyansCnYkFCGTwb+u60lCQgKbN2/G17d7Sz1IQEFBAX/++SdJiQlE+jhhpZCj1WrZJXZITpgwwcwWSpgKmUzG3p8+YdUnbwHw1FNPdbvZsBKGITl1Ei0hRewslAEDBnD77bczefJk/Fzs+OjWWO797hhKuYxHJ0fRO8AVcO1QfZ3ElccLL7zAsmXLWLp0KR988AEAcXFxlJWV4eTkxKBBg8xroIRJefDBB/H398fV1ZVbb73V3OZISEhYEJJjZ6FMnTqVqVOnNv3/xBgf9jw1AQcbZbdW2JdomcmTJ7Ns2TK2bt3a9Nq2bdsAGDt2LEql9FO+mvDz8+OBBx4wtxkSEhIWiMWmYhMSErj++uvx9PTEy8uLhQsXUlZW1v6B3Rh/V7smp+6bb77hxRdf5OTJk+Y1SsIkTJw4EZlMxtmzZ8nNFQrnf/31V0AQ7ZSQkJCQkAALduwqKiqYN28eKSkppKen09jYyJNPPmlus0xKY2MjiYmJVFdXX7Jt1apVvPTSS5Jjd5Xg7u7O4MHCIPjff/8dgHXr1vHBBx9w8803m9M0CQkJCQkLwmIdu6FDh7Jo0SJcXFxwcHDg3nvv5fDhw+Y2y6QMGTKE6Oho9u7de8m2xMREAKKiokxtloSZmDdvHgBvvfUWarUaHx8fli5dire3t5ktk5CQkJCwFCzWsbuY/fv307t371a3NzQ0UFlZecHflU6PHsKYr5SUlAter62tJUuUOpEcu6uH+++/H09PT7KystizZ4+5zZGQkJCQsECuCMfu5MmTfPTRRzz//POt7vP666/j4uLS9KcfnH4lo+94vdixS0hIAIT0nKS6ffXg6OjIBx98gFar5amnnqKwsNDcJklISEhIWBhmc+ymTp2Kra1ti3+vvvpq035paWnMmDGDr776qs2I3TPPPENFRUXTnz6idSWjd+ySk5MveD0uLg6Afv36STpGVxkLFiygqKiITz75BC8vL3ObIyEhISFhYZhNI2HLli3t7pOfn8+UKVN4/vnnmT17dpv72tjYYGNj00nWWQbR0dEAnD179oLX9Y5d//79TW6ThPlxdXVl6NCh5jZDQkJCQsICsdhUbEVFBddccw2LFi1i8eLF5jbHLOgdt5SUlAtqBs+dO3fBdgkJCQkJCQkJsGDHbt26dZw6dYq33noLR0fHpr+rCU9PTwIDAwE4depU0+t//PEH58+fZ9asWeYyTUJCQkJCQsICkel0Op25jegKKisrcXFxoaKiAmdnZ3Ob02HeeOMN1Go1CxYsaOqSlZCQkJCQkLh6MMankRw7CQkJCQkJCQkLxhifxmJTsRIt89hjjzF//nyOHTtmblMkJCQkJCQkLAzJsbsCKC0tZcWKFaSmpvLTTz/x448/dgsBZgkJCQkJCYnOxWxyJxKGc8cdd7Bx40YmTJhAfn4+9vb2jBw50txmSUhISEhISFgYUsTuCmDOnDkA7NixA4BFixZ1O80+CQkJCQkJictHcuyuAG655RYmTZoEgI+PD6+99pqZLZKQkJCQkJCwRKRU7BWAra0tW7Zs4c8//6RXr164ubmZ2yQJCQkJCQkJC0Ry7K4Q5HI506dPN7cZEhISEhISEhaMlIqVkJCQkJCQkOgmdNuInV53WZIFkZCQkJCQkLiS0fsyhsyU6LaOXVVVFQBBQUFmtkRCQkJCQkJC4vKpqqrCxcWlzX267UgxrVZLbm4uTk5OyGSyLjtPZWUlQUFBZGVlSaPLLAzpu7FMpO/FcpG+G8tF+m4sE1N9LzqdjqqqKvz9/ZHL266i67YRO7lcTmBgoMnO5+zsLP3YLBTpu7FMpO/FcpG+G8tF+m4sE1N8L+1F6vRIzRMSEhISEhISEt0EybGTkJCQkJCQkOgmSI7dZWJjY8MLL7wgjfiyQKTvxjKRvhfLRfpuLBfpu7FMLPF76bbNExISEhISEhISVxtSxE5CQkJCQkJCopsgOXYSEhISEhISEt0EybGTkJCQkJCQkOgmSI6dhISEhISEhEQ3QXLsJCQkJCQkJCS6CZJjJyEhISEhISHRTZAcOwkJCQkJCQmJboLk2ElISEhISEhIdBMkx05CQkJCQkJCopsgOXYSEhISEhISEt0EybGTkJCQkJCQkOgmSI6dhISEhISEhEQ3QXLsJCQkJCQkJCS6CZJjJyEhISEhISHRTZAcOwkJCQkJCQmJboJFO3YNDQ3ceeedBAYG4uLiwvjx4zl9+rS5zZKQkJCQkJCQsEiU5jagLdRqNWFhYRw8eBA/Pz8+/PBDZs+eTUpKSrvHarVacnNzcXJyQiaTmcBaCQkJCQkJCYnOR6fTUVVVhb+/P3J52zE5mU6n05nIrsumsbERW1tbioqK8PDwuGBbQ0MDDQ0NTf+fk5NDr169TG2ihISEhISEhESXkJWVRWBgYJv7WHTE7mIOHDiAj4/PJU4dwOuvv85LL710yetZWVk4OzubwjwJCQkJCQkJiU6nsrKSoKAgnJyc2t33ionYVVRUMGzYMJ566inuuuuuS7ZfHLHT/yNUVFRIjp2EhISEhITEFUtlZSUuLi4G+TQW3Tyhp76+ntmzZzN9+vQWnToAGxsbnJ2dL/gzFxlxpziyYYPZzm9uSjIzST16FK1Wa25TzEbNocOocnPNbYbZ0DY0UL1rF9r6enObYjY0FRWoi4rMbYbZyDx9mj0//nhV3wd2r1rF0Q0bzW2G2dCo1Rxet46yq/heaA4s3rFTq9Xccsst+Pv7884775jbnHZ5/557iRoQy7BZs9j0yafmNsfklGZn0zMigvAhQxjo5YXqKnywl69bR+qiRdzSrz+7vvvO3OaYHE1FBZm330HWffeTs/RRc5tjFs68+SaJw4aTNGYsVdu3m9sck1NTVsbIoUOZsmABxxbfh06tNrdJJufb555j3MKFjJg9i4O//mpuc0xOfnIyfT08GHbDDUztH3tVO/imxuIdu3vvvZe6ujpWrFhh8d2tVUVFPPfN1zTqdOiAF1568aq7mBv/+osnPDwBiCst5fePPzazRaZFU15O4Rtv8nVpKWtLirlzyZKr7hrI+uxz6k6eRKvT8e2G9Wx4911zm2RSaisqGP3cc8xNTyNfpaLkq6/NbZLJeXvxYnLq62nU6bDfs4eqbVeXc1uQksL9r78OgFqn44dnnkF3ld0HPnriCc5VVgJwtLiIvz773MwWXT1YtGOXkZHBihUr2L17N25ubjg6OuLo6MiePXvMbVqLVO/bx788vRjs4ICVTMbRoiL2/PCDuc0yGTqtluqffmamiwu3uLkD8N1V9lCr2rYNTXk5t0VGogTSams5u2OHuc0yKZNffZV5Gem8WFHO8/n5fPDhh+Y2yaT8+Op/qdBoKNNo8bKyYs+ePZzbts3cZpkMrVbLp+vXA/BaWBhymYzq7VfP5wfY8Omn1Ot0eFpZ8V5wCEvkChqSks1tlsnQarWsFiPVVmJA5r233jSnSVcVFu3YhYSEoNPpqKuro7q6uulvzJgx5jatRTQ7d3GLmxt/vPQSk3uEAbB93TrzGmVCGpKSUGVnI7e357633wbgr4Tz1JSVmdky0/Hka6/zRG4ORSOGM8zPD4CN33xjZqtMx5lt20ipqeZ8fT13/Oc/ABzNzUXd2Ghmy0zHT2tWA7Bg0iTetbZiUVYmK95/38xWmY4z27ZRpFJhI5Ox+KuvADi3eQuNtbVmtsx0/PnnnwDcMno0cydPBqD20CFzmmRSjm7YQEp1NdYyGQdXr2ZFUBBvurhelSl5c2DRjt2VhE6no2bfPgCcpkxh2KCBABw6ftycZpmUTd9+x9elJaQHBzH2zjvwsLKiXqfj6O+/m9s0k6DVatl45jR/VlWhCQtnqrgA2bxzl5ktMx2/fbkcgOH+/ky95x7s5XKqNBpO/PWXmS0zDVqtluM5OQDMvG0hUf36A3D8zBlzmmVStq1eA8AAb2/cxozh3vw8JsedZNeqVWa2zDRo1Gp2JiUBMH3ePOyHDQOg+tBBc5plUs5t24arQsGowEAGzJnDcG8f7BsbaUi+eqKW5kRy7DqJ7GPHWJudTYpWi23Pnky4/npG2tszUCc4fVcDP23cwDtFRWyuq0Mul9PHxweAo1dJKvLMtm0UNjZiLZMxfuECrluwAIBjeXlXTZ3d0RPCQmb80GFY2doyyNcXgB1r15rTLJORdPAgpWo1SpmMITNnMmzSJABO5eWZ2TLTsXe/sMAdGRuLTKHAztUVgGO7LbOEprM5t307VWo19nI5ExctojYyggezsxn11VdorpKI1WRbO/aFR/DV0qXI5HJs+/YBoC7ulJktuzqQHLtOYtuvv/Jsfh7PFRchs7Ji9Lx5LA8L5zYbG1TiCr67czQ1FYBREycCMDCmJ2HW1ugKrw7Jh73rBYmbWC8v7F1c6DdxIkqgWqsh/SqJ3J4Rr/VBo0cBMHKgELnef/DqSEPtF2WOerm6YufkxNCZM5ADhY2NZFwlD7VD4n1gwnXXAdAnKgqAM/FXR9QyqKGBo5FRbLxuGtb29viNGMG+2hqyGxpIPHDA3OaZhPr4eGQyGV6DBwOQ6unFm4UFvLNMaqAwBZJj10kcE+snYsOE2jq5rS220dEA1J+JN5tdpqIoLY00sYZm7Lx5ALzw+GP83iOMuba25jTNZJw+FQdAX/EasHF0pIeTEzYyGUlXwQ29srCQtJoaAIaID/WBI0YAkJh3dehYHRLLMQZFRgLg6OFBpKgUf3Bj99e21FRW8qaXNy/6+DL6xhsB6D9oEADxGRnmNM1k1J8/j7VcTq+hQwCwsrWlh6MjAKcttPGvM1FXVdGYlgaAbe/eABS6OPNtWRm/HD1qTtOuGiTHrpM4mZAAwADxJgZgHRZGiVpN+okT5jLLZJz8+28AAmxt8erRAwA7cVZvQ2IiOpXKbLaZinOpws2sd9++Ta/9fMstHI2MYrC9g7nMMhnHNm1CB3hZWxMgfve9RccurarqqkhDDVYoWeDqxvXXXNv0Wv/QUACO7e/+zn1DcgoD7e2ZHx2Na0AAAAPGjwcgobz8qrgGGhISAbCJim56LVpspDp7FTwLtv3wA5OSk3i1qhKlpyB9NWCS0ECSVl19VTVSmQvJsesk4gsLARgycVLTa8uSEhmTksyrP3T/ouEzh48AECH+kAGsgoKQ2dujbWig/ipYrZ8vEq6B/iNHNr0WGBuLQiajQSym7s4o8/OZ4ezMtRERTa9FjRjB/4JD+CUkFE1+vhmtMw1j1Wqe8/HhuptuanotWozepWSkm8kq09GQLFznNs2ugV7jxmEtk1Gn1ZIgRjS7MwvX/srz+XlUens1vRYdLvx7nE9MNJdZJiPuwAHy1GoKlFZNr4UNGoiVTIZKpyP12NVRlmJOJMeuEyhISaFMXIn2mzih6fVwMWqRchUUTp8/dxaAmJCQptdkcjlPFhQwNDmJ39esMZdpJqGmoAA3mQxrmYwBU6c2vW4jPtSvBscusrGRN/38eafZ2D8rW1uu69OHMBsbGtO7t3OvqahAU1wMgLUYtQaYNGEiT3h5cZO3t7lMMxm/rV/PmvJy8t3dm16zsrUlUhzxeGpX9+4Qz0tMZE9ZGWsrKnAT05AAvfr3AyDxKqi3Tk1JASAiKLDpNaW1NcEOQtbi3FVQlmJuJMeuEzgr1k342tjg5PXPKq3nEKHGIrWiwix2mZIE8aEd0+xmBqCxtaFGqyU5/qw5zDIZsuxs1ob2IG7cONz8/Zter3Z357HcHOZt2dztO2Mb09MBsBY1HPVY9wgVtot1N92V7GPHiKuro9bDA4XjP6n3YZMncbe7BwPruv94vW927uSFgnwOVVdd8Pq0Xr2Y5+KKVzf/DZwRHdcAW1ucRVUAgD4jhCh+cnl5t78PpInOa4/w8AteDxOzOQliLbJE1yE5dp3AuaPHAAhvtkoF6DlK6AwsV6spEFcx3ZUPwnqwJiSUWXPnXvB6j0Bh1ZaS2r0/f4PYCWgfEXnB664REWyuquJodTWF4j7dlZRz51DrdFiHhlzweqqtHV+UlLBq3W9mssw0/L52LbdmZrA07cLv2TooCBDGzWm6+SIvUYxY9hNrK/U8OWcOL/r60rtZeq47kiR2Poe4uV3weu8J4/FWKgmzsqIiM9MMlpmODPEaiLxokR8RHAxAUmL3z16YG8mx6wTGeXnxWUAgS5ul4ACcvLzwtbEB4Hw3ri3R1tRgU1hEb1tbgsQopZ4IsdYmrZunIFSZWQBYizcvPfYuLnhbWwOQ3I07wmrKyph48CADExOodnG5YFt8fT0fFBex5mD3Fmg9f1aISkeJjpweuYMD6Q72/F1VRbq4COyOVBUVUSgWxvceN+6CbVbiAk+VnW1yu0xJcpJQQ9dDbJbQY+fkxN5Ro/kxJBTbbuzca7VassTO+KhmjYQAkWIzSdZV0iFvTiTHrhNwKS5mnKMjk0T9tub0EFduCSdPmtgq09EorkAVbm4oL1qpRogdohklJSa3y5Q88s3X3JCext/Fl2r2BYn1RSmnT5vaLJNxbu9eAOwVCjyb1ZcB9BBrTbPLy01tlklJFFPR0TExl2z7v6wsHsnNYdfm7juBI1GUfHJRKPAMuTBqaxUYRI1Ww/nz58xhmslIzRDuhWEX/QYArMQuYVVu93Vscs+epVarRQaEDx16wbabb7mFPeER/C8ysuWDJToNybHrBP6pLbr0xxwk1txlpHTfNNyezZt5tSCfP1uYsBElClRm1dZ2a6mDs7m5JDQ0oPTwuGSb/hpIFSVxuiPnjwhd0aHOzsjlF95WevQTnPvcurpuXV+UJqagovv1u2RbmK8QwUk8130dmxRx8Rok6vY1J0ujZkhSEjN37+7W10C62Bkf0bPnJdusxNrb7ixYX5KURF9bW3o6OmJ30XXgFRONh1KJJi8fXTe+BiwBybG7TDRqNR8fPcIflZVwUfgdYPLAQSxyc6O/GLXpjhw4cIAfysvZUV52ybbwwYNRAo06HRndOGqZWVkJQETsgEu2hYppqIxuLPmSIM5CDRdHiDWnR2wsMqBBpyPv/HkTW2YatFot2WIKKlKcttGcEPEayOrGqcgU8bsNaSZ5pCds0CBkQL1OR+7Z7ttI1VAvNMhEDbj0PrA6K5OpqSn8e/lyU5tlMkLlcn4OCWXTnLmXbFN6e4Ncjk6lQtPNMzjmRnLsLpPs+LO8n5/P03m5WDfrhtRz0/XTedrbh+F23Xf6QlpaOvDPw6s5Vra2jHR3Z6yDA3XdVPalOCODCo0GgKjhwy7ZHiZ2h6V3Yx23FLExJDwk9JJtNo6OTXWGqXHdsyMu7/x56sQUVMRFdaYAIWI0P6eo2MSWmQ79NRDawn3AxtERP7HeOKmb1ppqqqtZGxTMicioJkHe5shcXMhWqUjpxqnYRrHW2Cro0mtAZmXFZ7W1PJyTTdxVMIHDnEiO3WWSfFRIQfnZ2mJtb3/JdqUYxevOdRXp+YLDFtZMlLQ53069hs8Dgwiy6p4dcYmHDwPgaWWFcwtaZWG9e6MEtPXdV+4io0BIQYVHtVw/EyhGrNO66Xg9/TXga2ODjTg+qjkh4r9LTkW5Kc0yKUsiIlkRFMRt11/f4vZAsakmtZtKH6nEWmN7T0+sXS7N0ISJ6dnsskszG92FhkwhK2EdFNzi9v011Wyrrubs8e4/gcOcSI7dZZIsFsQHu7q1uF3p60eJWs2J5ORuW1uSWVoKQPhF7e16lKKek7qgwGQ2mZLkk0IUKriVdPvE66/nZFQ0X/gHoGuhDrE7kCmm4cObjVNrTqBYe5ie3D2lDnx08LSXN/e28vlDevcBILe2ttveB9zKShlq70DvYcNb3O7nJshB5WR2z5IEfbTK+qKuaD3h/fsDkNONr4GbvvuO61JTONLKAkZ/H8hISTahVVcfkmN3maQmCRdoqK9Pi9s17m6MSUnmpoQESrOyTGmaSdCo1WTX1gIQeVF7ux4r0bGry+2eqdiUBLG2qJXJAjZ+fshlMnQNDWi7odSBTq1mhqMT052ciRl2aSoa4KlZs1gf2oOF0Zd2jHYHvOvrWOTuzn2Tp7S4vceAWABqtVrKumGdnU6nozFL+FzWLaThAPx9hN9HbjdtHvhy5fcsyszg51Yicj3E2ss6rbbbalqmlJWRoVLh3EJJBkCgmMHK7OZafuZGcuwukzRx9dmjlQvZwc0Nd6USgFQLHQCt02qpT0jsUKdS1pl4GnU6FEBYC0XjAKuTEhmSlMiD3664PEO7EG1NTVN3s7HY1NQSYW1Nr4uU1vXIbWxQiGkolThT2BLRNjaiE2sFjUGVn8/DHh68ExpKYCtR256xsUTa2GBVbLk1ZtlnzlDZwahyo+istVRbBODo4cF/gkN4188fmRjhtjS0Wi2H1q6lUVyoGUNRUhLvZ2WxtqICqxaayAACRbmPHAv+Dajq68kQRYaN5cTZsxytq6NQLmtxu72LCx5iOUqmhUofqRsbKc/rWC1wVWEhRSoVADEt1BoDBIk6n9n5lpu9ST950mK/H0ORHLvLJF18EIS1UlsE4C/W3KSJnYOWRt5z/yFt1ix+mzGDEiNXUinHhEJoP1s7rGxbbhBx9PKiRqslz0JrS+rPnydl+vWkXDeNyj//NPr4hX6+bOgRxpN33d3qPm8VF3FLRjq7tmy9HFO7BI1azZTQUF4KCSFl6jVoqquNOl4vOmsVEIBM3vItRSl2y6ottIFk38+rCe/Xj8CAAD5futTo43cfOcqZ+jo0zUYKXsydAwdynbMzVhao56fVarl35EiGz53LqNBQao2MLMfv388XpSV8WlaKTGyUuZjBgwZxs4sr490vlQSyBHasWEGoqyuhsf35+IEHjD4+TXSIIqKiWt3HV6zDzkpM7JiRXYhWq2VKRARu/n4M9famyMgRgAkH/9Ex9LhIx1BPqKhhl11mmYubuM2b6T1oEJH9+7Py//7P3OZ0GMmxu0yyKkSZixa0q/QEiKK9mRY4Vqxq504qfvuNHJWKu/76i+cWLDDq+EEeHhyIiOT7665rdZ8g8UaXV1XV6j7mQqvV8vqNN5KQkQE6HXn/9wKNRq4m20tBASQ3NnKqvp5kC9Qx++Pjj/k7I4P91dWocnIo+/57o47POn2aHJUKeQtd4XoqbWz4vKSY1w4fulxzOx2tVstjDz9Eo06HFeDyxyY0onyNoTyxexfzMjI4W13T6j5WFuzcfvvss3wtCgwfLSpi6bRpRh3/T62xa6v7jJ4wkRd8fZnl4NDqPubkkcceI7ehAYDnv/zSaMcmQ6wzjRRr6Vqit48v/WxtkRm5eDIFmz9fxk6xXOhIURFvPfCgUccnnxQyUq3VGgOEimLluRb4+XVaLW/cfz+1Wi2NOh13v/oquVeoPJPk2F0G2oYGvggI4NOAQPqNHdvqfv7iKt4Sa0t2vP+BoAjvYE+FVsuvhw6hMqJ7U5Wdg4tCQXTvXq3uEyR2gxXU11tc0fDprVt5OT6eORnprJfBLWfO8ObSRww+XqtS0Sh+r1atdIIB+IlFwzlZlldbsuzjjwFwEud4lnyzAq0R6bjPV69mSmoKL51uI4Xl6spHxcV8lZPToVRfV7JjxQqOFBVhLZPxQ6/exCqVVGzcaPDxDTU15IsOQeSQwa3uV2Bny7aqKvbvP3DZNnc2q9esAaCHvT3jHBzoU1iEzghB8RQxAhXi03KtMYg6ZoC6sNDimojiNm/mjBhJdVIoKFeref/RRw0+vqG6mjy9ht1FExea88G8efwUEsqYFvQezc2br70GgLVMSCV/s+1v6oxYjCeJ3c6hbUStg/sITUQ1ajWNNa0vgsxBfXw8z1rb8IivL97W1jTqdKx84w1zm9UhJMfuMpApFEz+bS3zl32OSyudUAB+oup8roWt1Buqq7n1942MSEpi1Kuv4qZUUqxSsfGjjwx+D5W4wrMKaD1aFSx2CjbqdEavgrua1Z9+BsDowEBkQ4dysr6ODTt2GHx84qFDDEs4z61ZmSg8W08x+Ys38lwL0/Iry81lixhJfvbbFey3seb5xAS2fPWVwe+RLkYsQ1tJvwD4RkWhBHRAjoWtgrf+9hsAUyMiGPLkkwCU//qrwcenHDuGFrCVyQjo1foCZ11aGg/n5rBi+/bLsrezqauqYrdYX/r98uV80acvUxUKasVpIoaQLpZw9GjjGrDy9qJKoyGpuppaC+uQ/+addwCYFBzCS3fdRaCVFQojmt2SDh1CC9jJ5a3WmUIz57bAsuoMtfX1PGVvz6u+vpzcuhVva2tKVCrWv/++we+RkiY0hPRo41noExHBgZieHIuMQmZhJQk1+/ajlMl4cs5cHp4zB4DVf/xhZqs6huTYXQYypRK7/v1xmT4dmazlglmAwBAhkpNvYUXTe1evplarxUmppN+0acwWmx/WrV5t8Hu8tHEDrxbkk976x8fOyampgSTTwuoMN+0VhDJnXncdNz70EADHi4rIM7AGJvnYMaq1WqrlcuQKRav7BYiF47kW1jxw4Ld1qIEAW1sGz5zJdrmcNRUVRl0DGW2MUdKjUCrxEgVqMy1s8sC+48cBGDtqFM7XT2d/TQ1v7dpNfpJh0izJ4vGBDg6XjFNrToD4wMu3sPqibStWUKvV4mllxfCbbsJpiiCuW7lli8HvkS6WL4RFtl5fJrO2ZlpGOjPT04g/ePDyjO5kxmp1POLpydJ77mbJ88+zuUcY8+sb0BhYa5gkNsa1dw0off6JWloS9WfO0EMuZ15EJDETJ3Jt377Yy+Rkite2IfjodPSxtaVPG46tXC7H098fmUxmcY1kNQeESLrDyBHc9vQzyIBjxcVXZCOF5NiZgAGDBnG7mxsz2ghRm4Mta9cCMKpHDxRKJZOuvRaAQ0YU9q5PSeGH8nJq2qmb8RW3Z1pQtKaxtpZ4saHjuttvJ3zIEHq5uKAF/vjiC4PeI0kU3A1xd29zv8AeYQDkl1uW3MnB7dsAGBAoOB2Tr7kGgJ1GTIhoGqfWRm0RgK/YRJRtoMNkChprazkmRtInzp2L0t2dNyvKWVZawraVKw16j5R44RoIbucaCBK7pvOMrN/rcpKTmeXszG1DhqBQKrEfN46UhgZ+Wbfe4LfIFHXLIvr2aXO/puYBC5qbrFOr6VVSwv0enkxdsBDboCBsIiNAq2162LdHZU4O3kolPTwuHafWnGOFhUxJTWH2GsMXTqagVnRM7QYMQCaT8fK//sWByEhupI0V+0Xc6+7O6pBQ5s9vu07bEqOW1SUlXLf6Z94oLEAxaBAh/fsR7eyMDDi6YYO5zTMaybEzAQNGjeLf3j7MsGq5W8xcHD4l1ESNGTkSgPE33wxAYlUVxQbMNa2tqKCgsRFou64EYFhQEOMcHLCrs5zpC6f+3oZKp8NRriByuCCqOkJMpR3ct8+g90gVBXd7iBG51ggUG0gKai2rruSY6MANihWcsuvuuQcZkFBVRY4BkbXKggJKxVqsqFY07PT4urgCkN1BWZmuIGPvXiKsrfFQKokVFzYjo6MB2L1tm0HvkSqmsnv4td48AhAUI2j4FdTWddTcLqG/Ss3rfv785/4lANSFhDAjPY1HTp4w6D7QUF3dVGPY3n3AR5T9ybIgHbeG1FR09fXI7e2xDhVSyY6jRqHW6Ug38Bq41s+fneERrLin9c54AEc/P3JUKrIsrJHsg2++4fuyUspDQwEImDQJK5mMhoQENAakTHVqNaocYbpSW01kAL8UF/NwTja/bLQch+nEli2cq6/n96oqnMR79bLbbuNARCSj5K1nYiwVybEzAfrJC9rKSrR1lnNTTyoqAgTHEyCgVy9CxRX1rp9+avf4lCOC1Im9XI5PK+PE9Pz3hjl8FhjEkHaiGqbk+E6hli7Gw70pfTJcdHKPGhi1TBXry8LDwtrcL7hXT6xlMmx1oBGdYUsgQ6x1GjZhIgC+ERFEODkBsG/dunaPTzwkjNJyUShxb2FGaHP8vYWIdY4FCfR6lpfzc0goBxcsRCGWC4wZPwGA/fGGjT9LFz9PaI/QNvfT15pWazUd1svrChrEa91GfKD5RkYSIt4H9ohNFW2hKyxkS1g434RH4BfTtgC1v6cQ0crJtByx9sMbf2drVRVloSFNcj0HtVqGJSVym4G1pvpaY5vg1msM4Z9GsmKVyqgmta5m2dGjvF5YSJG7oOCg9PTEOjwcdDpqjh1r9/j6nFw0KhUyK6um511rJNbWsK26mhMWVJJxet9+AKI9PZueBf2nTsVZoaD2pGXqz7aF5NiZALmjI8VKJfH19ZRbSPNAZUEBOeKNpd/EiU2vLxg6jHvdPfApK2/3PZKOCz/4oHbqSgCU4mQOVaHlPNBOHBXs7ysOaAcYNWsWAGfLygzqCMsoFpzj8DaK5gH8Y2I4EdOTv8LC0FpIraW6rIzV/gFsDQtn7Lybml7vLz6cjuzZ2+57pMSdBCBYdAbbwk8Urs2zoBRMY6rwe3RspkM54eZ5AJwrL6fGAO3FBX7+/NvLm0kTJrS5n6ufH/bi78RSak1rS0o5l5KCWqfDptm/wWDxN7F/W/uNHuqcXAKsrBjbM6bd+4C/n76RzHKaiL779ReW5ubwTbOar+hx46jT6ThXXk6DAdIc7QlU62neRJRtIY5NcUYGxaKw8IBrrm16fbVaxfS0VF4zoIFi24b1DExKZElBPrI2ao0B/EVZpBwLWtycETv6ezZr/rETa87rz5674uZ8S46dCZDJZCxKS+WmjHSO7NljbnMAiBM78zytrPBpNjHhicX38piXF8GlJe2+R4qoyRbs0b7gqH6sWHW25Ui+PBAUyHdBwdwzb17Ta9GjRhFlZ8doBwfyxcHuraHVasmoEm76UQMGtLmvXKnEytuyCqcb09KQyWSEhATj4PlPbdAg8bMYsqIOlMu5192DuW3oOOpZOGMG60N78H/929/XVNSLMyttwv75DYQOHIi70goNcGzTpnbfY2BDA7e7uzNg3Lg295PJZPiIIt6Z5y2jxuzIpj+YmZ7GdRnpKN3+mXc9QkypHjGgcFyVIzo1ga13Q+oJFBtIckvav7+YingxLTyg2UjEqJEjcVUoUel0HPm97c5IrVbLtX/9yR2ZmZS2ItKup3kTUZaFaFrG79oFgLe1Na5+zWRYPD1Ja2zkpAH3geTTZ1DpdMja+fwAgaLzlF9qOYL158SAS58+/8x6tgoI4LPaGu5ISeHgesPrTS0BybEzET6OQkQjO9kyRIq9auv4j7cPS/rHXvC6jZhKaTjXfpNDU21RO/VlAIfy8hmSlMiMtYbLSHQ1NpmZDLa3Z8DkyU2vyeVytt52G58EBOLSzmid2oICBtnZEmZtTUQ79WXwT0ecykJWqo3pQv2U9UUSFUMnCRHc4rLSdvXGwnXwmJcXS2Zc3+75Anv2JNLGBnsLaiCZ/OOP3JCeRmqzGnG5XE5fMcJ8pB1pEk11dVMNUluSP3qeHDyEd/38iXJy7LDNncmJvUJUNuyixdkQ8TdxuiC/Xe3Jnzds4MOiIk7p2teoDBQXkfkWNDM5Xfz+ejZz7ORyOf3Ea+DQls1tHl+UlkZSfT2H62rxFOsz28JHbCTLSU7uoMWdy9mjQklN+EVlMrGjRgMQb4BEU4pYaxzm3/6zIEBcROVXWU4TUYK42O47YnjTazKZjDitlsN1tRz6+29zmdYhJMfORPiJP5ocA4qRTYFbaQnz3dx4YObMC163jYqiRKNhd3o6pe2kjfNFByU0tEeb+wF49QilRqulwELEabW1tWiKBOkRa7FgWI+tWAfTkNC2c6soLuazwCD+GjYcRwNqB5dnZ3NzRjorjdBI60o+W7mSh3Ky2XbRdzJqzhz2RUXzo38A6nac0CYdQwOiNf90w1mGY1tTVkZKTQ0JDQ34XdTRGysucOJOnmzzPbJPnmRzVSVJ1tYoHNufqDBr2FCuc3bGVWw2MDfnxWhMzx4X/oYHXnstcqBUrSa7nVrD348cYVlpCacMSFn2GjCAW1xducHNMmpta8rKmho/osX6Wj0DxPvA8XZmfCeKkX1va2scmkU9W8NXnM6RZSFlOfprIPKiGtkh04XpI9n19e2OmkwT7wNhYe0/C4J7ik1EFiJYX5abS554DcQ2W+QD9BNHoJ1s5z5gaUiOnYnwF6M1ubmWkYrU1xZZh19Y9C93cGBRbg73ZGex57d1bb7HOz17sT8ikttuntfmfgAhYuF4hUZDtQWkYZIOHeLdokI2NjaiEDv19NjG9ESn05Fzsm3Jj0bxZmcV3L5TA5Cv0XC6vp4EC1mp7z99mu3V1eReVBdl4+yMr1hvVd+OPM2R+DPkqlQoAtruCAWQeXjyeUkxLyQnU2UB6ej4XbvQAa4KJb4Xzfe8d9HtbAjtwf+1Uwy/f9s2HsvN5flsw5oB9CUJ6sKiDtnc2aSLU1PCmpVjADi6u9NDlKc52s785AxRmzE8pnUdQz3hAwfyfz6+3GZri9YCmogSRTkTJ4UC74saoAYMGQLAmXYW48liZ3nwRfeR1ugVFEx/W1scNRpjze0SktLSAYi+6DfgGRJCgJhaPdJOSUK6eA1EtFNrDP9Mn6jVainPzTXW3E4n8/hxvJVKvKys8LwoexE7WLwGLMQJNxTJsTMRAfqCUQt4oAFsOXaMuLo6tN6XdjD1FlOrx/e3Lvmh0+lQZ2fjqlDg1U4nHIBbYCB2ogORcaqN0VMm4sSePXxVWsr3LYjFlnu4MyYlmVF//N7m+KtafSrTgGgVgL8oh5Fr5CzariJVFBaO6tv3km020WJKvo1aMK1Wy/z9+5mcmkKOASOirF2cWV5ays8V5WRYgOjnOXGyQpiryyVF/5ETxhNhY4MqORldGw5IsliyEGKgRmWJtQ1/V1Wx1UJm5maKi6ywFn7DT0+cJIxLFEfNtfoeYpNR5MC260wBFK6uyKwF2SdLqDVNENOQIU5Ol1wDAydOAiCxvBx1G9dAiqjJF9rCvbQlnrn1Vn4MCWW2gfeNriZZvA/ExMZesq232OyiT9m3hFarbboGopqls1vDycsLJ4UCG5mMPAvQM+xhZc3O8Aj2io1zzRk8WbgGzpeVtXkNWBqSY2ciAsRUR74FjFHRarU8cuwot2ZmkNvCA7m/qBwe10YKRltRgVZMvVi1I3MBQs2KvnDcEsRJk8XC5ZbmGgYMGECDTkejTsfpNmqsHv/yC4YnJfJjpmHp9YBgy5k8oNVqSRdvxj2HXlofeAQdd2dl8vhnn7b6Htnx8TTodMiBcANu6AA+tnYAZFrANZAmynwEe3lfss0qIAC5iwuoVDS0EWFNTxdW8qEG/AYAjhYW8EhuDu8bqJPYlWi1WjLF33CU2AHYnBtmXM94R0ds25hvXJyRQaUYeYoQI1xtIZPJqPNwJ6mhgSILEKpOFO9xYd6XXgM9x47hWhdX7nJ3pyqp9WsgTdRl7BHc+qzo5igtqIlKp9PxQ3AIPweHMHb69Eu29xMXeCfbWIwXpaVRJV4Dej3Q9tg9aRLHI6MItbPrgNWdi0qMWju0MOu794QJ2Mpk1Gq1nLeQxkdDkBw7ExEk6rzlV7Vfh9LVFKWlUSvWNoS3MLR84IgRAJxpQ2/s5M5dLMnO4uOaGuQG/jj1DSQ5KeYXJ00VO+FaeiArlEp6isXkx9pw7NLy86nUanEWV7XtEWhBkwey4+Op1WqRA1HDL3XsdL6+HKitZX9K680+SaKOoZ+tLdbtTB7R4+usvwbM30SUIabYQgIvLfiWyWTsdLDnqdxcVn/9davvkSamksLa0XHUEyzW7OQbUI/W1RSlpVEj3gciWhAWthFTq201Up0VIzne1tY4GRi1vC8+nlnpaWzZ3HZTgim4zs+P9/z9uXPKlEu2Ka2t+XTKFB7y9ELRxuJNP04tvJlcTFsoRT1HS2ii0pSU4KBW09feHo/IS+0fMnoU0TY2+IlyKC1RlZrKVEcnRru6GVRjCOAeECiMFbMA6SOVXqqmhWeB0tqaaPEzHb2CGigkx85ERMYO4HY3N+5wc2u307Cr0T+QvayscBALeZszSNQySquublXHK27/PnbV1HDYCH0fX/EHkttOIa4pSBM7vcJbuJkB9Bc7t06IqZqWyBSjrxEtpDJbIkjsmCu0AJHqNLEg3NfGBhvHSzs0B4mjxdJqalq9BlLOCOnUYHGihCHom4iyLaCJKFPf/NOKuPRpnY7fqyrZLspBtPgeoiahIbVFAEHifkUNDWjEiR3mQpWbywMeHiz082vxgWwTHcXfVVW8d/Roq4LKCeIs0dAW7iOt4W9BjWQ+lVVc6+TM+AkTW9xuGyP8ZuvbiDC7aDV4KZREGCD5A5Dd0MCU1BRGtNNtawpU4sJE6e2NzOrSlPsNd9zBb6E9uE9p1WpJgldjIx8EBPD9tGkGn9eSopaP/rCKOzIzOdxKp3bf0FDcFQrKU6+cOjvJsTMRAb178W9vH+Y7Oxs0oqUrSRXrmwKdnVvcHtS3D+5KJVrg+F9/tbhPgthJFe5vWLQKYHBkJOMcHPBVmn9ES4ZYWxTZilMWK9YLnW4lDVdXVfVPN52B6Qf95IEqjfknD6SJqWj/Vq6BwN69270GUsRUZoivYbVFAP7iDT3HAoqmA+Vyom1siO7T8nzTQWIUK66V8Veq+noyxBrMGAOvgcBevZABaiDfiJnMXYFzdTUPeXrxyrjxLW638vLi1eIiPi0p5lgrDRRJYnxLpRQAAF7ESURBVDQv3MCoNYCvGNnLtYBrQB81s2qu39YM66ho8lQqDu9uOQ2nU6l418OTXRERjL2+fckfAM/wcHJUKopUKoMEsLuSPVu38t+CAv5qJSKn9PdH7uwslCS09jsQF+rWQYbXDG4rLuKhnGz+ZwHyV8dycjhcV4vWzbXF7W8vXcqe8AjmuBrWHGMJSI6diZBbW6MQV8XmXqWkJYm1RZ4tp07kcjm9xQfwsZ07W9wnSfyRR17UTdcWS+YIY8Wm+bR8EzUVWq2WXDFqFtbK4PqB4lip+MKiFlvykw8dRocwTu3ijsrWcPH1xU2pJMjKimIzz8qsyMvHViYjsJWh5XK5nJ7iA/jk7t0t7pMmRlxCDawtAvAXtd7yRakZc6HT6XjWzZ3fQnswsZUH8lBxduy5srIWxz8lHTqEWqfDViajh4E1htb29niIkZEMA0eWdRWNWe1PS+gldvEeb+UaeKhnT7b0CONJcc60IegnD+RZQGfw92dOs7WqCnUrEccT9XVMSk1h8aaWRYpVeXmg1SKzsUFpYCraPSgIW5kgnGjuCSQHDh1mVXkZ21oRpJfJZNjGxKDS6Sg40nL2Iv/sWbQ6HdY92pc6aTpGo2F7dTWHzawQoNVqyREXZxEtNI8AOPfti0wmM0jb1VKQHDsTUu7qypn6OnLNrDjeVm2RnrunTOEVX19GiTMjLyYlXxDvjTEwDQn/1JaY27EtzcykQUyHh7SSPom9ZipKoFyjJl1MNzVHP04t2NGx3TFKeuRyOYcnTGRzWDg+7Yzd6WpujojgWGQUH9x+e6v79BGd9ta03JLFdHZMKxGvlggQNQPzzByp0JSXoxVv6Fb+LUu1xIwZg4NcToNOR9yWrZdsd62p4SP/AF7o179pzqwh+Ir1iNlmbh5IOHOa9MZGNG04JP3ERcvJuJalfzRZWQRaWxNhoGMLECBKSuSZuYmosqCAlzIzWZqbg7YVx26AWJKQ19BAcQup44YMUfIoUKgZMwS5XI632EiW3Y6cUFeTJTbGBLXyGwD4qqiQwUmJvLbs8xa3z1+1ikFJiewrN/w3Hai/D5g5e5WfmEidVosMWl2c2UZFgUyGurAQVbF5F6SGIjl2JuTf8WeYl5HB72YuGs4QnbLQHq0Prp8990bmurji1UJxq1arJU1sAOhlwMQFPfqRWpUGKJl3Jfb19fwdFs7qXr2xb0V7yt7FhTnBwdzu5kZjC80eSWK0JcSAcWrNsfIVopVqMxcNq/LykMlkOIe0Hm2LFTslT7cQXdTpdCx0duEuN3eGTZpk8HknT53ChtAeLDewJq2raMjKRqfTofDyRC6OeLoYhVJJH3HU2pG/L3XsbAoLmezkxMJ2RoldjK94zeWYuWbnpY0bmZaWyi9tOBd6LbfTYufnxTRm6KeXhBp83iBxwVBgwCzmrkQfLbOXy3FrxbHxCA5u0nJrSc/v42WfMyklmc9KjHvg+4qzlbPN3ESUqX8WXCTS3hy/yEhUOh2nWoiuabVaUisqaNDpWl0kt0SguGAwdxNRqqhV6mVtjV0r867lDg68Wl3NxJRk/ly50pTmdRijHDt7e/t2/+zs7PAw8mF3teAv/rvkZBkmZtpV3BUYxHPePowbN7bVfWx76nXMzqO7KBWZn5hIlUaDDGG2qqHkNDYyODGR4Tt3mFVxXFtUjL+VFYPaSSO/f8st/NvbB48WbtpeDQ2McXBgmJEOiqUUDatF51rZRm3UgHHjcJbLcW5UXdLwoy4s4jpra5709SVqzBiDz+sVEUGEjQ22ZWVmbSL67Zc1DElK4l/tNPLEis01x1poomkUhV2te4Qade57x47lXT9/xrURMTcF+mhJYBsptMGiEv/5FtLRhampPHHuHJ8UF7eZzr2YQFEzr9DMkwf083p92+nq7ymmo0/svVSiJv7sWfLUajSt1Kq2hp84eSM73bwNJNli809oG6PQBo4fD8DZ4uJLvq+s02eo0WpRANGjRxt83uBegqRWcWOjWfXhshPFa6Cdrv5KO1vy1WqOtyBTVPrtt+Q+8yw17cwWNyVGOXZyuZxz5861+6exEEVtS8PfV3iI5po5YjVEp2OBmxu9Bl8qdaLHOjSUUxo1q3JyyDx27IJtWSdO4CyX429r22rEqyV8o6Ko1Wmp1WqpyG97DmtXoi4UCqaVLWhXNactuYeJSiXLAoN4/L77jDr3r1mZ3JyRzps//mDUcZ3N/G1/83BONsVtpIQHT5/OgZiefOTj0+QI6mlMFSIN1kFByEXBWUNQihEwVCo0ZkzHZqelU6vTomnH9oFDhgijtVqYG/zztm1sqaqk1rPlOsXWmDB8ONc5O+PbhoSEKSioqQEgMKr1h3rMmDHYi+nosxd1Bx/7azN/VFWyobYGRSslGy0R3Ls3t7q6co+7OyozSv/kiNewTyuRGj19Racn7vSlWm4JYp1in4tmbreHn1iWkpPTuqSUKcgVr4GwNkpqYqdORSmTUanRkHbRs+DMHqH2MsjevtWIV0v4R0chBzRArhk1LfVj3fza6eru11NYwMedubQu9t9vvcXTyz7n/JXq2L3yyiuEhIS0+RcaGspLL73UVfZe0fiLOjm5ZszTaxsb0YirNKVP692MMqWS/5aU8EphAbvWrb9gW6RWy4GISP6Y1/4oseboFccBssxYOP7733/zXlEhB2pr2tzPtmcMVRoNBw4euOB1nU5HQ6JQH2XTilxKa1QrFJyur+ecGSVfqktKOFhZybbqapxDWh+ZpbCxwVbUZ6u76Ps6sWMnh2trqDFQmFePzNqalQ0NvJSfT2IbUjJdjX60n287Be/z7r6bI5FRvObmdkHkWqvV8t/Dh3g0N5dMA2ur9FjCWLHG2lqKRccypHfrUWeltTUxYtPXka0X6nid3L8fgF6+xjVDOXp68kJEJPd7eEKp+ers9JI7fu3MeR4iZiVOXjRWSqvVkijWCfYdbXjmAqBnRAT9bW3xNWOtbXVJCRViEKatNKqNoyORotN25KJ09FnR0YswsHFEj5WtLZ7ioioz/qxRx3Ym2qoqvBRKAttZ5A8cqdd2vTDbptVq+SUhke/LylAZ0Rne1Rjl2D322GMG7bd06dIOGdPdCRQHJBe0opfTGnVVVTw4fjyzY2L48TKd5tKUFP6qqiROpULRziqlr/jQP3HkwpVI/blzyGQy/FpQq28PbzHtYez0ib0//cwgT0/+ff311F1mbc7fR4+yvLSUw+08VOr9/BiWnMS8o0cvGIJdlZFBcWkpKBRYt6KB1hodnUCi1Wr5v7lz6ePqyrY2BHMNIe3ESUCsLWrHMbPrJ874PX7hIPTlv63ljqwslqcbXye2rkwYK3auneHqF3NkwwZu6tOXm/r0paro8pyiXFHmwr+dm7FLz57Y29ujq62lsVmdWdbpMxSrVMiBgWL3rKHU2Dvwd1UVay+KfrSHurGR2wcPZkZUFNu/WWHUsReTfe4cOkApk+HbRhoO4L9z5vB7aA+me1/48D4t6hj2aSPi1xpKcXa22kjZn+qEBMb6+rJk7NjLTuHpJXf821jgAoycNRuApKqqC6677Ph4KjUa5EAfMV1pKHfcdBM/hoRyR4BxCyOdRkPpt9+Sfut8qi9zEkLzGkPXNponAPqJNXiHLhotdkaUzooxoiNWj6+DAzYyGSUGTu7RU3PwENmPPkbBG2+iucxnwW2RUeyKiOD12+9oc7/B114HQOpF2q7px49TrlGjBPpPnXpZtnQmHWqeqKioYOXKlTzzzDM8/PDDPPPMM6xcuZIKIx2Wq40gfcFoG/NHW+KDJUv4dNcu1ickcP8rr1B9Ganc04cO8XhuLk/m5rTbxRUrtn8fuyhao09N2how9Pti9EXDOUbIfZTn5XPrnXdyvKSEt/74g4daUIk3hrxiobU/IKDtGiePkBD8xcL65jpe2375hdEpydyel2dUGhL+mT5h7ASS/9xwA6+sXUt8RQU33XcfaS106hqK3qn2s7Nrt6P3sFzO1NQUbv7g/QtePy1GL/q2IhHQFvrmgSwjroGqoiKmzp3LL/Fn+CX+DK+0cyNuj3zRqQ9oR6pFplBgK9aE1TeLLBz643cAIpyccDSypjhfo+aR3BxeMnJm8p+vvsr3x47xe1IS0+6+i+zLkMrIEnUova2t2+3oHTZpMmE2NqgumhscLy52+rcwvaY96lxdSWpoMKorVF1WRt79S7hJoWT5nj0smTDB6PM2J1900tq7D4T078cDQcG84edHQ7MF6SlxKk2Ivb1RJSnwT7bE2Frbki+XU/D6G1QcP86LCxZScBnNFyF2duwNj2Dt8BHt3geGiWMHD5++8Jo7LjaWDR450ujzr7nxJo5HRjHWCP27mrIy3l+0iJI//6R0xQpKvvjC6PM2R7+wsGon6txc2/Xoxo1Nrytzc3nZx5eHo2OMSkV3NUY7dtu3bycsLIzly5dTU1ODi4sLNTU1fPnll4SHh7Njx45ONbCoqIjp06djb29PdHQ027Zt69T3NyXB4gzWEpWqzeHyzamtqOCDNWua/v/XkBDqfl3bYRuyxJmHvi1MG7iYceJQ5GP5+U2F0421tUz+43ceycmmIaDtVV5L+OpV541IRb59//1k1/8zreHnI0eoLmlZd8kQ/ikabz/aph+CfbiZnt/JgwcB8PE0vkkoqKfgDBc2GF443lhby7JNm5r+v0yt5r0nnjD63Hr+qS1qv+A7YNBgslUqThUWNkVI6qqqOCM6RqNmzjT6/H6idl5uluH1RV8/9xzlzSY1fLL5L/IuQ+A3X1zpBxqgw7hFq+XWjHReaubcHhUL6fsFt57Kbo0QsZ6pXKM2WKBWW1tL9OYtLBcHxzfodHz+/PNGn1tPlii1Ysh9wFa8b9WdPo1OTN2pGxtJFH9HA4yMVgG8dDKOWelprPr9d4OPqfhtHeq8POp0WtTA9wcOXJbQ90PhEbzv7881E1ueOtGcp2fO4HpnF2TNZsbuE59FfY0sR4B/6nsb8/ONug/sW7YMgKdyc3k3K5MX7rjD6HPrURcV4a5UEmNA1mHCDTcw0dGRCUpF0zWgbWxkio01ExwcGTljhtHndw4MEMeKGf4dvrfkAZ5PSuReUaZl11dfU95C/auhqIsEx1rp3XYqWS6XM0j8nnc2u2btc3K40dWVJ2Ya//m7EqMduwcffJCvv/6anTt38tFHH/Hqq6/y0UcfsWvXLr7++muWLFnSqQY++OCD+Pv7U1xczJtvvslNN91EmZk1sDqKT0QEd3h48pSXN/UGRt3WvPUWhY2N+NnYUPjLLwRYWVO+enWHOwpzMtIB8HVtf6bfwGnTcFIoqNVqOSTW2Z36exupDQ0cqK3FXYxkGIOfeEPLMyLquFEs0H3vnnu5JziEzwMCUe/bb/S59RSItXUBBtTHjRC1jXYd+KfObr9YGza4A9GqIFHzrVGnoyjNsDTmxv/9j1K1Gnelki/Ecog/L6M+Te9U+7SitN6c2GuvwV4up0arJW7LFkBIiTbqdLgqlcQY0RGrx19cHeca0UBzTVY27/j5881DDxHj7EytVsu6jz82+tx69GPdgtpJQwKovDyJq6/n4Nl/InYnzwn/PTC2ZYHrtnAPCsLGSIHamgMH0FRUMDYyks8eegiAFX/91eGxZD3s7Fji4cFcA3QobWOiWd9QzyPnz3N43ToATvz1F/U6HfZyeYeuAT8xFWtMI1nmeuEedPdbbxFsZ0eDTsdvH31k9Ln1hDc2cI2TMzEDBrS7r11foQatrlmk3K28nH62toztQLQKd3empKbQ/8xpg+8Dq994g9knT/BgYQELH3kYgM2XEbnX13i210QG0P+aqXwaFc0CB0caxEVBQ0Ii97q68XmfPkQYIXulp0khwAjpp+9Foej5Cxbyn6pK5p07y4oXXzD63HpmbN/OoswMDCnsmDhyFP1tbXFqJq5efxnZq67EaMcuMzOTa1upKZk6dSpZnSjlUV1dzfr163n55Zext7dn9uzZ9OnTh43NQqF6GhoaqKysvODP0lAolTzbry93uLtjZWBtwBYxUjNz8GA8pk9HZmUlCCV2sPg+N0coGvdrZ4UCQuH0YLH2Yuf6dQBsF0fA9PXyMkqUVU//mJ6Md3Ag3LZtiQE96SdPcrqsDBkw77FHee2JJxhkb09VB+csqurrKRYjT20VjeuZepPQIHIgKwtVfT3qxkYOiUOjJ82Za/T57ZyccBP/3bIMLBoOy8jgSS8vHp4yhbmPPcY8NzeedHGloYOzNvVOtb8BN3SltTWDREds08pVAOz7UxgxNtDf32Bx5uYEBAtRpzwDtb9UOTno0tKY5ubGba+9xlRRW2379o5lBxorKxloa0uMjQ1BYjSqLYaIqf8zRUVo1Gq0Wi0nxd/RoA44NXK5HB9RGy3LQLHyqp1CR6rjxInc9vLL2Mnl5NTXc3rrpfp6hhBpbc3Dnl7cNbn9sgaZUsk2nY4t1VX89fPPAMRv2YKdTMZQ/wCURpYjAPj7C+nPfANrJRP272fIhvXck52Fw+TJzBQdiV+aZTOMQafVGuXY2A0bSlxdHW+v/ZXG2lp0Wi2z6hv4KSSUB5580ujzW9vbU6vToQYyDWwkW7dWyNTE9O3L9CVLUADptbUk7O/YIveHP//kvwUFHDTgWSlTKLDrLzi3teIEivp4YVFi26ePweLMzTlRXs5DOdk8t2F9+zsDacePk1RVhRxY8OwzhIudyLtaGffWHtUlJZytreVoXR0uBkzPWfr00/wYEsr06mp0Gg3qxkY+Xr+O8/X12PYzXKjfFBh9V54wYQKPPPIIhRfVBhQWFvLYY48xvgNh+dZISkrCxcUFv2YFzv379ye+hR/C66+/jouLS9NfkBF5e1Ni5S3UVqgMWKVotVp2iTUoU2fORG5ry043Vx7NyWFVB1equeL3FtBOsaye0eJD9MiRIwD8JdaVTDFCv64582bO5NPAIG5up2BZT/3Bg9zv4cGc0FACevXCQew+q4uL61DUMufcObSAAvAzIFoz7IbZuCqU1Gi17P3pJw6vX0+VRoOjXMGw2bOMPj9AqJMTQVZWVOW1PytTp9PheCaeu9w9+NcLL+IeFMRbM2cxztGRml0tj3lqD1VNDbYyWdNop/a4Tqxl+nPXTgD2Hz4EwBAjBEmbo9dNyzewJrfmkNC8Y9enDwonJ66dPZuhdvb0amjoWOS6tJTPAoNY16t3q8K0zYmdOhV7uZwqjYZd36/k2G/rKFKpsJfLGX3LLcafn2YCtQaMVNJqtYx7712W5mRT2asnDm5u9BO7EPds2NCh8zfNSPVp36kBGDtc6ArcJZYhTFSpOBARyf8efbRD5w8I1U+fMCz78vvy5WgAjYMjtr6+zBEnpuxNTeuQFl5ZZiYriwr5u7rKoFFgNr16sSQ3h49yc9m1ahUNycloKyuR2dtja8B9pCV8RYmYLAPqDLVaLfvFa2XanDm4BwYyQBwJuamDjTTbTp1iVXkZ56oNCzI4jBpNnkrFT199BcC671dSpFZj18pYxvaos7Vle3U1+wwMBv2xfDkA/dzd8erRg7HXCQGmw2kdG8+YITZ+2MnluLZTZwmCSoLc3h5tVRX1Z8+xf80a3szM5PbsLKyNmL5jCox27FasWEFZWRnBwcH4+voSFRWFr68vISEhlJaW8u2333aacdXV1ThfJPzo7OxMdQtq1c888wwVFRVNf50ZOexMqpydOV1XR7J4UbVFxfnz9FZa4alUMuXOOwFIsLVlS3UVf4ppMWPJM7BoXM+CpY/yWUAgr1rbUJ6YyH4xWjVLtMdYjBXodUpK5hFPLz4TV8W2PXuyrbaG/2/vvMPbKs/+/5Es2fK24723nT3JIGQCSdiEGQJJwygrQMtLGS3Q0tIyXt7Cj9ECpWUTCCmEsDPIIpCQkEF24r3t2PHetqTz+0OPjOxIlmRsS1Gez3XluhJJx+dxztE597nH9/u/h49QbMPqqi/MGZIIHx+8hGdnn+v19uYcIUC7+q23+ep9k/7c5LhYtCLr4ixrrrqadalpTHBA/6yrrMz0f6XVdk+o+k0xBdtth+yfQ9b406hR7MnI5M7rr3fo81fccQcAP1ZWUrxvH5tEw/aF11zTr/2by58nHOwz/d8Xnuelk9VUCemVBbfcwltpaVzn5UVXP7KWXVXmvhrHghpvPz8uFL2RK/71KkklxWxITeWfF1/sdNO8mRghIVLqwPoPbthAbmsr37a0ECeC7KmjRxPi5UVdP7029x89RmFnJ0Y7Uh9mLhTnyrbSUkoPHqTtx914q9WkX3xRv/afKNogzFp69tghNMJmikn8aVddhUalot6gJ3en8/ph2Xv28GRVFY9XVaFy8DowR5x/n73/Phtf/Rf1BgN+48eh6kflAiwcSBwoxeb88AMVHR1ogNniWJw7dQoAW77d2seWtjFbujl6L2gaN5bz8vO4Z+sWjm7bxu1ff8WcvFyq+9GSA5AorgNVDl4HNoiexnNFsmHWtdfiBZR3dJAnEg/OYA6oo3Q6hyoPKo0G/1mzaDAYWPvC83z5wQcAzExO7ve9YLBwOrALDw9n1apV1NTUsHbtWt544w3Wrl3LyZMn+fDDDwl3UqyzLwICAk4pqTY2NhJgpeHXx8eHoKCgHn/ckRcPHWJRcRFv2jCVtkR19BjPx8Xx47WLCBRPlXPE2PUP/ZyGOtHdNJ7u0OezZs3kghkz0BiNPH/RRXQoCjE+Pky4qH8XdPPNtKGiwqEn7TYxOegr+tnUvr682tjIG3W1bP3oY6f3P2rYML5JTeMNJ2ygbrv9dn4XEcHi0lL+JdoALrvwQqf3bcYZz9xv33+fzxoaqEtORi2kYpS0NL5vaeGttWv7tX99VTUqlQofB3WXhs+YQUZgIHrg2UsvY3VSMr8fPoKZS5b0a/+jpk7ls+QUvkpKRnFAsuKtnTt5paaGqmhTllft64uveEJuc+ABqTedotna0cAO4Ppf/QqANXv2kP/2O8Rpvbni1lud3rcZc69peZn9rO32L03XirHh4fiLgPDRBx7g+7R0bvC2bodmj7s3beSignx2VzrWuH7WZZcyftgw9IrClVOn0tHSgiYiAh8x6e8s8SJQPtHR4dB14EcxQT1DlMV9AwPJCg7GR6Xi2FbnA5tiMd0a5YSw8sIrrwTgH1u2cMn/e465ebnk/oJMTYyYpi51IAnxzQrTA+XYsLDue8E0UR07LNoCnOWESJDEpzt2L0g+6yxGBQdjBOacfz5dikKqvz/Dz7M/fGKNRPF/12Aw0OKAnuEe0X503qWmga2gqChGie/DppUrnd5/aa4YIgtwfJq1fMIE5uTlcu3rr/OKuP7OF+4s7kS/vWL9/f0ZP348M2bMYPz48fjbseToDxkZGTQ0NFBp0WS9f/9+RjnQF+OuxMYK9wkHJoHaxROFr0WD8/QrFgJQ0tbWr2mgB6OieSQyitFOSBSEXH0VekXhkBBWvuPyy/vVWwWghIZwVnY2Zx05TI2dbEVNYSEbjx6l3mDo8X8wSVyIfviuH70VNTXEarWMceKGdOl997F86jRCNRpeiovniuho7nz2Wef3LTAL1DoyDfb2hx/y+8oK3rUoW7ZFx3BraQl/OnjQ6elgRVG6A0pnAptH7rmHCwMDuVqnI9Hbm/sfeKDf54BfdDQZAQEEenmhtyPWXXbkCJUdHagwPaGb8RmeRZ1ez3GLoRZHeXHFe0zOyeapo44Lo15y993E6XR4Y7JW8goNJeAXtJ1cc+65PBcby/Xp9qdyd4ny5ySLzEjolCmoVCo68/IwONlPbDQaOSGm3BNHOJ5tWS4mMH9sa+PSgnw6Fi9G1c9zwBzY6RWFKjuyN2VHjlDc1oYKmHntz1nid5fdyK6MTKb1o8fPnCWLDg5xeJsbHnuMkRYZ2mCtlhkim90fnHEi2r7dNIU93aL9YdKCBQAUtLQ4PF1txmg0UiXOgXgnMm533mB6mKsSD2SLzju/39eBkLg4fMW29oaIThYVUSbWO/XSS7pfnyyyfnv7kbErK3ZMoNqSSdcvJsLHBz3QZDAQ6+PDssf+7PS+Bxunjsi0adMc+twMJzzj+iIgIIDLLruMxx57jLa2Nj777DMOHTrEpf0YrXYX4kXau7LW/hex4qd9KIrS7dsKEJGSQqS4kO3f+I2tTa1iaG5mllbLDaGhxDnhcRp85ZUE/epXbG9pYV5yMo+sWOHUfi3x8ffH20t8me00DW98/33uKCtlaWUFXhYZ2ClTTefhfuH+4AzmCSxNpGM9fgAqtZqoh/+Ad1ISE8LDeevtt/FxQCbCFlsrKlhUVMj/iEb0vtgrMrMz5s7pfi1hzGjCtFqM9NTXc4TakhKuOX6Mu0pLUTlxQfvV3/7Gv2+9jXBvb4IXLiT0BsfKuNZQqVTdQaW94Hb3WtOQTIq/P0EWfZkflZdzTl4uv++HKXdFebnJ39KJh1FvPz++XLMGo1rNpo4OEl59Ba9foFt11tSpXBAYRGKX/anWPaLcOtWir1UTGtrt89vuZDm2vrycVpElc8a4/cYnnuCWadPwUamICR3G8F/f4tR+LfEJCOCW2Dh+Gx6OYufh5FsxIJERGMgwC2mR5GnT0KpUtPfDxaZMSO3ERjheYdJ4e/Pic8/hr1aT4OvLM/ffj7cTGb/edA8ROeBEdFA8BE8+++cJ3PhRo3h1+HDWpaaicrL1qL68nDZxDiQ4cS+486UXWSasKBeNHcufVjj//TNjOURUbGeIyKesnC+SU3h5zFjCLErHo0TW76iDk8WWlHcLVDv+gOul1fLBm2+R4ueHj0rFv599jpAY55xXhgKnmgN++uknnnnmGbufO9iP8ogtXn75ZZYtW0ZYWBjx8fGsWrWK0FD7Uh3uijntXdnU91O2Qa9n1urVeKtUfB8QgGUnT1Z4OFXl5Rzcvp3ZTpTDzJkadUAAaiduaiq1mqRHHmbfdYsIT0rq1zSsJVG+vtQ3NVF87BiTLrnE5ud2fmsaDpjUS9V8zPSz4eV/klvrvJbd2+vXk11dxaK2Vpz5OvqffTZp6/pX+uyN0T+Ag+3tKHbKYB3NzWSLbMxUi9K3Wq1mdGQUW8tK2bN5s1PnQMnhwxxqbyfYywutM+eASkXs/z1DzJNPoPbpX/nPkk9bmvmxspJbtmxhXh9yE3u+Nyndj+7V3DxiyhR47TWOOinwClBhFqZ1cHjEzLgFCyioqqKtoQFfB/uSbNEtUGsnsG1taOCo0IubflnPYZ2X6ur4KC+XP/z739zzyisO79ts5xfk5eWUuLJWp+M/O3bwSns7KrW6X9Owljw8ZQodR48S2NHR5+d2i6zshF7XAZ2Yam93cLLYkvJKMRnupA3UeTffTOONN/Y7S2VJ1ogRjNf5kmbn+6To9dwSGMgBVJxzycXdr6vVai48+2xatu+gMzsbPyeC9P6eA2q1mrd+/JEnjx0jtp+9dZZEBQRS2NraXRa1RWf2cVJ9fBg3e1aP12fNn8+vV69moh0XJWtoOzqI1GhIcFKHcMZ1i8i5+ioaKit7PGi4E07doRcvXsxRB75EixYt6veCehMREcFXFuKspzvmxvHKtrY+P5fzww80G41oVSpSzz67x3sjU1LYVl7OISeV6wv27+frxkbSw8PpzxxXlANiro4QFxrK8aYminL6zrjtFxpBE3vd+EeL/riqzk5qS0ud+nJ9fvAA22prmdTSgqs6I8z6eVV2vGr3f/MNekUhyMuLVNEwbGZMRjpby0o5ZMWUui9KhahvZD8yDSq1GtUABHUAW2pr+aKhnnF79tKX4MYBcQMa26v9YoLotars6OBkURHhfXje9qZclK3ihE2SM/gFB/d7YMISY0goG5qaqD5yhL8YjTYDhT1ffYVeUQjVaEgXzfJmOgL8qdDrOXyw7zJWb7qbxn0dkxzqzUA1imsiI+g4etRu1jZLgYsCAzl3es9JfE1SEg+Ul1NQWMBWZ88BkSWM68eNeSCCOoC5Cxbw/qv/wstOYNVZUMB8Xz8uCAsntZdenE9mFi3bd9DeyxXEHmb3mch+ngMDEdSBqQyqq66iscrOQ664F/j02u/4+fO5LyISuvQYmpvxcqKScn9GJnc1NRPXj15hL43GbYM6cDKwe/PNNwdrHWcM5obRVqOR+opKm2nc3etMU69ZwcGnpPtHjRmDdvt2mqrtp/At2bp1K7+rKGcGCs77BQwcCZGRUFxMsYX3pjUOnzD1EE6a1fMpbVh8PJHe3lR1dnJw82ZmL13q8L4rRAbM0YbhwSBhpKm/6GRnJ13t7TZvlHuEtMyIsPBTbiZZI0bAli3kljpXgikV/UzRLra/iY6IgJwcysv7bvw+JKawJ/YSgQ1LTCTGx4eKjg72rV/PPCcGGZxtGh8MvCLC+a343e/Kzyfaxlpa8vKY6OtLVGTkKefAiFGj4PvvOSZExx2lRJT3o108YNYVOoycjg4a9h9gWh8T1nM7OpgZG0fSLTf3eF0bFMTujnZOdHZyeOtWZosBF0eobBDXgQF6WO0P5qytoaYGpbMTlY0MqLnX2mf48FN6GitDQvj3yZP4rV7NM48+4vC+ZyQm8n1aOsYJ4/u3+AHihaVLaX77bcLsBIp/+fhjAhsbuCsmFktxGq/gYDQREeirq+nMze0esnMEc7bcmV7j04V+P3qsWrXK5h+JbYKioghQewFQ3IdcxV6hFTbGylPokptuYk9GJn928oQsFVNF0cOct8IaSMwagyV9TARWFxRQKUo0k6xMoGaIp9xDO3c6te9ux4F+TvMNBLFZWWhUKoxASR9Nwz8JVfkxVhrshwtHjHwHpsksKRO9ONFO9NcNBuYyaF9DRO319ZwQx2uyFVH0TKHjdcwJ9f3+No0PND2Eqo/YHuIYj4r3EpN4887lp7w3RmRvchwU+TVTWmS6DsQ46XE70Lxx9CiXFxbwnBA/t4a+rg69GJ7rna0BSBPXsiNOOrE8mZTI87GxnHXOwPSD9wev0FBUWi1GRaGzj+/B5q++4oeWFjqtZJhrfHz4Z81J3jviXOZeX11NqEZDaqrrAluAgLhYk61YHy0VrQ0NvJ6Xy7PV1SiJp+rTNifEs6OlhX3iQdgReg6ROd5vfbrQ72apV3r1dFRWVpKXl8c555zDtRbTa5JTWZ6Rjqq2jsA+pB4OiNT6OCvij0HDh6NRqdBXV2NsbUXtYFmt23HAQXHgwSJZXExKT9q+If34lWkoIF7na1VE9omrrqbzyy8Z7USvU/PJkzQKn8NEB6yUBguNtzexOh3FbW3k7dtH6lnWJ5QPiszKhAkTT3lvlHA8KG9vp6WurlsGwx7mcyDGxedAnANDREpRET+kZ1AWEEC8FVmJlNgYtpaVkmunpG9JTXExHULU2BHXicEk2s+PusZGSo4dY7INz90Oc7Ym69QHkbFC0668o6PP7H9vpkaEc2dYGFN6lfeHmmTxwFLSR2BavH07RZ2dpKalWS2zZcTHsb2ygqNODFAYOzoY0aVnRGAQMcP7Jy48EKhUKpaWlPBTQz1rN2zgvJtvtvq55z77jG3l5Txfc5LeZ8HImabAtKKjw6nrQLfrhouvAz/3mtoO7PatXYcBCNFoSJ546rXwn4WFvF5awp2ffsr0++5zaL9V+fnMP36MKI2GHf3w/HZ3+p2x27x5c48/R48e5T//+Q/j++Gfeaax/OyzWTZsGMP68Hk8XGm9DAngFRSEWvT5dJY6bqReLr7MsfH2VbYHkxETJzDb358pfrab9/cKKZPRsdabm8dMm0q8tzf6gkKH92tWGvdTqwmOdu0kU4Jo9s23cUNSFIWnoqP5Z1wcF1xz9SnvR6Wn8/fUNFYmJqGUO+63aR4ciHVAaX0wMesoVjTadp9oP3oMtUpl08vTnG3Id8Jer6mkhDn+/kwJDBqQXrlfglmgttTGRJ/RaKRWZPN0VrJVESkp3Vm/4zsct5WaoNNxT3gEl7tYfytFTGOWNtgeJHvvnXe4sCCfB2yUm7NEz3KOnbYOS/TiO6Dy9sarH033A4mXkM6wdR0wGo0cFpmlSSKQtyQyNZUgL1MF6Oh33zm833+s/Zq/najkkIMC0YNFSWcnd5WVcssG24L7e7aYrANHRURY7W9ME9eBIif0/IoOHqSkq4v8ri40biYuPBAMTBeoYMmSJbz11lsD+SM9km5bMRtTkZU5OVR2msuQ1oWAX6iv55rCQr7+5BOH91vZUA9AnBNNxoPB5LlzeSU+gTv8/VFEBq035wWH8NeoaJbNX2D1fW9RlugscfymXiKyoI4qjQ8maTExJGq16G3oT+krKohsa2du6DBSeg3PgKmB+8qzJjHG1xejM1IHnZ34qlQuPwfSRLBW2tZm08i+/ZhpUEtnQ2tt+vSzWRoaygInysqRKjUvxyfwwQBaH/YXcym0zMbxK9izh0n79nJpYQFaG4MeiaJPLscJFxZnPFIHk1RRjahsb0Nvo3qxX7QqjLBh2zVCnEe5TkxH5+7dy7t1tezQePXL43QgSRJSG/k2ss4lBw9Rq9fjBUxYcOq1UK1WkyzOgWNOlKO/PnqU9+vrKe+yLxA+mGgjItnc3My2mhqbQtXmlpSxYuisN2miZ7nIAdkYM79kiOx0oN93t6qqqh5/CgsLeeqpp4h2cSbkdKAjPJyDbW38uMt6f1jrsWMsCw3lspgYQm1krEoUI4c72jm6f7/D+60UTeOu7C8DTN6MXl7Q1YX+pHXJkqiKCq4KCeFiK9kqgK7wcF46Wc3vtm+3GRj0pjTPpPcVHeh6V5Jnf/1r1qamcYWNyaruhum0NJtN1T7m4LbQcQ2nF9Iz2J2RyRULFzq13oEmZcJ4NECXotjsM7zzzTd5oLycQhuyLOcsuIA/REYxzwm72P6IMw8W9gRq96xbj4KpdO9lI6swPiGB8TpfvBz03QXYlZNDYWcnuLjPMnHsWDSAHtu9podF0DtxqnUN1RHioae4pcXh68DO7dt5qqqKV5yodgwWyaIlodBG1nn3WlNLSkpAgM0ya2qU6Z6bY0fk1xKzlVuCCweIAJLGjEaN6TpQZqPX9GC+6fo2wUbLSqYI7ouamh32Df5ZoNq1WfvBot+BXXR0NDExMURHRxMdHc3o0aNZu3Yt77777kCuzyNZV1rCouIi/mpDxiW4qpqHIqP45zW2exWTRUCQZ0e13YxBr6daDCMkuLBpHEyee16RETQaDDTknSquauzspEP0l1krQQH4JyTwWk0Nn9bVUXrYMQeBSzIy+CY1jWf60M4bKrSiFNplY4DkwxUr+MfJarL7uPAU63S8V1fLf9etc2ifiqLQVW2yE/N28QOYVqfji+nn8GNGJpFWbsj6zk7Wl5byZVMjOhuTi94Jpu+AsaEBg4OBTXuF6f9b44Qo6WCRJDyIi200zu8TD36jEk5tGDfzzLIbeT8pibkODkIYurq44ad9XFSQT60Y4nIVGm9vonUmuY18KxnH1oYG8oQF4lkXWM/cp06ciK9KRbRGQ5V4GLJHmRD7jXVxYAuQKgKrIhvnwN7tJg2/0fG2z4F08YCX46DNpNFopErcC8wOIK7CJyCAGPHQkm1lEM6g13NE6JVOttE6kCGGiJqNBk46WJI327jFuniAaLDod2BnNBoxGAwYjUaMRiPNzc1s27aNs2xE1ZKfyRAliCIbZTiz4Kaujy+d+YJQUG7faxKg6+RJnouN449RUcQ7oTQ+WNyenc203Bw++fhUv9fsLVv4oLqaI15e3er6vfH28yNWaDBl28h89kZVW0usVkumCxumzfwc2FnvC/l4yxZerqlhd6dt8dYDTU08WVXFuzt+cGifhvp66OoCQDOAns79ZURWFv5qNZ0lp2ZOjn67jTajEZ1KxUgbvr5qPz8ag4PZ39ZGhYNabr9/6y3Oys7m9T4mUYeK8y+4gGdjYnkowfoA0CERqPTW8LPEHNxa+z+0RsXx4xgwXfhjrQxkDDXxwabseYGV4/HT+vXogRAv603zYHpA2Hf++axNTSOoxTEz+TLxnYtxg6xtuhjiKhEi1L05IGzvxo22fQ5kilJkvoMWkydyc+kSA0SuDuwAkkUmMttK9Sn/hx/oMBrxUakYZaXHEMB/2LBuN6bjDqokVIge9pgoz6wwurbR6Awla4pJaLSio4M28URqyc4fdtBkMNjsLQLIEBeEYkflLmprOT8wkCXpGb/IBmegiBUX1eNWsm3frFnDX6tO8FxtbZ89MEnigpDroNNJ1wn3KcM1+fmxqKiQGVs2W+0vOiTKcxNnzLT5M9JGi3NA9E7a49COHVxTWMgfampslneHEq3IRHVZ6THbLZqph4eG9ulwcFd+HouLi/jmyy8c2mfFyRpaFSP+bpCtSZs2jQuDgkhpaECxcg6Yzd3HTzu1x9KMVmTu2x0cIDFbN4V7ew+Y0PAv4eqJk/if8AhGWSm379uyBYCRNprmzfiKwLjLQU3HClGOj4117QARQLpIhFS0t9NpRbT+kDgHJp5zzinvmbn48sv5KiWV1x30/TX7soZptb/IGnGgSBEP77nHTxVZjmpqYndGJp/Nm9/n+ZooKhu5DrYmmW3c4hLcV2T4lyADOxcQO3Ikfmo1CpC7c1eP95pra7nm+++ZmptDfR9p4oxJpgtCSWurQ70lZnV3rRsENQAZIuOYa6U/bF+3flvf/R/dFwTRCGuP5zZu5O9VVRQ54M852ISlpXGso4MTej15vZqea0tLKTXrt110qoafmcwpJrmKivZ2OlvtZysKjhzhcEc7x/vIAg4lB7s6+XNlJc9//NEp7+0T/ydjetlI9SZZyCXkOliGqxBBcHxKqhMrHRw0ERGo/PzAaKSztGfmtqm6mkJxTG2VIQHqdL7Mz89jwjcbMIhsbF+UiiZ9d2kaX3b55dwaFkailaBm3959AIxJ6/tYaYW2WWexg4GdkNiJT/pltnADQfyoUUz082N+YCD1vdpqDM0t/G9EJH+OiubsPvzRI0eOJNnbG/WJKqsPCL0pFgFUtJucA2mppuObV3Tqw0n70WP4qNXdmo22uHX6dP4aHc04B/ung4wKURoNCamuvw4MBjKwcwFqtZoE8YSavafnTX3v119jxPQ0FdtHyTR14gS8gE5FodgBa7EDu/fwdWMjeb/Q53WgGC4yjnlWeksO5JkucBMn913WTxG9JQUOZiv+m5vDG3W11Ll4IhZM/UUp4mn50LZtPd7b87WpYTrWx4eIPgKbuJEj8RFCx3l79tjdZ5m4cUS52HHATIVKxaqGetZZKcMdOG4K1ieMt+0jC5Aqms8LHOytMTeNx9mYsBtKVCoV+/39eKe2ln1C0sHM3nXrUDBdB+L6uA5EjxhOZVcXrUajQ9eBMiEP5C5N496ppvO7M//UB7x5AQHcPiyMi+bP7/NnbK2rZ1FRIb978w2H9lnZbKqSJLjBOeCl0bBq1myejY3Dv1efaEd2NmN0Oq7PzCSiD4cMr7AwVL6+oCh02RjEscR8DkS5yTmQPmIEOpUKoxWLRXuT8WauOH8eVwWHENPet1WnmaeSEtmcls7FNvQjT3dcf4c7Q0kWPU45vbw+92zeAtgvP2h1OpL9/UnWelNtJYXdmzWbNvK7inLeyXeswXawGSGewAoaG3tMMjnSLGsmXfSHFDogdWA0GjkhGoYTR7q+rwQgUwwwHO7lnLDnW1OgN8qOQblarSZePCDkOhLYiSlAVzsOmMkQmpf59fWnTLMdFt6RE+dY768zky5kMAoq7fcXdbW3c1JkNJJGub7PFODdykqerq5i0zff9HhdU1rKFUHBXGwn+ND4+HT3muY4IHdRLsqVMW7QYwmgTU4mv6ODr/fu7XEOKIrC+Lo6fhsRwXw73uNKaAgH29vZ78CUq9Fo5IRwHklwg/4yAG+RkezI7TlIZg5qfOwENSqVio8Meh6uKGf7Ott6cGauHzWK79PS+b8rr+rnigeWhddey56MTP4e01OI3mg0snjVKv5SWUmznWEvZ3pNFb0eg1Bj0LpYoHmwkIGdi8gQOmLHxJfXzK7dPwIw0YEBh43XXMtXqalk+tjvlSkXN75YN5GjyZw+HTXQYjRSbpGxObZtG62iWXb0uef2+TPMgUFpo22BUzNV+fndDcOudhwwkyWENY/1Csz3HzRlXsZm2e+ZSQo19YrlOjAZbG4YdrXziJlx55+PGqjT63tIHdTm5RGhVuOtUjHRinaXJelj+x5EsqTs6FGMmOx2Ylw8GW7GnHHM6XVTT2lo5ImYGP5+++12f8bPvab2B0jM14EYN7kOqGNiuLywgLsK8ntkHLvKyjA2N6PSavGxU47PEHIXxQ5cB4wNDbyVkMgLsXEkjR37yxY/QPhkZKBXFMp6TQZ/sOq/fFhfx0kHGvy3NDSyprGRvTvtD1Lpq6sI1WhIyHCt1IkZv5QUVCoVhoaGHrqe+bt3s72hgY8b6gmzMTxjxhAZyc7WFlb/+KPd/elPngRFAS8vvNyg13YwkIGdi7jsoot5MCKSK0N7nlh7Rbls2kzbTfNmvEWPiCMivRU1pieUuD6kE4YS38BAYoXUwSELxfTdInORFRJit7l73Ny5rE1J5ZuUVAzNfSuomxuGh2k0btEwDDBqvCkoOd4r05Av/j1+in3Lp2Rht1bgQCbW3DAca0M7b6jxDw0lRWQc91hItmhy8/g4OYWfLrmUwIgIW5sDkDX150GkdqHTaItiETxG+Pjg5S4tCSNMD3DHerUTtIvzVWfFSq03yUIPLy/XvrXagpgYloeFMdtOz9JQ4RMQQILo9TpscR04uH493zY30xgfb3fQJ1P8Lg0GAyeFlIkt9NXVjPP15YL4eHSBgb9w9QPDdw0NTMrJ5sZ33unx+kubNvKXEyc4gn2hxuQ483XAvvyV2b7LXbJVan//7kGqjqM/Jzq2f/oZAMNDQuy6xDQHBHBTSQn3Hzlst99415YtLMjP476qKlRu0JYzGHjmb3UaMOvKK7hx2DAyT57sdl+or6gkR0zJTr/iCrs/w7t7qtB++rlS9G/Eu1Gz6MVZmSwJCSXEYtR/nxgmGWNDad8S32HDSImKQqtS2Z2IMyuNR9sQu3UFo6dPByDXohRpaG7hjYhIPklKZsENN9j9GbdefhnvJyZxqwP9QpX1pnPA1a4TlowQgelPP/ycaWgTk20hE/vurwNT5u3nQaS+pQ7UTU3M8fdnup0S91Ayef48AA5Vn+wegmqtreWnw4fpUhR0o+wHdinJpuPpSK/pDB8dd4dHMMuGhIwrSBfB+2GLUvLbK1ZwR1kpz5Xbt4kKiooiXKsF4PgPfZ8D5qDG1R6plqROnkyXonC8rq77OlBXXk62uBfMccB73TyAUOBAOfqxjRv524lKSvXWXX9cwcquTi4vKOD/vfBC92s7vzcF+hP76C80Ez9qlKlPD8i105JQfPw4JV1dVBrd5/cfaGRg5yK8k5NR6XQora10igvyjk8+QQFifHwcMqk/0NLCoqJClqxYYfezJ8RTTLyLXScs+dvtt/NwVBQJ1T9bwdwZEcGbCQncdLV1x4nemJ/0Ou3YapW42eAAwKjZsxnm5cUUX19axDnQtv8n1IrC6LQ0ImzYKPX4GVOmMN7XlwAbDh6W+BgNJjsxN2gaNzNG9DkdtCjFNu0zTUP6jhtvd3u1Ws3yzCz+EBmJv52M3ciAQF6OT+D5S2xPGA414+fPx0elotlo4OjWrQB899//clVBPpcUF6F1QEg5VZSVixzoNXUn5w0zo8U1abdFn+j3Irif2YfcjyVJIqOT89O+Pj+3a/v3vFtXyz7FCbuSQWbk7NloVCpajEbyRSlx26pVKEC8zpd4B7K23eeAA7Zaa4qLeb++ng5/95iKBWgLDiGns4Pd+38ux+8Rk+6ThTxYX6jVahJEJSZnd9/9xiXCdSLGxT7Bg4kM7FyEysuLythYvmhs4McvvwQgvr6OP0VGsdyGfU5v/OLjOdjezsGavr/MbU1N1IpsQKIDF4mhQjfG1OPSJnprjK2tGA8dYqqfPzMdyFYBbGhr46GKclb+91TJDEvKRVYz1k2axgH8goP58dLLeDEuHqMYomkTNzffSX33lJgx65h12XlSV4xG3o6LZ3dGJlNtCH26gvGTTRftamHM3tHczKT/rmJRUSEtDmpM/ea8c1kaOozgpr4DO72YwHanbI1Wp2OU6PP5QUxDr129GoCJDmZWR0ycxDidjpF2SpZdbW1sLymmoLMDLzsl7qFk5jxT1nKncE5oqq7mgGgdOW/xdQ79jGQRqNqTvdnw3Xc8VVXFx054TA823n5+pImgZKdwI/petKSc5UDlAiBd9BuXWNFFtaSlro56g+lekORA8mComHSOqXpxSFReWurq+Ek8hMzoQ+rFkiTxPco73HevablZoDrcfb4DA40M7FzI6ycqebCigvdWrgQgYP9+rgsN5a67lju0feY0UwBY09VFfR+q4+bGdG+Vqk/5jKHGd+wYWo1Gdhw/Tm1hIa179kJXF5rYGLSJjmlMHe1o5/PGRrbv29vn5+4aP55NqWk8eMWVA7H0AcNf9Ae17jKVoG9//gUeqiinxMHmdk1sHO/X1fFk9nHq+5A6MNTWgsGASq1G60bB7QVLlvBDegb/CgtHX1vL2tf+TaPBQIXBQLS4WdnDW9gt2QtuW8rLURTFrbJVABNEBnW3KEdvEqWk+efPc2j7SeefxwdJyTwcFIzRih6cmeJDh7ippITLCwtRu1G2YvZ116ECitvaKD54kK0rV6IHon18yBTtCvbITEklQatFYydra/bldZfhETPniGG59eIhf7O4Hpw91X62CiBjsqkf116fobnXWKdSMcxN+q0Bpgqbx/yWFurKy1n373/ToSjE+Pgw1s4AlZlk4eaT22sQqTcV4gEvxoYPuycgAzsXMv+iiwD4cs9euqqraT9gclAImD3Hoe1DY2MJEU3gOT/usvm5oC49L8XG8bfhI/qUUBlqvIKCWFxRzq9Kivn6zTf5f8/8L0+eOEFBckqfjhOWpGeYyjgFdvSbVCdPEq3VkuAGNkqW+E2ZgqIoZG/dQumhQ3xWkM/njY0ETrDfXwagCQzgn7U1vF1XZ9Vr0Yy5BOcVHobKTQYHAIKSk4gYMxoUheYtW1n9vqmt4IIxYxwecOiMCOdgWxvf2bGWW/rB+5yVk83aAsf8lYeKO5YuZVVSEr8JCKQqP58Dwk3mol/f4tD2XsHBqEWLQV8tCaWiMT3S2xsvL9f6xFoSlpBAllj/lpUf8vmHHwIwPS3N4evVQ7f+mnWpadxkx02iXJQq4/vwXnUFC4Se2qbDRyjaf4DdIoN99d13O7R9UFQUYVotKqBgr+2H3BLxkB/l6+tW94K4kSNJ9fdHAT569llO7tlDnFbL+aNHO7zOVCE6Xljad1+meZAw3oaVnyfgPkf2DOSKe+/FV62mtL2NBxZewYraWmpSUhzqqzGTJC6I2X3omOmamzkvMJBrhEetO3GeCGCef+UVnvt6Le/V13EiLtbOVj+TPtZUTiiq7VvuoqvavSbBzHiNGc28gnzmfPst/7N4MQZgYliYTV9EayQItfWcfbb7i9Z98QVXFRbwfzbMxl1J4BzT71r+xResFfZwVy5e7PD2u2trWVRcxKMbN/b5ucrGRtoUhZBYx8+voWDC0qWMCQlFVVjISzfeiAJkBgaS7GDGEsA7Ph69otDYR7aiRLwX7SZT4Zbcf9HFvBgbx/DcXFaKzOUNy5Y5vL02wTGFAPMQWVyq+1QuAC645RY0KhWl7W28/dCDeAGTwsNJdcJ7/dP5C9iXkUmWn+0BsZJcU7k7OsA9JoItuXLmLABWfriKuRWVrE9J5e9//avD26cLbcoiO61JlaJcHW/H0eR0RgZ2LiQgLIxzxTTTC9u/54mqE2xzUjw2WfTK5PXRW6Kvcr/eIjO/ffJJVMDu6mrqDXoSfX257pFHHN4+U5Qgytpa6RLCo9Z4ZPsO/l5VRb2YnnMX/CIiGCtKIh+JMsnSq5wTDk2OMJVW84/btlbLPnKEox0dlDhgPzfUdE2axG0lJYx68w2qOjsJ1Wi46I47HN4+Q2hcFTc3nyJ0bEmlKFMmDncPYVozXoGBBC2YT6fRyN+EC8k9S5Y49TP+mp/PxOzjvPbuuzY/UyZKdNFuVIY1s+SJv3F+cDBte3aTrNUSr/Pl8vvuc3h7b2Erpq+oxNiHrVaFcB5JdGAwaSgJiopibkIij0VFcU1hEdvSM3jxvt859TOSs7LwVqv7VAgoLSoEIMYN9duW3PtbADaVlVJRXo42NpZwJx5wp5x7Ln+JiubhqCiUPoZjIlQqojUaEtzsHBhIZGDnYh5+8kmCRVlk3LBh3P+ffzu1fYroRcvvQ7/oux9+4OvGRkq93O9wD58xgwstxtkfuvkWp8zJE8eOxVulQg8U2MhYdTQ3s/JEJW/U1aJ2wwvaC++tQCdKz6OCg7n58ced2j5JDFAU9FFiLBElung3DO5jz5lOmnADUQH/evxxp7QG0yZPRgW0Go2cyLGu5VZfUUmLCPrcaYDITOivfkWRuA5kBAZy+3PPObV9YFgYevq+DnQ3jbvR4IQZn9RUgi+/nEiNlquCQ3jktlud0hr0CgvjlvIyZuRkc7iXRZ+Z1oYGaoSfbooT2dChYs3mTSxOSgYgesoUpolAx1G0ZveFPnpNK0XLSqwTVaGhYtyCBSwcPoJLAoOI0GiI/uOjqJ24FySMH881oaFMVKkx2BAsN7a385+YWDalpXfLTXki7tNsc4Yy/Zpr+Ckjg/efeYZljz7qVFADkJWVRcKWLQT0YQD+1pYtfFJRzmPFxTg2bzu0rNyxg80rVuAXGMi5N93k1LZeGg0Jfn7ktbSQvXs3mWeffcpnCsXUrbdKRbQbyb2YyTx7Gl++/gbH9uzhlqefclpAOTU9HTZtorCPPsNS8V68m4gT9+aNnTuZ8pvfEpUQz1UPPeTUtr5BQUT7+FDR0UH2zp3EWHkSLzlsmjr2V6sJiXGvxnkA31GjmPrJJ7zx1pvMufZap68DaZkZsGkj+WXlNj9TXimaxt1Ix8+S6D/9Eb/Jk3lw3Fh0DmiXWaJSqagFag0GcvbsYcx5553ymULh7OCrVrvVEJkZv9RUkt55h5bvviNk0SLUwirOUY53dfFcRQVRH3/Mq489ZvUzD0+cxK8qTxDhgDaeK/j48CFObtyIr0pFoB3nod6ofXzQREWhr6ykq6QEjZWHeHOvscrXF7WbCFQPBjKwcwOSx4/n4fff79e2S5cuZfaGb/rsHasUTy9xDk6aDjWBERFcdu+9/d4+aVgYBS0tlNkoReb/ZNLEinWzhmFLzr3pRs696cZ+bZsuLNKKRdO9NcpEw3CiG97QQOjR/eOlfm+fGBxMRVUVOQcOYE16t1hY90U7ebMcSqKGZ3HT00/3a9sMIR1UWGf7HCg9aWrId9tzwNeXkCvtC7PbIjk8nONNTeQcPmz1/ShUrExMosWOD7cr0WVlouvngFeTnz9rGhtIzrPtQmOoqiLEy4vIVOcC56FCrVYTOc+xaXBr5Pj7s6+hntlbv2WGlZ7ybsmjyAiHB/ROR9zz7JY4TLf7RFl5t4NFbyqEh2J8mnt4Aw40Ly1Zwt7MLK5ISbb6ftFxU/9hvBv2Fg0E6aLHrLS1tdu9oDfl4hxIchOP1IEmWfhp5mVbD+5LxeBAVKD7CFQPJFnCfq6s1Xav6Q2RkSwPC2Oyh5agUkQ2Ot9GYONVW8tYX1/OdxOP2IEm86xJAJS1taG30Wf4s5aj+5ViB4J3Skt4tLKSLzast/r+yg8/ZF5+Hk/bEbQ/3ZGB3WmOJjISlVYLej2d5aeW4gx6PWWiaTzNQQmN043o4Vl4q1R0Flv/shaKvqN4N+wtGgjSJk1iRXIK61PTMFhxoDDo9VR2dACQ7EaipANJt62WDQ2vMFTM9Q/gLBvB/+lO0vjx3b2m+VbkLhSDgfOMCneHRzDKQwO7tHTTg2u+DbkLfaXp+qhxw1L8QJA8YQIaoEtRKD5w4JT3DXo9y/fs5q8nKml3I2vFgSRVCDrnF1q/DuRmZ1PW1UWzmw3RDTQysDvNUXl58cDJk5yTm8M3n6455f3Sw0foUhS8gJQJ44d6eUOCNt5sK2Zd6qBENBMnxPWtcXW6ovX1ZUpqKhEaDXor3pq1BQUkaLX4q9UOWdWdjiyYM5ffR0RyTbT1/rFzgoL4Z3w8j17nmJPB6Ya51xQgR9hSWaKvrga9HjQatxNoHigyhK9uUa11e73V32zkndpa8vHMEpzG25tY0WqQa0X+qjI7mw2NjaysryfATXttfylpor+28IR1wf4ikalLciNx5sFABnYeQLNWQ53BQI7QALMkTzy9R+t0Tjdkny40BQbwh4pyln6z0arcRZkQ+0xO8VzdIrNnbpeVEkNAWxtfpqSy95wZHnsOTD13Lr8aNoyxNkpQZlcKbZxn3tAAzk1N46LAQHybW055r+TAAba3tHAiKAiVG4kTDyQZohRZYkP25v2dO3m6uoqf+uhDPN1JCg0FIPfgqbZaucLRJNrHB28/9/GJHUgyRF9dkdAr7E2xKEWnODmcc7ohAzsPIFUIruZbESfNF755CcIk2xMJTk/n08ZGtjc1Ui0Mni15bfQYNqamsXDhwqFf3BDxo0HP/1VV8dFnn53ynjmo8fbQjCX8HNjqK63rmFUXFqAoSre3rify+KJF/D02jtHep5aZNq1fz69LS/h9vu3G+tOd9MmTSdBqGe/rS31B4SnvlzXUAya9N08lSVil5eWeKvuTI8qzicEhQ7mkISVL2GxWdXbSYkXypEQEfKnCws1TkYGdB5AiptwKrGRrzo6I4J9xcfx2jvsYvw80/qGhRAoD9OO9bLUURcFYWUmMVkt4ZoYrljck7Gts4s26WtbuPNVaztx76O2mU9EDgdewYRwD1jY2UHGgZ+Za39nJtC1bmJKbQ43Wc4UAugeprPSaFoqBggQP7TMF8PbzY9PMWbyRkIiuV1bOoNdTIsSJU91Qw26gSBE9ZjVC1sMS82BRcrT7aVkOFBEpKQSoTRnpHOFgYqZHv7kYOPNUZGDnAWQIuYtCUXK0ZFhzM3MDAjlv5oyhXtaQkigykrlCq8qM4eRJlPZ2UKnQuJnx90CSJUR3cypO1TH7/ev/4arCAtbasdo5nVGpVPyhvIz7ysvZtXlTj/eK9u9Hryh0KAqxbihOPFBoExMwKAolViaDi4pN/adJHlyKBtP/AUBnSU+R3uIDB+hQFDRAxtSpLljZ0HDHDTewLyOTP2eempU0DxalJCUN9bKGDLVaTVKgSQc0e0/PIaKSQ4fQi3PAU3uNzcjAzgNIF0+gxcIDz5IuoTav9eAyHECK0PHLPX68x+s7167lf8rLWGEwoBZZPU9kzDmmSce8+oZT+osOFhdztKMDo4fKvZhJCjdZqx3vlbHLEY3ksTodGg8+B0pRMSH7OOd/s+GUc6Cw0tRM7um9Rd7CM7ZdWGeZObrDlL2J9/Pz2D5TgGEjRuCjVtNRUHCKrVahEKhOc0OR9oHk4blz+U98AmeF9RQobigoYLzOl9FBwR59DoAM7DyCLOG20GgwUNmrz+6177ezvqmRLje00hpIzGPuveUufvzuO9Y1NfFdW6sLVjV0DJ85EzXQbDRQcqhn43RhfT0AmR5cggLIEi0JR48e6fF6/iGTYG2iaCz3VJInTcSAyVqtslfWLlcIVI+c7Lip/OnImvIyZubmsPy113q8flxk8pOd9OI+3fBOSQG1GmNDA4aantPBJ5tND/4ZHqrjZ+b8c85hur8/fr2kn5IUeD8piTVX9F8E+3RBBnYeQEBYGBODg5nh58/J/T/rFzXX1PBk9nHuLS+ny0MlDsyYn0LNmQkzx4+aHAfSPXy83TcwsFvu4tC333a/3lxTQ5UYJsiyYrfmSYwWE3FHi3vK3uRkm7K4SW7okzuQ+AYGEisyEUe+/7779fqKym4dw9GzrflyeA4BMTHUGAwUiOlHM+ZzIM2Dh2cA1Dodr3S0s7S4iC0fr+5+3djZyWcJiXyfls7kX+DscDrgLVw1OnolOTryTP/28fCsNcjAzmNYc/0NvJaQQGzLz1IHBzZtQgFCNRpiPdRxwEzWxImoAL1ojjWTK27ymR5efgDIEI3xRyw0rI7v2AFAkJeXW/pjDiTjzjkHgJzauh6lyCPiAj/KwyfhADLFObDvu++6XzssAv0wrdbjzwFzVrpYOK2YuSsjk5WJSdx2xZUuWNXQcsxoZE9bG3u3b+9+rbOwEBUQFhyMrwf3GgMYExP5rKGBZzZ+0+M60JptmhT2SZeBneQ0wUdMfHbk/FyCOSie2jM9vAwLMPXSS9mVmcU7MbF0WUyE5YmBkpEe6rphSaYoR+daeOYeEVPCSUFBbuuPOVCMOe881EC9QU+phV/oMZHFHSukEDyZsULK44CF80B0VydPRsdw3wTPngQEyJpuCu5r9XpOWFiL6SorGOvry8gz4BwYnmrS6zxy5OfvQIfoPfbJyvJoj1QAn7RUHq6s4OWysh7Xgbkr3mNBfh6nCsF4Hp59pT+D0ImMVLXFiXxIXNyHe7DMhRmfoCBChaVQhyi/dra2UtJq6q0bKbI5nsxvli1jS1oavxcBHsBukbEba/Gap+IXEkKSKEcf2GSajDW0tDDP15cZfv5MnD/flcsbEsafZeqhO1hY2P1a4MmTLAwO5qYLLnDRqoaOkJjo7nNgx5o1ABg7OugUFlM+qZ6dsQQYJSY+LVsSnn31Ve4sLWEriq3NPAb/0FBShGXa3vUmz9jmmhqK29oo6eoiYdIkVy5vSJCBnYdQFRjIrNwczv70024j+GPCI3WkkEPxdHQjRgDQLgK7fWvXogf81WqSPHxwACB59hwiNVo6Dh9G6eoCwLehkWStN5POgIsZwO9nzeIfcXEMF1pWnXl5/C4iktcnTCDmDCjHTzrvfACO19V1G8F3iszVmRDUAIwXk7E7t24FYM/nn/Pn0lLWGY1ohJi7JzPx3HMBOHTyJF3t7QB8u/8AW1taqPVQj9jejBDH+SehZXdoy5butqQz4TogAzsPIWnyZJqMRlqNRn5auxaAo6KBePTkKa5c2pDxo1rF0uIi7nzhBQCOffstOpWKSTExeGk8V5jWjHdyEuqgIJSODtqzs1EUhZs1Gr5KTeX2e+5x9fKGhCuvvJJzAwLxOX4MgA4xHeqTke7KZQ0ZI2bN5IKQEG4ZFkZTTg5Go5F/fvkl+9pa0Xqw44IlZwlP7D3CYnHjp5+xqqGez9rbPL4MCTDhggsIUHvRajSy58svAThSZboXTJw1y5VLGzJGi57yfaJqtXvjRgAyzoC2JJCBnceg1emYEmMyQF+/ciW5O3ZQ1t6OBph+pec3DANoExPZ09bGtvx8jEYj52u07MzI5J/L73L10oYElVrNBn9/7iwt4a1//AN9ZSWGujrQaNCdITd1/ymmh5i2H3ejGI1s+/JL6g0GdFmePTxkRuPtzSsXX8xd4eFw8CBHNm/m6YICbiopwUtktD2dmQsWcJavL6NF1XH3PpNQ7aRRnitObYnG25tJsaZ7weZP1lB66BDVXV2ogIlnQDke4NxLLwVgW34+Br2eTVu2ADBdTM57OjKw8yDmCjmLjdu+I6yoiA2pqbw8azYhMZ49BWVm9uLF+KrVnOjsZMd/P6Jt7160KhUpczxb4sGSQh8ftra0sG7zZo5//TXtRiM+6emofXxcvbQhQTdyJLsMBv6em8uBr7/mlhUrmJGbw5Fhnq1hZ0nATFNWpnnLVr5ZtQqA8RER+HmwX7QlM6+9lndS07hVp6MjJ4e9wj962hl0HZg+cSKRGg0dJcV89NJLAIwKCSHQgy3lLJl9ww34q9XU6fX88PFqvhOT8fMXer6GHcjAzqNYsGgRAD+UllC9bj1xWm+uWHydi1c1dASEhTFXyDn848+P0VJaCmo1urFnxlMawKXXLwZgbXY2199/P7PzctkeeWZczAFUWi1vtrfx79oaHrrnHqq7utCp1UxbvNjVSxsyAubMoUavZ8WXX/K5aMuYcQZMhZtR+/kRIEqOX//mtxS0tqJVqZh17bUuXtnQ8dDvf8/m1DQWt7TyyWefAXD5nDmuXdQQ4u3nxznCOu3fTz1pug6oVMxZusTFKxsaZGDnQUy94gqifXxoMRr54yefAOB/BkyDWnKl0KlaeewYE3KyOTR8OF4BZ0bDMMDMJUsYERREh6JwsK6OJqORyTfe6OplDSnXidaDdSJTMzs5Bd/AQFcuaUjxzkhncVkpfygtYZOYjFy4bJmLVzW0BC+8nGaDgSvWfg3AFaNHe7yGnyVBU6eiy8ygoq6Ob4Xcz3V33+3iVQ0tj999D5tS07ivrZ2loaFMi48/Y64DbhvYHT9+nEsuuYTw8HAiIiJYsmQJdXV1rl6WW+Ol0fDiY48BcKi9DWXePHQebHpujWsffID0AJMJtBoYvny5axc0xKjVam63yE7NjI0la/p0F65o6Ln12WcZLXxxNcDvHnnYpesZatRqNY//9rfd/148bhwzz6CMJYDfrFlcU1rS/e/f/vGPLlzN0KNSqwm/806itFom+/oyJyGB0eed5+plDSlT7r6L5LMmoVWpeDghkVWffurqJQ0ZKqW3U7CbsGvXLo4dO8bll1+ORqPhpptuIjAwkNdff92h7RsbGwkODqahoYGgoKBBXq178Y/lyynML+CJj/6LjwhyziQaT5zgmTvuIHPkKH71xN9cvZwhx6DXs+KxP9Pe1sply5cTnX5mTIRacnTbNl74wx/49YMPcdZll7p6OS5h1ZNPsunrr3nygw8Y5uFWWtYoO3KET/7xDyJj47j20UdcvZwhR1EU6leu5ER7O6mLFuEt9P3OJLqqqqh59V8EXXgBfpMnu3o5vwhnYhq3Dex6s2HDBu677z4OihF2e5zJgZ1EIpFIJBLPwZmY5rQR99q+fTuj+hDa7ejooEMYXYPpP0EikUgkEonkTOK0COx++uknXnzxRb4VZtbWeOqpp/jLX/4yhKuSSCQSiUQicS9cVoqdP3++zUDt0Ucf5dFHHwWgoKCAWbNm8dJLL7Fw4UKbP693xq6hoYHExERKSkpkKVYikUgkEslpS2NjIwkJCdTX1xNsR5PSrXvsKisrmTFjBg8++CC33XabU9uWlpaSkJAwSCuTSCQSiUQiGVpKSkqItzMM5baBXUNDA7NmzeKqq67iT3/6k9PbG41GysvLCQwMHFR/QHMULTOD7oc8Nu6JPC7uizw27os8Nu7JUB0XRVFoamoiNjYWtbpvpTq37bFbs2YNBw4cIC8vj2eeeab79ebmZoe2V6vVdqPagSQoKEh+2dwUeWzcE3lc3Bd5bNwXeWzck6E4LvZKsGbcVqB42bJlKIpCc3Nzjz8SiUQikUgkEuu4bWAnkUgkEolEInEOGdj9Qnx8fHjsscfw8fFx9VIkvZDHxj2Rx8V9kcfGfZHHxj1xx+PitsMTEolEIpFIJBLnkBk7iUQikUgkEg9BBnYSiUQikUgkHoIM7CQSiUQikUg8BBnYSSQSiUQikXgIMrD7BVRXV3PxxRfj5+dHVlYWGzdudPWSzlgee+wxRo4ciVqtZuXKlT3ee/rpp4mIiGDYsGE8+OCDyHmhoaOjo4ObbrqJ+Ph4goODmTNnDgcPHux+Xx4b13LbbbcRExNDUFAQY8aM4Ysvvuh+Tx4b17Njxw7UajVPP/1092vyuLiWOXPmoNPpCAgIICAggAsvvLD7Pbc5Noqk31xzzTXKr3/9a6WlpUX55JNPlNDQUKW2ttbVyzojeffdd5X169crU6dOVT744IPu17/88kslMTFRycvLU8rLy5URI0Yor7/+ugtXembR3NysPP7440pJSYmi1+uVZ599VklNTVUURR4bd+Do0aNKe3u7oiiKsmvXLiU4OFipra2Vx8YNMBgMytSpU5UpU6YoTz31lKIo8jvjDsyePbvHPcaMOx0bmbHrJ83NzXz66ac8/vjj+Pn5sXDhQkaPHs3nn3/u6qWdkSxZsoR58+ah0+l6vP7uu++yfPlyUlNTiYmJ4f777+e9995z0SrPPPz9/fnjH/9IfHw8Xl5e3H333RQUFFBTUyOPjRswfPjwbv0tlUpFe3s7FRUV8ti4Aa+99hpTp05lxIgR3a/J4+K+uNOxkYFdP8nJySE4OJiYmJju18aNG8fhw4dduCpJb44cOcKYMWO6/y2PkWvZsWMHUVFRhIWFyWPjJixfvhxfX18mT57MBRdcwMiRI+WxcTG1tbU8//zz/PnPf+7xujwu7sE999xDREQE8+bN48CBA4B7HRsZ2PWT5ubmUwx/g4KCpJ+tm9H7OMlj5DoaGhq4/fbbeeKJJwB5bNyFl19+mebmZjZs2MDs2bMBeWxczcMPP8y9995LaGhoj9flcXE9zzzzDAUFBRQXFzNv3jwuuuiibi97dzk2MrDrJwEBATQ2NvZ4rbGxkYCAABetSGKN3sdJHiPX0N7ezsKFC7n44ou5+eabAXls3AkvLy/OP/98Nm7cyLp16+SxcSH79u1j165d3Hrrrae8J4+L65kyZQoBAQH4+vry4IMPEhAQwK5du9zq2MjArp9kZGTQ0NBAZWVl92v79+9n1KhRLlyVpDcjR47sMYUpj9HQo9frue6664iNjeXvf/979+vy2LgfRqORvLw8eWxcyNatW8nOziYuLo7o6Gg+/PBDnnjiCW699VZ5XNwQtdoURrnVsXHJyIaHcPXVVyu33Xab0traqnz66adyKtaFdHZ2Km1tbcrMmTOVd955R2lra1MMBoPyxRdfKElJSUp+fr5SUVGhjBo1Sk6RDTE33nijMn/+fKWzs7PH6/LYuJampiblvffeU5qampSuri7lo48+UnQ6nXLgwAF5bFxIS0uLUlFR0f3n2muvVR555BGlrq5OHhcXU1dXp6xfv15pb29XOjo6lOeee06JiopSGhoa3OrYyMDuF1BVVaVceOGFiq+vr5KRkaFs2LDB1Us6Y1m2bJkC9PizefNmRVEU5cknn1TCwsKUkJAQ5YEHHlCMRqNrF3sGUVhYqACKTqdT/P39u/98++23iqLIY+NKmpublblz5yrBwcFKUFCQMnHiRGX16tXd78tj4x4sW7asW+5EUeRxcSVVVVXKpEmTFH9/fyU0NFSZO3eusmfPnu733eXYqBRFqhtKJBKJRCKReAKyx04ikUgkEonEQ5CBnUQikUgkEomHIAM7iUQikUgkEg9BBnYSiUQikUgkHoIM7CQSiUQikUg8BBnYSSQSiUQikXgIMrCTSCQSiUQi8RBkYCeRSCQSiUTiIcjATiKRnNEUFxcTHh4+qPsoLCxEpVIREBDAmjVr+vzsxx9/TEBAACqVqocXtUQikTiCdJ6QSCQeT0BAQPffW1pa8PPzQ6VSAXDkyBESExMHdf+FhYUMHz6c9vZ2h7dRqVRUVFQQHR09iCuTSCSehsbVC5BIJJLBprm5ufvvOp2Ow4cPk5yc7LoFSSQSySAhS7ESieSMprCwEJ1O1/1vlUrFK6+8QmJiIuHh4Xz44Yd88cUXpKamEhkZyYcfftj92draWq6//noiIyNJTU3l7bffdni/P/zwAxMmTCAwMJDo6Giee+65Af29JBLJmYnM2EkkEkkvvv/+e7Kzs/n888+54447uOyyyzh06BAbN27k5ptv5uqrr8bLy4ulS5cyevRoSkpKKCgo4Nxzz2X8+PGMGzfO7j7uvfdeHnjgAa6//nrq6uooLCwc/F9MIpF4PDJjJ5FIJL148MEH0el0XHnlldTX17N8+XL8/Py49NJLaWpqory8nMrKSrZt28aTTz6Jj48Pw4cP5/rrr2f16tUO7UOr1XL8+HFqa2sJDQ1lwoQJg/xbSSSSMwEZ2EkkEkkvIiMjAfDy8kKr1RIREdH9nk6no6WlheLiYlpaWggLCyMkJISQkBD+9a9/ceLECYf28Z///IejR4+Snp7O9OnT2bFjx6D8LhKJ5MxClmIlEomkH8TFxRESEkJNTU2/ts/KymLVqlXo9XpeffVVlixZQl5e3gCvUiKRnGnIjJ1EIpH0g7i4OCZPnsyf/vQnWltb0ev17N27lyNHjji0/YoVK6ipqUGj0RAYGIiXl9cgr1gikZwJyMBOIpFI+smKFSsoKirqnpi99957aWtrc2jbr776iqysLAIDA3nxxRd58803B3m1EonkTEAKFEskEskgU1RUxPDhw/Hx8eGdd97hsssus/nZ1atXc/PNN9Pe3k5RURFRUVFDuFKJRHK6IwM7iUQikUgkEg9BlmIlEolEIpFIPAQZ2EkkEolEIpF4CDKwk0gkEolEIvEQZGAnkUgkEolE4iHIwE4ikUgkEonEQ5CBnUQikUgkEomHIAM7iUQikUgkEg9BBnYSiUQikUgkHoIM7CQSiUQikUg8BBnYSSQSiUQikXgI/x9QyKoT988MVgAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Show that the forced response from non-zero initial condition is not linear\n", + "X0 = [1, 0, 0, 0]\n", + "resp1 = ct.forced_response(sys, T, U1, X0=X0)\n", + "resp2 = ct.forced_response(sys, T, U2, X0=X0)\n", + "resp3 = ct.forced_response(sys, T, U1 + U2, X0=X0)\n", + "\n", + "cplt = resp3.plot()\n", + "cplt.axes[0, 0].plot(resp1.time, resp1.outputs[0] + resp2.outputs[0], 'k--')\n", + "cplt.axes[1, 0].plot(resp1.time, resp1.outputs[1] + resp2.outputs[1], 'k--')\n", + "cplt.axes[2, 0].plot(resp1.time, resp1.inputs[0] + resp2.inputs[0], 'k--');" + ] + }, + { + "cell_type": "markdown", + "id": "891204fe", + "metadata": { + "id": "mo7hpvPQkKke" + }, + "source": [ + "### Frequency response" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "b2966a99", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Manual computation of the frequency response\n", + "resp = ct.input_output_response(sys, T, np.sin(1.35 * T))\n", + "\n", + "cplt = resp.plot(\n", + " plot_inputs='overlay', \n", + " legend_map=np.array([['lower left'], ['lower left']]),\n", + " label=[['q1', 'u[0]'], ['q2', None]])" + ] + }, + { + "cell_type": "markdown", + "id": "75fa2659", + "metadata": { + "id": "muqeLlJJ6s8F" + }, + "source": [ + "The magnitude and phase of the frequency response is controlled by the transfer function,\n", + "\n", + "$$\n", + "G(s) = C (sI - A)^{-1} B + D\n", + "$$\n", + "\n", + "which can be computed using the `ss2tf` function:" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "443764af", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + ": u to q1, q2\n", + "Inputs (1): ['u[0]']\n", + "Outputs (2): ['q1', 'q2']\n", + "\n", + "\n", + "Input 1 to output 1:\n", + " 4\n", + "-------------------------------------\n", + "s^4 + 0.2 s^3 + 8.01 s^2 + 0.8 s + 12\n", + "\n", + "Input 1 to output 2:\n", + " 2 s^2 + 0.2 s + 8\n", + "-------------------------------------\n", + "s^4 + 0.2 s^3 + 8.01 s^2 + 0.8 s + 12\n", + "\n" + ] + } + ], + "source": [ + "try:\n", + " G = ct.ss2tf(sys, name='u to q1, q2')\n", + "except ct.ControlMIMONotImplemented:\n", + " # Create SISO transfer functions, in case we don't have slycot\n", + " G = ct.ss2tf(sys[0, 0], name='u to q1')\n", + "print(G)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "fd2df9a9", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "G(1.35j)=array([[3.33005647-2.70686327j],\n", + " [3.80831226-2.72231858j]])\n", + "Gain: [[4.29143157]\n", + " [4.681267 ]]\n", + "Phase: [[-0.6825322 ]\n", + " [-0.62061375]] ( [[-39.10621449]\n", + " [-35.55854848]] deg)\n" + ] + } + ], + "source": [ + "# Gain and phase for the simulation above\n", + "from math import pi\n", + "val = G(1.35j)\n", + "print(f\"{G(1.35j)=}\")\n", + "print(f\"Gain: {np.absolute(val)}\")\n", + "print(f\"Phase: {np.angle(val)}\", \" (\", np.angle(val) * 180/pi, \"deg)\")" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "bf710831", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "G(0)=array([[0.33333333+0.j],\n", + " [0.66666667+0.j]])\n", + "Final value of step response: 0.33297541813724874\n" + ] + } + ], + "source": [ + "# Gain and phase at s = 0 (= steady state step response)\n", + "print(f\"{G(0)=}\")\n", + "print(\"Final value of step response:\", stepresp.outputs[0, 0, -1])" + ] + }, + { + "cell_type": "markdown", + "id": "5108e6c6", + "metadata": { + "id": "I9eFoXm92Jgj" + }, + "source": [ + "The frequency response across all frequencies can be computed using the `frequency_response` function:" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "41429d56", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "freqresp = ct.frequency_response(sys)\n", + "cplt = freqresp.plot()" + ] + }, + { + "cell_type": "markdown", + "id": "5ec3b52c", + "metadata": { + "id": "pylQb07G2cqe" + }, + "source": [ + "By default, frequency responses are plotted using a \"Bode plot\", which plots the log of the magnitude and the (linear) phase against the log of the forcing frequency.\n", + "\n", + "You can also call the Bode plot command directly, and change the way the data are presented:" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "456ad3a9", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "cplt = ct.bode_plot(sys, overlay_outputs=True)" + ] + }, + { + "cell_type": "markdown", + "id": "190f59c6", + "metadata": { + "id": "I_LTjP2J6gqx" + }, + "source": [ + "Note the \"dip\" in the frequency response for $q_2$ at frequency 2 rad/sec, which corresponds to a \"zero\" of the transfer function." + ] + }, + { + "cell_type": "markdown", + "id": "2f27f767-e012-45f9-8b76-cc040cfc89e2", + "metadata": {}, + "source": [ + "## Example 2: Trajectory tracking for a kinematic vehicle model\n", + "\n", + "This example illustrates the use of python-control to model, analyze, and design nonlinear control systems.\n", + "\n", + "We make use of a simple model for a vehicle navigating in the plane, known as the \"bicycle model\". The kinematics of this vehicle can be written in terms of the contact point $(x, y)$ and the angle $\\theta$ of the vehicle with respect to the horizontal axis:\n", + "\n", + "\n", + "\n", + " \n", + " \n", + "\n", + "
\n", + "$$\n", + "\\large\\begin{aligned}\n", + " \\dot x &= \\cos\\theta\\, v \\\\\n", + " \\dot y &= \\sin\\theta\\, v \\\\\n", + " \\dot\\theta &= \\frac{v}{l} \\tan \\delta\n", + "\\end{aligned}\n", + "$$\n", + "
\n", + "\n", + "The input $v$ represents the velocity of the vehicle and the input $\\delta$ represents the turning rate. The parameter $l$ is the wheelbase." + ] + }, + { + "cell_type": "markdown", + "id": "novel-geology", + "metadata": {}, + "source": [ + "### System Definiton\n", + "\n", + "We define the dynamics of the system that we are going to use for the control design. The dynamics of the system will be of the form\n", + "\n", + "$$\n", + "\\dot x = f(x, u), \\qquad y = h(x, u)\n", + "$$\n", + "\n", + "where $x$ is the state vector for the system, $u$ represents the vector of inputs, and $y$ represents the vector of outputs.\n", + "\n", + "The python-control package allows definition of input/output sytems using the `InputOutputSystem` class and its various subclasess, including the `NonlinearIOSystem` class that we use here. A `NonlinearIOSystem` object is created by defining the update law ($f(x, u)$) and the output map ($h(x, u)$), and then calling the factory function `ct.nlsys`.\n", + "\n", + "For the example in this notebook, we will be controlling the steering of a vehicle, using a \"bicycle\" model for the dynamics of the vehicle. A more complete description of the dynamics of this system are available in [Example 3.11](https://fbswiki.org/wiki/index.php/System_Modeling) of [_Feedback Systems_](https://fbswiki.org/wiki/index.php/FBS) by Astrom and Murray (2020)." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "sufficient-douglas", + "metadata": {}, + "outputs": [], + "source": [ + "# Define the update rule for the system, f(x, u)\n", + "# States: x, y, theta (postion and angle of the center of mass)\n", + "# Inputs: v (forward velocity), delta (steering angle)\n", + "def vehicle_update(t, x, u, params):\n", + " # Get the parameters for the model\n", + " a = params.get('refoffset', 1.5) # offset to vehicle reference point\n", + " b = params.get('wheelbase', 3.) # vehicle wheelbase\n", + " maxsteer = params.get('maxsteer', 0.5) # max steering angle (rad)\n", + "\n", + " # Saturate the steering input\n", + " delta = np.clip(u[1], -maxsteer, maxsteer)\n", + " alpha = np.arctan2(a * np.tan(delta), b)\n", + "\n", + " # Return the derivative of the state\n", + " return np.array([\n", + " u[0] * np.cos(x[2] + alpha), # xdot = cos(theta + alpha) v\n", + " u[0] * np.sin(x[2] + alpha), # ydot = sin(theta + alpha) v\n", + " (u[0] / a) * np.sin(alpha) # thdot = v sin(alpha) / a\n", + " ])\n", + "\n", + "# Define the readout map for the system, h(x, u)\n", + "# Outputs: x, y (planar position of the center of mass)\n", + "def vehicle_output(t, x, u, params):\n", + " return x\n", + "\n", + "# Default vehicle parameters (including nominal velocity)\n", + "vehicle_params={'refoffset': 1.5, 'wheelbase': 3, 'velocity': 15, \n", + " 'maxsteer': 0.5}\n", + "\n", + "# Define the vehicle steering dynamics as an input/output system\n", + "vehicle = ct.nlsys(\n", + " vehicle_update, vehicle_output, states=3, name='vehicle',\n", + " inputs=['v', 'delta'], outputs=['x', 'y', 'theta'], params=vehicle_params)" + ] + }, + { + "cell_type": "markdown", + "id": "intellectual-democrat", + "metadata": {}, + "source": [ + "### Open loop simulation\n", + "\n", + "After these operations, the `vehicle` object references the nonlinear model for the system. This system can be simulated to compute a trajectory for the system. Here we command a velocity of 10 m/s and turn the wheel back and forth at one Hertz." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "likely-hindu", + "metadata": {}, + "outputs": [], + "source": [ + "# Define the time interval that we want to use for the simualation\n", + "timepts = np.linspace(0, 10, 1000)\n", + "\n", + "# Define the inputs\n", + "U = [\n", + " 10 * np.ones_like(timepts), # velocity\n", + " 0.1 * np.sin(timepts * 2*np.pi) # steering angle\n", + "]\n", + "\n", + "# Simulate the system dynamics, starting from the origin\n", + "response = ct.input_output_response(vehicle, timepts, U, 0)\n", + "time, outputs, inputs = response.time, response.outputs, response.inputs" + ] + }, + { + "cell_type": "markdown", + "id": "dutch-charm", + "metadata": {}, + "source": [ + "We can plot the results using standard `matplotlib` commands:" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "piano-algeria", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Create a figure to plot the results\n", + "fig, ax = plt.subplots(2, 1)\n", + "\n", + "# Plot the results in the xy plane\n", + "ax[0].plot(outputs[0], outputs[1])\n", + "ax[0].set_xlabel(\"$x$ [m]\")\n", + "ax[0].set_ylabel(\"$y$ [m]\")\n", + "\n", + "# Plot the inputs\n", + "ax[1].plot(timepts, U[0])\n", + "ax[1].set_ylim(0, 12)\n", + "ax[1].set_xlabel(\"Time $t$ [s]\")\n", + "ax[1].set_ylabel(\"Velocity $v$ [m/s]\")\n", + "ax[1].yaxis.label.set_color('blue')\n", + "\n", + "rightax = ax[1].twinx() # Create an axis in the right\n", + "rightax.plot(timepts, U[1], color='red')\n", + "rightax.set_ylim(None, 0.5)\n", + "rightax.set_ylabel(r\"Steering angle $\\phi$ [rad]\")\n", + "rightax.yaxis.label.set_color('red')\n", + "\n", + "fig.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "id": "alone-worry", + "metadata": {}, + "source": [ + "Notice that there is a small drift in the $y$ position despite the fact that the steering wheel is moved back and forth symmetrically around zero. Exercise: explain what might be happening." + ] + }, + { + "cell_type": "markdown", + "id": "portable-rubber", + "metadata": {}, + "source": [ + "### Linearize the system around a trajectory\n", + "\n", + "We choose a straight path along the $x$ axis at a speed of 10 m/s as our desired trajectory and then linearize the dynamics around the initial point in that trajectory." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "surprising-algorithm", + "metadata": {}, + "outputs": [], + "source": [ + "# Create the desired trajectory \n", + "Ud = np.array([10 * np.ones_like(timepts), np.zeros_like(timepts)])\n", + "Xd = np.array([10 * timepts, 0 * timepts, np.zeros_like(timepts)])\n", + "\n", + "# Now linizearize the system around this trajectory\n", + "linsys = vehicle.linearize(Xd[:, 0], Ud[:, 0])" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "protecting-committee", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0., 0., 0.])" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Check on the eigenvalues of the open loop system\n", + "np.linalg.eigvals(linsys.A)" + ] + }, + { + "cell_type": "markdown", + "id": "trying-stereo", + "metadata": {}, + "source": [ + "We see that all eigenvalues are zero, corresponding to a single integrator in the $x$ (longitudinal) direction and a double integrator in the $y$ (lateral) direction." + ] + }, + { + "cell_type": "markdown", + "id": "pressed-delta", + "metadata": {}, + "source": [ + "### Compute a state space (LQR) control law\n", + "\n", + "We can now compute a feedback controller around the trajectory. We choose a simple LQR controller here, but any method can be used." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "auburn-caribbean", + "metadata": {}, + "outputs": [], + "source": [ + "# Compute LQR controller\n", + "K, S, E = ct.lqr(linsys, np.diag([1, 1, 1]), np.diag([1, 1]))" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "independent-lafayette", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([-1. +0.j , -5.06896878+2.76385399j,\n", + " -5.06896878-2.76385399j])" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Check on the eigenvalues of the closed loop system\n", + "np.linalg.eigvals(linsys.A - linsys.B @ K)" + ] + }, + { + "cell_type": "markdown", + "id": "handmade-moral", + "metadata": {}, + "source": [ + "The closed loop eigenvalues have negative real part, so the closed loop (linear) system will be stable about the operating trajectory." + ] + }, + { + "cell_type": "markdown", + "id": "handy-virgin", + "metadata": {}, + "source": [ + "### Create a controller with feedforward and feedback\n", + "\n", + "We now create an I/O system representing the control law. The controller takes as an input the desired state space trajectory $x_\\text{d}$ and the nominal input $u_\\text{d}$. It outputs the control law\n", + "\n", + "$$\n", + "u = u_\\text{d} - K(x - x_\\text{d}).\n", + "$$" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "negative-scope", + "metadata": {}, + "outputs": [], + "source": [ + "# Define the output rule for the controller\n", + "# States: none (=> no update rule required)\n", + "# Inputs: z = [xd, ud, x]\n", + "# Outputs: v (forward velocity), delta (steering angle)\n", + "def control_output(t, x, z, params):\n", + " # Get the parameters for the model\n", + " K = params.get('K', np.zeros((2, 3))) # nominal gain\n", + " \n", + " # Split up the input to the controller into the desired state and nominal input\n", + " xd_vec = z[0:3] # desired state ('xd', 'yd', 'thetad')\n", + " ud_vec = z[3:5] # nominal input ('vd', 'deltad')\n", + " x_vec = z[5:8] # current state ('x', 'y', 'theta')\n", + " \n", + " # Compute the control law\n", + " return ud_vec - K @ (x_vec - xd_vec)\n", + "\n", + "# Define the controller system\n", + "control = ct.nlsys(\n", + " None, control_output, name='control',\n", + " inputs=['xd', 'yd', 'thetad', 'vd', 'deltad', 'x', 'y', 'theta'], \n", + " outputs=['v', 'delta'], params={'K': K})" + ] + }, + { + "cell_type": "markdown", + "id": "affected-motor", + "metadata": {}, + "source": [ + "Because we have named the signals in both the vehicle model and the controller in a compatible way, we can use the autoconnect feature of the `interconnect()` function to create the closed loop system." + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "stock-regression", + "metadata": {}, + "outputs": [], + "source": [ + "# Build the closed loop system\n", + "vehicle_closed = ct.interconnect(\n", + " (vehicle, control),\n", + " inputs=['xd', 'yd', 'thetad', 'vd', 'deltad'],\n", + " outputs=['x', 'y', 'theta']\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "hispanic-monroe", + "metadata": {}, + "source": [ + "### Closed loop simulation\n", + "\n", + "We now command the system to follow in trajectory and use the linear controller to correct for any errors. \n", + "\n", + "The desired trajectory is a given by a longitudinal position that tracks a velocity of 10 m/s for the first 5 seconds and then increases to 12 m/s and a lateral position that varies sinusoidally by $\\pm 0.5$ m around the centerline. The nominal inputs are not modified, so that feedback is required to obtained proper trajectory tracking." + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "american-return", + "metadata": {}, + "outputs": [], + "source": [ + "Xd = np.array([\n", + " 10 * timepts + 2 * (timepts-5) * (timepts > 5), \n", + " 0.5 * np.sin(timepts * 2*np.pi), \n", + " np.zeros_like(timepts)\n", + "])\n", + "\n", + "Ud = np.array([10 * np.ones_like(timepts), np.zeros_like(timepts)])\n", + "\n", + "# Simulate the system dynamics, starting from the origin\n", + "resp = ct.input_output_response(\n", + " vehicle_closed, timepts, np.vstack((Xd, Ud)), 0)\n", + "time, outputs = resp.time, resp.outputs" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "indirect-longitude", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the results in the xy plane\n", + "plt.plot(Xd[0], Xd[1], 'b--') # desired trajectory\n", + "plt.plot(outputs[0], outputs[1]) # actual trajectory\n", + "plt.xlabel(\"$x$ [m]\")\n", + "plt.ylabel(\"$y$ [m]\")\n", + "plt.ylim(-1, 2)\n", + "\n", + "# Add a legend\n", + "plt.legend(['desired', 'actual'], loc='upper left')\n", + "\n", + "# Compute and plot the velocity\n", + "rightax = plt.twinx() # Create an axis in the right\n", + "rightax.plot(Xd[0, :-1], np.diff(Xd[0]) / np.diff(timepts), 'r--')\n", + "rightax.plot(outputs[0, :-1], np.diff(outputs[0]) / np.diff(timepts), 'r-')\n", + "rightax.set_ylim(0, 13)\n", + "rightax.set_ylabel(\"$x$ velocity [m/s]\")\n", + "rightax.yaxis.label.set_color('red')" + ] + }, + { + "cell_type": "markdown", + "id": "weighted-directory", + "metadata": {}, + "source": [ + "We see that there is a small error in each axis. By adjusting the weights in the LQR controller we can adjust the steady state error (try it!)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f31dd981-161a-49f0-a637-84128f7ec5ff", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.1" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From bb3aeb07e3f12a55ce23d3563ad5d44704386edb Mon Sep 17 00:00:00 2001 From: Richard Murray Date: Fri, 27 Dec 2024 10:26:33 -0800 Subject: [PATCH 26/93] updated intro text --- control/__init__.py | 53 +++++++++++---------------------------------- doc/intro.rst | 46 +++++++++++++++++++++++---------------- 2 files changed, 40 insertions(+), 59 deletions(-) diff --git a/control/__init__.py b/control/__init__.py index 3f78452fa..4bbb81c3f 100644 --- a/control/__init__.py +++ b/control/__init__.py @@ -1,50 +1,23 @@ # __init__.py - initialization for control systems toolbox # # Author: Richard M. Murray -# Date: 24 May 09 -# -# This file contains the initialization information from the control package. -# -# Copyright (c) 2009 by California Institute of Technology -# All rights reserved. -# -# Redistribution and use in source and binary forms, with or without -# modification, are permitted provided that the following conditions -# are met: -# -# 1. Redistributions of source code must retain the above copyright -# notice, this list of conditions and the following disclaimer. -# -# 2. Redistributions in binary form must reproduce the above copyright -# notice, this list of conditions and the following disclaimer in the -# documentation and/or other materials provided with the distribution. -# -# 3. Neither the name of the California Institute of Technology nor -# the names of its contributors may be used to endorse or promote -# products derived from this software without specific prior -# written permission. -# -# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS -# "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT -# LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS -# FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL CALTECH -# OR THE CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, -# SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT -# LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF -# USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND -# ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, -# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT -# OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF -# SUCH DAMAGE. -# -# $Id$ +# Date: 24 May 2009 -""" -The Python Control Systems Library :mod:`control` provides common functions +"""The Python Control Systems Library :mod:`control` provides common functions for analyzing and designing feedback control systems. +The initial goal for the package is to implement all of the +functionality required to work through the examples in the textbook +`Feedback Systems `_ by Astrom and Murray. In +addition to standard techniques available for linear control systems, +support for nonlinear systems (including trajectory generation, gain +scheduling, phase plane diagrams, and describing functions) is also +included. A :ref:`matlab-module` is available that provides many of +the common functions corresponding to commands available in the MATLAB +Control Systems Toolbox. + Documentation is available in two forms: docstrings provided with the code, -and the python-control users guide, available from `the python-control +and the python-control User Guide, available from the `python-control homepage `_. The docstring examples assume that the following import commands:: diff --git a/doc/intro.rst b/doc/intro.rst index dac2a9280..b56904f4f 100644 --- a/doc/intro.rst +++ b/doc/intro.rst @@ -10,15 +10,10 @@ examples illustrating their use. Package overview ================ -The python-control package is a set of python classes and functions that -implement common operations for the analysis and design of feedback control -systems. The initial goal is to implement all of the functionality required -to work through the examples in the textbook `Feedback Systems -`_ by Astrom and Murray. A :ref:`matlab-module` is -available that provides many of the common functions corresponding to -commands available in the MATLAB Control Systems Toolbox. - -.. todo:: Add information from :module:`control`? +.. automodule:: control + :no-members: + :no-inherited-members: + :no-special-members: Installation ============ @@ -51,7 +46,7 @@ To install using pip:: .. note:: If you install Slycot using pip you'll need a development - environment (e.g., Python development files, C and Fortran compilers). + environment (e.g., Python development files, C, and Fortran compilers). Pip installation can be particularly complicated for Windows. Many parts of `python-control` will work without `slycot`, but some @@ -65,9 +60,10 @@ and verifying that no error message appears. More information on the Slycot package can be obtained from the `Slycot project page `_. -Alternatively, to install from source, first `download the source -`_ and unpack it. -To install in your home directory, use:: +Alternatively, to install `python-control` from source, first +`download the source code +`_ and +unpack it. To install in your Python environment, use:: pip install . @@ -87,17 +83,29 @@ functionality may not be available. Some differences from MATLAB ============================ -The python-control package makes use of `NumPy `_ and -`SciPy `_. A list of general differences between -NumPy and MATLAB can be found `here + +Users familiar with the MATLAB control systems toolbox will find much +of the functionality implemented in `python-control`, though using +Python constructs and coding conventions. The python-control package +makes heavy use of `NumPy `_ and `SciPy +`_ and many differences are reflected in the +use of those . A list of general differences between NumPy and MATLAB +can be found `here `_. In terms of the python-control package more specifically, here are some things to keep in mind: -* You must include commas in vectors. So [1 2 3] must be [1, 2, 3]. -* Functions that return multiple values use objects (with elements for - each return value) or tuples. +* Vectors and matrices used as arguments to functions can be written + using lists, with commas required between elements and column + vectors implemented as nested list . So [1 2 3] must be written as + [1, 2, 3] and matrices are written using 2D nested lists, e.g., [[1, + 2], [3, 4]]. +* Functions that return multiple values use either objects (with + elements for each return value) or tuples. The number of elements + in a tuple is fixed and so functions that return variable numbers of + return values will have a parameter of the form ``return_`` + that is used to return additional data. * You cannot use braces for collections; use tuples instead. * Time series data have time as the final index (see :ref:`time-series-convention`). From 2de0e89cd0384a7f9a855ec0d9fe22e91dd3acd0 Mon Sep 17 00:00:00 2001 From: Richard Murray Date: Fri, 27 Dec 2024 10:48:32 -0800 Subject: [PATCH 27/93] add version info to Jupyter tutorial --- examples/python-control_tutorial.ipynb | 34 ++++++++++++++++++++------ 1 file changed, 27 insertions(+), 7 deletions(-) diff --git a/examples/python-control_tutorial.ipynb b/examples/python-control_tutorial.ipynb index b256b1483..be7565aed 100644 --- a/examples/python-control_tutorial.ipynb +++ b/examples/python-control_tutorial.ipynb @@ -5,9 +5,7 @@ "id": "numerous-rochester", "metadata": {}, "source": [ - "# Introduction to the Python Control Systems Library (python-control)\n", - "\n", - "Richard M. Murray, 13 Nov 2021 (updated 26 Dec 2024)\n", + "# Python Control Systems Library (python-control) Tutorial\n", "\n", "This Jupyter notebook contains an introduction to the basic operations in the Python Control Systems Library (python-control), a Python package for control system design. The tutorial consists of two examples:\n", "\n", @@ -1209,13 +1207,35 @@ "We see that there is a small error in each axis. By adjusting the weights in the LQR controller we can adjust the steady state error (try it!)" ] }, + { + "cell_type": "markdown", + "id": "03b1fd75-579c-47da-805d-68f155957084", + "metadata": {}, + "source": [ + "## Computing environment" + ] + }, { "cell_type": "code", - "execution_count": null, - "id": "f31dd981-161a-49f0-a637-84128f7ec5ff", + "execution_count": 31, + "id": "280d8d0e-38bc-484c-8ed5-fd6a7f2b56b5", "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Control version: 0.10.1.dev324+g2fd3802a.d20241218\n", + "Slycot installed: True\n", + "NumPy version: 2.2.0\n" + ] + } + ], + "source": [ + "print(\"Control version:\", ct.__version__)\n", + "print(\"Slycot installed:\", ct.slycot_check())\n", + "print(\"NumPy version:\", np.__version__)" + ] } ], "metadata": { From b158cef77ac2663849f13dde9391d812fd4057f9 Mon Sep 17 00:00:00 2001 From: Richard Murray Date: Fri, 27 Dec 2024 12:46:13 -0800 Subject: [PATCH 28/93] set default_role to py:obj to allow use of single backticks --- doc/conf.py | 3 +++ doc/intro.rst | 2 +- doc/iosys.rst | 20 ++++++++-------- doc/linear.rst | 12 +++++----- doc/optimal.rst | 2 +- doc/response.rst | 62 ++++++++++++++++++++++++------------------------ 6 files changed, 52 insertions(+), 49 deletions(-) diff --git a/doc/conf.py b/doc/conf.py index ff5da5436..fa22c5f18 100644 --- a/doc/conf.py +++ b/doc/conf.py @@ -118,6 +118,9 @@ # html_theme = 'sphinx_rtd_theme' +# Set the default role to render items in backticks as code +default_role = 'py:obj' + # Theme options are theme-specific and customize the look and feel of a theme # further. For a list of options available for each theme, see the # documentation. diff --git a/doc/intro.rst b/doc/intro.rst index b56904f4f..0d9bc7c98 100644 --- a/doc/intro.rst +++ b/doc/intro.rst @@ -104,7 +104,7 @@ some things to keep in mind: * Functions that return multiple values use either objects (with elements for each return value) or tuples. The number of elements in a tuple is fixed and so functions that return variable numbers of - return values will have a parameter of the form ``return_`` + return values will have a parameter of the form `return_` that is used to return additional data. * You cannot use braces for collections; use tuples instead. * Time series data have time as the final index (see diff --git a/doc/iosys.rst b/doc/iosys.rst index 00ff8efbf..949655454 100644 --- a/doc/iosys.rst +++ b/doc/iosys.rst @@ -252,14 +252,14 @@ In this command, the states and the inputs are broadcast to the size of the state and input vectors, respectively. If we want to linearize the closed loop system around a process state -``x0`` (with two elements) and an estimator state ``0`` (for both states), +`x0` (with two elements) and an estimator state `0` (for both states), we can use the list processing feature:: H = clsys.linearize([x0, 0], 0) Note that this also utilizes the zero-padding functionality, since the -second argument in the list ``[x0, 0]`` is a scalar and so the vector -``[x0, 0]`` only has three elements instead of the required four. +second argument in the list `[x0, 0]` is a scalar and so the vector +`[x0, 0]` only has three elements instead of the required four. To run an input/output simulation with a sinusoidal signal for the first input, a constant for the second input, and no external disturbance, we can @@ -285,8 +285,8 @@ use the command sumblk = ct.summing_junction(3) -By default, the name of the inputs will be of the form ``u[i]`` and the output -will be ``y``. This can be changed by giving an explicit list of names:: +By default, the name of the inputs will be of the form `u[i]` and the output +will be `y`. This can be changed by giving an explicit list of names:: sumblk = ct.summing_junction(inputs=['a', 'b', 'c'], output='d') @@ -306,16 +306,16 @@ the command sumblk = ct.summing_junction(inputs=['r', '-y'], output='e', dimension=2) -will produce an input/output block that implements ``e[0] = r[0] - y[0]`` and -``e[1] = r[1] - y[1]``. +will produce an input/output block that implements `e[0] = r[0] - y[0]` and +`e[1] = r[1] - y[1]`. Automatic connections using signal names ---------------------------------------- The :func:`~control.interconnect` function allows the interconnection of -multiple systems by using signal names of the form ``sys.signal``. In many +multiple systems by using signal names of the form `sys.signal`. In many situations, it can be cumbersome to explicitly connect all of the appropriate -inputs and outputs. As an alternative, if the ``connections`` keyword is +inputs and outputs. As an alternative, if the `connections` keyword is omitted, the :func:`~control.interconnect` function will connect all signals of the same name to each other. This can allow for simplified methods of interconnecting systems, especially when combined with the @@ -331,7 +331,7 @@ If a signal name appears in multiple outputs then that signal will be summed when it is interconnected. Similarly, if a signal name appears in multiple inputs then all systems using that signal name will receive the same input. The :func:`~control.interconnect` function will generate an error if a signal -listed in ``inplist`` or ``outlist`` (corresponding to the inputs and outputs +listed in `inplist` or `outlist` (corresponding to the inputs and outputs of the interconnected system) is not found, but inputs and outputs of individual systems that are not connected to other systems are left unconnected (so be careful!). diff --git a/doc/linear.rst b/doc/linear.rst index be52b2079..3dc9ce07d 100644 --- a/doc/linear.rst +++ b/doc/linear.rst @@ -109,8 +109,8 @@ argument:: mag, phase, omega = response(squeeze=False) Frequency response objects are also available as named properties of the -``response`` object: ``response.magnitude``, ``response.phase``, and -``response.response`` (for the complex response). For MIMO systems, these +`response` object: `response.magnitude`, `response.phase`, and +`response.response` (for the complex response). For MIMO systems, these elements of the frequency response can be accessed using the names of the inputs and outputs:: @@ -119,8 +119,8 @@ inputs and outputs:: where the signal names are based on the system that generated the frequency response. -Note: The ``fresp`` data member is stored as a NumPy array and cannot be -accessed with signal names. Use ``response.response`` to access the +Note: The `fresp` data member is stored as a NumPy array and cannot be +accessed with signal names. Use `response.response` to access the complex frequency response using signal names. Multi-input, multi-output (MIMO) systems @@ -137,8 +137,8 @@ names:: Signal names for an indexed subsystem are preserved from the original system and the subsystem name is set according to the values of -``config.defaults['iosys.indexed_system_name_prefix']`` and -``config.defaults['iosys.indexed_system_name_suffix']``. The default +`config.defaults['iosys.indexed_system_name_prefix']` and +`config.defaults['iosys.indexed_system_name_suffix']`. The default subsystem name is the original system name with '$indexed' appended. .. include:: statesp.rst diff --git a/doc/optimal.rst b/doc/optimal.rst index d65c1a61f..56228dcff 100644 --- a/doc/optimal.rst +++ b/doc/optimal.rst @@ -468,7 +468,7 @@ Module classes and functions ---------------------------- The following classes and functions are defined in the -``optimal`` module: +`optimal` module: .. autosummary:: :template: custom-class-template.rst diff --git a/doc/response.rst b/doc/response.rst index 8298d5e23..c20acd929 100644 --- a/doc/response.rst +++ b/doc/response.rst @@ -275,13 +275,13 @@ array of frequencies as a second argument (after the list of systems):: .. image:: figures/freqplot-siso_bode-omega.png Alternatively, frequency ranges can be specified by passing a list of the -form ``[wmin, wmax]``, where ``wmin`` and ``wmax`` are the minimum and +form `[wmin, wmax]`, where `wmin` and `wmax` are the minimum and maximum frequencies in the (log-spaced) frequency range:: response = ct.frequency_response([sys1, sys2], [1e-2, 1e2]) The number of (log-spaced) points in the frequency can be specified using -the ``omega_num`` keyword parameter. +the `omega_num` keyword parameter. Frequency response data can also be accessed directly and plotted manually:: @@ -290,9 +290,9 @@ Frequency response data can also be accessed directly and plotted manually:: plt.loglog(fresp.omega, fresp.magnitude['y[1]', 'u[0]']) Access to frequency response data is available via the attributes -``omega``, ``magnitude``,` `phase``, and ``response``, where ``response`` +`omega`, `magnitude`,` `phase`, and `response`, where `response` represents the complex value of the frequency response at each frequency. -The ``magnitude``, ``phase``, and ``response`` arrays can be indexed using +The `magnitude`, `phase`, and `response` arrays can be indexed using either input/output indices or signal names, with the first index corresponding to the output signal and the second input corresponding to the input signal. @@ -464,20 +464,20 @@ various ways. The following general rules apply: sequentially, the :func:`matplotlib.pyplot.figure` command should be used to explicitly create a new figure. -* The ``ax`` keyword argument can be used to direct the plotting function - to use a specific axes or array of axes. The value of the ``ax`` keyword +* The `ax` keyword argument can be used to direct the plotting function + to use a specific axes or array of axes. The value of the `ax` keyword must have the proper number of axes for the plot (so a plot generating a - 2x2 array of subplots should be given a 2x2 array of axes for the ``ax`` + 2x2 array of subplots should be given a 2x2 array of axes for the `ax` keyword). -* The ``color``, ``linestyle``, ``linewidth``, and other matplotlib line +* The `color`, `linestyle`, `linewidth`, and other matplotlib line property arguments can be used to override the default line properties. If these arguments are absent, the default matplotlib line properties are used and the color cycles through the default matplotlib color cycle. The :func:`~control.bode_plot`, :func:`~control.time_response_plot`, and selected other commands can also accept a matplotlib format - string (e.g., ``'r--'``). The format string must appear as a positional + string (e.g., `'r--'`). The format string must appear as a positional argument right after the required data argument. Note that line property arguments are the same for all lines generated as @@ -486,9 +486,9 @@ various ways. The following general rules apply: often easiest to call multiple plot commands in sequence, with each command setting the line properties for that system/trace. -* The ``label`` keyword argument can be used to override the line labels +* The `label` keyword argument can be used to override the line labels that are used in generating the title and legend. If more than one line - is being plotted in a given call to a plot command, the ``label`` + is being plotted in a given call to a plot command, the `label` argument value should be a list of labels, one for each line, in the order they will appear in the legend. @@ -502,33 +502,33 @@ various ways. The following general rules apply: For non-input/output plots (e.g., Nyquist plots, pole/zero plots, root locus plots), the default labels are the system name. - If ``label`` is set to ``False``, individual lines are still given + If `label` is set to `False`, individual lines are still given labels, but no legend is generated in the plot. (This can also be - accomplished by setting ``legend_map`` to ``False``). + accomplished by setting `legend_map` to `False`). - Note: the ``label`` keyword argument is not implemented for describing + Note: the `label` keyword argument is not implemented for describing function plots or phase plane plots, since these plots are primarily - intended to be for a single system. Standard ``matplotlib`` commands can + intended to be for a single system. Standard `matplotlib` commands can be used to customize these plots for displaying information for multiple systems. -* The ``legend_loc``, ``legend_map`` and ``show_legend`` keyword arguments +* The `legend_loc`, `legend_map` and `show_legend` keyword arguments can be used to customize the locations for legends. By default, a minimal number of legends are used such that lines can be uniquely identified and no legend is generated if there is only one line in the - plot. Setting ``show_legend`` to ``False`` will suppress the legend and - setting it to ``True`` will force the legend to be displayed even if + plot. Setting `show_legend` to `False` will suppress the legend and + setting it to `True` will force the legend to be displayed even if there is only a single line in each axes. In addition, if the value of - the ``legend_loc`` keyword argument is set to a string or integer, it + the `legend_loc` keyword argument is set to a string or integer, it will set the position of the legend as described in the - :func:`matplotlib.legend` documentation. Finally, ``legend_map`` can be + :func:`matplotlib.legend` documentation. Finally, `legend_map` can be set to an array that matches the shape of the subplots, with each item being a string indicating the location of the legend for that axes (or - ``None`` for no legend). + `None` for no legend). -* The ``rcParams`` keyword argument can be used to override the default +* The `rcParams` keyword argument can be used to override the default matplotlib style parameters used when creating a plot. The default - parameters for all control plots are given by the ``ct.rcParams`` + parameters for all control plots are given by the `ct.rcParams` dictionary and have the following values: .. list-table:: @@ -551,8 +551,8 @@ various ways. The following general rules apply: - 'small' Only those values that should be changed from the default need to be - specified in the ``rcParams`` keyword argument. To override the defaults - for all control plots, update the ``ct.rcParams`` dictionary entries. + specified in the `rcParams` keyword argument. To override the defaults + for all control plots, update the `ct.rcParams` dictionary entries. The default values for style parameters for control plots can be restored using :func:`~control.reset_rcParams`. @@ -570,15 +570,15 @@ various ways. The following general rules apply: and phase portions of the plot, and `share_frequency` can be used instead of `sharex`. -* The ``title`` keyword can be used to override the automatic creation of +* The `title` keyword can be used to override the automatic creation of the plot title. The default title is a string of the form " plot for " where is a list of the sys names contained in the plot (which can be updated if the plotting is called multiple times). - Use ``title=False`` to suppress the title completely. The title can also + Use `title=False` to suppress the title completely. The title can also be updated using the :func:`~control.ControlPlot.set_plot_title` method for the returned control plot object. - The plot title is only generated if ``ax`` is ``None``. + The plot title is only generated if `ax` is `None`. The following code illustrates the use of some of these customization features:: @@ -632,8 +632,8 @@ example:: .. image:: figures/ctrlplot-pole_zero_subplots.png -Alternatively, turning off the omega-damping grid (using ``grid=False`` or -``grid='empty'``) allows use of Matplotlib layout commands. +Alternatively, turning off the omega-damping grid (using `grid=False` or +`grid='empty'`) allows use of Matplotlib layout commands. Response and plotting functions @@ -645,7 +645,7 @@ Response functions Response functions take a system or list of systems and return a response object that can be used to retrieve information about the system (e.g., the number of encirclements for a Nyquist plot) as well as plotting (via the -``plot`` method). +`plot` method). .. autosummary:: From ec89ffbcf3f84f2a82c1a38795c44626606a8adb Mon Sep 17 00:00:00 2001 From: Richard Murray Date: Fri, 27 Dec 2024 17:32:08 -0800 Subject: [PATCH 29/93] main LTI systems chapter content --- doc/functions.rst | 3 + doc/linear.rst | 393 +++++++++++++++++++++++++++++++++++++++------- 2 files changed, 341 insertions(+), 55 deletions(-) diff --git a/doc/functions.rst b/doc/functions.rst index ebb3cd257..5dfb4f1ad 100644 --- a/doc/functions.rst +++ b/doc/functions.rst @@ -42,8 +42,11 @@ Systems can also be created by transforming existing systems: tf2ss tfdata +.. _interconnections-ref: + System interconnections ======================= + .. autosummary:: :toctree: generated/ diff --git a/doc/linear.rst b/doc/linear.rst index 3dc9ce07d..ada8667c9 100644 --- a/doc/linear.rst +++ b/doc/linear.rst @@ -9,11 +9,27 @@ state space, transfer function, or frequency response data (FRD) form. Most functions in the toolbox will operate on any of these data types, and functions for converting between compatible types are provided. + Creating LTI systems ==================== +LTI systems are created using "factory functions" that accept the +parameters required to define the system. Three factory functions are +available for LTI systems: + +.. autosummary:: + + ss + tf + frd + +Each of these functions returns an object of an appropriate class to +represent the system. + + State space systems ------------------- + The :class:`StateSpace` class is used to represent state-space realizations of linear time-invariant (LTI) systems: @@ -22,30 +38,44 @@ of linear time-invariant (LTI) systems: \frac{dx}{dt} &= A x + B u \\ y &= C x + D u -where u is the input, y is the output, and x is the state. +where :math:`u` is the input, :math:`y` is the output, and :math:`x` +is the state. To create a state space system, use the :func:`ss` function:: sys = ct.ss(A, B, C, D) -State space systems can be manipulated using standard arithmetic operations -as well as the :func:`feedback`, :func:`parallel`, and :func:`series` -function. A full list of functions can be found in :ref:`function-ref`. +State space systems can be manipulated using standard arithmetic +operations as well as the :func:`feedback`, :func:`parallel`, and +:func:`series` function. A full list of "block diagram algebra" +functions can be found in the :ref:`interconnections-ref` section of +the :ref:`function-ref`. Systems, inputs, outputs, and states can be given labels to allow more customized access to system information:: sys = ct.ss( A, B, C, D, name='sys', - states=['x1', 'x2'], inputs=['u'], outputs=['y']) + states=['x1', 'x2'], inputs=['u1', 'u2'], outputs=['y']) State space can be manipulated using standard arithmetic operations as well as the :func:`feedback`, :func:`parallel`, and :func:`series` function. A full list of functions can be found in :ref:`function-ref`. +The :func:`rss` function can be used to create a random state space +system with a desired numbef or inputs, outputs, and states:: + + sys = ct.rss(states=4, outputs=1, inputs=1], strictly_proper=True) + +The `states`, `inputs`, and `output` parameters can also be +given as lists of strings to create named signals. All systems +generated by :func:`rss` are stable. + + Transfer functions ------------------ + The :class:`TransferFunction` class is used to represent input/output transfer functions @@ -65,24 +95,33 @@ To create a transfer function, use the :func:`tf` function:: The system name as well as input and output labels can be specified in the same way as state space systems:: - sys = ct.tf(A, B, C, D, name='sys', inputs=['u'], outputs=['y']) + sys = ct.tf(num, den, name='sys', inputs=['u'], outputs=['y']) + +Transfer functions can be manipulated using standard arithmetic +operations as well as the :func:`feedback`, :func:`parallel`, and +:func:`series` functions. A full list of "block diagram algebra" +functions can be found in the :ref:`interconnections-ref` section of the +:ref:`function-ref`. -Transfer functions can be manipulated using standard arithmetic operations -as well as the :func:`feedback`, :func:`parallel`, and :func:`series` -function. A full list of functions can be found in :ref:`function-ref`. Frequency response data (FRD) systems ------------------------------------- -The :class:`FrequencyResponseData` (FRD) class is used to represent systems in -frequency response data form. -The main data members are `omega` and `fresp`, where `omega` is a 1D array -with the frequency points of the response, and `fresp` is a 3D array, with -the first dimension corresponding to the output index of the system, the -second dimension corresponding to the input index, and the 3rd dimension -corresponding to the frequency points in omega. +The :class:`FrequencyResponseData` (FRD) class is used to represent +systems in frequency response data form. The main data attributes are +`omega` and `fresp`, where `omega` is a 1D array of frequencies and +`fresp` is the (complex-value) value of the transfer function at each +frequency point. + +FRD systems can be created with the :func:`frd` factory function:: + + sys = ct.frd(fresp, omega) + +FRD systems can also be created by evaluating an LTI system at a given +set of frequencies:: + + frd_sys = ct.frd(lti_sys, omega) -FRD systems can be created with the :func:`frd` factory function. Frequency response data systems have a somewhat more limited set of functions that are available, although all of the standard algebraic manipulations can be performed. @@ -97,36 +136,40 @@ object can be assigned to a tuple using:: where `mag` is the magnitude (absolute value, not dB or log10) of the system frequency response, `phase` is the wrapped phase in radians of the system frequency response, and `omega` is the (sorted) frequencies -at which the response was evaluated. If the system is SISO and the -`squeeze` argument to :func:`frequency_response` is not True, -`magnitude` and `phase` are 1D, indexed by frequency. If the system -is not SISO or `squeeze` is False, the array is 3D, indexed by the -output, input, and frequency. If `squeeze` is True then -single-dimensional axes are removed. The processing of the `squeeze` -keyword can be changed by calling the response function with a new -argument:: - - mag, phase, omega = response(squeeze=False) - -Frequency response objects are also available as named properties of the -`response` object: `response.magnitude`, `response.phase`, and -`response.response` (for the complex response). For MIMO systems, these -elements of the frequency response can be accessed using the names of the -inputs and outputs:: +at which the response was evaluated. - response.magnitude['y[0]', 'u[1]'] +Frequency response properties are also available as named attributes of +the `response` object: `response.magnitude`, `response.phase`, +and `response.response` (for the complex response). -where the signal names are based on the system that generated the frequency -response. - -Note: The `fresp` data member is stored as a NumPy array and cannot be -accessed with signal names. Use `response.response` to access the -complex frequency response using signal names. Multi-input, multi-output (MIMO) systems ---------------------------------------- -.. todo:: Add information on building MIMO systems +Multi-input, multi-output (MIMO) sytems are created by providing +parameters of the appropriate dimensions to the relevant factory +function. For transfer function, this is done by providing a 2D list +of numerator and denominator polynomials to the :func:`tf` function, +e.g.:: + + sys = ct.tf( + [[num11, num12], [num21, num22]], + [[den11, den12], [den21, denm22]]) + +Similarly, MIMO frequency response data systems are created by +providing the :func:`frd` function with a 3D array of response +values,with the first dimension corresponding to the output index +of the system, the second dimension corresponding to the input index, +and the 3rd dimension corresponding to the frequency points in omega. + +Signal names for MIMO systems are specified using lists of labels:: + + sys = ct.ss(A, B, C, D, inputs=['u1', 'u2'], outputs=['y1' 'y2']) + +Signals that are not given explicit labels are given labels of the +form 's[i]' where the default value of 's' is 'x' for states, 'u' for +inputs, and 'y' for outputs, and 'i' ranges over the dimension of the +signal (starting at 0). Subsets of input/output pairs for LTI systems can be obtained by indexing the system using either numerical indices (including slices) or signal @@ -138,20 +181,43 @@ names:: Signal names for an indexed subsystem are preserved from the original system and the subsystem name is set according to the values of `config.defaults['iosys.indexed_system_name_prefix']` and -`config.defaults['iosys.indexed_system_name_suffix']`. The default -subsystem name is the original system name with '$indexed' appended. +`config.defaults['iosys.indexed_system_name_suffix']` (see +:ref:`package-configuration-parameters` for more information). The +default subsystem name is the original system name with '$indexed' +appended. -.. include:: statesp.rst +For FRD objects, the frequency response properties for MIMO systems +can be accessed using the names of the inputs and outputs:: -.. include:: xferfcn.rst + response.magnitude['y[0]', 'u[1]'] + +where the signal names are based on the system that generated the frequency +response. + +.. note:: If an LTI system is SISO, `magnitude` and `phase` default to + 1D arrays, indexed by frequency. If the system is not SISO + or `squeeze` is set to `False`, the array is 3D, indexed by + the output, input, and frequency. If `squeeze` is `True` + for a MIMO system then single-dimensional axes are removed. + The processing of the `squeeze` keyword can be changed by + calling the response function with a new argument:: + + mag, phase, omega = response(squeeze=False) + +.. note:: The `fresp` data member is stored as a NumPy array and + cannot be accessed with signal names. Use + `response.response` to access the complex frequency response + using signal names. + + +.. _discrete_time_systems: Discrete time systems ===================== -.. todo:: add anchor for time base (?) - -A discrete time system is created by specifying a nonzero 'timebase', dt. -The timebase argument can be given when a system is constructed: +A discrete time system is created by specifying a nonzero "timebase", +`dt`. The timebase argument can be given when a system is +constructed: * `dt = 0`: continuous time system (default) * `dt > 0`: discrete time system with sampling period 'dt' @@ -170,18 +236,235 @@ time system from a continuous time system. See :ref:`utility-and-conversions`. The default value of `dt` can be changed by changing the value of `config.defaults['control.default_dt']`. +Functions operating on LTI systems will take into account whether a +system is continous time or discrete time when carrying out operations +that depend on this difference. For example, the :func:`rss` function +will place all system eigenvalues within the unit circle when called +using `dt` corresponding to a discrete time system:: + + >>> sys = ct.rss(2, 1, 1, dt=True) + >>> sys.poles() + array([-0.78096961+0.j, 0.09347055+0.j]) + + +.. include:: statesp.rst + +.. include:: xferfcn.rst + Model conversion and reduction -=============================== +============================== + +A variety of functions are available to manipulate LTI systems, +including functions for convering between state space and frequency +domain, sampling systems in time and frequency domain, and creating +reduced order models. Conversion between representations ---------------------------------- -LTI systems can be converted between representations either by calling the -constructor for the desired data type using the original system as the sole -argument or using the explicit conversion functions :func:`ss2tf` and -:func:`tf2ss`. + +LTI systems can be converted between representations either by calling +the constructor for the desired data type using the original system as +the sole argument or using the explicit conversion functions +:func:`ss2tf` and :func:`tf2ss`. In most cases these types of +explicit conversions are not necessary, since functions designed to +operate on LTI systems will work on any subclass. + +To explicitly convert a state space system into a transfer function +represetation, the state space system can be passed as an argument to +the :func:`tf` factory functions:: + + sys_ss = ct.rss(4, 2, 2, name='sys_ss') + sys_tf = ct.tf(sys_ss, name='sys_tf') + +The :func:`ss2tf` function can also be used, passing either the state +space system or the matrices that represent the state space systems:: + + sys_tf = ct.ss2tf(A, B, C, D) + +In either form, system and signal names can be changed by passing the +appropriate keyword arguments. + +Conversion of transfer functions to state space form is also possible:: + + sys_ss = ct.ss(sys_tf) + sys_ss = ct.tf2ss(sys_tf) + sys_ss = ct.tf2ss(num, den) + +.. note:: State space realizations of transfer functions are not + unique and the state space representation obtained via these + functions may not match realizations obtained by other + algorithms. + + +Time sampling +------------- + +Continous time systems can be converted to discrete time systems using +the :func:`sample_system` function and specifying a sampling time:: + + >> csys = ct.rss(4, 2, 2, name='csys') + >> dsys = ct.sample_system(csys, 0.1, method='bilinear') + >> print(dsys) + : cys$sampled + Inputs (2): ['u[0]', 'u[1]'] + Outputs (2): ['y[0]', 'y[1]'] + States (4): ['x[0]', 'x[1]', 'x[2]', 'x[3]'] + + A = [[ 1.80851713 3.70669158 2.01645278 4.11088725] + [-1.08922089 -1.56305649 -0.52225931 -1.44683994] + [ 0.09926437 0.31372724 1.01817342 0.4445761 ] + [ 0.1936853 0.3376456 0.0166808 0.96877128]] + + B = [[-0.07840985 -0.39220354] + [-0.00693812 0.12585167] + [-0.14077857 0.02673465] + [ 0.05062632 -0.08018257]] + + C = [[-0.17214877 -0.06055247 -0.09715775 0.03881976] + [-1.60336436 -2.11612637 -1.15117992 -2.34687907]] + + D = [[0.00704611 0.01107279] + [0.04476368 0.22390648]] + + dt = 0.1 + +Note that the system name for the discrete time system is the name of +the origal system with the string '$sampled' appended. + +Discrete time systems can also be created using the +:func:`StateSpace.sample` or :func:`TransferFunction.sample` methods +applied directly to the system:: + + dsys = csys.sample(0.1) + + +Frequency sampling +------------------ + +Transfer functions can be sampled at a selected set of frequencies to +obtain a frequency response data representation of a system by calling +the :func:`frd` factory function with an LTI system and an +array of frequencies:: + + >>> sys_ss = ct.ss(4, 1, 1, name='sys_ss') + >>> sys_frd = ct.frd(sys_ss, np.logspace(-1, 1, 5)) + >>> print(sys_frd) + : sys_ss$sampled + Inputs (1): ['u[0]'] + Outputs (1): ['y[0]'] + + Freq [rad/s] Response + ------------ --------------------- + 0.100 2.357 -3.909j + 0.316 -0.6068 -2.216j + 1.000 -1.383 -0.9971j + 3.162 -1.666 -0.3708j + 10.000 -1.703 -0.1186j + +The :func:`frequency_response` function an also be used for this +purpose, although in that case the output is usually used for plotting +the frequency response, as described in more detail in the +:ref:`frequency_response` section. Model reduction --------------- -.. automodule:: modelsimp +Reduced order models for LTI systems can be obtained by approximating +the system by a system of lower order that has similar input/output +properties. A variety of functions are available in the +python-control package that perform various types of model +simplification: + +.. autosummary:: + + balanced_reduction + minimal_realization + model_reduction + +The :func:`balanced_reduction` function eliminate states based on the +Hankel singular values of a system. Intuitively, a system (or +subsystem) with small Hankel singular values correspond to a situation +in which it is difficult to observe a state and/or difficult to +control that state. Eliminating states corresponding to small Hankel +singular values thus represents a good approximation in terms of the +input/output properties of a system. For systems with unstable modes, +:func:`balanced_reduction` first removes the states corresponding to +the unstable subspace from the system, then carries out a balanced +realization on the stable part, then reinserts the unstable modes. + +The :func:`minimal_realization` function eliminates uncontrollable or +unobservable states in state space models or cancels pole-zero pairs +in transfer functions. The resuling output system has minimal order +and the same input/output response characteristics as the original +model system. Unlike the :func:`balanced_reduction` function, the +:func:`minimal_realization` eliminates all uncontrollable and/or +unobservable modes, so should be used with caution if applied to an +unstable system. + +The :func:`model_reduction` function produces a reduced-order model of +a system by eliminating specified inputs, outputs, and/or states from +the original system. The specific states, inputs, or outputs that are +eliminated can be specified by either listing the states, inputs, or +outputs to be eliminated or those to be kept. Two methods of state +reduction are possible: 'truncate' removes the states marked for +elimination, while 'matchdc' replaces the eliminated states with their +equilibrium values (thereby keeping the input/output gain unchanged at +zero frequency ["DC"]). + + +Displaying LTI system information +================================= + +Information about an LTI system can be obtained using the Python +`print()` function:: + + >>> sys = ct.rss(4, 2, 2, name='sys_2x2') + >>> print(sys) + : sys_2x2 + Inputs (2): ['u[0]', 'u[1]'] + Outputs (2): ['y[0]', 'y[1]'] + States (4): ['x[0]', 'x[1]', 'x[2]', 'x[3]'] + + A = [[ 64.81683274 -26.26662488 71.02177207 -107.97639093] + [ 39.61359988 -16.12926876 41.82185164 -65.62112231] + [ -30.6112503 12.44992996 -34.00905316 49.74919788] + [ 10.43751558 -4.08781262 10.64062604 -18.15145265]] + + B = [[ 2.13327172 -0. ] + [ 0.98132238 0. ] + [-1.22635806 -1.57449988] + [ 0. 0.97986024]] + + C = [[-0. 1.34197324 0.28470822 0. ] + [-0.2777818 0. -0.60024615 -0.19122287]] + + D = [[-0. 0.] + [ 0. 0.]] + +For a more compact represent, the LTI objects can be evaluated to +return a summary of the system input/output properties:: + + >>> sys = ct.rss(4, 2, 2, name='sys_2x2') + >>> sys + ['y[0]', 'y[1]']> + +Alternative representations of the system are available using the +:func:iosys_repr` function. + +Transfer functions are displayed as ratios of polynomials, using +either 's' or 'z' depending on whether the systems is continuous or +discrete time:: + + >>> sys_tf = ct.tf([1, 0], [1, 2, 1]) + >>> print(sys_tf) + : sys[1] + Inputs (1): ['u[0]'] + Outputs (1): ['y[0]'] + + z + ------------- + z^2 + 2 z + 1 + + dt = 1 + From 76346d8052de30ceda921f89d35087d1d6ab5d67 Mon Sep 17 00:00:00 2001 From: Richard Murray Date: Fri, 27 Dec 2024 17:34:06 -0800 Subject: [PATCH 30/93] state space systems section content --- control/iosys.py | 7 +- control/modelsimp.py | 6 +- control/passivity.py | 7 +- control/statefbk.py | 39 +--------- doc/response.rst | 3 + doc/statesp.rst | 177 +++++++++++++++++++++++++++++++++++++++++-- 6 files changed, 191 insertions(+), 48 deletions(-) diff --git a/control/iosys.py b/control/iosys.py index cedcc4ff6..7ab15966a 100644 --- a/control/iosys.py +++ b/control/iosys.py @@ -243,6 +243,11 @@ def _generic_name_check(self): #: :meta hide-value: nstates = None + #: System timebase. + #: + #: :meta hide-value: + dt = None + # # System representation # @@ -256,7 +261,7 @@ def __str__(self): out += f"\nStates ({self.nstates}): {self.state_labels}" out += self._dt_repr(separator="\n", space=" ") return out - + def __repr__(self): return iosys_repr(self, format=self.repr_format) diff --git a/control/modelsimp.py b/control/modelsimp.py index ecb15008b..b7b94b23a 100644 --- a/control/modelsimp.py +++ b/control/modelsimp.py @@ -3,8 +3,10 @@ # Author: Steve Brunton, Kevin Chen, Lauren Padilla # Date: 30 Nov 2010 # -# This file contains routines for obtaining reduced order models -# +"""This :mod:`modelsimp` modules contains routines for obtaining +reduced order models for state space systems. + +""" import warnings diff --git a/control/passivity.py b/control/passivity.py index 3f2b2ea22..59e2fde2f 100644 --- a/control/passivity.py +++ b/control/passivity.py @@ -1,8 +1,11 @@ +# passivity.py +# +# Author: Mark Yeatman +#Date: July 17, 2022 + """ Functions for passive control. -Author: Mark Yeatman -Date: July 17, 2022 """ import numpy as np diff --git a/control/statefbk.py b/control/statefbk.py index 7d7aa7f5b..89b906083 100644 --- a/control/statefbk.py +++ b/control/statefbk.py @@ -3,41 +3,10 @@ # Author: Richard M. Murray, Roberto Bucher # Date: 31 May 2010 # -# This file contains routines for designing state space controllers -# -# Copyright (c) 2010 by California Institute of Technology -# All rights reserved. -# -# Redistribution and use in source and binary forms, with or without -# modification, are permitted provided that the following conditions -# are met: -# -# 1. Redistributions of source code must retain the above copyright -# notice, this list of conditions and the following disclaimer. -# -# 2. Redistributions in binary form must reproduce the above copyright -# notice, this list of conditions and the following disclaimer in the -# documentation and/or other materials provided with the distribution. -# -# 3. Neither the name of the California Institute of Technology nor -# the names of its contributors may be used to endorse or promote -# products derived from this software without specific prior -# written permission. -# -# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS -# "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT -# LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS -# FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL CALTECH -# OR THE CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, -# SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT -# LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF -# USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND -# ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, -# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT -# OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF -# SUCH DAMAGE. -# -# $Id$ +"""The statefbk module contains routines for designing state space +controllers. + +""" import warnings diff --git a/doc/response.rst b/doc/response.rst index c20acd929..06dda6347 100644 --- a/doc/response.rst +++ b/doc/response.rst @@ -162,6 +162,9 @@ and styles for various signals and traces:: .. image:: figures/timeplot-mimo_step-linestyle.png + +.. _frequency_response: + Frequency response data ======================= diff --git a/doc/statesp.rst b/doc/statesp.rst index bcaf1f514..93b5cd5d1 100644 --- a/doc/statesp.rst +++ b/doc/statesp.rst @@ -3,21 +3,182 @@ State space analysis and design =============================== +This section describes the functions the are available to analyze +state space systems and design state feedback controllers. The +functionality described here is mainly specific to state space system +representations; additional functions for analysis of linear +input/output systems are defined in the next section and can also be +applied to LTI systems in state space form. + + State space properties ---------------------- +The following basic attributes and methods are available for +:class:`StateSpace` objects: + +.. autosummary:: + + ~StateSpace.A + ~StateSpace.B + ~StateSpace.C + ~StateSpace.D + ~StateSpace.dt + ~StateSpace.shape + ~StateSpace.nstates + ~StateSpace.poles + ~StateSpace.zeros + ~StateSpace.dcgain + ~StateSpace.sample + ~StateSpace.returnScipySignalLTI + +A complete list of attributes, methods, and properties is available in +the :class:`~control.StateSpace` class documentation. + + +Similarity transformations and canonical forms +---------------------------------------------- + +State space systems can be transformed into different internal +representations representing a variety of standard cananonical forms +that have the same input/output properties. The +:func:`similarity_transform` function allows a change of internal +state variable via similarity transformation and the +:func:`canonical_form` function converts sytems into different +canonical forms. Additional information is available on the +documentation pages for the individual functions: + +.. autosummary:: + + canonical_form + observable_form + modal_form + reachable_form + similarity_transform + + State feedback design --------------------- -Canonical forms ---------------- +State feedback controllers for a linear system are controllers of the form + +.. math:: + + u = -K x + +where :math:`K \in {\mathbb R}^{m \times n}` is a matrix of feedback +gains. Assuming the systems is controllable, the resulting closed +loop system will have dynamics matrix :math:`A - B K` with stable +eigenvalues. + +Feedback controllers can be designed using one of several +methods: + +.. autosummary:: + + acker + lqr + place + place_varga + +The :func:`acker`, :func:`place`, and :func:`place_varga` place the +eigenvalues of the closed loop system to a desired set of values. +Each takes the `A` and `B` matrices of the state space system and the +desired locaton of the eigenvalues and returns a gain matrix `K`:: + + K = ct.place(sys.A, sys.B, E) + +where `E` is a 1D array of desired eigenvalues. + +The :func:`lqr` function computes the optimal state feedback controller +that minimizes the quadratic cost + +.. math:: + + J = \int_0^\infty (x' Q x + u' R u + 2 x' N u) dt + +by solving the approriate Riccati equation. It returns the gain +matrix `K`, the solution to the Riccati equation `S`, and the location +of the closed loop eigenvalues `E`. It can be called in one of +several forms: + + * `K, S, E = ct.lqr(sys, Q, R)` + * `K, S, E = ct.lqr(sys, Q, R, N)` + * `K, S, E = ct.lqr(A, B, Q, R)` + * `K, S, E = ct.lqr(A, B, Q, R, N)` + +If `sys` is a discrete time system, the first two forms will compute +the discrete time optimal controller. For the second two forms, the +:func:`dlqr` function can be used. Additional arguments and details +are given on the :func:`lqr` and :func:`dlqr` documentation pages. State estimation ---------------- -Passive systems ---------------- -.. automodule:: passive - :no-members: - :no-inherited-members: - :no-special-members: +State estimators (or observers) are dynamical systems that estimate +the state of the system given a model of the dynamics and the input +and output signals as a function of time. Linear state estimators +have the form + +.. math:: + + \frac{d\hat x}{dt} = A \hat x + B u + L(y - C\hat x - D u), + +where :math:`\hat x` is an estimate of the state and :math:`L \in +{\mathbb R}^{n \times p}` represents the estimator gain. The gain +:math:`L` is chosen such that the eigenvalues of the matrix :math:`A - +L C` are stable, resulting in an estimate that converges to the value +of the system state. + +The gain matrix :math:`L` can be chosen using eigenvalue placement by +calling the :func:`place` function:: + + L = ct.place(sys.A.T, sys.C.T, E).T + +where `E` is the desired location of the eigenvalues and `.T` computes +the transpose of a matrix. + +Alternatively, an optimal estimator can be computed using the +:func:`lqe` function. We consider a continuous time, state space +system + +.. math:: + + \frac{dx}{dt} &= Ax + Bu + Gw \\ + y &= Cx + Du + v + +with unbiased process noise :math:`w` and measurement noise :math:`v` +with covariances satisfying + +.. math:: + + {\mathbb E}\{w w^T\} = QN,\qquad + {\mathbb E}\{v v^T\} = RN,\qquad + {\mathbb E}\{w v^T\} = NN + +where :math:`{\mathbb E}\{\cdot\}` represents the expectation of a +quantity. + +The :func:`lqe` function computes the observer gain matrix L such that the +stationary (non-time-varying) Kalman filter + +.. math:: + + \frac{d\hat x}{dt} = A \hat x + B u + L(y - C\hat x - D u), + +produces a state estimate :math:`\hat x` that minimizes the expected +squared error using the sensor measurements :math:`y`. + +As with the :func:`lqr` function, the :func:`lqe` function can be called in several forms: + + * `L, P, E = lqe(sys, QN, RN)` + * `L, P, E = lqe(sys, QN, RN, NN)` + * `L, P, E = lqe(A, G, C, QN, RN)` + * `L, P, E = lqe(A, G, C, QN, RN, NN)` + +where `sys` is an :class:`LTI` object, and `A`, `G`, `C`, `QN`, `RN`, +and `NN` are 2D arrays of appropriate dimension. If `sys` is a +discrete time system, the first two forms will compute the discrete +time optimal controller. For the second two forms, the :func:`dlqr` +function can be used. Additional arguments and details are given on +the :func:`lqr` and :func:`dlqr` documentation pages. From 2bd2bf4b8d3b14d3fb0dc765f26199b1e93a0401 Mon Sep 17 00:00:00 2001 From: Richard Murray Date: Fri, 27 Dec 2024 17:34:29 -0800 Subject: [PATCH 31/93] initial transfer fucntion section content --- control/xferfcn.py | 10 ++++++++++ doc/xferfcn.rst | 23 +++++++++++++++++++++++ 2 files changed, 33 insertions(+) diff --git a/control/xferfcn.py b/control/xferfcn.py index 98edf54b7..a91534819 100644 --- a/control/xferfcn.py +++ b/control/xferfcn.py @@ -306,6 +306,16 @@ def __init__(self, *args, **kwargs): #: :meta hide-value: noutputs = 1 + #: Numerator polynomial coefficients as a 2D array of 1D coefficents. + #: + #: :meta hide-value: + num_array = None + + #: Denominator polynomial coefficients as a 2D array of 1D coefficents. + #: + #: :meta hide-value: + den_array = None + # Numerator and denominator as lists of lists of lists @property def num_list(self): diff --git a/doc/xferfcn.rst b/doc/xferfcn.rst index fcd678500..3b521caa8 100644 --- a/doc/xferfcn.rst +++ b/doc/xferfcn.rst @@ -4,22 +4,45 @@ Frequency domain analysis and design Transfer function properties ---------------------------- +The following basic attributes and methods are available for +:class:`StateSpace` objects: + +.. autosummary:: + + ~StateSpace.num_array + ~StateSpace.den_array + ~StateSpace.shape + ~StateSpace.poles + ~StateSpace.zeros + ~StateSpace.dcgain + ~StateSpace.sample + ~StateSpace.returnScipySignalLTI + +A complete list of attributes, methods, and properties is available in +the :class:`~control.StateSpace` class documentation. + + Frequency domain methods ------------------------ + Input/output norms ------------------ + Stability margins ----------------- + Frequency domain synthesis -------------------------- + Transfer functions from data ---------------------------- .. todo:: rename to match FBS2e termincology + Systems with time delays ------------------------ From 5c4a26b936d08e4f7606b137c041a3343cf552e7 Mon Sep 17 00:00:00 2001 From: Richard Murray Date: Fri, 27 Dec 2024 20:54:22 -0800 Subject: [PATCH 32/93] add missing classes to reference section --- doc/classes.rst | 15 ++++++++++++++- doc/functions.rst | 22 ++++++++++++++++++++-- doc/test_sphinxdocs.py | 2 +- 3 files changed, 35 insertions(+), 4 deletions(-) diff --git a/doc/classes.rst b/doc/classes.rst index d9a1bbc1f..971cd4630 100644 --- a/doc/classes.rst +++ b/doc/classes.rst @@ -9,7 +9,7 @@ The classes listed below are used to represent models of input/output systems (both linear time-invariant and nonlinear). They are usually created from factory functions such as :func:`tf` and :func:`ss`, so the user should normally not need to instantiate these directly. - + .. autosummary:: :toctree: generated/ :template: custom-class-template.rst @@ -32,21 +32,34 @@ another: Additional classes ================== + +.. todo:: Break these up into more useful sections + .. autosummary:: + :toctree: generated/ :template: custom-class-template.rst :nosignatures: + ControlPlot DescribingFunctionNonlinearity DescribingFunctionResponse flatsys.BasisFamily + flatsys.BezierFamily + flatsys.BSplineFamily flatsys.FlatSystem flatsys.LinearFlatSystem flatsys.PolyFamily flatsys.SystemTrajectory + FrequencyResponseList + NyquistResponseData + OperatingPoint optimal.OptimalControlProblem optimal.OptimalControlResult optimal.OptimalEstimationProblem optimal.OptimalEstimationResult + PoleZeroData + TimeResponseData + TimeResponseList The use of these classes is described in more detail in the :ref:`flatsys-module` module and the :ref:`optimal-module` module diff --git a/doc/functions.rst b/doc/functions.rst index 5dfb4f1ad..d456a0d3f 100644 --- a/doc/functions.rst +++ b/doc/functions.rst @@ -75,6 +75,15 @@ Time domain simulation phase_plane_plot step_response time_response_plot + combine_time_responses + phaseplot.boxgrid + phaseplot.circlegrid + phaseplot.equilpoints + phaseplot.meshgrid + phaseplot.separatrices + phaseplot.streamlines + phaseplot.vectorfield + Frequency response ================== @@ -84,8 +93,11 @@ Frequency response bode_plot describing_function_plot + describing_function_response frequency_response + nyquist_response nyquist_plot + gangof4_response gangof4_plot nichols_plot nichols_grid @@ -110,8 +122,13 @@ Control system analysis phase_crossover_frequencies poles zeros - pzmap - root_locus + pole_zero_map + pole_zero_plot + pole_zero_subplots + root_locus_map + root_locus_plot + singular_values_plot + singular_values_response sisotool StateSpace.__call__ TransferFunction.__call__ @@ -233,6 +250,7 @@ Utility functions issiso mag2db reset_defaults + reset_rcParams sample_system set_defaults ssdata diff --git a/doc/test_sphinxdocs.py b/doc/test_sphinxdocs.py index 955533c0d..9d4c10198 100644 --- a/doc/test_sphinxdocs.py +++ b/doc/test_sphinxdocs.py @@ -42,7 +42,7 @@ # Functons that we can skip object_skiplist = [ - control.NamedSignal, # users don't need to know about this + control.NamedSignal, # np.ndarray members cause errors control.common_timebase, # mainly internal use control.cvxopt_check, # mainly internal use control.pandas_check, # mainly internal use From b90661760002e0f659de8e0fcadc4c6890c2cebd Mon Sep 17 00:00:00 2001 From: Richard Murray Date: Fri, 27 Dec 2024 22:24:02 -0800 Subject: [PATCH 33/93] fix up warnings in make html, make doctest, pytest --- control/statesp.py | 20 +-- control/xferfcn.py | 16 +- doc/Makefile | 1 - doc/config.rst | 4 +- doc/descfcn.rst | 18 ++- doc/flatsys.rst | 48 +++--- doc/freqresp.rst | 9 -- doc/functions.rst | 11 +- doc/intro.rst | 1 + doc/linear.rst | 2 +- doc/matlab.rst | 2 - doc/nonlinear.rst | 1 + doc/optimal.rst | 74 ++++----- doc/pzmap.rst | 11 -- doc/response.rst | 380 ++++++++++++++++++++++++++------------------- doc/statesp.rst | 6 +- doc/timeresp.rst | 157 ------------------- doc/xferfcn.rst | 22 +-- 18 files changed, 337 insertions(+), 446 deletions(-) delete mode 100644 doc/freqresp.rst delete mode 100644 doc/pzmap.rst delete mode 100644 doc/timeresp.rst diff --git a/control/statesp.py b/control/statesp.py index f03a4eb59..938f6dc69 100644 --- a/control/statesp.py +++ b/control/statesp.py @@ -1270,18 +1270,19 @@ def sample(self, Ts, method='zoh', alpha=None, prewarp_frequency=None, ---------- Ts : float Sampling period. - method : {"gbt", "bilinear", "euler", "backward_diff", "zoh"} - Which method to use: - - * gbt: generalized bilinear transformation - * bilinear: Tustin's approximation ("gbt" with alpha=0.5) - * euler: Euler (or forward differencing) method ("gbt" with + method : {'gbt', 'bilinear', 'euler', 'backward_diff', 'zoh'} + Method to use for sampling: + + * 'gbt': generalized bilinear transformation + * 'backward_diff': Backwards differencing ('gbt' with alpha=1.0) + * 'bilinear' (or 'tustin'): Tustin's approximation ('gbt' with + alpha=0.5) + * 'euler': Euler (or forward differencing) method ('gbt' with alpha=0) - * backward_diff: Backwards differencing ("gbt" with alpha=1.0) - * zoh: zero-order hold (default) + * 'zoh': zero-order hold (default) alpha : float within [0, 1] The generalized bilinear transformation weighting parameter, which - should only be specified with method="gbt", and is ignored + should only be specified with method='gbt', and is ignored otherwise. prewarp_frequency : float within [0, infinity) The frequency [rad/s] at which to match with the input continuous- @@ -1612,6 +1613,7 @@ def ss(*args, **kwargs): systems, `B` and `C` can be given as 1D arrays and D can be given as a scalar. + ``ss(*args, inputs=['u1', ..., 'up'], outputs=['y1', ..., 'yq'], states=['x1', ..., 'xn'])`` Create a system with named input, output, and state signals. diff --git a/control/xferfcn.py b/control/xferfcn.py index a91534819..978eed3d0 100644 --- a/control/xferfcn.py +++ b/control/xferfcn.py @@ -1155,15 +1155,17 @@ def sample(self, Ts, method='zoh', alpha=None, prewarp_frequency=None, ---------- Ts : float Sampling period. - method : {"gbt", "bilinear", "euler", "backward_diff", - "zoh", "matched"} + method : {'gbt', 'bilinear', 'euler', 'backward_diff', 'zoh', 'matched'} Method to use for sampling: - * gbt: generalized bilinear transformation - * bilinear or tustin: Tustin's approximation ("gbt" with alpha=0.5) - * euler: Euler (or forward difference) method ("gbt" with alpha=0) - * backward_diff: Backwards difference ("gbt" with alpha=1.0) - * zoh: zero-order hold (default) + * 'gbt': generalized bilinear transformation + * 'backward_diff': Backwards differencing ('gbt' with alpha=1.0) + * 'bilinear' (or 'tustin'): Tustin's approximation ('gbt' with + alpha=0.5) + * 'euler': Euler (or forward differencing) method ('gbt' with + alpha=0) + * 'matched': pole-zero match method + * 'zoh': zero-order hold (default) alpha : float within [0, 1] The generalized bilinear transformation weighting parameter, which should only be specified with method="gbt", and is ignored diff --git a/doc/Makefile b/doc/Makefile index ff8e16ca7..478c9386b 100644 --- a/doc/Makefile +++ b/doc/Makefile @@ -24,5 +24,4 @@ clean: @$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) distclean: clean - make -C figures clean /bin/rm -rf generated diff --git a/doc/config.rst b/doc/config.rst index 15337b056..b357c2a06 100644 --- a/doc/config.rst +++ b/doc/config.rst @@ -10,11 +10,11 @@ for various types of plots and establishing the underlying representation for state space matrices. To set the default value of a configuration variable, set the appropriate -element of the `control.config.defaults` dictionary:: +element of the `config.defaults` dictionary:: ct.config.defaults['module.parameter'] = value -The :func:`~control.set_defaults` function can also be used to +The :func:`set_defaults` function can also be used to set multiple configuration parameters at the same time:: ct.set_defaults('module', param1=val1, param2=val2, ...] diff --git a/doc/descfcn.rst b/doc/descfcn.rst index 45d5b668a..101a43204 100644 --- a/doc/descfcn.rst +++ b/doc/descfcn.rst @@ -1,5 +1,7 @@ .. _descfcn-module: +.. currentmodule:: control + Describing functions ==================== @@ -29,7 +31,7 @@ because it assumes that higher harmonics can be neglected. Module usage ------------ -The function :func:`~control.describing_function` can be used to +The function :func:`describing_function` can be used to compute the describing function of a nonlinear function:: N = ct.describing_function(F, A) @@ -44,7 +46,7 @@ amplitudes :math:`a` and frequencies :math`\omega` such that These points can be determined by generating a Nyquist plot in which the transfer function :math:`H(j\omega)` intersections the negative reciprocal of the describing function :math:`N(A)`. The -:func:`~control.describing_function_response` function computes the +:func:`describing_function_response` function computes the amplitude and frequency of any points of intersection:: response = ct.describing_function_response(H, F, amp_range[, omega_range]) @@ -52,7 +54,7 @@ amplitude and frequency of any points of intersection:: A Nyquist plot showing the describing function and the intersections with the Nyquist curve can be generated using `response.plot()`, which -calls the :func:`~control.describing_function_plot` function. +calls the :func:`describing_function_plot` function. Pre-defined nonlinearities @@ -76,7 +78,7 @@ nonlinearity:: F = ct.saturation_nonlinearity(1) These functions use the -:class:`~control.DescribingFunctionNonlinearity`, which allows an +:class:`DescribingFunctionNonlinearity`, which allows an analytical description of the describing function. Module classes and functions @@ -84,7 +86,7 @@ Module classes and functions .. autosummary:: :template: custom-class-template.rst - ~control.DescribingFunctionNonlinearity - ~control.friction_backlash_nonlinearity - ~control.relay_hysteresis_nonlinearity - ~control.saturation_nonlinearity + DescribingFunctionNonlinearity + friction_backlash_nonlinearity + relay_hysteresis_nonlinearity + saturation_nonlinearity diff --git a/doc/flatsys.rst b/doc/flatsys.rst index de7fcdc5e..716cf1d3d 100644 --- a/doc/flatsys.rst +++ b/doc/flatsys.rst @@ -110,8 +110,8 @@ Module usage ============ To create a trajectory for a differentially flat system, a -:class:`~control.flatsys.FlatSystem` object must be created. This is done -using the :func:`~control.flatsys.flatsys` function: +:class:`~flatsys.FlatSystem` object must be created. This is done +using the :func:`~flatsys.flatsys` function: import control.flatsys as fs sys = fs.flatsys(forward, reverse) @@ -120,12 +120,12 @@ The `forward` and `reverse` parameters describe the mappings between the system state/input and the differentially flat outputs and their derivatives ("flat flag"). -The :func:`~control.flatsys.FlatSystem.forward` method computes the +The :func:`~flatsys.FlatSystem.forward` method computes the flat flag given a state and input: zflag = sys.forward(x, u) -The :func:`~control.flatsys.FlatSystem.reverse` method computes the state +The :func:`~flatsys.FlatSystem.reverse` method computes the state and input given the flat flag: x, u = sys.reverse(zflag) @@ -139,11 +139,11 @@ The number of flat outputs must match the number of system inputs. For a linear system, a flat system representation can be generated by passing a :class:`~control.StateSpace` system to the -:func:`~control.flatsys.flatsys` factory function:: +:func:`~flatsys.flatsys` factory function:: sys = fs.flatsys(linsys) -The :func:`~control.flatsys.flatsys` function also supports the use of +The :func:`~flatsys.flatsys` function also supports the use of named input, output, and state signals:: sys = fs.flatsys( @@ -153,23 +153,23 @@ In addition to the flat system description, a set of basis functions :math:`\phi_i(t)` must be chosen. The `FlatBasis` class is used to represent the basis functions. A polynomial basis function of the form 1, :math:`t`, :math:`t^2`, ... can be computed using the -:class:`~control.flatsys.PolyFamily` class, which is initialized by +:class:`~flatsys.PolyFamily` class, which is initialized by passing the desired order of the polynomial basis set:: basis = fs.PolyFamily(N) Additional basis function families include Bezier curves -(:class:`~control.flatsys.BezierFamily`) and B-splines -(:class:`~control.flatsys.BSplineFamily`). +(:class:`~flatsys.BezierFamily`) and B-splines +(:class:`~flatsys.BSplineFamily`). Once the system and basis function have been defined, the -:func:`~control.flatsys.point_to_point` function can be used to compute a +:func:`~flatsys.point_to_point` function can be used to compute a trajectory between initial and final states and inputs:: traj = fs.point_to_point( sys, Tf, x0, u0, xf, uf, basis=basis) -The returned object has class :class:`~control.flatsys.SystemTrajectory` and +The returned object has class :class:`~flatsys.SystemTrajectory` and can be used to compute the state and input trajectory between the initial and final condition:: @@ -178,11 +178,11 @@ final condition:: where `T` is a list of times on which the trajectory should be evaluated (e.g., `T = numpy.linspace(0, Tf, M)`. -The :func:`~control.flatsys.point_to_point` function also allows the +The :func:`~flatsys.point_to_point` function also allows the specification of a cost function and/or constraints, in the same format as :func:`~control.optimal.solve_ocp`. -The :func:`~control.flatsys.solve_flat_ocp` function can be used to +The :func:`~flatsys.solve_flat_ocp` function can be used to solve an optimal control problem without a final state:: traj = fs.solve_flat_ocp( @@ -311,19 +311,21 @@ cost:` Module classes and functions ============================ +.. currentmodule:: control + .. autosummary:: :template: custom-class-template.rst - ~control.flatsys.BasisFamily - ~control.flatsys.BezierFamily - ~control.flatsys.BSplineFamily - ~control.flatsys.FlatSystem - ~control.flatsys.LinearFlatSystem - ~control.flatsys.PolyFamily - ~control.flatsys.SystemTrajectory + flatsys.BasisFamily + flatsys.BezierFamily + flatsys.BSplineFamily + flatsys.FlatSystem + flatsys.LinearFlatSystem + flatsys.PolyFamily + flatsys.SystemTrajectory .. autosummary:: - ~control.flatsys.flatsys - ~control.flatsys.point_to_point - ~control.flatsys.solve_flat_ocp + flatsys.flatsys + flatsys.point_to_point + flatsys.solve_flat_ocp diff --git a/doc/freqresp.rst b/doc/freqresp.rst deleted file mode 100644 index c072715f0..000000000 --- a/doc/freqresp.rst +++ /dev/null @@ -1,9 +0,0 @@ -Frequency responses -=================== - -Bode plots ----------- - -Nyquist plots -------------- - diff --git a/doc/functions.rst b/doc/functions.rst index d456a0d3f..ca5996d22 100644 --- a/doc/functions.rst +++ b/doc/functions.rst @@ -58,6 +58,7 @@ System interconnections append combine_tf split_tf + summing_junction connection_table combine_tf split_tf @@ -110,7 +111,6 @@ Control system analysis bandwidth damp dcgain - describing_function get_input_ff_index get_output_fb_index ispassive @@ -130,8 +130,7 @@ Control system analysis singular_values_plot singular_values_response sisotool - StateSpace.__call__ - TransferFunction.__call__ + Control system synthesis ======================== @@ -140,7 +139,6 @@ Control system synthesis acker create_statefbk_iosystem - create_estimator_iosystem dlqr h2syn hinfsyn @@ -167,12 +165,8 @@ Nonlinear system support .. autosummary:: :toctree: generated/ - describing_function find_operating_point linearize - input_output_response - summing_junction - flatsys.point_to_point Stochastic system support ========================= @@ -209,6 +203,7 @@ Describing functions .. autosummary:: :toctree: generated/ + describing_function friction_backlash_nonlinearity relay_hysteresis_nonlinearity saturation_nonlinearity diff --git a/doc/intro.rst b/doc/intro.rst index 0d9bc7c98..964575f92 100644 --- a/doc/intro.rst +++ b/doc/intro.rst @@ -14,6 +14,7 @@ Package overview :no-members: :no-inherited-members: :no-special-members: + :no-index: Installation ============ diff --git a/doc/linear.rst b/doc/linear.rst index ada8667c9..fcfb7e908 100644 --- a/doc/linear.rst +++ b/doc/linear.rst @@ -1,4 +1,4 @@ -.. module:: control +.. currentmodule:: control ******************************************** Linear System Modeling, Analysis, and Design diff --git a/doc/matlab.rst b/doc/matlab.rst index 9dd129768..33def56e6 100644 --- a/doc/matlab.rst +++ b/doc/matlab.rst @@ -21,8 +21,6 @@ Creating linear models tf ss frd - rss - drss Utility functions and conversions ================================= diff --git a/doc/nonlinear.rst b/doc/nonlinear.rst index a5ae87723..3b58f9c46 100644 --- a/doc/nonlinear.rst +++ b/doc/nonlinear.rst @@ -12,3 +12,4 @@ Nonlinear System Modeling, Analysis, and Design phaseplot optimal descfcn + flatsys diff --git a/doc/optimal.rst b/doc/optimal.rst index 56228dcff..790a9e93a 100644 --- a/doc/optimal.rst +++ b/doc/optimal.rst @@ -1,5 +1,7 @@ .. _optimal-module: +.. currentmodule:: control + Optimization-based control ========================== @@ -220,11 +222,11 @@ state and/or input, either along the trajectory and at the terminal time. The optimal control module operates by converting the optimal control problem into a standard optimization problem that can be solved by :func:`scipy.optimize.minimize`. The optimal control problem can be solved -by using the :func:`~control.optimal.solve_ocp` function:: +by using the :func:`optimal.solve_ocp` function:: res = obc.solve_ocp(sys, timepts, X0, cost, constraints) -The `sys` parameter should be an :class:`~control.InputOutputSystem` and the +The `sys` parameter should be an :class:`InputOutputSystem` and the `timepts` parameter should represent a time vector that gives the list of times at which the cost and constraints should be evaluated. @@ -261,7 +263,7 @@ all points on the trajectory. The `terminal_constraint` parameter can be used to specify a constraint that only holds at the final point of the trajectory. -The return value for :func:`~control.optimal.solve_ocp` is a bundle object +The return value for :func:`optimal.solve_ocp` is a bundle object that has the following elements: * `res.success`: `True` if the optimization was successfully solved @@ -273,35 +275,37 @@ In addition, the results from :func:`scipy.optimize.minimize` are also available. To simplify the specification of cost functions and constraints, the -:mod:`~control.ios` module defines a number of utility functions for +:mod:`optimal` module defines a number of utility functions for optimal control problems: +.. currentmodule:: control + .. autosummary:: - ~control.optimal.quadratic_cost - ~control.optimal.input_poly_constraint - ~control.optimal.input_range_constraint - ~control.optimal.output_poly_constraint - ~control.optimal.output_range_constraint - ~control.optimal.state_poly_constraint - ~control.optimal.state_range_constraint + optimal.quadratic_cost + optimal.input_poly_constraint + optimal.input_range_constraint + optimal.output_poly_constraint + optimal.output_range_constraint + optimal.state_poly_constraint + optimal.state_range_constraint The optimization-based control module also implements functions for solving optimal estimation problems. The -:class:`~control.optimal.OptimalEstimationProblem` class is used to define +:class:`optimal.OptimalEstimationProblem` class is used to define an optimal estimation problem over a finite horizon:: oep = OptimalEstimationProblem(sys, timepts, cost[, constraints]) Given noisy measurements :math:`y` and control inputs :math:`u`, an estimate of the states over the time points can be computed using the -:func:`~control.optimal.OptimalEstimationProblem.compute_estimate` method:: +:func:`optimal.OptimalEstimationProblem.compute_estimate` method:: estim = oep.compute_optimal(Y, U[, X0=x0, initial_guess=(xhat, v)]) xhat, v, w = estim.states, estim.inputs, estim.outputs For discrete time systems, the -:func:`~control.optimal.OptimalEstimationProblem.create_mhe_iosystem` +:func:`optimal.OptimalEstimationProblem.create_mhe_iosystem` method can be used to generate an input/output system that implements a moving horizon estimator. @@ -310,8 +314,8 @@ problems: .. autosummary:: - ~control.optimal.gaussian_likelihood_cost - ~control.optimal.disturbance_range_constraint + optimal.gaussian_likelihood_cost + optimal.disturbance_range_constraint Example ------- @@ -415,7 +419,7 @@ yields An example showing the use of the optimal estimation problem and moving horizon estimation (MHE) is given in the :doc:`mhe-pvtol Jupyter -notebook `. +notebook `. Optimization Tips ----------------- @@ -443,12 +447,12 @@ solutions do not seem close to optimal, here are a few things to try: * Use a smooth basis: as an alternative to parameterizing the optimal control inputs using the value of the control at the listed time points, you can specify a set of basis functions using the `basis` keyword in - :func:`~control.solve_ocp` and then parameterize the controller by linear + :func:`solve_ocp` and then parameterize the controller by linear combination of the basis functions. The :mod:`!control.flatsys` module defines several sets of basis functions that can be used. * Tweak the optimizer: by using the `minimize_method`, `minimize_options`, - and `minimize_kwargs` keywords in :func:`~control.solve_ocp`, you can + and `minimize_kwargs` keywords in :func:`solve_ocp`, you can choose the SciPy optimization function that you use and set many parameters. See :func:`scipy.optimize.minimize` for more information on the optimizers that are available and the options and keywords that they @@ -473,22 +477,22 @@ The following classes and functions are defined in the .. autosummary:: :template: custom-class-template.rst - ~control.optimal.OptimalControlProblem - ~control.optimal.OptimalControlResult - ~control.optimal.OptimalEstimationProblem - ~control.optimal.OptimalEstimationResult + optimal.OptimalControlProblem + optimal.OptimalControlResult + optimal.OptimalEstimationProblem + optimal.OptimalEstimationResult .. autosummary:: - ~control.optimal.create_mpc_iosystem - ~control.optimal.disturbance_range_constraint - ~control.optimal.gaussian_likelihood_cost - ~control.optimal.input_poly_constraint - ~control.optimal.input_range_constraint - ~control.optimal.output_poly_constraint - ~control.optimal.output_range_constraint - ~control.optimal.quadratic_cost - ~control.optimal.solve_ocp - ~control.optimal.solve_oep - ~control.optimal.state_poly_constraint - ~control.optimal.state_range_constraint + optimal.create_mpc_iosystem + optimal.disturbance_range_constraint + optimal.gaussian_likelihood_cost + optimal.input_poly_constraint + optimal.input_range_constraint + optimal.output_poly_constraint + optimal.output_range_constraint + optimal.quadratic_cost + optimal.solve_ocp + optimal.solve_oep + optimal.state_poly_constraint + optimal.state_range_constraint diff --git a/doc/pzmap.rst b/doc/pzmap.rst deleted file mode 100644 index 554c7fab1..000000000 --- a/doc/pzmap.rst +++ /dev/null @@ -1,11 +0,0 @@ -Pole/zero maps -============== - -Pole/zero plots ---------------- - -Root locus maps ---------------- - -Nichols charts --------------- diff --git a/doc/response.rst b/doc/response.rst index 06dda6347..080cb85db 100644 --- a/doc/response.rst +++ b/doc/response.rst @@ -2,14 +2,14 @@ .. currentmodule:: control -************* -Plotting data -************* +********************************** +Input/output Response and Plotting +********************************** The Python Control Systems Toolbox contains a number of functions for -plotting input/output responses in the time and frequency domain, root -locus diagrams, and other standard charts used in control system analysis, -for example:: +computing and plotting input/output responses in the time and +frequency domain, root locus diagrams, and other standard charts used +in control system analysis, for example:: bode_plot(sys) nyquist_plot([sys1, sys2]) @@ -28,7 +28,7 @@ resulting in the following standard pattern:: count = ct.response.count # number of encirclements of -1 cplt = ct.nyquist_plot(response) # Nyquist plot -Plotting commands return a :class:`~control.ControlPlot` object that +Plotting commands return a :class:`ControlPlot` object that provides access to the individual lines in the generated plot using `cplt.lines`, allowing various aspects of the plot to be modified to suit specific needs. @@ -49,15 +49,170 @@ these response and plotting functions can be customized. Time response data ================== +LTI response functions +---------------------- + +A number of functions are available for computing the output (and +state) response of an LTI systems: + +.. autosummary:: + + initial_response + step_response + impulse_response + forced_response + +Each of these functions returns a :class:`TimeResponseData` object +that contains the data for the time response (described in more detail +in the next section). + +The :func:`forced_response` system is the most general and allows by +the zero initial state response to be simulated as well as the +response from a non-zero initial condition. + +For linear time invariant (LTI) systems, the :func:`impulse_response`, +:func:`initial_response`, and :func:`step_response` functions will +automatically compute the time vector based on the poles and zeros of +the system. If a list of systems is passed, a common time vector will be +computed and a list of responses will be returned in the form of a +:class:`TimeResponseList` object. The :func:`forced_response` function can +also take a list of systems, to which a single common input is applied. +The :class:`TimeResponseList` object has a `plot()` method that will plot +each of the responses in turn, using a sequence of different colors with +appropriate titles and legends. + +In addition the :func:`input_output_response` function, which handles +simulation of nonlinear systems and interconnected systems, can be +used. For an LTI system, results are generally more accurate using +the LTI simulation functions above. The :func:`input_output_response` +function is described in more detail in the :ref:`iosys-module` section. + +.. _time-series-convention: + +Time series data +---------------- +A variety of functions in the library return time series data: sequences of +values that change over time. A common set of conventions is used for +returning such data: columns represent different points in time, rows are +different components (e.g., inputs, outputs or states). For return +arguments, an array of times is given as the first returned argument, +followed by one or more arrays of variable values. This convention is used +throughout the library, for example in the functions +:func:`forced_response`, :func:`step_response`, :func:`impulse_response`, +and :func:`initial_response`. + +.. note:: + The convention used by python-control is different from the convention + used in the `scipy.signal + `_ library. In + Scipy's convention the meaning of rows and columns is interchanged. + Thus, all 2D values must be transposed when they are used with functions + from `scipy.signal`_. + +The time vector is a 1D array with shape (n, ):: + + T = [t1, t2, t3, ..., tn ] + +Input, state, and output all follow the same convention. Columns are different +points in time, rows are different components:: + + U = [[u1(t1), u1(t2), u1(t3), ..., u1(tn)] + [u2(t1), u2(t2), u2(t3), ..., u2(tn)] + ... + ... + [ui(t1), ui(t2), ui(t3), ..., ui(tn)]] + +(and similarly for `X`, `Y`). So, `U[:, 2]` is the system's input at the +third point in time; and `U[1]` or `U[1, :]` is the sequence of values for +the system's second input. + +When there is only one row, a 1D object is accepted or returned, which adds +convenience for SISO systems: + +The initial conditions are either 1D, or 2D with shape (j, 1):: + + X0 = [[x1] + [x2] + ... + ... + [xj]] + +Functions that return time responses (e.g., :func:`forced_response`, +:func:`impulse_response`, :func:`input_output_response`, +:func:`initial_response`, and :func:`step_response`) return a +:class:`TimeResponseData` object that contains the data for the time +response. These data can be accessed via the +:attr:`~TimeResponseData.time`, :attr:`~TimeResponseData.outputs`, +:attr:`~TimeResponseData.states` and :attr:`~TimeResponseData.inputs` +properties:: + + sys = ct.rss(4, 1, 1) + response = ct.step_response(sys) + plt.plot(response.time, response.outputs) + +The dimensions of the response properties depend on the function being +called and whether the system is SISO or MIMO. In addition, some time +response function can return multiple "traces" (input/output pairs), +such as the :func:`step_response` function applied to a MIMO system, +which will compute the step response for each input/output pair. See +:class:`TimeResponseData` for more details. + +The input, output, and state elements of the response can be accessed using +signal names in place of integer offsets:: + + plt.plot(response. time, response.states['x[1]'] + +For multi-trace systems generated by :func:`step_response` and +:func:`impulse_response`, the input name used to generate the trace can be +used to access the appropriate input output pair:: + + plt.plot(response.time, response.outputs['y[0]', 'u[1]']) + +The time response functions can also be assigned to a tuple, which extracts +the time and output (and optionally the state, if the `return_x` keyword is +used). This allows simple commands for plotting:: + + t, y = ct.step_response(sys) + plot(t, y) + +The output of a MIMO LTI system can be plotted like this:: + + t, y = ct.forced_response(sys, t, u) + plot(t, y[0], label='y_0') + plot(t, y[1], label='y_1') + +The convention also works well with the state space form of linear +systems. If `D` is the feedthrough matrix (2D array) of a linear system, +and `U` is its input (array), then the feedthrough part of the system's +response, can be computed like this:: + + ft = D @ U + +Finally, the `to_pandas()` function can be used to create a pandas dataframe:: + + df = response.to_pandas() + +The column labels for the data frame are `time` and the labels for the input, +output, and state signals (`u[i]`, `y[i]`, and `x[i]` by default, but these +can be changed using the `inputs`, `outputs`, and `states` keywords when +constructing the system, as described in :func:`ss`, :func:`tf`, and other +system creation function. Note that when exporting to pandas, "rows" in the +data frame correspond to time and "cols" (DataSeries) correspond to signals. + +Time response plots +------------------- + +.. todo:: Improve flow between previous section and this one + Input/output time responses are produced one of several python-control -functions: :func:`~control.forced_response`, -:func:`~control.impulse_response`, :func:`~control.initial_response`, -:func:`~control.input_output_response`, :func:`~control.step_response`. -Each of these return a :class:`~control.TimeResponseData` object, which +functions: :func:`forced_response`, +:func:`impulse_response`, :func:`initial_response`, +:func:`input_output_response`, :func:`step_response`. +Each of these return a :class:`TimeResponseData` object, which contains the time, input, state, and output vectors associated with the simulation. Time response data can be plotted with the -:func:`~control.time_response_plot` function, which is also available as -the :func:`~control.TimeResponseData.plot` method. For example, the step +:func:`time_response_plot` function, which is also available as +the :func:`TimeResponseData.plot` method. For example, the step response for a two-input, two-output can be plotted using the commands:: sys_mimo = ct.tf2ss( @@ -70,7 +225,7 @@ which produces the following plot: .. image:: figures/timeplot-mimo_step-default.png -The :class:`~control.TimeResponseData` object can also be used to access +The :class:`TimeResponseData` object can also be used to access the data from the simulation:: time, outputs, inputs = response.time, response.outputs, response.inputs @@ -86,7 +241,7 @@ names can allow be used:: A number of options are available in the `plot` method to customize the appearance of input output data. For data produced by the -:func:`~control.impulse_response` and :func:`~control.step_response` +:func:`impulse_response` and :func:`step_response` commands, the inputs are not shown. This behavior can be changed using the `plot_inputs` keyword. It is also possible to combine multiple lines onto a single graph, using either the `overlay_signals` @@ -107,8 +262,8 @@ following plot:: .. image:: figures/timeplot-mimo_step-pi_cs.png Input/output response plots created with either the -:func:`~control.forced_response` or the -:func:`~control.input_output_response` functions include the input signals by +:func:`forced_response` or the +:func:`input_output_response` functions include the input signals by default. These can be plotted on separate axes, but also "overlaid" on the output axes (useful when the input and output signals are being compared to each other). The following plot shows the use of `plot_inputs='overlay'` @@ -145,10 +300,10 @@ following figure:: .. image:: figures/timeplot-mimo_ioresp-mt_tr.png This figure also illustrates the ability to create "multi-trace" plots -using the :func:`~control.combine_time_responses` function. The line +using the :func:`combine_time_responses` function. The line properties that are used when combining signals and traces are set by the `input_props`, `output_props` and `trace_props` parameters for -:func:`~control.time_response_plot`. +:func:`time_response_plot`. Additional customization is possible using the `input_props`, `output_props`, and `trace_props` keywords to set complementary line colors @@ -171,7 +326,7 @@ Frequency response data Linear time invariant (LTI) systems can be analyzed in terms of their frequency response and python-control provides a variety of tools for carrying out frequency response analysis. The most basic of these is -the :func:`~control.frequency_response` function, which will compute +the :func:`frequency_response` function, which will compute the frequency response for one or more linear systems:: sys1 = ct.tf([1], [1, 2, 1], name='sys1') @@ -179,7 +334,7 @@ the frequency response for one or more linear systems:: response = ct.frequency_response([sys1, sys2]) A Bode plot provide a graphical view of the response an LTI system and can -be generated using the :func:`~control.bode_plot` function:: +be generated using the :func:`bode_plot` function:: ct.bode_plot(response, initial_phase=0) @@ -189,7 +344,7 @@ Computing the response for multiple systems at the same time yields a common frequency range that covers the features of all listed systems. Bode plots can also be created directly using the -:meth:`~control.FrequencyResponseData.plot` method:: +:meth:`FrequencyResponseData.plot` method:: sys_mimo = ct.tf( [[[1], [0.1]], [[0.2], [1]]], @@ -207,7 +362,7 @@ overlaying the inputs or outputs:: .. image:: figures/freqplot-mimo_bode-magonly.png -The :func:`~control.singular_values_response` function can be used to +The :func:`singular_values_response` function can be used to generate Bode plots that show the singular values of a transfer function:: @@ -224,7 +379,7 @@ plot, use `plot_type='nichols'`:: .. image:: figures/freqplot-siso_nichols-default.png Another response function that can be used to generate Bode plots is the -:func:`~control.gangof4_response` function, which computes the four primary +:func:`gangof4_response` function, which computes the four primary sensitivity functions for a feedback control system in standard form:: proc = ct.tf([1], [1, 1, 1], name="process") @@ -234,16 +389,16 @@ sensitivity functions for a feedback control system in standard form:: .. image:: figures/freqplot-gangof4.png -Nyquist analysis can be done using the :func:`~control.nyquist_response` +Nyquist analysis can be done using the :func:`nyquist_response` function, which evaluates an LTI system along the Nyquist contour, and -the :func:`~control.nyquist_plot` function, which generates a Nyquist plot:: +the :func:`nyquist_plot` function, which generates a Nyquist plot:: sys = ct.tf([1, 0.2], [1, 1, 3, 1, 1], name='sys') nyquist_plot(sys) .. image:: figures/freqplot-nyquist-default.png -The :func:`~control.nyquist_response` function can be used to compute +The :func:`nyquist_response` function can be used to compute the number of encirclements of the -1 point and can return the Nyquist contour that was used to generate the Nyquist curve. @@ -253,7 +408,7 @@ curve that are far from the origin are scaled to a maximum value, while the line style is changed to reflect the scaling, and it is possible to offset the scaled portions to separate out the portions of the Nyquist curve at :math:`\infty`. A number of keyword parameters for both are available for -:func:`~control.nyquist_response` and :func:`~control.nyquist_plot` to tune +:func:`nyquist_response` and :func:`nyquist_plot` to tune the computation of the Nyquist curve and the way the data are plotted:: sys = ct.tf([1, 0.2], [1, 0, 1]) * ct.tf([1], [1, 0]) @@ -305,7 +460,7 @@ Pole/zero data Pole/zero maps and root locus diagrams provide insights into system response based on the locations of system poles and zeros in the complex -plane. The :func:`~control.pole_zero_map` function returns the poles and +plane. The :func:`pole_zero_map` function returns the poles and zeros and can be used to generate a pole/zero plot:: sys = ct.tf([1, 2], [1, 2, 3], name='SISO transfer function') @@ -352,101 +507,6 @@ for each system is plotted in different colors:: .. image:: figures/rlocus-siso_multiple-nogrid.png -Phase plane plots -================= -Insight into nonlinear systems can often be obtained by looking at phase -plane diagrams. The :func:`~control.phase_plane_plot` function allows the -creation of a 2-dimensional phase plane diagram for a system. This -functionality is supported by a set of mapping functions that are part of -the `phaseplot` module. - -The default method for generating a phase plane plot is to provide a -2D dynamical system along with a range of coordinates and time limit:: - - sys = ct.nlsys( - lambda t, x, u, params: np.array([[0, 1], [-1, -1]]) @ x, - states=['position', 'velocity'], inputs=0, name='damped oscillator') - axis_limits = [-1, 1, -1, 1] - T = 8 - ct.phase_plane_plot(sys, axis_limits, T) - -.. image:: figures/phaseplot-dampedosc-default.png - -By default, the plot includes streamlines generated from starting -points on limits of the plot, with arrows showing the flow of the -system, as well as any equilibrium points for the system. A variety -of options are available to modify the information that is plotted, -including plotting a grid of vectors instead of streamlines and -turning on and off various features of the plot. - -To illustrate some of these possibilities, consider a phase plane plot for -an inverted pendulum system, which is created using a mesh grid:: - - def invpend_update(t, x, u, params): - m, l, b, g = params['m'], params['l'], params['b'], params['g'] - return [x[1], -b/m * x[1] + (g * l / m) * np.sin(x[0]) + u[0]/m] - invpend = ct.nlsys(invpend_update, states=2, inputs=1, name='invpend') - - ct.phase_plane_plot( - invpend, [-2*pi, 2*pi, -2, 2], 5, - gridtype='meshgrid', gridspec=[5, 8], arrows=3, - plot_equilpoints={'gridspec': [12, 9]}, - params={'m': 1, 'l': 1, 'b': 0.2, 'g': 1}) - plt.xlabel(r"$\theta$ [rad]") - plt.ylabel(r"$\dot\theta$ [rad/sec]") - -.. image:: figures/phaseplot-invpend-meshgrid.png - -This figure shows several features of more complex phase plane plots: -multiple equilibrium points are shown, with saddle points showing -separatrices, and streamlines generated along a 5x8 mesh of initial -conditions. At each mesh point, a streamline is created that goes 5 time -units forward and backward in time. A separate grid specification is used -to find equilibrium points and separatrices (since the course grid spacing -of 5x8 does not find all possible equilibrium points). Together, the -multiple features in the phase plane plot give a good global picture of the -topological structure of solutions of the dynamical system. - -Phase plots can be built up by hand using a variety of helper functions that -are part of the :mod:`~control.phaseplot` (pp) module:: - - import control.phaseplot as pp - - def oscillator_update(t, x, u, params): - return [x[1] + x[0] * (1 - x[0]**2 - x[1]**2), - -x[0] + x[1] * (1 - x[0]**2 - x[1]**2)] - oscillator = ct.nlsys( - oscillator_update, states=2, inputs=0, name='nonlinear oscillator') - - ct.phase_plane_plot(oscillator, [-1.5, 1.5, -1.5, 1.5], 0.9) - pp.streamlines( - oscillator, np.array([[0, 0]]), 1.5, - gridtype='circlegrid', gridspec=[0.5, 6], dir='both') - pp.streamlines( - oscillator, np.array([[1, 0]]), 2*pi, arrows=6, color='b') - plt.gca().set_aspect('equal') - -.. image:: figures/phaseplot-oscillator-helpers.png - -The following helper functions are available: - -.. autosummary:: - - phaseplot.equilpoints - phaseplot.separatrices - phaseplot.streamlines - phaseplot.vectorfield - -The :func:`~control.phase_plane_plot` function calls these helper functions -based on the options it is passed. - -Note that unlike other plotting functions, phase plane plots do not involve -computing a response and then plotting the result via a `plot()` method. -Instead, the plot is generated directly be a call to the -:func:`~control.phase_plane_plot` function (or one of the -:mod:`~control.phaseplot` helper functions. - - Customizing control plots ========================= @@ -478,7 +538,7 @@ various ways. The following general rules apply: If these arguments are absent, the default matplotlib line properties are used and the color cycles through the default matplotlib color cycle. - The :func:`~control.bode_plot`, :func:`~control.time_response_plot`, + The :func:`bode_plot`, :func:`time_response_plot`, and selected other commands can also accept a matplotlib format string (e.g., `'r--'`). The format string must appear as a positional argument right after the required data argument. @@ -558,7 +618,7 @@ various ways. The following general rules apply: for all control plots, update the `ct.rcParams` dictionary entries. The default values for style parameters for control plots can be restored - using :func:`~control.reset_rcParams`. + using :func:`reset_rcParams`. * For multi-input, multi-output time and frequency domain plots, the `sharex` and `sharey` keyword arguments can be used to determine whether @@ -578,7 +638,7 @@ various ways. The following general rules apply: for " where is a list of the sys names contained in the plot (which can be updated if the plotting is called multiple times). Use `title=False` to suppress the title completely. The title can also - be updated using the :func:`~control.ControlPlot.set_plot_title` method + be updated using the :func:`ControlPlot.set_plot_title` method for the returned control plot object. The plot title is only generated if `ax` is `None`. @@ -622,7 +682,7 @@ Matplotlib plotting functions can generally be intermixed. One type of plot for which this does not currently work is pole/zero plots with a continuous time omega-damping grid (including root locus diagrams), due to the way that axes grids are implemented. As a workaround, the -:func:`~control.pole_zero_subplots` command can be used to create an array +:func:`pole_zero_subplots` command can be used to create an array of subplots with different grid types, as illustrated in the following example:: @@ -652,39 +712,39 @@ number of encirclements for a Nyquist plot) as well as plotting (via the .. autosummary:: - ~control.describing_function_response - ~control.frequency_response - ~control.forced_response - ~control.gangof4_response - ~control.impulse_response - ~control.initial_response - ~control.input_output_response - ~control.nyquist_response - ~control.pole_zero_map - ~control.root_locus_map - ~control.singular_values_response - ~control.step_response + describing_function_response + frequency_response + forced_response + gangof4_response + impulse_response + initial_response + input_output_response + nyquist_response + pole_zero_map + root_locus_map + singular_values_response + step_response Plotting functions ------------------ .. autosummary:: - ~control.bode_plot - ~control.describing_function_plot - ~control.nichols_plot - ~control.nyquist_plot - ~control.phase_plane_plot + bode_plot + describing_function_plot + nichols_plot + nyquist_plot + phase_plane_plot phaseplot.circlegrid phaseplot.equilpoints phaseplot.meshgrid phaseplot.separatrices phaseplot.streamlines phaseplot.vectorfield - ~control.pole_zero_plot - ~control.root_locus_plot - ~control.singular_values_plot - ~control.time_response_plot + pole_zero_plot + root_locus_plot + singular_values_plot + time_response_plot Utility functions @@ -696,9 +756,9 @@ carry out other operations in creating control plots. .. autosummary:: phaseplot.boxgrid - ~control.combine_time_responses - ~control.pole_zero_subplots - ~control.reset_rcParams + combine_time_responses + pole_zero_subplots + reset_rcParams Response and plotting classes @@ -708,11 +768,11 @@ The following classes are used in generating response data. .. autosummary:: - ~control.ControlPlot - ~control.DescribingFunctionResponse - ~control.FrequencyResponseData - ~control.FrequencyResponseList - ~control.NyquistResponseData - ~control.PoleZeroData - ~control.TimeResponseData - ~control.TimeResponseList + ControlPlot + DescribingFunctionResponse + FrequencyResponseData + FrequencyResponseList + NyquistResponseData + PoleZeroData + TimeResponseData + TimeResponseList diff --git a/doc/statesp.rst b/doc/statesp.rst index 93b5cd5d1..86a416a35 100644 --- a/doc/statesp.rst +++ b/doc/statesp.rst @@ -1,5 +1,5 @@ -.. module: control - +.. currentmodule:: control + State space analysis and design =============================== @@ -33,7 +33,7 @@ The following basic attributes and methods are available for ~StateSpace.returnScipySignalLTI A complete list of attributes, methods, and properties is available in -the :class:`~control.StateSpace` class documentation. +the :class:`StateSpace` class documentation. Similarity transformations and canonical forms diff --git a/doc/timeresp.rst b/doc/timeresp.rst deleted file mode 100644 index 29dd08483..000000000 --- a/doc/timeresp.rst +++ /dev/null @@ -1,157 +0,0 @@ -Time responses -============== - -LTI response functions ----------------------- - -A number of functions are available for computing the output (and -state) response of an LTI systems: - -.. autosummary:: - - initial_response - step_response - impulse_response - forced_response - -Each of these functions returns a :class:`TimeResponseData` object -that contains the data for the time response (described in more detail -in the next section). - -The :func:`forced_response` system is the most general and allows by -the zero initial state response to be simulated as well as the -response from a non-zero initial condition. - -For linear time invariant (LTI) systems, the :func:`impulse_response`, -:func:`initial_response`, and :func:`step_response` functions will -automatically compute the time vector based on the poles and zeros of -the system. If a list of systems is passed, a common time vector will be -computed and a list of responses will be returned in the form of a -:class:`TimeResponseList` object. The :func:`forced_response` function can -also take a list of systems, to which a single common input is applied. -The :class:`TimeResponseList` object has a `plot()` method that will plot -each of the responses in turn, using a sequence of different colors with -appropriate titles and legends. - -In addition the :func:`input_output_response` function, which handles -simulation of nonlinear systems and interconnected systems, can be -used. For an LTI system, results are generally more accurate using -the LTI simulation functions above. The :func:`input_output_response` -function is described in more detail in the :ref:`iosys-module` section. - -.. currentmodule:: control -.. _time-series-convention: - -Time series data ----------------- -A variety of functions in the library return time series data: sequences of -values that change over time. A common set of conventions is used for -returning such data: columns represent different points in time, rows are -different components (e.g., inputs, outputs or states). For return -arguments, an array of times is given as the first returned argument, -followed by one or more arrays of variable values. This convention is used -throughout the library, for example in the functions -:func:`forced_response`, :func:`step_response`, :func:`impulse_response`, -and :func:`initial_response`. - -.. note:: - The convention used by python-control is different from the convention - used in the `scipy.signal - `_ library. In - Scipy's convention the meaning of rows and columns is interchanged. - Thus, all 2D values must be transposed when they are used with functions - from `scipy.signal`_. - -The time vector is a 1D array with shape (n, ):: - - T = [t1, t2, t3, ..., tn ] - -Input, state, and output all follow the same convention. Columns are different -points in time, rows are different components:: - - U = [[u1(t1), u1(t2), u1(t3), ..., u1(tn)] - [u2(t1), u2(t2), u2(t3), ..., u2(tn)] - ... - ... - [ui(t1), ui(t2), ui(t3), ..., ui(tn)]] - -(and similarly for `X`, `Y`). So, `U[:, 2]` is the system's input at the -third point in time; and `U[1]` or `U[1, :]` is the sequence of values for -the system's second input. - -When there is only one row, a 1D object is accepted or returned, which adds -convenience for SISO systems: - -The initial conditions are either 1D, or 2D with shape (j, 1):: - - X0 = [[x1] - [x2] - ... - ... - [xj]] - -Functions that return time responses (e.g., :func:`forced_response`, -:func:`impulse_response`, :func:`input_output_response`, -:func:`initial_response`, and :func:`step_response`) return a -:class:`TimeResponseData` object that contains the data for the time -response. These data can be accessed via the -:attr:`~TimeResponseData.time`, :attr:`~TimeResponseData.outputs`, -:attr:`~TimeResponseData.states` and :attr:`~TimeResponseData.inputs` -properties:: - - sys = ct.rss(4, 1, 1) - response = ct.step_response(sys) - plt.plot(response.time, response.outputs) - -The dimensions of the response properties depend on the function being -called and whether the system is SISO or MIMO. In addition, some time -response function can return multiple "traces" (input/output pairs), -such as the :func:`step_response` function applied to a MIMO system, -which will compute the step response for each input/output pair. See -:class:`TimeResponseData` for more details. - -The input, output, and state elements of the response can be accessed using -signal names in place of integer offsets:: - - plt.plot(response. time, response.states['x[1]'] - -For multi-trace systems generated by :func:`step_response` and -:func:`impulse_response`, the input name used to generate the trace can be -used to access the appropriate input output pair:: - - plt.plot(response.time, response.outputs['y[0]', 'u[1]']) - -The time response functions can also be assigned to a tuple, which extracts -the time and output (and optionally the state, if the `return_x` keyword is -used). This allows simple commands for plotting:: - - t, y = ct.step_response(sys) - plot(t, y) - -The output of a MIMO LTI system can be plotted like this:: - - t, y = ct.forced_response(sys, t, u) - plot(t, y[0], label='y_0') - plot(t, y[1], label='y_1') - -The convention also works well with the state space form of linear -systems. If `D` is the feedthrough matrix (2D array) of a linear system, -and `U` is its input (array), then the feedthrough part of the system's -response, can be computed like this:: - - ft = D @ U - -Finally, the `to_pandas()` function can be used to create a pandas dataframe:: - - df = response.to_pandas() - -The column labels for the data frame are `time` and the labels for the input, -output, and state signals (`u[i]`, `y[i]`, and `x[i]` by default, but these -can be changed using the `inputs`, `outputs`, and `states` keywords when -constructing the system, as described in :func:`ss`, :func:`tf`, and other -system creation function. Note that when exporting to pandas, "rows" in the -data frame correspond to time and "cols" (DataSeries) correspond to signals. - -Input/output response ---------------------- - diff --git a/doc/xferfcn.rst b/doc/xferfcn.rst index 3b521caa8..750b12414 100644 --- a/doc/xferfcn.rst +++ b/doc/xferfcn.rst @@ -1,3 +1,5 @@ +.. currentmodule:: control + Frequency domain analysis and design ==================================== @@ -5,21 +7,21 @@ Transfer function properties ---------------------------- The following basic attributes and methods are available for -:class:`StateSpace` objects: +:class:`TransferFunction` objects: .. autosummary:: - ~StateSpace.num_array - ~StateSpace.den_array - ~StateSpace.shape - ~StateSpace.poles - ~StateSpace.zeros - ~StateSpace.dcgain - ~StateSpace.sample - ~StateSpace.returnScipySignalLTI + ~TransferFunction.num_array + ~TransferFunction.den_array + ~TransferFunction.shape + ~TransferFunction.poles + ~TransferFunction.zeros + ~TransferFunction.dcgain + ~TransferFunction.sample + ~TransferFunction.returnScipySignalLTI A complete list of attributes, methods, and properties is available in -the :class:`~control.StateSpace` class documentation. +the :class:`TransferFunction` class documentation. Frequency domain methods From 6d6cf25bf4c5a020e7559b1907868c30575ee469 Mon Sep 17 00:00:00 2001 From: Richard Murray Date: Fri, 27 Dec 2024 22:55:33 -0800 Subject: [PATCH 34/93] add missing files + tweak noindex for readthedocs --- doc/examples/vehicle-steering.png | 1 + doc/intro.rst | 2 +- doc/phaseplot.rst | 97 +++++++++++++++++++++++++++++++ 3 files changed, 99 insertions(+), 1 deletion(-) create mode 120000 doc/examples/vehicle-steering.png create mode 100644 doc/phaseplot.rst diff --git a/doc/examples/vehicle-steering.png b/doc/examples/vehicle-steering.png new file mode 120000 index 000000000..c568707da --- /dev/null +++ b/doc/examples/vehicle-steering.png @@ -0,0 +1 @@ +../../examples/vehicle-steering.png \ No newline at end of file diff --git a/doc/intro.rst b/doc/intro.rst index 964575f92..582830ea8 100644 --- a/doc/intro.rst +++ b/doc/intro.rst @@ -11,10 +11,10 @@ Package overview ================ .. automodule:: control + :noindex: :no-members: :no-inherited-members: :no-special-members: - :no-index: Installation ============ diff --git a/doc/phaseplot.rst b/doc/phaseplot.rst new file mode 100644 index 000000000..ded98e365 --- /dev/null +++ b/doc/phaseplot.rst @@ -0,0 +1,97 @@ +.. currentmodule:: control + +Phase plane plots +================= +Insight into nonlinear systems can often be obtained by looking at phase +plane diagrams. The :func:`~control.phase_plane_plot` function allows the +creation of a 2-dimensional phase plane diagram for a system. This +functionality is supported by a set of mapping functions that are part of +the `phaseplot` module. + +The default method for generating a phase plane plot is to provide a +2D dynamical system along with a range of coordinates and time limit:: + + sys = ct.nlsys( + lambda t, x, u, params: np.array([[0, 1], [-1, -1]]) @ x, + states=['position', 'velocity'], inputs=0, name='damped oscillator') + axis_limits = [-1, 1, -1, 1] + T = 8 + ct.phase_plane_plot(sys, axis_limits, T) + +.. image:: figures/phaseplot-dampedosc-default.png + +By default, the plot includes streamlines generated from starting +points on limits of the plot, with arrows showing the flow of the +system, as well as any equilibrium points for the system. A variety +of options are available to modify the information that is plotted, +including plotting a grid of vectors instead of streamlines and +turning on and off various features of the plot. + +To illustrate some of these possibilities, consider a phase plane plot for +an inverted pendulum system, which is created using a mesh grid:: + + def invpend_update(t, x, u, params): + m, l, b, g = params['m'], params['l'], params['b'], params['g'] + return [x[1], -b/m * x[1] + (g * l / m) * np.sin(x[0]) + u[0]/m] + invpend = ct.nlsys(invpend_update, states=2, inputs=1, name='invpend') + + ct.phase_plane_plot( + invpend, [-2*pi, 2*pi, -2, 2], 5, + gridtype='meshgrid', gridspec=[5, 8], arrows=3, + plot_equilpoints={'gridspec': [12, 9]}, + params={'m': 1, 'l': 1, 'b': 0.2, 'g': 1}) + plt.xlabel(r"$\theta$ [rad]") + plt.ylabel(r"$\dot\theta$ [rad/sec]") + +.. image:: figures/phaseplot-invpend-meshgrid.png + +This figure shows several features of more complex phase plane plots: +multiple equilibrium points are shown, with saddle points showing +separatrices, and streamlines generated along a 5x8 mesh of initial +conditions. At each mesh point, a streamline is created that goes 5 time +units forward and backward in time. A separate grid specification is used +to find equilibrium points and separatrices (since the course grid spacing +of 5x8 does not find all possible equilibrium points). Together, the +multiple features in the phase plane plot give a good global picture of the +topological structure of solutions of the dynamical system. + +Phase plots can be built up by hand using a variety of helper functions that +are part of the :mod:`~control.phaseplot` (pp) module:: + + import control.phaseplot as pp + + def oscillator_update(t, x, u, params): + return [x[1] + x[0] * (1 - x[0]**2 - x[1]**2), + -x[0] + x[1] * (1 - x[0]**2 - x[1]**2)] + oscillator = ct.nlsys( + oscillator_update, states=2, inputs=0, name='nonlinear oscillator') + + ct.phase_plane_plot(oscillator, [-1.5, 1.5, -1.5, 1.5], 0.9) + pp.streamlines( + oscillator, np.array([[0, 0]]), 1.5, + gridtype='circlegrid', gridspec=[0.5, 6], dir='both') + pp.streamlines( + oscillator, np.array([[1, 0]]), 2*pi, arrows=6, color='b') + plt.gca().set_aspect('equal') + +.. image:: figures/phaseplot-oscillator-helpers.png + +The following helper functions are available: + +.. autosummary:: + + phaseplot.equilpoints + phaseplot.separatrices + phaseplot.streamlines + phaseplot.vectorfield + +The :func:`~control.phase_plane_plot` function calls these helper functions +based on the options it is passed. + +Note that unlike other plotting functions, phase plane plots do not involve +computing a response and then plotting the result via a `plot()` method. +Instead, the plot is generated directly be a call to the +:func:`~control.phase_plane_plot` function (or one of the +:mod:`~control.phaseplot` helper functions. + + From e4fd04f07cd91523fe2fb6c64ab1e9871043e08e Mon Sep 17 00:00:00 2001 From: Richard Murray Date: Sat, 28 Dec 2024 10:10:00 -0800 Subject: [PATCH 35/93] update linear.rst and associated doc files and function docstrings --- control/lti.py | 5 +- control/margins.py | 15 +-- control/robust.py | 40 +------- control/statesp.py | 7 +- control/sysnorm.py | 23 +++-- control/xferfcn.py | 2 +- doc/conf.py | 3 + doc/functions.rst | 3 +- doc/intro.rst | 41 +++++++- doc/linear.rst | 67 ++++++++----- doc/optimal.rst | 16 ++-- doc/response.rst | 2 +- doc/statesp.rst | 43 +++++++-- doc/test_sphinxdocs.py | 1 + doc/xferfcn.rst | 127 ++++++++++++++++++++++++- examples/.gitignore | 1 + examples/python-control_tutorial.ipynb | 8 +- 17 files changed, 284 insertions(+), 120 deletions(-) diff --git a/control/lti.py b/control/lti.py index 4e0127c60..066230697 100644 --- a/control/lti.py +++ b/control/lti.py @@ -133,9 +133,8 @@ def frequency_response(self, omega=None, squeeze=None): outputs=self.output_labels, plot_type='bode') def dcgain(self): - """Return the zero-frequency gain""" - raise NotImplementedError("dcgain not implemented for %s objects" % - str(self.__class__)) + """Return the zero-frequency (DC) gain.""" + return NotImplemented def _dcgain(self, warn_infinite): zeroresp = self(0 if self.isctime() else 1, diff --git a/control/margins.py b/control/margins.py index 528191002..87b0302f8 100644 --- a/control/margins.py +++ b/control/margins.py @@ -271,13 +271,14 @@ def stability_margins(sysdata, returnall=False, epsw=0.0, method='best'): and not returned as margin. method : string, optional Method to use (default is 'best'): - 'poly': use polynomial method if passed a :class:`LTI` system. - 'frd': calculate crossover frequencies using numerical interpolation - of a :class:`FrequencyResponseData` representation of the system if - passed a :class:`LTI` system. - 'best': use the 'poly' method if possible, reverting to 'frd' if it is - detected that numerical inaccuracy is likey to arise in the 'poly' - method for for discrete-time systems. + + * 'poly': use polynomial method if passed a :class:`LTI` system. + * 'frd': calculate crossover frequencies using numerical + interpolation of a :class:`FrequencyResponseData` representation + of the system if passed a :class:`LTI` system. + * 'best': use the 'poly' method if possible, reverting to 'frd' if + it is detected that numerical inaccuracy is likey to arise in the + 'poly' method for for discrete-time systems. Returns ------- diff --git a/control/robust.py b/control/robust.py index d2fff7c0a..85d86bb6d 100644 --- a/control/robust.py +++ b/control/robust.py @@ -2,42 +2,10 @@ # # Author: Steve Brunton, Kevin Chen, Lauren Padilla # Date: 24 Dec 2010 -# -# This file contains routines for obtaining reduced order models -# -# Copyright (c) 2010 by California Institute of Technology -# All rights reserved. -# -# Redistribution and use in source and binary forms, with or without -# modification, are permitted provided that the following conditions -# are met: -# -# 1. Redistributions of source code must retain the above copyright -# notice, this list of conditions and the following disclaimer. -# -# 2. Redistributions in binary form must reproduce the above copyright -# notice, this list of conditions and the following disclaimer in the -# documentation and/or other materials provided with the distribution. -# -# 3. Neither the name of the California Institute of Technology nor -# the names of its contributors may be used to endorse or promote -# products derived from this software without specific prior -# written permission. -# -# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS -# "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT -# LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS -# FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL CALTECH -# OR THE CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, -# SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT -# LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF -# USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND -# ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, -# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT -# OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF -# SUCH DAMAGE. -# -# $Id$ + +""" +This file contains routines for obtaining reduced order models. +""" import warnings diff --git a/control/statesp.py b/control/statesp.py index 938f6dc69..0cc3a6b15 100644 --- a/control/statesp.py +++ b/control/statesp.py @@ -1350,7 +1350,7 @@ def sample(self, Ts, method='zoh', alpha=None, prewarp_frequency=None, return StateSpace(sysd, **kwargs) def dcgain(self, warn_infinite=False): - """Return the zero-frequency gain. + """Return the zero-frequency ("DC") gain. The zero-frequency gain of a continuous-time state-space system is given by: @@ -1931,9 +1931,6 @@ def ssdata(sys): def linfnorm(sys, tol=1e-10): """L-infinity norm of a linear system. - .. deprecated:: 0.10.2 - This functionality is now available in :func:`sysnorm`. - Parameters ---------- sys : LTI (StateSpace or TransferFunction) @@ -1957,8 +1954,6 @@ def linfnorm(sys, tol=1e-10): -------- slycot.ab13dd : the Slycot routine linfnorm that does the calculation """ - warn("linfnorm() is deprecated; use sysnorm(sys, p='inf')", FutureWarning) - if ab13dd is None: raise ControlSlycot("Can't find slycot module 'ab13dd'") diff --git a/control/sysnorm.py b/control/sysnorm.py index 67183c29f..ddba5faca 100644 --- a/control/sysnorm.py +++ b/control/sysnorm.py @@ -18,7 +18,7 @@ import control as ct -__all__ = ['norm'] +__all__ = ['system_norm', 'norm'] #------------------------------------------------------------------------------ @@ -83,24 +83,26 @@ def _h2norm_slycot(sys, print_warning=True): #------------------------------------------------------------------------------ -def norm(system, p=2, tol=1e-6, print_warning=True, method=None): - """Computes norm of system. +def system_norm(system, p=2, tol=1e-6, print_warning=True, method=None): + """Computes the input/output norm of system. Parameters ---------- system : LTI (:class:`StateSpace` or :class:`TransferFunction`) - System in continuous or discrete time for which the norm should be computed. + System in continuous or discrete time for which the norm should + be computed. p : int or str - Type of norm to be computed. ``p=2`` gives the H2 norm, and ``p='inf'`` gives the L-infinity norm. + Type of norm to be computed. `p=2` gives the H2 norm, and + `p='inf'` gives the L-infinity norm. tol : float Relative tolerance for accuracy of L-infinity norm computation. Ignored - unless ``p='inf'``. + unless `p='inf'`. print_warning : bool Print warning message in case norm value may be uncertain. method : str, optional Set the method used for computing the result. Current methods are - ``'slycot'`` and ``'scipy'``. If set to ``None`` (default), try ``'slycot'`` first - and then ``'scipy'``. + 'slycot' and 'scipy'. If set to `None` (default), try 'slycot' first + and then 'scipy'. Returns ------- @@ -121,7 +123,8 @@ def norm(system, p=2, tol=1e-6, print_warning=True, method=None): """ if not isinstance(system, (ct.StateSpace, ct.TransferFunction)): - raise TypeError('Parameter ``system``: must be a ``StateSpace`` or ``TransferFunction``') + raise TypeError( + "Parameter `system`: must be a `StateSpace` or `TransferFunction`") G = ct.ss(system) A = G.A @@ -292,3 +295,5 @@ def _Hamilton_matrix(gamma): else: raise ct.ControlArgument(f"Norm computation for p={p} currently not supported.") + +norm = system_norm diff --git a/control/xferfcn.py b/control/xferfcn.py index 978eed3d0..3c133b797 100644 --- a/control/xferfcn.py +++ b/control/xferfcn.py @@ -1243,7 +1243,7 @@ def sample(self, Ts, method='zoh', alpha=None, prewarp_frequency=None, return TransferFunction(sysd, name=name, **kwargs) def dcgain(self, warn_infinite=False): - """Return the zero-frequency (or DC) gain. + """Return the zero-frequency ("DC") gain. For a continous-time transfer function G(s), the DC gain is G(0) For a discrete-time transfer function G(z), the DC gain is G(1) diff --git a/doc/conf.py b/doc/conf.py index fa22c5f18..300c9186f 100644 --- a/doc/conf.py +++ b/doc/conf.py @@ -121,6 +121,9 @@ # Set the default role to render items in backticks as code default_role = 'py:obj' +# Align inline math with text +imgmath_use_preview = True + # Theme options are theme-specific and customize the look and feel of a theme # further. For a list of options available for each theme, see the # documentation. diff --git a/doc/functions.rst b/doc/functions.rst index ca5996d22..b9bb4f504 100644 --- a/doc/functions.rst +++ b/doc/functions.rst @@ -114,11 +114,12 @@ Control system analysis get_input_ff_index get_output_fb_index ispassive + linfnorm margin - norm solve_passivity_LMI stability_margins step_info + system_norm phase_crossover_frequencies poles zeros diff --git a/doc/intro.rst b/doc/intro.rst index 582830ea8..93c32fa14 100644 --- a/doc/intro.rst +++ b/doc/intro.rst @@ -7,6 +7,7 @@ Guide. This guide contains information on using the python-control package, including documentation for all functions in the package and examples illustrating their use. + Package overview ================ @@ -16,6 +17,7 @@ Package overview :no-inherited-members: :no-special-members: + Installation ============ @@ -82,6 +84,35 @@ control package:: Note that Google colab does not currently support Slycot, so some functionality may not be available. + +Package conventions +=================== + +The python-control package makes use of a few naming and calling conventions: + +* Function names are written in lower case with underscores between + words ('frequency_response'). + +* Class names use camel case ('StateSpace', 'ControlPlot', etc) and + instances of the class are created with "factory functions" ('ss') + or as the output of an operation ('bode_plot'). + +* Functions that return multiple values use either objects (with + elements for each return value) or tuples. For those functions that + return tuples, the underscore variable can be used if only some of + the return values are needed:: + + K, _, _ = lqr(sys) + +* Python-control supports both single-input, single-output (SISO) + systems and mullti-input, multi-output (MIMO) systems, including + time and frequency responses. By default, SISO systems will + typically generate objects that have the input and output dimensions + supressed (using the NumPy :func:`~numpy.squeeze` function). The + `squeeze` keyword can be set to `False` to force functions to return + objects that include the input and output dimensions. + + Some differences from MATLAB ============================ @@ -102,11 +133,11 @@ some things to keep in mind: vectors implemented as nested list . So [1 2 3] must be written as [1, 2, 3] and matrices are written using 2D nested lists, e.g., [[1, 2], [3, 4]]. -* Functions that return multiple values use either objects (with - elements for each return value) or tuples. The number of elements - in a tuple is fixed and so functions that return variable numbers of - return values will have a parameter of the form `return_` - that is used to return additional data. +* Functions that in MATLAB would return variable numbers of values + will have a parameter of the form `return_` that is used to + return additional data. (These functions usually return an object of + a class that has attributpes that can be used to access the + information and this is the preferred usage pattern.) * You cannot use braces for collections; use tuples instead. * Time series data have time as the final index (see :ref:`time-series-convention`). diff --git a/doc/linear.rst b/doc/linear.rst index fcfb7e908..5f2fda208 100644 --- a/doc/linear.rst +++ b/doc/linear.rst @@ -58,9 +58,9 @@ customized access to system information:: A, B, C, D, name='sys', states=['x1', 'x2'], inputs=['u1', 'u2'], outputs=['y']) -State space can be manipulated using standard arithmetic operations as -well as the :func:`feedback`, :func:`parallel`, and :func:`series` -function. A full list of functions can be found in +State space models can be manipulated using standard arithmetic +operations as well as the :func:`feedback`, :func:`parallel`, and +:func:`series` function. A full list of functions can be found in :ref:`function-ref`. The :func:`rss` function can be used to create a random state space @@ -85,8 +85,8 @@ transfer functions = \frac{a_0 s^m + a_1 s^{m-1} + \cdots + a_m} {b_0 s^n + b_1 s^{n-1} + \cdots + b_n}, -where n is generally greater than or equal to m (for a proper transfer -function). +where :math:`n` is greater than or equal to :math:`m` for a proper +transfer function. Improper transfer functions are also allowed. To create a transfer function, use the :func:`tf` function:: @@ -103,6 +103,20 @@ operations as well as the :func:`feedback`, :func:`parallel`, and functions can be found in the :ref:`interconnections-ref` section of the :ref:`function-ref`. +To aid in the construction of transfer functions, the :func:`tf` +factory function can used to create transfer function corresponding +to the derivative or difference operator:: + + s = ct.tf('s') + +Standard algebraic operations can be used to construct more +complicated transfer functions:: + + sys = 5 * (s + 10)/(s**2 + 2*s + 1) + +Discrete time transfer functions (described in more detail below) can +be created using `z = ct.tf('z')`. + Frequency response data (FRD) systems ------------------------------------- @@ -127,11 +141,11 @@ functions that are available, although all of the standard algebraic manipulations can be performed. The FRD class is also used as the return type for the -:func:`frequency_response` function (and the equivalent method for the -:class:`StateSpace` and :class:`TransferFunction` classes). This -object can be assigned to a tuple using:: +:func:`frequency_response` function. This object can be assigned to a +tuple using:: - mag, phase, omega = response + response = ct.frequency_response(sys) + mag, phase, omega = response where `mag` is the magnitude (absolute value, not dB or log10) of the system frequency response, `phase` is the wrapped phase in radians of @@ -148,7 +162,7 @@ Multi-input, multi-output (MIMO) systems Multi-input, multi-output (MIMO) sytems are created by providing parameters of the appropriate dimensions to the relevant factory -function. For transfer function, this is done by providing a 2D list +function. For transfer functions, this is done by providing a 2D list of numerator and denominator polynomials to the :func:`tf` function, e.g.:: @@ -156,11 +170,11 @@ e.g.:: [[num11, num12], [num21, num22]], [[den11, den12], [den21, denm22]]) -Similarly, MIMO frequency response data systems are created by +Similarly, MIMO frequency response data (FRD) systems are created by providing the :func:`frd` function with a 3D array of response -values,with the first dimension corresponding to the output index -of the system, the second dimension corresponding to the input index, -and the 3rd dimension corresponding to the frequency points in omega. +values,with the first dimension corresponding to the output index of +the system, the second dimension corresponding to the input index, and +the 3rd dimension corresponding to the frequency points in omega. Signal names for MIMO systems are specified using lists of labels:: @@ -194,13 +208,14 @@ can be accessed using the names of the inputs and outputs:: where the signal names are based on the system that generated the frequency response. -.. note:: If an LTI system is SISO, `magnitude` and `phase` default to - 1D arrays, indexed by frequency. If the system is not SISO - or `squeeze` is set to `False`, the array is 3D, indexed by - the output, input, and frequency. If `squeeze` is `True` - for a MIMO system then single-dimensional axes are removed. - The processing of the `squeeze` keyword can be changed by - calling the response function with a new argument:: +.. note:: If a system is single-input, single-output (SISO), + `magnitude` and `phase` default to 1D arrays, indexed by + frequency. If the system is not SISO or `squeeze` is set to + `False`, the array is 3D, indexed by the output, input, and + frequency. If `squeeze` is `True` for a MIMO system then + single-dimensional axes are removed. The processing of the + `squeeze` keyword can be changed by calling the response + function with a new argument:: mag, phase, omega = response(squeeze=False) @@ -442,21 +457,21 @@ Information about an LTI system can be obtained using the Python D = [[-0. 0.] [ 0. 0.]] -For a more compact represent, the LTI objects can be evaluated to -return a summary of the system input/output properties:: +For a more compact represention, LTI objects can be evaluated directly +to return a summary of the system input/output properties:: >>> sys = ct.rss(4, 2, 2, name='sys_2x2') >>> sys ['y[0]', 'y[1]']> Alternative representations of the system are available using the -:func:iosys_repr` function. +:func:`iosys_repr` function. Transfer functions are displayed as ratios of polynomials, using either 's' or 'z' depending on whether the systems is continuous or discrete time:: - >>> sys_tf = ct.tf([1, 0], [1, 2, 1]) + >>> sys_tf = ct.tf([1, 0], [1, 2, 1], 0.1) >>> print(sys_tf) : sys[1] Inputs (1): ['u[0]'] @@ -466,5 +481,5 @@ discrete time:: ------------- z^2 + 2 z + 1 - dt = 1 + dt = 0.1 diff --git a/doc/optimal.rst b/doc/optimal.rst index 790a9e93a..d3948033e 100644 --- a/doc/optimal.rst +++ b/doc/optimal.rst @@ -211,10 +211,10 @@ optimal trajectories for nonlinear systems and implementing optimization-based controllers, including model predictive control and moving horizon estimation. It follows the basic problem setups described above, but carries out all computations in *discrete time* -(so that integrals become sums) and over a *finite horizon*. To local -the optimal control modules, import `control.optimal`: +(so that integrals become sums) and over a *finite horizon*. To access +the optimal control modules, import `control.optimal`:: - import control.optimal as obc + import control.optimal as opt To describe an optimal control problem we need an input/output system, a time horizon, a cost function, and (optionally) a set of constraints on the @@ -224,7 +224,7 @@ problem into a standard optimization problem that can be solved by :func:`scipy.optimize.minimize`. The optimal control problem can be solved by using the :func:`optimal.solve_ocp` function:: - res = obc.solve_ocp(sys, timepts, X0, cost, constraints) + res = opt.solve_ocp(sys, timepts, X0, cost, constraints) The `sys` parameter should be an :class:`InputOutputSystem` and the `timepts` parameter should represent a time vector that gives the list of @@ -367,18 +367,18 @@ penalizes the state and input using quadratic cost functions:: Q = np.diag([0, 0, 0.1]) # don't turn too sharply R = np.diag([1, 1]) # keep inputs small P = np.diag([1000, 1000, 1000]) # get close to final point - traj_cost = obc.quadratic_cost(vehicle, Q, R, x0=xf, u0=uf) - term_cost = obc.quadratic_cost(vehicle, P, 0, x0=xf) + traj_cost = opt.quadratic_cost(vehicle, Q, R, x0=xf, u0=uf) + term_cost = opt.quadratic_cost(vehicle, P, 0, x0=xf) We also constrain the maximum turning rate to 0.1 radians (about 6 degrees) and constrain the velocity to be in the range of 9 m/s to 11 m/s:: - constraints = [ obc.input_range_constraint(vehicle, [8, -0.1], [12, 0.1]) ] + constraints = [ opt.input_range_constraint(vehicle, [8, -0.1], [12, 0.1]) ] Finally, we solve for the optimal inputs:: timepts = np.linspace(0, Tf, 10, endpoint=True) - result = obc.solve_ocp( + result = opt.solve_ocp( vehicle, timepts, x0, traj_cost, constraints, terminal_cost=term_cost, initial_guess=u0) diff --git a/doc/response.rst b/doc/response.rst index 080cb85db..283c326bc 100644 --- a/doc/response.rst +++ b/doc/response.rst @@ -1,4 +1,4 @@ -.. _plotting-module: +.. _response-chapter: .. currentmodule:: control diff --git a/doc/statesp.rst b/doc/statesp.rst index 86a416a35..cbf96963e 100644 --- a/doc/statesp.rst +++ b/doc/statesp.rst @@ -57,6 +57,28 @@ documentation pages for the individual functions: similarity_transform +Time domain properties +---------------------- + +The following functions are available to analyze the time domain +properties of a linear systems: + +.. autosummary:: + + damp + forced_response + impulse_response + initial_response + ssdata + step_info + step_response + +The time response functions (:func:`forced_response`, +:func:`impulse_response`, :func:`initial_response`, and +:func:`step_response) are described in more detail in the +:ref:`response-chapter` chapter. + + State feedback design --------------------- @@ -81,10 +103,11 @@ methods: place place_varga -The :func:`acker`, :func:`place`, and :func:`place_varga` place the -eigenvalues of the closed loop system to a desired set of values. -Each takes the `A` and `B` matrices of the state space system and the -desired locaton of the eigenvalues and returns a gain matrix `K`:: +The :func:`acker`, :func:`place`, and :func:`place_varga` functions +place the eigenvalues of the closed loop system to a desired set of +values. Each takes the `A` and `B` matrices of the state space system +and the desired location of the eigenvalues and returns a gain matrix +`K`:: K = ct.place(sys.A, sys.B, E) @@ -109,8 +132,9 @@ several forms: If `sys` is a discrete time system, the first two forms will compute the discrete time optimal controller. For the second two forms, the -:func:`dlqr` function can be used. Additional arguments and details -are given on the :func:`lqr` and :func:`dlqr` documentation pages. +:func:`dlqr` function can be used to compute the discrete time optimal +controller. Additional arguments and details are given on the +:func:`lqr` and :func:`dlqr` documentation pages. State estimation ---------------- @@ -156,11 +180,10 @@ with covariances satisfying {\mathbb E}\{v v^T\} = RN,\qquad {\mathbb E}\{w v^T\} = NN -where :math:`{\mathbb E}\{\cdot\}` represents the expectation of a -quantity. +where :math:`{\mathbb E}\{\cdot\}` represents expectation. -The :func:`lqe` function computes the observer gain matrix L such that the -stationary (non-time-varying) Kalman filter +The :func:`lqe` function computes the observer gain matrix :math:`L` +such that the stationary (non-time-varying) Kalman filter .. math:: diff --git a/doc/test_sphinxdocs.py b/doc/test_sphinxdocs.py index 9d4c10198..0b2422ec9 100644 --- a/doc/test_sphinxdocs.py +++ b/doc/test_sphinxdocs.py @@ -33,6 +33,7 @@ 'minreal', # minimal_realization 'modred', # model_reduction 'nichols', # nichols_plot + 'norm', # system_norm 'nyquist', # nyquist_plot 'pzmap', # pole_zero_plot 'rlocus', # root_locus_plot diff --git a/doc/xferfcn.rst b/doc/xferfcn.rst index 750b12414..389f30a22 100644 --- a/doc/xferfcn.rst +++ b/doc/xferfcn.rst @@ -24,27 +24,144 @@ A complete list of attributes, methods, and properties is available in the :class:`TransferFunction` class documentation. -Frequency domain methods ------------------------- +Frequency domain properties +--------------------------- + +The following functions are available to analyze the frequency +domain properties of a linear systems: + +.. autosummary:: + + bandwidth + dcgain + frequency_response + phase_crossover_frequencies + singular_values_response + tfdata + +These functions work on both state space and transfer function models. +The :func:`frequency_response` and :func:`singular_values_response` +functions are described in more detail in the :ref:`response-chapter` +chapter. Input/output norms ------------------ +Continuous and discrete time signals can be represented as a normed +linear space with the appropriate choice of signal norm. For +continous time signals, the three most common norms are the 2-norm and +the :math:`\infty`-norm: + +.. list-table:: + :header-rows: 1 + + * - Name + - Continuous time + - Discrete time + * - 1-norm + - :math:`\int_{-\infty}^\infty |u(\tau)|, d\tau` + - :math:`\sum_k \|x[k]\|` + * - 2-norm + - :math:`\left(\int_{-\infty}^\infty |u(\tau)|^2, d\tau \right)^{1/2}` + - :math:`\left(\sum_k \|x[k]\|^2 \right)^{1/2}` + * - :math:`\infty`-norm + - :math:`\sup_t |u(t)|` + - :math:`\max_k \|x[k]\|` + +Given a norm for input signals and a norm for output signals, we can +define the \emph{induced norm} for an input/output system. The +following table summarizes the induced norms for a transfer function +:math:`G(s)` with impulse response :math:`g(t)`: + +.. list-table:: + :header-rows: 1 + + * - + - :math:`\|u\|_2` + - :math:`\| u \|_\infty` + * - :math:`\| y \|_2` + - :math:`\| G \|_\infty` + - :math:`\infty` + * - :math:`\| y \|_\infty` + - :math:`\| G \|_2` + - :math:`\| g \|_1` + +The 2-norm and :math:`\infty`-norm can be computed using :func:`system_norm`:: + + sysnorm = ct.system_norm(sys, p=) + +where `val` is either 2 or 'inf'. + Stability margins ----------------- +The stability margin of a system indicates the robustness of a +feedback system to perturbations that might cause the system to become +unstable. Standard measures of robustness include gain margin, phase +margin, and stability margin (distance to the -1 point on the Nyquist +curve). These margins are computed based on the loop transfer +function for a feedback system, assuming the loop will be closed using +negative feedback with gain 1. + +The :func:`stability_margins` function computes all three of these +margins as well as the frequencies at which they occur:: + + >>> sys = ct.tf(10, [1, 2, 3, 4]) + >>> gm, pm, sm, wpc, wgc, wms = ct.stability(sys) + >>> print(f"Gain margin: {gm:2.2} at omega = {wpc:2.2} rad/sec") + Gain margin: 0.2 at omega = 1.7 rad/sec + Frequency domain synthesis -------------------------- +Synthesis of feedback controllers in the frequency domain can be done +using the following functions: -Transfer functions from data ----------------------------- +.. autosummary:: + + h2syn + hinfsyn + mixsyn + +The :func:`mixsyn` function computes a feedback controller +:math:`C(s)` that minimizes the mixed sensitivity gain -.. todo:: rename to match FBS2e termincology +.. math:: + + \| W_1 S \|_\infty + \| W_2 C \|_\infty + \| W_3 T \|_\infty, + +where + +.. math:: + + S = \frac{1}{1 + P C}, \qquad T = \frac{P C}{1 + P C}, + +are the sensitivty function and complementary sensitivity function, +and :math:`P(s)` represents the process dynamics. + +The :func:`h2syn` and :func:`hinfsyn` functions compute a feedback +controller :math:`C(s)` that minimizes the 2-norm and the +:math:`\infty`-norm of the sensitity function for the closed loop +system, respectively. Systems with time delays ------------------------ + +Time delays are not directly representable in `python-control`, but +the :func:`pade` function generates a linear system that approximates +a time delay to a given order:: + + >> num, den = ct.pade(0.1, 3) + >> delay = ct.tf(num, den, name='delay') + >> print(delay) + : delay + Inputs (1): ['u[0]'] + Outputs (1): ['y[0]'] + + -s^3 + 120 s^2 - 6000 s + 1.2e+05 + --------------------------------- + s^3 + 120 s^2 + 6000 s + 1.2e+05 diff --git a/examples/.gitignore b/examples/.gitignore index ad3049346..3d8dcfb71 100644 --- a/examples/.gitignore +++ b/examples/.gitignore @@ -1 +1,2 @@ .ipynb-clean +.ipynb_checkpoints/ diff --git a/examples/python-control_tutorial.ipynb b/examples/python-control_tutorial.ipynb index be7565aed..a6f668b4b 100644 --- a/examples/python-control_tutorial.ipynb +++ b/examples/python-control_tutorial.ipynb @@ -1226,14 +1226,18 @@ "output_type": "stream", "text": [ "Control version: 0.10.1.dev324+g2fd3802a.d20241218\n", - "Slycot installed: True\n", + "Slycot version: 0.6.0\n", "NumPy version: 2.2.0\n" ] } ], "source": [ "print(\"Control version:\", ct.__version__)\n", - "print(\"Slycot installed:\", ct.slycot_check())\n", + "if ct.slycot_check():\n", + " import slycot\n", + " print(\"Slycot version:\", slycot.__version__)\n", + "else:\n", + " print(\"Slycot version: not installed\")\n", "print(\"NumPy version:\", np.__version__)" ] } From aa055377883a33b9ba7b76514f73cb1388eccf49 Mon Sep 17 00:00:00 2001 From: Richard Murray Date: Sat, 28 Dec 2024 11:03:20 -0800 Subject: [PATCH 36/93] update response.rst and associated function docstrings --- control/ctrlplot.py | 6 +++-- doc/examples/.gitignore | 1 + doc/response.rst | 59 +++++++++++++++-------------------------- 3 files changed, 26 insertions(+), 40 deletions(-) create mode 100644 doc/examples/.gitignore diff --git a/control/ctrlplot.py b/control/ctrlplot.py index 310a2d9c4..a26f706c5 100644 --- a/control/ctrlplot.py +++ b/control/ctrlplot.py @@ -1,7 +1,9 @@ # ctrlplot.py - utility functions for plotting # Richard M. Murray, 14 Jun 2024 # -# Collection of functions that are used by various plotting functions. +""" +Collection of functions that are used by various plotting functions. +""" # Code pattern for control system plotting functions: # @@ -116,7 +118,7 @@ # class ControlPlot(object): - """A class for returning control figures. + """A class for representing control figures. This class is used as the return type for control plotting functions. It contains the information required to access portions of the plot diff --git a/doc/examples/.gitignore b/doc/examples/.gitignore new file mode 100644 index 000000000..87620ac7e --- /dev/null +++ b/doc/examples/.gitignore @@ -0,0 +1 @@ +.ipynb_checkpoints/ diff --git a/doc/response.rst b/doc/response.rst index 283c326bc..e390868d8 100644 --- a/doc/response.rst +++ b/doc/response.rst @@ -3,7 +3,7 @@ .. currentmodule:: control ********************************** -Input/output Response and Plotting +Input/Output Response and Plotting ********************************** The Python Control Systems Toolbox contains a number of functions for @@ -160,7 +160,7 @@ which will compute the step response for each input/output pair. See The input, output, and state elements of the response can be accessed using signal names in place of integer offsets:: - plt.plot(response. time, response.states['x[1]'] + plt.plot(response.time, response.states['x[1]'] For multi-trace systems generated by :func:`step_response` and :func:`impulse_response`, the input name used to generate the trace can be @@ -202,18 +202,15 @@ data frame correspond to time and "cols" (DataSeries) correspond to signals. Time response plots ------------------- -.. todo:: Improve flow between previous section and this one - -Input/output time responses are produced one of several python-control -functions: :func:`forced_response`, +The input/output time response functions ( :func:`forced_response`, :func:`impulse_response`, :func:`initial_response`, -:func:`input_output_response`, :func:`step_response`. -Each of these return a :class:`TimeResponseData` object, which -contains the time, input, state, and output vectors associated with the -simulation. Time response data can be plotted with the -:func:`time_response_plot` function, which is also available as -the :func:`TimeResponseData.plot` method. For example, the step -response for a two-input, two-output can be plotted using the commands:: +:func:`input_output_response`, :func:`step_response`) return a +:class:`TimeResponseData` object, which contains the time, input, +state, and output vectors associated with the simulation, as described +above. Time response data can be plotted with the +:func:`time_response_plot` function, which is also available as the +:func:`TimeResponseData.plot` method. For example, the step response +for a two-input, two-output can be plotted using the commands:: sys_mimo = ct.tf2ss( [[[1], [0.1]], [[0.2], [1]]], @@ -225,31 +222,17 @@ which produces the following plot: .. image:: figures/timeplot-mimo_step-default.png -The :class:`TimeResponseData` object can also be used to access -the data from the simulation:: - - time, outputs, inputs = response.time, response.outputs, response.inputs - fig, axs = plt.subplots(2, 2) - for i in range(2): - for j in range(2): - axs[i, j].plot(time, outputs[i, j]) - -In addition to accessing time response data via integer indices, signal -names can allow be used:: - - plt.plot(response.time, response.outputs['y[0]', 'u[1]']) - -A number of options are available in the `plot` method to customize -the appearance of input output data. For data produced by the -:func:`impulse_response` and :func:`step_response` -commands, the inputs are not shown. This behavior can be changed -using the `plot_inputs` keyword. It is also possible to combine -multiple lines onto a single graph, using either the `overlay_signals` -keyword (which puts all outputs out a single graph and all inputs on a -single graph) or the `overlay_traces` keyword, which puts different -traces (e.g., corresponding to step inputs in different channels) on -the same graph, with appropriate labeling via a legend on selected -axes. +A number of options are available in the :func:`time_response_plot` +function (and associated :func:`TimeResponseData.plot` method) to +customize the appearance of input output data. For data produced by +the :func:`impulse_response` and :func:`step_response` commands, the +inputs are not shown. This behavior can be changed using the +`plot_inputs` keyword. It is also possible to combine multiple lines +onto a single graph, using either the `overlay_signals` keyword (which +puts all outputs out a single graph and all inputs on a single graph) +or the `overlay_traces` keyword, which puts different traces (e.g., +corresponding to step inputs in different channels) on the same graph, +with appropriate labeling via a legend on selected axes. For example, using `plot_input=True` and `overlay_signals=True` yields the following plot:: From 01e8c3cca445e901c94188a786326bd6ab60954d Mon Sep 17 00:00:00 2001 From: Richard Murray Date: Sat, 28 Dec 2024 13:31:38 -0800 Subject: [PATCH 37/93] update nonlinear.rst and associated doc files --- control/flatsys/__init__.py | 36 +------- doc/conf.py | 4 +- doc/descfcn.rst | 4 +- doc/figures/servomech-diagram.png | Bin 0 -> 53101 bytes doc/flatsys.rst | 32 ++++--- doc/iosys.rst | 59 ++---------- doc/nlsys.rst | 149 ++++++++++++++++++++++++++++++ doc/nonlinear.rst | 24 ++--- doc/optimal.rst | 147 ++--------------------------- doc/phaseplot.rst | 12 +-- doc/stochastic.rst | 103 +++++++++++++++++++++ 11 files changed, 315 insertions(+), 255 deletions(-) create mode 100644 doc/figures/servomech-diagram.png diff --git a/control/flatsys/__init__.py b/control/flatsys/__init__.py index 16374b589..53d0b12b1 100644 --- a/control/flatsys/__init__.py +++ b/control/flatsys/__init__.py @@ -1,43 +1,9 @@ # flatsys/__init__.py: flat systems package initialization file # -# Copyright (c) 2019 by California Institute of Technology -# All rights reserved. -# -# Redistribution and use in source and binary forms, with or without -# modification, are permitted provided that the following conditions -# are met: -# -# 1. Redistributions of source code must retain the above copyright -# notice, this list of conditions and the following disclaimer. -# -# 2. Redistributions in binary form must reproduce the above copyright -# notice, this list of conditions and the following disclaimer in the -# documentation and/or other materials provided with the distribution. -# -# 3. Neither the name of the California Institute of Technology nor -# the names of its contributors may be used to endorse or promote -# products derived from this software without specific prior -# written permission. -# -# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS -# "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT -# LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS -# FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL CALTECH -# OR THE CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, -# SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT -# LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF -# USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND -# ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, -# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT -# OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF -# SUCH DAMAGE. -# # Author: Richard M. Murray # Date: 1 Jul 2019 -r"""Differentially flat systems sub-package. - -The :mod:`control.flatsys` sub-package contains a set of classes and +r"""The :mod:`control.flatsys` sub-package contains a set of classes and functions to compute trajectories for differentially flat systems. A differentially flat system is defined by creating an object using the diff --git a/doc/conf.py b/doc/conf.py index 300c9186f..0ae0eed61 100644 --- a/doc/conf.py +++ b/doc/conf.py @@ -29,7 +29,7 @@ # -- Project information ----------------------------------------------------- project = u'Python Control Systems Library' -copyright = u'2023, python-control.org' +copyright = u'2024, python-control.org' author = u'Python Control Developers' # Version information - read from the source code @@ -285,7 +285,7 @@ def linkcode_resolve(domain, info): doctest_global_setup = """ import numpy as np import control as ct -import control.optimal as obc +import control.optimal as opt import control.flatsys as fs import control.phaseplot as pp ct.reset_defaults() diff --git a/doc/descfcn.rst b/doc/descfcn.rst index 101a43204..48fbbe60d 100644 --- a/doc/descfcn.rst +++ b/doc/descfcn.rst @@ -1,7 +1,7 @@ -.. _descfcn-module: - .. currentmodule:: control +.. _descfcn-module: + Describing functions 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zB`ow1Sl}$5a={tm3>BhQdsifAAfVy0w?O5V@?*NpP=V==M{yZK-@f|_F7Q=EbU}Rb zz=e{S)eze+Ju?fvjT(i*2<7X7M*N9Fn?fyc4oqZb0{TA?zGhcw_SpW2x3t01)gxT`G4)^>7=k29-*|a-KjbQl# zVSHV!ik4QqQ<9||97btKS5w_LF9`{%kl2iH2H6#7-X;`x|4*>SgB+i^?|9R^o array + +where `x` is a 1-D array with shape (n,), `u` is a 1-D array +with shape (m,), `t` is a float representing the currrent time, +and `params` is a dict containing the values of parameters used by the +function. The dynamics of the system can be in continuous or discrete +time (use the `dt` keyword to create a discrte time system). + +The output function `outfcn` is used to specify the outputs of the +system and has the same calling signature as `updfcn`. If it is not +specified, then the output of the system is set equal to the system +state. Otherwise, it should return an output of shape (p,). + +Note that the number of states, inputs, and outputs should generally +be explicitly specified, although some operations can infer the +dimensions if they are not given when the system is created. The +`inputs`, `outputs`, and `states` keywords can also be given as lists +of strings, in which case the various signals will be given the +appropriate names. + +To illustrate the creation of a nonlinear I/O system model, consider a +simple model of a spring loaded arm driven by a motor: + +.. image:: figures/servomech-diagram.png + :width: 240 + +The dynamics of this system can be modeling using the following code:: + + # Parameter values + servomech_params = { + 'J': 100, # Moment of inertia of the motor + 'b': 10, # Angular damping of the arm + 'k': 1, # Spring constant + 'r': 1, # Location of spring contact on arm + 'l': 2, # Distance to the read head + 'eps': 0.01, # Magnitude of velocity-dependent perturbation + } + + # State derivative + def servomech_update(t, x, u, params): + # Extract the configuration and velocity variables from the state vector + theta = x[0] # Angular position of the disk drive arm + thetadot = x[1] # Angular velocity of the disk drive arm + tau = u[0] # Torque applied at the base of the arm + + # Get the parameter values + J, b, k, r = map(params.get, ['J', 'b', 'k', 'r']) + + # Compute the angular acceleration + dthetadot = 1/J * ( + -b * thetadot - k * r * np.sin(theta) + tau) + + # Return the state update law + return np.array([thetadot, dthetadot]) + + # System output (tip radial position + angular velocity) + def servomech_output(t, x, u, params): + l = params['l'] + return np.array([l * x[0], x[1]]) + + # System dynamics + servomech = ct.nlsys( + servomech_update, servomech_output, name='servomech', + params=servomech_params, states=['theta', 'thdot'], + outputs=['y', 'thdot'], inputs=['tau']) + +A summary of the model can be obtained using the string representation +of the model (via the Python `print()` function):: + + >>> print(servomech) + : servomech + Inputs (1): ['tau'] + Outputs (2): ['y', 'thdot'] + States (2): ['theta', 'thdot'] + Parameters: ['J', 'b', 'k', 'r', 'l', 'eps'] + + Update: + Output: + + Operating points and linearization ---------------------------------- +A nonlinear input/output system can be linearized around an equilibrium point +to obtain a :class:`~control.StateSpace` linear system:: + + sys_ss = ct.linearize(sys_nl, xeq, ueq) + +If the equilibrium point is not known, the +:func:`find_operating_point` function can be used to obtain an +equilibrium point. In its simplest form, `find_operating_point` finds +an equilibrium point given either the desired input or desired +output:: + + xeq, ueq = find_operating_point(sys, x0, u0) + xeq, ueq = find_operating_point(sys, x0, u0, y0) + +The first form finds an equilibrium point for a given input `u0` based +on an initial guess `x0`. The second form fixes the desired output +values `y0` and uses x0 and u0 as an initial guess to find the +equilibrium point. If no equilibrium point can be found, the function +returns the operating point that minimizes the state update (state +derivative for continuous time systems, state difference for discrete +time systems). + +More complex operating points can be found by specifying which states, +inputs, or outputs should be used in computing the operating point, as +well as desired values of the states, inputs, outputs, or state +updates. See the :func:`find_operating_point` documentation for more deatils. + + Simulations and plotting ------------------------ + +To simulate an input/output system, use the +:func:`~control.input_output_response` function:: + + resp = ct.input_output_response(io_sys, T, U, x0, params) + t, y, x = resp.time, resp.outputs, resp.states + +Time responses can be plotted using the :func:`time_response_plot` +function or (equivalently) the :func:`TimeResponseData.plot` +method:: + + cplt = ct.time_response_plot(resp) + +The resulting :class:`ControlPlot` object can be used to access +different plot elements. The :func:`combine_time_responses` function +an be used to combine multiple time responses into a single +`TimeResponseData` object. See the :ref:`response-chapter` chapter +for more information on this functionality. diff --git a/doc/nonlinear.rst b/doc/nonlinear.rst index 3b58f9c46..fef12d98b 100644 --- a/doc/nonlinear.rst +++ b/doc/nonlinear.rst @@ -2,14 +2,16 @@ Nonlinear System Modeling, Analysis, and Design *********************************************** -.. todo:: Figure out how to get these sections to display in a good - way without making the chapter too long. - -.. toctree:: - :maxdepth: 1 - - nlsys - phaseplot - optimal - descfcn - flatsys +The Python Control Systems Library contains a variety of tools for +modeling, analyzing, and designing nonlinear feedback systems, +including support for simulation and optimization. + +.. include:: nlsys.rst + +.. include:: phaseplot.rst + +.. include:: optimal.rst + +.. include:: descfcn.rst + +.. include:: flatsys.rst diff --git a/doc/optimal.rst b/doc/optimal.rst index d3948033e..57e654918 100644 --- a/doc/optimal.rst +++ b/doc/optimal.rst @@ -1,15 +1,17 @@ .. _optimal-module: -.. currentmodule:: control - Optimization-based control ========================== .. automodule:: control.optimal + :noindex: :no-members: :no-inherited-members: :no-special-members: +.. currentmodule:: control + + Optimal control problem setup ----------------------------- @@ -100,119 +102,16 @@ approach can be shown to generate stabilizing control laws under suitable conditions (see, for example, the FBS2e supplement on `Optimization-Based Control `_. -Optimal estimation problem setup --------------------------------- - -Consider a nonlinear system with discrete time dynamics of the form - -.. math:: - :label: eq_fusion_nlsys-oep - - X[k+1] = f(X[k], u[k], V[k]), \qquad Y[k] = h(X[k]) + W[k], - -where :math:`X[k] \in \mathbb{R}^n`, :math:`u[k] \in \mathbb{R}^m`, and -:math:`Y[k] \in \mathbb{R}^p`, and :math:`V[k] \in \mathbb{R}^q` and -:math:`W[k] \in \mathbb{R}^p` represent random processes that are not -necessarily Gaussian white noise processes. The estimation problem that we -wish to solve is to find the estimate :math:`\hat x[\cdot]` that matches -the measured outputs :math:`y[\cdot]` with "likely" disturbances and -noise. - -For a fixed horizon of length :math:`N`, this problem can be formulated as -an optimization problem where we define the likelihood of a given estimate -(and the resulting noise and disturbances predicted by the model) as a cost -function. Suppose we model the likelihood using a conditional probability -density function :math:`p(x[0], \dots, x[N] \mid y[0], \dots, y[N-1])`. -Then we can pose the state estimation problem as - -.. math:: - :label: eq_fusion_oep - - \hat x[0], \dots, \hat x[N] = - \arg \max_{\hat x[0], \dots, \hat x[N]} - p(\hat x[0], \dots, \hat x[N] \mid y[0], \dots, y[N-1]) - -subject to the constraints given by equation :eq:`eq_fusion_nlsys-oep`. -The result of this optimization gives us the estimated state for the -previous :math:`N` steps in time, including the "current" time -:math:`x[N]`. The basic idea is thus to compute the state estimate that is -most consistent with our model and penalize the noise and disturbances -according to how likely they are (based on the given stochastic system -model for each). - -Given a solution to this fixed-horizon optimal estimation problem, we can -create an estimator for the state over all times by repeatedly applying the -optimization problem :eq:`eq_fusion_oep` over a moving horizon. At each -time :math:`k`, we take the measurements for the last :math:`N` time steps -along with the previously estimated state at the start of the horizon, -:math:`x[k-N]` and reapply the optimization in equation -:eq:`eq_fusion_oep`. This approach is known as a \define{moving horizon -estimator} (MHE). - -The formulation for the moving horizon estimation problem is very general -and various situations can be captured using the conditional probability -function :math:`p(x[0], \dots, x[N] \mid y[0], \dots, y[N-1]`. We start by -noting that if the disturbances are independent of the underlying states of -the system, we can write the conditional probability as - -.. math:: - - p \bigl(x[0], \dots, x[N] \mid y[0], \dots, y[N-1]\bigr) = - p_{X[0]}(x[0])\, \prod_{k=0}^{N-1} p_V\bigl(y[k] - h(x[k])\bigr)\, - p\bigl(x[k+1] \mid x[k]\bigr). - -This expression can be further simplified by taking the log of the -expression and maximizing the function - -.. math:: - :label: eq_fusion_log-likelihood - - \log p_{X[0]}(x[0]) + \sum_{k=0}^{N-1} \log - p_W \bigl(y[k] - h(x[k])\bigr) + \log p_V(v[k]). - -The first term represents the likelihood of the initial state, the -second term captures the likelihood of the noise signal, and the final -term captures the likelihood of the disturbances. - -If we return to the case where :math:`V` and :math:`W` are modeled as -Gaussian processes, then it can be shown that maximizing equation -:eq:`eq_fusion_log-likelihood` is equivalent to solving the optimization -problem given by - -.. math:: - :label: eq_fusion_oep-gaussian - - \min_{x[0], \{v[0], \dots, v[N-1]\}} - \|x[0] - \bar x[0]\|_{P_0^{-1}} + \sum_{k=0}^{N-1} - \|y[k] - h(x_k)\|_{R_W^{-1}}^2 + - \|v[k] \|_{R_V^{-1}}^2, - -where :math:`P_0`, :math:`R_V`, and :math:`R_W` are the covariances of the -initial state, disturbances, and measurement noise. - -Note that while the optimization is carried out only over the estimated -initial state :math:`\hat x[0]`, the entire history of estimated states can -be reconstructed using the system dynamics: - -.. math:: - - \hat x[k+1] = f(\hat x[k], u[k], v[k]), \quad k = 0, \dots, N-1. - -In particular, we can obtain the estimated state at the end of the moving -horizon window, corresponding to the current time, and we can thus -implement an estimator by repeatedly solving the optimization of a window -of length :math:`N` backwards in time. - Module usage ------------ The optimization-based control module provides a means of computing optimal trajectories for nonlinear systems and implementing -optimization-based controllers, including model predictive control and -moving horizon estimation. It follows the basic problem setups -described above, but carries out all computations in *discrete time* -(so that integrals become sums) and over a *finite horizon*. To access -the optimal control modules, import `control.optimal`:: +optimization-based controllers, including model predictive control. +It follows the basic problem setups described above, but carries out +all computations in *discrete time* (so that integrals become sums) +and over a *finite horizon*. To access the optimal control modules, +import `control.optimal`:: import control.optimal as opt @@ -278,8 +177,6 @@ To simplify the specification of cost functions and constraints, the :mod:`optimal` module defines a number of utility functions for optimal control problems: -.. currentmodule:: control - .. autosummary:: optimal.quadratic_cost @@ -290,32 +187,6 @@ optimal control problems: optimal.state_poly_constraint optimal.state_range_constraint -The optimization-based control module also implements functions for solving -optimal estimation problems. The -:class:`optimal.OptimalEstimationProblem` class is used to define -an optimal estimation problem over a finite horizon:: - - oep = OptimalEstimationProblem(sys, timepts, cost[, constraints]) - -Given noisy measurements :math:`y` and control inputs :math:`u`, an -estimate of the states over the time points can be computed using the -:func:`optimal.OptimalEstimationProblem.compute_estimate` method:: - - estim = oep.compute_optimal(Y, U[, X0=x0, initial_guess=(xhat, v)]) - xhat, v, w = estim.states, estim.inputs, estim.outputs - -For discrete time systems, the -:func:`optimal.OptimalEstimationProblem.create_mhe_iosystem` -method can be used to generate an input/output system that implements a -moving horizon estimator. - -Several functions are available to help set up standard optimal estimation -problems: - -.. autosummary:: - - optimal.gaussian_likelihood_cost - optimal.disturbance_range_constraint Example ------- diff --git a/doc/phaseplot.rst b/doc/phaseplot.rst index ded98e365..0a9240ea1 100644 --- a/doc/phaseplot.rst +++ b/doc/phaseplot.rst @@ -1,9 +1,9 @@ .. currentmodule:: control - + Phase plane plots ================= Insight into nonlinear systems can often be obtained by looking at phase -plane diagrams. The :func:`~control.phase_plane_plot` function allows the +plane diagrams. The :func:`phase_plane_plot` function allows the creation of a 2-dimensional phase plane diagram for a system. This functionality is supported by a set of mapping functions that are part of the `phaseplot` module. @@ -56,7 +56,7 @@ multiple features in the phase plane plot give a good global picture of the topological structure of solutions of the dynamical system. Phase plots can be built up by hand using a variety of helper functions that -are part of the :mod:`~control.phaseplot` (pp) module:: +are part of the :mod:`phaseplot` (pp) module:: import control.phaseplot as pp @@ -85,13 +85,13 @@ The following helper functions are available: phaseplot.streamlines phaseplot.vectorfield -The :func:`~control.phase_plane_plot` function calls these helper functions +The :func:`phase_plane_plot` function calls these helper functions based on the options it is passed. Note that unlike other plotting functions, phase plane plots do not involve computing a response and then plotting the result via a `plot()` method. Instead, the plot is generated directly be a call to the -:func:`~control.phase_plane_plot` function (or one of the -:mod:`~control.phaseplot` helper functions. +:func:`phase_plane_plot` function (or one of the +:mod:`phaseplot` helper functions. diff --git a/doc/stochastic.rst b/doc/stochastic.rst index f9a8dbb51..7c53dde26 100644 --- a/doc/stochastic.rst +++ b/doc/stochastic.rst @@ -1,3 +1,106 @@ ****************** Stochastic Systems ****************** + +Optimal estimation problem setup +-------------------------------- + +Consider a nonlinear system with discrete time dynamics of the form + +.. math:: + :label: eq_fusion_nlsys-oep + + X[k+1] = f(X[k], u[k], V[k]), \qquad Y[k] = h(X[k]) + W[k], + +where :math:`X[k] \in \mathbb{R}^n`, :math:`u[k] \in \mathbb{R}^m`, and +:math:`Y[k] \in \mathbb{R}^p`, and :math:`V[k] \in \mathbb{R}^q` and +:math:`W[k] \in \mathbb{R}^p` represent random processes that are not +necessarily Gaussian white noise processes. The estimation problem that we +wish to solve is to find the estimate :math:`\hat x[\cdot]` that matches +the measured outputs :math:`y[\cdot]` with "likely" disturbances and +noise. + +For a fixed horizon of length :math:`N`, this problem can be formulated as +an optimization problem where we define the likelihood of a given estimate +(and the resulting noise and disturbances predicted by the model) as a cost +function. Suppose we model the likelihood using a conditional probability +density function :math:`p(x[0], \dots, x[N] \mid y[0], \dots, y[N-1])`. +Then we can pose the state estimation problem as + +.. math:: + :label: eq_fusion_oep + + \hat x[0], \dots, \hat x[N] = + \arg \max_{\hat x[0], \dots, \hat x[N]} + p(\hat x[0], \dots, \hat x[N] \mid y[0], \dots, y[N-1]) + +subject to the constraints given by equation :eq:`eq_fusion_nlsys-oep`. +The result of this optimization gives us the estimated state for the +previous :math:`N` steps in time, including the "current" time +:math:`x[N]`. The basic idea is thus to compute the state estimate that is +most consistent with our model and penalize the noise and disturbances +according to how likely they are (based on the given stochastic system +model for each). + +Given a solution to this fixed-horizon optimal estimation problem, we can +create an estimator for the state over all times by repeatedly applying the +optimization problem :eq:`eq_fusion_oep` over a moving horizon. At each +time :math:`k`, we take the measurements for the last :math:`N` time steps +along with the previously estimated state at the start of the horizon, +:math:`x[k-N]` and reapply the optimization in equation +:eq:`eq_fusion_oep`. This approach is known as a \define{moving horizon +estimator} (MHE). + +The formulation for the moving horizon estimation problem is very general +and various situations can be captured using the conditional probability +function :math:`p(x[0], \dots, x[N] \mid y[0], \dots, y[N-1]`. We start by +noting that if the disturbances are independent of the underlying states of +the system, we can write the conditional probability as + +.. math:: + + p \bigl(x[0], \dots, x[N] \mid y[0], \dots, y[N-1]\bigr) = + p_{X[0]}(x[0])\, \prod_{k=0}^{N-1} p_V\bigl(y[k] - h(x[k])\bigr)\, + p\bigl(x[k+1] \mid x[k]\bigr). + +This expression can be further simplified by taking the log of the +expression and maximizing the function + +.. math:: + :label: eq_fusion_log-likelihood + + \log p_{X[0]}(x[0]) + \sum_{k=0}^{N-1} \log + p_W \bigl(y[k] - h(x[k])\bigr) + \log p_V(v[k]). + +The first term represents the likelihood of the initial state, the +second term captures the likelihood of the noise signal, and the final +term captures the likelihood of the disturbances. + +If we return to the case where :math:`V` and :math:`W` are modeled as +Gaussian processes, then it can be shown that maximizing equation +:eq:`eq_fusion_log-likelihood` is equivalent to solving the optimization +problem given by + +.. math:: + :label: eq_fusion_oep-gaussian + + \min_{x[0], \{v[0], \dots, v[N-1]\}} + \|x[0] - \bar x[0]\|_{P_0^{-1}} + \sum_{k=0}^{N-1} + \|y[k] - h(x_k)\|_{R_W^{-1}}^2 + + \|v[k] \|_{R_V^{-1}}^2, + +where :math:`P_0`, :math:`R_V`, and :math:`R_W` are the covariances of the +initial state, disturbances, and measurement noise. + +Note that while the optimization is carried out only over the estimated +initial state :math:`\hat x[0]`, the entire history of estimated states can +be reconstructed using the system dynamics: + +.. math:: + + \hat x[k+1] = f(\hat x[k], u[k], v[k]), \quad k = 0, \dots, N-1. + +In particular, we can obtain the estimated state at the end of the moving +horizon window, corresponding to the current time, and we can thus +implement an estimator by repeatedly solving the optimization of a window +of length :math:`N` backwards in time. From eb70e962eaa35754189e4a779b726233d96361c6 Mon Sep 17 00:00:00 2001 From: Richard Murray Date: Sat, 28 Dec 2024 16:38:30 -0800 Subject: [PATCH 38/93] update iosys.rst and associated doc files and docstrings --- control/lti.py | 5 ++ doc/figures/bdalg-feedback.png | Bin 0 -> 32280 bytes doc/functions.rst | 2 + doc/index.rst | 1 - doc/iosys.rst | 137 ++++++++++++++++++++++++++++----- 5 files changed, 126 insertions(+), 19 deletions(-) create mode 100644 doc/figures/bdalg-feedback.png diff --git a/control/lti.py b/control/lti.py index 066230697..2f2848177 100644 --- a/control/lti.py +++ b/control/lti.py @@ -61,6 +61,11 @@ def damp(self): zeta = -real(splane_poles)/wn return wn, zeta, poles + def feedback(self, other=1, sign=-1): + """Feedback interconnection between two LTI objects.""" + # Implemented in subclasses, but documented here + return NotImplemented + def frequency_response(self, omega=None, squeeze=None): """Evaluate LTI system response at an array of frequencies. diff --git 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ziW!lH06WjVN7?Bg{?-k4qx*N!<~8ILav{V3Y=m^z#ZBA(;cqiAK{M+2*T7d`5Ww5V zBId0${gUqLuWNs+f__wSp8JPx{OQ{Nhtz6U7%QELuPB+n;GZ2bFzD+TX%~=eDgOub CJX^2; literal 0 HcmV?d00001 diff --git a/doc/functions.rst b/doc/functions.rst index b9bb4f504..e101ce8ac 100644 --- a/doc/functions.rst +++ b/doc/functions.rst @@ -10,6 +10,7 @@ Function reference :no-inherited-members: :no-special-members: + System creation =============== @@ -243,6 +244,7 @@ Utility functions db2mag isctime isdtime + iosys_repr issiso mag2db reset_defaults diff --git a/doc/index.rst b/doc/index.rst index dc3e05097..7cb110db4 100644 --- a/doc/index.rst +++ b/doc/index.rst @@ -94,4 +94,3 @@ Your contributions are welcome! Simply fork the `GitHub repository `_. @@ -123,7 +220,7 @@ lynxes as the desired output (following `Feedback Systems To construct the control law, we build a simple input/output system that applies a corrective input based on deviations from the equilibrium point. This system has no dynamics, since it is a static (affine) map, and can -constructed using :func:`~control.nlsys` with no update function: +constructed using :func:`nlsys` with no update function: .. code-block:: python @@ -169,13 +266,13 @@ Finally, we simulate the closed loop system: plt.legend(['input']) plt.show(block=False) -Additional features -=================== +This example shows the standard operations that would be used to build +up an interconnected nonlinear system. The I/O systems module has a +number of other features that can be used to simplify the creation and +use of interconnected input/output systems. -The I/O systems module has a number of other features that can be used to -simplify the creation and use of interconnected input/output systems. -Vector elements processing +Vector element processing -------------------------- Several I/O system commands perform processing of vector elements @@ -183,7 +280,7 @@ Several I/O system commands perform processing of vector elements proper shape. For static elements, such as the initial state in a simulation or the -nominal state and input for a linearization), the following processing +nominal state and input for a linearization, the following processing is done: * Scalars are automatically converted to a vector of the appropriate @@ -254,6 +351,7 @@ use the list processing feature combined with time series broadcasting:: In this command, the second and third arguments will be broadcast to match the number of time points. + Summing junction ---------------- @@ -290,6 +388,7 @@ the command will produce an input/output block that implements `e[0] = r[0] - y[0]` and `e[1] = r[1] - y[1]`. + Automatic connections using signal names ---------------------------------------- @@ -317,6 +416,7 @@ of the interconnected system) is not found, but inputs and outputs of individual systems that are not connected to other systems are left unconnected (so be careful!). + Advanced specification of signal names -------------------------------------- @@ -430,6 +530,7 @@ complicated to debug error message when things go wrong. Setting information about how the arguments are processed that may be helpful in understanding what is going wrong. + Automated creation of state feedback systems -------------------------------------------- From ecc3b5a1b7e1e25340000b9273b6a52eb9b74e6e Mon Sep 17 00:00:00 2001 From: Richard Murray Date: Sat, 28 Dec 2024 17:09:33 -0800 Subject: [PATCH 39/93] update stochastic.rst --- doc/statesp.rst | 48 +--------- doc/stochastic.rst | 227 ++++++++++++++++++++++++++++++++++++++++++++- 2 files changed, 229 insertions(+), 46 deletions(-) diff --git a/doc/statesp.rst b/doc/statesp.rst index cbf96963e..0eb3e67d3 100644 --- a/doc/statesp.rst +++ b/doc/statesp.rst @@ -162,46 +162,8 @@ calling the :func:`place` function:: where `E` is the desired location of the eigenvalues and `.T` computes the transpose of a matrix. -Alternatively, an optimal estimator can be computed using the -:func:`lqe` function. We consider a continuous time, state space -system - -.. math:: - - \frac{dx}{dt} &= Ax + Bu + Gw \\ - y &= Cx + Du + v - -with unbiased process noise :math:`w` and measurement noise :math:`v` -with covariances satisfying - -.. math:: - - {\mathbb E}\{w w^T\} = QN,\qquad - {\mathbb E}\{v v^T\} = RN,\qquad - {\mathbb E}\{w v^T\} = NN - -where :math:`{\mathbb E}\{\cdot\}` represents expectation. - -The :func:`lqe` function computes the observer gain matrix :math:`L` -such that the stationary (non-time-varying) Kalman filter - -.. math:: - - \frac{d\hat x}{dt} = A \hat x + B u + L(y - C\hat x - D u), - -produces a state estimate :math:`\hat x` that minimizes the expected -squared error using the sensor measurements :math:`y`. - -As with the :func:`lqr` function, the :func:`lqe` function can be called in several forms: - - * `L, P, E = lqe(sys, QN, RN)` - * `L, P, E = lqe(sys, QN, RN, NN)` - * `L, P, E = lqe(A, G, C, QN, RN)` - * `L, P, E = lqe(A, G, C, QN, RN, NN)` - -where `sys` is an :class:`LTI` object, and `A`, `G`, `C`, `QN`, `RN`, -and `NN` are 2D arrays of appropriate dimension. If `sys` is a -discrete time system, the first two forms will compute the discrete -time optimal controller. For the second two forms, the :func:`dlqr` -function can be used. Additional arguments and details are given on -the :func:`lqr` and :func:`dlqr` documentation pages. +More sophisticated estimators can be constructed by modeling noise and +disturbances as stochastic signals generated by a random process. +Estimators constructed using these models are described in more detail +in the :ref:kalman-filter section of the :ref:stochastic-systems +chapter. diff --git a/doc/stochastic.rst b/doc/stochastic.rst index 7c53dde26..86f09f7bf 100644 --- a/doc/stochastic.rst +++ b/doc/stochastic.rst @@ -1,11 +1,204 @@ +.. currentmodule:: control + +.. _stochastic-systems: + ****************** Stochastic Systems ****************** -Optimal estimation problem setup --------------------------------- +The Python Control Systems Library has support for basic operations +involving linear and nonlinear I/O systems with Gaussian white noise +as an input. + + +Stochastic signals +================== + +A stochastic signal is a representation of the output of a random +process. NumPy and SciPy have a number of functions to calculate the +covariance and correlation of random signals: + + * :func:`numpy.cov` - returns the sample variance of vector random + variable :math:`X \in \mathbb{R}^n` where the input argument + represents samples of :math:`X`. + + * :func:`numpy.cov` - returns the (cross-)covariance of the + variables :math:`X` and :math:`Y` where the input arguments + represent samples of the given random variables. + + * :func:`scipy.signal.correlate` - the "cross-correlation" between two + random (1D) sequences. If these sequences came from a random + process, this is a single sample approximation of the (discrete + time) correlation function. Use the function + :func:`scipy.signal.correlation_lags` to compute the lag + :math:`\tau` and :func:`scipy.signal.correlate` to get the (auto) + correlation function :math:`r_X(\tau)`. + +The python-control package has variants of these functions that do +appropriate processing for continuous time models. + +The :func:`white_noise` function generates a (multi-variable) white +noise signal of specified intensity as either a sampled continuous time +signal or a discrete time signal. A white noise signal along a 1D +array of linearly spaced set of times `timepts` can be computing using + +.. code:: + + V = ct.white_noise(timepts, Q[, dt]) + +where `Q` is a positive definite matrix providing the noise +intensity and `dt` is the sampling time (or 0 for continuous time). + +In continuous time, the white noise signal is scaled such that the +integral of the covariance over a sample period is `Q`, thus +approximating a white noise signal. In discrete time, the white noise +signal has covariance `Q` at each point in time (without any +scaling based on the sample time). + +The python-control :func:`correlation` function computes the +correlation matrix :math:`{\mathbb E}\{X^\mathsf{T}(t+\tau) X(t)\}` or the +cross-correlation matrix :math:`{\mathbb E}\{X^\mathsf{T}(t+\tau) Y(t)\}`: + +.. code:: + + tau, Rtau = ct.correlation(timepts, X[, Y]) + +where :math:`\mathbb{E}` represents expectation. The signal `X` (and +`Y`, if present) represents a continuous time signal sampled at +regularly spaced times `timepts`. The return value provides the +correlation :math:`R_\tau` between :math:`X(t+\tau)` and :math:`X(t)` +at a set of time offsets :math:`\tau` (determined based on the spacing +of entries in the `timepts` vector. -Consider a nonlinear system with discrete time dynamics of the form +Note that the computation of the correlation function is based on a +single time signal (or pair of time signals) and is thus a very crude +approximation to the true correlation function between two random +processes. + +To compute the response of a linear (or nonlinear) system to a white +noise input, use the :func:`forced_response` (or +:func:`input_output_response`) function: + +.. code:: + + a, c = 1, 1 + sys = ct.ss([[-a]], [[1]], [[c]], 0) + timepts = np.linspace(0, 5, 1000) + Q = np.array([[0.1]]) + V = ct.white_noise(timepts, Q) + resp = ct.forced_response(sys, timepts, V) + +The correlation function for the output can be computed using the +:func:`correlation` function and compared to the analytical expression: + +.. code:: + + tau, r_Y = ct.correlation(timepts, resp.outputs) + plt.plot(tau, r_Y) + plt.plot(tau, c**2 * Q.item() / (2 * a) * np.exp(-a * np.abs(tau))) + +.. _kalman-filter: + +Linear quadratic estimation (Kalman filter) +=========================================== + +A standard application of stochastic linear systems is the computation +of the linear estimator under the assumption of white Gaussian +measurement and process noise. This estimator is called the linear +quadratic estimator (LQE) and its gains can be computed using the +:func:`lqe` function. + +We consider a continuous time, state space system + +.. math:: + + \frac{dx}{dt} &= Ax + Bu + Gw \\ + y &= Cx + Du + v + +with unbiased process noise :math:`w` and measurement noise :math:`v` +with covariances satisfying + +.. math:: + + {\mathbb E}\{w w^T\} = QN,\qquad + {\mathbb E}\{v v^T\} = RN,\qquad + {\mathbb E}\{w v^T\} = NN + +where :math:`{\mathbb E}\{\cdot\}` represents expectation. + +The :func:`lqe` function computes the observer gain matrix :math:`L` +such that the stationary (non-time-varying) Kalman filter + +.. math:: + + \frac{d\hat x}{dt} = A \hat x + B u + L(y - C\hat x - D u), + +produces a state estimate :math:`\hat x` that minimizes the expected +squared error using the sensor measurements :math:`y`. + +As with the :func:`lqr` function, the :func:`lqe` function can be +called in several forms: + + * `L, P, E = lqe(sys, QN, RN)` + * `L, P, E = lqe(sys, QN, RN, NN)` + * `L, P, E = lqe(A, G, C, QN, RN)` + * `L, P, E = lqe(A, G, C, QN, RN, NN)` + +where `sys` is an :class:`LTI` object, and `A`, `G`, `C`, `QN`, `RN`, +and `NN` are 2D arrays of appropriate dimension. If `sys` is a +discrete time system, the first two forms will compute the discrete +time optimal controller. For the second two forms, the :func:`dlqr` +function can be used. Additional arguments and details are given on +the :func:`lqr` and :func:`dlqr` documentation pages. + +The :func:`create_estimator_iosystem` function can be used to create +an I/O system implementing a Kalman filter, including integration of +the Riccati ODE. The command has the form + +.. code:: + + estim = ct.create_estimator_iosystem(sys, Qv, Qw) + +The input to the estimator is the measured outputs `Y` and the system +input `U`. To run the estimator on a noisy signal, use the command + +.. code:: + + resp = ct.input_output_response(est, timepts, [Y, U], [X0, P0]) + +If desired, the :func:`correct` parameter can be set to :func:`False` +to allow prediction with no additional sensor information:: + + resp = ct.input_output_response( + estim, timepts, 0, [X0, P0], param={'correct': False}) + +The :func:`create_statefbk_iosystem` function can be used to combine +an estimator with a state feedback controller:: + + K, _, _ = ct.lqr(sys, Qx, Qu) + estim = ct.create_estimator_iosystem(sys, Qv, Qw, P0) + ctrl, clsys = ct.create_statefbk_iosystem(sys, K, estimator=estim) + +The controller will have the same form as a full state feedback +controller, but with the system state :math:`x` input replaced by the +estimated state :math:`\hat x` (output of `estim`): + +.. math:: + + u = u_\text{d} - K (\hat x - x_\text{d}). + +The closed loop controller `clsys` includes both the state +feedback and the estimator dynamics and takes as its input the desired +state :math:`x_\text{d}` and input :math:`u_\text{d}`:: + + resp = ct.input_output_response( + clsys, timepts, [Xd, Ud], [X0, np.zeros_like(X0), P0]) + + +Maximum likelihood estimation +============================= + +Consider a *nonlinear* system with discrete time dynamics of the form .. math:: :label: eq_fusion_nlsys-oep @@ -104,3 +297,31 @@ In particular, we can obtain the estimated state at the end of the moving horizon window, corresponding to the current time, and we can thus implement an estimator by repeatedly solving the optimization of a window of length :math:`N` backwards in time. + +The :mod:`optimal` module described in the :ref:`optimal-module` +section implements functions for solving optimal estimation problems +using maximum likelihood estimation. The +:class:`optimal.OptimalEstimationProblem` class is used to define an +optimal estimation problem over a finite horizon:: + + oep = opt.OptimalEstimationProblem(sys, timepts, cost[, constraints]) + +Given noisy measurements :math:`y` and control inputs :math:`u`, an +estimate of the states over the time points can be computed using the +:func:`optimal.OptimalEstimationProblem.compute_estimate` method:: + + estim = oep.compute_optimal(Y, U[, X0=x0, initial_guess=(xhat, v)]) + xhat, v, w = estim.states, estim.inputs, estim.outputs + +For discrete time systems, the +:func:`optimal.OptimalEstimationProblem.create_mhe_iosystem` method +can be used to generate an input/output system that implements a +moving horizon estimator. + +Several functions are available to help set up standard optimal estimation +problems: + +.. autosummary:: + + optimal.gaussian_likelihood_cost + optimal.disturbance_range_constraint From 02f7a13a8450e2ce55b6fdc21787de83248ce09a Mon Sep 17 00:00:00 2001 From: Richard Murray Date: Sat, 28 Dec 2024 17:36:13 -0800 Subject: [PATCH 40/93] spelling corrections --- doc/intro.rst | 18 +++++++++--------- doc/iosys.rst | 15 ++++++++------- doc/linear.rst | 20 ++++++++++---------- doc/nlsys.rst | 7 ++++--- doc/optimal.rst | 2 +- doc/stochastic.rst | 19 +++++++++---------- 6 files changed, 41 insertions(+), 40 deletions(-) diff --git a/doc/intro.rst b/doc/intro.rst index 93c32fa14..6db3563c3 100644 --- a/doc/intro.rst +++ b/doc/intro.rst @@ -49,7 +49,7 @@ To install using pip:: .. note:: If you install Slycot using pip you'll need a development - environment (e.g., Python development files, C, and Fortran compilers). + environment (e.g., Python development files, C, and FORTRAN compilers). Pip installation can be particularly complicated for Windows. Many parts of `python-control` will work without `slycot`, but some @@ -81,7 +81,7 @@ control package:: !pip install control import control as ct -Note that Google colab does not currently support Slycot, so some +Note that Google Colab does not currently support Slycot, so some functionality may not be available. @@ -91,11 +91,11 @@ Package conventions The python-control package makes use of a few naming and calling conventions: * Function names are written in lower case with underscores between - words ('frequency_response'). + words (`frequency_response`). -* Class names use camel case ('StateSpace', 'ControlPlot', etc) and - instances of the class are created with "factory functions" ('ss') - or as the output of an operation ('bode_plot'). +* Class names use camel case (`StateSpace`, `ControlPlot`, etc) and + instances of the class are created with "factory functions" (`ss`) + or as the output of an operation (`bode_plot`). * Functions that return multiple values use either objects (with elements for each return value) or tuples. For those functions that @@ -105,10 +105,10 @@ The python-control package makes use of a few naming and calling conventions: K, _, _ = lqr(sys) * Python-control supports both single-input, single-output (SISO) - systems and mullti-input, multi-output (MIMO) systems, including + systems and multi-input, multi-output (MIMO) systems, including time and frequency responses. By default, SISO systems will typically generate objects that have the input and output dimensions - supressed (using the NumPy :func:`~numpy.squeeze` function). The + suppressed (using the NumPy :func:`~numpy.squeeze` function). The `squeeze` keyword can be set to `False` to force functions to return objects that include the input and output dimensions. @@ -136,7 +136,7 @@ some things to keep in mind: * Functions that in MATLAB would return variable numbers of values will have a parameter of the form `return_` that is used to return additional data. (These functions usually return an object of - a class that has attributpes that can be used to access the + a class that has attributes that can be used to access the information and this is the preferred usage pattern.) * You cannot use braces for collections; use tuples instead. * Time series data have time as the final index (see diff --git a/doc/iosys.rst b/doc/iosys.rst index 2886b5166..7ee889a2b 100644 --- a/doc/iosys.rst +++ b/doc/iosys.rst @@ -8,7 +8,7 @@ Interconnected I/O Systems Input/output systems can be interconnected in a variety of ways, including operator overloading, block diagram algebra functions, and -using the :func:`interconnect` function to build a hiearchical system +using the :func:`interconnect` function to build a hierarchical system description. This chapter provides more detailed information on operator overloading and block diagram algebra, as well as a description of the :class:`InterconnectedSystem` class, which can be @@ -23,7 +23,7 @@ The following operators are defined to operate between I/O systems: :header-rows: 1 * - Operation - - Desription + - Description - Equivalent command * - `sys1 + sys2` - Add the outputs of two systems receiving the same input @@ -104,7 +104,7 @@ signals to be specified using the usual `name`, `inputs`, and `outputs` keywords, as described in the :class:`InputOutputSystem` class. For state space systems, the names of the states can also be given, but caution should be used since the order of states in the -combined system is not gauranteed. +combined system is not guaranteed. Signal-based interconnection @@ -444,9 +444,9 @@ is not specified, then it defaults to the minimum or maximum value of the signal range. Note that despite the similarity to slice notation, negative indices and step specifications are not supported. -Using these various forms can simplfy the specification of +Using these various forms can simplify the specification of interconnections. For example, consider a process with inputs 'u' and -'v', each of dimension 2, and two outputs 'w' and 'y', each of +'v', each of dimension 2, and two outputs 'w' and 'y', each of dimension 2:: P = ct.rss( @@ -472,7 +472,7 @@ the closed loop system to consist of all system outputs `y` and `z`, as well as the controller input `u`. This collection of systems can be combined in a variety of ways. The -most explict would specify every signal:: +most explicit would specify every signal:: clsys1 = ct.interconnect( [C, P, sumblk], @@ -569,7 +569,8 @@ feedforward gain (normally chosen so that the steady state output A reference gain controller can be created with the command:: - ctrl, clsys = ct.create_statefbk_iosystem(sys, K, kf, feedfwd_pattern='refgain') + ctrl, clsys = ct.create_statefbk_iosystem( + sys, K, kf, feedfwd_pattern='refgain') This reference gain design pattern is described in more detail in Section 7.2 of `Feedback Systems `_ (Stabilization diff --git a/doc/linear.rst b/doc/linear.rst index 5f2fda208..5f4128a11 100644 --- a/doc/linear.rst +++ b/doc/linear.rst @@ -64,7 +64,7 @@ operations as well as the :func:`feedback`, :func:`parallel`, and :ref:`function-ref`. The :func:`rss` function can be used to create a random state space -system with a desired numbef or inputs, outputs, and states:: +system with a desired number or inputs, outputs, and states:: sys = ct.rss(states=4, outputs=1, inputs=1], strictly_proper=True) @@ -160,7 +160,7 @@ and `response.response` (for the complex response). Multi-input, multi-output (MIMO) systems ---------------------------------------- -Multi-input, multi-output (MIMO) sytems are created by providing +Multi-input, multi-output (MIMO) systems are created by providing parameters of the appropriate dimensions to the relevant factory function. For transfer functions, this is done by providing a 2D list of numerator and denominator polynomials to the :func:`tf` function, @@ -168,7 +168,7 @@ e.g.:: sys = ct.tf( [[num11, num12], [num21, num22]], - [[den11, den12], [den21, denm22]]) + [[den11, den12], [den21, den22]]) Similarly, MIMO frequency response data (FRD) systems are created by providing the :func:`frd` function with a 3D array of response @@ -252,7 +252,7 @@ time system from a continuous time system. See changing the value of `config.defaults['control.default_dt']`. Functions operating on LTI systems will take into account whether a -system is continous time or discrete time when carrying out operations +system is continuous time or discrete time when carrying out operations that depend on this difference. For example, the :func:`rss` function will place all system eigenvalues within the unit circle when called using `dt` corresponding to a discrete time system:: @@ -271,7 +271,7 @@ Model conversion and reduction ============================== A variety of functions are available to manipulate LTI systems, -including functions for convering between state space and frequency +including functions for converting between state space and frequency domain, sampling systems in time and frequency domain, and creating reduced order models. @@ -286,7 +286,7 @@ explicit conversions are not necessary, since functions designed to operate on LTI systems will work on any subclass. To explicitly convert a state space system into a transfer function -represetation, the state space system can be passed as an argument to +representation, the state space system can be passed as an argument to the :func:`tf` factory functions:: sys_ss = ct.rss(4, 2, 2, name='sys_ss') @@ -315,7 +315,7 @@ Conversion of transfer functions to state space form is also possible:: Time sampling ------------- -Continous time systems can be converted to discrete time systems using +Continuous time systems can be converted to discrete time systems using the :func:`sample_system` function and specifying a sampling time:: >> csys = ct.rss(4, 2, 2, name='csys') @@ -345,7 +345,7 @@ the :func:`sample_system` function and specifying a sampling time:: dt = 0.1 Note that the system name for the discrete time system is the name of -the origal system with the string '$sampled' appended. +the original system with the string '$sampled' appended. Discrete time systems can also be created using the :func:`StateSpace.sample` or :func:`TransferFunction.sample` methods @@ -410,7 +410,7 @@ realization on the stable part, then reinserts the unstable modes. The :func:`minimal_realization` function eliminates uncontrollable or unobservable states in state space models or cancels pole-zero pairs -in transfer functions. The resuling output system has minimal order +in transfer functions. The resulting output system has minimal order and the same input/output response characteristics as the original model system. Unlike the :func:`balanced_reduction` function, the :func:`minimal_realization` eliminates all uncontrollable and/or @@ -457,7 +457,7 @@ Information about an LTI system can be obtained using the Python D = [[-0. 0.] [ 0. 0.]] -For a more compact represention, LTI objects can be evaluated directly +For a more compact representation, LTI objects can be evaluated directly to return a summary of the system input/output properties:: >>> sys = ct.rss(4, 2, 2, name='sys_2x2') diff --git a/doc/nlsys.rst b/doc/nlsys.rst index 9b0b8aee3..68e5d8632 100644 --- a/doc/nlsys.rst +++ b/doc/nlsys.rst @@ -30,10 +30,10 @@ The `updfcn` argument is a function returning the state update function:: updfcn(t, x, u, params) -> array where `x` is a 1-D array with shape (n,), `u` is a 1-D array -with shape (m,), `t` is a float representing the currrent time, +with shape (m,), `t` is a float representing the current time, and `params` is a dict containing the values of parameters used by the function. The dynamics of the system can be in continuous or discrete -time (use the `dt` keyword to create a discrte time system). +time (use the `dt` keyword to create a discrete time system). The output function `outfcn` is used to specify the outputs of the system and has the same calling signature as `updfcn`. If it is not @@ -135,7 +135,8 @@ time systems). More complex operating points can be found by specifying which states, inputs, or outputs should be used in computing the operating point, as well as desired values of the states, inputs, outputs, or state -updates. See the :func:`find_operating_point` documentation for more deatils. +updates. See the :func:`find_operating_point` documentation for more +details. Simulations and plotting diff --git a/doc/optimal.rst b/doc/optimal.rst index 57e654918..f15422ab7 100644 --- a/doc/optimal.rst +++ b/doc/optimal.rst @@ -279,7 +279,7 @@ Plotting the results:: plt.xlabel("t [sec]") plt.ylabel("u2 [rad/s]") - plt.suptitle("Lane change manuever") + plt.suptitle("Lane change maneuver") plt.tight_layout() plt.show() diff --git a/doc/stochastic.rst b/doc/stochastic.rst index 86f09f7bf..c6d871145 100644 --- a/doc/stochastic.rst +++ b/doc/stochastic.rst @@ -15,16 +15,15 @@ Stochastic signals ================== A stochastic signal is a representation of the output of a random -process. NumPy and SciPy have a number of functions to calculate the -covariance and correlation of random signals: - - * :func:`numpy.cov` - returns the sample variance of vector random - variable :math:`X \in \mathbb{R}^n` where the input argument - represents samples of :math:`X`. - - * :func:`numpy.cov` - returns the (cross-)covariance of the - variables :math:`X` and :math:`Y` where the input arguments - represent samples of the given random variables. +process. NumPy and SciPy have a functions to calculate the covariance +and correlation of random signals: + + * :func:`numpy.cov` - with a single argument, returns the sample + variance of a vector random variable :math:`X \in \mathbb{R}^n` + where the input argument represents samples of :math:`X`. With + two arguments, returns the (cross-)covariance of random variables + :math:`X` and :math:`Y` where the input arguments represent + samples of the given random variables. * :func:`scipy.signal.correlate` - the "cross-correlation" between two random (1D) sequences. If these sequences came from a random From 5d7b328ace37a17050c741d6afe5b95757a050ec Mon Sep 17 00:00:00 2001 From: Richard Murray Date: Sat, 28 Dec 2024 21:45:00 -0800 Subject: [PATCH 41/93] fix doctest errors in {combine,split}_tf (by suppressing output) --- control/bdalg.py | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/control/bdalg.py b/control/bdalg.py index 365a414e6..2410065ed 100644 --- a/control/bdalg.py +++ b/control/bdalg.py @@ -550,7 +550,7 @@ def combine_tf(tf_array, **kwargs): Examples -------- - Combine two transfer functions + Combine two transfer functions: >>> s = ct.tf('s') >>> ct.combine_tf( @@ -565,7 +565,7 @@ def combine_tf(tf_array, **kwargs): [array([1, 2])]], name='G', outputs=2, inputs=1) - Combine NumPy arrays with transfer functions + Combine NumPy arrays with transfer functions: >>> ct.combine_tf( ... [[np.eye(2), np.zeros((2, 1))], @@ -639,6 +639,7 @@ def combine_tf(tf_array, **kwargs): return tf.TransferFunction(num, den, dt=dt, **kwargs) + def split_tf(transfer_function): """Split MIMO transfer function into SISO transfer functions. @@ -657,7 +658,7 @@ def split_tf(transfer_function): Examples -------- - Split a MIMO transfer function + Split a MIMO transfer function: >>> G = ct.tf( ... [ [[87.8], [-86.4]], From 322dba6ec23dd92c191212989232a56c610ee8ff Mon Sep 17 00:00:00 2001 From: Richard Murray Date: Sat, 28 Dec 2024 23:04:30 -0800 Subject: [PATCH 42/93] reorganize functions.rst, classes.rst --- doc/classes.rst | 43 +++++++++++----- doc/functions.rst | 121 +++++++++++++++++++++++++++++++--------------- doc/nonlinear.rst | 2 + 3 files changed, 115 insertions(+), 51 deletions(-) diff --git a/doc/classes.rst b/doc/classes.rst index 971cd4630..1a49d5079 100644 --- a/doc/classes.rst +++ b/doc/classes.rst @@ -5,6 +5,9 @@ Control system classes ********************** +Input/output system classes +=========================== + The classes listed below are used to represent models of input/output systems (both linear time-invariant and nonlinear). They are usually created from factory functions such as :func:`tf` and :func:`ss`, so the @@ -13,13 +16,14 @@ user should normally not need to instantiate these directly. .. autosummary:: :toctree: generated/ :template: custom-class-template.rst + :nosignatures: InputOutputSystem + NonlinearIOSystem LTI StateSpace TransferFunction FrequencyResponseData - NonlinearIOSystem InterconnectedSystem LinearICSystem @@ -30,10 +34,12 @@ another: .. image:: figures/classes.pdf :width: 800 -Additional classes -================== -.. todo:: Break these up into more useful sections +Response and plotting classes +============================= + +These classes are used as the outputs of `_response`, `_map`, and +`_plot` functions: .. autosummary:: :toctree: generated/ @@ -41,6 +47,26 @@ Additional classes :nosignatures: ControlPlot + FrequencyResponseData + TimeResponseData + NyquistResponseData + PoleZeroData + +More informaton on the functions used to create these classes can be +found in the :ref:iosys-module chapter. + + +Nonlinear system classes +======================== + +These classes are used for various nonlinear input/output system +operations: + +.. autosummary:: + :toctree: generated/ + :template: custom-class-template.rst + :nosignatures: + DescribingFunctionNonlinearity DescribingFunctionResponse flatsys.BasisFamily @@ -50,16 +76,11 @@ Additional classes flatsys.LinearFlatSystem flatsys.PolyFamily flatsys.SystemTrajectory - FrequencyResponseList - NyquistResponseData OperatingPoint optimal.OptimalControlProblem optimal.OptimalControlResult optimal.OptimalEstimationProblem optimal.OptimalEstimationResult - PoleZeroData - TimeResponseData - TimeResponseList -The use of these classes is described in more detail in the -:ref:`flatsys-module` module and the :ref:`optimal-module` module +More informaton on the functions used to create these classes can be +found in the :ref:nonlinear-systems chapter. diff --git a/doc/functions.rst b/doc/functions.rst index e101ce8ac..b2d7b5045 100644 --- a/doc/functions.rst +++ b/doc/functions.rst @@ -14,8 +14,8 @@ Function reference System creation =============== -The control toolbox makes use of "factory functions" to create input/output -systems of different types (classes): +Functions that create input/output systems from a description of the +system properties: .. autosummary:: :toctree: generated/ @@ -25,10 +25,11 @@ systems of different types (classes): frd nlsys zpk + pade rss drss -Systems can also be created by transforming existing systems: +Functions that transform systems from one form to another: .. autosummary:: :toctree: generated/ @@ -38,7 +39,6 @@ Systems can also be created by transforming existing systems: observable_form reachable_form similarity_transform - pade ss2tf tf2ss tfdata @@ -64,8 +64,9 @@ System interconnections combine_tf split_tf -Time domain simulation -====================== + +Time response +============= .. autosummary:: :toctree: generated/ @@ -78,6 +79,12 @@ Time domain simulation step_response time_response_plot combine_time_responses + +Additional functions for customizing phase plots: + +.. autosummary:: + :toctree: generated/ + phaseplot.boxgrid phaseplot.circlegrid phaseplot.equilpoints @@ -104,24 +111,39 @@ Frequency response nichols_plot nichols_grid + Control system analysis ======================= + +Time domain analysis: + .. autosummary:: :toctree: generated/ - bandwidth damp + step_info + +Frequency domain analysis: + +.. autosummary:: + :toctree: generated/ + + bandwidth dcgain - get_input_ff_index - get_output_fb_index - ispassive linfnorm margin - solve_passivity_LMI stability_margins - step_info system_norm phase_crossover_frequencies + singular_values_plot + singular_values_response + sisotool + +Pole/zero-based analysis: + +.. autosummary:: + :toctree: generated/ + poles zeros pole_zero_map @@ -129,27 +151,44 @@ Control system analysis pole_zero_subplots root_locus_map root_locus_plot - singular_values_plot - singular_values_response - sisotool + +Passive systems analysis: + +.. autosummary:: + :toctree: generated/ + + get_input_ff_index + get_output_fb_index + ispassive + solve_passivity_LMI Control system synthesis ======================== + +State space synthesis: + .. autosummary:: :toctree: generated/ acker create_statefbk_iosystem dlqr - h2syn - hinfsyn lqr - mixsyn place place_varga + +Frequency domain synthesis: + +.. autosummary:: + :toctree: generated/ + + h2syn + hinfsyn + mixsyn rootlocus_pid_designer + System ID and model reduction ============================= .. autosummary:: @@ -162,6 +201,7 @@ System ID and model reduction eigensys_realization markov + Nonlinear system support ======================== .. autosummary:: @@ -170,19 +210,27 @@ Nonlinear system support find_operating_point linearize -Stochastic system support -========================= +Describing functions +-------------------- .. autosummary:: :toctree: generated/ - correlation - create_estimator_iosystem - dlqe - lqe - white_noise + describing_function + friction_backlash_nonlinearity + relay_hysteresis_nonlinearity + saturation_nonlinearity + +Differentially flat systems +--------------------------- +.. autosummary:: + :toctree: generated/ + + flatsys.flatsys + flatsys.point_to_point + flatsys.solve_flat_ocp Optimal control -=============== +--------------- .. autosummary:: :toctree: generated/ @@ -200,24 +248,17 @@ Optimal control optimal.state_range_constraint -Describing functions -==================== +Stochastic system support +========================= .. autosummary:: :toctree: generated/ - describing_function - friction_backlash_nonlinearity - relay_hysteresis_nonlinearity - saturation_nonlinearity - -Differentially flat systems -=========================== -.. autosummary:: - :toctree: generated/ + correlation + create_estimator_iosystem + dlqe + lqe + white_noise - flatsys.flatsys - flatsys.point_to_point - flatsys.solve_flat_ocp Matrix computations =================== diff --git a/doc/nonlinear.rst b/doc/nonlinear.rst index fef12d98b..b28ebb56f 100644 --- a/doc/nonlinear.rst +++ b/doc/nonlinear.rst @@ -1,3 +1,5 @@ +.. _nonlinear-systems: + *********************************************** Nonlinear System Modeling, Analysis, and Design *********************************************** From b750119bed7a968e7c194afe72d442728651d238 Mon Sep 17 00:00:00 2001 From: Richard Murray Date: Sat, 28 Dec 2024 23:05:08 -0800 Subject: [PATCH 43/93] Restructure config.rst to document all defaults + update docstrings, code --- control/frdata.py | 9 +- control/freqplot.py | 34 ++- control/nichols.py | 3 +- control/nlsys.py | 7 +- control/phaseplot.py | 26 ++ control/pzmap.py | 33 ++- control/timeplot.py | 2 +- doc/config.rst | 633 ++++++++++++++++++++++++++++++++++++++--- doc/response.rst | 10 + doc/test_sphinxdocs.py | 66 ++++- 10 files changed, 756 insertions(+), 67 deletions(-) diff --git a/control/frdata.py b/control/frdata.py index baa2513dc..5b687d54f 100644 --- a/control/frdata.py +++ b/control/frdata.py @@ -1014,8 +1014,13 @@ def frd(*args, **kwargs): input_prefix, output_prefix : string, optional Set the prefix for input and output signals. Defaults = 'u', 'y'. name : string, optional - System name. If unspecified, a generic name is generated - with a unique integer id. + Set the name of the system. If unspecified and the system is + sampled from an existing system, the new system name is determined + by adding the prefix and suffix strings in + config.defaults['iosys.sampled_system_name_prefix'] and + config.defaults['iosys.sampled_system_name_suffix'], with the + default being to add the suffix '$ampled'. Otherwise, a generic + name is generated with a unique integer id See Also -------- diff --git a/control/freqplot.py b/control/freqplot.py index d44edad28..7d9d202b0 100644 --- a/control/freqplot.py +++ b/control/freqplot.py @@ -213,7 +213,8 @@ def bode_plot( Set the frame of reference used to center the plot title. If set to 'axes' (default), the horizontal position of the title will be centered relative to the axes. If set to 'figure', it will be - centered with respect to the figure (faster execution). + centered with respect to the figure (faster execution). The + default value can be set using config.defaults['freqplot.title_frame']. wrap_phase : bool or float If wrap_phase is `False` (default), then the phase will be unwrapped so that it is continuously increasing or decreasing. If wrap_phase is @@ -1200,14 +1201,17 @@ def nyquist_response( be set using config.defaults['nyquist.encirclement_threshold']. indent_direction : str, optional For poles on the imaginary axis, set the direction of indentation to - be 'right' (default), 'left', or 'none'. + be 'right' (default), 'left', or 'none'. The default value can + be set using config.defaults['nyquist.indent_direction']. indent_points : int, optional Number of points to insert in the Nyquist contour around poles that are at or near the imaginary axis. indent_radius : float, optional Amount to indent the Nyquist contour around poles on or near the - imaginary axis. Portions of the Nyquist plot corresponding to indented - portions of the contour are plotted using a different line style. + imaginary axis. Portions of the Nyquist plot corresponding to + indented portions of the contour are plotted using a different line + style. The default value can be set using + config.defaults['nyquist.indent_radius']. omega_limits : array_like of two values Set limits for plotted frequency range. If Hz=True the limits are in Hz otherwise in rad/s. Specifying ``omega`` as a list of two @@ -1539,7 +1543,9 @@ def nyquist_plot( ``omega`` as a list of two elements is equivalent to providing ``omega_limits``. unit_circle : bool, optional - If ``True``, display the unit circle, to read gain crossover frequency. + If ``True``, display the unit circle, to read gain crossover + frequency. The circle style is determined by + config.defaults['nyquist.circle_style']. mt_circles : array_like, optional Draw circles corresponding to the given magnitudes of sensitivity. ms_circles : array_like, optional @@ -1577,12 +1583,13 @@ def nyquist_plot( arrows : int or 1D/2D array of floats, optional Specify the number of arrows to plot on the Nyquist curve. If an integer is passed. that number of equally spaced arrows will be - plotted on each of the primary segment and the mirror image. If a 1D - array is passed, it should consist of a sorted list of floats between - 0 and 1, indicating the location along the curve to plot an arrow. If - a 2D array is passed, the first row will be used to specify arrow - locations for the primary curve and the second row will be used for - the mirror image. + plotted on each of the primary segment and the mirror image. If a + 1D array is passed, it should consist of a sorted list of floats + between 0 and 1, indicating the location along the curve to plot an + arrow. If a 2D array is passed, the first row will be used to + specify arrow locations for the primary curve and the second row + will be used for the mirror image. Default value is 2 and can be + set using config.defaults['nyquist.arrows']. arrow_size : float, optional Arrowhead width and length (in display coordinates). Default value is 8 and can be set using config.defaults['nyquist.arrow_size']. @@ -1619,11 +1626,14 @@ def nyquist_plot( max_curve_magnitude : float, optional Restrict the maximum magnitude of the Nyquist plot to this value. Portions of the Nyquist plot whose magnitude is restricted are - plotted using a different line style. + plotted using a different line style. The default value is 20 and + can be set using config.defaults['nyquist.max_curve_magnitude']. max_curve_offset : float, optional When plotting scaled portion of the Nyquist plot, increase/decrease the magnitude by this fraction of the max_curve_magnitude to allow any overlaps between the primary and mirror curves to be avoided. + The default value is 0.02 and can be set using + config.defaults['nyquist.max_curve_magnitude']. mirror_style : [str, str] or False Linestyles for mirror image of the Nyquist curve. The first element is used for unscaled portions of the Nyquist curve, the second element diff --git a/control/nichols.py b/control/nichols.py index 1006c0c6e..650c57e6b 100644 --- a/control/nichols.py +++ b/control/nichols.py @@ -51,7 +51,8 @@ def nichols_plot( Passed to `matplotlib` as the format string for all lines in the plot. The `omega` parameter must be present (use omega=None if needed). grid : boolean, optional - True if the plot should include a Nichols-chart grid. Default is True. + True if the plot should include a Nichols-chart grid. Default is + True and can be set using config.defaults['nichols.grid']. **kwargs : :func:`matplotlib.pyplot.plot` keyword properties, optional Additional keywords passed to `matplotlib` to specify line properties. diff --git a/control/nlsys.py b/control/nlsys.py index 76e65e8dd..26cd904e3 100644 --- a/control/nlsys.py +++ b/control/nlsys.py @@ -2320,9 +2320,10 @@ def interconnect( states : int, list of str, or None, optional Description of the system states. Same format as `inputs`. The - default is `None`, in which case the states will be given names of the - form '.', for each subsys in syslist and each - state_name of each subsys. + default is `None`, in which case the states will be given names of + the form '', for each subsys in + syslist and each state_name of each subsys, where is the + value of config.defaults['iosys.state_name_delim']. params : dict, optional Parameter values for the systems. Passed to the evaluation functions diff --git a/control/phaseplot.py b/control/phaseplot.py index 59d340182..2ec8fbe8b 100644 --- a/control/phaseplot.py +++ b/control/phaseplot.py @@ -48,6 +48,7 @@ _phaseplot_defaults = { 'phaseplot.arrows': 2, # number of arrows around curve 'phaseplot.arrow_size': 8, # pixel size for arrows + 'phaseplot.arrow_style': None, # set arrow style 'phaseplot.separatrices_radius': 0.1 # initial radius for separatrices } @@ -120,6 +121,15 @@ def phase_plane_plot( Other Parameters ---------------- + arrows : int + Set the number of arrows to plot along the streamlines. The default + value can be set in config.defaults['phaseplot.arrows']. + arrow_size : float + Set the size of arrows to plot along the streamlines. The default + value can be set in config.defaults['phaseplot.arrow_size']. + arrow_style : matplotlib patch + Set the style of arrows to plot along the streamlines. The default + value can be set in config.defaults['phaseplot.arrow_style']. dir : str, optional Direction to draw streamlines: 'forward' to flow forward in time from the reference points, 'reverse' to flow backward in time, or @@ -383,6 +393,15 @@ def streamlines( Other Parameters ---------------- + arrows : int + Set the number of arrows to plot along the streamlines. The default + value can be set in config.defaults['phaseplot.arrows']. + arrow_size : float + Set the size of arrows to plot along the streamlines. The default + value can be set in config.defaults['phaseplot.arrow_size']. + arrow_style : matplotlib patch + Set the style of arrows to plot along the streamlines. The default + value can be set in config.defaults['phaseplot.arrow_style']. rcParams : dict Override the default parameters used for generating plots. Default is set by config.default['ctrlplot.rcParams']. @@ -593,6 +612,13 @@ def separatrices( suppress_warnings : bool, optional If set to `True`, suppress warning messages in generating trajectories. + Notes + ----- + The value of config.defaults['separatrices_radius'] is used to set the + offset from the equlibrium point to the starting point of the separatix + traces, in the direction of the eigenvectors evaluated at that + equilibrium point. + """ # Process keywords rcParams = config._get_param('ctrlplot', 'rcParams', kwargs, pop=True) diff --git a/control/pzmap.py b/control/pzmap.py index 546e39cbc..0a47ef2e4 100644 --- a/control/pzmap.py +++ b/control/pzmap.py @@ -31,7 +31,7 @@ # Define default parameter values for this module _pzmap_defaults = { - 'pzmap.grid': None, # Plot omega-damping grid + 'pzmap.grid': False, # Plot omega-damping grid 'pzmap.marker_size': 6, # Size of the markers 'pzmap.marker_width': 1.5, # Width of the markers 'pzmap.expansion_factor': 1.8, # Amount to scale plots beyond features @@ -273,16 +273,27 @@ def pole_zero_plot( Notes ----- - 1. By default, the pzmap function calls matplotlib.pyplot.axis('equal'), - which means that trying to reset the axis limits may not behave as - expected. To change the axis limits, use the `scaling` keyword of - use matplotlib.pyplot.gca().axis('auto') and then set the axis - limits to the desired values. - - 2. Pole/zero plots that use the continuous time omega-damping grid do - not work with the ``ax`` keyword argument, due to the way that axes - grids are implemented. The ``grid`` argument must be set to - ``False`` or ``'empty'`` when using the ``ax`` keyword argument. + By default, the pzmap function calls matplotlib.pyplot.axis('equal'), + which means that trying to reset the axis limits may not behave as + expected. To change the axis limits, use the `scaling` keyword of use + matplotlib.pyplot.gca().axis('auto') and then set the axis limits to + the desired values. + + Pole/zero plots that use the continuous time omega-damping grid do not + work with the ``ax`` keyword argument, due to the way that axes grids + are implemented. The ``grid`` argument must be set to ``False`` or + ``'empty'`` when using the ``ax`` keyword argument. + + The limits of the pole/zero plot are set based on the location features + in the plot, including the location of poles, zeros, and local maxima + of root locus curves. The locations of local maxima are expanded by a + buffer factor set by config.defaults['phaseplot.buffer_factor'] that is + applied to the locations of the local maxima. The final axis limits + are set to by the largest features in the plot multiplied by an + expansion factor set by config.defaults['phaseplot.expansion_factor']. + The default value for the buffer factor is 1.05 (5% buffer around local + maxima) and the default value for the expansion factor is 1.8 (80% + increase in limits around the most distant features). """ # Get parameter values diff --git a/control/timeplot.py b/control/timeplot.py index 25417dcf3..23c599b24 100644 --- a/control/timeplot.py +++ b/control/timeplot.py @@ -153,7 +153,7 @@ def time_response_plot( trace_labels : list of str, optional Replace the default trace labels with the given labels. trace_props : array of dicts - List of line properties to use when plotting combined outputs. The + List of line properties to use when plotting multiple traces. The default values are set by config.defaults['timeplot.trace_props']. Notes diff --git a/doc/config.rst b/doc/config.rst index b357c2a06..a4215e71a 100644 --- a/doc/config.rst +++ b/doc/config.rst @@ -1,57 +1,626 @@ .. currentmodule:: control + .. _package-configuration-parameters: Package configuration parameters ================================ -The python-control library can be customized to allow for different default -values for selected parameters. This includes the ability to set the style -for various types of plots and establishing the underlying representation for -state space matrices. +The python-control package can be customized to allow for different default +values for selected parameters. This includes the ability to change the +way system names are created, set the style for various types of plots, and +determine default solvers and parameters to use in solving optimization +problems. -To set the default value of a configuration variable, set the appropriate +To set the default value of a configuration parameter, set the appropriate element of the `config.defaults` dictionary:: - ct.config.defaults['module.parameter'] = value + ct.config.defaults['module.parameter'] = value -The :func:`set_defaults` function can also be used to -set multiple configuration parameters at the same time:: +The :func:`set_defaults` function can also be used to set multiple +configuration parameters at the same time:: - ct.set_defaults('module', param1=val1, param2=val2, ...] + ct.set_defaults('module', param1=val1, param2=val2, ...] -Finally, there are also functions available set collections of variables based -on standard configurations. +Finally, there are also functions available to set collections of +parameters based on standard configurations: -Selected variables that can be configured, along with their default values: +.. autosummary:: - * freqplot.dB (False): Bode plot magnitude plotted in dB (otherwise powers - of 10) + reset_defaults + use_fbs_defaults + use_matlab_defaults + use_legacy_defaults - * freqplot.deg (True): Bode plot phase plotted in degrees (otherwise radians) - * freqplot.Hz (False): Bode plot frequency plotted in Hertz (otherwise - rad/sec) +System creation parameters +-------------------------- - * freqplot.grid (True): Include grids for magnitude and phase plots +.. py:data:: control.default_dt + :type: int + :value: 0 - * freqplot.number_of_samples (1000): Number of frequency points in Bode plots + Default value of `dt` when constructing new I/O systems. If `dt` + is not specified explicitly this value will be used. Set to `None` + to leave the timebase unspecified, 0 for continuous time systems, + `True` for discrete time systems. - * freqplot.feature_periphery_decade (1.0): How many decades to include in - the frequency range on both sides of features (poles, zeros). +.. py:data:: iosys.converted_system_name_prefix + :type: str + :value: '' - * statesp.default_dt and xferfcn.default_dt (None): set the default value - of dt when constructing new LTI systems + Prefix to add to system name when converting a system from one + representation to another. - * statesp.remove_useless_states (True): remove states that have no effect - on the input-output dynamics of the system +.. py:data:: iosys.converted_system_name_suffix + :type: str + :value: '$converted' -Additional parameter variables are documented in individual functions + Suffix to add to system name when converting a system from one + representation to another. -Functions that can be used to set standard configurations: +.. py:data:: iosys.duplicate_system_name_prefix + :type: str + :value: '' -.. autosummary:: + Prefix to add to system name when making a copy of a system. - reset_defaults - use_fbs_defaults - use_matlab_defaults - use_legacy_defaults +.. py:data:: iosys.duplicate_system_name_suffix + :type: str + :value: '$copy' + + Suffix to add to system name when making a copy of a system. + +.. py:data:: iosys.indexed_system_name_prefix + :type: str + :value: '' + + Prefix to add to system name when extracting a subset of the inputs + and outputs. + +.. py:data:: iosys.indexed_system_name_suffix + :type: str + :value: '$indexed' + + Suffix to add to system name when extracting a subset of the inputs + and outputs. + +.. py:data:: iosys.linearized_system_name_prefix + :type: str + :value: '' + + Prefix to add to system name when linearizing a system using + :func:`linearize`. + +.. py:data:: iosys.linearized_system_name_suffix + :type: str + :value: '$linearized' + + Suffix to add to system name when linearizing a system using + :func:`linearize`. + +.. py:data:: iosys.sampled_system_name_prefix + :type: str + :value: '' + + Prefix to add to system name when sampling a system at a set of + frequency points in :func:`frd`. + +.. py:data:: iosys.sampled_system_name_suffix + :type: str + :value: '$sampled' + + Suffix to add to system name when sampling a system at a set of + frequency points in :func:`frd`. + +.. py:data:: iosys.state_name_delim + :type: str + :value: '_' + + Used by :func:`interconnect` to set the names of the states of the + interconnected system. If the state names are not explicitly given, + the states will be given names of the form + '', for each subsys in syslist and + each state_name of each subsys, where is the value of + config.defaults['iosys.state_name_delim']. + +.. py:data:: statesp.remove_useless_states + :type: bool + :value: False + + When creating a :class:`StateSpace` system, remove states that have no + effect on the input-output dynamics of the system. + + +System display parameters +------------------------- + +.. py:data:: iosys.repr_format + :type: str + :value: 'info' + + Set the default format used by :func:`iosys_repr` to create the + representation of an :class:`InputOutputSystem`: + + * 'info' : [outputs] + * 'eval' : system specific, loadable representation + * 'latex' : latex representation of the object + +.. py:data:: xferfcn.display_format + :type: str + :value: 'poly' + + Set the display format used in printing the + :class:`TransferFunction` object: + + * 'poly': Single polynomial for numerator and denominator. + * 'zpk': Product of factors, showing poles and zeros. + +.. py:data:: xferfcn.floating_point_format + :type: str + :value: '.4g' + + Format to use for displaying coefficients in + :class:`TransferFunction` objects when generating string + representations. + +.. py:data:: statesp.latex_num_format + :type: str + :value: '.3g' + + Format to use for displaying coefficients in :class:`StateSpace` systems + when generating LaTeX representations. + +.. py:data:: statesp.latex_repr_type + :type: str + :value: 'partitioned' + + Used when generating LaTeX representations of :class:`StateSpace` + systems. If 'partitioned', the A, B, C, D matrices are shown as + a single, partitioned matrix; if 'separate', the matrices are + shown separately. + +.. py:data:: statesp.latex_maxsize + :type: int + :value: 10 + + Maximum number of states plus inputs or outputs for which the LaTeX + representation of the system dynamics will be generated. + + +Response parameters +------------------- + +.. py:data:: control.squeeze_frequency_response + :type: bool + :value: None + + Set the default value of the `squeeze` parameter for + :func:`frequency_response` and :class:`FrequencyResponseData` + objects. If `None` then if a system is single-input, single-output + (SISO) the outputs (and inputs) are returned as a 1D array (indexed by + frequency), and if a system is multi-input or multi-output, then the + outputs are returned as a 2D array (indexed by output and frequency) or + a 3D array (indexed by output, input (or trace), and frequency). If + `squeeze=True`, access to the output response will remove + single-dimensional entries from the shape of the inputs and outputs even + if the system is not SISO. If `squeeze=False`, the output is returned as + a 3D array (indexed by the output, input, and frequency) even if the + system is SISO. + +.. py:data:: control.squeeze_time_response + :type: bool + :value: None + + Set the default value of the `squeeze` parameter for + :func:`input_output_response` and other time response objects. By + default, if a system is single-input, single-output (SISO) then the + outputs (and inputs) are returned as a 1D array (indexed by time) and if + a system is multi-input or multi-output, then the outputs are returned + as a 2D array (indexed by output and time) or a 3D array (indexed by + output, input, and time). If `squeeze=True`, access to the output + response will remove single-dimensional entries from the shape of the + inputs and outputs even if the system is not SISO. If `squeeze=False`, + the output is returned as a 3D array (indexed by the output, input, and + time) even if the system is SISO. + +.. py:data:: forced_response.return_x + :type: bool + :value: False + + Determine whether :func:`forced_response` returns the values of the + states when the :class:`TimeResponseData` object is evaluated. The + default value was `True` before version 0.9 and is `False` since then. + + +Plotting parameters +------------------- + +.. py:data:: ctrlplot.rcParams + :type: dict + :value: {'axes.labelsize': 'small', 'axes.titlesize': 'small', 'figure.titlesize': 'medium', 'legend.fontsize': 'x-small', 'xtick.labelsize': 'small', 'ytick.labelsize': 'small'} + + Overrides the default Matplotlib parameters used for generating plots. + +.. py:data:: freqplot.dB + :type: bool + :value: False + + If `True`, the magnitude in :func:`bode_plot` is plotted in dB + (otherwise powers of 10). + +.. py:data:: freqplot.deg + :type: bool + :value: True + + If `True`, the phase in :func:`bode_plot` is plotted in degrees + (otherwise radians). + +.. py:data:: freqplot.feature_periphery_decades + :type: int + :value: 1 + + Number of decades in frequency to include on both sides of features + (poles, zeros) when generating frequency plots. + +.. py:data:: freqplot.freq_label + :type: bool + :value: 'Frequency [{units}]' + + Label for the frequency axis in frequency plots, with `units` set to + either 'rad/sec' or 'Hz' when the label is created. + +.. py:data:: freqplot.grid + :type: bool + :value: True + + Include grids for magnitude and phase in frequency plots. + +.. py:data:: freqplot.Hz + :type: bool + :value: False + + If `True`, use Hertz for frequency response plots (otherwise rad/sec). + +.. py:data:: freqplot.number_of_samples + :type: int + :value: 1000 + + Number of frequency points to use in in frequency plots. + +.. py:data:: freqplot.share_magnitude + :type: str + :value: 'row' + + Determine whether and how axis limits are shared between the magnitude + variables in :func:`bode_plot`. Can be set set to 'row' to share across + all subplots in a row, 'col' to set across all subplots in a column, or + `False` to allow independent limits. + +.. py:data:: freqplot.share_phase + :type: str + :value: 'row' + + Determine whether and how axis limits are shared between the phase + variables in :func:`bode_plot`. Can be set set to 'row' to share across + all subplots in a row, 'col' to set across all subplots in a column, or + `False` to allow independent limits. + +.. py:data:: freqplot.share_frequency + :type: str + :value: 'col' + + Determine whether and how axis limits are shared between the frequency + axes in :func:`bode_plot`. Can be set set to 'row' to share across all + subplots in a row, 'col' to set across all subplots in a column, or + `False` to allow independent limits. + +.. py:data:: freqplot.title_frame + :type: str + :value: 'axes' + + Set the frame of reference used to center the plot title. If set to + 'axes', the horizontal position of the title will be centered relative + to the axes. If set to 'figure', it will be centered with respect to + the figure (faster execution). + +.. py:data:: freqplot.wrap_phase + :type: bool + :value: False + + If wrap_phase is `False`, then the phase will be unwrapped so that it + is continuously increasing or decreasing. If wrap_phase is `True` the + phase will be restricted to the range [-180, 180) (or [:math:`-\\pi`, + :math:`\\pi`) radians). If `wrap_phase` is specified as a float, the + phase will be offset by 360 degrees if it falls below the specified + value. + +.. py:data:: nichols.grid + :type: bool + :value: True + + Set to `True` if :func:`nichols_plot` should include a Nichols-chart + grid. + +.. py:data:: nyquist.arrows + :type: int + :value: 2 + + Specify the number of arrows for :func:`nyquist_plot`. If an integer is + passed. that number of equally spaced arrows will be plotted on each of + the primary segment and the mirror image. If a 1D array is passed, it + should consist of a sorted list of floats between 0 and 1, indicating + the location along the curve to plot an arrow. If a 2D array is passed, + the first row will be used to specify arrow locations for the primary + curve and the second row will be used for the mirror image. + +.. py:data:: nyquist.arrow_size + :type: float + :value: 8 + + Arrowhead width and length (in display coordinates) for + :func:`nyquist_plot`. + +.. py:data:: nyquist.circle_style + :type: dict + :value: {'color': 'black', 'linestyle': 'dashed', 'linewidth': 1} + + Style for unit circle in :func:`nyquist_plot`. + +.. py:data:: nyquist.encirclement_threshold + :type: float + :value: 0.05 + + Define the threshold in :func:`nyquist_response` for generating a + warning if the number of net encirclements is a non-integer value. + +.. py:data:: nyquist.indent_direction + :type: str + :value: 'right' + + Set the direction of indentation in :func:`nyquist_response` for poles + on the imaginary axis. Valid values are 'right', 'left', or 'none'. + +.. py:data:: nyquist.indent_points + :type: int + :value: 50 + + Set the number of points to insert in the Nyquist contour around poles + that are at or near the imaginary axis in :func:`nyquist_response`. + +.. py:data:: nyquist.indent_radius + :type: float + :value: 0.0001 + + Amount to indent the Nyquist contour around poles on or near the + imaginary axis in :func:`nyquist_response`. Portions of the Nyquist plot + corresponding to indented portions of the contour are plotted using a + different line style. + +.. py:data:: nyquist.max_curve_magnitude + :type: float + :value: 20 + + Restrict the maximum magnitude of the Nyquist plot in + :func:`nyquist_plot`. Portions of the Nyquist plot whose magnitude is + restricted are plotted using a different line style. + +.. py:data:: nyquist.max_curve_offset + :type: float + :value: 0.02 + + When plotting scaled portion of the Nyquist plot in + :func:`nyquist_plot`, increase/decrease the magnitude by this fraction + of the max_curve_magnitude to allow any overlaps between the primary and + mirror curves to be avoided. + +.. py:data:: nyquist.mirror_style + :type: list of str + :value: ['--', ':'] + + Linestyles for mirror image of the Nyquist curve in + :func:`nyquist_plot`. The first element is used for unscaled portions + of the Nyquist curve, the second element is used for portions that are + scaled (using max_curve_magnitude). If `False` then omit completely. + +.. py:data:: nyquist.primary_style + :type: list of str + :value: ['-', '-.'] + + Linestyles for primary image of the Nyquist curve in + :func:`nyquist_plot`. The first element is used for unscaled portions + of the Nyquist curve, the second element is used for portions that are + scaled (using max_curve_magnitude). + +.. py:data:: nyquist.start_marker + :type: str + :value: 'o' + + Matplotlib marker to use to mark the starting point of the Nyquist plot + in :func:`nyquist_plot`. + +.. py:data:: nyquist.start_marker_size + :type: float + :value: 4 + + Start marker size (in display coordinates) in :func:`nyquist_plot`. + +.. py:data:: phaseplot.arrows + :type: int + :value: 2 + + Set the default number of arrows in :func:`phase_plane_plot` and + :func:`phaseplot.streamlines`. + +.. py:data:: phaseplot.arrow_size + :type: float + :value: 8 + + Set the default size of arrows in :func:`phase_plane_plot` and + :func:`phaseplot.streamlines`. + +.. py:data:: phaseplot.arrow_style + :type: matplotlib patch + :value: None + + Set the default style for arrows in :func:`phase_plane_plot` and + :func:`phaseplot.streamlines`. If set to `None`, defaults to + + .. code:: + + mpl.patches.ArrowStyle( + 'simple', head_width=int(2 * arrow_size / 3), + head_length=arrow_size) + +.. py:data:: phaseplot.separatrices_radius + :type: float + :value: 0.1 + + In :func:`phaseplot.separatrices`, set the offset from the equlibrium + point to the starting point of the separatix traces, in the direction of + the eigenvectors evaluated at that equilibrium point. + +.. py:data:: pzmap.buffer_factor + :type: float + :value: 1.05 + + The limits of the pole/zero plot generated by :func:`pole_zero_plot` + are set based on the location features in the plot, including the + location of poles, zeros, and local maxima of root locus curves. The + locations of local maxima are expanded by the buffer factor set by + `buffer_factor`. + +.. py:data:: pzmap.expansion_factor + :type: float + :value: 1.8 + + The final axis limits of the pole/zero plot generated by + :func:`pole_zero_plot` are set to by the largest features in the plot + multiplied by an expansion factor set by `expansion_factor`. + +.. py:data:: pzmap.grid + :type: bool + :value: False + + If `True` plot omega-damping grid in :func:`pole_zero_plot`. If `False` + or None show imaginary axis for continuous time systems, unit circle for + discrete time systems. If `empty`, do not draw any additonal lines. + + Note: this setting only applies to pole/zero plots. For root locus + plots, the 'rlocus.grid' parameter value is used as teh default. + +.. py:data:: pzmap.marker_size + :type: float + :value: 6 + + Set the size of the markers used for poles and zeros in + :func:`pole_zero_plot`. + +.. py:data:: pzmap.marker_width + :type: float + :value: 1.5 + + Set the line width of the markers used for poles and zeros in + :func:`pole_zero_plot`. + +.. py:data:: rlocus.grid + :type: bool + :value: True + + If `True` plot omega-damping grid in :func:`root_locus_plot`. If `False` + or None show imaginary axis for continuous time systems, unit circle for + discrete time systems. If `empty`, do not draw any additonal lines. + +.. py:data:: sisotool.initial_gain + :type: float + :value: 1 + + Initial gain to use for plotting root locus in :func:`sisotool`. + +.. py:data:: timeplot.input_props + :type: list of dict + :value: [{'color': 'tab:red'}, {'color': 'tab:purple'}, {'color': 'tab:brown'}, {'color': 'tab:olive'}, {'color': 'tab:cyan'}] + + List of line properties to use when plotting combined inputs in + :func:`time_response_plot`. The line properties for each input will be + cycled through this list. + +.. py:data:: timeplot.output_props + :type: list of dict + :value: [{'color': 'tab:blue'}, {'color': 'tab:orange'}, {'color': 'tab:green'}, {'color': 'tab:pink'}, {'color': 'tab:gray'}] + + List of line properties to use when plotting combined outputs in + :func:`time_response_plot`. The line properties for each input will be + cycled through this list. + +.. py:data:: timeplot.trace_props + :type: list of dict + :value: [{'linestyle': '-'}, {'linestyle': '--'}, {'linestyle': ':'}, {'linestyle': '-.'}] + + List of line properties to use when plotting multiple traces in + :func:`time_response_plot`. The line properties for each input will be + cycled through this list. + +.. py:data:: timeplot.sharex + :type: str + :value: 'col' + + Determine whether and how x-axis limits are shared between subplots in + :func:`time_response_plot`. Can be set set to 'row' to share across all + subplots in a row, 'col' to set across all subplots in a column, 'all' + to share across all subplots, or `False` to allow independent limits. + +.. py:data:: timeplot.sharey + :type: bool + :value: False + + Determine whether and how y-axis limits are shared between subplots in + :func:`time_response_plot`. Can be set set to 'row' to share across all + subplots in a row, 'col' to set across all subplots in a column, 'all' + to share across all subplots, or `False` to allow independent limits. + +.. py:data:: timeplot.time_label + :type: str + :value: 'Time [s]' + + Label to use for the time axis in :func:`time_response_plot`. + + +Optimization parameters +----------------------- + +.. py:data:: optimal.minimize_method + :type: str + :value: None + + Set the method used by :func:`scipy.optimize.minimize` when called in + :func:`solve_ocp` and :func:`solve_oep`. + +.. py:data:: optimal.minimize_options + :type: dict + :value: {} + + Set the value of the options keyword used by + :func:`scipy.optimize.minimize` when called in :func:`solve_ocp` and + :func:`solve_oep`. + +.. py:data:: optimal.minimize_kwargs + :type: dict + :value: {} + + Set the keyword arguments passed to :func:`scipy.optimize.minimize` when + called in :func:`solve_ocp` and :func:`solve_oep`. + +.. py:data:: optimal.solve_ivp_method + :type: str + :value: None + + Set the method used by :func:`scipy.integrate.solve_ivp` when called in + :func:`solve_ocp` and :func:`solve_oep`. + +.. py:data:: optimal.solve_ivp_options + :type: dict + :value: {} + + Set the value of the options keyword used by + :func:`scipy.integrate.solve_ivp` when called in :func:`solve_ocp` and + :func:`solve_oep`. diff --git a/doc/response.rst b/doc/response.rst index e390868d8..2529c9266 100644 --- a/doc/response.rst +++ b/doc/response.rst @@ -759,3 +759,13 @@ The following classes are used in generating response data. PoleZeroData TimeResponseData TimeResponseList + +.. _controlplot-params: + +Plot parameters +--------------- + +Matplotlib users the :func:`matplotlib.rcParams` to specify default +properties of figures and axes. The `python-control` plotting +functions make use of a set of default values for all plots that +override the default Matplotlib values. diff --git a/doc/test_sphinxdocs.py b/doc/test_sphinxdocs.py index 0b2422ec9..84fb0fb9f 100644 --- a/doc/test_sphinxdocs.py +++ b/doc/test_sphinxdocs.py @@ -6,6 +6,7 @@ import inspect import os +import re import sys import warnings from importlib import resources @@ -43,11 +44,13 @@ # Functons that we can skip object_skiplist = [ - control.NamedSignal, # np.ndarray members cause errors - control.common_timebase, # mainly internal use - control.cvxopt_check, # mainly internal use - control.pandas_check, # mainly internal use - control.slycot_check, # mainly internal use + control.NamedSignal, # np.ndarray members cause errors + control.FrequencyResponseList, # Use FrequencyResponseData + control.TimeResponseList, # Use TimeResponseData + control.common_timebase, # mainly internal use + control.cvxopt_check, # mainly internal use + control.pandas_check, # mainly internal use + control.slycot_check, # mainly internal use ] # Global list of objects we have checked @@ -105,6 +108,57 @@ def test_sphinx_functions(module): _fail(f"{objname} not referenced in sphinx docs") +defaults_skiplist = [] +def test_config_defaults(): + # Keep track of params we found and params we have checked + config_rstdocs = dict() + config_defaults = control.config.defaults + + # Read the documentation file and extract the keys + with open('config.rst', 'r') as file: + for line in file: + if (key_match := re.search(r"py:data:: ([\w]+\.[\w]+)", line)): + if (key := key_match.group(1)) in defaults_skiplist: + _info(f"skipping config param {key}", 2) + continue + else: + _info(f"checking config param {key}", 2) + + if key in config_rstdocs: + _warn(f"config param '{key}' listed multiple times") + + # Get the default value and check it + while not re.match(r"^$|^\.\.", line := next(file)): + if (val_match := re.search(r":value: (.*)", line)): + _info(f"found value for config param {key}", 3) + config_rstdocs[key] = val_match.group(1) + + # Check to make sure (almost) all keys in config.defaults were documented + for key in config_defaults: + if key in defaults_skiplist: + config_rstdocs.pop(key, None) + continue + + if key not in config_rstdocs: + # TODO: change to _fail once everything is set up + _warn(f"config param '{key}' not documented") + continue + + # Make sure the listed default value is correct + try: + if (defval := config_defaults[key]) != eval(config_rstdocs[key]): + _warn(f"config param '{key}' has different default value: " + f"{config_rstdocs[key]} instead of {defval}") + except SyntaxError: + _warn(f"could not evaluate default value for config param '{key}'") + + # Done processing this key + config_rstdocs.pop(key, None) + + if config_rstdocs: + _warn(f"Unknown params in config.rst: {config_rstdocs}") + + def _check_deprecated(obj): with warnings.catch_warnings(): warnings.simplefilter('ignore') # debug via sphinx, not here @@ -138,3 +192,5 @@ def _fail(str, level=-1): for module in control_module_list: test_sphinx_functions(module) + + test_config_defaults() From 199e0ddf250626eceb1ddd694cf304a3d0241b4c Mon Sep 17 00:00:00 2001 From: Richard Murray Date: Sun, 29 Dec 2024 17:25:36 -0800 Subject: [PATCH 44/93] update copybutton to skip prompts, outputs --- doc/conf.py | 4 ++++ doc/linear.rst | 6 +++--- doc/xferfcn.rst | 6 +++--- 3 files changed, 10 insertions(+), 6 deletions(-) diff --git a/doc/conf.py b/doc/conf.py index 0ae0eed61..638f803fa 100644 --- a/doc/conf.py +++ b/doc/conf.py @@ -124,6 +124,10 @@ # Align inline math with text imgmath_use_preview = True +# Skip prompts when using copy button +copybutton_prompt_text = r">>> |\.\.\. " +copybutton_prompt_is_regexp = True + # Theme options are theme-specific and customize the look and feel of a theme # further. For a list of options available for each theme, see the # documentation. diff --git a/doc/linear.rst b/doc/linear.rst index 5f4128a11..b7f3d2253 100644 --- a/doc/linear.rst +++ b/doc/linear.rst @@ -318,9 +318,9 @@ Time sampling Continuous time systems can be converted to discrete time systems using the :func:`sample_system` function and specifying a sampling time:: - >> csys = ct.rss(4, 2, 2, name='csys') - >> dsys = ct.sample_system(csys, 0.1, method='bilinear') - >> print(dsys) + >>> csys = ct.rss(4, 2, 2, name='csys') + >>> dsys = ct.sample_system(csys, 0.1, method='bilinear') + >>> print(dsys) : cys$sampled Inputs (2): ['u[0]', 'u[1]'] Outputs (2): ['y[0]', 'y[1]'] diff --git a/doc/xferfcn.rst b/doc/xferfcn.rst index 389f30a22..b1ca4ea3a 100644 --- a/doc/xferfcn.rst +++ b/doc/xferfcn.rst @@ -155,9 +155,9 @@ Time delays are not directly representable in `python-control`, but the :func:`pade` function generates a linear system that approximates a time delay to a given order:: - >> num, den = ct.pade(0.1, 3) - >> delay = ct.tf(num, den, name='delay') - >> print(delay) + >>> num, den = ct.pade(0.1, 3) + >>> delay = ct.tf(num, den, name='delay') + >>> print(delay) : delay Inputs (1): ['u[0]'] Outputs (1): ['y[0]'] From 4e066703c251274251d7db6772bd3bcf16ba62e3 Mon Sep 17 00:00:00 2001 From: Richard Murray Date: Sun, 29 Dec 2024 19:28:41 -0800 Subject: [PATCH 45/93] add documentation on __call__ functions --- control/flatsys/basis.py | 4 ++-- control/frdata.py | 2 +- control/nlsys.py | 2 +- control/xferfcn.py | 1 - doc/classes.rst | 3 ++- doc/descfcn.rst | 5 ++++- doc/examples.rst | 3 ++- doc/iosys.rst | 4 ++-- doc/linear.rst | 8 ++++++++ doc/nlsys.rst | 35 ++++++++++++++++++++++++++++++++--- doc/statesp.rst | 1 + doc/xferfcn.rst | 1 + 12 files changed, 56 insertions(+), 13 deletions(-) diff --git a/control/flatsys/basis.py b/control/flatsys/basis.py index cd0e0194c..ca7e6a292 100644 --- a/control/flatsys/basis.py +++ b/control/flatsys/basis.py @@ -71,11 +71,11 @@ def __repr__(self): f'N={self.N}>' def __call__(self, i, t, var=None): - """Evaluate the ith basis function at a point in time""" + """Evaluate the ith basis function at a point in time.""" return self.eval_deriv(i, 0, t, var=var) def var_ncoefs(self, var): - """Get the number of coefficients for a variable""" + """Get the number of coefficients for a variable.""" return self.N if self.nvars is None else self.coef_length[var] def eval(self, coeffs, tlist, var=None): diff --git a/control/frdata.py b/control/frdata.py index 5b687d54f..a23566390 100644 --- a/control/frdata.py +++ b/control/frdata.py @@ -130,7 +130,7 @@ class constructor, using the :func:`~control.frd` factory function, or A frequency response data object is callable and returns the value of the transfer function evaluated at a point in the complex plane (must be on - the imaginary access). See :meth:`~control.FrequencyResponseData.__call__` + the imaginary axis). See :meth:`~control.FrequencyResponseData.__call__` for a more detailed description. A state space system is callable and returns the value of the transfer diff --git a/control/nlsys.py b/control/nlsys.py index 26cd904e3..dad42c7f7 100644 --- a/control/nlsys.py +++ b/control/nlsys.py @@ -163,7 +163,7 @@ def __str__(self): # Return the value of a static nonlinear system def __call__(sys, u, params=None, squeeze=None): - """Evaluate a (static) nonlinearity at a given input value + """Evaluate a (static) nonlinearity at a given input value. If a nonlinear I/O system has no internal state, then evaluating the system at an input `u` gives the output `y = F(u)`, determined by the diff --git a/control/xferfcn.py b/control/xferfcn.py index 3c133b797..716424be3 100644 --- a/control/xferfcn.py +++ b/control/xferfcn.py @@ -331,7 +331,6 @@ def den_list(self): num, den = num_list, den_list def __call__(self, x, squeeze=None, warn_infinite=True): - """Evaluate system's transfer function at complex frequencies. Returns the complex frequency response `sys(x)` where `x` is `s` for diff --git a/doc/classes.rst b/doc/classes.rst index 1a49d5079..d5758b3aa 100644 --- a/doc/classes.rst +++ b/doc/classes.rst @@ -1,6 +1,7 @@ -.. _class-ref: .. currentmodule:: control +.. _class-ref: + ********************** Control system classes ********************** diff --git a/doc/descfcn.rst b/doc/descfcn.rst index 48fbbe60d..4ab6c750e 100644 --- a/doc/descfcn.rst +++ b/doc/descfcn.rst @@ -26,7 +26,8 @@ If such an intersection exists, it indicates that there may be a limit cycle of amplitude :math:`A` with frequency :math:`\omega`. Describing function analysis is a simple method, but it is approximate -because it assumes that higher harmonics can be neglected. +because it assumes that higher harmonics can be neglected. + Module usage ------------ @@ -90,3 +91,5 @@ Module classes and functions friction_backlash_nonlinearity relay_hysteresis_nonlinearity saturation_nonlinearity + ~DescribingFunctionNonlinearity.__call__ + diff --git a/doc/examples.rst b/doc/examples.rst index 65ac68695..a345a927d 100644 --- a/doc/examples.rst +++ b/doc/examples.rst @@ -1,6 +1,7 @@ -.. _examples: .. currentmodule:: control +.. _examples: + ******** Examples ******** diff --git a/doc/iosys.rst b/doc/iosys.rst index 7ee889a2b..f63ba3061 100644 --- a/doc/iosys.rst +++ b/doc/iosys.rst @@ -1,7 +1,7 @@ -.. _iosys-module: - .. currentmodule:: control +.. _iosys-module: + ************************** Interconnected I/O Systems ************************** diff --git a/doc/linear.rst b/doc/linear.rst index b7f3d2253..55f9f775f 100644 --- a/doc/linear.rst +++ b/doc/linear.rst @@ -114,6 +114,11 @@ complicated transfer functions:: sys = 5 * (s + 10)/(s**2 + 2*s + 1) +Transfer functions can be evaluated at a point in the complex plane by +calling the transfer function object:: + + val = sys(s) + Discrete time transfer functions (described in more detail below) can be created using `z = ct.tf('z')`. @@ -227,6 +232,7 @@ response. .. _discrete_time_systems: + Discrete time systems ===================== @@ -275,6 +281,7 @@ including functions for converting between state space and frequency domain, sampling systems in time and frequency domain, and creating reduced order models. + Conversion between representations ---------------------------------- @@ -382,6 +389,7 @@ purpose, although in that case the output is usually used for plotting the frequency response, as described in more detail in the :ref:`frequency_response` section. + Model reduction --------------- diff --git a/doc/nlsys.rst b/doc/nlsys.rst index 68e5d8632..fd94561f5 100644 --- a/doc/nlsys.rst +++ b/doc/nlsys.rst @@ -1,4 +1,4 @@ -..currentmodule control +.. currentmodule:: control Nonlinear system models ======================= @@ -111,7 +111,7 @@ Operating points and linearization ---------------------------------- A nonlinear input/output system can be linearized around an equilibrium point -to obtain a :class:`~control.StateSpace` linear system:: +to obtain a :class:`StateSpace` linear system:: sys_ss = ct.linearize(sys_nl, xeq, ueq) @@ -143,7 +143,7 @@ Simulations and plotting ------------------------ To simulate an input/output system, use the -:func:`~control.input_output_response` function:: +:func:`input_output_response` function:: resp = ct.input_output_response(io_sys, T, U, x0, params) t, y, x = resp.time, resp.outputs, resp.states @@ -159,3 +159,32 @@ different plot elements. The :func:`combine_time_responses` function an be used to combine multiple time responses into a single `TimeResponseData` object. See the :ref:`response-chapter` chapter for more information on this functionality. + +Nonlinear system properties +--------------------------- + +The following basic attributes and methods are available for +:class:`NonlinearIOSystem` objects: + +.. autosummary:: + + ~NonlinearIOSystem.dynamics + ~NonlinearIOSystem.output + ~NonlinearIOSystem.linearize + ~NonlinearIOSystem.__call__ + +The :func:`~NonlinearIOSystem.dynamics` method returns the right hand +side of the differential or difference equation, evaluated at the +current time, state, input, and (optionally) parameter values. The +:func:`~NonlinearIOSystem.output` method returns the system output. +For static nonlinear systems, it is also possible to obtain the value +of the output by directly calling the system with the value of the +input:: + + >>> sys = ct.nlsys( + ... None, lambda t, x, u, params: np.sin(u), inputs=1, outputs=1) + >>> sys(1) + np.float64(0.8414709848078965) + +The :func:`~NonlinearIOSystem.linearize` method is equivalent to the +:func:`linearize` function. diff --git a/doc/statesp.rst b/doc/statesp.rst index 0eb3e67d3..e9e36d683 100644 --- a/doc/statesp.rst +++ b/doc/statesp.rst @@ -31,6 +31,7 @@ The following basic attributes and methods are available for ~StateSpace.dcgain ~StateSpace.sample ~StateSpace.returnScipySignalLTI + ~StateSpace.__call__ A complete list of attributes, methods, and properties is available in the :class:`StateSpace` class documentation. diff --git a/doc/xferfcn.rst b/doc/xferfcn.rst index b1ca4ea3a..85a25d5c1 100644 --- a/doc/xferfcn.rst +++ b/doc/xferfcn.rst @@ -19,6 +19,7 @@ The following basic attributes and methods are available for ~TransferFunction.dcgain ~TransferFunction.sample ~TransferFunction.returnScipySignalLTI + ~TransferFunction.__call__ A complete list of attributes, methods, and properties is available in the :class:`TransferFunction` class documentation. From 87222ef90ffd6adff3fb8daef9b8b651a7099e50 Mon Sep 17 00:00:00 2001 From: Richard Murray Date: Mon, 30 Dec 2024 09:37:31 -0800 Subject: [PATCH 46/93] small formatting fixes in flatsys.rst --- doc/flatsys.rst | 108 ++++++++++++++++++++++++------------------------ 1 file changed, 54 insertions(+), 54 deletions(-) diff --git a/doc/flatsys.rst b/doc/flatsys.rst index 9bc43c4b9..3caabc29b 100644 --- a/doc/flatsys.rst +++ b/doc/flatsys.rst @@ -18,23 +18,23 @@ Overview of differential flatness A nonlinear differential equation of the form .. math:: - - \dot x = f(x, u), \qquad x \in R^n, u \in R^m + + \dot x = f(x, u), \qquad x \in R^n, u \in R^m is *differentially flat* if there exists a function :math:`\alpha` such that .. math:: - - z = \alpha(x, u, \dot u\, \dots, u^{(p)}) + + z = \alpha(x, u, \dot u\, \dots, u^{(p)}) and we can write the solutions of the nonlinear system as functions of :math:`z` and a finite number of derivatives .. math:: - :label: flat2state - - x &= \beta(z, \dot z, \dots, z^{(q)}) \\ - u &= \gamma(z, \dot z, \dots, z^{(q)}). + :label: flat2state + + x &= \beta(z, \dot z, \dots, z^{(q)}) \\ + u &= \gamma(z, \dot z, \dots, z^{(q)}). For a differentially flat system, all of the feasible trajectories for the system can be written as functions of a flat output :math:`z(\cdot)` and @@ -48,15 +48,15 @@ space, and then map these to appropriate inputs. Suppose we wish to generate a feasible trajectory for the nonlinear system .. math:: - - \dot x = f(x, u), \qquad x(0) = x_0,\, x(T) = x_f. + + \dot x = f(x, u), \qquad x(0) = x_0,\, x(T) = x_f. If the system is differentially flat then .. math:: - - x(0) &= \beta\bigl(z(0), \dot z(0), \dots, z^{(q)}(0) \bigr) = x_0, \\ - x(T) &= \gamma\bigl(z(T), \dot z(T), \dots, z^{(q)}(T) \bigr) = x_f, + + x(0) &= \beta\bigl(z(0), \dot z(0), \dots, z^{(q)}(0) \bigr) = x_0, \\ + x(T) &= \gamma\bigl(z(T), \dot z(T), \dots, z^{(q)}(T) \bigr) = x_f, and we see that the initial and final condition in the full state space depends on just the output :math:`z` and its derivatives at the @@ -72,8 +72,8 @@ trajectory of the system. We can parameterize the flat output trajectory using a set of smooth basis functions :math:`\psi_i(t)`: .. math:: - - z(t) = \sum_{i=1}^N c_i \psi_i(t), \qquad c_i \in R + + z(t) = \sum_{i=1}^N c_i \psi_i(t), \qquad c_i \in R We seek a set of coefficients :math:`c_i`, :math:`i = 1, \dots, N` such that :math:`z(t)` satisfies the boundary conditions for :math:`x(0)` and @@ -81,36 +81,36 @@ that :math:`z(t)` satisfies the boundary conditions for :math:`x(0)` and the derivatives of the basis functions: .. math:: - - \dot z(t) &= \sum_{i=1}^N c_i \dot \psi_i(t) \\ - &\,\vdots \\ - \dot z^{(q)}(t) &= \sum_{i=1}^N c_i \psi^{(q)}_i(t). + + \dot z(t) &= \sum_{i=1}^N c_i \dot \psi_i(t) \\ + &\, \vdots \\ + \dot z^{(q)}(t) &= \sum_{i=1}^N c_i \psi^{(q)}_i(t). We can thus write the conditions on the flat outputs and their derivatives as .. math:: - - \begin{bmatrix} - \psi_1(0) & \psi_2(0) & \dots & \psi_N(0) \\ - \dot \psi_1(0) & \dot \psi_2(0) & \dots & \dot \psi_N(0) \\ - \vdots & \vdots & & \vdots \\ - \psi^{(q)}_1(0) & \psi^{(q)}_2(0) & \dots & \psi^{(q)}_N(0) \\[1ex] - \psi_1(T) & \psi_2(T) & \dots & \psi_N(T) \\ - \dot \psi_1(T) & \dot \psi_2(T) & \dots & \dot \psi_N(T) \\ - \vdots & \vdots & & \vdots \\ - \psi^{(q)}_1(T) & \psi^{(q)}_2(T) & \dots & \psi^{(q)}_N(T) \\ - \end{bmatrix} - \begin{bmatrix} c_1 \\ \vdots \\ c_N \end{bmatrix} = - \begin{bmatrix} - z(0) \\ \dot z(0) \\ \vdots \\ z^{(q)}(0) \\[1ex] - z(T) \\ \dot z(T) \\ \vdots \\ z^{(q)}(T) \\ - \end{bmatrix} + + \begin{bmatrix} + \psi_1(0) & \psi_2(0) & \dots & \psi_N(0) \\ + \dot \psi_1(0) & \dot \psi_2(0) & \dots & \dot \psi_N(0) \\ + \vdots & \vdots & & \vdots \\ + \psi^{(q)}_1(0) & \psi^{(q)}_2(0) & \dots & \psi^{(q)}_N(0) \\[1ex] + \psi_1(T) & \psi_2(T) & \dots & \psi_N(T) \\ + \dot \psi_1(T) & \dot \psi_2(T) & \dots & \dot \psi_N(T) \\ + \vdots & \vdots & & \vdots \\ + \psi^{(q)}_1(T) & \psi^{(q)}_2(T) & \dots & \psi^{(q)}_N(T) \\ + \end{bmatrix} + \begin{bmatrix} c_1 \\ \vdots \\ c_N \end{bmatrix} = + \begin{bmatrix} + z(0) \\ \dot z(0) \\ \vdots \\ z^{(q)}(0) \\[1ex] + z(T) \\ \dot z(T) \\ \vdots \\ z^{(q)}(T) \\ + \end{bmatrix} This equation is a *linear* equation of the form .. math:: - + M c = \begin{bmatrix} \bar z(0) \\ \bar z(T) \end{bmatrix} where :math:`\bar z` is called the *flat flag* for the system. @@ -123,29 +123,29 @@ Module usage To create a trajectory for a differentially flat system, a :class:`~flatsys.FlatSystem` object must be created. This is done -using the :func:`~flatsys.flatsys` function: +using the :func:`~flatsys.flatsys` function:: - import control.flatsys as fs - sys = fs.flatsys(forward, reverse) + import control.flatsys as fs + sys = fs.flatsys(forward, reverse) The `forward` and `reverse` parameters describe the mappings between the system state/input and the differentially flat outputs and their derivatives ("flat flag"). The :func:`~flatsys.FlatSystem.forward` method computes the -flat flag given a state and input: +flat flag given a state and input:: - zflag = sys.forward(x, u) + zflag = sys.forward(x, u) The :func:`~flatsys.FlatSystem.reverse` method computes the state -and input given the flat flag: +and input given the flat flag:: - x, u = sys.reverse(zflag) + x, u = sys.reverse(zflag) The flag :math:`\bar z` is implemented as a list of flat outputs :math:`z_i` and their derivatives up to order :math:`q_i`: - zflag[i][j] = :math:`z_i^{(j)}` + `zflag[i][j]` = :math:`z_i^{(j)}` The number of flat outputs must match the number of system inputs. @@ -153,13 +153,13 @@ For a linear system, a flat system representation can be generated by passing a :class:`StateSpace` system to the :func:`~flatsys.flatsys` factory function:: - sys = fs.flatsys(linsys) + sys = fs.flatsys(linsys) The :func:`~flatsys.flatsys` function also supports the use of named input, output, and state signals:: - sys = fs.flatsys( - forward, reverse, states=['x1', ..., 'xn'], inputs=['u1', ..., 'um']) + sys = fs.flatsys( + forward, reverse, states=['x1', ..., 'xn'], inputs=['u1', ..., 'um']) In addition to the flat system description, a set of basis functions :math:`\phi_i(t)` must be chosen. The `FlatBasis` class is used to @@ -168,7 +168,7 @@ form 1, :math:`t`, :math:`t^2`, ... can be computed using the :class:`~flatsys.PolyFamily` class, which is initialized by passing the desired order of the polynomial basis set:: - basis = fs.PolyFamily(N) + basis = fs.PolyFamily(N) Additional basis function families include Bezier curves (:class:`~flatsys.BezierFamily`) and B-splines @@ -178,14 +178,14 @@ Once the system and basis function have been defined, the :func:`~flatsys.point_to_point` function can be used to compute a trajectory between initial and final states and inputs:: - traj = fs.point_to_point( - sys, Tf, x0, u0, xf, uf, basis=basis) + traj = fs.point_to_point( + sys, Tf, x0, u0, xf, uf, basis=basis) The returned object has class :class:`~flatsys.SystemTrajectory` and can be used to compute the state and input trajectory between the initial and final condition:: - xd, ud = traj.eval(T) + xd, ud = traj.eval(T) where `T` is a list of times on which the trajectory should be evaluated (e.g., `T = numpy.linspace(0, Tf, M)`. @@ -197,8 +197,8 @@ format as :func:`optimal.solve_ocp`. The :func:`~flatsys.solve_flat_ocp` function can be used to solve an optimal control problem without a final state:: - traj = fs.solve_flat_ocp( - sys, timepts, x0, u0, cost, basis=basis) + traj = fs.solve_flat_ocp( + sys, timepts, x0, u0, cost, basis=basis) The `cost` parameter is a function with call signature `cost(x, u)` and should return the (incremental) cost at the given @@ -295,7 +295,7 @@ the endpoints. Alternatively, we can solve an optimal control problem in which we minimize a cost function along the trajectory as well as a terminal -cost:` +cost: .. code-block:: python From 3706083a6763a4f64ef7d827962040fcbc0ef5aa Mon Sep 17 00:00:00 2001 From: Richard Murray Date: Tue, 31 Dec 2024 21:00:32 -0800 Subject: [PATCH 47/93] rebase cleanup + remove unneeded spaces + header updates --- benchmarks/optestim_bench.py | 1 - control/canonical.py | 2 +- control/ctrlutil.py | 49 ++------- control/iosys.py | 2 +- control/modelsimp.py | 10 +- control/passivity.py | 4 +- control/robust.py | 4 +- control/sysnorm.py | 105 +++++++++---------- control/tests/docstrings_test.py | 19 +--- control/tests/phaseplot_test.py | 4 +- control/tests/sysnorm_test.py | 14 +-- control/tests/timebase_test.py | 2 +- doc/config.rst | 2 +- doc/examples.rst | 1 - doc/figures/phaseplot-dampedosc-default.png | Bin 102936 -> 102937 bytes doc/figures/phaseplot-invpend-meshgrid.png | Bin 189738 -> 189739 bytes doc/figures/phaseplot-oscillator-helpers.png | Bin 76107 -> 76108 bytes doc/index.rst | 2 +- doc/linear.rst | 24 ++--- doc/nlsys.rst | 18 ++-- doc/phaseplot.rst | 2 +- doc/xferfcn.rst | 2 +- examples/cruise-control.py | 14 +-- examples/markov.py | 8 +- examples/mrac_siso_lyapunov.py | 14 +-- examples/mrac_siso_mit.py | 10 +- examples/plot_gallery.py | 2 +- 27 files changed, 131 insertions(+), 184 deletions(-) diff --git a/benchmarks/optestim_bench.py b/benchmarks/optestim_bench.py index fdc4dc824..612ee6bb3 100644 --- a/benchmarks/optestim_bench.py +++ b/benchmarks/optestim_bench.py @@ -64,7 +64,6 @@ def time_oep_minimizer_methods(minimizer_name, noise_name, initial_guess): initial_guess = (res.states, V) else: initial_guess = None - # Set up optimal estimation function using Gaussian likelihoods for cost traj_cost = opt.gaussian_likelihood_cost(sys, Rv, Rw) diff --git a/control/canonical.py b/control/canonical.py index fee7ab52b..2c700a55d 100644 --- a/control/canonical.py +++ b/control/canonical.py @@ -188,7 +188,7 @@ def observable_form(xsys): def similarity_transform(xsys, T, timescale=1, inverse=False): - """Similarity transformation, with option time rescaling. + """Similarity transformation, with optional time rescaling. Transform a linear state space system to a new state space representation z = T x, or x = T z, where T is an invertible matrix. diff --git a/control/ctrlutil.py b/control/ctrlutil.py index c9e1c471b..cc2dbb423 100644 --- a/control/ctrlutil.py +++ b/control/ctrlutil.py @@ -1,52 +1,17 @@ # ctrlutil.py - control system utility functions # -# Author: Richard M. Murray -# Date: 24 May 09 -# -# These are some basic utility functions that are used in the control -# systems library and that didn't naturally fit anyplace else. -# -# Copyright (c) 2009 by California Institute of Technology -# All rights reserved. -# -# Redistribution and use in source and binary forms, with or without -# modification, are permitted provided that the following conditions -# are met: -# -# 1. Redistributions of source code must retain the above copyright -# notice, this list of conditions and the following disclaimer. -# -# 2. Redistributions in binary form must reproduce the above copyright -# notice, this list of conditions and the following disclaimer in the -# documentation and/or other materials provided with the distribution. -# -# 3. Neither the name of the California Institute of Technology nor -# the names of its contributors may be used to endorse or promote -# products derived from this software without specific prior -# written permission. -# -# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS -# "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT -# LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS -# FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL CALTECH -# OR THE CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, -# SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT -# LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF -# USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND -# ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, -# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT -# OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF -# SUCH DAMAGE. -# -# $Id$ +# Original author: Richard M. Murray +# Creation date: 24 May 2009 +# Use `git shortlog -n -s ctrlutil.py` for full list of contributors + +"""Control system utility functions.""" import math import warnings import numpy as np -# Packages that we need access to -from . import lti +from .lti import LTI __all__ = ['unwrap', 'issys', 'db2mag', 'mag2db'] @@ -96,7 +61,7 @@ def issys(obj): """ warnings.warn("issys() is deprecated; use isinstance(obj, ct.LTI)", FutureWarning, stacklevel=2) - return isinstance(obj, lti.LTI) + return isinstance(obj, LTI) def db2mag(db): """Convert a gain in decibels (dB) to a magnitude. diff --git a/control/iosys.py b/control/iosys.py index 7ab15966a..fc78bd6ca 100644 --- a/control/iosys.py +++ b/control/iosys.py @@ -261,7 +261,7 @@ def __str__(self): out += f"\nStates ({self.nstates}): {self.state_labels}" out += self._dt_repr(separator="\n", space=" ") return out - + def __repr__(self): return iosys_repr(self, format=self.repr_format) diff --git a/control/modelsimp.py b/control/modelsimp.py index b7b94b23a..8e78d4bee 100644 --- a/control/modelsimp.py +++ b/control/modelsimp.py @@ -1,10 +1,10 @@ # modelsimp.py - tools for model simplification # -# Author: Steve Brunton, Kevin Chen, Lauren Padilla -# Date: 30 Nov 2010 -# -"""This :mod:`modelsimp` modules contains routines for obtaining -reduced order models for state space systems. +# Original authors: Steve Brunton, Kevin Chen, Lauren Padilla +# Creation date: 30 Nov 2010 + +"""The :mod:`modelsimp` module contains routines for obtaining reduced +order models for state space systems. """ diff --git a/control/passivity.py b/control/passivity.py index 59e2fde2f..f7bee7bd0 100644 --- a/control/passivity.py +++ b/control/passivity.py @@ -1,7 +1,7 @@ # passivity.py # -# Author: Mark Yeatman -#Date: July 17, 2022 +# Original author: Mark Yeatman +# Creation date: July 17, 2022 """ Functions for passive control. diff --git a/control/robust.py b/control/robust.py index 85d86bb6d..e5ea795bb 100644 --- a/control/robust.py +++ b/control/robust.py @@ -1,7 +1,7 @@ # robust.py - tools for robust control # -# Author: Steve Brunton, Kevin Chen, Lauren Padilla -# Date: 24 Dec 2010 +# Original authors: Steve Brunton, Kevin Chen, Lauren Padilla +# Creation date: 24 Dec 2010 """ This file contains routines for obtaining reduced order models. diff --git a/control/sysnorm.py b/control/sysnorm.py index ddba5faca..271ded7b3 100644 --- a/control/sysnorm.py +++ b/control/sysnorm.py @@ -29,8 +29,8 @@ def _h2norm_slycot(sys, print_warning=True): -------- ``slycot.ab13bd`` : the Slycot routine that does the calculation https://github.com/python-control/Slycot/issues/199 : Post on issue with ``ab13bf`` + """ - try: from slycot import ab13bd except ImportError: @@ -38,7 +38,7 @@ def _h2norm_slycot(sys, print_warning=True): try: from slycot.exceptions import SlycotArithmeticError - except ImportError: + except ImportError: raise ct.ControlSlycot("Can't find slycot class ``SlycotArithmeticError``!") A, B, C, D = ct.ssdata(ct.ss(sys)) @@ -46,9 +46,9 @@ def _h2norm_slycot(sys, print_warning=True): n = A.shape[0] m = B.shape[1] p = C.shape[0] - + dico = 'C' if sys.isctime() else 'D' # Continuous or discrete time - jobn = 'H' # H2 (and not L2 norm) + jobn = 'H' # H2 (and not L2 norm) if n == 0: # ab13bd does not accept empty A, B, C @@ -61,7 +61,7 @@ def _h2norm_slycot(sys, print_warning=True): return 0.0 elif dico == 'D': return np.sqrt(D@D.T) - + try: norm = ab13bd(dico, jobn, n, m, p, A, B, C, D) except SlycotArithmeticError as e: @@ -85,7 +85,7 @@ def _h2norm_slycot(sys, print_warning=True): def system_norm(system, p=2, tol=1e-6, print_warning=True, method=None): """Computes the input/output norm of system. - + Parameters ---------- system : LTI (:class:`StateSpace` or :class:`TransferFunction`) @@ -103,16 +103,16 @@ def system_norm(system, p=2, tol=1e-6, print_warning=True, method=None): Set the method used for computing the result. Current methods are 'slycot' and 'scipy'. If set to `None` (default), try 'slycot' first and then 'scipy'. - + Returns ------- norm_value : float Norm value of system. - + Notes ----- Does not yet compute the L-infinity norm for discrete time systems with pole(s) in z=0 unless Slycot is used. - + Examples -------- >>> Gc = ct.tf([1], [1, 2, 1]) @@ -120,32 +120,32 @@ def system_norm(system, p=2, tol=1e-6, print_warning=True, method=None): 0.5 >>> round(ct.norm(Gc, 'inf', tol=1e-5, method='scipy'), 3) np.float64(1.0) + """ - if not isinstance(system, (ct.StateSpace, ct.TransferFunction)): raise TypeError( "Parameter `system`: must be a `StateSpace` or `TransferFunction`") - + G = ct.ss(system) A = G.A B = G.B C = G.C D = G.D - + # Decide what method to use method = ct.mateqn._slycot_or_scipy(method) - + # ------------------- # H2 norm computation # ------------------- - if p == 2: + if p == 2: # -------------------- # Continuous time case # -------------------- if G.isctime(): - + # Check for cases with infinite norm - poles_real_part = G.poles().real + poles_real_part = G.poles().real if any(np.isclose(poles_real_part, 0.0)): # Poles on imaginary axis if print_warning: warnings.warn("Poles close to, or on, the imaginary axis. Norm value may be uncertain.", UserWarning) @@ -157,35 +157,35 @@ def system_norm(system, p=2, tol=1e-6, print_warning=True, method=None): elif any(D.flat != 0): # System has direct feedthrough if print_warning: warnings.warn("System has a direct feedthrough term!", UserWarning) - return float('inf') - - else: + return float('inf') + + else: # Use slycot, if available, to compute (finite) norm if method == 'slycot': - return _h2norm_slycot(G, print_warning) - - # Else use scipy + return _h2norm_slycot(G, print_warning) + + # Else use scipy else: P = ct.lyap(A, B@B.T, method=method) # Solve for controllability Gramian - - # System is stable to reach this point, and P should be positive semi-definite. + + # System is stable to reach this point, and P should be positive semi-definite. # Test next is a precaution in case the Lyapunov equation is ill conditioned. - if any(la.eigvals(P).real < 0.0): + if any(la.eigvals(P).real < 0.0): if print_warning: warnings.warn("There appears to be poles close to the imaginary axis. Norm value may be uncertain.", UserWarning) return float('inf') else: norm_value = np.sqrt(np.trace(C@P@C.T)) # Argument in sqrt should be non-negative if np.isnan(norm_value): - raise ct.ControlArgument("Norm computation resulted in NaN.") + raise ct.ControlArgument("Norm computation resulted in NaN.") else: return norm_value - + # ------------------ # Discrete time case # ------------------ elif G.isdtime(): - + # Check for cases with infinite norm poles_abs = abs(G.poles()) if any(np.isclose(poles_abs, 1.0)): # Poles on imaginary axis @@ -196,34 +196,33 @@ def system_norm(system, p=2, tol=1e-6, print_warning=True, method=None): if print_warning: warnings.warn("System is unstable!", UserWarning) return float('inf') - else: # Use slycot, if available, to compute (finite) norm if method == 'slycot': - return _h2norm_slycot(G, print_warning) - - # Else use scipy + return _h2norm_slycot(G, print_warning) + + # Else use scipy else: P = ct.dlyap(A, B@B.T, method=method) - - # System is stable to reach this point, and P should be positive semi-definite. + + # System is stable to reach this point, and P should be positive semi-definite. # Test next is a precaution in case the Lyapunov equation is ill conditioned. if any(la.eigvals(P).real < 0.0): if print_warning: warnings.warn("Warning: There appears to be poles close to the complex unit circle. Norm value may be uncertain.", UserWarning) return float('inf') else: - norm_value = np.sqrt(np.trace(C@P@C.T + D@D.T)) # Argument in sqrt should be non-negative + norm_value = np.sqrt(np.trace(C@P@C.T + D@D.T)) # Argument in sqrt should be non-negative if np.isnan(norm_value): - raise ct.ControlArgument("Norm computation resulted in NaN.") + raise ct.ControlArgument("Norm computation resulted in NaN.") else: - return norm_value - + return norm_value + # --------------------------- # L-infinity norm computation # --------------------------- - elif p == "inf": - + elif p == "inf": + # Check for cases with infinite norm poles = G.poles() if G.isdtime(): # Discrete time @@ -236,15 +235,15 @@ def system_norm(system, p=2, tol=1e-6, print_warning=True, method=None): if print_warning: warnings.warn("Poles close to, or on, the imaginary axis. Norm value may be uncertain.", UserWarning) return float('inf') - + # Use slycot, if available, to compute (finite) norm if method == 'slycot': return ct.linfnorm(G, tol)[0] - + # Else use scipy else: - - # ------------------ + + # ------------------ # Discrete time case # ------------------ # Use inverse bilinear transformation of discrete time system to s-plane if no poles on |z|=1 or z=0. @@ -255,8 +254,8 @@ def system_norm(system, p=2, tol=1e-6, print_warning=True, method=None): Cd = C Dd = D if any(np.isclose(la.eigvals(Ad), 0.0)): - raise ct.ControlArgument("L-infinity norm computation for discrete time system with pole(s) in z=0 currently not supported unless Slycot installed.") - + raise ct.ControlArgument("L-infinity norm computation for discrete time system with pole(s) in z=0 currently not supported unless Slycot installed.") + # Inverse bilinear transformation In = np.eye(len(Ad)) Adinv = la.inv(Ad+In) @@ -264,7 +263,7 @@ def system_norm(system, p=2, tol=1e-6, print_warning=True, method=None): B = 2*Adinv@Bd C = 2*Cd@Adinv D = Dd - Cd@Adinv@Bd - + # -------------------- # Continuous time case # -------------------- @@ -272,15 +271,15 @@ def _Hamilton_matrix(gamma): """Constructs Hamiltonian matrix. For internal use.""" R = Ip*gamma**2 - D.T@D invR = la.inv(R) - return np.block([[A+B@invR@D.T@C, B@invR@B.T], [-C.T@(Ip+D@invR@D.T)@C, -(A+B@invR@D.T@C).T]]) + return np.block([[A+B@invR@D.T@C, B@invR@B.T], [-C.T@(Ip+D@invR@D.T)@C, -(A+B@invR@D.T@C).T]]) gaml = la.norm(D,ord=2) # Lower bound gamu = max(1.0, 2.0*gaml) # Candidate upper bound - Ip = np.eye(len(D)) - + Ip = np.eye(len(D)) + while any(np.isclose(la.eigvals(_Hamilton_matrix(gamu)).real, 0.0)): # Find actual upper bound gamu *= 2.0 - + while (gamu-gaml)/gamu > tol: gam = (gamu+gaml)/2.0 if any(np.isclose(la.eigvals(_Hamilton_matrix(gam)).real, 0.0)): @@ -288,12 +287,12 @@ def _Hamilton_matrix(gamma): else: gamu = gam return gam - + # ---------------------- # Other norm computation # ---------------------- else: - raise ct.ControlArgument(f"Norm computation for p={p} currently not supported.") + raise ct.ControlArgument(f"Norm computation for p={p} currently not supported.") norm = system_norm diff --git a/control/tests/docstrings_test.py b/control/tests/docstrings_test.py index e28c16ea0..cedfd5b09 100644 --- a/control/tests/docstrings_test.py +++ b/control/tests/docstrings_test.py @@ -89,11 +89,6 @@ def test_parameter_docs(module, prefix): objname = name _info(f"Checking object {objname}", 4) - # Parse the docstring using numpydoc - with warnings.catch_warnings(): - warnings.simplefilter('ignore') # debug via sphinx, not here - doc = None if obj is None else npd.FunctionDoc(obj) - # Parse the docstring using numpydoc with warnings.catch_warnings(): warnings.simplefilter('ignore') # debug via sphinx, not here @@ -121,7 +116,7 @@ def test_parameter_docs(module, prefix): test_parameter_docs(obj, prefix + name + '.') continue - # Skip anything that is inherited, hidden, deprecated, or checked + # Skip anything that is inherited, hidden, or already checked if not inspect.isfunction(obj) or \ inspect.isclass(module) and name not in module.__dict__ \ or name.startswith('_') or obj in function_skiplist \ @@ -152,7 +147,7 @@ def test_parameter_docs(module, prefix): docstring = inspect.getdoc(obj) source = inspect.getsource(obj) - # Skip deprecated functions + # Skip deprecated functions (and check for proper annotation) doc_extended = "\n".join(doc["Extended Summary"]) if ".. deprecated::" in doc_extended: _info(" [deprecated]", 2) @@ -162,7 +157,6 @@ def test_parameter_docs(module, prefix): _info(" [deprecated, but not numpydoc compliant]", 2) _warn(f"{objname} deprecated, but not numpydoc compliant", 0) continue - elif re.search(name + r"(\(\))? is deprecated", source): _warn(f"{objname} is deprecated, but not documented", 1) continue @@ -240,15 +234,6 @@ def test_parameter_docs(module, prefix): f"{obj} return value '{retname}' " "docstring missing space") - # Look at the return values - for val in doc["Returns"]: - if val.name == '' and \ - (match := re.search(r"([\w]+):", val.type)) is not None: - retname = match.group(1) - _warn( - f"{obj} return value '{retname}' " - "docstring missing space") - @pytest.mark.parametrize("module, prefix", [ (control, ""), (control.flatsys, "flatsys."), diff --git a/control/tests/phaseplot_test.py b/control/tests/phaseplot_test.py index 5e7a31651..106dee6f0 100644 --- a/control/tests/phaseplot_test.py +++ b/control/tests/phaseplot_test.py @@ -149,10 +149,10 @@ def invpend_ode(t, x, m=0, l=0, b=0, g=0): def test_phaseplane_errors(): with pytest.raises(ValueError, match="invalid grid specification"): ct.phase_plane_plot(ct.rss(2, 1, 1), gridspec='bad') - + with pytest.raises(ValueError, match="unknown grid type"): ct.phase_plane_plot(ct.rss(2, 1, 1), gridtype='bad') - + with pytest.raises(ValueError, match="system must be planar"): ct.phase_plane_plot(ct.rss(3, 1, 1)) diff --git a/control/tests/sysnorm_test.py b/control/tests/sysnorm_test.py index 68edad230..4b4c6c0e4 100644 --- a/control/tests/sysnorm_test.py +++ b/control/tests/sysnorm_test.py @@ -14,11 +14,11 @@ def test_norm_1st_order_stable_system(): """First-order stable continuous-time system""" s = ct.tf('s') - + G1 = 1/(s+1) assert np.allclose(ct.norm(G1, p='inf'), 1.0) # Comparison to norm computed in MATLAB assert np.allclose(ct.norm(G1, p=2), 0.707106781186547) # Comparison to norm computed in MATLAB - + Gd1 = ct.sample_system(G1, 0.1) assert np.allclose(ct.norm(Gd1, p='inf'), 1.0) # Comparison to norm computed in MATLAB assert np.allclose(ct.norm(Gd1, p=2), 0.223513699524858) # Comparison to norm computed in MATLAB @@ -27,12 +27,12 @@ def test_norm_1st_order_stable_system(): def test_norm_1st_order_unstable_system(): """First-order unstable continuous-time system""" s = ct.tf('s') - + G2 = 1/(1-s) assert np.allclose(ct.norm(G2, p='inf'), 1.0) # Comparison to norm computed in MATLAB with pytest.warns(UserWarning, match="System is unstable!"): assert ct.norm(G2, p=2) == float('inf') # Comparison to norm computed in MATLAB - + Gd2 = ct.sample_system(G2, 0.1) assert np.allclose(ct.norm(Gd2, p='inf'), 1.0) # Comparison to norm computed in MATLAB with pytest.warns(UserWarning, match="System is unstable!"): @@ -41,13 +41,13 @@ def test_norm_1st_order_unstable_system(): def test_norm_2nd_order_system_imag_poles(): """Second-order continuous-time system with poles on imaginary axis""" s = ct.tf('s') - + G3 = 1/(s**2+1) with pytest.warns(UserWarning, match="Poles close to, or on, the imaginary axis."): assert ct.norm(G3, p='inf') == float('inf') # Comparison to norm computed in MATLAB with pytest.warns(UserWarning, match="Poles close to, or on, the imaginary axis."): assert ct.norm(G3, p=2) == float('inf') # Comparison to norm computed in MATLAB - + Gd3 = ct.sample_system(G3, 0.1) with pytest.warns(UserWarning, match="Poles close to, or on, the complex unit circle."): assert ct.norm(Gd3, p='inf') == float('inf') # Comparison to norm computed in MATLAB @@ -68,7 +68,7 @@ def test_norm_3rd_order_mimo_system(): G4 = ct.ss(A,B,C,D) # Random system generated in MATLAB assert np.allclose(ct.norm(G4, p='inf'), 4.276759162964244) # Comparison to norm computed in MATLAB assert np.allclose(ct.norm(G4, p=2), 2.237461821810309) # Comparison to norm computed in MATLAB - + Gd4 = ct.sample_system(G4, 0.1) assert np.allclose(ct.norm(Gd4, p='inf'), 4.276759162964228) # Comparison to norm computed in MATLAB assert np.allclose(ct.norm(Gd4, p=2), 0.707434962289554) # Comparison to norm computed in MATLAB diff --git a/control/tests/timebase_test.py b/control/tests/timebase_test.py index c0e02d3b8..c416d3fee 100644 --- a/control/tests/timebase_test.py +++ b/control/tests/timebase_test.py @@ -57,7 +57,7 @@ def test_composition(dt1, dt2, dt3, op, type): sys1 = Karray elif dt1 == 'float': sys1 = kfloat - + if isinstance(dt2, (int, float)) or dt2 is None: sys2 = ct.StateSpace(A, B, C, D, dt2) sys2 = type(sys2) diff --git a/doc/config.rst b/doc/config.rst index a4215e71a..27959f9a8 100644 --- a/doc/config.rst +++ b/doc/config.rst @@ -136,7 +136,7 @@ System display parameters .. py:data:: iosys.repr_format :type: str - :value: 'info' + :value: 'eval' Set the default format used by :func:`iosys_repr` to create the representation of an :class:`InputOutputSystem`: diff --git a/doc/examples.rst b/doc/examples.rst index a345a927d..3c24cf16e 100644 --- a/doc/examples.rst +++ b/doc/examples.rst @@ -20,7 +20,6 @@ other sources. .. toctree:: :maxdepth: 1 - :glob: examples/secord-matlab examples/pvtol-nested diff --git a/doc/figures/phaseplot-dampedosc-default.png b/doc/figures/phaseplot-dampedosc-default.png index 64ca7bd362a9a7585d2ce085f4fd56390e459ee4..69a28254f31d37793bd93e9f1b04ea30c811b576 100644 GIT binary patch delta 54 zcmbQSgl*;$wh1bXRufef6%7sa40IGSN=gcft@QPC6H5wm@=J0ull1b7()FhlMHDrr KZB1j`G!X#jpA$9! delta 53 zcmbQagl)zWwh1bXmJ?MK6)g0Obrdp6N(zdt^!0NSOA2!GOL8)k^zw_+^^ZS`2yRT@ Jn$EasA^_s66PN%1 diff --git a/doc/figures/phaseplot-invpend-meshgrid.png b/doc/figures/phaseplot-invpend-meshgrid.png index eba45c15345bc516666c9f387dcd6781ef488bc3..118c364be08dec7c084a3548d5be9312ea5f9d2b 100644 GIT binary patch delta 57 zcmZ2=ihK1b?g=W4Rufef6%7sa40IGSN=gcft@QPC6H5wm@=J0ull1b7()FhlMHDrr NwWcv{O=CLp1OPj@6#@VN delta 56 zcmZ2|ihI>5?g=W4mJ?MK6)g0Obrdp6N(zdt^!0NSOA2!GOL8)k^zw_+^^ZS`2yRSo MO=sMi&UEAn05^0MWdHyG diff --git a/doc/figures/phaseplot-oscillator-helpers.png b/doc/figures/phaseplot-oscillator-helpers.png index beceb948b297bcc97ffd35e7cd54a625381d8d1b..829d94d7401f60a127bca66d374f0cdce51d6c28 100644 GIT binary patch delta 54 zcmX?oiRH{CmI*41Rufef6%7sa40IGSN=gcft@QPC6H5wm@=J0ull1b7()FhlMHDrr KZB1i*ssR8FP7}TW delta 53 zcmX?eiRJVqmI*41mJ?MK6)g0Obrdp6N(zdt^!0NSOA2!GOL8)k^zw_+^^ZS`2yRT@ Jn$GxC0{{q|6d(Wq diff --git a/doc/index.rst b/doc/index.rst index 7cb110db4..95c208365 100644 --- a/doc/index.rst +++ b/doc/index.rst @@ -47,7 +47,7 @@ or the GitHub site: https://github.com/python-control/python-control. :caption: User Guide :maxdepth: 1 :numbered: 2 - + intro Tutorial Linear systems diff --git a/doc/linear.rst b/doc/linear.rst index 55f9f775f..fac13e8bb 100644 --- a/doc/linear.rst +++ b/doc/linear.rst @@ -332,23 +332,23 @@ the :func:`sample_system` function and specifying a sampling time:: Inputs (2): ['u[0]', 'u[1]'] Outputs (2): ['y[0]', 'y[1]'] States (4): ['x[0]', 'x[1]', 'x[2]', 'x[3]'] - + A = [[ 1.80851713 3.70669158 2.01645278 4.11088725] [-1.08922089 -1.56305649 -0.52225931 -1.44683994] [ 0.09926437 0.31372724 1.01817342 0.4445761 ] [ 0.1936853 0.3376456 0.0166808 0.96877128]] - + B = [[-0.07840985 -0.39220354] [-0.00693812 0.12585167] [-0.14077857 0.02673465] [ 0.05062632 -0.08018257]] - + C = [[-0.17214877 -0.06055247 -0.09715775 0.03881976] [-1.60336436 -2.11612637 -1.15117992 -2.34687907]] - + D = [[0.00704611 0.01107279] [0.04476368 0.22390648]] - + dt = 0.1 Note that the system name for the discrete time system is the name of @@ -375,7 +375,7 @@ array of frequencies:: : sys_ss$sampled Inputs (1): ['u[0]'] Outputs (1): ['y[0]'] - + Freq [rad/s] Response ------------ --------------------- 0.100 2.357 -3.909j @@ -448,20 +448,20 @@ Information about an LTI system can be obtained using the Python Inputs (2): ['u[0]', 'u[1]'] Outputs (2): ['y[0]', 'y[1]'] States (4): ['x[0]', 'x[1]', 'x[2]', 'x[3]'] - + A = [[ 64.81683274 -26.26662488 71.02177207 -107.97639093] [ 39.61359988 -16.12926876 41.82185164 -65.62112231] [ -30.6112503 12.44992996 -34.00905316 49.74919788] [ 10.43751558 -4.08781262 10.64062604 -18.15145265]] - + B = [[ 2.13327172 -0. ] [ 0.98132238 0. ] [-1.22635806 -1.57449988] [ 0. 0.97986024]] - + C = [[-0. 1.34197324 0.28470822 0. ] [-0.2777818 0. -0.60024615 -0.19122287]] - + D = [[-0. 0.] [ 0. 0.]] @@ -484,10 +484,10 @@ discrete time:: : sys[1] Inputs (1): ['u[0]'] Outputs (1): ['y[0]'] - + z ------------- z^2 + 2 z + 1 - + dt = 0.1 diff --git a/doc/nlsys.rst b/doc/nlsys.rst index fd94561f5..228e0110a 100644 --- a/doc/nlsys.rst +++ b/doc/nlsys.rst @@ -26,7 +26,7 @@ A nonlinear system is created using the :func:`nlsys` factory function:: updfcn[, outfcn], inputs=m, states=n, outputs=p, [, params=params]) The `updfcn` argument is a function returning the state update function:: - + updfcn(t, x, u, params) -> array where `x` is a 1-D array with shape (n,), `u` is a 1-D array @@ -64,29 +64,29 @@ The dynamics of this system can be modeling using the following code:: 'l': 2, # Distance to the read head 'eps': 0.01, # Magnitude of velocity-dependent perturbation } - + # State derivative def servomech_update(t, x, u, params): # Extract the configuration and velocity variables from the state vector theta = x[0] # Angular position of the disk drive arm thetadot = x[1] # Angular velocity of the disk drive arm tau = u[0] # Torque applied at the base of the arm - + # Get the parameter values J, b, k, r = map(params.get, ['J', 'b', 'k', 'r']) - + # Compute the angular acceleration dthetadot = 1/J * ( -b * thetadot - k * r * np.sin(theta) + tau) - + # Return the state update law return np.array([thetadot, dthetadot]) - + # System output (tip radial position + angular velocity) def servomech_output(t, x, u, params): l = params['l'] return np.array([l * x[0], x[1]]) - + # System dynamics servomech = ct.nlsys( servomech_update, servomech_output, name='servomech', @@ -102,10 +102,10 @@ of the model (via the Python `print()` function):: Outputs (2): ['y', 'thdot'] States (2): ['theta', 'thdot'] Parameters: ['J', 'b', 'k', 'r', 'l', 'eps'] - + Update: Output: - + Operating points and linearization ---------------------------------- diff --git a/doc/phaseplot.rst b/doc/phaseplot.rst index 0a9240ea1..862469871 100644 --- a/doc/phaseplot.rst +++ b/doc/phaseplot.rst @@ -79,7 +79,7 @@ are part of the :mod:`phaseplot` (pp) module:: The following helper functions are available: .. autosummary:: - + phaseplot.equilpoints phaseplot.separatrices phaseplot.streamlines diff --git a/doc/xferfcn.rst b/doc/xferfcn.rst index 85a25d5c1..a64663d16 100644 --- a/doc/xferfcn.rst +++ b/doc/xferfcn.rst @@ -162,7 +162,7 @@ a time delay to a given order:: : delay Inputs (1): ['u[0]'] Outputs (1): ['y[0]'] - + -s^3 + 120 s^2 - 6000 s + 1.2e+05 --------------------------------- s^3 + 120 s^2 + 6000 s + 1.2e+05 diff --git a/examples/cruise-control.py b/examples/cruise-control.py index 074be8fa8..5bb263830 100644 --- a/examples/cruise-control.py +++ b/examples/cruise-control.py @@ -50,7 +50,7 @@ def vehicle_update(t, x, u, params={}): """ from math import copysign, sin sign = lambda x: copysign(1, x) # define the sign() function - + # Set up the system parameters m = params.get('m', 1600.) g = params.get('g', 9.8) @@ -80,13 +80,13 @@ def vehicle_update(t, x, u, params={}): # Letting the slope of the road be \theta (theta), gravity gives the # force Fg = m g sin \theta. - + Fg = m * g * sin(theta) # A simple model of rolling friction is Fr = m g Cr sgn(v), where Cr is # the coefficient of rolling friction and sgn(v) is the sign of v (+/- 1) or # zero if v = 0. - + Fr = m * g * Cr * sign(v) # The aerodynamic drag is proportional to the square of the speed: Fa = @@ -95,11 +95,11 @@ def vehicle_update(t, x, u, params={}): # of the car. Fa = 1/2 * rho * Cd * A * abs(v) * v - + # Final acceleration on the car Fd = Fg + Fr + Fa dv = (F - Fd) / m - + return dv # Engine model: motor_torque @@ -108,7 +108,7 @@ def vehicle_update(t, x, u, params={}): # the rate of fuel injection, which is itself proportional to a control # signal 0 <= u <= 1 that controls the throttle position. The torque also # depends on engine speed omega. - + def motor_torque(omega, params={}): # Set up the system parameters Tm = params.get('Tm', 190.) # engine torque constant @@ -166,7 +166,7 @@ def motor_torque(omega, params={}): for m in (1200, 1600, 2000): # Compute the equilibrium state for the system X0, U0 = ct.find_operating_point( - cruise_tf, [0, vref[0]], [vref[0], gear[0], theta0[0]], + cruise_tf, [0, vref[0]], [vref[0], gear[0], theta0[0]], iu=[1, 2], y0=[vref[0], 0], iy=[0], params={'m': m}) t, y = ct.input_output_response( diff --git a/examples/markov.py b/examples/markov.py index 6c02499bd..5444e4cff 100644 --- a/examples/markov.py +++ b/examples/markov.py @@ -12,17 +12,17 @@ def create_impulse_response(H, time, transpose, dt): """Helper function to use TimeResponseData type for plotting""" - + H = np.array(H, ndmin=3) if transpose: H = np.transpose(H) - + q, p, m = H.shape inputs = np.zeros((p,p,m)) issiso = True if (q == 1 and p == 1) else False - + input_labels = [] trace_labels, trace_types = [], [] for i in range(p): @@ -105,4 +105,4 @@ def create_impulse_response(H, time, transpose, dt): plt.show() if 'PYCONTROL_TEST_EXAMPLES' not in os.environ: - plt.show() \ No newline at end of file + plt.show() diff --git a/examples/mrac_siso_lyapunov.py b/examples/mrac_siso_lyapunov.py index 60550a8d9..52dc2610c 100644 --- a/examples/mrac_siso_lyapunov.py +++ b/examples/mrac_siso_lyapunov.py @@ -42,27 +42,27 @@ def adaptive_controller_state(_t, xc, uc, params): """Internal state of adaptive controller, f(t,x,u;p)""" - + # Parameters gam = params["gam"] signB = params["signB"] - + # Controller inputs r = uc[0] xm = uc[1] x = uc[2] - + # Controller states # x1 = xc[0] # kr # x2 = xc[1] # kx - + # Algebraic relationships e = xm - x # Controller dynamics d_x1 = gam*r*e*signB d_x2 = gam*x*e*signB - + return [d_x1, d_x2] def adaptive_controller_output(_t, xc, uc, params): @@ -72,7 +72,7 @@ def adaptive_controller_output(_t, xc, uc, params): r = uc[0] #xm = uc[1] x = uc[2] - + # Controller state kr = xc[0] kx = xc[1] @@ -112,7 +112,7 @@ def adaptive_controller_output(_t, xc, uc, params): Tend = 100 dt = 0.1 -# Define simulation time +# Define simulation time t_vec = np.arange(0, Tend, dt) # Define control reference input diff --git a/examples/mrac_siso_mit.py b/examples/mrac_siso_mit.py index f901478cb..f8940e694 100644 --- a/examples/mrac_siso_mit.py +++ b/examples/mrac_siso_mit.py @@ -42,7 +42,7 @@ def adaptive_controller_state(t, xc, uc, params): """Internal state of adaptive controller, f(t,x,u;p)""" - + # Parameters gam = params["gam"] Am = params["Am"] @@ -59,7 +59,7 @@ def adaptive_controller_state(t, xc, uc, params): # x2 = xc[1] # kr x3 = xc[2] # # x4 = xc[3] # kx - + # Algebraic relationships e = xm - x @@ -78,11 +78,11 @@ def adaptive_controller_output(t, xc, uc, params): r = uc[0] # xm = uc[1] x = uc[2] - + # Controller state kr = xc[1] kx = xc[3] - + # Control law u = kx*x + kr*r @@ -118,7 +118,7 @@ def adaptive_controller_output(t, xc, uc, params): Tend = 100 dt = 0.1 -# Define simulation time +# Define simulation time t_vec = np.arange(0, Tend, dt) # Define control reference input diff --git a/examples/plot_gallery.py b/examples/plot_gallery.py index d7a700c91..5d7163952 100644 --- a/examples/plot_gallery.py +++ b/examples/plot_gallery.py @@ -147,7 +147,7 @@ def invpend_update(t, x, u, params): # time response with create_figure("time response"): timepts = np.linspace(0, 10) - + U = np.vstack([np.sin(timepts), np.cos(2*timepts)]) resp1 = ct.input_output_response(sys_mimo1, timepts, U) From fecc773745bedfedf0cd9791f1f3aec368c64a6e Mon Sep 17 00:00:00 2001 From: Richard Murray Date: Wed, 1 Jan 2025 07:55:31 -0800 Subject: [PATCH 48/93] update module headers and docstrings --- control/__init__.py | 14 +++++--- control/bdalg.py | 61 +++++-------------------------- control/canonical.py | 7 ++++ control/config.py | 13 ++++--- control/ctrlplot.py | 10 ++++-- control/ctrlutil.py | 2 +- control/delay.py | 9 +++-- control/descfcn.py | 5 --- control/dtime.py | 52 +++------------------------ control/exception.py | 42 +++------------------- control/flatsys/basis.py | 41 ++++----------------- control/flatsys/bezier.py | 47 ++++++------------------ control/flatsys/bspline.py | 13 ++++--- control/flatsys/flatsys.py | 4 +++ control/flatsys/linflat.py | 38 +++----------------- control/flatsys/poly.py | 43 +++++----------------- control/flatsys/systraj.py | 42 ++++------------------ control/frdata.py | 6 ++-- control/freqplot.py | 16 +++++---- control/grid.py | 14 +++++--- control/iosys.py | 15 +++++--- control/lti.py | 9 +++-- control/margins.py | 54 ++++------------------------ control/mateqn.py | 45 ++++++----------------- control/modelsimp.py | 8 +++-- control/nichols.py | 13 ++----- control/nlsys.py | 4 +-- control/optimal.py | 10 +++--- control/passivity.py | 9 ++--- control/phaseplot.py | 44 +++++++++++------------ control/pzmap.py | 19 +++++----- control/rlocus.py | 13 +++---- control/robust.py | 6 ++-- control/sisotool.py | 4 +++ control/statefbk.py | 9 ++--- control/statesp.py | 11 +++--- control/stochsys.py | 13 +++---- control/sysnorm.py | 16 +++------ control/timeplot.py | 15 ++++---- control/timeresp.py | 74 +++++++++++--------------------------- control/xferfcn.py | 9 +++-- 41 files changed, 264 insertions(+), 615 deletions(-) diff --git a/control/__init__.py b/control/__init__.py index 4bbb81c3f..1bc81f816 100644 --- a/control/__init__.py +++ b/control/__init__.py @@ -1,7 +1,8 @@ # __init__.py - initialization for control systems toolbox # -# Author: Richard M. Murray -# Date: 24 May 2009 +# Initial author: Richard M. Murray +# Creation date: 24 May 2009 +# Use `git shortlog -n -s` for full list of contributors """The Python Control Systems Library :mod:`control` provides common functions for analyzing and designing feedback control systems. @@ -20,7 +21,7 @@ and the python-control User Guide, available from the `python-control homepage `_. -The docstring examples assume that the following import commands:: +The docstring examples assume the following import commands:: >>> import numpy as np >>> import control as ct @@ -30,14 +31,17 @@ The main control package includes the most common functions used in analysis, design, and simulation of feedback control systems. Several -additional subpackages are available that provide more specialized -functionality: +additional subpackages and modules are available that provide more +specialized functionality: * :mod:`~control.flatsys`: Differentially flat systems * :mod:`~control.matlab`: MATLAB compatibility module * :mod:`~control.optimal`: Optimization-based control * :mod:`~control.phaseplot`: 2D phase plane diagrams +These subpackages and modules are described in more detail in the +subpackage and module docstrings and in the User Guide. + """ # Import functions from within the control system library diff --git a/control/bdalg.py b/control/bdalg.py index 2410065ed..569bc45f7 100644 --- a/control/bdalg.py +++ b/control/bdalg.py @@ -1,57 +1,14 @@ -"""bdalg.py +# bdalg.py - block diagram algebra +# +# Initial author: Richard M. Murray +# Creation date: 24 May 09 +# Pre-2014 revisions: Kevin K. Chen, Dec 2010 +# Use `git shortlog -n -s bdalg.py` for full list of contributors -This file contains some standard block diagram algebra. +"""Block diagram algebra. -Routines in this module: - -append -series -parallel -negate -feedback -connect -combine_tf -split_tf - -""" - -"""Copyright (c) 2010 by California Institute of Technology -All rights reserved. - -Redistribution and use in source and binary forms, with or without -modification, are permitted provided that the following conditions -are met: - -1. Redistributions of source code must retain the above copyright - notice, this list of conditions and the following disclaimer. - -2. Redistributions in binary form must reproduce the above copyright - notice, this list of conditions and the following disclaimer in the - documentation and/or other materials provided with the distribution. - -3. Neither the name of the California Institute of Technology nor - the names of its contributors may be used to endorse or promote - products derived from this software without specific prior - written permission. - -THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS -"AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT -LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS -FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL CALTECH -OR THE CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, -SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT -LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF -USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND -ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, -OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT -OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF -SUCH DAMAGE. - -Author: Richard M. Murray -Date: 24 May 09 -Revised: Kevin K. Chen, Dec 10 - -$Id$ +The :mod:`control.bdalg` module contains some standard block diagram +algebra, including series, parallel, and feedback functions. """ diff --git a/control/canonical.py b/control/canonical.py index 2c700a55d..3bff39ca2 100644 --- a/control/canonical.py +++ b/control/canonical.py @@ -1,6 +1,10 @@ # canonical.py - functions for converting systems to canonical forms # RMM, 10 Nov 2012 +"""Functions for converting systems to canonical forms. + +""" + import numpy as np from numpy import poly, transpose, zeros_like from numpy.linalg import matrix_rank, solve @@ -210,6 +214,9 @@ def similarity_transform(xsys, T, timescale=1, inverse=False): zsys : StateSpace object System in transformed coordinates, with state 'z'. + See Also + -------- + canonical_form Examples -------- diff --git a/control/config.py b/control/config.py index e1d50884e..1b3823779 100644 --- a/control/config.py +++ b/control/config.py @@ -1,12 +1,15 @@ # config.py - package defaults # RMM, 4 Nov 2012 # -# This file contains default values and utility functions for setting -# variables that control the behavior of the control package. -# Eventually it will be possible to read and write configuration -# files. For now, you can just choose between MATLAB and FBS default -# values + tweak a few other things. +# TODO: add ability to read/write configuration files (ala Matplotlib) +"""Functions to access default parameter values. + +The :mod:`control.config` module contains default values and utility +functions for setting parameters that control the behavior of the +control package. + +""" import collections import warnings diff --git a/control/ctrlplot.py b/control/ctrlplot.py index a26f706c5..722cea9b4 100644 --- a/control/ctrlplot.py +++ b/control/ctrlplot.py @@ -1,8 +1,12 @@ # ctrlplot.py - utility functions for plotting -# Richard M. Murray, 14 Jun 2024 +# RMM, 14 Jun 2024 # -""" -Collection of functions that are used by various plotting functions. + +"""Utility functions for plotting. + +The :mod:`control.ctrlplot` module contains a collection of functions +that are used by various plotting functions. + """ # Code pattern for control system plotting functions: diff --git a/control/ctrlutil.py b/control/ctrlutil.py index cc2dbb423..8bfe0e7f0 100644 --- a/control/ctrlutil.py +++ b/control/ctrlutil.py @@ -1,6 +1,6 @@ # ctrlutil.py - control system utility functions # -# Original author: Richard M. Murray +# Initial author: Richard M. Murray # Creation date: 24 May 2009 # Use `git shortlog -n -s ctrlutil.py` for full list of contributors diff --git a/control/delay.py b/control/delay.py index 5828ded2e..acc57cdb3 100644 --- a/control/delay.py +++ b/control/delay.py @@ -1,10 +1,9 @@ -# -*-coding: utf-8-*- -#! TODO: add module docstring # delay.py - functions involving time delays # -# Author: Sawyer Fuller -# Date: 26 Aug 2010 -# +# Initial author: Sawyer Fuller +# Creation date: 26 Aug 2010 + +"""Functions to implement time delays (pade).""" __all__ = ['pade'] diff --git a/control/descfcn.py b/control/descfcn.py index 09f0480a4..79a5b0f8b 100644 --- a/control/descfcn.py +++ b/control/descfcn.py @@ -1,10 +1,5 @@ # descfcn.py - describing function analysis -# # RMM, 23 Jan 2021 -# -# This module adds functions for carrying out analysis of systems with -# memoryless nonlinear feedback functions using describing functions. -# """The :mod:~control.descfcn` module contains function for performing closed loop analysis of systems with memoryless nonlinearities using diff --git a/control/dtime.py b/control/dtime.py index aada8c574..13fea7146 100644 --- a/control/dtime.py +++ b/control/dtime.py @@ -1,51 +1,9 @@ -"""dtime.py +# dtime.py - functions for manipulating discrete time systems +# +# Initial author: Richard M. Murray +# Creation date: 6 October 2012 -Functions for manipulating discrete time systems. - -Routines in this module: - -sample_system() -c2d() -""" - -"""Copyright (c) 2012 by California Institute of Technology -All rights reserved. - -Redistribution and use in source and binary forms, with or without -modification, are permitted provided that the following conditions -are met: - -1. Redistributions of source code must retain the above copyright - notice, this list of conditions and the following disclaimer. - -2. Redistributions in binary form must reproduce the above copyright - notice, this list of conditions and the following disclaimer in the - documentation and/or other materials provided with the distribution. - -3. Neither the name of the California Institute of Technology nor - the names of its contributors may be used to endorse or promote - products derived from this software without specific prior - written permission. - -THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS -"AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT -LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS -FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL CALTECH -OR THE CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, -SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT -LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF -USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND -ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, -OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT -OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF -SUCH DAMAGE. - -Author: Richard M. Murray -Date: 6 October 2012 - -$Id: dtime.py 185 2012-08-30 05:44:32Z murrayrm $ - -""" +"""Functions for manipulating discrete time systems.""" from .iosys import isctime diff --git a/control/exception.py b/control/exception.py index 95dfc3ef7..31c09769a 100644 --- a/control/exception.py +++ b/control/exception.py @@ -1,43 +1,9 @@ # exception.py - exception definitions for the control package # -# Author: Richard M. Murray -# Date: 31 May 2010 -# -# This file contains definitions of standard exceptions for the control package -# -# Copyright (c) 2010 by California Institute of Technology -# All rights reserved. -# -# Redistribution and use in source and binary forms, with or without -# modification, are permitted provided that the following conditions -# are met: -# -# 1. Redistributions of source code must retain the above copyright -# notice, this list of conditions and the following disclaimer. -# -# 2. Redistributions in binary form must reproduce the above copyright -# notice, this list of conditions and the following disclaimer in the -# documentation and/or other materials provided with the distribution. -# -# 3. Neither the name of the California Institute of Technology nor -# the names of its contributors may be used to endorse or promote -# products derived from this software without specific prior -# written permission. -# -# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS -# "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT -# LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS -# FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL CALTECH -# OR THE CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, -# SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT -# LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF -# USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND -# ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, -# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT -# OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF -# SUCH DAMAGE. -# -# $Id$ +# Initial author: Richard M. Murray +# Creation date: 31 May 2010 + +"""Exception definitions for the control package.""" class ControlSlycot(ImportError): """Slycot import failed.""" diff --git a/control/flatsys/basis.py b/control/flatsys/basis.py index ca7e6a292..0a747ebe8 100644 --- a/control/flatsys/basis.py +++ b/control/flatsys/basis.py @@ -1,40 +1,11 @@ # basis.py - BasisFamily class # RMM, 10 Nov 2012 -# -# The BasisFamily class is used to specify a set of basis functions for -# implementing differential flatness computations. -# -# Copyright (c) 2012 by California Institute of Technology -# All rights reserved. -# -# Redistribution and use in source and binary forms, with or without -# modification, are permitted provided that the following conditions -# are met: -# -# 1. Redistributions of source code must retain the above copyright -# notice, this list of conditions and the following disclaimer. -# -# 2. Redistributions in binary form must reproduce the above copyright -# notice, this list of conditions and the following disclaimer in the -# documentation and/or other materials provided with the distribution. -# -# 3. Neither the name of the California Institute of Technology nor -# the names of its contributors may be used to endorse or promote -# products derived from this software without specific prior -# written permission. -# -# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS -# "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT -# LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS -# FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL CALTECH -# OR THE CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, -# SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT -# LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF -# USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND -# ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, -# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT -# OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF -# SUCH DAMAGE. + +"""The :mod:`control.flatsys.basis` module defines the +:class:`flatsys.BasisFamily` class that used to specify a set of basis +functions for implementing differential flatness computations. + +""" import numpy as np diff --git a/control/flatsys/bezier.py b/control/flatsys/bezier.py index b3de70dff..e146b1c74 100644 --- a/control/flatsys/bezier.py +++ b/control/flatsys/bezier.py @@ -1,42 +1,15 @@ # bezier.m - 1D Bezier curve basis functions # RMM, 24 Feb 2021 -# -# This class implements a set of basis functions based on Bezier curves: -# -# \phi_i(t) = \sum_{i=0}^n {n \choose i} (T - t)^{n-i} t^i -# - -# Copyright (c) 2012 by California Institute of Technology -# All rights reserved. -# -# Redistribution and use in source and binary forms, with or without -# modification, are permitted provided that the following conditions -# are met: -# -# 1. Redistributions of source code must retain the above copyright -# notice, this list of conditions and the following disclaimer. -# -# 2. Redistributions in binary form must reproduce the above copyright -# notice, this list of conditions and the following disclaimer in the -# documentation and/or other materials provided with the distribution. -# -# 3. Neither the name of the California Institute of Technology nor -# the names of its contributors may be used to endorse or promote -# products derived from this software without specific prior -# written permission. -# -# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS -# "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT -# LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS -# FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL CALTECH -# OR THE CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, -# SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT -# LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF -# USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND -# ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, -# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT -# OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF -# SUCH DAMAGE. + +"""1D Bezier curve basis functions. + +The :mod:`control.flatsys.bezier` module defines the +:class:`BezierFamily` class, which implements a set of basis functions +based on Bezier curves: + +.. math:: \phi_i(t) = \sum_{i=0}^n {n \choose i} (T - t)^{n-i} t^i + +""" import numpy as np from scipy.special import binom, factorial diff --git a/control/flatsys/bspline.py b/control/flatsys/bspline.py index f580834bf..9b296142b 100644 --- a/control/flatsys/bspline.py +++ b/control/flatsys/bspline.py @@ -1,10 +1,13 @@ # bspline.py - B-spline basis functions # RMM, 2 Aug 2022 -# -# This class implements a set of B-spline basis functions that implement a -# piecewise polynomial at a set of breakpoints t0, ..., tn with given orders -# and smoothness. -# + +"""B-spline basis functions. + +This module implements a set of B-spline basis functions that +implement a piecewise polynomial at a set of breakpoints t0, ..., tn +with given orders and smoothness. + +""" import numpy as np from scipy.interpolate import BSpline diff --git a/control/flatsys/flatsys.py b/control/flatsys/flatsys.py index 86cfd997d..9c74c2117 100644 --- a/control/flatsys/flatsys.py +++ b/control/flatsys/flatsys.py @@ -1,6 +1,10 @@ # flatsys.py - trajectory generation for differentially flat systems # RMM, 10 Nov 2012 +"""Trajectory generation for differentially flat systems. + +""" + import itertools import warnings diff --git a/control/flatsys/linflat.py b/control/flatsys/linflat.py index 6996b8a92..1b4e3cc37 100644 --- a/control/flatsys/linflat.py +++ b/control/flatsys/linflat.py @@ -1,39 +1,9 @@ # linflat.py - FlatSystem subclass for linear systems # RMM, 10 November 2012 -# -# This file defines a FlatSystem class for a linear system. -# -# Copyright (c) 2012 by California Institute of Technology -# All rights reserved. -# -# Redistribution and use in source and binary forms, with or without -# modification, are permitted provided that the following conditions -# are met: -# -# 1. Redistributions of source code must retain the above copyright -# notice, this list of conditions and the following disclaimer. -# -# 2. Redistributions in binary form must reproduce the above copyright -# notice, this list of conditions and the following disclaimer in the -# documentation and/or other materials provided with the distribution. -# -# 3. Neither the name of the California Institute of Technology nor -# the names of its contributors may be used to endorse or promote -# products derived from this software without specific prior -# written permission. -# -# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS -# "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT -# LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS -# FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL CALTECH -# OR THE CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, -# SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT -# LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF -# USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND -# ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, -# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT -# OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF -# SUCH DAMAGE. + +"""FlatSystem class for a linear system. + +""" import numpy as np diff --git a/control/flatsys/poly.py b/control/flatsys/poly.py index a24bf6fde..4e925aa2c 100644 --- a/control/flatsys/poly.py +++ b/control/flatsys/poly.py @@ -1,41 +1,14 @@ # poly.m - simple set of polynomial basis functions -# TODO: rename this as taylor.m # RMM, 10 Nov 2012 # -# This class implements a set of simple basis functions consisting of powers -# of t: 1, t, t^2, ... -# -# Copyright (c) 2012 by California Institute of Technology -# All rights reserved. -# -# Redistribution and use in source and binary forms, with or without -# modification, are permitted provided that the following conditions -# are met: -# -# 1. Redistributions of source code must retain the above copyright -# notice, this list of conditions and the following disclaimer. -# -# 2. Redistributions in binary form must reproduce the above copyright -# notice, this list of conditions and the following disclaimer in the -# documentation and/or other materials provided with the distribution. -# -# 3. Neither the name of the California Institute of Technology nor -# the names of its contributors may be used to endorse or promote -# products derived from this software without specific prior -# written permission. -# -# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS -# "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT -# LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS -# FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL CALTECH -# OR THE CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, -# SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT -# LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF -# USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND -# ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, -# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT -# OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF -# SUCH DAMAGE. +# TODO: rename this as taylor.m + +"""Simple set of polynomial basis functions. + +This class implements a set of simple basis functions consisting of +powers of t: 1, t, t^2, ... + +""" import numpy as np from scipy.special import factorial diff --git a/control/flatsys/systraj.py b/control/flatsys/systraj.py index 21d493557..bb74de694 100644 --- a/control/flatsys/systraj.py +++ b/control/flatsys/systraj.py @@ -1,40 +1,12 @@ # systraj.py - SystemTrajectory class # RMM, 10 November 2012 -# -# The SystemTrajetory class is used to store a feasible trajectory for -# the state and input of a (nonlinear) control system. -# -# Copyright (c) 2012 by California Institute of Technology -# All rights reserved. -# -# Redistribution and use in source and binary forms, with or without -# modification, are permitted provided that the following conditions -# are met: -# -# 1. Redistributions of source code must retain the above copyright -# notice, this list of conditions and the following disclaimer. -# -# 2. Redistributions in binary form must reproduce the above copyright -# notice, this list of conditions and the following disclaimer in the -# documentation and/or other materials provided with the distribution. -# -# 3. Neither the name of the California Institute of Technology nor -# the names of its contributors may be used to endorse or promote -# products derived from this software without specific prior -# written permission. -# -# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS -# "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT -# LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS -# FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL CALTECH -# OR THE CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, -# SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT -# LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF -# USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND -# ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, -# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT -# OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF -# SUCH DAMAGE. + +"""SystemTrajectory class. + +The SystemTrajetory class is used to store a feasible trajectory for +the state and input of a (nonlinear) control system. + +""" import numpy as np diff --git a/control/frdata.py b/control/frdata.py index a23566390..4b7ccf896 100644 --- a/control/frdata.py +++ b/control/frdata.py @@ -1,11 +1,11 @@ # frdata.py - frequency response data representation and functions # -# Author: M.M. (Rene) van Paassen (using xferfcn.py as basis) -# Date: 02 Oct 12 +# Initial author: M.M. (Rene) van Paassen (using xferfcn.py as basis) +# Creation date: 02 Oct 2012 """Frequency response data representation and functions. -This module contains the FrequencyResponseData (FRD) class and also +This module contains the `FrequencyResponseData` (FRD) class and also functions that operate on FRD data. """ diff --git a/control/freqplot.py b/control/freqplot.py index 7d9d202b0..70b0c4227 100644 --- a/control/freqplot.py +++ b/control/freqplot.py @@ -1,12 +1,16 @@ # freqplot.py - frequency domain plots for control systems # # Initial author: Richard M. Murray -# Date: 24 May 09 -# -# This file contains some standard control system plots: Bode plots, -# Nyquist plots and other frequency response plots. The code for Nichols -# charts is in nichols.py. The code for pole-zero diagrams is in pzmap.py -# and rlocus.py. +# Creation date: 24 May 2009 + +"""Frequency domain plots for control systems. + +This module contains some standard control system plots: Bode plots, +Nyquist plots and other frequency response plots. The code for +Nichols charts is in nichols.py. The code for pole-zero diagrams is +in pzmap.py and rlocus.py. + +""" import itertools import math diff --git a/control/grid.py b/control/grid.py index 54a1940c9..145fbb4ce 100644 --- a/control/grid.py +++ b/control/grid.py @@ -1,9 +1,13 @@ # grid.py - code to add gridlines to root locus and pole-zero diagrams -# -# This code generates grids for pole-zero diagrams (including root locus -# diagrams). Rather than just draw a grid in place, it uses the AxisArtist -# package to generate a custom grid that will scale with the figure. -# + +"""Functions to add gridlines to root locus and pole-zero diagrams. + +This code generates grids for pole-zero diagrams (including root locus +diagrams). Rather than just draw a grid in place, it uses the +AxisArtist package to generate a custom grid that will scale with the +figure. + +""" import matplotlib.pyplot as plt import mpl_toolkits.axisartist.angle_helper as angle_helper diff --git a/control/iosys.py b/control/iosys.py index fc78bd6ca..9a5426400 100644 --- a/control/iosys.py +++ b/control/iosys.py @@ -1,10 +1,15 @@ # iosys.py - I/O system class and helper functions # RMM, 13 Mar 2022 -# -# This file implements the InputOutputSystem class, which is used as a -# parent class for StateSpace, TransferFunction, NonlinearIOSystem, LTI, -# FrequencyResponseData, InterconnectedSystem and other similar classes -# that allow naming of signals. + +"""I/O system class and helper functions. + +Theis module implements the :class:`InputOutputSystem` class, which is used +as a parent class for :class`LTI`, :class:`StateSpace`, +:class:`TransferFunction`, :class:`NonlinearIOSystem`, +class:`FrequencyResponseData`, :class:`InterconnectedSystem` and other +similar classes that allow naming of signals. + +""" import re from copy import deepcopy diff --git a/control/lti.py b/control/lti.py index 2f2848177..c402c131c 100644 --- a/control/lti.py +++ b/control/lti.py @@ -1,7 +1,10 @@ -"""lti.py +# lti.py - LTI class and functions for linear systems + +"""LTI class and functions for linear systems. + +This module contains the LTI parent class to the child classes +StateSpace and TransferFunction. -The lti module contains the LTI parent class to the child classes StateSpace -and TransferFunction. It is designed for use in the python-control library. """ import math diff --git a/control/margins.py b/control/margins.py index 87b0302f8..72b1b863c 100644 --- a/control/margins.py +++ b/control/margins.py @@ -1,51 +1,9 @@ -"""margins.py - -Functions for computing stability margins and related functions. - -Routines in this module: - -margins.stability_margins -margins.phase_crossover_frequencies -margins.margin -""" - -"""Copyright (c) 2011 by California Institute of Technology -All rights reserved. - -Redistribution and use in source and binary forms, with or without -modification, are permitted provided that the following conditions -are met: - -1. Redistributions of source code must retain the above copyright - notice, this list of conditions and the following disclaimer. - -2. Redistributions in binary form must reproduce the above copyright - notice, this list of conditions and the following disclaimer in the - documentation and/or other materials provided with the distribution. - -3. Neither the name of the California Institute of Technology nor - the names of its contributors may be used to endorse or promote - products derived from this software without specific prior - written permission. - -THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS -"AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT -LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS -FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL CALTECH -OR THE CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, -SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT -LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF -USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND -ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, -OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT -OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF -SUCH DAMAGE. - -Author: Richard M. Murray -Date: 14 July 2011 - -$Id$ -""" +# margins.py - functions for computing stability margins +# +# Initial author: Richard M. Murray +# Creation date: 14 July 2011 + +"""Functions for computing stability margins and related functions.""" import math from warnings import warn diff --git a/control/mateqn.py b/control/mateqn.py index f16b5aa9c..870490bb2 100644 --- a/control/mateqn.py +++ b/control/mateqn.py @@ -1,39 +1,14 @@ -# mateqn.py - Matrix equation solvers (Lyapunov, Riccati) +# mateqn.py - matrix equation solvers (Lyapunov, Riccati) # -# Implementation of the functions lyap, dlyap, care and dare -# for solution of Lyapunov and Riccati equations. -# -# Original author: Bjorn Olofsson - -# Copyright (c) 2011, All rights reserved. - -# Redistribution and use in source and binary forms, with or without -# modification, are permitted provided that the following conditions -# are met: - -# 1. Redistributions of source code must retain the above copyright -# notice, this list of conditions and the following disclaimer. - -# 2. Redistributions in binary form must reproduce the above copyright -# notice, this list of conditions and the following disclaimer in the -# documentation and/or other materials provided with the distribution. - -# 3. Neither the name of the project author nor the names of its -# contributors may be used to endorse or promote products derived -# from this software without specific prior written permission. - -# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS -# "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT -# LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS -# FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL CALTECH -# OR THE CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, -# SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT -# LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF -# USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND -# ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, -# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT -# OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF -# SUCH DAMAGE. +# Initial author: Bjorn Olofsson +# Creation date: 2011 + +"""Matrix equation solvers (Lyapunov, Riccati). + +This module contains implementation of the functions lyap, dlyap, care +and dare for solution of Lyapunov and Riccati equations. + +""" import warnings diff --git a/control/modelsimp.py b/control/modelsimp.py index 8e78d4bee..b1ca3062f 100644 --- a/control/modelsimp.py +++ b/control/modelsimp.py @@ -1,10 +1,12 @@ # modelsimp.py - tools for model simplification # -# Original authors: Steve Brunton, Kevin Chen, Lauren Padilla +# Initial authors: Steve Brunton, Kevin Chen, Lauren Padilla # Creation date: 30 Nov 2010 -"""The :mod:`modelsimp` module contains routines for obtaining reduced -order models for state space systems. +"""Tools for model simplification. + +This module contains routines for obtaining reduced order models for state +space systems. """ diff --git a/control/nichols.py b/control/nichols.py index 650c57e6b..3e42b1acd 100644 --- a/control/nichols.py +++ b/control/nichols.py @@ -1,17 +1,8 @@ # nichols.py - Nichols plot # -# Contributed by Allan McInnes -# - -"""nichols.py - -Functions for plotting Black-Nichols charts. - -Routines in this module: +# Initial author: Allan McInnes -nichols.nichols_plot aliased as nichols.nichols -nichols.nichols_grid -""" +"""Functions for plotting Black-Nichols charts.""" import matplotlib.pyplot as plt import matplotlib.transforms diff --git a/control/nlsys.py b/control/nlsys.py index dad42c7f7..0a725d9be 100644 --- a/control/nlsys.py +++ b/control/nlsys.py @@ -1,13 +1,11 @@ # nlsys.py - input/output system module # RMM, 28 April 2019 # -# Additional features to add +# Additional features to add: # * Allow constant inputs for MIMO input_output_response (w/out ones) -# * Add support for constants/matrices as part of operators (1 + P) # * Add unit tests (and example?) for time-varying systems # * Allow time vector for discrete time simulations to be multiples of dt # * Check the way initial outputs for discrete time systems are handled -# """The :mod:`~control.nlsys` module contains the :class:`~control.NonlinearIOSystem` class that represents (possibly nonlinear) diff --git a/control/optimal.py b/control/optimal.py index 03e87953a..03cbe3e1f 100644 --- a/control/optimal.py +++ b/control/optimal.py @@ -3,14 +3,16 @@ # RMM, 11 Feb 2021 # -"""The :mod:`control.optimal` module provides support for optimization-based -controllers for nonlinear systems with state and input constraints. +"""Optimization-based control module. -The docstring examples assume that the following import commands:: +This module provides support for optimization-based controllers for +nonlinear systems with state and input constraints. + +The docstring examples assume the following import commands:: >>> import numpy as np >>> import control as ct - >>> import control.optimal as obc + >>> import control.optimal as opt """ diff --git a/control/passivity.py b/control/passivity.py index f7bee7bd0..b06cc89fc 100644 --- a/control/passivity.py +++ b/control/passivity.py @@ -1,12 +1,9 @@ -# passivity.py +# passivity.py - functions for passive control # -# Original author: Mark Yeatman +# Initial author: Mark Yeatman # Creation date: July 17, 2022 -""" -Functions for passive control. - -""" +"""Functions for passive control.""" import numpy as np diff --git a/control/phaseplot.py b/control/phaseplot.py index 2ec8fbe8b..bd75bd95f 100644 --- a/control/phaseplot.py +++ b/control/phaseplot.py @@ -1,29 +1,25 @@ # phaseplot.py - generate 2D phase portraits # -# Author: Richard M. Murray -# Date: 23 Mar 2024 (legacy version information below) -# -# TODO -# * Allow multiple timepoints (and change timespec name to T?) -# * Update linestyles (color -> linestyle?) -# * Check for keyword compatibility with other plot routines -# * Set up configuration parameters (nyquist --> phaseplot) - -"""Module for generating 2D phase plane plots. - -The :mod:`control.phaseplot` module contains functions for generating 2D -phase plots. The base function for creating phase plane portraits is -:func:`~control.phase_plane_plot`, which generates a phase plane portrait -for a 2 state I/O system (with no inputs). In addition, several other -functions are available to create customized phase plane plots: - -* boxgrid: Generate a list of points along the edge of a box -* circlegrid: Generate list of points around a circle -* equilpoints: Plot equilibrium points in the phase plane -* meshgrid: Generate a list of points forming a mesh -* separatrices: Plot separatrices in the phase plane -* streamlines: Plot stream lines in the phase plane -* vectorfield: Plot a vector field in the phase plane +# Initial author: Richard M. Murray +# Creation date: 24 July 2011, converted from MATLAB version (2002); +# based on an original version by Kristi Morgansen + +"""Generate 2D phase portraits. + +This module contains functions for generating 2D phase plots. The base +function for creating phase plane portraits is +:func:`~control.phase_plane_plot`, which generates a phase plane +portrait for a 2 state I/O system (with no inputs). In addition, +several other functions are available to create customized phase plane +plots: + +* `phaseplot.boxgrid`: Generate a list of points along the edge of a box +* `phaseplot.circlegrid`: Generate list of points around a circle +* `phaseplot.equilpoints`: Plot equilibrium points in the phase plane +* `phaseplot.meshgrid`: Generate a list of points forming a mesh +* `phaseplot.separatrices`: Plot separatrices in the phase plane +* `phaseplot.streamlines`: Plot stream lines in the phase plane +* `phaseplot.vectorfield`: Plot a vector field in the phase plane """ diff --git a/control/pzmap.py b/control/pzmap.py index 0a47ef2e4..9e84914fa 100644 --- a/control/pzmap.py +++ b/control/pzmap.py @@ -1,13 +1,16 @@ # pzmap.py - computations involving poles and zeros # -# Original author: Richard M. Murray -# Date: 7 Sep 2009 -# -# This file contains functions that compute poles, zeros and related -# quantities for a linear system, as well as the main functions for -# storing and plotting pole/zero and root locus diagrams. (The actual -# computation of root locus diagrams is in rlocus.py.) -# +# Initial author: Richard M. Murray +# Creation date: 7 Sep 2009 + +"""Computations involving poles and zeros. + +This module contains functions that compute poles, zeros and related +quantities for a linear system, as well as the main functions for +storing and plotting pole/zero and root locus diagrams. (The actual +computation of root locus diagrams is in rlocus.py.) + +""" import itertools import warnings diff --git a/control/rlocus.py b/control/rlocus.py index 065fbc10d..ac8dd7d08 100644 --- a/control/rlocus.py +++ b/control/rlocus.py @@ -1,19 +1,16 @@ # rlocus.py - code for computing a root locus plot -# Code contributed by Ryan Krauss, 2010 +# +# Initial author: Ryan Krauss +# Creation date: 2010 # # RMM, 17 June 2010: modified to be a standalone piece of code -# * Added BSD copyright info to file (per Ryan) -# * Added code to convert (num, den) to poly1d's if they aren't already. -# This allows Ryan's code to run on a standard signal.ltisys object -# or a control.TransferFunction object. -# * Added some comments to make sure I understand the code # # RMM, 2 April 2011: modified to work with new LTI structure (see ChangeLog) -# * Not tested: should still work on signal.ltisys objects # # Sawyer B. Fuller (minster@uw.edu) 21 May 2020: # * added compatibility with discrete-time systems. -# + +"""Code for computing a root locus plot.""" import warnings diff --git a/control/robust.py b/control/robust.py index e5ea795bb..2101445c1 100644 --- a/control/robust.py +++ b/control/robust.py @@ -1,11 +1,9 @@ # robust.py - tools for robust control # -# Original authors: Steve Brunton, Kevin Chen, Lauren Padilla +# Initial authors: Steve Brunton, Kevin Chen, Lauren Padilla # Creation date: 24 Dec 2010 -""" -This file contains routines for obtaining reduced order models. -""" +"""Robust control synthesis algorithms.""" import warnings diff --git a/control/sisotool.py b/control/sisotool.py index a2c31cc86..db896cad2 100644 --- a/control/sisotool.py +++ b/control/sisotool.py @@ -1,3 +1,7 @@ +# sisotool.py - interactive tool for SISO control design + +"""Interactive tool for SISO control design.""" + __all__ = ['sisotool', 'rootlocus_pid_designer'] import warnings diff --git a/control/statefbk.py b/control/statefbk.py index 89b906083..2a598c110 100644 --- a/control/statefbk.py +++ b/control/statefbk.py @@ -1,12 +1,9 @@ # statefbk.py - tools for state feedback control # -# Author: Richard M. Murray, Roberto Bucher -# Date: 31 May 2010 -# -"""The statefbk module contains routines for designing state space -controllers. +# Initial authors: Richard M. Murray, Roberto Bucher +# Creation date: 31 May 2010 -""" +"""Routines for designing state space controllers.""" import warnings diff --git a/control/statesp.py b/control/statesp.py index 0cc3a6b15..d15f5a18e 100644 --- a/control/statesp.py +++ b/control/statesp.py @@ -1,15 +1,14 @@ # statesp.py - state space class and related functions # -# Original author: Richard M. Murray +# Initial author: Richard M. Murray # Creation date: 24 May 2009 -# Pre-2014 revisions: Kevin K. Chen, Dec 10 +# Pre-2014 revisions: Kevin K. Chen, Dec 2010 # Use `git shortlog -n -s statesp.py` for full list of contributors -"""State space representation and functions. +"""State space class and related functions. -This module contains the StateSpace class, which is used to represent -linear systems in state space. This is the primary representation for the -python-control library. +This module contains the `StateSpace class`, which is used to +represent linear systems in state space. """ diff --git a/control/stochsys.py b/control/stochsys.py index 80cf72661..08457d9d0 100644 --- a/control/stochsys.py +++ b/control/stochsys.py @@ -1,14 +1,11 @@ # stochsys.py - stochastic systems module # RMM, 16 Mar 2022 -# -# This module contains functions that are intended to be used for analysis -# and design of stochastic control systems, mainly involving Kalman -# filtering and its variants. -# -"""The :mod:`~control.stochsys` module contains functions for analyzing and -designing stochastic (control) systems, including white noise processes and -Kalman filtering. +"""Stochastic systems module. + +This module contains functions for analyzing and designing stochastic +(control) systems, including white noise processes and Kalman +filtering. """ diff --git a/control/sysnorm.py b/control/sysnorm.py index 271ded7b3..7848e222c 100644 --- a/control/sysnorm.py +++ b/control/sysnorm.py @@ -1,15 +1,9 @@ -# -*- coding: utf-8 -*- -"""sysnorm.py +# sysnorm.py - functions for computing system norms +# +# Initial author: Henrik Sandberg +# Creation date: 21 Dec 2023 -Functions for computing system norms. - -Routine in this module: - -norm - -Created on Thu Dec 21 08:06:12 2023 -Author: Henrik Sandberg -""" +"""Functions for computing system norms.""" import warnings diff --git a/control/timeplot.py b/control/timeplot.py index 23c599b24..41a778cd4 100644 --- a/control/timeplot.py +++ b/control/timeplot.py @@ -1,12 +1,13 @@ # timeplot.py - time plotting functions # RMM, 20 Jun 2023 -# -# This file contains routines for plotting out time responses. These -# functions can be called either as standalone functions or access from the -# TimeDataResponse class. -# -# Note: It might eventually make sense to put the functions here -# directly into timeresp.py. + +"""Time plotting functions. + +This module contains routines for plotting out time responses. These +functions can be called either as standalone functions or access from +the TimeDataResponse class. + +""" import itertools from warnings import warn diff --git a/control/timeresp.py b/control/timeresp.py index 1342390e2..d73cfbfd5 100644 --- a/control/timeresp.py +++ b/control/timeresp.py @@ -1,9 +1,24 @@ -""" -timeresp.py - time-domain simulation routines. +# timeresp.py - time-domain simulation routines. +# +# Initial author: Eike Welk +# Creation date: 12 May 2011 +# +# Modified: Sawyer B. Fuller (minster@uw.edu) to add discrete-time +# capability and better automatic time vector creation +# Date: June 2020 +# +# Modified by Ilhan Polat to improve automatic time vector creation +# Date: August 17, 2020 +# +# Modified by Richard Murray to add TimeResponseData class +# Date: August 2021 +# +# Use `git shortlog -n -s statesp.py` for full list of contributors + +"""Time domain simulation routines. -The :mod:`~control.timeresp` module contains a collection of -functions that are used to compute time-domain simulations of LTI -systems. +This module contains a collection of functions that are used to +compute time-domain simulations of LTI systems. Arguments to time-domain simulations include a time vector, an input vector (when needed), and an initial condition vector. The most @@ -19,55 +34,6 @@ See :ref:`time-series-convention` for more information on how time series data are represented. -Copyright (c) 2011 by California Institute of Technology -All rights reserved. - -Copyright (c) 2011 by Eike Welk -Copyright (c) 2010 by SciPy Developers - -Redistribution and use in source and binary forms, with or without -modification, are permitted provided that the following conditions -are met: - -1. Redistributions of source code must retain the above copyright - notice, this list of conditions and the following disclaimer. - -2. Redistributions in binary form must reproduce the above copyright - notice, this list of conditions and the following disclaimer in the - documentation and/or other materials provided with the distribution. - -3. Neither the name of the California Institute of Technology nor - the names of its contributors may be used to endorse or promote - products derived from this software without specific prior - written permission. - -THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS -"AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT -LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS -FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL CALTECH -OR THE CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, -SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT -LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF -USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND -ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, -OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT -OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF -SUCH DAMAGE. - -Initial Author: Eike Welk -Date: 12 May 2011 - -Modified: Sawyer B. Fuller (minster@uw.edu) to add discrete-time -capability and better automatic time vector creation -Date: June 2020 - -Modified by Ilhan Polat to improve automatic time vector creation -Date: August 17, 2020 - -Modified by Richard Murray to add TimeResponseData class -Date: August 2021 - -$Id$ """ import warnings diff --git a/control/xferfcn.py b/control/xferfcn.py index 716424be3..5cb0e1e35 100644 --- a/control/xferfcn.py +++ b/control/xferfcn.py @@ -1,15 +1,14 @@ # xferfcn.py - transfer function class and related functions # -# Original author: Richard M. Murray +# Initial author: Richard M. Murray # Creation date: 24 May 2009 # Pre-2014 revisions: Kevin K. Chen, Dec 2010 # Use `git shortlog -n -s xferfcn.py` for full list of contributors -"""Transfer function representation and functions. +"""Transfer function class and related functions. -This module contains the TransferFunction class and also functions that -operate on transfer functions. This is the primary representation for the -python-control library. +This module contains the :class:`TransferFunction` class and also +functions that operate on transfer functions. """ From 06b462c25190d84c9052a74c166b8d375744cec8 Mon Sep 17 00:00:00 2001 From: Richard Murray Date: Wed, 1 Jan 2025 08:53:38 -0800 Subject: [PATCH 49/93] update MATLAB module headers and docstrings --- control/matlab/__init__.py | 348 +------------------------------------ control/matlab/timeresp.py | 9 +- control/matlab/wrappers.py | 6 +- doc/matlab.rst | 5 +- 4 files changed, 22 insertions(+), 346 deletions(-) diff --git a/control/matlab/__init__.py b/control/matlab/__init__.py index dca522ec5..d0d87cb8e 100644 --- a/control/matlab/__init__.py +++ b/control/matlab/__init__.py @@ -1,52 +1,14 @@ -# -*- coding: utf-8 -*- -""" -The :mod:`control.matlab` module contains a number of functions that emulate -some of the functionality of MATLAB. The intent of these functions is to -provide a simple interface to the python control systems library -(python-control) for people who are familiar with the MATLAB Control Systems -Toolbox (tm). -""" - -"""Copyright (c) 2009 by California Institute of Technology -All rights reserved. - -Copyright (c) 2011 by Eike Welk - - -Redistribution and use in source and binary forms, with or without -modification, are permitted provided that the following conditions -are met: - -1. Redistributions of source code must retain the above copyright - notice, this list of conditions and the following disclaimer. - -2. Redistributions in binary form must reproduce the above copyright - notice, this list of conditions and the following disclaimer in the - documentation and/or other materials provided with the distribution. +# Original author: Richard M. Murray +# Creation date: 29 May 09 +# Pre-2014 revisions: Kevin K. Chen, Dec 2010 -3. Neither the name of the California Institute of Technology nor - the names of its contributors may be used to endorse or promote - products derived from this software without specific prior - written permission. +"""MATLAB compatibility sub-package. -THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS -"AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT -LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS -FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL CALTECH -OR THE CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, -SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT -LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF -USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND -ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, -OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT -OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF -SUCH DAMAGE. - -Author: Richard M. Murray -Date: 29 May 09 -Revised: Kevin K. Chen, Dec 10 - -$Id$ +The :mod:`control.matlab` sub-package contains a number of functions +that emulate some of the functionality of MATLAB. The intent of these +functions is to provide a simple interface to the python control +systems library (python-control) for people who are familiar with the +MATLAB Control Systems Toolbox (tm). """ @@ -101,295 +63,3 @@ # Set up defaults corresponding to MATLAB conventions from ..config import * use_matlab_defaults() - -r""" -The following tables give an overview of the module ``control.matlab``. -They also show the implementation progress and the planned features of the -module. - -The symbols in the first column show the current state of a feature: - -* ``*`` : The feature is currently implemented. -* ``-`` : The feature is not planned for implementation. -* ``s`` : A similar feature from another library (Scipy) is imported into - the module, until the feature is implemented here. - - -Creating linear models ----------------------------------------------------------------------------- - -== ========================== ============================================ -\* :func:`tf` create transfer function (TF) models -\* :func:`zpk` create zero/pole/gain (ZPK) models. -\* :func:`ss` create state-space (SS) models -\ dss create descriptor state-space models -\ delayss create state-space models with delayed terms -\* :func:`frd` create frequency response data (FRD) models -\ lti/exp create pure continuous-time delays (TF and - ZPK only) -\ filt specify digital filters -\- lti/set set/modify properties of LTI models -\- setdelaymodel specify internal delay model (state space - only) -\* :func:`rss` create a random continuous state space model -\* :func:`drss` create a random discrete state space model -== ========================== ============================================ - - -Data extraction ----------------------------------------------------------------------------- - -== ========================== ============================================ -\* :func:`tfdata` extract numerators and denominators -\ lti/zpkdata extract zero/pole/gain data -\ lti/ssdata extract state-space matrices -\ lti/dssdata descriptor version of SSDATA -\ frd/frdata extract frequency response data -\ lti/get access values of LTI model properties -\ ss/getDelayModel access internal delay model (state space) -== ========================== ============================================ - - -Conversions ----------------------------------------------------------------------------- - -== ============================ ============================================ -\* :func:`tf` conversion to transfer function -\ zpk conversion to zero/pole/gain -\* :func:`ss` conversion to state space -\* :func:`frd` conversion to frequency data -\* :func:`c2d` continuous to discrete conversion -\ d2c discrete to continuous conversion -\ d2d resample discrete-time model -\ upsample upsample discrete-time LTI systems -\* :func:`ss2tf` state space to transfer function -\s :func:`~scipy.signal.ss2zpk` transfer function to zero-pole-gain -\* :func:`tf2ss` transfer function to state space -\s :func:`~scipy.signal.tf2zpk` transfer function to zero-pole-gain -\s :func:`~scipy.signal.zpk2ss` zero-pole-gain to state space -\s :func:`~scipy.signal.zpk2tf` zero-pole-gain to transfer function -== ============================ ============================================ - - -System interconnections ----------------------------------------------------------------------------- - -== ========================== ============================================ -\* :func:`~control.append` group LTI models by appending inputs/outputs -\* :func:`~control.parallel` connect LTI models in parallel - (see also overloaded ``+``) -\* :func:`~control.series` connect LTI models in series - (see also overloaded ``*``) -\* :func:`~control.feedback` connect lti models with a feedback loop -\ lti/lft generalized feedback interconnection -\* :func:`~control.connect` arbitrary interconnection of lti models -\ sumblk summing junction (for use with connect) -\ strseq builds sequence of indexed strings - (for I/O naming) -== ========================== ============================================ - - -System gain and dynamics ----------------------------------------------------------------------------- - -== ========================== ============================================ -\* :func:`dcgain` steady-state (D.C.) gain -\* :func:`bandwidth` system bandwidth -\ lti/norm h2 and Hinfinity norms of LTI models -\* :func:`pole` system poles -\* :func:`zero` system (transmission) zeros -\ lti/order model order (number of states) -\* :func:`~control.pzmap` pole-zero map (TF only) -\ lti/iopzmap input/output pole-zero map -\* :func:`damp` natural frequency, damping of system poles -\ esort sort continuous poles by real part -\ dsort sort discrete poles by magnitude -\ lti/stabsep stable/unstable decomposition -\ lti/modsep region-based modal decomposition -== ========================== ============================================ - - -Time-domain analysis ----------------------------------------------------------------------------- - -== ========================== ============================================ -\* :func:`step` step response -\ stepinfo step response characteristics -\* :func:`impulse` impulse response -\* :func:`initial` free response with initial conditions -\* :func:`lsim` response to user-defined input signal -\ lsiminfo linear response characteristics -\ gensig generate input signal for LSIM -\ covar covariance of response to white noise -== ========================== ============================================ - - -Frequency-domain analysis ----------------------------------------------------------------------------- - -== ========================== ============================================ -\* :func:`bode` Bode plot of the frequency response -\ lti/bodemag Bode magnitude diagram only -\ sigma singular value frequency plot -\* :func:`~control.nyquist` Nyquist plot -\* :func:`~control.nichols` Nichols plot -\* :func:`margin` gain and phase margins -\ lti/allmargin all crossover frequencies and margins -\* :func:`freqresp` frequency response -\* :func:`evalfr` frequency response at complex frequency s -== ========================== ============================================ - - -Model simplification ----------------------------------------------------------------------------- - -== ========================== ============================================ -\* :func:`~control.minreal` minimal realization; pole/zero cancellation -\ ss/sminreal structurally minimal realization -\* :func:`~control.hsvd` hankel singular values (state contributions) -\* :func:`~control.balred` reduced-order approximations of LTI models -\* :func:`~control.modred` model order reduction -== ========================== ============================================ - - -Compensator design ----------------------------------------------------------------------------- - -== ========================== ============================================ -\* :func:`rlocus` evans root locus -\* :func:`sisotool` SISO controller design -\* :func:`~control.place` pole placement -\ estim form estimator given estimator gain -\ reg form regulator given state-feedback and - estimator gains -== ========================== ============================================ - - -LQR/LQG design ----------------------------------------------------------------------------- - -== ========================== ============================================ -\ ss/lqg single-step LQG design -\* :func:`~control.lqr` linear quadratic (LQ) state-fbk regulator -\ dlqr discrete-time LQ state-feedback regulator -\ lqry LQ regulator with output weighting -\ lqrd discrete LQ regulator for continuous plant -\ ss/lqi Linear-Quadratic-Integral (LQI) controller -\ ss/kalman Kalman state estimator -\ ss/kalmd discrete Kalman estimator for cts plant -\ ss/lqgreg build LQG regulator from LQ gain and Kalman - estimator -\ ss/lqgtrack build LQG servo-controller -\ augstate augment output by appending states -== ========================== ============================================ - - -State-space (SS) models ----------------------------------------------------------------------------- - -== ========================== ============================================ -\* :func:`rss` random stable cts-time state-space models -\* :func:`drss` random stable disc-time state-space models -\ ss2ss state coordinate transformation -\ canon canonical forms of state-space models -\* :func:`~control.ctrb` controllability matrix -\* :func:`~control.obsv` observability matrix -\* :func:`~control.gram` controllability and observability gramians -\ ss/prescale optimal scaling of state-space models. -\ balreal gramian-based input/output balancing -\ ss/xperm reorder states. -== ========================== ============================================ - - -Frequency response data (FRD) models ----------------------------------------------------------------------------- - -== ========================== ============================================ -\ frd/chgunits change frequency vector units -\ frd/fcat merge frequency responses -\ frd/fselect select frequency range or subgrid -\ frd/fnorm peak gain as a function of frequency -\ frd/abs entrywise magnitude of frequency response -\ frd/real real part of the frequency response -\ frd/imag imaginary part of the frequency response -\ frd/interp interpolate frequency response data -\* :func:`~control.mag2db` convert magnitude to decibels (dB) -\* :func:`~control.db2mag` convert decibels (dB) to magnitude -== ========================== ============================================ - - -Time delays ----------------------------------------------------------------------------- - -== ========================== ============================================ -\ lti/hasdelay true for models with time delays -\ lti/totaldelay total delay between each input/output pair -\ lti/delay2z replace delays by poles at z=0 or FRD phase - shift -\* :func:`~control.pade` pade approximation of time delays -== ========================== ============================================ - - -Model dimensions and characteristics ----------------------------------------------------------------------------- - -== ========================== ============================================ -\ class model type ('tf', 'zpk', 'ss', or 'frd') -\ isa test if model is of given type -\ tf/size model sizes -\ lti/ndims number of dimensions -\ lti/isempty true for empty models -\ lti/isct true for continuous-time models -\ lti/isdt true for discrete-time models -\ lti/isproper true for proper models -\ lti/issiso true for single-input/single-output models -\ lti/isstable true for models with stable dynamics -\ lti/reshape reshape array of linear models -== ========================== ============================================ - -Overloaded arithmetic operations ----------------------------------------------------------------------------- - -== ========================== ============================================ -\* \+ and - add, subtract systems (parallel connection) -\* \* multiply systems (series connection) -\ / right divide -- sys1\*inv(sys2) -\- \\ left divide -- inv(sys1)\*sys2 -\ ^ powers of a given system -\ ' pertransposition -\ .' transposition of input/output map -\ .\* element-by-element multiplication -\ [..] concatenate models along inputs or outputs -\ lti/stack stack models/arrays along some dimension -\ lti/inv inverse of an LTI system -\ lti/conj complex conjugation of model coefficients -== ========================== ============================================ - -Matrix equation solvers and linear algebra ----------------------------------------------------------------------------- - -== ========================== ============================================ -\* :func:`~control.lyap` solve continuous-time Lyapunov equations -\* :func:`~control.dlyap` solve discrete-time Lyapunov equations -\ lyapchol, dlyapchol square-root Lyapunov solvers -\* :func:`~control.care` solve continuous-time algebraic Riccati - equations -\* :func:`~control.dare` solve disc-time algebraic Riccati equations -\ gcare, gdare generalized Riccati solvers -\ bdschur block diagonalization of a square matrix -== ========================== ============================================ - - -Additional functions ----------------------------------------------------------------------------- - -== ========================== ============================================ -\* :func:`~control.gangof4` generate the Gang of 4 sensitivity plots -\* :func:`~numpy.linspace` generate a set of numbers that are linearly - spaced -\* :func:`~numpy.logspace` generate a set of numbers that are - logarithmically spaced -\* :func:`~control.unwrap` unwrap phase angle to give continuous curve -== ========================== ============================================ - -""" diff --git a/control/matlab/timeresp.py b/control/matlab/timeresp.py index 348e625ea..39ec75735 100644 --- a/control/matlab/timeresp.py +++ b/control/matlab/timeresp.py @@ -1,7 +1,10 @@ -""" -Time response routines in the Matlab compatibility package +# timeresp.py - time response routines in the MATLAB compatibility package + +"""Time response routines in the MATLAB compatibility package. + +Note that the return arguments are different than in the standard +control package.. -Note that the return arguments are different than in the standard control package. """ __all__ = ['step', 'stepinfo', 'impulse', 'initial', 'lsim'] diff --git a/control/matlab/wrappers.py b/control/matlab/wrappers.py index 0042b5d09..93910d39a 100644 --- a/control/matlab/wrappers.py +++ b/control/matlab/wrappers.py @@ -1,5 +1,7 @@ -""" -Wrappers for the MATLAB compatibility module +# wrappers.py - Wrappers for the MATLAB compatibility module. + +"""Wrappers for the MATLAB compatibility module. + """ import numpy as np diff --git a/doc/matlab.rst b/doc/matlab.rst index 33def56e6..846824e51 100644 --- a/doc/matlab.rst +++ b/doc/matlab.rst @@ -21,6 +21,7 @@ Creating linear models tf ss frd + zpk Utility functions and conversions ================================= @@ -140,8 +141,8 @@ Additional functions gangof4 unwrap -Functions imported from other modules -===================================== +Functions imported from other packages +====================================== .. autosummary:: ~numpy.linspace From bbfcfa3d96199c6323483dceefcc240a2fc33d08 Mon Sep 17 00:00:00 2001 From: Richard Murray Date: Wed, 1 Jan 2025 09:42:27 -0800 Subject: [PATCH 50/93] fix: config.default[] to config.defaults[] --- control/flatsys/bezier.py | 2 +- control/freqplot.py | 6 +++--- control/nichols.py | 2 +- control/phaseplot.py | 10 +++++----- control/pzmap.py | 4 ++-- control/rlocus.py | 2 +- control/timeplot.py | 2 +- doc/config.rst | 10 ++++++++-- 8 files changed, 22 insertions(+), 16 deletions(-) diff --git a/control/flatsys/bezier.py b/control/flatsys/bezier.py index e146b1c74..439dfd8db 100644 --- a/control/flatsys/bezier.py +++ b/control/flatsys/bezier.py @@ -1,7 +1,7 @@ # bezier.m - 1D Bezier curve basis functions # RMM, 24 Feb 2021 -"""1D Bezier curve basis functions. +r"""1D Bezier curve basis functions. The :mod:`control.flatsys.bezier` module defines the :class:`BezierFamily` class, which implements a set of basis functions diff --git a/control/freqplot.py b/control/freqplot.py index 70b0c4227..4a583d5d6 100644 --- a/control/freqplot.py +++ b/control/freqplot.py @@ -196,7 +196,7 @@ def bode_plot( If set to `False`, don't plot the magnitude or phase, respectively. rcParams : dict Override the default parameters used for generating plots. - Default is set by config.default['ctrlplot.rcParams']. + Default is set by config.defaults['ctrlplot.rcParams']. share_frequency, share_magnitude, share_phase : str or bool, optional Determine whether and how axis limits are shared between the indicated variables. Can be set set to 'row' to share across all @@ -1663,7 +1663,7 @@ def nyquist_plot( determined by config.defaults['nyquist.mirror_style']. rcParams : dict Override the default parameters used for generating plots. - Default is set by config.default['ctrlplot.rcParams']. + Default is set by config.defaults['ctrlplot.rcParams']. return_contour : bool, optional (legacy) If 'True', return the encirclement count and Nyquist contour used to generate the Nyquist plot. @@ -2413,7 +2413,7 @@ def singular_values_plot( the values with no plot. rcParams : dict Override the default parameters used for generating plots. - Default is set up config.default['ctrlplot.rcParams']. + Default is set up config.defaults['ctrlplot.rcParams']. show_legend : bool, optional Force legend to be shown if ``True`` or hidden if ``False``. If ``None``, then show legend when there is more than one line on an diff --git a/control/nichols.py b/control/nichols.py index 3e42b1acd..ea026dfb1 100644 --- a/control/nichols.py +++ b/control/nichols.py @@ -81,7 +81,7 @@ def nichols_plot( with no legend for a single response. Use False to suppress legend. rcParams : dict Override the default parameters used for generating plots. - Default is set by config.default['ctrlplot.rcParams']. + Default is set by config.defaults['ctrlplot.rcParams']. show_legend : bool, optional Force legend to be shown if ``True`` or hidden if ``False``. If ``None``, then show legend when there is more than one line on the diff --git a/control/phaseplot.py b/control/phaseplot.py index bd75bd95f..1caba2c6b 100644 --- a/control/phaseplot.py +++ b/control/phaseplot.py @@ -149,7 +149,7 @@ def phase_plane_plot( in the dict as keywords to :func:`~control.phaseplot.separatrices`. rcParams : dict Override the default parameters used for generating plots. - Default is set by config.default['ctrlplot.rcParams']. + Default is set by config.defaults['ctrlplot.rcParams']. suppress_warnings : bool, optional If set to `True`, suppress warning messages in generating trajectories. title : str, optional @@ -290,7 +290,7 @@ def vectorfield( ---------------- rcParams : dict Override the default parameters used for generating plots. - Default is set by config.default['ctrlplot.rcParams']. + Default is set by config.defaults['ctrlplot.rcParams']. suppress_warnings : bool, optional If set to `True`, suppress warning messages in generating trajectories. @@ -400,7 +400,7 @@ def streamlines( value can be set in config.defaults['phaseplot.arrow_style']. rcParams : dict Override the default parameters used for generating plots. - Default is set by config.default['ctrlplot.rcParams']. + Default is set by config.defaults['ctrlplot.rcParams']. suppress_warnings : bool, optional If set to `True`, suppress warning messages in generating trajectories. @@ -512,7 +512,7 @@ def equilpoints( ---------------- rcParams : dict Override the default parameters used for generating plots. - Default is set by config.default['ctrlplot.rcParams']. + Default is set by config.defaults['ctrlplot.rcParams']. """ # Process keywords @@ -604,7 +604,7 @@ def separatrices( ---------------- rcParams : dict Override the default parameters used for generating plots. - Default is set by config.default['ctrlplot.rcParams']. + Default is set by config.defaults['ctrlplot.rcParams']. suppress_warnings : bool, optional If set to `True`, suppress warning messages in generating trajectories. diff --git a/control/pzmap.py b/control/pzmap.py index 9e84914fa..9e8a1a859 100644 --- a/control/pzmap.py +++ b/control/pzmap.py @@ -199,7 +199,7 @@ def pole_zero_plot( If `True` plot omega-damping grid, if `False` show imaginary axis for continuous time systems, unit circle for discrete time systems. If `empty`, do not draw any additonal lines. Default value is set - by config.default['pzmap.grid'] or config.default['rlocus.grid']. + by config.defaults['pzmap.grid'] or config.defaults['rlocus.grid']. plot : bool, optional (legacy) If ``True`` a graph is generated with Matplotlib, otherwise the poles and zeros are only computed and returned. @@ -259,7 +259,7 @@ def pole_zero_plot( Set the line width of the markers used for poles and zeros. rcParams : dict Override the default parameters used for generating plots. - Default is set by config.default['ctrlplot.rcParams']. + Default is set by config.defaults['ctrlplot.rcParams']. scaling : str or list, optional Set the type of axis scaling. Can be 'equal' (default), 'auto', or a list of the form [xmin, xmax, ymin, ymax]. diff --git a/control/rlocus.py b/control/rlocus.py index ac8dd7d08..f74665467 100644 --- a/control/rlocus.py +++ b/control/rlocus.py @@ -119,7 +119,7 @@ def root_locus_plot( If `True` plot omega-damping grid, if `False` show imaginary axis for continuous time systems, unit circle for discrete time systems. If `empty`, do not draw any additonal lines. Default value is set - by config.default['rlocus.grid']. + by config.defaults['rlocus.grid']. initial_gain : float, optional Mark the point on the root locus diagram corresponding to the given gain. diff --git a/control/timeplot.py b/control/timeplot.py index 41a778cd4..021ef7f93 100644 --- a/control/timeplot.py +++ b/control/timeplot.py @@ -138,7 +138,7 @@ def time_response_plot( default values are set by config.defaults['timeplot.output_props']. rcParams : dict Override the default parameters used for generating plots. - Default is set by config.default['ctrlplot.rcParams']. + Default is set by config.defaults['ctrlplot.rcParams']. relabel : bool, optional (deprecated) By default, existing figures and axes are relabeled when new data are added. If set to `False`, just plot new data on diff --git a/doc/config.rst b/doc/config.rst index 27959f9a8..0a0b5339a 100644 --- a/doc/config.rst +++ b/doc/config.rst @@ -31,6 +31,12 @@ parameters based on standard configurations: use_matlab_defaults use_legacy_defaults +.. Set the current module to be control.config.defaults so that each of + the parameters gets a link of the form control.config.defaults.. + This can be linked from the documentation using a the construct + `config.defaults['param'] `. + +.. currentmodule:: control.config.defaults System creation parameters -------------------------- @@ -329,8 +335,8 @@ Plotting parameters If wrap_phase is `False`, then the phase will be unwrapped so that it is continuously increasing or decreasing. If wrap_phase is `True` the - phase will be restricted to the range [-180, 180) (or [:math:`-\\pi`, - :math:`\\pi`) radians). If `wrap_phase` is specified as a float, the + phase will be restricted to the range [-180, 180) (or [:math:`-\pi`, + :math:`\pi`) radians). If `wrap_phase` is specified as a float, the phase will be offset by 360 degrees if it falls below the specified value. From 9664acffa380535e321264048c22a111ddf2fca2 Mon Sep 17 00:00:00 2001 From: Richard Murray Date: Wed, 1 Jan 2025 11:11:37 -0800 Subject: [PATCH 51/93] remove unnecessary :role:, ~control. and `` from docstrings --- control/__init__.py | 4 +- control/bdalg.py | 66 +++++----- control/config.py | 5 +- control/ctrlplot.py | 28 ++--- control/descfcn.py | 47 ++++--- control/dtime.py | 12 +- control/flatsys/__init__.py | 25 ++-- control/flatsys/basis.py | 2 +- control/flatsys/bezier.py | 2 +- control/flatsys/flatsys.py | 56 ++++----- control/flatsys/linflat.py | 4 +- control/flatsys/systraj.py | 16 +-- control/frdata.py | 97 ++++++++------ control/freqplot.py | 144 ++++++++++----------- control/iosys.py | 13 +- control/lti.py | 54 ++++---- control/margins.py | 10 +- control/matlab/timeresp.py | 4 +- control/matlab/wrappers.py | 18 +-- control/modelsimp.py | 18 +-- control/nichols.py | 28 ++--- control/nlsys.py | 126 +++++++++---------- control/optimal.py | 84 ++++++------- control/phaseplot.py | 34 ++--- control/pzmap.py | 28 ++--- control/rlocus.py | 16 +-- control/sisotool.py | 10 +- control/statefbk.py | 30 ++--- control/statesp.py | 106 ++++++++-------- control/stochsys.py | 18 +-- control/sysnorm.py | 10 +- control/tests/bdalg_test.py | 4 +- control/tests/docstrings_test.py | 13 +- control/tests/matlab2_test.py | 14 +-- control/tests/matlab_test.py | 6 +- control/tests/timeresp_test.py | 10 +- control/timeplot.py | 30 ++--- control/timeresp.py | 209 +++++++++++++++++-------------- control/xferfcn.py | 86 ++++++------- doc/descfcn.rst | 3 + doc/iosys.rst | 24 ++-- doc/optimal.rst | 11 +- 42 files changed, 779 insertions(+), 746 deletions(-) diff --git a/control/__init__.py b/control/__init__.py index 1bc81f816..e0d74bd6d 100644 --- a/control/__init__.py +++ b/control/__init__.py @@ -4,8 +4,8 @@ # Creation date: 24 May 2009 # Use `git shortlog -n -s` for full list of contributors -"""The Python Control Systems Library :mod:`control` provides common functions -for analyzing and designing feedback control systems. +"""The Python Control Systems Library (python-control) provides common +functions for analyzing and designing feedback control systems. The initial goal for the package is to implement all of the functionality required to work through the examples in the textbook diff --git a/control/bdalg.py b/control/bdalg.py index 569bc45f7..df27b80d2 100644 --- a/control/bdalg.py +++ b/control/bdalg.py @@ -7,8 +7,8 @@ """Block diagram algebra. -The :mod:`control.bdalg` module contains some standard block diagram -algebra, including series, parallel, and feedback functions. +This module contains some standard block diagram algebra, including +series, parallel, and feedback functions. """ @@ -33,12 +33,12 @@ def series(*sys, **kwargs): Parameters ---------- - sys1, sys2, ..., sysn : scalar, array, or :class:`InputOutputSystem` + sys1, sys2, ..., sysn : scalar, array, or `InputOutputSystem` I/O systems to combine. Returns ------- - out : scalar, array, or :class:`InputOutputSystem` + out : scalar, array, or `InputOutputSystem` Series interconnection of the systems. Other Parameters @@ -46,7 +46,7 @@ def series(*sys, **kwargs): inputs, outputs : str, or list of str, optional List of strings that name the individual signals. If not given, signal names will be of the form `s[i]` (where `s` is one of `u`, - or `y`). See :class:`InputOutputSystem` for more information. + or `y`). See `InputOutputSystem` for more information. states : str, or list of str, optional List of names for system states. If not given, state names will be of of the form `x[i]` for interconnections of linear systems or @@ -68,10 +68,9 @@ def series(*sys, **kwargs): Notes ----- This function is a wrapper for the __mul__ function in the appropriate - :class:`NonlinearIOSystem`, :class:`StateSpace`, - :class:`TransferFunction`, or other I/O system class. The output type - is the type of `sys1` unless a more general type is required based on - type type of `sys2`. + `NonlinearIOSystem`, `StateSpace`, `TransferFunction`, or other I/O + system class. The output type is the type of `sys1` unless a more + general type is required based on type type of `sys2`. If both systems have a defined timebase (dt = 0 for continuous time, dt > 0 for discrete time), then the timebase for both systems must @@ -105,12 +104,12 @@ def parallel(*sys, **kwargs): Parameters ---------- - sys1, sys2, ..., sysn : scalar, array, or :class:`InputOutputSystem` + sys1, sys2, ..., sysn : scalar, array, or `InputOutputSystem` I/O systems to combine. Returns ------- - out : scalar, array, or :class:`InputOutputSystem` + out : scalar, array, or `InputOutputSystem` Parallel interconnection of the systems. Other Parameters @@ -118,7 +117,7 @@ def parallel(*sys, **kwargs): inputs, outputs : str, or list of str, optional List of strings that name the individual signals. If not given, signal names will be of the form `s[i]` (where `s` is one of `u`, - or `y`). See :class:`InputOutputSystem` for more information. + or `y`). See `InputOutputSystem` for more information. states : str, or list of str, optional List of names for system states. If not given, state names will be of the form `x[i]` for interconnections of linear systems or @@ -173,12 +172,12 @@ def negate(sys, **kwargs): Parameters ---------- - sys : scalar, array, or :class:`InputOutputSystem` + sys : scalar, array, or `InputOutputSystem` I/O systems to negate. Returns ------- - out : scalar, array, or :class:`InputOutputSystem` + out : scalar, array, or `InputOutputSystem` Negated system. Other Parameters @@ -186,7 +185,7 @@ def negate(sys, **kwargs): inputs, outputs : str, or list of str, optional List of strings that name the individual signals. If not given, signal names will be of the form `s[i]` (where `s` is one of `u`, - or `y`). See :class:`InputOutputSystem` for more information. + or `y`). See `InputOutputSystem` for more information. states : str, or list of str, optional List of names for system states. If not given, state names will be of of the form `x[i]` for interconnections of linear systems or @@ -225,7 +224,7 @@ def feedback(sys1, sys2=1, sign=-1, **kwargs): Parameters ---------- - sys1, sys2 : scalar, array, or :class:`InputOutputSystem` + sys1, sys2 : scalar, array, or `InputOutputSystem` I/O systems to combine. sign : scalar The sign of feedback. `sign` = -1 indicates negative feedback, and @@ -234,7 +233,7 @@ def feedback(sys1, sys2=1, sign=-1, **kwargs): Returns ------- - out : scalar, array, or :class:`InputOutputSystem` + out : scalar, array, or `InputOutputSystem` Feedback interconnection of the systems. Other Parameters @@ -242,7 +241,7 @@ def feedback(sys1, sys2=1, sign=-1, **kwargs): inputs, outputs : str, or list of str, optional List of strings that name the individual signals. If not given, signal names will be of the form `s[i]` (where `s` is one of `u`, - or `y`). See :class:`InputOutputSystem` for more information. + or `y`). See `InputOutputSystem` for more information. states : str, or list of str, optional List of names for system states. If not given, state names will be of of the form `x[i]` for interconnections of linear systems or @@ -320,15 +319,22 @@ def append(*sys, **kwargs): Parameters ---------- - sys1, sys2, ..., sysn : scalar, array, or :class:`LTI` + sys1, sys2, ..., sysn : scalar, array, or `LTI` I/O systems to combine. + Returns + ------- + out : `LTI` + Combined system, with input/output vectors consisting of all + input/output vectors appended. Specific type returned is the type of + the first argument. + Other Parameters ---------------- inputs, outputs : str, or list of str, optional List of strings that name the individual signals. If not given, signal names will be of the form `s[i]` (where `s` is one of `u`, - or `y`). See :class:`InputOutputSystem` for more information. + or `y`). See `InputOutputSystem` for more information. states : str, or list of str, optional List of names for system states. If not given, state names will be of of the form `x[i]` for interconnections of linear systems or @@ -337,13 +343,6 @@ def append(*sys, **kwargs): System name (used for specifying signals). If unspecified, a generic name is generated with a unique integer id. - Returns - ------- - out : :class:`LTI` - Combined system, with input/output vectors consisting of all - input/output vectors appended. Specific type returned is the type of - the first argument. - See Also -------- interconnect, feedback, negate, parallel, series @@ -374,7 +373,7 @@ def connect(sys, Q, inputv, outputv): .. deprecated:: 0.10.0 `connect` will be removed in a future version of python-control. - Use :func:`interconnect` instead, which works with named signals. + Use `interconnect` instead, which works with named signals. The system `sys` is a system typically constructed with `append`, with multiple inputs and outputs. The inputs and outputs are connected @@ -387,7 +386,7 @@ def connect(sys, Q, inputv, outputv): Parameters ---------- - sys : :class:`InputOutputSystem` + sys : `InputOutputSystem` System to be connected. Q : 2D array Interconnection matrix. First column gives the input to be connected. @@ -403,7 +402,7 @@ def connect(sys, Q, inputv, outputv): Returns ------- - out : :class:`InputOutputSystem` + out : `InputOutputSystem` Connected and trimmed I/O system. See Also @@ -412,8 +411,7 @@ def connect(sys, Q, inputv, outputv): Notes ----- - The :func:`~control.interconnect` function in the :ref:`input/output - systems ` module allows the use of named signals and + The `interconnect` function allows the use of named signals and provides an alternative method for interconnecting multiple systems. Examples @@ -490,7 +488,7 @@ def combine_tf(tf_array, **kwargs): inputs, outputs : str, or list of str, optional List of strings that name the individual signals. If not given, signal names will be of the form `s[i]` (where `s` is one of `u`, - or `y`). See :class:`InputOutputSystem` for more information. + or `y`). See `InputOutputSystem` for more information. name : string, optional System name (used for specifying signals). If unspecified, a generic name is generated with a unique integer id. @@ -500,7 +498,7 @@ def combine_tf(tf_array, **kwargs): ValueError If timesteps of transfer functions do not match. ValueError - If ``tf_array`` has incorrect dimensions. + If `tf_array` has incorrect dimensions. ValueError If the transfer functions in a row have mismatched output or input dimensions. diff --git a/control/config.py b/control/config.py index 1b3823779..e9ed25411 100644 --- a/control/config.py +++ b/control/config.py @@ -5,9 +5,8 @@ """Functions to access default parameter values. -The :mod:`control.config` module contains default values and utility -functions for setting parameters that control the behavior of the -control package. +This module contains default values and utility functions for setting +parameters that control the behavior of the control package. """ diff --git a/control/ctrlplot.py b/control/ctrlplot.py index 722cea9b4..6bd3ae174 100644 --- a/control/ctrlplot.py +++ b/control/ctrlplot.py @@ -4,8 +4,8 @@ """Utility functions for plotting. -The :mod:`control.ctrlplot` module contains a collection of functions -that are used by various plotting functions. +This module contains a collection of functions that are used by +various plotting functions. """ @@ -129,24 +129,24 @@ class ControlPlot(object): that the user might want to adjust, as well as providing methods to modify some of the properties of the plot. - A control figure consists of a :class:`matplotlib.figure.Figure` with - an array of :class:`matplotlib.axes.Axes`. Each axes in the figure has + A control figure consists of a `matplotlib.figure.Figure` with + an array of `matplotlib.axes.Axes`. Each axes in the figure has a number of lines that represent the data for the plot. There may also be a legend present in one or more of the axes. Attributes ---------- - lines : array of list of :class:`matplotlib:Line2D` + lines : array of list of `matplotlib:Line2D` Array of Line2D objects for each line in the plot. Generally, the shape of the array matches the subplots shape and the value of the array is a list of Line2D objects in that subplot. Some plotting functions will return variants of this structure, as described in the individual documentation for the functions. - axes : 2D array of :class:`matplotlib:Axes` + axes : 2D array of `matplotlib:Axes` Array of Axes objects for each subplot in the plot. - figure : :class:`matplotlib:Figure` + figure : `matplotlib:Figure` Figure on which the Axes are drawn. - legend : :class:`matplotlib:.legend.Legend` (instance or ndarray) + legend : `matplotlib:.legend.Legend` (instance or ndarray) Legend object(s) for the plot. If more than one legend is included, this will be an array with each entry being either None (for no legend) or a legend object. @@ -182,7 +182,7 @@ def set_plot_title(self, title, frame='axes'): """Set the title for a control plot. This is a wrapper for the matplotlib `suptitle` function, but by - setting ``frame`` to 'axes' (default) then the title is centered on + setting `frame` to 'axes' (default) then the title is centered on the midpoint of the axes in the figure, rather than the center of the figure. This usually looks better (particularly with multi-panel plots), though it takes longer to render. @@ -195,7 +195,7 @@ def set_plot_title(self, title, frame='axes'): Matplotlib figure. Defaults to current figure. frame : str, optional Coordinate frame to use for centering: 'axes' (default) or 'figure'. - **kwargs : :func:`matplotlib.pyplot.suptitle` keywords, optional + **kwargs : `matplotlib.pyplot.suptitle` keywords, optional Additional keywords (passed to matplotlib). """ @@ -214,7 +214,7 @@ def suptitle( """Add a centered title to a figure. .. deprecated:: 0.10.1 - Use :func:`ControlPlot.set_plot_title`. + Use `ControlPlot.set_plot_title`. """ warnings.warn( @@ -229,7 +229,7 @@ def get_plot_axes(line_array): .. deprecated:: 0.10.1 This function will be removed in a future version of python-control. - Use `cplt.axes` to obtain axes for an instance of :class:`ControlPlot`. + Use `cplt.axes` to obtain axes for an instance of `ControlPlot`. This function can be used to return the set of axes corresponding to the line array that is returned by `time_response_plot`. This @@ -278,11 +278,11 @@ def pole_zero_subplots( Timebase for each subplot (or a list of timebases). scaling : 'auto', 'equal', or None Scaling to apply to the subplots. - fig : :class:`matplotlib.figure.Figure` + fig : `matplotlib.figure.Figure` Figure to use for creating subplots. rcParams : dict Override the default parameters used for generating plots. - Default is set up config.default['ctrlplot.rcParams']. + Default is set by config.defaults['ctrlplot.rcParams']. Returns ------- diff --git a/control/descfcn.py b/control/descfcn.py index 79a5b0f8b..bed94893b 100644 --- a/control/descfcn.py +++ b/control/descfcn.py @@ -1,9 +1,8 @@ # descfcn.py - describing function analysis # RMM, 23 Jan 2021 -"""The :mod:~control.descfcn` module contains function for performing -closed loop analysis of systems with memoryless nonlinearities using -describing function analysis. +"""This module contains functions for performing closed loop analysis of +systems with memoryless nonlinearities using describing function analysis. """ @@ -91,7 +90,7 @@ def describing_function( If the function is an object with a method `describing_function` then this method will be used to computing the describing function instead of a nonlinear computation. Some common nonlinearities - use the :class:`~control.DescribingFunctionNonlinearity` class, + use the `DescribingFunctionNonlinearity` class, which provides this functionality. A : array_like @@ -111,7 +110,7 @@ def describing_function( If `True` (default), check the `F` argument to see if it is an object with a `describing_function` method and use this to compute the describing function. More information in the `describing_function` - method for the :class:`~control.DescribingFunctionNonlinearity` class. + method for the `DescribingFunctionNonlinearity` class. Returns ------- @@ -218,7 +217,7 @@ class DescribingFunctionResponse: Describing functions allow analysis of a linear I/O systems with a static nonlinear feedback function. The DescribingFunctionResponse - class is used by the :func:`~control.describing_function_response` + class is used by the `describing_function_response` function to return the results of a describing function analysis. The response object can be used to obtain information about the describing function analysis or generate a Nyquist plot showing the frequency @@ -227,7 +226,7 @@ class is used by the :func:`~control.describing_function_response` Attributes ---------- - response : :class:`~control.FrequencyResponseData` + response : `FrequencyResponseData` Frequency response of the linear system component of the system. intersections : 1D array of 2-tuples or None A list of all amplitudes and frequencies in which @@ -252,7 +251,7 @@ def __init__(self, response, N_vals, positions, intersections): def plot(self, **kwargs): """Plot the results of a describing function analysis. - See :func:`~control.describing_function_plot` for details. + See `describing_function_plot` for details. """ return describing_function_plot(self, **kwargs) @@ -294,13 +293,13 @@ def describing_function_response( to turn off warnings. If not set (or set to None), warnings are turned off if omega is specified, otherwise they are turned on. refine : bool, optional - If `True`, :func:`scipy.optimize.minimize` to refine the estimate + If `True`, `scipy.optimize.minimize` to refine the estimate of the intersection of the frequency response and the describing function. Returns ------- - response : :class:`~control.DescribingFunctionResponse` object + response : `DescribingFunctionResponse` object Response object that contains the result of the describing function analysis. The following information can be retrieved from this object: @@ -399,14 +398,14 @@ def describing_function_plot( describing_function_plot(H, F, A[, omega[, options]]) In the first form, the response should be generated using the - :func:`~control.describing_function_response` function. In the second + `describing_function_response` function. In the second form, that function is called internally, with the listed arguments. Parameters ---------- - data : :class:`~control.DescribingFunctionResponse` + data : `DescribingFunctionResponse` A describing function response data object created by - :func:`~control.describing_function_response`. + `describing_function_response`. H : LTI system Linear time-invariant (LTI) system (state space, transfer function, or FRD). @@ -438,27 +437,27 @@ def describing_function_plot( Set to True to turn on warnings generated by `nyquist_plot` or False to turn off warnings. If not set (or set to None), warnings are turned off if omega is specified, otherwise they are turned on. - **kwargs : :func:`matplotlib.pyplot.plot` keyword properties, optional + **kwargs : `matplotlib.pyplot.plot` keyword properties, optional Additional keywords passed to `matplotlib` to specify line properties for Nyquist curve. Returns ------- - cplt : :class:`ControlPlot` object + cplt : `ControlPlot` object Object containing the data that were plotted: - * cplt.lines: Array of :class:`matplotlib.lines.Line2D` objects - for each line in the plot. The first element of the array is a - list of lines (typically only one) for the Nyquist plot of the - linear I/O system. The second element of the array is a list - of lines (typically only one) for the describing function - curve. + * cplt.lines: Array of `matplotlib.lines.Line2D` objects for + each line in the plot. The first element of the array is + a list of lines (typically only one) for the Nyquist plot + of the linear I/O system. The second element of the array + is a list of lines (typically only one) for the describing + function curve. - * cplt.axes: 2D array of :class:`matplotlib.axes.Axes` for the plot. + * cplt.axes: 2D array of `matplotlib.axes.Axes` for the plot. - * cplt.figure: :class:`matplotlib.figure.Figure` containing the plot. + * cplt.figure: `matplotlib.figure.Figure` containing the plot. - See :class:`ControlPlot` for more detailed information. + See `ControlPlot` for more detailed information. Examples -------- diff --git a/control/dtime.py b/control/dtime.py index 13fea7146..1451d8d06 100644 --- a/control/dtime.py +++ b/control/dtime.py @@ -16,7 +16,7 @@ def sample_system(sysc, Ts, method='zoh', alpha=None, prewarp_frequency=None, Parameters ---------- - sysc : LTI (:class:`StateSpace` or :class:`TransferFunction`) + sysc : LTI (`StateSpace` or `TransferFunction`) Continuous time system to be converted. Ts : float > 0 Sampling period. @@ -25,7 +25,7 @@ def sample_system(sysc, Ts, method='zoh', alpha=None, prewarp_frequency=None, alpha : float within [0, 1] The generalized bilinear transformation weighting parameter, which should only be specified with method="gbt", and is ignored - otherwise. See :func:`scipy.signal.cont2discrete`. + otherwise. See `scipy.signal.cont2discrete`. prewarp_frequency : float within [0, infinity) The frequency [rad/s] at which to match with the input continuous- time system's magnitude and phase (only valid for method='bilinear', @@ -40,13 +40,13 @@ def sample_system(sysc, Ts, method='zoh', alpha=None, prewarp_frequency=None, ---------------- inputs : int, list of str or None, optional Description of the system inputs. If not specified, the original - system inputs are used. See :class:`InputOutputSystem` for more + system inputs are used. See `InputOutputSystem` for more information. outputs : int, list of str or None, optional Description of the system outputs. Same format as `inputs`. states : int, list of str, or None, optional Description of the system states. Same format as `inputs`. Only - available if the system is :class:`StateSpace`. + available if the system is `StateSpace`. name : string, optional Set the name of the sampled system. If not specified and if `copy_names` is `False`, a generic name is generated @@ -61,8 +61,8 @@ def sample_system(sysc, Ts, method='zoh', alpha=None, prewarp_frequency=None, Notes ----- - See :meth:`StateSpace.sample` or :meth:`TransferFunction.sample` for - further details. + See `StateSpace.sample` or `TransferFunction.sample` for further + details. Examples -------- diff --git a/control/flatsys/__init__.py b/control/flatsys/__init__.py index 53d0b12b1..c06023cc8 100644 --- a/control/flatsys/__init__.py +++ b/control/flatsys/__init__.py @@ -3,20 +3,21 @@ # Author: Richard M. Murray # Date: 1 Jul 2019 -r"""The :mod:`control.flatsys` sub-package contains a set of classes and -functions to compute trajectories for differentially flat systems. +r"""Flat systems sub-package. + +This sub-package contains a set of classes and functions to compute +trajectories for differentially flat systems. A differentially flat system is defined by creating an object using the -:class:`~control.flatsys.FlatSystem` class, which has member functions for -mapping the system state and input into and out of flat coordinates. The -:func:`~control.flatsys.point_to_point` function can be used to create a -trajectory between two endpoints, written in terms of a set of basis functions -defined using the :class:`~control.flatsys.BasisFamily` class. The resulting -trajectory is return as a :class:`~control.flatsys.SystemTrajectory` object -and can be evaluated using the :func:`~control.flatsys.SystemTrajectory.eval` -member function. Alternatively, the :func:`~control.flatsys.solve_flat_ocp` -function can be used to solve an optimal control problem with trajectory and -final costs or constraints. +`flatsys.FlatSystem` class, which has member functions for mapping the +system state and input into and out of flat coordinates. The +`flatsys.point_to_point` function can be used to create a trajectory +between two endpoints, written in terms of a set of basis functions defined +using the `flatsys.BasisFamily` class. The resulting trajectory is return +as a `flatsys.SystemTrajectory` object and can be evaluated using the +`flatsys.SystemTrajectory.eval` member function. Alternatively, the +`flatsys.solve_flat_ocp` function can be used to solve an optimal control +problem with trajectory and final costs or constraints. The docstring examples assume that the following import commands:: diff --git a/control/flatsys/basis.py b/control/flatsys/basis.py index 0a747ebe8..c6ea3af9e 100644 --- a/control/flatsys/basis.py +++ b/control/flatsys/basis.py @@ -2,7 +2,7 @@ # RMM, 10 Nov 2012 """The :mod:`control.flatsys.basis` module defines the -:class:`flatsys.BasisFamily` class that used to specify a set of basis +`flatsys.BasisFamily` class that used to specify a set of basis functions for implementing differential flatness computations. """ diff --git a/control/flatsys/bezier.py b/control/flatsys/bezier.py index 439dfd8db..3b74375cf 100644 --- a/control/flatsys/bezier.py +++ b/control/flatsys/bezier.py @@ -4,7 +4,7 @@ r"""1D Bezier curve basis functions. The :mod:`control.flatsys.bezier` module defines the -:class:`BezierFamily` class, which implements a set of basis functions +`BezierFamily` class, which implements a set of basis functions based on Bezier curves: .. math:: \phi_i(t) = \sum_{i=0}^n {n \choose i} (T - t)^{n-i} t^i diff --git a/control/flatsys/flatsys.py b/control/flatsys/flatsys.py index 9c74c2117..2434b6723 100644 --- a/control/flatsys/flatsys.py +++ b/control/flatsys/flatsys.py @@ -24,11 +24,11 @@ class FlatSystem(NonlinearIOSystem): """Base class for representing a differentially flat system. - The FlatSystem class is used as a base class to describe differentially - flat systems for trajectory generation. The output of the system does - not need to be the differentially flat output. Flat systems are - usually created with the :func:`~control.flatsys.flatsys` factory - function. + The FlatSystem class is used as a base class to describe + differentially flat systems for trajectory generation. The output + of the system does not need to be the differentially flat output. + Flat systems are usually created with the `~control.flatsys.flatsys` + factory function. Parameters ---------- @@ -175,13 +175,13 @@ def flatsys(*args, updfcn=None, outfcn=None, **kwargs): that also represents a differentially flat system. It can be used in a variety of forms: - ``fs.flatsys(forward, reverse)`` + `fs.flatsys(forward, reverse)` Create a flat system with mapings to/from flat flag. - ``fs.flatsys(forward, reverse, updfcn[, outfcn])`` + `fs.flatsys(forward, reverse, updfcn[, outfcn])` Create a flat system that is also a nonlinear I/O system. - ``fs.flatsys(linsys)`` + `fs.flatsys(linsys)` Create a flat system from a linear (StateSpace) system. Parameters @@ -241,7 +241,7 @@ def flatsys(*args, updfcn=None, outfcn=None, **kwargs): Returns ------- - sys : :class:`FlatSystem` + sys : `FlatSystem` Flat system. Other Parameters @@ -290,7 +290,7 @@ def flatsys(*args, updfcn=None, outfcn=None, **kwargs): def _basis_flag_matrix(sys, basis, flag, t): """Compute the matrix of basis functions and their derivatives - This function computes the matrix ``M`` that is used to solve for the + This function computes the matrix `M` that is used to solve for the coefficients of the basis functions given the state and input. Each column of the matrix corresponds to a basis function and each row is a derivative, with the derivatives (flag) for each output stacked on top @@ -343,9 +343,9 @@ def point_to_point( The initial time for the trajectory (corresponding to x0). If not specified, its value is taken to be zero. - basis : :class:`~control.flatsys.BasisFamily` object, optional + basis : `flatsys.BasisFamily` object, optional The basis functions to use for generating the trajectory. If not - specified, the :class:`~control.flatsys.PolyFamily` basis family + specified, the `flatsys.PolyFamily` basis family will be used, with the minimal number of elements required to find a feasible trajectory (twice the number of system states) @@ -356,8 +356,8 @@ def point_to_point( trajectory_constraints : list of tuples, optional List of constraints that should hold at each point in the time vector. Each element of the list should consist of a tuple with first element - given by :class:`scipy.optimize.LinearConstraint` or - :class:`scipy.optimize.NonlinearConstraint` and the remaining + given by `scipy.optimize.LinearConstraint` or + `scipy.optimize.NonlinearConstraint` and the remaining elements of the tuple are the arguments that would be passed to those functions. The following tuples are supported: @@ -380,17 +380,17 @@ def point_to_point( defaults. minimize_method : str, optional - Set the method used by :func:`scipy.optimize.minimize`. + Set the method used by `scipy.optimize.minimize`. minimize_options : str, optional - Set the options keyword used by :func:`scipy.optimize.minimize`. + Set the options keyword used by `scipy.optimize.minimize`. minimize_kwargs : str, optional - Pass additional keywords to :func:`scipy.optimize.minimize`. + Pass additional keywords to `scipy.optimize.minimize`. Returns ------- - traj : :class:`~control.flatsys.SystemTrajectory` object + traj : `flatsys.SystemTrajectory` object The system trajectory is returned as an object that implements the `eval()` function, we can be used to compute the value of the state and input and a given time t. @@ -399,7 +399,7 @@ def point_to_point( ----- Additional keyword parameters can be used to fine tune the behavior of the underlying optimization function. See `minimize_*` keywords in - :func:`OptimalControlProblem` for more information. + `OptimalControlProblem` for more information. """ # @@ -672,9 +672,9 @@ def solve_flat_ocp( values are given as None, they are replaced by a vector of zeros of the appropriate dimension. - basis : :class:`~control.flatsys.BasisFamily` object, optional + basis : `flatsys.BasisFamily` object, optional The basis functions to use for generating the trajectory. If not - specified, the :class:`~control.flatsys.PolyFamily` basis family + specified, the `flatsys.PolyFamily` basis family will be used, with the minimal number of elements required to find a feasible trajectory (twice the number of system states) @@ -689,8 +689,8 @@ def solve_flat_ocp( trajectory_constraints : list of tuples, optional List of constraints that should hold at each point in the time vector. Each element of the list should consist of a tuple with first element - given by :class:`scipy.optimize.LinearConstraint` or - :class:`scipy.optimize.NonlinearConstraint` and the remaining + given by `scipy.optimize.LinearConstraint` or + `scipy.optimize.NonlinearConstraint` and the remaining elements of the tuple are the arguments that would be passed to those functions. The following tuples are supported: @@ -713,17 +713,17 @@ def solve_flat_ocp( defaults. minimize_method : str, optional - Set the method used by :func:`scipy.optimize.minimize`. + Set the method used by `scipy.optimize.minimize`. minimize_options : str, optional - Set the options keyword used by :func:`scipy.optimize.minimize`. + Set the options keyword used by `scipy.optimize.minimize`. minimize_kwargs : str, optional - Pass additional keywords to :func:`scipy.optimize.minimize`. + Pass additional keywords to `scipy.optimize.minimize`. Returns ------- - traj : :class:`~control.flatsys.SystemTrajectory` object + traj : `flatsys.SystemTrajectory` object The system trajectory is returned as an object that implements the `eval()` function, we can be used to compute the value of the state and input and a given time t. @@ -732,7 +732,7 @@ def solve_flat_ocp( ----- 1. Additional keyword parameters can be used to fine tune the behavior of the underlying optimization function. See `minimize_*` keywords - in :func:`~control.optimal.OptimalControlProblem` for more information. + in `optimal.OptimalControlProblem` for more information. 2. The return data structure includes the following additional attributes: * success : bool indicating whether the optimization succeeded diff --git a/control/flatsys/linflat.py b/control/flatsys/linflat.py index 1b4e3cc37..87c02a48a 100644 --- a/control/flatsys/linflat.py +++ b/control/flatsys/linflat.py @@ -85,7 +85,7 @@ def __init__(self, linsys, **kwargs): def forward(self, x, u, params): """Compute the flat flag given the states and input. - See :func:`control.flatsys.FlatSystem.forward` for more info. + See `control.flatsys.FlatSystem.forward` for more info. """ x = np.reshape(x, (-1, 1)) @@ -102,7 +102,7 @@ def forward(self, x, u, params): def reverse(self, zflag, params): """Compute the states and input given the flat flag. - See :func:`control.flatsys.FlatSystem.reverse` for more info. + See `control.flatsys.FlatSystem.reverse` for more info. """ z = zflag[0][0:-1] diff --git a/control/flatsys/systraj.py b/control/flatsys/systraj.py index bb74de694..6e9a104d4 100644 --- a/control/flatsys/systraj.py +++ b/control/flatsys/systraj.py @@ -18,7 +18,7 @@ class SystemTrajectory: The `SystemTrajectory` class is used to represent the trajectory of a (differentially flat) system. Used by the - :func:`~control.trajsys.point_to_point` function to return a trajectory. + `trajsys.point_to_point` function to return a trajectory. Parameters ---------- @@ -110,12 +110,12 @@ def response(self, tlist, transpose=False, return_x=False, squeeze=None): transpose : bool, optional If True, transpose all input and output arrays (for backward - compatibility with MATLAB and :func:`scipy.signal.lsim`). + compatibility with MATLAB and `scipy.signal.lsim`). Default value is False. return_x : bool, optional If True, return the state vector when assigning to a tuple - (default = False). See :func:`forced_response` for more details. + (default = False). See `forced_response` for more details. squeeze : bool, optional By default, if a system is single-input, single-output (SISO) then @@ -129,27 +129,27 @@ def response(self, tlist, transpose=False, return_x=False, squeeze=None): Returns ------- results : TimeResponseData - Time response represented as a :class:`TimeResponseData` object + Time response represented as a `TimeResponseData` object containing the following properties: * time (array): Time values of the output. * outputs (array): Response of the system. If the system is SISO and squeeze is not True, the array is 1D (indexed by time). If - the system is not SISO or ``squeeze`` is False, the array is 3D + the system is not SISO or `squeeze` is False, the array is 3D (indexed by the output, trace, and time). * states (array): Time evolution of the state vector, represented as either a 2D array indexed by state and time (if SISO) or a 3D array indexed by state, trace, and time. Not affected by - ``squeeze``. + `squeeze`. * inputs (array): Input(s) to the system, indexed in the same - manner as ``outputs``. + manner as `outputs`. The return value of the system can also be accessed by assigning the function to a tuple of length 2 (time, output) or of length 3 - (time, output, state) if ``return_x`` is ``True``. + (time, output, state) if `return_x` is `True`. """ # Compute the state and input response using the eval function diff --git a/control/frdata.py b/control/frdata.py index 4b7ccf896..b00b01a41 100644 --- a/control/frdata.py +++ b/control/frdata.py @@ -34,8 +34,8 @@ class FrequencyResponseData(LTI): The FrequencyResponseData (FRD) class is used to represent systems in frequency response data form. It can be created manually using the - class constructor, using the :func:`~control.frd` factory function, or - via the :func:`~control.frequency_response` function. + class constructor, using the `frd` factory function, or + via the `frequency_response` function. Parameters ---------- @@ -48,10 +48,10 @@ class constructor, using the :func:`~control.frd` factory function, or omega : iterable of real frequencies List of frequency points for which data are available. smooth : bool, optional - If ``True``, create an interpolation function that allows the + If `True`, create an interpolation function that allows the frequency response to be computed at any frequency within the range of - frequencies give in ``w``. If ``False`` (default), frequency response - can only be obtained at the frequencies specified in ``w``. + frequencies give in `w`. If `False` (default), frequency response + can only be obtained at the frequencies specified in `w`. dt : None, True or float, optional System timebase. 0 (default) indicates continuous time, True indicates discrete time with unspecified sampling time, positive @@ -63,9 +63,9 @@ class constructor, using the :func:`~control.frd` factory function, or frequency) and if a system is multi-input or multi-output, then the outputs are returned as a 2D array (indexed by output and frequency) or a 3D array (indexed by output, trace, and frequency). - If ``squeeze=True``, access to the output response will remove + If `squeeze=True`, access to the output response will remove single-dimensional entries from the shape of the inputs and outputs - even if the system is not SISO. If ``squeeze=False``, the output is + even if the system is not SISO. If `squeeze=False`, the output is returned as a 3D array (indexed by the output, input, and frequency) even if the system is SISO. The default value can be set using config.defaults['control.squeeze_frequency_response']. @@ -87,7 +87,7 @@ class constructor, using the :func:`~control.frd` factory function, or Names for the input and output signals. name : str System name. For data generated using - :func:`~control.frequency_response`, stores the name of the + `frequency_response`, stores the name of the system that created the data. magnitude : array Magnitude of the frequency response, indexed by frequency. @@ -97,17 +97,17 @@ class constructor, using the :func:`~control.frd` factory function, or Other Parameters ---------------- plot_type : str, optional - Set the type of plot to generate with ``plot()`` ('bode', 'nichols'). + Set the type of plot to generate with `plot()` ('bode', 'nichols'). title : str, optional Set the title to use when plotting. plot_magnitude, plot_phase : bool, optional If set to `False`, don't plot the magnitude or phase, respectively. return_magphase : bool, optional If True, then a frequency response data object will enumerate as a - tuple of the form (mag, phase, omega) where where ``mag`` is the + tuple of the form (mag, phase, omega) where where `mag` is the magnitude (absolute value, not dB or log10) of the system - frequency response, ``phase`` is the wrapped phase in radians of - the system frequency response, and ``omega`` is the (sorted) + frequency response, `phase` is the wrapped phase in radians of + the system frequency response, and `omega` is the (sorted) frequencies at which the response was evaluated. See Also @@ -130,12 +130,12 @@ class constructor, using the :func:`~control.frd` factory function, or A frequency response data object is callable and returns the value of the transfer function evaluated at a point in the complex plane (must be on - the imaginary axis). See :meth:`~control.FrequencyResponseData.__call__` + the imaginary axis). See `FrequencyResponseData.__call__` for a more detailed description. A state space system is callable and returns the value of the transfer function evaluated at a point in the complex plane. See - :meth:`~control.StateSpace.__call__` for a more detailed description. + `StateSpace.__call__` for a more detailed description. Subsystem response corresponding to selected input/output pairs can be created by indexing the frequency response data object:: @@ -165,6 +165,23 @@ class constructor, using the :func:`~control.frd` factory function, or #: :meta hide-value: noutputs = 1 + #: Squeeze processing parameter. + #: + #: By default, if a system is single-input, single-output (SISO) then + #: the outputs (and inputs) are returned as a 1D array (indexed by + #: frequency) and if a system is multi-input or multi-output, then the + #: outputs are returned as a 2D array (indexed by output and frequency) + #: or a 3D array (indexed by output, trace, and frequency). If + #: `squeeze=True`, access to the output response will remove + #: single-dimensional entries from the shape of the inputs and outputs + #: even if the system is not SISO. If `squeeze=False`, the output is + #: returned as a 3D array (indexed by the output, input, and frequency) + #: even if the system is SISO. The default value can be set using + #: config.defaults['control.squeeze_frequency_response']. + #: + #: :meta hide-value: + squeeze = None + _epsw = 1e-8 #: Bound for exact frequency match def __init__(self, *args, **kwargs): @@ -183,10 +200,10 @@ def __init__(self, *args, **kwargs): To construct frequency response data for an existing LTI object, other than an FRD, call FrequencyResponseData(sys, omega). This - functionality can also be obtained using :func:`frequency_response` + functionality can also be obtained using `frequency_response` (which has additional options available). - See :class:`FrequencyResponseData` and :func:`frd` for more + See `FrequencyResponseData` and `frd` for more information. """ @@ -334,7 +351,7 @@ def magnitude(self): Magnitude of the frequency response, indexed by either the output and frequency (if only a single input is given) or the output, input, and frequency (for multi-input systems). See - :attr:`FrequencyResponseData.squeeze` for a description of how this + `FrequencyResponseData.squeeze` for a description of how this can be modified using the `squeeze` keyword. Input and output signal names can be used to index the data in @@ -353,7 +370,7 @@ def phase(self): Phase of the frequency response in radians/sec, indexed by either the output and frequency (if only a single input is given) or the output, input, and frequency (for multi-input systems). See - :attr:`FrequencyResponseData.squeeze` for a description of how this + `FrequencyResponseData.squeeze` for a description of how this can be modified using the `squeeze` keyword. Input and output signal names can be used to index the data in @@ -381,7 +398,7 @@ def response(self): Value of the frequency response as a complex number, indexed by either the output and frequency (if only a single input is given) or the output, input, and frequency (for multi-input systems). See - :attr:`FrequencyResponseData.squeeze` for a description of how this + `FrequencyResponseData.squeeze` for a description of how this can be modified using the `squeeze` keyword. Input and output signal names can be used to index the data in @@ -601,7 +618,7 @@ def __pow__(self, other): # Define the `eval` function to evaluate an FRD at a given (real) # frequency. Note that we choose to use `eval` instead of `evalfr` to - # avoid confusion with :func:`evalfr`, which takes a complex number as its + # avoid confusion with `evalfr`, which takes a complex number as its # argument. Similarly, we don't use `__call__` to avoid confusion between # G(s) for a transfer function and G(omega) for an FRD object. # update Sawyer B. Fuller 2020.08.14: __call__ added to provide a uniform @@ -631,7 +648,7 @@ def eval(self, omega, squeeze=None): squeeze is not True, the shape of the array matches the shape of omega. If the system is not SISO or squeeze is False, the first two dimensions of the array are indices for the output and input - and the remaining dimensions match omega. If ``squeeze`` is True + and the remaining dimensions match omega. If `squeeze` is True then single-dimensional axes are removed. """ @@ -672,12 +689,12 @@ def __call__(self, s=None, squeeze=None, return_magphase=None): outputs. To evaluate at a frequency omega in radians per second, enter - ``s = omega * 1j`` or use ``sys.eval(omega)`` + `s = omega * 1j` or use `sys.eval(omega)` For a frequency response data object, the argument must be an imaginary number (since only the frequency response is defined). - If ``s`` is not given, this function creates a copy of a frequency + If `s` is not given, this function creates a copy of a frequency response data object with a different set of output settings. Parameters @@ -685,7 +702,7 @@ def __call__(self, s=None, squeeze=None, return_magphase=None): s : complex scalar or 1D array_like Complex frequencies. If not specified, return a copy of the frequency response data object with updated settings for output - processing (``squeeze``, ``return_magphase``). + processing (`squeeze`, `return_magphase`). squeeze : bool, optional If squeeze=True, remove single-dimensional entries from the shape @@ -696,10 +713,10 @@ def __call__(self, s=None, squeeze=None, return_magphase=None): return_magphase : bool, optional If True, then a frequency response data object will enumerate as a - tuple of the form (mag, phase, omega) where where ``mag`` is the + tuple of the form (mag, phase, omega) where where `mag` is the magnitude (absolute value, not dB or log10) of the system - frequency response, ``phase`` is the wrapped phase in radians of - the system frequency response, and ``omega`` is the (sorted) + frequency response, `phase` is the wrapped phase in radians of + the system frequency response, and `omega` is the (sorted) frequencies at which the response was evaluated. Returns @@ -709,14 +726,14 @@ def __call__(self, s=None, squeeze=None, return_magphase=None): squeeze is not True, the shape of the array matches the shape of omega. If the system is not SISO or squeeze is False, the first two dimensions of the array are indices for the output and input - and the remaining dimensions match omega. If ``squeeze`` is True + and the remaining dimensions match omega. If `squeeze` is True then single-dimensional axes are removed. Raises ------ ValueError If `s` is not purely imaginary, because - :class:`FrequencyResponseData` systems are only defined at + `FrequencyResponseData` systems are only defined at imaginary values (corresponding to real frequencies). """ @@ -785,8 +802,8 @@ def freqresp(self, omega): .. deprecated::0.9.0 Method has been given the more pythonic name - :meth:`FrequencyResponseData.frequency_response`. Or use - :func:`freqresp` in the MATLAB compatibility module. + `FrequencyResponseData.frequency_response`. Or use + `freqresp` in the MATLAB compatibility module. """ warn("FrequencyResponseData.freqresp(omega) will be removed in a " @@ -842,8 +859,8 @@ def plot(self, plot_type=None, *args, **kwargs): Plot the frequency response using either a standard Bode plot (default) or using a singular values plot (by setting `plot_type` - to 'svplot'). See :func:`~control.bode_plot` and - :func:`~control.singular_values_plot` for more detailed + to 'svplot'). See `bode_plot` and + `singular_values_plot` for more detailed descriptions. """ @@ -974,13 +991,13 @@ def frd(*args, **kwargs): A frequency response data model stores the (measured) frequency response of a system. This factory function can be called in different ways: - ``frd(response, omega)`` + `frd(response, omega)` Create an frd model with the given response data, in the form of - complex response vector, at matching frequencies ``omega`` [in rad/s]. + complex response vector, at matching frequencies `omega` [in rad/s]. - ``frd(sys, omega)`` + `frd(sys, omega)` Convert an LTI system into an frd model with data at frequencies - ``omega``. + `omega`. Parameters ---------- @@ -994,11 +1011,11 @@ def frd(*args, **kwargs): dt : float, True, or None System timebase. smooth : bool, optional - If ``True``, create an interpolation function that allows the + If `True`, create an interpolation function that allows the frequency response to be computed at any frequency within the range - of frequencies give in ``omega``. If ``False`` (default), + of frequencies give in `omega`. If `False` (default), frequency response can only be obtained at the frequencies - specified in ``omega``. + specified in `omega`. Returns ------- diff --git a/control/freqplot.py b/control/freqplot.py index 4a583d5d6..7b568abec 100644 --- a/control/freqplot.py +++ b/control/freqplot.py @@ -99,13 +99,13 @@ def bode_plot( Parameters ---------- data : list of `FrequencyResponseData` or `LTI` - List of LTI systems or :class:`FrequencyResponseData` objects. A + List of LTI systems or `FrequencyResponseData` objects. A single system or frequency response can also be passed. omega : array_like, optoinal Set of frequencies in rad/sec to plot over. If not specified, this will be determined from the proporties of the systems. Ignored if `data` is not a list of systems. - *fmt : :func:`matplotlib.pyplot.plot` format string, optional + *fmt : `matplotlib.pyplot.plot` format string, optional Passed to `matplotlib` as the format string for all lines in the plot. The `omega` parameter must be present (use omega=None if needed). dB : bool @@ -121,26 +121,26 @@ def bode_plot( graphs and display the margins at the top of the graph. If set to 'overlay', the values for the gain and phase margin are placed on the graph. Setting display_margins turns off the axes grid. - **kwargs : :func:`matplotlib.pyplot.plot` keyword properties, optional + **kwargs : `matplotlib.pyplot.plot` keyword properties, optional Additional keywords passed to `matplotlib` to specify line properties. Returns ------- - cplt : :class:`ControlPlot` object + cplt : `ControlPlot` object Object containing the data that were plotted: - * cplt.lines: Array of :class:`matplotlib.lines.Line2D` objects + * cplt.lines: Array of `matplotlib.lines.Line2D` objects for each line in the plot. The shape of the array matches the subplots shape and the value of the array is a list of Line2D objects in that subplot. - * cplt.axes: 2D array of :class:`matplotlib.axes.Axes` for the plot. + * cplt.axes: 2D array of `matplotlib.axes.Axes` for the plot. - * cplt.figure: :class:`matplotlib.figure.Figure` containing the plot. + * cplt.figure: `matplotlib.figure.Figure` containing the plot. * cplt.legend: legend object(s) contained in the plot - See :class:`ControlPlot` for more detailed information. + See `ControlPlot` for more detailed information. Other Parameters ---------------- @@ -170,16 +170,16 @@ def bode_plot( Location of the legend for multi-axes plots. Specifies an array of legend location strings matching the shape of the subplots, with each entry being either None (for no legend) or a legend location - string (see :func:`~matplotlib.pyplot.legend`). + string (see `~matplotlib.pyplot.legend`). legend_loc : int or str, optional Include a legend in the given location. Default is 'center right', with no legend for a single response. Use False to suppress legend. margins_method : str, optional - Method to use in computing margins (see :func:`stability_margins`). + Method to use in computing margins (see `stability_margins`). omega_limits : array_like of two values Set limits for plotted frequency range. If Hz=True the limits are - in Hz otherwise in rad/s. Specifying ``omega`` as a list of two - elements is equivalent to providing ``omega_limits``. Ignored if + in Hz otherwise in rad/s. Specifying `omega` as a list of two + elements is equivalent to providing `omega_limits`. Ignored if data is not a list of systems. omega_num : int Number of samples to use for the frequeny range. Defaults to @@ -208,9 +208,9 @@ def bode_plot( 'col', for `share_frequency`, and can be set using config.defaults['freqplot.share_']. show_legend : bool, optional - Force legend to be shown if ``True`` or hidden if ``False``. If - ``None``, then show legend when there is more than one line on an - axis or ``legend_loc`` or ``legend_map`` has been specified. + Force legend to be shown if `True` or hidden if `False`. If + `None`, then show legend when there is more than one line on an + axis or `legend_loc` or `legend_map` has been specified. title : str, optional Set the title of the plot. Defaults to plot type and system name(s). title_frame : str, optional @@ -235,17 +235,17 @@ def bode_plot( Notes ----- Starting with python-control version 0.10, `bode_plot` returns a - :class:`ControlPlot` object instead of magnitude, phase, and + `ControlPlot` object instead of magnitude, phase, and frequency. To recover the old behavior, call `bode_plot` with `plot=True`, which will force the legacy values (mag, phase, omega) to be returned (with a warning). To obtain just the frequency response of a system (or list of systems) without plotting, use the - :func:`~control.frequency_response` command. + `frequency_response` command. If a discrete time model is given, the frequency response is plotted - along the upper branch of the unit circle, using the mapping ``z = - exp(1j * omega * dt)`` where `omega` ranges from 0 to `pi/dt` and `dt` - is the discrete timebase. If timebase not specified (``dt=True``), + along the upper branch of the unit circle, using the mapping `z = + exp(1j * omega * dt)` where `omega` ranges from 0 to `pi/dt` and `dt` + is the discrete timebase. If timebase not specified (`dt=True`), `dt` is set to 1. The default values for Bode plot configuration parameters can be reset @@ -1102,7 +1102,7 @@ class NyquistResponseData: Nyquist contour analysis allows the stability and robustness of a closed loop linear system to be evaluated using the open loop response of the loop transfer function. The NyquistResponseData class is used - by the :func:`~control.nyquist_response` function to return the + by the `nyquist_response` function to return the response of a linear system along the Nyquist 'D' contour. The response object can be used to obtain information about the Nyquist response or to generate a Nyquist plot. @@ -1186,7 +1186,7 @@ def nyquist_response( Returns ------- - responses : list of :class:`~control.NyquistResponseData` + responses : list of `NyquistResponseData` For each system, a Nyquist response data object is returned. If `sysdata` is a single system, a single elemeent is returned (not a list). For each response, the following information is available: @@ -1218,8 +1218,8 @@ def nyquist_response( config.defaults['nyquist.indent_radius']. omega_limits : array_like of two values Set limits for plotted frequency range. If Hz=True the limits are - in Hz otherwise in rad/s. Specifying ``omega`` as a list of two - elements is equivalent to providing ``omega_limits``. + in Hz otherwise in rad/s. Specifying `omega` as a list of two + elements is equivalent to providing `omega_limits`. omega_num : int, optional Number of samples to use for the frequeny range. Defaults to config.defaults['freqplot.number_of_samples']. @@ -1232,9 +1232,9 @@ def nyquist_response( Notes ----- 1. If a discrete time model is given, the frequency response is computed - along the upper branch of the unit circle, using the mapping ``z = - exp(1j * omega * dt)`` where `omega` ranges from 0 to `pi/dt` and `dt` - is the discrete timebase. If timebase not specified (``dt=True``), + along the upper branch of the unit circle, using the mapping `z = + exp(1j * omega * dt)` where `omega` ranges from 0 to `pi/dt` and `dt` + is the discrete timebase. If timebase not specified (`dt=True`), `dt` is set to 1. 2. If a continuous-time system contains poles on or near the imaginary @@ -1540,14 +1540,14 @@ def nyquist_plot( ---------- data : list of LTI or NyquistResponseData List of linear input/output systems (single system is OK) or - Nyquist ersponses (computed using :func:`~control.nyquist_response`). + Nyquist ersponses (computed using `nyquist_response`). Nyquist curves for each system are plotted on the same graph. omega : array_like, optional Set of frequencies to be evaluated, in rad/sec. Specifying - ``omega`` as a list of two elements is equivalent to providing - ``omega_limits``. + `omega` as a list of two elements is equivalent to providing + `omega_limits`. unit_circle : bool, optional - If ``True``, display the unit circle, to read gain crossover + If `True`, display the unit circle, to read gain crossover frequency. The circle style is determined by config.defaults['nyquist.circle_style']. mt_circles : array_like, optional @@ -1555,15 +1555,15 @@ def nyquist_plot( ms_circles : array_like, optional Draw circles corresponding to the given magnitudes of complementary sensitivity. - **kwargs : :func:`matplotlib.pyplot.plot` keyword properties, optional + **kwargs : `matplotlib.pyplot.plot` keyword properties, optional Additional keywords passed to `matplotlib` to specify line properties. Returns ------- - cplt : :class:`ControlPlot` object + cplt : `ControlPlot` object Object containing the data that were plotted: - * cplt.lines: 2D array of :class:`matplotlib.lines.Line2D` + * cplt.lines: 2D array of `matplotlib.lines.Line2D` objects for each line in the plot. The shape of the array is given by (nsys, 4) where nsys is the number of systems or Nyquist responses passed to the function. The second index @@ -1574,13 +1574,13 @@ def nyquist_plot( - lines[idx, 2]: unscaled portion of the mirror curve - lines[idx, 3]: scaled portion of the mirror curve - * cplt.axes: 2D array of :class:`matplotlib.axes.Axes` for the plot. + * cplt.axes: 2D array of `matplotlib.axes.Axes` for the plot. - * cplt.figure: :class:`matplotlib.figure.Figure` containing the plot. + * cplt.figure: `matplotlib.figure.Figure` containing the plot. * cplt.legend: legend object(s) contained in the plot - See :class:`ControlPlot` for more detailed information. + See `ControlPlot` for more detailed information. Other Parameters ---------------- @@ -1646,8 +1646,8 @@ def nyquist_plot( determined by config.defaults['nyquist.mirror_style']. omega_limits : array_like of two values Set limits for plotted frequency range. If Hz=True the limits are - in Hz otherwise in rad/s. Specifying ``omega`` as a list of two - elements is equivalent to providing ``omega_limits``. + in Hz otherwise in rad/s. Specifying `omega` as a list of two + elements is equivalent to providing `omega_limits`. omega_num : int, optional Number of samples to use for the frequeny range. Defaults to config.defaults['freqplot.number_of_samples']. Ignored if data is @@ -1668,9 +1668,9 @@ def nyquist_plot( (legacy) If 'True', return the encirclement count and Nyquist contour used to generate the Nyquist plot. show_legend : bool, optional - Force legend to be shown if ``True`` or hidden if ``False``. If - ``None``, then show legend when there is more than one line on the - plot or ``legend_loc`` has been specified. + Force legend to be shown if `True` or hidden if `False`. If + `None`, then show legend when there is more than one line on the + plot or `legend_loc` has been specified. start_marker : str, optional Matplotlib marker to use to mark the starting point of the Nyquist plot. Defaults value is 'o' and can be set using @@ -1698,9 +1698,9 @@ def nyquist_plot( Notes ----- 1. If a discrete time model is given, the frequency response is computed - along the upper branch of the unit circle, using the mapping ``z = - exp(1j * omega * dt)`` where `omega` ranges from 0 to `pi/dt` and `dt` - is the discrete timebase. If timebase not specified (``dt=True``), + along the upper branch of the unit circle, using the mapping `z = + exp(1j * omega * dt)` where `omega` ranges from 0 to `pi/dt` and `dt` + is the discrete timebase. If timebase not specified (`dt=True`), `dt` is set to 1. 2. If a continuous-time system contains poles on or near the imaginary @@ -2120,8 +2120,8 @@ def gangof4_response( Range of frequencies (list or bounds) in rad/sec. omega_limits : array_like of two values Set limits for plotted frequency range. If Hz=True the limits are - in Hz otherwise in rad/s. Specifying ``omega`` as a list of two - elements is equivalent to providing ``omega_limits``. Ignored if + in Hz otherwise in rad/s. Specifying `omega` as a list of two + elements is equivalent to providing `omega_limits`. Ignored if data is not a list of systems. omega_num : int Number of samples to use for the frequeny range. Defaults to @@ -2133,7 +2133,7 @@ def gangof4_response( Returns ------- - response : :class:`~control.FrequencyResponseData` + response : `FrequencyResponseData` Frequency response with inputs 'r' and 'd' and outputs 'y', and 'u' representing the 2x2 matrix of transfer functions in the Gang of 4. @@ -2207,8 +2207,8 @@ def gangof4_plot( Range of frequencies (list or bounds) in rad/sec. omega_limits : array_like of two values Set limits for plotted frequency range. If Hz=True the limits are - in Hz otherwise in rad/s. Specifying ``omega`` as a list of two - elements is equivalent to providing ``omega_limits``. Ignored if + in Hz otherwise in rad/s. Specifying `omega` as a list of two + elements is equivalent to providing `omega_limits`. Ignored if data is not a list of systems. omega_num : int Number of samples to use for the frequeny range. Defaults to @@ -2220,20 +2220,20 @@ def gangof4_plot( Returns ------- - cplt : :class:`ControlPlot` object + cplt : `ControlPlot` object Object containing the data that were plotted: - * cplt.lines: 2x2 array of :class:`matplotlib.lines.Line2D` + * cplt.lines: 2x2 array of `matplotlib.lines.Line2D` objects for each line in the plot. The value of each array entry is a list of Line2D objects in that subplot. - * cplt.axes: 2D array of :class:`matplotlib.axes.Axes` for the plot. + * cplt.axes: 2D array of `matplotlib.axes.Axes` for the plot. - * cplt.figure: :class:`matplotlib.figure.Figure` containing the plot. + * cplt.figure: `matplotlib.figure.Figure` containing the plot. * cplt.legend: legend object(s) contained in the plot - See :class:`ControlPlot` for more detailed information. + See `ControlPlot` for more detailed information. """ if len(args) == 1 and isinstance(args[0], FrequencyResponseData): @@ -2284,8 +2284,8 @@ def singular_values_response( ---------------- omega_limits : array_like of two values Set limits for plotted frequency range. If Hz=True the limits are - in Hz otherwise in rad/s. Specifying ``omega`` as a list of two - elements is equivalent to providing ``omega_limits``. + in Hz otherwise in rad/s. Specifying `omega` as a list of two + elements is equivalent to providing `omega_limits`. omega_num : int, optional Number of samples to use for the frequeny range. Defaults to config.defaults['freqplot.number_of_samples']. @@ -2349,11 +2349,11 @@ def singular_values_plot( Parameters ---------- data : list of `FrequencyResponseData` - List of :class:`FrequencyResponseData` objects. For backward + List of `FrequencyResponseData` objects. For backward compatibility, a list of LTI systems can also be given. omega : array_like List of frequencies in rad/sec over to plot over. - *fmt : :func:`matplotlib.pyplot.plot` format string, optional + *fmt : `matplotlib.pyplot.plot` format string, optional Passed to `matplotlib` as the format string for all lines in the plot. The `omega` parameter must be present (use omega=None if needed). dB : bool @@ -2361,25 +2361,25 @@ def singular_values_plot( Hz : bool If True, plot frequency in Hz (omega must be provided in rad/sec). Default value (False) set by config.defaults['freqplot.Hz']. - **kwargs : :func:`matplotlib.pyplot.plot` keyword properties, optional + **kwargs : `matplotlib.pyplot.plot` keyword properties, optional Additional keywords passed to `matplotlib` to specify line properties. Returns ------- - cplt : :class:`ControlPlot` object + cplt : `ControlPlot` object Object containing the data that were plotted: - * cplt.lines: 1-D array of :class:`matplotlib.lines.Line2D` objects. + * cplt.lines: 1-D array of `matplotlib.lines.Line2D` objects. The size of the array matches the number of systems and the value of the array is a list of Line2D objects for that system. - * cplt.axes: 2D array of :class:`matplotlib.axes.Axes` for the plot. + * cplt.axes: 2D array of `matplotlib.axes.Axes` for the plot. - * cplt.figure: :class:`matplotlib.figure.Figure` containing the plot. + * cplt.figure: `matplotlib.figure.Figure` containing the plot. * cplt.legend: legend object(s) contained in the plot - See :class:`ControlPlot` for more detailed information. + See `ControlPlot` for more detailed information. Other Parameters ---------------- @@ -2401,8 +2401,8 @@ def singular_values_plot( with no legend for a single response. Use False to suppress legend. omega_limits : array_like of two values Set limits for plotted frequency range. If Hz=True the limits are - in Hz otherwise in rad/s. Specifying ``omega`` as a list of two - elements is equivalent to providing ``omega_limits``. + in Hz otherwise in rad/s. Specifying `omega` as a list of two + elements is equivalent to providing `omega_limits`. omega_num : int, optional Number of samples to use for the frequeny range. Defaults to config.defaults['freqplot.number_of_samples']. Ignored if data is @@ -2415,9 +2415,9 @@ def singular_values_plot( Override the default parameters used for generating plots. Default is set up config.defaults['ctrlplot.rcParams']. show_legend : bool, optional - Force legend to be shown if ``True`` or hidden if ``False``. If - ``None``, then show legend when there is more than one line on an - axis or ``legend_loc`` or ``legend_map`` has been specified. + Force legend to be shown if `True` or hidden if `False`. If + `None`, then show legend when there is more than one line on an + axis or `legend_loc` or `legend_map` has been specified. title : str, optional Set the title of the plot. Defaults to plot type and system name(s). title_frame : str, optional @@ -2700,12 +2700,12 @@ def _default_frequency_range(syslist, Hz=None, number_of_samples=None, scale in Hz otherwise in rad/s. Omega is always returned in rad/sec. number_of_samples : int, optional Number of samples to generate. The default value is read from - ``config.defaults['freqplot.number_of_samples']. If None, then the + `config.defaults['freqplot.number_of_samples']. If None, then the default from `numpy.logspace` is used. feature_periphery_decades : float, optional Defines how many decades shall be included in the frequency range on both sides of features (poles, zeros). The default value is read from - ``config.defaults['freqplot.feature_periphery_decades']``. + `config.defaults['freqplot.feature_periphery_decades']`. Returns ------- diff --git a/control/iosys.py b/control/iosys.py index 9a5426400..6da367776 100644 --- a/control/iosys.py +++ b/control/iosys.py @@ -3,11 +3,10 @@ """I/O system class and helper functions. -Theis module implements the :class:`InputOutputSystem` class, which is used -as a parent class for :class`LTI`, :class:`StateSpace`, -:class:`TransferFunction`, :class:`NonlinearIOSystem`, -class:`FrequencyResponseData`, :class:`InterconnectedSystem` and other -similar classes that allow naming of signals. +This module implements the `InputOutputSystem` class, which is used as a +parent class for `LTI`, `StateSpace`, `TransferFunction`, +`NonlinearIOSystem`, class:`FrequencyResponseData`, `InterconnectedSystem` +and other similar classes that allow naming of signals. """ @@ -170,7 +169,7 @@ class InputOutputSystem(object): state_prefix : string, optional Set the prefix for state signals. Default = 'x'. repr_format : str - String representation format. See :func:`control.iosys_repr`. + String representation format. See `control.iosys_repr`. """ # Allow NDarray * IOSystem to give IOSystem._rmul_() priority @@ -564,7 +563,7 @@ def update_names(self, **kwargs): inputs : list of str, int, or None, optional List of strings that name the individual input signals. If given as an integer or None, signal names default to the form - `u[i]`. See :class:`InputOutputSystem` for more information. + `u[i]`. See `InputOutputSystem` for more information. outputs : list of str, int, or None, optional Description of output signals; defaults to `y[i]`. states : int, list of str, int, or None, optional diff --git a/control/lti.py b/control/lti.py index c402c131c..a990bb56c 100644 --- a/control/lti.py +++ b/control/lti.py @@ -100,21 +100,21 @@ def frequency_response(self, omega=None, squeeze=None): Returns ------- - response : :class:`FrequencyResponseData` + response : `FrequencyResponseData` Frequency response data object representing the frequency response. This object can be assigned to a tuple using mag, phase, omega = response - where ``mag`` is the magnitude (absolute value, not dB or log10) - of the system frequency response, ``phase`` is the wrapped phase - in radians of the system frequency response, and ``omega`` is + where `mag` is the magnitude (absolute value, not dB or log10) + of the system frequency response, `phase` is the wrapped phase + in radians of the system frequency response, and `omega` is the (sorted) frequencies at which the response was evaluated. - If the system is SISO and squeeze is not True, ``magnitude`` and - ``phase`` are 1D, indexed by frequency. If the system is not + If the system is SISO and squeeze is not True, `magnitude` and + `phase` are 1D, indexed by frequency. If the system is not SISO or squeeze is False, the array is 3D, indexed by the output, input, and, if omega is array_like, frequency. If - ``squeeze`` is True then single-dimensional axes are removed. + `squeeze` is True then single-dimensional axes are removed. """ from .frdata import FrequencyResponseData @@ -367,9 +367,9 @@ def evalfr(sys, x, squeeze=None): outputs. To evaluate at a frequency omega in radians per second, enter - ``x = omega * 1j`` for continuous-time systems, or - ``x = exp(1j * omega * dt)`` for discrete-time systems, or use - ``freqresp(sys, omega)``. + `x = omega * 1j` for continuous-time systems, or + `x = exp(1j * omega * dt)` for discrete-time systems, or use + `freqresp(sys, omega)`. Parameters ---------- @@ -391,7 +391,7 @@ def evalfr(sys, x, squeeze=None): squeeze is not True, the shape of the array matches the shape of omega. If the system is not SISO or squeeze is False, the first two dimensions of the array are indices for the output and input and the - remaining dimensions match omega. If ``squeeze`` is True then + remaining dimensions match omega. If `squeeze` is True then single-dimensional axes are removed. See Also @@ -401,8 +401,8 @@ def evalfr(sys, x, squeeze=None): Notes ----- - This function is a wrapper for :meth:`StateSpace.__call__` and - :meth:`TransferFunction.__call__`. + This function is a wrapper for `StateSpace.__call__` and + `TransferFunction.__call__`. Examples -------- @@ -436,8 +436,8 @@ def frequency_response( of frequencies that works across all given systems is computed. omega_limits : array_like of two values, optional Limits to the range of frequencies, in rad/sec. Specifying - ``omega`` as a list of two elements is equivalent to providing - ``omega_limits``. Ignored if omega is provided. + `omega` as a list of two elements is equivalent to providing + `omega_limits`. Ignored if omega is provided. omega_num : int, optional Number of frequency samples at which to compute the response. Defaults to config.defaults['freqplot.number_of_samples']. Ignored @@ -445,23 +445,23 @@ def frequency_response( Returns ------- - response : :class:`FrequencyResponseData` + response : `FrequencyResponseData` Frequency response data object representing the frequency response. This object can be assigned to a tuple using mag, phase, omega = response - where ``mag`` is the magnitude (absolute value, not dB or log10) of - the system frequency response, ``phase`` is the wrapped phase in - radians of the system frequency response, and ``omega`` is the + where `mag` is the magnitude (absolute value, not dB or log10) of + the system frequency response, `phase` is the wrapped phase in + radians of the system frequency response, and `omega` is the (sorted) frequencies at which the response was evaluated. If the - system is SISO and squeeze is not False, ``magnitude`` and ``phase`` + system is SISO and squeeze is not False, `magnitude` and `phase` are 1D, indexed by frequency. If the system is not SISO or squeeze is False, the array is 3D, indexed by the output, input, and - frequency. If ``squeeze`` is True then single-dimensional axes are + frequency. If `squeeze` is True then single-dimensional axes are removed. - Returns a list of :class:`FrequencyResponseData` objects if sys is + Returns a list of `FrequencyResponseData` objects if sys is a list of systems. Other Parameters @@ -484,10 +484,10 @@ def frequency_response( Notes ----- - 1. This function is a wrapper for :meth:`StateSpace.frequency_response` - and :meth:`TransferFunction.frequency_response`. + 1. This function is a wrapper for `StateSpace.frequency_response` + and `TransferFunction.frequency_response`. - 2. You can also use the lower-level methods ``sys(s)`` or ``sys(z)`` to + 2. You can also use the lower-level methods `sys(s)` or `sys(z)` to generate the frequency response for a single system. 3. All frequency data should be given in rad/sec. If frequency limits @@ -496,8 +496,8 @@ def frequency_response( `Hz=True` look better. 4. The frequency response data can be plotted by calling the - :func:`~control_bode_plot` function or using the `plot` method of - the :class:`~control.FrequencyResponseData` class. + `bode_plot` function or using the `plot` method of + the `FrequencyResponseData` class. Examples -------- diff --git a/control/margins.py b/control/margins.py index 72b1b863c..044203926 100644 --- a/control/margins.py +++ b/control/margins.py @@ -230,10 +230,10 @@ def stability_margins(sysdata, returnall=False, epsw=0.0, method='best'): method : string, optional Method to use (default is 'best'): - * 'poly': use polynomial method if passed a :class:`LTI` system. + * 'poly': use polynomial method if passed a `LTI` system. * 'frd': calculate crossover frequencies using numerical - interpolation of a :class:`FrequencyResponseData` representation - of the system if passed a :class:`LTI` system. + interpolation of a `FrequencyResponseData` representation + of the system if passed a `LTI` system. * 'best': use the 'poly' method if possible, reverting to 'frd' if it is detected that numerical inaccuracy is likey to arise in the 'poly' method for for discrete-time systems. @@ -472,8 +472,8 @@ def margin(*args): Gain and phase margins and associated crossover frequencies. - Can be called as ``margin(sys)`` where ``sys`` is a SISO LTI system or - ``margin(mag, phase, omega)``. + Can be called as `margin(sys)` where `sys` is a SISO LTI system or + `margin(mag, phase, omega)`. Parameters ---------- diff --git a/control/matlab/timeresp.py b/control/matlab/timeresp.py index 39ec75735..fae6c9675 100644 --- a/control/matlab/timeresp.py +++ b/control/matlab/timeresp.py @@ -112,7 +112,7 @@ def stepinfo(sysdata, T=None, yfinal=None, SettlingTimeThreshold=0.02, If `sysdata` corresponds to a MIMO system, `S` is a 2D list of dicts. To get the step response characteristics from the j-th input to the - i-th output, access ``S[i][j]`` + i-th output, access `S[i][j]` See Also -------- @@ -253,7 +253,7 @@ def lsim(sys, U=0., T=None, X0=0.): U : array-like or number, optional Input array giving input at each time `T` (default = 0). - If `U` is ``None`` or ``0``, a special algorithm is used. This special + If `U` is `None` or `0`, a special algorithm is used. This special algorithm is faster than the general algorithm, which is used otherwise. T : array-like, optional for discrete LTI `sys` Time steps at which the input is defined; values must be evenly spaced. diff --git a/control/matlab/wrappers.py b/control/matlab/wrappers.py index 93910d39a..12d425d24 100644 --- a/control/matlab/wrappers.py +++ b/control/matlab/wrappers.py @@ -112,8 +112,8 @@ def nyquist(*args, plot=True, **kwargs): Set of frequencies to be evaluated, in rad/sec. omega_limits : array_like of two values Set limits for plotted frequency range. If Hz=True the limits are - in Hz otherwise in rad/s. Specifying ``omega`` as a list of two - elements is equivalent to providing ``omega_limits``. + in Hz otherwise in rad/s. Specifying `omega` as a list of two + elements is equivalent to providing `omega_limits`. plot : bool If `False`, do not generate a plot. @@ -243,7 +243,7 @@ def rlocus(*args, **kwargs): Notes ----- - This function is a wrapper for :func:`~control.root_locus_plot`, + This function is a wrapper for `root_locus_plot`, with legacy return arguments. """ @@ -275,7 +275,7 @@ def pzmap(*args, **kwargs): sys : LTI (StateSpace or TransferFunction) Linear system for which poles and zeros are computed. plot : bool, optional - If ``True`` a graph is generated with Matplotlib, + If `True` a graph is generated with Matplotlib, otherwise the poles and zeros are only computed and returned. grid : boolean (default = False) If True plot omega-damping grid. @@ -289,7 +289,7 @@ def pzmap(*args, **kwargs): Notes ----- - This function is a wrapper for :func:`~control.pole_zero_plot`, + This function is a wrapper for `pole_zero_plot`, with legacy return arguments. """ @@ -351,7 +351,7 @@ def dcgain(*args): Notes ----- This function is only useful for systems with invertible system - matrix ``A``. + matrix `A`. All systems are first converted to state space form. The function then computes: @@ -373,7 +373,7 @@ def dcgain(*args): sys, = args return sys.dcgain() else: - raise ValueError("Function ``dcgain`` needs either 1, 2, 3 or 4 " + raise ValueError("Function `dcgain` needs either 1, 2, 3 or 4 " "arguments.") @@ -394,7 +394,7 @@ def connect(*args): Parameters ---------- - sys : :class:`InputOutputSystem` + sys : `InputOutputSystem` System to be connected. Q : 2D array Interconnection matrix. First column gives the input to be connected. @@ -410,7 +410,7 @@ def connect(*args): Returns ------- - out : :class:`InputOutputSystem` + out : `InputOutputSystem` Connected and trimmed I/O system. See Also diff --git a/control/modelsimp.py b/control/modelsimp.py index b1ca3062f..b3f89b072 100644 --- a/control/modelsimp.py +++ b/control/modelsimp.py @@ -151,7 +151,7 @@ def model_reduction( States, inputs, and outputs can be specified using integer offsets or using signal names. Slices can also be specified, but must use the - Python ``slice()`` function. + Python `slice()` function. """ if not isinstance(sys, StateSpace): @@ -275,7 +275,7 @@ def balanced_reduction(sys, orders, method='truncate', alpha=None): Desired order of reduced order model (if a vector, returns a vector of systems). method : string - Method of removing states, either ``'truncate'`` or ``'matchdc'``.. + Method of removing states, either `'truncate'` or `'matchdc'`.. alpha : float Redefines the stability boundary for eigenvalues of the system matrix A. By default for continuous-time systems, alpha <= 0 @@ -293,7 +293,7 @@ def balanced_reduction(sys, orders, method='truncate', alpha=None): Raises ------ ValueError - If `method` is not ``'truncate'`` or ``'matchdc'`` + If `method` is not `'truncate'` or `'matchdc'` ImportError if slycot routine ab09ad, ab09md, or ab09nd is not found @@ -441,8 +441,8 @@ def eigensys_realization(arg, r, m=None, n=None, dt=True, transpose=False): The function can be called with 2 arguments: - * ``sysd, S = eigensys_realization(data, r)`` - * ``sysd, S = eigensys_realization(YY, r)`` + * `sysd, S = eigensys_realization(data, r)` + * `sysd, S = eigensys_realization(YY, r)` where `data` is a `TimeResponseData` object, `YY` is a 1D or 3D array, and r is an integer. @@ -554,10 +554,10 @@ def markov(*args, m=None, transpose=False, dt=None, truncate=False): The function can be called with either 1, 2 or 3 arguments: - * ``H = markov(data)`` - * ``H = markov(data, m)`` - * ``H = markov(Y, U)`` - * ``H = markov(Y, U, m)`` + * `H = markov(data)` + * `H = markov(data, m)` + * `H = markov(Y, U)` + * `H = markov(Y, U, m)` where `data` is a `TimeResponseData` object, `YY` is a 1D or 3D array, and r is an integer. diff --git a/control/nichols.py b/control/nichols.py index ea026dfb1..f04b30ccc 100644 --- a/control/nichols.py +++ b/control/nichols.py @@ -34,35 +34,35 @@ def nichols_plot( Parameters ---------- data : list of `FrequencyResponseData` or `LTI` - List of LTI systems or :class:`FrequencyResponseData` objects. A + List of LTI systems or `FrequencyResponseData` objects. A single system or frequency response can also be passed. omega : array_like Range of frequencies (list or bounds) in rad/sec. - *fmt : :func:`matplotlib.pyplot.plot` format string, optional + *fmt : `matplotlib.pyplot.plot` format string, optional Passed to `matplotlib` as the format string for all lines in the plot. The `omega` parameter must be present (use omega=None if needed). grid : boolean, optional True if the plot should include a Nichols-chart grid. Default is True and can be set using config.defaults['nichols.grid']. - **kwargs : :func:`matplotlib.pyplot.plot` keyword properties, optional + **kwargs : `matplotlib.pyplot.plot` keyword properties, optional Additional keywords passed to `matplotlib` to specify line properties. Returns ------- - cplt : :class:`ControlPlot` object + cplt : `ControlPlot` object Object containing the data that were plotted: - * cplt.lines: 1D array of :class:`matplotlib.lines.Line2D` objects. + * cplt.lines: 1D array of `matplotlib.lines.Line2D` objects. The size of the array matches the number of systems and the value of the array is a list of Line2D objects for that system. - * cplt.axes: 2D array of :class:`matplotlib.axes.Axes` for the plot. + * cplt.axes: 2D array of `matplotlib.axes.Axes` for the plot. - * cplt.figure: :class:`matplotlib.figure.Figure` containing the plot. + * cplt.figure: `matplotlib.figure.Figure` containing the plot. * cplt.legend: legend object(s) contained in the plot - See :class:`ControlPlot` for more detailed information. + See `ControlPlot` for more detailed information. lines : array of Line2D @@ -83,9 +83,9 @@ def nichols_plot( Override the default parameters used for generating plots. Default is set by config.defaults['ctrlplot.rcParams']. show_legend : bool, optional - Force legend to be shown if ``True`` or hidden if ``False``. If - ``None``, then show legend when there is more than one line on the - plot or ``legend_loc`` has been specified. + Force legend to be shown if `True` or hidden if `False`. If + `None`, then show legend when there is more than one line on the + plot or `legend_loc` has been specified. title : str, optional Set the title of the plot. Defaults to plot type and system name(s). @@ -194,7 +194,7 @@ def nichols_grid(cl_mags=None, cl_phases=None, line_style='dotted', ax=None, :doc:`Matplotlib linestyle \ `. ax : matplotlib.axes.Axes, optional - Axes to add grid to. If ``None``, use ``matplotlib.pyplot.gca()``. + Axes to add grid to. If `None`, use `matplotlib.pyplot.gca()`. label_cl_phases : bool, optional If True, closed-loop phase lines will be labelled. @@ -206,10 +206,10 @@ def nichols_grid(cl_mags=None, cl_phases=None, line_style='dotted', ax=None, The constant closed-loop phase contours. cl_mag_labels : list of `matplotlib.text.Text` Magnitude contour labels; each entry corresponds to the respective entry. - in ``cl_mag_lines``. + in `cl_mag_lines`. cl_phase_labels : list of `matplotlib.text.Text` Phase contour labels; each entry corresponds to the respective entry - in ``cl_phase_lines``. + in `cl_phase_lines`. """ if ax is None: ax = plt.gca() diff --git a/control/nlsys.py b/control/nlsys.py index 0a725d9be..d7bc90661 100644 --- a/control/nlsys.py +++ b/control/nlsys.py @@ -7,12 +7,12 @@ # * Allow time vector for discrete time simulations to be multiples of dt # * Check the way initial outputs for discrete time systems are handled -"""The :mod:`~control.nlsys` module contains the -:class:`~control.NonlinearIOSystem` class that represents (possibly nonlinear) -input/output systems. The :class:`~control.NonlinearIOSystem` class is a -general class that defines any continuous or discrete time dynamical system. -Input/output systems can be simulated and also used to compute operating -points and linearizations. +"""This module contains the `NonlinearIOSystem` class that +represents (possibly nonlinear) input/output systems. The +`NonlinearIOSystem` class is a general class that defines any +continuous or discrete time dynamical system. Input/output systems +can be simulated and also used to compute operating points and +linearizations. """ @@ -36,10 +36,10 @@ class NonlinearIOSystem(InputOutputSystem): """Nonlinear I/O system. - Creates an :class:`~control.InputOutputSystem` for a nonlinear system + Creates an `InputOutputSystem` for a nonlinear system by specifying a state update function and an output function. The new system can be a continuous or discrete time system. Nonlinear I/O - systems are usually created with the :func:`~control.nlsys` factory + systems are usually created with the `nlsys` factory function. Parameters @@ -63,7 +63,7 @@ class NonlinearIOSystem(InputOutputSystem): inputs, outputs, states : int, list of str or None, optional Description of the system inputs, outputs, and states. See - :func:`control.nlsys` for more details. + `control.nlsys` for more details. params : dict, optional Parameter values for the systems. Passed to the evaluation functions @@ -96,7 +96,7 @@ class NonlinearIOSystem(InputOutputSystem): Notes ----- - The :class:`~control.InputOuputSystem` class (and its subclasses) makes + The `InputOuputSystem` class (and its subclasses) makes use of two special methods for implementing much of the work of the class: * _rhs(t, x, u): compute the right hand side of the differential or @@ -365,7 +365,7 @@ def _rhs(self, t, x, u): Private function used to compute the right hand side of an input/output system model. Intended for fast evaluation; for a more - user-friendly interface you may want to use :meth:`dynamics`. + user-friendly interface you may want to use `dynamics`. """ return np.asarray( @@ -415,7 +415,7 @@ def _out(self, t, x, u): Private function used to compute the output of of an input/output system model given the state, input, parameters. Intended for fast evaluation; for a more user-friendly interface you may want to use - :meth:`output`. + `output`. """ # @@ -497,7 +497,7 @@ def linearize(self, x0, u0=None, t=0, params=None, eps=1e-6, Return the linearization of an input/output system at a given operating point (or state and input value) as a StateSpace system. - See :func:`~control.linearize` for complete documentation. + See `linearize` for complete documentation. """ # @@ -567,7 +567,7 @@ class InterconnectedSystem(NonlinearIOSystem): whose inputs and outputs are connected via a connection map. The overall system inputs and outputs are subsets of the subsystem inputs and outputs. - The function :func:`~control.interconnect` should be used to create an + The `interconnect` factory function should be used to create an interconnected I/O system since it performs additional argument processing and checking. @@ -1060,7 +1060,7 @@ def unused_signals(self): def connection_table(self, show_names=False, column_width=32): """Table of connections inside an interconnected system. - Intended primarily for :class:`InterconnectedSystem`'s that have been + Intended primarily for `InterconnectedSystem`'s that have been connected implicitly using signal names. Parameters @@ -1069,7 +1069,7 @@ def connection_table(self, show_names=False, column_width=32): Instead of printing out the system number, print out the name of each system. Default is False because system name is not usually specified when performing implicit interconnection using - :func:`interconnect`. + `interconnect`. column_width : int, optional Character width of printed columns. @@ -1272,7 +1272,7 @@ def check_unused_signals( def nlsys(updfcn, outfcn=None, **kwargs): """Create a nonlinear input/output system. - Creates an :class:`~control.InputOutputSystem` for a nonlinear system by + Creates an `InputOutputSystem` for a nonlinear system by specifying a state update function and an output function. The new system can be a continuous or discrete time system. @@ -1288,7 +1288,7 @@ def nlsys(updfcn, outfcn=None, **kwargs): time, and `params` is a dict containing the values of parameters used by the function. - If a :class:`StateSpace` system is passed as the update function, + If a `StateSpace` system is passed as the update function, then a nonlinear I/O system is created that implements the linear dynamics of the state space system. @@ -1334,7 +1334,7 @@ def nlsys(updfcn, outfcn=None, **kwargs): Returns ------- - sys : :class:`NonlinearIOSystem` + sys : `NonlinearIOSystem` Nonlinear input/output system. Other Parameters @@ -1424,10 +1424,10 @@ def input_output_response( with zeros. t_eval : array-list, optional List of times at which the time response should be computed. - Defaults to ``T``. + Defaults to `T`. return_x : bool, optional If True, return the state vector when assigning to a tuple (default = - False). See :func:`forced_response` for more details. + False). See `forced_response` for more details. If True, return the values of the state at each time (default = False). params : dict, optional Parameter values for the system. Passed to the evaluation functions @@ -1441,7 +1441,7 @@ def input_output_response( Returns ------- results : TimeResponseData - Time response represented as a :class:`TimeResponseData` object + Time response represented as a `TimeResponseData` object containing the following properties: * time (array): Time values of the output. @@ -1460,25 +1460,25 @@ def input_output_response( The return value of the system can also be accessed by assigning the function to a tuple of length 2 (time, output) or of length 3 (time, - output, state) if ``return_x`` is ``True``. If the input/output + output, state) if `return_x` is `True`. If the input/output system signals are named, these names will be used as labels for the time response. Other Parameters ---------------- ignore_errors : bool, optional - If ``False`` (default), errors during computation of the trajectory - will raise a ``RuntimeError`` exception. If ``True``, do not raise - an exception and instead set ``results.success`` to ``False`` and - place an error message in ``results.message``. + If `False` (default), errors during computation of the trajectory + will raise a `RuntimeError` exception. If `True`, do not raise + an exception and instead set `results.success` to `False` and + place an error message in `results.message`. solve_ivp_method : str, optional - Set the method used by :func:`scipy.integrate.solve_ivp`. Defaults + Set the method used by `scipy.integrate.solve_ivp`. Defaults to 'RK45'. solve_ivp_kwargs : dict, optional - Pass additional keywords to :func:`scipy.integrate.solve_ivp`. + Pass additional keywords to `scipy.integrate.solve_ivp`. transpose : bool, default=False If True, transpose all input and output arrays (for backward - compatibility with MATLAB and :func:`scipy.signal.lsim`). + compatibility with MATLAB and `scipy.signal.lsim`). Raises ------ @@ -1607,7 +1607,7 @@ def input_output_response( legal_shapes = [(ninputs, ntimepts)] U = _check_convert_array( - U, legal_shapes, 'Parameter ``U``: ', squeeze=False) + U, legal_shapes, 'Parameter `U`: ', squeeze=False) # Always store the input as a 2D array U = U.reshape(-1, ntimepts) @@ -1702,7 +1702,7 @@ def ivp_rhs(t, x): # Make sure the time vector is uniformly spaced dt = t_eval[1] - t_eval[0] if not np.allclose(t_eval[1:] - t_eval[:-1], dt): - raise ValueError("parameter ``t_eval``: time values must be " + raise ValueError("parameter `t_eval`: time values must be " "equally spaced") # Make sure the sample time matches the given time @@ -1713,11 +1713,11 @@ def ivp_rhs(t, x): # TODO: this test is brittle if dt = sys.dt # First make sure that time increment is bigger than sampling time # if dt < sys.dt: - # raise ValueError("Time steps ``T`` must match sampling time") + # raise ValueError("Time steps `T` must match sampling time") # Check to make sure sampling time matches time increments if not np.isclose(dt, sys.dt): - raise ValueError("Time steps ``T`` must be equal to " + raise ValueError("Time steps `T` must be equal to " "sampling time") # Compute the solution @@ -1758,11 +1758,11 @@ def ivp_rhs(t, x): class OperatingPoint(): """Class for representing operating point of nonlinear I/O system. - The ``OperatingPoint`` class stores the operating point of a nonlinear + The `OperatingPoint` class stores the operating point of a nonlinear system, consisting of the state and input vectors for the system. The main use for this class is as the return object for the - :func:`find_operating_point` function and as an input to the - :func:`linearize` function. + `find_operating_point` function and as an input to the + `linearize` function. Attributes ---------- @@ -1772,8 +1772,8 @@ class OperatingPoint(): Input vector at the operating point. outputs : array, optional Output vector at the operating point. - result : :class:`scipy.optimize.OptimizeResult`, optional - Result from the :func:`scipy.optimize.root` function, if available. + result : `scipy.optimize.OptimizeResult`, optional + Result from the `scipy.optimize.root` function, if available. """ def __init__( @@ -1885,14 +1885,14 @@ def find_operating_point( fixed at `dx0` in searching for an operating point. root_method : str, optional Method to find the operating point. If specified, this parameter - is passed to the :func:`scipy.optimize.root` function. + is passed to the `scipy.optimize.root` function. root_kwargs : dict, optional - Additional keyword arguments to pass :func:`scipy.optimize.root`. + Additional keyword arguments to pass `scipy.optimize.root`. return_outputs : bool, optional If True, return the value of outputs at the operating point. return_result : bool, optional If True, return the `result` option from the - :func:`scipy.optimize.root` function used to compute the + `scipy.optimize.root` function used to compute the operating point. Returns @@ -1901,7 +1901,7 @@ def find_operating_point( The solution represented as an `OperatingPoint` object. The main attributes are `states` and `inputs`, which represent the state and input arrays at the operating point. See - :class:`OperatingPoint` for a description of other attributes. + `OperatingPoint` for a description of other attributes. If accessed as a tuple, returns `states`, `inputs`, and optionally `outputs` and `result` based on the `return_outputs` and @@ -1916,12 +1916,12 @@ def find_operating_point( difference equation returns the current state: :math:`f(t, x_e, u_e) = x_e`. - Operating points are found using the :func:`scipy.optimize.root` + Operating points are found using the `scipy.optimize.root` function, which will attempt to find states and inputs that satisfy the specified constraints. If no solution is found and `return_result` is `False`, the returned state and input for the operating point will be `None`. If `return_result` is `True`, then the return values from - :func:`scipy.optimize.root` will be returned (but may not be valid). + `scipy.optimize.root` will be returned (but may not be valid). If `root_method` is set to 'lm', then the least squares solution (in the free variables) will be returned. @@ -2134,20 +2134,20 @@ def linearize(sys, xeq, ueq=None, t=0, params=None, **kw): """Linearize an input/output system at a given state and input. Compute the linearization of an I/O system at an operating point (state - and input) and returns a :class:`~control.StateSpace` object. The + and input) and returns a `StateSpace` object. The operating point need not be an equilibrium point. Parameters ---------- sys : InputOutputSystem The system to be linearized. - xeq : array or :class:`~control.OperatingPoint` + xeq : array or `OperatingPoint` The state or operating point at which the linearization will be evaluated (does not need to be an equilibrium state). ueq : array, optional The input at which the linearization will be evaluated (does not need to correspond to an equlibrium state). Can be omitted if `xeq` is - an :class:`~control.OperatingPoint`. Defaults to 0. + an `OperatingPoint`. Defaults to 0. t : float, optional The time at which the linearization will be computed (for time-varying systems). @@ -2169,14 +2169,14 @@ def linearize(sys, xeq, ueq=None, t=0, params=None, **kw): Returns ------- ss_sys : StateSpace - The linearization of the system, as a :class:`~control.StateSpace` + The linearization of the system, as a `StateSpace` object. Other Parameters ---------------- inputs : int, list of str or None, optional Description of the system inputs. If not specified, the original - system inputs are used. See :class:`InputOutputSystem` for more + system inputs are used. See `InputOutputSystem` for more information. outputs : int, list of str or None, optional Description of the system outputs. Same format as `inputs`. @@ -2218,10 +2218,10 @@ def interconnect( This function creates a new system that is an interconnection of a set of input/output systems. If all of the input systems are linear I/O systems - (type :class:`~control.StateSpace`) then the resulting system will be - a linear interconnected I/O system (type :class:`~control.LinearICSystem`) + (type `StateSpace`) then the resulting system will be + a linear interconnected I/O system (type `LinearICSystem`) with the appropriate inputs, outputs, and states. Otherwise, an - interconnected I/O system (type :class:`~control.InterconnectedSystem`) + interconnected I/O system (type `InterconnectedSystem`) will be created. Parameters @@ -2273,7 +2273,7 @@ def interconnect( The `connections` keyword can also be set to `False`, which will leave the connection map empty and it can be specified instead using the - low-level :func:`~control.InterconnectedSystem.set_connect_map` + low-level `InterconnectedSystem.set_connect_map` method. inplist : list of input connections, optional @@ -2302,7 +2302,7 @@ def interconnect( term) to form the system output. If omitted, the output map can be specified using the - :func:`~control.InterconnectedSystem.set_output_map` method. + `InterconnectedSystem.set_output_map` method. inputs : int, list of str or None, optional Description of the system inputs. This can be given as an integer @@ -2410,7 +2410,7 @@ def interconnect( ... inplist=['C'], outlist=['P']) A feedback system can also be constructed using the - :func:`~control.summing_junction` function and the ability to + `summing_junction` function and the ability to automatically interconnect signals with the same names: >>> P = ct.tf(1, [1, 0], inputs='u', outputs='y') @@ -2443,11 +2443,11 @@ def interconnect( you should be using a list. In addition to its use for general nonlinear I/O systems, the - :func:`~control.interconnect` function allows linear systems to be + `interconnect` function allows linear systems to be interconnected using named signals (compared with the - :func:`~control.connect` function, which uses signal indices) and to be - treated as both a :class:`~control.StateSpace` system as well as an - :class:`~control.InputOutputSystem`. + `connect` function, which uses signal indices) and to be + treated as both a `StateSpace` system as well as an + `InputOutputSystem`. The `input` and `output` keywords can be used instead of `inputs` and `outputs`, for more natural naming of SISO systems. @@ -2855,18 +2855,18 @@ def _convert_static_iosystem(sys): def connection_table(sys, show_names=False, column_width=32): """Print table of connections inside interconnected system. - Intended primarily for :class:`InterconnectedSystems` that have been + Intended primarily for `InterconnectedSystems` that have been connected implicitly using signal names. Parameters ---------- - sys : :class:`InterconnectedSystem` + sys : `InterconnectedSystem` Interconnected system object. show_names : bool, optional Instead of printing out the system number, print out the name of each system. Default is False because system name is not usually specified when performing implicit interconnection using - :func:`interconnect`. + `interconnect`. column_width : int, optional Character width of printed columns. diff --git a/control/optimal.py b/control/optimal.py index 03cbe3e1f..0af09409e 100644 --- a/control/optimal.py +++ b/control/optimal.py @@ -62,8 +62,8 @@ class OptimalControlProblem(): trajectory_constraints : list of constraints, optional List of constraints that should hold at each point in the time vector. Each element of the list should be an object of type - :class:`~scipy.optimize.LinearConstraint` with arguments `(A, lb, - ub)` or :class:`~scipy.optimize.NonlinearConstraint` with arguments + `~scipy.optimize.LinearConstraint` with arguments `(A, lb, + ub)` or `~scipy.optimize.NonlinearConstraint` with arguments `(fun, lb, ub)`. The constraints will be applied at each time point along the trajectory. terminal_cost : callable, optional @@ -84,7 +84,7 @@ class OptimalControlProblem(): will be broadcast by extension of the time axis. log : bool, optional If `True`, turn on logging messages (using Python logging module). - Use :py:func:`logging.basicConfig` to enable logging output + Use :py`logging.basicConfig` to enable logging output (e.g., to a file). Returns @@ -102,15 +102,15 @@ class OptimalControlProblem(): List of constraints that should hold at the terminal point in time, in the same form as `trajectory_constraints`. solve_ivp_method : str, optional - Set the method used by :func:`scipy.integrate.solve_ivp`. + Set the method used by `scipy.integrate.solve_ivp`. solve_ivp_kwargs : str, optional - Pass additional keywords to :func:`scipy.integrate.solve_ivp`. + Pass additional keywords to `scipy.integrate.solve_ivp`. minimize_method : str, optional - Set the method used by :func:`scipy.optimize.minimize`. + Set the method used by `scipy.optimize.minimize`. minimize_options : str, optional - Set the options keyword used by :func:`scipy.optimize.minimize`. + Set the options keyword used by `scipy.optimize.minimize`. minimize_kwargs : str, optional - Pass additional keywords to :func:`scipy.optimize.minimize`. + Pass additional keywords to `scipy.optimize.minimize`. Notes ----- @@ -121,7 +121,7 @@ class OptimalControlProblem(): optimization over the inputs at each point in time, using the integral and terminal costs as well as the trajectory and terminal constraints. The `compute_trajectory` method sets up an optimization problem that - can be solved using :func:`scipy.optimize.minimize`. + can be solved using `scipy.optimize.minimize`. The `_cost_function` method takes the information computes the cost of the trajectory generated by the proposed input. It does this by calling a @@ -141,8 +141,8 @@ class OptimalControlProblem(): values of the input at the specified times (using linear interpolation for continuous systems). - The default values for ``minimize_method``, ``minimize_options``, - ``minimize_kwargs``, ``solve_ivp_method``, and ``solve_ivp_options`` can + The default values for `minimize_method`, `minimize_options`, + `minimize_kwargs`, `solve_ivp_method`, and `solve_ivp_options` can be set using config.defaults['optimal.']. """ @@ -899,7 +899,7 @@ def _output(t, x, u, params={}): class OptimalControlResult(sp.optimize.OptimizeResult): """Result from solving an optimal control problem. - This class is a subclass of :class:`scipy.optimize.OptimizeResult` with + This class is a subclass of `scipy.optimize.OptimizeResult` with additional attributes associated with solving optimal control problems. Attributes @@ -1005,8 +1005,8 @@ def solve_ocp( trajectory_constraints : list of tuples, optional List of constraints that should hold at each point in the time vector. Each element of the list should consist of a tuple with first element - given by :meth:`scipy.optimize.LinearConstraint` or - :meth:`scipy.optimize.NonlinearConstraint` and the remaining + given by `scipy.optimize.LinearConstraint` or + `scipy.optimize.NonlinearConstraint` and the remaining elements of the tuple are the arguments that would be passed to those functions. The following tuples are supported: @@ -1088,7 +1088,7 @@ def solve_ocp( Other Parameters ---------------- minimize_method : str, optional - Set the method used by :func:`scipy.optimize.minimize`. + Set the method used by `scipy.optimize.minimize`. Notes ----- @@ -1104,7 +1104,7 @@ def solve_ocp( 2. Additional keyword parameters can be used to fine-tune the behavior of the underlying optimization and integration functions. See - :func:`OptimalControlProblem` for more information. + `OptimalControlProblem` for more information. """ # Process keyword arguments @@ -1171,7 +1171,7 @@ def create_mpc_iosystem( constraints : list of tuples, optional List of constraints that should hold at each point in the time vector. - See :func:`~control.optimal.solve_ocp` for more details. + See `optimal.solve_ocp` for more details. terminal_cost : callable, optional Function that returns the terminal cost given the final state @@ -1182,8 +1182,8 @@ def create_mpc_iosystem( Same format as `constraints`. **kwargs - Additional parameters, passed to :func:`scipy.optimal.minimize` and - :class:`NonlinearIOSystem`. + Additional parameters, passed to `scipy.optimal.minimize` and + `NonlinearIOSystem`. Returns ------- @@ -1196,10 +1196,10 @@ def create_mpc_iosystem( ---------------- inputs, outputs, states : int or list of str, optional Set the names of the inputs, outputs, and states, as described in - :func:`~control.InputOutputSystem`. + `InputOutputSystem`. log : bool, optional If `True`, turn on logging messages (using Python logging module). - Use :py:func:`logging.basicConfig` to enable logging output + Use :py`logging.basicConfig` to enable logging output (e.g., to a file). name : string, optional System name (used for specifying signals). If unspecified, a generic @@ -1209,7 +1209,7 @@ def create_mpc_iosystem( ----- Additional keyword parameters can be used to fine-tune the behavior of the underlying optimization and integrations functions. See - :func:`OptimalControlProblem` for more information. + `OptimalControlProblem` for more information. """ from .iosys import InputOutputSystem @@ -1256,8 +1256,8 @@ class OptimalEstimationProblem(): trajectory_constraints : list of constraints, optional List of constraints that should hold at each point in the time vector. Each element of the list should be an object of type - :class:`~scipy.optimize.LinearConstraint` with arguments `(A, lb, - ub)` or :class:`~scipy.optimize.NonlinearConstraint` with arguments + `~scipy.optimize.LinearConstraint` with arguments `(A, lb, + ub)` or `~scipy.optimize.NonlinearConstraint` with arguments `(fun, lb, ub)`. The constraints will be applied at each time point along the trajectory. terminal_cost : callable, optional @@ -1292,15 +1292,15 @@ class OptimalEstimationProblem(): List of constraints that should hold at the terminal point in time, in the same form as `trajectory_constraints`. solve_ivp_method : str, optional - Set the method used by :func:`scipy.integrate.solve_ivp`. + Set the method used by `scipy.integrate.solve_ivp`. solve_ivp_kwargs : str, optional - Pass additional keywords to :func:`scipy.integrate.solve_ivp`. + Pass additional keywords to `scipy.integrate.solve_ivp`. minimize_method : str, optional - Set the method used by :func:`scipy.optimize.minimize`. + Set the method used by `scipy.optimize.minimize`. minimize_options : str, optional - Set the options keyword used by :func:`scipy.optimize.minimize`. + Set the options keyword used by `scipy.optimize.minimize`. minimize_kwargs : str, optional - Pass additional keywords to :func:`scipy.optimize.minimize`. + Pass additional keywords to `scipy.optimize.minimize`. Notes ----- @@ -1310,9 +1310,9 @@ class OptimalEstimationProblem(): along the trajectory (and at the terminal time). This class sets up an optimization over the state and disturbances at each point in time, using the integral and terminal costs as well as the trajectory constraints. - The :func:`~control.optimal.OptimalEstimationProblem.compute_estimate` + The `optimal.OptimalEstimationProblem.compute_estimate` method solves the underling optimization problem using - :func:`scipy.optimize.minimize`. + `scipy.optimize.minimize`. The control input and disturbance indices can be specified using the `control_indices` and `disturbance_indices` keywords. If only one is @@ -1335,8 +1335,8 @@ class OptimalEstimationProblem(): bounds. The constraint function is processed in the class initializer, so that it only needs to be computed once. - The default values for ``minimize_method``, ``minimize_options``, - ``minimize_kwargs``, ``solve_ivp_method``, and ``solve_ivp_options`` + The default values for `minimize_method`, `minimize_options`, + `minimize_kwargs`, `solve_ivp_method`, and `solve_ivp_options` can be set using config.defaults['optimal.']. """ @@ -1774,21 +1774,21 @@ def create_mhe_iosystem( estimate_labels : str or list of str, optional Set the name of the signals to use for the estimated state (estimator outputs). If a single string is specified, it - should be a format string using the variable ``i`` as an index. + should be a format string using the variable `i` as an index. Otherwise, a list of strings matching the size of the estimated state should be used. Default is "xhat[{i}]". These settings can also be overriden using the `outputs` keyword. measurement_labels, control_labels : str or list of str, optional Set the name of the measurement and control signal names (estimator inputs). If a single string is specified, it should - be a format string using the variable ``i`` as an index. + be a format string using the variable `i` as an index. Otherwise, a list of strings matching the size of the system inputs and outputs should be used. Default is the signal names for the system outputs and control inputs. These settings can also be overriden using the `inputs` keyword. **kwargs, optional Additional keyword arguments to set system, input, and output - signal names; see :func:`~control.InputOutputSystem`. + signal names; see `InputOutputSystem`. Returns ------- @@ -1803,7 +1803,7 @@ def create_mhe_iosystem( based on the signal names for the system model used in the optimal estimation problem. The system name and signal names can be overridden using the `name`, `input`, and `output` keywords, as - described in :func:`~control.InputOutputSystem`. + described in `InputOutputSystem`. """ # Check to make sure we are in discrete time @@ -1888,7 +1888,7 @@ def _mhe_output(t, xvec, uvec, params={}): class OptimalEstimationResult(sp.optimize.OptimizeResult): """Result from solving an optimal estimationproblem. - This class is a subclass of :class:`scipy.optimize.OptimizeResult` with + This class is a subclass of `scipy.optimize.OptimizeResult` with additional attributes associated with solving optimal estimation problems. @@ -1983,15 +1983,15 @@ def solve_oep( Mean value of the initial condition (defaults to 0). trajectory_constraints : list of tuples, optional List of constraints that should hold at each point in the time vector. - See :func:`solve_ocp` for more information. + See `solve_ocp` for more information. control_indices : int, slice, or list of int or string, optional Specify the indices in the system input vector that correspond to the control inputs. For more information on possible values, see - :func:`~control.optimal.OptimalEstimationProblem` + `optimal.OptimalEstimationProblem` disturbance_indices : int, list of int, or slice, optional Specify the indices in the system input vector that correspond to the input disturbances. For more information on possible values, see - :func:`~control.optimal.OptimalEstimationProblem` + `optimal.OptimalEstimationProblem` initial_guess : 2D array_like, optional Initial guess for the state estimate at each time point. print_summary : bool, optional @@ -2027,7 +2027,7 @@ def solve_oep( ----- Additional keyword parameters can be used to fine-tune the behavior of the underlying optimization and integration functions. See - :func:`~control.optimal.OptimalControlProblem` for more information. + `optimal.OptimalControlProblem` for more information. """ # Set up the optimal control problem diff --git a/control/phaseplot.py b/control/phaseplot.py index 1caba2c6b..ae9182432 100644 --- a/control/phaseplot.py +++ b/control/phaseplot.py @@ -8,7 +8,7 @@ This module contains functions for generating 2D phase plots. The base function for creating phase plane portraits is -:func:`~control.phase_plane_plot`, which generates a phase plane +`phase_plane_plot`, which generates a phase plane portrait for a 2 state I/O system (with no inputs). In addition, several other functions are available to create customized phase plane plots: @@ -98,21 +98,21 @@ def phase_plane_plot( Returns ------- - cplt : :class:`ControlPlot` object + cplt : `ControlPlot` object Object containing the data that were plotted: - * cplt.lines: array of list of :class:`matplotlib.artist.Artist` + * cplt.lines: array of list of `matplotlib.artist.Artist` objects: - lines[0] = list of Line2D objects (streamlines, separatrices). - lines[1] = Quiver object (vector field arrows). - lines[2] = list of Line2D objects (equilibrium points). - * cplt.axes: 2D array of :class:`matplotlib.axes.Axes` for the plot. + * cplt.axes: 2D array of `matplotlib.axes.Axes` for the plot. - * cplt.figure: :class:`matplotlib.figure.Figure` containing the plot. + * cplt.figure: `matplotlib.figure.Figure` containing the plot. - See :class:`ControlPlot` for more detailed information. + See `ControlPlot` for more detailed information. Other Parameters @@ -130,23 +130,23 @@ def phase_plane_plot( Direction to draw streamlines: 'forward' to flow forward in time from the reference points, 'reverse' to flow backward in time, or 'both' to flow both forward and backward. The amount of time to - simulate in each direction is given by the ``timedata`` argument. + simulate in each direction is given by the `timedata` argument. plot_streamlines : bool or dict, optional If `True` (default) then plot streamlines based on the pointdata and gridtype. If set to a dict, pass on the key-value pairs in - the dict as keywords to :func:`~control.phaseplot.streamlines`. + the dict as keywords to `phaseplot.streamlines`. plot_vectorfield : bool or dict, optional If `True` (default) then plot the vector field based on the pointdata and gridtype. If set to a dict, pass on the key-value pairs in - the dict as keywords to :func:`~control.phaseplot.vectorfield`. + the dict as keywords to `phaseplot.vectorfield`. plot_equilpoints : bool or dict, optional If `True` (default) then plot equilibrium points based in the phase plot boundary. If set to a dict, pass on the key-value pairs in the - dict as keywords to :func:`~control.phaseplot.equilpoints`. + dict as keywords to `phaseplot.equilpoints`. plot_separatrices : bool or dict, optional If `True` (default) then plot separatrices starting from each equilibrium point. If set to a dict, pass on the key-value pairs - in the dict as keywords to :func:`~control.phaseplot.separatrices`. + in the dict as keywords to `phaseplot.separatrices`. rcParams : dict Override the default parameters used for generating plots. Default is set by config.defaults['ctrlplot.rcParams']. @@ -373,7 +373,7 @@ def streamlines( Direction to draw streamlines: 'forward' to flow forward in time from the reference points, 'reverse' to flow backward in time, or 'both' to flow both forward and backward. The amount of time to - simulate in each direction is given by the ``timedata`` argument. + simulate in each direction is given by the `timedata` argument. params : dict or list, optional Parameters to pass to system. For an I/O system, `params` should be a dict of parameters and values. For a callable, `params` should be @@ -1034,11 +1034,11 @@ def phase_plot(odefun, X=None, Y=None, scale=1, X0=None, T=None, """(legacy) Phase plot for 2D dynamical systems. .. deprecated:: 0.10.1 - This function is deprecated; use :func:`phase_plane_plot` instead. + This function is deprecated; use `phase_plane_plot` instead. Produces a vector field or stream line plot for a planar system. This - function has been replaced by the :func:`~control.phase_plane_map` and - :func:`~control.phase_plane_plot` functions. + function has been replaced by the `phase_plane_map` and + `phase_plane_plot` functions. Call signatures: phase_plot(func, X, Y, ...) - display vector field on meshgrid @@ -1052,7 +1052,7 @@ def phase_plot(odefun, X=None, Y=None, scale=1, X0=None, T=None, ---------- func : callable(x, t, ...) Computes the time derivative of y (compatible with odeint). The - function should be the same for as used for :mod:`scipy.integrate`. + function should be the same for as used for `scipy.integrate`. Namely, it should be a function of the form dxdt = F(t, x) that accepts a state x of dimension 2 and returns a derivative dx/dt of dimension 2. @@ -1298,7 +1298,7 @@ def box_grid(xlimp, ylimp): """Generate list of points on edge of box. .. deprecated:: 0.10.0 - Use :func:`phaseplot.boxgrid` instead. + Use `phaseplot.boxgrid` instead. list = box_grid([xmin xmax xnum], [ymin ymax ynum]) generates a list of points that correspond to a uniform grid at the end of the diff --git a/control/pzmap.py b/control/pzmap.py index 9e8a1a859..3ef62a8bc 100644 --- a/control/pzmap.py +++ b/control/pzmap.py @@ -123,7 +123,7 @@ def __iter__(self): def plot(self, *args, **kwargs): """Plot the pole/zero data. - See :func:`~control.pole_zero_plot` for description of arguments + See `pole_zero_plot` for description of arguments and keywords. """ @@ -135,7 +135,7 @@ class PoleZeroList(list): def plot(self, *args, **kwargs): """Plot pole/zero data. - See :func:`~control.pole_zero_plot` for description of arguments + See `pole_zero_plot` for description of arguments and keywords. """ @@ -201,17 +201,17 @@ def pole_zero_plot( If `empty`, do not draw any additonal lines. Default value is set by config.defaults['pzmap.grid'] or config.defaults['rlocus.grid']. plot : bool, optional - (legacy) If ``True`` a graph is generated with Matplotlib, + (legacy) If `True` a graph is generated with Matplotlib, otherwise the poles and zeros are only computed and returned. If this argument is present, the legacy value of poles and zeros is returned. Returns ------- - cplt : :class:`ControlPlot` object + cplt : `ControlPlot` object Object containing the data that were plotted: - * cplt.lines: Array of :class:`matplotlib.lines.Line2D` objects + * cplt.lines: Array of `matplotlib.lines.Line2D` objects for each set of markers in the plot. The shape of the array is given by (`nsys`, 2) where `nsys` is the number of systems or responses passed to the function. The second index specifies @@ -220,13 +220,13 @@ def pole_zero_plot( - lines[idx, 0]: poles - lines[idx, 1]: zeros - * cplt.axes: 2D array of :class:`matplotlib.axes.Axes` for the plot. + * cplt.axes: 2D array of `matplotlib.axes.Axes` for the plot. - * cplt.figure: :class:`matplotlib.figure.Figure` containing the plot. + * cplt.figure: `matplotlib.figure.Figure` containing the plot. * cplt.legend: legend object(s) contained in the plot - See :class:`ControlPlot` for more detailed information. + See `ControlPlot` for more detailed information. poles, zeros : list of arrays (legacy) If the `plot` keyword is given, the system poles and zeros @@ -264,9 +264,9 @@ def pole_zero_plot( Set the type of axis scaling. Can be 'equal' (default), 'auto', or a list of the form [xmin, xmax, ymin, ymax]. show_legend : bool, optional - Force legend to be shown if ``True`` or hidden if ``False``. If - ``None``, then show legend when there is more than one line on the - plot or ``legend_loc`` has been specified. + Force legend to be shown if `True` or hidden if `False`. If + `None`, then show legend when there is more than one line on the + plot or `legend_loc` has been specified. title : str, optional Set the title of the plot. Defaults to plot type and system name(s). xlim : list, optional @@ -283,9 +283,9 @@ def pole_zero_plot( the desired values. Pole/zero plots that use the continuous time omega-damping grid do not - work with the ``ax`` keyword argument, due to the way that axes grids - are implemented. The ``grid`` argument must be set to ``False`` or - ``'empty'`` when using the ``ax`` keyword argument. + work with the `ax` keyword argument, due to the way that axes grids + are implemented. The `grid` argument must be set to `False` or + `'empty'` when using the `ax` keyword argument. The limits of the pole/zero plot are set based on the location features in the plot, including the location of poles, zeros, and local maxima diff --git a/control/rlocus.py b/control/rlocus.py index f74665467..a47f473b4 100644 --- a/control/rlocus.py +++ b/control/rlocus.py @@ -128,10 +128,10 @@ def root_locus_plot( Returns ------- - cplt : :class:`ControlPlot` object + cplt : `ControlPlot` object Object containing the data that were plotted: - * cplt.lines: Array of :class:`matplotlib.lines.Line2D` objects + * cplt.lines: Array of `matplotlib.lines.Line2D` objects for each set of markers in the plot. The shape of the array is given by (nsys, 3) where nsys is the number of systems or responses passed to the function. The second index specifies @@ -141,11 +141,11 @@ def root_locus_plot( - lines[idx, 1]: zeros - lines[idx, 2]: loci - * cplt.axes: 2D array of :class:`matplotlib.axes.Axes` for the plot. + * cplt.axes: 2D array of `matplotlib.axes.Axes` for the plot. - * cplt.figure: :class:`matplotlib.figure.Figure` containing the plot. + * cplt.figure: `matplotlib.figure.Figure` containing the plot. - See :class:`ControlPlot` for more detailed information. + See `ControlPlot` for more detailed information. roots, gains : ndarray (legacy) If the `plot` keyword is given, returns the closed-loop @@ -166,9 +166,9 @@ def root_locus_plot( Include a legend in the given location. Default is 'center right', with no legend for a single response. Use False to suppress legend. show_legend : bool, optional - Force legend to be shown if ``True`` or hidden if ``False``. If - ``None``, then show legend when there is more than one line on the - plot or ``legend_loc`` has been specified. + Force legend to be shown if `True` or hidden if `False`. If + `None`, then show legend when there is more than one line on the + plot or `legend_loc` has been specified. title : str, optional Set the title of the plot. Defaults to plot type and system name(s). diff --git a/control/sisotool.py b/control/sisotool.py index db896cad2..87c779f8e 100644 --- a/control/sisotool.py +++ b/control/sisotool.py @@ -45,7 +45,7 @@ def sisotool(sys, initial_gain=None, xlim_rlocus=None, ylim_rlocus=None, the root locus and step response. If it is desired to see a different step response than feedback(K*sys,1), such as a disturbance response, sys can be provided as a two-input, two-output system (e.g. by using - :func:`bdgalg.connect' or :func:`iosys.interconnect`). For two-input, + `bdgalg.connect' or `iosys.interconnect`). For two-input, two-output system, sisotool inserts the negative of the selected gain K between the first output and first input and uses the second input and output for computing the step response. To see the disturbance @@ -63,7 +63,7 @@ def sisotool(sys, initial_gain=None, xlim_rlocus=None, ylim_rlocus=None, (see :doc:`matplotlib:api/axes_api`). ylim_rlocus : tuple or list, optional Control of y-axis range. - plotstr_rlocus : :func:`matplotlib.pyplot.plot` format string, optional + plotstr_rlocus : `matplotlib.pyplot.plot` format string, optional Plotting style for the root locus plot(color, linestyle, etc). rlocus_grid : boolean (default = False) If True plot s- or z-plane grid. @@ -326,7 +326,7 @@ def rootlocus_pid_designer(plant, gain='P', sign=+1, input_signal='r', Parameters ---------- - plant : :class:`LTI` (:class:`TransferFunction` or :class:`StateSpace` system) + plant : `LTI` (`TransferFunction` or `StateSpace` system) The dynamical system to be controlled. gain : string, optional Which gain to vary by `deltaK`. Must be one of `'P'`, `'I'`, or `'D'` @@ -345,8 +345,8 @@ def rootlocus_pid_designer(plant, gain='P', sign=+1, input_signal='r', The time constant associated with the pole in the continuous-time derivative term. This is required to make the derivative transfer function proper. - C_ff : float or :class:`LTI` system, optional - Feedforward controller. If :class:`LTI`, must have timebase that is + C_ff : float or `LTI` system, optional + Feedforward controller. If `LTI`, must have timebase that is compatible with plant. derivative_in_feedback_path : bool, optional Whether to place the derivative term in feedback transfer function diff --git a/control/statefbk.py b/control/statefbk.py index 2a598c110..a185dfe76 100644 --- a/control/statefbk.py +++ b/control/statefbk.py @@ -66,10 +66,10 @@ def place(A, B, p): Notes ----- Algorithm - This is a wrapper function for :func:`scipy.signal.place_poles`, which + This is a wrapper function for `scipy.signal.place_poles`, which implements the Tits and Yang algorithm [1]_. It will handle SISO, MISO, and MIMO systems. If you want more control over the algorithm, - use :func:`scipy.signal.place_poles` directly. + use `scipy.signal.place_poles` directly. Limitations The algorithm will not place poles at the same location more @@ -267,10 +267,10 @@ def lqr(*args, **kwargs): The function can be called with either 3, 4, or 5 arguments: - * ``K, S, E = lqr(sys, Q, R)`` - * ``K, S, E = lqr(sys, Q, R, N)`` - * ``K, S, E = lqr(A, B, Q, R)`` - * ``K, S, E = lqr(A, B, Q, R, N)`` + * `K, S, E = lqr(sys, Q, R)` + * `K, S, E = lqr(sys, Q, R, N)` + * `K, S, E = lqr(A, B, Q, R)` + * `K, S, E = lqr(A, B, Q, R, N)` where `sys` is an `LTI` object, and `A`, `B`, `Q`, `R`, and `N` are 2D arrays or matrices of appropriate dimension. @@ -315,7 +315,7 @@ def lqr(*args, **kwargs): ----- If the first argument is an LTI object, then this object will be used to define the dynamics and input matrices. Furthermore, if the LTI - object corresponds to a discrete time system, the ``dlqr()`` function + object corresponds to a discrete time system, the `dlqr()` function will be called. Examples @@ -413,12 +413,12 @@ def dlqr(*args, **kwargs): The function can be called with either 3, 4, or 5 arguments: - * ``dlqr(dsys, Q, R)`` - * ``dlqr(dsys, Q, R, N)`` - * ``dlqr(A, B, Q, R)`` - * ``dlqr(A, B, Q, R, N)`` + * `dlqr(dsys, Q, R)` + * `dlqr(dsys, Q, R, N)` + * `dlqr(A, B, Q, R)` + * `dlqr(A, B, Q, R, N)` - where `dsys` is a discrete-time :class:`StateSpace` system, and `A`, `B`, + where `dsys` is a discrete-time `StateSpace` system, and `A`, `B`, `Q`, `R`, and `N` are 2d arrays of appropriate dimension (`dsys.dt` must not be 0.) @@ -426,7 +426,7 @@ def dlqr(*args, **kwargs): ---------- A, B : 2D array Dynamics and input matrices. - dsys : LTI :class:`StateSpace` + dsys : LTI `StateSpace` Discrete-time linear system. Q, R : 2D array State and input weight matrices. @@ -639,14 +639,14 @@ def create_statefbk_iosystem( gainsched_method : str, optional The method to use for gain scheduling. Possible values are 'linear' (default), 'nearest', and 'cubic'. More information is available in - :func:`scipy.interpolate.griddata`. For points outside of the convex + `scipy.interpolate.griddata`. For points outside of the convex hull of the scheduling points, the gain at the nearest point is used. controller_type : 'linear' or 'nonlinear', optional Set the type of controller to create. The default for a linear gain is a linear controller implementing the LQR regulator. If the type - is 'nonlinear', a :class:NonlinearIOSystem is created instead, with + is 'nonlinear', a NonlinearIOSystem is created instead, with the gain `K` as a parameter (allowing modifications of the gain at runtime). If the gain parameter is a tuple, then a nonlinear, gain-scheduled controller is created. diff --git a/control/statesp.py b/control/statesp.py index d15f5a18e..cce13c131 100644 --- a/control/statesp.py +++ b/control/statesp.py @@ -72,7 +72,7 @@ class StateSpace(NonlinearIOSystem, LTI): y &= C x + D u where `u` is the input, `y` is the output, and `x` is the state. State - space systems are usually created with the :func:`~control.ss` factory + space systems are usually created with the `ss` factory function. Parameters @@ -98,7 +98,7 @@ class StateSpace(NonlinearIOSystem, LTI): Notes ----- - The main data members in the ``StateSpace`` class are the A, B, C, and D + The main data members in the `StateSpace` class are the A, B, C, and D matrices. The class also keeps track of the number of states (i.e., the size of A). @@ -117,11 +117,11 @@ class StateSpace(NonlinearIOSystem, LTI): a system with timebase `None` can be combined with a system having any timebase; the result will have the timebase of the latter system. The default value of dt can be changed by changing the value of - ``control.config.defaults['control.default_dt']``. + `control.config.defaults['control.default_dt']`. A state space system is callable and returns the value of the transfer function evaluated at a point in the complex plane. See - :meth:`~control.StateSpace.__call__` for a more detailed description. + `StateSpace.__call__` for a more detailed description. Subsystems corresponding to selected input/output pairs can be created by indexing the state space system:: @@ -163,7 +163,7 @@ def __init__(self, *args, **kwargs): True for unspecified sampling time). To call the copy constructor, call StateSpace(sys), where sys is a StateSpace object. - See :class:`StateSpace` and :func:`ss` for more information. + See `StateSpace` and `ss` for more information. """ # @@ -333,11 +333,11 @@ def _set_states(self, value): FutureWarning, stacklevel=2) self.nstates = value - #: Deprecated attribute; use :attr:`nstates` instead. + #: Deprecated attribute; use `nstates` instead. #: - #: The ``state`` attribute was used to store the number of states for : a + #: The `state` attribute was used to store the number of states for : a #: state space system. It is no longer used. If you need to access the - #: number of states, use :attr:`nstates`. + #: number of states, use `nstates`. states = property(_get_states, _set_states) def _remove_useless_states(self): @@ -764,9 +764,9 @@ def __call__(self, x, squeeze=None, warn_infinite=True): continuous-time systems and `z` for discrete-time systems. To evaluate at a frequency omega in radians per second, enter - ``x = omega * 1j``, for continuous-time systems, or - ``x = exp(1j * omega * dt)`` for discrete-time systems. Or use - :meth:`StateSpace.frequency_response`. + `x = omega * 1j`, for continuous-time systems, or + `x = exp(1j * omega * dt)` for discrete-time systems. Or use + `StateSpace.frequency_response`. Parameters ---------- @@ -788,7 +788,7 @@ def __call__(self, x, squeeze=None, warn_infinite=True): squeeze is not True, the shape of the array matches the shape of omega. If the system is not SISO or squeeze is False, the first two dimensions of the array are indices for the output and input - and the remaining dimensions match omega. If ``squeeze`` is True + and the remaining dimensions match omega. If `squeeze` is True then single-dimensional axes are removed. """ @@ -801,7 +801,7 @@ def slycot_laub(self, x): Evaluate transfer function at complex frequency using Laub's method from Slycot. Expects inputs and outputs to be - formatted correctly. Use ``sys(x)`` for a more user-friendly + formatted correctly. Use `sys(x)` for a more user-friendly interface. Parameters @@ -860,7 +860,7 @@ def horner(self, x, warn_infinite=True): Evaluates `sys(x)` where `x` is `s` for continuous-time systems and `z` for discrete-time systems. - Expects inputs and outputs to be formatted correctly. Use ``sys(x)`` + Expects inputs and outputs to be formatted correctly. Use `sys(x)` for a more user-friendly interface. Notes @@ -924,8 +924,8 @@ def freqresp(self, omega): .. deprecated::0.9.0 Method has been given the more pythonic name - :meth:`StateSpace.frequency_response`. Or use - :func:`freqresp` in the MATLAB compatibility module. + `StateSpace.frequency_response`. Or use + `freqresp` in the MATLAB compatibility module. """ warn("StateSpace.freqresp(omega) will be removed in a " "future release of python-control; use " @@ -1161,14 +1161,14 @@ def minreal(self, tol=0.0): return StateSpace(self) def returnScipySignalLTI(self, strict=True): - """Return a list of a list of :class:`scipy.signal.lti` objects. + """Return a list of a list of `scipy.signal.lti` objects. For instance, >>> out = ssobject.returnScipySignalLTI() # doctest: +SKIP >>> out[3][5] # doctest: +SKIP - is a :class:`scipy.signal.lti` object corresponding to the transfer + is a `scipy.signal.lti` object corresponding to the transfer function from the 6th input to the 4th output. Parameters @@ -1179,13 +1179,13 @@ def returnScipySignalLTI(self, strict=True): be continuous (0) or discrete (True or > 0). False: If `ssobject.dt` is None, continuous time - :class:`scipy.signal.lti` objects are returned. + `scipy.signal.lti` objects are returned. Returns ------- - out : list of list of :class:`scipy.signal.StateSpace` - Continuous time (inheriting from :class:`scipy.signal.lti`) - or discrete time (inheriting from :class:`scipy.signal.dlti`) + out : list of list of `scipy.signal.StateSpace` + Continuous time (inheriting from `scipy.signal.lti`) + or discrete time (inheriting from `scipy.signal.dlti`) SISO objects. """ if strict and self.dt is None: @@ -1309,7 +1309,7 @@ def sample(self, Ts, method='zoh', alpha=None, prewarp_frequency=None, ---------------- inputs : int, list of str or None, optional Description of the system inputs. If not specified, the origional - system inputs are used. See :class:`InputOutputSystem` for more + system inputs are used. See `InputOutputSystem` for more information. outputs : int, list of str or None, optional Description of the system outputs. Same format as `inputs`. @@ -1318,7 +1318,7 @@ def sample(self, Ts, method='zoh', alpha=None, prewarp_frequency=None, Notes ----- - Uses :func:`scipy.signal.cont2discrete` + Uses `scipy.signal.cont2discrete` Examples -------- @@ -1398,10 +1398,10 @@ def dynamics(self, t, x, u=None, params=None): The inputs `x` and `u` must be of the correct length for the system. - The first argument `t` is ignored because :class:`StateSpace` systems + The first argument `t` is ignored because `StateSpace` systems are time-invariant. It is included so that the dynamics can be passed - to numerical integrators, such as :func:`scipy.integrate.solve_ivp` - and for consistency with :class:`IOSystem` systems. + to numerical integrators, such as `scipy.integrate.solve_ivp` + and for consistency with `IOSystem` systems. Parameters ---------- @@ -1443,10 +1443,10 @@ def output(self, t, x, u=None, params=None): where A and B are the state-space matrices of the system. - The first argument `t` is ignored because :class:`StateSpace` systems + The first argument `t` is ignored because `StateSpace` systems are time-invariant. It is included so that the dynamics can be passed to most numerical integrators, such as scipy's `integrate.solve_ivp` - and for consistency with :class:`IOSystem` systems. + and for consistency with `IOSystem` systems. The inputs `x` and `u` must be of the correct length for the system. @@ -1488,11 +1488,11 @@ class LinearICSystem(InterconnectedSystem, StateSpace): This class is used to implement a system that is an interconnection of linear input/output systems. It has all of the structure of an - :class:`~control.InterconnectedSystem`, but also maintains the required - elements of the :class:`StateSpace` class structure, allowing it to be - passed to functions that expect a :class:`StateSpace` system. + `InterconnectedSystem`, but also maintains the required + elements of the `StateSpace` class structure, allowing it to be + passed to functions that expect a `StateSpace` system. - This class is generated using :func:`~control.interconnect` and + This class is generated using `interconnect` and not called directly. """ @@ -1570,11 +1570,11 @@ def _repr_html_(self): # this entry to show up properly in sphinx doccumentation (not sure why, # but it was the only way to get it to work). # - #: Deprecated attribute; use :attr:`nstates` instead. + #: Deprecated attribute; use `nstates` instead. #: - #: The ``state`` attribute was used to store the number of states for : a + #: The `state` attribute was used to store the number of states for : a #: state space system. It is no longer used. If you need to access the - #: number of states, use :attr:`nstates`. + #: number of states, use `nstates`. states = property(StateSpace._get_states, StateSpace._set_states) @@ -1586,11 +1586,11 @@ def ss(*args, **kwargs): The function accepts either 1, 4 or 5 positional parameters: - ``ss(sys)`` + `ss(sys)` Convert a linear system into space system form. Always creates a new system, even if sys is already a state space system. - ``ss(A, B, C, D)`` + `ss(A, B, C, D)` Create a state space system from the matrices of its state and output equations: @@ -1599,7 +1599,7 @@ def ss(*args, **kwargs): dx/dt &= A x + B u \\ y &= C x + D u - ``ss(A, B, C, D, dt)`` + `ss(A, B, C, D, dt)` Create a discrete-time state space system from the matrices of its state and output equations: @@ -1648,7 +1648,7 @@ def ss(*args, **kwargs): List of strings that name the individual signals. If this parameter is not given or given as `None`, the signal names will be of the form `s[i]` (where `s` is one of `u`, `y`, or `x`). See - :class:`InputOutputSystem` for more information. + `InputOutputSystem` for more information. input_prefix, output_prefix, state_prefix : string, optional Set the prefix for input, output, and state signals. Defaults = 'u', 'y', 'x'. @@ -1669,7 +1669,7 @@ def ss(*args, **kwargs): ----- If a transfer function is passed as the sole positional argument, the system will be converted to state space form in the same way as calling - :func:`~control.tf2ss`. The `method` keyword can be used to select the + `tf2ss`. The `method` keyword can be used to select the method for conversion. Examples @@ -1734,8 +1734,8 @@ def ss2io(*args, **kwargs): This function will be removed in a future version of python-control. The `ss` function can be used directly to produce an I/O system. - Create an :class:`~control.StateSpace` system with the given signal - and system names. See :func:`~control.ss` for more details. + Create an `StateSpace` system with the given signal + and system names. See `ss` for more details. """ warn("ss2io() is deprecated; use ss()", FutureWarning) return StateSpace(*args, **kwargs) @@ -1753,15 +1753,15 @@ def tf2io(*args, **kwargs): The function accepts either 1 or 2 parameters: - ``tf2io(sys)`` + `tf2io(sys)` Convert a linear system into space space form. Always creates a new system, even if sys is already a StateSpace object. - ``tf2io(num, den)`` + `tf2io(num, den)` Create a linear I/O system from its numerator and denominator polynomial coefficients. - For details see: :func:`tf` + For details see: `tf` Parameters ---------- @@ -1824,15 +1824,15 @@ def tf2ss(*args, **kwargs): The function accepts either 1 or 2 parameters: - ``tf2ss(sys)`` + `tf2ss(sys)` Convert a transfer function into space space form. Equivalent to `ss(sys)`. - ``tf2ss(num, den)`` + `tf2ss(num, den)` Create a state space system from its numerator and denominator polynomial coefficients. - For details see: :func:`tf` + For details see: `tf` Parameters ---------- @@ -1879,10 +1879,10 @@ def tf2ss(*args, **kwargs): Notes ----- - The ``slycot`` routine used to convert a transfer function into state + The `slycot` routine used to convert a transfer function into state space form appears to have a bug and in some (rare) instances may not return a system with the same poles as the input transfer function. - For SISO systems, setting ``method=scipy`` can be used as an alternative. + For SISO systems, setting `method=scipy` can be used as an alternative. Examples -------- @@ -2051,7 +2051,7 @@ def drss(*args, **kwargs): Create a stable, discrete-time, random state space system. Create a stable *discrete time* random state space object. This - function calls :func:`rss` using either the `dt` keyword provided by + function calls `rss` using either the `dt` keyword provided by the user or `dt=True` if not specified. Examples @@ -2095,7 +2095,7 @@ def summing_junction( This function creates a static input/output system that outputs the sum of the inputs, potentially with a change in sign for each individual input. The input/output system that is created by this function can be used as a - component in the :func:`~control.interconnect` function. + component in the `interconnect` function. Parameters ---------- diff --git a/control/stochsys.py b/control/stochsys.py index 08457d9d0..9d89ba651 100644 --- a/control/stochsys.py +++ b/control/stochsys.py @@ -60,10 +60,10 @@ def lqe(*args, **kwargs): The function can be called with either 3, 4, 5, or 6 arguments: - * ``L, P, E = lqe(sys, QN, RN)`` - * ``L, P, E = lqe(sys, QN, RN, NN)`` - * ``L, P, E = lqe(A, G, C, QN, RN)`` - * ``L, P, E = lqe(A, G, C, QN, RN, NN)`` + * `L, P, E = lqe(sys, QN, RN)` + * `L, P, E = lqe(sys, QN, RN, NN)` + * `L, P, E = lqe(A, G, C, QN, RN)` + * `L, P, E = lqe(A, G, C, QN, RN, NN)` where `sys` is an `LTI` object, and `A`, `G`, `C`, `QN`, `RN`, and `NN` are 2D arrays or matrices of appropriate dimension. @@ -102,7 +102,7 @@ def lqe(*args, **kwargs): ----- If the first argument is an LTI object, then this object will be used to define the dynamics, noise and output matrices. Furthermore, if the - LTI object corresponds to a discrete time system, the ``dlqe()`` + LTI object corresponds to a discrete time system, the `dlqe()` function will be called. Examples @@ -399,21 +399,21 @@ def create_estimator_iosystem( measurement_labels, control_labels : str or list of str, optional Set the name of the measurement and control signal names (estimator inputs). If a single string is specified, it should be a format - string using the variable ``i`` as an index. Otherwise, a list of + string using the variable `i` as an index. Otherwise, a list of strings matching the size of the system inputs and outputs should be used. Default is the signal names for the system measurements and known control inputs. These settings can also be overriden using the `inputs` keyword. inputs, outputs, states : int or list of str, optional Set the names of the inputs, outputs, and states, as described in - :func:`~control.InputOutputSystem`. Overrides signal labels. + `InputOutputSystem`. Overrides signal labels. name : string, optional System name (used for specifying signals). If unspecified, a generic name is generated with a unique integer id. Notes ----- - This function can be used with the ``create_statefbk_iosystem()`` function + This function can be used with the `create_statefbk_iosystem()` function to create a closed loop, output-feedback, state space controller:: K, _, _ = ct.lqr(sys, Q, R) @@ -424,7 +424,7 @@ def create_estimator_iosystem( resp = ct.input_output_response(est, T, [Y, U], [X0, P0]) - If desired, the ``correct`` parameter can be set to ``False`` to allow + If desired, the `correct` parameter can be set to `False` to allow prediction with no additional measurement information:: resp = ct.input_output_response( diff --git a/control/sysnorm.py b/control/sysnorm.py index 7848e222c..b25bbae8c 100644 --- a/control/sysnorm.py +++ b/control/sysnorm.py @@ -21,19 +21,19 @@ def _h2norm_slycot(sys, print_warning=True): See Also -------- - ``slycot.ab13bd`` : the Slycot routine that does the calculation - https://github.com/python-control/Slycot/issues/199 : Post on issue with ``ab13bf`` + `slycot.ab13bd` : the Slycot routine that does the calculation + https://github.com/python-control/Slycot/issues/199 : Post on issue with `ab13bf` """ try: from slycot import ab13bd except ImportError: - ct.ControlSlycot("Can't find slycot module ``ab13bd``!") + ct.ControlSlycot("Can't find slycot module `ab13bd`!") try: from slycot.exceptions import SlycotArithmeticError except ImportError: - raise ct.ControlSlycot("Can't find slycot class ``SlycotArithmeticError``!") + raise ct.ControlSlycot("Can't find slycot class `SlycotArithmeticError`!") A, B, C, D = ct.ssdata(ct.ss(sys)) @@ -82,7 +82,7 @@ def system_norm(system, p=2, tol=1e-6, print_warning=True, method=None): Parameters ---------- - system : LTI (:class:`StateSpace` or :class:`TransferFunction`) + system : LTI (`StateSpace` or `TransferFunction`) System in continuous or discrete time for which the norm should be computed. p : int or str diff --git a/control/tests/bdalg_test.py b/control/tests/bdalg_test.py index 576b91d77..50ae9e8a9 100644 --- a/control/tests/bdalg_test.py +++ b/control/tests/bdalg_test.py @@ -363,7 +363,7 @@ def test_bdalg_udpate_names_errors(): class TestEnsureTf: - """Test ``_ensure_tf``.""" + """Test `_ensure_tf`.""" @pytest.mark.parametrize( "arraylike_or_tf, dt, tf", @@ -463,7 +463,7 @@ def test_error_ensure(self, arraylike_or_tf, dt, exception): class TestTfCombineSplit: - """Test ``combine_tf`` and ``split_tf``.""" + """Test `combine_tf` and `split_tf`.""" @pytest.mark.parametrize( "tf_array, tf", diff --git a/control/tests/docstrings_test.py b/control/tests/docstrings_test.py index cedfd5b09..64c62b815 100644 --- a/control/tests/docstrings_test.py +++ b/control/tests/docstrings_test.py @@ -396,9 +396,8 @@ def test_iosys_primary_classes(cls, fcn, args): # Make sure we reference the factory function if re.search( - r"created.*(with|by|using).*the[\s]*" - f":func:`~control\\..*{fcn.__name__}`" - r"[\s]factory[\s]function", "\n".join(doc["Extended Summary"]), + f"`(~[\\w.]*)*{fcn.__name__}`" + r"[\s]+factory[\s]+function", "\n".join(doc["Extended Summary"]), re.DOTALL) is None: _fail( f"{cls.__name__} does not reference factory function " @@ -510,14 +509,6 @@ def test_iosys_intermediate_classes(cls): _fail(f"intermediate {cls} docstring contains Parameters section") return - # Make sure we reference the factory function - # TODO: check extended summary - fcn = class_factory_function[cls] - if re.search(f":func:`~control.{fcn.__name__}`", docstring) is None: - _fail( - f"{cls.__name__} does not reference factory function " - f"{fcn.__name__}") - @pytest.mark.parametrize("fcn", factory_args.keys()) def test_iosys_factory_functions(fcn): diff --git a/control/tests/matlab2_test.py b/control/tests/matlab2_test.py index d7c0c1ce1..7f5bf945c 100644 --- a/control/tests/matlab2_test.py +++ b/control/tests/matlab2_test.py @@ -87,7 +87,7 @@ def test_dcgain_2(self, SISO_mats): Z, P, k = scipy.signal.tf2zpk(num[0][-1], den) sys_ss = ss(A, B, C, D) - #Compute the gain with ``dcgain`` + #Compute the gain with `dcgain` gain_abcd = dcgain(A, B, C, D) gain_zpk = dcgain(Z, P, k) gain_numden = dcgain(np.squeeze(num), den) @@ -109,7 +109,7 @@ def test_dcgain_2(self, SISO_mats): decimal=6) def test_step(self, SISO_mats, MIMO_mats, mplcleanup): - """Test function ``step``.""" + """Test function `step`.""" figure(); plot_shape = (1, 3) #Test SISO system @@ -320,12 +320,12 @@ def test_lsim(self, SISO_mats, MIMO_mats): #T is None; - special handling: Value error self.assertRaises(ValueError, lsim(sys, U=0, T=None, x0=0)) #T="hello" : Wrong type - #TODO: better wording of error messages of ``lsim`` and - # ``_check_convert_array``, when wrong type is given. + #TODO: better wording of error messages of `lsim` and + # `_check_convert_array`, when wrong type is given. # Current error message is too cryptic. self.assertRaises(TypeError, lsim(sys, U=0, T="hello", x0=0)) #T=0; - T can not be zero dimensional, it determines the size of the - # input vector ``U`` + # input vector `U` self.assertRaises(ValueError, lsim(sys, U=0, T=0, x0=0)) #T is not monotonically increasing self.assertRaises(ValueError, lsim(sys, U=0, T=[0., 1., 2., 2., 3.], x0=0)) @@ -333,7 +333,7 @@ def test_lsim(self, SISO_mats, MIMO_mats): def assert_systems_behave_equal(self, sys1, sys2): ''' - Test if the behavior of two LTI systems is equal. Raises ``AssertionError`` + Test if the behavior of two LTI systems is equal. Raises `AssertionError` if the systems are not equal. Works only for SISO systems. @@ -343,7 +343,7 @@ def assert_systems_behave_equal(self, sys1, sys2): #gain of both systems must be the same assert_array_almost_equal(dcgain(sys1), dcgain(sys2)) - #Results of ``step`` simulation must be the same too + #Results of `step` simulation must be the same too y1, t1 = step(sys1) y2, t2 = step(sys2, t1) assert_array_almost_equal(y1, y2) diff --git a/control/tests/matlab_test.py b/control/tests/matlab_test.py index 26ff16774..83527cf76 100644 --- a/control/tests/matlab_test.py +++ b/control/tests/matlab_test.py @@ -111,7 +111,7 @@ def siso(self): @pytest.fixture def mimo(self): - """Create MIMO system, contains ``siso_ss1`` twice""" + """Create MIMO system, contains `siso_ss1` twice""" m = tsystems() A = np.array([[1., -2., 0., 0.], [3., -4., 0., 0.], @@ -314,7 +314,7 @@ def testLsim(self, siso): yout, _t, _xout = lsim(siso.tf3, u, t) np.testing.assert_array_almost_equal(yout, youttrue, decimal=4) - # test with initial value and special algorithm for ``U=0`` + # test with initial value and special algorithm for `U=0` u = 0 x0 = np.array([[.5], [1.]]) youttrue = np.array([11., 8.1494, 5.9361, 4.2258, 2.9118, 1.9092, @@ -378,7 +378,7 @@ def testDcgain(self, siso): num, den = sp.signal.ss2tf(A, B, C, D) sys_ss = siso.ss1 - # Compute the gain with ``dcgain`` + # Compute the gain with `dcgain` gain_abcd = dcgain(A, B, C, D) gain_zpk = dcgain(Z, P, k) gain_numden = dcgain(np.squeeze(num), den) diff --git a/control/tests/timeresp_test.py b/control/tests/timeresp_test.py index ae28975ee..1174947d8 100644 --- a/control/tests/timeresp_test.py +++ b/control/tests/timeresp_test.py @@ -68,7 +68,7 @@ def tsystem(self, request): siso_tf2 = copy(siso_ss1) siso_tf2.sys = ss2tf(siso_ss1.sys) - """MIMO system, contains ``siso_ss1`` twice""" + """MIMO system, contains `siso_ss1` twice""" mimo_ss1 = copy(siso_ss1) A = np.zeros((4, 4)) A[:2, :2] = siso_ss1.sys.A @@ -84,7 +84,7 @@ def tsystem(self, request): D[1:, 1:] = siso_ss1.sys.D mimo_ss1.sys = StateSpace(A, B, C, D) - """MIMO system, contains ``siso_ss2`` twice""" + """MIMO system, contains `siso_ss2` twice""" mimo_ss2 = copy(siso_ss2) A = np.zeros((4, 4)) A[:2, :2] = siso_ss2.sys.A @@ -336,7 +336,7 @@ def test_step_response_siso(self, tsystem, kwargs): @pytest.mark.parametrize("tsystem", ["mimo_ss1"], indirect=True) def test_step_response_mimo(self, tsystem): - """Test MIMO system, which contains ``siso_ss1`` twice.""" + """Test MIMO system, which contains `siso_ss1` twice.""" sys = tsystem.sys t = tsystem.t yref = tsystem.ystep @@ -734,10 +734,10 @@ def test_forced_response_invalid_d(self, tsystem): """Test invalid parameters dtime with sys.dt > 0.""" with pytest.raises(ValueError, match="can't both be zero"): forced_response(tsystem.sys) - with pytest.raises(ValueError, match="Parameter ``U``: Wrong shape"): + with pytest.raises(ValueError, match="Parameter `U`: Wrong shape"): forced_response(tsystem.sys, T=tsystem.t, U=np.random.randn(1, 12)) - with pytest.raises(ValueError, match="Parameter ``U``: Wrong shape"): + with pytest.raises(ValueError, match="Parameter `U`: Wrong shape"): forced_response(tsystem.sys, T=tsystem.t, U=np.random.randn(12)) with pytest.raises(ValueError, match="must match sampling time"): diff --git a/control/timeplot.py b/control/timeplot.py index 021ef7f93..294dba6d8 100644 --- a/control/timeplot.py +++ b/control/timeplot.py @@ -83,28 +83,28 @@ def time_response_plot( signals are plotted from left to right, starting with the inputs (if plotted) and then the outputs. Multi-trace responses are stacked vertically. - *fmt : :func:`matplotlib.pyplot.plot` format string, optional + *fmt : `matplotlib.pyplot.plot` format string, optional Passed to `matplotlib` as the format string for all lines in the plot. - **kwargs : :func:`matplotlib.pyplot.plot` keyword properties, optional + **kwargs : `matplotlib.pyplot.plot` keyword properties, optional Additional keywords passed to `matplotlib` to specify line properties. Returns ------- - cplt : :class:`ControlPlot` object + cplt : `ControlPlot` object Object containing the data that were plotted: - * cplt.lines: Array of :class:`matplotlib.lines.Line2D` objects + * cplt.lines: Array of `matplotlib.lines.Line2D` objects for each line in the plot. The shape of the array matches the subplots shape and the value of the array is a list of Line2D objects in that subplot. - * cplt.axes: 2D array of :class:`matplotlib.axes.Axes` for the plot. + * cplt.axes: 2D array of `matplotlib.axes.Axes` for the plot. - * cplt.figure: :class:`matplotlib.figure.Figure` containing the plot. + * cplt.figure: `matplotlib.figure.Figure` containing the plot. * cplt.legend: legend object(s) contained in the plot - See :class:`ControlPlot` for more detailed information. + See `ControlPlot` for more detailed information. Other Parameters ---------------- @@ -129,7 +129,7 @@ def time_response_plot( Location of the legend for multi-axes plots. Specifies an array of legend location strings matching the shape of the subplots, with each entry being either None (for no legend) or a legend location - string (see :func:`~matplotlib.pyplot.legend`). + string (see `~matplotlib.pyplot.legend`). legend_loc : int or str, optional Include a legend in the given location. Default is 'center right', with no legend for a single response. Use False to suppress legend. @@ -144,9 +144,9 @@ def time_response_plot( when new data are added. If set to `False`, just plot new data on existing axes. show_legend : bool, optional - Force legend to be shown if ``True`` or hidden if ``False``. If - ``None``, then show legend when there is more than one line on an - axis or ``legend_loc`` or ``legend_map`` has been specified. + Force legend to be shown if `True` or hidden if `False`. If + `None`, then show legend when there is more than one line on an + axis or `legend_loc` or `legend_map` has been specified. time_label : str, optional Label to use for the time axis. title : str, optional @@ -674,12 +674,12 @@ def _make_line_label(signal_index, signal_labels, trace_index): def combine_time_responses(response_list, trace_labels=None, title=None): """Combine individual time responses into multi-trace response. - This function combines multiple instances of :class:`TimeResponseData` - into a multi-trace :class:`TimeResponseData` object. + This function combines multiple instances of `TimeResponseData` + into a multi-trace `TimeResponseData` object. Parameters ---------- - response_list : list of :class:`TimeResponseData` objects + response_list : list of `TimeResponseData` objects Reponses to be combined. trace_labels : list of str, optional List of labels for each trace. If not specified, trace names are @@ -690,7 +690,7 @@ def combine_time_responses(response_list, trace_labels=None, title=None): Returns ------- - data : :class:`TimeResponseData` + data : `TimeResponseData` Multi-trace input/output data. """ diff --git a/control/timeresp.py b/control/timeresp.py index d73cfbfd5..8d9a063ab 100644 --- a/control/timeresp.py +++ b/control/timeresp.py @@ -23,7 +23,7 @@ Arguments to time-domain simulations include a time vector, an input vector (when needed), and an initial condition vector. The most general function for simulating LTI systems the -:func:`forced_response` function, which has the form:: +`forced_response` function, which has the form:: t, y = forced_response(sys, T, U, X0) @@ -72,38 +72,37 @@ class TimeResponseData: step responses for linear systems. For multi-trace responses, the same time vector must be used for all traces. - Time responses are accessed through either the raw data, stored as - :attr:`t`, :attr:`y`, :attr:`x`, :attr:`u`, or using a set of properties - :attr:`time`, :attr:`outputs`, :attr:`states`, :attr:`inputs`. When - accessing time responses via their properties, squeeze processing is - applied so that (by default) single-input, single-output systems will have - the output and input indices suppressed. This behavior is set using the - ``squeeze`` keyword. + Time responses are accessed through either the raw data, stored as `t`, + `y`, `x`, `u`, or using a set of properties `time`, `outputs`, + `states`, `inputs`. When accessing time responses via their + properties, squeeze processing is applied so that (by default) + single-input, single-output systems will have the output and input + indices suppressed. This behavior is set using the `squeeze` parameter. Attributes ---------- t : 1D array Time values of the input/output response(s). This attribute is - normally accessed via the :attr:`time` property. + normally accessed via the `time` property. y : 2D or 3D array Output response data, indexed either by output index and time (for single trace responses) or output, trace, and time (for multi-trace - responses). These data are normally accessed via the :attr:`outputs` + responses). These data are normally accessed via the `outputs` property, which performs squeeze processing. x : 2D or 3D array, or None State space data, indexed either by output number and time (for single trace responses) or output, trace, and time (for multi-trace - responses). If no state data are present, value is ``None``. These - data are normally accessed via the :attr:`states` property, which + responses). If no state data are present, value is `None`. These + data are normally accessed via the `states` property, which performs squeeze processing. u : 2D or 3D array, or None Input signal data, indexed either by input index and time (for single trace responses) or input, trace, and time (for multi-trace - responses). If no input data are present, value is ``None``. These - data are normally accessed via the :attr:`inputs` property, which + responses). If no input data are present, value is `None`. These + data are normally accessed via the `inputs` property, which performs squeeze processing. squeeze : bool, optional @@ -112,22 +111,22 @@ class TimeResponseData: (indexed by time) and if a system is multi-input or multi-output, then the outputs are returned as a 2D array (indexed by output and time) or a 3D array (indexed by output, - trace, and time). If ``squeeze=True``, access to the output + trace, and time). If `squeeze=True`, access to the output response will remove single-dimensional entries from the shape of the inputs and outputs even if the system is not SISO. If - ``squeeze=False``, the output is returned as a 2D or 3D array + `squeeze=False`, the output is returned as a 2D or 3D array (indexed by the output [if multi-input], trace [if multi-trace] and time) even if the system is SISO. The default value can be set using config.defaults['control.squeeze_time_response']. transpose : bool, optional If True, transpose all input and output arrays (for backward - compatibility with MATLAB and :func:`scipy.signal.lsim`). Default + compatibility with MATLAB and `scipy.signal.lsim`). Default value is False. issiso : bool, optional - Set to ``True`` if the system generating the data is single-input, - single-output. If passed as ``None`` (default), the input data + Set to `True` if the system generating the data is single-input, + single-output. If passed as `None` (default), the input data will be used to set the value. ninputs, noutputs, nstates : int @@ -137,11 +136,11 @@ class TimeResponseData: Names for the input, output, and state variables. success : bool, optional - If ``False``, result may not be valid (see - :func:`~control.input_output_response`). Defaults to ``True``. + If `False`, result may not be valid (see + `input_output_response`). Defaults to `True`. message : str, optional - Informational message if ``success`` is ``False``. + Informational message if `success` is `False`. sysname : str, optional Name of the system that created the data. @@ -187,14 +186,14 @@ class TimeResponseData: plt.plot(resp.time, resp.outputs[['y[0]', 'y[1]'], 'u[0]'].T) For backward compatibility with earlier versions of python-control, - this class has an ``__iter__`` method that allows it to be assigned to + this class has an `__iter__` method that allows it to be assigned to a tuple with a variable number of elements. This allows the following patterns to work:: t, y = step_response(sys) t, y, x = step_response(sys, return_x=True) - Similarly, the class has ``__getitem__`` and ``__len__`` methods that + Similarly, the class has `__getitem__` and `__len__` methods that allow the return value to be indexed: * response[0]: returns the time vector @@ -204,14 +203,40 @@ class TimeResponseData: When using this (legacy) interface, the state vector is not affected by the `squeeze` parameter. - The default settings for ``return_x``, ``squeeze`` and ``transpose`` + The default settings for `return_x`, `squeeze` and `transpose` can be changed by calling the class instance and passing new values:: response(tranpose=True).input - See :meth:`TimeResponseData.__call__` for more information. + See `TimeResponseData.__call__` for more information. """ + # + # Class attributes + # + # These attributes are defined as class attributes so that they are + # documented properly. They are "overwritten" in __init__. + # + + #: Squeeze processing parameter. + #: + #: By default, if a system is single-input, single-output (SISO) + #: then the inputs and outputs are returned as a 1D array (indexed + #: by time) and if a system is multi-input or multi-output, then + #: the inputs are returned as a 2D array (indexed by input and + #: time) and the outputs are returned as either a 2D array (indexed + #: by output and time) or a 3D array (indexed by output, trace, and + #: time). If squeeze=True, access to the output response will + #: remove single-dimensional entries from the shape of the inputs + #: and outputs even if the system is not SISO. If squeeze=False, + #: keep the input as a 2D or 3D array (indexed by the input (if + #: multi-input), trace (if single input) and time) and the output + #: as a 3D array (indexed by the output, trace, and time) even if + #: the system is SISO. The default value can be set using + #: config.defaults['control.squeeze_time_response']. + #: + #: :meta hide-value: + squeeze = None def __init__( self, time, outputs, states=None, inputs=None, issiso=None, @@ -280,7 +305,7 @@ def __init__( transpose : bool, optional If True, transpose all input and output arrays (for backward - compatibility with MATLAB and :func:`scipy.signal.lsim`). + compatibility with MATLAB and `scipy.signal.lsim`). Default value is False. return_x : bool, optional @@ -292,16 +317,16 @@ def __init__( in the plot() method) multi_trace : bool, optional - If ``True``, then 2D input array represents multiple traces. For - a MIMO system, the ``input`` attribute should then be set to - indicate which trace is being specified. Default is ``False``. + If `True`, then 2D input array represents multiple traces. For + a MIMO system, the `input` attribute should then be set to + indicate which trace is being specified. Default is `False`. success : bool, optional - If ``False``, result may not be valid (see - :func:`~control.input_output_response`). + If `False`, result may not be valid (see + `input_output_response`). message : str, optional - Informational message if ``success`` is ``False``. + Informational message if `success` is `False`. """ # @@ -464,8 +489,8 @@ def __call__(self, **kwargs): """Change value of processing keywords. Calling the time response object will create a copy of the object and - change the values of the keywords used to control the ``outputs``, - ``states``, and ``inputs`` properties. + change the values of the keywords used to control the `outputs`, + `states`, and `inputs` properties. Parameters ---------- @@ -480,7 +505,7 @@ def __call__(self, **kwargs): transpose : bool, optional If True, transpose all input and output arrays (for backward - compatibility with MATLAB and :func:`scipy.signal.lsim`). + compatibility with MATLAB and `scipy.signal.lsim`). Default value is False. return_x : bool, optional @@ -539,7 +564,7 @@ def outputs(self): Output response of the system, indexed by either the output and time (if only a single input is given) or the output, trace, and time - (for multiple traces). See :attr:`TimeResponseData.squeeze` for a + (for multiple traces). See `TimeResponseData.squeeze` for a description of how this can be modified using the `squeeze` keyword. Input and output signal names can be used to index the data in @@ -562,7 +587,7 @@ def states(self): Time evolution of the state vector, indexed indexed by either the state and time (if only a single trace is given) or the state, trace, - and time (for multiple traces). See :attr:`TimeResponseData.squeeze` + and time (for multiple traces). See `TimeResponseData.squeeze` for a description of how this can be modified using the `squeeze` keyword. @@ -595,7 +620,7 @@ def inputs(self): and time. If a 1D vector is passed, the input corresponds to a scalar-valued input. If a 2D vector is passed, then it can either represent multiple single-input traces or a single multi-input trace. - The optional ``multi_trace`` keyword should be used to disambiguate + The optional `multi_trace` keyword should be used to disambiguate the two. If a 3D vector is passed, then it represents a multi-trace, multi-input signal, indexed by input, trace, and time. @@ -603,7 +628,7 @@ def inputs(self): place of integer offsets, with the input signal names being used to access multi-input data. - See :attr:`TimeResponseData.squeeze` for a description of how the + See `TimeResponseData.squeeze` for a description of how the dimensions of the input vector can be modified using the `squeeze` keyword. @@ -719,7 +744,7 @@ def to_pandas(self): def plot(self, *args, **kwargs): """Plot the time response data objects. - This method calls :func:`time_response_plot`, passing all arguments + This method calls `time_response_plot`, passing all arguments and keywords. """ @@ -736,9 +761,9 @@ def plot(self, *args, **kwargs): class TimeResponseList(list): """List of TimeResponseData objects with plotting capability. - This class consists of a list of :class:`TimeResponseData` objects. + This class consists of a list of `TimeResponseData` objects. It is a subclass of the Python `list` class, with a `plot` method that - plots the individual :class:`TimeResponseData` objects. + plots the individual `TimeResponseData` objects. """ def plot(self, *args, **kwargs): @@ -816,10 +841,10 @@ def _check_convert_array(in_obj, legal_shapes, err_msg_start, squeeze=False, """Helper function for checking array_like parameters. - * Check type and shape of ``in_obj``. - * Convert ``in_obj`` to an array if necessary. - * Change shape of ``in_obj`` according to parameter ``squeeze``. - * If ``in_obj`` is a scalar (number) it is converted to an array with + * Check type and shape of `in_obj`. + * Convert `in_obj` to an array if necessary. + * Change shape of `in_obj` according to parameter `squeeze`. + * If `in_obj` is a scalar (number) it is converted to an array with a legal shape, that is filled with the scalar value. The function raises an exception when it detects an error. @@ -834,8 +859,8 @@ def _check_convert_array(in_obj, legal_shapes, err_msg_start, squeeze=False, The special value "any" means that there can be any number of elements in a certain dimension. - * ``(2, 3)`` describes an array with 2 rows and 3 columns - * ``(2, "any")`` describes an array with 2 rows and any number of + * `(2, 3)` describes an array with 2 rows and 3 columns + * `(2, "any")` describes an array with 2 rows and any number of columns err_msg_start : str @@ -848,7 +873,7 @@ def _check_convert_array(in_obj, legal_shapes, err_msg_start, squeeze=False, array. If False the array's shape is unmodified. For example: - ``array([[1,2,3]])`` is converted to ``array([1, 2, 3])`` + `array([[1,2,3]])` is converted to `array([1, 2, 3])` transpose : bool, optional If True, assume that 2D input arrays are transposed from the standard @@ -858,7 +883,7 @@ def _check_convert_array(in_obj, legal_shapes, err_msg_start, squeeze=False, ------- out_array : array - The checked and converted contents of ``in_obj``. + The checked and converted contents of `in_obj`. """ # convert nearly everything to an array. @@ -958,7 +983,7 @@ def forced_response(sysdata, T=None, U=0., X0=0., transpose=False, params=None, transpose : bool, default=False If True, transpose all input and output arrays (for backward - compatibility with MATLAB and :func:`scipy.signal.lsim`). + compatibility with MATLAB and `scipy.signal.lsim`). interpolate : bool, default=False If True and system is a discrete time system, the input will @@ -989,9 +1014,9 @@ def forced_response(sysdata, T=None, U=0., X0=0., transpose=False, params=None, Returns ------- - results : :class:`TimeResponseData` or :class:`TimeResponseList` - Time response represented as a :class:`TimeResponseData` object or - list of :class:`TimeResponseData` objects containing the following + results : `TimeResponseData` or `TimeResponseList` + Time response represented as a `TimeResponseData` object or + list of `TimeResponseData` objects containing the following properties: * time (array): Time values of the output. @@ -1007,7 +1032,7 @@ def forced_response(sysdata, T=None, U=0., X0=0., transpose=False, params=None, * inputs (array): Input(s) to the system, indexed by input and time. The `plot()` method can be used to create a plot of the time - response(s) (see :func:`time_response_plot` for more information). + response(s) (see `time_response_plot` for more information). See Also -------- @@ -1016,7 +1041,7 @@ def forced_response(sysdata, T=None, U=0., X0=0., transpose=False, params=None, Notes ----- 1. For discrete time systems, the input/output response is computed - using the :func:`scipy.signal.dlsim` function. + using the `scipy.signal.dlsim` function. 2. For continuous time systems, the output is computed using the matrix exponential `exp(A t)` and assuming linear interpolation of the @@ -1030,7 +1055,7 @@ def forced_response(sysdata, T=None, U=0., X0=0., transpose=False, params=None, 4. (legacy) The return value of the system can also be accessed by assigning the function to a tuple of length 2 (time, output) or of - length 3 (time, output, state) if ``return_x`` is ``True``. + length 3 (time, output, state) if `return_x` is `True`. Examples -------- @@ -1066,8 +1091,8 @@ def forced_response(sysdata, T=None, U=0., X0=0., transpose=False, params=None, sys, T, U, X0, params=params, transpose=transpose, return_x=return_x, squeeze=squeeze) else: - raise TypeError('Parameter ``sys``: must be a ``StateSpace`` or' - ' ``TransferFunction``)') + raise TypeError('Parameter `sys`: must be a `StateSpace` or' + ' `TransferFunction`)') # If return_x was not specified, figure out the default if return_x is None: @@ -1105,7 +1130,7 @@ def forced_response(sysdata, T=None, U=0., X0=0., transpose=False, params=None, if isdtime(sys): if T is None: if U is None or (U.ndim == 0 and U == 0.): - raise ValueError('Parameters ``T`` and ``U`` can\'t both be ' + raise ValueError('Parameters `T` and `U` can\'t both be ' 'zero for discrete-time simulation') # Set T to equally spaced samples with same length as U if U.ndim == 1: @@ -1119,12 +1144,12 @@ def forced_response(sysdata, T=None, U=0., X0=0., transpose=False, params=None, U = np.full((n_inputs, T.shape[0]), U) else: if T is None: - raise ValueError('Parameter ``T`` is mandatory for continuous ' + raise ValueError('Parameter `T` is mandatory for continuous ' 'time systems.') # Test if T has shape (n,) or (1, n); T = _check_convert_array(T, [('any',), (1, 'any')], - 'Parameter ``T``: ', squeeze=True, + 'Parameter `T`: ', squeeze=True, transpose=transpose) n_steps = T.shape[0] # number of simulation steps @@ -1132,18 +1157,18 @@ def forced_response(sysdata, T=None, U=0., X0=0., transpose=False, params=None, # equally spaced also implies strictly monotonic increase, dt = (T[-1] - T[0]) / (n_steps - 1) if not np.allclose(np.diff(T), dt): - raise ValueError("Parameter ``T``: time values must be equally " + raise ValueError("Parameter `T`: time values must be equally " "spaced.") # create X0 if not given, test if X0 has correct shape X0 = _check_convert_array(X0, [(n_states,), (n_states, 1)], - 'Parameter ``X0``: ', squeeze=True) + 'Parameter `X0`: ', squeeze=True) # Test if U has correct shape and type legal_shapes = [(n_steps,), (1, n_steps)] if n_inputs == 1 else \ [(n_inputs, n_steps)] U = _check_convert_array(U, legal_shapes, - 'Parameter ``U``: ', squeeze=False, + 'Parameter `U`: ', squeeze=False, transpose=transpose) xout = np.zeros((n_states, n_steps)) @@ -1206,13 +1231,13 @@ def forced_response(sysdata, T=None, U=0., X0=0., transpose=False, params=None, # First make sure that time increment is bigger than sampling time # (with allowance for small precision errors) if dt < sys.dt and not np.isclose(dt, sys.dt): - raise ValueError("Time steps ``T`` must match sampling time") + raise ValueError("Time steps `T` must match sampling time") # Now check to make sure it is a multiple (with check against # sys.dt because floating point mod can have small errors if not (np.isclose(dt % sys.dt, 0) or np.isclose(dt % sys.dt, sys.dt)): - raise ValueError("Time steps ``T`` must be multiples of " + raise ValueError("Time steps `T` must be multiples of " "sampling time") sys_dt = sys.dt @@ -1275,12 +1300,12 @@ def _process_time_response( forced response) or a 3D array indexed by output, input, and time. issiso : bool, optional - If ``True``, process data as single-input, single-output data. - Default is ``False``. + If `True`, process data as single-input, single-output data. + Default is `False`. transpose : bool, optional If True, transpose data (for backward compatibility with MATLAB and - :func:`scipy.signal.lsim`). Default value is False. + `scipy.signal.lsim`). Default value is False. squeeze : bool, optional By default, if a system is single-input, single-output (SISO) then the @@ -1380,12 +1405,12 @@ def step_response( transpose : bool, optional If True, transpose all input and output arrays (for backward - compatibility with MATLAB and :func:`scipy.signal.lsim`). Default + compatibility with MATLAB and `scipy.signal.lsim`). Default value is False. return_x : bool, optional If True, return the state vector when assigning to a tuple (default = - False). See :func:`forced_response` for more details. + False). See `forced_response` for more details. squeeze : bool, optional By default, if a system is single-input, single-output (SISO) then the @@ -1399,9 +1424,9 @@ def step_response( Returns ------- results : `TimeResponseData` or `TimeResponseList` - Time response represented as a :class:`TimeResponseData` object or - list of :class:`TimeResponseData` objects. See - :func:`forced_response` for additional information. + Time response represented as a `TimeResponseData` object or + list of `TimeResponseData` objects. See + `forced_response` for additional information. See Also -------- @@ -1511,7 +1536,7 @@ def step_info(sysdata, T=None, T_num=None, yfinal=None, params=None, TransferFunction), or a time series of step response data. T : array_like or float, optional Time vector, or simulation time duration if a number (time vector is - autocomputed if not given, see :func:`step_response` for more detail). + autocomputed if not given, see `step_response` for more detail). Required, if sysdata is a time series of response data. T_num : int, optional Number of time steps to use in simulation if T is not provided as an @@ -1556,7 +1581,7 @@ def step_info(sysdata, T=None, T_num=None, yfinal=None, params=None, If `sysdata` corresponds to a MIMO system, `S` is a 2D list of dicts. To get the step response characteristics from the j-th input to the - i-th output, access ``S[i][j]`` + i-th output, access `S[i][j]` See Also @@ -1749,7 +1774,7 @@ def initial_response( T : array_like or float, optional Time vector, or simulation time duration if a number (time vector is - autocomputed if not given; see :func:`step_response` for more detail). + autocomputed if not given; see `step_response` for more detail). X0 : array_like or float, optional Initial condition (default = 0). Numbers are converted to constant @@ -1768,12 +1793,12 @@ def initial_response( transpose : bool, optional If True, transpose all input and output arrays (for backward - compatibility with MATLAB and :func:`scipy.signal.lsim`). Default + compatibility with MATLAB and `scipy.signal.lsim`). Default value is False. return_x : bool, optional If True, return the state vector when assigning to a tuple (default = - False). See :func:`forced_response` for more details. + False). See `forced_response` for more details. squeeze : bool, optional By default, if a system is single-input, single-output (SISO) then the @@ -1787,9 +1812,9 @@ def initial_response( Returns ------- results : `TimeResponseData` or `TimeResponseList` - Time response represented as a :class:`TimeResponseData` object or - list of :class:`TimeResponseData` objects. See - :func:`forced_response` for additional information. + Time response represented as a `TimeResponseData` object or + list of `TimeResponseData` objects. See + `forced_response` for additional information. See Also -------- @@ -1866,7 +1891,7 @@ def impulse_response( T : array_like or float, optional Time vector, or simulation time duration if a scalar (time vector is - autocomputed if not given; see :func:`step_response` for more detail). + autocomputed if not given; see `step_response` for more detail). input : int, optional Only compute the impulse response for the listed input. If not @@ -1883,12 +1908,12 @@ def impulse_response( transpose : bool, optional If True, transpose all input and output arrays (for backward - compatibility with MATLAB and :func:`scipy.signal.lsim`). Default + compatibility with MATLAB and `scipy.signal.lsim`). Default value is False. return_x : bool, optional If True, return the state vector when assigning to a tuple (default = - False). See :func:`forced_response` for more details. + False). See `forced_response` for more details. squeeze : bool, optional By default, if a system is single-input, single-output (SISO) then the @@ -1902,9 +1927,9 @@ def impulse_response( Returns ------- results : `TimeResponseData` or `TimeResponseList` - Time response represented as a :class:`TimeResponseData` object or - list of :class:`TimeResponseData` objects. See - :func:`forced_response` for additional information. + Time response represented as a `TimeResponseData` object or + list of `TimeResponseData` objects. See + `forced_response` for additional information. See Also -------- @@ -1955,8 +1980,8 @@ def impulse_response( # Check to make sure there is not a direct term if np.any(sys.D != 0) and isctime(sys): - warnings.warn("System has direct feedthrough: ``D != 0``. The " - "infinite impulse at ``t=0`` does not appear in the " + warnings.warn("System has direct feedthrough: `D != 0`. The " + "infinite impulse at `t=0` does not appear in the " "output.\n" "Results may be meaningless!") diff --git a/control/xferfcn.py b/control/xferfcn.py index 5cb0e1e35..1732eff4e 100644 --- a/control/xferfcn.py +++ b/control/xferfcn.py @@ -7,8 +7,8 @@ """Transfer function class and related functions. -This module contains the :class:`TransferFunction` class and also -functions that operate on transfer functions. +This module contains the `TransferFunction` class and also functions +that operate on transfer functions. """ @@ -52,7 +52,7 @@ class TransferFunction(LTI): The TransferFunction class is used to represent systems in transfer function form. Transfer functions are usually created with the - :func:`~control.tf` factory function. + `tf` factory function. Parameters ---------- @@ -95,7 +95,7 @@ class TransferFunction(LTI): ----- The numerator and denominator polynomials are stored as 2D ndarrays with each element containing a 1D ndarray of coefficients. These data - structures can be retrieved using ``num_array`` and ``den_array``. For + structures can be retrieved using `num_array` and `den_array`. For example, >>> sys.num_array[2, 5] # doctest: +SKIP @@ -105,14 +105,14 @@ class TransferFunction(LTI): the numerators and denominators can have different numbers of coefficients in each entry.) - The attributes ``num_list`` and ``den_list`` are properties that return + The attributes `num_list` and `den_list` are properties that return 2D nested lists containing MIMO numerator and denominator coefficients. For example, >>> sys.num_list[2][5] # doctest: +SKIP For legacy purposes, this list-based representation can also be - obtained using ``num`` and ``den``. + obtained using `num` and `den`. A discrete time transfer function is created by specifying a nonzero 'timebase' dt when the system is constructed: @@ -129,11 +129,11 @@ class TransferFunction(LTI): a system with timebase `None` can be combined with a system having any timebase; the result will have the timebase of the latter system. The default value of dt can be changed by changing the value of - ``control.config.defaults['control.default_dt']``. + `control.config.defaults['control.default_dt']`. A transfer function is callable and returns the value of the transfer function evaluated at a point in the complex plane. See - :meth:`~control.TransferFunction.__call__` for a more detailed description. + `TransferFunction.__call__` for a more detailed description. Subsystems corresponding to selected input/output pairs can be created by indexing the transfer function:: @@ -145,7 +145,7 @@ class TransferFunction(LTI): of the signals can be used and will be replaced with the equivalent signal offsets. - The TransferFunction class defines two constants ``s`` and ``z`` that + The TransferFunction class defines two constants `s` and `z` that represent the differentiation and delay operators in continuous and discrete time. These can be used to create variables that allow algebraic creation of transfer functions. For example, @@ -167,7 +167,7 @@ def __init__(self, *args, **kwargs): TransferFunction(sys), where sys is a TransferFunction object (continuous or discrete). - See :class:`TransferFunction` and :func:`tf` for more information. + See `TransferFunction` and `tf` for more information. """ # @@ -340,9 +340,9 @@ def __call__(self, x, squeeze=None, warn_infinite=True): self.noutputs` number of outputs. To evaluate at a frequency omega in radians per second, enter - ``x = omega * 1j``, for continuous-time systems, or - ``x = exp(1j * omega * dt)`` for discrete-time systems. Or use - :meth:`TransferFunction.frequency_response`. + `x = omega * 1j`, for continuous-time systems, or + `x = exp(1j * omega * dt)` for discrete-time systems. Or use + `TransferFunction.frequency_response`. Parameters ---------- @@ -367,7 +367,7 @@ def __call__(self, x, squeeze=None, warn_infinite=True): squeeze is not True, the shape of the array matches the shape of omega. If the system is not SISO or squeeze is False, the first two dimensions of the array are indices for the output and input - and the remaining dimensions match omega. If ``squeeze`` is True + and the remaining dimensions match omega. If `squeeze` is True then single-dimensional axes are removed. """ @@ -381,7 +381,7 @@ def horner(self, x, warn_infinite=True): Evaluates `sys(x)` where `x` is `s` for continuous-time systems and `z` for discrete-time systems. - Expects inputs and outputs to be formatted correctly. Use ``sys(x)`` + Expects inputs and outputs to be formatted correctly. Use `sys(x)` for a more user-friendly interface. """ @@ -822,8 +822,8 @@ def freqresp(self, omega): .. deprecated::0.9.0 Method has been given the more pythonic name - :meth:`TransferFunction.frequency_response`. Or use - :func:`freqresp` in the MATLAB compatibility module. + `TransferFunction.frequency_response`. Or use + `freqresp` in the MATLAB compatibility module. """ warn("TransferFunction.freqresp(omega) will be removed in a " "future release of python-control; use " @@ -931,14 +931,14 @@ def minreal(self, tol=None): return TransferFunction(num, den, self.dt) def returnScipySignalLTI(self, strict=True): - """Return a 2D array of :class:`scipy.signal.lti` objects. + """Return a 2D array of `scipy.signal.lti` objects. For instance, >>> out = tfobject.returnScipySignalLTI() # doctest: +SKIP >>> out[3, 5] # doctest: +SKIP - is a :class:`scipy.signal.lti` object corresponding to the + is a `scipy.signal.lti` object corresponding to the transfer function from the 6th input to the 4th output. Parameters @@ -949,13 +949,13 @@ def returnScipySignalLTI(self, strict=True): continuous (0) or discrete (True or > 0). False: if `tfobject.dt` is None, continuous time - :class:`scipy.signal.lti` objects are returned + `scipy.signal.lti` objects are returned Returns ------- - out : list of list of :class:`scipy.signal.TransferFunction` - Continuous time (inheriting from :class:`scipy.signal.lti`) - or discrete time (inheriting from :class:`scipy.signal.dlti`) + out : list of list of `scipy.signal.TransferFunction` + Continuous time (inheriting from `scipy.signal.lti`) + or discrete time (inheriting from `scipy.signal.dlti`) SISO objects. """ if strict and self.dt is None: @@ -1167,7 +1167,7 @@ def sample(self, Ts, method='zoh', alpha=None, prewarp_frequency=None, alpha : float within [0, 1] The generalized bilinear transformation weighting parameter, which should only be specified with method="gbt", and is ignored - otherwise. See :func:`scipy.signal.cont2discrete`. + otherwise. See `scipy.signal.cont2discrete`. prewarp_frequency : float within [0, infinity) The frequency [rad/s] at which to match with the input continuous- time system's magnitude and phase (the gain=1 crossover frequency, @@ -1194,7 +1194,7 @@ def sample(self, Ts, method='zoh', alpha=None, prewarp_frequency=None, ---------------- inputs : int, list of str or None, optional Description of the system inputs. If not specified, the origional - system inputs are used. See :class:`InputOutputSystem` for more + system inputs are used. See `InputOutputSystem` for more information. outputs : int, list of str or None, optional Description of the system outputs. Same format as `inputs`. @@ -1203,7 +1203,7 @@ def sample(self, Ts, method='zoh', alpha=None, prewarp_frequency=None, ----- 1. Available only for SISO systems - 2. Uses :func:`scipy.signal.cont2discrete` + 2. Uses `scipy.signal.cont2discrete` Examples -------- @@ -1295,7 +1295,7 @@ def _isstatic(self): #: Differentation operator (continuous time). #: - #: The ``s`` constant can be used to create continuous time transfer + #: The `s` constant can be used to create continuous time transfer #: functions using algebraic expressions. #: #: Examples @@ -1308,7 +1308,7 @@ def _isstatic(self): #: Delay operator (discrete time). #: - #: The ``z`` constant can be used to create discrete time transfer + #: The `z` constant can be used to create discrete time transfer #: functions using algebraic expressions. #: #: Examples @@ -1579,11 +1579,11 @@ def tf(*args, **kwargs): The function accepts either 1, 2, or 3 parameters: - ``tf(sys)`` + `tf(sys)` Convert a linear system into transfer function form. Always creates a new system, even if sys is already a TransferFunction object. - ``tf(num, den)`` + `tf(num, den)` Create a transfer function system from its numerator and denominator polynomial coefficients. @@ -1595,16 +1595,16 @@ def tf(*args, **kwargs): for details see note below). If the denominator for all transfer function is the same, `den` can be specified as a 1D array. - ``tf(num, den, dt)`` + `tf(num, den, dt)` Create a discrete time transfer function system; dt can either be a positive number indicating the sampling time or 'True' if no specific timebase is given. - ``tf([[G11, ..., G1m], ..., [Gp1, ..., Gpm]][, dt])`` + `tf([[G11, ..., G1m], ..., [Gp1, ..., Gpm]][, dt])` Create a pxm MIMO system from SISO transfer functions Gij. See - :func:`combine_tf` for more details. + `combine_tf` for more details. - ``tf('s')`` or ``tf('z')`` + `tf('s')` or `tf('z')` Create a transfer function representing the differential operator ('s') or delay operator ('z'). @@ -1662,13 +1662,13 @@ def tf(*args, **kwargs): Notes ----- MIMO transfer functions are created by passing a 2D array of coeffients: - ``num[i][j]`` contains the polynomial coefficients of the numerator + `num[i][j]` contains the polynomial coefficients of the numerator for the transfer function from the (j+1)st input to the (i+1)st output, - and ``den[i][j]`` works the same way. + and `den[i][j]` works the same way. - The list ``[2, 3, 4]`` denotes the polynomial :math:`2s^2 + 3s + 4`. + The list `[2, 3, 4]` denotes the polynomial :math:`2s^2 + 3s + 4`. - The special forms ``tf('s')`` and ``tf('z')`` can be used to create + The special forms `tf('s')` and `tf('z')` can be used to create transfer functions for differentiation and unit delays. Examples @@ -1779,7 +1779,7 @@ def zpk(zeros, poles, gain, *args, **kwargs): List of strings that name the individual signals. If this parameter is not given or given as `None`, the signal names will be of the form `s[i]` (where `s` is one of `u`, `y`, or `x`). See - :class:`InputOutputSystem` for more information. + `InputOutputSystem` for more information. name : string, optional System name (used for specifying signals). If unspecified, a generic name is generated with a unique integer id. @@ -1815,15 +1815,15 @@ def ss2tf(*args, **kwargs): The function accepts either 1 or 4 parameters: - ``ss2tf(sys)`` + `ss2tf(sys)` Convert a linear system from state space into transfer function form. Always creates a new system. - ``ss2tf(A, B, C, D)`` + `ss2tf(A, B, C, D)` Create a transfer function system from the matrices of its state and output equations. - For details see: :func:`tf` + For details see: `tf` Parameters ---------- @@ -1838,7 +1838,7 @@ def ss2tf(*args, **kwargs): D : array_like or string Feedthrough matrix. **kwargs : keyword arguments - Additional arguments passed to :func:`tf` (e.g., signal names). + Additional arguments passed to `tf` (e.g., signal names). Returns ------- diff --git a/doc/descfcn.rst b/doc/descfcn.rst index 4ab6c750e..b08267974 100644 --- a/doc/descfcn.rst +++ b/doc/descfcn.rst @@ -87,6 +87,9 @@ Module classes and functions .. autosummary:: :template: custom-class-template.rst + describing_function + describing_function_response + describing_function_plot DescribingFunctionNonlinearity friction_backlash_nonlinearity relay_hysteresis_nonlinearity diff --git a/doc/iosys.rst b/doc/iosys.rst index f63ba3061..b4fac9ddb 100644 --- a/doc/iosys.rst +++ b/doc/iosys.rst @@ -233,7 +233,7 @@ The input to the controller is `u`, consisting of the vector of hare and lynx populations followed by the desired lynx population. To connect the controller to the predatory-prey model, we use the -:func:`~control.interconnect` function: +:func:`interconnect` function: .. code-block:: python @@ -298,8 +298,8 @@ is done: given vector is non-zero, a warning is issued.) Similar processing is done for input time series, used for the -:func:`~control.input_output_response` and -:func:`~control.forced_response` commands, with the following +:func:`input_output_response` and +:func:`forced_response` commands, with the following additional feature: * Time series elements are broadcast to match the number of time points @@ -355,7 +355,7 @@ the number of time points. Summing junction ---------------- -The :func:`~control.summing_junction` function can be used to create an +The :func:`summing_junction` function can be used to create an input/output system that takes the sum of an arbitrary number of inputs. For example, to create an input/output system that takes the sum of three inputs, use the command @@ -392,14 +392,14 @@ will produce an input/output block that implements `e[0] = r[0] - y[0]` and Automatic connections using signal names ---------------------------------------- -The :func:`~control.interconnect` function allows the interconnection of +The :func:`interconnect` function allows the interconnection of multiple systems by using signal names of the form `sys.signal`. In many situations, it can be cumbersome to explicitly connect all of the appropriate inputs and outputs. As an alternative, if the `connections` keyword is -omitted, the :func:`~control.interconnect` function will connect all signals +omitted, the :func:`interconnect` function will connect all signals of the same name to each other. This can allow for simplified methods of interconnecting systems, especially when combined with the -:func:`~control.summing_junction` function. For example, the following code +:func:`summing_junction` function. For example, the following code will create a unity gain, negative feedback system:: P = ct.tf([1], [1, 0], inputs='u', outputs='y') @@ -410,7 +410,7 @@ will create a unity gain, negative feedback system:: If a signal name appears in multiple outputs then that signal will be summed when it is interconnected. Similarly, if a signal name appears in multiple inputs then all systems using that signal name will receive the same input. -The :func:`~control.interconnect` function will generate an error if a signal +The :func:`interconnect` function will generate an error if a signal listed in `inplist` or `outlist` (corresponding to the inputs and outputs of the interconnected system) is not found, but inputs and outputs of individual systems that are not connected to other systems are left @@ -422,7 +422,7 @@ Advanced specification of signal names In addition to manual specification of signal names and automatic connection of signals with the same name, the -:func:`~control.interconnect` has a variety of other mechanisms +:func:`interconnect` has a variety of other mechanisms available for specifying signal names. The following forms are recognized for the `connections`, `inplist`, and `outlist` parameters:: @@ -517,7 +517,7 @@ of an individual system are used in a given specification:: ) And finally, since we have named the signals throughout the system in a -consistent way, we could let :func:`~control.interconnect` do all of the +consistent way, we could let :func:`interconnect` do all of the work:: clsys5 = ct.interconnect( @@ -526,7 +526,7 @@ work:: Various other simplifications are possible, but it can sometimes be complicated to debug error message when things go wrong. Setting -`debug=True` when calling :func:`~control.interconnect` prints out +`debug=True` when calling :func:`interconnect` prints out information about how the arguments are processed that may be helpful in understanding what is going wrong. @@ -534,7 +534,7 @@ in understanding what is going wrong. Automated creation of state feedback systems -------------------------------------------- -The :func:`~control.create_statefbk_iosystem` function can be used to +The :func:`create_statefbk_iosystem` function can be used to create an I/O system consisting of a state feedback gain (with optional integral action and gain scheduling) and an estimator. A basic state feedback controller of the form diff --git a/doc/optimal.rst b/doc/optimal.rst index f15422ab7..1fb15436f 100644 --- a/doc/optimal.rst +++ b/doc/optimal.rst @@ -316,11 +316,12 @@ solutions do not seem close to optimal, here are a few things to try: `input_output_response` (as done above). * Use a smooth basis: as an alternative to parameterizing the optimal - control inputs using the value of the control at the listed time points, - you can specify a set of basis functions using the `basis` keyword in - :func:`solve_ocp` and then parameterize the controller by linear - combination of the basis functions. The :mod:`!control.flatsys` module - defines several sets of basis functions that can be used. + control inputs using the value of the control at the listed time + points, you can specify a set of basis functions using the `basis` + keyword in :func:`solve_ocp` and then parameterize the controller by + linear combination of the basis functions. The :ref:`flatsys + sub-package ` defines several sets of basis + functions that can be used. * Tweak the optimizer: by using the `minimize_method`, `minimize_options`, and `minimize_kwargs` keywords in :func:`solve_ocp`, you can From 9edd4228807af07f6154714ed10be1f700ef2b01 Mon Sep 17 00:00:00 2001 From: Richard Murray Date: Wed, 1 Jan 2025 12:27:31 -0800 Subject: [PATCH 52/93] fix up sub-package/module references --- control/phaseplot.py | 22 +++++++++------------- doc/functions.rst | 31 ++++++++++++++++++++++++++++++- 2 files changed, 39 insertions(+), 14 deletions(-) diff --git a/control/phaseplot.py b/control/phaseplot.py index ae9182432..a684aa9bc 100644 --- a/control/phaseplot.py +++ b/control/phaseplot.py @@ -7,19 +7,15 @@ """Generate 2D phase portraits. This module contains functions for generating 2D phase plots. The base -function for creating phase plane portraits is -`phase_plane_plot`, which generates a phase plane -portrait for a 2 state I/O system (with no inputs). In addition, -several other functions are available to create customized phase plane -plots: - -* `phaseplot.boxgrid`: Generate a list of points along the edge of a box -* `phaseplot.circlegrid`: Generate list of points around a circle -* `phaseplot.equilpoints`: Plot equilibrium points in the phase plane -* `phaseplot.meshgrid`: Generate a list of points forming a mesh -* `phaseplot.separatrices`: Plot separatrices in the phase plane -* `phaseplot.streamlines`: Plot stream lines in the phase plane -* `phaseplot.vectorfield`: Plot a vector field in the phase plane +function for creating phase plane portraits is `~control.phase_plane_plot`, +which generates a phase plane portrait for a 2 state I/O system (with no +inputs). + +The docstring examples assume the following import commands:: + + >>> import numpy as np + >>> import control as ct + >>> import control.phaseplot as pp """ diff --git a/doc/functions.rst b/doc/functions.rst index b2d7b5045..677736149 100644 --- a/doc/functions.rst +++ b/doc/functions.rst @@ -80,7 +80,17 @@ Time response time_response_plot combine_time_responses -Additional functions for customizing phase plots: + +Phase plane plots +----------------- + +.. automodule:: control.phaseplot + :no-members: + :no-inherited-members: + :no-special-members: + +.. Reset current module to main package to force reference to use prefix +.. currentmodule:: control .. autosummary:: :toctree: generated/ @@ -210,6 +220,7 @@ Nonlinear system support find_operating_point linearize + Describing functions -------------------- .. autosummary:: @@ -220,8 +231,17 @@ Describing functions relay_hysteresis_nonlinearity saturation_nonlinearity + Differentially flat systems --------------------------- +.. automodule:: control.flatsys + :no-members: + :no-inherited-members: + :no-special-members: + +.. Reset current module to main package to force reference to use prefix +.. currentmodule:: control + .. autosummary:: :toctree: generated/ @@ -229,8 +249,17 @@ Differentially flat systems flatsys.point_to_point flatsys.solve_flat_ocp + Optimal control --------------- +.. automodule:: control.optimal + :no-members: + :no-inherited-members: + :no-special-members: + +.. Reset current module to main package to force reference to use prefix +.. currentmodule:: control + .. autosummary:: :toctree: generated/ From 88643853644213ecde1d0f8685dfd9054e0aa32d Mon Sep 17 00:00:00 2001 From: Richard Murray Date: Thu, 2 Jan 2025 08:16:01 -0800 Subject: [PATCH 53/93] fix up class docstrings + See Also --- control/ctrlplot.py | 4 +- control/descfcn.py | 2 +- control/flatsys/basis.py | 2 +- control/flatsys/flatsys.py | 4 + control/frdata.py | 20 ++-- control/freqplot.py | 2 +- control/grid.py | 2 +- control/iosys.py | 2 +- control/lti.py | 24 ++-- control/matlab/wrappers.py | 2 +- control/modelsimp.py | 3 +- control/nlsys.py | 76 ++++++++++++- control/optimal.py | 32 +++--- control/phaseplot.py | 2 +- control/pzmap.py | 24 +--- control/robust.py | 2 + control/statesp.py | 14 ++- control/sysnorm.py | 6 +- control/tests/docstrings_test.py | 36 +++++- control/timeresp.py | 184 +++++++++++++------------------ control/xferfcn.py | 13 +-- 21 files changed, 254 insertions(+), 202 deletions(-) diff --git a/control/ctrlplot.py b/control/ctrlplot.py index 6bd3ae174..bcb5e3b3d 100644 --- a/control/ctrlplot.py +++ b/control/ctrlplot.py @@ -121,7 +121,7 @@ # Control figure # -class ControlPlot(object): +class ControlPlot(): """A class for representing control figures. This class is used as the return type for control plotting functions. @@ -134,7 +134,7 @@ class ControlPlot(object): a number of lines that represent the data for the plot. There may also be a legend present in one or more of the axes. - Attributes + Parameters ---------- lines : array of list of `matplotlib:Line2D` Array of Line2D objects for each line in the plot. Generally, the diff --git a/control/descfcn.py b/control/descfcn.py index bed94893b..2862c64ac 100644 --- a/control/descfcn.py +++ b/control/descfcn.py @@ -224,7 +224,7 @@ class is used by the `describing_function_response` response of the linear systems and the describing function for the nonlinear element. - Attributes + Parameters ---------- response : `FrequencyResponseData` Frequency response of the linear system component of the system. diff --git a/control/flatsys/basis.py b/control/flatsys/basis.py index c6ea3af9e..a9e028bb6 100644 --- a/control/flatsys/basis.py +++ b/control/flatsys/basis.py @@ -24,7 +24,7 @@ class BasisFamily: each flat output (nvars = None) or a different variable for different flat outputs (nvars > 0). - Attributes + Parameters ---------- N : int Order of the basis set. diff --git a/control/flatsys/flatsys.py b/control/flatsys/flatsys.py index 2434b6723..e9c73b7dc 100644 --- a/control/flatsys/flatsys.py +++ b/control/flatsys/flatsys.py @@ -50,6 +50,10 @@ class FlatSystem(NonlinearIOSystem): name : string, optional System name. + See Also + -------- + flatsys + Notes ----- The class must implement two functions: diff --git a/control/frdata.py b/control/frdata.py index b00b01a41..85fa86d92 100644 --- a/control/frdata.py +++ b/control/frdata.py @@ -112,7 +112,7 @@ class constructor, using the `frd` factory function, or See Also -------- - frd + frd, InputOutputSystem, TransferFunction, TimeResponseData Notes ----- @@ -189,17 +189,17 @@ def __init__(self, *args, **kwargs): Construct a frequency response data (FRD) object. - The default constructor is FrequencyResponseData(d, w), where w is - an iterable of frequency points, and d is the matching frequency - data. If d is a single list, 1D array, or tuple, a SISO system - description is assumed. d can also be a 2D array, in which case a - MIMO response is created. To call the copy constructor, call - FrequencyResponseData(sys), where sys is a FRD object. The - timebase for the frequency response can be provided using an - optional third argument or the 'dt' keyword. + The default constructor is `FrequencyResponseData(d, w)`, where `w` + is an iterable of frequency points and `d` is the matching + frequency data. If `d` is a single list, 1D array, or tuple, a + SISO system description is assumed. `d` can also be a 2D array, in + which case a MIMO response is created. To call the copy + constructor, call `FrequencyResponseData(sys)`, where `sys` is a + FRD object. The timebase for the frequency response can be + provided using an optional third argument or the `dt` keyword. To construct frequency response data for an existing LTI object, - other than an FRD, call FrequencyResponseData(sys, omega). This + other than an FRD, call `FrequencyResponseData(sys, omega)`. This functionality can also be obtained using `frequency_response` (which has additional options available). diff --git a/control/freqplot.py b/control/freqplot.py index 7b568abec..52e016c5e 100644 --- a/control/freqplot.py +++ b/control/freqplot.py @@ -1107,7 +1107,7 @@ class NyquistResponseData: response object can be used to obtain information about the Nyquist response or to generate a Nyquist plot. - Attributes + Parameters ---------- count : integer Number of encirclements of the -1 point by the Nyquist curve for diff --git a/control/grid.py b/control/grid.py index 145fbb4ce..2c6b31389 100644 --- a/control/grid.py +++ b/control/grid.py @@ -22,7 +22,7 @@ from .iosys import isdtime -class FormatterDMS(object): +class FormatterDMS(): '''Transforms angle ticks to damping ratios''' def __call__(self, direction, factor, values): angles_deg = np.asarray(values)/factor diff --git a/control/iosys.py b/control/iosys.py index 6da367776..19757fcf0 100644 --- a/control/iosys.py +++ b/control/iosys.py @@ -103,7 +103,7 @@ def __getitem__(self, key): return super().__getitem__(self._parse_key(key)) -class InputOutputSystem(object): +class InputOutputSystem(): """A class for representing input/output systems. The InputOutputSystem class allows (possibly nonlinear) input/output diff --git a/control/lti.py b/control/lti.py index a990bb56c..8c1629195 100644 --- a/control/lti.py +++ b/control/lti.py @@ -30,11 +30,9 @@ class LTI(InputOutputSystem): contains the number of inputs and outputs, and the timebase (dt) for the system. This function is not generally called directly by the user. - When two LTI systems are combined, their timebases much match. A system - with timebase None can be combined with a system having a specified - timebase, and the result will have the timebase of the latter system. - - Note: dt processing has been moved to the InputOutputSystem class. + See Also + -------- + InputOutputSystem, StateSpace, TransferFunction, FrequencyResponseData """ def __init__(self, inputs=1, outputs=1, states=None, name=None, **kwargs): @@ -264,9 +262,7 @@ def poles(sys): See Also -------- - zeros - TransferFunction.poles - StateSpace.poles + zeros, StateSpace.poles, TransferFunction.poles """ @@ -289,9 +285,7 @@ def zeros(sys): See Also -------- - poles - StateSpace.zeros - TransferFunction.zeros + poles, StateSpace.zeros, TransferFunction.zeros """ @@ -319,7 +313,7 @@ def damp(sys, doprint=True): See Also -------- - pole + poles Notes ----- @@ -396,8 +390,7 @@ def evalfr(sys, x, squeeze=None): See Also -------- - freqresp - bode + frequency_response, bode_plot Notes ----- @@ -479,8 +472,7 @@ def frequency_response( See Also -------- - evalfr - bode_plot + evalfr, bode_plot Notes ----- diff --git a/control/matlab/wrappers.py b/control/matlab/wrappers.py index 12d425d24..9738dcf5b 100644 --- a/control/matlab/wrappers.py +++ b/control/matlab/wrappers.py @@ -415,7 +415,7 @@ def connect(*args): See Also -------- - append, feedback, interconnect, negate, parallel, series + append, feedback, connect, negate, parallel, series Examples -------- diff --git a/control/modelsimp.py b/control/modelsimp.py index b3f89b072..00281b5df 100644 --- a/control/modelsimp.py +++ b/control/modelsimp.py @@ -138,8 +138,7 @@ def model_reduction( See Also -------- - balanced_reduction : Eliminate states using Hankel singular values. - minimal_realization : Eliminate unreachable or unobservable states. + balanced_reduction, minimal_realization Notes ----- diff --git a/control/nlsys.py b/control/nlsys.py index d7bc90661..d31a441c2 100644 --- a/control/nlsys.py +++ b/control/nlsys.py @@ -92,7 +92,7 @@ class NonlinearIOSystem(InputOutputSystem): See Also -------- - InputOutputSystem : Input/output system class. + nlsys, InputOutputSystem Notes ----- @@ -571,6 +571,52 @@ class InterconnectedSystem(NonlinearIOSystem): interconnected I/O system since it performs additional argument processing and checking. + Parameters + ---------- + syslist : list of NonlinearIOsystem + List of state space systems to interconnect. + connections : list of connections + Description of the internal connections between the subsystem. See + `interconnect` for details. + inplist, outlist : list of input and output connections + Description of the inputs and outputs for the overall system. See + `interconnect` for details. + inputs, outputs, states : int, list of str or None, optional + Description of the system inputs, outputs, and states. See + `control.nlsys` for more details. + params : dict, optional + Parameter values for the systems. Passed to the evaluation functions + for the system as default values, overriding internal defaults. + connection_type : str + Type of connection: 'explicit' (or `None`) for explicitly listed + set of connections, 'implicit' for connections made via signal names. + + Attributes + ---------- + ninputs, noutputs, nstates : int + Number of input, output and state variables. + shape : tuple + 2-tuple of I/O system dimension, (noutputs, ninputs). + name : string, optional + System name. + connect_map : 2D array + Mapping of subsystem outputs to subsystem inputs. + input_map : 2D array + Mapping of system inputs to subsystem inputs. + output_map : 2D array + Mapping of (stacked) subsystem outputs and inputs to system outputs. + input_labels, output_labels, state_labels : list of str + Names for the input, output, and state variables. + input_offset, output_offset, state_offset : list of int + Offset to the subsystem inputs, outputs, and states in the overal + system input, output, and state arrays. + syslist_index : dict + Index of the subsytem with key given by the name of the subsystem. + + See Also + -------- + interconnect, NonlinearIOSystem, LinearICSystem + """ def __init__(self, syslist, connections=None, inplist=None, outlist=None, params=None, warn_duplicate=None, connection_type=None, @@ -1764,7 +1810,7 @@ class OperatingPoint(): `find_operating_point` function and as an input to the `linearize` function. - Attributes + Parameters ---------- states : array State vector at the operating point. @@ -1774,6 +1820,16 @@ class OperatingPoint(): Output vector at the operating point. result : `scipy.optimize.OptimizeResult`, optional Result from the `scipy.optimize.root` function, if available. + return_outputs, return_result : bool, optional + If set to `True`, then when accessed a tuple the output values + and/or result of the root finding function will be returned. + + Notes + ----- + In addition to accessing the elements of the operating point as + attributes, if accessed as a list then the object will return `(x0, + u0[, y0, res])`, where `y0` and `res` are returned depending on the + `return_outputs` and `return_result` parameters. """ def __init__( @@ -2226,8 +2282,8 @@ def interconnect( Parameters ---------- - syslist : list of InputOutputSystems - The list of input/output systems to be connected. + syslist : list of NonlinearIOSystems + The list of (state-based) input/output systems to be connected. connections : list of connections, optional Description of the internal connections between the subsystems: @@ -2342,6 +2398,17 @@ def interconnect( System name (used for specifying signals). If unspecified, a generic name is generated with a unique integer id. + Returns + ------- + sys : InterconnectedSystem + `NonlinearIOSystem` consisting of the interconnected subsystems. + + Other Parameters + ---------------- + input_prefix, output_prefix, state_prefix : string, optional + Set the prefix for input, output, and state signals. Defaults = + 'u', 'y', 'x'. + check_unused : bool, optional If True, check for unused sub-system signals. This check is not done if connections is False, and neither input nor output @@ -2381,7 +2448,6 @@ def interconnect( Print out information about how signals are being processed that may be useful in understanding why something is not working. - Examples -------- >>> P = ct.rss(2, 2, 2, strictly_proper=True, name='P') diff --git a/control/optimal.py b/control/optimal.py index 0af09409e..074bfe120 100644 --- a/control/optimal.py +++ b/control/optimal.py @@ -87,11 +87,13 @@ class OptimalControlProblem(): Use :py`logging.basicConfig` to enable logging output (e.g., to a file). - Returns - ------- - ocp : OptimalControlProblem - Optimal control problem object, to be used in computing optimal - controllers. + Attributes + ---------- + constraint: list of SciPy constraint objects + List of `~scipy.optimize.LinearConstraint` or + `~scipy.optimize.NonlinearConstraint` objects. + constraint_lb, constrain_ub, eqconst_value : list of float + List of constraint bounds. Other Parameters ---------------- @@ -902,7 +904,7 @@ class OptimalControlResult(sp.optimize.OptimizeResult): This class is a subclass of `scipy.optimize.OptimizeResult` with additional attributes associated with solving optimal control problems. - Attributes + Parameters ---------- inputs : ndarray The optimal inputs associated with the optimal control problem. @@ -919,7 +921,7 @@ class OptimalControlResult(sp.optimize.OptimizeResult): Number of system simulations and evaluations of the cost function, (inequality) constraint function, and equality constraint function performed during the optimzation. - {cost, constraint, eqconst}_process_time : float + cost_process_time, constraint_process_time, eqconst_process_time : float If logging was enabled, the amount of time spent evaluating the cost and constraint functions. @@ -1280,11 +1282,13 @@ class OptimalEstimationProblem(): specified, defaults to the complement of the control indicies (see also notes below). - Returns - ------- - oep : OptimalEstimationProblem - Optimal estimation problem object, to be used in computing optimal - estimates. + Attributes + ---------- + constraint: list of SciPy constraint objects + List of `~scipy.optimize.LinearConstraint` or + `~scipy.optimize.NonlinearConstraint` objects. + constraint_lb, constrain_ub, eqconst_value : list of float + List of constraint bounds. Other Parameters ---------------- @@ -1892,7 +1896,7 @@ class OptimalEstimationResult(sp.optimize.OptimizeResult): additional attributes associated with solving optimal estimation problems. - Attributes + Parameters ---------- states : ndarray Estimated state trajectory. @@ -1910,7 +1914,7 @@ class OptimalEstimationResult(sp.optimize.OptimizeResult): Number of system simulations and evaluations of the cost function, (inequality) constraint function, and equality constraint function performed during the optimzation. - {cost, constraint, eqconst}_process_time : float + cost_process_time, constraint_process_time, eqconst_process_time : float If logging was enabled, the amount of time spent evaluating the cost and constraint functions. diff --git a/control/phaseplot.py b/control/phaseplot.py index a684aa9bc..157709bd7 100644 --- a/control/phaseplot.py +++ b/control/phaseplot.py @@ -1086,7 +1086,7 @@ def phase_plot(odefun, X=None, Y=None, scale=1, X0=None, T=None, See Also -------- - box_grid : construct box-shaped grid of initial conditions + box_grid """ # Generate a deprecation warning diff --git a/control/pzmap.py b/control/pzmap.py index 3ef62a8bc..423b5da59 100644 --- a/control/pzmap.py +++ b/control/pzmap.py @@ -65,7 +65,7 @@ class PoleZeroData: system poles and zeros, as well as the gains and loci for root locus diagrams. - Attributes + Parameters ---------- poles : ndarray 1D array of system poles. @@ -73,11 +73,11 @@ class PoleZeroData: 1D array of system zeros. gains : ndarray, optional 1D array of gains for root locus plots. - loci : ndarray, optiona + loci : ndarray, optional 2D array of poles, with each row corresponding to a gain. sysname : str, optional System name. - sys : StateSpace or TransferFunction + sys : `StateSpace` or `TransferFunction`, optional System corresponding to the data. sort_loci : bool, optional Set to False to turn off sorting of loci into unique branches. @@ -86,24 +86,6 @@ class PoleZeroData: def __init__( self, poles, zeros, gains=None, loci=None, dt=None, sysname=None, sys=None, sort_loci=True): - """Create a pole/zero map object. - - Parameters - ---------- - poles : ndarray - 1D array of system poles. - zeros : ndarray - 1D array of system zeros. - gains : ndarray, optional - 1D array of gains for root locus plots. - loci : ndarray, optiona - 2D array of poles, with each row corresponding to a gain. - sysname : str, optional - System name. - sys : StateSpace or TransferFunction - System corresponding to the data. - - """ from .rlocus import _RLSortRoots self.poles = poles self.zeros = zeros diff --git a/control/robust.py b/control/robust.py index 2101445c1..c42d5b448 100644 --- a/control/robust.py +++ b/control/robust.py @@ -201,6 +201,7 @@ def _size_as_needed(w, wname, n): See Also -------- augw + """ from . import append, ss if w is not None: @@ -252,6 +253,7 @@ def augw(g, w1=None, w2=None, w3=None): See Also -------- h2syn, hinfsyn, mixsyn + """ from . import append, connect, ss diff --git a/control/statesp.py b/control/statesp.py index cce13c131..534310808 100644 --- a/control/statesp.py +++ b/control/statesp.py @@ -96,6 +96,10 @@ class StateSpace(NonlinearIOSystem, LTI): name : string, optional System name. + See Also + -------- + ss, InputOutputSystem, NonlinearIOSystem + Notes ----- The main data members in the `StateSpace` class are the A, B, C, and D @@ -1798,8 +1802,7 @@ def tf2io(*args, **kwargs): See Also -------- - ss2io - tf2ss + ss2io, tf2ss Examples -------- @@ -1873,9 +1876,7 @@ def tf2ss(*args, **kwargs): See Also -------- - ss - tf - ss2tf + ss, tf, ss2tf Notes ----- @@ -1951,7 +1952,8 @@ def linfnorm(sys, tol=1e-10): See Also -------- - slycot.ab13dd : the Slycot routine linfnorm that does the calculation + slycot.ab13dd + """ if ab13dd is None: raise ControlSlycot("Can't find slycot module 'ab13dd'") diff --git a/control/sysnorm.py b/control/sysnorm.py index b25bbae8c..58ca93cf9 100644 --- a/control/sysnorm.py +++ b/control/sysnorm.py @@ -21,10 +21,10 @@ def _h2norm_slycot(sys, print_warning=True): See Also -------- - `slycot.ab13bd` : the Slycot routine that does the calculation - https://github.com/python-control/Slycot/issues/199 : Post on issue with `ab13bf` - + slycot.ab13bd + """ + # See: https://github.com/python-control/Slycot/issues/199 try: from slycot import ab13bd except ImportError: diff --git a/control/tests/docstrings_test.py b/control/tests/docstrings_test.py index 64c62b815..f15803d66 100644 --- a/control/tests/docstrings_test.py +++ b/control/tests/docstrings_test.py @@ -112,6 +112,11 @@ def test_parameter_docs(module, prefix): if inspect.isclass(obj): _info(f"Checking class {name}", 1) _check_numpydoc_style(obj, doc) + + # Make sure there is no Returns section + if doc["Returns"] != []: + _fail(f"Class {name} should not have Returns section") + # Check member functions within the class test_parameter_docs(obj, prefix + name + '.') continue @@ -316,7 +321,7 @@ def test_deprecated_functions(module, prefix): # # These are the minimal arguments needed to initialize the class. Optional # arguments should be documented in the factory functions and do not need -# to be duplicated in the class documentation. +# to be duplicated in the class documentation (=> don't list here). # class_args = { fs.FlatSystem: ['forward', 'reverse'], @@ -325,6 +330,8 @@ def test_deprecated_functions(module, prefix): 'updfcn', 'outfcn', 'inputs', 'outputs', 'states', 'params', 'dt'], ct.StateSpace: ['A', 'B', 'C', 'D', 'dt'], ct.TransferFunction: ['num', 'den', 'dt'], + ct.InterconnectedSystem: [ + 'syslist', 'connections', 'inplist', 'outlist', 'params'] } # @@ -350,6 +357,10 @@ def test_deprecated_functions(module, prefix): ct.NonlinearIOSystem: ['nstates', 'state_labels'], ct.StateSpace: ['nstates', 'state_labels'], ct.TransferFunction: [], + ct.InterconnectedSystem: [ + 'connect_map', 'input_map', 'output_map', + 'input_offset', 'output_offset', 'state_offset', 'syslist_index', + 'nstates', 'state_labels' ] } # List of attributes defined in a parent class (no need to warn) @@ -376,6 +387,7 @@ def test_deprecated_functions(module, prefix): ct.nlsys: ['state_prefix'], ct.ss: ['sys', 'states', 'state_prefix'], ct.tf: ['sys'], + ct.interconnect: ['dt'] } @@ -400,9 +412,24 @@ def test_iosys_primary_classes(cls, fcn, args): r"[\s]+factory[\s]+function", "\n".join(doc["Extended Summary"]), re.DOTALL) is None: _fail( - f"{cls.__name__} does not reference factory function " + f"{cls.__name__} summary does not reference factory function " f"{fcn.__name__}") + if doc["See Also"] == []: + _fail( + f'{cls.__name__} does not have "See Also" section; ' + f"must include and reference {fcn.__name__}") + else: + found_factory_function = False + for name, _ in doc["See Also"][0][0]: + if name == f"{fcn.__name__}": + found_factory_function = True + break; + if not found_factory_function: + _fail( + f'{cls.__name__} "See Also" section does not reference ' + f"factory function {fcn.__name__}") + # Make sure we don't reference parameters from the factory function for argname in factory_args[fcn]: if re.search(f"[\\s]+{argname}(, .*)*[\\s]*:", docstring) is not None: @@ -425,6 +452,9 @@ def test_iosys_attribute_lists(cls, ignore_future_warning): case ct.frd: sys = ct.frd(sys, [0.1, 1, 10]) ignore_args = ['state_labels'] + case ct.interconnect: + sys = ct.nlsys(sys, name='sys') + sys = ct.interconnect([sys], inplist='sys.u', outlist='sys.y') case ct.nlsys: sys = ct.nlsys(sys) case fs.flatsys: @@ -495,7 +525,7 @@ def test_iosys_container_classes(cls): break -@pytest.mark.parametrize("cls", [ct.InterconnectedSystem, ct.LinearICSystem]) +@pytest.mark.parametrize("cls", [ct.LTI, ct.LinearICSystem]) def test_iosys_intermediate_classes(cls): docstring = inspect.getdoc(cls) with warnings.catch_warnings(): diff --git a/control/timeresp.py b/control/timeresp.py index 8d9a063ab..b6db861c6 100644 --- a/control/timeresp.py +++ b/control/timeresp.py @@ -79,6 +79,46 @@ class TimeResponseData: single-input, single-output systems will have the output and input indices suppressed. This behavior is set using the `squeeze` parameter. + Parameters + ---------- + time : 1D array + Time values of the output. Ignored if None. + outputs : ndarray + Output response of the system. This can either be a 1D array + indexed by time (for SISO systems or MISO systems with a specified + input), a 2D array indexed by output and time (for MIMO systems + with no input indexing, such as initial_response or forced + response) or trace and time (for SISO systems with multiple + traces), or a 3D array indexed by output, trace, and time (for + multi-trace input/output responses). + states : array, optional + Individual response of each state variable. This should be a 2D + array indexed by the state index and time (for single trace + systems) or a 3D array indexed by state, trace, and time. + inputs : array, optional + Inputs used to generate the output. This can either be a 1D array + indexed by time (for SISO systems or MISO/MIMO systems with a + specified input), a 2D array indexed either by input and time (for + a multi-input system) or trace and time (for a single-input, + multi-trace response), or a 3D array indexed by input, trace, and + time. + title : str, optonal + Title of the data set (used as figure title in plotting). + squeeze : bool, optional + By default, if a system is single-input, single-output (SISO) then + the inputs and outputs are returned as a 1D array (indexed by time) + and if a system is multi-input or multi-output, then the inputs are + returned as a 2D array (indexed by input and time) and the outputs + are returned as either a 2D array (indexed by output and time) or a + 3D array (indexed by output, trace, and time). If squeeze=True, + access to the output response will remove single-dimensional + entries from the shape of the inputs and outputs even if the system + is not SISO. If squeeze=False, keep the input as a 2D or 3D array + (indexed by the input (if multi-input), trace (if single input) and + time) and the output as a 3D array (indexed by the output, trace, + and time) even if the system is SISO. The default value can be set + using config.defaults['control.squeeze_time_response']. + Attributes ---------- t : 1D array @@ -104,69 +144,74 @@ class TimeResponseData: responses). If no input data are present, value is `None`. These data are normally accessed via the `inputs` property, which performs squeeze processing. - - squeeze : bool, optional - By default, if a system is single-input, single-output (SISO) - then the outputs (and inputs) are returned as a 1D array - (indexed by time) and if a system is multi-input or - multi-output, then the outputs are returned as a 2D array - (indexed by output and time) or a 3D array (indexed by output, - trace, and time). If `squeeze=True`, access to the output - response will remove single-dimensional entries from the shape - of the inputs and outputs even if the system is not SISO. If - `squeeze=False`, the output is returned as a 2D or 3D array - (indexed by the output [if multi-input], trace [if multi-trace] - and time) even if the system is SISO. The default value can be - set using config.defaults['control.squeeze_time_response']. - transpose : bool, optional If True, transpose all input and output arrays (for backward compatibility with MATLAB and `scipy.signal.lsim`). Default value is False. - issiso : bool, optional Set to `True` if the system generating the data is single-input, single-output. If passed as `None` (default), the input data will be used to set the value. - ninputs, noutputs, nstates : int Number of inputs, outputs, and states of the underlying system. - input_labels, output_labels, state_labels : array of str Names for the input, output, and state variables. - success : bool, optional If `False`, result may not be valid (see `input_output_response`). Defaults to `True`. - message : str, optional Informational message if `success` is `False`. - sysname : str, optional Name of the system that created the data. - params : dict, optional If system is a nonlinear I/O system, set parameter values. - plot_inputs : bool, optional Whether or not to plot the inputs by default (can be overridden in the plot() method) - ntraces : int, optional Number of independent traces represented in the input/output response. If ntraces is 0 (default) then the data represents a single trace with the trace index surpressed in the data. - title : str, optional Set the title to use when plotting. - trace_labels : array of string, optional Labels to use for traces (set to sysname it ntraces is 0) - trace_types : array of string, optional Type of trace. Currently only 'step' is supported, which controls the way in which the signal is plotted. + Other Parameters + ---------------- + input_labels, output_labels, state_labels: array of str, optional + Optional labels for the inputs, outputs, and states, given as a + list of strings matching the appropriate signal dimension. + sysname : str, optional + Name of the system that created the data. + transpose : bool, optional + If True, transpose all input and output arrays (for backward + compatibility with MATLAB and `scipy.signal.lsim`). Default value + is False. + return_x : bool, optional + If True, return the state vector when enumerating result by + assigning to a tuple (default = False). + plot_inputs : bool, optional + Whether or not to plot the inputs by default (can be overridden + in the plot() method) + multi_trace : bool, optional + If `True`, then 2D input array represents multiple traces. For + a MIMO system, the `input` attribute should then be set to + indicate which trace is being specified. Default is `False`. + success : bool, optional + If `False`, result may not be valid (see + `input_output_response`). + message : str, optional + Informational message if `success` is `False`. + + See Also + -------- + input_output_response, forced_response, impulse_response, \ + initial_response, step_response, FrequencyResponseData + Notes ----- The responses for individual elements of the time response can be @@ -248,85 +293,12 @@ def __init__( ): """Create an input/output time response object. - Parameters - ---------- - time : 1D array - Time values of the output. Ignored if None. - - outputs : ndarray - Output response of the system. This can either be a 1D array - indexed by time (for SISO systems or MISO systems with a specified - input), a 2D array indexed by output and time (for MIMO systems - with no input indexing, such as initial_response or forced - response) or trace and time (for SISO systems with multiple - traces), or a 3D array indexed by output, trace, and time (for - multi-trace input/output responses). - - states : array, optional - Individual response of each state variable. This should be a 2D - array indexed by the state index and time (for single trace - systems) or a 3D array indexed by state, trace, and time. - - inputs : array, optional - Inputs used to generate the output. This can either be a 1D - array indexed by time (for SISO systems or MISO/MIMO systems - with a specified input), a 2D array indexed either by input and - time (for a multi-input system) or trace and time (for a - single-input, multi-trace response), or a 3D array indexed by - input, trace, and time. - - title : str, optonal - Title of the data set (used as figure title in plotting). - - squeeze : bool, optional - By default, if a system is single-input, single-output (SISO) - then the inputs and outputs are returned as a 1D array (indexed - by time) and if a system is multi-input or multi-output, then - the inputs are returned as a 2D array (indexed by input and - time) and the outputs are returned as either a 2D array (indexed - by output and time) or a 3D array (indexed by output, trace, and - time). If squeeze=True, access to the output response will - remove single-dimensional entries from the shape of the inputs - and outputs even if the system is not SISO. If squeeze=False, - keep the input as a 2D or 3D array (indexed by the input (if - multi-input), trace (if single input) and time) and the output - as a 3D array (indexed by the output, trace, and time) even if - the system is SISO. The default value can be set using - config.defaults['control.squeeze_time_response']. - - Other Parameters - ---------------- - input_labels, output_labels, state_labels: array of str, optional - Optional labels for the inputs, outputs, and states, given as a - list of strings matching the appropriate signal dimension. - - sysname : str, optional - Name of the system that created the data. - - transpose : bool, optional - If True, transpose all input and output arrays (for backward - compatibility with MATLAB and `scipy.signal.lsim`). - Default value is False. - - return_x : bool, optional - If True, return the state vector when enumerating result by - assigning to a tuple (default = False). - - plot_inputs : bool, optional - Whether or not to plot the inputs by default (can be overridden - in the plot() method) - - multi_trace : bool, optional - If `True`, then 2D input array represents multiple traces. For - a MIMO system, the `input` attribute should then be set to - indicate which trace is being specified. Default is `False`. - - success : bool, optional - If `False`, result may not be valid (see - `input_output_response`). + This function is used by the various time response functions, such + as `input_output_response` and `step_response` to store the + response of a simulation. It can be passed to `plot_time_response` + to plot the data, or the `~TimeResponseData.plot` method can be used. - message : str, optional - Informational message if `success` is `False`. + See `TimeResponseData` for more information on parameters. """ # @@ -1586,7 +1558,7 @@ def step_info(sysdata, T=None, T_num=None, yfinal=None, params=None, See Also -------- - step, lsim, initial, impulse + step_response, forced_response, initial_response, impulse_response Examples -------- diff --git a/control/xferfcn.py b/control/xferfcn.py index 1732eff4e..e6ffe87ce 100644 --- a/control/xferfcn.py +++ b/control/xferfcn.py @@ -91,6 +91,10 @@ class TransferFunction(LTI): z : TransferFunction Represents the discrete time delay operator. + See Also + -------- + tf, InputOutputSystem, FrequencyResponseData + Notes ----- The numerator and denominator polynomials are stored as 2D ndarrays @@ -1654,10 +1658,7 @@ def tf(*args, **kwargs): See Also -------- - TransferFunction - ss - ss2tf - tf2ss + TransferFunction, ss, ss2tf, tf2ss Notes ----- @@ -1865,9 +1866,7 @@ def ss2tf(*args, **kwargs): See Also -------- - tf - ss - tf2ss + tf, ss, tf2ss Examples -------- From a8d5ff98906ebf3ccc94de5268adc10136dd55f0 Mon Sep 17 00:00:00 2001 From: Richard Murray Date: Thu, 2 Jan 2025 16:37:30 -0800 Subject: [PATCH 54/93] add checks for backticks + class docstrings and fix up docstrings --- control/bdalg.py | 6 +- control/canonical.py | 12 +-- control/config.py | 4 +- control/ctrlplot.py | 6 +- control/descfcn.py | 37 +++++--- control/dtime.py | 2 +- control/flatsys/bspline.py | 2 +- control/flatsys/flatsys.py | 9 +- control/flatsys/linflat.py | 8 +- control/flatsys/systraj.py | 14 +-- control/frdata.py | 34 ++++---- control/freqplot.py | 58 ++++++------- control/iosys.py | 26 +++--- control/lti.py | 36 ++++---- control/margins.py | 2 +- control/mateqn.py | 8 +- control/matlab/timeresp.py | 6 +- control/matlab/wrappers.py | 10 +-- control/modelsimp.py | 10 +-- control/nichols.py | 8 +- control/nlsys.py | 50 ++++++----- control/optimal.py | 87 ++++++++++++------- control/passivity.py | 27 +++--- control/phaseplot.py | 2 +- control/pzmap.py | 8 +- control/rlocus.py | 4 +- control/sisotool.py | 10 +-- control/statefbk.py | 12 +-- control/statesp.py | 46 +++++----- control/stochsys.py | 8 +- control/tests/docstrings_test.py | 145 ++++++++++++++++++++----------- control/timeplot.py | 16 ++-- control/timeresp.py | 133 ++++++++++++---------------- control/xferfcn.py | 27 +++--- doc/classes.rst | 1 - 35 files changed, 473 insertions(+), 401 deletions(-) diff --git a/control/bdalg.py b/control/bdalg.py index df27b80d2..0aa4fe2c9 100644 --- a/control/bdalg.py +++ b/control/bdalg.py @@ -666,10 +666,10 @@ def _ensure_tf(arraylike_or_tf, dt=None): Array-like or transfer function. dt : None, True or float, optional System timebase. 0 (default) indicates continuous - time, True indicates discrete time with unspecified sampling + time, `True` indicates discrete time with unspecified sampling time, positive number is discrete time with specified - sampling time, None indicates unspecified timebase (either - continuous or discrete time). If None, timestep is not validated. + sampling time, `None` indicates unspecified timebase (either + continuous or discrete time). If `None`, timestep is not validated. Returns ------- diff --git a/control/canonical.py b/control/canonical.py index 3bff39ca2..6b2bf5748 100644 --- a/control/canonical.py +++ b/control/canonical.py @@ -206,7 +206,7 @@ def similarity_transform(xsys, T, timescale=1, inverse=False): timescale : float, optional If present, also rescale the time unit to tau = timescale * t. inverse : bool, optional - If False (default), transform so z = T x. If True, transform + If `False` (default), transform so z = T x. If `True`, transform so x = T z. Returns @@ -270,7 +270,7 @@ def _bdschur_defective(blksizes, eigvals): Returns ------- - True iff Schur blocks are defective. + `True` iff Schur blocks are defective. blksizes, eigvals are the 3rd and 4th results returned by mb03rd. """ @@ -404,7 +404,7 @@ def bdschur(a, condmax=None, sort=None): a : (M, M) array_like Real matrix to decompose. condmax : None or float, optional - If None (default), use 1/sqrt(eps), which is approximately 1e8. + If `None` (default), use 1/sqrt(eps), which is approximately 1e8. sort : {None, 'continuous', 'discrete'} Block sorting; see below. @@ -419,7 +419,7 @@ def bdschur(a, condmax=None, sort=None): Notes ----- - If `sort` is None, the blocks are not sorted. + If `sort` is `None`, the blocks are not sorted. If `sort` is 'continuous', the blocks are sorted according to associated eigenvalues. The ordering is first by real part of @@ -493,10 +493,10 @@ def modal_form(xsys, condmax=None, sort=False): xsys : StateSpace object System to be transformed, with state `x`. condmax : None or float, optional - An upper bound on individual transformations. If None, use + An upper bound on individual transformations. If `None`, use `bdschur` default. sort : bool, optional - If False (default), Schur blocks will not be sorted. See `bdschur` + If `False` (default), Schur blocks will not be sorted. See `bdschur` for sort order. Returns diff --git a/control/config.py b/control/config.py index e9ed25411..ebeb50aa2 100644 --- a/control/config.py +++ b/control/config.py @@ -217,12 +217,12 @@ def _get_param(module, param, argval=None, defval=None, pop=False, last=False): `module.param` is used to determine the default value. Defaults to None. pop : bool, optional - If True and if argval is a dict, then pop the remove the parameter + If `True` and if argval is a dict, then pop the remove the parameter entry from the argval dict after retreiving it. This allows the use of a keyword argument list to be passed through to other functions internal to the function being called. last : bool, optional - If True, check to make sure dictionary is empy after processing. + If `True`, check to make sure dictionary is empy after processing. """ diff --git a/control/ctrlplot.py b/control/ctrlplot.py index bcb5e3b3d..a68a1522d 100644 --- a/control/ctrlplot.py +++ b/control/ctrlplot.py @@ -148,7 +148,7 @@ class ControlPlot(): Figure on which the Axes are drawn. legend : `matplotlib:.legend.Legend` (instance or ndarray) Legend object(s) for the plot. If more than one legend is - included, this will be an array with each entry being either None + included, this will be an array with each entry being either` None` (for no legend) or a legend object. """ @@ -350,7 +350,7 @@ def _process_ax_keyword( created with axes of the desired shape. If `create_axes` is False and a new/empty figure is returned, then axs - is an array of the proper shape but None for each element. This allows + is an array of the proper shape but `None` for each element. This allows the calling function to do the actual axis creation (needed for curvilinear grids that use the AxisArtist module). @@ -735,7 +735,7 @@ def _get_color( Returns ------- color : matplotlib color spec - Color to use for this line (or None for matplotlib default). + Color to use for this line (or `None` for matplotlib default). """ # See if the color was explicitly specified by the user diff --git a/control/descfcn.py b/control/descfcn.py index 2862c64ac..55da1e1a0 100644 --- a/control/descfcn.py +++ b/control/descfcn.py @@ -54,7 +54,7 @@ def describing_function(self, A): "describing function not implemented for this function") def _isstatic(self): - """Return True if the function has no internal state (memoryless). + """Return `True` if the function has no internal state (memoryless). This internal function is used to optimize numerical computation of the describing function. It can be set to `True` if the instance @@ -289,9 +289,9 @@ def describing_function_response( omega : list, optional List of frequencies to be used for the linear system Nyquist curve. warn_nyquist : bool, optional - Set to True to turn on warnings generated by `nyquist_plot` or False - to turn off warnings. If not set (or set to None), warnings are - turned off if omega is specified, otherwise they are turned on. + Set to `True` to turn on warnings generated by `nyquist_plot` or + `False` to turn off warnings. If not set (or set to `None`), warnings + are turned off if omega is specified, otherwise they are turned on. refine : bool, optional If `True`, `scipy.optimize.minimize` to refine the estimate of the intersection of the frequency response and the describing @@ -419,7 +419,7 @@ def describing_function_plot( curve. If not specified (or None), frequencies are computed automatically based on the properties of the linear system. refine : bool, optional - If True (default), refine the location of the intersection of the + If `True` (default), refine the location of the intersection of the Nyquist curve for the linear system and the describing function to determine the intersection point. label : str or array_like of str, optional @@ -434,9 +434,9 @@ def describing_function_plot( title : str, optional Set the title of the plot. Defaults to plot type and system name(s). warn_nyquist : bool, optional - Set to True to turn on warnings generated by `nyquist_plot` or False - to turn off warnings. If not set (or set to None), warnings are - turned off if omega is specified, otherwise they are turned on. + Set to `True` to turn on warnings generated by `nyquist_plot` or + `False` to turn off warnings. If not set (or set to `None`), warnings + are turned off if omega is specified, otherwise they are turned on. **kwargs : `matplotlib.pyplot.plot` keyword properties, optional Additional keywords passed to `matplotlib` to specify line properties for Nyquist curve. @@ -560,6 +560,11 @@ class saturation_nonlinearity(DescribingFunctionNonlinearity): functions will not have zero bias and hence care must be taken in using the nonlinearity for analysis. + Parameters + ---------- + lb, ub : float + Upper and lower saturation bounds. + Examples -------- >>> nl = ct.saturation_nonlinearity(5) @@ -615,7 +620,7 @@ class relay_hysteresis_nonlinearity(DescribingFunctionNonlinearity): of width `c` (using the notation from [FBS2e](https://fbsbook.org), Example 10.12, including the describing function for the nonlinearity. The following call creates a nonlinear function suitable for describing - function analysis: + function analysis:: F = relay_hysteresis_nonlinearity(b, c) @@ -623,6 +628,13 @@ class relay_hysteresis_nonlinearity(DescribingFunctionNonlinearity): `-c <= x <= c`, the value depends on the branch of the hysteresis loop (as illustrated in Figure 10.20 of FBS2e). + Parameters + ---------- + b : float + Hysteresis bound. + c : float + Width of hysteresis region. + Examples -------- >>> nl = ct.relay_hysteresis_nonlinearity(1, 2) @@ -683,7 +695,7 @@ class friction_backlash_nonlinearity(DescribingFunctionNonlinearity): This class creates a nonlinear function representing a friction-dominated backlash nonlinearity ,including the describing function for the nonlinearity. The following call creates a nonlinear function suitable - for describing function analysis: + for describing function analysis:: F = friction_backlash_nonlinearity(b) @@ -692,6 +704,11 @@ class friction_backlash_nonlinearity(DescribingFunctionNonlinearity): center, the output is unchanged. Otherwise, the output is given by the input shifted by `b/2`. + Parameters + ---------- + b : float + Backlash amount. + Examples -------- >>> nl = ct.friction_backlash_nonlinearity(2) # backlash of +/- 1 diff --git a/control/dtime.py b/control/dtime.py index 1451d8d06..03e6e92c2 100644 --- a/control/dtime.py +++ b/control/dtime.py @@ -56,7 +56,7 @@ def sample_system(sysc, Ts, method='zoh', alpha=None, prewarp_frequency=None, config.defaults['iosys.sampled_system_name_suffix'], with the default being to add the suffix '$sampled'. copy_names : bool, Optional - If True, copy the names of the input signals, output + If `True`, copy the names of the input signals, output signals, and states to the sampled system. Notes diff --git a/control/flatsys/bspline.py b/control/flatsys/bspline.py index 9b296142b..e214efe22 100644 --- a/control/flatsys/bspline.py +++ b/control/flatsys/bspline.py @@ -40,7 +40,7 @@ class BSplineFamily(BasisFamily): derivatives that should match). vars : None or int, optional - The number of spline variables. If specified as None (default), + The number of spline variables. If specified as `None` (default), then the spline basis describes a single variable, with no indexing. If the number of spine variables is > 0, then the spline basis is index using the `var` keyword. diff --git a/control/flatsys/flatsys.py b/control/flatsys/flatsys.py index e9c73b7dc..7863e7da8 100644 --- a/control/flatsys/flatsys.py +++ b/control/flatsys/flatsys.py @@ -173,7 +173,10 @@ def _flat_outfcn(self, t, x, u, params=None): def flatsys(*args, updfcn=None, outfcn=None, **kwargs): - """Create a differentially flat I/O system. + """flatsys(forward, reverse[, updfcn, outfcn]) \ + flatsys(linsys) + + Create a differentially flat I/O system. The flatsys() function is used to create an input/output system object that also represents a differentially flat system. It can be used in a @@ -231,7 +234,7 @@ def flatsys(*args, updfcn=None, outfcn=None, **kwargs): Description of the system states. Same format as `inputs`. dt : None, True or float, optional - System timebase. None (default) indicates continuous time, True + System timebase. `None` (default) indicates continuous time, `True` indicates discrete time with undefined sampling time, positive number is discrete time with specified sampling time. @@ -741,7 +744,7 @@ def solve_flat_ocp( 2. The return data structure includes the following additional attributes: * success : bool indicating whether the optimization succeeded * cost : computed cost of the returned trajectory - * message : message returned by optimization if success if False + * message : message returned by optimization if success if `False` 3. A common failure in solving optimal control problem is that the default initial guess violates the constraints and the optimizer diff --git a/control/flatsys/linflat.py b/control/flatsys/linflat.py index 87c02a48a..597a6de22 100644 --- a/control/flatsys/linflat.py +++ b/control/flatsys/linflat.py @@ -22,7 +22,7 @@ class LinearFlatSystem(FlatSystem, StateSpace): Parameters ---------- linsys : StateSpace - LTI StateSpace system to be converted + LTI StateSpace system to be converted. inputs : int, list of str or None, optional Description of the system inputs. This can be given as an integer count or as a list of strings that name the individual signals. @@ -36,8 +36,8 @@ class LinearFlatSystem(FlatSystem, StateSpace): states : int, list of str, or None, optional Description of the system states. Same format as `inputs`. dt : None, True or float, optional - System timebase. None (default) indicates continuous - time, True indicates discrete time with undefined sampling + System timebase. `None` (default) indicates continuous + time, `True` indicates discrete time with undefined sampling time, positive number is discrete time with specified sampling time. params : dict, optional @@ -45,7 +45,7 @@ class LinearFlatSystem(FlatSystem, StateSpace): functions for the system as default values, overriding internal defaults. name : string, optional - System name (used for specifying signals) + System name (used for specifying signals). """ diff --git a/control/flatsys/systraj.py b/control/flatsys/systraj.py index 6e9a104d4..c39f69c74 100644 --- a/control/flatsys/systraj.py +++ b/control/flatsys/systraj.py @@ -34,6 +34,8 @@ class SystemTrajectory: For each flat output, the number of derivatives of the flat output used to define the trajectory. Defaults to an empty list. + params : dict, optional + Parameter values used for the trajectory. """ @@ -109,19 +111,19 @@ def response(self, tlist, transpose=False, return_x=False, squeeze=None): List of times to evaluate the trajectory. transpose : bool, optional - If True, transpose all input and output arrays (for backward + If `True`, transpose all input and output arrays (for backward compatibility with MATLAB and `scipy.signal.lsim`). Default value is False. return_x : bool, optional - If True, return the state vector when assigning to a tuple + If `True`, return the state vector when assigning to a tuple (default = False). See `forced_response` for more details. squeeze : bool, optional By default, if a system is single-input, single-output (SISO) then the output response is returned as a 1D array (indexed by time). - If squeeze=True, remove single-dimensional entries from the shape - of the output even if the system is not SISO. If squeeze=False, + If squeeze=`True`, remove single-dimensional entries from the shape + of the output even if the system is not SISO. If squeeze=`False`, keep the output as a 3D array (indexed by the output, input, and time) even if the system is SISO. The default value can be set using config.defaults['control.squeeze_time_response']. @@ -135,8 +137,8 @@ def response(self, tlist, transpose=False, return_x=False, squeeze=None): * time (array): Time values of the output. * outputs (array): Response of the system. If the system is SISO - and squeeze is not True, the array is 1D (indexed by time). If - the system is not SISO or `squeeze` is False, the array is 3D + and squeeze is not `True`, the array is 1D (indexed by time). If + the system is not SISO or `squeeze` is `False`, the array is 3D (indexed by the output, trace, and time). * states (array): Time evolution of the state vector, represented diff --git a/control/frdata.py b/control/frdata.py index 85fa86d92..06be5f018 100644 --- a/control/frdata.py +++ b/control/frdata.py @@ -28,7 +28,7 @@ class FrequencyResponseData(LTI): - """FrequencyResponseData(d, w[, smooth]) + """FrequencyResponseData(response, omega[, smooth]) A class for models defined by frequency response data (FRD). @@ -53,9 +53,9 @@ class constructor, using the `frd` factory function, or frequencies give in `w`. If `False` (default), frequency response can only be obtained at the frequencies specified in `w`. dt : None, True or float, optional - System timebase. 0 (default) indicates continuous time, True + System timebase. 0 (default) indicates continuous time, `True` indicates discrete time with unspecified sampling time, positive - number is discrete time with specified sampling time, None + number is discrete time with specified sampling time, `None` indicates unspecified timebase (either continuous or discrete time). squeeze : bool By default, if a system is single-input, single-output (SISO) then @@ -103,7 +103,7 @@ class constructor, using the `frd` factory function, or plot_magnitude, plot_phase : bool, optional If set to `False`, don't plot the magnitude or phase, respectively. return_magphase : bool, optional - If True, then a frequency response data object will enumerate as a + If `True`, then a frequency response data object will enumerate as a tuple of the form (mag, phase, omega) where where `mag` is the magnitude (absolute value, not dB or log10) of the system frequency response, `phase` is the wrapped phase in radians of @@ -635,8 +635,8 @@ def eval(self, omega, squeeze=None): omega : float or 1D array_like Frequencies in radians per second. squeeze : bool, optional - If squeeze=True, remove single-dimensional entries from the shape - of the output even if the system is not SISO. If squeeze=False, + If `squeeze=True`, remove single-dimensional entries from the shape + of the output even if the system is not SISO. If `squeeze=False`, keep all indices (output, input and, if omega is array_like, frequency) even if the system is SISO. The default value can be set using config.defaults['control.squeeze_frequency_response']. @@ -644,12 +644,12 @@ def eval(self, omega, squeeze=None): Returns ------- fresp : complex ndarray - The frequency response of the system. If the system is SISO and - squeeze is not True, the shape of the array matches the shape of - omega. If the system is not SISO or squeeze is False, the first - two dimensions of the array are indices for the output and input - and the remaining dimensions match omega. If `squeeze` is True - then single-dimensional axes are removed. + The frequency response of the system. If the system is SISO + and `squeeze` is not `True`, the shape of the array matches the + shape of omega. If the system is not SISO or `squeeze` is + `False`, the first two dimensions of the array are indices for + the output and input and the remaining dimensions match omega. + If `squeeze` is `True` then single-dimensional axes are removed. """ omega_array = np.array(omega, ndmin=1) # array-like version of omega @@ -705,14 +705,14 @@ def __call__(self, s=None, squeeze=None, return_magphase=None): processing (`squeeze`, `return_magphase`). squeeze : bool, optional - If squeeze=True, remove single-dimensional entries from the shape - of the output even if the system is not SISO. If squeeze=False, + If squeeze=`True`, remove single-dimensional entries from the shape + of the output even if the system is not SISO. If squeeze=`False`, keep all indices (output, input and, if omega is array_like, frequency) even if the system is SISO. The default value can be set using config.defaults['control.squeeze_frequency_response']. return_magphase : bool, optional - If True, then a frequency response data object will enumerate as a + If `True`, then a frequency response data object will enumerate as a tuple of the form (mag, phase, omega) where where `mag` is the magnitude (absolute value, not dB or log10) of the system frequency response, `phase` is the wrapped phase in radians of @@ -723,8 +723,8 @@ def __call__(self, s=None, squeeze=None, return_magphase=None): ------- fresp : complex ndarray The frequency response of the system. If the system is SISO and - squeeze is not True, the shape of the array matches the shape of - omega. If the system is not SISO or squeeze is False, the first + squeeze is not `True`, the shape of the array matches the shape of + omega. If the system is not SISO or squeeze is `False`, the first two dimensions of the array are indices for the output and input and the remaining dimensions match omega. If `squeeze` is True then single-dimensional axes are removed. diff --git a/control/freqplot.py b/control/freqplot.py index 52e016c5e..5d4b4dd58 100644 --- a/control/freqplot.py +++ b/control/freqplot.py @@ -109,15 +109,15 @@ def bode_plot( Passed to `matplotlib` as the format string for all lines in the plot. The `omega` parameter must be present (use omega=None if needed). dB : bool - If True, plot result in dB. Default is False. + If `True`, plot result in dB. Default is False. Hz : bool - If True, plot frequency in Hz (omega must be provided in rad/sec). + If `True`, plot frequency in Hz (omega must be provided in rad/sec). Default value (False) set by config.defaults['freqplot.Hz']. deg : bool - If True, plot phase in degrees (else radians). Default value (True) + If `True`, plot phase in degrees (else radians). Default value (`True`) set by config.defaults['freqplot.deg']. display_margins : bool or str - If True, draw gain and phase margin lines on the magnitude and phase + If `True`, draw gain and phase margin lines on the magnitude and phase graphs and display the margins at the top of the graph. If set to 'overlay', the values for the gain and phase margin are placed on the graph. Setting display_margins turns off the axes grid. @@ -154,13 +154,13 @@ def bode_plot( Labels to use for the frequency, magnitude, and phase axes. Defaults are set by `config.defaults['freqplot.']`. grid : bool, optional - If True, plot grid lines on gain and phase plots. Default is set by + If `True`, plot grid lines on gain and phase plots. Default is set by `config.defaults['freqplot.grid']`. initial_phase : float, optional Set the reference phase to use for the lowest frequency. If set, the initial phase of the Bode plot will be set to the value closest to the value specified. Units are in either degrees or radians, depending on - the `deg` parameter. Default is -180 if wrap_phase is False, 0 if + the `deg` parameter. Default is -180 if wrap_phase is `False`, 0 if wrap_phase is True. label : str or array_like of str, optional If present, replace automatically generated label(s) with the given @@ -169,11 +169,11 @@ def bode_plot( legend_map : array of str, optional Location of the legend for multi-axes plots. Specifies an array of legend location strings matching the shape of the subplots, with - each entry being either None (for no legend) or a legend location + each entry being either `None` (for no legend) or a legend location string (see `~matplotlib.pyplot.legend`). legend_loc : int or str, optional Include a legend in the given location. Default is 'center right', - with no legend for a single response. Use False to suppress legend. + with no legend for a single response. Use `False` to suppress legend. margins_method : str, optional Method to use in computing margins (see `stability_margins`). omega_limits : array_like of two values @@ -186,11 +186,11 @@ def bode_plot( config.defaults['freqplot.number_of_samples']. Ignored if data is not a list of systems. overlay_inputs, overlay_outputs : bool, optional - If set to True, combine input and/or output signals onto a single + If set to `True`, combine input and/or output signals onto a single plot and use line colors, labels, and a legend to distinguish them. plot : bool, optional (legacy) If given, `bode_plot` returns the legacy return values - of magnitude, phase, and frequency. If False, just return the + of magnitude, phase, and frequency. If `False`, just return the values with no plot. plot_magnitude, plot_phase : bool, optional If set to `False`, don't plot the magnitude or phase, respectively. @@ -1121,7 +1121,7 @@ class NyquistResponseData: The system timebase. sysname : str The name of the system being analyzed. - return_contour: bool + return_contour : bool If true, when the object is accessed as an iterable return two elements": `count` (number of encirlements) and `contour`. If false (default), then return only `count`. @@ -1252,7 +1252,7 @@ def nyquist_response( primary curve use a dotted line style and the scaled portion of the mirror image use a dashdot line style. - 4. If the legacy keyword `return_contour` is specified as True, the + 4. If the legacy keyword `return_contour` is specified as `True`, the response object can be iterated over to return `count, contour`. This behavior is deprecated and will be removed in a future release. @@ -1626,7 +1626,7 @@ def nyquist_plot( are generated. legend_loc : int or str, optional Include a legend in the given location. Default is 'upper right', - with no legend for a single response. Use False to suppress legend. + with no legend for a single response. Use `False` to suppress legend. max_curve_magnitude : float, optional Restrict the maximum magnitude of the Nyquist plot to this value. Portions of the Nyquist plot whose magnitude is restricted are @@ -1654,7 +1654,7 @@ def nyquist_plot( not a list of systems. plot : bool, optional (legacy) If given, `nyquist_plot` returns the legacy return values - of (counts, contours). If False, return the values with no plot. + of (counts, contours). If `False`, return the values with no plot. primary_style : [str, str], optional Linestyles for primary image of the Nyquist curve. The first element is used for unscaled portions of the Nyquist curve, @@ -1709,7 +1709,7 @@ def nyquist_plot( right of stable poles and the left of unstable poles. If a pole is exactly on the imaginary axis, the `indent_direction` parameter can be used to set the direction of indentation. Setting `indent_direction` - to `none` will turn off indentation. If `return_contour` is True, the + to `none` will turn off indentation. If `return_contour` is `True`, the exact contour used for evaluation is returned. 3. For those portions of the Nyquist plot in which the contour is @@ -2128,7 +2128,7 @@ def gangof4_response( config.defaults['freqplot.number_of_samples']. Ignored if data is not a list of systems. Hz : bool, optional - If True, when computing frequency limits automatically set + If `True`, when computing frequency limits automatically set limits to full decades in Hz instead of rad/s. Returns @@ -2215,7 +2215,7 @@ def gangof4_plot( config.defaults['freqplot.number_of_samples']. Ignored if data is not a list of systems. Hz : bool, optional - If True, when computing frequency limits automatically set + If `True`, when computing frequency limits automatically set limits to full decades in Hz instead of rad/s. Returns @@ -2271,7 +2271,7 @@ def singular_values_response( omega : array_like List of frequencies in rad/sec to be used for frequency response. Hz : bool, optional - If True, when computing frequency limits automatically set + If `True`, when computing frequency limits automatically set limits to full decades in Hz instead of rad/s. Returns @@ -2357,9 +2357,9 @@ def singular_values_plot( Passed to `matplotlib` as the format string for all lines in the plot. The `omega` parameter must be present (use omega=None if needed). dB : bool - If True, plot result in dB. Default is False. + If `True`, plot result in dB. Default is False. Hz : bool - If True, plot frequency in Hz (omega must be provided in rad/sec). + If `True`, plot frequency in Hz (omega must be provided in rad/sec). Default value (False) set by config.defaults['freqplot.Hz']. **kwargs : `matplotlib.pyplot.plot` keyword properties, optional Additional keywords passed to `matplotlib` to specify line properties. @@ -2388,9 +2388,9 @@ def singular_values_plot( the current figure has a single axes, that axes is used. Otherwise, a new figure is created. color : matplotlib color spec - Color to use for singular values (or None for matplotlib default). + Color to use for singular values (or `None` for matplotlib default). grid : bool - If True, plot grid lines on gain and phase plots. Default is set by + If `True`, plot grid lines on gain and phase plots. Default is set by `config.defaults['freqplot.grid']`. label : str or array_like of str, optional If present, replace automatically generated label(s) with the given @@ -2398,7 +2398,7 @@ def singular_values_plot( system. legend_loc : int or str, optional Include a legend in the given location. Default is 'center right', - with no legend for a single response. Use False to suppress legend. + with no legend for a single response. Use `False` to suppress legend. omega_limits : array_like of two values Set limits for plotted frequency range. If Hz=True the limits are in Hz otherwise in rad/s. Specifying `omega` as a list of two @@ -2409,7 +2409,7 @@ def singular_values_plot( not a list of systems. plot : bool, optional (legacy) If given, `singular_values_plot` returns the legacy return - values of magnitude, phase, and frequency. If False, just return + values of magnitude, phase, and frequency. If `False`, just return the values with no plot. rcParams : dict Override the default parameters used for generating plots. @@ -2432,7 +2432,7 @@ def singular_values_plot( Notes ----- - 1. If plot==False, the following legacy values are returned: + 1. If plot==`False`, the following legacy values are returned: * mag : ndarray (or list of ndarray if len(data) > 1)) Magnitude of the response (deprecated). * phase : ndarray (or list of ndarray if len(data) > 1)) @@ -2637,7 +2637,7 @@ def _determine_omega_vector(syslist, omega_in, omega_limits, omega_num, Number of points to be used for the frequency range (if the frequency range is not user-specified). Hz : bool, optional - If True, the limits (first and last value) of the frequencies + If `True`, the limits (first and last value) of the frequencies are set to full decades in Hz so it fits plotting with logarithmic scale in Hz otherwise in rad/s. Omega is always returned in rad/sec. @@ -2646,7 +2646,7 @@ def _determine_omega_vector(syslist, omega_in, omega_limits, omega_num, omega_out : 1D array Frequency range to be used. omega_range_given : bool - True if the frequency range was specified by the user, either through + `True` if the frequency range was specified by the user, either through omega_in or through omega_limits. False if both omega_in and omega_limits are None. @@ -2695,12 +2695,12 @@ def _default_frequency_range(syslist, Hz=None, number_of_samples=None, syslist : list of LTI List of linear input/output systems (single system is OK) Hz : bool, optional - If True, the limits (first and last value) of the frequencies + If `True`, the limits (first and last value) of the frequencies are set to full decades in Hz so it fits plotting with logarithmic scale in Hz otherwise in rad/s. Omega is always returned in rad/sec. number_of_samples : int, optional Number of samples to generate. The default value is read from - `config.defaults['freqplot.number_of_samples']. If None, then the + `config.defaults['freqplot.number_of_samples']. If `None`, then the default from `numpy.logspace` is used. feature_periphery_decades : float, optional Defines how many decades shall be included in the frequency range on diff --git a/control/iosys.py b/control/iosys.py index 19757fcf0..fabb3ea46 100644 --- a/control/iosys.py +++ b/control/iosys.py @@ -106,7 +106,7 @@ def __getitem__(self, key): class InputOutputSystem(): """A class for representing input/output systems. - The InputOutputSystem class allows (possibly nonlinear) input/output + The `InputOutputSystem` class allows (possibly nonlinear) input/output systems to be represented in Python. It is used as a parent class for a set of subclasses that are used to implement specific structures and operations for different types of input/output dynamical systems. @@ -115,10 +115,10 @@ class InputOutputSystem(): is operating in continuous or discrete time. It can have the following values: - * dt = None No timebase specified + * dt = `None` No timebase specified * dt = 0 Continuous time system * dt > 0 Discrete time system with sampling time dt - * dt = True Discrete time system with unspecified sampling time + * dt = `True` Discrete time system with unspecified sampling time Parameters ---------- @@ -137,9 +137,9 @@ class InputOutputSystem(): Description of the system states. Same format as `inputs`, with the prefix given by state_prefix (defaults to 'x'). dt : None, True or float, optional - System timebase. 0 (default) indicates continuous time, True + System timebase. 0 (default) indicates continuous time, `True` indicates discrete time with unspecified sampling time, positive - number is discrete time with specified sampling time, None indicates + number is discrete time with specified sampling time, `None` indicates unspecified timebase (either continuous or discrete time). name : string, optional System name (used for specifying signals). If unspecified, a generic @@ -426,7 +426,7 @@ def copy(self, name=None, use_prefix_suffix=True): A copy of the system is made, with a new name. The `name` keyword can be used to specify a specific name for the system. If no name - is given and `use_prefix_suffix` is True, the name is constructed + is given and `use_prefix_suffix` is `True`, the name is constructed by prepending config.defaults['iosys.duplicate_system_name_prefix'] and appending config.defaults['iosys.duplicate_system_name_suffix']. Otherwise, a generic system name of the form `sys[]` is used, @@ -610,7 +610,7 @@ def isctime(self, strict=False): sys : Named I/O system System to be checked. strict : bool, optional - If strict is True, make sure that timebase is not None. Default + If strict is `True`, make sure that timebase is not None. Default is False. """ # If no timebase is given, answer depends on strict flag @@ -625,7 +625,7 @@ def isdtime(self, strict=False): Parameters ---------- strict : bool, optional - If strict is True, make sure that timebase is not None. Default + If strict is `True`, make sure that timebase is not None. Default is False. """ @@ -655,7 +655,7 @@ def issiso(sys, strict=False): sys : I/O or LTI system System to be checked. strict : bool (default = False) - If strict is True, do not treat scalars as SISO. + If strict is `True`, do not treat scalars as SISO. """ if isinstance(sys, (int, float, complex, np.number)) and not strict: return True @@ -672,7 +672,7 @@ def timebase(sys, strict=True): dt = timebase(sys) returns the timebase for a system 'sys'. If the strict option is - set to `True`, dt = True will be returned as 1. + set to `True`, dt = `True` will be returned as 1. Parameters ---------- @@ -764,7 +764,7 @@ def isdtime(sys=None, strict=False, dt=None): dt : None or number, optional Timebase to be checked. strict : bool, default=False - If strict is True, make sure that timebase is not None. + If strict is `True`, make sure that timebase is not None. """ # See if we were passed a timebase instead of a system @@ -796,7 +796,7 @@ def isctime(sys=None, dt=None, strict=False): dt : None or number, optional Timebase to be checked. strict : bool (default = False) - If strict is True, make sure that timebase is not None. + If strict is `True`, make sure that timebase is not None. """ # See if we were passed a timebase instead of a system @@ -868,7 +868,7 @@ def _process_iosys_keywords( `default` dictionary can also be set to an InputOutputSystem object, which is useful for copy constructors that change system/signal names. - If `end` is True, then generate an error if there are any remaining + If `end` is `True`, then generate an error if there are any remaining keywords. """ diff --git a/control/lti.py b/control/lti.py index 8c1629195..8babc1e28 100644 --- a/control/lti.py +++ b/control/lti.py @@ -90,8 +90,8 @@ def frequency_response(self, omega=None, squeeze=None): radians/sec at which the system will be evaluated. If None (default), a set of frequencies is computed based on the system dynamics. squeeze : bool, optional - If squeeze=True, remove single-dimensional entries from the shape - of the output even if the system is not SISO. If squeeze=False, + If squeeze=`True`, remove single-dimensional entries from the shape + of the output even if the system is not SISO. If squeeze=`False`, keep all indices (output, input and, if omega is array_like, frequency) even if the system is SISO. The default value can be set using config.defaults['control.squeeze_frequency_response']. @@ -108,11 +108,11 @@ def frequency_response(self, omega=None, squeeze=None): of the system frequency response, `phase` is the wrapped phase in radians of the system frequency response, and `omega` is the (sorted) frequencies at which the response was evaluated. - If the system is SISO and squeeze is not True, `magnitude` and + If the system is SISO and squeeze is not `True`, `magnitude` and `phase` are 1D, indexed by frequency. If the system is not - SISO or squeeze is False, the array is 3D, indexed by the + SISO or squeeze is `False`, the array is 3D, indexed by the output, input, and, if omega is array_like, frequency. If - `squeeze` is True then single-dimensional axes are removed. + `squeeze` is `True` then single-dimensional axes are removed. """ from .frdata import FrequencyResponseData @@ -300,7 +300,7 @@ def damp(sys, doprint=True): sys : LTI (StateSpace or TransferFunction) A linear system object. doprint : bool (optional) - If True, print table with values. + If `True`, print table with values. Returns ------- @@ -372,8 +372,8 @@ def evalfr(sys, x, squeeze=None): x : complex scalar or 1D array_like Complex frequency(s). squeeze : bool, optional (default=True) - If squeeze=True, remove single-dimensional entries from the shape of - the output even if the system is not SISO. If squeeze=False, keep all + If squeeze=`True`, remove single-dimensional entries from the shape of + the output even if the system is not SISO. If squeeze=`False`, keep all indices (output, input and, if omega is array_like, frequency) even if the system is SISO. The default value can be set using config.defaults['control.squeeze_frequency_response']. @@ -382,10 +382,10 @@ def evalfr(sys, x, squeeze=None): ------- fresp : complex ndarray The frequency response of the system. If the system is SISO and - squeeze is not True, the shape of the array matches the shape of - omega. If the system is not SISO or squeeze is False, the first two + squeeze is not `True`, the shape of the array matches the shape of + omega. If the system is not SISO or squeeze is `False`, the first two dimensions of the array are indices for the output and input and the - remaining dimensions match omega. If `squeeze` is True then + remaining dimensions match omega. If `squeeze` is `True` then single-dimensional axes are removed. See Also @@ -425,7 +425,7 @@ def frequency_response( omega : float or 1D array_like, optional Frequencies in radians/sec at which the system should be evaluated. Can be a single frequency or array of frequencies, which - will be sorted before evaluation. If None (default), a common set + will be sorted before evaluation. If `None` (default), a common set of frequencies that works across all given systems is computed. omega_limits : array_like of two values, optional Limits to the range of frequencies, in rad/sec. Specifying @@ -448,10 +448,10 @@ def frequency_response( the system frequency response, `phase` is the wrapped phase in radians of the system frequency response, and `omega` is the (sorted) frequencies at which the response was evaluated. If the - system is SISO and squeeze is not False, `magnitude` and `phase` + system is SISO and squeeze is not `False`, `magnitude` and `phase` are 1D, indexed by frequency. If the system is not SISO or squeeze - is False, the array is 3D, indexed by the output, input, and - frequency. If `squeeze` is True then single-dimensional axes are + is `False`, the array is 3D, indexed by the output, input, and + frequency. If `squeeze` is `True` then single-dimensional axes are removed. Returns a list of `FrequencyResponseData` objects if sys is @@ -460,12 +460,12 @@ def frequency_response( Other Parameters ---------------- Hz : bool, optional - If True, when computing frequency limits automatically set + If `True`, when computing frequency limits automatically set limits to full decades in Hz instead of rad/s. Omega is always returned in rad/sec. squeeze : bool, optional - If squeeze=True, remove single-dimensional entries from the shape of - the output even if the system is not SISO. If squeeze=False, keep all + If squeeze=`True`, remove single-dimensional entries from the shape of + the output even if the system is not SISO. If squeeze=`False`, keep all indices (output, input and, if omega is array_like, frequency) even if the system is SISO. The default value can be set using config.defaults['control.squeeze_frequency_response']. diff --git a/control/margins.py b/control/margins.py index 044203926..7187f41ac 100644 --- a/control/margins.py +++ b/control/margins.py @@ -221,7 +221,7 @@ def stability_margins(sysdata, returnall=False, epsw=0.0, method='best'): Alternatively, a three tuple of the form (mag, phase, omega) providing the frequency response can be passed. returnall : bool, optional - If true, return all margins found. If False (default), return only the + If true, return all margins found. If `False` (default), return only the minimum stability margins. For frequency data or FRD systems, only margins in the given frequency region can be found and returned. epsw : float, optional diff --git a/control/mateqn.py b/control/mateqn.py index 870490bb2..0541feb92 100644 --- a/control/mateqn.py +++ b/control/mateqn.py @@ -95,7 +95,7 @@ def lyap(A, Q, C=None, E=None, method=None): If present, solve the generalized Lyapunov equation. method : str, optional Set the method used for computing the result. Current methods are - 'slycot' and 'scipy'. If set to None (default), try 'slycot' first + 'slycot' and 'scipy'. If set to `None` (default), try 'slycot' first and then 'scipy'. Returns @@ -221,7 +221,7 @@ def dlyap(A, Q, C=None, E=None, method=None): If present, solve the generalized Lyapunov equation. method : str, optional Set the method used for computing the result. Current methods are - 'slycot' and 'scipy'. If set to None (default), try 'slycot' first + 'slycot' and 'scipy'. If set to `None` (default), try 'slycot' first and then 'scipy'. Returns @@ -347,7 +347,7 @@ def care(A, B, Q, R=None, S=None, E=None, stabilizing=True, method=None, Input matrices for generalized Riccati equation. method : str, optional Set the method used for computing the result. Current methods are - 'slycot' and 'scipy'. If set to None (default), try 'slycot' first + 'slycot' and 'scipy'. If set to `None` (default), try 'slycot' first and then 'scipy'. stabilizing : bool, optional If `method` is 'slycot', unstabilized eigenvalues will be returned @@ -503,7 +503,7 @@ def dare(A, B, Q, R, S=None, E=None, stabilizing=True, method=None, Input matrices for generalized Riccati equation. method : str, optional Set the method used for computing the result. Current methods are - 'slycot' and 'scipy'. If set to None (default), try 'slycot' first + 'slycot' and 'scipy'. If set to `None` (default), try 'slycot' first and then 'scipy'. stabilizing : bool, optional If `method` is 'slycot', unstabilized eigenvalues will be returned diff --git a/control/matlab/timeresp.py b/control/matlab/timeresp.py index fae6c9675..29104d1fe 100644 --- a/control/matlab/timeresp.py +++ b/control/matlab/timeresp.py @@ -30,7 +30,7 @@ def step(sys, T=None, input=0, output=None, return_x=False): output : int If given, index of the output that is returned by this simulation. return_x : bool, optional - If True, return the state vector in addition to outputs. + If `True`, return the state vector in addition to outputs. Returns ------- @@ -156,7 +156,7 @@ def impulse(sys, T=None, input=0, output=None, return_x=False): output : int Index of the output that will be used in this simulation. return_x : bool, optional - If True, return the state vector in addition to outputs. + If `True`, return the state vector in addition to outputs. Returns ------- @@ -208,7 +208,7 @@ def initial(sys, T=None, X0=0., input=None, output=None, return_x=False): output : int If given, index of the output that is returned by this simulation. return_x : bool, optional - If True, return the state vector in addition to outputs. + If `True`, return the state vector in addition to outputs. Returns ------- diff --git a/control/matlab/wrappers.py b/control/matlab/wrappers.py index 9738dcf5b..52dee324b 100644 --- a/control/matlab/wrappers.py +++ b/control/matlab/wrappers.py @@ -34,13 +34,13 @@ def bode(*args, **kwargs): omega : array Range of frequencies in rad/s. dB : boolean - If True, plot result in dB. + If `True`, plot result in dB. Hz : boolean - If True, plot frequency in Hz (omega must be provided in rad/sec). + If `True`, plot frequency in Hz (omega must be provided in rad/sec). deg : boolean - If True, return phase in degrees (else radians). + If `True`, return phase in degrees (else radians). plot : boolean - If True, plot magnitude and phase. + If `True`, plot magnitude and phase. Returns ------- @@ -278,7 +278,7 @@ def pzmap(*args, **kwargs): If `True` a graph is generated with Matplotlib, otherwise the poles and zeros are only computed and returned. grid : boolean (default = False) - If True plot omega-damping grid. + If `True`, plot omega-damping grid. Returns ------- diff --git a/control/modelsimp.py b/control/modelsimp.py index 00281b5df..5422a1281 100644 --- a/control/modelsimp.py +++ b/control/modelsimp.py @@ -460,10 +460,10 @@ def eigensys_realization(arg, r, m=None, n=None, dt=True, transpose=False): n : integer, optional Number of columns in Hankel matrix. Default is 2*r. dt : True or float, optional - True indicates discrete time with unspecified sampling time and a + `True` indicates discrete time with unspecified sampling time and a positive float is discrete time with the specified sampling time. It can be used to scale the StateSpace model in order to match the - unit-area impulse response of python-control. Default is True. + unit-area impulse response of python-control. Default is `True`. transpose : bool, optional Assume that input data is transposed relative to the standard :ref:`time-series-convention`. For TimeResponseData this parameter @@ -565,7 +565,7 @@ def markov(*args, m=None, transpose=False, dt=None, truncate=False): ---------- Y : array_like Output data. If the array is 1D, the system is assumed to be - single input. If the array is 2D and transpose=False, the columns + single input. If the array is 2D and transpose=`False`, the columns of `Y` are taken as time points, otherwise the rows of `Y` are taken as time points. U : array_like @@ -576,10 +576,10 @@ def markov(*args, m=None, transpose=False, dt=None, truncate=False): m : int, optional Number of Markov parameters to output. Defaults to len(U). dt : True of float, optional - True indicates discrete time with unspecified sampling time and a + `True` indicates discrete time with unspecified sampling time and a positive float is discrete time with the specified sampling time. It can be used to scale the Markov parameters in order to match - the unit-area impulse response of python-control. Default is True + the unit-area impulse response of python-control. Default is `True` for array_like and dt=data.time[1]-data.time[0] for TimeResponseData as input. truncate : bool, optional diff --git a/control/nichols.py b/control/nichols.py index f04b30ccc..c69b88be4 100644 --- a/control/nichols.py +++ b/control/nichols.py @@ -42,8 +42,8 @@ def nichols_plot( Passed to `matplotlib` as the format string for all lines in the plot. The `omega` parameter must be present (use omega=None if needed). grid : boolean, optional - True if the plot should include a Nichols-chart grid. Default is - True and can be set using config.defaults['nichols.grid']. + `True` if the plot should include a Nichols-chart grid. Default is + `True` and can be set using config.defaults['nichols.grid']. **kwargs : `matplotlib.pyplot.plot` keyword properties, optional Additional keywords passed to `matplotlib` to specify line properties. @@ -78,7 +78,7 @@ def nichols_plot( system. legend_loc : int or str, optional Include a legend in the given location. Default is 'upper left', - with no legend for a single response. Use False to suppress legend. + with no legend for a single response. Use `False` to suppress legend. rcParams : dict Override the default parameters used for generating plots. Default is set by config.defaults['ctrlplot.rcParams']. @@ -196,7 +196,7 @@ def nichols_grid(cl_mags=None, cl_phases=None, line_style='dotted', ax=None, ax : matplotlib.axes.Axes, optional Axes to add grid to. If `None`, use `matplotlib.pyplot.gca()`. label_cl_phases : bool, optional - If True, closed-loop phase lines will be labelled. + If `True`, closed-loop phase lines will be labelled. Returns ------- diff --git a/control/nlsys.py b/control/nlsys.py index d31a441c2..9725e583e 100644 --- a/control/nlsys.py +++ b/control/nlsys.py @@ -173,8 +173,8 @@ def __call__(sys, u, params=None, squeeze=None): Parameter values for the system. Passed to the evaluation function for the system as default values, overriding internal defaults. squeeze : bool, optional - If True and if the system has a single output, return the system - output as a 1D array rather than a 2D array. If False, return the + If `True` and if the system has a single output, return the system + output as a 1D array rather than a 2D array. If `False`, return the system output as a 2D array even if the system is SISO. Default value set by config.defaults['control.squeeze_time_response']. @@ -1112,10 +1112,10 @@ def connection_table(self, show_names=False, column_width=32): Parameters ---------- show_names : bool, optional - Instead of printing out the system number, print out the name of - each system. Default is False because system name is not usually - specified when performing implicit interconnection using - `interconnect`. + Instead of printing out the system number, print out the name + of each system. Default is `False` because system name is not + usually specified when performing implicit interconnection + using `interconnect`. column_width : int, optional Character width of printed columns. @@ -1130,6 +1130,7 @@ def connection_table(self, show_names=False, column_width=32): e | input | C u | C | P y | P | output + """ print('signal'.ljust(10) + '| source'.ljust(column_width) + \ @@ -1210,8 +1211,8 @@ def check_unused_signals( """Check for unused subsystem inputs and outputs. Check to see if there are any unused signals and return a list of - unused input and output signal descriptions. If `warning` is True - and any unused inputs or outputs are found, emit a warning. + unused input and output signal descriptions. If `warning` is + `True` and any unused inputs or outputs are found, emit a warning. Parameters ---------- @@ -1472,15 +1473,15 @@ def input_output_response( List of times at which the time response should be computed. Defaults to `T`. return_x : bool, optional - If True, return the state vector when assigning to a tuple (default = + If `True`, return the state vector when assigning to a tuple (default = False). See `forced_response` for more details. - If True, return the values of the state at each time (default = False). + If `True`, return the values of the state at each time (default = False). params : dict, optional Parameter values for the system. Passed to the evaluation functions for the system as default values, overriding internal defaults. squeeze : bool, optional - If True and if the system has a single output, return the system - output as a 1D array rather than a 2D array. If False, return the + If `True` and if the system has a single output, return the system + output as a 1D array rather than a 2D array. If `False`, return the system output as a 2D array even if the system is SISO. Default value set by config.defaults['control.squeeze_time_response']. @@ -1493,8 +1494,8 @@ def input_output_response( * time (array): Time values of the output. * outputs (array): Response of the system. If the system is SISO and - `squeeze` is not True, the array is 1D (indexed by time). If the - system is not SISO or `squeeze` is False, the array is 2D (indexed + `squeeze` is not `True`, the array is 1D (indexed by time). If the + system is not SISO or `squeeze` is `False`, the array is 2D (indexed by output and time). * states (array): Time evolution of the state vector, represented as @@ -1523,7 +1524,7 @@ def input_output_response( solve_ivp_kwargs : dict, optional Pass additional keywords to `scipy.integrate.solve_ivp`. transpose : bool, default=False - If True, transpose all input and output arrays (for backward + If `True`, transpose all input and output arrays (for backward compatibility with MATLAB and `scipy.signal.lsim`). Raises @@ -1821,7 +1822,7 @@ class OperatingPoint(): result : `scipy.optimize.OptimizeResult`, optional Result from the `scipy.optimize.root` function, if available. return_outputs, return_result : bool, optional - If set to `True`, then when accessed a tuple the output values + If set to `True`, then when accessed a tuple the output values and/or result of the root finding function will be returned. Notes @@ -1945,9 +1946,9 @@ def find_operating_point( root_kwargs : dict, optional Additional keyword arguments to pass `scipy.optimize.root`. return_outputs : bool, optional - If True, return the value of outputs at the operating point. + If `True`, return the value of outputs at the operating point. return_result : bool, optional - If True, return the `result` option from the + If `True`, return the `result` option from the `scipy.optimize.root` function used to compute the operating point. @@ -2219,7 +2220,7 @@ def linearize(sys, xeq, ueq=None, t=0, params=None, **kw): config.defaults['iosys.linearized_system_name_suffix'], with the default being to add the suffix '$linearized'. copy_names : bool, Optional - If True, Copy the names of the input signals, output signals, and + If `True`, Copy the names of the input signals, output signals, and states to the linearized system. Returns @@ -2410,12 +2411,12 @@ def interconnect( 'u', 'y', 'x'. check_unused : bool, optional - If True, check for unused sub-system signals. This check is - not done if connections is False, and neither input nor output + If `True`, check for unused sub-system signals. This check is + not done if connections is `False`, and neither input nor output mappings are specified. add_unused : bool, optional - If True, subsystem signals that are not connected to other components + If `True`, subsystem signals that are not connected to other components are added as inputs and outputs of the interconnected system. ignore_inputs : list of input-spec, optional @@ -2859,7 +2860,7 @@ def _process_vector_argument(arg, name, size): name : string Name of the argument being processed. Used in errors/warnings. size : int or None - Size of the element. If None, size is determined by arg. + Size of the element. If `None`, size is determined by arg. Returns ------- @@ -2930,7 +2931,7 @@ def connection_table(sys, show_names=False, column_width=32): Interconnected system object. show_names : bool, optional Instead of printing out the system number, print out the name of - each system. Default is False because system name is not usually + each system. Default is `False` because system name is not usually specified when performing implicit interconnection using `interconnect`. column_width : int, optional @@ -2947,6 +2948,7 @@ def connection_table(sys, show_names=False, column_width=32): e | input | C u | C | P y | P | output + """ assert isinstance(sys, InterconnectedSystem), "system must be"\ "an InterconnectedSystem." diff --git a/control/optimal.py b/control/optimal.py index 074bfe120..ee41c88b1 100644 --- a/control/optimal.py +++ b/control/optimal.py @@ -151,7 +151,7 @@ class OptimalControlProblem(): def __init__( self, sys, timepts, integral_cost, trajectory_constraints=None, terminal_cost=None, terminal_constraints=None, initial_guess=None, - trajectory_method=None, basis=None, log=False, kwargs_check=True, + trajectory_method=None, basis=None, log=False, _kwargs_check=True, **kwargs): """Set up an optimal control problem.""" # Save the basic information for use later @@ -195,7 +195,7 @@ def __init__( " discrete time systems") # Make sure there were no extraneous keywords - if kwargs_check and kwargs: + if _kwargs_check and kwargs: raise TypeError("unrecognized keyword(s): ", str(kwargs)) self.trajectory_constraints = _process_constraints( @@ -771,14 +771,14 @@ def compute_trajectory( 2D vector of shape (ninputs, ntimepts) or a 1D input of shape (ninputs,) that will be broadcast by extension of the time axis. return_states : bool, optional - If True (default), return the values of the state at each time. + If `True` (default), return the values of the state at each time. squeeze : bool, optional - If True and if the system has a single output, return the system - output as a 1D array rather than a 2D array. If False, return the + If `True` and if the system has a single output, return the system + output as a 1D array rather than a 2D array. If `False`, return the system output as a 2D array even if the system is SISO. Default value set by config.defaults['control.squeeze_time_response']. transpose : bool, optional - If True, assume that 2D input arrays are transposed from the + If `True`, assume that 2D input arrays are transposed from the standard format. Used to convert MATLAB-style inputs to our format. print_summary : bool, optional @@ -795,8 +795,8 @@ def compute_trajectory( Time values of the input. res.inputs : array Optimal inputs for the system. If the system is SISO and squeeze - is not True, the array is 1D (indexed by time). If the system is - not SISO or squeeze is False, the array is 2D (indexed by the + is not `True`, the array is 1D (indexed by time). If the system is + not SISO or squeeze is `False`, the array is 2D (indexed by the output number and time). res.states : array Time evolution of the state vector (if return_states=True). @@ -837,8 +837,8 @@ def compute_mpc(self, x, squeeze=None): x : array-like or number, optional Initial state for the system. squeeze : bool, optional - If True and if the system has a single output, return the system - output as a 1D array rather than a 2D array. If False, return the + If `True` and if the system has a single output, return the system + output as a 1D array rather than a 2D array. If `False`, return the system output as a 2D array even if the system is SISO. Default value set by config.defaults['control.squeeze_time_response']. @@ -846,8 +846,8 @@ def compute_mpc(self, x, squeeze=None): ------- input : array Optimal input for the system at the current time. If the system - is SISO and squeeze is not True, the array is 1D (indexed by - time). If the system is not SISO or squeeze is False, the array + is SISO and squeeze is not `True`, the array is 1D (indexed by + time). If the system is not SISO or squeeze is `False`, the array is 2D (indexed by the output number and time). Set to `None` if the optimization failed. @@ -906,6 +906,20 @@ class OptimalControlResult(sp.optimize.OptimizeResult): Parameters ---------- + ocp : OptimalControlProblem + Optimal control problem that generated this solution. + res : scipy.minimize.OptimizeResult + Result of optimization. + print_summary : bool, optional + If `True` (default), print a short summary of the computation. + squeeze : bool, optional + If `True` and if the system has a single output, return the system + output as a 1D array rather than a 2D array. If `False`, return the + system output as a 2D array even if the system is SISO. Default value + set by config.defaults['control.squeeze_time_response']. + + Attributes + ---------- inputs : ndarray The optimal inputs associated with the optimal control problem. states : ndarray @@ -913,11 +927,10 @@ class OptimalControlResult(sp.optimize.OptimizeResult): associated with the optimal input. success : bool Whether or not the optimizer exited successful. - problem : OptimalControlProblem - Optimal control problem that generated this solution. cost : float Final cost of the return solution. - system_simulations, {cost, constraint, eqconst}_evaluations : int + system_simulations, cost_evaluations, constraint_evaluations, \ + eqconst_evaluations : int Number of system simulations and evaluations of the cost function, (inequality) constraint function, and equality constraint function performed during the optimzation. @@ -1055,16 +1068,16 @@ def solve_ocp( If `True` (default), print a short summary of the computation. return_states : bool, optional - If True, return the values of the state at each time (default = True). + If `True`, return the values of the state at each time (default = True). squeeze : bool, optional - If True and if the system has a single output, return the system - output as a 1D array rather than a 2D array. If False, return the + If `True` and if the system has a single output, return the system + output as a 1D array rather than a 2D array. If `False`, return the system output as a 2D array even if the system is SISO. Default value set by config.defaults['control.squeeze_time_response']. transpose : bool, optional - If True, assume that 2D input arrays are transposed from the standard + If `True`, assume that 2D input arrays are transposed from the standard format. Used to convert MATLAB-style inputs to our format. Returns @@ -1080,8 +1093,8 @@ def solve_ocp( res.inputs : array Optimal inputs for the system. If the system is SISO and squeeze is - not True, the array is 1D (indexed by time). If the system is not - SISO or squeeze is False, the array is 2D (indexed by the output + not `True`, the array is 1D (indexed by time). If the system is not + SISO or squeeze is `False`, the array is 2D (indexed by the output number and time). res.states : array @@ -1247,7 +1260,7 @@ class OptimalEstimationProblem(): ---------- sys : InputOutputSystem I/O system for which the optimal input will be computed. - timepts: 1D array + timepts : 1D array Set up time points at which the inputs and outputs are given. integral_cost : callable Function that returns the integral cost given the estimated state, @@ -1692,9 +1705,9 @@ def compute_estimate( A 2-tuple consisting of the estimated states and disturbance values to use as a guess for the optimal estimated trajectory. squeeze : bool, optional - If True and if the system has a single disturbance input and + If `True` and if the system has a single disturbance input and single measured output, return the system input and output as a - 1D array rather than a 2D array. If False, return the system + 1D array rather than a 2D array. If `False`, return the system output as a 2D array even if the system is SISO. Default value set by config.defaults['control.squeeze_time_response']. print_summary : bool, optional @@ -1898,6 +1911,20 @@ class OptimalEstimationResult(sp.optimize.OptimizeResult): Parameters ---------- + oep : OptimalEstimationProblem + Optimal estimation problem that generated this solution. + res : scipy.minimize.OptimizeResult + Result of optimization. + print_summary : bool, optional + If `True` (default), print a short summary of the computation. + squeeze : bool, optional + If `True` and if the system has a single output, return the system + output as a 1D array rather than a 2D array. If `False`, return the + system output as a 2D array even if the system is SISO. Default value + set by config.defaults['control.squeeze_time_response']. + + Attributes + ---------- states : ndarray Estimated state trajectory. inputs : ndarray @@ -2001,8 +2028,8 @@ def solve_oep( print_summary : bool, optional If `True` (default), print a short summary of the computation. squeeze : bool, optional - If True and if the system has a single output, return the system - output as a 1D array rather than a 2D array. If False, return the + If `True` and if the system has a single output, return the system + output as a 1D array rather than a 2D array. If `False`, return the system output as a 2D array even if the system is SISO. Default value set by config.defaults['control.squeeze_time_response']. @@ -2016,15 +2043,15 @@ def solve_oep( Time values of the input. res.inputs : array Disturbance values corresponding to the estimated state. If the - system is SISO and squeeze is not True, the array is 1D (indexed by - time). If the system is not SISO or squeeze is False, the array is + system is SISO and squeeze is not `True`, the array is 1D (indexed by + time). If the system is not SISO or squeeze is `False`, the array is 2D (indexed by the output number and time). res.states : array Estimated state vector over the given time points. res.outputs : array Noise values corresponding to the estimated state. If the system - is SISO and squeeze is not True, the array is 1D (indexed by time). - If the system is not SISO or squeeze is False, the array is 2D + is SISO and squeeze is not `True`, the array is 1D (indexed by time). + If the system is not SISO or squeeze is `False`, the array is 2D (indexed by the output number and time). Notes diff --git a/control/passivity.py b/control/passivity.py index b06cc89fc..101ae4427 100644 --- a/control/passivity.py +++ b/control/passivity.py @@ -23,13 +23,14 @@ def solve_passivity_LMI(sys, rho=None, nu=None): """Compute passivity indices and/or solves feasiblity via a LMI. - Constructs a linear matrix inequality (LMI) such that if a solution exists - and the last element of the solution is positive, the system `sys` is - passive. Inputs of None for `rho` or `nu` indicate that the function should - solve for that index (they are mutually exclusive, they can't both be - None, otherwise you're trying to solve a nonconvex bilinear matrix - inequality.) The last element of the output `solution` is either the output or input - passivity index, for `rho` = None and `nu` = None respectively. + Constructs a linear matrix inequality (LMI) such that if a solution + exists and the last element of the solution is positive, the system + `sys` is passive. Inputs of `None` for `rho` or `nu` indicate that the + function should solve for that index (they are mutually exclusive, they + can't both be `None`, otherwise you're trying to solve a nonconvex + bilinear matrix inequality.) The last element of the output `solution` + is either the output or input passivity index, for `rho` = `None` and + `nu` = `None` respectively. The sources for the algorithm are: @@ -54,6 +55,7 @@ def solve_passivity_LMI(sys, rho=None, nu=None): ------- solution : ndarray The LMI solution. + """ if cvx is None: raise ModuleNotFoundError("cvxopt required for passivity module") @@ -280,11 +282,11 @@ def ispassive(sys, ofp_index=0, ifp_index=0): .. math:: V(x) >= 0 \land \dot{V}(x) <= y^T u - is equivalent to the default case of `ofp_index` = 0 and `ifp_index` = 0. - Note that computing the `ofp_index` and `ifp_index` for a system, then - using both values simultaneously as inputs to this function is not - guaranteed to have an output of True (the system might not be passive with - both indices at the same time). + is equivalent to the default case of `ofp_index` = 0 and `ifp_index` = + 0. Note that computing the `ofp_index` and `ifp_index` for a system, + then using both values simultaneously as inputs to this function is not + guaranteed to have an output of `True` (the system might not be passive + with both indices at the same time). For more details, see [1]_. @@ -293,5 +295,6 @@ def ispassive(sys, ofp_index=0, ifp_index=0): .. [1] McCourt, Michael J., and Panos J. Antsaklis "Demonstrating passivity and dissipativity using computational methods." + """ return solve_passivity_LMI(sys, rho=ofp_index, nu=ifp_index) is not None diff --git a/control/phaseplot.py b/control/phaseplot.py index 157709bd7..425ee642d 100644 --- a/control/phaseplot.py +++ b/control/phaseplot.py @@ -1080,7 +1080,7 @@ def phase_plot(odefun, X=None, Y=None, scale=1, X0=None, T=None, timepts : array-like, optional Draw arrows at the given list times [t1, t2, ...] tfirst : bool, optional - If True, call `func` with signature `func(t, x, ...)`. + If `True`, call `func` with signature `func(t, x, ...)`. params: tuple, optional List of parameters to pass to vector field: `func(x, t, *params)` diff --git a/control/pzmap.py b/control/pzmap.py index 423b5da59..0a57d8845 100644 --- a/control/pzmap.py +++ b/control/pzmap.py @@ -79,8 +79,10 @@ class PoleZeroData: System name. sys : `StateSpace` or `TransferFunction`, optional System corresponding to the data. + dt : None, True or float, optional + System timebase (used for showing stability boundary). sort_loci : bool, optional - Set to False to turn off sorting of loci into unique branches. + Set to `False` to turn off sorting of loci into unique branches. """ def __init__( @@ -169,7 +171,7 @@ def pole_zero_plot( system is plotted. When the root locus for a single system is plotted, clicking on a location on the root locus will mark the gain on all branches of the diagram and show the system gain and damping for the - given pole in the axes title. Set to False to turn off this behavior. + given pole in the axes title. Set to `False` to turn off this behavior. Parameters ---------- @@ -232,7 +234,7 @@ def pole_zero_plot( system. legend_loc : int or str, optional Include a legend in the given location. Default is 'upper right', - with no legend for a single response. Use False to suppress legend. + with no legend for a single response. Use `False` to suppress legend. marker_color : str, optional Set the color of the markers used for poles and zeros. marker_size : int, optional diff --git a/control/rlocus.py b/control/rlocus.py index a47f473b4..b9162f485 100644 --- a/control/rlocus.py +++ b/control/rlocus.py @@ -114,7 +114,7 @@ def root_locus_plot( (see :doc:`matplotlib:api/axes_api`). plot : bool, optional (legacy) If given, `root_locus_plot` returns the legacy return values - of roots and gains. If False, just return the values with no plot. + of roots and gains. If `False`, just return the values with no plot. grid : bool or str, optional If `True` plot omega-damping grid, if `False` show imaginary axis for continuous time systems, unit circle for discrete time systems. @@ -164,7 +164,7 @@ def root_locus_plot( system. legend_loc : int or str, optional Include a legend in the given location. Default is 'center right', - with no legend for a single response. Use False to suppress legend. + with no legend for a single response. Use `False` to suppress legend. show_legend : bool, optional Force legend to be shown if `True` or hidden if `False`. If `None`, then show legend when there is more than one line on the diff --git a/control/sisotool.py b/control/sisotool.py index 87c779f8e..f35d2d08b 100644 --- a/control/sisotool.py +++ b/control/sisotool.py @@ -66,16 +66,16 @@ def sisotool(sys, initial_gain=None, xlim_rlocus=None, ylim_rlocus=None, plotstr_rlocus : `matplotlib.pyplot.plot` format string, optional Plotting style for the root locus plot(color, linestyle, etc). rlocus_grid : boolean (default = False) - If True plot s- or z-plane grid. + If `True`, plot s- or z-plane grid. omega : array_like List of frequencies in rad/sec to be used for bode plot. dB : boolean - If True, plot result in dB for the bode plot. + If `True`, plot result in dB for the bode plot. Hz : boolean - If True, plot frequency in Hz for the bode plot (omega must be + If `True`, plot frequency in Hz for the bode plot (omega must be provided in rad/sec). deg : boolean - If True, plot phase in degrees for the bode plot (else radians). + If `True`, plot phase in degrees for the bode plot (else radians). omega_limits : array_like of two values Limits of the to generate frequency vector. If Hz=True the limits are in Hz otherwise in rad/s. Ignored if omega is provided, and @@ -84,7 +84,7 @@ def sisotool(sys, initial_gain=None, xlim_rlocus=None, ylim_rlocus=None, Number of samples to plot. Defaults to config.defaults['freqplot.number_of_samples']. margins_bode : boolean - If True, plot gain and phase margin in the bode plot. + If `True`, plot gain and phase margin in the bode plot. tvect : list or ndarray, optional List of timesteps to use for closed loop step response. diff --git a/control/statefbk.py b/control/statefbk.py index a185dfe76..735f58626 100644 --- a/control/statefbk.py +++ b/control/statefbk.py @@ -120,8 +120,8 @@ def place_varga(A, B, p, dtime=False, alpha=None): p : 1D array_like Desired eigenvalue locations. dtime : bool, optional - False for continuous time pole placement or True for discrete time. - The default is dtime=False. + `False` (default) for continuous time pole placement or `True` + for discrete time. alpha : float, optional If `dtime` is false then place_varga will leave the eigenvalues with real part less than alpha untouched. If `dtime` is true then @@ -295,8 +295,8 @@ def lqr(*args, **kwargs): additional rows and columns in the `Q` matrix. method : str, optional Set the method used for computing the result. Current methods are - 'slycot' and 'scipy'. If set to None (default), try 'slycot' first - and then 'scipy'. + 'slycot' and 'scipy'. If set to `None` (default), try 'slycot' + first and then 'scipy'. Returns ------- @@ -442,8 +442,8 @@ def dlqr(*args, **kwargs): additional rows and columns in the `Q` matrix. method : str, optional Set the method used for computing the result. Current methods are - 'slycot' and 'scipy'. If set to None (default), try 'slycot' first - and then 'scipy'. + 'slycot' and 'scipy'. If set to `None` (default), try 'slycot' + first and then 'scipy'. Returns ------- diff --git a/control/statesp.py b/control/statesp.py index 534310808..56c0f01dd 100644 --- a/control/statesp.py +++ b/control/statesp.py @@ -80,9 +80,9 @@ class StateSpace(NonlinearIOSystem, LTI): A, B, C, D : array_like System matrices of the appropriate dimensions. dt : None, True or float, optional - System timebase. 0 (default) indicates continuous time, True + System timebase. 0 (default) indicates continuous time, `True` indicates discrete time with unspecified sampling time, positive - number is discrete time with specified sampling time, None + number is discrete time with specified sampling time, `None` indicates unspecified timebase (either continuous or discrete time). Attributes @@ -777,8 +777,8 @@ def __call__(self, x, squeeze=None, warn_infinite=True): x : complex or complex 1D array_like Complex frequencies squeeze : bool, optional - If squeeze=True, remove single-dimensional entries from the shape - of the output even if the system is not SISO. If squeeze=False, + If squeeze=`True`, remove single-dimensional entries from the shape + of the output even if the system is not SISO. If squeeze=`False`, keep all indices (output, input and, if omega is array_like, frequency) even if the system is SISO. The default value can be set using config.defaults['control.squeeze_frequency_response']. @@ -789,8 +789,8 @@ def __call__(self, x, squeeze=None, warn_infinite=True): ------- fresp : complex ndarray The frequency response of the system. If the system is SISO and - squeeze is not True, the shape of the array matches the shape of - omega. If the system is not SISO or squeeze is False, the first + squeeze is not `True`, the shape of the array matches the shape of + omega. If the system is not SISO or squeeze is `False`, the first two dimensions of the array are indices for the output and input and the remaining dimensions match omega. If `squeeze` is True then single-dimensional axes are removed. @@ -1301,7 +1301,7 @@ def sample(self, Ts, method='zoh', alpha=None, prewarp_frequency=None, config.defaults['iosys.sampled_system_name_suffix'], with the default being to add the suffix '$sampled'. copy_names : bool, Optional - If True, copy the names of the input signals, output + If `True`, copy the names of the input signals, output signals, and states to the sampled system. Returns @@ -1617,7 +1617,7 @@ def ss(*args, **kwargs): as a scalar. - ``ss(*args, inputs=['u1', ..., 'up'], outputs=['y1', ..., 'yq'], states=['x1', ..., 'xn'])`` + `ss(*args, inputs=['u1', ..., 'up'], outputs=['y1', ..., 'yq'], states=['x1', ..., 'xn'])` Create a system with named input, output, and state signals. Parameters @@ -1627,11 +1627,10 @@ def ss(*args, **kwargs): A, B, C, D : array_like or string System, control, output, and feed forward matrices. dt : None, True or float, optional - System timebase. 0 (default) indicates continuous - time, True indicates discrete time with unspecified sampling - time, positive number is discrete time with specified - sampling time, None indicates unspecified timebase (either - continuous or discrete time). + System timebase. 0 (default) indicates continuous time, `True` + indicates discrete time with unspecified sampling time, positive + number is discrete time with specified sampling time, `None` + indicates unspecified timebase (either continuous or discrete time). remove_useless_states : bool, optional If `True`, remove states that have no effect on the input/output dynamics. If not specified, the value is read from @@ -1639,7 +1638,7 @@ def ss(*args, **kwargs): method : str, optional Set the method used for converting a transfer function to a state space system. Current methods are 'slycot' and 'scipy'. If set to - None (default), try 'slycot' first and then 'scipy' (SISO only). + `None` (default), try 'slycot' first and then 'scipy' (SISO only). Returns ------- @@ -1862,8 +1861,8 @@ def tf2ss(*args, **kwargs): with a unique integer id. method : str, optional Set the method used for computing the result. Current methods are - 'slycot' and 'scipy'. If set to None (default), try 'slycot' first - and then 'scipy' (SISO only). + 'slycot' and 'scipy'. If set to `None` (default), try 'slycot' + first and then 'scipy' (SISO only). Raises ------ @@ -1998,13 +1997,12 @@ def rss(states=1, outputs=1, inputs=1, strictly_proper=False, **kwargs): the signal will be of the form 's[i]' (where 's' is one of 'x', 'y', or 'u'). strictly_proper : bool, optional - If set to 'True', returns a proper system (no direct term). + If set to `True`, returns a proper system (no direct term). dt : None, True or float, optional - System timebase. 0 (default) indicates continuous - time, True indicates discrete time with unspecified sampling - time, positive number is discrete time with specified - sampling time, None indicates unspecified timebase (either - continuous or discrete time). + System timebase. 0 (default) indicates continuous time, `True` + indicates discrete time with unspecified sampling time, positive + number is discrete time with specified sampling time, `None` + indicates unspecified timebase (either continuous or discrete time). name : string, optional System name (used for specifying signals). If unspecified, a generic name is generated with a unique integer id. @@ -2023,8 +2021,8 @@ def rss(states=1, outputs=1, inputs=1, strictly_proper=False, **kwargs): ----- If the number of states, inputs, or outputs is not specified, then the missing numbers are assumed to be 1. If dt is not specified or is given - as 0 or None, the poles of the returned system will always have a - negative real part. If dt is True or a postive float, the poles of the + as 0 or `None`, the poles of the returned system will always have a + negative real part. If dt is `True` or a postive float, the poles of the returned system will have magnitude less than 1. """ diff --git a/control/stochsys.py b/control/stochsys.py index 9d89ba651..bb828bbcf 100644 --- a/control/stochsys.py +++ b/control/stochsys.py @@ -81,7 +81,7 @@ def lqe(*args, **kwargs): Cross covariance matrix. Not currently implemented. method : str, optional Set the method used for computing the result. Current methods are - 'slycot' and 'scipy'. If set to None (default), try 'slycot' first + 'slycot' and 'scipy'. If set to `None` (default), try 'slycot' first and then 'scipy'. Returns @@ -218,8 +218,8 @@ def dlqe(*args, **kwargs): Cross covariance matrix (not yet supported). method : str, optional Set the method used for computing the result. Current methods are - 'slycot' and 'scipy'. If set to None (default), try 'slycot' first - and then 'scipy'. + 'slycot' and 'scipy'. If set to `None` (default), try 'slycot' + first and then 'scipy'. Returns ------- @@ -675,7 +675,7 @@ def correlation(T, X, Y=None, squeeze=True): If present, the signal with which to compute the correlation. Defaults to X. squeeze : bool, optional - If True, squeeze Rtau to remove extra dimensions (useful if the + If `True`, squeeze Rtau to remove extra dimensions (useful if the signals are scalars). Returns diff --git a/control/tests/docstrings_test.py b/control/tests/docstrings_test.py index f15803d66..ff88b4fa0 100644 --- a/control/tests/docstrings_test.py +++ b/control/tests/docstrings_test.py @@ -32,10 +32,14 @@ control.ControlPlot.reshape, # needed for legacy interface control.phase_plot, # legacy function control.drss, # documention in rss - control.flatsys.flatsys, # tested separately below - control.frd, # tested separately below - control.ss, # tested separately below - control.tf, # tested separately below + control.LinearICSystem, # intermediate I/O class + control.LTI, # intermediate I/O class + control.NamedSignal, # internal I/O class + control.TimeResponseList, # internal response class + control.FrequencyResponseList, # internal response class + control.FrequencyResponseData, # check separately (iosys) + control.InterconnectedSystem, # check separately (iosys) + control.flatsys.FlatSystem, # check separately (iosys) ] # List of keywords that we can skip testing (special cases) @@ -58,9 +62,19 @@ control.flatsys.solve_flat_ocp: [ 'method', 'options', # minimize_kwargs ], + control.optimal.OptimalControlProblem: [ + 'method', 'options' # solve_ivp_kwargs, minimize_kwargs + ], + control.optimal.OptimalControlResult: [ + 'return_x', 'return_states', 'transpose'], # legacy control.optimal.OptimalControlProblem.compute_trajectory: [ 'return_x', # legacy ], + control.optimal.OptimalEstimationProblem: [ + 'method', 'options' # solve_ivp_kwargs, minimize_kwargs + ], + control.optimal.OptimalEstimationResult: [ + 'return_x', 'return_states', 'transpose'], # legacy control.optimal.OptimalEstimationProblem.create_mhe_iosystem: [ 'inputs', 'outputs', 'states', # doc'd elsewhere ], @@ -104,29 +118,26 @@ def test_parameter_docs(module, prefix): # Skip non-top-level functions without parameter lists if prefix != "" and inspect.getmodule(obj) != module and \ doc is not None and doc["Parameters"] == []: - _info(f"skipping {prefix}.{name}", 2) + _info(f"skipping {objname} [no doc or no Paramaters]", 2) continue # If this is a class, recurse through methods # TODO: check top level documenation here (__init__, attributes?) if inspect.isclass(obj): - _info(f"Checking class {name}", 1) - _check_numpydoc_style(obj, doc) - - # Make sure there is no Returns section - if doc["Returns"] != []: - _fail(f"Class {name} should not have Returns section") - + _info(f"Checking class {objname}", 1) + # Check member functions within the class test_parameter_docs(obj, prefix + name + '.') - continue + + # Drop through and continue checks as a function # Skip anything that is inherited, hidden, or already checked - if not inspect.isfunction(obj) or \ + if not (inspect.isfunction(obj) or inspect.isclass(obj) and + not issubclass(obj, Exception)) or \ inspect.isclass(module) and name not in module.__dict__ \ or name.startswith('_') or obj in function_skiplist \ or obj in checked: - _info(f"skipping {prefix}.{name}", 6) + _info(f"skipping {objname} [inherited, hidden, or checked]", 4) continue # Skip non-top-level functions without parameter lists @@ -136,20 +147,27 @@ def test_parameter_docs(module, prefix): _info(f"skipping {prefix}{name} (not top-level, no params)", 2) continue + _info(f"Checking function {objname} against numpydoc", 2) _check_numpydoc_style(obj, doc) # Add this to the list of functions we have checked checked.add(obj) # Get the docstring (skip w/ warning if there isn't one) - _info(f"Checking function {name}", 2) + _info(f"Checking function {objname} against python-control", 2) if obj.__doc__ is None: _warn(f"{objname} is missing docstring", 2) continue elif doc is None: _fail(f"{objname} docstring not parseable", 2) + continue else: docstring = inspect.getdoc(obj) + + if inspect.isclass(obj): + # Just check __init__() + source = inspect.getsource(obj.__init__) + else: source = inspect.getsource(obj) # Skip deprecated functions (and check for proper annotation) @@ -199,7 +217,8 @@ def test_parameter_docs(module, prefix): for _, kwargname in re.findall( argname + r"(\[|\.pop\(|\.get\()'([\w]+)'", source): _info(f"Found direct keyword argument {kwargname}", 2) - kwargnames.add(kwargname) + if not kwargname.startswith('_'): + kwargnames.add(kwargname) # Look for kwargs accessed via _get_param for kwargname in re.findall( @@ -639,46 +658,70 @@ def _check_numpydoc_style(obj, doc): if len(summary) > max_summary_len: _warn(f"{name} summary is longer than {max_summary_len} characters") + # Look for Python objects that are not marked properly + python_objects = ['True', 'False', 'None'] + for pyobj in python_objects: + for section in ["Extended Summary", "Notes"]: + text = "\n".join(doc[section]) + if re.search(f"[\\s]{pyobj}([\\s]|$)", text) is not None: + _warn(f"{pyobj} appears in {section} for {name} " + "without backticks") + if inspect.isclass(obj): # Specialized checks for classes - pass + if doc["Returns"] != []: + _fail(f'Class {name} should not have "Returns" section') + elif inspect.isfunction(obj): # Specialized checks for functions - def _check_param(param, empty_ok=False, noname_ok=False, section="??"): - param_desc = "\n".join(param.desc) - param_name = f"{name} '{param.name}'" - - # Check for empty section - if param.name == "" and param.type == '': - _fail(f"Empty {section} section in {name}") - - # Make sure we have a name and description - if param.name == "" and not noname_ok: - _fail(f"{param_name} has improperly formatted parameter") - return - elif param_desc == "": - if not empty_ok: - _warn(f"{param_name} isn't documented") - return - - # Description should end in a period (colon also allowed) - if re.search(r"\.$|\.[\s]|:$", param_desc, re.MULTILINE) is None: - _warn(f"{param_name} description doesn't contain period") - - if param_desc[0:1].islower(): - _warn(f"{param_name} description starts with lower case letter") - - for param in doc["Parameters"] + doc["Other Parameters"]: - _check_param(param, section="Parameters") - for param in doc["Returns"]: - _check_param( - param, empty_ok=True, noname_ok=True, section="Returns") - for param in doc["Yields"]: - _check_param( - param, empty_ok=True, noname_ok=True, section="Yields") + if doc["Returns"] == [] and obj.__doc__ and 'return' in obj.__doc__: + _fail(f'Class {name} does not have a "Returns" section') + else: raise TypeError("unknown object type for {obj}") + for param in doc["Parameters"] + doc["Other Parameters"]: + _check_numpydoc_param(name, param, section="Parameters") + for param in doc["Returns"]: + _check_numpydoc_param( + name, param, empty_ok=True, noname_ok=True, section="Returns") + for param in doc["Yields"]: + _check_numpydoc_param( + name, param, empty_ok=True, noname_ok=True, section="Yields") + + +# Utility function for checking NumPyDoc parametres +def _check_numpydoc_param( + name, param, empty_ok=False, noname_ok=False, section="??"): + param_desc = "\n".join(param.desc) + param_name = f"{name} '{param.name}'" + + # Check for empty section + if param.name == "" and param.type == '': + _fail(f"Empty {section} section in {name}") + + # Make sure we have a name and description + if param.name == "" and not noname_ok: + _fail(f"{param_name} has improperly formatted parameter") + return + elif param_desc == "": + if not empty_ok: + _warn(f"{param_name} isn't documented") + return + + # Description should end in a period (colon also allowed) + if re.search(r"\.$|\.[\s]|:$", param_desc, re.MULTILINE) is None: + _warn(f"{param_name} description doesn't contain period") + if param_desc[0:1].islower(): + _warn(f"{param_name} description starts with lower case letter") + + # Look for Python objects that are not marked properly + python_objects = ['True', 'False', 'None'] + for pyobj in python_objects: + if re.search(f"[\\s]{pyobj}([\\s]|$)", param_desc) is not None: + _warn(f"{pyobj} appears in {param_name} description " + "without backticks") + # Utility function to replace positional signature with docstring signature def _replace_var_positional_with_docstring(sig, doc): @@ -833,7 +876,7 @@ def test_check_parameter_docs(docstring, exception, match): _info(f"--- test_iosys_container_classes(): {cls.__name__} ----", 0) test_iosys_container_classes(cls) - for cls in [ct.InterconnectedSystem, ct.LinearICSystem]: + for cls in [ct.LTI, ct.LinearICSystem]: _info(f"--- test_iosys_intermediate_classes(): {cls.__name__} ----", 0) test_iosys_intermediate_classes(cls) diff --git a/control/timeplot.py b/control/timeplot.py index 294dba6d8..22ecbd4f4 100644 --- a/control/timeplot.py +++ b/control/timeplot.py @@ -61,12 +61,12 @@ def time_response_plot( * 'overlay`: plot inputs overlaid with outputs * True: plot the inputs on their own axes plot_outputs : bool, optional - If False, suppress plotting of the outputs. + If `False`, suppress plotting of the outputs. overlay_traces : bool, optional - If set to True, combine all traces onto a single row instead of + If set to `True`, combine all traces onto a single row instead of plotting a separate row for each trace. overlay_signals : bool, optional - If set to True, combine all input and output signals onto a single + If set to `True`, combine all input and output signals onto a single plot (for each). sharex, sharey : str or bool, optional Determine whether and how x- and y-axis limits are shared between @@ -77,9 +77,9 @@ def time_response_plot( can be set using config.defaults['timeplot.sharex'] and config.defaults['timeplot.sharey']. transpose : bool, optional - If transpose is False (default), signals are plotted from top to + If transpose is `False` (default), signals are plotted from top to bottom, starting with outputs (if plotted) and then inputs. - Multi-trace plots are stacked horizontally. If transpose is True, + Multi-trace plots are stacked horizontally. If transpose is `True`, signals are plotted from left to right, starting with the inputs (if plotted) and then the outputs. Multi-trace responses are stacked vertically. @@ -110,7 +110,7 @@ def time_response_plot( ---------------- add_initial_zero : bool Add an initial point of zero at the first time point for all - inputs with type 'step'. Default is True. + inputs with type 'step'. Default is `True`. ax : array of matplotlib.axes.Axes, optional The matplotlib axes to draw the figure on. If not specified, the axes for the current figure are used or, if there is no current @@ -128,11 +128,11 @@ def time_response_plot( legend_map : array of str, optional Location of the legend for multi-axes plots. Specifies an array of legend location strings matching the shape of the subplots, with - each entry being either None (for no legend) or a legend location + each entry being either `None` (for no legend) or a legend location string (see `~matplotlib.pyplot.legend`). legend_loc : int or str, optional Include a legend in the given location. Default is 'center right', - with no legend for a single response. Use False to suppress legend. + with no legend for a single response. Use `False` to suppress legend. output_props : array of dicts, optional List of line properties to use when plotting combined outputs. The default values are set by config.defaults['timeplot.output_props']. diff --git a/control/timeresp.py b/control/timeresp.py index b6db861c6..c07e52ee2 100644 --- a/control/timeresp.py +++ b/control/timeresp.py @@ -110,10 +110,10 @@ class TimeResponseData: and if a system is multi-input or multi-output, then the inputs are returned as a 2D array (indexed by input and time) and the outputs are returned as either a 2D array (indexed by output and time) or a - 3D array (indexed by output, trace, and time). If squeeze=True, + 3D array (indexed by output, trace, and time). If squeeze=`True`, access to the output response will remove single-dimensional entries from the shape of the inputs and outputs even if the system - is not SISO. If squeeze=False, keep the input as a 2D or 3D array + is not SISO. If squeeze=`False`, keep the input as a 2D or 3D array (indexed by the input (if multi-input), trace (if single input) and time) and the output as a 3D array (indexed by the output, trace, and time) even if the system is SISO. The default value can be set @@ -124,56 +124,35 @@ class TimeResponseData: t : 1D array Time values of the input/output response(s). This attribute is normally accessed via the `time` property. - y : 2D or 3D array Output response data, indexed either by output index and time (for single trace responses) or output, trace, and time (for multi-trace responses). These data are normally accessed via the `outputs` property, which performs squeeze processing. - x : 2D or 3D array, or None State space data, indexed either by output number and time (for single trace responses) or output, trace, and time (for multi-trace responses). If no state data are present, value is `None`. These data are normally accessed via the `states` property, which performs squeeze processing. - u : 2D or 3D array, or None Input signal data, indexed either by input index and time (for single trace responses) or input, trace, and time (for multi-trace responses). If no input data are present, value is `None`. These data are normally accessed via the `inputs` property, which performs squeeze processing. - transpose : bool, optional - If True, transpose all input and output arrays (for backward - compatibility with MATLAB and `scipy.signal.lsim`). Default - value is False. issiso : bool, optional Set to `True` if the system generating the data is single-input, single-output. If passed as `None` (default), the input data will be used to set the value. ninputs, noutputs, nstates : int Number of inputs, outputs, and states of the underlying system. - input_labels, output_labels, state_labels : array of str - Names for the input, output, and state variables. - success : bool, optional - If `False`, result may not be valid (see - `input_output_response`). Defaults to `True`. - message : str, optional - Informational message if `success` is `False`. - sysname : str, optional - Name of the system that created the data. params : dict, optional If system is a nonlinear I/O system, set parameter values. - plot_inputs : bool, optional - Whether or not to plot the inputs by default (can be overridden in - the plot() method) ntraces : int, optional Number of independent traces represented in the input/output response. If ntraces is 0 (default) then the data represents a single trace with the trace index surpressed in the data. - title : str, optional - Set the title to use when plotting. trace_labels : array of string, optional Labels to use for traces (set to sysname it ntraces is 0) trace_types : array of string, optional @@ -182,28 +161,27 @@ class TimeResponseData: Other Parameters ---------------- - input_labels, output_labels, state_labels: array of str, optional + input_labels, output_labels, state_labels : array of str, optional Optional labels for the inputs, outputs, and states, given as a list of strings matching the appropriate signal dimension. sysname : str, optional Name of the system that created the data. transpose : bool, optional - If True, transpose all input and output arrays (for backward + If `True`, transpose all input and output arrays (for backward compatibility with MATLAB and `scipy.signal.lsim`). Default value is False. return_x : bool, optional - If True, return the state vector when enumerating result by + If `True`, return the state vector when enumerating result by assigning to a tuple (default = False). plot_inputs : bool, optional Whether or not to plot the inputs by default (can be overridden - in the plot() method) + in the plot() method). multi_trace : bool, optional If `True`, then 2D input array represents multiple traces. For a MIMO system, the `input` attribute should then be set to indicate which trace is being specified. Default is `False`. success : bool, optional - If `False`, result may not be valid (see - `input_output_response`). + If `False`, result may not be valid (see `input_output_response`). message : str, optional Informational message if `success` is `False`. @@ -467,21 +445,21 @@ def __call__(self, **kwargs): Parameters ---------- squeeze : bool, optional - If squeeze=True, access to the output response will remove + If squeeze=`True`, access to the output response will remove single-dimensional entries from the shape of the inputs, outputs, - and states even if the system is not SISO. If squeeze=False, keep + and states even if the system is not SISO. If squeeze=`False`, keep the input as a 2D or 3D array (indexed by the input (if multi-input), trace (if single input) and time) and the output and states as a 3D array (indexed by the output/state, trace, and time) even if the system is SISO. transpose : bool, optional - If True, transpose all input and output arrays (for backward + If `True`, transpose all input and output arrays (for backward compatibility with MATLAB and `scipy.signal.lsim`). Default value is False. return_x : bool, optional - If True, return the state vector when enumerating result by + If `True`, return the state vector when enumerating result by assigning to a tuple (default = False). input_labels, output_labels, state_labels: array of str @@ -841,14 +819,14 @@ def _check_convert_array(in_obj, legal_shapes, err_msg_start, squeeze=False, is currently checked. squeeze : bool - If True, all dimensions with only one element are removed from the - array. If False the array's shape is unmodified. + If `True`, all dimensions with only one element are removed from the + array. If `False` the array's shape is unmodified. For example: `array([[1,2,3]])` is converted to `array([1, 2, 3])` transpose : bool, optional - If True, assume that 2D input arrays are transposed from the standard + If `True`, assume that 2D input arrays are transposed from the standard format. Used to convert MATLAB-style inputs to our format. Returns @@ -934,18 +912,17 @@ def forced_response(sysdata, T=None, U=0., X0=0., transpose=False, params=None, I/O system(s) for which forced response is computed. T : array_like, optional for discrete LTI `sys` - Time steps at which the input is defined; values must be evenly spaced. - - If None, `U` must be given and `len(U)` time steps of sys.dt are - simulated. If sys.dt is None or True (undetermined time step), a time - step of 1.0 is assumed. + Time steps at which the input is defined; values must be evenly + spaced. If `None`, `U` must be given and `len(U)` time steps of + sys.dt are simulated. If sys.dt is `None` or `True` (undetermined + time step), a time step of 1.0 is assumed. U : array_like or float, optional - Input array giving input at each time `T`. - If `U` is None or 0, `T` must be given, even for discrete - time systems. In this case, for continuous time systems, a direct - calculation of the matrix exponential is used, which is faster than the - general interpolating algorithm used otherwise. + Input array giving input at each time `T`. If `U` is `None` or 0, + `T` must be given, even for discrete time systems. In this case, + for continuous time systems, a direct calculation of the matrix + exponential is used, which is faster than the general interpolating + algorithm used otherwise. X0 : array_like or float, default=0. Initial condition. @@ -954,11 +931,11 @@ def forced_response(sysdata, T=None, U=0., X0=0., transpose=False, params=None, If system is a nonlinear I/O system, set parameter values. transpose : bool, default=False - If True, transpose all input and output arrays (for backward + If `True`, transpose all input and output arrays (for backward compatibility with MATLAB and `scipy.signal.lsim`). interpolate : bool, default=False - If True and system is a discrete time system, the input will + If `True` and system is a discrete time system, the input will be interpolated between the given time steps and the output will be given at system sampling rate. Otherwise, only return the output at the times given in `T`. No effect on continuous @@ -967,19 +944,19 @@ def forced_response(sysdata, T=None, U=0., X0=0., transpose=False, params=None, return_x : bool, default=None Used if the time response data is assigned to a tuple: - * If False, return only the time and output vectors. + * If `False`, return only the time and output vectors. - * If True, also return the the state vector. + * If `True`, also return the the state vector. - * If None, determine the returned variables by - config.defaults['forced_response.return_x'], which was True - before version 0.9 and is False since then. + * If `None`, determine the returned variables by + config.defaults['forced_response.return_x'], which was `True` + before version 0.9 and is `False` since then. squeeze : bool, optional By default, if a system is single-input, single-output (SISO) then the output response is returned as a 1D array (indexed by time). If - `squeeze` is True, remove single-dimensional entries from the shape of - the output even if the system is not SISO. If `squeeze` is False, keep + `squeeze` is `True`, remove single-dimensional entries from the shape of + the output even if the system is not SISO. If `squeeze` is `False`, keep the output as a 2D array (indexed by the output number and time) even if the system is SISO. The default behavior can be overridden by config.defaults['control.squeeze_time_response']. @@ -994,8 +971,8 @@ def forced_response(sysdata, T=None, U=0., X0=0., transpose=False, params=None, * time (array): Time values of the output. * outputs (array): Response of the system. If the system is SISO and - `squeeze` is not True, the array is 1D (indexed by time). If the - system is not SISO or `squeeze` is False, the array is 2D (indexed + `squeeze` is not `True`, the array is 1D (indexed by time). If the + system is not SISO or `squeeze` is `False`, the array is 2D (indexed by output and time). * states (array): Time evolution of the state vector, represented as @@ -1276,14 +1253,14 @@ def _process_time_response( Default is `False`. transpose : bool, optional - If True, transpose data (for backward compatibility with MATLAB and + If `True`, transpose data (for backward compatibility with MATLAB and `scipy.signal.lsim`). Default value is False. squeeze : bool, optional By default, if a system is single-input, single-output (SISO) then the signals are returned as a 1D array (indexed by time). If - squeeze=True, remove single-dimensional entries from the shape of the - signal even if the system is not SISO. If squeeze=False, keep the + squeeze=`True`, remove single-dimensional entries from the shape of the + signal even if the system is not SISO. If squeeze=`False`, keep the signal as a 3D array (indexed by the output, input, and time) even if the system is SISO. The default value can be set using config.defaults['control.squeeze_time_response']. @@ -1291,9 +1268,9 @@ def _process_time_response( Returns ------- output : ndarray - Processed signal. If the system is SISO and squeeze is not True, + Processed signal. If the system is SISO and squeeze is not `True`, the array is 1D (indexed by time). If the system is not SISO or - squeeze is False, the array is either 2D (indexed by output and + squeeze is `False`, the array is either 2D (indexed by output and time) or 3D (indexed by input, output, and time). """ @@ -1376,19 +1353,19 @@ def step_response( array (autocomputed if not given); ignored if sys is discrete-time. transpose : bool, optional - If True, transpose all input and output arrays (for backward + If `True`, transpose all input and output arrays (for backward compatibility with MATLAB and `scipy.signal.lsim`). Default value is False. return_x : bool, optional - If True, return the state vector when assigning to a tuple (default = + If `True`, return the state vector when assigning to a tuple (default = False). See `forced_response` for more details. squeeze : bool, optional By default, if a system is single-input, single-output (SISO) then the output response is returned as a 1D array (indexed by time). If - squeeze=True, remove single-dimensional entries from the shape of the - output even if the system is not SISO. If squeeze=False, keep the + squeeze=`True`, remove single-dimensional entries from the shape of the + output even if the system is not SISO. If squeeze=`False`, keep the output as a 3D array (indexed by the output, input, and time) even if the system is SISO. The default value can be set using config.defaults['control.squeeze_time_response']. @@ -1753,8 +1730,8 @@ def initial_response( arrays with the correct shape. output : int - Index of the output that will be used in this simulation. Set to None - to not trim outputs. + Index of the output that will be used in this simulation. Set + to `None` to not trim outputs. T_num : int, optional Number of time steps to use in simulation if T is not provided as an @@ -1764,19 +1741,19 @@ def initial_response( If system is a nonlinear I/O system, set parameter values. transpose : bool, optional - If True, transpose all input and output arrays (for backward + If `True`, transpose all input and output arrays (for backward compatibility with MATLAB and `scipy.signal.lsim`). Default value is False. return_x : bool, optional - If True, return the state vector when assigning to a tuple (default = + If `True`, return the state vector when assigning to a tuple (default = False). See `forced_response` for more details. squeeze : bool, optional By default, if a system is single-input, single-output (SISO) then the output response is returned as a 1D array (indexed by time). If - squeeze=True, remove single-dimensional entries from the shape of the - output even if the system is not SISO. If squeeze=False, keep the + squeeze=`True`, remove single-dimensional entries from the shape of the + output even if the system is not SISO. If squeeze=`False`, keep the output as a 2D array (indexed by the output number and time) even if the system is SISO. The default value can be set using config.defaults['control.squeeze_time_response']. @@ -1879,19 +1856,19 @@ def impulse_response( array (autocomputed if not given); ignored if sys is discrete-time. transpose : bool, optional - If True, transpose all input and output arrays (for backward + If `True`, transpose all input and output arrays (for backward compatibility with MATLAB and `scipy.signal.lsim`). Default value is False. return_x : bool, optional - If True, return the state vector when assigning to a tuple (default = - False). See `forced_response` for more details. + If `True`, return the state vector when assigning to a tuple + (default = `False`). See `forced_response` for more details. squeeze : bool, optional By default, if a system is single-input, single-output (SISO) then the output response is returned as a 1D array (indexed by time). If - squeeze=True, remove single-dimensional entries from the shape of the - output even if the system is not SISO. If squeeze=False, keep the + squeeze=`True`, remove single-dimensional entries from the shape of the + output even if the system is not SISO. If squeeze=`False`, keep the output as a 2D array (indexed by the output number and time) even if the system is SISO. The default value can be set using config.defaults['control.squeeze_time_response']. @@ -2035,7 +2012,7 @@ def _ideal_tfinal_and_dt(sys, is_step=True): Scales the dc value by the magnitude of the nonzero mode since integrating the impulse response gives :math:`\\int e^{-\\lambda t} = -e^{-\\lambda t}/ \\lambda` - Default is True. + Default is `True`. Returns ------- diff --git a/control/xferfcn.py b/control/xferfcn.py index e6ffe87ce..8bf4115bb 100644 --- a/control/xferfcn.py +++ b/control/xferfcn.py @@ -63,7 +63,7 @@ class TransferFunction(LTI): dt : None, True or float, optional System timebase. 0 (default) indicates continuous time, `True` indicates discrete time with unspecified sampling time, positive - number is discrete time with specified sampling time, None indicates + number is discrete time with specified sampling time, `None` indicates unspecified timebase (either continuous or discrete time). Attributes @@ -353,13 +353,13 @@ def __call__(self, x, squeeze=None, warn_infinite=True): x : complex or complex 1D array_like Complex frequencies squeeze : bool, optional - If squeeze=True, remove single-dimensional entries from the shape - of the output even if the system is not SISO. If squeeze=False, + If squeeze=`True`, remove single-dimensional entries from the shape + of the output even if the system is not SISO. If squeeze=`False`, keep all indices (output, input and, if omega is array_like, frequency) even if the system is SISO. The default value can be set using config.defaults['control.squeeze_frequency_response']. - If True and the system is single-input single-output (SISO), - return a 1D array rather than a 3D array. Default value (True) + If `True` and the system is single-input single-output (SISO), + return a 1D array rather than a 3D array. Default value (`True`) set by config.defaults['control.squeeze_frequency_response']. warn_infinite : bool, optional If set to `False`, turn off divide by zero warning. @@ -368,8 +368,8 @@ def __call__(self, x, squeeze=None, warn_infinite=True): ------- fresp : complex ndarray The frequency response of the system. If the system is SISO and - squeeze is not True, the shape of the array matches the shape of - omega. If the system is not SISO or squeeze is False, the first + squeeze is not `True`, the shape of the array matches the shape of + omega. If the system is not SISO or squeeze is `False`, the first two dimensions of the array are indices for the output and input and the remaining dimensions match omega. If `squeeze` is True then single-dimensional axes are removed. @@ -1186,7 +1186,7 @@ def sample(self, Ts, method='zoh', alpha=None, prewarp_frequency=None, config.defaults['iosys.sampled_system_name_suffix'], with the default being to add the suffix '$sampled'. copy_names : bool, Optional - If True, copy the names of the input signals, output + If `True`, copy the names of the input signals, output signals, and states to the sampled system. Returns @@ -1625,7 +1625,7 @@ def tf(*args, **kwargs): dt : None, True or float, optional System timebase. 0 (default) indicates continuous time, `True` indicates discrete time with unspecified sampling time, positive - number is discrete time with specified sampling time, None indicates + number is discrete time with specified sampling time, `None` indicates unspecified timebase (either continuous or discrete time). display_format : None, 'poly' or 'zpk' Set the display format used in printing the TransferFunction object. @@ -1771,11 +1771,10 @@ def zpk(zeros, poles, gain, *args, **kwargs): gain : float System gain. dt : None, True or float, optional - System timebase. 0 (default) indicates continuous - time, True indicates discrete time with unspecified sampling - time, positive number is discrete time with specified - sampling time, None indicates unspecified timebase (either - continuous or discrete time). + System timebase. 0 (default) indicates continuous time, `True` + indicates discrete time with unspecified sampling time, positive + number is discrete time with specified sampling time, `None` + indicates unspecified timebase (either continuous or discrete time). inputs, outputs, states : str, or list of str, optional List of strings that name the individual signals. If this parameter is not given or given as `None`, the signal names will be of the diff --git a/doc/classes.rst b/doc/classes.rst index d5758b3aa..69ab50503 100644 --- a/doc/classes.rst +++ b/doc/classes.rst @@ -48,7 +48,6 @@ These classes are used as the outputs of `_response`, `_map`, and :nosignatures: ControlPlot - FrequencyResponseData TimeResponseData NyquistResponseData PoleZeroData From c888ccc188ec1f8f57f950bf31c751453df76bce Mon Sep 17 00:00:00 2001 From: Richard Murray Date: Thu, 2 Jan 2025 23:03:47 -0800 Subject: [PATCH 55/93] initial set of developer notes --- control/config.py | 38 ++++- control/xferfcn.py | 25 +-- doc/config.rst | 9 ++ doc/develop.rst | 328 ++++++++++++++++++++++++++++++++++++++ doc/examples/template.py | 167 +++++++++++++++++++ doc/examples/template.rst | 95 +++++++++++ doc/index.rst | 1 + 7 files changed, 644 insertions(+), 19 deletions(-) create mode 100644 doc/develop.rst create mode 100644 doc/examples/template.py create mode 100644 doc/examples/template.rst diff --git a/control/config.py b/control/config.py index ebeb50aa2..7c0b19d7d 100644 --- a/control/config.py +++ b/control/config.py @@ -376,15 +376,37 @@ def use_legacy_defaults(version): return (major, minor, patch) -# -# Utility function for processing legacy keywords -# -# Use this function to handle a legacy keyword that has been renamed. This -# function pops the old keyword off of the kwargs dictionary and issues a -# warning. If both the old and new keyword are present, a ControlArgument -# exception is raised. -# def _process_legacy_keyword(kwargs, oldkey, newkey, newval, warn_oldkey=True): + """Utility function for processing legacy keywords. + + Use this function to handle a legacy keyword that has been renamed. + This function pops the old keyword off of the kwargs dictionary and + issues a warning. If both the old and new keyword are present, a + ControlArgument exception is raised. + + Parameters + ---------- + kwargs : dict + Dictionary of keword arguments (from function call). + oldkey : str + Old (legacy) parameter name. + newkey : str + Current name of the parameter. + newval : object + Value of the current parameter (from the function signature). + warn_oldkey : bool + If set to `False`, suppress generation of a warning about using a + legacy keyword. This is useful if you have two versions of a + keyword and you want to allow either to be used (see the `cost` and + `trajectory_cost` keywords in `flatsys.point_to_point` for an + example of this). + + Returns + ------- + val : object + Value of the (new) keyword. + + """ if oldkey in kwargs: if warn_oldkey: warnings.warn( diff --git a/control/xferfcn.py b/control/xferfcn.py index 8bf4115bb..768ebd1f6 100644 --- a/control/xferfcn.py +++ b/control/xferfcn.py @@ -1480,29 +1480,32 @@ def _convert_to_transfer_function( sys, inputs=1, outputs=1, use_prefix_suffix=False): """Convert a system to transfer function form (if needed). - If sys is already a transfer function, then it is returned. If sys is a - state space object, then it is converted to a transfer function and - returned. If sys is a scalar, then the number of inputs and outputs can be - specified manually, as in: + If `sys` is already a transfer function, then it is returned. If `sys` + is a state space object, then it is converted to a transfer function + and returned. If `sys` is a scalar, then the number of inputs and + outputs can be specified manually, as in:: >>> from control.xferfcn import _convert_to_transfer_function >>> sys = _convert_to_transfer_function(3.) # Assumes inputs = outputs = 1 >>> sys = _convert_to_transfer_function(1., inputs=3, outputs=2) - In the latter example, sys's matrix transfer function is [[1., 1., 1.] - [1., 1., 1.]]. + In the latter example, the matrix transfer function for `sys` is:: - If sys is an array-like type, then it is converted to a constant-gain + [[1., 1., 1.] + [1., 1., 1.]]. + + If `sys` is an array-like type, then it is converted to a constant-gain transfer function. Note: no renaming of inputs and outputs is performed; this should be done by the calling function. - >>> sys = _convert_to_transfer_function([[1., 0.], [2., 3.]]) + Arrays can also be passed as an argument. For example:: + + sys = _convert_to_transfer_function([[1., 0.], [2., 3.]]) - In this example, the numerator matrix will be - [[[1.0], [0.0]], [[2.0], [3.0]]] - and the denominator matrix [[[1.0], [1.0]], [[1.0], [1.0]]] + will give a system with numerator matrix `[[[1.0], [0.0]], [[2.0], + [3.0]]]` and denominator matrix `[[[1.0], [1.0]], [[1.0], [1.0]]]`. """ from .statesp import StateSpace diff --git a/doc/config.rst b/doc/config.rst index 0a0b5339a..20dde9834 100644 --- a/doc/config.rst +++ b/doc/config.rst @@ -151,6 +151,15 @@ System display parameters * 'eval' : system specific, loadable representation * 'latex' : latex representation of the object +.. py:data:: iosys.repr_show_count + :type: bool + :value: True + + If `True`, show the input, output, and state count when using + `iosys_repr()` and the 'eval' format. Otherwise, the input, + output, and state values are repressed from the output unless + non-generic signal names are present. + .. py:data:: xferfcn.display_format :type: str :value: 'poly' diff --git a/doc/develop.rst b/doc/develop.rst new file mode 100644 index 000000000..649c56d69 --- /dev/null +++ b/doc/develop.rst @@ -0,0 +1,328 @@ +.. currentmodule:: control + +*************** +Developer Notes +*************** + +This chapter contains notes for developers who wish to contribute to +the Python Control Systems Library (python-control). It is mainly a +listing of the practices that have evolved over the course of +development since the package was created in 2009. + + +Package structure +================= + +The python-control package is maintained on GitHub, with documentation +hosted by ReadTheDocs and a mailing list on SourceForge: + + * Project home page: http://python-control.org + * Source code repository: https://github.com/python-control/python-control + * Documentation: http://python-control.readthedocs.org/ + * Issue tracker: https://github.com/python-control/python-control/issues + * Mailing list: http://sourceforge.net/p/python-control/mailman/ + +GitHub repository file and directory layout: + - **python-control/** - main repository + + * LICENSE, Manifest, pyproject.toml, README.rst - package information + + * **control/** - primary package source code + + + __init__.py, _version.py, config.py - package definition + and configuration + + + iosys.py, nlsys.py, lti.py, statesp.py, xferfcn.py, + frdata.py - I/O system classes + + + bdalg.py, delay.py, canonical.py, margins.py, + sysnorm.py, modelsimp.py, passivity.py, robust.py, + statefbk.py, stochsys.py - analysis and synthesis routintes + + + ctrlplot.py, descfcn.py, freqplot.py, grid.py, + nichols.py, pzmap.py, rlocus.py, sisotool.py, + timeplot.py, timeresp.py - response and plotting rouintes + + + ctrlutil.py, dtime.py, exception.py, mateqn.py - utility funcitons + + + phaseplot.py - phase plot module + + + optimal.py - optimal control module + + + **flatsys/** - flat systems sub-package + + - __init__.py, basis.py, bezier.py, bspline.py, + flatsys.py, linflat.py, poly.py, systraj.py - + sub-package files + + + **matlab/** - MATLAB compatibility subpackage + + - __init.py, timeresp.py, wrappers.py - subpackage files + + + **tests/** - unit tests + + * **.github/** - GitHub workflows + + * **benchmarks/** - benchmarking files (not well-maintained) + + * **doc/** - user guide and reference manual + + + index.rst - main documentation index + + + conf.py, Makefile - sphinx configuration files + + + intro.rst, linear.rst, statesp.rst, xferfcn.rst, nonlinear.rst, + flatsys.rst, iosys.rst, nlsys.rst, optimal.rst, phaseplot.rst, + response.rst, descfcn.rst, stochastic.rst, examples.rst - User + Guide + + + functions.rst, classes.rst, config.rst, matlab.rst, develop.rst - + Reference Manual + + + **examples/** + + - \*.py, \*.rst - Python scripts (linked to ../examples/\*.py) + + - \*.ipynb - Jupytern notebooks (linked to ../examples.ipynb) + + + **figures/** + + - \*.pdf, \*.png - Figures for inclusion in documentation + + * **examples/** + + + \*.py - Python scripts + + + \*.ipynb - Jupytern notebooks + + +Naming conventions +================== + +Generally speaking, standard Python and NumPy naming conventions are +used through the package. + + +Filenames +--------- + +* Source files are lower case, usually less than 10 characters + +* Unit tests (in `control/tests/`) are of the form `module_test.py` or + `module_function.py`. + + +Class names +----------- + +* Most class names are in camel case, with long form descriptions of + the contents (`TimeResponseData`). + +* Input/output class names are written out as long as they aren't too + long (`StateSpace`, `TransferFunciton`), but for very long names + 'IO' can be used in place of 'InputOutput' (`NonlinearIOSystem`) and + 'IC' can be used in place of 'Interconnected' (`LinearICSystem`). + +* Some older classes don't follow these guidlines (e.g., `LTI` instead + of `LinearTimeInvariantSystem`). + + +Function names +-------------- + +* Function names are lower case with words separated by underscores. + +* Function names usuall describe what they do + (`create_statefbk_iosys`, `find_operating_points`) or what they + generate (`input_output_response`, `find_operating_point`). + +* Some abbreviations and shorted versions are used when names get very + long (e.g., `create_statefbk_iosys` instead of + `create_state_feedback_input_output_system`. + +* Factory functions for I/O systems use short names (partly from MATLAB + conventions, partly because they are pretty frequenctly used): + `frd`, `flatsys`, `nlsys`, `ss`, and `tf`. + +* Short versions of common commands are created by creating an object + with the shorter name as a copy of the main object: `bode = + bode_plot`, `step = step_response`, etc. + +* The MATLAB compatibility library (`control.matlab`) uses names that + line up with MATLAB (e.g., `lsim` instead of `forced_response`). + + +Parameter names +--------------- + +Parameter names are not (yet) very uniform across the package. A few +general patterns are emerging: + +* Use longer description parameter names that describe the action or + role (e.g., `trajectory_constraints` and `print_summary` in + `optimal.solve_ocp` (which probably should be named + `optimal.`find_optimal_trajectory`...). + +System creating commands: + +* Commands that create an I/O system should allow the use of the + following standard parameters: + + - `name`: system name + + - `inputs`, `outputs`, `states`: number or names of inputs, outputs, state + + These can be parsed in a consistent way using the + `iosys._process_iosys_keywords` function. + +* Commands + +System arguments: + +* `sys` when an argument is a single input/output system (e.g. `bandwidth`). + +* `syslist` when an argument is a list of systems (e.g., + `interconnect`). A single system should also be OK. + +* `sysdata` when an argument can either be a system, a list of + systems ,or data describing a response (e.g, + `nyquist_response`). + +Signal arguments: + +* Factory functions use `inputs`, `outputs`, and `states` to provide + either the number of each signal or a list of labels for the + signals. + + +Documentation guidelines +======================== + +The python-control package is documented using docstrings and Sphinx. +Reference documentation (class and function descriptions, with details +on parameters) should all go in docstrings. User documentation in +more narrative form should be in the `.rst` files in `doc/`, where it +can be incorporated into the User Guide. All significant +functionality should have a narrative description in the User Guide in +addition to docstrings. + + +General docstring info +---------------------- + +* Use single backticks around all Python objects. Double backticks + should never be used. (The `doc/conf.py` file defines + `default_role` to be `py:obj`, so everything in a single backtick + will be rendered in code form and linked to the appropriate + documentation if it exists.) + +* All function names, parameter names, and Python objects (`True`, + `False`, `None`) should be written as code (enclose in backticks). + + +Function docstrings +------------------- + +Follow NumPyDoc format with the following additional detals: + +* All functions should have a short (< 64 character) summary line that + starts with a capital letter and ends with a period. + +* All paramter descriptions should start with a capital letter and end + with a period. An exception is paramters that have a list of + possible values, in which case a phrase sending in `:` followed by a + list (without punctuation) is OK. + +* All parameters and keywords must be documented. The + `docstrings_test.py` unit test tries to flag as many of these as + possible. + +* Include an "Examples" section for all non-trivial functions, in a + form that can be checked by running `make doctest` in the `doc` + directory. This is also part of the CI checks. + + +Class docstrings +---------------- + +Follow NumpyDoc format with the follow additional details: + +* Paramaters used in creating an object go in the class docstring and + not in the `__init__` docstring (which is not included in the + Sphinx-based documentation). OK for the `__init__` function to have + no docstring. + +* Parameters that are also attributes only need to be documented once + (in the "Parameters" or "Additional Parameters" section of the class + docstring). + +* Attributes that are created within a class and might be of interest + to the user should be documented in the "Attributes" section of the + class docstring. + +* Classes should not include a "Returns" section (since they always + return an instance of the class). + +* Functions and attributes that are not intended to be accessed by + users should start with an underscore. + +I/O system classes: + +* Subclasses of `InputOutputSystem` should always have a factory + function that is used ot create them. The class documentation only + needs to documen the required parameters; the full list of + parameters (and optional keywords) can and should be documented in + the factory function docstring. + + +Sphinx documentation: + +* Each file should declare the `currentmodule` at or near the top of + the file. Excpt for sub-packages (`control.flatsys`) and modules + that need to be impored separatly (`control.optimal`), + `currentmodule` should be set to control. + + +Utility functions +================= + +The following utility functions can be used to help with standard +processing and parsing operations: + +.. autosummary:: + :toctree: generated/ + + config._process_legacy_keyword + exception.cvxopt_check + exception.pandas_check + exception.slycot_check + iosys._process_iosys_keywords + statesp._convert_to_statespace + xferfcn._convert_to_transfer_function + + +Sample files +============ + + +Code template +------------- + +The following file is a template for a python-control module. It can +be found in `python-control/doc/examples/template.py`. + +.. literalinclude:: examples/template.py + :language: python + :linenos: + + +Documentation template +---------------------- + +The following file is a template for a documentation file. It can be +found in `python-control/doc/examples/template.rst`. + +.. literalinclude:: examples/template.rst + :language: text + :linenos: + :lines: 3- diff --git a/doc/examples/template.py b/doc/examples/template.py new file mode 100644 index 000000000..30f6ef0f4 --- /dev/null +++ b/doc/examples/template.py @@ -0,0 +1,167 @@ +# template.py - template file for python-control module +# RMM, 3 Jan 2024 + +"""Template file for python-control module. + +This file provides a template that can be used when creating a new +file/module in python-control. The key elements of a module are included +in this template, following the suggestions in the Developer Guidelines. + +The first line of a module file should be the name of the file and a short +description. The next few lines can contain information about who created +the file (your name/initials and date). For this file I used the short +version (initials, date), but a longer version would be to do something of +the form:: + + # filename.py - short one line description + # + # Initial author: Full name + # Creation date: date the file was created + +After the header comments, the next item is the module docstring, which +should be a multi-line comment, like this one. The first line of the +comment is a one line summary phrase, starting with a capital letter and +ending in a period (often the same as the line at the very top). The rest +of the docstring is an extended summary (this one is a bit longer than +would be typical). + +After the docstring, you should have the following elements (in Python): + + * Package imports, using the `isort -m2` format (library, standard, custom) + * __all__ command, listing public objects in the file + * Class definitions (if any) + * Public function definitions + * Internal function definitions (starting with '_') + * Function aliases (short = long_name) + +The rest of this file contains examples of these elements. + +""" + +import warnings # Python packages + +import numpy as np # Standard external packages + +from . import config # Other modules/packages in python-control +from .lti import LTI # Public function or class from a module + +__all__ = ['SampleClass', 'sample_function'] + + +class SampleClass(): + """Sample class in the python-control package. + + This is an example of a class definition. The docstring follows + numpydoc format. The first line should be a summary (which will show + up in `autosummary` entries in the Sphinx documentation) and then an + extended summary describing what the class does. Then the usual + sections, per numpydoc. + + Additional guidelines on what should be listed in the various sections + can be found in the 'Class docstrings' section of the Developer + Guidelines. + + Parameters + ---------- + sys : InputOutputSystem + Short description of the parameter. + + Attributes + ---------- + data : array + Short description of an attribute. + + """ + def __init__(self, sys): + # No docstring required here + self.sys = sys # Parameter passed as argument + self.data = sys.name # Attribute created within class + + def sample_method(self, data): + """Sample method within in a class. + + This is an example of a method within a class. Document using + numpydoc format. + + """ + return None + + +def sample_function(data, option=False, **kwargs): + """Sample function in the template module. + + This is an example of a public function within the template module. + This function will usually be placed in the `control` namespace by + updating `__init.py` to import the function (often by importing the + entire module). + + Docstring should be in standard numpy doc format. The extended summary + (this text) should describe the basic operation of the function, with + technical details in the "Notes" section. + + Parameters + ---------- + data : array + Sample parameter for sample function, with short docstring. + option : bool, optional + Optional parameter, with default value `False`. + + Returns + ------- + out : float + Short description of the function output. + + Additional Parameters + --------------------- + inputs : int, str, or list of str + Parameters that are less commonly used, in this case a keyword + parameter. + + See Also + -------- + function1, function2 + + Notes + ----- + This section can contain a more detailed description of how the system + works. OK to include some limited mathematics, either via inline math + directions for a short formula (like this: ..math: a = b c) or via a + displayed equation: + + ..math:: + + a = \int_0^t f(t) dt + + The trick in the docstring is to write something that looks good in + pure text format but is also processed by sphinx correctly. + + If you refer to parameters, such as the `data` argument to this + function, but them in single backticks (which will render them in code + style in Sphinx). You should also do this for Python contains like + `True, `False`, and `None`. + + """ + inputs = kwargs['inputs'] + if option is True: + return data + else: + return None + +# +# Internal functions +# +# Functions that are not intended for public use can go anyplace, but I +# usually put them at the bottom of the file (out of the way). Their name +# should start with an underscore. Docstrings are optional, but if you +# don't include a docstring, make sure to include comments describing how +# the function works. +# + + +# Sample internal function to process data +def _internal_function(data): + return None + + +# Aliases (short versions of long function names) +sf = sample_function diff --git a/doc/examples/template.rst b/doc/examples/template.rst new file mode 100644 index 000000000..b7596fdd9 --- /dev/null +++ b/doc/examples/template.rst @@ -0,0 +1,95 @@ +:orphan: remove this line and the next bbefore use (supresses toctree warning) + +.. currentmodule:: control + +************** +Sample Chapter +************** + +This is an example of a top-level documentation file, which serves a +chapter in the User Guide or Reference Manual in the Sphinx +documentation. It is not that likely we will create a lot more files +of this sort, so it is probably the internal structure of the file +that is most useful. + +The file in which a chapter is contained will usual start by declaring +`currentmodule` to be `control`, which will allow text enclosed in +backticks to be searched for class and function names and appropriate +links inserted. The next element of the file is the chapter name, +with asterisks above and below. Chapters should have a capitalized +title and an introductory paragraph. If you need to add a reference +to a chapter, insert a sphinx reference (`.. _ch-sample:`) above +the chapter title. + +.. _sec-sample: + +Sample Section +============== + +A chapter is made of up of multiple sections. Sections use equal +signs below the section title. Following FBS2e, the section title +should be capitalized. If you need to insert a reference to the +section, put that above the section title (`.. _sec-sample:`), as +shown here. + + +Sample subsection +----------------- + +Subsections use dashes below the subsection title. The first word of +the title should be capitalized, but the rest of the subsection title +is lower case (unless it has a proper noun). I usually leave two +blank lines before the start up a subection and one blank line after +the section markers. + + +Mathematics +----------- + +Mathematics can be uncluded using the `math` directive. This can be +done inline using `:math:short formula` (e.g. :math:`a = b`) or as a +displayed equation, using the `.. math::` directive:: + +.. math:: + + a(t) = \int_0^t b(\tau) d\tau + + +Function summaries +------------------ + +Use the `autosummary` directive to include a table with a list of +function sinatures and summary descriptions:: + +.. autosummary:: + + input_output_response + describing_function + some_other_function + + +Module summaries +---------------- + +If you have a docstring at the top of a module that you want to pull +into the documentation, you can do that with the `automodule` +directive: + +.. automodule:: control.optimal + :noindex: + :no-members: + :no-inherited-members: + :no-special-members: + +.. currentmodule:: control + +The `:noindex:` option gets rid of warnings about a module being +indexed twice. The next three options are used to just bring in the +summary and extended summary in the module docstring, without +including all of the documentation of the classes and functions in the +module. + +Note that we `automodule` will set the current module to the one for +which you just generated documentation, so the `currentmodule` should +be reset to control afterwards (otherwise references to functions in +the `control` namespace won't be recognized. diff --git a/doc/index.rst b/doc/index.rst index 95c208365..bea410060 100644 --- a/doc/index.rst +++ b/doc/index.rst @@ -66,6 +66,7 @@ or the GitHub site: https://github.com/python-control/python-control. classes config matlab + develop *********** Development From e657edc0ecc6cb56202e83eeca1a0e9a34f6e2dc Mon Sep 17 00:00:00 2001 From: Richard Murray Date: Tue, 7 Jan 2025 16:13:59 -0800 Subject: [PATCH 56/93] fix small typos --- doc/develop.rst | 105 +++++++++++++++++++++++++++++++++--------------- 1 file changed, 72 insertions(+), 33 deletions(-) diff --git a/doc/develop.rst b/doc/develop.rst index 649c56d69..d20464491 100644 --- a/doc/develop.rst +++ b/doc/develop.rst @@ -83,7 +83,7 @@ GitHub repository file and directory layout: - \*.py, \*.rst - Python scripts (linked to ../examples/\*.py) - - \*.ipynb - Jupytern notebooks (linked to ../examples.ipynb) + - \*.ipynb - Jupyter notebooks (linked to ../examples.ipynb) + **figures/** @@ -93,20 +93,23 @@ GitHub repository file and directory layout: + \*.py - Python scripts - + \*.ipynb - Jupytern notebooks + + \*.ipynb - Jupyter notebooks Naming conventions ================== Generally speaking, standard Python and NumPy naming conventions are -used through the package. +used throughout the package. + +* Python PEP 8 (code style): https://peps.python.org/pep-0008/ Filenames --------- -* Source files are lower case, usually less than 10 characters +* Source files are lower case, usually less than 10 characters (and 8 + or less is better). * Unit tests (in `control/tests/`) are of the form `module_test.py` or `module_function.py`. @@ -116,15 +119,15 @@ Class names ----------- * Most class names are in camel case, with long form descriptions of - the contents (`TimeResponseData`). + the object purpose/contents (`TimeResponseData`). -* Input/output class names are written out as long as they aren't too - long (`StateSpace`, `TransferFunciton`), but for very long names +* Input/output class names are written out in long form as they aren't + too long (`StateSpace`, `TransferFunction`), but for very long names 'IO' can be used in place of 'InputOutput' (`NonlinearIOSystem`) and 'IC' can be used in place of 'Interconnected' (`LinearICSystem`). -* Some older classes don't follow these guidlines (e.g., `LTI` instead - of `LinearTimeInvariantSystem`). +* Some older classes don't follow these guidelines (e.g., `LTI` instead + of `LinearTimeInvariantSystem` or `LTISystem`). Function names @@ -132,24 +135,24 @@ Function names * Function names are lower case with words separated by underscores. -* Function names usuall describe what they do +* Function names usually describe what they do (`create_statefbk_iosys`, `find_operating_points`) or what they generate (`input_output_response`, `find_operating_point`). -* Some abbreviations and shorted versions are used when names get very - long (e.g., `create_statefbk_iosys` instead of +* Some abbreviations and shortened versions are used when names get + very long (e.g., `create_statefbk_iosys` instead of `create_state_feedback_input_output_system`. * Factory functions for I/O systems use short names (partly from MATLAB - conventions, partly because they are pretty frequenctly used): + conventions, partly because they are pretty frequently used): `frd`, `flatsys`, `nlsys`, `ss`, and `tf`. -* Short versions of common commands are created by creating an object - with the shorter name as a copy of the main object: `bode = - bode_plot`, `step = step_response`, etc. +* Short versions of common commands with longer names are created by + creating an object with the shorter name as a copy of the main + object: `bode = bode_plot`, `step = step_response`, etc. * The MATLAB compatibility library (`control.matlab`) uses names that - line up with MATLAB (e.g., `lsim` instead of `forced_response`). + try to line up with MATLAB (e.g., `lsim` instead of `forced_response`). Parameter names @@ -163,7 +166,7 @@ general patterns are emerging: `optimal.solve_ocp` (which probably should be named `optimal.`find_optimal_trajectory`...). -System creating commands: +System-creating commands: * Commands that create an I/O system should allow the use of the following standard parameters: @@ -172,10 +175,16 @@ System creating commands: - `inputs`, `outputs`, `states`: number or names of inputs, outputs, state - These can be parsed in a consistent way using the - `iosys._process_iosys_keywords` function. + - `input_prefix`, `output_prefix`, `state_prefix`: change the default + prefixes used for naming signals. -* Commands + - `dt`: set the timebase. This one takes a bit of care, since if it + is not specified then it defaults to + `config.defaults['control.default_dt']`. This is different than + setting `dt=None`, so you `dt` should always be part of **kwargs. + + These keywords can be parsed in a consistent way using the + `iosys._process_iosys_keywords` function. System arguments: @@ -185,8 +194,10 @@ System arguments: `interconnect`). A single system should also be OK. * `sysdata` when an argument can either be a system, a list of - systems ,or data describing a response (e.g, - `nyquist_response`). + systems, or data describing a response (e.g, `nyquist_response`). + + .. todo:: For a future release (v 0.11.x?) we should make this more + consistent across the package. Signal arguments: @@ -206,6 +217,12 @@ can be incorporated into the User Guide. All significant functionality should have a narrative description in the User Guide in addition to docstrings. +Generally speaking, standard Python and NumPy documentation +conventions are used throughout the package: + +* Python PEP 257 (docstrings): https://peps.python.org/pep-0257/ +* Numpydoc Style guide: https://numpydoc.readthedocs.io/en/latest/format.html + General docstring info ---------------------- @@ -223,13 +240,13 @@ General docstring info Function docstrings ------------------- -Follow NumPyDoc format with the following additional detals: +Follow numpydoc format with the following additional details: * All functions should have a short (< 64 character) summary line that starts with a capital letter and ends with a period. -* All paramter descriptions should start with a capital letter and end - with a period. An exception is paramters that have a list of +* All parameter descriptions should start with a capital letter and end + with a period. An exception is parameters that have a list of possible values, in which case a phrase sending in `:` followed by a list (without punctuation) is OK. @@ -245,9 +262,9 @@ Follow NumPyDoc format with the following additional detals: Class docstrings ---------------- -Follow NumpyDoc format with the follow additional details: +Follow numpydoc format with the follow additional details: -* Paramaters used in creating an object go in the class docstring and +* Parameters used in creating an object go in the class docstring and not in the `__init__` docstring (which is not included in the Sphinx-based documentation). OK for the `__init__` function to have no docstring. @@ -269,20 +286,42 @@ Follow NumpyDoc format with the follow additional details: I/O system classes: * Subclasses of `InputOutputSystem` should always have a factory - function that is used ot create them. The class documentation only - needs to documen the required parameters; the full list of + function that is used to create them. The class documentation only + needs to document the required parameters; the full list of parameters (and optional keywords) can and should be documented in the factory function docstring. -Sphinx documentation: +User Guide +---------- + +The purpose of the User Guide is provide a *narrative* description of +the key functions of the package. It is not expected to cover every +command, but should allow someone who knows about control system +design to get up and running quickly. + +The User Guide consists of chapters that are each their own separate +`.rst` file and each of them generates a separate page. Chapters are +divided into sections whose names appear in the indexo on the left of +the web page when that chapter is being viewed. In some cases a +section may be in its own file, including in the chapter page by using +the `include` directive (see `nlsys.py` for an example). + +Sphinx files guidlines: * Each file should declare the `currentmodule` at or near the top of - the file. Excpt for sub-packages (`control.flatsys`) and modules - that need to be impored separatly (`control.optimal`), + the file. Except for sub-packages (`control.flatsys`) and modules + that need to be imported separately (`control.optimal`), `currentmodule` should be set to control. +Reference Manual +---------------- + +The reference manual should provide a fairly comprehensive description +of every class, function and configuration variable in the package. + + Utility functions ================= From 6d5496c92349f39404ca97757135ede5ce9dfa82 Mon Sep 17 00:00:00 2001 From: Richard Murray Date: Fri, 10 Jan 2025 15:55:21 -0800 Subject: [PATCH 57/93] update backtick guidelines, docstrings, CSS processing --- control/bdalg.py | 64 ++++---- control/canonical.py | 36 +++-- control/config.py | 11 +- control/ctrlplot.py | 10 +- control/ctrlutil.py | 2 +- control/delay.py | 8 +- control/descfcn.py | 50 +++--- control/dtime.py | 10 +- control/flatsys/bspline.py | 2 +- control/flatsys/flatsys.py | 80 +++++----- control/flatsys/linflat.py | 8 +- control/flatsys/systraj.py | 37 ++--- control/frdata.py | 101 ++++++------ control/freqplot.py | 266 +++++++++++++++---------------- control/iosys.py | 92 +++++------ control/lti.py | 79 ++++----- control/margins.py | 11 +- control/mateqn.py | 10 +- control/matlab/timeresp.py | 21 ++- control/matlab/wrappers.py | 30 ++-- control/modelsimp.py | 34 ++-- control/nichols.py | 21 +-- control/nlsys.py | 197 +++++++++++------------ control/optimal.py | 253 ++++++++++++++--------------- control/passivity.py | 29 ++-- control/phaseplot.py | 64 ++++---- control/pzmap.py | 39 ++--- control/rlocus.py | 20 +-- control/robust.py | 20 +-- control/sisotool.py | 68 ++++---- control/statefbk.py | 62 +++---- control/statesp.py | 217 +++++++++++++------------ control/stochsys.py | 34 ++-- control/sysnorm.py | 103 ++++++++---- control/tests/docstrings_test.py | 15 +- control/timeplot.py | 46 +++--- control/timeresp.py | 248 ++++++++++++++-------------- control/xferfcn.py | 177 ++++++++++---------- doc/conf.py | 3 +- doc/config.rst | 43 ++--- doc/descfcn.rst | 2 +- doc/develop.rst | 111 +++++++++++-- doc/examples.rst | 7 +- doc/flatsys.rst | 20 +-- doc/index.rst | 10 +- doc/intro.rst | 8 +- doc/iosys.rst | 67 ++++---- doc/linear.rst | 46 +++--- doc/nlsys.rst | 2 +- doc/optimal.rst | 14 +- doc/phaseplot.rst | 2 +- doc/response.rst | 67 ++++---- doc/statesp.rst | 8 +- doc/stochastic.rst | 10 +- doc/xferfcn.rst | 3 +- 55 files changed, 1583 insertions(+), 1415 deletions(-) diff --git a/control/bdalg.py b/control/bdalg.py index 0aa4fe2c9..8125e2702 100644 --- a/control/bdalg.py +++ b/control/bdalg.py @@ -45,11 +45,11 @@ def series(*sys, **kwargs): ---------------- inputs, outputs : str, or list of str, optional List of strings that name the individual signals. If not given, - signal names will be of the form `s[i]` (where `s` is one of `u`, - or `y`). See `InputOutputSystem` for more information. + signal names will be of the form 's[i]' (where 's' is one of 'u, + or 'y'). See `InputOutputSystem` for more information. states : str, or list of str, optional List of names for system states. If not given, state names will be - of of the form `x[i]` for interconnections of linear systems or + of the form 'x[i]' for interconnections of linear systems or '.' for interconnected nonlinear systems. name : string, optional System name (used for specifying signals). If unspecified, a generic @@ -58,8 +58,8 @@ def series(*sys, **kwargs): Raises ------ ValueError - if `sys2.ninputs` does not equal `sys1.noutputs` - if `sys1.dt` is not compatible with `sys2.dt` + if ``sys2.ninputs`` does not equal ``sys1.noutputs`` + if ``sys1.dt`` is not compatible with ``sys2.dt`` See Also -------- @@ -116,11 +116,11 @@ def parallel(*sys, **kwargs): ---------------- inputs, outputs : str, or list of str, optional List of strings that name the individual signals. If not given, - signal names will be of the form `s[i]` (where `s` is one of `u`, - or `y`). See `InputOutputSystem` for more information. + signal names will be of the form 's[i'` (where 's' is one of 'u', + or 'y'). See `InputOutputSystem` for more information. states : str, or list of str, optional List of names for system states. If not given, state names will be - of the form `x[i]` for interconnections of linear systems or + of the form 'x[i]' for interconnections of linear systems or '.' for interconnected nonlinear systems. name : string, optional System name (used for specifying signals). If unspecified, a generic @@ -129,7 +129,8 @@ def parallel(*sys, **kwargs): Raises ------ ValueError - if `sys1` and `sys2` do not have the same numbers of inputs and outputs + If `sys1` and `sys2` do not have the same numbers of inputs and + outputs. See Also -------- @@ -184,11 +185,11 @@ def negate(sys, **kwargs): ---------------- inputs, outputs : str, or list of str, optional List of strings that name the individual signals. If not given, - signal names will be of the form `s[i]` (where `s` is one of `u`, - or `y`). See `InputOutputSystem` for more information. + signal names will be of the form 's[i]' (where 's' is one of 'u', + or 'y'). See `InputOutputSystem` for more information. states : str, or list of str, optional List of names for system states. If not given, state names will be - of of the form `x[i]` for interconnections of linear systems or + of of the form 'x[i]' for interconnections of linear systems or '.' for interconnected nonlinear systems. name : string, optional System name (used for specifying signals). If unspecified, a generic @@ -226,10 +227,9 @@ def feedback(sys1, sys2=1, sign=-1, **kwargs): ---------- sys1, sys2 : scalar, array, or `InputOutputSystem` I/O systems to combine. - sign : scalar - The sign of feedback. `sign` = -1 indicates negative feedback, and - `sign` = 1 indicates positive feedback. `sign` is an optional - argument; it assumes a value of -1 if not specified. + sign : scalar, optional + The sign of feedback. `sign=-1` indicates negative feedback + (default), and `sign=1` indicates positive feedback. Returns ------- @@ -240,11 +240,11 @@ def feedback(sys1, sys2=1, sign=-1, **kwargs): ---------------- inputs, outputs : str, or list of str, optional List of strings that name the individual signals. If not given, - signal names will be of the form `s[i]` (where `s` is one of `u`, - or `y`). See `InputOutputSystem` for more information. + signal names will be of the form 's[i]' (where 's' is one of 'u', + or 'y'). See `InputOutputSystem` for more information. states : str, or list of str, optional List of names for system states. If not given, state names will be - of of the form `x[i]` for interconnections of linear systems or + of of the form 'x[i]' for interconnections of linear systems or '.' for interconnected nonlinear systems. name : string, optional System name (used for specifying signals). If unspecified, a generic @@ -333,11 +333,11 @@ def append(*sys, **kwargs): ---------------- inputs, outputs : str, or list of str, optional List of strings that name the individual signals. If not given, - signal names will be of the form `s[i]` (where `s` is one of `u`, - or `y`). See `InputOutputSystem` for more information. + signal names will be of the form 's[i]' (where 's' is one of 'u', + or 'y'). See `InputOutputSystem` for more information. states : str, or list of str, optional List of names for system states. If not given, state names will be - of of the form `x[i]` for interconnections of linear systems or + of of the form 'x[i]' for interconnections of linear systems or '.' for interconnected nonlinear systems. name : string, optional System name (used for specifying signals). If unspecified, a generic @@ -473,10 +473,10 @@ def combine_tf(tf_array, **kwargs): Parameters ---------- tf_array : list of list of TransferFunction or array_like - Transfer matrix represented as a two-dimensional array or list-of-lists - containing TransferFunction objects. The TransferFunction objects can - have multiple outputs and inputs, as long as the dimensions are - compatible. + Transfer matrix represented as a two-dimensional array or + list-of-lists containing TransferFunction objects. The + `TransferFunction` objects can have multiple outputs and inputs, as + long as the dimensions are compatible. Returns ------- @@ -487,8 +487,8 @@ def combine_tf(tf_array, **kwargs): ---------------- inputs, outputs : str, or list of str, optional List of strings that name the individual signals. If not given, - signal names will be of the form `s[i]` (where `s` is one of `u`, - or `y`). See `InputOutputSystem` for more information. + signal names will be of the form 's[i]' (where 's' is one of 'u', + or 'y'). See `InputOutputSystem` for more information. name : string, optional System name (used for specifying signals). If unspecified, a generic name is generated with a unique integer id. @@ -535,7 +535,7 @@ def combine_tf(tf_array, **kwargs): [array([1.]), array([1.]), array([1.])], [array([1.]), array([1.]), array([1, 0])]], name='G', outputs=3, inputs=3) - + """ # Find common timebase or raise error dt_list = [] @@ -666,10 +666,10 @@ def _ensure_tf(arraylike_or_tf, dt=None): Array-like or transfer function. dt : None, True or float, optional System timebase. 0 (default) indicates continuous - time, `True` indicates discrete time with unspecified sampling + time, True indicates discrete time with unspecified sampling time, positive number is discrete time with specified - sampling time, `None` indicates unspecified timebase (either - continuous or discrete time). If `None`, timestep is not validated. + sampling time, None indicates unspecified timebase (either + continuous or discrete time). If None, timestep is not validated. Returns ------- diff --git a/control/canonical.py b/control/canonical.py index 6b2bf5748..0587c8d29 100644 --- a/control/canonical.py +++ b/control/canonical.py @@ -15,8 +15,8 @@ from .statefbk import ctrb, obsv from .statesp import StateSpace, _convert_to_statespace -__all__ = ['canonical_form', 'reachable_form', 'observable_form', 'modal_form', - 'similarity_transform', 'bdschur'] +__all__ = ['canonical_form', 'reachable_form', 'observable_form', + 'modal_form', 'similarity_transform', 'bdschur'] def canonical_form(xsys, form='reachable'): @@ -126,10 +126,12 @@ def reachable_form(xsys): # Check to make sure inversion was OK. Note that since we are inverting # Wrx and we already checked its rank, this exception should never occur if matrix_rank(Tzx) != xsys.nstates: # pragma: no cover - raise ValueError("Transformation matrix singular to working precision.") + raise ValueError( + "Transformation matrix singular to working precision.") # Finally, compute the output matrix - zsys.C = solve(Tzx.T, xsys.C.T).T # matrix right division, zsys.C = xsys.C * inv(Tzx) + # matrix right division, zsys.C = xsys.C * inv(Tzx) + zsys.C = solve(Tzx.T, xsys.C.T).T return zsys, Tzx @@ -183,7 +185,8 @@ def observable_form(xsys): Tzx = solve(Wrz, Wrx) # matrix left division, Tzx = inv(Wrz) * Wrx if matrix_rank(Tzx) != xsys.nstates: - raise ValueError("Transformation matrix singular to working precision.") + raise ValueError( + "Transformation matrix singular to working precision.") # Finally, compute the output matrix zsys.B = Tzx @ xsys.B @@ -206,7 +209,7 @@ def similarity_transform(xsys, T, timescale=1, inverse=False): timescale : float, optional If present, also rescale the time unit to tau = timescale * t. inverse : bool, optional - If `False` (default), transform so z = T x. If `True`, transform + If False (default), transform so z = T x. If True, transform so x = T z. Returns @@ -270,7 +273,7 @@ def _bdschur_defective(blksizes, eigvals): Returns ------- - `True` iff Schur blocks are defective. + True iff Schur blocks are defective. blksizes, eigvals are the 3rd and 4th results returned by mb03rd. """ @@ -393,7 +396,9 @@ def _bdschur_condmax_search(aschur, tschur, condmax): # hit search limit return reslower else: - raise ValueError('bisection failed to converge; pmaxlower={}, pmaxupper={}'.format(pmaxlower, pmaxupper)) + raise ValueError( + "bisection failed to converge; " + "pmaxlower={}, pmaxupper={}".format(pmaxlower, pmaxupper)) def bdschur(a, condmax=None, sort=None): @@ -404,7 +409,7 @@ def bdschur(a, condmax=None, sort=None): a : (M, M) array_like Real matrix to decompose. condmax : None or float, optional - If `None` (default), use 1/sqrt(eps), which is approximately 1e8. + If None (default), use 1/sqrt(eps), which is approximately 1e8. sort : {None, 'continuous', 'discrete'} Block sorting; see below. @@ -419,7 +424,7 @@ def bdschur(a, condmax=None, sort=None): Notes ----- - If `sort` is `None`, the blocks are not sorted. + If `sort` is None, the blocks are not sorted. If `sort` is 'continuous', the blocks are sorted according to associated eigenvalues. The ordering is first by real part of @@ -443,7 +448,8 @@ def bdschur(a, condmax=None, sort=None): condmax = np.finfo(np.float64).eps ** -0.5 if not (np.isscalar(condmax) and condmax >= 1.0): - raise ValueError('condmax="{}" must be a scalar >= 1.0'.format(condmax)) + raise ValueError( + 'condmax="{}" must be a scalar >= 1.0'.format(condmax)) a = np.atleast_2d(a) if a.shape[0] == 0 or a.shape[1] == 0: @@ -491,18 +497,18 @@ def modal_form(xsys, condmax=None, sort=False): Parameters ---------- xsys : StateSpace object - System to be transformed, with state `x`. + System to be transformed, with state ``x``. condmax : None or float, optional - An upper bound on individual transformations. If `None`, use + An upper bound on individual transformations. If None, use `bdschur` default. sort : bool, optional - If `False` (default), Schur blocks will not be sorted. See `bdschur` + If False (default), Schur blocks will not be sorted. See `bdschur` for sort order. Returns ------- zsys : StateSpace object - System in modal canonical form, with state `z`. + System in modal canonical form, with state ``z``. T : (M, M) ndarray Coordinate transformation: z = T * x. diff --git a/control/config.py b/control/config.py index 7c0b19d7d..c0b0804fc 100644 --- a/control/config.py +++ b/control/config.py @@ -133,6 +133,7 @@ def set_defaults(module, **keywords): defaults[module + '.' + key] = val +# TODO: allow individual modules and individaul parameters to be reset def reset_defaults(): """Reset configuration values to their default (initial) values. @@ -199,7 +200,7 @@ def _get_param(module, param, argval=None, defval=None, pop=False, last=False): parameter for a module based on the default parameter settings and any arguments passed to the function. The precedence order for parameters is the value passed to the function (as a keyword), the value from the - config.defaults dictionary, and the default value `defval`. + `config.defaults` dictionary, and the default value `defval`. Parameters ---------- @@ -214,15 +215,15 @@ def _get_param(module, param, argval=None, defval=None, pop=False, last=False): defval : object Default value of the parameter to use, if it is not located in the `config.defaults` dictionary. If a dictionary is provided, then - `module.param` is used to determine the default value. Defaults to + 'module.param' is used to determine the default value. Defaults to None. pop : bool, optional - If `True` and if argval is a dict, then pop the remove the parameter + If True and if argval is a dict, then pop the remove the parameter entry from the argval dict after retreiving it. This allows the use of a keyword argument list to be passed through to other functions internal to the function being called. last : bool, optional - If `True`, check to make sure dictionary is empy after processing. + If True, check to make sure dictionary is empy after processing. """ @@ -395,7 +396,7 @@ def _process_legacy_keyword(kwargs, oldkey, newkey, newval, warn_oldkey=True): newval : object Value of the current parameter (from the function signature). warn_oldkey : bool - If set to `False`, suppress generation of a warning about using a + If set to False, suppress generation of a warning about using a legacy keyword. This is useful if you have two versions of a keyword and you want to allow either to be used (see the `cost` and `trajectory_cost` keywords in `flatsys.point_to_point` for an diff --git a/control/ctrlplot.py b/control/ctrlplot.py index a68a1522d..d4bb85482 100644 --- a/control/ctrlplot.py +++ b/control/ctrlplot.py @@ -148,7 +148,7 @@ class ControlPlot(): Figure on which the Axes are drawn. legend : `matplotlib:.legend.Legend` (instance or ndarray) Legend object(s) for the plot. If more than one legend is - included, this will be an array with each entry being either` None` + included, this will be an array with each entry being either`` None`` (for no legend) or a legend object. """ @@ -194,7 +194,7 @@ def set_plot_title(self, title, frame='axes'): fig : Figure, optional Matplotlib figure. Defaults to current figure. frame : str, optional - Coordinate frame to use for centering: 'axes' (default) or 'figure'. + Coordinate frame for centering: 'axes' (default) or 'figure'. **kwargs : `matplotlib.pyplot.suptitle` keywords, optional Additional keywords (passed to matplotlib). @@ -282,7 +282,7 @@ def pole_zero_subplots( Figure to use for creating subplots. rcParams : dict Override the default parameters used for generating plots. - Default is set by config.defaults['ctrlplot.rcParams']. + Default is set by `config.defaults['ctrlplot.rcParams']`. Returns ------- @@ -350,7 +350,7 @@ def _process_ax_keyword( created with axes of the desired shape. If `create_axes` is False and a new/empty figure is returned, then axs - is an array of the proper shape but `None` for each element. This allows + is an array of the proper shape but None for each element. This allows the calling function to do the actual axis creation (needed for curvilinear grids that use the AxisArtist module). @@ -735,7 +735,7 @@ def _get_color( Returns ------- color : matplotlib color spec - Color to use for this line (or `None` for matplotlib default). + Color to use for this line (or None for matplotlib default). """ # See if the color was explicitly specified by the user diff --git a/control/ctrlutil.py b/control/ctrlutil.py index 8bfe0e7f0..9df6672df 100644 --- a/control/ctrlutil.py +++ b/control/ctrlutil.py @@ -24,7 +24,7 @@ def unwrap(angle, period=2*math.pi): angle : array_like Array of angles to be unwrapped. period : float, optional - Period (defaults to `2*pi`). + Period (defaults to ``2*pi``). Returns ------- diff --git a/control/delay.py b/control/delay.py index acc57cdb3..66a6b10a4 100644 --- a/control/delay.py +++ b/control/delay.py @@ -8,10 +8,10 @@ __all__ = ['pade'] def pade(T, n=1, numdeg=None): - """ - Create a linear system that approximates a delay. + """Create a linear system that approximates a delay. - Return the numerator and denominator coefficients of the Pade approximation. + Return the numerator and denominator coefficients of the Pade + approximation of the given order. Parameters ---------- @@ -20,7 +20,7 @@ def pade(T, n=1, numdeg=None): n : positive integer Degree of denominator of approximation. numdeg : integer, or None (the default) - If numdeg is `None`, numerator degree equals denominator degree. + If numdeg is None, numerator degree equals denominator degree. If numdeg >= 0, specifies degree of numerator. If numdeg < 0, numerator degree is n+numdeg. diff --git a/control/descfcn.py b/control/descfcn.py index 55da1e1a0..93b9c8f16 100644 --- a/control/descfcn.py +++ b/control/descfcn.py @@ -29,7 +29,7 @@ class DescribingFunctionNonlinearity(): This class is intended to be used as a base class for nonlinear functions that have an analytically defined describing function. Subclasses should override the `__call__` and `describing_function` methods and (optionally) - the `_isstatic` method (should be `False` if `__call__` updates the + the `_isstatic` method (should be False if `__call__` updates the instance state). """ @@ -54,10 +54,10 @@ def describing_function(self, A): "describing function not implemented for this function") def _isstatic(self): - """Return `True` if the function has no internal state (memoryless). + """Return True if the function has no internal state (memoryless). This internal function is used to optimize numerical computation of - the describing function. It can be set to `True` if the instance + the describing function. It can be set to True if the instance maintains no internal memory of the instance state. Assumed False by default. @@ -101,13 +101,13 @@ def describing_function( 100). zero_check : bool, optional - If `True` (default) then `A` is zero, the function will be evaluated + If True (default) then `A` is zero, the function will be evaluated and checked to make sure it is zero. If not, a `TypeError` exception - is raised. If zero_check is `False`, no check is made on the value of + is raised. If zero_check is False, no check is made on the value of the function at zero. try_method : bool, optional - If `True` (default), check the `F` argument to see if it is an object + If True (default), check the `F` argument to see if it is an object with a `describing_function` method and use this to compute the describing function. More information in the `describing_function` method for the `DescribingFunctionNonlinearity` class. @@ -231,7 +231,7 @@ class is used by the `describing_function_response` intersections : 1D array of 2-tuples or None A list of all amplitudes and frequencies in which :math:`H(j\\omega) N(a) = -1`, where :math:`N(a)` is the describing - function associated with `F`, or `None` if there are no such + function associated with `F`, or None if there are no such points. Each pair represents a potential limit cycle for the closed loop system with amplitude given by the first value of the tuple and frequency given by the second value. @@ -289,11 +289,12 @@ def describing_function_response( omega : list, optional List of frequencies to be used for the linear system Nyquist curve. warn_nyquist : bool, optional - Set to `True` to turn on warnings generated by `nyquist_plot` or - `False` to turn off warnings. If not set (or set to `None`), warnings - are turned off if omega is specified, otherwise they are turned on. + Set to True to turn on warnings generated by `nyquist_plot` or + False to turn off warnings. If not set (or set to None), + warnings are turned off if omega is specified, otherwise they are + turned on. refine : bool, optional - If `True`, `scipy.optimize.minimize` to refine the estimate + If True, `scipy.optimize.minimize` to refine the estimate of the intersection of the frequency response and the describing function. @@ -306,7 +307,7 @@ def describing_function_response( response.intersections : 1D array of 2-tuples or None A list of all amplitudes and frequencies in which :math:`H(j\\omega) N(a) = -1`, where :math:`N(a)` is the describing - function associated with `F`, or `None` if there are no such + function associated with `F`, or None if there are no such points. Each pair represents a potential limit cycle for the closed loop system with amplitude given by the first value of the tuple and frequency given by the second value. @@ -419,14 +420,14 @@ def describing_function_plot( curve. If not specified (or None), frequencies are computed automatically based on the properties of the linear system. refine : bool, optional - If `True` (default), refine the location of the intersection of the + If True (default), refine the location of the intersection of the Nyquist curve for the linear system and the describing function to determine the intersection point. label : str or array_like of str, optional If present, replace automatically generated label with the given label. point_label : str, optional Formatting string used to label intersection points on the Nyquist - plot. Defaults to "%5.2g @ %-5.2g". Set to `None` to omit labels. + plot. Defaults to "%5.2g @ %-5.2g". Set to None to omit labels. ax : matplotlib.axes.Axes, optional The matplotlib axes to draw the figure on. If not specified and the current figure has a single axes, that axes is used. @@ -434,9 +435,10 @@ def describing_function_plot( title : str, optional Set the title of the plot. Defaults to plot type and system name(s). warn_nyquist : bool, optional - Set to `True` to turn on warnings generated by `nyquist_plot` or - `False` to turn off warnings. If not set (or set to `None`), warnings - are turned off if omega is specified, otherwise they are turned on. + Set to True to turn on warnings generated by `nyquist_plot` or + False to turn off warnings. If not set (or set to None), + warnings are turned off if omega is specified, otherwise they are + turned on. **kwargs : `matplotlib.pyplot.plot` keyword properties, optional Additional keywords passed to `matplotlib` to specify line properties for Nyquist curve. @@ -624,9 +626,9 @@ class relay_hysteresis_nonlinearity(DescribingFunctionNonlinearity): F = relay_hysteresis_nonlinearity(b, c) - The output of this function is `b` if `x > c` and `-b` if `x < -c`. For - `-c <= x <= c`, the value depends on the branch of the hysteresis loop (as - illustrated in Figure 10.20 of FBS2e). + The output of this function is ``b`` if ``x > c`` and ``-b`` if ``x < + -c``. For ``-c <= x <= c``, the value depends on the branch of the + hysteresis loop (as illustrated in Figure 10.20 of FBS2e). Parameters ---------- @@ -699,10 +701,10 @@ class friction_backlash_nonlinearity(DescribingFunctionNonlinearity): F = friction_backlash_nonlinearity(b) - This function maintains an internal state representing the 'center' of a - mechanism with backlash. If the new input is within `b/2` of the current - center, the output is unchanged. Otherwise, the output is given by the - input shifted by `b/2`. + This function maintains an internal state representing the 'center' of + a mechanism with backlash. If the new input is within ``b/2`` of the + current center, the output is unchanged. Otherwise, the output is + given by the input shifted by ``b/2``. Parameters ---------- diff --git a/control/dtime.py b/control/dtime.py index 03e6e92c2..783ba93f8 100644 --- a/control/dtime.py +++ b/control/dtime.py @@ -49,14 +49,14 @@ def sample_system(sysc, Ts, method='zoh', alpha=None, prewarp_frequency=None, available if the system is `StateSpace`. name : string, optional Set the name of the sampled system. If not specified and - if `copy_names` is `False`, a generic name is generated - with a unique integer id. If `copy_names` is `True`, the new system + if `copy_names` is False, a generic name is generated + with a unique integer id. If `copy_names` is True, the new system name is determined by adding the prefix and suffix strings in - config.defaults['iosys.sampled_system_name_prefix'] and - config.defaults['iosys.sampled_system_name_suffix'], with the + `config.defaults['iosys.sampled_system_name_prefix']` and + `config.defaults['iosys.sampled_system_name_suffix']`, with the default being to add the suffix '$sampled'. copy_names : bool, Optional - If `True`, copy the names of the input signals, output + If True, copy the names of the input signals, output signals, and states to the sampled system. Notes diff --git a/control/flatsys/bspline.py b/control/flatsys/bspline.py index e214efe22..9b296142b 100644 --- a/control/flatsys/bspline.py +++ b/control/flatsys/bspline.py @@ -40,7 +40,7 @@ class BSplineFamily(BasisFamily): derivatives that should match). vars : None or int, optional - The number of spline variables. If specified as `None` (default), + The number of spline variables. If specified as None (default), then the spline basis describes a single variable, with no indexing. If the number of spine variables is > 0, then the spline basis is index using the `var` keyword. diff --git a/control/flatsys/flatsys.py b/control/flatsys/flatsys.py index 7863e7da8..8ab7df1f2 100644 --- a/control/flatsys/flatsys.py +++ b/control/flatsys/flatsys.py @@ -182,13 +182,13 @@ def flatsys(*args, updfcn=None, outfcn=None, **kwargs): that also represents a differentially flat system. It can be used in a variety of forms: - `fs.flatsys(forward, reverse)` + ``fs.flatsys(forward, reverse)`` Create a flat system with mapings to/from flat flag. - `fs.flatsys(forward, reverse, updfcn[, outfcn])` + ``fs.flatsys(forward, reverse, updfcn[, outfcn])`` Create a flat system that is also a nonlinear I/O system. - `fs.flatsys(linsys)` + ``fs.flatsys(linsys)`` Create a flat system from a linear (StateSpace) system. Parameters @@ -202,7 +202,7 @@ def flatsys(*args, updfcn=None, outfcn=None, **kwargs): updfcn : callable, optional Function returning the state update function - `updfcn(t, x, u[, param]) -> array` + ``updfcn(t, x, u[, param]) -> array`` where `x` is a 1-D array with shape (nstates,), `u` is a 1-D array with shape (ninputs,), `t` is a float representing the currrent @@ -213,7 +213,7 @@ def flatsys(*args, updfcn=None, outfcn=None, **kwargs): outfcn : callable, optional Function returning the output at the given state - `outfcn(t, x, u[, param]) -> array` + ``outfcn(t, x, u[, param]) -> array`` where the arguments are the same as for `upfcn`. If not specified, the output will be the flat outputs. @@ -222,8 +222,8 @@ def flatsys(*args, updfcn=None, outfcn=None, **kwargs): Description of the system inputs. This can be given as an integer count or as a list of strings that name the individual signals. If an integer count is specified, the names of the signal will be - of the form `s[i]` (where `s` is one of `u`, `y`, or `x`). If - this parameter is not given or given as `None`, the relevant + of the form 's[i]' (where 's' is one of 'u', 'y', or 'x'). If + this parameter is not given or given as None, the relevant quantity will be determined when possible based on other information provided to functions using the system. @@ -234,7 +234,7 @@ def flatsys(*args, updfcn=None, outfcn=None, **kwargs): Description of the system states. Same format as `inputs`. dt : None, True or float, optional - System timebase. `None` (default) indicates continuous time, `True` + System timebase. None (default) indicates continuous time, True indicates discrete time with undefined sampling time, positive number is discrete time with specified sampling time. @@ -331,8 +331,8 @@ def point_to_point( ---------- sys : FlatSystem object Description of the differentially flat system. This object must - define a function `flatsys.forward()` that takes the system state and - produceds the flag of flat outputs and a system `flatsys.reverse()` + define a function `flatsys.forward` that takes the system state and + produceds the flag of flat outputs and a system `flatsys.reverse` that takes the flag of the flat output and prodes the state and input. @@ -358,23 +358,23 @@ def point_to_point( cost : callable Function that returns the integral cost given the current state - and input. Called as `cost(x, u)`. + and input. Called as ``cost(x, u)``. trajectory_constraints : list of tuples, optional - List of constraints that should hold at each point in the time vector. - Each element of the list should consist of a tuple with first element - given by `scipy.optimize.LinearConstraint` or - `scipy.optimize.NonlinearConstraint` and the remaining - elements of the tuple are the arguments that would be passed to those + List of constraints that should hold at each point in the time + vector. Each element of the list should consist of a tuple with + first element given by `scipy.optimize.LinearConstraint` or + `scipy.optimize.NonlinearConstraint` and the remaining elements of + the tuple are the arguments that would be passed to those functions. The following tuples are supported: - * (LinearConstraint, A, lb, ub): The matrix A is multiplied by stacked - vector of the state and input at each point on the trajectory for - comparison against the upper and lower bounds. + * (LinearConstraint, A, lb, ub): The matrix A is multiplied by + stacked vector of the state and input at each point on the + trajectory for comparison against the upper and lower bounds. * (NonlinearConstraint, fun, lb, ub): a user-specific constraint - function `fun(x, u)` is called at each point along the trajectory - and compared against the upper and lower bounds. + function ``fun(x, u)`` is called at each point along the + trajectory and compared against the upper and lower bounds. The constraints are applied at each time point along the trajectory. @@ -399,8 +399,8 @@ def point_to_point( ------- traj : `flatsys.SystemTrajectory` object The system trajectory is returned as an object that implements the - `eval()` function, we can be used to compute the value of the state - and input and a given time t. + `~flatsys.SystemTrajectory.eval` function, we can be used to + compute the value of the state and input and a given time t. Notes ----- @@ -664,8 +664,8 @@ def solve_flat_ocp( ---------- sys : FlatSystem object Description of the differentially flat system. This object must - define a function `flatsys.forward()` that takes the system state and - produceds the flag of flat outputs and a system `flatsys.reverse()` + define a function `flatsys.forward` that takes the system state and + produceds the flag of flat outputs and a system `flatsys.reverse` that takes the flag of the flat output and prodes the state and input. @@ -687,27 +687,27 @@ def solve_flat_ocp( trajectory_cost : callable Function that returns the integral cost given the current state - and input. Called as `cost(x, u)`. + and input. Called as ``cost(x, u)``. terminal_cost : callable Function that returns the terminal cost given the state and input. - Called as `cost(x, u)`. + Called as ``cost(x, u)``. trajectory_constraints : list of tuples, optional - List of constraints that should hold at each point in the time vector. - Each element of the list should consist of a tuple with first element - given by `scipy.optimize.LinearConstraint` or - `scipy.optimize.NonlinearConstraint` and the remaining - elements of the tuple are the arguments that would be passed to those + List of constraints that should hold at each point in the time + vector. Each element of the list should consist of a tuple with + first element given by `scipy.optimize.LinearConstraint` or + `scipy.optimize.NonlinearConstraint` and the remaining elements of + the tuple are the arguments that would be passed to those functions. The following tuples are supported: - * (LinearConstraint, A, lb, ub): The matrix A is multiplied by stacked - vector of the state and input at each point on the trajectory for - comparison against the upper and lower bounds. + * (LinearConstraint, A, lb, ub): The matrix A is multiplied by + stacked vector of the state and input at each point on the + trajectory for comparison against the upper and lower bounds. * (NonlinearConstraint, fun, lb, ub): a user-specific constraint - function `fun(x, u)` is called at each point along the trajectory - and compared against the upper and lower bounds. + function ``fun(x, u)`` is called at each point along the + trajectory and compared against the upper and lower bounds. The constraints are applied at each time point along the trajectory. @@ -732,8 +732,8 @@ def solve_flat_ocp( ------- traj : `flatsys.SystemTrajectory` object The system trajectory is returned as an object that implements the - `eval()` function, we can be used to compute the value of the state - and input and a given time t. + `~flatsys.SystemTrajectory.eval` function, we can be used to + compute the value of the state and input and a given time t. Notes ----- @@ -744,7 +744,7 @@ def solve_flat_ocp( 2. The return data structure includes the following additional attributes: * success : bool indicating whether the optimization succeeded * cost : computed cost of the returned trajectory - * message : message returned by optimization if success if `False` + * message : message returned by optimization if success if False 3. A common failure in solving optimal control problem is that the default initial guess violates the constraints and the optimizer diff --git a/control/flatsys/linflat.py b/control/flatsys/linflat.py index 597a6de22..724e7bf67 100644 --- a/control/flatsys/linflat.py +++ b/control/flatsys/linflat.py @@ -27,8 +27,8 @@ class LinearFlatSystem(FlatSystem, StateSpace): Description of the system inputs. This can be given as an integer count or as a list of strings that name the individual signals. If an integer count is specified, the names of the signal will be - of the form `s[i]` (where `s` is one of `u`, `y`, or `x`). If - this parameter is not given or given as `None`, the relevant + of the form 's[i]' (where 's' is one of 'u', 'y', or 'x'). If + this parameter is not given or given as None, the relevant quantity will be determined when possible based on other information provided to functions using the system. outputs : int, list of str or None, optional @@ -36,8 +36,8 @@ class LinearFlatSystem(FlatSystem, StateSpace): states : int, list of str, or None, optional Description of the system states. Same format as `inputs`. dt : None, True or float, optional - System timebase. `None` (default) indicates continuous - time, `True` indicates discrete time with undefined sampling + System timebase. None (default) indicates continuous + time, True indicates discrete time with undefined sampling time, positive number is discrete time with specified sampling time. params : dict, optional diff --git a/control/flatsys/systraj.py b/control/flatsys/systraj.py index c39f69c74..1783c2a1b 100644 --- a/control/flatsys/systraj.py +++ b/control/flatsys/systraj.py @@ -111,22 +111,23 @@ def response(self, tlist, transpose=False, return_x=False, squeeze=None): List of times to evaluate the trajectory. transpose : bool, optional - If `True`, transpose all input and output arrays (for backward + If True, transpose all input and output arrays (for backward compatibility with MATLAB and `scipy.signal.lsim`). Default value is False. return_x : bool, optional - If `True`, return the state vector when assigning to a tuple + If True, return the state vector when assigning to a tuple (default = False). See `forced_response` for more details. squeeze : bool, optional - By default, if a system is single-input, single-output (SISO) then - the output response is returned as a 1D array (indexed by time). - If squeeze=`True`, remove single-dimensional entries from the shape - of the output even if the system is not SISO. If squeeze=`False`, - keep the output as a 3D array (indexed by the output, input, and - time) even if the system is SISO. The default value can be set - using config.defaults['control.squeeze_time_response']. + By default, if a system is single-input, single-output (SISO) + then the output response is returned as a 1D array (indexed by + time). If ``squeeze=True`, remove single-dimensional entries + from the shape of the output even if the system is not SISO. If + ``squeeze=False``, keep the output as a 3D array (indexed by + the output, input, and time) even if the system is SISO. The + default value can be set using + `config.defaults['control.squeeze_time_response']`. Returns ------- @@ -136,22 +137,22 @@ def response(self, tlist, transpose=False, return_x=False, squeeze=None): * time (array): Time values of the output. - * outputs (array): Response of the system. If the system is SISO - and squeeze is not `True`, the array is 1D (indexed by time). If - the system is not SISO or `squeeze` is `False`, the array is 3D - (indexed by the output, trace, and time). + * outputs (array): Response of the system. If the system is + SISO and squeeze is not True, the array is 1D (indexed by + time). If the system is not SISO or `squeeze` is False, + the array is 3D (indexed by the output, trace, and time). - * states (array): Time evolution of the state vector, represented - as either a 2D array indexed by state and time (if SISO) or a 3D - array indexed by state, trace, and time. Not affected by - `squeeze`. + * states (array): Time evolution of the state vector, + represented as either a 2D array indexed by state and time + (if SISO) or a 3D array indexed by state, trace, and time. + Not affected by `squeeze`. * inputs (array): Input(s) to the system, indexed in the same manner as `outputs`. The return value of the system can also be accessed by assigning the function to a tuple of length 2 (time, output) or of length 3 - (time, output, state) if `return_x` is `True`. + (time, output, state) if `return_x` is True. """ # Compute the state and input response using the eval function diff --git a/control/frdata.py b/control/frdata.py index 06be5f018..e23db8a0c 100644 --- a/control/frdata.py +++ b/control/frdata.py @@ -48,14 +48,14 @@ class constructor, using the `frd` factory function, or omega : iterable of real frequencies List of frequency points for which data are available. smooth : bool, optional - If `True`, create an interpolation function that allows the + If True, create an interpolation function that allows the frequency response to be computed at any frequency within the range of - frequencies give in `w`. If `False` (default), frequency response + frequencies give in `w`. If False (default), frequency response can only be obtained at the frequencies specified in `w`. dt : None, True or float, optional - System timebase. 0 (default) indicates continuous time, `True` + System timebase. 0 (default) indicates continuous time, True indicates discrete time with unspecified sampling time, positive - number is discrete time with specified sampling time, `None` + number is discrete time with specified sampling time, None indicates unspecified timebase (either continuous or discrete time). squeeze : bool By default, if a system is single-input, single-output (SISO) then @@ -63,12 +63,12 @@ class constructor, using the `frd` factory function, or frequency) and if a system is multi-input or multi-output, then the outputs are returned as a 2D array (indexed by output and frequency) or a 3D array (indexed by output, trace, and frequency). - If `squeeze=True`, access to the output response will remove + If ``squeeze=True``, access to the output response will remove single-dimensional entries from the shape of the inputs and outputs - even if the system is not SISO. If `squeeze=False`, the output is + even if the system is not SISO. If ``squeeze=False``, the output is returned as a 3D array (indexed by the output, input, and frequency) even if the system is SISO. The default value can be set - using config.defaults['control.squeeze_frequency_response']. + using `config.defaults['control.squeeze_frequency_response']`. sysname : str or None Name of the system that generated the data. @@ -101,14 +101,14 @@ class constructor, using the `frd` factory function, or title : str, optional Set the title to use when plotting. plot_magnitude, plot_phase : bool, optional - If set to `False`, don't plot the magnitude or phase, respectively. + If set to False, don't plot the magnitude or phase, respectively. return_magphase : bool, optional - If `True`, then a frequency response data object will enumerate as a - tuple of the form (mag, phase, omega) where where `mag` is the - magnitude (absolute value, not dB or log10) of the system - frequency response, `phase` is the wrapped phase in radians of - the system frequency response, and `omega` is the (sorted) - frequencies at which the response was evaluated. + If True, then a frequency response data object will enumerate + as a tuple of the form ``(mag, phase, omega)`` where where ``mag`` + is the magnitude (absolute value, not dB or log10) of the system + frequency response, ``phase`` is the wrapped phase in radians of the + system frequency response, and ``omega`` is the (sorted) frequencies + at which the response was evaluated. See Also -------- @@ -172,9 +172,9 @@ class constructor, using the `frd` factory function, or #: frequency) and if a system is multi-input or multi-output, then the #: outputs are returned as a 2D array (indexed by output and frequency) #: or a 3D array (indexed by output, trace, and frequency). If - #: `squeeze=True`, access to the output response will remove + #: ``squeeze=True``, access to the output response will remove #: single-dimensional entries from the shape of the inputs and outputs - #: even if the system is not SISO. If `squeeze=False`, the output is + #: even if the system is not SISO. If ``squeeze=False``, the output is #: returned as a 3D array (indexed by the output, input, and frequency) #: even if the system is SISO. The default value can be set using #: config.defaults['control.squeeze_frequency_response']. @@ -635,21 +635,22 @@ def eval(self, omega, squeeze=None): omega : float or 1D array_like Frequencies in radians per second. squeeze : bool, optional - If `squeeze=True`, remove single-dimensional entries from the shape - of the output even if the system is not SISO. If `squeeze=False`, - keep all indices (output, input and, if omega is array_like, - frequency) even if the system is SISO. The default value can be - set using config.defaults['control.squeeze_frequency_response']. + If ``squeeze=True``, remove single-dimensional entries from + the shape of the output even if the system is not SISO. If + ``squeeze=False``, keep all indices (output, input and, if omega + is array_like, frequency) even if the system is SISO. The + default value can be set using + `config.defaults['control.squeeze_frequency_response']`. Returns ------- fresp : complex ndarray The frequency response of the system. If the system is SISO - and `squeeze` is not `True`, the shape of the array matches the + and `squeeze` is not True, the shape of the array matches the shape of omega. If the system is not SISO or `squeeze` is - `False`, the first two dimensions of the array are indices for + False, the first two dimensions of the array are indices for the output and input and the remaining dimensions match omega. - If `squeeze` is `True` then single-dimensional axes are removed. + If `squeeze` is True then single-dimensional axes are removed. """ omega_array = np.array(omega, ndmin=1) # array-like version of omega @@ -684,12 +685,12 @@ def eval(self, omega, squeeze=None): def __call__(self, s=None, squeeze=None, return_magphase=None): """Evaluate system's transfer function at complex frequencies. - Returns the complex frequency response `sys(s)` of system `sys` with - `m = sys.ninputs` number of inputs and `p = sys.noutputs` number of - outputs. + Returns the complex frequency response ``sys(s)`` of system `sys` + with ``m = sys.ninputs`` number of inputs and ``p = sys.noutputs`` + number of outputs. To evaluate at a frequency omega in radians per second, enter - `s = omega * 1j` or use `sys.eval(omega)` + ``s = omega * 1j`` or use ``sys.eval(omega)``. For a frequency response data object, the argument must be an imaginary number (since only the frequency response is defined). @@ -705,26 +706,27 @@ def __call__(self, s=None, squeeze=None, return_magphase=None): processing (`squeeze`, `return_magphase`). squeeze : bool, optional - If squeeze=`True`, remove single-dimensional entries from the shape - of the output even if the system is not SISO. If squeeze=`False`, - keep all indices (output, input and, if omega is array_like, - frequency) even if the system is SISO. The default value can be - set using config.defaults['control.squeeze_frequency_response']. + If ``squeeze=True``, remove single-dimensional entries from the + shape of the output even if the system is not SISO. If + ``squeeze=False``, keep all indices (output, input and, if + omega is array_like, frequency) even if the system is SISO. The + default value can be set using + `config.defaults['control.squeeze_frequency_response']`. return_magphase : bool, optional - If `True`, then a frequency response data object will enumerate as a - tuple of the form (mag, phase, omega) where where `mag` is the - magnitude (absolute value, not dB or log10) of the system - frequency response, `phase` is the wrapped phase in radians of - the system frequency response, and `omega` is the (sorted) - frequencies at which the response was evaluated. + If True, then a frequency response data object will + enumerate as a tuple of the form ``(mag, phase, omega)`` where + where ``mag`` is the magnitude (absolute value, not dB or log10) + of the system frequency response, ``phase`` is the wrapped phase + in radians of the system frequency response, and ``omega`` is + the (sorted) frequencies at which the response was evaluated. Returns ------- fresp : complex ndarray The frequency response of the system. If the system is SISO and - squeeze is not `True`, the shape of the array matches the shape of - omega. If the system is not SISO or squeeze is `False`, the first + squeeze is not True, the shape of the array matches the shape of + omega. If the system is not SISO or squeeze is False, the first two dimensions of the array are indices for the output and input and the remaining dimensions match omega. If `squeeze` is True then single-dimensional axes are removed. @@ -859,9 +861,8 @@ def plot(self, plot_type=None, *args, **kwargs): Plot the frequency response using either a standard Bode plot (default) or using a singular values plot (by setting `plot_type` - to 'svplot'). See `bode_plot` and - `singular_values_plot` for more detailed - descriptions. + to 'svplot'). See `bode_plot` and `singular_values_plot` for more + detailed descriptions. """ from .freqplot import bode_plot, singular_values_plot @@ -991,11 +992,11 @@ def frd(*args, **kwargs): A frequency response data model stores the (measured) frequency response of a system. This factory function can be called in different ways: - `frd(response, omega)` + ``frd(response, omega)`` Create an frd model with the given response data, in the form of complex response vector, at matching frequencies `omega` [in rad/s]. - `frd(sys, omega)` + ``frd(sys, omega)`` Convert an LTI system into an frd model with data at frequencies `omega`. @@ -1011,9 +1012,9 @@ def frd(*args, **kwargs): dt : float, True, or None System timebase. smooth : bool, optional - If `True`, create an interpolation function that allows the + If True, create an interpolation function that allows the frequency response to be computed at any frequency within the range - of frequencies give in `omega`. If `False` (default), + of frequencies give in `omega`. If False (default), frequency response can only be obtained at the frequencies specified in `omega`. @@ -1034,8 +1035,8 @@ def frd(*args, **kwargs): Set the name of the system. If unspecified and the system is sampled from an existing system, the new system name is determined by adding the prefix and suffix strings in - config.defaults['iosys.sampled_system_name_prefix'] and - config.defaults['iosys.sampled_system_name_suffix'], with the + `config.defaults['iosys.sampled_system_name_prefix']` and + `config.defaults['iosys.sampled_system_name_suffix']`, with the default being to add the suffix '$ampled'. Otherwise, a generic name is generated with a unique integer id diff --git a/control/freqplot.py b/control/freqplot.py index 5d4b4dd58..2473cd0c3 100644 --- a/control/freqplot.py +++ b/control/freqplot.py @@ -109,15 +109,15 @@ def bode_plot( Passed to `matplotlib` as the format string for all lines in the plot. The `omega` parameter must be present (use omega=None if needed). dB : bool - If `True`, plot result in dB. Default is False. + If True, plot result in dB. Default is False. Hz : bool - If `True`, plot frequency in Hz (omega must be provided in rad/sec). - Default value (False) set by config.defaults['freqplot.Hz']. + If True, plot frequency in Hz (omega must be provided in rad/sec). + Default value (False) set by `config.defaults['freqplot.Hz']`. deg : bool - If `True`, plot phase in degrees (else radians). Default value (`True`) - set by config.defaults['freqplot.deg']. + If True, plot phase in degrees (else radians). Default + value (True) set by `config.defaults['freqplot.deg']`. display_margins : bool or str - If `True`, draw gain and phase margin lines on the magnitude and phase + If True, draw gain and phase margin lines on the magnitude and phase graphs and display the margins at the top of the graph. If set to 'overlay', the values for the gain and phase margin are placed on the graph. Setting display_margins turns off the axes grid. @@ -154,13 +154,13 @@ def bode_plot( Labels to use for the frequency, magnitude, and phase axes. Defaults are set by `config.defaults['freqplot.']`. grid : bool, optional - If `True`, plot grid lines on gain and phase plots. Default is set by + If True, plot grid lines on gain and phase plots. Default is set by `config.defaults['freqplot.grid']`. initial_phase : float, optional Set the reference phase to use for the lowest frequency. If set, the initial phase of the Bode plot will be set to the value closest to the value specified. Units are in either degrees or radians, depending on - the `deg` parameter. Default is -180 if wrap_phase is `False`, 0 if + the `deg` parameter. Default is -180 if wrap_phase is False, 0 if wrap_phase is True. label : str or array_like of str, optional If present, replace automatically generated label(s) with the given @@ -169,11 +169,11 @@ def bode_plot( legend_map : array of str, optional Location of the legend for multi-axes plots. Specifies an array of legend location strings matching the shape of the subplots, with - each entry being either `None` (for no legend) or a legend location + each entry being either None (for no legend) or a legend location string (see `~matplotlib.pyplot.legend`). legend_loc : int or str, optional Include a legend in the given location. Default is 'center right', - with no legend for a single response. Use `False` to suppress legend. + with no legend for a single response. Use False to suppress legend. margins_method : str, optional Method to use in computing margins (see `stability_margins`). omega_limits : array_like of two values @@ -183,33 +183,33 @@ def bode_plot( data is not a list of systems. omega_num : int Number of samples to use for the frequeny range. Defaults to - config.defaults['freqplot.number_of_samples']. Ignored if data is + `config.defaults['freqplot.number_of_samples']`. Ignored if data is not a list of systems. overlay_inputs, overlay_outputs : bool, optional - If set to `True`, combine input and/or output signals onto a single + If set to True, combine input and/or output signals onto a single plot and use line colors, labels, and a legend to distinguish them. plot : bool, optional (legacy) If given, `bode_plot` returns the legacy return values - of magnitude, phase, and frequency. If `False`, just return the + of magnitude, phase, and frequency. If False, just return the values with no plot. plot_magnitude, plot_phase : bool, optional - If set to `False`, don't plot the magnitude or phase, respectively. + If set to False, do not plot the magnitude or phase, respectively. rcParams : dict Override the default parameters used for generating plots. - Default is set by config.defaults['ctrlplot.rcParams']. + Default is set by `config.defaults['ctrlplot.rcParams']`. share_frequency, share_magnitude, share_phase : str or bool, optional Determine whether and how axis limits are shared between the indicated variables. Can be set set to 'row' to share across all subplots in a row, 'col' to set across all subplots in a column, or - `False` to allow independent limits. Note: if `sharex` is given, + False to allow independent limits. Note: if `sharex` is given, it sets the value of `share_frequency`; if `sharey` is given, it sets the value of both `share_magnitude` and `share_phase`. - Default values are 'row' for `share_magnitude` and `share_phase', + Default values are 'row' for `share_magnitude` and `share_phase`, 'col', for `share_frequency`, and can be set using - config.defaults['freqplot.share_']. + `config.defaults['freqplot.share_']`. show_legend : bool, optional - Force legend to be shown if `True` or hidden if `False`. If - `None`, then show legend when there is more than one line on an + Force legend to be shown if True or hidden if False. If + None, then show legend when there is more than one line on an axis or `legend_loc` or `legend_map` has been specified. title : str, optional Set the title of the plot. Defaults to plot type and system name(s). @@ -217,16 +217,16 @@ def bode_plot( Set the frame of reference used to center the plot title. If set to 'axes' (default), the horizontal position of the title will be centered relative to the axes. If set to 'figure', it will be - centered with respect to the figure (faster execution). The - default value can be set using config.defaults['freqplot.title_frame']. + centered with respect to the figure (faster execution). The default + value can be set using `config.defaults['freqplot.title_frame']`. wrap_phase : bool or float - If wrap_phase is `False` (default), then the phase will be unwrapped + If wrap_phase is False (default), then the phase will be unwrapped so that it is continuously increasing or decreasing. If wrap_phase is - `True` the phase will be restricted to the range [-180, 180) (or + True the phase will be restricted to the range [-180, 180) (or [:math:`-\\pi`, :math:`\\pi`) radians). If `wrap_phase` is specified as a float, the phase will be offset by 360 degrees if it falls below - the specified value. Default value is `False` and can be set using - config.defaults['freqplot.wrap_phase']. + the specified value. Default value is False and can be set using + `config.defaults['freqplot.wrap_phase']`. See Also -------- @@ -237,15 +237,15 @@ def bode_plot( Starting with python-control version 0.10, `bode_plot` returns a `ControlPlot` object instead of magnitude, phase, and frequency. To recover the old behavior, call `bode_plot` with - `plot=True`, which will force the legacy values (mag, phase, omega) to + ``plot=True``, which will force the legacy values (mag, phase, omega) to be returned (with a warning). To obtain just the frequency response of a system (or list of systems) without plotting, use the `frequency_response` command. If a discrete time model is given, the frequency response is plotted - along the upper branch of the unit circle, using the mapping `z = - exp(1j * omega * dt)` where `omega` ranges from 0 to `pi/dt` and `dt` - is the discrete timebase. If timebase not specified (`dt=True`), + along the upper branch of the unit circle, using the mapping ``z = + exp(1j * omega * dt)`` where `omega` ranges from 0 to ``pi/dt`` and `dt` + is the discrete timebase. If timebase not specified (``dt=True``), `dt` is set to 1. The default values for Bode plot configuration parameters can be reset @@ -965,7 +965,7 @@ def gen_zero_centered_series(val_min, val_max, period): mag_bbox = inv_transform.transform( ax_mag.get_tightbbox(fig.canvas.get_renderer())) - # Figure out location for the text (center left in figure frame) + # Figure out location for text (center left in figure frame) xpos = mag_bbox[0, 0] # left edge # Put a centered label as text outside the box @@ -989,10 +989,10 @@ def gen_zero_centered_series(val_min, val_max, period): # list of systems (e.g., "Step response for sys[1], sys[2]"). # - # Set the initial title for the data (unique system names, preserving order) + # Set initial title for the data (unique system names, preserving order) seen = set() - sysnames = [response.sysname for response in data \ - if not (response.sysname in seen or seen.add(response.sysname))] + sysnames = [response.sysname for response in data if not + (response.sysname in seen or seen.add(response.sysname))] if ax is None and title is None: if data[0].title is None: @@ -1202,11 +1202,11 @@ def nyquist_response( encirclement_threshold : float, optional Define the threshold for generating a warning if the number of net encirclements is a non-integer value. Default value is 0.05 and can - be set using config.defaults['nyquist.encirclement_threshold']. + be set using `config.defaults['nyquist.encirclement_threshold']`. indent_direction : str, optional For poles on the imaginary axis, set the direction of indentation to be 'right' (default), 'left', or 'none'. The default value can - be set using config.defaults['nyquist.indent_direction']. + be set using `config.defaults['nyquist.indent_direction']`. indent_points : int, optional Number of points to insert in the Nyquist contour around poles that are at or near the imaginary axis. @@ -1215,14 +1215,14 @@ def nyquist_response( imaginary axis. Portions of the Nyquist plot corresponding to indented portions of the contour are plotted using a different line style. The default value can be set using - config.defaults['nyquist.indent_radius']. + `config.defaults['nyquist.indent_radius']`. omega_limits : array_like of two values Set limits for plotted frequency range. If Hz=True the limits are in Hz otherwise in rad/s. Specifying `omega` as a list of two elements is equivalent to providing `omega_limits`. omega_num : int, optional Number of samples to use for the frequeny range. Defaults to - config.defaults['freqplot.number_of_samples']. + `config.defaults['freqplot.number_of_samples']`. warn_nyquist : bool, optional If set to 'False', turn off warnings about frequencies above Nyquist. warn_encirclements : bool, optional @@ -1231,30 +1231,30 @@ def nyquist_response( Notes ----- - 1. If a discrete time model is given, the frequency response is computed - along the upper branch of the unit circle, using the mapping `z = - exp(1j * omega * dt)` where `omega` ranges from 0 to `pi/dt` and `dt` - is the discrete timebase. If timebase not specified (`dt=True`), - `dt` is set to 1. - - 2. If a continuous-time system contains poles on or near the imaginary - axis, a small indentation will be used to avoid the pole. The radius - of the indentation is given by `indent_radius` and it is taken to the - right of stable poles and the left of unstable poles. If a pole is - exactly on the imaginary axis, the `indent_direction` parameter can be - used to set the direction of indentation. Setting `indent_direction` - to `none` will turn off indentation. - - 3. For those portions of the Nyquist plot in which the contour is - indented to avoid poles, resuling in a scaling of the Nyquist plot, - the line styles are according to the settings of the `primary_style` - and `mirror_style` keywords. By default the scaled portions of the - primary curve use a dotted line style and the scaled portion of the - mirror image use a dashdot line style. - - 4. If the legacy keyword `return_contour` is specified as `True`, the - response object can be iterated over to return `count, contour`. - This behavior is deprecated and will be removed in a future release. + If a discrete time model is given, the frequency response is computed + along the upper branch of the unit circle, using the mapping ``z = + exp(1j * omega * dt)`` where `omega` ranges from 0 to ``pi/dt`` and + `dt` is the discrete timebase. If timebase not specified + (``dt=True``), `dt` is set to 1. + + If a continuous-time system contains poles on or near the imaginary + axis, a small indentation will be used to avoid the pole. The radius + of the indentation is given by `indent_radius` and it is taken to the + right of stable poles and the left of unstable poles. If a pole is + exactly on the imaginary axis, the `indent_direction` parameter can be + used to set the direction of indentation. Setting `indent_direction` + to 'none' will turn off indentation. + + For those portions of the Nyquist plot in which the contour is indented + to avoid poles, resuling in a scaling of the Nyquist plot, the line + styles are according to the settings of the `primary_style` and + `mirror_style` keywords. By default the scaled portions of the primary + curve use a dotted line style and the scaled portion of the mirror + image use a dashdot line style. + + If the legacy keyword `return_contour` is specified as True, the + response object can be iterated over to return `count, contour`. This + behavior is deprecated and will be removed in a future release. See Also -------- @@ -1502,8 +1502,8 @@ def nyquist_response( "number of encirclements does not match Nyquist criterion;" " check frequency range and indent radius/direction", UserWarning, stacklevel=2) - elif indent_direction == 'none' and any(sys.poles().real == 0) and \ - warn_encirclements: + elif indent_direction == 'none' and any(sys.poles().real == 0) \ + and warn_encirclements: warnings.warn( "system has pure imaginary poles but indentation is" " turned off; results may be meaningless", @@ -1547,9 +1547,9 @@ def nyquist_plot( `omega` as a list of two elements is equivalent to providing `omega_limits`. unit_circle : bool, optional - If `True`, display the unit circle, to read gain crossover + If True, display the unit circle, to read gain crossover frequency. The circle style is determined by - config.defaults['nyquist.circle_style']. + `config.defaults['nyquist.circle_style']`. mt_circles : array_like, optional Draw circles corresponding to the given magnitudes of sensitivity. ms_circles : array_like, optional @@ -1593,10 +1593,10 @@ def nyquist_plot( arrow. If a 2D array is passed, the first row will be used to specify arrow locations for the primary curve and the second row will be used for the mirror image. Default value is 2 and can be - set using config.defaults['nyquist.arrows']. + set using `config.defaults['nyquist.arrows']`. arrow_size : float, optional Arrowhead width and length (in display coordinates). Default value is - 8 and can be set using config.defaults['nyquist.arrow_size']. + 8 and can be set using `config.defaults['nyquist.arrow_size']`. arrow_style : matplotlib.patches.ArrowStyle, optional Define style used for Nyquist curve arrows (overrides `arrow_size`). ax : matplotlib.axes.Axes, optional @@ -1606,7 +1606,7 @@ def nyquist_plot( encirclement_threshold : float, optional Define the threshold for generating a warning if the number of net encirclements is a non-integer value. Default value is 0.05 and can - be set using config.defaults['nyquist.encirclement_threshold']. + be set using `config.defaults['nyquist.encirclement_threshold']`. indent_direction : str, optional For poles on the imaginary axis, set the direction of indentation to be 'right' (default), 'left', or 'none'. @@ -1626,58 +1626,58 @@ def nyquist_plot( are generated. legend_loc : int or str, optional Include a legend in the given location. Default is 'upper right', - with no legend for a single response. Use `False` to suppress legend. + with no legend for a single response. Use False to suppress legend. max_curve_magnitude : float, optional Restrict the maximum magnitude of the Nyquist plot to this value. Portions of the Nyquist plot whose magnitude is restricted are plotted using a different line style. The default value is 20 and - can be set using config.defaults['nyquist.max_curve_magnitude']. + can be set using `config.defaults['nyquist.max_curve_magnitude']`. max_curve_offset : float, optional When plotting scaled portion of the Nyquist plot, increase/decrease the magnitude by this fraction of the max_curve_magnitude to allow any overlaps between the primary and mirror curves to be avoided. The default value is 0.02 and can be set using - config.defaults['nyquist.max_curve_magnitude']. + `config.defaults['nyquist.max_curve_magnitude']`. mirror_style : [str, str] or False Linestyles for mirror image of the Nyquist curve. The first element is used for unscaled portions of the Nyquist curve, the second element is used for portions that are scaled (using max_curve_magnitude). If - `False` then omit completely. Default linestyle (['--', ':']) is - determined by config.defaults['nyquist.mirror_style']. + False then omit completely. Default linestyle (['--', ':']) is + determined by `config.defaults['nyquist.mirror_style']`. omega_limits : array_like of two values Set limits for plotted frequency range. If Hz=True the limits are in Hz otherwise in rad/s. Specifying `omega` as a list of two elements is equivalent to providing `omega_limits`. omega_num : int, optional Number of samples to use for the frequeny range. Defaults to - config.defaults['freqplot.number_of_samples']. Ignored if data is + `config.defaults['freqplot.number_of_samples']`. Ignored if data is not a list of systems. plot : bool, optional (legacy) If given, `nyquist_plot` returns the legacy return values - of (counts, contours). If `False`, return the values with no plot. + of (counts, contours). If False, return the values with no plot. primary_style : [str, str], optional Linestyles for primary image of the Nyquist curve. The first element is used for unscaled portions of the Nyquist curve, the second element is used for portions that are scaled (using max_curve_magnitude). Default linestyle (['-', '-.']) is - determined by config.defaults['nyquist.mirror_style']. + determined by `config.defaults['nyquist.mirror_style']`. rcParams : dict Override the default parameters used for generating plots. - Default is set by config.defaults['ctrlplot.rcParams']. + Default is set by `config.defaults['ctrlplot.rcParams']`. return_contour : bool, optional (legacy) If 'True', return the encirclement count and Nyquist contour used to generate the Nyquist plot. show_legend : bool, optional - Force legend to be shown if `True` or hidden if `False`. If - `None`, then show legend when there is more than one line on the + Force legend to be shown if True or hidden if False. If + None, then show legend when there is more than one line on the plot or `legend_loc` has been specified. start_marker : str, optional Matplotlib marker to use to mark the starting point of the Nyquist plot. Defaults value is 'o' and can be set using - config.defaults['nyquist.start_marker']. + `config.defaults['nyquist.start_marker']`. start_marker_size : float, optional Start marker size (in display coordinates). Default value is - 4 and can be set using config.defaults['nyquist.start_marker_size']. + 4 and can be set using `config.defaults['nyquist.start_marker_size']`. title : str, optional Set the title of the plot. Defaults to plot type and system name(s). title_frame : str, optional @@ -1697,27 +1697,27 @@ def nyquist_plot( Notes ----- - 1. If a discrete time model is given, the frequency response is computed - along the upper branch of the unit circle, using the mapping `z = - exp(1j * omega * dt)` where `omega` ranges from 0 to `pi/dt` and `dt` - is the discrete timebase. If timebase not specified (`dt=True`), - `dt` is set to 1. - - 2. If a continuous-time system contains poles on or near the imaginary - axis, a small indentation will be used to avoid the pole. The radius - of the indentation is given by `indent_radius` and it is taken to the - right of stable poles and the left of unstable poles. If a pole is - exactly on the imaginary axis, the `indent_direction` parameter can be - used to set the direction of indentation. Setting `indent_direction` - to `none` will turn off indentation. If `return_contour` is `True`, the - exact contour used for evaluation is returned. - - 3. For those portions of the Nyquist plot in which the contour is - indented to avoid poles, resuling in a scaling of the Nyquist plot, - the line styles are according to the settings of the `primary_style` - and `mirror_style` keywords. By default the scaled portions of the - primary curve use a dotted line style and the scaled portion of the - mirror image use a dashdot line style. + If a discrete time model is given, the frequency response is computed + along the upper branch of the unit circle, using the mapping ``z = + exp(1j * omega * dt)`` where `omega` ranges from 0 to ``pi/dt`` and + `dt` is the discrete timebase. If timebase not specified + (``dt=True``), `dt` is set to 1. + + If a continuous-time system contains poles on or near the imaginary + axis, a small indentation will be used to avoid the pole. The radius + of the indentation is given by `indent_radius` and it is taken to the + right of stable poles and the left of unstable poles. If a pole is + exactly on the imaginary axis, the `indent_direction` parameter can be + used to set the direction of indentation. Setting `indent_direction` + to 'none' will turn off indentation. If `return_contour` is True, + the exact contour used for evaluation is returned. + + For those portions of the Nyquist plot in which the contour is indented + to avoid poles, resuling in a scaling of the Nyquist plot, the line + styles are according to the settings of the `primary_style` and + `mirror_style` keywords. By default the scaled portions of the primary + curve use a dotted line style and the scaled portion of the mirror + image use a dashdot line style. Examples -------- @@ -2125,10 +2125,10 @@ def gangof4_response( data is not a list of systems. omega_num : int Number of samples to use for the frequeny range. Defaults to - config.defaults['freqplot.number_of_samples']. Ignored if data is + `config.defaults['freqplot.number_of_samples']`. Ignored if data is not a list of systems. Hz : bool, optional - If `True`, when computing frequency limits automatically set + If True, when computing frequency limits automatically set limits to full decades in Hz instead of rad/s. Returns @@ -2212,10 +2212,10 @@ def gangof4_plot( data is not a list of systems. omega_num : int Number of samples to use for the frequeny range. Defaults to - config.defaults['freqplot.number_of_samples']. Ignored if data is + `config.defaults['freqplot.number_of_samples']`. Ignored if data is not a list of systems. Hz : bool, optional - If `True`, when computing frequency limits automatically set + If True, when computing frequency limits automatically set limits to full decades in Hz instead of rad/s. Returns @@ -2271,7 +2271,7 @@ def singular_values_response( omega : array_like List of frequencies in rad/sec to be used for frequency response. Hz : bool, optional - If `True`, when computing frequency limits automatically set + If True, when computing frequency limits automatically set limits to full decades in Hz instead of rad/s. Returns @@ -2288,7 +2288,7 @@ def singular_values_response( elements is equivalent to providing `omega_limits`. omega_num : int, optional Number of samples to use for the frequeny range. Defaults to - config.defaults['freqplot.number_of_samples']. + `config.defaults['freqplot.number_of_samples']`. See Also -------- @@ -2357,10 +2357,10 @@ def singular_values_plot( Passed to `matplotlib` as the format string for all lines in the plot. The `omega` parameter must be present (use omega=None if needed). dB : bool - If `True`, plot result in dB. Default is False. + If True, plot result in dB. Default is False. Hz : bool - If `True`, plot frequency in Hz (omega must be provided in rad/sec). - Default value (False) set by config.defaults['freqplot.Hz']. + If True, plot frequency in Hz (omega must be provided in rad/sec). + Default value (False) set by `config.defaults['freqplot.Hz']`. **kwargs : `matplotlib.pyplot.plot` keyword properties, optional Additional keywords passed to `matplotlib` to specify line properties. @@ -2388,35 +2388,35 @@ def singular_values_plot( the current figure has a single axes, that axes is used. Otherwise, a new figure is created. color : matplotlib color spec - Color to use for singular values (or `None` for matplotlib default). + Color to use for singular values (or None for matplotlib default). grid : bool - If `True`, plot grid lines on gain and phase plots. Default is set by - `config.defaults['freqplot.grid']`. + If True, plot grid lines on gain and phase plots. Default is + set by `config.defaults['freqplot.grid']`. label : str or array_like of str, optional If present, replace automatically generated label(s) with the given label(s). If sysdata is a list, strings should be specified for each system. legend_loc : int or str, optional Include a legend in the given location. Default is 'center right', - with no legend for a single response. Use `False` to suppress legend. + with no legend for a single response. Use False to suppress legend. omega_limits : array_like of two values Set limits for plotted frequency range. If Hz=True the limits are in Hz otherwise in rad/s. Specifying `omega` as a list of two elements is equivalent to providing `omega_limits`. omega_num : int, optional Number of samples to use for the frequeny range. Defaults to - config.defaults['freqplot.number_of_samples']. Ignored if data is + `config.defaults['freqplot.number_of_samples']`. Ignored if data is not a list of systems. plot : bool, optional (legacy) If given, `singular_values_plot` returns the legacy return - values of magnitude, phase, and frequency. If `False`, just return + values of magnitude, phase, and frequency. If False, just return the values with no plot. rcParams : dict Override the default parameters used for generating plots. - Default is set up config.defaults['ctrlplot.rcParams']. + Default is set up `config.defaults['ctrlplot.rcParams']`. show_legend : bool, optional - Force legend to be shown if `True` or hidden if `False`. If - `None`, then show legend when there is more than one line on an + Force legend to be shown if True or hidden if False. If + None, then show legend when there is more than one line on an axis or `legend_loc` or `legend_map` has been specified. title : str, optional Set the title of the plot. Defaults to plot type and system name(s). @@ -2432,13 +2432,13 @@ def singular_values_plot( Notes ----- - 1. If plot==`False`, the following legacy values are returned: - * mag : ndarray (or list of ndarray if len(data) > 1)) - Magnitude of the response (deprecated). - * phase : ndarray (or list of ndarray if len(data) > 1)) - Phase in radians of the response (deprecated). - * omega : ndarray (or list of ndarray if len(data) > 1)) - Frequency in rad/sec (deprecated). + If ``plot=False``, the following legacy values are returned: + * mag : ndarray (or list of ndarray if len(data) > 1)) + Magnitude of the response (deprecated). + * phase : ndarray (or list of ndarray if len(data) > 1)) + Phase in radians of the response (deprecated). + * omega : ndarray (or list of ndarray if len(data) > 1)) + Frequency in rad/sec (deprecated). """ # Keyword processing @@ -2637,7 +2637,7 @@ def _determine_omega_vector(syslist, omega_in, omega_limits, omega_num, Number of points to be used for the frequency range (if the frequency range is not user-specified). Hz : bool, optional - If `True`, the limits (first and last value) of the frequencies + If True, the limits (first and last value) of the frequencies are set to full decades in Hz so it fits plotting with logarithmic scale in Hz otherwise in rad/s. Omega is always returned in rad/sec. @@ -2646,7 +2646,7 @@ def _determine_omega_vector(syslist, omega_in, omega_limits, omega_num, omega_out : 1D array Frequency range to be used. omega_range_given : bool - `True` if the frequency range was specified by the user, either through + True if the frequency range was specified by the user, either through omega_in or through omega_limits. False if both omega_in and omega_limits are None. @@ -2695,13 +2695,13 @@ def _default_frequency_range(syslist, Hz=None, number_of_samples=None, syslist : list of LTI List of linear input/output systems (single system is OK) Hz : bool, optional - If `True`, the limits (first and last value) of the frequencies + If True, the limits (first and last value) of the frequencies are set to full decades in Hz so it fits plotting with logarithmic scale in Hz otherwise in rad/s. Omega is always returned in rad/sec. number_of_samples : int, optional Number of samples to generate. The default value is read from - `config.defaults['freqplot.number_of_samples']. If `None`, then the - default from `numpy.logspace` is used. + `config.defaults['freqplot.number_of_samples']`. If None, + then the default from `numpy.logspace` is used. feature_periphery_decades : float, optional Defines how many decades shall be included in the frequency range on both sides of features (poles, zeros). The default value is read from diff --git a/control/iosys.py b/control/iosys.py index fabb3ea46..fe954957b 100644 --- a/control/iosys.py +++ b/control/iosys.py @@ -115,10 +115,10 @@ class InputOutputSystem(): is operating in continuous or discrete time. It can have the following values: - * dt = `None` No timebase specified - * dt = 0 Continuous time system - * dt > 0 Discrete time system with sampling time dt - * dt = `True` Discrete time system with unspecified sampling time + * ``dt = None`` No timebase specified + * ``dt = 0`` Continuous time system + * ``dt > 0`` Discrete time system with sampling time dt + * ``dt = True`` Discrete time system with unspecified sampling time Parameters ---------- @@ -128,7 +128,7 @@ class InputOutputSystem(): integer count is specified, the names of the signal will be of the form 's[i]' (where 's' is given by the `input_prefix` parameter and has default value 'u'). If this parameter is not given or given as - `None`, the relevant quantity will be determined when possible + None, the relevant quantity will be determined when possible based on other information provided to functions using the system. outputs : int, list of str, or None Description of the system outputs. Same format as `inputs`, with @@ -137,9 +137,9 @@ class InputOutputSystem(): Description of the system states. Same format as `inputs`, with the prefix given by state_prefix (defaults to 'x'). dt : None, True or float, optional - System timebase. 0 (default) indicates continuous time, `True` + System timebase. 0 (default) indicates continuous time, True indicates discrete time with unspecified sampling time, positive - number is discrete time with specified sampling time, `None` indicates + number is discrete time with specified sampling time, None indicates unspecified timebase (either continuous or discrete time). name : string, optional System name (used for specifying signals). If unspecified, a generic @@ -313,7 +313,7 @@ def repr_format(self): This value can be changed for an individual system by setting the `repr_format` parameter when the system is created or by setting the `repr_format` property after system creation. Set - config.defaults['iosys.repr_format'] to change for all I/O systems + `config.defaults['iosys.repr_format']` to change for all I/O systems or use the `repr_format` parameter/attribute for a single system. """ @@ -426,11 +426,11 @@ def copy(self, name=None, use_prefix_suffix=True): A copy of the system is made, with a new name. The `name` keyword can be used to specify a specific name for the system. If no name - is given and `use_prefix_suffix` is `True`, the name is constructed - by prepending config.defaults['iosys.duplicate_system_name_prefix'] - and appending config.defaults['iosys.duplicate_system_name_suffix']. - Otherwise, a generic system name of the form `sys[]` is used, - where `` is based on an internal counter. + is given and `use_prefix_suffix` is True, the name is constructed + by prepending `config.defaults['iosys.duplicate_system_name_prefix']` + and appending `config.defaults['iosys.duplicate_system_name_suffix']`. + Otherwise, a generic system name of the form 'sys[]' is used, + where '' is based on an internal counter. """ # Create a copy of the system @@ -455,23 +455,23 @@ def set_inputs(self, inputs, prefix='u'): Description of the system inputs. This can be given as an integer count or as a list of strings that name the individual signals. If an integer count is specified, the names of the signal will be - of the form `u[i]` (where the prefix `u` can be changed using the + of the form 'u[i]' (where the prefix 'u' can be changed using the optional prefix parameter). prefix : string, optional If `inputs` is an integer, create the names of the states using the given prefix (default = 'u'). The names of the input will be - of the form `prefix[i]`. + of the form 'prefix[i]'. """ self.ninputs, self.input_index = \ _process_signal_list(inputs, prefix=prefix) def find_input(self, name): - """Find the index for an input given its name (`None` if not found)""" + """Find the index for an input given its name (None if not found)""" return self.input_index.get(name, None) def find_inputs(self, name_list): - """Return list of indices matching input spec (`None` if not found)""" + """Return list of indices matching input spec (None if not found)""" return self._find_signals(name_list, self.input_index) # Property for getting and setting list of input signals @@ -485,26 +485,26 @@ def set_outputs(self, outputs, prefix='y'): Parameters ---------- outputs : int, list of str, or None - Description of the system outputs. This can be given as an integer - count or as a list of strings that name the individual signals. - If an integer count is specified, the names of the signal will be - of the form `u[i]` (where the prefix `u` can be changed using the - optional prefix parameter). + Description of the system outputs. This can be given as an + integer count or as a list of strings that name the individual + signals. If an integer count is specified, the names of the + signal will be of the form 'u[i]' (where the prefix 'u' can be + changed using the optional prefix parameter). prefix : string, optional If `outputs` is an integer, create the names of the states using the given prefix (default = 'y'). The names of the input will be - of the form `prefix[i]`. + of the form 'prefix[i]'. """ self.noutputs, self.output_index = \ _process_signal_list(outputs, prefix=prefix) def find_output(self, name): - """Find the index for an output given its name (`None` if not found)""" + """Find the index for an output given its name (None if not found)""" return self.output_index.get(name, None) def find_outputs(self, name_list): - """Return list of indices matching output spec (`None` if not found)""" + """Return list of indices matching output spec (None if not found)""" return self._find_signals(name_list, self.output_index) # Property for getting and setting list of output signals @@ -521,23 +521,23 @@ def set_states(self, states, prefix='x'): Description of the system states. This can be given as an integer count or as a list of strings that name the individual signals. If an integer count is specified, the names of the signal will be - of the form `u[i]` (where the prefix `u` can be changed using the + of the form 'u[i]' (where the prefix 'u' can be changed using the optional prefix parameter). prefix : string, optional If `states` is an integer, create the names of the states using the given prefix (default = 'x'). The names of the input will be - of the form `prefix[i]`. + of the form 'prefix[i]'. """ self.nstates, self.state_index = \ _process_signal_list(states, prefix=prefix, allow_dot=True) def find_state(self, name): - """Find the index for a state given its name (`None` if not found)""" + """Find the index for a state given its name (None if not found)""" return self.state_index.get(name, None) def find_states(self, name_list): - """Return list of indices matching state spec (`None` if not found)""" + """Return list of indices matching state spec (None if not found)""" return self._find_signals(name_list, self.state_index) # Property for getting and setting list of state signals @@ -563,11 +563,11 @@ def update_names(self, **kwargs): inputs : list of str, int, or None, optional List of strings that name the individual input signals. If given as an integer or None, signal names default to the form - `u[i]`. See `InputOutputSystem` for more information. + 'u[i]'. See `InputOutputSystem` for more information. outputs : list of str, int, or None, optional - Description of output signals; defaults to `y[i]`. + Description of output signals; defaults to 'y[i]'. states : int, list of str, int, or None, optional - Description of system states; defaults to `x[i]`. + Description of system states; defaults to 'x[i]'. input_prefix : string, optional Set the prefix for input signals. Default = 'u'. output_prefix : string, optional @@ -610,7 +610,7 @@ def isctime(self, strict=False): sys : Named I/O system System to be checked. strict : bool, optional - If strict is `True`, make sure that timebase is not None. Default + If strict is True, make sure that timebase is not None. Default is False. """ # If no timebase is given, answer depends on strict flag @@ -625,7 +625,7 @@ def isdtime(self, strict=False): Parameters ---------- strict : bool, optional - If strict is `True`, make sure that timebase is not None. Default + If strict is True, make sure that timebase is not None. Default is False. """ @@ -655,7 +655,7 @@ def issiso(sys, strict=False): sys : I/O or LTI system System to be checked. strict : bool (default = False) - If strict is `True`, do not treat scalars as SISO. + If strict is True, do not treat scalars as SISO. """ if isinstance(sys, (int, float, complex, np.number)) and not strict: return True @@ -672,20 +672,20 @@ def timebase(sys, strict=True): dt = timebase(sys) returns the timebase for a system 'sys'. If the strict option is - set to `True`, dt = `True` will be returned as 1. + set to True, ``dt = True`` will be returned as 1. Parameters ---------- sys : InputOutputSystem or float System whose timebase is to be determined. strict : bool, optional - Whether to implement strict checking. If set to `True` (default), - a float will always be returned (dt = `True` will be returned as 1). + Whether to implement strict checking. If set to True (default), + a float will always be returned (``dt = True`` will be returned as 1). Returns ------- dt : timebase - Timebase for the system (0 = continuous time, `None` = unspecified). + Timebase for the system (0 = continuous time, None = unspecified). """ # System needs to be either a constant or an I/O or LTI system @@ -764,7 +764,7 @@ def isdtime(sys=None, strict=False, dt=None): dt : None or number, optional Timebase to be checked. strict : bool, default=False - If strict is `True`, make sure that timebase is not None. + If strict is True, make sure that timebase is not None. """ # See if we were passed a timebase instead of a system @@ -796,7 +796,7 @@ def isctime(sys=None, dt=None, strict=False): dt : None or number, optional Timebase to be checked. strict : bool (default = False) - If strict is `True`, make sure that timebase is not None. + If strict is True, make sure that timebase is not None. """ # See if we were passed a timebase instead of a system @@ -838,7 +838,7 @@ def iosys_repr(sys, format=None): Notes ----- By default, the representation for an input/output is set to 'eval'. - Set config.defaults['iosys.repr_format'] to change for all I/O systems + Set `config.defaults['iosys.repr_format']` to change for all I/O systems or use the `repr_format` parameter for a single system. Jupyter will automatically use the 'latex' representation for I/O @@ -863,12 +863,12 @@ def _process_iosys_keywords( """Process iosys specification. This function processes the standard keywords used in initializing an - I/O system. It first looks in the `keyword` dictionary to see if a - value is specified. If not, the `default` dictionary is used. The - `default` dictionary can also be set to an InputOutputSystem object, + I/O system. It first looks in the `keywords` dictionary to see if a + value is specified. If not, the `defaults` dictionary is used. The + `defaults` dictionary can also be set to an InputOutputSystem object, which is useful for copy constructors that change system/signal names. - If `end` is `True`, then generate an error if there are any remaining + If `end` is True, then generate an error if there are any remaining keywords. """ diff --git a/control/lti.py b/control/lti.py index 8babc1e28..96dcf0701 100644 --- a/control/lti.py +++ b/control/lti.py @@ -80,7 +80,7 @@ def frequency_response(self, omega=None, squeeze=None): G(exp(j*omega*dt)) = mag * exp(j*phase). In general the system may be multiple input, multiple output (MIMO), - where `m = self.ninputs` number of inputs and `p = self.noutputs` + where ``m = self.ninputs`` number of inputs and ``p = self.noutputs`` number of outputs. Parameters @@ -90,11 +90,12 @@ def frequency_response(self, omega=None, squeeze=None): radians/sec at which the system will be evaluated. If None (default), a set of frequencies is computed based on the system dynamics. squeeze : bool, optional - If squeeze=`True`, remove single-dimensional entries from the shape - of the output even if the system is not SISO. If squeeze=`False`, - keep all indices (output, input and, if omega is array_like, - frequency) even if the system is SISO. The default value can be - set using config.defaults['control.squeeze_frequency_response']. + If ``squeeze=True``, remove single-dimensional entries from the + shape of the output even if the system is not SISO. If + ``squeeze=False``, keep all indices (output, input and, if + omega is array_like, frequency) even if the system is SISO. The + default value can be set using + `config.defaults['control.squeeze_frequency_response']`. Returns ------- @@ -108,11 +109,11 @@ def frequency_response(self, omega=None, squeeze=None): of the system frequency response, `phase` is the wrapped phase in radians of the system frequency response, and `omega` is the (sorted) frequencies at which the response was evaluated. - If the system is SISO and squeeze is not `True`, `magnitude` and + If the system is SISO and squeeze is not True, `magnitude` and `phase` are 1D, indexed by frequency. If the system is not - SISO or squeeze is `False`, the array is 3D, indexed by the + SISO or squeeze is False, the array is 3D, indexed by the output, input, and, if omega is array_like, frequency. If - `squeeze` is `True` then single-dimensional axes are removed. + `squeeze` is True then single-dimensional axes are removed. """ from .frdata import FrequencyResponseData @@ -300,7 +301,7 @@ def damp(sys, doprint=True): sys : LTI (StateSpace or TransferFunction) A linear system object. doprint : bool (optional) - If `True`, print table with values. + If True, print table with values. Returns ------- @@ -355,15 +356,15 @@ def damp(sys, doprint=True): def evalfr(sys, x, squeeze=None): """Evaluate transfer function of LTI system at complex frequency. - Returns the complex frequency response `sys(x)` where `x` is `s` for + Returns the complex frequency response ``sys(x)`` where `x` is `s` for continuous-time systems and `z` for discrete-time systems, with - `m = sys.ninputs` number of inputs and `p = sys.noutputs` number of + ``m = sys.ninputs`` number of inputs and ``p = sys.noutputs`` number of outputs. To evaluate at a frequency omega in radians per second, enter - `x = omega * 1j` for continuous-time systems, or - `x = exp(1j * omega * dt)` for discrete-time systems, or use - `freqresp(sys, omega)`. + ``x = omega * 1j`` for continuous-time systems, or + ``x = exp(1j * omega * dt)`` for discrete-time systems, or use + ``freqresp(sys, omega)``. Parameters ---------- @@ -372,20 +373,21 @@ def evalfr(sys, x, squeeze=None): x : complex scalar or 1D array_like Complex frequency(s). squeeze : bool, optional (default=True) - If squeeze=`True`, remove single-dimensional entries from the shape of - the output even if the system is not SISO. If squeeze=`False`, keep all - indices (output, input and, if omega is array_like, frequency) even if - the system is SISO. The default value can be set using - config.defaults['control.squeeze_frequency_response']. + If ``squeeze=True``, remove single-dimensional entries from the + shape of the output even if the system is not SISO. If + ``squeeze=False``, keep all indices (output, input and, if omega is + array_like, frequency) even if the system is SISO. The default + value can be set using + `config.defaults['control.squeeze_frequency_response']`. Returns ------- fresp : complex ndarray The frequency response of the system. If the system is SISO and - squeeze is not `True`, the shape of the array matches the shape of - omega. If the system is not SISO or squeeze is `False`, the first two + squeeze is not True, the shape of the array matches the shape of + omega. If the system is not SISO or squeeze is False, the first two dimensions of the array are indices for the output and input and the - remaining dimensions match omega. If `squeeze` is `True` then + remaining dimensions match omega. If `squeeze` is True then single-dimensional axes are removed. See Also @@ -415,8 +417,8 @@ def frequency_response( Computes the frequency response of an LTI system at a list of frequencies. In general the system may be multiple input, multiple - output (MIMO), where `m = sys.ninputs` number of inputs and `p = - sys.noutputs` number of outputs. + output (MIMO), where ``m = sys.ninputs`` number of inputs and ``p = + sys.noutputs`` number of outputs. Parameters ---------- @@ -425,7 +427,7 @@ def frequency_response( omega : float or 1D array_like, optional Frequencies in radians/sec at which the system should be evaluated. Can be a single frequency or array of frequencies, which - will be sorted before evaluation. If `None` (default), a common set + will be sorted before evaluation. If None (default), a common set of frequencies that works across all given systems is computed. omega_limits : array_like of two values, optional Limits to the range of frequencies, in rad/sec. Specifying @@ -433,7 +435,7 @@ def frequency_response( `omega_limits`. Ignored if omega is provided. omega_num : int, optional Number of frequency samples at which to compute the response. - Defaults to config.defaults['freqplot.number_of_samples']. Ignored + Defaults to `config.defaults['freqplot.number_of_samples']`. Ignored if omega is provided. Returns @@ -448,10 +450,10 @@ def frequency_response( the system frequency response, `phase` is the wrapped phase in radians of the system frequency response, and `omega` is the (sorted) frequencies at which the response was evaluated. If the - system is SISO and squeeze is not `False`, `magnitude` and `phase` + system is SISO and squeeze is not False, `magnitude` and `phase` are 1D, indexed by frequency. If the system is not SISO or squeeze - is `False`, the array is 3D, indexed by the output, input, and - frequency. If `squeeze` is `True` then single-dimensional axes are + is False, the array is 3D, indexed by the output, input, and + frequency. If `squeeze` is True then single-dimensional axes are removed. Returns a list of `FrequencyResponseData` objects if sys is @@ -460,15 +462,16 @@ def frequency_response( Other Parameters ---------------- Hz : bool, optional - If `True`, when computing frequency limits automatically set + If True, when computing frequency limits automatically set limits to full decades in Hz instead of rad/s. Omega is always returned in rad/sec. squeeze : bool, optional - If squeeze=`True`, remove single-dimensional entries from the shape of - the output even if the system is not SISO. If squeeze=`False`, keep all - indices (output, input and, if omega is array_like, frequency) even if - the system is SISO. The default value can be set using - config.defaults['control.squeeze_frequency_response']. + If ``squeeze=True``, remove single-dimensional entries from the + shape of the output even if the system is not SISO. If + ``squeeze=False``, keep all indices (output, input and, if omega is + array_like, frequency) even if the system is SISO. The default + value can be set using + `config.defaults['control.squeeze_frequency_response']`. See Also -------- @@ -479,13 +482,13 @@ def frequency_response( 1. This function is a wrapper for `StateSpace.frequency_response` and `TransferFunction.frequency_response`. - 2. You can also use the lower-level methods `sys(s)` or `sys(z)` to + 2. You can also use the lower-level methods ``sys(s)`` or ``sys(z)`` to generate the frequency response for a single system. 3. All frequency data should be given in rad/sec. If frequency limits are computed automatically, the `Hz` keyword can be used to ensure that limits are in factors of decades in Hz, so that Bode plots with - `Hz=True` look better. + ``Hz=True`` look better. 4. The frequency response data can be plotted by calling the `bode_plot` function or using the `plot` method of diff --git a/control/margins.py b/control/margins.py index 7187f41ac..1817c7d70 100644 --- a/control/margins.py +++ b/control/margins.py @@ -221,7 +221,7 @@ def stability_margins(sysdata, returnall=False, epsw=0.0, method='best'): Alternatively, a three tuple of the form (mag, phase, omega) providing the frequency response can be passed. returnall : bool, optional - If true, return all margins found. If `False` (default), return only the + If true, return all margins found. If False (default), return only the minimum stability margins. For frequency data or FRD systems, only margins in the given frequency region can be found and returned. epsw : float, optional @@ -387,9 +387,8 @@ def _dstab(w): # find all stab margins? widx, = np.where(np.diff(np.sign(np.diff(_dstab(sys.omega)))) > 0) wstab = np.array( - [sp.optimize.minimize_scalar(_dstab, - bracket=(sys.omega[i], sys.omega[i+1]) - ).x + [sp.optimize.minimize_scalar( + _dstab, bracket=(sys.omega[i], sys.omega[i+1])).x for i in widx]) wstab = wstab[(wstab >= sys.omega[0]) * (wstab <= sys.omega[-1])] ws_resp = sys(1j * wstab) @@ -472,8 +471,8 @@ def margin(*args): Gain and phase margins and associated crossover frequencies. - Can be called as `margin(sys)` where `sys` is a SISO LTI system or - `margin(mag, phase, omega)`. + Can be called as ``margin(sys)`` where `sys` is a SISO LTI system or + ``margin(mag, phase, omega)``. Parameters ---------- diff --git a/control/mateqn.py b/control/mateqn.py index 0541feb92..e24c01d2f 100644 --- a/control/mateqn.py +++ b/control/mateqn.py @@ -95,7 +95,7 @@ def lyap(A, Q, C=None, E=None, method=None): If present, solve the generalized Lyapunov equation. method : str, optional Set the method used for computing the result. Current methods are - 'slycot' and 'scipy'. If set to `None` (default), try 'slycot' first + 'slycot' and 'scipy'. If set to None (default), try 'slycot' first and then 'scipy'. Returns @@ -221,7 +221,7 @@ def dlyap(A, Q, C=None, E=None, method=None): If present, solve the generalized Lyapunov equation. method : str, optional Set the method used for computing the result. Current methods are - 'slycot' and 'scipy'. If set to `None` (default), try 'slycot' first + 'slycot' and 'scipy'. If set to None (default), try 'slycot' first and then 'scipy'. Returns @@ -347,11 +347,11 @@ def care(A, B, Q, R=None, S=None, E=None, stabilizing=True, method=None, Input matrices for generalized Riccati equation. method : str, optional Set the method used for computing the result. Current methods are - 'slycot' and 'scipy'. If set to `None` (default), try 'slycot' first + 'slycot' and 'scipy'. If set to None (default), try 'slycot' first and then 'scipy'. stabilizing : bool, optional If `method` is 'slycot', unstabilized eigenvalues will be returned - in the initial elements of `L`. Not supported for 'scipy'. + in the initial elements of ``L``. Not supported for 'scipy'. Returns ------- @@ -503,7 +503,7 @@ def dare(A, B, Q, R, S=None, E=None, stabilizing=True, method=None, Input matrices for generalized Riccati equation. method : str, optional Set the method used for computing the result. Current methods are - 'slycot' and 'scipy'. If set to `None` (default), try 'slycot' first + 'slycot' and 'scipy'. If set to None (default), try 'slycot' first and then 'scipy'. stabilizing : bool, optional If `method` is 'slycot', unstabilized eigenvalues will be returned diff --git a/control/matlab/timeresp.py b/control/matlab/timeresp.py index 29104d1fe..5574deb24 100644 --- a/control/matlab/timeresp.py +++ b/control/matlab/timeresp.py @@ -30,7 +30,7 @@ def step(sys, T=None, input=0, output=None, return_x=False): output : int If given, index of the output that is returned by this simulation. return_x : bool, optional - If `True`, return the state vector in addition to outputs. + If True, return the state vector in addition to outputs. Returns ------- @@ -112,7 +112,7 @@ def stepinfo(sysdata, T=None, yfinal=None, SettlingTimeThreshold=0.02, If `sysdata` corresponds to a MIMO system, `S` is a 2D list of dicts. To get the step response characteristics from the j-th input to the - i-th output, access `S[i][j]` + i-th output, access ``S[i][j]`` See Also -------- @@ -156,7 +156,7 @@ def impulse(sys, T=None, input=0, output=None, return_x=False): output : int Index of the output that will be used in this simulation. return_x : bool, optional - If `True`, return the state vector in addition to outputs. + If True, return the state vector in addition to outputs. Returns ------- @@ -208,7 +208,7 @@ def initial(sys, T=None, X0=0., input=None, output=None, return_x=False): output : int If given, index of the output that is returned by this simulation. return_x : bool, optional - If `True`, return the state vector in addition to outputs. + If True, return the state vector in addition to outputs. Returns ------- @@ -242,19 +242,18 @@ def initial(sys, T=None, X0=0., input=None, output=None, return_x=False): def lsim(sys, U=0., T=None, X0=0.): '''Simulate the output of a linear system. - As a convenience for parameters `U`, `X0`: - Numbers (scalars) are converted to constant arrays with the correct shape. - The correct shape is inferred from arguments `sys` and `T`. + As a convenience for parameters `U` and `X0`, numbers (scalars) are + converted to constant arrays with the correct shape. The correct + shape is inferred from arguments `sys` and `T`. Parameters ---------- sys : LTI (StateSpace, or TransferFunction) LTI system to simulate. U : array-like or number, optional - Input array giving input at each time `T` (default = 0). - - If `U` is `None` or `0`, a special algorithm is used. This special - algorithm is faster than the general algorithm, which is used otherwise. + Input array giving input at each time `T` (default = 0). If `U` is + None or 0, a special algorithm is used. This special algorithm is + faster than the general algorithm, which is used otherwise. T : array-like, optional for discrete LTI `sys` Time steps at which the input is defined; values must be evenly spaced. X0 : array-like or number, optional diff --git a/control/matlab/wrappers.py b/control/matlab/wrappers.py index 52dee324b..b11c01769 100644 --- a/control/matlab/wrappers.py +++ b/control/matlab/wrappers.py @@ -34,13 +34,13 @@ def bode(*args, **kwargs): omega : array Range of frequencies in rad/s. dB : boolean - If `True`, plot result in dB. + If True, plot result in dB. Hz : boolean - If `True`, plot frequency in Hz (omega must be provided in rad/sec). + If True, plot frequency in Hz (omega must be provided in rad/sec). deg : boolean - If `True`, return phase in degrees (else radians). + If True, return phase in degrees (else radians). plot : boolean - If `True`, plot magnitude and phase. + If True, plot magnitude and phase. Returns ------- @@ -115,7 +115,7 @@ def nyquist(*args, plot=True, **kwargs): in Hz otherwise in rad/s. Specifying `omega` as a list of two elements is equivalent to providing `omega_limits`. plot : bool - If `False`, do not generate a plot. + If False, do not generate a plot. Returns ------- @@ -226,12 +226,12 @@ def rlocus(*args, **kwargs): Gains to use in computing plot of closed-loop poles. xlim : tuple or list, optional Set limits of x axis, normally with tuple - (see :doc:`matplotlib:api/axes_api`). + (see `matplotlib:api/axes_api`). ylim : tuple or list, optional Set limits of y axis, normally with tuple - (see :doc:`matplotlib:api/axes_api`). + (see `matplotlib:api/axes_api`). plot : bool - If `False`, do not generate a plot. + If False, do not generate a plot. Returns ------- @@ -275,10 +275,10 @@ def pzmap(*args, **kwargs): sys : LTI (StateSpace or TransferFunction) Linear system for which poles and zeros are computed. plot : bool, optional - If `True` a graph is generated with Matplotlib, + If True a graph is generated with Matplotlib, otherwise the poles and zeros are only computed and returned. grid : boolean (default = False) - If `True`, plot omega-damping grid. + If True, plot omega-damping grid. Returns ------- @@ -385,9 +385,9 @@ def connect(*args): The system `sys` is a system typically constructed with `append`, with multiple inputs and outputs. The inputs and outputs are connected - according to the interconnection matrix `Q`, and then the final inputs and - outputs are trimmed according to the inputs and outputs listed in `inputv` - and `outputv`. + according to the interconnection matrix `Q`, and then the final inputs + and outputs are trimmed according to the inputs and outputs listed in + `inputv` and `outputv`. NOTE: Inputs and outputs are indexed starting at 1 and negative values correspond to a negative feedback interconnection. @@ -400,8 +400,8 @@ def connect(*args): Interconnection matrix. First column gives the input to be connected. The second column gives the index of an output that is to be fed into that input. Each additional column gives the index of an additional - input that may be optionally added to that input. Negative - values mean the feedback is negative. A zero value is ignored. Inputs + input that may be optionally added to that input. Negative values + mean the feedback is negative. A zero value is ignored. Inputs and outputs are indexed starting at 1 to communicate sign information. inputv : 1D array List of final external inputs, indexed starting at 1. diff --git a/control/modelsimp.py b/control/modelsimp.py index 5422a1281..43f410b34 100644 --- a/control/modelsimp.py +++ b/control/modelsimp.py @@ -110,7 +110,7 @@ def model_reduction( method : string Method of removing states: either 'truncate' or 'matchdc' (default). warn_unstable : bool, option - If `False`, don't warn if system is unstable. + If False, don't warn if system is unstable. Returns ------- @@ -150,7 +150,7 @@ def model_reduction( States, inputs, and outputs can be specified using integer offsets or using signal names. Slices can also be specified, but must use the - Python `slice()` function. + Python `slice` function. """ if not isinstance(sys, StateSpace): @@ -274,7 +274,7 @@ def balanced_reduction(sys, orders, method='truncate', alpha=None): Desired order of reduced order model (if a vector, returns a vector of systems). method : string - Method of removing states, either `'truncate'` or `'matchdc'`.. + Method of removing states, either 'truncate' or 'matchdc'. alpha : float Redefines the stability boundary for eigenvalues of the system matrix A. By default for continuous-time systems, alpha <= 0 @@ -292,9 +292,9 @@ def balanced_reduction(sys, orders, method='truncate', alpha=None): Raises ------ ValueError - If `method` is not `'truncate'` or `'matchdc'` + If `method` is not 'truncate' or 'matchdc'. ImportError - if slycot routine ab09ad, ab09md, or ab09nd is not found + if slycot routine ab09ad, ab09md, or ab09nd is not found. ValueError if there are more unstable modes than any value in orders @@ -436,12 +436,12 @@ def eigensys_realization(arg, r, m=None, n=None, dt=True, transpose=False): x[k+1] &= A x[k] + B u[k] \\\\ y[k] &= C x[k] + D u[k] - of order `r` for a given impulse-response data (see [1]_). + of order :math:`r` for a given impulse-response data (see [1]_). The function can be called with 2 arguments: - * `sysd, S = eigensys_realization(data, r)` - * `sysd, S = eigensys_realization(YY, r)` + * ``sysd, S = eigensys_realization(data, r)`` + * ``sysd, S = eigensys_realization(YY, r)`` where `data` is a `TimeResponseData` object, `YY` is a 1D or 3D array, and r is an integer. @@ -460,10 +460,10 @@ def eigensys_realization(arg, r, m=None, n=None, dt=True, transpose=False): n : integer, optional Number of columns in Hankel matrix. Default is 2*r. dt : True or float, optional - `True` indicates discrete time with unspecified sampling time and a + True indicates discrete time with unspecified sampling time and a positive float is discrete time with the specified sampling time. It can be used to scale the StateSpace model in order to match the - unit-area impulse response of python-control. Default is `True`. + unit-area impulse response of python-control. Default is True. transpose : bool, optional Assume that input data is transposed relative to the standard :ref:`time-series-convention`. For TimeResponseData this parameter @@ -553,10 +553,10 @@ def markov(*args, m=None, transpose=False, dt=None, truncate=False): The function can be called with either 1, 2 or 3 arguments: - * `H = markov(data)` - * `H = markov(data, m)` - * `H = markov(Y, U)` - * `H = markov(Y, U, m)` + * ``H = markov(data)`` + * ``H = markov(data, m)`` + * ``H = markov(Y, U)`` + * ``H = markov(Y, U, m)`` where `data` is a `TimeResponseData` object, `YY` is a 1D or 3D array, and r is an integer. @@ -565,7 +565,7 @@ def markov(*args, m=None, transpose=False, dt=None, truncate=False): ---------- Y : array_like Output data. If the array is 1D, the system is assumed to be - single input. If the array is 2D and transpose=`False`, the columns + single input. If the array is 2D and ``transpose=False``, the columns of `Y` are taken as time points, otherwise the rows of `Y` are taken as time points. U : array_like @@ -576,10 +576,10 @@ def markov(*args, m=None, transpose=False, dt=None, truncate=False): m : int, optional Number of Markov parameters to output. Defaults to len(U). dt : True of float, optional - `True` indicates discrete time with unspecified sampling time and a + True indicates discrete time with unspecified sampling time and a positive float is discrete time with the specified sampling time. It can be used to scale the Markov parameters in order to match - the unit-area impulse response of python-control. Default is `True` + the unit-area impulse response of python-control. Default is True for array_like and dt=data.time[1]-data.time[0] for TimeResponseData as input. truncate : bool, optional diff --git a/control/nichols.py b/control/nichols.py index c69b88be4..9ef31302d 100644 --- a/control/nichols.py +++ b/control/nichols.py @@ -42,8 +42,8 @@ def nichols_plot( Passed to `matplotlib` as the format string for all lines in the plot. The `omega` parameter must be present (use omega=None if needed). grid : boolean, optional - `True` if the plot should include a Nichols-chart grid. Default is - `True` and can be set using config.defaults['nichols.grid']. + True if the plot should include a Nichols-chart grid. Default is + True and can be set using `config.defaults['nichols.grid']`. **kwargs : `matplotlib.pyplot.plot` keyword properties, optional Additional keywords passed to `matplotlib` to specify line properties. @@ -78,13 +78,13 @@ def nichols_plot( system. legend_loc : int or str, optional Include a legend in the given location. Default is 'upper left', - with no legend for a single response. Use `False` to suppress legend. + with no legend for a single response. Use False to suppress legend. rcParams : dict Override the default parameters used for generating plots. - Default is set by config.defaults['ctrlplot.rcParams']. + Default is set by `config.defaults['ctrlplot.rcParams']`. show_legend : bool, optional - Force legend to be shown if `True` or hidden if `False`. If - `None`, then show legend when there is more than one line on the + Force legend to be shown if True or hidden if False. If + None, then show legend when there is more than one line on the plot or `legend_loc` has been specified. title : str, optional Set the title of the plot. Defaults to plot type and system name(s). @@ -194,9 +194,9 @@ def nichols_grid(cl_mags=None, cl_phases=None, line_style='dotted', ax=None, :doc:`Matplotlib linestyle \ `. ax : matplotlib.axes.Axes, optional - Axes to add grid to. If `None`, use `matplotlib.pyplot.gca()`. + Axes to add grid to. If None, use `matplotlib.pyplot.gca()`. label_cl_phases : bool, optional - If `True`, closed-loop phase lines will be labelled. + If True, closed-loop phase lines will be labelled. Returns ------- @@ -205,11 +205,12 @@ def nichols_grid(cl_mags=None, cl_phases=None, line_style='dotted', ax=None, cl_phase_lines : list of `matplotlib.line.Line2D` The constant closed-loop phase contours. cl_mag_labels : list of `matplotlib.text.Text` - Magnitude contour labels; each entry corresponds to the respective entry. - in `cl_mag_lines`. + Magnitude contour labels; each entry corresponds to the respective + entry in `cl_mag_lines`. cl_phase_labels : list of `matplotlib.text.Text` Phase contour labels; each entry corresponds to the respective entry in `cl_phase_lines`. + """ if ax is None: ax = plt.gca() diff --git a/control/nlsys.py b/control/nlsys.py index 9725e583e..e6e445b73 100644 --- a/control/nlsys.py +++ b/control/nlsys.py @@ -47,7 +47,7 @@ class NonlinearIOSystem(InputOutputSystem): updfcn : callable Function returning the state update function - `updfcn(t, x, u, params) -> array` + ``updfcn(t, x, u, params) -> array`` where `x` is a 1-D array with shape (nstates,), `u` is a 1-D array with shape (ninputs,), `t` is a float representing the currrent @@ -96,8 +96,8 @@ class NonlinearIOSystem(InputOutputSystem): Notes ----- - The `InputOuputSystem` class (and its subclasses) makes - use of two special methods for implementing much of the work of the class: + The `InputOuputSystem` class (and its subclasses) makes use of two + special methods for implementing much of the work of the class: * _rhs(t, x, u): compute the right hand side of the differential or difference equation for the system. If not specified, the system @@ -163,9 +163,9 @@ def __str__(self): def __call__(sys, u, params=None, squeeze=None): """Evaluate a (static) nonlinearity at a given input value. - If a nonlinear I/O system has no internal state, then evaluating the - system at an input `u` gives the output `y = F(u)`, determined by the - output function. + If a nonlinear I/O system has no internal state, then evaluating + the system at an input `u` gives the output ``y = F(u)``, + determined by the output function. Parameters ---------- @@ -173,10 +173,11 @@ def __call__(sys, u, params=None, squeeze=None): Parameter values for the system. Passed to the evaluation function for the system as default values, overriding internal defaults. squeeze : bool, optional - If `True` and if the system has a single output, return the system - output as a 1D array rather than a 2D array. If `False`, return the - system output as a 2D array even if the system is SISO. Default - value set by config.defaults['control.squeeze_time_response']. + If True and if the system has a single output, return the + system output as a 1D array rather than a 2D array. If + False, return the system output as a 2D array even if the + system is SISO. Default value set by + `config.defaults['control.squeeze_time_response']`. """ # Make sure the call makes sense @@ -376,12 +377,12 @@ def dynamics(self, t, x, u, params=None): Given time `t`, input `u` and state `x`, returns the value of the right hand side of the dynamical system. If the system is continuous, - returns the time derivative + returns the time derivative:: dx/dt = f(t, x, u[, params]) where `f` is the system's (possibly nonlinear) dynamics function. - If the system is discrete-time, returns the next value of `x`: + If the system is discrete-time, returns the next value of `x`:: x[t+dt] = f(t, x[t], u[t][, params]) @@ -588,7 +589,7 @@ class InterconnectedSystem(NonlinearIOSystem): Parameter values for the systems. Passed to the evaluation functions for the system as default values, overriding internal defaults. connection_type : str - Type of connection: 'explicit' (or `None`) for explicitly listed + Type of connection: 'explicit' (or None) for explicitly listed set of connections, 'implicit' for connections made via signal names. Attributes @@ -1113,7 +1114,7 @@ def connection_table(self, show_names=False, column_width=32): ---------- show_names : bool, optional Instead of printing out the system number, print out the name - of each system. Default is `False` because system name is not + of each system. Default is False because system name is not usually specified when performing implicit interconnection using `interconnect`. column_width : int, optional @@ -1212,7 +1213,7 @@ def check_unused_signals( Check to see if there are any unused signals and return a list of unused input and output signal descriptions. If `warning` is - `True` and any unused inputs or outputs are found, emit a warning. + True and any unused inputs or outputs are found, emit a warning. Parameters ---------- @@ -1231,7 +1232,7 @@ def check_unused_signals( name are considered ignored. warning : bool, optional - If `True`, print a warning listing any unused signals. + If True, print a warning listing any unused signals. Returns ------- @@ -1319,16 +1320,16 @@ def check_unused_signals( def nlsys(updfcn, outfcn=None, **kwargs): """Create a nonlinear input/output system. - Creates an `InputOutputSystem` for a nonlinear system by - specifying a state update function and an output function. The new system - can be a continuous or discrete time system. + Creates an `InputOutputSystem` for a nonlinear system by specifying a + state update function and an output function. The new system can be a + continuous or discrete time system. Parameters ---------- updfcn : callable (or StateSpace) Function returning the state update function - `updfcn(t, x, u, params) -> array` + ``updfcn(t, x, u, params) -> array`` where `x` is a 1-D array with shape (nstates,), `u` is a 1-D array with shape (ninputs,), `t` is a float representing the currrent @@ -1342,7 +1343,7 @@ def nlsys(updfcn, outfcn=None, **kwargs): outfcn : callable Function returning the output at the given state - `outfcn(t, x, u, params) -> array` + ``outfcn(t, x, u, params) -> array`` where the arguments are the same as for `upfcn`. @@ -1351,7 +1352,7 @@ def nlsys(updfcn, outfcn=None, **kwargs): count or as a list of strings that name the individual signals. If an integer count is specified, the names of the signal will be of the form 's[i]' (where 's' is one of 'u', 'y', or 'x'). If - this parameter is not given or given as `None`, the relevant + this parameter is not given or given as None, the relevant quantity will be determined when possible based on other information provided to functions using the system. @@ -1473,17 +1474,17 @@ def input_output_response( List of times at which the time response should be computed. Defaults to `T`. return_x : bool, optional - If `True`, return the state vector when assigning to a tuple (default = - False). See `forced_response` for more details. - If `True`, return the values of the state at each time (default = False). + If True, return the state vector when assigning to a tuple. See + `forced_response` for more details. If True, return the values of + the state at each time Default is False. params : dict, optional Parameter values for the system. Passed to the evaluation functions for the system as default values, overriding internal defaults. squeeze : bool, optional - If `True` and if the system has a single output, return the system - output as a 1D array rather than a 2D array. If `False`, return the - system output as a 2D array even if the system is SISO. Default value - set by config.defaults['control.squeeze_time_response']. + If True and if the system has a single output, return the system + output as a 1D array rather than a 2D array. If False, return the + system output as a 2D array even if the system is SISO. Default + value set by `config.defaults['control.squeeze_time_response']`. Returns ------- @@ -1494,8 +1495,8 @@ def input_output_response( * time (array): Time values of the output. * outputs (array): Response of the system. If the system is SISO and - `squeeze` is not `True`, the array is 1D (indexed by time). If the - system is not SISO or `squeeze` is `False`, the array is 2D (indexed + `squeeze` is not True, the array is 1D (indexed by time). If the + system is not SISO or `squeeze` is False, the array is 2D (indexed by output and time). * states (array): Time evolution of the state vector, represented as @@ -1507,16 +1508,16 @@ def input_output_response( The return value of the system can also be accessed by assigning the function to a tuple of length 2 (time, output) or of length 3 (time, - output, state) if `return_x` is `True`. If the input/output + output, state) if `return_x` is True. If the input/output system signals are named, these names will be used as labels for the time response. Other Parameters ---------------- ignore_errors : bool, optional - If `False` (default), errors during computation of the trajectory - will raise a `RuntimeError` exception. If `True`, do not raise - an exception and instead set `results.success` to `False` and + If False (default), errors during computation of the trajectory + will raise a `RuntimeError` exception. If True, do not raise + an exception and instead set `results.success` to False and place an error message in `results.message`. solve_ivp_method : str, optional Set the method used by `scipy.integrate.solve_ivp`. Defaults @@ -1524,7 +1525,7 @@ def input_output_response( solve_ivp_kwargs : dict, optional Pass additional keywords to `scipy.integrate.solve_ivp`. transpose : bool, default=False - If `True`, transpose all input and output arrays (for backward + If True, transpose all input and output arrays (for backward compatibility with MATLAB and `scipy.signal.lsim`). Raises @@ -1550,7 +1551,7 @@ def input_output_response( `ivp_keywords` parameters can be used to tune the ODE solver and produce better results. In particular, using 'LSODA' as the `ivp_method` or setting the `rtol` parameter to a smaller value - (e.g. using `ivp_kwargs={'rtol': 1e-4}`) can provide more accurate + (e.g. using ``ivp_kwargs={'rtol': 1e-4}``) can provide more accurate results. """ @@ -1822,14 +1823,14 @@ class OperatingPoint(): result : `scipy.optimize.OptimizeResult`, optional Result from the `scipy.optimize.root` function, if available. return_outputs, return_result : bool, optional - If set to `True`, then when accessed a tuple the output values + If set to True, then when accessed a tuple the output values and/or result of the root finding function will be returned. Notes ----- In addition to accessing the elements of the operating point as - attributes, if accessed as a list then the object will return `(x0, - u0[, y0, res])`, where `y0` and `res` are returned depending on the + attributes, if accessed as a list then the object will return ``(x0, + u0[, y0, res])``, where `y0` and `res` are returned depending on the `return_outputs` and `return_result` parameters. """ @@ -1924,8 +1925,8 @@ def find_operating_point( the specified values in solving for an operating point. All other inputs will be varied. Input indices can be listed in any order. iy : list of output indices, optional - If specified, only the outputs with the given indices will be fixed at - the specified values in solving for an operating point. All other + If specified, only the outputs with the given indices will be fixed + at the specified values in solving for an operating point. All other outputs will be varied. Output indices can be listed in any order. ix : list of state indices, optional If specified, states with the given indices will be fixed at the @@ -1946,9 +1947,9 @@ def find_operating_point( root_kwargs : dict, optional Additional keyword arguments to pass `scipy.optimize.root`. return_outputs : bool, optional - If `True`, return the value of outputs at the operating point. + If True, return the value of outputs at the operating point. return_result : bool, optional - If `True`, return the `result` option from the + If True, return the `result` option from the `scipy.optimize.root` function used to compute the operating point. @@ -1976,8 +1977,8 @@ def find_operating_point( Operating points are found using the `scipy.optimize.root` function, which will attempt to find states and inputs that satisfy the specified constraints. If no solution is found and `return_result` is - `False`, the returned state and input for the operating point will be - `None`. If `return_result` is `True`, then the return values from + False, the returned state and input for the operating point will be + None. If `return_result` is True, then the return values from `scipy.optimize.root` will be returned (but may not be valid). If `root_method` is set to 'lm', then the least squares solution (in the free variables) will be returned. @@ -2081,10 +2082,10 @@ def rootfun(z): # Construct the index lists for mapping variables and constraints # - # The mechanism by which we implement the root finding function is to - # map the subset of variables we are searching over into the inputs - # and states, and then return a function that represents the equations - # we are trying to solve. + # The mechanism by which we implement the root finding function is + # to map the subset of variables we are searching over into the + # inputs and states, and then return a function that represents the + # equations we are trying to solve. # # To do this, we need to carry out the following operations: # @@ -2213,14 +2214,14 @@ def linearize(sys, xeq, ueq=None, t=0, params=None, **kw): for the system as default values, overriding internal defaults. name : string, optional Set the name of the linearized system. If not specified and - if `copy_names` is `False`, a generic name is generated - with a unique integer id. If `copy_names` is `True`, the new system + if `copy_names` is False, a generic name is generated + with a unique integer id. If `copy_names` is True, the new system name is determined by adding the prefix and suffix strings in - config.defaults['iosys.linearized_system_name_prefix'] and - config.defaults['iosys.linearized_system_name_suffix'], with the + `config.defaults['iosys.linearized_system_name_prefix']` and + `config.defaults['iosys.linearized_system_name_suffix']`, with the default being to add the suffix '$linearized'. copy_names : bool, Optional - If `True`, Copy the names of the input signals, output signals, and + If True, Copy the names of the input signals, output signals, and states to the linearized system. Returns @@ -2287,17 +2288,17 @@ def interconnect( The list of (state-based) input/output systems to be connected. connections : list of connections, optional - Description of the internal connections between the subsystems: + Description of the internal connections between the subsystems:: [connection1, connection2, ...] - Each connection is itself a list that describes an input to one of the - subsystems. The entries are of the form: + Each connection is itself a list that describes an input to one of + the subsystems. The entries are of the form:: [input-spec, output-spec1, output-spec2, ...] The input-spec can be in a number of different forms. The lowest - level representation is a tuple of the form `(subsys_i, inp_j)` + level representation is a tuple of the form ``(subsys_i, inp_j)`` where `subsys_i` is the index into `syslist` and `inp_j` is the index into the input vector for the subsystem. If the signal index is omitted, then all subsystem inputs are used. If systems and @@ -2305,14 +2306,14 @@ def interconnect( are also recognized. Finally, for multivariable systems the signal index can be given as a list, for example '(subsys_i, [inp_j1, ..., inp_jn])'; or as a slice, for example, 'sys.sig[i:j]'; or as a base - name `sys.sig` (which matches `sys.sig[i]`). + name 'sys.sig' (which matches 'sys.sig[i]'). Similarly, each output-spec should describe an output signal from one of the subsystems. The lowest level representation is a tuple - of the form `(subsys_i, out_j, gain)`. The input will be + of the form ``(subsys_i, out_j, gain)``. The input will be constructed by summing the listed outputs after multiplying by the gain term. If the gain term is omitted, it is assumed to be 1. If - the subsystem index `subsys_i` is omitted, then all outputs of the + the subsystem index 'subsys_i' is omitted, then all outputs of the subsystem are used. If systems and signals are given names, then the form 'sys.sig', ('sys', 'sig') or ('sys', 'sig', gain) are also recognized, and the special form '-sys.sig' can be used to specify @@ -2321,14 +2322,14 @@ def interconnect( mataches the input spec. If omitted, the `interconnect` function will attempt to create the - interconnection map by connecting all signals with the same base names - (ignoring the system name). Specifically, for each input signal name - in the list of systems, if that signal name corresponds to the output - signal in any of the systems, it will be connected to that input (with - a summation across all signals if the output name occurs in more than - one system). - - The `connections` keyword can also be set to `False`, which will leave + interconnection map by connecting all signals with the same base + names (ignoring the system name). Specifically, for each input + signal name in the list of systems, if that signal name corresponds + to the output signal in any of the systems, it will be connected to + that input (with a summation across all signals if the output name + occurs in more than one system). + + The `connections` keyword can also be set to False, which will leave the connection map empty and it can be specified instead using the low-level `InterconnectedSystem.set_connect_map` method. @@ -2336,7 +2337,7 @@ def interconnect( inplist : list of input connections, optional List of connections for how the inputs for the overall system are mapped to the subsystem inputs. The entries for a connection are - of the form: + of the form:: [input-spec1, input-spec2, ...] @@ -2350,7 +2351,7 @@ def interconnect( outlist : list of output connections, optional List of connections for how the outputs from the subsystems are mapped to overall system outputs. The entries for a connection are - of the form: + of the form:: [output-spec1, output-spec2, ...] @@ -2363,11 +2364,11 @@ def interconnect( inputs : int, list of str or None, optional Description of the system inputs. This can be given as an integer - count or as a list of strings that name the individual signals. If an - integer count is specified, the names of the signal will be of the - form 's[i]' (where 's' is one of 'u', 'y', or 'x'). If this parameter - is not given or given as `None`, the relevant quantity will be - determined when possible based on other information provided to + count or as a list of strings that name the individual signals. If + an integer count is specified, the names of the signal will be of + the form 's[i]' (where 's' is one of 'u', 'y', or 'x'). If this + parameter is not given or given as None, the relevant quantity will + be determined when possible based on other information provided to functions using the system. outputs : int, list of str or None, optional @@ -2375,10 +2376,10 @@ def interconnect( states : int, list of str, or None, optional Description of the system states. Same format as `inputs`. The - default is `None`, in which case the states will be given names of + default is None, in which case the states will be given names of the form '', for each subsys in syslist and each state_name of each subsys, where is the - value of config.defaults['iosys.state_name_delim']. + value of `config.defaults['iosys.state_name_delim']`. params : dict, optional Parameter values for the systems. Passed to the evaluation functions @@ -2390,10 +2391,10 @@ def interconnect( operating in continuous or discrete time. It can have the following values: - * dt = 0: continuous time system (default) - * dt > 0: discrete time system with sampling period 'dt' - * dt = True: discrete time with unspecified sampling period - * dt = None: no timebase specified + * ``dt=0``: continuous time system (default) + * ``dt>0``: discrete time system with sampling period 'dt' + * ``dt=True``: discrete time with unspecified sampling period + * ``dt=None``: no timebase specified name : string, optional System name (used for specifying signals). If unspecified, a generic @@ -2411,12 +2412,12 @@ def interconnect( 'u', 'y', 'x'. check_unused : bool, optional - If `True`, check for unused sub-system signals. This check is - not done if connections is `False`, and neither input nor output + If True, check for unused sub-system signals. This check is + not done if connections is False, and neither input nor output mappings are specified. add_unused : bool, optional - If `True`, subsystem signals that are not connected to other components + If True, subsystem signals that are not connected to other components are added as inputs and outputs of the interconnected system. ignore_inputs : list of input-spec, optional @@ -2441,9 +2442,9 @@ def interconnect( warn_duplicate : None, True, or False, optional Control how warnings are generated if duplicate objects or names are - detected. In `None` (default), then warnings are generated for - systems that have non-generic names. If `False`, warnings are not - generated and if `True` then warnings are always generated. + detected. In None (default), then warnings are generated for + systems that have non-generic names. If False, warnings are not + generated and if True then warnings are always generated. debug : bool, default=False Print out information about how signals are being processed that @@ -2490,21 +2491,21 @@ def interconnect( If a system is duplicated in the list of systems to be connected, a warning is generated and a copy of the system is created with the name of the new system determined by adding the prefix and suffix - strings in config.defaults['iosys.duplicate_system_name_prefix'] - and config.defaults['iosys.duplicate_system_name_suffix'], with the + strings in `config.defaults['iosys.duplicate_system_name_prefix']` + and `config.defaults['iosys.duplicate_system_name_suffix']`, with the default being to add the suffix '$copy' to the system name. In addition to explicit lists of system signals, it is possible to lists vectors of signals, using one of the following forms:: - (subsys, [i1, ..., iN], gain) signals with indices i1, ..., in - 'sysname.signal[i:j]' range of signal names, i through j-1 - 'sysname.signal[:]' all signals with given prefix + (subsys, [i1, ..., iN], gain) # signals with indices i1, ..., in + 'sysname.signal[i:j]' # range of signal names, i through j-1 + 'sysname.signal[:]' # all signals with given prefix While in many Python functions tuples can be used in place of lists, for the interconnect() function the only use of tuples should be in the specification of an input- or output-signal via the tuple notation - `(subsys_i, signal_j, gain)` (where `gain` is optional). If you get an + ``(subsys_i, signal_j, gain)`` (where `gain` is optional). If you get an unexpected error message about a specification being of the wrong type or not being found, check to make sure you are not using a tuple where you should be using a list. @@ -2512,7 +2513,7 @@ def interconnect( In addition to its use for general nonlinear I/O systems, the `interconnect` function allows linear systems to be interconnected using named signals (compared with the - `connect` function, which uses signal indices) and to be + legacy `connect` function, which uses signal indices) and to be treated as both a `StateSpace` system as well as an `InputOutputSystem`. @@ -2860,7 +2861,7 @@ def _process_vector_argument(arg, name, size): name : string Name of the argument being processed. Used in errors/warnings. size : int or None - Size of the element. If `None`, size is determined by arg. + Size of the element. If None, size is determined by arg. Returns ------- @@ -2931,7 +2932,7 @@ def connection_table(sys, show_names=False, column_width=32): Interconnected system object. show_names : bool, optional Instead of printing out the system number, print out the name of - each system. Default is `False` because system name is not usually + each system. Default is False because system name is not usually specified when performing implicit interconnection using `interconnect`. column_width : int, optional diff --git a/control/optimal.py b/control/optimal.py index ee41c88b1..46fa65f24 100644 --- a/control/optimal.py +++ b/control/optimal.py @@ -43,12 +43,12 @@ class OptimalControlProblem(): """Description of a finite horizon, optimal control problem. - The `OptimalControlProblem` class holds all of the information required to - specify an optimal control problem: the system dynamics, cost function, - and constraints. As much as possible, the information used to specify an - optimal control problem matches the notation and terminology of the SciPy - `optimize.minimize` module, with the hope that this makes it easier to - remember how to describe a problem. + The `OptimalControlProblem` class holds all of the information required + to specify an optimal control problem: the system dynamics, cost + function, and constraints. As much as possible, the information used + to specify an optimal control problem matches the notation and + terminology of the SciPy `optimize.minimize` module, with the hope that + this makes it easier to remember how to describe a problem. Parameters ---------- @@ -62,9 +62,9 @@ class OptimalControlProblem(): trajectory_constraints : list of constraints, optional List of constraints that should hold at each point in the time vector. Each element of the list should be an object of type - `~scipy.optimize.LinearConstraint` with arguments `(A, lb, - ub)` or `~scipy.optimize.NonlinearConstraint` with arguments - `(fun, lb, ub)`. The constraints will be applied at each time point + `scipy.optimize.LinearConstraint` with arguments ``(A, lb, + ub)`` or `scipy.optimize.NonlinearConstraint` with arguments + ``(fun, lb, ub)``. The constraints will be applied at each time point along the trajectory. terminal_cost : callable, optional Function that returns the terminal cost given the final state @@ -83,15 +83,15 @@ class OptimalControlProblem(): shape (ninputs, ntimepts) or a 1D input of shape (ninputs,) that will be broadcast by extension of the time axis. log : bool, optional - If `True`, turn on logging messages (using Python logging module). - Use :py`logging.basicConfig` to enable logging output + If True, turn on logging messages (using Python logging module). + Use `python.logging.basicConfig` to enable logging output (e.g., to a file). Attributes ---------- constraint: list of SciPy constraint objects - List of `~scipy.optimize.LinearConstraint` or - `~scipy.optimize.NonlinearConstraint` objects. + List of `scipy.optimize.LinearConstraint` or + `scipy.optimize.NonlinearConstraint` objects. constraint_lb, constrain_ub, eqconst_value : list of float List of constraint bounds. @@ -125,11 +125,11 @@ class OptimalControlProblem(): The `compute_trajectory` method sets up an optimization problem that can be solved using `scipy.optimize.minimize`. - The `_cost_function` method takes the information computes the cost of the - trajectory generated by the proposed input. It does this by calling a - user-defined function for the integral_cost given the current states and - inputs at each point along the trajectory and then adding the value of a - user-defined terminal cost at the final point in the trajectory. + The `_cost_function` method takes the information computes the cost of + the trajectory generated by the proposed input. It does this by calling + a user-defined function for the integral_cost given the current states + and inputs at each point along the trajectory and then adding the value + of a user-defined terminal cost at the final point in the trajectory. The `_constraint_function` method evaluates the constraint functions along the trajectory generated by the proposed input. As in the case of the @@ -140,12 +140,12 @@ class OptimalControlProblem(): If `basis` is specified, then the optimization is done over coefficients of the basis elements. Otherwise, the optimization is performed over the - values of the input at the specified times (using linear interpolation for - continuous systems). + values of the input at the specified times (using linear interpolation + for continuous systems). The default values for `minimize_method`, `minimize_options`, `minimize_kwargs`, `solve_ivp_method`, and `solve_ivp_options` can - be set using config.defaults['optimal.']. + be set using `config.defaults['optimal.']`. """ def __init__( @@ -290,8 +290,8 @@ def __init__( # time point and we use a trapezoidal approximation to compute the # integral cost, then add on the terminal cost. # - # For shooting methods, given the input U = [u[t_0], ... u[t_N]] we need to - # compute the cost of the trajectory generated by that input. This + # For shooting methods, given the input U = [u[t_0], ... u[t_N]] we need + # to compute the cost of the trajectory generated by that input. This # means we have to simulate the system to get the state trajectory X = # [x[t_0], ..., x[t_N]] and then compute the cost at each point: # @@ -771,18 +771,19 @@ def compute_trajectory( 2D vector of shape (ninputs, ntimepts) or a 1D input of shape (ninputs,) that will be broadcast by extension of the time axis. return_states : bool, optional - If `True` (default), return the values of the state at each time. + If True (default), return the values of the state at each time. squeeze : bool, optional - If `True` and if the system has a single output, return the system - output as a 1D array rather than a 2D array. If `False`, return the - system output as a 2D array even if the system is SISO. Default - value set by config.defaults['control.squeeze_time_response']. + If True and if the system has a single output, return + the system output as a 1D array rather than a 2D array. If + False, return the system output as a 2D array even if + the system is SISO. Default value set by + `config.defaults['control.squeeze_time_response']`. transpose : bool, optional - If `True`, assume that 2D input arrays are transposed from the + If True, assume that 2D input arrays are transposed from the standard format. Used to convert MATLAB-style inputs to our format. print_summary : bool, optional - If `True` (default), print a short summary of the computation. + If True (default), print a short summary of the computation. Returns @@ -794,10 +795,10 @@ def compute_trajectory( res.time : array Time values of the input. res.inputs : array - Optimal inputs for the system. If the system is SISO and squeeze - is not `True`, the array is 1D (indexed by time). If the system is - not SISO or squeeze is `False`, the array is 2D (indexed by the - output number and time). + Optimal inputs for the system. If the system is SISO and + squeeze is not True, the array is 1D (indexed by time). + If the system is not SISO or squeeze is False, the array + is 2D (indexed by the output number and time). res.states : array Time evolution of the state vector (if return_states=True). @@ -837,18 +838,19 @@ def compute_mpc(self, x, squeeze=None): x : array-like or number, optional Initial state for the system. squeeze : bool, optional - If `True` and if the system has a single output, return the system - output as a 1D array rather than a 2D array. If `False`, return the - system output as a 2D array even if the system is SISO. Default - value set by config.defaults['control.squeeze_time_response']. + If True and if the system has a single output, return + the system output as a 1D array rather than a 2D array. If + False, return the system output as a 2D array even if + the system is SISO. Default value set by + `config.defaults['control.squeeze_time_response']`. Returns ------- input : array Optimal input for the system at the current time. If the system - is SISO and squeeze is not `True`, the array is 1D (indexed by - time). If the system is not SISO or squeeze is `False`, the array - is 2D (indexed by the output number and time). Set to `None` + is SISO and squeeze is not True, the array is 1D (indexed by + time). If the system is not SISO or squeeze is False, the array + is 2D (indexed by the output number and time). Set to None if the optimization failed. """ @@ -911,12 +913,12 @@ class OptimalControlResult(sp.optimize.OptimizeResult): res : scipy.minimize.OptimizeResult Result of optimization. print_summary : bool, optional - If `True` (default), print a short summary of the computation. + If True (default), print a short summary of the computation. squeeze : bool, optional - If `True` and if the system has a single output, return the system - output as a 1D array rather than a 2D array. If `False`, return the - system output as a 2D array even if the system is SISO. Default value - set by config.defaults['control.squeeze_time_response']. + If True and if the system has a single output, return the system + output as a 1D array rather than a 2D array. If False, return the + system output as a 2D array even if the system is SISO. Default + value set by `config.defaults['control.squeeze_time_response']`. Attributes ---------- @@ -980,10 +982,10 @@ def __init__( # Compute the input for a nonlinear, (constrained) optimal control problem def solve_ocp( - sys, timepts, X0, cost, trajectory_constraints=None, terminal_cost=None, - terminal_constraints=None, initial_guess=None, basis=None, squeeze=None, - transpose=None, return_states=True, print_summary=True, log=False, - **kwargs): + sys, timepts, X0, cost, trajectory_constraints=None, + terminal_cost=None, terminal_constraints=None, initial_guess=None, + basis=None, squeeze=None, transpose=None, return_states=True, + print_summary=True, log=False, **kwargs): r"""Compute the solution to an optimal control problem. @@ -1015,22 +1017,22 @@ def solve_ocp( cost : callable Function that returns the integral cost (L) given the current state - and input. Called as `cost(x, u)`. + and input. Called as ``cost(x, u)``. trajectory_constraints : list of tuples, optional - List of constraints that should hold at each point in the time vector. - Each element of the list should consist of a tuple with first element - given by `scipy.optimize.LinearConstraint` or - `scipy.optimize.NonlinearConstraint` and the remaining - elements of the tuple are the arguments that would be passed to those - functions. The following tuples are supported: + List of constraints that should hold at each point in the time + vector. Each element of the list should consist of a tuple with + first element given by `scipy.optimize.LinearConstraint` or + `scipy.optimize.NonlinearConstraint` and the remaining elements of + the tuple are the arguments that would be passed to those functions. + The following tuples are supported: - * (LinearConstraint, A, lb, ub): The matrix A is multiplied by stacked - vector of the state and input at each point on the trajectory for - comparison against the upper and lower bounds. + * (LinearConstraint, A, lb, ub): The matrix A is multiplied by + stacked vector of the state and input at each point on the + trajectory for comparison against the upper and lower bounds. * (NonlinearConstraint, fun, lb, ub): a user-specific constraint - function `fun(x, u)` is called at each point along the trajectory + function ``fun(x, u)`` is called at each point along the trajectory and compared against the upper and lower bounds. The constraints are applied at each time point along the trajectory. @@ -1062,22 +1064,22 @@ def solve_ocp( 'collocation' for continuous time systems. log : bool, optional - If `True`, turn on logging messages (using Python logging module). + If True, turn on logging messages (using Python logging module). print_summary : bool, optional - If `True` (default), print a short summary of the computation. + If True (default), print a short summary of the computation. return_states : bool, optional - If `True`, return the values of the state at each time (default = True). + If True (default), return the values of the state at each time. squeeze : bool, optional - If `True` and if the system has a single output, return the system - output as a 1D array rather than a 2D array. If `False`, return the - system output as a 2D array even if the system is SISO. Default value - set by config.defaults['control.squeeze_time_response']. + If True and if the system has a single output, return the system + output as a 1D array rather than a 2D array. If False, return the + system output as a 2D array even if the system is SISO. Default + value set by `config.defaults['control.squeeze_time_response']`. transpose : bool, optional - If `True`, assume that 2D input arrays are transposed from the standard + If True, assume that 2D input arrays are transposed from the standard format. Used to convert MATLAB-style inputs to our format. Returns @@ -1093,8 +1095,8 @@ def solve_ocp( res.inputs : array Optimal inputs for the system. If the system is SISO and squeeze is - not `True`, the array is 1D (indexed by time). If the system is not - SISO or squeeze is `False`, the array is 2D (indexed by the output + not True, the array is 1D (indexed by time). If the system is not + SISO or squeeze is False, the array is 2D (indexed by the output number and time). res.states : array @@ -1185,8 +1187,8 @@ def create_mpc_iosystem( and input. Called as cost(x, u). constraints : list of tuples, optional - List of constraints that should hold at each point in the time vector. - See `optimal.solve_ocp` for more details. + List of constraints that should hold at each point in the time + vector. See `optimal.solve_ocp` for more details. terminal_cost : callable, optional Function that returns the terminal cost given the final state @@ -1213,8 +1215,8 @@ def create_mpc_iosystem( Set the names of the inputs, outputs, and states, as described in `InputOutputSystem`. log : bool, optional - If `True`, turn on logging messages (using Python logging module). - Use :py`logging.basicConfig` to enable logging output + If True, turn on logging messages (using Python logging module). + Use `python.logging.basicConfig` to enable logging output (e.g., to a file). name : string, optional System name (used for specifying signals). If unspecified, a generic @@ -1271,9 +1273,9 @@ class OptimalEstimationProblem(): trajectory_constraints : list of constraints, optional List of constraints that should hold at each point in the time vector. Each element of the list should be an object of type - `~scipy.optimize.LinearConstraint` with arguments `(A, lb, - ub)` or `~scipy.optimize.NonlinearConstraint` with arguments - `(fun, lb, ub)`. The constraints will be applied at each time point + `scipy.optimize.LinearConstraint` with arguments ``(A, lb, + ub)`` or `scipy.optimize.NonlinearConstraint` with arguments + ``(fun, lb, ub)``. The constraints will be applied at each time point along the trajectory. terminal_cost : callable, optional Function that returns the terminal cost given the initial estimated @@ -1298,8 +1300,8 @@ class OptimalEstimationProblem(): Attributes ---------- constraint: list of SciPy constraint objects - List of `~scipy.optimize.LinearConstraint` or - `~scipy.optimize.NonlinearConstraint` objects. + List of `scipy.optimize.LinearConstraint` or + `scipy.optimize.NonlinearConstraint` objects. constraint_lb, constrain_ub, eqconst_value : list of float List of constraint bounds. @@ -1321,15 +1323,14 @@ class OptimalEstimationProblem(): Notes ----- - To describe an optimal estimation problem we need an input/output system, - a set of time points, applied inputs and measured outputs, a cost - function, and (optionally) a set of constraints on the state and/or inputs - along the trajectory (and at the terminal time). This class sets up an - optimization over the state and disturbances at each point in time, using - the integral and terminal costs as well as the trajectory constraints. - The `optimal.OptimalEstimationProblem.compute_estimate` - method solves the underling optimization problem using - `scipy.optimize.minimize`. + To describe an optimal estimation problem we need an input/output + system, a set of time points, applied inputs and measured outputs, a + cost function, and (optionally) a set of constraints on the state + and/or inputs along the trajectory (and at the terminal time). This + class sets up an optimization over the state and disturbances at + each point in time, using the integral and terminal costs as well as + the trajectory constraints. The `compute_estimate` method solves + the underling optimization problem using `scipy.optimize.minimize`. The control input and disturbance indices can be specified using the `control_indices` and `disturbance_indices` keywords. If only one is @@ -1354,7 +1355,7 @@ class OptimalEstimationProblem(): The default values for `minimize_method`, `minimize_options`, `minimize_kwargs`, `solve_ivp_method`, and `solve_ivp_options` - can be set using config.defaults['optimal.']. + can be set using `config.defaults['optimal.']`. """ def __init__( @@ -1705,13 +1706,13 @@ def compute_estimate( A 2-tuple consisting of the estimated states and disturbance values to use as a guess for the optimal estimated trajectory. squeeze : bool, optional - If `True` and if the system has a single disturbance input and + If True and if the system has a single disturbance input and single measured output, return the system input and output as a - 1D array rather than a 2D array. If `False`, return the system + 1D array rather than a 2D array. If False, return the system output as a 2D array even if the system is SISO. Default value - set by config.defaults['control.squeeze_time_response']. + set by `config.defaults['control.squeeze_time_response']`. print_summary : bool, optional - If `True` (default), print a short summary of the computation. + If True (default), print a short summary of the computation. Returns ------- @@ -1916,12 +1917,12 @@ class OptimalEstimationResult(sp.optimize.OptimizeResult): res : scipy.minimize.OptimizeResult Result of optimization. print_summary : bool, optional - If `True` (default), print a short summary of the computation. + If True (default), print a short summary of the computation. squeeze : bool, optional - If `True` and if the system has a single output, return the system - output as a 1D array rather than a 2D array. If `False`, return the - system output as a 2D array even if the system is SISO. Default value - set by config.defaults['control.squeeze_time_response']. + If True and if the system has a single output, return the system + output as a 1D array rather than a 2D array. If False, return the + system output as a 2D array even if the system is SISO. Default + value set by `config.defaults['control.squeeze_time_response']`. Attributes ---------- @@ -2009,29 +2010,29 @@ def solve_oep( Values of the outputs and inputs at each time point. trajectory_cost : callable Function that returns the cost given the current state - and input. Called as `cost(y, u, x0)`. + and input. Called as ``cost(y, u, x0)``. X0 : 1D array_like, optional Mean value of the initial condition (defaults to 0). trajectory_constraints : list of tuples, optional - List of constraints that should hold at each point in the time vector. - See `solve_ocp` for more information. + List of constraints that should hold at each point in the time + vector. See `solve_ocp` for more information. control_indices : int, slice, or list of int or string, optional Specify the indices in the system input vector that correspond to the control inputs. For more information on possible values, see - `optimal.OptimalEstimationProblem` + `optimal.OptimalEstimationProblem`. disturbance_indices : int, list of int, or slice, optional Specify the indices in the system input vector that correspond to the input disturbances. For more information on possible values, see - `optimal.OptimalEstimationProblem` + `optimal.OptimalEstimationProblem`. initial_guess : 2D array_like, optional Initial guess for the state estimate at each time point. print_summary : bool, optional - If `True` (default), print a short summary of the computation. + If True (default), print a short summary of the computation. squeeze : bool, optional - If `True` and if the system has a single output, return the system - output as a 1D array rather than a 2D array. If `False`, return the - system output as a 2D array even if the system is SISO. Default value - set by config.defaults['control.squeeze_time_response']. + If True and if the system has a single output, return the system + output as a 1D array rather than a 2D array. If False, return the + system output as a 2D array even if the system is SISO. Default + value set by `config.defaults['control.squeeze_time_response']`. Returns ------- @@ -2043,15 +2044,15 @@ def solve_oep( Time values of the input. res.inputs : array Disturbance values corresponding to the estimated state. If the - system is SISO and squeeze is not `True`, the array is 1D (indexed by - time). If the system is not SISO or squeeze is `False`, the array is + system is SISO and squeeze is not True, the array is 1D (indexed by + time). If the system is not SISO or squeeze is False, the array is 2D (indexed by the output number and time). res.states : array Estimated state vector over the given time points. res.outputs : array Noise values corresponding to the estimated state. If the system - is SISO and squeeze is not `True`, the array is 1D (indexed by time). - If the system is not SISO or squeeze is `False`, the array is 2D + is SISO and squeeze is not True, the array is 1D (indexed by time). + If the system is not SISO or squeeze is False, the array is 2D (indexed by the output number and time). Notes @@ -2184,11 +2185,11 @@ def gaussian_likelihood_cost(sys, Rv, Rw=None): # Functions to create constraints: either polytopes (A x <= b) or ranges # (lb # <= x <= ub). # -# As in the cost function evaluation, the main "trick" in creating a constrain -# on the state or input is to properly evaluate the constraint on the stacked -# state and input vector at the current time point. The constraint itself -# will be called at each point along the trajectory (or the endpoint) via the -# constrain_function() method. +# As in the cost function evaluation, the main "trick" in creating a +# constraint on the state or input is to properly evaluate the constraint on +# the stacked state and input vector at the current time point. The +# constraint itself will be called at each point along the trajectory (or the +# endpoint) via the constrain_function() method. # # Note that these functions to not actually evaluate the constraint, they # simply return the information required to do so. We use the SciPy @@ -2235,7 +2236,7 @@ def state_range_constraint(sys, lb, ub): Creates a linear constraint on the system state that bounds the range of the individual states to be between `lb` and `ub`. The upper and lower - bounds can be set of `inf` and `-inf` to indicate there is no constraint + bounds can be set of 'inf' and '-inf' to indicate there is no constraint or to the same value to describe an equality constraint. Parameters @@ -2308,7 +2309,7 @@ def input_range_constraint(sys, lb, ub): Creates a linear constraint on the system input that bounds the range of the individual states to be between `lb` and `ub`. The upper and lower - bounds can be set of `inf` and `-inf` to indicate there is no constraint + bounds can be set of 'inf' and '-inf' to indicate there is no constraint or to the same value to describe an equality constraint. Parameters @@ -2342,12 +2343,12 @@ def input_range_constraint(sys, lb, ub): # # Create a constraint polytope/range constraint on the system output # -# Unlike the state and input constraints, for the output constraint we need to -# do a function evaluation before applying the constraints. +# Unlike the state and input constraints, for the output constraint we need +# to do a function evaluation before applying the constraints. # -# TODO: for the special case of an LTI system, we can avoid the extra function -# call by multiplying the state by the C matrix for the system and then -# imposing a linear constraint: +# TODO: for the special case of an LTI system, we can avoid the extra +# function call by multiplying the state by the C matrix for the system and +# then imposing a linear constraint: # # np.hstack( # [A @ sys.C, np.zeros((A.shape[0], sys.ninputs))]) @@ -2396,7 +2397,7 @@ def output_range_constraint(sys, lb, ub): Creates a linear constraint on the system output that bounds the range of the individual states to be between `lb` and `ub`. The upper and lower - bounds can be set of `inf` and `-inf` to indicate there is no constraint + bounds can be set of 'inf' and '-inf' to indicate there is no constraint or to the same value to describe an equality constraint. Parameters diff --git a/control/passivity.py b/control/passivity.py index 101ae4427..3dde17bd0 100644 --- a/control/passivity.py +++ b/control/passivity.py @@ -25,12 +25,12 @@ def solve_passivity_LMI(sys, rho=None, nu=None): Constructs a linear matrix inequality (LMI) such that if a solution exists and the last element of the solution is positive, the system - `sys` is passive. Inputs of `None` for `rho` or `nu` indicate that the + `sys` is passive. Inputs of None for `rho` or `nu` indicate that the function should solve for that index (they are mutually exclusive, they - can't both be `None`, otherwise you're trying to solve a nonconvex + can't both be None, otherwise you're trying to solve a nonconvex bilinear matrix inequality.) The last element of the output `solution` - is either the output or input passivity index, for `rho` = `None` and - `nu` = `None` respectively. + is either the output or input passivity index, for ``rho = None`` and + ``nu = None`` respectively. The sources for the algorithm are: @@ -101,8 +101,9 @@ def make_P_basis_matrices(n, rho, nu): """Make list of matrix constraints for passivity LMI. Utility function to make basis matrices for a LMI from a - symmetric matrix P of size n by n representing a parametrized symbolic - matrix + symmetric matrix P of size n by n representing a parametrized + symbolic matrix. + """ matrix_list = [] for i in range(0, n): @@ -178,12 +179,12 @@ def P_pos_def_constraint(n): return sol["x"] except ZeroDivisionError as e: - raise ValueError("The system is probably ill conditioned. " - "Consider perturbing the system matrices by a small amount." + raise ValueError( + "The system is probably ill conditioned. Consider perturbing " + "the system matrices by a small amount." ) from e - def get_output_fb_index(sys): """Return the output feedback passivity (OFP) index for the system. @@ -259,8 +260,8 @@ def get_directional_index(sys): def ispassive(sys, ofp_index=0, ifp_index=0): r"""Indicate if a linear time invariant (LTI) system is passive. - Checks if system is passive with the given output feedback (OFP) and input - feedforward (IFP) passivity indices. + Checks if system is passive with the given output feedback (OFP) + and input feedforward (IFP) passivity indices. Parameters ---------- @@ -282,10 +283,10 @@ def ispassive(sys, ofp_index=0, ifp_index=0): .. math:: V(x) >= 0 \land \dot{V}(x) <= y^T u - is equivalent to the default case of `ofp_index` = 0 and `ifp_index` = - 0. Note that computing the `ofp_index` and `ifp_index` for a system, + is equivalent to the default case of ``ofp_index = 0`` and ``ifp_index` = + 0``. Note that computing the `ofp_index` and `ifp_index` for a system, then using both values simultaneously as inputs to this function is not - guaranteed to have an output of `True` (the system might not be passive + guaranteed to have an output of True (the system might not be passive with both indices at the same time). For more details, see [1]_. diff --git a/control/phaseplot.py b/control/phaseplot.py index 425ee642d..f0da39bc8 100644 --- a/control/phaseplot.py +++ b/control/phaseplot.py @@ -7,7 +7,7 @@ """Generate 2D phase portraits. This module contains functions for generating 2D phase plots. The base -function for creating phase plane portraits is `~control.phase_plane_plot`, +function for creating phase plane portraits is `phase_plane_plot`, which generates a phase plane portrait for a 2 state I/O system (with no inputs). @@ -85,7 +85,7 @@ def phase_plane_plot( a dict of parameters and values. For a callable, `params` should be dict with key 'args' and value given by a tuple (passed to callable). color : matplotlib color spec, optional - Plot all elements in the given color (use `plot_={'color': c}` + Plot all elements in the given color (use ``plot_={'color': c}`` to set the color in one element of the phase plot. ax : matplotlib.axes.Axes, optional The matplotlib axes to draw the figure on. If not specified and @@ -115,39 +115,39 @@ def phase_plane_plot( ---------------- arrows : int Set the number of arrows to plot along the streamlines. The default - value can be set in config.defaults['phaseplot.arrows']. + value can be set in `config.defaults['phaseplot.arrows']`. arrow_size : float Set the size of arrows to plot along the streamlines. The default - value can be set in config.defaults['phaseplot.arrow_size']. + value can be set in `config.defaults['phaseplot.arrow_size']`. arrow_style : matplotlib patch Set the style of arrows to plot along the streamlines. The default - value can be set in config.defaults['phaseplot.arrow_style']. + value can be set in `config.defaults['phaseplot.arrow_style']`. dir : str, optional Direction to draw streamlines: 'forward' to flow forward in time from the reference points, 'reverse' to flow backward in time, or 'both' to flow both forward and backward. The amount of time to simulate in each direction is given by the `timedata` argument. plot_streamlines : bool or dict, optional - If `True` (default) then plot streamlines based on the pointdata + If True (default) then plot streamlines based on the pointdata and gridtype. If set to a dict, pass on the key-value pairs in the dict as keywords to `phaseplot.streamlines`. plot_vectorfield : bool or dict, optional - If `True` (default) then plot the vector field based on the pointdata + If True (default) then plot the vector field based on the pointdata and gridtype. If set to a dict, pass on the key-value pairs in the dict as keywords to `phaseplot.vectorfield`. plot_equilpoints : bool or dict, optional - If `True` (default) then plot equilibrium points based in the phase + If True (default) then plot equilibrium points based in the phase plot boundary. If set to a dict, pass on the key-value pairs in the dict as keywords to `phaseplot.equilpoints`. plot_separatrices : bool or dict, optional - If `True` (default) then plot separatrices starting from each + If True (default) then plot separatrices starting from each equilibrium point. If set to a dict, pass on the key-value pairs in the dict as keywords to `phaseplot.separatrices`. rcParams : dict Override the default parameters used for generating plots. - Default is set by config.defaults['ctrlplot.rcParams']. + Default is set by `config.defaults['ctrlplot.rcParams']`. suppress_warnings : bool, optional - If set to `True`, suppress warning messages in generating trajectories. + If set to True, suppress warning messages in generating trajectories. title : str, optional Set the title of the plot. Defaults to plot type and system name(s). @@ -286,9 +286,9 @@ def vectorfield( ---------------- rcParams : dict Override the default parameters used for generating plots. - Default is set by config.defaults['ctrlplot.rcParams']. + Default is set by `config.defaults['ctrlplot.rcParams']`. suppress_warnings : bool, optional - If set to `True`, suppress warning messages in generating trajectories. + If set to True, suppress warning messages in generating trajectories. """ # Process keywords @@ -387,18 +387,18 @@ def streamlines( ---------------- arrows : int Set the number of arrows to plot along the streamlines. The default - value can be set in config.defaults['phaseplot.arrows']. + value can be set in `config.defaults['phaseplot.arrows']`. arrow_size : float Set the size of arrows to plot along the streamlines. The default - value can be set in config.defaults['phaseplot.arrow_size']. + value can be set in `config.defaults['phaseplot.arrow_size']`. arrow_style : matplotlib patch Set the style of arrows to plot along the streamlines. The default - value can be set in config.defaults['phaseplot.arrow_style']. + value can be set in `config.defaults['phaseplot.arrow_style']`. rcParams : dict Override the default parameters used for generating plots. - Default is set by config.defaults['ctrlplot.rcParams']. + Default is set by `config.defaults['ctrlplot.rcParams']`. suppress_warnings : bool, optional - If set to `True`, suppress warning messages in generating trajectories. + If set to True, suppress warning messages in generating trajectories. """ # Process keywords @@ -463,8 +463,8 @@ def streamlines( def equilpoints( - sys, pointdata, gridspec=None, color='k', ax=None, _check_kwargs=True, - **kwargs): + sys, pointdata, gridspec=None, color='k', ax=None, + _check_kwargs=True, **kwargs): """Plot equilibrium points in the phase plane. This function plots the equilibrium points for a planar dynamical system. @@ -508,7 +508,7 @@ def equilpoints( ---------------- rcParams : dict Override the default parameters used for generating plots. - Default is set by config.defaults['ctrlplot.rcParams']. + Default is set by `config.defaults['ctrlplot.rcParams']`. """ # Process keywords @@ -600,13 +600,13 @@ def separatrices( ---------------- rcParams : dict Override the default parameters used for generating plots. - Default is set by config.defaults['ctrlplot.rcParams']. + Default is set by `config.defaults['ctrlplot.rcParams']`. suppress_warnings : bool, optional - If set to `True`, suppress warning messages in generating trajectories. + If set to True, suppress warning messages in generating trajectories. Notes ----- - The value of config.defaults['separatrices_radius'] is used to set the + The value of `config.defaults['separatrices_radius']` is used to set the offset from the equlibrium point to the starting point of the separatix traces, in the direction of the eigenvectors evaluated at that equilibrium point. @@ -1069,20 +1069,20 @@ def phase_plot(odefun, X=None, Y=None, scale=1, X0=None, T=None, len(X0) that gives the simulation time for each initial condition. Default value = 50. lingrid : integer or 2-tuple of integers, optional - Argument is either N or (N, M). If X0 is given and X, Y are missing, - a grid of arrows is produced using the limits of the initial - conditions, with N grid points in each dimension or N grid points in x - and M grid points in y. + Argument is either N or (N, M). If X0 is given and X, Y are + missing, a grid of arrows is produced using the limits of the + initial conditions, with N grid points in each dimension or N grid + points in x and M grid points in y. lintime : integer or tuple (integer, float), optional - If a single integer N is given, draw N arrows using equally space time - points. If a tuple (N, lambda) is given, draw N arrows using + If a single integer N is given, draw N arrows using equally space + time points. If a tuple (N, lambda) is given, draw N arrows using exponential time constant lambda timepts : array-like, optional Draw arrows at the given list times [t1, t2, ...] tfirst : bool, optional - If `True`, call `func` with signature `func(t, x, ...)`. + If True, call `func` with signature ``func(t, x, ...)``. params: tuple, optional - List of parameters to pass to vector field: `func(x, t, *params)` + List of parameters to pass to vector field: ``func(x, t, *params)``. See Also -------- diff --git a/control/pzmap.py b/control/pzmap.py index 0a57d8845..bfe89b9f5 100644 --- a/control/pzmap.py +++ b/control/pzmap.py @@ -82,7 +82,7 @@ class PoleZeroData: dt : None, True or float, optional System timebase (used for showing stability boundary). sort_loci : bool, optional - Set to `False` to turn off sorting of loci into unique branches. + Set to False to turn off sorting of loci into unique branches. """ def __init__( @@ -139,7 +139,7 @@ def pole_zero_map(sysdata): ------- pzmap_data : PoleZeroMap Pole/zero map containing the poles and zeros of the system. Use - `pzmap_data.plot()` or `pole_zero_plot(pzmap_data)` to plot the + ``pzmap_data.plot()`` or ``pole_zero_plot(pzmap_data)`` to plot the pole/zero map. """ @@ -159,8 +159,8 @@ def pole_zero_map(sysdata): # TODO: Implement more elegant cross-style axes. See: -# https://matplotlib.org/2.0.2/examples/axes_grid/demo_axisline_style.html -# https://matplotlib.org/2.0.2/examples/axes_grid/demo_curvelinear_grid.html +# https://matplotlib.org/2.0.2/examples/axes_grid/demo_axisline_style.html +# https://matplotlib.org/2.0.2/examples/axes_grid/demo_curvelinear_grid.html def pole_zero_plot( data, plot=None, grid=None, title=None, color=None, marker_size=None, marker_width=None, xlim=None, ylim=None, interactive=None, ax=None, @@ -171,7 +171,7 @@ def pole_zero_plot( system is plotted. When the root locus for a single system is plotted, clicking on a location on the root locus will mark the gain on all branches of the diagram and show the system gain and damping for the - given pole in the axes title. Set to `False` to turn off this behavior. + given pole in the axes title. Set to False to turn off this behavior. Parameters ---------- @@ -180,12 +180,13 @@ def pole_zero_plot( or root_locus_map() that are to be plotted. If a list of systems is given, the poles and zeros of those systems will be plotted. grid : bool or str, optional - If `True` plot omega-damping grid, if `False` show imaginary axis - for continuous time systems, unit circle for discrete time systems. - If `empty`, do not draw any additonal lines. Default value is set - by config.defaults['pzmap.grid'] or config.defaults['rlocus.grid']. + If True plot omega-damping grid, if False show imaginary + axis for continuous time systems, unit circle for discrete time + systems. If 'empty', do not draw any additonal lines. Default + value is set by `config.defaults['pzmap.grid']` or + `config.defaults['rlocus.grid']`. plot : bool, optional - (legacy) If `True` a graph is generated with Matplotlib, + (legacy) If True a graph is generated with Matplotlib, otherwise the poles and zeros are only computed and returned. If this argument is present, the legacy value of poles and zeros is returned. @@ -197,7 +198,7 @@ def pole_zero_plot( * cplt.lines: Array of `matplotlib.lines.Line2D` objects for each set of markers in the plot. The shape of the array is - given by (`nsys`, 2) where `nsys` is the number of systems or + given by (nsys, 2) where nsys is the number of systems or responses passed to the function. The second index specifies the pzmap object type: @@ -234,7 +235,7 @@ def pole_zero_plot( system. legend_loc : int or str, optional Include a legend in the given location. Default is 'upper right', - with no legend for a single response. Use `False` to suppress legend. + with no legend for a single response. Use False to suppress legend. marker_color : str, optional Set the color of the markers used for poles and zeros. marker_size : int, optional @@ -243,13 +244,13 @@ def pole_zero_plot( Set the line width of the markers used for poles and zeros. rcParams : dict Override the default parameters used for generating plots. - Default is set by config.defaults['ctrlplot.rcParams']. + Default is set by `config.defaults['ctrlplot.rcParams']`. scaling : str or list, optional Set the type of axis scaling. Can be 'equal' (default), 'auto', or a list of the form [xmin, xmax, ymin, ymax]. show_legend : bool, optional - Force legend to be shown if `True` or hidden if `False`. If - `None`, then show legend when there is more than one line on the + Force legend to be shown if True or hidden if False. If + None, then show legend when there is more than one line on the plot or `legend_loc` has been specified. title : str, optional Set the title of the plot. Defaults to plot type and system name(s). @@ -268,16 +269,16 @@ def pole_zero_plot( Pole/zero plots that use the continuous time omega-damping grid do not work with the `ax` keyword argument, due to the way that axes grids - are implemented. The `grid` argument must be set to `False` or - `'empty'` when using the `ax` keyword argument. + are implemented. The `grid` argument must be set to False or + 'empty' when using the `ax` keyword argument. The limits of the pole/zero plot are set based on the location features in the plot, including the location of poles, zeros, and local maxima of root locus curves. The locations of local maxima are expanded by a - buffer factor set by config.defaults['phaseplot.buffer_factor'] that is + buffer factor set by `config.defaults['phaseplot.buffer_factor']` that is applied to the locations of the local maxima. The final axis limits are set to by the largest features in the plot multiplied by an - expansion factor set by config.defaults['phaseplot.expansion_factor']. + expansion factor set by `config.defaults['phaseplot.expansion_factor']`. The default value for the buffer factor is 1.05 (5% buffer around local maxima) and the default value for the expansion factor is 1.8 (80% increase in limits around the most distant features). diff --git a/control/rlocus.py b/control/rlocus.py index b9162f485..a913a84b4 100644 --- a/control/rlocus.py +++ b/control/rlocus.py @@ -57,7 +57,7 @@ def root_locus_map(sysdata, gains=None): Notes ----- For backward compatibility, the `rldata` return object can be - assigned to the tuple `roots, gains`. + assigned to the tuple ``(roots, gains)``. """ from .pzmap import PoleZeroData, PoleZeroList @@ -108,18 +108,18 @@ def root_locus_plot( gains are chosen to include the main features of the root locus map. xlim : tuple or list, optional Set limits of x axis, normally with tuple - (see :doc:`matplotlib:api/axes_api`). + (see `matplotlib:api/axes_api`). ylim : tuple or list, optional Set limits of y axis, normally with tuple - (see :doc:`matplotlib:api/axes_api`). + (see `matplotlib:api/axes_api`). plot : bool, optional (legacy) If given, `root_locus_plot` returns the legacy return values - of roots and gains. If `False`, just return the values with no plot. + of roots and gains. If False, just return the values with no plot. grid : bool or str, optional - If `True` plot omega-damping grid, if `False` show imaginary axis + If True plot omega-damping grid, if False show imaginary axis for continuous time systems, unit circle for discrete time systems. - If `empty`, do not draw any additonal lines. Default value is set - by config.defaults['rlocus.grid']. + If 'empty', do not draw any additonal lines. Default value is set + by `config.defaults['rlocus.grid']`. initial_gain : float, optional Mark the point on the root locus diagram corresponding to the given gain. @@ -164,10 +164,10 @@ def root_locus_plot( system. legend_loc : int or str, optional Include a legend in the given location. Default is 'center right', - with no legend for a single response. Use `False` to suppress legend. + with no legend for a single response. Use False to suppress legend. show_legend : bool, optional - Force legend to be shown if `True` or hidden if `False`. If - `None`, then show legend when there is more than one line on the + Force legend to be shown if True or hidden if False. If + None, then show legend when there is more than one line on the plot or `legend_loc` has been specified. title : str, optional Set the title of the plot. Defaults to plot type and system name(s). diff --git a/control/robust.py b/control/robust.py index c42d5b448..04deb4f5d 100644 --- a/control/robust.py +++ b/control/robust.py @@ -226,7 +226,7 @@ def augw(g, w1=None, w2=None, w3=None): one weighting must not be None. If a weighting w is scalar, it will be replaced by I*w, where I is - ny-by-ny for w1 and w3, and nu-by-nu for w2. + ny-by-ny for `w1` and `w3`, and nu-by-nu for `w2`. Parameters ---------- @@ -242,8 +242,8 @@ def augw(g, w1=None, w2=None, w3=None): Returns ------- p : StateSpace - Plant augmented with weightings, suitable for submission to hinfsyn or - h2syn. + Plant augmented with weightings, suitable for submission to + `hinfsyn` or `h2syn`. Raises ------ @@ -305,12 +305,12 @@ def augw(g, w1=None, w2=None, w3=None): 1 + now1 + now2 + now3 + 2 * ny + niw2) # y -> w3 - q[niw1 + niw2:niw1 + niw2 + niw3, 1] = np.arange(1 + now1 + now2 + now3 + ny, - 1 + now1 + now2 + now3 + ny + niw3) + q[niw1 + niw2:niw1 + niw2 + niw3, 1] = np.arange( + 1 + now1 + now2 + now3 + ny, 1 + now1 + now2 + now3 + ny + niw3) # -y -> Iy; note the leading - - q[niw1 + niw2 + niw3:niw1 + niw2 + niw3 + ny, 1] = -np.arange(1 + now1 + now2 + now3 + ny, - 1 + now1 + now2 + now3 + 2 * ny) + q[niw1 + niw2 + niw3:niw1 + niw2 + niw3 + ny, 1] = -np.arange( + 1 + now1 + now2 + now3 + ny, 1 + now1 + now2 + now3 + 2 * ny) # Iu -> G q[niw1 + niw2 + niw3 + ny:niw1 + niw2 + niw3 + ny + nu, 1] = np.arange( @@ -364,10 +364,10 @@ def mixsyn(g, w1=None, w2=None, w3=None): [z] = [p11 p12] [w] [y] [p21 g] [u] - then cl is the system from w->z with `u = -k*y`. + then cl is the system from w->z with ``u = -k*y``. info : tuple - Two-tuple `(gamma, rcond)` containing additional information: + Two-tuple ``(gamma, rcond)`` containing additional information: * gamma (scalar): H-infinity norm of cl. * rcond (array): Estimates of reciprocal condition numbers computed @@ -379,7 +379,7 @@ def mixsyn(g, w1=None, w2=None, w3=None): Notes ----- - If a weighting `w` is scalar, it will be replaced by `I*w`, where `I` is + If a weighting w is scalar, it will be replaced by I*w, where I is ny-by-ny for `w1` and `w3`, and nu-by-nu for `w2`. """ diff --git a/control/sisotool.py b/control/sisotool.py index f35d2d08b..3fb959cec 100644 --- a/control/sisotool.py +++ b/control/sisotool.py @@ -57,7 +57,7 @@ def sisotool(sys, initial_gain=None, xlim_rlocus=None, ylim_rlocus=None, system with a gain in the feedback. initial_gain : float, optional Initial gain to use for plotting root locus. Defaults to 1 - (config.defaults['sisotool.initial_gain']). + (`config.defaults['sisotool.initial_gain']`). xlim_rlocus : tuple or list, optional Control of x-axis range, normally with tuple (see :doc:`matplotlib:api/axes_api`). @@ -66,25 +66,25 @@ def sisotool(sys, initial_gain=None, xlim_rlocus=None, ylim_rlocus=None, plotstr_rlocus : `matplotlib.pyplot.plot` format string, optional Plotting style for the root locus plot(color, linestyle, etc). rlocus_grid : boolean (default = False) - If `True`, plot s- or z-plane grid. + If True, plot s- or z-plane grid. omega : array_like List of frequencies in rad/sec to be used for bode plot. dB : boolean - If `True`, plot result in dB for the bode plot. + If True, plot result in dB for the bode plot. Hz : boolean - If `True`, plot frequency in Hz for the bode plot (omega must be + If True, plot frequency in Hz for the bode plot (omega must be provided in rad/sec). deg : boolean - If `True`, plot phase in degrees for the bode plot (else radians). + If True, plot phase in degrees for the bode plot (else radians). omega_limits : array_like of two values Limits of the to generate frequency vector. If Hz=True the limits are in Hz otherwise in rad/s. Ignored if omega is provided, and auto-generated if omitted. omega_num : int Number of samples to plot. Defaults to - config.defaults['freqplot.number_of_samples']. + `config.defaults['freqplot.number_of_samples']`. margins_bode : boolean - If `True`, plot gain and phase margin in the bode plot. + If True, plot gain and phase margin in the bode plot. tvect : list or ndarray, optional List of timesteps to use for closed loop step response. @@ -260,32 +260,32 @@ def rootlocus_pid_designer(plant, gain='P', sign=+1, input_signal='r', """Manual PID controller design based on root locus using Sisotool. Uses `sisotool` to investigate the effect of adding or subtracting an - amount `deltaK` to the proportional, integral, or derivative (PID) gains of - a controller. One of the PID gains, `Kp`, `Ki`, or `Kd`, respectively, can - be modified at a time. `Sisotool` plots the step response, frequency + amount `deltaK` to the proportional, integral, or derivative (PID) gains + of a controller. One of the PID gains, `Kp`, `Ki`, or `Kd`, respectively, + can be modified at a time. `sisotool` plots the step response, frequency response, and root locus of the closed-loop system controlling the dynamical system specified by `plant`. Can be used with either non- interactive plots (e.g. in a Jupyter Notebook), or interactive plots. To use non-interactively, choose starting-point PID gains `Kp0`, `Ki0`, - and `Kd0` (you might want to start with all zeros to begin with), select - which gain you would like to vary (e.g. gain=`'P'`, `'I'`, or `'D'`), and - choose a value of `deltaK` (default 0.001) to specify by how much you - would like to change that gain. Repeatedly run `rootlocus_pid_designer` - with different values of `deltaK` until you are satisfied with the - performance for that gain. Then, to tune a different gain, e.g. `'I'`, - make sure to add your chosen `deltaK` to the previous gain you you were - tuning. + and `Kd0` (you might want to start with all zeros to begin with), + select which gain you would like to vary (e.g. ``gain='P'``, ``'I'``, + or ``'D'``), and choose a value of `deltaK` (default 0.001) to specify + by how much you would like to change that gain. Repeatedly run + `rootlocus_pid_designer` with different values of `deltaK` until you + are satisfied with the performance for that gain. Then, to tune a + different gain, e.g. 'I', make sure to add your chosen `deltaK` to + the previous gain you you were tuning. Example: to examine the effect of varying `Kp` starting from an intial - value of 10, use the arguments `gain='P', Kp0=10` and try varying values + value of 10, use the arguments ``gain='P', Kp0=10`` and try varying values of `deltaK`. Suppose a `deltaK` of 5 gives satisfactory performance. Then, to tune the derivative gain, add your selected `deltaK` to `Kp0` in the - next call using the arguments `gain='D', Kp0=15`, to see how adding + next call using the arguments ``gain='D', Kp0=15``, to see how adding different values of `deltaK` to your derivative gain affects performance. To use with interactive plots, you will need to enable interactive mode - if you are in a Jupyter Notebook, e.g. using `%matplotlib`. See + if you are in a Jupyter Notebook, e.g. using ``%matplotlib``. See `Interactive Plots `_ for more information. Click on a branch of the root locus plot to try different values of `deltaK`. Each click updates plots and prints the @@ -293,11 +293,11 @@ def rootlocus_pid_designer(plant, gain='P', sign=+1, input_signal='r', glass on the plot to get more locations to click. Just make sure to deactivate magnification mode when you are done by clicking the magnifying glass. Otherwise you will not be able to be able to choose a gain on the - root locus plot. When you are done, `%matplotlib inline` returns to inline, - non-interactive ploting. + root locus plot. When you are done, ``%matplotlib inline`` returns to + inline, non-interactive ploting. - By default, all three PID terms are in the forward path C_f in the diagram - shown below, that is, + By default, all three PID terms are in the forward path C_f in the + diagram shown below, that is, C_f = Kp + Ki/s + Kd*s/(tau*s + 1). @@ -312,12 +312,12 @@ def rootlocus_pid_designer(plant, gain='P', sign=+1, input_signal='r', | ----- C_b <-------| --------------------------------- - If `plant` is a discrete-time system, then the proportional, integral, and - derivative terms are given instead by Kp, Ki*dt/2*(z+1)/(z-1), and + If `plant` is a discrete-time system, then the proportional, integral, + and derivative terms are given instead by Kp, Ki*dt/2*(z+1)/(z-1), and Kd/dt*(z-1)/z, respectively. It is also possible to move the derivative term into the feedback path - `C_b` using `derivative_in_feedback_path=True`. This may be desired to + `C_b` using ``derivative_in_feedback_path=True``. This may be desired to avoid that the plant is subject to an impulse function when the reference `r` is a step input. `C_b` is otherwise set to zero. @@ -329,13 +329,13 @@ def rootlocus_pid_designer(plant, gain='P', sign=+1, input_signal='r', plant : `LTI` (`TransferFunction` or `StateSpace` system) The dynamical system to be controlled. gain : string, optional - Which gain to vary by `deltaK`. Must be one of `'P'`, `'I'`, or `'D'` - (proportional, integral, or derative). + Which gain to vary by `deltaK`. Must be one of 'P', 'I', or 'D' + (proportional, integral, or derivative). sign : int, optional The sign of deltaK gain perturbation. input_signal : string, optional - The input used for the step response; must be `'r'` (reference) or - `'d'` (disturbance) (see figure above). + The input used for the step response; must be 'r' (reference) or + 'd' (disturbance) (see figure above). Kp0, Ki0, Kd0 : float, optional Initial values for proportional, integral, and derivative gains, respectively. @@ -356,12 +356,12 @@ def rootlocus_pid_designer(plant, gain='P', sign=+1, input_signal='r', Returns ------- - closedloop : class:`StateSpace` system + closedloop : `StateSpace` system The closed-loop system using initial gains. Notes ----- - When running using iPython or Jupyter, use `%matplotlib` to configure + When running using iPython or Jupyter, use ``%matplotlib`` to configure the session for interactive support. """ diff --git a/control/statefbk.py b/control/statefbk.py index 735f58626..eaf2f81f8 100644 --- a/control/statefbk.py +++ b/control/statefbk.py @@ -120,8 +120,8 @@ def place_varga(A, B, p, dtime=False, alpha=None): p : 1D array_like Desired eigenvalue locations. dtime : bool, optional - `False` (default) for continuous time pole placement or `True` - for discrete time. + False (default) for continuous time pole placement or True + for discrete time. alpha : float, optional If `dtime` is false then place_varga will leave the eigenvalues with real part less than alpha untouched. If `dtime` is true then @@ -143,20 +143,22 @@ def place_varga(A, B, p, dtime=False, alpha=None): Notes ----- This function is a wrapper for the slycot function sb01bd, which - implements the pole placement algorithm of Varga [1]_. In contrast to the - algorithm used by place(), the Varga algorithm can place multiple poles at - the same location. The placement, however, may not be as robust. + implements the pole placement algorithm of Varga [1]_. In contrast + to the algorithm used by `place`, the Varga algorithm can place + multiple poles at the same location. The placement, however, may + not be as robust. References ---------- - .. [1] Varga A. "A Schur method for pole assignment." IEEE Trans. Automatic - Control, Vol. AC-26, pp. 517-519, 1981. + .. [1] Varga A. "A Schur method for pole assignment." IEEE Trans. + Automatic Control, Vol. AC-26, pp. 517-519, 1981. Examples -------- >>> A = [[-1, -1], [0, 1]] >>> B = [[0], [1]] >>> K = ct.place_varga(A, B, [-2, -5]) + """ # Make sure that SLICOT is installed @@ -267,10 +269,10 @@ def lqr(*args, **kwargs): The function can be called with either 3, 4, or 5 arguments: - * `K, S, E = lqr(sys, Q, R)` - * `K, S, E = lqr(sys, Q, R, N)` - * `K, S, E = lqr(A, B, Q, R)` - * `K, S, E = lqr(A, B, Q, R, N)` + * ``K, S, E = lqr(sys, Q, R)`` + * ``K, S, E = lqr(sys, Q, R, N)`` + * ``K, S, E = lqr(A, B, Q, R)`` + * ``K, S, E = lqr(A, B, Q, R, N)`` where `sys` is an `LTI` object, and `A`, `B`, `Q`, `R`, and `N` are 2D arrays or matrices of appropriate dimension. @@ -295,7 +297,7 @@ def lqr(*args, **kwargs): additional rows and columns in the `Q` matrix. method : str, optional Set the method used for computing the result. Current methods are - 'slycot' and 'scipy'. If set to `None` (default), try 'slycot' + 'slycot' and 'scipy'. If set to None (default), try 'slycot' first and then 'scipy'. Returns @@ -315,7 +317,7 @@ def lqr(*args, **kwargs): ----- If the first argument is an LTI object, then this object will be used to define the dynamics and input matrices. Furthermore, if the LTI - object corresponds to a discrete time system, the `dlqr()` function + object corresponds to a discrete time system, the `dlqr` function will be called. Examples @@ -413,13 +415,13 @@ def dlqr(*args, **kwargs): The function can be called with either 3, 4, or 5 arguments: - * `dlqr(dsys, Q, R)` - * `dlqr(dsys, Q, R, N)` - * `dlqr(A, B, Q, R)` - * `dlqr(A, B, Q, R, N)` + * ``dlqr(dsys, Q, R)`` + * ``dlqr(dsys, Q, R, N)`` + * ``dlqr(A, B, Q, R)`` + * ``dlqr(A, B, Q, R, N)`` where `dsys` is a discrete-time `StateSpace` system, and `A`, `B`, - `Q`, `R`, and `N` are 2d arrays of appropriate dimension (`dsys.dt` must + `Q`, `R`, and `N` are 2d arrays of appropriate dimension (`dsys.dt`` must not be 0.) Parameters @@ -442,7 +444,7 @@ def dlqr(*args, **kwargs): additional rows and columns in the `Q` matrix. method : str, optional Set the method used for computing the result. Current methods are - 'slycot' and 'scipy'. If set to `None` (default), try 'slycot' + 'slycot' and 'scipy'. If set to None (default), try 'slycot' first and then 'scipy'. Returns @@ -596,7 +598,7 @@ def create_statefbk_iosystem( represent the gains of the integral states of the controller. If a tuple is given, then it specifies a gain schedule. The tuple - should be of the form `(gains, points)` where gains is a list of + should be of the form ``(gains, points)`` where gains is a list of gains `K_j` and points is a list of values `mu_j` at which the gains are computed. The `gainsched_indices` parameter should be used to specify the scheduling variables. @@ -631,7 +633,7 @@ def create_statefbk_iosystem( the controller is the desired state `x_d`, the desired input `u_d`, and the system state `x` (or state estimate `xhat`, if an estimator is given). If value is an integer `q`, the first `q` - values of the `[x_d, u_d, x]` vector are used. Otherwise, the + values of the ``[x_d, u_d, x]`` vector are used. Otherwise, the value should be a slice or a list of indices. The list of indices can be specified as either integer offsets or as signal names. The default is to use the desired state `x_d`. @@ -658,7 +660,7 @@ def create_statefbk_iosystem( design pattern (default), this system takes as inputs the desired state `x_d`, the desired input `u_d`, and either the system state `x` or the estimated state `xhat`. It outputs the controller - action `u` according to the formula `u = u_d - K(x - x_d)`. For + action `u` according to the formula ``u = u_d - K(x - x_d)``. For the 'refgain' design pattern, the system takes as inputs the reference input `r` and the system or estimated state. If the keyword `integral_action` is specified, then an additional set of @@ -670,7 +672,7 @@ def create_statefbk_iosystem( clsys : NonlinearIOSystem Input/output system representing the closed loop system. This - system takes as inputs the desired trajectory `(x_d, u_d)` and + system takes as inputs the desired trajectory ``(x_d, u_d)`` and outputs the system state `x` and the applied input `u` (vertically stacked). @@ -678,8 +680,8 @@ def create_statefbk_iosystem( ---------------- control_indices : int, slice, or list of int or str, optional Specify the indices of the system inputs that should be determined - by the state feedback controller. If value is an integer `m`, the - first `m` system inputs are used. Otherwise, the value should be a + by the state feedback controller. If value is an integer ``m``, the + first ``m`` system inputs are used. Otherwise, the value should be a slice or a list of indices. The list of indices can be specified as either integer offsets or as system input signal names. If not specified, defaults to the system inputs. @@ -687,7 +689,7 @@ def create_statefbk_iosystem( state_indices : int, slice, or list of int or str, optional Specify the indices of the system (or estimator) outputs that should be used by the state feedback controller. If value is an integer - `n`, the first `n` system states are used. Otherwise, the value + ``n``, the first ``n`` system states are used. Otherwise, the value should be a slice or a list of indices. The list of indices can be specified as either integer offsets or as estimator/system output signal names. If not specified, defaults to the system states. @@ -696,10 +698,10 @@ def create_statefbk_iosystem( Set the name of the signals to use for the desired state and inputs or the reference inputs (for the 'refgain' design pattern). If a single string is specified, it should be a format string using the - variable `i` as an index. Otherwise, a list of strings matching - the size of `x_d` and `u_d`, respectively, should be used. Default - is "xd[{i}]" for xd_labels and "ud[{i}]" for ud_labels. These - settings can also be overridden using the `inputs` keyword. + variable ``i`` as an index. Otherwise, a list of strings matching + the size of ``x_d`` and ``u_d``, respectively, should be used. + Default is "xd[{i}]" for xd_labels and "ud[{i}]" for ud_labels. + These settings can also be overridden using the `inputs` keyword. inputs, outputs, states : str, or list of str, optional List of strings that name the individual signals of the transformed diff --git a/control/statesp.py b/control/statesp.py index 56c0f01dd..615477e48 100644 --- a/control/statesp.py +++ b/control/statesp.py @@ -46,8 +46,8 @@ except ImportError: ab13dd = None -__all__ = ['StateSpace', 'LinearICSystem', 'ss2io', 'tf2io', 'tf2ss', 'ssdata', - 'linfnorm', 'ss', 'rss', 'drss', 'summing_junction'] +__all__ = ['StateSpace', 'LinearICSystem', 'ss2io', 'tf2io', 'tf2ss', + 'ssdata', 'linfnorm', 'ss', 'rss', 'drss', 'summing_junction'] # Define module default parameter values _statesp_defaults = { @@ -71,18 +71,18 @@ class StateSpace(NonlinearIOSystem, LTI): dx/dt &= A x + B u \\ y &= C x + D u - where `u` is the input, `y` is the output, and `x` is the state. State - space systems are usually created with the `ss` factory - function. + where :math:`u` is the input, :math:`y` is the output, and + :math:`x` is the state. State space systems are usually created + with the `ss` factory function. Parameters ---------- A, B, C, D : array_like System matrices of the appropriate dimensions. dt : None, True or float, optional - System timebase. 0 (default) indicates continuous time, `True` + System timebase. 0 (default) indicates continuous time, True indicates discrete time with unspecified sampling time, positive - number is discrete time with specified sampling time, `None` + number is discrete time with specified sampling time, None indicates unspecified timebase (either continuous or discrete time). Attributes @@ -114,14 +114,14 @@ class StateSpace(NonlinearIOSystem, LTI): * dt = True: discrete time with unspecified sampling period * dt = None: no timebase specified - Systems must have compatible timebases in order to be combined. A discrete - time system with unspecified sampling time (`dt = True`) can be combined - with a system having a specified sampling time; the result will be a - discrete time system with the sample time of the latter system. Similarly, - a system with timebase `None` can be combined with a system having any - timebase; the result will have the timebase of the latter system. - The default value of dt can be changed by changing the value of - `control.config.defaults['control.default_dt']`. + Systems must have compatible timebases in order to be combined. A + discrete time system with unspecified sampling time (``dt=True``) can + be combined with a system having a specified sampling time; the result + will be a discrete time system with the sample time of the latter + system. Similarly, a system with timebase None can be combined with a + system having any timebase; the result will have the timebase of the + latter system. The default value of dt can be changed by changing the + value of `control.config.defaults['control.default_dt']`. A state space system is callable and returns the value of the transfer function evaluated at a point in the complex plane. See @@ -146,14 +146,14 @@ class StateSpace(NonlinearIOSystem, LTI): may look odd when typeset by non-MathJax LaTeX systems. `control.config.defaults['statesp.latex_num_format']` is a format string - fragment, specifically the part of the format string after `'{:'` + fragment, specifically the part of the format string after ``'{:'`` used to convert floating-point numbers to strings. By default it - is `'.3g'`. + is '.3g'. `control.config.defaults['statesp.latex_repr_type']` must either be - `'partitioned'` or `'separate'`. If `'partitioned'`, the A, B, C, D + 'partitioned' or 'separate'. If 'partitioned', the A, B, C, D matrices are shown as a single, partitioned matrix; if - `'separate'`, the matrices are shown separately. + 'separate', the matrices are shown separately. """ def __init__(self, *args, **kwargs): @@ -162,10 +162,11 @@ def __init__(self, *args, **kwargs): Construct a state space object. The default constructor is StateSpace(A, B, C, D), where A, B, C, D - are matrices or equivalent objects. To create a discrete time system, - use StateSpace(A, B, C, D, dt) where `dt` is the sampling time (or - True for unspecified sampling time). To call the copy constructor, - call StateSpace(sys), where sys is a StateSpace object. + are matrices or equivalent objects. To create a discrete time + system, use StateSpace(A, B, C, D, dt) where `dt` is the sampling + time (or True for unspecified sampling time). To call the copy + constructor, call StateSpace(sys), where sys is a StateSpace + object. See `StateSpace` and `ss` for more information. @@ -764,12 +765,12 @@ def __pow__(self, other): def __call__(self, x, squeeze=None, warn_infinite=True): """Evaluate system's frequency response at complex frequencies. - Returns the complex frequency response `sys(x)` where `x` is `s` for + Returns the complex frequency response ``sys(x)`` where `x` is `s` for continuous-time systems and `z` for discrete-time systems. To evaluate at a frequency omega in radians per second, enter - `x = omega * 1j`, for continuous-time systems, or - `x = exp(1j * omega * dt)` for discrete-time systems. Or use + ``x = omega * 1j``, for continuous-time systems, or + ``x = exp(1j * omega * dt)`` for discrete-time systems. Or use `StateSpace.frequency_response`. Parameters @@ -777,20 +778,21 @@ def __call__(self, x, squeeze=None, warn_infinite=True): x : complex or complex 1D array_like Complex frequencies squeeze : bool, optional - If squeeze=`True`, remove single-dimensional entries from the shape - of the output even if the system is not SISO. If squeeze=`False`, - keep all indices (output, input and, if omega is array_like, - frequency) even if the system is SISO. The default value can be - set using config.defaults['control.squeeze_frequency_response']. + If ``squeeze=True``, remove single-dimensional entries from the + shape of the output even if the system is not SISO. If + ``squeeze=False``, keep all indices (output, input and, if + omega is array_like, frequency) even if the system is SISO. The + default value can be set using + `config.defaults['control.squeeze_frequency_response']`. warn_infinite : bool, optional - If set to `False`, don't warn if frequency response is infinite. + If set to False, don't warn if frequency response is infinite. Returns ------- fresp : complex ndarray The frequency response of the system. If the system is SISO and - squeeze is not `True`, the shape of the array matches the shape of - omega. If the system is not SISO or squeeze is `False`, the first + squeeze is not True, the shape of the array matches the shape of + omega. If the system is not SISO or squeeze is False, the first two dimensions of the array are indices for the output and input and the remaining dimensions match omega. If `squeeze` is True then single-dimensional axes are removed. @@ -805,7 +807,7 @@ def slycot_laub(self, x): Evaluate transfer function at complex frequency using Laub's method from Slycot. Expects inputs and outputs to be - formatted correctly. Use `sys(x)` for a more user-friendly + formatted correctly. Use ``sys(x)`` for a more user-friendly interface. Parameters @@ -861,10 +863,10 @@ def horner(self, x, warn_infinite=True): """Evaluate system's transfer function at complex frequency using Laub's or Horner's method. - Evaluates `sys(x)` where `x` is `s` for continuous-time systems and `z` - for discrete-time systems. + Evaluates ``sys(x)`` where `x` is `s` for continuous-time systems + and `z` for discrete-time systems. - Expects inputs and outputs to be formatted correctly. Use `sys(x)` + Expects inputs and outputs to be formatted correctly. Use ``sys(x)`` for a more user-friendly interface. Notes @@ -1179,10 +1181,10 @@ def returnScipySignalLTI(self, strict=True): ---------- strict : bool, optional True (default): - The timebase `ssobject.dt` cannot be None; it must + The timebase ``ssobject.dt`` cannot be None; it must be continuous (0) or discrete (True or > 0). False: - If `ssobject.dt` is None, continuous time + If ``ssobject.dt`` is None, continuous time `scipy.signal.lti` objects are returned. Returns @@ -1284,24 +1286,26 @@ def sample(self, Ts, method='zoh', alpha=None, prewarp_frequency=None, alpha=0) * 'zoh': zero-order hold (default) alpha : float within [0, 1] - The generalized bilinear transformation weighting parameter, which - should only be specified with method='gbt', and is ignored - otherwise. + The generalized bilinear transformation weighting parameter, + which should only be specified with method='gbt', and is + ignored otherwise. prewarp_frequency : float within [0, infinity) - The frequency [rad/s] at which to match with the input continuous- - time system's magnitude and phase (the gain=1 crossover frequency, - for example). Should only be specified with method='bilinear' or - 'gbt' with alpha=0.5 and ignored otherwise. + The frequency [rad/s] at which to match with the input + continuous-time system's magnitude and phase (the gain = 1 + crossover frequency, for example). Should only be specified + with `method` = 'bilinear' or 'gbt' with ``alpha=0.5`` and + ignored otherwise. name : string, optional - Set the name of the sampled system. If not specified and - if `copy_names` is `False`, a generic name is generated - with a unique integer id. If `copy_names` is `True`, the new system - name is determined by adding the prefix and suffix strings in - config.defaults['iosys.sampled_system_name_prefix'] and - config.defaults['iosys.sampled_system_name_suffix'], with the - default being to add the suffix '$sampled'. + Set the name of the sampled system. If not specified and if + `copy_names` is False, a generic name is + generated with a unique integer id. If `copy_names` is + True, the new system name is determined by adding the + prefix and suffix strings in + `config.defaults['iosys.sampled_system_name_prefix']` and + `config.defaults['iosys.sampled_system_name_suffix']`, with + the default being to add the suffix '$sampled'. copy_names : bool, Optional - If `True`, copy the names of the input signals, output + If True, copy the names of the input signals, output signals, and states to the sampled system. Returns @@ -1312,9 +1316,9 @@ def sample(self, Ts, method='zoh', alpha=None, prewarp_frequency=None, Other Parameters ---------------- inputs : int, list of str or None, optional - Description of the system inputs. If not specified, the origional - system inputs are used. See `InputOutputSystem` for more - information. + Description of the system inputs. If not specified, the + origional system inputs are used. See `InputOutputSystem` for + more information. outputs : int, list of str or None, optional Description of the system outputs. Same format as `inputs`. states : int, list of str, or None, optional @@ -1368,21 +1372,22 @@ def dcgain(self, warn_infinite=False): ---------- warn_infinite : bool, optional By default, don't issue a warning message if the zero-frequency - gain is infinite. Setting `warn_infinite` to generate the warning - message. + gain is infinite. Setting `warn_infinite` to generate the + warning message. Returns ------- gain : (noutputs, ninputs) ndarray or scalar Array or scalar value for SISO systems, depending on - config.defaults['control.squeeze_frequency_response']. - The value of the array elements or the scalar is either the - zero-frequency (or DC) gain, or `inf`, if the frequency response - is singular. + `config.defaults['control.squeeze_frequency_response']`. The + value of the array elements or the scalar is either the + zero-frequency (or DC) gain, or ``inf``, if the frequency + response is singular. For real valued systems, the empty imaginary part of the complex zero-frequency response is discarded and a real array or scalar is returned. + """ return self._dcgain(warn_infinite) @@ -1590,11 +1595,11 @@ def ss(*args, **kwargs): The function accepts either 1, 4 or 5 positional parameters: - `ss(sys)` + ``ss(sys)`` Convert a linear system into space system form. Always creates a new system, even if sys is already a state space system. - `ss(A, B, C, D)` + ``ss(A, B, C, D)`` Create a state space system from the matrices of its state and output equations: @@ -1603,7 +1608,7 @@ def ss(*args, **kwargs): dx/dt &= A x + B u \\ y &= C x + D u - `ss(A, B, C, D, dt)` + ``ss(A, B, C, D, dt)`` Create a discrete-time state space system from the matrices of its state and output equations: @@ -1617,7 +1622,7 @@ def ss(*args, **kwargs): as a scalar. - `ss(*args, inputs=['u1', ..., 'up'], outputs=['y1', ..., 'yq'], states=['x1', ..., 'xn'])` + ``ss(*args, inputs=['u1', ..., 'up'], outputs=['y1', ..., 'yq'], states=['x1', ..., 'xn'])`` Create a system with named input, output, and state signals. Parameters @@ -1627,18 +1632,18 @@ def ss(*args, **kwargs): A, B, C, D : array_like or string System, control, output, and feed forward matrices. dt : None, True or float, optional - System timebase. 0 (default) indicates continuous time, `True` + System timebase. 0 (default) indicates continuous time, True indicates discrete time with unspecified sampling time, positive - number is discrete time with specified sampling time, `None` + number is discrete time with specified sampling time, None indicates unspecified timebase (either continuous or discrete time). remove_useless_states : bool, optional - If `True`, remove states that have no effect on the input/output + If True, remove states that have no effect on the input/output dynamics. If not specified, the value is read from `config.defaults['statesp.remove_useless_states']` (default = False). method : str, optional Set the method used for converting a transfer function to a state space system. Current methods are 'slycot' and 'scipy'. If set to - `None` (default), try 'slycot' first and then 'scipy' (SISO only). + None (default), try 'slycot' first and then 'scipy' (SISO only). Returns ------- @@ -1649,8 +1654,8 @@ def ss(*args, **kwargs): ---------------- inputs, outputs, states : str, or list of str, optional List of strings that name the individual signals. If this parameter - is not given or given as `None`, the signal names will be of the - form `s[i]` (where `s` is one of `u`, `y`, or `x`). See + is not given or given as None, the signal names will be of the + form 's[i]' (where 's' is one of 'u', 'y', or 'x'). See `InputOutputSystem` for more information. input_prefix, output_prefix, state_prefix : string, optional Set the prefix for input, output, and state signals. Defaults = @@ -1756,15 +1761,15 @@ def tf2io(*args, **kwargs): The function accepts either 1 or 2 parameters: - `tf2io(sys)` + ``tf2io(sys)`` Convert a linear system into space space form. Always creates a new system, even if sys is already a StateSpace object. - `tf2io(num, den)` + ``tf2io(num, den)`` Create a linear I/O system from its numerator and denominator polynomial coefficients. - For details see: `tf` + For details see: `tf`. Parameters ---------- @@ -1826,15 +1831,15 @@ def tf2ss(*args, **kwargs): The function accepts either 1 or 2 parameters: - `tf2ss(sys)` + ``tf2ss(sys)`` Convert a transfer function into space space form. Equivalent to `ss(sys)`. - `tf2ss(num, den)` + ``tf2ss(num, den)`` Create a state space system from its numerator and denominator polynomial coefficients. - For details see: `tf` + For details see: `tf`. Parameters ---------- @@ -1861,17 +1866,17 @@ def tf2ss(*args, **kwargs): with a unique integer id. method : str, optional Set the method used for computing the result. Current methods are - 'slycot' and 'scipy'. If set to `None` (default), try 'slycot' + 'slycot' and 'scipy'. If set to None (default), try 'slycot' first and then 'scipy' (SISO only). Raises ------ ValueError if `num` and `den` have invalid or unequal dimensions, or if an - invalid number of arguments is passed in + invalid number of arguments is passed in. TypeError if `num` or `den` are of incorrect type, or if sys is not a - TransferFunction object + TransferFunction object. See Also -------- @@ -1879,10 +1884,10 @@ def tf2ss(*args, **kwargs): Notes ----- - The `slycot` routine used to convert a transfer function into state - space form appears to have a bug and in some (rare) instances may not - return a system with the same poles as the input transfer function. - For SISO systems, setting `method=scipy` can be used as an alternative. + The `slycot` routine used to convert a transfer function into state space + form appears to have a bug and in some (rare) instances may not return + a system with the same poles as the input transfer function. For SISO + systems, setting ``method='scipy'`` can be used as an alternative. Examples -------- @@ -1997,11 +2002,11 @@ def rss(states=1, outputs=1, inputs=1, strictly_proper=False, **kwargs): the signal will be of the form 's[i]' (where 's' is one of 'x', 'y', or 'u'). strictly_proper : bool, optional - If set to `True`, returns a proper system (no direct term). + If set to True, returns a proper system (no direct term). dt : None, True or float, optional - System timebase. 0 (default) indicates continuous time, `True` + System timebase. 0 (default) indicates continuous time, True indicates discrete time with unspecified sampling time, positive - number is discrete time with specified sampling time, `None` + number is discrete time with specified sampling time, None indicates unspecified timebase (either continuous or discrete time). name : string, optional System name (used for specifying signals). If unspecified, a generic @@ -2021,8 +2026,8 @@ def rss(states=1, outputs=1, inputs=1, strictly_proper=False, **kwargs): ----- If the number of states, inputs, or outputs is not specified, then the missing numbers are assumed to be 1. If dt is not specified or is given - as 0 or `None`, the poles of the returned system will always have a - negative real part. If dt is `True` or a postive float, the poles of the + as 0 or None, the poles of the returned system will always have a + negative real part. If dt is True or a postive float, the poles of the returned system will have magnitude less than 1. """ @@ -2052,7 +2057,7 @@ def drss(*args, **kwargs): Create a stable *discrete time* random state space object. This function calls `rss` using either the `dt` keyword provided by - the user or `dt=True` if not specified. + the user or ``dt=True`` if not specified. Examples -------- @@ -2092,33 +2097,33 @@ def summing_junction( inputs=None, output=None, dimension=None, prefix='u', **kwargs): """Create a summing junction as an input/output system. - This function creates a static input/output system that outputs the sum of - the inputs, potentially with a change in sign for each individual input. - The input/output system that is created by this function can be used as a - component in the `interconnect` function. + This function creates a static input/output system that outputs the sum + of the inputs, potentially with a change in sign for each individual + input. The input/output system that is created by this function can be + used as a component in the `interconnect` function. Parameters ---------- inputs : int, string or list of strings - Description of the inputs to the summing junction. This can be given - as an integer count, a string, or a list of strings. If an integer - count is specified, the names of the input signals will be of the form - `u[i]`. + Description of the inputs to the summing junction. This can be + given as an integer count, a string, or a list of strings. If an + integer count is specified, the names of the input signals will be + of the form 'u[i]'. output : string, optional Name of the system output. If not specified, the output will be 'y'. dimension : int, optional The dimension of the summing junction. If the dimension is set to a positive integer, a multi-input, multi-output summing junction will be created. The input and output signal names will be of the form - `[i]` where `signal` is the input/output signal name specified - by the `inputs` and `output` keywords. Default value is `None`. + '[i]' where 'signal' is the input/output signal name specified + by the `inputs` and `output` keywords. Default value is None. name : string, optional System name (used for specifying signals). If unspecified, a generic name is generated with a unique integer id. prefix : string, optional If `inputs` is an integer, create the names of the states using the given prefix (default = 'u'). The names of the input will be of the - form `prefix[i]`. + form 'prefix[i]'. Returns ------- @@ -2228,8 +2233,8 @@ def _ssmatrix(data, axis=1, square=None, rows=None, cols=None, name=None): data : array, list, or string Input data defining the contents of the 2D array axis : 0 or 1 - If input data is 1D, which axis to use for return object. The default - is 1, corresponding to a row matrix. + If input data is 1D, which axis to use for return object. The + default is 1, corresponding to a row matrix. square : bool, optional If set to True, check that the input matrix is square. rows : int, optional diff --git a/control/stochsys.py b/control/stochsys.py index bb828bbcf..3b76f6d49 100644 --- a/control/stochsys.py +++ b/control/stochsys.py @@ -60,10 +60,10 @@ def lqe(*args, **kwargs): The function can be called with either 3, 4, 5, or 6 arguments: - * `L, P, E = lqe(sys, QN, RN)` - * `L, P, E = lqe(sys, QN, RN, NN)` - * `L, P, E = lqe(A, G, C, QN, RN)` - * `L, P, E = lqe(A, G, C, QN, RN, NN)` + * ``L, P, E = lqe(sys, QN, RN)`` + * ``L, P, E = lqe(sys, QN, RN, NN)`` + * ``L, P, E = lqe(A, G, C, QN, RN)`` + * ``L, P, E = lqe(A, G, C, QN, RN, NN)`` where `sys` is an `LTI` object, and `A`, `G`, `C`, `QN`, `RN`, and `NN` are 2D arrays or matrices of appropriate dimension. @@ -81,7 +81,7 @@ def lqe(*args, **kwargs): Cross covariance matrix. Not currently implemented. method : str, optional Set the method used for computing the result. Current methods are - 'slycot' and 'scipy'. If set to `None` (default), try 'slycot' first + 'slycot' and 'scipy'. If set to None (default), try 'slycot' first and then 'scipy'. Returns @@ -102,7 +102,7 @@ def lqe(*args, **kwargs): ----- If the first argument is an LTI object, then this object will be used to define the dynamics, noise and output matrices. Furthermore, if the - LTI object corresponds to a discrete time system, the `dlqe()` + LTI object corresponds to a discrete time system, the `dlqe` function will be called. Examples @@ -204,9 +204,9 @@ def dlqe(*args, **kwargs): .. math:: x_e[n+1] = A x_e[n] + B u[n] + L(y[n] - C x_e[n] - D u[n]) - produces a state estimate x_e[n] that minimizes the expected squared error - using the sensor measurements y. The noise cross-correlation `NN` is - set to zero when omitted. + produces a state estimate x_e[n] that minimizes the expected squared + error using the sensor measurements y. The noise cross-correlation `NN` + is set to zero when omitted. Parameters ---------- @@ -218,7 +218,7 @@ def dlqe(*args, **kwargs): Cross covariance matrix (not yet supported). method : str, optional Set the method used for computing the result. Current methods are - 'slycot' and 'scipy'. If set to `None` (default), try 'slycot' + 'slycot' and 'scipy'. If set to None (default), try 'slycot' first and then 'scipy'. Returns @@ -413,7 +413,7 @@ def create_estimator_iosystem( Notes ----- - This function can be used with the `create_statefbk_iosystem()` function + This function can be used with the `create_statefbk_iosystem` function to create a closed loop, output-feedback, state space controller:: K, _, _ = ct.lqr(sys, Q, R) @@ -424,7 +424,7 @@ def create_estimator_iosystem( resp = ct.input_output_response(est, T, [Y, U], [X0, P0]) - If desired, the `correct` parameter can be set to `False` to allow + If desired, the `correct` parameter can be set to False to allow prediction with no additional measurement information:: resp = ct.input_output_response( @@ -618,7 +618,7 @@ def white_noise(T, Q, dt=0): Returns ------- V : array - Noise signal indexed as `V[i, j]` where `i` is the signal index and + Noise signal indexed as ``V[i, j]`` where `i` is the signal index and `j` is the time index. """ @@ -654,9 +654,9 @@ def white_noise(T, Q, dt=0): def correlation(T, X, Y=None, squeeze=True): """Compute the correlation of time signals. - For a time series X(t) (and optionally Y(t)), the correlation() function - computes the correlation matrix E(X'(t+tau) X(t)) or the cross-correlation - matrix E(X'(t+tau) Y(t)]: + For a time series X(t) (and optionally Y(t)), the correlation() + function computes the correlation matrix E(X'(t+tau) X(t)) or the + cross-correlation matrix E(X'(t+tau) Y(t)]: tau, Rtau = correlation(T, X[, Y]) @@ -675,7 +675,7 @@ def correlation(T, X, Y=None, squeeze=True): If present, the signal with which to compute the correlation. Defaults to X. squeeze : bool, optional - If `True`, squeeze Rtau to remove extra dimensions (useful if the + If True, squeeze Rtau to remove extra dimensions (useful if the signals are scalars). Returns diff --git a/control/sysnorm.py b/control/sysnorm.py index 58ca93cf9..cdf673921 100644 --- a/control/sysnorm.py +++ b/control/sysnorm.py @@ -22,7 +22,7 @@ def _h2norm_slycot(sys, print_warning=True): See Also -------- slycot.ab13bd - + """ # See: https://github.com/python-control/Slycot/issues/199 try: @@ -33,7 +33,8 @@ def _h2norm_slycot(sys, print_warning=True): try: from slycot.exceptions import SlycotArithmeticError except ImportError: - raise ct.ControlSlycot("Can't find slycot class `SlycotArithmeticError`!") + raise ct.ControlSlycot( + "Can't find slycot class `SlycotArithmeticError`!") A, B, C, D = ct.ssdata(ct.ss(sys)) @@ -49,7 +50,8 @@ def _h2norm_slycot(sys, print_warning=True): if dico == 'C': if any(D.flat != 0): if print_warning: - warnings.warn("System has a direct feedthrough term!", UserWarning) + warnings.warn( + "System has a direct feedthrough term!", UserWarning) return float("inf") else: return 0.0 @@ -61,11 +63,14 @@ def _h2norm_slycot(sys, print_warning=True): except SlycotArithmeticError as e: if e.info == 3: if print_warning: - warnings.warn("System has pole(s) on the stability boundary!", UserWarning) + warnings.warn( + "System has pole(s) on the stability boundary!", + UserWarning) return float("inf") elif e.info == 5: if print_warning: - warnings.warn("System has a direct feedthrough term!", UserWarning) + warnings.warn( + "System has a direct feedthrough term!", UserWarning) return float("inf") elif e.info == 6: if print_warning: @@ -86,16 +91,16 @@ def system_norm(system, p=2, tol=1e-6, print_warning=True, method=None): System in continuous or discrete time for which the norm should be computed. p : int or str - Type of norm to be computed. `p=2` gives the H2 norm, and - `p='inf'` gives the L-infinity norm. + Type of norm to be computed. ``p=2`` gives the H2 norm, and + ``p='inf'`` gives the L-infinity norm. tol : float - Relative tolerance for accuracy of L-infinity norm computation. Ignored - unless `p='inf'`. + Relative tolerance for accuracy of L-infinity norm + computation. Ignored unless ``p='inf'``. print_warning : bool Print warning message in case norm value may be uncertain. method : str, optional Set the method used for computing the result. Current methods are - 'slycot' and 'scipy'. If set to `None` (default), try 'slycot' first + 'slycot' and 'scipy'. If set to None (default), try 'slycot' first and then 'scipy'. Returns @@ -105,7 +110,8 @@ def system_norm(system, p=2, tol=1e-6, print_warning=True, method=None): Notes ----- - Does not yet compute the L-infinity norm for discrete time systems with pole(s) in z=0 unless Slycot is used. + Does not yet compute the L-infinity norm for discrete time systems + with pole(s) in ``z=0`` unless Slycot is used. Examples -------- @@ -142,7 +148,9 @@ def system_norm(system, p=2, tol=1e-6, print_warning=True, method=None): poles_real_part = G.poles().real if any(np.isclose(poles_real_part, 0.0)): # Poles on imaginary axis if print_warning: - warnings.warn("Poles close to, or on, the imaginary axis. Norm value may be uncertain.", UserWarning) + warnings.warn( + "Poles close to, or on, the imaginary axis. " + "Norm value may be uncertain.", UserWarning) return float('inf') elif any(poles_real_part > 0.0): # System unstable if print_warning: @@ -150,7 +158,8 @@ def system_norm(system, p=2, tol=1e-6, print_warning=True, method=None): return float('inf') elif any(D.flat != 0): # System has direct feedthrough if print_warning: - warnings.warn("System has a direct feedthrough term!", UserWarning) + warnings.warn( + "System has a direct feedthrough term!", UserWarning) return float('inf') else: @@ -160,18 +169,25 @@ def system_norm(system, p=2, tol=1e-6, print_warning=True, method=None): # Else use scipy else: - P = ct.lyap(A, B@B.T, method=method) # Solve for controllability Gramian + # Solve for controllability Gramian + P = ct.lyap(A, B@B.T, method=method) - # System is stable to reach this point, and P should be positive semi-definite. - # Test next is a precaution in case the Lyapunov equation is ill conditioned. + # System is stable to reach this point, and P should be + # positive semi-definite. Test next is a precaution in + # case the Lyapunov equation is ill conditioned. if any(la.eigvals(P).real < 0.0): if print_warning: - warnings.warn("There appears to be poles close to the imaginary axis. Norm value may be uncertain.", UserWarning) + warnings.warn( + "There appears to be poles close to the " + "imaginary axis. Norm value may be uncertain.", + UserWarning) return float('inf') else: - norm_value = np.sqrt(np.trace(C@P@C.T)) # Argument in sqrt should be non-negative + # Argument in sqrt should be non-negative + norm_value = np.sqrt(np.trace(C@P@C.T)) if np.isnan(norm_value): - raise ct.ControlArgument("Norm computation resulted in NaN.") + raise ct.ControlArgument( + "Norm computation resulted in NaN.") else: return norm_value @@ -184,7 +200,9 @@ def system_norm(system, p=2, tol=1e-6, print_warning=True, method=None): poles_abs = abs(G.poles()) if any(np.isclose(poles_abs, 1.0)): # Poles on imaginary axis if print_warning: - warnings.warn("Poles close to, or on, the complex unit circle. Norm value may be uncertain.", UserWarning) + warnings.warn( + "Poles close to, or on, the complex unit circle. " + "Norm value may be uncertain.", UserWarning) return float('inf') elif any(poles_abs > 1.0): # System unstable if print_warning: @@ -199,16 +217,22 @@ def system_norm(system, p=2, tol=1e-6, print_warning=True, method=None): else: P = ct.dlyap(A, B@B.T, method=method) - # System is stable to reach this point, and P should be positive semi-definite. - # Test next is a precaution in case the Lyapunov equation is ill conditioned. + # System is stable to reach this point, and P should be + # positive semi-definite. Test next is a precaution in + # case the Lyapunov equation is ill conditioned. if any(la.eigvals(P).real < 0.0): if print_warning: - warnings.warn("Warning: There appears to be poles close to the complex unit circle. Norm value may be uncertain.", UserWarning) + warnings.warn( + "There appears to be poles close to the complex " + "unit circle. Norm value may be uncertain.", + UserWarning) return float('inf') else: - norm_value = np.sqrt(np.trace(C@P@C.T + D@D.T)) # Argument in sqrt should be non-negative + # Argument in sqrt should be non-negative + norm_value = np.sqrt(np.trace(C@P@C.T + D@D.T)) if np.isnan(norm_value): - raise ct.ControlArgument("Norm computation resulted in NaN.") + raise ct.ControlArgument( + "Norm computation resulted in NaN.") else: return norm_value @@ -222,12 +246,16 @@ def system_norm(system, p=2, tol=1e-6, print_warning=True, method=None): if G.isdtime(): # Discrete time if any(np.isclose(abs(poles), 1.0)): # Poles on unit circle if print_warning: - warnings.warn("Poles close to, or on, the complex unit circle. Norm value may be uncertain.", UserWarning) + warnings.warn( + "Poles close to, or on, the complex unit circle. " + "Norm value may be uncertain.", UserWarning) return float('inf') else: # Continuous time if any(np.isclose(poles.real, 0.0)): # Poles on imaginary axis if print_warning: - warnings.warn("Poles close to, or on, the imaginary axis. Norm value may be uncertain.", UserWarning) + warnings.warn( + "Poles close to, or on, the imaginary axis. " + "Norm value may be uncertain.", UserWarning) return float('inf') # Use slycot, if available, to compute (finite) norm @@ -240,15 +268,19 @@ def system_norm(system, p=2, tol=1e-6, print_warning=True, method=None): # ------------------ # Discrete time case # ------------------ - # Use inverse bilinear transformation of discrete time system to s-plane if no poles on |z|=1 or z=0. - # Allows us to use test for continuous time systems next. + # Use inverse bilinear transformation of discrete time system + # to s-plane if no poles on |z|=1 or z=0. Allows us to use + # test for continuous time systems next. if G.isdtime(): Ad = A Bd = B Cd = C Dd = D if any(np.isclose(la.eigvals(Ad), 0.0)): - raise ct.ControlArgument("L-infinity norm computation for discrete time system with pole(s) in z=0 currently not supported unless Slycot installed.") + raise ct.ControlArgument( + "L-infinity norm computation for discrete time " + "system with pole(s) in z=0 currently not supported " + "unless Slycot installed.") # Inverse bilinear transformation In = np.eye(len(Ad)) @@ -265,13 +297,17 @@ def _Hamilton_matrix(gamma): """Constructs Hamiltonian matrix. For internal use.""" R = Ip*gamma**2 - D.T@D invR = la.inv(R) - return np.block([[A+B@invR@D.T@C, B@invR@B.T], [-C.T@(Ip+D@invR@D.T)@C, -(A+B@invR@D.T@C).T]]) + return np.block([ + [A+B@invR@D.T@C, B@invR@B.T], + [-C.T@(Ip+D@invR@D.T)@C, -(A+B@invR@D.T@C).T]]) gaml = la.norm(D,ord=2) # Lower bound gamu = max(1.0, 2.0*gaml) # Candidate upper bound Ip = np.eye(len(D)) - while any(np.isclose(la.eigvals(_Hamilton_matrix(gamu)).real, 0.0)): # Find actual upper bound + while any(np.isclose( + la.eigvals(_Hamilton_matrix(gamu)).real, 0.0)): + # Find actual upper bound gamu *= 2.0 while (gamu-gaml)/gamu > tol: @@ -286,7 +322,8 @@ def _Hamilton_matrix(gamma): # Other norm computation # ---------------------- else: - raise ct.ControlArgument(f"Norm computation for p={p} currently not supported.") + raise ct.ControlArgument( + f"Norm computation for p={p} currently not supported.") norm = system_norm diff --git a/control/tests/docstrings_test.py b/control/tests/docstrings_test.py index ff88b4fa0..f47612dbe 100644 --- a/control/tests/docstrings_test.py +++ b/control/tests/docstrings_test.py @@ -125,7 +125,7 @@ def test_parameter_docs(module, prefix): # TODO: check top level documenation here (__init__, attributes?) if inspect.isclass(obj): _info(f"Checking class {objname}", 1) - + # Check member functions within the class test_parameter_docs(obj, prefix + name + '.') @@ -386,7 +386,8 @@ def test_deprecated_functions(module, prefix): iosys_parent_attributes = [ 'input_index', 'output_index', 'state_index', # rarely used 'states', 'nstates', 'state_labels', # not need in TF, FRD - 'params', 'outfcn', 'updfcn' # NL I/O, SS overlap + 'params', 'outfcn', 'updfcn', # NL I/O, SS overlap + 'repr_format' # rarely used ] # @@ -663,9 +664,8 @@ def _check_numpydoc_style(obj, doc): for pyobj in python_objects: for section in ["Extended Summary", "Notes"]: text = "\n".join(doc[section]) - if re.search(f"[\\s]{pyobj}([\\s]|$)", text) is not None: - _warn(f"{pyobj} appears in {section} for {name} " - "without backticks") + if re.search(f"`{pyobj}`", text) is not None: + _warn(f"{pyobj} appears in {section} for {name} with backticks") if inspect.isclass(obj): # Specialized checks for classes @@ -718,9 +718,8 @@ def _check_numpydoc_param( # Look for Python objects that are not marked properly python_objects = ['True', 'False', 'None'] for pyobj in python_objects: - if re.search(f"[\\s]{pyobj}([\\s]|$)", param_desc) is not None: - _warn(f"{pyobj} appears in {param_name} description " - "without backticks") + if re.search(f"`{pyobj}`", param_desc) is not None: + _warn(f"{pyobj} appears in {param_name} description with backticks") # Utility function to replace positional signature with docstring signature diff --git a/control/timeplot.py b/control/timeplot.py index 22ecbd4f4..4af39231b 100644 --- a/control/timeplot.py +++ b/control/timeplot.py @@ -58,28 +58,28 @@ def time_response_plot( Sets how and where to plot the inputs: * False: don't plot the inputs * None: use value from time response data (default) - * 'overlay`: plot inputs overlaid with outputs + * 'overlay': plot inputs overlaid with outputs * True: plot the inputs on their own axes plot_outputs : bool, optional - If `False`, suppress plotting of the outputs. + If False, suppress plotting of the outputs. overlay_traces : bool, optional - If set to `True`, combine all traces onto a single row instead of + If set to True, combine all traces onto a single row instead of plotting a separate row for each trace. overlay_signals : bool, optional - If set to `True`, combine all input and output signals onto a single + If set to True, combine all input and output signals onto a single plot (for each). sharex, sharey : str or bool, optional Determine whether and how x- and y-axis limits are shared between subplots. Can be set set to 'row' to share across all subplots in a row, 'col' to set across all subplots in a column, 'all' to share - across all subplots, or `False` to allow independent limits. - Default values are `False` for `sharex' and 'col' for `sharey`, and - can be set using config.defaults['timeplot.sharex'] and - config.defaults['timeplot.sharey']. + across all subplots, or False to allow independent limits. + Default values are False for `sharex' and 'col' for `sharey`, and + can be set using `config.defaults['timeplot.sharex']` and + `config.defaults['timeplot.sharey']`. transpose : bool, optional - If transpose is `False` (default), signals are plotted from top to + If transpose is False (default), signals are plotted from top to bottom, starting with outputs (if plotted) and then inputs. - Multi-trace plots are stacked horizontally. If transpose is `True`, + Multi-trace plots are stacked horizontally. If transpose is True, signals are plotted from left to right, starting with the inputs (if plotted) and then the outputs. Multi-trace responses are stacked vertically. @@ -110,7 +110,7 @@ def time_response_plot( ---------------- add_initial_zero : bool Add an initial point of zero at the first time point for all - inputs with type 'step'. Default is `True`. + inputs with type 'step'. Default is True. ax : array of matplotlib.axes.Axes, optional The matplotlib axes to draw the figure on. If not specified, the axes for the current figure are used or, if there is no current @@ -119,7 +119,7 @@ def time_response_plot( plotted data. input_props : array of dicts List of line properties to use when plotting combined inputs. The - default values are set by config.defaults['timeplot.input_props']. + default values are set by `config.defaults['timeplot.input_props']`. label : str or array_like of str, optional If present, replace automatically generated label(s) with the given label(s). If more than one line is being generated, an array of @@ -128,24 +128,24 @@ def time_response_plot( legend_map : array of str, optional Location of the legend for multi-axes plots. Specifies an array of legend location strings matching the shape of the subplots, with - each entry being either `None` (for no legend) or a legend location + each entry being either None (for no legend) or a legend location string (see `~matplotlib.pyplot.legend`). legend_loc : int or str, optional Include a legend in the given location. Default is 'center right', - with no legend for a single response. Use `False` to suppress legend. + with no legend for a single response. Use False to suppress legend. output_props : array of dicts, optional List of line properties to use when plotting combined outputs. The - default values are set by config.defaults['timeplot.output_props']. + default values are set by `config.defaults['timeplot.output_props']`. rcParams : dict Override the default parameters used for generating plots. - Default is set by config.defaults['ctrlplot.rcParams']. + Default is set by `config.defaults['ctrlplot.rcParams']`. relabel : bool, optional (deprecated) By default, existing figures and axes are relabeled - when new data are added. If set to `False`, just plot new data on + when new data are added. If set to False, just plot new data on existing axes. show_legend : bool, optional - Force legend to be shown if `True` or hidden if `False`. If - `None`, then show legend when there is more than one line on an + Force legend to be shown if True or hidden if False. If + None, then show legend when there is more than one line on an axis or `legend_loc` or `legend_map` has been specified. time_label : str, optional Label to use for the time axis. @@ -155,14 +155,14 @@ def time_response_plot( Replace the default trace labels with the given labels. trace_props : array of dicts List of line properties to use when plotting multiple traces. The - default values are set by config.defaults['timeplot.trace_props']. + default values are set by `config.defaults['timeplot.trace_props']`. Notes ----- 1. A new figure will be generated if there is no current figure or the current figure has an incompatible number of axes. To - force the creation of a new figures, use `plt.figure()`. To reuse - a portion of an existing figure, use the `ax` keyword. + force the creation of a new figures, use `plt.figure`. To reuse + a portion of an existing figure, use the ``ax`` keyword. 2. The line properties (color, linestyle, etc) can be set for the entire plot using the `fmt` and/or `kwargs` parameter, which @@ -174,7 +174,7 @@ def time_response_plot( the kwarg properties to determine the final line properties. 3. The default plot properties, such as font sizes, can be set using - config.defaults[''timeplot.rcParams']. + `config.defaults[''timeplot.rcParams']`. """ from .ctrlplot import _process_ax_keyword, _process_line_labels diff --git a/control/timeresp.py b/control/timeresp.py index c07e52ee2..b90cb270d 100644 --- a/control/timeresp.py +++ b/control/timeresp.py @@ -110,14 +110,14 @@ class TimeResponseData: and if a system is multi-input or multi-output, then the inputs are returned as a 2D array (indexed by input and time) and the outputs are returned as either a 2D array (indexed by output and time) or a - 3D array (indexed by output, trace, and time). If squeeze=`True`, + 3D array (indexed by output, trace, and time). If ``squeeze=True``, access to the output response will remove single-dimensional entries from the shape of the inputs and outputs even if the system - is not SISO. If squeeze=`False`, keep the input as a 2D or 3D array + is not SISO. If squeeze=False, keep the input as a 2D or 3D array (indexed by the input (if multi-input), trace (if single input) and time) and the output as a 3D array (indexed by the output, trace, and time) even if the system is SISO. The default value can be set - using config.defaults['control.squeeze_time_response']. + using `config.defaults['control.squeeze_time_response']`. Attributes ---------- @@ -130,20 +130,20 @@ class TimeResponseData: responses). These data are normally accessed via the `outputs` property, which performs squeeze processing. x : 2D or 3D array, or None - State space data, indexed either by output number and time (for single - trace responses) or output, trace, and time (for multi-trace - responses). If no state data are present, value is `None`. These + State space data, indexed either by output number and time (for + single trace responses) or output, trace, and time (for multi-trace + responses). If no state data are present, value is None. These data are normally accessed via the `states` property, which performs squeeze processing. u : 2D or 3D array, or None Input signal data, indexed either by input index and time (for single trace responses) or input, trace, and time (for multi-trace - responses). If no input data are present, value is `None`. These + responses). If no input data are present, value is None. These data are normally accessed via the `inputs` property, which performs squeeze processing. issiso : bool, optional - Set to `True` if the system generating the data is single-input, - single-output. If passed as `None` (default), the input data + Set to True if the system generating the data is single-input, + single-output. If passed as None (default), the input data will be used to set the value. ninputs, noutputs, nstates : int Number of inputs, outputs, and states of the underlying system. @@ -167,23 +167,23 @@ class TimeResponseData: sysname : str, optional Name of the system that created the data. transpose : bool, optional - If `True`, transpose all input and output arrays (for backward + If True, transpose all input and output arrays (for backward compatibility with MATLAB and `scipy.signal.lsim`). Default value is False. return_x : bool, optional - If `True`, return the state vector when enumerating result by + If True, return the state vector when enumerating result by assigning to a tuple (default = False). plot_inputs : bool, optional Whether or not to plot the inputs by default (can be overridden in the plot() method). multi_trace : bool, optional - If `True`, then 2D input array represents multiple traces. For + If True, then 2D input array represents multiple traces. For a MIMO system, the `input` attribute should then be set to - indicate which trace is being specified. Default is `False`. + indicate which trace is being specified. Default is False. success : bool, optional - If `False`, result may not be valid (see `input_output_response`). + If False, result may not be valid (see `input_output_response`). message : str, optional - Informational message if `success` is `False`. + Informational message if `success` is False. See Also -------- @@ -445,21 +445,21 @@ def __call__(self, **kwargs): Parameters ---------- squeeze : bool, optional - If squeeze=`True`, access to the output response will remove - single-dimensional entries from the shape of the inputs, outputs, - and states even if the system is not SISO. If squeeze=`False`, keep - the input as a 2D or 3D array (indexed by the input (if - multi-input), trace (if single input) and time) and the output and - states as a 3D array (indexed by the output/state, trace, and - time) even if the system is SISO. + If ``squeeze=True``, access to the output response will remove + single-dimensional entries from the shape of the inputs, + outputs, and states even if the system is not SISO. If + ``squeeze=False``, keep the input as a 2D or 3D array (indexed + by the input (if multi-input), trace (if single input) and + time) and the output and states as a 3D array (indexed by the + output/state, trace, and time) even if the system is SISO. transpose : bool, optional - If `True`, transpose all input and output arrays (for backward + If True, transpose all input and output arrays (for backward compatibility with MATLAB and `scipy.signal.lsim`). Default value is False. return_x : bool, optional - If `True`, return the state vector when enumerating result by + If True, return the state vector when enumerating result by assigning to a tuple (default = False). input_labels, output_labels, state_labels: array of str @@ -809,8 +809,8 @@ def _check_convert_array(in_obj, legal_shapes, err_msg_start, squeeze=False, The special value "any" means that there can be any number of elements in a certain dimension. - * `(2, 3)` describes an array with 2 rows and 3 columns - * `(2, "any")` describes an array with 2 rows and any number of + * ``(2, 3)`` describes an array with 2 rows and 3 columns + * ``(2, 'any')`` describes an array with 2 rows and any number of columns err_msg_start : str @@ -819,15 +819,16 @@ def _check_convert_array(in_obj, legal_shapes, err_msg_start, squeeze=False, is currently checked. squeeze : bool - If `True`, all dimensions with only one element are removed from the - array. If `False` the array's shape is unmodified. + If True, all dimensions with only one element are removed from the + array. If False the array's shape is unmodified. - For example: - `array([[1,2,3]])` is converted to `array([1, 2, 3])` + For example: ``array([[1,2,3]])`` is converted to ``array([1, 2, + 3])``. transpose : bool, optional - If `True`, assume that 2D input arrays are transposed from the standard - format. Used to convert MATLAB-style inputs to our format. + If True, assume that 2D input arrays are transposed from the + standard format. Used to convert MATLAB-style inputs to our + format. Returns ------- @@ -899,9 +900,9 @@ def forced_response(sysdata, T=None, U=0., X0=0., transpose=False, params=None, interpolate=False, return_x=None, squeeze=None): """Compute the output of a linear system given the input. - As a convenience for parameters `U`, `X0`: - Numbers (scalars) are converted to constant arrays with the correct shape. - The correct shape is inferred from arguments `sys` and `T`. + As a convenience for parameters `U`, `X0`: Numbers (scalars) are + converted to constant arrays with the correct shape. The correct shape + is inferred from arguments `sys` and `T`. For information on the **shape** of parameters `U`, `T`, `X0` and return values `T`, `yout`, `xout`, see :ref:`time-series-convention`. @@ -913,12 +914,12 @@ def forced_response(sysdata, T=None, U=0., X0=0., transpose=False, params=None, T : array_like, optional for discrete LTI `sys` Time steps at which the input is defined; values must be evenly - spaced. If `None`, `U` must be given and `len(U)` time steps of - sys.dt are simulated. If sys.dt is `None` or `True` (undetermined + spaced. If None, `U` must be given and ``len(U)`` time steps of + sys.dt are simulated. If sys.dt is None or True (undetermined time step), a time step of 1.0 is assumed. U : array_like or float, optional - Input array giving input at each time `T`. If `U` is `None` or 0, + Input array giving input at each time `T`. If `U` is None or 0, `T` must be given, even for discrete time systems. In this case, for continuous time systems, a direct calculation of the matrix exponential is used, which is faster than the general interpolating @@ -931,11 +932,11 @@ def forced_response(sysdata, T=None, U=0., X0=0., transpose=False, params=None, If system is a nonlinear I/O system, set parameter values. transpose : bool, default=False - If `True`, transpose all input and output arrays (for backward + If True, transpose all input and output arrays (for backward compatibility with MATLAB and `scipy.signal.lsim`). interpolate : bool, default=False - If `True` and system is a discrete time system, the input will + If True and system is a discrete time system, the input will be interpolated between the given time steps and the output will be given at system sampling rate. Otherwise, only return the output at the times given in `T`. No effect on continuous @@ -944,22 +945,23 @@ def forced_response(sysdata, T=None, U=0., X0=0., transpose=False, params=None, return_x : bool, default=None Used if the time response data is assigned to a tuple: - * If `False`, return only the time and output vectors. + * If False, return only the time and output vectors. - * If `True`, also return the the state vector. + * If True, also return the the state vector. - * If `None`, determine the returned variables by - config.defaults['forced_response.return_x'], which was `True` - before version 0.9 and is `False` since then. + * If None, determine the returned variables by + `config.defaults['forced_response.return_x']`, which was True + before version 0.9 and is False since then. squeeze : bool, optional By default, if a system is single-input, single-output (SISO) then - the output response is returned as a 1D array (indexed by time). If - `squeeze` is `True`, remove single-dimensional entries from the shape of - the output even if the system is not SISO. If `squeeze` is `False`, keep - the output as a 2D array (indexed by the output number and time) - even if the system is SISO. The default behavior can be overridden by - config.defaults['control.squeeze_time_response']. + the output response is returned as a 1D array (indexed by time). + If `squeeze` is True, remove single-dimensional entries from + the shape of the output even if the system is not SISO. If + `squeeze` is False, keep the output as a 2D array (indexed by + the output number and time) even if the system is SISO. The default + behavior can be overridden by + `config.defaults['control.squeeze_time_response']`. Returns ------- @@ -971,8 +973,8 @@ def forced_response(sysdata, T=None, U=0., X0=0., transpose=False, params=None, * time (array): Time values of the output. * outputs (array): Response of the system. If the system is SISO and - `squeeze` is not `True`, the array is 1D (indexed by time). If the - system is not SISO or `squeeze` is `False`, the array is 2D (indexed + `squeeze` is not True, the array is 1D (indexed by time). If the + system is not SISO or `squeeze` is False, the array is 2D (indexed by output and time). * states (array): Time evolution of the state vector, represented as @@ -980,8 +982,9 @@ def forced_response(sysdata, T=None, U=0., X0=0., transpose=False, params=None, * inputs (array): Input(s) to the system, indexed by input and time. - The `plot()` method can be used to create a plot of the time - response(s) (see `time_response_plot` for more information). + The `~TimeResponseData.plot` method can be used to create a plot of + the time response(s) (see `time_response_plot` for more + information). See Also -------- @@ -993,7 +996,7 @@ def forced_response(sysdata, T=None, U=0., X0=0., transpose=False, params=None, using the `scipy.signal.dlsim` function. 2. For continuous time systems, the output is computed using the matrix - exponential `exp(A t)` and assuming linear interpolation of the + exponential ``exp(A t)`` and assuming linear interpolation of the inputs between time points. 3. If a nonlinear I/O system is passed to `forced_response`, the @@ -1004,7 +1007,7 @@ def forced_response(sysdata, T=None, U=0., X0=0., transpose=False, params=None, 4. (legacy) The return value of the system can also be accessed by assigning the function to a tuple of length 2 (time, output) or of - length 3 (time, output, state) if `return_x` is `True`. + length 3 (time, output, state) if `return_x` is True. Examples -------- @@ -1249,28 +1252,28 @@ def _process_time_response( forced response) or a 3D array indexed by output, input, and time. issiso : bool, optional - If `True`, process data as single-input, single-output data. - Default is `False`. + If True, process data as single-input, single-output data. + Default is False. transpose : bool, optional - If `True`, transpose data (for backward compatibility with MATLAB and + If True, transpose data (for backward compatibility with MATLAB and `scipy.signal.lsim`). Default value is False. squeeze : bool, optional - By default, if a system is single-input, single-output (SISO) then the - signals are returned as a 1D array (indexed by time). If - squeeze=`True`, remove single-dimensional entries from the shape of the - signal even if the system is not SISO. If squeeze=`False`, keep the - signal as a 3D array (indexed by the output, input, and time) even if - the system is SISO. The default value can be set using - config.defaults['control.squeeze_time_response']. + By default, if a system is single-input, single-output (SISO) then + the signals are returned as a 1D array (indexed by time). If + ``squeeze=True``, remove single-dimensional entries from the shape + of the signal even if the system is not SISO. If ``squeeze=False``, + keep the signal as a 3D array (indexed by the output, input, and + time) even if the system is SISO. The default value can be set + using `config.defaults['control.squeeze_time_response']`. Returns ------- output : ndarray - Processed signal. If the system is SISO and squeeze is not `True`, + Processed signal. If the system is SISO and squeeze is not True, the array is 1D (indexed by time). If the system is not SISO or - squeeze is `False`, the array is either 2D (indexed by output and + squeeze is False, the array is either 2D (indexed by output and time) or 3D (indexed by input, output, and time). """ @@ -1308,10 +1311,10 @@ def step_response( """Compute the step response for a linear system. If the system has multiple inputs and/or multiple outputs, the step - response is computed for each input/output pair, with all other inputs set - to zero. Optionally, a single input and/or single output can be selected, - in which case all other inputs are set to 0 and all other outputs are - ignored. + response is computed for each input/output pair, with all other inputs + set to zero. Optionally, a single input and/or single output can be + selected, in which case all other inputs are set to 0 and all other + outputs are ignored. For information on the **shape** of parameters `T`, `X0` and return values `T`, `yout`, see :ref:`time-series-convention`. @@ -1353,22 +1356,23 @@ def step_response( array (autocomputed if not given); ignored if sys is discrete-time. transpose : bool, optional - If `True`, transpose all input and output arrays (for backward + If True, transpose all input and output arrays (for backward compatibility with MATLAB and `scipy.signal.lsim`). Default value is False. return_x : bool, optional - If `True`, return the state vector when assigning to a tuple (default = - False). See `forced_response` for more details. + If True, return the state vector when assigning to a tuple + (default = False). See `forced_response` for more details. squeeze : bool, optional - By default, if a system is single-input, single-output (SISO) then the - output response is returned as a 1D array (indexed by time). If - squeeze=`True`, remove single-dimensional entries from the shape of the - output even if the system is not SISO. If squeeze=`False`, keep the - output as a 3D array (indexed by the output, input, and time) even if - the system is SISO. The default value can be set using - config.defaults['control.squeeze_time_response']. + By default, if a system is single-input, single-output (SISO) then + the output response is returned as a 1D array (indexed by time). + If ``squeeze=True``, remove single-dimensional entries from the + shape of the output even if the system is not SISO. If + ``squeeze=False``, keep the output as a 3D array (indexed by the + output, input, and time) even if the system is SISO. The default + value can be set using + `config.defaults['control.squeeze_time_response']`. Returns ------- @@ -1530,7 +1534,7 @@ def step_info(sysdata, T=None, T_num=None, yfinal=None, params=None, If `sysdata` corresponds to a MIMO system, `S` is a 2D list of dicts. To get the step response characteristics from the j-th input to the - i-th output, access `S[i][j]` + i-th output, access ``S[i][j]`` See Also @@ -1731,7 +1735,7 @@ def initial_response( output : int Index of the output that will be used in this simulation. Set - to `None` to not trim outputs. + to None to not trim outputs. T_num : int, optional Number of time steps to use in simulation if T is not provided as an @@ -1741,22 +1745,23 @@ def initial_response( If system is a nonlinear I/O system, set parameter values. transpose : bool, optional - If `True`, transpose all input and output arrays (for backward + If True, transpose all input and output arrays (for backward compatibility with MATLAB and `scipy.signal.lsim`). Default value is False. return_x : bool, optional - If `True`, return the state vector when assigning to a tuple (default = - False). See `forced_response` for more details. + If True, return the state vector when assigning to a tuple + (default = False). See `forced_response` for more details. squeeze : bool, optional - By default, if a system is single-input, single-output (SISO) then the - output response is returned as a 1D array (indexed by time). If - squeeze=`True`, remove single-dimensional entries from the shape of the - output even if the system is not SISO. If squeeze=`False`, keep the - output as a 2D array (indexed by the output number and time) even if - the system is SISO. The default value can be set using - config.defaults['control.squeeze_time_response']. + By default, if a system is single-input, single-output (SISO) then + the output response is returned as a 1D array (indexed by time). + If ``squeeze=True``, remove single-dimensional entries from the + shape of the output even if the system is not SISO. If + ``squeeze=False``, keep the output as a 2D array (indexed by the + output number and time) even if the system is SISO. The default + value can be set using + `config.defaults['control.squeeze_time_response']`. Returns ------- @@ -1825,10 +1830,10 @@ def impulse_response( """Compute the impulse response for a linear system. If the system has multiple inputs and/or multiple outputs, the impulse - response is computed for each input/output pair, with all other inputs set - to zero. Optionally, a single input and/or single output can be selected, - in which case all other inputs are set to 0 and all other outputs are - ignored. + response is computed for each input/output pair, with all other inputs + set to zero. Optionally, a single input and/or single output can be + selected, in which case all other inputs are set to 0 and all other + outputs are ignored. For information on the **shape** of parameters `T`, `X0` and return values `T`, `yout`, see :ref:`time-series-convention`. @@ -1856,22 +1861,23 @@ def impulse_response( array (autocomputed if not given); ignored if sys is discrete-time. transpose : bool, optional - If `True`, transpose all input and output arrays (for backward + If True, transpose all input and output arrays (for backward compatibility with MATLAB and `scipy.signal.lsim`). Default value is False. return_x : bool, optional - If `True`, return the state vector when assigning to a tuple - (default = `False`). See `forced_response` for more details. + If True, return the state vector when assigning to a tuple + (default = False). See `forced_response` for more details. squeeze : bool, optional - By default, if a system is single-input, single-output (SISO) then the - output response is returned as a 1D array (indexed by time). If - squeeze=`True`, remove single-dimensional entries from the shape of the - output even if the system is not SISO. If squeeze=`False`, keep the - output as a 2D array (indexed by the output number and time) even if - the system is SISO. The default value can be set using - config.defaults['control.squeeze_time_response']. + By default, if a system is single-input, single-output (SISO) then + the output response is returned as a 1D array (indexed by time). + If ``squeeze=True``, remove single-dimensional entries from the + shape of the output even if the system is not SISO. If + ``squeeze=False``, keep the output as a 2D array (indexed by the + output number and time) even if the system is SISO. The default + value can be set using + `config.defaults['control.squeeze_time_response']`. Returns ------- @@ -1998,9 +2004,9 @@ def impulse_response( # utility function to find time period and time increment using pole locations def _ideal_tfinal_and_dt(sys, is_step=True): - """helper function to compute ideal simulation duration tfinal and dt, the - time increment. Usually called by _default_time_vector, whose job it is to - choose a realistic time vector. Considers both poles and zeros. + """Helper function to compute ideal simulation duration tfinal and dt, + the time increment. Usually called by _default_time_vector, whose job + it is to choose a realistic time vector. Considers both poles and zeros. For discrete-time models, dt is inherent and only tfinal is computed. @@ -2012,7 +2018,7 @@ def _ideal_tfinal_and_dt(sys, is_step=True): Scales the dc value by the magnitude of the nonzero mode since integrating the impulse response gives :math:`\\int e^{-\\lambda t} = -e^{-\\lambda t}/ \\lambda` - Default is `True`. + Default is True. Returns ------- @@ -2030,14 +2036,14 @@ def _ideal_tfinal_and_dt(sys, is_step=True): and the simulation would be unnecessarily long and the plot is virtually an L shape since the decay is so fast. - Instead, a modal decomposition in time domain hence a truncated ZIR and ZSR - can be used such that only the modes that have significant effect on the - time response are taken. But the sensitivity of the eigenvalues complicate - the matter since dlambda = with = 1. Hence we can only work - with simple poles with this formulation. See Golub, Van Loan Section 7.2.2 - for simple eigenvalue sensitivity about the nonunity of . The size of - the response is dependent on the size of the eigenshapes rather than the - eigenvalues themselves. + Instead, a modal decomposition in time domain hence a truncated ZIR and + ZSR can be used such that only the modes that have significant effect + on the time response are taken. But the sensitivity of the eigenvalues + complicate the matter since dlambda = with = 1. Hence + we can only work with simple poles with this formulation. See Golub, + Van Loan Section 7.2.2 for simple eigenvalue sensitivity about the + nonunity of . The size of the response is dependent on the size of + the eigenshapes rather than the eigenvalues themselves. By Ilhan Polat, with modifications by Sawyer Fuller to integrate into python-control 2020.08.17 diff --git a/control/xferfcn.py b/control/xferfcn.py index 768ebd1f6..d8da0afd7 100644 --- a/control/xferfcn.py +++ b/control/xferfcn.py @@ -61,9 +61,9 @@ class TransferFunction(LTI): den : 2D list of coefficient arrays Polynomial coefficients of the denominator. dt : None, True or float, optional - System timebase. 0 (default) indicates continuous time, `True` + System timebase. 0 (default) indicates continuous time, True indicates discrete time with unspecified sampling time, positive - number is discrete time with specified sampling time, `None` indicates + number is discrete time with specified sampling time, None indicates unspecified timebase (either continuous or discrete time). Attributes @@ -85,7 +85,7 @@ class TransferFunction(LTI): display_format : None, 'poly' or 'zpk' Display format used in printing the TransferFunction object. Default behavior is polynomial display and can be changed by - changing config.defaults['xferfcn.display_format']. + changing `config.defaults['xferfcn.display_format']`. s : TransferFunction Represents the continuous time differential operator. z : TransferFunction @@ -126,14 +126,14 @@ class TransferFunction(LTI): * dt = True: discrete time with unspecified sampling period * dt = None: no timebase specified - Systems must have compatible timebases in order to be combined. A discrete - time system with unspecified sampling time (`dt = True`) can be combined - with a system having a specified sampling time; the result will be a - discrete time system with the sample time of the latter system. Similarly, - a system with timebase `None` can be combined with a system having any - timebase; the result will have the timebase of the latter system. - The default value of dt can be changed by changing the value of - `control.config.defaults['control.default_dt']`. + Systems must have compatible timebases in order to be combined. A + discrete time system with unspecified sampling time (``dt = True``) can + be combined with a system having a specified sampling time; the result + will be a discrete time system with the sample time of the latter + system. Similarly, a system with timebase None can be combined with a + system having any timebase; the result will have the timebase of the + latter system. The default value of dt can be changed by changing the + value of `control.config.defaults['control.default_dt']`. A transfer function is callable and returns the value of the transfer function evaluated at a point in the complex plane. See @@ -151,8 +151,8 @@ class TransferFunction(LTI): The TransferFunction class defines two constants `s` and `z` that represent the differentiation and delay operators in continuous and - discrete time. These can be used to create variables that allow algebraic - creation of transfer functions. For example, + discrete time. These can be used to create variables that allow + algebraic creation of transfer functions. For example, >>> s = ct.TransferFunction.s >>> G = (s + 1)/(s**2 + 2*s + 1) @@ -336,16 +336,16 @@ def den_list(self): def __call__(self, x, squeeze=None, warn_infinite=True): """Evaluate system's transfer function at complex frequencies. - Returns the complex frequency response `sys(x)` where `x` is `s` for - continuous-time systems and `z` for discrete-time systems. + Returns the complex frequency response ``sys(x)`` where `x` is `s` + for continuous-time systems and `z` for discrete-time systems. In general the system may be multiple input, multiple output - (MIMO), where `m = self.ninputs` number of inputs and `p = - self.noutputs` number of outputs. + (MIMO), where ``m = self.ninputs`` number of inputs and ``p = + self.noutputs`` number of outputs. To evaluate at a frequency omega in radians per second, enter - `x = omega * 1j`, for continuous-time systems, or - `x = exp(1j * omega * dt)` for discrete-time systems. Or use + ``x = omega * 1j``, for continuous-time systems, or + ``x = exp(1j * omega * dt)`` for discrete-time systems. Or use `TransferFunction.frequency_response`. Parameters @@ -353,26 +353,29 @@ def __call__(self, x, squeeze=None, warn_infinite=True): x : complex or complex 1D array_like Complex frequencies squeeze : bool, optional - If squeeze=`True`, remove single-dimensional entries from the shape - of the output even if the system is not SISO. If squeeze=`False`, - keep all indices (output, input and, if omega is array_like, - frequency) even if the system is SISO. The default value can be - set using config.defaults['control.squeeze_frequency_response']. - If `True` and the system is single-input single-output (SISO), - return a 1D array rather than a 3D array. Default value (`True`) - set by config.defaults['control.squeeze_frequency_response']. - warn_infinite : bool, optional - If set to `False`, turn off divide by zero warning. + If ``squeeze=True``, remove single-dimensional entries from the + shape of the output even if the system is not SISO. If + ``squeeze=False``, keep all indices (output, input and, if + omega is array_like, frequency) even if the system is SISO. The + default value can be set using + `config.defaults['control.squeeze_frequency_response']`. If + True and the system is single-input single-output (SISO), + return a 1D array rather than a 3D array. Default value + (True) set by + `config.defaults['control.squeeze_frequency_response']`. + warn_infinite : bool, optional If set to False, turn off + divide by zero warning. Returns ------- fresp : complex ndarray - The frequency response of the system. If the system is SISO and - squeeze is not `True`, the shape of the array matches the shape of - omega. If the system is not SISO or squeeze is `False`, the first - two dimensions of the array are indices for the output and input - and the remaining dimensions match omega. If `squeeze` is True - then single-dimensional axes are removed. + The frequency response of the system. If the system is SISO + and squeeze is not True, the shape of the array matches the + shape of omega. If the system is not SISO or squeeze is + False, the first two dimensions of the array are indices + for the output and input and the remaining dimensions match + omega. If `squeeze` is True then single-dimensional axes + are removed. """ out = self.horner(x, warn_infinite=warn_infinite) @@ -382,10 +385,10 @@ def horner(self, x, warn_infinite=True): """Evaluate system's transfer function at complex frequency using Horner's method. - Evaluates `sys(x)` where `x` is `s` for continuous-time systems and `z` - for discrete-time systems. + Evaluates ``sys(x)`` where `x` is `s` for continuous-time systems + and `z` for discrete-time systems. - Expects inputs and outputs to be formatted correctly. Use `sys(x)` + Expects inputs and outputs to be formatted correctly. Use ``sys(x)`` for a more user-friendly interface. """ @@ -1169,24 +1172,26 @@ def sample(self, Ts, method='zoh', alpha=None, prewarp_frequency=None, * 'matched': pole-zero match method * 'zoh': zero-order hold (default) alpha : float within [0, 1] - The generalized bilinear transformation weighting parameter, which - should only be specified with method="gbt", and is ignored - otherwise. See `scipy.signal.cont2discrete`. + The generalized bilinear transformation weighting parameter, + which should only be specified with ``method='gbt'``, and is + ignored otherwise. See `scipy.signal.cont2discrete`. prewarp_frequency : float within [0, infinity) - The frequency [rad/s] at which to match with the input continuous- - time system's magnitude and phase (the gain=1 crossover frequency, - for example). Should only be specified with method='bilinear' or - 'gbt' with alpha=0.5 and ignored otherwise. + The frequency [rad/s] at which to match with the input + continuous- time system's magnitude and phase (the gain=1 + crossover frequency, for example). Should only be specified + with `method` = 'bilinear' or 'gbt' with ``alpha=0.5`` and + ignored otherwise. name : string, optional - Set the name of the sampled system. If not specified and - if `copy_names` is `False`, a generic name is generated - with a unique integer id. If `copy_names` is `True`, the new system + Set the name of the sampled system. If not specified and if + `copy_names` is False, a generic name is generated with + a unique integer id. If `copy_names` is True, the new system name is determined by adding the prefix and suffix strings in - config.defaults['iosys.sampled_system_name_prefix'] and - config.defaults['iosys.sampled_system_name_suffix'], with the + `config.defaults['iosys.sampled_system_name_prefix']` and + `config.defaults['iosys.sampled_system_name_suffix']`, with the default being to add the suffix '$sampled'. + copy_names : bool, Optional - If `True`, copy the names of the input signals, output + If True, copy the names of the input signals, output signals, and states to the sampled system. Returns @@ -1197,9 +1202,9 @@ def sample(self, Ts, method='zoh', alpha=None, prewarp_frequency=None, Other Parameters ---------------- inputs : int, list of str or None, optional - Description of the system inputs. If not specified, the origional - system inputs are used. See `InputOutputSystem` for more - information. + Description of the system inputs. If not specified, the + original system inputs are used. See `InputOutputSystem` for + more information. outputs : int, list of str or None, optional Description of the system outputs. Same format as `inputs`. @@ -1254,17 +1259,17 @@ def dcgain(self, warn_infinite=False): ---------- warn_infinite : bool, optional By default, don't issue a warning message if the zero-frequency - gain is infinite. Setting `warn_infinite` to generate the warning - message. + gain is infinite. Setting `warn_infinite` to generate the + warning message. Returns ------- gain : (noutputs, ninputs) ndarray or scalar Array or scalar value for SISO systems, depending on - config.defaults['control.squeeze_frequency_response']. - The value of the array elements or the scalar is either the - zero-frequency (or DC) gain, or `inf`, if the frequency response - is singular. + `config.defaults['control.squeeze_frequency_response']`. The + value of the array elements or the scalar is either the + zero-frequency (or DC) gain, or ``inf``, if the frequency + response is singular. For real valued systems, the empty imaginary part of the complex zero-frequency response is discarded and a real array or @@ -1293,9 +1298,9 @@ def _isstatic(self): # Attributes for differentiation and delay # # These attributes are created here with sphinx docstrings so that the - # autodoc generated documentation has a description. The actual values of - # the class attributes are set at the bottom of the file to avoid problems - # with recursive calls. + # autodoc generated documentation has a description. The actual values + # of the class attributes are set at the bottom of the file to avoid + # problems with recursive calls. #: Differentation operator (continuous time). #: @@ -1504,8 +1509,8 @@ def _convert_to_transfer_function( sys = _convert_to_transfer_function([[1., 0.], [2., 3.]]) - will give a system with numerator matrix `[[[1.0], [0.0]], [[2.0], - [3.0]]]` and denominator matrix `[[[1.0], [1.0]], [[1.0], [1.0]]]`. + will give a system with numerator matrix ``[[[1.0], [0.0]], [[2.0], + [3.0]]]`` and denominator matrix ``[[[1.0], [1.0]], [[1.0], [1.0]]]``. """ from .statesp import StateSpace @@ -1586,11 +1591,11 @@ def tf(*args, **kwargs): The function accepts either 1, 2, or 3 parameters: - `tf(sys)` + ``tf(sys)`` Convert a linear system into transfer function form. Always creates a new system, even if sys is already a TransferFunction object. - `tf(num, den)` + ``tf(num, den)`` Create a transfer function system from its numerator and denominator polynomial coefficients. @@ -1602,16 +1607,16 @@ def tf(*args, **kwargs): for details see note below). If the denominator for all transfer function is the same, `den` can be specified as a 1D array. - `tf(num, den, dt)` + ``tf(num, den, dt)`` Create a discrete time transfer function system; dt can either be a positive number indicating the sampling time or 'True' if no specific timebase is given. - `tf([[G11, ..., G1m], ..., [Gp1, ..., Gpm]][, dt])` + ``tf([[G11, ..., G1m], ..., [Gp1, ..., Gpm]][, dt])`` Create a pxm MIMO system from SISO transfer functions Gij. See `combine_tf` for more details. - `tf('s')` or `tf('z')` + ``tf('s')`` or ``tf('z')`` Create a transfer function representing the differential operator ('s') or delay operator ('z'). @@ -1626,14 +1631,14 @@ def tf(*args, **kwargs): den : array_like, or list of list of array_like Polynomial coefficients of the denominator. dt : None, True or float, optional - System timebase. 0 (default) indicates continuous time, `True` + System timebase. 0 (default) indicates continuous time, True indicates discrete time with unspecified sampling time, positive - number is discrete time with specified sampling time, `None` indicates + number is discrete time with specified sampling time, None indicates unspecified timebase (either continuous or discrete time). display_format : None, 'poly' or 'zpk' Set the display format used in printing the TransferFunction object. Default behavior is polynomial display and can be changed by - changing config.defaults['xferfcn.display_format']. + changing `config.defaults['xferfcn.display_format']`. Returns ------- @@ -1666,13 +1671,13 @@ def tf(*args, **kwargs): Notes ----- MIMO transfer functions are created by passing a 2D array of coeffients: - `num[i][j]` contains the polynomial coefficients of the numerator + ``num[i][j]`` contains the polynomial coefficients of the numerator for the transfer function from the (j+1)st input to the (i+1)st output, - and `den[i][j]` works the same way. + and ``den[i][j]`` works the same way. - The list `[2, 3, 4]` denotes the polynomial :math:`2s^2 + 3s + 4`. + The list ``[2, 3, 4]`` denotes the polynomial :math:`2s^2 + 3s + 4`. - The special forms `tf('s')` and `tf('z')` can be used to create + The special forms ``tf('s')`` and ``tf('z')`` can be used to create transfer functions for differentiation and unit delays. Examples @@ -1774,14 +1779,14 @@ def zpk(zeros, poles, gain, *args, **kwargs): gain : float System gain. dt : None, True or float, optional - System timebase. 0 (default) indicates continuous time, `True` + System timebase. 0 (default) indicates continuous time, True indicates discrete time with unspecified sampling time, positive - number is discrete time with specified sampling time, `None` + number is discrete time with specified sampling time, None indicates unspecified timebase (either continuous or discrete time). inputs, outputs, states : str, or list of str, optional List of strings that name the individual signals. If this parameter - is not given or given as `None`, the signal names will be of the - form `s[i]` (where `s` is one of `u`, `y`, or `x`). See + is not given or given as None, the signal names will be of the + form 's[i]' (where 's' is one of 'u', 'y', or 'x'). See `InputOutputSystem` for more information. name : string, optional System name (used for specifying signals). If unspecified, a generic @@ -1789,7 +1794,7 @@ def zpk(zeros, poles, gain, *args, **kwargs): display_format : None, 'poly' or 'zpk', optional Set the display format used in printing the TransferFunction object. Default behavior is polynomial display and can be changed by - changing config.defaults['xferfcn.display_format']. + changing `config.defaults['xferfcn.display_format']`. Returns ------- @@ -1818,15 +1823,15 @@ def ss2tf(*args, **kwargs): The function accepts either 1 or 4 parameters: - `ss2tf(sys)` + ``ss2tf(sys)`` Convert a linear system from state space into transfer function form. Always creates a new system. - `ss2tf(A, B, C, D)` + ``ss2tf(A, B, C, D)`` Create a transfer function system from the matrices of its state and output equations. - For details see: `tf` + For details see: `tf`. Parameters ---------- @@ -1864,7 +1869,7 @@ def ss2tf(*args, **kwargs): if matrix sizes are not self-consistent, or if an invalid number of arguments is passed in TypeError - if `sys` is not a StateSpace object + if `sys` is not a StateSpace object. See Also -------- diff --git a/doc/conf.py b/doc/conf.py index 638f803fa..ab208abcf 100644 --- a/doc/conf.py +++ b/doc/conf.py @@ -139,8 +139,7 @@ # so a file named "default.css" will overwrite the builtin "default.css". html_static_path = ['_static'] -def setup(app): - app.add_css_file('css/custom.css') +html_css_files = ['css/custom.css'] # Custom sidebar templates, must be a dictionary that maps document names # to template names. diff --git a/doc/config.rst b/doc/config.rst index 20dde9834..a42350493 100644 --- a/doc/config.rst +++ b/doc/config.rst @@ -46,9 +46,9 @@ System creation parameters :value: 0 Default value of `dt` when constructing new I/O systems. If `dt` - is not specified explicitly this value will be used. Set to `None` + is not specified explicitly this value will be used. Set to None to leave the timebase unspecified, 0 for continuous time systems, - `True` for discrete time systems. + True for discrete time systems. .. py:data:: iosys.converted_system_name_prefix :type: str @@ -64,6 +64,9 @@ System creation parameters Suffix to add to system name when converting a system from one representation to another. + +.. _config.defaults['iosys.duplicate_system_name_prefix']: + .. py:data:: iosys.duplicate_system_name_prefix :type: str :value: '' @@ -155,7 +158,7 @@ System display parameters :type: bool :value: True - If `True`, show the input, output, and state count when using + If True, show the input, output, and state count when using `iosys_repr()` and the 'eval' format. Otherwise, the input, output, and state values are repressed from the output unless non-generic signal names are present. @@ -211,7 +214,7 @@ Response parameters Set the default value of the `squeeze` parameter for :func:`frequency_response` and :class:`FrequencyResponseData` - objects. If `None` then if a system is single-input, single-output + objects. If None then if a system is single-input, single-output (SISO) the outputs (and inputs) are returned as a 1D array (indexed by frequency), and if a system is multi-input or multi-output, then the outputs are returned as a 2D array (indexed by output and frequency) or @@ -244,7 +247,7 @@ Response parameters Determine whether :func:`forced_response` returns the values of the states when the :class:`TimeResponseData` object is evaluated. The - default value was `True` before version 0.9 and is `False` since then. + default value was True before version 0.9 and is False since then. Plotting parameters @@ -260,14 +263,14 @@ Plotting parameters :type: bool :value: False - If `True`, the magnitude in :func:`bode_plot` is plotted in dB + If True, the magnitude in :func:`bode_plot` is plotted in dB (otherwise powers of 10). .. py:data:: freqplot.deg :type: bool :value: True - If `True`, the phase in :func:`bode_plot` is plotted in degrees + If True, the phase in :func:`bode_plot` is plotted in degrees (otherwise radians). .. py:data:: freqplot.feature_periphery_decades @@ -294,7 +297,7 @@ Plotting parameters :type: bool :value: False - If `True`, use Hertz for frequency response plots (otherwise rad/sec). + If True, use Hertz for frequency response plots (otherwise rad/sec). .. py:data:: freqplot.number_of_samples :type: int @@ -309,7 +312,7 @@ Plotting parameters Determine whether and how axis limits are shared between the magnitude variables in :func:`bode_plot`. Can be set set to 'row' to share across all subplots in a row, 'col' to set across all subplots in a column, or - `False` to allow independent limits. + False to allow independent limits. .. py:data:: freqplot.share_phase :type: str @@ -318,7 +321,7 @@ Plotting parameters Determine whether and how axis limits are shared between the phase variables in :func:`bode_plot`. Can be set set to 'row' to share across all subplots in a row, 'col' to set across all subplots in a column, or - `False` to allow independent limits. + False to allow independent limits. .. py:data:: freqplot.share_frequency :type: str @@ -327,7 +330,7 @@ Plotting parameters Determine whether and how axis limits are shared between the frequency axes in :func:`bode_plot`. Can be set set to 'row' to share across all subplots in a row, 'col' to set across all subplots in a column, or - `False` to allow independent limits. + False to allow independent limits. .. py:data:: freqplot.title_frame :type: str @@ -342,8 +345,8 @@ Plotting parameters :type: bool :value: False - If wrap_phase is `False`, then the phase will be unwrapped so that it - is continuously increasing or decreasing. If wrap_phase is `True` the + If wrap_phase is False, then the phase will be unwrapped so that it + is continuously increasing or decreasing. If wrap_phase is True the phase will be restricted to the range [-180, 180) (or [:math:`-\pi`, :math:`\pi`) radians). If `wrap_phase` is specified as a float, the phase will be offset by 360 degrees if it falls below the specified @@ -353,7 +356,7 @@ Plotting parameters :type: bool :value: True - Set to `True` if :func:`nichols_plot` should include a Nichols-chart + Set to True if :func:`nichols_plot` should include a Nichols-chart grid. .. py:data:: nyquist.arrows @@ -435,7 +438,7 @@ Plotting parameters Linestyles for mirror image of the Nyquist curve in :func:`nyquist_plot`. The first element is used for unscaled portions of the Nyquist curve, the second element is used for portions that are - scaled (using max_curve_magnitude). If `False` then omit completely. + scaled (using max_curve_magnitude). If False then omit completely. .. py:data:: nyquist.primary_style :type: list of str @@ -478,7 +481,7 @@ Plotting parameters :value: None Set the default style for arrows in :func:`phase_plane_plot` and - :func:`phaseplot.streamlines`. If set to `None`, defaults to + :func:`phaseplot.streamlines`. If set to None, defaults to .. code:: @@ -516,7 +519,7 @@ Plotting parameters :type: bool :value: False - If `True` plot omega-damping grid in :func:`pole_zero_plot`. If `False` + If True plot omega-damping grid in :func:`pole_zero_plot`. If False or None show imaginary axis for continuous time systems, unit circle for discrete time systems. If `empty`, do not draw any additonal lines. @@ -541,7 +544,7 @@ Plotting parameters :type: bool :value: True - If `True` plot omega-damping grid in :func:`root_locus_plot`. If `False` + If True plot omega-damping grid in :func:`root_locus_plot`. If False or None show imaginary axis for continuous time systems, unit circle for discrete time systems. If `empty`, do not draw any additonal lines. @@ -582,7 +585,7 @@ Plotting parameters Determine whether and how x-axis limits are shared between subplots in :func:`time_response_plot`. Can be set set to 'row' to share across all subplots in a row, 'col' to set across all subplots in a column, 'all' - to share across all subplots, or `False` to allow independent limits. + to share across all subplots, or False to allow independent limits. .. py:data:: timeplot.sharey :type: bool @@ -591,7 +594,7 @@ Plotting parameters Determine whether and how y-axis limits are shared between subplots in :func:`time_response_plot`. Can be set set to 'row' to share across all subplots in a row, 'col' to set across all subplots in a column, 'all' - to share across all subplots, or `False` to allow independent limits. + to share across all subplots, or False to allow independent limits. .. py:data:: timeplot.time_label :type: str diff --git a/doc/descfcn.rst b/doc/descfcn.rst index b08267974..e7b64530d 100644 --- a/doc/descfcn.rst +++ b/doc/descfcn.rst @@ -54,7 +54,7 @@ amplitude and frequency of any points of intersection:: response.intersections # frequency, amplitude pairs A Nyquist plot showing the describing function and the intersections -with the Nyquist curve can be generated using `response.plot()`, which +with the Nyquist curve can be generated using ``response.plot()``, which calls the :func:`describing_function_plot` function. diff --git a/doc/develop.rst b/doc/develop.rst index d20464491..06dc5a2ae 100644 --- a/doc/develop.rst +++ b/doc/develop.rst @@ -180,8 +180,8 @@ System-creating commands: - `dt`: set the timebase. This one takes a bit of care, since if it is not specified then it defaults to - `config.defaults['control.default_dt']`. This is different than - setting `dt=None`, so you `dt` should always be part of **kwargs. + ``config.defaults['control.default_dt']``. This is different than + setting ``dt=None``, so `dt` should always be part of `**kwargs`. These keywords can be parsed in a consistent way using the `iosys._process_iosys_keywords` function. @@ -227,14 +227,99 @@ conventions are used throughout the package: General docstring info ---------------------- -* Use single backticks around all Python objects. Double backticks - should never be used. (The `doc/conf.py` file defines - `default_role` to be `py:obj`, so everything in a single backtick - will be rendered in code form and linked to the appropriate - documentation if it exists.) +The guiding principle used to guide how docstrings are written is +similar to NumPy (as articuated in the `numpydoc style guide +`_: -* All function names, parameter names, and Python objects (`True`, - `False`, `None`) should be written as code (enclose in backticks). + A guiding principle is that human readers of the text are given + precedence over contorting docstrings so our tools produce nice + output. Rather than sacrificing the readability of the docstrings, + we have written pre-processors to assist Sphinx in its task. + +To that end, docstrings should use the following guidelines: + +* Use single backticks around all Python objects. The Sphinx + configuration file (`doc/conf.py`) defines `default_role` to be + `py:obj`, so everything in a single backtick will be rendered in + code form and linked to the appropriate documentation if it exists. + + - Note: consistent with numpydoc recommendations, parameters names + for functions should be in single backticks, even though they + don't generate a link (but the font will still be OK). + + - The `doc/_static/custom.css` file defines the style for Python + objects and is configured so that linked objects will appear in a + bolder type, so that it is easier to see what things you can click + on to get more information. + +* Use double backticks for inline code, such as a Python statement, + and formulas that could potentially be computed using Python (\`\`dt + > 0\`\` which reders as ``dt > 0``). Keyword variable assignments + that appear as part of documentation (``squeeze=True``) should also + be written as code. + + - In principle single backticks might actually work OK given the way + that the `py:obj` processing works in Sphinx, but the inclusion of + code is somewhat rare and the extra two backticks seem like a + small sacrifice (and far from a "contortion"). + +* Built-in objects (True, False, None) should be written with no + backticks and should be properly capitalized. + + - This guideline combined with the previous one imples that font + choices can look slight different depending on how you word + things. For exmaple, you can say `squeeze` is True or ``squeeze=True``. + + - Another possibility here is to use a single backtick around + built-in objects, and the `py:obj` processing will then generate a + link back to the primary Python documentation. That seems + distracting for built-ins like `True`, `False` and `None` (written + here in single backticks) and using double backticks looks fine in + Sphinx (``True``, ``False``, ``None``), but seemed to cross the + "contortions" threshold. + +* Strings used as arguments to parameters should be in single + (forward) ticks ('eval', 'rows', etc) and don't need to be rendered + as code if just listed as part of a docstring. + + - The rationale here is similar to built-ins: adding 4 backticks + just to get them in a code font seems unnecessary. + + - Note that if a string is is included in Python assignment + statement (e.g., ``method='slycot'``) it should be enclosed in + double backticks (\`\`method='slycot'\`\`). + +* References to the `defaults` dictionary should be of the form + \`config.defaults['module.param']\` (like a parameter), which + renders as `config.defaults['module.param']` in Sphinx. + + - It would be nice to have the term show up as a link to the + documentation for that parameter (in the + :ref:`package-configuration-parameters` section of the Reference + Manual), but the special processing to do that hasn't been + implemented. If we go that route at some point, we could perhaps + due a global change to single backticks. + + - Depending on placement, you can end up with lots of white space + around defaults parameters (also true in the docstrings). + +* Math formulas can be written as plain text unless the require + special symbols (this is consistent with numpydoc) or include Python + code. Use the ``:math:`` directive to handle symbols. + +Examples of different styles: + +* Single backticks to a a function: `interconnect` + +* Single backticks to a parameter (no link): `squeeze` + +* Double backticks to a code snippet: ``squeeze=True`` + +* Built-in Python objects: True, False, None + +* Defaults parameter: `config.defaults['control.squeeze_time_response']` + +* Inline math: :math:`\eta = m \xi + \beta` Function docstrings @@ -245,10 +330,10 @@ Follow numpydoc format with the following additional details: * All functions should have a short (< 64 character) summary line that starts with a capital letter and ends with a period. -* All parameter descriptions should start with a capital letter and end - with a period. An exception is parameters that have a list of - possible values, in which case a phrase sending in `:` followed by a - list (without punctuation) is OK. +* All parameter descriptions should start with a capital letter and + end with a period. An exception is parameters that have a list of + possible values, in which case a phrase sending in a colon (:) + followed by a list (without punctuation) is OK. * All parameters and keywords must be documented. The `docstrings_test.py` unit test tries to flag as many of these as diff --git a/doc/examples.rst b/doc/examples.rst index 3c24cf16e..0a66b6e4e 100644 --- a/doc/examples.rst +++ b/doc/examples.rst @@ -6,9 +6,10 @@ Examples ******** -The source code for the examples below are available in the `examples/` -subdirectory of the source code distribution. They can also be accessed online -via the `python-control GitHub repository `_. +The source code for the examples below are available in the +`examples/` subdirectory of the source code distribution. They can +also be accessed online via the `python-control GitHub repository +`_. Python scripts diff --git a/doc/flatsys.rst b/doc/flatsys.rst index 3caabc29b..41f9870f3 100644 --- a/doc/flatsys.rst +++ b/doc/flatsys.rst @@ -114,15 +114,15 @@ This equation is a *linear* equation of the form M c = \begin{bmatrix} \bar z(0) \\ \bar z(T) \end{bmatrix} where :math:`\bar z` is called the *flat flag* for the system. -Assuming that :math:`M` has a sufficient number of columns and that it is full -column rank, we can solve for a (possibly non-unique) :math:`\alpha` that -solves the trajectory generation problem. +Assuming that :math:`M` has a sufficient number of columns and that it +is full column rank, we can solve for a (possibly non-unique) +:math:`\alpha` that solves the trajectory generation problem. Module usage ------------ To create a trajectory for a differentially flat system, a -:class:`~flatsys.FlatSystem` object must be created. This is done +:class:`flatsys.FlatSystem` object must be created. This is done using the :func:`~flatsys.flatsys` function:: import control.flatsys as fs @@ -145,7 +145,7 @@ and input given the flat flag:: The flag :math:`\bar z` is implemented as a list of flat outputs :math:`z_i` and their derivatives up to order :math:`q_i`: - `zflag[i][j]` = :math:`z_i^{(j)}` + ``zflag[i][j]`` = :math:`z_i^{(j)}` The number of flat outputs must match the number of system inputs. @@ -270,11 +270,11 @@ derived in *Feedback Systems* by Astrom and Murray, Example 3.11. vehicle_flat_forward, vehicle_flat_reverse, inputs=('v', 'delta'), outputs=('x', 'y'), states=('x', 'y', 'theta')) -To find a trajectory from an initial state :math:`x_0` to a final state -:math:`x_\text{f}` in time :math:`T_\text{f}` we solve a point-to-point -trajectory generation problem. We also set the initial and final inputs, which -sets the vehicle velocity :math:`v` and steering wheel angle :math:`\delta` at -the endpoints. +To find a trajectory from an initial state :math:`x_0` to a final +state :math:`x_\text{f}` in time :math:`T_\text{f}` we solve a +point-to-point trajectory generation problem. We also set the initial +and final inputs, which sets the vehicle velocity :math:`v` and +steering wheel angle :math:`\delta` at the endpoints. .. code-block:: python diff --git a/doc/index.rst b/doc/index.rst index bea410060..4e36461e3 100644 --- a/doc/index.rst +++ b/doc/index.rst @@ -2,8 +2,9 @@ Python Control Systems Library ############################## -The Python Control Systems Library (`python-control`) is a Python package that -implements basic operations for analysis and design of feedback control systems. +The Python Control Systems Library (`python-control`) is a Python +package that implements basic operations for analysis and design of +feedback control systems. .. rubric:: Features @@ -87,8 +88,9 @@ or to test the installed package:: .. _pytest: https://docs.pytest.org/ -Your contributions are welcome! Simply fork the `GitHub repository `_ and send a -`pull request`_. +Your contributions are welcome! Simply fork the `GitHub repository +`_ and send a `pull +request`_. .. _pull request: https://github.com/python-control/python-control/pulls diff --git a/doc/intro.rst b/doc/intro.rst index 6db3563c3..289346218 100644 --- a/doc/intro.rst +++ b/doc/intro.rst @@ -59,7 +59,7 @@ correctly by running the command:: python -c "import slycot" -and verifying that no error message appears. More information on the +and verifying that no error message appears. More information on the Slycot package can be obtained from the `Slycot project page `_. @@ -108,8 +108,8 @@ The python-control package makes use of a few naming and calling conventions: systems and multi-input, multi-output (MIMO) systems, including time and frequency responses. By default, SISO systems will typically generate objects that have the input and output dimensions - suppressed (using the NumPy :func:`~numpy.squeeze` function). The - `squeeze` keyword can be set to `False` to force functions to return + suppressed (using the NumPy :func:`numpy.squeeze` function). The + `squeeze` keyword can be set to False to force functions to return objects that include the input and output dimensions. @@ -134,7 +134,7 @@ some things to keep in mind: [1, 2, 3] and matrices are written using 2D nested lists, e.g., [[1, 2], [3, 4]]. * Functions that in MATLAB would return variable numbers of values - will have a parameter of the form `return_` that is used to + will have a parameter of the form ``return_`` that is used to return additional data. (These functions usually return an object of a class that has attributes that can be used to access the information and this is the preferred usage pattern.) diff --git a/doc/iosys.rst b/doc/iosys.rst index b4fac9ddb..bc1322a28 100644 --- a/doc/iosys.rst +++ b/doc/iosys.rst @@ -25,20 +25,20 @@ The following operators are defined to operate between I/O systems: * - Operation - Description - Equivalent command - * - `sys1 + sys2` + * - ``sys1 + sys2`` - Add the outputs of two systems receiving the same input - - `parallel(sys1, sys2)` - * - `sys1 * sys2` + - ``parallel(sys1, sys2)`` + * - ``sys1 * sys2`` - Connect output(s) of sys2 to input(s) of sys1 - - `series(sys2, sys1)` - * - `-sys` + - ``series(sys2, sys1)`` + * - ``-sys`` - Multiply the output(s) of the system by -1 - - `negate(sys)` - * - `tf1 / tf2` + - ``negate(sys)`` + * - ``tf1 / tf2`` - Divide one SISO transfer function by another - N/A - * - `tf**n` - - Multiply a transfer function by itself `n` times + * - ``tf**n`` + - Multiply a transfer function by itself ``n`` times - N/A If either of the systems is a number or an array of appropriate @@ -178,8 +178,8 @@ We now create an input/output system using these dynamics: Note that since we have not specified an output function, the entire state will be used as the output of the system. -The `io_predprey` system can now be simulated to obtain the open loop dynamics -of the system: +The `io_predprey` system can now be simulated to obtain the open loop +dynamics of the system: .. code-block:: python @@ -336,8 +336,8 @@ we can use the list processing feature:: H = clsys.linearize([x0, 0], 0) Note that this also utilizes the zero-padding functionality, since the -second argument in the list `[x0, 0]` is a scalar and so the vector -`[x0, 0]` only has three elements instead of the required four. +second argument in the list ``[x0, 0]`` is a scalar and so the vector +``[x0, 0]`` only has three elements instead of the required four. To run an input/output simulation with a sinusoidal signal for the first input, a constant for the second input, and no external disturbance, we can @@ -364,29 +364,30 @@ use the command sumblk = ct.summing_junction(3) -By default, the name of the inputs will be of the form `u[i]` and the output -will be `y`. This can be changed by giving an explicit list of names:: +By default, the name of the inputs will be of the form 'u[i]' and the output +will be 'y'. This can be changed by giving an explicit list of names:: sumblk = ct.summing_junction(inputs=['a', 'b', 'c'], output='d') -A more typical usage would be to define an input/output system that compares a -reference signal to the output of the process and computes the error:: +A more typical usage would be to define an input/output system that +compares a reference signal to the output of the process and computes +the error:: sumblk = ct.summing_junction(inputs=['r', '-y'], output='e') -Note the use of the minus sign as a means of setting the sign of the input 'y' -to be negative instead of positive. +Note the use of the minus sign as a means of setting the sign of the +input 'y' to be negative instead of positive. It is also possible to define "vector" summing blocks that take -multi-dimensional inputs and produce a multi-dimensional output. For example, -the command +multi-dimensional inputs and produce a multi-dimensional output. For +example, the command .. code-block:: python sumblk = ct.summing_junction(inputs=['r', '-y'], output='e', dimension=2) -will produce an input/output block that implements `e[0] = r[0] - y[0]` and -`e[1] = r[1] - y[1]`. +will produce an input/output block that implements ``e[0] = r[0] - y[0]`` and +``e[1] = r[1] - y[1]``. Automatic connections using signal names @@ -439,7 +440,7 @@ parameters:: For tuple forms, mixed specifications using integer indices and strings are possible. -For the index range form `sysname.signal[i:j]`, if either `i` or `j` +For the index range form ``sysname.signal[i:j]``, if either `i` or `j` is not specified, then it defaults to the minimum or maximum value of the signal range. Note that despite the similarity to slice notation, negative indices and step specifications are not supported. @@ -455,21 +456,21 @@ dimension 2:: outputs=['y[0]', 'y[1]', 'z[0]', 'z[1]']) Suppose we construct a controller with 2 inputs and 2 outputs that -takes the (2-dimensional) error `e` and outputs and control signal `u`:: +takes the (2-dimensional) error 'e' and outputs and control signal 'u':: C = ct.rss(4, 2, 2, name='C', input_prefix='e', output_prefix='u') Finally, we include a summing block that will take the difference between -the reference input `r` and the measured output `y`:: +the reference input 'r' and the measured output 'y':: sumblk = ct.summing_junction( inputs=['r', '-y'], outputs='e', dimension=2, name='sum') The closed loop system should close the loop around the process -outputs `y` and inputs `u`, leaving the process inputs `v` and outputs -'w', as well as the reference input `r`. We would like the output of -the closed loop system to consist of all system outputs `y` and `z`, -as well as the controller input `u`. +outputs 'y' and inputs 'u', leaving the process inputs 'v' and outputs +'w', as well as the reference input 'r'. We would like the output of +the closed loop system to consist of all system outputs 'y' and 'z', +as well as the controller input 'u'. This collection of systems can be combined in a variety of ways. The most explicit would specify every signal:: @@ -526,7 +527,7 @@ work:: Various other simplifications are possible, but it can sometimes be complicated to debug error message when things go wrong. Setting -`debug=True` when calling :func:`interconnect` prints out +``debug=True`` when calling :func:`interconnect` prints out information about how the arguments are processed that may be helpful in understanding what is going wrong. @@ -607,8 +608,8 @@ and the control action will be given by u = u_\text{d} - K\text{p} (x - x_\text{d}) - K_\text{i} \int C (x - x_\text{d}) dt. -If `integral_action` is a function `h`, that function will be called -with the signature `h(t, x, u, params)` to obtain the outputs that +If `integral_action` is a function ``h``, that function will be called +with the signature ``h(t, x, u, params)`` to obtain the outputs that should be integrated. The number of outputs that are to be integrated must match the number of additional columns in the `K` matrix. If an estimator is specified, :math:`\hat x` will be used in place of diff --git a/doc/linear.rst b/doc/linear.rst index fac13e8bb..7efae985a 100644 --- a/doc/linear.rst +++ b/doc/linear.rst @@ -120,7 +120,7 @@ calling the transfer function object:: val = sys(s) Discrete time transfer functions (described in more detail below) can -be created using `z = ct.tf('z')`. +be created using ``z = ct.tf('z')``. Frequency response data (FRD) systems @@ -199,8 +199,8 @@ names:: Signal names for an indexed subsystem are preserved from the original system and the subsystem name is set according to the values of -`config.defaults['iosys.indexed_system_name_prefix']` and -`config.defaults['iosys.indexed_system_name_suffix']` (see +``config.defaults['iosys.indexed_system_name_prefix']`` and +``config.defaults['iosys.indexed_system_name_suffix']`` (see :ref:`package-configuration-parameters` for more information). The default subsystem name is the original system name with '$indexed' appended. @@ -216,8 +216,8 @@ response. .. note:: If a system is single-input, single-output (SISO), `magnitude` and `phase` default to 1D arrays, indexed by frequency. If the system is not SISO or `squeeze` is set to - `False`, the array is 3D, indexed by the output, input, and - frequency. If `squeeze` is `True` for a MIMO system then + False, the array is 3D, indexed by the output, input, and + frequency. If `squeeze` is True for a MIMO system then single-dimensional axes are removed. The processing of the `squeeze` keyword can be changed by calling the response function with a new argument:: @@ -240,22 +240,24 @@ A discrete time system is created by specifying a nonzero "timebase", `dt`. The timebase argument can be given when a system is constructed: -* `dt = 0`: continuous time system (default) -* `dt > 0`: discrete time system with sampling period 'dt' -* `dt = True`: discrete time with unspecified sampling period -* `dt = None`: no timebase specified - -Systems must have compatible timebases in order to be combined. A discrete -time system with unspecified sampling time (`dt = True`) can be combined with -a system having a specified sampling time; the result will be a discrete time -system with the sample time of the latter system. Similarly, a system with -timebase `None` can be combined with a system having a specified timebase; the -result will have the timebase of the latter system. For continuous time -systems, the :func:`sample_system` function or the :meth:`StateSpace.sample` -and :meth:`TransferFunction.sample` methods can be used to create a discrete -time system from a continuous time system. See -:ref:`utility-and-conversions`. The default value of `dt` can be changed by -changing the value of `config.defaults['control.default_dt']`. +* ``dt = 0``: continuous time system (default) +* ``dt > 0``: discrete time system with sampling period 'dt' +* ``dt = True``: discrete time with unspecified sampling period +* ``dt = None``: no timebase specified + +Systems must have compatible timebases in order to be combined. A +discrete time system with unspecified sampling time (``dt = True``) +can be combined with a system having a specified sampling time; the +result will be a discrete time system with the sample time of the +latter system. Similarly, a system with timebase None can be combined +with a system having a specified timebase; the result will have the +timebase of the latter system. For continuous time systems, the +:func:`sample_system` function or the :meth:`StateSpace.sample` and +:meth:`TransferFunction.sample` methods can be used to create a +discrete time system from a continuous time system. See +:ref:`utility-and-conversions`. The default value of `dt` can be +changed by changing the value of +``config.defaults['control.default_dt']``. Functions operating on LTI systems will take into account whether a system is continuous time or discrete time when carrying out operations @@ -440,7 +442,7 @@ Displaying LTI system information ================================= Information about an LTI system can be obtained using the Python -`print()` function:: +`~python.print` function:: >>> sys = ct.rss(4, 2, 2, name='sys_2x2') >>> print(sys) diff --git a/doc/nlsys.rst b/doc/nlsys.rst index 228e0110a..9d8599eeb 100644 --- a/doc/nlsys.rst +++ b/doc/nlsys.rst @@ -94,7 +94,7 @@ The dynamics of this system can be modeling using the following code:: outputs=['y', 'thdot'], inputs=['tau']) A summary of the model can be obtained using the string representation -of the model (via the Python `print()` function):: +of the model (via the Python `~python.print` function):: >>> print(servomech) : servomech diff --git a/doc/optimal.rst b/doc/optimal.rst index 1fb15436f..eecd40b4b 100644 --- a/doc/optimal.rst +++ b/doc/optimal.rst @@ -129,11 +129,11 @@ The `sys` parameter should be an :class:`InputOutputSystem` and the `timepts` parameter should represent a time vector that gives the list of times at which the cost and constraints should be evaluated. -The `cost` function has call signature `cost(t, x, u)` and should return the -(incremental) cost at the given time, state, and input. It will be -evaluated at each point in the `timepts` vector. The `terminal_cost` -parameter can be used to specify a cost function for the final point in the -trajectory. +The `cost` function has call signature ``cost(t, x, u)`` and should +return the (incremental) cost at the given time, state, and input. It +will be evaluated at each point in the `timepts` vector. The +`terminal_cost` parameter can be used to specify a cost function for +the final point in the trajectory. The `constraints` parameter is a list of constraints similar to that used by the :func:`scipy.optimize.minimize` function. Each constraint is specified @@ -165,9 +165,9 @@ trajectory. The return value for :func:`optimal.solve_ocp` is a bundle object that has the following elements: - * `res.success`: `True` if the optimization was successfully solved + * `res.success`: True if the optimization was successfully solved * `res.inputs`: optimal input - * `res.states`: state trajectory (if `return_x` was `True`) + * `res.states`: state trajectory (if `return_x` was True) * `res.time`: copy of the time timepts vector In addition, the results from :func:`scipy.optimize.minimize` are also diff --git a/doc/phaseplot.rst b/doc/phaseplot.rst index 862469871..27a4f4856 100644 --- a/doc/phaseplot.rst +++ b/doc/phaseplot.rst @@ -89,7 +89,7 @@ The :func:`phase_plane_plot` function calls these helper functions based on the options it is passed. Note that unlike other plotting functions, phase plane plots do not involve -computing a response and then plotting the result via a `plot()` method. +computing a response and then plotting the result via a ``plot()`` method. Instead, the plot is generated directly be a call to the :func:`phase_plane_plot` function (or one of the :mod:`phaseplot` helper functions. diff --git a/doc/response.rst b/doc/response.rst index 2529c9266..a944f0bbc 100644 --- a/doc/response.rst +++ b/doc/response.rst @@ -30,10 +30,10 @@ resulting in the following standard pattern:: Plotting commands return a :class:`ControlPlot` object that provides access to the individual lines in the generated plot using -`cplt.lines`, allowing various aspects of the plot to be modified to +``cplt.lines``, allowing various aspects of the plot to be modified to suit specific needs. -The plotting function is also available via the `plot()` method of the +The plotting function is also available via the ``plot()`` method of the analysis object, allowing the following type of calls:: step_response(sys).plot() @@ -77,7 +77,7 @@ the system. If a list of systems is passed, a common time vector will be computed and a list of responses will be returned in the form of a :class:`TimeResponseList` object. The :func:`forced_response` function can also take a list of systems, to which a single common input is applied. -The :class:`TimeResponseList` object has a `plot()` method that will plot +The :class:`TimeResponseList` object has a ``plot()`` method that will plot each of the responses in turn, using a sequence of different colors with appropriate titles and legends. @@ -113,8 +113,8 @@ The time vector is a 1D array with shape (n, ):: T = [t1, t2, t3, ..., tn ] -Input, state, and output all follow the same convention. Columns are different -points in time, rows are different components:: +Input, state, and output all follow the same convention. Columns are +different points in time, rows are different components:: U = [[u1(t1), u1(t2), u1(t3), ..., u1(tn)] [u2(t1), u2(t2), u2(t3), ..., u2(tn)] @@ -122,9 +122,9 @@ points in time, rows are different components:: ... [ui(t1), ui(t2), ui(t3), ..., ui(tn)]] -(and similarly for `X`, `Y`). So, `U[:, 2]` is the system's input at the -third point in time; and `U[1]` or `U[1, :]` is the sequence of values for -the system's second input. +(and similarly for `X`, `Y`). So, ``U[:, 2]`` is the system's input +at the third point in time; and ``U[1]`` or ``U[1, :]`` is the +sequence of values for the system's second input. When there is only one row, a 1D object is accepted or returned, which adds convenience for SISO systems: @@ -188,12 +188,13 @@ response, can be computed like this:: ft = D @ U -Finally, the `to_pandas()` function can be used to create a pandas dataframe:: +Finally, the `~TimeResponseData.to_pandas` method can be used to create +a pandas dataframe:: df = response.to_pandas() The column labels for the data frame are `time` and the labels for the input, -output, and state signals (`u[i]`, `y[i]`, and `x[i]` by default, but these +output, and state signals ('u[i]', 'y[i]', and 'x[i]' by default, but these can be changed using the `inputs`, `outputs`, and `states` keywords when constructing the system, as described in :func:`ss`, :func:`tf`, and other system creation function. Note that when exporting to pandas, "rows" in the @@ -234,8 +235,8 @@ or the `overlay_traces` keyword, which puts different traces (e.g., corresponding to step inputs in different channels) on the same graph, with appropriate labeling via a legend on selected axes. -For example, using `plot_input=True` and `overlay_signals=True` yields the -following plot:: +For example, using ``plot_input=True`` and ``overlay_signals=True`` +yields the following plot:: ct.step_response(sys_mimo).plot( plot_inputs=True, overlay_signals=True, @@ -249,7 +250,7 @@ Input/output response plots created with either the :func:`input_output_response` functions include the input signals by default. These can be plotted on separate axes, but also "overlaid" on the output axes (useful when the input and output signals are being compared to -each other). The following plot shows the use of `plot_inputs='overlay'` +each other). The following plot shows the use of ``plot_inputs='overlay'`` as well as the ability to reposition the legends using the `legend_map` keyword:: @@ -355,7 +356,7 @@ function:: Different types of plots can also be specified for a given frequency response. For example, to plot the frequency response using a a Nichols -plot, use `plot_type='nichols'`:: +plot, use ``plot_type='nichols'``:: response.plot(plot_type='nichols') @@ -416,7 +417,7 @@ array of frequencies as a second argument (after the list of systems):: .. image:: figures/freqplot-siso_bode-omega.png Alternatively, frequency ranges can be specified by passing a list of the -form `[wmin, wmax]`, where `wmin` and `wmax` are the minimum and +form ``[wmin, wmax]``, where `wmin` and `wmax` are the minimum and maximum frequencies in the (log-spaced) frequency range:: response = ct.frequency_response([sys1, sys2], [1e-2, 1e2]) @@ -461,9 +462,9 @@ as a function of the loop gain:: The grid in the left hand plane shows lines of constant damping ratio as well as arcs corresponding to the frequency of the complex pole. The grid -can be turned off using the `grid` keyword. Setting `grid` to `False` will +can be turned off using the `grid` keyword. Setting `grid` to False will turn off the grid but show the real and imaginary axis. To completely -remove all lines except the root loci, use `grid='empty'`. +remove all lines except the root loci, use ``grid='empty'``. On systems that support interactive plots, clicking on a location on the root locus diagram will mark the pole locations on all branches of the @@ -523,7 +524,7 @@ various ways. The following general rules apply: The :func:`bode_plot`, :func:`time_response_plot`, and selected other commands can also accept a matplotlib format - string (e.g., `'r--'`). The format string must appear as a positional + string (e.g., 'r--'). The format string must appear as a positional argument right after the required data argument. Note that line property arguments are the same for all lines generated as @@ -548,9 +549,9 @@ various ways. The following general rules apply: For non-input/output plots (e.g., Nyquist plots, pole/zero plots, root locus plots), the default labels are the system name. - If `label` is set to `False`, individual lines are still given + If `label` is set to False, individual lines are still given labels, but no legend is generated in the plot. (This can also be - accomplished by setting `legend_map` to `False`). + accomplished by setting `legend_map` to False). Note: the `label` keyword argument is not implemented for describing function plots or phase plane plots, since these plots are primarily @@ -562,20 +563,21 @@ various ways. The following general rules apply: can be used to customize the locations for legends. By default, a minimal number of legends are used such that lines can be uniquely identified and no legend is generated if there is only one line in the - plot. Setting `show_legend` to `False` will suppress the legend and - setting it to `True` will force the legend to be displayed even if + plot. Setting `show_legend` to False will suppress the legend and + setting it to True will force the legend to be displayed even if there is only a single line in each axes. In addition, if the value of the `legend_loc` keyword argument is set to a string or integer, it will set the position of the legend as described in the :func:`matplotlib.legend` documentation. Finally, `legend_map` can be set to an array that matches the shape of the subplots, with each item being a string indicating the location of the legend for that axes (or - `None` for no legend). + None for no legend). * The `rcParams` keyword argument can be used to override the default matplotlib style parameters used when creating a plot. The default - parameters for all control plots are given by the `ct.rcParams` - dictionary and have the following values: + parameters for all control plots are given by the + `config.defaults['rcParams'` dictionary and have the following + values: .. list-table:: :widths: 50 50 @@ -597,8 +599,9 @@ various ways. The following general rules apply: - 'small' Only those values that should be changed from the default need to be - specified in the `rcParams` keyword argument. To override the defaults - for all control plots, update the `ct.rcParams` dictionary entries. + specified in the `rcParams` keyword argument. To override the + defaults for all control plots, update the + `config.defaults['rcParams']` dictionary entries. The default values for style parameters for control plots can be restored using :func:`reset_rcParams`. @@ -608,7 +611,7 @@ various ways. The following general rules apply: and how axis limits are shared between the individual subplots. Setting the keyword to 'row' will share the axes limits across all subplots in a row, 'col' will share across all subplots in a column, 'all' will share - across all subplots in the figure, and `False` will allow independent + across all subplots in the figure, and False will allow independent limits for each subplot. For Bode plots, the `share_magnitude` and `share_phase` keyword arguments @@ -620,11 +623,11 @@ various ways. The following general rules apply: the plot title. The default title is a string of the form " plot for " where is a list of the sys names contained in the plot (which can be updated if the plotting is called multiple times). - Use `title=False` to suppress the title completely. The title can also + Use ``title=False`` to suppress the title completely. The title can also be updated using the :func:`ControlPlot.set_plot_title` method for the returned control plot object. - The plot title is only generated if `ax` is `None`. + The plot title is only generated if `ax` is None. The following code illustrates the use of some of these customization features:: @@ -678,8 +681,8 @@ example:: .. image:: figures/ctrlplot-pole_zero_subplots.png -Alternatively, turning off the omega-damping grid (using `grid=False` or -`grid='empty'`) allows use of Matplotlib layout commands. +Alternatively, turning off the omega-damping grid (using ``grid=False`` or +``grid='empty'``) allows use of Matplotlib layout commands. Response and plotting functions diff --git a/doc/statesp.rst b/doc/statesp.rst index e9e36d683..a05c03638 100644 --- a/doc/statesp.rst +++ b/doc/statesp.rst @@ -126,10 +126,10 @@ matrix `K`, the solution to the Riccati equation `S`, and the location of the closed loop eigenvalues `E`. It can be called in one of several forms: - * `K, S, E = ct.lqr(sys, Q, R)` - * `K, S, E = ct.lqr(sys, Q, R, N)` - * `K, S, E = ct.lqr(A, B, Q, R)` - * `K, S, E = ct.lqr(A, B, Q, R, N)` + * ``K, S, E = ct.lqr(sys, Q, R)`` + * ``K, S, E = ct.lqr(sys, Q, R, N)`` + * ``K, S, E = ct.lqr(A, B, Q, R)`` + * ``K, S, E = ct.lqr(A, B, Q, R, N)`` If `sys` is a discrete time system, the first two forms will compute the discrete time optimal controller. For the second two forms, the diff --git a/doc/stochastic.rst b/doc/stochastic.rst index c6d871145..b812ab358 100644 --- a/doc/stochastic.rst +++ b/doc/stochastic.rst @@ -138,10 +138,10 @@ squared error using the sensor measurements :math:`y`. As with the :func:`lqr` function, the :func:`lqe` function can be called in several forms: - * `L, P, E = lqe(sys, QN, RN)` - * `L, P, E = lqe(sys, QN, RN, NN)` - * `L, P, E = lqe(A, G, C, QN, RN)` - * `L, P, E = lqe(A, G, C, QN, RN, NN)` + * ``L, P, E = lqe(sys, QN, RN)`` + * ``L, P, E = lqe(sys, QN, RN, NN)`` + * ``L, P, E = lqe(A, G, C, QN, RN)`` + * ``L, P, E = lqe(A, G, C, QN, RN, NN)`` where `sys` is an :class:`LTI` object, and `A`, `G`, `C`, `QN`, `RN`, and `NN` are 2D arrays of appropriate dimension. If `sys` is a @@ -165,7 +165,7 @@ input `U`. To run the estimator on a noisy signal, use the command resp = ct.input_output_response(est, timepts, [Y, U], [X0, P0]) -If desired, the :func:`correct` parameter can be set to :func:`False` +If desired, the :func:`correct` parameter can be set to False to allow prediction with no additional sensor information:: resp = ct.input_output_response( diff --git a/doc/xferfcn.rst b/doc/xferfcn.rst index a64663d16..726e926a8 100644 --- a/doc/xferfcn.rst +++ b/doc/xferfcn.rst @@ -88,7 +88,8 @@ following table summarizes the induced norms for a transfer function - :math:`\| G \|_2` - :math:`\| g \|_1` -The 2-norm and :math:`\infty`-norm can be computed using :func:`system_norm`:: +The 2-norm and :math:`\infty`-norm can be computed using +:func:`system_norm`:: sysnorm = ct.system_norm(sys, p=) From 00d6f02aa0bb08c49d789fb99081df22d4a506ac Mon Sep 17 00:00:00 2001 From: Richard Murray Date: Fri, 10 Jan 2025 16:35:26 -0800 Subject: [PATCH 58/93] add custom CSS --- doc/.gitignore | 1 - doc/_static/css/custom.css | 20 ++++++++++++++++++++ 2 files changed, 20 insertions(+), 1 deletion(-) create mode 100644 doc/_static/css/custom.css diff --git a/doc/.gitignore b/doc/.gitignore index 38303de2b..d948f64d2 100644 --- a/doc/.gitignore +++ b/doc/.gitignore @@ -1,2 +1 @@ *.fig.bak -_static/ diff --git a/doc/_static/css/custom.css b/doc/_static/css/custom.css new file mode 100644 index 000000000..41bbb3f9e --- /dev/null +++ b/doc/_static/css/custom.css @@ -0,0 +1,20 @@ +/* Center equations with equation numbers on the right */ +.math { + text-align: center; +} +.eqno { + float: right; +} + +/* Make code blocks show up in in dark grey, rather than RTD default (red) */ +code.literal { + color: #404040 !important; +} +/* Make py:obj objects non-bold by default */ +.py-obj .pre { + font-weight: normal; +} +/* Turn bold back on for py:obj objects that actually link to something */ +a .py-obj .pre { + font-weight: bold; +} From 1506ab060a9091b575499ac463e1fa96b424722e Mon Sep 17 00:00:00 2001 From: Richard Murray Date: Fri, 10 Jan 2025 17:57:58 -0800 Subject: [PATCH 59/93] center figures + small fixes --- control/frdata.py | 11 ++++++----- control/nichols.py | 2 +- control/optimal.py | 4 ++-- doc/classes.rst | 3 ++- doc/conf.py | 1 + doc/config.rst | 2 +- doc/iosys.rst | 1 + doc/nlsys.rst | 3 ++- doc/optimal.rst | 4 +++- doc/phaseplot.rst | 3 +++ doc/response.rst | 21 +++++++++++++++++++++ 11 files changed, 43 insertions(+), 12 deletions(-) diff --git a/control/frdata.py b/control/frdata.py index e23db8a0c..eaffcd2d4 100644 --- a/control/frdata.py +++ b/control/frdata.py @@ -97,7 +97,8 @@ class constructor, using the `frd` factory function, or Other Parameters ---------------- plot_type : str, optional - Set the type of plot to generate with `plot()` ('bode', 'nichols'). + Set the type of plot to generate with `~FrequencyResponseData.plot` + ('bode', 'nichols'). title : str, optional Set the title to use when plotting. plot_magnitude, plot_phase : bool, optional @@ -857,12 +858,12 @@ def append(self, other): # Plotting interface def plot(self, plot_type=None, *args, **kwargs): - """Plot the frequency response using a Bode plot. + """Plot the frequency response using Bode or singular values plot. Plot the frequency response using either a standard Bode plot - (default) or using a singular values plot (by setting `plot_type` - to 'svplot'). See `bode_plot` and `singular_values_plot` for more - detailed descriptions. + (``plot_type='bode'``, default) or a singular values plot + (``plot_type='svplot'``). See `bode_plot` and + `singular_values_plot` for more detailed descriptions. """ from .freqplot import bode_plot, singular_values_plot diff --git a/control/nichols.py b/control/nichols.py index 9ef31302d..270bf462a 100644 --- a/control/nichols.py +++ b/control/nichols.py @@ -194,7 +194,7 @@ def nichols_grid(cl_mags=None, cl_phases=None, line_style='dotted', ax=None, :doc:`Matplotlib linestyle \ `. ax : matplotlib.axes.Axes, optional - Axes to add grid to. If None, use `matplotlib.pyplot.gca()`. + Axes to add grid to. If None, use `matplotlib.pyplot.gca`. label_cl_phases : bool, optional If True, closed-loop phase lines will be labelled. diff --git a/control/optimal.py b/control/optimal.py index 46fa65f24..4e2d2a6c1 100644 --- a/control/optimal.py +++ b/control/optimal.py @@ -84,7 +84,7 @@ class OptimalControlProblem(): will be broadcast by extension of the time axis. log : bool, optional If True, turn on logging messages (using Python logging module). - Use `python.logging.basicConfig` to enable logging output + Use `logging.basicConfig` to enable logging output (e.g., to a file). Attributes @@ -1216,7 +1216,7 @@ def create_mpc_iosystem( `InputOutputSystem`. log : bool, optional If True, turn on logging messages (using Python logging module). - Use `python.logging.basicConfig` to enable logging output + Use `logging.basicConfig` to enable logging output (e.g., to a file). name : string, optional System name (used for specifying signals). If unspecified, a generic diff --git a/doc/classes.rst b/doc/classes.rst index 69ab50503..de963566d 100644 --- a/doc/classes.rst +++ b/doc/classes.rst @@ -33,7 +33,8 @@ some of the functions that can be used to convert objects from one class to another: .. image:: figures/classes.pdf - :width: 800 + :width: 800 + :align: center Response and plotting classes diff --git a/doc/conf.py b/doc/conf.py index ab208abcf..0bbfa22f1 100644 --- a/doc/conf.py +++ b/doc/conf.py @@ -104,6 +104,7 @@ {'scipy': ('https://docs.scipy.org/doc/scipy', None), 'numpy': ('https://numpy.org/doc/stable', None), 'matplotlib': ('https://matplotlib.org/stable/', None), + 'python': ('https://docs.python.org/3/', None), } # If this is True, todo and todolist produce output, else they produce nothing. diff --git a/doc/config.rst b/doc/config.rst index a42350493..21e89e3b2 100644 --- a/doc/config.rst +++ b/doc/config.rst @@ -159,7 +159,7 @@ System display parameters :value: True If True, show the input, output, and state count when using - `iosys_repr()` and the 'eval' format. Otherwise, the input, + `iosys_repr` and the 'eval' format. Otherwise, the input, output, and state values are repressed from the output unless non-generic signal names are present. diff --git a/doc/iosys.rst b/doc/iosys.rst index bc1322a28..3edc8e732 100644 --- a/doc/iosys.rst +++ b/doc/iosys.rst @@ -83,6 +83,7 @@ diagram: .. image:: figures/bdalg-feedback.png :width: 240 + :align: center By default a gain of -1 is applied at the output of the second system, so the dynamics illustrate above can be created using the command diff --git a/doc/nlsys.rst b/doc/nlsys.rst index 9d8599eeb..4b4b64500 100644 --- a/doc/nlsys.rst +++ b/doc/nlsys.rst @@ -51,7 +51,8 @@ To illustrate the creation of a nonlinear I/O system model, consider a simple model of a spring loaded arm driven by a motor: .. image:: figures/servomech-diagram.png - :width: 240 + :width: 240 + :align: center The dynamics of this system can be modeling using the following code:: diff --git a/doc/optimal.rst b/doc/optimal.rst index eecd40b4b..0b5b414e5 100644 --- a/doc/optimal.rst +++ b/doc/optimal.rst @@ -84,6 +84,8 @@ length. This process is then repeated, resulting in a sampled data feedback law. This approach is illustrated in the following figure: .. image:: figures/mpc-overview.png + :width: 640 + :align: center Every :math:`\Delta T` seconds, an optimal control problem is solved over a :math:`T` second horizon, starting from the current state. The first @@ -286,7 +288,7 @@ Plotting the results:: yields .. image:: figures/steering-optimal.png - + :align: center An example showing the use of the optimal estimation problem and moving horizon estimation (MHE) is given in the :doc:`mhe-pvtol Jupyter diff --git a/doc/phaseplot.rst b/doc/phaseplot.rst index 27a4f4856..543ef9f73 100644 --- a/doc/phaseplot.rst +++ b/doc/phaseplot.rst @@ -19,6 +19,7 @@ The default method for generating a phase plane plot is to provide a ct.phase_plane_plot(sys, axis_limits, T) .. image:: figures/phaseplot-dampedosc-default.png + :align: center By default, the plot includes streamlines generated from starting points on limits of the plot, with arrows showing the flow of the @@ -44,6 +45,7 @@ an inverted pendulum system, which is created using a mesh grid:: plt.ylabel(r"$\dot\theta$ [rad/sec]") .. image:: figures/phaseplot-invpend-meshgrid.png + :align: center This figure shows several features of more complex phase plane plots: multiple equilibrium points are shown, with saddle points showing @@ -75,6 +77,7 @@ are part of the :mod:`phaseplot` (pp) module:: plt.gca().set_aspect('equal') .. image:: figures/phaseplot-oscillator-helpers.png + :align: center The following helper functions are available: diff --git a/doc/response.rst b/doc/response.rst index a944f0bbc..912c61267 100644 --- a/doc/response.rst +++ b/doc/response.rst @@ -222,6 +222,7 @@ for a two-input, two-output can be plotted using the commands:: which produces the following plot: .. image:: figures/timeplot-mimo_step-default.png + :align: center A number of options are available in the :func:`time_response_plot` function (and associated :func:`TimeResponseData.plot` method) to @@ -244,6 +245,7 @@ yields the following plot:: "[plot_inputs, overlay_signals]") .. image:: figures/timeplot-mimo_step-pi_cs.png + :align: center Input/output response plots created with either the :func:`forced_response` or the @@ -263,6 +265,7 @@ keyword:: "[plot_inputs='overlay', legend_map]") .. image:: figures/timeplot-mimo_ioresp-ov_lm.png + :align: center Another option that is available is to use the `transpose` keyword so that instead of plotting the outputs on the top and inputs on the bottom, the @@ -282,6 +285,7 @@ following figure:: "[transpose]") .. image:: figures/timeplot-mimo_ioresp-mt_tr.png + :align: center This figure also illustrates the ability to create "multi-trace" plots using the :func:`combine_time_responses` function. The line @@ -300,6 +304,7 @@ and styles for various signals and traces:: trace_props=[{'linestyle': s} for s in ['-', '--']]) .. image:: figures/timeplot-mimo_step-linestyle.png + :align: center .. _frequency_response: @@ -323,6 +328,7 @@ be generated using the :func:`bode_plot` function:: ct.bode_plot(response, initial_phase=0) .. image:: figures/freqplot-siso_bode-default.png + :align: center Computing the response for multiple systems at the same time yields a common frequency range that covers the features of all listed systems. @@ -336,6 +342,7 @@ Bode plots can also be created directly using the ct.frequency_response(sys_mimo).plot() .. image:: figures/freqplot-mimo_bode-default.png + :align: center A variety of options are available for customizing Bode plots, for example allowing the display of the phase to be turned off or @@ -345,6 +352,7 @@ overlaying the inputs or outputs:: plot_phase=False, overlay_inputs=True, overlay_outputs=True) .. image:: figures/freqplot-mimo_bode-magonly.png + :align: center The :func:`singular_values_response` function can be used to generate Bode plots that show the singular values of a transfer @@ -353,6 +361,7 @@ function:: ct.singular_values_response(sys_mimo).plot() .. image:: figures/freqplot-mimo_svplot-default.png + :align: center Different types of plots can also be specified for a given frequency response. For example, to plot the frequency response using a a Nichols @@ -361,6 +370,7 @@ plot, use ``plot_type='nichols'``:: response.plot(plot_type='nichols') .. image:: figures/freqplot-siso_nichols-default.png + :align: center Another response function that can be used to generate Bode plots is the :func:`gangof4_response` function, which computes the four primary @@ -372,6 +382,7 @@ sensitivity functions for a feedback control system in standard form:: ct.bode_plot(response) # or response.plot() .. image:: figures/freqplot-gangof4.png + :align: center Nyquist analysis can be done using the :func:`nyquist_response` function, which evaluates an LTI system along the Nyquist contour, and @@ -381,6 +392,7 @@ the :func:`nyquist_plot` function, which generates a Nyquist plot:: nyquist_plot(sys) .. image:: figures/freqplot-nyquist-default.png + :align: center The :func:`nyquist_response` function can be used to compute the number of encirclements of the -1 point and can return the Nyquist @@ -403,6 +415,7 @@ the computation of the Nyquist curve and the way the data are plotted:: print("Encirclements =", nyqresp.count) .. image:: figures/freqplot-nyquist-custom.png + :align: center All frequency domain plotting functions will automatically compute the range of frequencies to plot based on the poles and zeros of the frequency @@ -415,6 +428,7 @@ array of frequencies as a second argument (after the list of systems):: ct.frequency_response([sys1, sys2], omega).plot(initial_phase=0) .. image:: figures/freqplot-siso_bode-omega.png + :align: center Alternatively, frequency ranges can be specified by passing a list of the form ``[wmin, wmax]``, where `wmin` and `wmax` are the minimum and @@ -452,6 +466,7 @@ zeros and can be used to generate a pole/zero plot:: ct.pole_zero_plot(response) .. image:: figures/pzmap-siso_ctime-default.png + :align: center A root locus plot shows the location of the closed loop poles of a system as a function of the loop gain:: @@ -459,6 +474,7 @@ as a function of the loop gain:: ct.root_locus_map(sys).plot() .. image:: figures/rlocus-siso_ctime-default.png + :align: center The grid in the left hand plane shows lines of constant damping ratio as well as arcs corresponding to the frequency of the complex pole. The grid @@ -472,6 +488,7 @@ diagram and display the gain and damping ratio for the clicked point below the plot title: .. image:: figures/rlocus-siso_ctime-clicked.png + :align: center Root locus diagrams are also supported for discrete time systems, in which case the grid is show inside the unit circle:: @@ -480,6 +497,7 @@ case the grid is show inside the unit circle:: ct.root_locus_plot(sysd) .. image:: figures/rlocus-siso_dtime-default.png + :align: center Lists of systems can also be given, in which case the root locus diagram for each system is plotted in different colors:: @@ -489,6 +507,7 @@ for each system is plotted in different colors:: ct.root_locus_plot([sys1, sys2], grid=False) .. image:: figures/rlocus-siso_multiple-nogrid.png + :align: center Customizing control plots @@ -662,6 +681,7 @@ features:: plt.tight_layout() .. image:: figures/ctrlplot-servomech.png + :align: center As this example illustrates, python-control plotting functions and Matplotlib plotting functions can generally be intermixed. One type of @@ -680,6 +700,7 @@ example:: cplt.set_plot_title("Root locus plots (w/ specified axes)") .. image:: figures/ctrlplot-pole_zero_subplots.png + :align: center Alternatively, turning off the omega-damping grid (using ``grid=False`` or ``grid='empty'``) allows use of Matplotlib layout commands. From a69c014df392889874843d1fdfc9c2d0633ae2d8 Mon Sep 17 00:00:00 2001 From: Richard Murray Date: Fri, 10 Jan 2025 18:13:25 -0800 Subject: [PATCH 60/93] fix capitalization of chapter and section titles --- doc/classes.rst | 8 ++++---- doc/config.rst | 2 +- doc/descfcn.rst | 2 +- doc/develop.rst | 10 +++++----- doc/examples.rst | 4 ++-- doc/examples/template.py | 7 ++++--- doc/flatsys.rst | 2 +- doc/functions.rst | 22 +++++++++++----------- doc/index.rst | 10 +++++----- doc/intro.rst | 6 +++--- doc/iosys.rst | 6 +++--- doc/linear.rst | 8 ++++---- doc/matlab.rst | 28 ++++++++++++++-------------- doc/nlsys.rst | 2 +- doc/optimal.rst | 4 +++- doc/phaseplot.rst | 3 ++- doc/statesp.rst | 2 +- doc/stochastic.rst | 6 +++--- doc/xferfcn.rst | 2 +- 19 files changed, 69 insertions(+), 65 deletions(-) diff --git a/doc/classes.rst b/doc/classes.rst index de963566d..c9553f01a 100644 --- a/doc/classes.rst +++ b/doc/classes.rst @@ -3,10 +3,10 @@ .. _class-ref: ********************** -Control system classes +Control System Classes ********************** -Input/output system classes +Input/output System Classes =========================== The classes listed below are used to represent models of input/output @@ -37,7 +37,7 @@ another: :align: center -Response and plotting classes +Response and Plotting Classes ============================= These classes are used as the outputs of `_response`, `_map`, and @@ -57,7 +57,7 @@ More informaton on the functions used to create these classes can be found in the :ref:iosys-module chapter. -Nonlinear system classes +Nonlinear System Classes ======================== These classes are used for various nonlinear input/output system diff --git a/doc/config.rst b/doc/config.rst index 21e89e3b2..a88e09d48 100644 --- a/doc/config.rst +++ b/doc/config.rst @@ -2,7 +2,7 @@ .. _package-configuration-parameters: -Package configuration parameters +Package Configuration Parameters ================================ The python-control package can be customized to allow for different default diff --git a/doc/descfcn.rst b/doc/descfcn.rst index e7b64530d..65d8691f5 100644 --- a/doc/descfcn.rst +++ b/doc/descfcn.rst @@ -2,7 +2,7 @@ .. _descfcn-module: -Describing functions +Describing Functions ==================== For nonlinear systems consisting of a feedback connection between a diff --git a/doc/develop.rst b/doc/develop.rst index 06dc5a2ae..04507e481 100644 --- a/doc/develop.rst +++ b/doc/develop.rst @@ -10,7 +10,7 @@ listing of the practices that have evolved over the course of development since the package was created in 2009. -Package structure +Package Structure ================= The python-control package is maintained on GitHub, with documentation @@ -96,7 +96,7 @@ GitHub repository file and directory layout: + \*.ipynb - Jupyter notebooks -Naming conventions +Naming Conventions ================== Generally speaking, standard Python and NumPy naming conventions are @@ -206,7 +206,7 @@ Signal arguments: signals. -Documentation guidelines +Documentation Guidelines ======================== The python-control package is documented using docstrings and Sphinx. @@ -407,7 +407,7 @@ The reference manual should provide a fairly comprehensive description of every class, function and configuration variable in the package. -Utility functions +Utility Functions ================= The following utility functions can be used to help with standard @@ -425,7 +425,7 @@ processing and parsing operations: xferfcn._convert_to_transfer_function -Sample files +Sample Files ============ diff --git a/doc/examples.rst b/doc/examples.rst index 0a66b6e4e..5853a7f9b 100644 --- a/doc/examples.rst +++ b/doc/examples.rst @@ -12,7 +12,7 @@ also be accessed online via the `python-control GitHub repository `_. -Python scripts +Python Scripts ============== The following Python scripts document the use of a variety of methods in the @@ -40,7 +40,7 @@ other sources. examples/markov examples/era_msd -Jupyter notebooks +Jupyter Notebooks ================= The examples below use `python-control` in a Jupyter notebook environment. diff --git a/doc/examples/template.py b/doc/examples/template.py index 30f6ef0f4..29bd70e2b 100644 --- a/doc/examples/template.py +++ b/doc/examples/template.py @@ -125,7 +125,7 @@ def sample_function(data, option=False, **kwargs): ----- This section can contain a more detailed description of how the system works. OK to include some limited mathematics, either via inline math - directions for a short formula (like this: ..math: a = b c) or via a + directions for a short formula (like this: ..math:`x = \alpha y`) or via a displayed equation: ..math:: @@ -137,8 +137,9 @@ def sample_function(data, option=False, **kwargs): If you refer to parameters, such as the `data` argument to this function, but them in single backticks (which will render them in code - style in Sphinx). You should also do this for Python contains like - `True, `False`, and `None`. + style in Sphinx). Strings that should be interpreted as Python code + use double backticks: ``mag, phase, omega = response``. Python + built-in objects, like True, False, and None are written on their own. """ inputs = kwargs['inputs'] diff --git a/doc/flatsys.rst b/doc/flatsys.rst index 41f9870f3..f223e7a0a 100644 --- a/doc/flatsys.rst +++ b/doc/flatsys.rst @@ -1,6 +1,6 @@ .. _flatsys-module: -Differentially flat systems +Differentially Flat Systems =========================== .. automodule:: control.flatsys diff --git a/doc/functions.rst b/doc/functions.rst index 677736149..c26316e8f 100644 --- a/doc/functions.rst +++ b/doc/functions.rst @@ -1,7 +1,7 @@ .. _function-ref: ****************** -Function reference +Function Reference ****************** .. Include header information from the main control module @@ -11,7 +11,7 @@ Function reference :no-special-members: -System creation +System Creation =============== Functions that create input/output systems from a description of the @@ -65,7 +65,7 @@ System interconnections split_tf -Time response +Time Response ============= .. autosummary:: @@ -104,7 +104,7 @@ Phase plane plots phaseplot.vectorfield -Frequency response +Frequency Response ================== .. autosummary:: @@ -122,7 +122,7 @@ Frequency response nichols_grid -Control system analysis +Control System Analysis ======================= Time domain analysis: @@ -173,7 +173,7 @@ Passive systems analysis: solve_passivity_LMI -Control system synthesis +Control System Synthesis ======================== State space synthesis: @@ -199,7 +199,7 @@ Frequency domain synthesis: rootlocus_pid_designer -System ID and model reduction +System ID and Model Reduction ============================= .. autosummary:: :toctree: generated/ @@ -212,7 +212,7 @@ System ID and model reduction markov -Nonlinear system support +Nonlinear System Support ======================== .. autosummary:: :toctree: generated/ @@ -277,7 +277,7 @@ Optimal control optimal.state_range_constraint -Stochastic system support +Stochastic System Support ========================= .. autosummary:: :toctree: generated/ @@ -289,7 +289,7 @@ Stochastic system support white_noise -Matrix computations +Matrix Computations =================== .. autosummary:: :toctree: generated/ @@ -304,7 +304,7 @@ Matrix computations .. _utility-and-conversions: -Utility functions +Utility Functions ================= .. autosummary:: :toctree: generated/ diff --git a/doc/index.rst b/doc/index.rst index 4e36461e3..f61c0a92b 100644 --- a/doc/index.rst +++ b/doc/index.rst @@ -51,11 +51,11 @@ or the GitHub site: https://github.com/python-control/python-control. intro Tutorial - Linear systems - I/O response and plotting - Nonlinear systems - Interconnected I/O systems - Stochastic systems + Linear Systems + I/O Response and Plotting + Nonlinear Systems + Interconnected I/O Systems + Stochastic Systems examples genindex diff --git a/doc/intro.rst b/doc/intro.rst index 289346218..f852bb16b 100644 --- a/doc/intro.rst +++ b/doc/intro.rst @@ -8,7 +8,7 @@ package, including documentation for all functions in the package and examples illustrating their use. -Package overview +Package Overview ================ .. automodule:: control @@ -85,7 +85,7 @@ Note that Google Colab does not currently support Slycot, so some functionality may not be available. -Package conventions +Package Conventions =================== The python-control package makes use of a few naming and calling conventions: @@ -113,7 +113,7 @@ The python-control package makes use of a few naming and calling conventions: objects that include the input and output dimensions. -Some differences from MATLAB +Some Differences from MATLAB ============================ Users familiar with the MATLAB control systems toolbox will find much diff --git a/doc/iosys.rst b/doc/iosys.rst index 3edc8e732..fa3059d4e 100644 --- a/doc/iosys.rst +++ b/doc/iosys.rst @@ -14,7 +14,7 @@ operator overloading and block diagram algebra, as well as a description of the :class:`InterconnectedSystem` class, which can be created using the :func:`interconnect` function. -Operator overloading +Operator Overloading ==================== The following operators are defined to operate between I/O systems: @@ -64,7 +64,7 @@ with the following rules: and arrays. -Block diagram algebra +Block Diagram Algebra ===================== Block diagram algebra is implemented using the following functions: @@ -108,7 +108,7 @@ given, but caution should be used since the order of states in the combined system is not guaranteed. -Signal-based interconnection +Signal-Based Interconnection ============================ More complex input/output systems can be constructed by using the diff --git a/doc/linear.rst b/doc/linear.rst index 7efae985a..fda71993f 100644 --- a/doc/linear.rst +++ b/doc/linear.rst @@ -10,7 +10,7 @@ functions in the toolbox will operate on any of these data types, and functions for converting between compatible types are provided. -Creating LTI systems +Creating LTI Systems ==================== LTI systems are created using "factory functions" that accept the @@ -233,7 +233,7 @@ response. .. _discrete_time_systems: -Discrete time systems +Discrete Time Systems ===================== A discrete time system is created by specifying a nonzero "timebase", @@ -275,7 +275,7 @@ using `dt` corresponding to a discrete time system:: .. include:: xferfcn.rst -Model conversion and reduction +Model Conversion and Reduction ============================== A variety of functions are available to manipulate LTI systems, @@ -438,7 +438,7 @@ equilibrium values (thereby keeping the input/output gain unchanged at zero frequency ["DC"]). -Displaying LTI system information +Displaying LTI System Information ================================= Information about an LTI system can be obtained using the Python diff --git a/doc/matlab.rst b/doc/matlab.rst index 846824e51..e5429b99e 100644 --- a/doc/matlab.rst +++ b/doc/matlab.rst @@ -1,7 +1,7 @@ .. _matlab-module: **************************** - MATLAB compatibility module + MATLAB Compatibility Module **************************** .. automodule:: control.matlab @@ -13,7 +13,7 @@ in main control package may not be be available via the MATLAB compatibility module. -Creating linear models +Creating Linear Models ====================== .. autosummary:: :toctree: generated/ @@ -23,7 +23,7 @@ Creating linear models frd zpk -Utility functions and conversions +Utility Functions and Conversions ================================= .. autosummary:: :toctree: generated/ @@ -35,7 +35,7 @@ Utility functions and conversions tf2ss tfdata -System interconnections +System Interconnections ======================= .. autosummary:: :toctree: generated/ @@ -47,7 +47,7 @@ System interconnections connect append -System gain and dynamics +System Gain and Dynamics ======================== .. autosummary:: :toctree: generated/ @@ -58,7 +58,7 @@ System gain and dynamics damp pzmap -Time-domain analysis +Time-Domain Analysis ==================== .. autosummary:: :toctree: generated/ @@ -68,7 +68,7 @@ Time-domain analysis initial lsim -Frequency-domain analysis +Frequency-Domain Analysis ========================= .. autosummary:: :toctree: generated/ @@ -80,7 +80,7 @@ Frequency-domain analysis freqresp evalfr -Compensator design +Compensator Design ================== .. autosummary:: :toctree: generated/ @@ -93,7 +93,7 @@ Compensator design lqe dlqe -State-space (SS) models +State-space (SS) Models ======================= .. autosummary:: :toctree: generated/ @@ -104,7 +104,7 @@ State-space (SS) models obsv gram -Model simplification +Model Simplification ==================== .. autosummary:: :toctree: generated/ @@ -116,14 +116,14 @@ Model simplification era markov -Time delays +Time Delays =========== .. autosummary:: :toctree: generated/ pade -Matrix equation solvers and linear algebra +Matrix Equation Solvers and Linear Algebra ========================================== .. autosummary:: :toctree: generated/ @@ -133,7 +133,7 @@ Matrix equation solvers and linear algebra care dare -Additional functions +Additional Functions ==================== .. autosummary:: :toctree: generated/ @@ -141,7 +141,7 @@ Additional functions gangof4 unwrap -Functions imported from other packages +Functions Imported from Other Packages ====================================== .. autosummary:: diff --git a/doc/nlsys.rst b/doc/nlsys.rst index 4b4b64500..094e6abb2 100644 --- a/doc/nlsys.rst +++ b/doc/nlsys.rst @@ -1,6 +1,6 @@ .. currentmodule:: control -Nonlinear system models +Nonlinear System Models ======================= Nonlinear input/output systems are represented as state space systems diff --git a/doc/optimal.rst b/doc/optimal.rst index 0b5b414e5..14b81e425 100644 --- a/doc/optimal.rst +++ b/doc/optimal.rst @@ -1,6 +1,6 @@ .. _optimal-module: -Optimization-based control +Optimization-Based Control ========================== .. automodule:: control.optimal @@ -104,6 +104,7 @@ approach can be shown to generate stabilizing control laws under suitable conditions (see, for example, the FBS2e supplement on `Optimization-Based Control `_. + Module usage ------------ @@ -294,6 +295,7 @@ An example showing the use of the optimal estimation problem and moving horizon estimation (MHE) is given in the :doc:`mhe-pvtol Jupyter notebook `. + Optimization Tips ----------------- diff --git a/doc/phaseplot.rst b/doc/phaseplot.rst index 543ef9f73..1a749341e 100644 --- a/doc/phaseplot.rst +++ b/doc/phaseplot.rst @@ -1,7 +1,8 @@ .. currentmodule:: control -Phase plane plots +Phase Plane Plots ================= + Insight into nonlinear systems can often be obtained by looking at phase plane diagrams. The :func:`phase_plane_plot` function allows the creation of a 2-dimensional phase plane diagram for a system. This diff --git a/doc/statesp.rst b/doc/statesp.rst index a05c03638..abb199ff3 100644 --- a/doc/statesp.rst +++ b/doc/statesp.rst @@ -1,6 +1,6 @@ .. currentmodule:: control -State space analysis and design +State Space Analysis and Design =============================== This section describes the functions the are available to analyze diff --git a/doc/stochastic.rst b/doc/stochastic.rst index b812ab358..c81f01ada 100644 --- a/doc/stochastic.rst +++ b/doc/stochastic.rst @@ -11,7 +11,7 @@ involving linear and nonlinear I/O systems with Gaussian white noise as an input. -Stochastic signals +Stochastic Signals ================== A stochastic signal is a representation of the output of a random @@ -98,7 +98,7 @@ The correlation function for the output can be computed using the .. _kalman-filter: -Linear quadratic estimation (Kalman filter) +Linear Quadratic Estimation (Kalman Filter) =========================================== A standard application of stochastic linear systems is the computation @@ -194,7 +194,7 @@ state :math:`x_\text{d}` and input :math:`u_\text{d}`:: clsys, timepts, [Xd, Ud], [X0, np.zeros_like(X0), P0]) -Maximum likelihood estimation +Maximum Likelihood Estimation ============================= Consider a *nonlinear* system with discrete time dynamics of the form diff --git a/doc/xferfcn.rst b/doc/xferfcn.rst index 726e926a8..a93ce0c51 100644 --- a/doc/xferfcn.rst +++ b/doc/xferfcn.rst @@ -1,6 +1,6 @@ .. currentmodule:: control -Frequency domain analysis and design +Frequency Domain Analysis and Design ==================================== Transfer function properties From eb2247ca54a25de6d5a7f925e6e217e1cd510dc4 Mon Sep 17 00:00:00 2001 From: Richard Murray Date: Sat, 11 Jan 2025 15:58:30 -0800 Subject: [PATCH 61/93] User Guide copyedits --- control/__init__.py | 2 +- control/freqplot.py | 20 +- control/iosys.py | 2 +- control/nlsys.py | 2 +- control/pzmap.py | 3 +- control/statefbk.py | 18 +- control/statesp.py | 2 +- control/stochsys.py | 7 +- control/tests/docstrings_test.py | 2 + control/tests/flatsys_test.py | 145 +++++-- control/tests/kwargs_test.py | 2 + control/tests/rlocus_test.py | 32 +- control/tests/statefbk_test.py | 4 +- control/tests/timeplot_test.py | 55 +++ control/timeplot.py | 2 +- control/timeresp.py | 5 + doc/Makefile | 7 +- doc/classes.rst | 12 +- doc/config.rst | 3 +- doc/descfcn.rst | 22 +- doc/develop.rst | 19 +- doc/figures/flatsys-steering-compare.png | Bin 0 -> 52991 bytes doc/figures/iosys-predprey-closed.png | Bin 0 -> 79692 bytes doc/figures/iosys-predprey-open.png | Bin 0 -> 61181 bytes doc/figures/timeplot-mimo_ioresp-mt_tr.png | Bin 64044 -> 64045 bytes doc/figures/timeplot-mimo_ioresp-ov_lm.png | Bin 63747 -> 63748 bytes doc/figures/timeplot-mimo_step-default.png | Bin 31977 -> 31978 bytes doc/figures/timeplot-mimo_step-linestyle.png | Bin 41690 -> 41691 bytes doc/figures/timeplot-mimo_step-pi_cs.png | Bin 31880 -> 31881 bytes doc/figures/timeplot-servomech-combined.png | Bin 0 -> 57609 bytes doc/flatsys.rst | 74 ++-- doc/functions.rst | 4 +- doc/index.rst | 2 +- doc/iosys.rst | 402 ++++++++++++------- doc/linear.rst | 185 +++++---- doc/nlsys.rst | 72 +++- doc/nonlinear.rst | 4 +- doc/optimal.rst | 53 +-- doc/phaseplot.rst | 14 +- doc/response.rst | 101 +++-- doc/statesp.rst | 20 +- doc/stochastic.rst | 54 ++- doc/test_sphinxdocs.py | 3 +- doc/xferfcn.rst | 24 +- examples/cds112-L1_python-control.ipynb | 2 +- examples/python-control_tutorial.ipynb | 43 +- 46 files changed, 928 insertions(+), 495 deletions(-) create mode 100644 doc/figures/flatsys-steering-compare.png create mode 100644 doc/figures/iosys-predprey-closed.png create mode 100644 doc/figures/iosys-predprey-open.png create mode 100644 doc/figures/timeplot-servomech-combined.png diff --git a/control/__init__.py b/control/__init__.py index e0d74bd6d..d9f5003c4 100644 --- a/control/__init__.py +++ b/control/__init__.py @@ -12,7 +12,7 @@ `Feedback Systems `_ by Astrom and Murray. In addition to standard techniques available for linear control systems, support for nonlinear systems (including trajectory generation, gain -scheduling, phase plane diagrams, and describing functions) is also +scheduling, phase plane diagrams, and describing functions) is included. A :ref:`matlab-module` is available that provides many of the common functions corresponding to commands available in the MATLAB Control Systems Toolbox. diff --git a/control/freqplot.py b/control/freqplot.py index 2473cd0c3..cb62f5dd2 100644 --- a/control/freqplot.py +++ b/control/freqplot.py @@ -37,7 +37,8 @@ __all__ = ['bode_plot', 'NyquistResponseData', 'nyquist_response', 'nyquist_plot', 'singular_values_response', 'singular_values_plot', 'gangof4_plot', 'gangof4_response', - 'bode', 'nyquist', 'gangof4', 'FrequencyResponseList'] + 'bode', 'nyquist', 'gangof4', 'FrequencyResponseList', + 'NyquistResponseList'] # Default values for module parameter variables _freqplot_defaults = { @@ -65,6 +66,11 @@ class FrequencyResponseList(list): def plot(self, *args, plot_type=None, **kwargs): + """Plot a list of frequency responses. + + See `FrequencyResponseData.plot` for details. + + """ if plot_type == None: for response in self: if plot_type is not None and response.plot_type != plot_type: @@ -1157,7 +1163,19 @@ def plot(self, *args, **kwargs): class NyquistResponseList(list): + """List of NyquistResponseData objects with plotting capability. + + This class consists of a list of `NyquistResponseData` objects. + It is a subclass of the Python `list` class, with a `plot` method that + plots the individual `NyquistResponseData` objects. + + """ def plot(self, *args, **kwargs): + """Plot a list of Nyquist responses. + + See `nyquist_plot` for details. + + """ return nyquist_plot(self, *args, **kwargs) diff --git a/control/iosys.py b/control/iosys.py index fe954957b..e93c55631 100644 --- a/control/iosys.py +++ b/control/iosys.py @@ -305,7 +305,7 @@ def repr_format(self): Format used in creating the representation for the system: - * 'info' : [outputs] + * 'info' : [outputs]> * 'eval' : system specific, loadable representation * 'latex' : HTML/LaTeX representation of the object diff --git a/control/nlsys.py b/control/nlsys.py index e6e445b73..cd496f1c0 100644 --- a/control/nlsys.py +++ b/control/nlsys.py @@ -612,7 +612,7 @@ class InterconnectedSystem(NonlinearIOSystem): Offset to the subsystem inputs, outputs, and states in the overal system input, output, and state arrays. syslist_index : dict - Index of the subsytem with key given by the name of the subsystem. + Index of the subsystem with key given by the name of the subsystem. See Also -------- diff --git a/control/pzmap.py b/control/pzmap.py index bfe89b9f5..7a72b60e5 100644 --- a/control/pzmap.py +++ b/control/pzmap.py @@ -29,7 +29,8 @@ from .statesp import StateSpace from .xferfcn import TransferFunction -__all__ = ['pole_zero_map', 'pole_zero_plot', 'pzmap', 'PoleZeroData'] +__all__ = ['pole_zero_map', 'pole_zero_plot', 'pzmap', 'PoleZeroData', + 'PoleZeroList'] # Define default parameter values for this module diff --git a/control/statefbk.py b/control/statefbk.py index eaf2f81f8..a0d3b845e 100644 --- a/control/statefbk.py +++ b/control/statefbk.py @@ -40,7 +40,7 @@ def sb03md(n, C, A, U, dico, job='X',fact='N',trana='N',ldwork=None): __all__ = ['ctrb', 'obsv', 'gram', 'place', 'place_varga', 'lqr', - 'dlqr', 'acker', 'create_statefbk_iosystem'] + 'dlqr', 'acker', 'place_acker', 'create_statefbk_iosystem'] # Pole placement @@ -89,7 +89,7 @@ def place(A, B, p): See Also -------- - place_varga, acker + place_acker, place_varga """ from scipy.signal import place_poles @@ -107,7 +107,7 @@ def place(A, B, p): def place_varga(A, B, p, dtime=False, alpha=None): - """Place closed loop eigenvalues. + """Place closed loop eigenvalues using Varga method. K = place_varga(A, B, p, dtime=False, alpha=None) @@ -138,7 +138,7 @@ def place_varga(A, B, p, dtime=False, alpha=None): See Also -------- - place, acker + place, place_acker Notes ----- @@ -210,11 +210,11 @@ def place_varga(A, B, p, dtime=False, alpha=None): # Contributed by Roberto Bucher -def acker(A, B, poles): +def place_acker(A, B, poles): """Pole placement using Ackermann method. Call: - K = acker(A, B, poles) + K = place_acker(A, B, poles) Parameters ---------- @@ -540,7 +540,7 @@ def dlqr(*args, **kwargs): return K, S, E -# Function to create an I/O sytems representing a state feedback controller +# Function to create an I/O systems representing a state feedback controller def create_statefbk_iosystem( sys, gain, feedfwd_gain=None, integral_action=None, estimator=None, controller_type=None, xd_labels=None, ud_labels=None, ref_labels=None, @@ -1220,3 +1220,7 @@ def gram(sys, type): n, m, A, Q, C.transpose(), dico, fact='N', trans=tra) gram = X return gram + + +# Short versions of functions +acker = place_acker diff --git a/control/statesp.py b/control/statesp.py index 615477e48..eec80beb8 100644 --- a/control/statesp.py +++ b/control/statesp.py @@ -2035,7 +2035,7 @@ def rss(states=1, outputs=1, inputs=1, strictly_proper=False, **kwargs): kwargs.update({'states': states, 'outputs': outputs, 'inputs': inputs}) name, inputs, outputs, states, dt = _process_iosys_keywords(kwargs) - # Figure out the size of the sytem + # Figure out the size of the system nstates, _ = _process_signal_list(states) ninputs, _ = _process_signal_list(inputs) noutputs, _ = _process_signal_list(outputs) diff --git a/control/stochsys.py b/control/stochsys.py index 3b76f6d49..c84f9898f 100644 --- a/control/stochsys.py +++ b/control/stochsys.py @@ -660,9 +660,10 @@ def correlation(T, X, Y=None, squeeze=True): tau, Rtau = correlation(T, X[, Y]) - The signal X (and Y, if present) represent a continuous time signal - sampled at times T. The return value provides the correlation Rtau - between X(t+tau) and X(t) at a set of time offets tau. + The signal X (and Y, if present) represent a continuous or + discrete time signal sampled at times T. The return value + provides the correlation Rtau between X(t+tau) and X(t) at a set + of time offets tau. Parameters ---------- diff --git a/control/tests/docstrings_test.py b/control/tests/docstrings_test.py index f47612dbe..158798782 100644 --- a/control/tests/docstrings_test.py +++ b/control/tests/docstrings_test.py @@ -37,6 +37,8 @@ control.NamedSignal, # internal I/O class control.TimeResponseList, # internal response class control.FrequencyResponseList, # internal response class + control.NyquistResponseList, # internal response class + control.PoleZeroList, # internal response class control.FrequencyResponseData, # check separately (iosys) control.InterconnectedSystem, # check separately (iosys) control.flatsys.FlatSystem, # check separately (iosys) diff --git a/control/tests/flatsys_test.py b/control/tests/flatsys_test.py index 772dea7b0..7f4bcde69 100644 --- a/control/tests/flatsys_test.py +++ b/control/tests/flatsys_test.py @@ -24,6 +24,48 @@ atol = 1e-4 rtol = 1e-4 +# Define the kinematic car system +def vehicle_flat_forward(x, u, params={}): + b = params.get('wheelbase', 3.) # get parameter values + zflag = [np.zeros(3), np.zeros(3)] # list for flag arrays + zflag[0][0] = x[0] # flat outputs + zflag[1][0] = x[1] + zflag[0][1] = u[0] * np.cos(x[2]) # first derivatives + zflag[1][1] = u[0] * np.sin(x[2]) + thdot = (u[0]/b) * np.tan(u[1]) # dtheta/dt + zflag[0][2] = -u[0] * thdot * np.sin(x[2]) # second derivatives + zflag[1][2] = u[0] * thdot * np.cos(x[2]) + return zflag + +def vehicle_flat_reverse(zflag, params={}): + b = params.get('wheelbase', 3.) # get parameter values + x = np.zeros(3); u = np.zeros(2) # vectors to store x, u + x[0] = zflag[0][0] # x position + x[1] = zflag[1][0] # y position + x[2] = np.arctan2(zflag[1][1], zflag[0][1]) # angle + u[0] = zflag[0][1] * np.cos(x[2]) + zflag[1][1] * np.sin(x[2]) + thdot_v = zflag[1][2] * np.cos(x[2]) - zflag[0][2] * np.sin(x[2]) + u[1] = np.arctan2(thdot_v, u[0]**2 / b) + return x, u + +def vehicle_update(t, x, u, params): + b = params.get('wheelbase', 3.) # get parameter values + dx = np.array([ + np.cos(x[2]) * u[0], + np.sin(x[2]) * u[0], + (u[0]/b) * np.tan(u[1]) + ]) + return dx + +def vehicle_output(t, x, u, params): return x + +# Create differentially flat input/output system +vehicle_flat = fs.FlatSystem( + vehicle_flat_forward, vehicle_flat_reverse, vehicle_update, + vehicle_output, inputs=('v', 'delta'), outputs=('x', 'y', 'theta'), + states=('x', 'y', 'theta'), name='vehicle_flat') + + class TestFlatSys: """Test differential flat systems""" @@ -58,48 +100,9 @@ def test_double_integrator(self, xf, uf, Tf, basis): t, y, x = ct.forced_response(sys, T, ud, x1, return_x=True) np.testing.assert_array_almost_equal(x, xd, decimal=3) - @pytest.fixture - def vehicle_flat(self): - """Differential flatness for a kinematic car""" - def vehicle_flat_forward(x, u, params={}): - b = params.get('wheelbase', 3.) # get parameter values - zflag = [np.zeros(3), np.zeros(3)] # list for flag arrays - zflag[0][0] = x[0] # flat outputs - zflag[1][0] = x[1] - zflag[0][1] = u[0] * np.cos(x[2]) # first derivatives - zflag[1][1] = u[0] * np.sin(x[2]) - thdot = (u[0]/b) * np.tan(u[1]) # dtheta/dt - zflag[0][2] = -u[0] * thdot * np.sin(x[2]) # second derivatives - zflag[1][2] = u[0] * thdot * np.cos(x[2]) - return zflag - - def vehicle_flat_reverse(zflag, params={}): - b = params.get('wheelbase', 3.) # get parameter values - x = np.zeros(3); u = np.zeros(2) # vectors to store x, u - x[0] = zflag[0][0] # x position - x[1] = zflag[1][0] # y position - x[2] = np.arctan2(zflag[1][1], zflag[0][1]) # angle - u[0] = zflag[0][1] * np.cos(x[2]) + zflag[1][1] * np.sin(x[2]) - thdot_v = zflag[1][2] * np.cos(x[2]) - zflag[0][2] * np.sin(x[2]) - u[1] = np.arctan2(thdot_v, u[0]**2 / b) - return x, u - - def vehicle_update(t, x, u, params): - b = params.get('wheelbase', 3.) # get parameter values - dx = np.array([ - np.cos(x[2]) * u[0], - np.sin(x[2]) * u[0], - (u[0]/b) * np.tan(u[1]) - ]) - return dx - - def vehicle_output(t, x, u, params): return x - - # Create differentially flat input/output system - return fs.FlatSystem( - vehicle_flat_forward, vehicle_flat_reverse, vehicle_update, - vehicle_output, inputs=('v', 'delta'), outputs=('x', 'y', 'theta'), - states=('x', 'y', 'theta')) + @pytest.fixture(name='vehicle_flat') + def vehicle_flat_fixture(self): + return vehicle_flat @pytest.mark.parametrize("basis", [ fs.PolyFamily(6), fs.PolyFamily(8), fs.BezierFamily(6), @@ -839,3 +842,61 @@ def test_flatsys_factory_function(self, vehicle_flat): with pytest.raises(TypeError, match="incorrect number or type"): flatsys = fs.flatsys(1, 2, 3, 4, 5) + + +if __name__ == '__main__': + # Generate images for User Guide + import matplotlib.pyplot as plt + + # + # Point to point + # + # Define the endpoints of the trajectory + x0 = [0., -2., 0.]; u0 = [10., 0.] + xf = [100., 2., 0.]; uf = [10., 0.] + Tf = 10 + + # Define a set of basis functions to use for the trajectories + poly = fs.PolyFamily(6) + + # Find a trajectory between the initial condition and the final condition + traj = fs.point_to_point(vehicle_flat, Tf, x0, u0, xf, uf, basis=poly) + + # Create the trajectory + timepts = np.linspace(0, Tf, 100) + xd, ud = traj.eval(timepts) + resp_p2p = ct.input_output_response(vehicle_flat, timepts, ud, X0=xd[:, 0]) + + # + # Solve OCP + # + # Define the cost along the trajectory: penalize steering angle + traj_cost = ct.optimal.quadratic_cost( + vehicle_flat, None, np.diag([0.1, 10]), u0=uf) + + # Define the terminal cost: penalize distance from the end point + term_cost = ct.optimal.quadratic_cost( + vehicle_flat, np.diag([1e3, 1e3, 1e3]), None, x0=xf) + + # Use a straight line as the initial guess + evalpts = np.linspace(0, Tf, 10) + initial_guess = np.array( + [x0[i] + (xf[i] - x0[i]) * evalpts/Tf for i in (0, 1)]) + + # Solve the optimal control problem, evaluating cost at timepts + bspline = fs.BSplineFamily([0, Tf/2, Tf], 4) + traj = fs.solve_flat_ocp( + vehicle_flat, evalpts, x0, u0, traj_cost, + terminal_cost=term_cost, initial_guess=initial_guess, basis=bspline) + + xd, ud = traj.eval(timepts) + resp_ocp = ct.input_output_response(vehicle_flat, timepts, ud, X0=xd[:, 0]) + + # + # Plot the results + # + cplt = ct.time_response_plot( + ct.combine_time_responses([resp_p2p, resp_ocp]), + overlay_traces=True, trace_labels=['point_to_point', 'solve_ocp']) + + plt.savefig('flatsys-steering-compare.png') diff --git a/control/tests/kwargs_test.py b/control/tests/kwargs_test.py index d73df0bbd..142c46c77 100644 --- a/control/tests/kwargs_test.py +++ b/control/tests/kwargs_test.py @@ -320,7 +320,9 @@ def test_response_plot_kwargs(data_fcn, plot_fcn, mimo): 'flatsys.LinearFlatSystem.__init__': test_unrecognized_kwargs, 'NonlinearIOSystem.linearize': test_unrecognized_kwargs, 'NyquistResponseData.plot': test_response_plot_kwargs, + 'NyquistResponseList.plot': test_response_plot_kwargs, 'PoleZeroData.plot': test_response_plot_kwargs, + 'PoleZeroList.plot': test_response_plot_kwargs, 'InterconnectedSystem.__init__': interconnect_test.test_interconnect_exceptions, 'StateSpace.__init__': diff --git a/control/tests/rlocus_test.py b/control/tests/rlocus_test.py index 52546a428..2e74f8649 100644 --- a/control/tests/rlocus_test.py +++ b/control/tests/rlocus_test.py @@ -209,7 +209,7 @@ def test_root_locus_documentation(savefigs=False): plt.figure() cplt = ct.root_locus_map(sys).plot(initial_gain=3.506) ax = cplt.axes[0, 0] - freqplot_rcParams = ct.config._get_param('freqplot', 'rcParams') + freqplot_rcParams = ct.config._get_param('ctrlplot', 'rcParams') with plt.rc_context(freqplot_rcParams): ax.set_title( "Clicked at: -2.729+1.511j gain = 3.506 damping = 0.8748") @@ -230,6 +230,21 @@ def test_root_locus_documentation(savefigs=False): plt.savefig('rlocus-siso_multiple-nogrid.png') +# https://github.com/python-control/python-control/issues/1063 +def test_rlocus_singleton(): + # Generate a root locus map for a singleton + L = ct.tf([1, 1], [1, 2, 3]) + rldata = ct.root_locus_map(L, 1) + np.testing.assert_equal(rldata.gains, np.array([1])) + assert rldata.loci.shape == (1, 2) + + # Generate the root locus plot (no loci) + cplt = rldata.plot() + assert len(cplt.lines[0, 0]) == 1 # poles (one set of markers) + assert len(cplt.lines[0, 1]) == 1 # zeros + assert len(cplt.lines[0, 2]) == 2 # loci (two 0-length lines) + + if __name__ == "__main__": # # Interactive mode: generate plots for manual viewing @@ -287,18 +302,3 @@ def test_root_locus_documentation(savefigs=False): # Run tests that generate plots for the documentation test_root_locus_documentation(savefigs=True) - - -# https://github.com/python-control/python-control/issues/1063 -def test_rlocus_singleton(): - # Generate a root locus map for a singleton - L = ct.tf([1, 1], [1, 2, 3]) - rldata = ct.root_locus_map(L, 1) - np.testing.assert_equal(rldata.gains, np.array([1])) - assert rldata.loci.shape == (1, 2) - - # Generate the root locus plot (no loci) - cplt = rldata.plot() - assert len(cplt.lines[0, 0]) == 1 # poles (one set of markers) - assert len(cplt.lines[0, 1]) == 1 # zeros - assert len(cplt.lines[0, 2]) == 2 # loci (two 0-length lines) diff --git a/control/tests/statefbk_test.py b/control/tests/statefbk_test.py index 0714e6f3b..adc7d437d 100644 --- a/control/tests/statefbk_test.py +++ b/control/tests/statefbk_test.py @@ -15,7 +15,7 @@ ControlArgument, slycot_check from control.mateqn import care, dare from control.statefbk import (ctrb, obsv, place, place_varga, lqr, dlqr, - gram, acker) + gram, place_acker) from control.tests.conftest import slycotonly @@ -269,7 +269,7 @@ def testAcker(self, fixedseed): desired = poles(des) # Now place the poles using acker - K = acker(sys.A, sys.B, desired) + K = place_acker(sys.A, sys.B, desired) new = ss(sys.A - sys.B * K, sys.B, sys.C, sys.D) placed = poles(new) diff --git a/control/tests/timeplot_test.py b/control/tests/timeplot_test.py index 9525c7e02..809169fda 100644 --- a/control/tests/timeplot_test.py +++ b/control/tests/timeplot_test.py @@ -715,3 +715,58 @@ def test_legend_customization(): fig = plt.figure() cplt = ct.step_response([sys1, sys2]).plot() cplt.set_plot_title("[List] " + fig._suptitle._text) + + # + # Servomechanism example for documentation + # + + # Parameter values + servomech_params = { + 'J': 100, # Moment of inertia of the motor + 'b': 10, # Angular damping of the arm + 'k': 1, # Spring constant + 'r': 1, # Location of spring contact on arm + 'l': 2, # Distance to the read head + 'eps': 0.01, # Magnitude of velocity-dependent perturbation + } + + # State derivative + def servomech_update(t, x, u, params): + # Extract the configuration and velocity variables from the state vector + theta = x[0] # Angular position of the disk drive arm + thetadot = x[1] # Angular velocity of the disk drive arm + tau = u[0] # Torque applied at the base of the arm + + # Get the parameter values + J, b, k, r = map(params.get, ['J', 'b', 'k', 'r']) + + # Compute the angular acceleration + dthetadot = 1/J * ( + -b * thetadot - k * r * np.sin(theta) + tau) + + # Return the state update law + return np.array([thetadot, dthetadot]) + + # System output (tip radial position + angular velocity) + def servomech_output(t, x, u, params): + l = params['l'] + return np.array([l * x[0], x[1]]) + + # System dynamics + servomech = ct.nlsys( + servomech_update, servomech_output, name='servomech', + params=servomech_params, states=['theta', 'thdot'], + outputs=['y', 'thdot'], inputs=['tau']) + + timepts = np.linspace(0, 10) + + U1 = np.sin(timepts) + resp1 = ct.input_output_response(servomech, timepts, U1) + + U2 = np.cos(2*timepts) + resp2 = ct.input_output_response(servomech, timepts, U2) + + plt.figure() + cplt = ct.combine_time_responses( + [resp1, resp2], trace_labels=["Scenario #1", "Scenario #2"]).plot() + plt.savefig('timeplot-servomech-combined.png') diff --git a/control/timeplot.py b/control/timeplot.py index 4af39231b..8f37211ef 100644 --- a/control/timeplot.py +++ b/control/timeplot.py @@ -102,7 +102,7 @@ def time_response_plot( * cplt.figure: `matplotlib.figure.Figure` containing the plot. - * cplt.legend: legend object(s) contained in the plot + * cplt.legend: legend object(s) contained in the plot. See `ControlPlot` for more detailed information. diff --git a/control/timeresp.py b/control/timeresp.py index b90cb270d..d83aad312 100644 --- a/control/timeresp.py +++ b/control/timeresp.py @@ -717,6 +717,11 @@ class TimeResponseList(list): """ def plot(self, *args, **kwargs): + """Plot a list of time responses. + + See `time_response_plot` for details. + + """ from .ctrlplot import ControlPlot lines = None diff --git a/doc/Makefile b/doc/Makefile index 478c9386b..1f33e3ad8 100644 --- a/doc/Makefile +++ b/doc/Makefile @@ -12,14 +12,17 @@ BUILDDIR = _build help: @$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) -.PHONY: help Makefile +.PHONY: help Makefile doctest # Catch-all target: route all unknown targets to Sphinx using the new # "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS). -html latexpdf doctest: Makefile +html latexpdf: Makefile doctest make -C figures @$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) +doctest: Makefile + @$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) + clean: @$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) diff --git a/doc/classes.rst b/doc/classes.rst index c9553f01a..7cde1e76c 100644 --- a/doc/classes.rst +++ b/doc/classes.rst @@ -6,7 +6,7 @@ Control System Classes ********************** -Input/output System Classes +Input/Output System Classes =========================== The classes listed below are used to represent models of input/output @@ -49,12 +49,16 @@ These classes are used as the outputs of `_response`, `_map`, and :nosignatures: ControlPlot - TimeResponseData + FrequencyResponseList NyquistResponseData + NyquistResponseList PoleZeroData + PoleZeroList + TimeResponseData + TimeResponseList More informaton on the functions used to create these classes can be -found in the :ref:iosys-module chapter. +found in the :ref:`iosys-module` chapter. Nonlinear System Classes @@ -84,4 +88,4 @@ operations: optimal.OptimalEstimationResult More informaton on the functions used to create these classes can be -found in the :ref:nonlinear-systems chapter. +found in the :ref:`nonlinear-systems` chapter. diff --git a/doc/config.rst b/doc/config.rst index a88e09d48..0000fe507 100644 --- a/doc/config.rst +++ b/doc/config.rst @@ -257,7 +257,8 @@ Plotting parameters :type: dict :value: {'axes.labelsize': 'small', 'axes.titlesize': 'small', 'figure.titlesize': 'medium', 'legend.fontsize': 'x-small', 'xtick.labelsize': 'small', 'ytick.labelsize': 'small'} - Overrides the default Matplotlib parameters used for generating plots. + Overrides the default Matplotlib parameters used for generating + plots. This dictionary can also be accessed as ``ct.rcParams``. .. py:data:: freqplot.dB :type: bool diff --git a/doc/descfcn.rst b/doc/descfcn.rst index 65d8691f5..7aa7d80bb 100644 --- a/doc/descfcn.rst +++ b/doc/descfcn.rst @@ -26,7 +26,10 @@ If such an intersection exists, it indicates that there may be a limit cycle of amplitude :math:`A` with frequency :math:`\omega`. Describing function analysis is a simple method, but it is approximate -because it assumes that higher harmonics can be neglected. +because it assumes that higher harmonics can be neglected. More +information on describing functions can be found in `Feedback Systems +`_, Section 10.5 +(Generalized Notions of Gain and Phase). Module usage @@ -37,24 +40,26 @@ compute the describing function of a nonlinear function:: N = ct.describing_function(F, A) +where `F` is a scalar nonlinear function. + Stability analysis using describing functions is done by looking for -amplitudes :math:`a` and frequencies :math`\omega` such that +amplitudes :math:`A` and frequencies :math:`\omega` such that .. math:: H(j\omega) = \frac{-1}{N(A)} These points can be determined by generating a Nyquist plot in which -the transfer function :math:`H(j\omega)` intersections the negative +the transfer function :math:`H(j\omega)` intersects the negative reciprocal of the describing function :math:`N(A)`. The :func:`describing_function_response` function computes the amplitude and frequency of any points of intersection:: - response = ct.describing_function_response(H, F, amp_range[, omega_range]) - response.intersections # frequency, amplitude pairs + dfresp = ct.describing_function_response(H, F, amp_range[, omega_range]) + dfresp.intersections # frequency, amplitude pairs A Nyquist plot showing the describing function and the intersections -with the Nyquist curve can be generated using ``response.plot()``, which +with the Nyquist curve can be generated using ``dfresp.plot()``, which calls the :func:`describing_function_plot` function. @@ -78,9 +83,8 @@ nonlinearity:: F = ct.saturation_nonlinearity(1) -These functions use the -:class:`DescribingFunctionNonlinearity`, which allows an -analytical description of the describing function. +These functions use the :class:`DescribingFunctionNonlinearity` class, +which allows an analytical description of the describing function. Module classes and functions ---------------------------- diff --git a/doc/develop.rst b/doc/develop.rst index 04507e481..131f150ff 100644 --- a/doc/develop.rst +++ b/doc/develop.rst @@ -392,13 +392,30 @@ the web page when that chapter is being viewed. In some cases a section may be in its own file, including in the chapter page by using the `include` directive (see `nlsys.py` for an example). -Sphinx files guidlines: +Sphinx files guidelines: * Each file should declare the `currentmodule` at or near the top of the file. Except for sub-packages (`control.flatsys`) and modules that need to be imported separately (`control.optimal`), `currentmodule` should be set to control. +* When possible, sample code in the User Guide should use Sphinx + doctest directives so that the code is executed by `make doctest`. + Two styles are possible: doctest-style blocks (showing code with a + prompt and the expected response) and code blocks (using the + `testcode` directive). + +* When refering to the python-control package, several different forms + can be used: + + - Full name: "the Python Control Systems Library (python-control)" + (used sparingly, mainly at the tops of chapters). + + - Adjective form: "the python-control package" or "a python-control + module" (this is the most common form). + + - Noun form: "`python-control`" (only used occassionally). + Reference Manual ---------------- diff --git a/doc/figures/flatsys-steering-compare.png b/doc/figures/flatsys-steering-compare.png new file mode 100644 index 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This is done @@ -128,12 +128,10 @@ using the :func:`~flatsys.flatsys` function:: import control.flatsys as fs sys = fs.flatsys(forward, reverse) -The `forward` and `reverse` parameters describe the mappings between the -system state/input and the differentially flat outputs and their -derivatives ("flat flag"). - -The :func:`~flatsys.FlatSystem.forward` method computes the -flat flag given a state and input:: +The `forward` and `reverse` parameters describe the mappings between +the system state/input and the differentially flat outputs and their +derivatives ("flat flag"). The :func:`~flatsys.FlatSystem.forward` +method computes the flat flag given a state and input:: zflag = sys.forward(x, u) @@ -185,20 +183,21 @@ The returned object has class :class:`~flatsys.SystemTrajectory` and can be used to compute the state and input trajectory between the initial and final condition:: - xd, ud = traj.eval(T) + xd, ud = traj.eval(timepts) -where `T` is a list of times on which the trajectory should be evaluated -(e.g., `T = numpy.linspace(0, Tf, M)`. +where `timepts` is a list of times on which the trajectory should be evaluated +(e.g., `timepts = numpy.linspace(0, Tf, M)`. The :func:`~flatsys.point_to_point` function also allows the specification of a cost function and/or constraints, in the same format as :func:`optimal.solve_ocp`. -The :func:`~flatsys.solve_flat_ocp` function can be used to -solve an optimal control problem without a final state:: +The :func:`~flatsys.solve_flat_ocp` function can be used to solve an +optimal control problem for a differentially flat system without a +final state constraint:: traj = fs.solve_flat_ocp( - sys, timepts, x0, u0, cost, basis=basis) + sys, timepts, x0, u0, cost, basis=basis) The `cost` parameter is a function with call signature `cost(x, u)` and should return the (incremental) cost at the given @@ -209,16 +208,18 @@ function for the final point in the trajectory. Example ------- -To illustrate how we can use a two degree-of-freedom design to improve the -performance of the system, consider the problem of steering a car to change -lanes on a road. We use the non-normalized form of the dynamics, which are -derived in *Feedback Systems* by Astrom and Murray, Example 3.11. +To illustrate how we can differential flatness to generate a feasible +trajectory, consider the problem of steering a car to change lanes on +a road. We use the non-normalized form of the dynamics, which are +derived in `Feedback Systems +`, Example 3.11 (Vehicle +Steering). .. code-block:: python + import numpy as np import control as ct import control.flatsys as fs - import numpy as np # Function to take states, inputs and return the flat flag def vehicle_flat_forward(x, u, params={}): @@ -290,8 +291,9 @@ steering wheel angle :math:`\delta` at the endpoints. traj = fs.point_to_point(vehicle_flat, Tf, x0, u0, xf, uf, basis=poly) # Create the trajectory - t = np.linspace(0, Tf, 100) - x, u = traj.eval(t) + timepts = np.linspace(0, Tf, 100) + xd, ud = traj.eval(timepts) + resp_p2p = ct.input_output_response(vehicle_flat, timepts, ud, X0=xd[:, 0]) Alternatively, we can solve an optimal control problem in which we minimize a cost function along the trajectory as well as a terminal @@ -308,20 +310,35 @@ cost: vehicle_flat, np.diag([1e3, 1e3, 1e3]), None, x0=xf) # Use a straight line as the initial guess - timepts = np.linspace(0, Tf, 10) + evalpts = np.linspace(0, Tf, 10) initial_guess = np.array( - [x0[i] + (xf[i] - x0[i]) * timepts/Tf for i in (0, 1)]) + [x0[i] + (xf[i] - x0[i]) * evalpts/Tf for i in (0, 1)]) # Solve the optimal control problem, evaluating cost at timepts bspline = fs.BSplineFamily([0, Tf/2, Tf], 4) traj = fs.solve_flat_ocp( - vehicle_flat, timepts, x0, u0, traj_cost, + vehicle_flat, evalpts, x0, u0, traj_cost, terminal_cost=term_cost, initial_guess=initial_guess, basis=bspline) - x, u = traj.eval(t) + xd, ud = traj.eval(timepts) + resp_ocp = ct.input_output_response(vehicle_flat, timepts, ud, X0=xd[:, 0]) + +The results of the two approaches can be shown using the +`time_response_plot` function:: + + cplt = ct.time_response_plot( + ct.combine_time_responses([resp_p2p, resp_ocp]), + overlay_traces=True, trace_labels=['point_to_point', 'solve_ocp']) -Module classes and functions ----------------------------- +.. image:: figures/flatsys-steering-compare.png + :align: center + + +Sub-package classes and functions +--------------------------------- + +The flat systems sub-package `flatsys` utilizes a number of classes to +define the flatsystem, the basis functions, and the system trajetory: .. autosummary:: :template: custom-class-template.rst @@ -334,6 +351,9 @@ Module classes and functions flatsys.PolyFamily flatsys.SystemTrajectory +The following functions can be used to define a flat system and +compute trajectories: + .. autosummary:: flatsys.flatsys diff --git a/doc/functions.rst b/doc/functions.rst index c26316e8f..c92c0b18d 100644 --- a/doc/functions.rst +++ b/doc/functions.rst @@ -45,7 +45,7 @@ Functions that transform systems from one form to another: .. _interconnections-ref: -System interconnections +System Interconnections ======================= .. autosummary:: @@ -181,11 +181,11 @@ State space synthesis: .. autosummary:: :toctree: generated/ - acker create_statefbk_iosystem dlqr lqr place + place_acker place_varga Frequency domain synthesis: diff --git a/doc/index.rst b/doc/index.rst index f61c0a92b..ae6f7c33c 100644 --- a/doc/index.rst +++ b/doc/index.rst @@ -2,7 +2,7 @@ Python Control Systems Library ############################## -The Python Control Systems Library (`python-control`) is a Python +The Python Control Systems Library (python-control) is a Python package that implements basic operations for analysis and design of feedback control systems. diff --git a/doc/iosys.rst b/doc/iosys.rst index fa3059d4e..265779885 100644 --- a/doc/iosys.rst +++ b/doc/iosys.rst @@ -41,17 +41,17 @@ The following operators are defined to operate between I/O systems: - Multiply a transfer function by itself ``n`` times - N/A -If either of the systems is a number or an array of appropriate +If either of the systems is a scalar or an array of appropriate dimension, then the appropriate scalar or matrix operation is performed. Systems of different types can be combined using these operations, with the following rules: -* If the two systems can be converted into the type of the other, the +* If both systems can be converted into the type of the other, the leftmost system determines the type of the output. -* If one system can be converted into the other, then the more general +* If only one system can be converted into the other, then the more general system determines the type of the output. In particular: - State space and transfer function systems can be converted to @@ -94,8 +94,8 @@ so the dynamics illustrate above can be created using the command An optional `gain` parameter can be used to change the sign of the gain. -For LTI systems, the :func:`feedback` operation is also implemented -via the :func:`LTI.feedback` method, so if `G1` is an LTI system then +The :func:`feedback` function is also implemented via the +:func:`LTI.feedback` method, so if `G1` is an input/output system then the following command will also work:: Gyu = G1.feedback(G2) @@ -123,8 +123,10 @@ inputs and outputs could be constructed using the command clsys = ct.interconnect( [plant, controller], name='system', - connections=[['controller.e', '-plant.y']], - inplist=['controller.e'], inputs='r', + connections=[ + ['controller.u', '-plant.y'], + ['plant.u', 'controller.y']], + inplist=['controller.u'], inputs='r', outlist=['plant.y'], outputs='y') The remainder of this section provides a detailed description of the @@ -138,13 +140,18 @@ To illustrate the use of the :func:`interconnect` function, we create a model for a predator/prey system, following the notation and parameter values in `Feedback Systems `_. -We begin by defining the dynamics of the system +We begin by defining the dynamics of the system: -.. code-block:: python +.. testsetup:: predprey + + import matplotlib.pyplot as plt + plt.close('all') + +.. testcode:: predprey - import control as ct - import numpy as np import matplotlib.pyplot as plt + import numpy as np + import control as ct def predprey_rhs(t, x, u, params): # Parameter setup @@ -170,48 +177,50 @@ We begin by defining the dynamics of the system We now create an input/output system using these dynamics: -.. code-block:: python +.. testcode:: predprey - io_predprey = ct.nlsys( - predprey_rhs, None, inputs=('u'), outputs=('H', 'L'), - states=('H', 'L'), name='predprey') + predprey = ct.nlsys( + predprey_rhs, None, inputs=['u'], outputs=['Hares', 'Lynxes'], + states=['H', 'L'], name='predprey') Note that since we have not specified an output function, the entire state will be used as the output of the system. -The `io_predprey` system can now be simulated to obtain the open loop +The `predprey` system can now be simulated to obtain the open loop dynamics of the system: -.. code-block:: python +.. testcode:: predprey - X0 = [25, 20] # Initial H, L - T = np.linspace(0, 70, 500) # Simulation 70 years of time + X0 = [25, 20] # Initial H, L + timepts = np.linspace(0, 70, 500) # Simulation 70 years of time - # Simulate the system - t, y = ct.input_output_response(io_predprey, T, 0, X0) + # Simulate the system and plots the results + resp = ct.input_output_response(predprey, timepts, 0, X0) + resp.plot(plot_inputs=False, overlay_signals=True, legend_loc='upper left') + +.. testcode:: predprey + :hide: - # Plot the response - plt.figure(1) - plt.plot(t, y[0]) - plt.plot(t, y[1]) - plt.legend(['Hare', 'Lynx']) - plt.show(block=False) + plt.savefig('figures/iosys-predprey-open.png') + +.. image:: figures/iosys-predprey-open.png + :align: center We can also create a feedback controller to stabilize a desired population of the system. We begin by finding the (unstable) equilibrium point for the system and computing the linearization about that point. -.. code-block:: python +.. testcode:: predprey - eqpt = ct.find_operating_point(io_predprey, X0, 0) - lin_predprey = ct.linearize(io_predprey, eqpt) + xeq, ueq = ct.find_operating_point(predprey, X0, 0) + lin_predprey = ct.linearize(predprey, xeq, ueq) We next compute a controller that stabilizes the equilibrium point using eigenvalue placement and computing the feedforward gain using the number of lynxes as the desired output (following `Feedback Systems `_, Example 7.5): -.. code-block:: python +.. testcode:: predprey K = ct.place(lin_predprey.A, lin_predprey.B, [-0.1, -0.2]) A, B = lin_predprey.A, lin_predprey.B @@ -223,49 +232,48 @@ applies a corrective input based on deviations from the equilibrium point. This system has no dynamics, since it is a static (affine) map, and can constructed using :func:`nlsys` with no update function: -.. code-block:: python +.. testcode:: predprey - io_controller = ct.nlsys( + controller = ct.nlsys( None, - lambda t, x, u, params: -K @ (u[1:] - xeq) + kf * (u[0] - xeq[1]), - inputs=('Ld', 'u1', 'u2'), outputs=1, name='control') + lambda t, x, u, params: -K @ (u[1:] - xeq) + kf * (u[0] - ueq), + inputs=['Ld', 'H', 'L'], outputs=1, name='control') -The input to the controller is `u`, consisting of the vector of hare and lynx -populations followed by the desired lynx population. +The input to the controller is `u`, consisting of the desired lynx +population followed by the vector of hare and lynx populations. To connect the controller to the predatory-prey model, we use the :func:`interconnect` function: -.. code-block:: python +.. testcode:: predprey - io_closed = ct.interconnect( - [io_predprey, io_controller], # systems + closed = ct.interconnect( + [predprey, controller], # systems connections=[ ['predprey.u', 'control.y[0]'], - ['control.u1', 'predprey.H'], - ['control.u2', 'predprey.L'] + ['control.H', 'predprey.Hares'], + ['control.L', 'predprey.Lynxes'] ], - inplist=['control.Ld'], - outlist=['predprey.H', 'predprey.L', 'control.y[0]'] + inplist=['control.Ld'], inputs='Ld', + outlist=['predprey.Hares', 'predprey.Lynxes', 'control.y[0]'], + outputs=['Hares', 'Lynxes', 'u0'], name='closed' ) Finally, we simulate the closed loop system: -.. code-block:: python +.. testcode:: predprey # Simulate the system - t, y = ct.input_output_response(io_closed, T, 30, [15, 20]) - - # Plot the response - plt.figure(2) - plt.subplot(2, 1, 1) - plt.plot(t, y[0]) - plt.plot(t, y[1]) - plt.legend(['Hare', 'Lynx']) - plt.subplot(2, 1, 2) - plt.plot(t, y[2]) - plt.legend(['input']) - plt.show(block=False) + resp = ct.input_output_response(closed, timepts, 30, [15, 20]) + resp.plot(plot_inputs=False, overlay_signals=True, legend_loc='upper left') + +.. testcode:: predprey + :hide: + + plt.savefig('figures/iosys-predprey-closed.png') + +.. image:: figures/iosys-predprey-closed.png + :align: center This example shows the standard operations that would be used to build up an interconnected nonlinear system. The I/O systems module has a @@ -273,6 +281,72 @@ number of other features that can be used to simplify the creation and use of interconnected input/output systems. +Summing junction +---------------- + +The :func:`summing_junction` function can be used to create an +input/output system that takes the sum of an arbitrary number of inputs. For +example, to create an input/output system that takes the sum of three inputs, +use the command + +.. code-block:: python + + sumblk = ct.summing_junction(3) + +By default, the name of the inputs will be of the form 'u[i]' and the output +will be 'y'. This can be changed by giving an explicit list of names:: + + sumblk = ct.summing_junction(inputs=['a', 'b', 'c'], output='d') + +A more typical usage would be to define an input/output system that +compares a reference signal to the output of the process and computes +the error:: + + sumblk = ct.summing_junction(inputs=['r', '-y'], output='e') + +Note the use of the minus sign as a means of setting the sign of the +input 'y' to be negative instead of positive. + +It is also possible to define "vector" summing blocks that take +multi-dimensional inputs and produce a multi-dimensional output. For +example, the command + +.. code-block:: python + + sumblk = ct.summing_junction(inputs=['r', '-y'], output='e', dimension=2) + +will produce an input/output block that implements ``e[0] = r[0] - y[0]`` and +``e[1] = r[1] - y[1]``. + + +Automatic connections using signal names +---------------------------------------- + +The :func:`interconnect` function allows the interconnection of +multiple systems by using signal names of the form 'sys.signal'. In many +situations, it can be cumbersome to explicitly connect all of the appropriate +inputs and outputs. As an alternative, if the `connections` keyword is +omitted, the :func:`interconnect` function will connect all signals +of the same name to each other. This can allow for simplified methods of +interconnecting systems, especially when combined with the +:func:`summing_junction` function. For example, the following code +will create a unity gain, negative feedback system:: + + P = ct.tf([1], [1, 0], inputs='u', outputs='y') + C = ct.tf([10], [1, 1], inputs='e', outputs='u') + sumblk = ct.summing_junction(inputs=['r', '-y'], output='e') + T = ct.interconnect([P, C, sumblk], inplist='r', outlist='y') + +If a signal name appears in multiple outputs then that signal will be summed +when it is interconnected. Similarly, if a signal name appears in multiple +inputs then all systems using that signal name will receive the same input. +The :func:`interconnect` function will generate an error if a signal +listed in `inplist` or `outlist` (corresponding to the inputs and outputs +of the interconnected system) is not found, but inputs and outputs of +individual systems that are not connected to other systems are left +unconnected (so be careful!). + + Vector element processing -------------------------- @@ -353,72 +427,6 @@ In this command, the second and third arguments will be broadcast to match the number of time points. -Summing junction ----------------- - -The :func:`summing_junction` function can be used to create an -input/output system that takes the sum of an arbitrary number of inputs. For -example, to create an input/output system that takes the sum of three inputs, -use the command - -.. code-block:: python - - sumblk = ct.summing_junction(3) - -By default, the name of the inputs will be of the form 'u[i]' and the output -will be 'y'. This can be changed by giving an explicit list of names:: - - sumblk = ct.summing_junction(inputs=['a', 'b', 'c'], output='d') - -A more typical usage would be to define an input/output system that -compares a reference signal to the output of the process and computes -the error:: - - sumblk = ct.summing_junction(inputs=['r', '-y'], output='e') - -Note the use of the minus sign as a means of setting the sign of the -input 'y' to be negative instead of positive. - -It is also possible to define "vector" summing blocks that take -multi-dimensional inputs and produce a multi-dimensional output. For -example, the command - -.. code-block:: python - - sumblk = ct.summing_junction(inputs=['r', '-y'], output='e', dimension=2) - -will produce an input/output block that implements ``e[0] = r[0] - y[0]`` and -``e[1] = r[1] - y[1]``. - - -Automatic connections using signal names ----------------------------------------- - -The :func:`interconnect` function allows the interconnection of -multiple systems by using signal names of the form `sys.signal`. In many -situations, it can be cumbersome to explicitly connect all of the appropriate -inputs and outputs. As an alternative, if the `connections` keyword is -omitted, the :func:`interconnect` function will connect all signals -of the same name to each other. This can allow for simplified methods of -interconnecting systems, especially when combined with the -:func:`summing_junction` function. For example, the following code -will create a unity gain, negative feedback system:: - - P = ct.tf([1], [1, 0], inputs='u', outputs='y') - C = ct.tf([10], [1, 1], inputs='e', outputs='u') - sumblk = ct.summing_junction(inputs=['r', '-y'], output='e') - T = ct.interconnect([P, C, sumblk], inplist='r', outlist='y') - -If a signal name appears in multiple outputs then that signal will be summed -when it is interconnected. Similarly, if a signal name appears in multiple -inputs then all systems using that signal name will receive the same input. -The :func:`interconnect` function will generate an error if a signal -listed in `inplist` or `outlist` (corresponding to the inputs and outputs -of the interconnected system) is not found, but inputs and outputs of -individual systems that are not connected to other systems are left -unconnected (so be careful!). - - Advanced specification of signal names -------------------------------------- @@ -449,20 +457,28 @@ negative indices and step specifications are not supported. Using these various forms can simplify the specification of interconnections. For example, consider a process with inputs 'u' and 'v', each of dimension 2, and two outputs 'w' and 'y', each of -dimension 2:: +dimension 2: - P = ct.rss( - states=6, name='P', strictly_proper=True, - inputs=['u[0]', 'u[1]', 'v[0]', 'v[1]'], - outputs=['y[0]', 'y[1]', 'z[0]', 'z[1]']) +.. testcode:: interconnect + + P = ct.ss( + np.diag([-1, -2, -3, -4]), np.eye(4), np.eye(4), 0, name='P', + inputs=['u[0]', 'u[1]', 'v[0]', 'v[1]'], + outputs=['y[0]', 'y[1]', 'z[0]', 'z[1]']) Suppose we construct a controller with 2 inputs and 2 outputs that -takes the (2-dimensional) error 'e' and outputs and control signal 'u':: +takes the (2-dimensional) error 'e' and outputs and control signal 'u': + +.. testcode:: interconnect - C = ct.rss(4, 2, 2, name='C', input_prefix='e', output_prefix='u') + C = ct.ss( + [], [], [], [[3, 0], [0, 4]], + name='C', input_prefix='e', output_prefix='u') Finally, we include a summing block that will take the difference between -the reference input 'r' and the measured output 'y':: +the reference input 'r' and the measured output 'y': + +.. testcode:: interconnect sumblk = ct.summing_junction( inputs=['r', '-y'], outputs='e', dimension=2, name='sum') @@ -474,7 +490,9 @@ the closed loop system to consist of all system outputs 'y' and 'z', as well as the controller input 'u'. This collection of systems can be combined in a variety of ways. The -most explicit would specify every signal:: +most explicit would specify every signal: + +.. testcode:: interconnect clsys1 = ct.interconnect( [C, P, sumblk], @@ -487,7 +505,9 @@ most explicit would specify every signal:: outlist=['P.y[0]', 'P.y[1]', 'P.z[0]', 'P.z[1]', 'C.u[0]', 'C.u[1]'] ) -This connections can be simplified using signal ranges:: +This connections can be simplified using signal ranges: + +.. testcode:: interconnect clsys2 = ct.interconnect( [C, P, sumblk], @@ -501,7 +521,9 @@ This connections can be simplified using signal ranges:: ) An even simpler form can be used by omitting the range specification -when all signals with the same prefix are used:: +when all signals with the same prefix are used: + +.. testcode:: interconnect clsys3 = ct.interconnect( [C, P, sumblk], @@ -510,17 +532,21 @@ when all signals with the same prefix are used:: ) A further simplification is possible when all of the inputs or outputs -of an individual system are used in a given specification:: +of an individual system are used in a given specification: + +.. testcode:: interconnect clsys4 = ct.interconnect( - [C, P, sumblk], + [C, P, sumblk], name='clsys4', connections=[['P.u', 'C'], ['C', 'sum'], ['sum.y', 'P.y']], inplist=['sum.r', 'P.v'], outlist=['P', 'C.u'] ) And finally, since we have named the signals throughout the system in a consistent way, we could let :func:`interconnect` do all of the -work:: +work: + +.. testcode:: interconnect clsys5 = ct.interconnect( [C, P, sumblk], inplist=['sum.r', 'P.v'], outlist=['P', 'C.u'] @@ -532,14 +558,86 @@ complicated to debug error message when things go wrong. Setting information about how the arguments are processed that may be helpful in understanding what is going wrong. +If the system is constructed successfully but the system does not seem +to behave correctly, the `print` function can be used to show the +interconnections and outputs: + +.. doctest:: interconnect + + >>> print(clsys4) + : clsys4 + Inputs (4): ['u[0]', 'u[1]', 'u[2]', 'u[3]'] + Outputs (6): ['y[0]', 'y[1]', 'y[2]', 'y[3]', 'y[4]', 'y[5]'] + States (4): ['P_x[0]', 'P_x[1]', 'P_x[2]', 'P_x[3]'] + + Subsystems (3): + * ['u[0]', 'u[1]'], dt=None> + * ['y[0]', 'y[1]', 'z[0]', + 'z[1]']> + * ['e[0]', 'e[1]'], + dt=None> + + Connections: + * C.e[0] <- sum.e[0] + * C.e[1] <- sum.e[1] + * P.u[0] <- C.u[0] + * P.u[1] <- C.u[1] + * P.v[0] <- u[2] + * P.v[1] <- u[3] + * sum.r[0] <- u[0] + * sum.r[1] <- u[1] + * sum.y[0] <- P.y[0] + * sum.y[1] <- P.y[1] + + Outputs: + * y[0] <- P.y[0] + * y[1] <- P.y[1] + * y[2] <- P.z[0] + * y[3] <- P.z[1] + * y[4] <- C.u[0] + * y[5] <- C.u[1] + + A = [[-4. 0. 0. 0.] + [ 0. -6. 0. 0.] + [ 0. 0. -3. 0.] + [ 0. 0. 0. -4.]] + + B = [[3. 0. 0. 0.] + [0. 4. 0. 0.] + [0. 0. 1. 0.] + [0. 0. 0. 1.]] + + C = [[ 1. 0. 0. 0.] + [ 0. 1. 0. 0.] + [ 0. 0. 1. 0.] + [ 0. 0. 0. 1.] + [-3. 0. 0. 0.] + [ 0. -4. 0. 0.]] + + D = [[0. 0. 0. 0.] + [0. 0. 0. 0.] + [0. 0. 0. 0.] + [0. 0. 0. 0.] + [3. 0. 0. 0.] + [0. 4. 0. 0.]] + Automated creation of state feedback systems --------------------------------------------- +============================================ + +A common architecture in state space feedback control is to use a +linear control law to stabilize a system around a trajectory. The +python-control package can create input/output systems that help +implement this architecture. -The :func:`create_statefbk_iosystem` function can be used to -create an I/O system consisting of a state feedback gain (with -optional integral action and gain scheduling) and an estimator. A -basic state feedback controller of the form + +Standard design patterns +------------------------ + +The :func:`create_statefbk_iosystem` function can be used to create an +I/O system consisting of a state feedback gain (with optional integral +action and gain scheduling) and an estimator. A basic state feedback +controller of the form .. math:: @@ -575,10 +673,17 @@ A reference gain controller can be created with the command:: sys, K, kf, feedfwd_pattern='refgain') This reference gain design pattern is described in more detail in -Section 7.2 of `Feedback Systems `_ (Stabilization +`Feedback Systems `_, Section 7.2 (Stabilization by State Feedback) and the trajectory generation design pattern is described in Section 8.5 (State Space Controller Design). + +Adding state estimation +----------------------- + +.. todo:: + Add `create_estimator_iosystem` + If the full system state is not available, the output of a state estimator can be used to construct the controller using the command:: @@ -590,6 +695,10 @@ replaced by the estimated state :math:`\hat x` (output of `estim`). The closed loop controller will include both the state feedback and the estimator. + +Adding integral action +---------------------- + Integral action can be included using the `integral_action` keyword. The value of this keyword can either be a matrix (ndarray) or a function. If a matrix :math:`C` is specified, the difference between @@ -616,6 +725,10 @@ must match the number of additional columns in the `K` matrix. If an estimator is specified, :math:`\hat x` will be used in place of :math:`x`. + +Adding gain scheduling +---------------------- + Finally, for the trajectory generation design pattern, gain scheduling on the desired state, desired input, or system state can be implemented by setting the gain to a 2-tuple consisting of a list of gains and a list of @@ -641,3 +754,6 @@ bottom of the file). Integral action and state estimation can also be used with gain scheduled controllers. + +.. todo:: + Add example? diff --git a/doc/linear.rst b/doc/linear.rst index fda71993f..a5ce47902 100644 --- a/doc/linear.rst +++ b/doc/linear.rst @@ -4,7 +4,7 @@ Linear System Modeling, Analysis, and Design ******************************************** -Linear time invariant (LTI) systems are represented in python-control in +Linear time invariant (LTI) systems are represented in `python-control` in state space, transfer function, or frequency response data (FRD) form. Most functions in the toolbox will operate on any of these data types, and functions for converting between compatible types are provided. @@ -167,9 +167,11 @@ Multi-input, multi-output (MIMO) systems Multi-input, multi-output (MIMO) systems are created by providing parameters of the appropriate dimensions to the relevant factory -function. For transfer functions, this is done by providing a 2D list -of numerator and denominator polynomials to the :func:`tf` function, -e.g.:: +function. For state space systems, the input matrix `B`, output +matrix `C`, and direct term `D` should be 2D matrices of the +appropriate shape. For transfer functions, this is done by providing +a 2D list of numerator and denominator polynomials to the :func:`tf` +function, e.g.:: sys = ct.tf( [[num11, num12], [num21, num22]], @@ -232,18 +234,21 @@ response. .. _discrete_time_systems: - Discrete Time Systems ===================== A discrete time system is created by specifying a nonzero "timebase", -`dt`. The timebase argument can be given when a system is -constructed: +`dt` when the system is constructed:: + + sys_ss = ct.ss(A, B, C, D, dt) + sys_tf = ct.tf(num, den, dt) + +The timebase argument is interpreted as follows: * ``dt = 0``: continuous time system (default) * ``dt > 0``: discrete time system with sampling period 'dt' * ``dt = True``: discrete time with unspecified sampling period -* ``dt = None``: no timebase specified +* ``dt = None``: no timebase specified (see below) Systems must have compatible timebases in order to be combined. A discrete time system with unspecified sampling time (``dt = True``) @@ -256,18 +261,25 @@ timebase of the latter system. For continuous time systems, the :meth:`TransferFunction.sample` methods can be used to create a discrete time system from a continuous time system. See :ref:`utility-and-conversions`. The default value of `dt` can be -changed by changing the value of -``config.defaults['control.default_dt']``. +changed by changing the value of ``config.defaults['control.default_dt']``. Functions operating on LTI systems will take into account whether a system is continuous time or discrete time when carrying out operations that depend on this difference. For example, the :func:`rss` function will place all system eigenvalues within the unit circle when called -using `dt` corresponding to a discrete time system:: +using `dt` corresponding to a discrete time system: + +.. testsetup:: + + import random + random.seed(117) + np.random.seed(117) + +.. doctest:: >>> sys = ct.rss(2, 1, 1, dt=True) >>> sys.poles() - array([-0.78096961+0.j, 0.09347055+0.j]) + array([-0.53807661+0.j, 0.86313342+0.j]) .. include:: statesp.rst @@ -325,33 +337,34 @@ Time sampling ------------- Continuous time systems can be converted to discrete time systems using -the :func:`sample_system` function and specifying a sampling time:: +the :func:`sample_system` function and specifying a sampling time: + +.. doctest:: - >>> csys = ct.rss(4, 2, 2, name='csys') - >>> dsys = ct.sample_system(csys, 0.1, method='bilinear') - >>> print(dsys) - : cys$sampled + >>> sys_ct = ct.rss(4, 2, 2, name='sys') + >>> sys_dt = ct.sample_system(sys_ct, 0.1, method='bilinear') + >>> print(sys_dt) + : sys$sampled Inputs (2): ['u[0]', 'u[1]'] Outputs (2): ['y[0]', 'y[1]'] States (4): ['x[0]', 'x[1]', 'x[2]', 'x[3]'] - - A = [[ 1.80851713 3.70669158 2.01645278 4.11088725] - [-1.08922089 -1.56305649 -0.52225931 -1.44683994] - [ 0.09926437 0.31372724 1.01817342 0.4445761 ] - [ 0.1936853 0.3376456 0.0166808 0.96877128]] - - B = [[-0.07840985 -0.39220354] - [-0.00693812 0.12585167] - [-0.14077857 0.02673465] - [ 0.05062632 -0.08018257]] - - C = [[-0.17214877 -0.06055247 -0.09715775 0.03881976] - [-1.60336436 -2.11612637 -1.15117992 -2.34687907]] - - D = [[0.00704611 0.01107279] - [0.04476368 0.22390648]] - dt = 0.1 + + A = [[-0.79324497 -0.51484336 -1.09297036 -0.05363047] + [-3.5428559 -0.9340972 -1.85691838 -0.74843144] + [ 3.90565206 1.9409475 3.21968314 0.48558594] + [ 3.47315264 1.55258121 2.09562768 1.25466845]] + + B = [[-0.01098544 0.00485652] + [-0.41579876 0.02204956] + [ 0.45553908 -0.02459682] + [ 0.50510046 -0.05448362]] + + C = [[-2.74490135 -0.3064149 -2.27909612 -0.64793559] + [ 2.56376145 1.09663807 2.4332544 0.30768752]] + + D = [[-0.34680884 0.02138098] + [ 0.29124186 -0.01476461]] Note that the system name for the discrete time system is the name of the original system with the string '$sampled' appended. @@ -360,7 +373,7 @@ Discrete time systems can also be created using the :func:`StateSpace.sample` or :func:`TransferFunction.sample` methods applied directly to the system:: - dsys = csys.sample(0.1) + sys_dt = sys_ct.sample(0.1) Frequency sampling @@ -369,24 +382,26 @@ Frequency sampling Transfer functions can be sampled at a selected set of frequencies to obtain a frequency response data representation of a system by calling the :func:`frd` factory function with an LTI system and an -array of frequencies:: +array of frequencies: + +.. doctest:: - >>> sys_ss = ct.ss(4, 1, 1, name='sys_ss') + >>> sys_ss = ct.rss(4, 1, 1, name='sys_ss') >>> sys_frd = ct.frd(sys_ss, np.logspace(-1, 1, 5)) >>> print(sys_frd) : sys_ss$sampled Inputs (1): ['u[0]'] Outputs (1): ['y[0]'] - + Freq [rad/s] Response ------------ --------------------- - 0.100 2.357 -3.909j - 0.316 -0.6068 -2.216j - 1.000 -1.383 -0.9971j - 3.162 -1.666 -0.3708j - 10.000 -1.703 -0.1186j + 0.100 -0.2648+0.0006429j + 0.316 -0.2653 +0.003783j + 1.000 -0.2561 +0.008021j + 3.162 -0.2528 -0.001438j + 10.000 -0.2578 -0.002443j -The :func:`frequency_response` function an also be used for this +The :func:`frequency_response` function can also be used for this purpose, although in that case the output is usually used for plotting the frequency response, as described in more detail in the :ref:`frequency_response` section. @@ -409,14 +424,14 @@ simplification: The :func:`balanced_reduction` function eliminate states based on the Hankel singular values of a system. Intuitively, a system (or -subsystem) with small Hankel singular values correspond to a situation +subsystem) with small Hankel singular values corresponds to a situation in which it is difficult to observe a state and/or difficult to control that state. Eliminating states corresponding to small Hankel singular values thus represents a good approximation in terms of the input/output properties of a system. For systems with unstable modes, :func:`balanced_reduction` first removes the states corresponding to the unstable subspace from the system, then carries out a balanced -realization on the stable part, then reinserts the unstable modes. +realization on the stable part, and then reinserts the unstable modes. The :func:`minimal_realization` function eliminates uncontrollable or unobservable states in state space models or cancels pole-zero pairs @@ -442,7 +457,9 @@ Displaying LTI System Information ================================= Information about an LTI system can be obtained using the Python -`~python.print` function:: +`~python.print` function: + +.. doctest:: >>> sys = ct.rss(4, 2, 2, name='sys_2x2') >>> print(sys) @@ -450,46 +467,56 @@ Information about an LTI system can be obtained using the Python Inputs (2): ['u[0]', 'u[1]'] Outputs (2): ['y[0]', 'y[1]'] States (4): ['x[0]', 'x[1]', 'x[2]', 'x[3]'] - - A = [[ 64.81683274 -26.26662488 71.02177207 -107.97639093] - [ 39.61359988 -16.12926876 41.82185164 -65.62112231] - [ -30.6112503 12.44992996 -34.00905316 49.74919788] - [ 10.43751558 -4.08781262 10.64062604 -18.15145265]] - - B = [[ 2.13327172 -0. ] - [ 0.98132238 0. ] - [-1.22635806 -1.57449988] - [ 0. 0.97986024]] - - C = [[-0. 1.34197324 0.28470822 0. ] - [-0.2777818 0. -0.60024615 -0.19122287]] - - D = [[-0. 0.] - [ 0. 0.]] - -For a more compact representation, LTI objects can be evaluated directly -to return a summary of the system input/output properties:: - - >>> sys = ct.rss(4, 2, 2, name='sys_2x2') + + A = [[-2.06417506 0.28005277 0.49875395 -0.40364606] + [-0.18000232 -0.91682581 0.03179904 -0.16708786] + [-0.7963147 0.19042684 -0.72505525 -0.52196969] + [ 0.69457346 -0.20403756 -0.59611373 -0.94713748]] + + B = [[-2.3400013 -1.02252469] + [-0.76682007 -0. ] + [ 0.13399373 0.94404387] + [ 0.71412443 -0.45903835]] + + C = [[ 0.62432205 -0.55879494 -0.08717116 1.05092654] + [-0.94352373 0.19332285 1.05341936 0.60141772]] + + D = [[ 0. 0.] + [-0. 0.]] + +A loadable description of a system can be obtained just by displaying +the system object:: + +.. doctest:: + + >>> sys = ct.rss(2, 1, 1, name='sys_siso') >>> sys - ['y[0]', 'y[1]']> + StateSpace( + array([[ 0.91008302, -0.87770371], + [ 6.83039608, -5.19117213]]), + array([[0.9810374], + [0.516694 ]]), + array([[1.38255365, 0.96999883]]), + array([[-0.]]), + name='sys_siso', states=2, outputs=1, inputs=1) Alternative representations of the system are available using the -:func:`iosys_repr` function. +:func:`iosys_repr` function and can be configured using +`config.defaults['iosys.repr_format']`. Transfer functions are displayed as ratios of polynomials, using either 's' or 'z' depending on whether the systems is continuous or -discrete time:: +discrete time: + +.. doctest:: - >>> sys_tf = ct.tf([1, 0], [1, 2, 1], 0.1) + >>> sys_tf = ct.tf([1, 0], [1, 2, 1], 0.1, name='sys') >>> print(sys_tf) - : sys[1] + : sys Inputs (1): ['u[0]'] Outputs (1): ['y[0]'] - - z - ------------- - z^2 + 2 z + 1 - dt = 0.1 - + + z + ------------- + z^2 + 2 z + 1 diff --git a/doc/nlsys.rst b/doc/nlsys.rst index 094e6abb2..02c5fe746 100644 --- a/doc/nlsys.rst +++ b/doc/nlsys.rst @@ -8,15 +8,21 @@ of the form .. math:: - \frac{dx}{dt} &= f(t, x, u, \theta) \\ - y &= h(t, x, u, \theta) + \frac{dx}{dt} &= f(t, x, u, \theta), \\ + y &= h(t, x, u, \theta), where :math:`t` represents the current time, :math:`x` is the system -state, math:`u` is the system input, :math:`y` is the system output, +state, :math:`u` is the system input, :math:`y` is the system output, and :math:`\theta` represents a set of parameters. Discrete time systems are also supported and have dynamics of the form +.. math:: + + x[t+1] &= f(t, x[t], u[t], \theta), \\ + y[t] &= h(t, x[t], u[t], \theta). + + Creating nonlinear models ------------------------- @@ -38,7 +44,7 @@ time (use the `dt` keyword to create a discrete time system). The output function `outfcn` is used to specify the outputs of the system and has the same calling signature as `updfcn`. If it is not specified, then the output of the system is set equal to the system -state. Otherwise, it should return an output of shape (p,). +state. Otherwise, it should return an array of shape (p,). Note that the number of states, inputs, and outputs should generally be explicitly specified, although some operations can infer the @@ -54,7 +60,9 @@ simple model of a spring loaded arm driven by a motor: :width: 240 :align: center -The dynamics of this system can be modeling using the following code:: +The dynamics of this system can be modeling using the following code: + +.. testcode:: # Parameter values servomech_params = { @@ -95,7 +103,9 @@ The dynamics of this system can be modeling using the following code:: outputs=['y', 'thdot'], inputs=['tau']) A summary of the model can be obtained using the string representation -of the model (via the Python `~python.print` function):: +of the model (via the Python `~python.print` function): + +.. doctest:: >>> print(servomech) : servomech @@ -103,9 +113,9 @@ of the model (via the Python `~python.print` function):: Outputs (2): ['y', 'thdot'] States (2): ['theta', 'thdot'] Parameters: ['J', 'b', 'k', 'r', 'l', 'eps'] - - Update: - Output: + + Update: + Output: Operating points and linearization @@ -127,7 +137,7 @@ output:: The first form finds an equilibrium point for a given input `u0` based on an initial guess `x0`. The second form fixes the desired output -values `y0` and uses x0 and u0 as an initial guess to find the +values `y0` and uses `x0` and `u0` as an initial guess to find the equilibrium point. If no equilibrium point can be found, the function returns the operating point that minimizes the state update (state derivative for continuous time systems, state difference for discrete @@ -146,7 +156,7 @@ Simulations and plotting To simulate an input/output system, use the :func:`input_output_response` function:: - resp = ct.input_output_response(io_sys, T, U, x0, params) + resp = ct.input_output_response(sys_nl, T, U, x0, params) t, y, x = resp.time, resp.outputs, resp.states Time responses can be plotted using the :func:`time_response_plot` @@ -156,10 +166,36 @@ method:: cplt = ct.time_response_plot(resp) The resulting :class:`ControlPlot` object can be used to access -different plot elements. The :func:`combine_time_responses` function -an be used to combine multiple time responses into a single -`TimeResponseData` object. See the :ref:`response-chapter` chapter -for more information on this functionality. +different plot elements: + +* `cplt.lines`: Array of `matplotlib.lines.Line2D` objects for each + line in the plot. The shape of the array matches the subplots shape + and the value of the array is a list of Line2D objects in that + subplot. + +* `cplt.axes`: 2D array of `matplotlib.axes.Axes` for the plot. + +* `cplt.figure`: `matplotlib.figure.Figure` containing the plot. + +* `cplt.legend`: legend object(s) contained in the plot. + +The :func:`combine_time_responses` function an be used to combine +multiple time responses into a single `TimeResponseData` object:: + + timepts = np.linspace(0, 10) + + U1 = np.sin(timepts) + resp1 = ct.input_output_response(servomech, timepts, U1) + + U2 = np.cos(2*timepts) + resp2 = ct.input_output_response(servomech, timepts, U2) + + cplt.ct.combine_time_responses( + [resp1, resp2], trace_labels=["Scenario #1", "Scenario #2"]).plot() + +.. image:: figures/timeplot-servomech-combined.png + :align: center + Nonlinear system properties --------------------------- @@ -180,12 +216,14 @@ current time, state, input, and (optionally) parameter values. The :func:`~NonlinearIOSystem.output` method returns the system output. For static nonlinear systems, it is also possible to obtain the value of the output by directly calling the system with the value of the -input:: +input: + +.. doctest:: >>> sys = ct.nlsys( ... None, lambda t, x, u, params: np.sin(u), inputs=1, outputs=1) >>> sys(1) np.float64(0.8414709848078965) -The :func:`~NonlinearIOSystem.linearize` method is equivalent to the +The :func:`NonlinearIOSystem.linearize` method is equivalent to the :func:`linearize` function. diff --git a/doc/nonlinear.rst b/doc/nonlinear.rst index b28ebb56f..63be7e4f3 100644 --- a/doc/nonlinear.rst +++ b/doc/nonlinear.rst @@ -6,7 +6,9 @@ Nonlinear System Modeling, Analysis, and Design The Python Control Systems Library contains a variety of tools for modeling, analyzing, and designing nonlinear feedback systems, -including support for simulation and optimization. +including support for simulation and optimization. This chapter +describes the primary functionality available, both in the core +python-control package and in specalized modules and sub-packages. .. include:: nlsys.rst diff --git a/doc/optimal.rst b/doc/optimal.rst index 14b81e425..54e049699 100644 --- a/doc/optimal.rst +++ b/doc/optimal.rst @@ -75,13 +75,14 @@ that the cost function is given by J(x, u) = \sum_{k=0}^{N-1} L(x_k, u_k)\, dt + V(x_N). -A common use of optimization-based control techniques is the implementation -of model predictive control (also called receding horizon control). In -model predictive control, a finite horizon optimal control problem is solved, -generating open-loop state and control trajectories. The resulting control -trajectory is applied to the system for a fraction of the horizon -length. This process is then repeated, resulting in a sampled data feedback -law. This approach is illustrated in the following figure: +A common use of optimization-based control techniques is the +implementation of model predictive control (MPC, also called receding +horizon control). In model predictive control, a finite horizon +optimal control problem is solved, generating open-loop state and +control trajectories. The resulting control trajectory is applied to +the system for a fraction of the horizon length. This process is then +repeated, resulting in a sampled data feedback law. This approach is +illustrated in the following figure: .. image:: figures/mpc-overview.png :width: 640 @@ -93,7 +94,7 @@ Every :math:`\Delta T` seconds, an optimal control problem is solved over a x(t))` is then applied to the system. If we let :math:`x_T^{\*}(\cdot; x(t))` represent the optimal trajectory starting from :math:`x(t)` then the system state evolves from :math:`x(t)` at current time :math:`t` to -:math:`x_T^{*}(\delta T, x(t))` at the next sample time :math:`t + \Delta +:math:`x_T^{*}(\Delta T, x(t))` at the next sample time :math:`t + \Delta T`, assuming no model uncertainty. In reality, the system will not follow the predicted path exactly, so that @@ -102,7 +103,7 @@ recompute the optimal path from the new state at time :math:`t + \Delta T`, extending our horizon by an additional :math:`\Delta T` units of time. This approach can be shown to generate stabilizing control laws under suitable conditions (see, for example, the FBS2e supplement on `Optimization-Based -Control `_. +Control `_). Module usage @@ -120,7 +121,7 @@ import `control.optimal`:: To describe an optimal control problem we need an input/output system, a time horizon, a cost function, and (optionally) a set of constraints on the -state and/or input, either along the trajectory and at the terminal time. +state and/or input, along the trajectory and/or at the terminal time. The optimal control module operates by converting the optimal control problem into a standard optimization problem that can be solved by :func:`scipy.optimize.minimize`. The optimal control problem can be solved @@ -129,8 +130,9 @@ by using the :func:`optimal.solve_ocp` function:: res = opt.solve_ocp(sys, timepts, X0, cost, constraints) The `sys` parameter should be an :class:`InputOutputSystem` and the -`timepts` parameter should represent a time vector that gives the list of -times at which the cost and constraints should be evaluated. +`timepts` parameter should represent a time vector that gives the list +of times at which the cost and constraints should be evaluated (the +time points need not be uniformly spaced). The `cost` function has call signature ``cost(t, x, u)`` and should return the (incremental) cost at the given time, state, and input. It @@ -138,9 +140,9 @@ will be evaluated at each point in the `timepts` vector. The `terminal_cost` parameter can be used to specify a cost function for the final point in the trajectory. -The `constraints` parameter is a list of constraints similar to that used by -the :func:`scipy.optimize.minimize` function. Each constraint is specified -using one of the following forms:: +The `constraints` parameter is a list of constraints similar to that +used by the :func:`scipy.optimize.minimize` function. Each constraint +is specified using one of the following forms:: LinearConstraint(A, lb, ub) NonlinearConstraint(f, lb, ub) @@ -160,8 +162,8 @@ A nonlinear constraint is satisfied if lb <= f(x, u) <= ub -By default, `constraints` are taken to be trajectory constraints holding at -all points on the trajectory. The `terminal_constraint` parameter can be +The `constraints` are taken as trajectory constraints holding at all +points on the trajectory. The `terminal_constraint` parameter can be used to specify a constraint that only holds at the final point of the trajectory. @@ -171,10 +173,11 @@ that has the following elements: * `res.success`: True if the optimization was successfully solved * `res.inputs`: optimal input * `res.states`: state trajectory (if `return_x` was True) - * `res.time`: copy of the time timepts vector + * `res.time`: copy of the time `timepts` vector In addition, the results from :func:`scipy.optimize.minimize` are also -available. +available as additional attributes, as described in +`scipy.optimize.OptimizeResult`. To simplify the specification of cost functions and constraints, the :mod:`optimal` module defines a number of utility functions for @@ -194,9 +197,11 @@ optimal control problems: Example ------- -Consider the vehicle steering example described in FBS2e. The dynamics of -the system can be defined as a nonlinear input/output system using the -following code:: +Consider the vehicle steering example described in Example 2.3 of +`Optimization-Based Control (OBC) +`_. The +dynamics of the system can be defined as a nonlinear input/output +system using the following code:: import numpy as np import control as ct @@ -291,10 +296,6 @@ yields .. image:: figures/steering-optimal.png :align: center -An example showing the use of the optimal estimation problem and moving -horizon estimation (MHE) is given in the :doc:`mhe-pvtol Jupyter -notebook `. - Optimization Tips ----------------- diff --git a/doc/phaseplot.rst b/doc/phaseplot.rst index 1a749341e..4832a4f34 100644 --- a/doc/phaseplot.rst +++ b/doc/phaseplot.rst @@ -1,5 +1,7 @@ .. currentmodule:: control +.. _phase-plane-plots: + Phase Plane Plots ================= @@ -92,10 +94,8 @@ The following helper functions are available: The :func:`phase_plane_plot` function calls these helper functions based on the options it is passed. -Note that unlike other plotting functions, phase plane plots do not involve -computing a response and then plotting the result via a ``plot()`` method. -Instead, the plot is generated directly be a call to the -:func:`phase_plane_plot` function (or one of the -:mod:`phaseplot` helper functions. - - +Note that unlike other plotting functions, phase plane plots do not +involve computing a response and then plotting the result via a +``plot()`` method. Instead, the plot is generated directly be a call +to the :func:`phase_plane_plot` function (or one of the +:mod:`phaseplot` helper functions). diff --git a/doc/response.rst b/doc/response.rst index 912c61267..55eb03161 100644 --- a/doc/response.rst +++ b/doc/response.rst @@ -20,13 +20,13 @@ in control system analysis, for example:: While plotting functions can be called directly, the standard pattern used in the toolbox is to provide a function that performs the basic computation or analysis (e.g., computation of the time or frequency response) and -returns and object representing the output data. A separate plotting -function, typically ending in `_plot` is then used to plot the data, +returns an object representing the output data. A separate plotting +function, typically ending in `_plot`, is then used to plot the data, resulting in the following standard pattern:: response = ct.nyquist_response([sys1, sys2]) count = ct.response.count # number of encirclements of -1 - cplt = ct.nyquist_plot(response) # Nyquist plot + cplt = ct.nyquist_plot(response) # Nyquist plot Plotting commands return a :class:`ControlPlot` object that provides access to the individual lines in the generated plot using @@ -46,9 +46,26 @@ The remainder of this chapter provides additional documentation on how these response and plotting functions can be customized. -Time response data +Time Response Data ================== +Time responses are used to provide information on the behavior of a +system in response to a standard input (such as a step function or +impulse function), the initial state with no input, a custom function +of time, or any combination of the above. Time responses are useful +for evaluating system performance of either linear or nonlinear +systems, in continous or discrete time. The time response for a +linear system to a standard input can be often computed exactly while +the responses of nonlinear systems or linear systems with arbitary +input signals must be computed numerically. + +Continous time signals in `python-control` are represented by the +value of the signal at a set of specified time points, with linear +interpolation between the time points. The time points need not be +uniformly spaced. Discrete time signals are represented by the value +of the signal at a uniformly-spaced sequence of times. + + LTI response functions ---------------------- @@ -66,9 +83,9 @@ Each of these functions returns a :class:`TimeResponseData` object that contains the data for the time response (described in more detail in the next section). -The :func:`forced_response` system is the most general and allows by -the zero initial state response to be simulated as well as the -response from a non-zero initial condition. +The :func:`forced_response` system is the most general and computes +the response of the system to a given input from a zero or non-zero +initial condition. For linear time invariant (LTI) systems, the :func:`impulse_response`, :func:`initial_response`, and :func:`step_response` functions will @@ -81,7 +98,7 @@ The :class:`TimeResponseList` object has a ``plot()`` method that will plot each of the responses in turn, using a sequence of different colors with appropriate titles and legends. -In addition the :func:`input_output_response` function, which handles +In addition, the :func:`input_output_response` function, which handles simulation of nonlinear systems and interconnected systems, can be used. For an LTI system, results are generally more accurate using the LTI simulation functions above. The :func:`input_output_response` @@ -91,6 +108,7 @@ function is described in more detail in the :ref:`iosys-module` section. Time series data ---------------- + A variety of functions in the library return time series data: sequences of values that change over time. A common set of conventions is used for returning such data: columns represent different points in time, rows are @@ -101,13 +119,12 @@ throughout the library, for example in the functions :func:`forced_response`, :func:`step_response`, :func:`impulse_response`, and :func:`initial_response`. -.. note:: - The convention used by python-control is different from the convention - used in the `scipy.signal - `_ library. In - Scipy's convention the meaning of rows and columns is interchanged. - Thus, all 2D values must be transposed when they are used with functions - from `scipy.signal`_. +.. note:: The convention used by `python-control` is different from + the convention used in the `scipy.signal + `_ + library. In Scipy's convention the meaning of rows and columns is + interchanged. Thus, all 2D values must be transposed when they + are used with functions from `scipy.signal`_. The time vector is a 1D array with shape (n, ):: @@ -197,7 +214,7 @@ The column labels for the data frame are `time` and the labels for the input, output, and state signals ('u[i]', 'y[i]', and 'x[i]' by default, but these can be changed using the `inputs`, `outputs`, and `states` keywords when constructing the system, as described in :func:`ss`, :func:`tf`, and other -system creation function. Note that when exporting to pandas, "rows" in the +system creation functions. Note that when exporting to pandas, "rows" in the data frame correspond to time and "cols" (DataSeries) correspond to signals. Time response plots @@ -211,12 +228,14 @@ state, and output vectors associated with the simulation, as described above. Time response data can be plotted with the :func:`time_response_plot` function, which is also available as the :func:`TimeResponseData.plot` method. For example, the step response -for a two-input, two-output can be plotted using the commands:: +for a two-input, two-output can be plotted using the commands: + +.. testcode:: sys_mimo = ct.tf2ss( [[[1], [0.1]], [[0.2], [1]]], [[[1, 0.6, 1], [1, 1, 1]], [[1, 0.4, 1], [1, 2, 1]]], name="sys_mimo") - response = ct.step_response(sys) + response = ct.step_response(sys_mimo) response.plot() which produces the following plot: @@ -237,7 +256,9 @@ corresponding to step inputs in different channels) on the same graph, with appropriate labeling via a legend on selected axes. For example, using ``plot_input=True`` and ``overlay_signals=True`` -yields the following plot:: +yields the following plot: + +.. testcode:: ct.step_response(sys_mimo).plot( plot_inputs=True, overlay_signals=True, @@ -309,11 +330,11 @@ and styles for various signals and traces:: .. _frequency_response: -Frequency response data +Frequency Response Data ======================= Linear time invariant (LTI) systems can be analyzed in terms of their -frequency response and python-control provides a variety of tools for +frequency response and `python-control` provides a variety of tools for carrying out frequency response analysis. The most basic of these is the :func:`frequency_response` function, which will compute the frequency response for one or more linear systems:: @@ -446,14 +467,14 @@ Frequency response data can also be accessed directly and plotted manually:: plt.loglog(fresp.omega, fresp.magnitude['y[1]', 'u[0]']) Access to frequency response data is available via the attributes -`omega`, `magnitude`,` `phase`, and `response`, where `response` +`omega`, `magnitude`, `phase`, and `response`, where `response` represents the complex value of the frequency response at each frequency. The `magnitude`, `phase`, and `response` arrays can be indexed using either input/output indices or signal names, with the first index corresponding to the output signal and the second input corresponding to the input signal. -Pole/zero data +Pole/Zero Data ============== Pole/zero maps and root locus diagrams provide insights into system @@ -510,7 +531,7 @@ for each system is plotted in different colors:: :align: center -Customizing control plots +Customizing Control Plots ========================= A set of common options are available to customize control plots in @@ -595,7 +616,7 @@ various ways. The following general rules apply: * The `rcParams` keyword argument can be used to override the default matplotlib style parameters used when creating a plot. The default parameters for all control plots are given by the - `config.defaults['rcParams'` dictionary and have the following + `config.defaults['ctrlplot.rcParams']` dictionary and have the following values: .. list-table:: @@ -620,7 +641,8 @@ various ways. The following general rules apply: Only those values that should be changed from the default need to be specified in the `rcParams` keyword argument. To override the defaults for all control plots, update the - `config.defaults['rcParams']` dictionary entries. + `config.defaults['ctrlplt.rcParams']` dictionary entries. For convenience, + this dictionary can alse be accessed as ``ct.rcParams``. The default values for style parameters for control plots can be restored using :func:`reset_rcParams`. @@ -641,7 +663,7 @@ various ways. The following general rules apply: * The `title` keyword can be used to override the automatic creation of the plot title. The default title is a string of the form " plot for " where is a list of the sys names contained in - the plot (which can be updated if the plotting is called multiple times). + the plot (which is updated if the plotting is called multiple times). Use ``title=False`` to suppress the title completely. The title can also be updated using the :func:`ControlPlot.set_plot_title` method for the returned control plot object. @@ -706,7 +728,7 @@ Alternatively, turning off the omega-damping grid (using ``grid=False`` or ``grid='empty'``) allows use of Matplotlib layout commands. -Response and plotting functions +Response and Plotting Reference =============================== Response functions @@ -735,6 +757,19 @@ number of encirclements for a Nyquist plot) as well as plotting (via the Plotting functions ------------------ +Plotting functions take a response or list of responses and return a +`ControlPlot` object that can be used to retrieve information about +the plot. Plotting functions can also be called with a system or list +of systems, in which case the appropriate response will be first +computed and then plotted. + +Note that the `phase_plane_plot` function is part of the +python-control namespace, but the individual functions for customizing +phase plots are contained in the `phaseplot` module, which should be +imported separately using ``import control.phaseplot as pp``. The +phase plane plotting functionality is described in more detail in the +:ref:`phase-plane-plots` section. + .. autosummary:: bode_plot @@ -783,13 +818,3 @@ The following classes are used in generating response data. PoleZeroData TimeResponseData TimeResponseList - -.. _controlplot-params: - -Plot parameters ---------------- - -Matplotlib users the :func:`matplotlib.rcParams` to specify default -properties of figures and axes. The `python-control` plotting -functions make use of a set of default values for all plots that -override the default Matplotlib values. diff --git a/doc/statesp.rst b/doc/statesp.rst index abb199ff3..f84066931 100644 --- a/doc/statesp.rst +++ b/doc/statesp.rst @@ -45,7 +45,7 @@ representations representing a variety of standard cananonical forms that have the same input/output properties. The :func:`similarity_transform` function allows a change of internal state variable via similarity transformation and the -:func:`canonical_form` function converts sytems into different +:func:`canonical_form` function converts systems into different canonical forms. Additional information is available on the documentation pages for the individual functions: @@ -62,7 +62,7 @@ Time domain properties ---------------------- The following functions are available to analyze the time domain -properties of a linear systems: +properties of a linear system: .. autosummary:: @@ -74,9 +74,9 @@ properties of a linear systems: step_info step_response -The time response functions (:func:`forced_response`, -:func:`impulse_response`, :func:`initial_response`, and -:func:`step_response) are described in more detail in the +The time response functions (:func:`impulse_response`, +:func:`initial_response`, :func:`forced_response`, and +:func:`step_response`) are described in more detail in the :ref:`response-chapter` chapter. @@ -99,12 +99,12 @@ methods: .. autosummary:: - acker lqr place + place_acker place_varga -The :func:`acker`, :func:`place`, and :func:`place_varga` functions +The :func:`place`, :func:`place_acker`, and :func:`place_varga` functions place the eigenvalues of the closed loop system to a desired set of values. Each takes the `A` and `B` matrices of the state space system and the desired location of the eigenvalues and returns a gain matrix @@ -141,7 +141,7 @@ State estimation ---------------- State estimators (or observers) are dynamical systems that estimate -the state of the system given a model of the dynamics and the input +the state of a system given a model of the dynamics and the input and output signals as a function of time. Linear state estimators have the form @@ -160,11 +160,11 @@ calling the :func:`place` function:: L = ct.place(sys.A.T, sys.C.T, E).T -where `E` is the desired location of the eigenvalues and `.T` computes +where `E` is the desired location of the eigenvalues and ``.T`` computes the transpose of a matrix. More sophisticated estimators can be constructed by modeling noise and disturbances as stochastic signals generated by a random process. Estimators constructed using these models are described in more detail -in the :ref:kalman-filter section of the :ref:stochastic-systems +in the :ref:`kalman-filter` section of the :ref:`stochastic-systems` chapter. diff --git a/doc/stochastic.rst b/doc/stochastic.rst index c81f01ada..264ad7856 100644 --- a/doc/stochastic.rst +++ b/doc/stochastic.rst @@ -55,19 +55,20 @@ signal has covariance `Q` at each point in time (without any scaling based on the sample time). The python-control :func:`correlation` function computes the -correlation matrix :math:`{\mathbb E}\{X^\mathsf{T}(t+\tau) X(t)\}` or the -cross-correlation matrix :math:`{\mathbb E}\{X^\mathsf{T}(t+\tau) Y(t)\}`: +correlation matrix :math:`{\mathbb E}\{X^\mathsf{T}(t+\tau) X(t)\}` or +the cross-correlation matrix :math:`{\mathbb E}\{X^\mathsf{T}(t+\tau) +Y(t)\}`, where :math:`\mathbb{E}` represents expectation: .. code:: tau, Rtau = ct.correlation(timepts, X[, Y]) -where :math:`\mathbb{E}` represents expectation. The signal `X` (and -`Y`, if present) represents a continuous time signal sampled at -regularly spaced times `timepts`. The return value provides the -correlation :math:`R_\tau` between :math:`X(t+\tau)` and :math:`X(t)` -at a set of time offsets :math:`\tau` (determined based on the spacing -of entries in the `timepts` vector. +The signal `X` (and `Y`, if present) represents a continuous or +discrete time signal sampled at regularly spaced times `timepts`. The +return value provides the correlation :math:`R_\tau` between +:math:`X(t+\tau)` and :math:`X(t)` at a set of time offsets +:math:`\tau` (determined based on the spacing of entries in the +`timepts` vector. Note that the computation of the correlation function is based on a single time signal (or pair of time signals) and is thus a very crude @@ -86,6 +87,10 @@ noise input, use the :func:`forced_response` (or Q = np.array([[0.1]]) V = ct.white_noise(timepts, Q) resp = ct.forced_response(sys, timepts, V) + resp.plot() + +.. todo:: + Add plot The correlation function for the output can be computed using the :func:`correlation` function and compared to the analytical expression: @@ -96,13 +101,17 @@ The correlation function for the output can be computed using the plt.plot(tau, r_Y) plt.plot(tau, c**2 * Q.item() / (2 * a) * np.exp(-a * np.abs(tau))) +.. todo:: + Add plot + + .. _kalman-filter: Linear Quadratic Estimation (Kalman Filter) =========================================== A standard application of stochastic linear systems is the computation -of the linear estimator under the assumption of white Gaussian +of the optimal linear estimator under the assumption of white Gaussian measurement and process noise. This estimator is called the linear quadratic estimator (LQE) and its gains can be computed using the :func:`lqe` function. @@ -193,6 +202,9 @@ state :math:`x_\text{d}` and input :math:`u_\text{d}`:: resp = ct.input_output_response( clsys, timepts, [Xd, Ud], [X0, np.zeros_like(X0), P0]) +.. todo:: + Add example (with plots?) + Maximum Likelihood Estimation ============================= @@ -234,14 +246,14 @@ most consistent with our model and penalize the noise and disturbances according to how likely they are (based on the given stochastic system model for each). -Given a solution to this fixed-horizon optimal estimation problem, we can -create an estimator for the state over all times by repeatedly applying the -optimization problem :eq:`eq_fusion_oep` over a moving horizon. At each -time :math:`k`, we take the measurements for the last :math:`N` time steps -along with the previously estimated state at the start of the horizon, -:math:`x[k-N]` and reapply the optimization in equation -:eq:`eq_fusion_oep`. This approach is known as a \define{moving horizon -estimator} (MHE). +Given a solution to this fixed-horizon optimal estimation problem, we +can create an estimator for the state over all times by repeatedly +applying the optimization problem :eq:`eq_fusion_oep` over a moving +horizon. At each time :math:`k`, we take the measurements for the +last :math:`N` time steps along with the previously estimated state at +the start of the horizon, :math:`x[k-N]` and reapply the optimization +in equation :eq:`eq_fusion_oep`. This approach is known as a *moving +horizon estimator* (MHE). The formulation for the moving horizon estimation problem is very general and various situations can be captured using the conditional probability @@ -317,6 +329,11 @@ For discrete time systems, the can be used to generate an input/output system that implements a moving horizon estimator. +An example showing the use of the optimal estimation problem and +moving horizon estimation (MHE) applied to a planar vertical takeoff +and landing (PVTOL) model is given in the :doc:`Moving Horizon +Estimation Jupyter notebook `. + Several functions are available to help set up standard optimal estimation problems: @@ -324,3 +341,6 @@ problems: optimal.gaussian_likelihood_cost optimal.disturbance_range_constraint + +.. todo:: + Add example: LQE vs MHE (from CDS 112) diff --git a/doc/test_sphinxdocs.py b/doc/test_sphinxdocs.py index 84fb0fb9f..34c148df7 100644 --- a/doc/test_sphinxdocs.py +++ b/doc/test_sphinxdocs.py @@ -22,13 +22,14 @@ # Functions that should not be referenced legacy_functions = [ - 'FRD', # FrequencyResponseData (or frd) + 'acker', # place_acker 'balred', # balanced_reduction 'bode', # bode_plot 'c2d', # sample_system 'era', # eigensys_realization 'evalfr', # use __call__() 'find_eqpt', # find_operating_point + 'FRD', # FrequencyResponseData (or frd) 'gangof4', # gangof4_plot 'hsvd', # hankel_singular_values 'minreal', # minimal_realization diff --git a/doc/xferfcn.rst b/doc/xferfcn.rst index a93ce0c51..933bc8851 100644 --- a/doc/xferfcn.rst +++ b/doc/xferfcn.rst @@ -71,7 +71,7 @@ the :math:`\infty`-norm: - :math:`\max_k \|x[k]\|` Given a norm for input signals and a norm for output signals, we can -define the \emph{induced norm} for an input/output system. The +define the *induced norm* for an input/output system. The following table summarizes the induced norms for a transfer function :math:`G(s)` with impulse response :math:`g(t)`: @@ -93,7 +93,7 @@ The 2-norm and :math:`\infty`-norm can be computed using sysnorm = ct.system_norm(sys, p=) -where `val` is either 2 or 'inf'. +where `val` is either 2 or 'inf' (the 1-norm is not yet implemented). Stability margins @@ -108,10 +108,12 @@ function for a feedback system, assuming the loop will be closed using negative feedback with gain 1. The :func:`stability_margins` function computes all three of these -margins as well as the frequencies at which they occur:: +margins as well as the frequencies at which they occur: + +.. doctest:: >>> sys = ct.tf(10, [1, 2, 3, 4]) - >>> gm, pm, sm, wpc, wgc, wms = ct.stability(sys) + >>> gm, pm, sm, wpc, wgc, wms = ct.stability_margins(sys) >>> print(f"Gain margin: {gm:2.2} at omega = {wpc:2.2} rad/sec") Gain margin: 0.2 at omega = 1.7 rad/sec @@ -139,7 +141,7 @@ where .. math:: - S = \frac{1}{1 + P C}, \qquad T = \frac{P C}{1 + P C}, + S = \frac{1}{1 + P C}, \qquad T = \frac{P C}{1 + P C} are the sensitivty function and complementary sensitivity function, and :math:`P(s)` represents the process dynamics. @@ -155,7 +157,9 @@ Systems with time delays Time delays are not directly representable in `python-control`, but the :func:`pade` function generates a linear system that approximates -a time delay to a given order:: +a time delay to a given order: + +.. doctest:: >>> num, den = ct.pade(0.1, 3) >>> delay = ct.tf(num, den, name='delay') @@ -163,7 +167,7 @@ a time delay to a given order:: : delay Inputs (1): ['u[0]'] Outputs (1): ['y[0]'] - - -s^3 + 120 s^2 - 6000 s + 1.2e+05 - --------------------------------- - s^3 + 120 s^2 + 6000 s + 1.2e+05 + + -s^3 + 120 s^2 - 6000 s + 1.2e+05 + --------------------------------- + s^3 + 120 s^2 + 6000 s + 1.2e+05 diff --git a/examples/cds112-L1_python-control.ipynb b/examples/cds112-L1_python-control.ipynb index 3f1a03487..140f32074 100644 --- a/examples/cds112-L1_python-control.ipynb +++ b/examples/cds112-L1_python-control.ipynb @@ -82,7 +82,7 @@ "\n", "where $x$ is the state vector for the system, $u$ represents the vector of inputs, and $y$ represents the vector of outputs.\n", "\n", - "The python-control package allows definition of input/output sytems using the `InputOutputSystem` class and its various subclasess, including the `NonlinearIOSystem` class that we use here. A `NonlinearIOSystem` object is created by defining the update law ($f(x, u)$) and the output map ($h(x, u)$), and then calling the factory function `ct.nlsys`.\n", + "The python-control package allows definition of input/output systems using the `InputOutputSystem` class and its various subclasess, including the `NonlinearIOSystem` class that we use here. A `NonlinearIOSystem` object is created by defining the update law ($f(x, u)$) and the output map ($h(x, u)$), and then calling the factory function `ct.nlsys`.\n", "\n", "For the example in this notebook, we will be controlling the steering of a vehicle, using a \"bicycle\" model for the dynamics of the vehicle. A more complete description of the dynamics of this system are available in [Example 3.11](https://fbswiki.org/wiki/index.php/System_Modeling) of [_Feedback Systems_](https://fbswiki.org/wiki/index.php/FBS) by Astrom and Murray (2020)." ] diff --git a/examples/python-control_tutorial.ipynb b/examples/python-control_tutorial.ipynb index a6f668b4b..d1fc446bf 100644 --- a/examples/python-control_tutorial.ipynb +++ b/examples/python-control_tutorial.ipynb @@ -60,7 +60,7 @@ "\n", "If you get an error importing the `control` package, it may be that it is not in your current Python path. You can fix this by setting the PYTHONPATH environment variable to include the directory where the python-control package is located. If you are invoking Jupyter from the command line, try using a command of the form\n", "\n", - " PYTHONPATH=/path/to/control jupyter notebook\n", + " PYTHONPATH=/path/to/python-control jupyter notebook\n", " \n", "If you are using [Google Colab](https://colab.research.google.com), use the following command at the top of the notebook to install the `control` package:\n", "\n", @@ -160,8 +160,7 @@ " [0. 1. 0. 0.]]\n", "\n", "D = [[0.]\n", - " [0.]]\n", - "\n" + " [0.]]\n" ] } ], @@ -512,7 +511,7 @@ "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -523,10 +522,11 @@ ], "source": [ "# Show that the system response is linear\n", - "cplt = resp3.plot()\n", - "cplt.axes[0, 0].plot(resp1.time, resp1.outputs[0] + resp2.outputs[0], 'k--')\n", + "cplt = resp3.plot(label=\"G(U1 + U2)\")\n", + "cplt.axes[0, 0].plot(resp1.time, resp1.outputs[0] + resp2.outputs[0], 'k--', label=\"G(U1) + G(U2)\")\n", "cplt.axes[1, 0].plot(resp1.time, resp1.outputs[1] + resp2.outputs[1], 'k--')\n", - "cplt.axes[2, 0].plot(resp1.time, resp1.inputs[0] + resp2.inputs[0], 'k--');" + "cplt.axes[2, 0].plot(resp1.time, resp1.inputs[0] + resp2.inputs[0], 'k--')\n", + "cplt.axes[0, 0].legend(loc='upper right', fontsize='x-small');" ] }, { @@ -537,7 +537,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -553,10 +553,11 @@ "resp2 = ct.forced_response(sys, T, U2, X0=X0)\n", "resp3 = ct.forced_response(sys, T, U1 + U2, X0=X0)\n", "\n", - "cplt = resp3.plot()\n", - "cplt.axes[0, 0].plot(resp1.time, resp1.outputs[0] + resp2.outputs[0], 'k--')\n", + "cplt = resp3.plot(label=\"G(U1 + U2)\")\n", + "cplt.axes[0, 0].plot(resp1.time, resp1.outputs[0] + resp2.outputs[0], 'k--', label=\"G(U1) + G(U2)\")\n", "cplt.axes[1, 0].plot(resp1.time, resp1.outputs[1] + resp2.outputs[1], 'k--')\n", - "cplt.axes[2, 0].plot(resp1.time, resp1.inputs[0] + resp2.inputs[0], 'k--');" + "cplt.axes[2, 0].plot(resp1.time, resp1.inputs[0] + resp2.inputs[0], 'k--')\n", + "cplt.axes[0, 0].legend(loc='upper right', fontsize='x-small');" ] }, { @@ -626,17 +627,17 @@ "Inputs (1): ['u[0]']\n", "Outputs (2): ['q1', 'q2']\n", "\n", - "\n", "Input 1 to output 1:\n", - " 4\n", - "-------------------------------------\n", - "s^4 + 0.2 s^3 + 8.01 s^2 + 0.8 s + 12\n", + "\n", + " 4\n", + " -------------------------------------\n", + " s^4 + 0.2 s^3 + 8.01 s^2 + 0.8 s + 12\n", "\n", "Input 1 to output 2:\n", - " 2 s^2 + 0.2 s + 8\n", - "-------------------------------------\n", - "s^4 + 0.2 s^3 + 8.01 s^2 + 0.8 s + 12\n", - "\n" + "\n", + " 2 s^2 + 0.2 s + 8\n", + " -------------------------------------\n", + " s^4 + 0.2 s^3 + 8.01 s^2 + 0.8 s + 12\n" ] } ], @@ -819,7 +820,7 @@ "\n", "where $x$ is the state vector for the system, $u$ represents the vector of inputs, and $y$ represents the vector of outputs.\n", "\n", - "The python-control package allows definition of input/output sytems using the `InputOutputSystem` class and its various subclasess, including the `NonlinearIOSystem` class that we use here. A `NonlinearIOSystem` object is created by defining the update law ($f(x, u)$) and the output map ($h(x, u)$), and then calling the factory function `ct.nlsys`.\n", + "The python-control package allows definition of input/output systems using the `InputOutputSystem` class and its various subclasess, including the `NonlinearIOSystem` class that we use here. A `NonlinearIOSystem` object is created by defining the update law ($f(x, u)$) and the output map ($h(x, u)$), and then calling the factory function `ct.nlsys`.\n", "\n", "For the example in this notebook, we will be controlling the steering of a vehicle, using a \"bicycle\" model for the dynamics of the vehicle. A more complete description of the dynamics of this system are available in [Example 3.11](https://fbswiki.org/wiki/index.php/System_Modeling) of [_Feedback Systems_](https://fbswiki.org/wiki/index.php/FBS) by Astrom and Murray (2020)." ] @@ -1136,7 +1137,7 @@ "source": [ "### Closed loop simulation\n", "\n", - "We now command the system to follow in trajectory and use the linear controller to correct for any errors. \n", + "We now command the system to follow a trajectory and use the linear controller to correct for any errors. \n", "\n", "The desired trajectory is a given by a longitudinal position that tracks a velocity of 10 m/s for the first 5 seconds and then increases to 12 m/s and a lateral position that varies sinusoidally by $\\pm 0.5$ m around the centerline. The nominal inputs are not modified, so that feedback is required to obtained proper trajectory tracking." ] From b91598ca05f930fb16cad22248c78888025b2883 Mon Sep 17 00:00:00 2001 From: Richard Murray Date: Sun, 12 Jan 2025 11:31:24 -0800 Subject: [PATCH 62/93] add doctest to user guide + internally generate figures --- control/tests/flatsys_test.py | 145 ++----- control/tests/timeplot_test.py | 55 --- doc/figures/Makefile | 19 +- doc/figures/ctrlplot-pole_zero_subplots.png | Bin 67170 -> 67171 bytes doc/figures/ctrlplot-servomech.png | Bin 63520 -> 63521 bytes doc/figures/flatsys-steering-compare.png | Bin 52991 -> 43922 bytes doc/figures/freqplot-gangof4.png | Bin 41638 -> 41639 bytes doc/figures/freqplot-mimo_bode-default.png | Bin 53337 -> 53338 bytes doc/figures/freqplot-mimo_bode-magonly.png | Bin 48091 -> 48092 bytes doc/figures/freqplot-mimo_svplot-default.png | Bin 33004 -> 33005 bytes doc/figures/freqplot-nyquist-custom.png | Bin 43976 -> 43943 bytes doc/figures/freqplot-nyquist-default.png | Bin 41642 -> 41643 bytes doc/figures/freqplot-siso_bode-default.png | Bin 46401 -> 46402 bytes doc/figures/freqplot-siso_bode-omega.png | Bin 45790 -> 45791 bytes doc/figures/freqplot-siso_nichols-default.png | Bin 96394 -> 96395 bytes doc/figures/pzmap-siso_ctime-default.png | Bin 15186 -> 15187 bytes doc/figures/rlocus-siso_ctime-clicked.png | Bin 88472 -> 85934 bytes doc/figures/rlocus-siso_ctime-default.png | Bin 81315 -> 81316 bytes doc/figures/rlocus-siso_dtime-default.png | Bin 89994 -> 89995 bytes doc/figures/rlocus-siso_multiple-nogrid.png | Bin 24424 -> 18600 bytes doc/figures/steering-optimal.png | Bin 28691 -> 29006 bytes .../stochastic-whitenoise-correlation.png | Bin 0 -> 33318 bytes .../stochastic-whitenoise-response.png | Bin 0 -> 59577 bytes doc/figures/timeplot-mimo_ioresp-ov_lm.png | Bin 63748 -> 64809 bytes doc/figures/timeplot-mimo_step-default.png | Bin 31978 -> 32326 bytes doc/figures/timeplot-mimo_step-linestyle.png | Bin 41691 -> 44356 bytes doc/figures/timeplot-mimo_step-pi_cs.png | Bin 31881 -> 42935 bytes doc/figures/timeplot-servomech-combined.png | Bin 57609 -> 60058 bytes doc/flatsys.rst | 31 +- doc/iosys.rst | 16 +- doc/linear.rst | 139 +++++-- doc/nlsys.rst | 17 +- doc/optimal.rst | 46 ++- doc/phaseplot.rst | 47 ++- doc/response.rst | 362 ++++++++++++++---- doc/stochastic.rst | 54 ++- 36 files changed, 610 insertions(+), 321 deletions(-) create mode 100644 doc/figures/stochastic-whitenoise-correlation.png create mode 100644 doc/figures/stochastic-whitenoise-response.png diff --git a/control/tests/flatsys_test.py b/control/tests/flatsys_test.py index 7f4bcde69..772dea7b0 100644 --- a/control/tests/flatsys_test.py +++ b/control/tests/flatsys_test.py @@ -24,48 +24,6 @@ atol = 1e-4 rtol = 1e-4 -# Define the kinematic car system -def vehicle_flat_forward(x, u, params={}): - b = params.get('wheelbase', 3.) # get parameter values - zflag = [np.zeros(3), np.zeros(3)] # list for flag arrays - zflag[0][0] = x[0] # flat outputs - zflag[1][0] = x[1] - zflag[0][1] = u[0] * np.cos(x[2]) # first derivatives - zflag[1][1] = u[0] * np.sin(x[2]) - thdot = (u[0]/b) * np.tan(u[1]) # dtheta/dt - zflag[0][2] = -u[0] * thdot * np.sin(x[2]) # second derivatives - zflag[1][2] = u[0] * thdot * np.cos(x[2]) - return zflag - -def vehicle_flat_reverse(zflag, params={}): - b = params.get('wheelbase', 3.) # get parameter values - x = np.zeros(3); u = np.zeros(2) # vectors to store x, u - x[0] = zflag[0][0] # x position - x[1] = zflag[1][0] # y position - x[2] = np.arctan2(zflag[1][1], zflag[0][1]) # angle - u[0] = zflag[0][1] * np.cos(x[2]) + zflag[1][1] * np.sin(x[2]) - thdot_v = zflag[1][2] * np.cos(x[2]) - zflag[0][2] * np.sin(x[2]) - u[1] = np.arctan2(thdot_v, u[0]**2 / b) - return x, u - -def vehicle_update(t, x, u, params): - b = params.get('wheelbase', 3.) # get parameter values - dx = np.array([ - np.cos(x[2]) * u[0], - np.sin(x[2]) * u[0], - (u[0]/b) * np.tan(u[1]) - ]) - return dx - -def vehicle_output(t, x, u, params): return x - -# Create differentially flat input/output system -vehicle_flat = fs.FlatSystem( - vehicle_flat_forward, vehicle_flat_reverse, vehicle_update, - vehicle_output, inputs=('v', 'delta'), outputs=('x', 'y', 'theta'), - states=('x', 'y', 'theta'), name='vehicle_flat') - - class TestFlatSys: """Test differential flat systems""" @@ -100,9 +58,48 @@ def test_double_integrator(self, xf, uf, Tf, basis): t, y, x = ct.forced_response(sys, T, ud, x1, return_x=True) np.testing.assert_array_almost_equal(x, xd, decimal=3) - @pytest.fixture(name='vehicle_flat') - def vehicle_flat_fixture(self): - return vehicle_flat + @pytest.fixture + def vehicle_flat(self): + """Differential flatness for a kinematic car""" + def vehicle_flat_forward(x, u, params={}): + b = params.get('wheelbase', 3.) # get parameter values + zflag = [np.zeros(3), np.zeros(3)] # list for flag arrays + zflag[0][0] = x[0] # flat outputs + zflag[1][0] = x[1] + zflag[0][1] = u[0] * np.cos(x[2]) # first derivatives + zflag[1][1] = u[0] * np.sin(x[2]) + thdot = (u[0]/b) * np.tan(u[1]) # dtheta/dt + zflag[0][2] = -u[0] * thdot * np.sin(x[2]) # second derivatives + zflag[1][2] = u[0] * thdot * np.cos(x[2]) + return zflag + + def vehicle_flat_reverse(zflag, params={}): + b = params.get('wheelbase', 3.) # get parameter values + x = np.zeros(3); u = np.zeros(2) # vectors to store x, u + x[0] = zflag[0][0] # x position + x[1] = zflag[1][0] # y position + x[2] = np.arctan2(zflag[1][1], zflag[0][1]) # angle + u[0] = zflag[0][1] * np.cos(x[2]) + zflag[1][1] * np.sin(x[2]) + thdot_v = zflag[1][2] * np.cos(x[2]) - zflag[0][2] * np.sin(x[2]) + u[1] = np.arctan2(thdot_v, u[0]**2 / b) + return x, u + + def vehicle_update(t, x, u, params): + b = params.get('wheelbase', 3.) # get parameter values + dx = np.array([ + np.cos(x[2]) * u[0], + np.sin(x[2]) * u[0], + (u[0]/b) * np.tan(u[1]) + ]) + return dx + + def vehicle_output(t, x, u, params): return x + + # Create differentially flat input/output system + return fs.FlatSystem( + vehicle_flat_forward, vehicle_flat_reverse, vehicle_update, + vehicle_output, inputs=('v', 'delta'), outputs=('x', 'y', 'theta'), + states=('x', 'y', 'theta')) @pytest.mark.parametrize("basis", [ fs.PolyFamily(6), fs.PolyFamily(8), fs.BezierFamily(6), @@ -842,61 +839,3 @@ def test_flatsys_factory_function(self, vehicle_flat): with pytest.raises(TypeError, match="incorrect number or type"): flatsys = fs.flatsys(1, 2, 3, 4, 5) - - -if __name__ == '__main__': - # Generate images for User Guide - import matplotlib.pyplot as plt - - # - # Point to point - # - # Define the endpoints of the trajectory - x0 = [0., -2., 0.]; u0 = [10., 0.] - xf = [100., 2., 0.]; uf = [10., 0.] - Tf = 10 - - # Define a set of basis functions to use for the trajectories - poly = fs.PolyFamily(6) - - # Find a trajectory between the initial condition and the final condition - traj = fs.point_to_point(vehicle_flat, Tf, x0, u0, xf, uf, basis=poly) - - # Create the trajectory - timepts = np.linspace(0, Tf, 100) - xd, ud = traj.eval(timepts) - resp_p2p = ct.input_output_response(vehicle_flat, timepts, ud, X0=xd[:, 0]) - - # - # Solve OCP - # - # Define the cost along the trajectory: penalize steering angle - traj_cost = ct.optimal.quadratic_cost( - vehicle_flat, None, np.diag([0.1, 10]), u0=uf) - - # Define the terminal cost: penalize distance from the end point - term_cost = ct.optimal.quadratic_cost( - vehicle_flat, np.diag([1e3, 1e3, 1e3]), None, x0=xf) - - # Use a straight line as the initial guess - evalpts = np.linspace(0, Tf, 10) - initial_guess = np.array( - [x0[i] + (xf[i] - x0[i]) * evalpts/Tf for i in (0, 1)]) - - # Solve the optimal control problem, evaluating cost at timepts - bspline = fs.BSplineFamily([0, Tf/2, Tf], 4) - traj = fs.solve_flat_ocp( - vehicle_flat, evalpts, x0, u0, traj_cost, - terminal_cost=term_cost, initial_guess=initial_guess, basis=bspline) - - xd, ud = traj.eval(timepts) - resp_ocp = ct.input_output_response(vehicle_flat, timepts, ud, X0=xd[:, 0]) - - # - # Plot the results - # - cplt = ct.time_response_plot( - ct.combine_time_responses([resp_p2p, resp_ocp]), - overlay_traces=True, trace_labels=['point_to_point', 'solve_ocp']) - - plt.savefig('flatsys-steering-compare.png') diff --git a/control/tests/timeplot_test.py b/control/tests/timeplot_test.py index 809169fda..9525c7e02 100644 --- a/control/tests/timeplot_test.py +++ b/control/tests/timeplot_test.py @@ -715,58 +715,3 @@ def test_legend_customization(): fig = plt.figure() cplt = ct.step_response([sys1, sys2]).plot() cplt.set_plot_title("[List] " + fig._suptitle._text) - - # - # Servomechanism example for documentation - # - - # Parameter values - servomech_params = { - 'J': 100, # Moment of inertia of the motor - 'b': 10, # Angular damping of the arm - 'k': 1, # Spring constant - 'r': 1, # Location of spring contact on arm - 'l': 2, # Distance to the read head - 'eps': 0.01, # Magnitude of velocity-dependent perturbation - } - - # State derivative - def servomech_update(t, x, u, params): - # Extract the configuration and velocity variables from the state vector - theta = x[0] # Angular position of the disk drive arm - thetadot = x[1] # Angular velocity of the disk drive arm - tau = u[0] # Torque applied at the base of the arm - - # Get the parameter values - J, b, k, r = map(params.get, ['J', 'b', 'k', 'r']) - - # Compute the angular acceleration - dthetadot = 1/J * ( - -b * thetadot - k * r * np.sin(theta) + tau) - - # Return the state update law - return np.array([thetadot, dthetadot]) - - # System output (tip radial position + angular velocity) - def servomech_output(t, x, u, params): - l = params['l'] - return np.array([l * x[0], x[1]]) - - # System dynamics - servomech = ct.nlsys( - servomech_update, servomech_output, name='servomech', - params=servomech_params, states=['theta', 'thdot'], - outputs=['y', 'thdot'], inputs=['tau']) - - timepts = np.linspace(0, 10) - - U1 = np.sin(timepts) - resp1 = ct.input_output_response(servomech, timepts, U1) - - U2 = np.cos(2*timepts) - resp2 = ct.input_output_response(servomech, timepts, U2) - - plt.figure() - cplt = ct.combine_time_responses( - [resp1, resp2], trace_labels=["Scenario #1", "Scenario #2"]).plot() - plt.savefig('timeplot-servomech-combined.png') diff --git a/doc/figures/Makefile b/doc/figures/Makefile index 7f96202f1..1ca54b372 100644 --- a/doc/figures/Makefile +++ b/doc/figures/Makefile @@ -2,9 +2,7 @@ # RMM, 26 Dec 2024 # List of figures that need to be created (first figure generated is OK) -FIGS = classes.pdf timeplot-mimo_step-default.png \ - freqplot-siso_bode-default.png rlocus-siso_ctime-default.png \ - phaseplot-dampedosc-default.png ctrlplot-servomech.png +FIGS = classes.pdf # Location of the control package SRCDIR = ../.. @@ -16,18 +14,3 @@ clean: classes.pdf: classes.fig fig2dev -Lpdf $< $@ - 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We also set the initial and final inputs, which sets the vehicle velocity :math:`v` and steering wheel angle :math:`\delta` at the endpoints. -.. code-block:: python +.. testcode:: flatsys # Define the endpoints of the trajectory x0 = [0., -2., 0.]; u0 = [10., 0.] @@ -299,7 +314,7 @@ Alternatively, we can solve an optimal control problem in which we minimize a cost function along the trajectory as well as a terminal cost: -.. code-block:: python +.. testcode:: flatsys # Define the cost along the trajectory: penalize steering angle traj_cost = ct.optimal.quadratic_cost( @@ -324,12 +339,20 @@ cost: resp_ocp = ct.input_output_response(vehicle_flat, timepts, ud, X0=xd[:, 0]) The results of the two approaches can be shown using the -`time_response_plot` function:: +`time_response_plot` function: + +.. testcode:: flatsys cplt = ct.time_response_plot( ct.combine_time_responses([resp_p2p, resp_ocp]), overlay_traces=True, trace_labels=['point_to_point', 'solve_ocp']) +.. testcode:: flatsys + :hide: + + import matplotlib.pyplot as plt + plt.savefig('figures/flatsys-steering-compare.png') + .. image:: figures/flatsys-steering-compare.png :align: center diff --git a/doc/iosys.rst b/doc/iosys.rst index 265779885..6d1fa775f 100644 --- a/doc/iosys.rst +++ b/doc/iosys.rst @@ -289,18 +289,22 @@ input/output system that takes the sum of an arbitrary number of inputs. For example, to create an input/output system that takes the sum of three inputs, use the command -.. code-block:: python +.. testcode:: summing sumblk = ct.summing_junction(3) By default, the name of the inputs will be of the form 'u[i]' and the output -will be 'y'. This can be changed by giving an explicit list of names:: +will be 'y'. This can be changed by giving an explicit list of names: + +.. testcode:: summing sumblk = ct.summing_junction(inputs=['a', 'b', 'c'], output='d') A more typical usage would be to define an input/output system that compares a reference signal to the output of the process and computes -the error:: +the error: + +.. testcode:: summing sumblk = ct.summing_junction(inputs=['r', '-y'], output='e') @@ -311,7 +315,7 @@ It is also possible to define "vector" summing blocks that take multi-dimensional inputs and produce a multi-dimensional output. For example, the command -.. code-block:: python +.. testcode:: summing sumblk = ct.summing_junction(inputs=['r', '-y'], output='e', dimension=2) @@ -330,7 +334,9 @@ omitted, the :func:`interconnect` function will connect all signals of the same name to each other. This can allow for simplified methods of interconnecting systems, especially when combined with the :func:`summing_junction` function. For example, the following code -will create a unity gain, negative feedback system:: +will create a unity gain, negative feedback system: + +.. testcode: autoconnect P = ct.tf([1], [1, 0], inputs='u', outputs='y') C = ct.tf([10], [1, 1], inputs='e', outputs='u') diff --git a/doc/linear.rst b/doc/linear.rst index a5ce47902..02689ad57 100644 --- a/doc/linear.rst +++ b/doc/linear.rst @@ -41,7 +41,16 @@ of linear time-invariant (LTI) systems: where :math:`u` is the input, :math:`y` is the output, and :math:`x` is the state. -To create a state space system, use the :func:`ss` function:: +To create a state space system, use the :func:`ss` function: + +.. testsetup:: statesp + + A = np.diag([-1, -2]) + B = np.eye(2) + C = np.eye(1, 2) + D = np.zeros((1, 2)) + +.. testcode:: statesp sys = ct.ss(A, B, C, D) @@ -52,7 +61,9 @@ functions can be found in the :ref:`interconnections-ref` section of the :ref:`function-ref`. Systems, inputs, outputs, and states can be given labels to allow more -customized access to system information:: +customized access to system information: + +.. testcode:: statesp sys = ct.ss( A, B, C, D, name='sys', @@ -64,9 +75,11 @@ operations as well as the :func:`feedback`, :func:`parallel`, and :ref:`function-ref`. The :func:`rss` function can be used to create a random state space -system with a desired number or inputs, outputs, and states:: +system with a desired number or inputs, outputs, and states: + +.. testcode:: statesp - sys = ct.rss(states=4, outputs=1, inputs=1], strictly_proper=True) + sys = ct.rss(states=4, outputs=1, inputs=1, strictly_proper=True) The `states`, `inputs`, and `output` parameters can also be given as lists of strings to create named signals. All systems @@ -88,12 +101,21 @@ transfer functions where :math:`n` is greater than or equal to :math:`m` for a proper transfer function. Improper transfer functions are also allowed. -To create a transfer function, use the :func:`tf` function:: +To create a transfer function, use the :func:`tf` function: + +.. testsetup:: xferfcn + + num = np.array([1, 2]) + den = np.array([3, 4]) + +.. testcode:: xferfcn sys = ct.tf(num, den) The system name as well as input and output labels can be specified in -the same way as state space systems:: +the same way as state space systems: + +.. testcode:: xferfcn sys = ct.tf(num, den, name='sys', inputs=['u'], outputs=['y']) @@ -105,19 +127,25 @@ functions can be found in the :ref:`interconnections-ref` section of the To aid in the construction of transfer functions, the :func:`tf` factory function can used to create transfer function corresponding -to the derivative or difference operator:: +to the derivative or difference operator: + +.. testcode:: xferfcn s = ct.tf('s') Standard algebraic operations can be used to construct more -complicated transfer functions:: +complicated transfer functions: + +.. testcode:: xferfcn sys = 5 * (s + 10)/(s**2 + 2*s + 1) Transfer functions can be evaluated at a point in the complex plane by -calling the transfer function object:: +calling the transfer function object: + +.. testcode:: xferfcn - val = sys(s) + val = sys(1 + 0.5j) Discrete time transfer functions (described in more detail below) can be created using ``z = ct.tf('z')``. @@ -132,14 +160,25 @@ systems in frequency response data form. The main data attributes are `fresp` is the (complex-value) value of the transfer function at each frequency point. -FRD systems can be created with the :func:`frd` factory function:: +FRD systems can be created with the :func:`frd` factory function: + +.. testsetup:: frdata + + sys_lti = ct.rss(2, 2, 2) + lti_resp = ct.frequency_response(sys_lti) + fresp = lti_resp.response + freqpts = lti_resp.frequency - sys = ct.frd(fresp, omega) +.. testcode:: frdata + + sys = ct.frd(fresp, freqpts) FRD systems can also be created by evaluating an LTI system at a given -set of frequencies:: +set of frequencies: + +.. testcode:: frdata - frd_sys = ct.frd(lti_sys, omega) + frd_sys = ct.frd(sys_lti, freqpts) Frequency response data systems have a somewhat more limited set of functions that are available, although all of the standard algebraic @@ -147,9 +186,11 @@ manipulations can be performed. The FRD class is also used as the return type for the :func:`frequency_response` function. This object can be assigned to a -tuple using:: +tuple using: - response = ct.frequency_response(sys) +.. testcode:: frdata + + response = ct.frequency_response(sys_lti) mag, phase, omega = response where `mag` is the magnitude (absolute value, not dB or log10) of the @@ -171,7 +212,17 @@ function. For state space systems, the input matrix `B`, output matrix `C`, and direct term `D` should be 2D matrices of the appropriate shape. For transfer functions, this is done by providing a 2D list of numerator and denominator polynomials to the :func:`tf` -function, e.g.:: +function, e.g.: + +.. testsetup:: mimo + + sys = ct.tf(ct.rss(4, 2, 2)) + [[num11, num12], [num21, num22]] = sys.num_list + [[den11, den12], [den21, den22]] = sys.den_list + + A, B, C, D = ct.ssdata(ct.rss(4, 3, 2)) # 3 output, 2 input + +.. testcode:: mimo sys = ct.tf( [[num11, num12], [num21, num22]], @@ -183,21 +234,25 @@ values,with the first dimension corresponding to the output index of the system, the second dimension corresponding to the input index, and the 3rd dimension corresponding to the frequency points in omega. -Signal names for MIMO systems are specified using lists of labels:: +Signal names for MIMO systems are specified using lists of labels: - sys = ct.ss(A, B, C, D, inputs=['u1', 'u2'], outputs=['y1' 'y2']) +.. testcode:: mimo + + sys = ct.ss(A, B, C, D, inputs=['u1', 'u2'], outputs=['y1', 'y2', 'y3']) Signals that are not given explicit labels are given labels of the form 's[i]' where the default value of 's' is 'x' for states, 'u' for inputs, and 'y' for outputs, and 'i' ranges over the dimension of the signal (starting at 0). -Subsets of input/output pairs for LTI systems can be obtained by indexing -the system using either numerical indices (including slices) or signal -names:: +Subsets of input/output pairs for LTI systems can be obtained by +indexing the system using either numerical indices (including slices) +or signal names: + +.. testcode:: mimo - subsys = sys[[0, 2], 0:2] - subsys = sys[['y[0]', 'y[2]'], ['u[0]', 'u[1]']] + subsys = sys[[0, 2], 0:2] + subsys = sys[['y1', 'y3'], ['u1', 'u2']] Signal names for an indexed subsystem are preserved from the original system and the subsystem name is set according to the values of @@ -208,7 +263,9 @@ default subsystem name is the original system name with '$indexed' appended. For FRD objects, the frequency response properties for MIMO systems -can be accessed using the names of the inputs and outputs:: +can be accessed using the names of the inputs and outputs: + +.. testcode:: frdata response.magnitude['y[0]', 'u[1]'] @@ -238,7 +295,15 @@ Discrete Time Systems ===================== A discrete time system is created by specifying a nonzero "timebase", -`dt` when the system is constructed:: +`dt` when the system is constructed: + +.. testsetup:: dtime + + A, B, C, D = ct.ssdata(ct.rss(2, 1, 1)) + num, den = ct.tfdata(ct.rss(2, 1, 1)) + dt = 0.1 + +.. testcode:: dtime sys_ss = ct.ss(A, B, C, D, dt) sys_tf = ct.tf(num, den, dt) @@ -308,20 +373,36 @@ operate on LTI systems will work on any subclass. To explicitly convert a state space system into a transfer function representation, the state space system can be passed as an argument to -the :func:`tf` factory functions:: +the :func:`tf` factory functions: + +.. testcode:: convert sys_ss = ct.rss(4, 2, 2, name='sys_ss') sys_tf = ct.tf(sys_ss, name='sys_tf') The :func:`ss2tf` function can also be used, passing either the state -space system or the matrices that represent the state space systems:: +space system or the matrices that represent the state space systems: + +.. testcode:: convert + :hide: + + A, B, C, D = ct.ssdata(sys_ss) + +.. testcode:: convert sys_tf = ct.ss2tf(A, B, C, D) In either form, system and signal names can be changed by passing the appropriate keyword arguments. -Conversion of transfer functions to state space form is also possible:: +Conversion of transfer functions to state space form is also possible: + +.. testcode:: convert + :hide: + + num, den = ct.tfdata(sys_tf) + +.. testcode:: convert sys_ss = ct.ss(sys_tf) sys_ss = ct.tf2ss(sys_tf) diff --git a/doc/nlsys.rst b/doc/nlsys.rst index 02c5fe746..d05c7884e 100644 --- a/doc/nlsys.rst +++ b/doc/nlsys.rst @@ -156,7 +156,7 @@ Simulations and plotting To simulate an input/output system, use the :func:`input_output_response` function:: - resp = ct.input_output_response(sys_nl, T, U, x0, params) + resp = ct.input_output_response(sys_nl, timepts, U, x0, params) t, y, x = resp.time, resp.outputs, resp.states Time responses can be plotted using the :func:`time_response_plot` @@ -180,7 +180,9 @@ different plot elements: * `cplt.legend`: legend object(s) contained in the plot. The :func:`combine_time_responses` function an be used to combine -multiple time responses into a single `TimeResponseData` object:: +multiple time responses into a single `TimeResponseData` object: + +.. testcode:: timepts = np.linspace(0, 10) @@ -190,8 +192,15 @@ multiple time responses into a single `TimeResponseData` object:: U2 = np.cos(2*timepts) resp2 = ct.input_output_response(servomech, timepts, U2) - cplt.ct.combine_time_responses( - [resp1, resp2], trace_labels=["Scenario #1", "Scenario #2"]).plot() + resp = ct.combine_time_responses( + [resp1, resp2], trace_labels=["Scenario #1", "Scenario #2"]) + resp.plot(legend_loc=False) + +.. testcode:: + :hide: + + import matplotlib.pyplot as plt + plt.savefig('figures/timeplot-servomech-combined.png') .. image:: figures/timeplot-servomech-combined.png :align: center diff --git a/doc/optimal.rst b/doc/optimal.rst index 54e049699..aa375893d 100644 --- a/doc/optimal.rst +++ b/doc/optimal.rst @@ -201,12 +201,19 @@ Consider the vehicle steering example described in Example 2.3 of `Optimization-Based Control (OBC) `_. The dynamics of the system can be defined as a nonlinear input/output -system using the following code:: +system using the following code: +.. testsetup:: optimal + + import matplotlib.pyplot as plt + plt.close('all') + +.. testcode:: optimal + + import matplotlib.pyplot as plt import numpy as np import control as ct import control.optimal as opt - import matplotlib.pyplot as plt def vehicle_update(t, x, u, params): # Get the parameters for the model @@ -234,14 +241,18 @@ system using the following code:: We consider an optimal control problem that consists of "changing lanes" by moving from the point x = 0 m, y = -2 m, :math:`\theta` = 0 to the point x = 100 m, y = 2 m, :math:`\theta` = 0) over a period of 10 seconds and -with a starting and ending velocity of 10 m/s:: +with a starting and ending velocity of 10 m/s: + +.. testcode:: optimal x0 = np.array([0., -2., 0.]); u0 = np.array([10., 0.]) xf = np.array([100., 2., 0.]); uf = np.array([10., 0.]) Tf = 10 To set up the optimal control problem we design a cost function that -penalizes the state and input using quadratic cost functions:: +penalizes the state and input using quadratic cost functions: + +.. testcode:: optimal Q = np.diag([0, 0, 0.1]) # don't turn too sharply R = np.diag([1, 1]) # keep inputs small @@ -250,18 +261,33 @@ penalizes the state and input using quadratic cost functions:: term_cost = opt.quadratic_cost(vehicle, P, 0, x0=xf) We also constrain the maximum turning rate to 0.1 radians (about 6 degrees) -and constrain the velocity to be in the range of 9 m/s to 11 m/s:: +and constrain the velocity to be in the range of 9 m/s to 11 m/s: + +.. testcode:: optimal constraints = [ opt.input_range_constraint(vehicle, [8, -0.1], [12, 0.1]) ] -Finally, we solve for the optimal inputs:: +Finally, we solve for the optimal inputs: + +.. testcode:: optimal timepts = np.linspace(0, Tf, 10, endpoint=True) result = opt.solve_ocp( vehicle, timepts, x0, traj_cost, constraints, terminal_cost=term_cost, initial_guess=u0) -Plotting the results:: +.. testoutput:: optimal + :hide: + + Summary statistics: + * Cost function calls: ... + * Constraint calls: ... + * System simulations: ... + * Final cost: ... + +Plotting the results: + +.. testcode:: optimal # Simulate the system dynamics (open loop) resp = ct.input_output_response( @@ -289,7 +315,11 @@ Plotting the results:: plt.suptitle("Lane change maneuver") plt.tight_layout() - plt.show() + +.. testcode:: optimal + :hide: + + plt.savefig('figures/steering-optimal.png') yields diff --git a/doc/phaseplot.rst b/doc/phaseplot.rst index 4832a4f34..45ecb387d 100644 --- a/doc/phaseplot.rst +++ b/doc/phaseplot.rst @@ -12,7 +12,14 @@ functionality is supported by a set of mapping functions that are part of the `phaseplot` module. The default method for generating a phase plane plot is to provide a -2D dynamical system along with a range of coordinates and time limit:: +2D dynamical system along with a range of coordinates and time limit: + +.. testsetup:: phaseplot + + import matplotlib.pyplot as plt + plt.close('all') + +.. testcode:: phaseplot sys = ct.nlsys( lambda t, x, u, params: np.array([[0, 1], [-1, -1]]) @ x, @@ -21,6 +28,12 @@ The default method for generating a phase plane plot is to provide a T = 8 ct.phase_plane_plot(sys, axis_limits, T) +.. testcode:: phaseplot + :hide: + + import matplotlib.pyplot as plt + plt.savefig('figures/phaseplot-dampedosc-default.png') + .. image:: figures/phaseplot-dampedosc-default.png :align: center @@ -32,7 +45,14 @@ including plotting a grid of vectors instead of streamlines and turning on and off various features of the plot. To illustrate some of these possibilities, consider a phase plane plot for -an inverted pendulum system, which is created using a mesh grid:: +an inverted pendulum system, which is created using a mesh grid: + +.. testcode:: phaseplot + :hide: + + plt.figure() + +.. testcode:: phaseplot def invpend_update(t, x, u, params): m, l, b, g = params['m'], params['l'], params['b'], params['g'] @@ -40,13 +60,18 @@ an inverted pendulum system, which is created using a mesh grid:: invpend = ct.nlsys(invpend_update, states=2, inputs=1, name='invpend') ct.phase_plane_plot( - invpend, [-2*pi, 2*pi, -2, 2], 5, + invpend, [-2 * np.pi, 2 * np.pi, -2, 2], 5, gridtype='meshgrid', gridspec=[5, 8], arrows=3, plot_equilpoints={'gridspec': [12, 9]}, params={'m': 1, 'l': 1, 'b': 0.2, 'g': 1}) plt.xlabel(r"$\theta$ [rad]") plt.ylabel(r"$\dot\theta$ [rad/sec]") +.. testcode:: phaseplot + :hide: + + plt.savefig('figures/phaseplot-invpend-meshgrid.png') + .. image:: figures/phaseplot-invpend-meshgrid.png :align: center @@ -61,7 +86,14 @@ multiple features in the phase plane plot give a good global picture of the topological structure of solutions of the dynamical system. Phase plots can be built up by hand using a variety of helper functions that -are part of the :mod:`phaseplot` (pp) module:: +are part of the :mod:`phaseplot` (pp) module: + +.. testcode:: phaseplot + :hide: + + plt.figure() + +.. testcode:: phaseplot import control.phaseplot as pp @@ -76,9 +108,14 @@ are part of the :mod:`phaseplot` (pp) module:: oscillator, np.array([[0, 0]]), 1.5, gridtype='circlegrid', gridspec=[0.5, 6], dir='both') pp.streamlines( - oscillator, np.array([[1, 0]]), 2*pi, arrows=6, color='b') + oscillator, np.array([[1, 0]]), 2 * np.pi, arrows=6, color='b') plt.gca().set_aspect('equal') +.. testcode:: phaseplot + :hide: + + plt.savefig('figures/phaseplot-oscillator-helpers.png') + .. image:: figures/phaseplot-oscillator-helpers.png :align: center diff --git a/doc/response.rst b/doc/response.rst index 55eb03161..1d3d1adb8 100644 --- a/doc/response.rst +++ b/doc/response.rst @@ -161,11 +161,19 @@ Functions that return time responses (e.g., :func:`forced_response`, response. These data can be accessed via the :attr:`~TimeResponseData.time`, :attr:`~TimeResponseData.outputs`, :attr:`~TimeResponseData.states` and :attr:`~TimeResponseData.inputs` -properties:: +properties: - sys = ct.rss(4, 1, 1) - response = ct.step_response(sys) - plt.plot(response.time, response.outputs) +.. testsetup:: time_series, timeplot, freqplot, pzmap, ctrlplot + + import matplotlib.pyplot as plt + import numpy as np + import control as ct + +.. testcode:: time_series + + sys = ct.rss(4, 1, 1) + response = ct.step_response(sys) + plt.plot(response.time, response.outputs) The dimensions of the response properties depend on the function being called and whether the system is SISO or MIMO. In addition, some time @@ -175,28 +183,42 @@ which will compute the step response for each input/output pair. See :class:`TimeResponseData` for more details. The input, output, and state elements of the response can be accessed using -signal names in place of integer offsets:: - - plt.plot(response.time, response.states['x[1]'] +signal names in place of integer offsets: -For multi-trace systems generated by :func:`step_response` and -:func:`impulse_response`, the input name used to generate the trace can be -used to access the appropriate input output pair:: +.. testcode:: time_series - plt.plot(response.time, response.outputs['y[0]', 'u[1]']) + plt.plot(response.time, response.states['x[1]']) The time response functions can also be assigned to a tuple, which extracts the time and output (and optionally the state, if the `return_x` keyword is -used). This allows simple commands for plotting:: +used). This allows simple commands for plotting: + +.. testcode:: time_series t, y = ct.step_response(sys) - plot(t, y) + plt.plot(t, y) + +The output of a MIMO LTI system can be plotted like this: -The output of a MIMO LTI system can be plotted like this:: +.. testcode:: time_series - t, y = ct.forced_response(sys, t, u) - plot(t, y[0], label='y_0') - plot(t, y[1], label='y_1') + sys = ct.rss(4, 2, 1) + + timepts = np.linspace(0, 10) + u = np.sin(timepts) + + t, y = ct.forced_response(sys, timepts, u) + plt.plot(t, y[0], label='y_0') + plt.plot(t, y[1], label='y_1') + +For multi-trace systems generated by :func:`step_response` and +:func:`impulse_response`, the input name used to generate the trace can be +used to access the appropriate input output pair: + +.. testcode:: time_series + + response = ct.step_response(sys) + plt.plot(response.time, response.outputs['y[1]', 'u[0]']) The convention also works well with the state space form of linear systems. If `D` is the feedthrough matrix (2D array) of a linear system, @@ -230,7 +252,7 @@ above. Time response data can be plotted with the :func:`TimeResponseData.plot` method. For example, the step response for a two-input, two-output can be plotted using the commands: -.. testcode:: +.. testcode:: timeplot sys_mimo = ct.tf2ss( [[[1], [0.1]], [[0.2], [1]]], @@ -238,6 +260,11 @@ for a two-input, two-output can be plotted using the commands: response = ct.step_response(sys_mimo) response.plot() +.. testcode:: timeplot + :hide: + + plt.savefig('figures/timeplot-mimo_step-default.png') + which produces the following plot: .. image:: figures/timeplot-mimo_step-default.png @@ -258,13 +285,18 @@ with appropriate labeling via a legend on selected axes. For example, using ``plot_input=True`` and ``overlay_signals=True`` yields the following plot: -.. testcode:: +.. testcode:: timeplot ct.step_response(sys_mimo).plot( plot_inputs=True, overlay_signals=True, title="Step response for 2x2 MIMO system " + "[plot_inputs, overlay_signals]") +.. testcode:: timeplot + :hide: + + plt.savefig('figures/timeplot-mimo_step-pi_cs.png') + .. image:: figures/timeplot-mimo_step-pi_cs.png :align: center @@ -275,7 +307,9 @@ default. These can be plotted on separate axes, but also "overlaid" on the output axes (useful when the input and output signals are being compared to each other). The following plot shows the use of ``plot_inputs='overlay'`` as well as the ability to reposition the legends using the `legend_map` -keyword:: +keyword: + +.. testcode:: timeplot timepts = np.linspace(0, 10, 100) U = np.vstack([np.sin(timepts), np.cos(2*timepts)]) @@ -285,13 +319,20 @@ keyword:: title="I/O response for 2x2 MIMO system " + "[plot_inputs='overlay', legend_map]") +.. testcode:: timeplot + :hide: + + plt.savefig('figures/timeplot-mimo_ioresp-ov_lm.png') + .. image:: figures/timeplot-mimo_ioresp-ov_lm.png :align: center Another option that is available is to use the `transpose` keyword so that instead of plotting the outputs on the top and inputs on the bottom, the inputs are plotted on the left and outputs on the right, as shown in the -following figure:: +following figure: + +.. testcode:: timeplot U1 = np.vstack([np.sin(timepts), np.cos(2*timepts)]) resp1 = ct.input_output_response(sys_mimo, timepts, U1) @@ -305,6 +346,11 @@ following figure:: title="I/O responses for 2x2 MIMO system, multiple traces " "[transpose]") +.. testcode:: timeplot + :hide: + + plt.savefig('figures/timeplot-mimo_ioresp-mt_tr.png') + .. image:: figures/timeplot-mimo_ioresp-mt_tr.png :align: center @@ -316,7 +362,9 @@ the `input_props`, `output_props` and `trace_props` parameters for Additional customization is possible using the `input_props`, `output_props`, and `trace_props` keywords to set complementary line colors -and styles for various signals and traces:: +and styles for various signals and traces: + +.. testcode:: timeplot cplt = ct.step_response(sys_mimo).plot( plot_inputs='overlay', overlay_signals=True, overlay_traces=True, @@ -324,6 +372,11 @@ and styles for various signals and traces:: input_props=[{'color': c} for c in ['red', 'green']], trace_props=[{'linestyle': s} for s in ['-', '--']]) +.. testcode:: timeplot + :hide: + + plt.savefig('figures/timeplot-mimo_step-linestyle.png') + .. image:: figures/timeplot-mimo_step-linestyle.png :align: center @@ -337,17 +390,27 @@ Linear time invariant (LTI) systems can be analyzed in terms of their frequency response and `python-control` provides a variety of tools for carrying out frequency response analysis. The most basic of these is the :func:`frequency_response` function, which will compute -the frequency response for one or more linear systems:: +the frequency response for one or more linear systems: + +.. testcode:: freqplot sys1 = ct.tf([1], [1, 2, 1], name='sys1') sys2 = ct.tf([1, 0.2], [1, 1, 3, 1, 1], name='sys2') response = ct.frequency_response([sys1, sys2]) A Bode plot provide a graphical view of the response an LTI system and can -be generated using the :func:`bode_plot` function:: +be generated using the :func:`bode_plot` function: + +.. testcode:: freqplot ct.bode_plot(response, initial_phase=0) +.. testcode:: freqplot + :hide: + + plt.savefig('figures/freqplot-siso_bode-default.png') + plt.close('all') + .. image:: figures/freqplot-siso_bode-default.png :align: center @@ -355,62 +418,110 @@ Computing the response for multiple systems at the same time yields a common frequency range that covers the features of all listed systems. Bode plots can also be created directly using the -:meth:`FrequencyResponseData.plot` method:: +:meth:`FrequencyResponseData.plot` method: + +.. testcode:: freqplot sys_mimo = ct.tf( [[[1], [0.1]], [[0.2], [1]]], [[[1, 0.6, 1], [1, 1, 1]], [[1, 0.4, 1], [1, 2, 1]]], name="sys_mimo") ct.frequency_response(sys_mimo).plot() +.. testcode:: freqplot + :hide: + + plt.savefig('figures/freqplot-mimo_bode-default.png') + plt.close('all') + .. image:: figures/freqplot-mimo_bode-default.png :align: center A variety of options are available for customizing Bode plots, for example allowing the display of the phase to be turned off or -overlaying the inputs or outputs:: +overlaying the inputs or outputs: + +.. testcode:: freqplot ct.frequency_response(sys_mimo).plot( plot_phase=False, overlay_inputs=True, overlay_outputs=True) +.. testcode:: freqplot + :hide: + + plt.savefig('figures/freqplot-mimo_bode-magonly.png') + plt.close('all') + .. image:: figures/freqplot-mimo_bode-magonly.png :align: center The :func:`singular_values_response` function can be used to generate Bode plots that show the singular values of a transfer -function:: +function: + +.. testcode:: freqplot ct.singular_values_response(sys_mimo).plot() +.. testcode:: freqplot + :hide: + + plt.savefig('figures/freqplot-mimo_svplot-default.png') + plt.close('all') + .. image:: figures/freqplot-mimo_svplot-default.png :align: center Different types of plots can also be specified for a given frequency response. For example, to plot the frequency response using a a Nichols -plot, use ``plot_type='nichols'``:: +plot, use ``plot_type='nichols'``: + +.. testcode:: freqplot response.plot(plot_type='nichols') +.. testcode:: freqplot + :hide: + + plt.savefig('figures/freqplot-siso_nichols-default.png') + plt.close('all') + .. image:: figures/freqplot-siso_nichols-default.png :align: center Another response function that can be used to generate Bode plots is the :func:`gangof4_response` function, which computes the four primary -sensitivity functions for a feedback control system in standard form:: +sensitivity functions for a feedback control system in standard form: + +.. testcode:: freqplot proc = ct.tf([1], [1, 1, 1], name="process") ctrl = ct.tf([100], [1, 5], name="control") - response = rect.gangof4_response(proc, ctrl) + response = ct.gangof4_response(proc, ctrl) ct.bode_plot(response) # or response.plot() +.. testcode:: freqplot + :hide: + + plt.savefig('figures/freqplot-gangof4.png') + plt.close('all') + .. image:: figures/freqplot-gangof4.png :align: center Nyquist analysis can be done using the :func:`nyquist_response` function, which evaluates an LTI system along the Nyquist contour, and -the :func:`nyquist_plot` function, which generates a Nyquist plot:: +the :func:`nyquist_plot` function, which generates a Nyquist plot: + +.. testcode:: freqplot sys = ct.tf([1, 0.2], [1, 1, 3, 1, 1], name='sys') - nyquist_plot(sys) + ct.nyquist_plot(sys) + +.. testcode:: freqplot + :hide: + + plt.savefig('figures/freqplot-nyquist-default.png') + plt.close('all') .. image:: figures/freqplot-nyquist-default.png :align: center @@ -426,41 +537,67 @@ line style is changed to reflect the scaling, and it is possible to offset the scaled portions to separate out the portions of the Nyquist curve at :math:`\infty`. A number of keyword parameters for both are available for :func:`nyquist_response` and :func:`nyquist_plot` to tune -the computation of the Nyquist curve and the way the data are plotted:: +the computation of the Nyquist curve and the way the data are plotted: + +.. testcode:: freqplot sys = ct.tf([1, 0.2], [1, 0, 1]) * ct.tf([1], [1, 0]) nyqresp = ct.nyquist_response(sys) nyqresp.plot( max_curve_magnitude=6, max_curve_offset=1, - arrows=[0, 0.15, 0.3, 0.6, 0.7, 0.925], label='sys') + arrows=[0, 0.15, 0.3, 0.6, 0.7, 0.925], + title='Custom Nyquist plot') print("Encirclements =", nyqresp.count) +.. testoutput:: freqplot + :hide: + + Encirclements = 2 + +.. testcode:: freqplot + :hide: + + plt.savefig('figures/freqplot-nyquist-custom.png') + plt.close('all') + .. image:: figures/freqplot-nyquist-custom.png :align: center All frequency domain plotting functions will automatically compute the range of frequencies to plot based on the poles and zeros of the frequency response. Frequency points can be explicitly specified by including an -array of frequencies as a second argument (after the list of systems):: +array of frequencies as a second argument (after the list of systems): + +.. testcode:: freqplot sys1 = ct.tf([1], [1, 2, 1], name='sys1') sys2 = ct.tf([1, 0.2], [1, 1, 3, 1, 1], name='sys2') omega = np.logspace(-2, 2, 500) ct.frequency_response([sys1, sys2], omega).plot(initial_phase=0) +.. testcode:: freqplot + :hide: + + plt.savefig('figures/freqplot-siso_bode-omega.png') + plt.close('all') + .. image:: figures/freqplot-siso_bode-omega.png :align: center Alternatively, frequency ranges can be specified by passing a list of the form ``[wmin, wmax]``, where `wmin` and `wmax` are the minimum and -maximum frequencies in the (log-spaced) frequency range:: +maximum frequencies in the (log-spaced) frequency range: + +.. testcode:: freqplot response = ct.frequency_response([sys1, sys2], [1e-2, 1e2]) The number of (log-spaced) points in the frequency can be specified using the `omega_num` keyword parameter. -Frequency response data can also be accessed directly and plotted manually:: +Frequency response data can also be accessed directly and plotted manually: + +.. testcode:: freqplot sys = ct.rss(4, 2, 2, strictly_proper=True) # 2x2 MIMO system fresp = ct.frequency_response(sys) @@ -480,20 +617,41 @@ Pole/Zero Data Pole/zero maps and root locus diagrams provide insights into system response based on the locations of system poles and zeros in the complex plane. The :func:`pole_zero_map` function returns the poles and -zeros and can be used to generate a pole/zero plot:: +zeros and can be used to generate a pole/zero plot: + +.. testcode:: pzmap + :hide: + + plt.close('all') + +.. testcode:: pzmap sys = ct.tf([1, 2], [1, 2, 3], name='SISO transfer function') response = ct.pole_zero_map(sys) ct.pole_zero_plot(response) +.. testcode:: pzmap + :hide: + + plt.savefig('figures/pzmap-siso_ctime-default.png') + plt.close('all') + .. image:: figures/pzmap-siso_ctime-default.png :align: center A root locus plot shows the location of the closed loop poles of a system -as a function of the loop gain:: +as a function of the loop gain: + +.. testcode:: pzmap ct.root_locus_map(sys).plot() +.. testcode:: pzmap + :hide: + + plt.savefig('figures/rlocus-siso_ctime-default.png') + plt.close('all') + .. image:: figures/rlocus-siso_ctime-default.png :align: center @@ -508,25 +666,54 @@ root locus diagram will mark the pole locations on all branches of the diagram and display the gain and damping ratio for the clicked point below the plot title: +.. testcode:: pzmap + :hide: + + cplt = ct.root_locus_map(sys).plot(initial_gain=3.506) + ax = cplt.axes[0, 0] + freqplot_rcParams = ct.config._get_param('ctrlplot', 'rcParams') + with plt.rc_context(freqplot_rcParams): + ax.set_title( + "Clicked at: -2.729+1.511j gain = 3.506 damping = 0.8748") + + plt.savefig('figures/rlocus-siso_ctime-clicked.png') + plt.close('all') + .. image:: figures/rlocus-siso_ctime-clicked.png :align: center Root locus diagrams are also supported for discrete time systems, in which -case the grid is show inside the unit circle:: +case the grid is show inside the unit circle: + +.. testcode:: pzmap sysd = sys.sample(0.1) ct.root_locus_plot(sysd) +.. testcode:: pzmap + :hide: + + plt.savefig('figures/rlocus-siso_dtime-default.png') + plt.close('all') + .. image:: figures/rlocus-siso_dtime-default.png :align: center Lists of systems can also be given, in which case the root locus diagram -for each system is plotted in different colors:: +for each system is plotted in different colors: + +.. testcode:: pzmap sys1 = ct.tf([1], [1, 2, 1], name='sys1') sys2 = ct.tf([1, 0.2], [1, 1, 3, 1, 1], name='sys2') ct.root_locus_plot([sys1, sys2], grid=False) +.. testcode:: pzmap + :hide: + + plt.savefig('figures/rlocus-siso_multiple-nogrid.png') + plt.close('all') + .. image:: figures/rlocus-siso_multiple-nogrid.png :align: center @@ -671,36 +858,44 @@ various ways. The following general rules apply: The plot title is only generated if `ax` is None. The following code illustrates the use of some of these customization -features:: - - P = ct.tf([0.02], [1, 0.1, 0.01]) # servomechanism - C1 = ct.tf([1, 1], [1, 0]) # unstable - L1 = P * C1 - C2 = ct.tf([1, 0.05], [1, 0]) # stable - L2 = P * C2 - - plt.rcParams.update(ct.rcParams) - fig = plt.figure(figsize=[7, 4]) - ax_mag = fig.add_subplot(2, 2, 1) - ax_phase = fig.add_subplot(2, 2, 3) - ax_nyquist = fig.add_subplot(1, 2, 2) - - ct.bode_plot( - [L1, L2], ax=[ax_mag, ax_phase], - label=["$L_1$ (unstable)", "$L_2$ (unstable)"], - show_legend=False) - ax_mag.set_title("Bode plot for $L_1$, $L_2$") - ax_mag.tick_params(labelbottom=False) - fig.align_labels() - - ct.nyquist_plot(L1, ax=ax_nyquist, label="$L_1$ (unstable)") - ct.nyquist_plot( - L2, ax=ax_nyquist, label="$L_2$ (stable)", - max_curve_magnitude=22, legend_loc='upper right') - ax_nyquist.set_title("Nyquist plot for $L_1$, $L_2$") - - fig.suptitle("Loop analysis for servomechanism control design") - plt.tight_layout() +features: + +.. testcode:: ctrlplot + + P = ct.tf([0.02], [1, 0.1, 0.01]) # servomechanism + C1 = ct.tf([1, 1], [1, 0]) # unstable + L1 = P * C1 + C2 = ct.tf([1, 0.05], [1, 0]) # stable + L2 = P * C2 + + plt.rcParams.update(ct.rcParams) + fig = plt.figure(figsize=[7, 4]) + ax_mag = fig.add_subplot(2, 2, 1) + ax_phase = fig.add_subplot(2, 2, 3) + ax_nyquist = fig.add_subplot(1, 2, 2) + + ct.bode_plot( + [L1, L2], ax=[ax_mag, ax_phase], + label=["$L_1$ (unstable)", "$L_2$ (unstable)"], + show_legend=False) + ax_mag.set_title("Bode plot for $L_1$, $L_2$") + ax_mag.tick_params(labelbottom=False) + fig.align_labels() + + ct.nyquist_plot(L1, ax=ax_nyquist, label="$L_1$ (unstable)") + ct.nyquist_plot( + L2, ax=ax_nyquist, label="$L_2$ (stable)", + max_curve_magnitude=22, legend_loc='upper right') + ax_nyquist.set_title("Nyquist plot for $L_1$, $L_2$") + + fig.suptitle("Loop analysis for servomechanism control design") + plt.tight_layout() + +.. testcode:: ctrlplot + :hide: + + plt.savefig('figures/ctrlplot-servomech.png') + plt.close('all') .. image:: figures/ctrlplot-servomech.png :align: center @@ -712,14 +907,23 @@ continuous time omega-damping grid (including root locus diagrams), due to the way that axes grids are implemented. As a workaround, the :func:`pole_zero_subplots` command can be used to create an array of subplots with different grid types, as illustrated in the following -example:: - - ax_array = ct.pole_zero_subplots(2, 1, grid=[True, False]) - sys1 = ct.tf([1, 2], [1, 2, 3], name='sys1') - sys2 = ct.tf([1, 0.2], [1, 1, 3, 1, 1], name='sys2') - ct.root_locus_plot([sys1, sys2], ax=ax_array[0, 0]) - cplt = ct.root_locus_plot([sys1, sys2], ax=ax_array[1, 0]) - cplt.set_plot_title("Root locus plots (w/ specified axes)") +example: + +.. testcode:: ctrlplot + + ax_array = ct.pole_zero_subplots(2, 1, grid=[True, False]) + sys1 = ct.tf([1, 2], [1, 2, 3], name='sys1') + sys2 = ct.tf([1, 0.2], [1, 1, 3, 1, 1], name='sys2') + ct.root_locus_plot([sys1, sys2], ax=ax_array[0, 0]) + cplt = ct.root_locus_plot([sys1, sys2], ax=ax_array[1, 0]) + cplt.set_plot_title("Root locus plots (w/ specified axes)") + cplt.figure.tight_layout() + +.. testcode:: ctrlplot + :hide: + + plt.savefig('figures/ctrlplot-pole_zero_subplots.png') + plt.close('all') .. image:: figures/ctrlplot-pole_zero_subplots.png :align: center diff --git a/doc/stochastic.rst b/doc/stochastic.rst index 264ad7856..ecb9f116f 100644 --- a/doc/stochastic.rst +++ b/doc/stochastic.rst @@ -79,30 +79,58 @@ To compute the response of a linear (or nonlinear) system to a white noise input, use the :func:`forced_response` (or :func:`input_output_response`) function: -.. code:: +.. testsetup:: + + import matplotlib.pyplot as plt + import numpy as np + import random + import control as ct + + random.seed(71) + np.random.seed(71) + +.. testcode:: a, c = 1, 1 - sys = ct.ss([[-a]], [[1]], [[c]], 0) + sys = ct.ss([[-a]], [[1]], [[c]], 0, name='sys') timepts = np.linspace(0, 5, 1000) Q = np.array([[0.1]]) V = ct.white_noise(timepts, Q) resp = ct.forced_response(sys, timepts, V) resp.plot() -.. todo:: - Add plot +.. testcode:: + :hide: + + plt.savefig('figures/stochastic-whitenoise-response.png') + plt.close('all') + +.. image:: figures/stochastic-whitenoise-response.png + :align: center The correlation function for the output can be computed using the :func:`correlation` function and compared to the analytical expression: -.. code:: +.. testcode:: tau, r_Y = ct.correlation(timepts, resp.outputs) - plt.plot(tau, r_Y) - plt.plot(tau, c**2 * Q.item() / (2 * a) * np.exp(-a * np.abs(tau))) + plt.plot(tau, r_Y, label='empirical') + plt.plot( + tau, c**2 * Q.item() / (2 * a) * np.exp(-a * np.abs(tau)), + label='approximation') + plt.xlabel(r"$\tau$") + plt.ylabel(r"$r_\tau$") + plt.title(f"Output correlation for {sys.name}") + plt.legend() -.. todo:: - Add plot +.. testcode:: + :hide: + + plt.savefig('figures/stochastic-whitenoise-correlation.png') + plt.close('all') + +.. image:: figures/stochastic-whitenoise-correlation.png + :align: center .. _kalman-filter: @@ -159,6 +187,9 @@ time optimal controller. For the second two forms, the :func:`dlqr` function can be used. Additional arguments and details are given on the :func:`lqr` and :func:`dlqr` documentation pages. +.. todo:: + Convert the following code to testcode + The :func:`create_estimator_iosystem` function can be used to create an I/O system implementing a Kalman filter, including integration of the Riccati ODE. The command has the form @@ -180,8 +211,9 @@ to allow prediction with no additional sensor information:: resp = ct.input_output_response( estim, timepts, 0, [X0, P0], param={'correct': False}) -The :func:`create_statefbk_iosystem` function can be used to combine -an estimator with a state feedback controller:: +The :func:`create_estimator_iosystem` and +:func:`create_statefbk_iosystem` functions can be used to combine an +estimator with a state feedback controller:: K, _, _ = ct.lqr(sys, Qx, Qu) estim = ct.create_estimator_iosystem(sys, Qv, Qw, P0) From a46f0b6d6d54d1950801b640f829a8572752a915 Mon Sep 17 00:00:00 2001 From: Richard Murray Date: Sun, 2 Feb 2025 20:57:36 -0800 Subject: [PATCH 63/93] updated isort's + remove extraneous spaces --- control/bdalg.py | 2 +- control/lti.py | 8 +++----- control/matlab/wrappers.py | 15 ++++++++++----- control/statesp.py | 3 +-- 4 files changed, 15 insertions(+), 13 deletions(-) diff --git a/control/bdalg.py b/control/bdalg.py index 8125e2702..3aba5a744 100644 --- a/control/bdalg.py +++ b/control/bdalg.py @@ -638,7 +638,7 @@ def split_tf(transfer_function): array([1, 1]), name='G', outputs=1, inputs=1)]], dtype=object) - + """ tf_split_lst = [] for i_out in range(transfer_function.noutputs): diff --git a/control/lti.py b/control/lti.py index 96dcf0701..140d72b7e 100644 --- a/control/lti.py +++ b/control/lti.py @@ -64,8 +64,9 @@ def damp(self): def feedback(self, other=1, sign=-1): """Feedback interconnection between two LTI objects.""" - # Implemented in subclasses, but documented here - return NotImplemented + raise NotImplementedError( + "feedback not implemented for base " + f"{self.__class__.__name__} objects") def frequency_response(self, omega=None, squeeze=None): """Evaluate LTI system response at an array of frequencies. @@ -217,9 +218,6 @@ def ispassive(self): # importing here prevents circular dependancy from control.passivity import ispassive return ispassive(self) - - def feedback(self, other=1, sign=-1): - raise NotImplementedError(f"feedback not implemented for base {self.__class__.__name__} objects") # convenience aliases # most function are only forward declaraed and patched in the __init__.py to avoid circular imports diff --git a/control/matlab/wrappers.py b/control/matlab/wrappers.py index b11c01769..a78e720d6 100644 --- a/control/matlab/wrappers.py +++ b/control/matlab/wrappers.py @@ -4,15 +4,16 @@ """ -import numpy as np -from scipy.signal import zpk2tf import warnings from warnings import warn +import numpy as np +from scipy.signal import zpk2tf + +from ..exception import ControlArgument +from ..lti import LTI from ..statesp import ss from ..xferfcn import tf -from ..lti import LTI -from ..exception import ControlArgument __all__ = ['bode', 'nyquist', 'ngrid', 'rlocus', 'pzmap', 'dcgain', 'connect'] @@ -127,7 +128,7 @@ def nyquist(*args, plot=True, **kwargs): Frequencies in rad/s. """ - from ..freqplot import nyquist_response, nyquist_plot + from ..freqplot import nyquist_plot, nyquist_response # If first argument is a list, assume python-control calling format if hasattr(args[0], '__iter__'): @@ -311,6 +312,8 @@ def pzmap(*args, **kwargs): from ..nichols import nichols_grid + + def ngrid(): return nichols_grid() ngrid.__doc__ = nichols_grid.__doc__ @@ -378,6 +381,8 @@ def dcgain(*args): from ..bdalg import connect as ct_connect + + def connect(*args): """connect(sys, Q, inputv, outputv) diff --git a/control/statesp.py b/control/statesp.py index eec80beb8..3df3bc194 100644 --- a/control/statesp.py +++ b/control/statesp.py @@ -20,7 +20,6 @@ import numpy as np import scipy as sp import scipy.linalg -# array needed in eval() call from numpy import array # noqa: F401 from numpy import any, asarray, concatenate, cos, delete, empty, exp, eye, \ isinf, pad, sin, squeeze, zeros @@ -1487,7 +1486,7 @@ def output(self, t, x, u=None, params=None): raise ValueError("len(u) must be equal to number of inputs") return (self.C @ x).reshape((-1,)) \ + (self.D @ u).reshape((-1,)) # return as row vector - + # convenience aliase, import needs to go over the submodule to avoid circular imports initial_response = control.timeresp.initial_response From b742bd262e1e9d6b75dc0ec66cd26fcb81b05a92 Mon Sep 17 00:00:00 2001 From: Richard Murray Date: Mon, 13 Jan 2025 08:23:50 -0800 Subject: [PATCH 64/93] add numpydoc dependency for pip OS/BLAS test matrix --- .github/workflows/os-blas-test-matrix.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/os-blas-test-matrix.yml b/.github/workflows/os-blas-test-matrix.yml index ea03e42df..242d89732 100644 --- a/.github/workflows/os-blas-test-matrix.yml +++ b/.github/workflows/os-blas-test-matrix.yml @@ -256,7 +256,7 @@ jobs: - name: Install Wheel run: | python -m pip install --upgrade pip - pip install matplotlib scipy pytest pytest-cov pytest-timeout coverage + pip install matplotlib scipy pytest pytest-cov pytest-timeout coverage numpydoc pip install slycot-wheels/${{ matrix.packagekey }}/slycot*.whl pip show slycot - name: Test with pytest From 34f39dc49e47b60089ef8a4dca92ff0b5949b14c Mon Sep 17 00:00:00 2001 From: Richard Murray Date: Mon, 13 Jan 2025 22:21:20 -0800 Subject: [PATCH 65/93] reformat numbered notes sections --- control/flatsys/flatsys.py | 33 +++++++++++++++++-------------- control/lti.py | 40 +++++++++++++++++++------------------- control/nlsys.py | 32 +++++++++++++++--------------- control/optimal.py | 26 ++++++++++++------------- control/statefbk.py | 16 +++++++-------- control/statesp.py | 2 +- control/timeplot.py | 31 ++++++++++++++--------------- control/timeresp.py | 32 +++++++++++++++--------------- control/xferfcn.py | 4 +--- 9 files changed, 107 insertions(+), 109 deletions(-) diff --git a/control/flatsys/flatsys.py b/control/flatsys/flatsys.py index 8ab7df1f2..07db5e700 100644 --- a/control/flatsys/flatsys.py +++ b/control/flatsys/flatsys.py @@ -58,7 +58,8 @@ class FlatSystem(NonlinearIOSystem): ----- The class must implement two functions: - zflag = flatsys.foward(x, u, params) + ``zflag = flatsys.foward(x, u, params)`` + This function computes the flag (derivatives) of the flat output. The inputs to this function are the state 'x' and inputs 'u' (both 1D arrays). The output should be a 2D array with the first @@ -66,7 +67,8 @@ class FlatSystem(NonlinearIOSystem): dimension of the length required to represent the full system dynamics (typically the number of states) - x, u = flatsys.reverse(zflag, params) + ``x, u = flatsys.reverse(zflag, params)`` + This function system state and inputs give the the flag (derivatives) of the flat output. The input to this function is an 2D array whose first dimension is equal to the number of system inputs and whose @@ -737,19 +739,20 @@ def solve_flat_ocp( Notes ----- - 1. Additional keyword parameters can be used to fine tune the behavior - of the underlying optimization function. See `minimize_*` keywords - in `optimal.OptimalControlProblem` for more information. - - 2. The return data structure includes the following additional attributes: - * success : bool indicating whether the optimization succeeded - * cost : computed cost of the returned trajectory - * message : message returned by optimization if success if False - - 3. A common failure in solving optimal control problem is that the - default initial guess violates the constraints and the optimizer - can't find a feasible solution. Using the `initial_guess` parameter - can often be used to overcome these errors. + Additional keyword parameters can be used to fine tune the behavior of + the underlying optimization function. See `minimize_*` keywords in + `optimal.OptimalControlProblem` for more information. + + The return data structure includes the following additional attributes: + + * success : bool indicating whether the optimization succeeded + * cost : computed cost of the returned trajectory + * message : message returned by optimization if success if False + + A common failure in solving optimal control problem is that the default + initial guess violates the constraints and the optimizer can't find a + feasible solution. Using the `initial_guess` parameter can often be + used to overcome these errors. """ # diff --git a/control/lti.py b/control/lti.py index 140d72b7e..66fd63baf 100644 --- a/control/lti.py +++ b/control/lti.py @@ -316,19 +316,20 @@ def damp(sys, doprint=True): Notes ----- - If the system is continuous, - wn = abs(poles) - zeta = -real(poles)/poles + If the system is continuous + + | ``wn = abs(poles)`` + | ``zeta = -real(poles)/poles`` If the system is discrete, the discrete poles are mapped to their equivalent location in the s-plane via - s = log(poles)/dt + | ``s = log(poles)/dt`` and - wn = abs(s) - zeta = -real(s)/wn. + | ``wn = abs(s)`` + | ``zeta = -real(s)/wn`` Examples -------- @@ -477,20 +478,19 @@ def frequency_response( Notes ----- - 1. This function is a wrapper for `StateSpace.frequency_response` - and `TransferFunction.frequency_response`. - - 2. You can also use the lower-level methods ``sys(s)`` or ``sys(z)`` to - generate the frequency response for a single system. - - 3. All frequency data should be given in rad/sec. If frequency limits - are computed automatically, the `Hz` keyword can be used to ensure - that limits are in factors of decades in Hz, so that Bode plots with - ``Hz=True`` look better. - - 4. The frequency response data can be plotted by calling the - `bode_plot` function or using the `plot` method of - the `FrequencyResponseData` class. + This function is a wrapper for `StateSpace.frequency_response` and + `TransferFunction.frequency_response`. You can also use the + lower-level methods ``sys(s)`` or ``sys(z)`` to generate the frequency + response for a single system. + + All frequency data should be given in rad/sec. If frequency limits are + computed automatically, the `Hz` keyword can be used to ensure that + limits are in factors of decades in Hz, so that Bode plots with + ``Hz=True`` look better. + + The frequency response data can be plotted by calling the `bode_plot` + function or using the `plot` method of the `FrequencyResponseData` + class. Examples -------- diff --git a/control/nlsys.py b/control/nlsys.py index cd496f1c0..cca7796c6 100644 --- a/control/nlsys.py +++ b/control/nlsys.py @@ -1537,22 +1537,22 @@ def input_output_response( Notes ----- - 1. If a smaller number of initial conditions are given than the number of - states in the system, the initial conditions will be padded with - zeros. This is often useful for interconnected control systems where - the process dynamics are the first system and all other components - start with zero initial condition since this can be specified as - [xsys_0, 0]. A warning is issued if the initial conditions are padded - and and the final listed initial state is not zero. - - 2. If discontinuous inputs are given, the underlying SciPy numerical - integration algorithms can sometimes produce erroneous results due - to the default tolerances that are used. The `ivp_method` and - `ivp_keywords` parameters can be used to tune the ODE solver and - produce better results. In particular, using 'LSODA' as the - `ivp_method` or setting the `rtol` parameter to a smaller value - (e.g. using ``ivp_kwargs={'rtol': 1e-4}``) can provide more accurate - results. + If a smaller number of initial conditions are given than the number of + states in the system, the initial conditions will be padded with zeros. + This is often useful for interconnected control systems where the + process dynamics are the first system and all other components start + with zero initial condition since this can be specified as [xsys_0, 0]. + A warning is issued if the initial conditions are padded and and the + final listed initial state is not zero. + + If discontinuous inputs are given, the underlying SciPy numerical + integration algorithms can sometimes produce erroneous results due to + the default tolerances that are used. The `ivp_method` and + `ivp_keywords` parameters can be used to tune the ODE solver and + produce better results. In particular, using 'LSODA' as the + `ivp_method` or setting the `rtol` parameter to a smaller value + (e.g. using ``ivp_kwargs={'rtol': 1e-4}``) can provide more accurate + results. """ # diff --git a/control/optimal.py b/control/optimal.py index 4e2d2a6c1..0e0d7d2cd 100644 --- a/control/optimal.py +++ b/control/optimal.py @@ -1109,19 +1109,19 @@ def solve_ocp( Notes ----- - 1. For discrete time systems, the final value of the timepts vector - specifies the final time t_N, and the trajectory cost is computed - from time t_0 to t_{N-1}. Note that the input u_N does not affect - the state x_N and so it should always be returned as 0. Further, if - neither a terminal cost nor a terminal constraint is given, then the - input at time point t_{N-1} does not affect the cost function and - hence u_{N-1} will also be returned as zero. If you want the - trajectory cost to include state costs at time t_{N}, then you can - set `terminal_cost` to be the same function as `cost`. - - 2. Additional keyword parameters can be used to fine-tune the behavior - of the underlying optimization and integration functions. See - `OptimalControlProblem` for more information. + For discrete time systems, the final value of the timepts vector + specifies the final time t_N, and the trajectory cost is computed from + time t_0 to t_{N-1}. Note that the input u_N does not affect the state + x_N and so it should always be returned as 0. Further, if neither a + terminal cost nor a terminal constraint is given, then the input at + time point t_{N-1} does not affect the cost function and hence u_{N-1} + will also be returned as zero. If you want the trajectory cost to + include state costs at time t_{N}, then you can set `terminal_cost` to + be the same function as `cost`. + + Additional keyword parameters can be used to fine-tune the behavior of + the underlying optimization and integration functions. See + `OptimalControlProblem` for more information. """ # Process keyword arguments diff --git a/control/statefbk.py b/control/statefbk.py index a0d3b845e..83b9159bc 100644 --- a/control/statefbk.py +++ b/control/statefbk.py @@ -65,15 +65,13 @@ def place(A, B, p): Notes ----- - Algorithm - This is a wrapper function for `scipy.signal.place_poles`, which - implements the Tits and Yang algorithm [1]_. It will handle SISO, - MISO, and MIMO systems. If you want more control over the algorithm, - use `scipy.signal.place_poles` directly. - - Limitations - The algorithm will not place poles at the same location more - than rank(B) times. + This is a wrapper function for `scipy.signal.place_poles`, which + implements the Tits and Yang algorithm [1]_. It will handle SISO, MISO, + and MIMO systems. If you want more control over the algorithm, use + `scipy.signal.place_poles` directly. + + Limitations: The algorithm will not place poles at the same location + more than rank(B) times. References ---------- diff --git a/control/statesp.py b/control/statesp.py index 3df3bc194..1e73464a7 100644 --- a/control/statesp.py +++ b/control/statesp.py @@ -1325,7 +1325,7 @@ def sample(self, Ts, method='zoh', alpha=None, prewarp_frequency=None, Notes ----- - Uses `scipy.signal.cont2discrete` + Uses `scipy.signal.cont2discrete`. Examples -------- diff --git a/control/timeplot.py b/control/timeplot.py index 8f37211ef..fee3d4df1 100644 --- a/control/timeplot.py +++ b/control/timeplot.py @@ -159,22 +159,21 @@ def time_response_plot( Notes ----- - 1. A new figure will be generated if there is no current figure or - the current figure has an incompatible number of axes. To - force the creation of a new figures, use `plt.figure`. To reuse - a portion of an existing figure, use the ``ax`` keyword. - - 2. The line properties (color, linestyle, etc) can be set for the - entire plot using the `fmt` and/or `kwargs` parameter, which - are passed on to `matplotlib`. When combining signals or - traces, the `input_props`, `output_props`, and `trace_props` - parameters can be used to pass a list of dictionaries - containing the line properties to use. These input/output - properties are combined with the trace properties and finally - the kwarg properties to determine the final line properties. - - 3. The default plot properties, such as font sizes, can be set using - `config.defaults[''timeplot.rcParams']`. + A new figure will be generated if there is no current figure or the + current figure has an incompatible number of axes. To force the + creation of a new figures, use `plt.figure`. To reuse a portion of an + existing figure, use the ``ax`` keyword. + + The line properties (color, linestyle, etc) can be set for the entire + plot using the `fmt` and/or `kwargs` parameter, which are passed on to + `matplotlib`. When combining signals or traces, the `input_props`, + `output_props`, and `trace_props` parameters can be used to pass a list + of dictionaries containing the line properties to use. These + input/output properties are combined with the trace properties and + finally the kwarg properties to determine the final line properties. + + The default plot properties, such as font sizes, can be set using + `config.defaults[''timeplot.rcParams']`. """ from .ctrlplot import _process_ax_keyword, _process_line_labels diff --git a/control/timeresp.py b/control/timeresp.py index d83aad312..d2c568f42 100644 --- a/control/timeresp.py +++ b/control/timeresp.py @@ -997,22 +997,22 @@ def forced_response(sysdata, T=None, U=0., X0=0., transpose=False, params=None, Notes ----- - 1. For discrete time systems, the input/output response is computed - using the `scipy.signal.dlsim` function. - - 2. For continuous time systems, the output is computed using the matrix - exponential ``exp(A t)`` and assuming linear interpolation of the - inputs between time points. - - 3. If a nonlinear I/O system is passed to `forced_response`, the - `input_output_response` function is called instead. The main - difference between `input_output_response` and `forced_response` is - that `forced_response` is specialized (and optimized) for linear - systems. - - 4. (legacy) The return value of the system can also be accessed by - assigning the function to a tuple of length 2 (time, output) or of - length 3 (time, output, state) if `return_x` is True. + For discrete time systems, the input/output response is computed + using the `scipy.signal.dlsim` function. + + For continuous time systems, the output is computed using the + matrix exponential ``exp(A t)`` and assuming linear interpolation + of the inputs between time points. + + If a nonlinear I/O system is passed to `forced_response`, the + `input_output_response` function is called instead. The main + difference between `input_output_response` and `forced_response` + is that `forced_response` is specialized (and optimized) for + linear systems. + + (legacy) The return value of the system can also be accessed by + assigning the function to a tuple of length 2 (time, output) or of + length 3 (time, output, state) if `return_x` is True. Examples -------- diff --git a/control/xferfcn.py b/control/xferfcn.py index d8da0afd7..c74abf179 100644 --- a/control/xferfcn.py +++ b/control/xferfcn.py @@ -1210,9 +1210,7 @@ def sample(self, Ts, method='zoh', alpha=None, prewarp_frequency=None, Notes ----- - 1. Available only for SISO systems - - 2. Uses `scipy.signal.cont2discrete` + Available only for SISO systems. Uses `scipy.signal.cont2discrete`. Examples -------- From 1ed88172fdab170a839c7d56613319cb9c1cd06b Mon Sep 17 00:00:00 2001 From: Richard Murray Date: Tue, 14 Jan 2025 16:11:41 -0800 Subject: [PATCH 66/93] regularize `dt` keyword use and related annotations (see develop.rst) --- control/bdalg.py | 12 ++++---- control/canonical.py | 4 +-- control/ctrlplot.py | 2 +- control/ctrlutil.py | 2 +- control/descfcn.py | 10 +++--- control/flatsys/flatsys.py | 3 ++ control/flatsys/systraj.py | 4 +-- control/frdata.py | 36 +++++++++++----------- control/freqplot.py | 22 +++++++------- control/iosys.py | 12 ++++---- control/lti.py | 14 ++++----- control/mateqn.py | 2 +- control/matlab/timeresp.py | 2 +- control/modelsimp.py | 2 +- control/nlsys.py | 24 +++++++-------- control/passivity.py | 8 ++--- control/phaseplot.py | 4 +-- control/robust.py | 8 ++--- control/sisotool.py | 6 ++-- control/statefbk.py | 16 +++++----- control/statesp.py | 35 ++++++++++++--------- control/sysnorm.py | 8 ++--- control/timeplot.py | 2 +- control/timeresp.py | 32 ++++++++++---------- control/xferfcn.py | 29 +++++++++++------- doc/config.rst | 2 +- doc/develop.rst | 62 +++++++++++++++++++++++++++----------- doc/intro.rst | 2 +- doc/iosys.rst | 2 +- doc/linear.rst | 16 +++++----- doc/response.rst | 18 +++++------ 31 files changed, 223 insertions(+), 178 deletions(-) diff --git a/control/bdalg.py b/control/bdalg.py index 3aba5a744..02d76a896 100644 --- a/control/bdalg.py +++ b/control/bdalg.py @@ -58,8 +58,8 @@ def series(*sys, **kwargs): Raises ------ ValueError - if ``sys2.ninputs`` does not equal ``sys1.noutputs`` - if ``sys1.dt`` is not compatible with ``sys2.dt`` + If `sys2.ninputs` does not equal `sys1.noutputs` or if `sys1.dt` is + not compatible with `sys2.dt`. See Also -------- @@ -72,8 +72,8 @@ def series(*sys, **kwargs): system class. The output type is the type of `sys1` unless a more general type is required based on type type of `sys2`. - If both systems have a defined timebase (dt = 0 for continuous time, - dt > 0 for discrete time), then the timebase for both systems must + If both systems have a defined timebase (`dt` = 0 for continuous time, + `dt` > 0 for discrete time), then the timebase for both systems must match. If only one of the system has a timebase, the return timebase will be set to match it. @@ -143,8 +143,8 @@ def parallel(*sys, **kwargs): the type of `sys1`. If `sys1` is a scalar, then the output type is the type of `sys2`. - If both systems have a defined timebase (dt = 0 for continuous time, - dt > 0 for discrete time), then the timebase for both systems must + If both systems have a defined timebase (`dt` = 0 for continuous time, + `dt` > 0 for discrete time), then the timebase for both systems must match. If only one of the system has a timebase, the return timebase will be set to match it. diff --git a/control/canonical.py b/control/canonical.py index 0587c8d29..6efa5f6c8 100644 --- a/control/canonical.py +++ b/control/canonical.py @@ -497,7 +497,7 @@ def modal_form(xsys, condmax=None, sort=False): Parameters ---------- xsys : StateSpace object - System to be transformed, with state ``x``. + System to be transformed, with state x. condmax : None or float, optional An upper bound on individual transformations. If None, use `bdschur` default. @@ -508,7 +508,7 @@ def modal_form(xsys, condmax=None, sort=False): Returns ------- zsys : StateSpace object - System in modal canonical form, with state ``z``. + System in modal canonical form, with state z. T : (M, M) ndarray Coordinate transformation: z = T * x. diff --git a/control/ctrlplot.py b/control/ctrlplot.py index d4bb85482..942efaed2 100644 --- a/control/ctrlplot.py +++ b/control/ctrlplot.py @@ -148,7 +148,7 @@ class ControlPlot(): Figure on which the Axes are drawn. legend : `matplotlib:.legend.Legend` (instance or ndarray) Legend object(s) for the plot. If more than one legend is - included, this will be an array with each entry being either`` None`` + included, this will be an array with each entry being either None (for no legend) or a legend object. """ diff --git a/control/ctrlutil.py b/control/ctrlutil.py index 9df6672df..3a951463f 100644 --- a/control/ctrlutil.py +++ b/control/ctrlutil.py @@ -24,7 +24,7 @@ def unwrap(angle, period=2*math.pi): angle : array_like Array of angles to be unwrapped. period : float, optional - Period (defaults to ``2*pi``). + Period (defaults to 2 pi). Returns ------- diff --git a/control/descfcn.py b/control/descfcn.py index 93b9c8f16..383df0149 100644 --- a/control/descfcn.py +++ b/control/descfcn.py @@ -626,9 +626,9 @@ class relay_hysteresis_nonlinearity(DescribingFunctionNonlinearity): F = relay_hysteresis_nonlinearity(b, c) - The output of this function is ``b`` if ``x > c`` and ``-b`` if ``x < - -c``. For ``-c <= x <= c``, the value depends on the branch of the - hysteresis loop (as illustrated in Figure 10.20 of FBS2e). + The output of this function is b if x > c and -b if x < -c. For -c <= + x <= c, the value depends on the branch of the hysteresis loop (as + illustrated in Figure 10.20 of FBS2e). Parameters ---------- @@ -702,9 +702,9 @@ class friction_backlash_nonlinearity(DescribingFunctionNonlinearity): F = friction_backlash_nonlinearity(b) This function maintains an internal state representing the 'center' of - a mechanism with backlash. If the new input is within ``b/2`` of the + a mechanism with backlash. If the new input is within b/2 of the current center, the output is unchanged. Otherwise, the output is - given by the input shifted by ``b/2``. + given by the input shifted by b/2. Parameters ---------- diff --git a/control/flatsys/flatsys.py b/control/flatsys/flatsys.py index 07db5e700..20990d5ae 100644 --- a/control/flatsys/flatsys.py +++ b/control/flatsys/flatsys.py @@ -185,12 +185,15 @@ def flatsys(*args, updfcn=None, outfcn=None, **kwargs): variety of forms: ``fs.flatsys(forward, reverse)`` + Create a flat system with mapings to/from flat flag. ``fs.flatsys(forward, reverse, updfcn[, outfcn])`` + Create a flat system that is also a nonlinear I/O system. ``fs.flatsys(linsys)`` + Create a flat system from a linear (StateSpace) system. Parameters diff --git a/control/flatsys/systraj.py b/control/flatsys/systraj.py index 1783c2a1b..3b68d05d5 100644 --- a/control/flatsys/systraj.py +++ b/control/flatsys/systraj.py @@ -122,9 +122,9 @@ def response(self, tlist, transpose=False, return_x=False, squeeze=None): squeeze : bool, optional By default, if a system is single-input, single-output (SISO) then the output response is returned as a 1D array (indexed by - time). If ``squeeze=True`, remove single-dimensional entries + time). If `squeeze` = True, remove single-dimensional entries from the shape of the output even if the system is not SISO. If - ``squeeze=False``, keep the output as a 3D array (indexed by + `squeeze` = False, keep the output as a 3D array (indexed by the output, input, and time) even if the system is SISO. The default value can be set using `config.defaults['control.squeeze_time_response']`. diff --git a/control/frdata.py b/control/frdata.py index eaffcd2d4..44713b0cd 100644 --- a/control/frdata.py +++ b/control/frdata.py @@ -63,9 +63,9 @@ class constructor, using the `frd` factory function, or frequency) and if a system is multi-input or multi-output, then the outputs are returned as a 2D array (indexed by output and frequency) or a 3D array (indexed by output, trace, and frequency). - If ``squeeze=True``, access to the output response will remove + If `squeeze` = True, access to the output response will remove single-dimensional entries from the shape of the inputs and outputs - even if the system is not SISO. If ``squeeze=False``, the output is + even if the system is not SISO. If `squeeze` = False, the output is returned as a 3D array (indexed by the output, input, and frequency) even if the system is SISO. The default value can be set using `config.defaults['control.squeeze_frequency_response']`. @@ -105,10 +105,10 @@ class constructor, using the `frd` factory function, or If set to False, don't plot the magnitude or phase, respectively. return_magphase : bool, optional If True, then a frequency response data object will enumerate - as a tuple of the form ``(mag, phase, omega)`` where where ``mag`` + as a tuple of the form ``(mag, phase, omega)`` where where `mag` is the magnitude (absolute value, not dB or log10) of the system - frequency response, ``phase`` is the wrapped phase in radians of the - system frequency response, and ``omega`` is the (sorted) frequencies + frequency response, `phase` is the wrapped phase in radians of the + system frequency response, and `omega` is the (sorted) frequencies at which the response was evaluated. See Also @@ -173,9 +173,9 @@ class constructor, using the `frd` factory function, or #: frequency) and if a system is multi-input or multi-output, then the #: outputs are returned as a 2D array (indexed by output and frequency) #: or a 3D array (indexed by output, trace, and frequency). If - #: ``squeeze=True``, access to the output response will remove + #: `squeeze` = True, access to the output response will remove #: single-dimensional entries from the shape of the inputs and outputs - #: even if the system is not SISO. If ``squeeze=False``, the output is + #: even if the system is not SISO. If `squeeze` = False, the output is #: returned as a 3D array (indexed by the output, input, and frequency) #: even if the system is SISO. The default value can be set using #: config.defaults['control.squeeze_frequency_response']. @@ -636,9 +636,9 @@ def eval(self, omega, squeeze=None): omega : float or 1D array_like Frequencies in radians per second. squeeze : bool, optional - If ``squeeze=True``, remove single-dimensional entries from + If `squeeze` = True, remove single-dimensional entries from the shape of the output even if the system is not SISO. If - ``squeeze=False``, keep all indices (output, input and, if omega + `squeeze` = False, keep all indices (output, input and, if omega is array_like, frequency) even if the system is SISO. The default value can be set using `config.defaults['control.squeeze_frequency_response']`. @@ -707,9 +707,9 @@ def __call__(self, s=None, squeeze=None, return_magphase=None): processing (`squeeze`, `return_magphase`). squeeze : bool, optional - If ``squeeze=True``, remove single-dimensional entries from the + If `squeeze` = True, remove single-dimensional entries from the shape of the output even if the system is not SISO. If - ``squeeze=False``, keep all indices (output, input and, if + `squeeze` = False, keep all indices (output, input and, if omega is array_like, frequency) even if the system is SISO. The default value can be set using `config.defaults['control.squeeze_frequency_response']`. @@ -717,9 +717,9 @@ def __call__(self, s=None, squeeze=None, return_magphase=None): return_magphase : bool, optional If True, then a frequency response data object will enumerate as a tuple of the form ``(mag, phase, omega)`` where - where ``mag`` is the magnitude (absolute value, not dB or log10) - of the system frequency response, ``phase`` is the wrapped phase - in radians of the system frequency response, and ``omega`` is + where `mag` is the magnitude (absolute value, not dB or log10) + of the system frequency response, `phase` is the wrapped phase + in radians of the system frequency response, and `omega` is the (sorted) frequencies at which the response was evaluated. Returns @@ -861,9 +861,9 @@ def plot(self, plot_type=None, *args, **kwargs): """Plot the frequency response using Bode or singular values plot. Plot the frequency response using either a standard Bode plot - (``plot_type='bode'``, default) or a singular values plot - (``plot_type='svplot'``). See `bode_plot` and - `singular_values_plot` for more detailed descriptions. + (plot_type='bode', default) or a singular values plot + (plot_type='svplot'). See `bode_plot` and `singular_values_plot` + for more detailed descriptions. """ from .freqplot import bode_plot, singular_values_plot @@ -994,10 +994,12 @@ def frd(*args, **kwargs): of a system. This factory function can be called in different ways: ``frd(response, omega)`` + Create an frd model with the given response data, in the form of complex response vector, at matching frequencies `omega` [in rad/s]. ``frd(sys, omega)`` + Convert an LTI system into an frd model with data at frequencies `omega`. diff --git a/control/freqplot.py b/control/freqplot.py index cb62f5dd2..0027bbab4 100644 --- a/control/freqplot.py +++ b/control/freqplot.py @@ -243,15 +243,15 @@ def bode_plot( Starting with python-control version 0.10, `bode_plot` returns a `ControlPlot` object instead of magnitude, phase, and frequency. To recover the old behavior, call `bode_plot` with - ``plot=True``, which will force the legacy values (mag, phase, omega) to + `plot` = True, which will force the legacy values (mag, phase, omega) to be returned (with a warning). To obtain just the frequency response of a system (or list of systems) without plotting, use the `frequency_response` command. If a discrete time model is given, the frequency response is plotted along the upper branch of the unit circle, using the mapping ``z = - exp(1j * omega * dt)`` where `omega` ranges from 0 to ``pi/dt`` and `dt` - is the discrete timebase. If timebase not specified (``dt=True``), + exp(1j * omega * dt)`` where `omega` ranges from 0 to pi/`dt` and `dt` + is the discrete timebase. If timebase not specified (`dt` = True), `dt` is set to 1. The default values for Bode plot configuration parameters can be reset @@ -1251,9 +1251,9 @@ def nyquist_response( ----- If a discrete time model is given, the frequency response is computed along the upper branch of the unit circle, using the mapping ``z = - exp(1j * omega * dt)`` where `omega` ranges from 0 to ``pi/dt`` and + exp(1j * omega * dt)`` where `omega` ranges from 0 to pi/`dt` and `dt` is the discrete timebase. If timebase not specified - (``dt=True``), `dt` is set to 1. + (`dt` = True), `dt` is set to 1. If a continuous-time system contains poles on or near the imaginary axis, a small indentation will be used to avoid the pole. The radius @@ -1717,9 +1717,9 @@ def nyquist_plot( ----- If a discrete time model is given, the frequency response is computed along the upper branch of the unit circle, using the mapping ``z = - exp(1j * omega * dt)`` where `omega` ranges from 0 to ``pi/dt`` and + exp(1j * omega * dt)`` where `omega` ranges from 0 to pi/`dt` and `dt` is the discrete timebase. If timebase not specified - (``dt=True``), `dt` is set to 1. + (`dt` = True), `dt` is set to 1. If a continuous-time system contains poles on or near the imaginary axis, a small indentation will be used to avoid the pole. The radius @@ -2450,12 +2450,12 @@ def singular_values_plot( Notes ----- - If ``plot=False``, the following legacy values are returned: - * mag : ndarray (or list of ndarray if len(data) > 1)) + If `plot` = `False`, the following legacy values are returned: + * `mag` : ndarray (or list of ndarray if len(data) > 1)) Magnitude of the response (deprecated). - * phase : ndarray (or list of ndarray if len(data) > 1)) + * `phase` : ndarray (or list of ndarray if len(data) > 1)) Phase in radians of the response (deprecated). - * omega : ndarray (or list of ndarray if len(data) > 1)) + * `omega` : ndarray (or list of ndarray if len(data) > 1)) Frequency in rad/sec (deprecated). """ diff --git a/control/iosys.py b/control/iosys.py index e93c55631..7336c1604 100644 --- a/control/iosys.py +++ b/control/iosys.py @@ -115,10 +115,10 @@ class InputOutputSystem(): is operating in continuous or discrete time. It can have the following values: - * ``dt = None`` No timebase specified - * ``dt = 0`` Continuous time system - * ``dt > 0`` Discrete time system with sampling time dt - * ``dt = True`` Discrete time system with unspecified sampling time + * `dt` = None: No timebase specified + * `dt` = 0: Continuous time system + * `dt` > 0: Discrete time system with sampling time dt + * `dt` = True: Discrete time system with unspecified sampling time Parameters ---------- @@ -672,7 +672,7 @@ def timebase(sys, strict=True): dt = timebase(sys) returns the timebase for a system 'sys'. If the strict option is - set to True, ``dt = True`` will be returned as 1. + set to True, `dt` = True will be returned as 1. Parameters ---------- @@ -680,7 +680,7 @@ def timebase(sys, strict=True): System whose timebase is to be determined. strict : bool, optional Whether to implement strict checking. If set to True (default), - a float will always be returned (``dt = True`` will be returned as 1). + a float will always be returned (`dt` = True will be returned as 1). Returns ------- diff --git a/control/lti.py b/control/lti.py index 66fd63baf..e87c1f01a 100644 --- a/control/lti.py +++ b/control/lti.py @@ -91,9 +91,9 @@ def frequency_response(self, omega=None, squeeze=None): radians/sec at which the system will be evaluated. If None (default), a set of frequencies is computed based on the system dynamics. squeeze : bool, optional - If ``squeeze=True``, remove single-dimensional entries from the + If `squeeze` = True, remove single-dimensional entries from the shape of the output even if the system is not SISO. If - ``squeeze=False``, keep all indices (output, input and, if + `squeeze` = False, keep all indices (output, input and, if omega is array_like, frequency) even if the system is SISO. The default value can be set using `config.defaults['control.squeeze_frequency_response']`. @@ -372,9 +372,9 @@ def evalfr(sys, x, squeeze=None): x : complex scalar or 1D array_like Complex frequency(s). squeeze : bool, optional (default=True) - If ``squeeze=True``, remove single-dimensional entries from the + If `squeeze` = True, remove single-dimensional entries from the shape of the output even if the system is not SISO. If - ``squeeze=False``, keep all indices (output, input and, if omega is + `squeeze` = False, keep all indices (output, input and, if omega is array_like, frequency) even if the system is SISO. The default value can be set using `config.defaults['control.squeeze_frequency_response']`. @@ -465,9 +465,9 @@ def frequency_response( limits to full decades in Hz instead of rad/s. Omega is always returned in rad/sec. squeeze : bool, optional - If ``squeeze=True``, remove single-dimensional entries from the + If `squeeze` = True, remove single-dimensional entries from the shape of the output even if the system is not SISO. If - ``squeeze=False``, keep all indices (output, input and, if omega is + `squeeze` = False, keep all indices (output, input and, if omega is array_like, frequency) even if the system is SISO. The default value can be set using `config.defaults['control.squeeze_frequency_response']`. @@ -486,7 +486,7 @@ def frequency_response( All frequency data should be given in rad/sec. If frequency limits are computed automatically, the `Hz` keyword can be used to ensure that limits are in factors of decades in Hz, so that Bode plots with - ``Hz=True`` look better. + `Hz` = True look better. The frequency response data can be plotted by calling the `bode_plot` function or using the `plot` method of the `FrequencyResponseData` diff --git a/control/mateqn.py b/control/mateqn.py index e24c01d2f..870490bb2 100644 --- a/control/mateqn.py +++ b/control/mateqn.py @@ -351,7 +351,7 @@ def care(A, B, Q, R=None, S=None, E=None, stabilizing=True, method=None, and then 'scipy'. stabilizing : bool, optional If `method` is 'slycot', unstabilized eigenvalues will be returned - in the initial elements of ``L``. Not supported for 'scipy'. + in the initial elements of `L`. Not supported for 'scipy'. Returns ------- diff --git a/control/matlab/timeresp.py b/control/matlab/timeresp.py index 5574deb24..f3ad29a1e 100644 --- a/control/matlab/timeresp.py +++ b/control/matlab/timeresp.py @@ -112,7 +112,7 @@ def stepinfo(sysdata, T=None, yfinal=None, SettlingTimeThreshold=0.02, If `sysdata` corresponds to a MIMO system, `S` is a 2D list of dicts. To get the step response characteristics from the j-th input to the - i-th output, access ``S[i][j]`` + i-th output, access ``S[i][j]``. See Also -------- diff --git a/control/modelsimp.py b/control/modelsimp.py index 43f410b34..c3f34dd9b 100644 --- a/control/modelsimp.py +++ b/control/modelsimp.py @@ -565,7 +565,7 @@ def markov(*args, m=None, transpose=False, dt=None, truncate=False): ---------- Y : array_like Output data. If the array is 1D, the system is assumed to be - single input. If the array is 2D and ``transpose=False``, the columns + single input. If the array is 2D and `transpose` = False, the columns of `Y` are taken as time points, otherwise the rows of `Y` are taken as time points. U : array_like diff --git a/control/nlsys.py b/control/nlsys.py index cca7796c6..3c184597a 100644 --- a/control/nlsys.py +++ b/control/nlsys.py @@ -74,10 +74,10 @@ class NonlinearIOSystem(InputOutputSystem): operating in continuous or discrete time. It can have the following values: - * dt = 0: continuous time system (default) - * dt > 0: discrete time system with sampling period 'dt' - * dt = True: discrete time with unspecified sampling period - * dt = None: no timebase specified + * `dt` = 0: continuous time system (default) + * `dt` > 0: discrete time system with sampling period 'dt' + * `dt` = True: discrete time with unspecified sampling period + * `dt` = None: no timebase specified Attributes ---------- @@ -1367,10 +1367,10 @@ def nlsys(updfcn, outfcn=None, **kwargs): operating in continuous or discrete time. It can have the following values: - * dt = 0: continuous time system (default) - * dt > 0: discrete time system with sampling period 'dt' - * dt = True: discrete time with unspecified sampling period - * dt = None: no timebase specified + * `dt` = 0: continuous time system (default) + * `dt` > 0: discrete time system with sampling period 'dt' + * `dt` = True: discrete time with unspecified sampling period + * `dt` = None: no timebase specified name : string, optional System name (used for specifying signals). If unspecified, a @@ -2391,10 +2391,10 @@ def interconnect( operating in continuous or discrete time. It can have the following values: - * ``dt=0``: continuous time system (default) - * ``dt>0``: discrete time system with sampling period 'dt' - * ``dt=True``: discrete time with unspecified sampling period - * ``dt=None``: no timebase specified + * `dt` = 0: continuous time system (default) + * `dt` > 0`: discrete time system with sampling period 'dt' + * `dt` = True: discrete time with unspecified sampling period + * `dt` = None: no timebase specified name : string, optional System name (used for specifying signals). If unspecified, a generic diff --git a/control/passivity.py b/control/passivity.py index 3dde17bd0..f8079a70d 100644 --- a/control/passivity.py +++ b/control/passivity.py @@ -29,8 +29,8 @@ def solve_passivity_LMI(sys, rho=None, nu=None): function should solve for that index (they are mutually exclusive, they can't both be None, otherwise you're trying to solve a nonconvex bilinear matrix inequality.) The last element of the output `solution` - is either the output or input passivity index, for ``rho = None`` and - ``nu = None`` respectively. + is either the output or input passivity index, for `rho` = None and + `nu` = None, respectively. The sources for the algorithm are: @@ -283,8 +283,8 @@ def ispassive(sys, ofp_index=0, ifp_index=0): .. math:: V(x) >= 0 \land \dot{V}(x) <= y^T u - is equivalent to the default case of ``ofp_index = 0`` and ``ifp_index` = - 0``. Note that computing the `ofp_index` and `ifp_index` for a system, + is equivalent to the default case of `ofp_index` = 0 and `ifp_index` = + 0. Note that computing the `ofp_index` and `ifp_index` for a system, then using both values simultaneously as inputs to this function is not guaranteed to have an output of True (the system might not be passive with both indices at the same time). diff --git a/control/phaseplot.py b/control/phaseplot.py index f0da39bc8..e2b199024 100644 --- a/control/phaseplot.py +++ b/control/phaseplot.py @@ -85,7 +85,7 @@ def phase_plane_plot( a dict of parameters and values. For a callable, `params` should be dict with key 'args' and value given by a tuple (passed to callable). color : matplotlib color spec, optional - Plot all elements in the given color (use ``plot_={'color': c}`` + Plot all elements in the given color (use `plot_` = {'color': c} to set the color in one element of the phase plot. ax : matplotlib.axes.Axes, optional The matplotlib axes to draw the figure on. If not specified and @@ -1049,7 +1049,7 @@ def phase_plot(odefun, X=None, Y=None, scale=1, X0=None, T=None, func : callable(x, t, ...) Computes the time derivative of y (compatible with odeint). The function should be the same for as used for `scipy.integrate`. - Namely, it should be a function of the form dxdt = F(t, x) that + Namely, it should be a function of the form dx/dt = F(t, x) that accepts a state x of dimension 2 and returns a derivative dx/dt of dimension 2. X, Y: 3-element sequences, optional, as [start, stop, npts] diff --git a/control/robust.py b/control/robust.py index 04deb4f5d..89735c5c6 100644 --- a/control/robust.py +++ b/control/robust.py @@ -364,13 +364,13 @@ def mixsyn(g, w1=None, w2=None, w3=None): [z] = [p11 p12] [w] [y] [p21 g] [u] - then cl is the system from w->z with ``u = -k*y``. + then cl is the system from w -> z with u = -k*y. info : tuple - Two-tuple ``(gamma, rcond)`` containing additional information: + Two-tuple (`gamma`, `rcond`) containing additional information: - * gamma (scalar): H-infinity norm of cl. - * rcond (array): Estimates of reciprocal condition numbers computed + * `gamma` (scalar): H-infinity norm of cl. + * `rcond` (array): Estimates of reciprocal condition numbers computed during synthesis. See hinfsyn for details. See Also diff --git a/control/sisotool.py b/control/sisotool.py index 3fb959cec..466f2b345 100644 --- a/control/sisotool.py +++ b/control/sisotool.py @@ -269,8 +269,8 @@ def rootlocus_pid_designer(plant, gain='P', sign=+1, input_signal='r', To use non-interactively, choose starting-point PID gains `Kp0`, `Ki0`, and `Kd0` (you might want to start with all zeros to begin with), - select which gain you would like to vary (e.g. ``gain='P'``, ``'I'``, - or ``'D'``), and choose a value of `deltaK` (default 0.001) to specify + select which gain you would like to vary (e.g. `gain` = 'P', 'I', + or 'D'), and choose a value of `deltaK` (default 0.001) to specify by how much you would like to change that gain. Repeatedly run `rootlocus_pid_designer` with different values of `deltaK` until you are satisfied with the performance for that gain. Then, to tune a @@ -317,7 +317,7 @@ def rootlocus_pid_designer(plant, gain='P', sign=+1, input_signal='r', Kd/dt*(z-1)/z, respectively. It is also possible to move the derivative term into the feedback path - `C_b` using ``derivative_in_feedback_path=True``. This may be desired to + `C_b` using `derivative_in_feedback_path` = True. This may be desired to avoid that the plant is subject to an impulse function when the reference `r` is a step input. `C_b` is otherwise set to zero. diff --git a/control/statefbk.py b/control/statefbk.py index 83b9159bc..1fbe8fe61 100644 --- a/control/statefbk.py +++ b/control/statefbk.py @@ -419,7 +419,7 @@ def dlqr(*args, **kwargs): * ``dlqr(A, B, Q, R, N)`` where `dsys` is a discrete-time `StateSpace` system, and `A`, `B`, - `Q`, `R`, and `N` are 2d arrays of appropriate dimension (`dsys.dt`` must + `Q`, `R`, and `N` are 2d arrays of appropriate dimension (`dsys.dt` must not be 0.) Parameters @@ -658,7 +658,7 @@ def create_statefbk_iosystem( design pattern (default), this system takes as inputs the desired state `x_d`, the desired input `u_d`, and either the system state `x` or the estimated state `xhat`. It outputs the controller - action `u` according to the formula ``u = u_d - K(x - x_d)``. For + action `u` according to the formula u = u_d - K(x - x_d). For the 'refgain' design pattern, the system takes as inputs the reference input `r` and the system or estimated state. If the keyword `integral_action` is specified, then an additional set of @@ -670,7 +670,7 @@ def create_statefbk_iosystem( clsys : NonlinearIOSystem Input/output system representing the closed loop system. This - system takes as inputs the desired trajectory ``(x_d, u_d)`` and + system takes as inputs the desired trajectory (x_d, u_d) and outputs the system state `x` and the applied input `u` (vertically stacked). @@ -678,8 +678,8 @@ def create_statefbk_iosystem( ---------------- control_indices : int, slice, or list of int or str, optional Specify the indices of the system inputs that should be determined - by the state feedback controller. If value is an integer ``m``, the - first ``m`` system inputs are used. Otherwise, the value should be a + by the state feedback controller. If value is an integer `m`, the + first `m` system inputs are used. Otherwise, the value should be a slice or a list of indices. The list of indices can be specified as either integer offsets or as system input signal names. If not specified, defaults to the system inputs. @@ -687,7 +687,7 @@ def create_statefbk_iosystem( state_indices : int, slice, or list of int or str, optional Specify the indices of the system (or estimator) outputs that should be used by the state feedback controller. If value is an integer - ``n``, the first ``n`` system states are used. Otherwise, the value + `n`, the first `n` system states are used. Otherwise, the value should be a slice or a list of indices. The list of indices can be specified as either integer offsets or as estimator/system output signal names. If not specified, defaults to the system states. @@ -696,8 +696,8 @@ def create_statefbk_iosystem( Set the name of the signals to use for the desired state and inputs or the reference inputs (for the 'refgain' design pattern). If a single string is specified, it should be a format string using the - variable ``i`` as an index. Otherwise, a list of strings matching - the size of ``x_d`` and ``u_d``, respectively, should be used. + variable `i` as an index. Otherwise, a list of strings matching + the size of x_d and u_d, respectively, should be used. Default is "xd[{i}]" for xd_labels and "ud[{i}]" for ud_labels. These settings can also be overridden using the `inputs` keyword. diff --git a/control/statesp.py b/control/statesp.py index 1e73464a7..33b422a89 100644 --- a/control/statesp.py +++ b/control/statesp.py @@ -108,13 +108,13 @@ class StateSpace(NonlinearIOSystem, LTI): A discrete time system is created by specifying a nonzero 'timebase', dt when the system is constructed: - * dt = 0: continuous time system (default) - * dt > 0: discrete time system with sampling period 'dt' - * dt = True: discrete time with unspecified sampling period - * dt = None: no timebase specified + * `dt` = 0: continuous time system (default) + * `dt` > 0: discrete time system with sampling period 'dt' + * `dt` = True: discrete time with unspecified sampling period + * `dt` = None: no timebase specified Systems must have compatible timebases in order to be combined. A - discrete time system with unspecified sampling time (``dt=True``) can + discrete time system with unspecified sampling time (`dt` = True) can be combined with a system having a specified sampling time; the result will be a discrete time system with the sample time of the latter system. Similarly, a system with timebase None can be combined with a @@ -145,7 +145,7 @@ class StateSpace(NonlinearIOSystem, LTI): may look odd when typeset by non-MathJax LaTeX systems. `control.config.defaults['statesp.latex_num_format']` is a format string - fragment, specifically the part of the format string after ``'{:'`` + fragment, specifically the part of the format string after '{:' used to convert floating-point numbers to strings. By default it is '.3g'. @@ -777,9 +777,9 @@ def __call__(self, x, squeeze=None, warn_infinite=True): x : complex or complex 1D array_like Complex frequencies squeeze : bool, optional - If ``squeeze=True``, remove single-dimensional entries from the + If `squeeze` = True, remove single-dimensional entries from the shape of the output even if the system is not SISO. If - ``squeeze=False``, keep all indices (output, input and, if + `squeeze` = False, keep all indices (output, input and, if omega is array_like, frequency) even if the system is SISO. The default value can be set using `config.defaults['control.squeeze_frequency_response']`. @@ -1180,10 +1180,10 @@ def returnScipySignalLTI(self, strict=True): ---------- strict : bool, optional True (default): - The timebase ``ssobject.dt`` cannot be None; it must + The timebase `ssobject.dt` cannot be None; it must be continuous (0) or discrete (True or > 0). False: - If ``ssobject.dt`` is None, continuous time + If `ssobject.dt` is None, continuous time `scipy.signal.lti` objects are returned. Returns @@ -1292,7 +1292,7 @@ def sample(self, Ts, method='zoh', alpha=None, prewarp_frequency=None, The frequency [rad/s] at which to match with the input continuous-time system's magnitude and phase (the gain = 1 crossover frequency, for example). Should only be specified - with `method` = 'bilinear' or 'gbt' with ``alpha=0.5`` and + with `method` = 'bilinear' or 'gbt' with `alpha` = 0.5 and ignored otherwise. name : string, optional Set the name of the sampled system. If not specified and if @@ -1380,7 +1380,7 @@ def dcgain(self, warn_infinite=False): Array or scalar value for SISO systems, depending on `config.defaults['control.squeeze_frequency_response']`. The value of the array elements or the scalar is either the - zero-frequency (or DC) gain, or ``inf``, if the frequency + zero-frequency (or DC) gain, or `inf`, if the frequency response is singular. For real valued systems, the empty imaginary part of the @@ -1595,10 +1595,12 @@ def ss(*args, **kwargs): The function accepts either 1, 4 or 5 positional parameters: ``ss(sys)`` + Convert a linear system into space system form. Always creates a new system, even if sys is already a state space system. ``ss(A, B, C, D)`` + Create a state space system from the matrices of its state and output equations: @@ -1608,6 +1610,7 @@ def ss(*args, **kwargs): y &= C x + D u ``ss(A, B, C, D, dt)`` + Create a discrete-time state space system from the matrices of its state and output equations: @@ -1761,10 +1764,12 @@ def tf2io(*args, **kwargs): The function accepts either 1 or 2 parameters: ``tf2io(sys)`` + Convert a linear system into space space form. Always creates a new system, even if sys is already a StateSpace object. ``tf2io(num, den)`` + Create a linear I/O system from its numerator and denominator polynomial coefficients. @@ -1831,10 +1836,12 @@ def tf2ss(*args, **kwargs): The function accepts either 1 or 2 parameters: ``tf2ss(sys)`` + Convert a transfer function into space space form. Equivalent to `ss(sys)`. ``tf2ss(num, den)`` + Create a state space system from its numerator and denominator polynomial coefficients. @@ -1886,7 +1893,7 @@ def tf2ss(*args, **kwargs): The `slycot` routine used to convert a transfer function into state space form appears to have a bug and in some (rare) instances may not return a system with the same poles as the input transfer function. For SISO - systems, setting ``method='scipy'`` can be used as an alternative. + systems, setting `method` = 'scipy' can be used as an alternative. Examples -------- @@ -2056,7 +2063,7 @@ def drss(*args, **kwargs): Create a stable *discrete time* random state space object. This function calls `rss` using either the `dt` keyword provided by - the user or ``dt=True`` if not specified. + the user or `dt` = True if not specified. Examples -------- diff --git a/control/sysnorm.py b/control/sysnorm.py index cdf673921..9f1745ab3 100644 --- a/control/sysnorm.py +++ b/control/sysnorm.py @@ -91,11 +91,11 @@ def system_norm(system, p=2, tol=1e-6, print_warning=True, method=None): System in continuous or discrete time for which the norm should be computed. p : int or str - Type of norm to be computed. ``p=2`` gives the H2 norm, and - ``p='inf'`` gives the L-infinity norm. + Type of norm to be computed. `p` = 2 gives the H2 norm, and + `p` = 'inf' gives the L-infinity norm. tol : float Relative tolerance for accuracy of L-infinity norm - computation. Ignored unless ``p='inf'``. + computation. Ignored unless `p` = 'inf'. print_warning : bool Print warning message in case norm value may be uncertain. method : str, optional @@ -111,7 +111,7 @@ def system_norm(system, p=2, tol=1e-6, print_warning=True, method=None): Notes ----- Does not yet compute the L-infinity norm for discrete time systems - with pole(s) in ``z=0`` unless Slycot is used. + with pole(s) at the origin unless Slycot is used. Examples -------- diff --git a/control/timeplot.py b/control/timeplot.py index fee3d4df1..ae84b7c6c 100644 --- a/control/timeplot.py +++ b/control/timeplot.py @@ -162,7 +162,7 @@ def time_response_plot( A new figure will be generated if there is no current figure or the current figure has an incompatible number of axes. To force the creation of a new figures, use `plt.figure`. To reuse a portion of an - existing figure, use the ``ax`` keyword. + existing figure, use the `ax` keyword. The line properties (color, linestyle, etc) can be set for the entire plot using the `fmt` and/or `kwargs` parameter, which are passed on to diff --git a/control/timeresp.py b/control/timeresp.py index d2c568f42..5603866c4 100644 --- a/control/timeresp.py +++ b/control/timeresp.py @@ -110,7 +110,7 @@ class TimeResponseData: and if a system is multi-input or multi-output, then the inputs are returned as a 2D array (indexed by input and time) and the outputs are returned as either a 2D array (indexed by output and time) or a - 3D array (indexed by output, trace, and time). If ``squeeze=True``, + 3D array (indexed by output, trace, and time). If `squeeze` = True, access to the output response will remove single-dimensional entries from the shape of the inputs and outputs even if the system is not SISO. If squeeze=False, keep the input as a 2D or 3D array @@ -445,10 +445,10 @@ def __call__(self, **kwargs): Parameters ---------- squeeze : bool, optional - If ``squeeze=True``, access to the output response will remove + If `squeeze` = True, access to the output response will remove single-dimensional entries from the shape of the inputs, outputs, and states even if the system is not SISO. If - ``squeeze=False``, keep the input as a 2D or 3D array (indexed + `squeeze` = False, keep the input as a 2D or 3D array (indexed by the input (if multi-input), trace (if single input) and time) and the output and states as a 3D array (indexed by the output/state, trace, and time) even if the system is SISO. @@ -814,8 +814,8 @@ def _check_convert_array(in_obj, legal_shapes, err_msg_start, squeeze=False, The special value "any" means that there can be any number of elements in a certain dimension. - * ``(2, 3)`` describes an array with 2 rows and 3 columns - * ``(2, 'any')`` describes an array with 2 rows and any number of + * (2, 3) describes an array with 2 rows and 3 columns + * (2, 'any') describes an array with 2 rows and any number of columns err_msg_start : str @@ -827,7 +827,7 @@ def _check_convert_array(in_obj, legal_shapes, err_msg_start, squeeze=False, If True, all dimensions with only one element are removed from the array. If False the array's shape is unmodified. - For example: ``array([[1,2,3]])`` is converted to ``array([1, 2, + For example: ``array([[1, 2, 3]])`` is converted to ``array([1, 2, 3])``. transpose : bool, optional @@ -1001,7 +1001,7 @@ def forced_response(sysdata, T=None, U=0., X0=0., transpose=False, params=None, using the `scipy.signal.dlsim` function. For continuous time systems, the output is computed using the - matrix exponential ``exp(A t)`` and assuming linear interpolation + matrix exponential exp(A t) and assuming linear interpolation of the inputs between time points. If a nonlinear I/O system is passed to `forced_response`, the @@ -1267,8 +1267,8 @@ def _process_time_response( squeeze : bool, optional By default, if a system is single-input, single-output (SISO) then the signals are returned as a 1D array (indexed by time). If - ``squeeze=True``, remove single-dimensional entries from the shape - of the signal even if the system is not SISO. If ``squeeze=False``, + `squeeze` = True, remove single-dimensional entries from the shape + of the signal even if the system is not SISO. If `squeeze` = False, keep the signal as a 3D array (indexed by the output, input, and time) even if the system is SISO. The default value can be set using `config.defaults['control.squeeze_time_response']`. @@ -1372,9 +1372,9 @@ def step_response( squeeze : bool, optional By default, if a system is single-input, single-output (SISO) then the output response is returned as a 1D array (indexed by time). - If ``squeeze=True``, remove single-dimensional entries from the + If `squeeze` = True, remove single-dimensional entries from the shape of the output even if the system is not SISO. If - ``squeeze=False``, keep the output as a 3D array (indexed by the + `squeeze` = False, keep the output as a 3D array (indexed by the output, input, and time) even if the system is SISO. The default value can be set using `config.defaults['control.squeeze_time_response']`. @@ -1539,7 +1539,7 @@ def step_info(sysdata, T=None, T_num=None, yfinal=None, params=None, If `sysdata` corresponds to a MIMO system, `S` is a 2D list of dicts. To get the step response characteristics from the j-th input to the - i-th output, access ``S[i][j]`` + i-th output, access ``S[i][j]``. See Also @@ -1761,9 +1761,9 @@ def initial_response( squeeze : bool, optional By default, if a system is single-input, single-output (SISO) then the output response is returned as a 1D array (indexed by time). - If ``squeeze=True``, remove single-dimensional entries from the + If `squeeze` = True, remove single-dimensional entries from the shape of the output even if the system is not SISO. If - ``squeeze=False``, keep the output as a 2D array (indexed by the + `squeeze` = False, keep the output as a 2D array (indexed by the output number and time) even if the system is SISO. The default value can be set using `config.defaults['control.squeeze_time_response']`. @@ -1877,9 +1877,9 @@ def impulse_response( squeeze : bool, optional By default, if a system is single-input, single-output (SISO) then the output response is returned as a 1D array (indexed by time). - If ``squeeze=True``, remove single-dimensional entries from the + If `squeeze` = True, remove single-dimensional entries from the shape of the output even if the system is not SISO. If - ``squeeze=False``, keep the output as a 2D array (indexed by the + `squeeze` = False, keep the output as a 2D array (indexed by the output number and time) even if the system is SISO. The default value can be set using `config.defaults['control.squeeze_time_response']`. diff --git a/control/xferfcn.py b/control/xferfcn.py index c74abf179..485d98408 100644 --- a/control/xferfcn.py +++ b/control/xferfcn.py @@ -121,13 +121,13 @@ class TransferFunction(LTI): A discrete time transfer function is created by specifying a nonzero 'timebase' dt when the system is constructed: - * dt = 0: continuous time system (default) - * dt > 0: discrete time system with sampling period 'dt' - * dt = True: discrete time with unspecified sampling period - * dt = None: no timebase specified + * `dt` = 0: continuous time system (default) + * `dt` > 0: discrete time system with sampling period 'dt' + * `dt` = True: discrete time with unspecified sampling period + * `dt` = None: no timebase specified Systems must have compatible timebases in order to be combined. A - discrete time system with unspecified sampling time (``dt = True``) can + discrete time system with unspecified sampling time (`dt` = True) can be combined with a system having a specified sampling time; the result will be a discrete time system with the sample time of the latter system. Similarly, a system with timebase None can be combined with a @@ -353,9 +353,9 @@ def __call__(self, x, squeeze=None, warn_infinite=True): x : complex or complex 1D array_like Complex frequencies squeeze : bool, optional - If ``squeeze=True``, remove single-dimensional entries from the + If `squeeze` = True, remove single-dimensional entries from the shape of the output even if the system is not SISO. If - ``squeeze=False``, keep all indices (output, input and, if + `squeeze` = False, keep all indices (output, input and, if omega is array_like, frequency) even if the system is SISO. The default value can be set using `config.defaults['control.squeeze_frequency_response']`. If @@ -1173,13 +1173,13 @@ def sample(self, Ts, method='zoh', alpha=None, prewarp_frequency=None, * 'zoh': zero-order hold (default) alpha : float within [0, 1] The generalized bilinear transformation weighting parameter, - which should only be specified with ``method='gbt'``, and is + which should only be specified with `method` = 'gbt', and is ignored otherwise. See `scipy.signal.cont2discrete`. prewarp_frequency : float within [0, infinity) The frequency [rad/s] at which to match with the input continuous- time system's magnitude and phase (the gain=1 crossover frequency, for example). Should only be specified - with `method` = 'bilinear' or 'gbt' with ``alpha=0.5`` and + with `method` = 'bilinear' or 'gbt' with `alpha` = 0.5 and ignored otherwise. name : string, optional Set the name of the sampled system. If not specified and if @@ -1266,7 +1266,7 @@ def dcgain(self, warn_infinite=False): Array or scalar value for SISO systems, depending on `config.defaults['control.squeeze_frequency_response']`. The value of the array elements or the scalar is either the - zero-frequency (or DC) gain, or ``inf``, if the frequency + zero-frequency (or DC) gain, or `inf`, if the frequency response is singular. For real valued systems, the empty imaginary part of the @@ -1590,10 +1590,12 @@ def tf(*args, **kwargs): The function accepts either 1, 2, or 3 parameters: ``tf(sys)`` + Convert a linear system into transfer function form. Always creates a new system, even if sys is already a TransferFunction object. ``tf(num, den)`` + Create a transfer function system from its numerator and denominator polynomial coefficients. @@ -1606,15 +1608,18 @@ def tf(*args, **kwargs): function is the same, `den` can be specified as a 1D array. ``tf(num, den, dt)`` + Create a discrete time transfer function system; dt can either be a positive number indicating the sampling time or 'True' if no specific timebase is given. ``tf([[G11, ..., G1m], ..., [Gp1, ..., Gpm]][, dt])`` + Create a pxm MIMO system from SISO transfer functions Gij. See `combine_tf` for more details. ``tf('s')`` or ``tf('z')`` + Create a transfer function representing the differential operator ('s') or delay operator ('z'). @@ -1673,7 +1678,7 @@ def tf(*args, **kwargs): for the transfer function from the (j+1)st input to the (i+1)st output, and ``den[i][j]`` works the same way. - The list ``[2, 3, 4]`` denotes the polynomial :math:`2s^2 + 3s + 4`. + The list ``[2, 3, 4]`` denotes the polynomial :math:`2 s^2 + 3 s + 4`. The special forms ``tf('s')`` and ``tf('z')`` can be used to create transfer functions for differentiation and unit delays. @@ -1822,10 +1827,12 @@ def ss2tf(*args, **kwargs): The function accepts either 1 or 4 parameters: ``ss2tf(sys)`` + Convert a linear system from state space into transfer function form. Always creates a new system. ``ss2tf(A, B, C, D)`` + Create a transfer function system from the matrices of its state and output equations. diff --git a/doc/config.rst b/doc/config.rst index 0000fe507..d246d62c3 100644 --- a/doc/config.rst +++ b/doc/config.rst @@ -258,7 +258,7 @@ Plotting parameters :value: {'axes.labelsize': 'small', 'axes.titlesize': 'small', 'figure.titlesize': 'medium', 'legend.fontsize': 'x-small', 'xtick.labelsize': 'small', 'ytick.labelsize': 'small'} Overrides the default Matplotlib parameters used for generating - plots. This dictionary can also be accessed as ``ct.rcParams``. + plots. This dictionary can also be accessed as `ct.rcParams`. .. py:data:: freqplot.dB :type: bool diff --git a/doc/develop.rst b/doc/develop.rst index 131f150ff..40d46e1b3 100644 --- a/doc/develop.rst +++ b/doc/develop.rst @@ -178,10 +178,10 @@ System-creating commands: - `input_prefix`, `output_prefix`, `state_prefix`: change the default prefixes used for naming signals. - - `dt`: set the timebase. This one takes a bit of care, since if it - is not specified then it defaults to - ``config.defaults['control.default_dt']``. This is different than - setting ``dt=None``, so `dt` should always be part of `**kwargs`. + - `dt`: set the timebase. This one takes a bit of care, since if it is + not specified then it defaults to + `config.defaults['control.default_dt']`. This is different than + setting `dt` = None, so `dt` should always be part of `**kwargs`. These keywords can be parsed in a consistent way using the `iosys._process_iosys_keywords` function. @@ -236,7 +236,8 @@ similar to NumPy (as articuated in the `numpydoc style guide output. Rather than sacrificing the readability of the docstrings, we have written pre-processors to assist Sphinx in its task. -To that end, docstrings should use the following guidelines: +To that end, docstrings in `python-control` should use the following +guidelines: * Use single backticks around all Python objects. The Sphinx configuration file (`doc/conf.py`) defines `default_role` to be @@ -252,23 +253,32 @@ To that end, docstrings should use the following guidelines: bolder type, so that it is easier to see what things you can click on to get more information. -* Use double backticks for inline code, such as a Python statement, - and formulas that could potentially be computed using Python (\`\`dt - > 0\`\` which reders as ``dt > 0``). Keyword variable assignments - that appear as part of documentation (``squeeze=True``) should also - be written as code. +* Use double backticks for inline code, such as a Python code fragments. - In principle single backticks might actually work OK given the way that the `py:obj` processing works in Sphinx, but the inclusion of code is somewhat rare and the extra two backticks seem like a small sacrifice (and far from a "contortion"). -* Built-in objects (True, False, None) should be written with no - backticks and should be properly capitalized. +* Avoid the use of backticks and \:math\: for simple formulas where + the additional annotation or formatting does not add anything. For + example "-c <= x <= c" (without the double quotes) in + `relay_hysteresis_nonlinearity`. + + - Some of these formulas might be interpreted as Python code + fragments, but they only need to be in double quotes if that makes + the documentation easier to understand. + + - Examples: + + * \`dt\` > 0 not \`\`dt > 0\`\` (`dt` is a parameter) + * \`squeeze\` = True not \`\`squeeze = True\`\` nor squeeze = True. + * -c <= x <= c not \`\`-c <= x <= c\`\` nor \:math\:\`-c \\leq x + \\leq c`. + * \:math\:\`|x| < \\epsilon\` (becomes :math:`|x| < \epsilon`) - - This guideline combined with the previous one imples that font - choices can look slight different depending on how you word - things. For exmaple, you can say `squeeze` is True or ``squeeze=True``. +* Built-in Python objects (True, False, None) should be written with no + backticks and should be properly capitalized. - Another possibility here is to use a single backtick around built-in objects, and the `py:obj` processing will then generate a @@ -286,8 +296,10 @@ To that end, docstrings should use the following guidelines: just to get them in a code font seems unnecessary. - Note that if a string is is included in Python assignment - statement (e.g., ``method='slycot'``) it should be enclosed in - double backticks (\`\`method='slycot'\`\`). + statement (e.g., ``method='slycot'``) it looks quite ugly in text + form to have it enclosed in double backticks + (\`\`method='slycot'\`\`), so OK to use method='slycot' (no + backticks). * References to the `defaults` dictionary should be of the form \`config.defaults['module.param']\` (like a parameter), which @@ -313,7 +325,7 @@ Examples of different styles: * Single backticks to a parameter (no link): `squeeze` -* Double backticks to a code snippet: ``squeeze=True`` +* Double backticks to a code fragment: ``subsys = sys[i][j]``. * Built-in Python objects: True, False, None @@ -416,6 +428,20 @@ Sphinx files guidelines: - Noun form: "`python-control`" (only used occassionally). +* Unlike docstrings, use backticks and \:math\: more liberally when it + is appropriate to highlight/format code properly. However, Python + built-ins should still just be written as True, False, and None (no + backticks). + + - The Sphinx documentation is not read in "raw" form, so OK to add + the additional annotations. + + - The Python built-ins occur frequently and are capitalized, and so + the additional formatting doesn't add much and would be + inconsistent if you jump from the User Guide to the Reference + Manual (eg, to look at a function more closely via a link in the + User Guide). + Reference Manual ---------------- diff --git a/doc/intro.rst b/doc/intro.rst index f852bb16b..871578174 100644 --- a/doc/intro.rst +++ b/doc/intro.rst @@ -134,7 +134,7 @@ some things to keep in mind: [1, 2, 3] and matrices are written using 2D nested lists, e.g., [[1, 2], [3, 4]]. * Functions that in MATLAB would return variable numbers of values - will have a parameter of the form ``return_`` that is used to + will have a parameter of the form `return_` that is used to return additional data. (These functions usually return an object of a class that has attributes that can be used to access the information and this is the preferred usage pattern.) diff --git a/doc/iosys.rst b/doc/iosys.rst index 6d1fa775f..c59a17c70 100644 --- a/doc/iosys.rst +++ b/doc/iosys.rst @@ -560,7 +560,7 @@ work: Various other simplifications are possible, but it can sometimes be complicated to debug error message when things go wrong. Setting -``debug=True`` when calling :func:`interconnect` prints out +`debug` = True when calling :func:`interconnect` prints out information about how the arguments are processed that may be helpful in understanding what is going wrong. diff --git a/doc/linear.rst b/doc/linear.rst index 02689ad57..f926857cf 100644 --- a/doc/linear.rst +++ b/doc/linear.rst @@ -256,8 +256,8 @@ or signal names: Signal names for an indexed subsystem are preserved from the original system and the subsystem name is set according to the values of -``config.defaults['iosys.indexed_system_name_prefix']`` and -``config.defaults['iosys.indexed_system_name_suffix']`` (see +`config.defaults['iosys.indexed_system_name_prefix']` and +`config.defaults['iosys.indexed_system_name_suffix']` (see :ref:`package-configuration-parameters` for more information). The default subsystem name is the original system name with '$indexed' appended. @@ -310,13 +310,13 @@ A discrete time system is created by specifying a nonzero "timebase", The timebase argument is interpreted as follows: -* ``dt = 0``: continuous time system (default) -* ``dt > 0``: discrete time system with sampling period 'dt' -* ``dt = True``: discrete time with unspecified sampling period -* ``dt = None``: no timebase specified (see below) +* `dt` = 0: continuous time system (default) +* `dt` > 0: discrete time system with sampling period 'dt' +* `dt` = True: discrete time with unspecified sampling period +* `dt` = None: no timebase specified (see below) Systems must have compatible timebases in order to be combined. A -discrete time system with unspecified sampling time (``dt = True``) +discrete time system with unspecified sampling time (`dt` = True) can be combined with a system having a specified sampling time; the result will be a discrete time system with the sample time of the latter system. Similarly, a system with timebase None can be combined @@ -326,7 +326,7 @@ timebase of the latter system. For continuous time systems, the :meth:`TransferFunction.sample` methods can be used to create a discrete time system from a continuous time system. See :ref:`utility-and-conversions`. The default value of `dt` can be -changed by changing the value of ``config.defaults['control.default_dt']``. +changed by changing the value of `config.defaults['control.default_dt']`. Functions operating on LTI systems will take into account whether a system is continuous time or discrete time when carrying out operations diff --git a/doc/response.rst b/doc/response.rst index 1d3d1adb8..574aa093f 100644 --- a/doc/response.rst +++ b/doc/response.rst @@ -30,7 +30,7 @@ resulting in the following standard pattern:: Plotting commands return a :class:`ControlPlot` object that provides access to the individual lines in the generated plot using -``cplt.lines``, allowing various aspects of the plot to be modified to +`cplt.lines`, allowing various aspects of the plot to be modified to suit specific needs. The plotting function is also available via the ``plot()`` method of the @@ -282,7 +282,7 @@ or the `overlay_traces` keyword, which puts different traces (e.g., corresponding to step inputs in different channels) on the same graph, with appropriate labeling via a legend on selected axes. -For example, using ``plot_input=True`` and ``overlay_signals=True`` +For example, using `plot_input` = True and `overlay_signals` = True yields the following plot: .. testcode:: timeplot @@ -305,7 +305,7 @@ Input/output response plots created with either the :func:`input_output_response` functions include the input signals by default. These can be plotted on separate axes, but also "overlaid" on the output axes (useful when the input and output signals are being compared to -each other). The following plot shows the use of ``plot_inputs='overlay'`` +each other). The following plot shows the use of `plot_inputs` = 'overlay' as well as the ability to reposition the legends using the `legend_map` keyword: @@ -473,7 +473,7 @@ function: Different types of plots can also be specified for a given frequency response. For example, to plot the frequency response using a a Nichols -plot, use ``plot_type='nichols'``: +plot, use `plot_type` = 'nichols': .. testcode:: freqplot @@ -659,7 +659,7 @@ The grid in the left hand plane shows lines of constant damping ratio as well as arcs corresponding to the frequency of the complex pole. The grid can be turned off using the `grid` keyword. Setting `grid` to False will turn off the grid but show the real and imaginary axis. To completely -remove all lines except the root loci, use ``grid='empty'``. +remove all lines except the root loci, use `grid` = 'empty'. On systems that support interactive plots, clicking on a location on the root locus diagram will mark the pole locations on all branches of the @@ -829,7 +829,7 @@ various ways. The following general rules apply: specified in the `rcParams` keyword argument. To override the defaults for all control plots, update the `config.defaults['ctrlplt.rcParams']` dictionary entries. For convenience, - this dictionary can alse be accessed as ``ct.rcParams``. + this dictionary can alse be accessed as `ct.rcParams`. The default values for style parameters for control plots can be restored using :func:`reset_rcParams`. @@ -851,7 +851,7 @@ various ways. The following general rules apply: the plot title. The default title is a string of the form " plot for " where is a list of the sys names contained in the plot (which is updated if the plotting is called multiple times). - Use ``title=False`` to suppress the title completely. The title can also + Use `title` = False to suppress the title completely. The title can also be updated using the :func:`ControlPlot.set_plot_title` method for the returned control plot object. @@ -928,8 +928,8 @@ example: .. image:: figures/ctrlplot-pole_zero_subplots.png :align: center -Alternatively, turning off the omega-damping grid (using ``grid=False`` or -``grid='empty'``) allows use of Matplotlib layout commands. +Alternatively, turning off the omega-damping grid (using `grid` = False or +`grid` = 'empty') allows use of Matplotlib layout commands. Response and Plotting Reference From cdc5fe806c3335484ab2df1e5d7e7f6ee99e8e3f Mon Sep 17 00:00:00 2001 From: Richard Murray Date: Wed, 15 Jan 2025 12:34:49 -0800 Subject: [PATCH 67/93] updated Makefile to avoid regenerating figures unless needed --- doc/.gitignore | 1 + doc/Makefile | 49 ++++++++++++++++++++++++++++++++++++++----------- 2 files changed, 39 insertions(+), 11 deletions(-) diff --git a/doc/.gitignore b/doc/.gitignore index d948f64d2..caaf55013 100644 --- a/doc/.gitignore +++ b/doc/.gitignore @@ -1 +1,2 @@ *.fig.bak +.docfigs diff --git a/doc/Makefile b/doc/Makefile index 1f33e3ad8..4821cf308 100644 --- a/doc/Makefile +++ b/doc/Makefile @@ -1,5 +1,17 @@ -# Minimal makefile for Sphinx documentation -# +# Makefile for python-control Sphinx documentation +# RMM, 15 Jan 2025 + +FIGS = figures/classes.pdf +RST_FIGS = figures/flatsys-steering-compare.png \ + figures/iosys-predprey-open.png \ + figures/timeplot-servomech-combined.png \ + figures/steering-optimal.png figures/ctrlplot-servomech.png \ + figures/phaseplot-dampedosc-default.png \ + figures/timeplot-mimo_step-default.png \ + figures/freqplot-siso_bode-default.png \ + figures/pzmap-siso_ctime-default.png \ + figures/rlocus-siso_ctime-default.png \ + figures/stochastic-whitenoise-response.png # You can set these variables from the command line. SPHINXOPTS = @@ -12,18 +24,33 @@ BUILDDIR = _build help: @$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) -.PHONY: help Makefile doctest +.PHONY: help Makefile html latexpdf doctest clean distclean -# Catch-all target: route all unknown targets to Sphinx using the new -# "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS). -html latexpdf: Makefile doctest - make -C figures - @$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) +# List of the first RST figure of each type in each file that is generated +figures/flatsys-steering-compare.png: flatsys.rst +figures/iosys-predprey-open.png: iosys.rst +figures/timeplot-servomech-combined.png: nlsys.rst +figures/steering-optimal.png: optimal.rst +figures/phaseplot-dampedosc-default.png: phaseplot.rst +figures/timeplot-mimo_step-default.png: response.rst +figures/freqplot-siso_bode-default.png: response.rst +figures/pzmap-siso_ctime-default.png: response.rst +figures/rlocus-siso_ctime-default.png: response.rst +figures/ctrlplot-servomech.png: response.rst +figures/stochastic-whitenoise-response.png: stochastic.rst -doctest: Makefile - @$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) +# Other figure rules +figure/classes.pdf: figure/classes.fig + make -C figures classes.pdf + +# Rule to run `make doctest` once for all figures +.docfigs: $(RST_FIGS) + @$(SPHINXBUILD) -M doctest "$(SOURCEDIR)" "$(BUILDDIR)" + touch $@ -clean: +# Catch-all target: route all unknown targets to Sphinx using the new +# "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS). +html latexpdf doctest clean: Makefile .docfigs $(FIGS) @$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) distclean: clean From 056110e085352ee45afee367bd21a659305c46ae Mon Sep 17 00:00:00 2001 From: Richard Murray Date: Thu, 16 Jan 2025 15:36:19 -0800 Subject: [PATCH 68/93] fix module/sub-package references (with developer notes) --- control/flatsys/__init__.py | 12 ++++---- control/flatsys/basis.py | 7 +++-- control/flatsys/bezier.py | 5 ++-- control/flatsys/flatsys.py | 55 ++++++++++++++++++------------------- control/flatsys/linflat.py | 4 +-- control/flatsys/systraj.py | 4 +-- control/matlab/__init__.py | 10 +++---- control/optimal.py | 23 ++++++++++------ control/phaseplot.py | 12 ++++---- doc/develop.rst | 36 ++++++++++++++++++++++++ doc/examples/template.rst | 2 +- doc/flatsys.rst | 20 ++++++++------ doc/functions.rst | 2 +- doc/optimal.rst | 22 ++++++++------- doc/phaseplot.rst | 2 +- 15 files changed, 133 insertions(+), 83 deletions(-) diff --git a/control/flatsys/__init__.py b/control/flatsys/__init__.py index c06023cc8..7de3f88ea 100644 --- a/control/flatsys/__init__.py +++ b/control/flatsys/__init__.py @@ -9,14 +9,14 @@ trajectories for differentially flat systems. A differentially flat system is defined by creating an object using the -`flatsys.FlatSystem` class, which has member functions for mapping the +`FlatSystem` class, which has member functions for mapping the system state and input into and out of flat coordinates. The -`flatsys.point_to_point` function can be used to create a trajectory +`point_to_point` function can be used to create a trajectory between two endpoints, written in terms of a set of basis functions defined -using the `flatsys.BasisFamily` class. The resulting trajectory is return -as a `flatsys.SystemTrajectory` object and can be evaluated using the -`flatsys.SystemTrajectory.eval` member function. Alternatively, the -`flatsys.solve_flat_ocp` function can be used to solve an optimal control +using the `BasisFamily` class. The resulting trajectory is return +as a `SystemTrajectory` object and can be evaluated using the +`SystemTrajectory.eval` member function. Alternatively, the +`solve_flat_ocp` function can be used to solve an optimal control problem with trajectory and final costs or constraints. The docstring examples assume that the following import commands:: diff --git a/control/flatsys/basis.py b/control/flatsys/basis.py index a9e028bb6..93243cc1c 100644 --- a/control/flatsys/basis.py +++ b/control/flatsys/basis.py @@ -1,9 +1,10 @@ # basis.py - BasisFamily class # RMM, 10 Nov 2012 -"""The :mod:`control.flatsys.basis` module defines the -`flatsys.BasisFamily` class that used to specify a set of basis -functions for implementing differential flatness computations. +"""Define base class for implementing basis functions. + +This module defines the `BasisFamily` class that used to specify a set +of basis functions for implementing differential flatness computations. """ diff --git a/control/flatsys/bezier.py b/control/flatsys/bezier.py index 3b74375cf..aa4fcf013 100644 --- a/control/flatsys/bezier.py +++ b/control/flatsys/bezier.py @@ -3,9 +3,8 @@ r"""1D Bezier curve basis functions. -The :mod:`control.flatsys.bezier` module defines the -`BezierFamily` class, which implements a set of basis functions -based on Bezier curves: +This module defines the `BezierFamily` class, which implements a set of +basis functions based on Bezier curves: .. math:: \phi_i(t) = \sum_{i=0}^n {n \choose i} (T - t)^{n-i} t^i diff --git a/control/flatsys/flatsys.py b/control/flatsys/flatsys.py index 20990d5ae..a9036792d 100644 --- a/control/flatsys/flatsys.py +++ b/control/flatsys/flatsys.py @@ -24,11 +24,10 @@ class FlatSystem(NonlinearIOSystem): """Base class for representing a differentially flat system. - The FlatSystem class is used as a base class to describe - differentially flat systems for trajectory generation. The output - of the system does not need to be the differentially flat output. - Flat systems are usually created with the `~control.flatsys.flatsys` - factory function. + The FlatSystem class is used as a base class to describe differentially + flat systems for trajectory generation. The output of the system does + not need to be the differentially flat output. Flat systems are + usually created with the `flatsys` factory function. Parameters ---------- @@ -334,12 +333,12 @@ def point_to_point( Parameters ---------- - sys : FlatSystem object + sys : `FlatSystem` object Description of the differentially flat system. This object must - define a function `flatsys.forward` that takes the system state and - produceds the flag of flat outputs and a system `flatsys.reverse` - that takes the flag of the flat output and prodes the state and - input. + define a function `~FlatSystem.forward` that takes the system state + and produces the flag of flat outputs and a function + `~FlatSystem.reverse` that takes the flag of the flat output and + prodes the state and input. timepts : float or 1D array_like The list of points for evaluating cost and constraints, as well as @@ -355,9 +354,9 @@ def point_to_point( The initial time for the trajectory (corresponding to x0). If not specified, its value is taken to be zero. - basis : `flatsys.BasisFamily` object, optional + basis : `BasisFamily` object, optional The basis functions to use for generating the trajectory. If not - specified, the `flatsys.PolyFamily` basis family + specified, the `PolyFamily` basis family will be used, with the minimal number of elements required to find a feasible trajectory (twice the number of system states) @@ -402,9 +401,9 @@ def point_to_point( Returns ------- - traj : `flatsys.SystemTrajectory` object + traj : `SystemTrajectory` object The system trajectory is returned as an object that implements the - `~flatsys.SystemTrajectory.eval` function, we can be used to + `~SystemTrajectory.eval` function, we can be used to compute the value of the state and input and a given time t. Notes @@ -667,12 +666,12 @@ def solve_flat_ocp( Parameters ---------- - sys : FlatSystem object + sys : `FlatSystem` object Description of the differentially flat system. This object must - define a function `flatsys.forward` that takes the system state and - produceds the flag of flat outputs and a system `flatsys.reverse` - that takes the flag of the flat output and prodes the state and - input. + define a function `~FlatSystem.forward` that takes the system state + and produces the flag of flat outputs and a function + `~FlatSystem.reverse` that takes the flag of the flat output and + prodes the state and input. timepts : float or 1D array_like The list of points for evaluating cost and constraints, as well as @@ -684,9 +683,9 @@ def solve_flat_ocp( values are given as None, they are replaced by a vector of zeros of the appropriate dimension. - basis : `flatsys.BasisFamily` object, optional + basis : `BasisFamily` object, optional The basis functions to use for generating the trajectory. If not - specified, the `flatsys.PolyFamily` basis family + specified, the `PolyFamily` basis family will be used, with the minimal number of elements required to find a feasible trajectory (twice the number of system states) @@ -735,22 +734,22 @@ def solve_flat_ocp( Returns ------- - traj : `flatsys.SystemTrajectory` object + traj : `SystemTrajectory` The system trajectory is returned as an object that implements the - `~flatsys.SystemTrajectory.eval` function, we can be used to - compute the value of the state and input and a given time t. + `SystemTrajectory.eval` function, we can be used to + compute the value of the state and input and a given time `t`. Notes ----- Additional keyword parameters can be used to fine tune the behavior of the underlying optimization function. See `minimize_*` keywords in - `optimal.OptimalControlProblem` for more information. + `control.optimal.OptimalControlProblem` for more information. The return data structure includes the following additional attributes: - * success : bool indicating whether the optimization succeeded - * cost : computed cost of the returned trajectory - * message : message returned by optimization if success if False + * `success` : bool indicating whether the optimization succeeded + * `cost` : computed cost of the returned trajectory + * `message` : message returned by optimization if success if False A common failure in solving optimal control problem is that the default initial guess violates the constraints and the optimizer can't find a diff --git a/control/flatsys/linflat.py b/control/flatsys/linflat.py index 724e7bf67..8ed57cd6c 100644 --- a/control/flatsys/linflat.py +++ b/control/flatsys/linflat.py @@ -85,7 +85,7 @@ def __init__(self, linsys, **kwargs): def forward(self, x, u, params): """Compute the flat flag given the states and input. - See `control.flatsys.FlatSystem.forward` for more info. + See `FlatSystem.forward` for more info. """ x = np.reshape(x, (-1, 1)) @@ -102,7 +102,7 @@ def forward(self, x, u, params): def reverse(self, zflag, params): """Compute the states and input given the flat flag. - See `control.flatsys.FlatSystem.reverse` for more info. + See `FlatSystem.reverse` for more info. """ z = zflag[0][0:-1] diff --git a/control/flatsys/systraj.py b/control/flatsys/systraj.py index 3b68d05d5..41c363bbe 100644 --- a/control/flatsys/systraj.py +++ b/control/flatsys/systraj.py @@ -18,7 +18,7 @@ class SystemTrajectory: The `SystemTrajectory` class is used to represent the trajectory of a (differentially flat) system. Used by the - `trajsys.point_to_point` function to return a trajectory. + `point_to_point` function to return a trajectory. Parameters ---------- @@ -131,7 +131,7 @@ def response(self, tlist, transpose=False, return_x=False, squeeze=None): Returns ------- - results : TimeResponseData + results : `control.TimeResponseData` Time response represented as a `TimeResponseData` object containing the following properties: diff --git a/control/matlab/__init__.py b/control/matlab/__init__.py index d0d87cb8e..8080b4cce 100644 --- a/control/matlab/__init__.py +++ b/control/matlab/__init__.py @@ -4,11 +4,11 @@ """MATLAB compatibility sub-package. -The :mod:`control.matlab` sub-package contains a number of functions -that emulate some of the functionality of MATLAB. The intent of these -functions is to provide a simple interface to the python control -systems library (python-control) for people who are familiar with the -MATLAB Control Systems Toolbox (tm). +This sub-package contains a number of functions that emulate some of +the functionality of MATLAB. The intent of these functions is to +provide a simple interface to the python control systems library +(python-control) for people who are familiar with the MATLAB Control +Systems Toolbox (tm). """ diff --git a/control/optimal.py b/control/optimal.py index 0e0d7d2cd..68285dec5 100644 --- a/control/optimal.py +++ b/control/optimal.py @@ -6,7 +6,14 @@ """Optimization-based control module. This module provides support for optimization-based controllers for -nonlinear systems with state and input constraints. +nonlinear systems with state and input constraints. An optimal +control problem can be solved using the `solve_ocp` function or set up +using the `OptimalControlProblem` class and then solved using the +`~OptimalControlProblem.compute_trajectory` method. Utility functions +are available to define common cost functions and input/state +constraints. Optimal estimation problems can be solved using the +`solve_oep` function or by using the `OptimalEstimationProblem` class +and the `~OptimalEstimationProblem.compute_estimate` method.. The docstring examples assume the following import commands:: @@ -47,7 +54,7 @@ class OptimalControlProblem(): to specify an optimal control problem: the system dynamics, cost function, and constraints. As much as possible, the information used to specify an optimal control problem matches the notation and - terminology of the SciPy `optimize.minimize` module, with the hope that + terminology of `scipy.optimize` module, with the hope that this makes it easier to remember how to describe a problem. Parameters @@ -1188,7 +1195,7 @@ def create_mpc_iosystem( constraints : list of tuples, optional List of constraints that should hold at each point in the time - vector. See `optimal.solve_ocp` for more details. + vector. See `solve_ocp` for more details. terminal_cost : callable, optional Function that returns the terminal cost given the final state @@ -1199,8 +1206,8 @@ def create_mpc_iosystem( Same format as `constraints`. **kwargs - Additional parameters, passed to `scipy.optimal.minimize` and - `NonlinearIOSystem`. + Additional parameters, passed to `scipy.optimize.minimize` and + `~control.NonlinearIOSystem`. Returns ------- @@ -2019,11 +2026,11 @@ def solve_oep( control_indices : int, slice, or list of int or string, optional Specify the indices in the system input vector that correspond to the control inputs. For more information on possible values, see - `optimal.OptimalEstimationProblem`. + `OptimalEstimationProblem`. disturbance_indices : int, list of int, or slice, optional Specify the indices in the system input vector that correspond to the input disturbances. For more information on possible values, see - `optimal.OptimalEstimationProblem`. + `OptimalEstimationProblem`. initial_guess : 2D array_like, optional Initial guess for the state estimate at each time point. print_summary : bool, optional @@ -2059,7 +2066,7 @@ def solve_oep( ----- Additional keyword parameters can be used to fine-tune the behavior of the underlying optimization and integration functions. See - `optimal.OptimalControlProblem` for more information. + `OptimalControlProblem` for more information. """ # Set up the optimal control problem diff --git a/control/phaseplot.py b/control/phaseplot.py index e2b199024..8037ef025 100644 --- a/control/phaseplot.py +++ b/control/phaseplot.py @@ -7,9 +7,10 @@ """Generate 2D phase portraits. This module contains functions for generating 2D phase plots. The base -function for creating phase plane portraits is `phase_plane_plot`, +function for creating phase plane portraits is `~control.phase_plane_plot`, which generates a phase plane portrait for a 2 state I/O system (with no -inputs). +inputs). Utility functions are available to customize the individual +elements of a phase plane portrait. The docstring examples assume the following import commands:: @@ -85,8 +86,9 @@ def phase_plane_plot( a dict of parameters and values. For a callable, `params` should be dict with key 'args' and value given by a tuple (passed to callable). color : matplotlib color spec, optional - Plot all elements in the given color (use `plot_` = {'color': c} - to set the color in one element of the phase plot. + Plot all elements in the given color (use ``plot_`` = + {'color': c} to set the color in one element of the phase + plot (equilpoints, separatrices, streamlines, etc). ax : matplotlib.axes.Axes, optional The matplotlib axes to draw the figure on. If not specified and the current figure has a single axes, that axes is used. @@ -130,7 +132,7 @@ def phase_plane_plot( plot_streamlines : bool or dict, optional If True (default) then plot streamlines based on the pointdata and gridtype. If set to a dict, pass on the key-value pairs in - the dict as keywords to `phaseplot.streamlines`. + the dict as keywords to `streamlines`. plot_vectorfield : bool or dict, optional If True (default) then plot the vector field based on the pointdata and gridtype. If set to a dict, pass on the key-value pairs in diff --git a/doc/develop.rst b/doc/develop.rst index 40d46e1b3..f74fb9207 100644 --- a/doc/develop.rst +++ b/doc/develop.rst @@ -450,6 +450,42 @@ The reference manual should provide a fairly comprehensive description of every class, function and configuration variable in the package. +Modules and sub-packages +------------------------ + +When documenting (independent) modules and sub-packages (refered to +here collectively as modules), use the following guidelines for +documentatation: + +* In module docstrings, refer to module functions and classes without + including the module prefix. This will let Sphinx set up the links + to the functions in the proper way and has the advantage that it + keeps the docstrings shorter. + +* Objects in the parent (`control`) package should be referenced using + the `~control` prefix, so that Sphinx generates the links properly + (otherwise it looks within the package). + +* In the User Guide, set ``currentmodule`` to ``control`` and refer to + the module objects using the prefix `~prefix` in the text portions + of the document but `px` (shortened prefix) in the code sections. + This will let users copy and past code from the examples and is + consistent with the use of the `ct` short prefix. Since this is in + the User Guide, the additional characters are not as big an issue. + +* If you include an `autosummary` of functions in the User Guide + section, list the functions using the regular prefix (without ``~``) + to remind everyone the function is in a module. + +* When referring to a module function or class in a docstring or User + Guide section that is not part of the module, use the fully + qualified function or class (\'prefix.function\'). + +The main overarching principle should be to make sure that references +to objects that have more detailed information should show up as a +link, not as code. + + Utility Functions ================= diff --git a/doc/examples/template.rst b/doc/examples/template.rst index b7596fdd9..f9abacede 100644 --- a/doc/examples/template.rst +++ b/doc/examples/template.rst @@ -1,4 +1,4 @@ -:orphan: remove this line and the next bbefore use (supresses toctree warning) +:orphan: remove this line and the next before use (supresses toctree warning) .. currentmodule:: control diff --git a/doc/flatsys.rst b/doc/flatsys.rst index f73091462..5195c9611 100644 --- a/doc/flatsys.rst +++ b/doc/flatsys.rst @@ -1,15 +1,16 @@ +.. currentmodule:: control + .. _flatsys-module: Differentially Flat Systems =========================== -.. automodule:: control.flatsys - :noindex: - :no-members: - :no-inherited-members: - :no-special-members: +The `flatsys` sub-package contains a set of classes and functions to +compute trajectories for differentially flat systems. The objects in +this sub-package must be explictly imported:: -.. currentmodule:: control + import control as ct + import control.flatsys as fs Overview of differential flatness @@ -121,11 +122,14 @@ is full column rank, we can solve for a (possibly non-unique) Sub-package usage ----------------- +To access the flat system modules, import `control.flatsys`:: + + import control.flatsys as fs + To create a trajectory for a differentially flat system, a -:class:`flatsys.FlatSystem` object must be created. This is done +:class:`~flatsys.FlatSystem` object must be created. This is done using the :func:`~flatsys.flatsys` function:: - import control.flatsys as fs sys = fs.flatsys(forward, reverse) The `forward` and `reverse` parameters describe the mappings between diff --git a/doc/functions.rst b/doc/functions.rst index c92c0b18d..033e850ae 100644 --- a/doc/functions.rst +++ b/doc/functions.rst @@ -75,7 +75,6 @@ Time Response impulse_response initial_response input_output_response - phase_plane_plot step_response time_response_plot combine_time_responses @@ -95,6 +94,7 @@ Phase plane plots .. autosummary:: :toctree: generated/ + phase_plane_plot phaseplot.boxgrid phaseplot.circlegrid phaseplot.equilpoints diff --git a/doc/optimal.rst b/doc/optimal.rst index aa375893d..468582423 100644 --- a/doc/optimal.rst +++ b/doc/optimal.rst @@ -1,15 +1,17 @@ +.. currentmodule:: control + .. _optimal-module: Optimization-Based Control ========================== -.. automodule:: control.optimal - :noindex: - :no-members: - :no-inherited-members: - :no-special-members: +The `optimal` module contains a set of classes and functions that can +be used to solve optimal control and optimal estimation problems for +linear or nonlinear systems. The objects in this module must be +explicitly imported:: -.. currentmodule:: control + import control as ct + import control.optimal as opt Optimal control problem setup @@ -125,7 +127,7 @@ state and/or input, along the trajectory and/or at the terminal time. The optimal control module operates by converting the optimal control problem into a standard optimization problem that can be solved by :func:`scipy.optimize.minimize`. The optimal control problem can be solved -by using the :func:`optimal.solve_ocp` function:: +by using the :func:`~optimal.solve_ocp` function:: res = opt.solve_ocp(sys, timepts, X0, cost, constraints) @@ -167,7 +169,7 @@ points on the trajectory. The `terminal_constraint` parameter can be used to specify a constraint that only holds at the final point of the trajectory. -The return value for :func:`optimal.solve_ocp` is a bundle object +The return value for :func:`~optimal.solve_ocp` is a bundle object that has the following elements: * `res.success`: True if the optimization was successfully solved @@ -353,13 +355,13 @@ solutions do not seem close to optimal, here are a few things to try: * Use a smooth basis: as an alternative to parameterizing the optimal control inputs using the value of the control at the listed time points, you can specify a set of basis functions using the `basis` - keyword in :func:`solve_ocp` and then parameterize the controller by + keyword in :func:`~optimal.solve_ocp` and then parameterize the controller by linear combination of the basis functions. The :ref:`flatsys sub-package ` defines several sets of basis functions that can be used. * Tweak the optimizer: by using the `minimize_method`, `minimize_options`, - and `minimize_kwargs` keywords in :func:`solve_ocp`, you can + and `minimize_kwargs` keywords in :func:`~optimal.solve_ocp`, you can choose the SciPy optimization function that you use and set many parameters. See :func:`scipy.optimize.minimize` for more information on the optimizers that are available and the options and keywords that they diff --git a/doc/phaseplot.rst b/doc/phaseplot.rst index 45ecb387d..612568843 100644 --- a/doc/phaseplot.rst +++ b/doc/phaseplot.rst @@ -135,4 +135,4 @@ Note that unlike other plotting functions, phase plane plots do not involve computing a response and then plotting the result via a ``plot()`` method. Instead, the plot is generated directly be a call to the :func:`phase_plane_plot` function (or one of the -:mod:`phaseplot` helper functions). +:mod:`~control.phaseplot` helper functions). From eedbf2ba9c437de573b8c17b1ad4af36e2b0d589 Mon Sep 17 00:00:00 2001 From: Richard Murray Date: Fri, 17 Jan 2025 17:40:48 -0800 Subject: [PATCH 69/93] update class documentation (+ unit tests) --- control/bdalg.py | 48 +++---- control/canonical.py | 22 +-- control/config.py | 2 +- control/ctrlplot.py | 15 ++- control/descfcn.py | 53 +++++++- control/dtime.py | 18 +-- control/flatsys/basis.py | 65 ++++++++- control/flatsys/bezier.py | 8 +- control/flatsys/bspline.py | 8 +- control/flatsys/flatsys.py | 11 +- control/flatsys/linflat.py | 6 +- control/flatsys/poly.py | 8 +- control/flatsys/systraj.py | 30 ++--- control/frdata.py | 45 ++++--- control/freqplot.py | 27 ++-- control/grid.py | 4 +- control/iosys.py | 162 +++++++++++++++++++---- control/lti.py | 53 +++++--- control/margins.py | 2 +- control/matlab/timeresp.py | 28 ++-- control/matlab/wrappers.py | 16 +-- control/modelsimp.py | 51 ++++--- control/nichols.py | 12 +- control/nlsys.py | 135 +++++++++++-------- control/optimal.py | 126 +++++++++++------- control/phaseplot.py | 20 +-- control/pzmap.py | 16 +-- control/rlocus.py | 10 +- control/robust.py | 30 ++--- control/sisotool.py | 7 +- control/statefbk.py | 32 ++--- control/statesp.py | 158 ++++++++++++++-------- control/stochsys.py | 26 ++-- control/sysnorm.py | 8 +- control/tests/discrete_test.py | 12 +- control/tests/docstrings_test.py | 59 +++++++-- control/tests/freqresp_test.py | 2 +- control/tests/iosys_test.py | 4 +- control/tests/lti_test.py | 4 +- control/tests/matlab2_test.py | 2 +- control/tests/modelsimp_test.py | 4 +- control/tests/nyquist_test.py | 2 +- control/tests/optimal_test.py | 4 +- control/tests/statefbk_test.py | 12 +- control/tests/statesp_test.py | 4 +- control/tests/stochsys_test.py | 6 +- control/tests/timeresp_test.py | 6 +- control/tests/xferfcn_test.py | 2 +- control/timeplot.py | 7 +- control/timeresp.py | 62 ++++----- control/xferfcn.py | 133 +++++++++++-------- doc/Makefile | 25 ++-- doc/_templates/custom-class-template.rst | 2 +- doc/classes.rst | 16 +-- doc/config.rst | 86 ++++++------ doc/figures/flatsys-steering-compare.png | Bin 43922 -> 40058 bytes doc/flatsys.rst | 12 +- doc/linear.rst | 22 +-- doc/matlab.rst | 6 +- doc/nlsys.rst | 4 +- doc/optimal.rst | 2 +- doc/response.rst | 4 +- doc/statesp.rst | 6 +- doc/stochastic.rst | 16 +-- doc/xferfcn.rst | 2 +- examples/cds110-L6c_doubleint-rhc.ipynb | 4 +- examples/cds112-L5_rhc-doubleint.ipynb | 4 +- examples/cds112-L8_fusion-kincar.ipynb | 6 +- examples/cds112-L9_mhe-pvtol.ipynb | 4 +- examples/kincar-fusion.ipynb | 4 +- examples/mhe-pvtol.ipynb | 6 +- 71 files changed, 1116 insertions(+), 702 deletions(-) diff --git a/control/bdalg.py b/control/bdalg.py index 02d76a896..8b066c293 100644 --- a/control/bdalg.py +++ b/control/bdalg.py @@ -53,7 +53,7 @@ def series(*sys, **kwargs): '.' for interconnected nonlinear systems. name : string, optional System name (used for specifying signals). If unspecified, a generic - name is generated with a unique integer id. + name 'sys[id]' is generated with a unique integer id. Raises ------ @@ -124,7 +124,7 @@ def parallel(*sys, **kwargs): '.' for interconnected nonlinear systems. name : string, optional System name (used for specifying signals). If unspecified, a generic - name is generated with a unique integer id. + name 'sys[id]' is generated with a unique integer id. Raises ------ @@ -139,7 +139,7 @@ def parallel(*sys, **kwargs): Notes ----- This function is a wrapper for the __add__ function in the - StateSpace and TransferFunction classes. The output type is usually + `StateSpace` and `TransferFunction` classes. The output type is usually the type of `sys1`. If `sys1` is a scalar, then the output type is the type of `sys2`. @@ -168,8 +168,7 @@ def parallel(*sys, **kwargs): return sys def negate(sys, **kwargs): - """ - Return the negative of a system. + """Return the negative of a system. Parameters ---------- @@ -193,7 +192,7 @@ def negate(sys, **kwargs): '.' for interconnected nonlinear systems. name : string, optional System name (used for specifying signals). If unspecified, a generic - name is generated with a unique integer id. + name 'sys[id]' is generated with a unique integer id. See Also -------- @@ -201,8 +200,9 @@ def negate(sys, **kwargs): Notes ----- - This function is a wrapper for the __neg__ function in the StateSpace and - TransferFunction classes. The output type is the same as the input type. + This function is a wrapper for the __neg__ function in the `StateSpace` + and `TransferFunction` classes. The output type is the same as the + input type. Examples -------- @@ -248,16 +248,16 @@ def feedback(sys1, sys2=1, sign=-1, **kwargs): '.' for interconnected nonlinear systems. name : string, optional System name (used for specifying signals). If unspecified, a generic - name is generated with a unique integer id. + name 'sys[id]' is generated with a unique integer id. Raises ------ ValueError - if `sys1` does not have as many inputs as `sys2` has outputs, or if - `sys2` does not have as many inputs as `sys1` has outputs + If `sys1` does not have as many inputs as `sys2` has outputs, or if + `sys2` does not have as many inputs as `sys1` has outputs. NotImplementedError - if an attempt is made to perform a feedback on a MIMO TransferFunction - object + If an attempt is made to perform a feedback on a MIMO `TransferFunction` + object. See Also -------- @@ -341,7 +341,7 @@ def append(*sys, **kwargs): '.' for interconnected nonlinear systems. name : string, optional System name (used for specifying signals). If unspecified, a generic - name is generated with a unique integer id. + name 'sys[id]' is generated with a unique integer id. See Also -------- @@ -472,16 +472,16 @@ def combine_tf(tf_array, **kwargs): Parameters ---------- - tf_array : list of list of TransferFunction or array_like + tf_array : list of list of `TransferFunction` or array_like Transfer matrix represented as a two-dimensional array or - list-of-lists containing TransferFunction objects. The + list-of-lists containing `TransferFunction` objects. The `TransferFunction` objects can have multiple outputs and inputs, as long as the dimensions are compatible. Returns ------- - TransferFunction - Transfer matrix represented as a single MIMO TransferFunction object. + `TransferFunction` + Transfer matrix represented as a single MIMO `TransferFunction` object. Other Parameters ---------------- @@ -491,7 +491,7 @@ def combine_tf(tf_array, **kwargs): or 'y'). See `InputOutputSystem` for more information. name : string, optional System name (used for specifying signals). If unspecified, a generic - name is generated with a unique integer id. + name 'sys[id]' is generated with a unique integer id. Raises ------ @@ -603,7 +603,7 @@ def split_tf(transfer_function): Parameters ---------- - transfer_function : TransferFunction + transfer_function : `TransferFunction` MIMO transfer function to split. Returns @@ -658,11 +658,11 @@ def split_tf(transfer_function): return np.array(tf_split_lst, dtype=object) def _ensure_tf(arraylike_or_tf, dt=None): - """Convert an array-like to a transfer function. + """Convert an array_like to a transfer function. Parameters ---------- - arraylike_or_tf : TransferFunction or array_like + arraylike_or_tf : `TransferFunction` or array_like Array-like or transfer function. dt : None, True or float, optional System timebase. 0 (default) indicates continuous @@ -673,7 +673,7 @@ def _ensure_tf(arraylike_or_tf, dt=None): Returns ------- - TransferFunction + `TransferFunction` Transfer function. Raises @@ -707,7 +707,7 @@ def _ensure_tf(arraylike_or_tf, dt=None): ) except TypeError: raise ValueError( - "`arraylike_or_tf` must only contain array-likes or transfer " + "`arraylike_or_tf` must only contain array_likes or transfer " "functions." ) return tfn diff --git a/control/canonical.py b/control/canonical.py index 6efa5f6c8..b6de75a73 100644 --- a/control/canonical.py +++ b/control/canonical.py @@ -24,7 +24,7 @@ def canonical_form(xsys, form='reachable'): Parameters ---------- - xsys : StateSpace object + xsys : `StateSpace` object System to be transformed, with state 'x'. form : str Canonical form for transformation. Chosen from: @@ -34,7 +34,7 @@ def canonical_form(xsys, form='reachable'): Returns ------- - zsys : StateSpace object + zsys : `StateSpace` object System in desired canonical form, with state 'z'. T : (M, M) real ndarray Coordinate transformation matrix, z = T * x. @@ -76,12 +76,12 @@ def reachable_form(xsys): Parameters ---------- - xsys : StateSpace object + xsys : `StateSpace` object System to be transformed, with state `x`. Returns ------- - zsys : StateSpace object + zsys : `StateSpace` object System in reachable canonical form, with state `z`. T : (M, M) real ndarray Coordinate transformation: z = T * x. @@ -141,12 +141,12 @@ def observable_form(xsys): Parameters ---------- - xsys : StateSpace object + xsys : `StateSpace` object System to be transformed, with state `x`. Returns ------- - zsys : StateSpace object + zsys : `StateSpace` object System in observable canonical form, with state `z`. T : (M, M) real ndarray Coordinate transformation: z = T * x. @@ -202,7 +202,7 @@ def similarity_transform(xsys, T, timescale=1, inverse=False): Parameters ---------- - xsys : StateSpace object + xsys : `StateSpace` object System to transform. T : (M, M) array_like The matrix `T` defines the new set of coordinates z = T x. @@ -214,7 +214,7 @@ def similarity_transform(xsys, T, timescale=1, inverse=False): Returns ------- - zsys : StateSpace object + zsys : `StateSpace` object System in transformed coordinates, with state 'z'. See Also @@ -496,7 +496,7 @@ def modal_form(xsys, condmax=None, sort=False): Parameters ---------- - xsys : StateSpace object + xsys : `StateSpace` object System to be transformed, with state x. condmax : None or float, optional An upper bound on individual transformations. If None, use @@ -507,9 +507,9 @@ def modal_form(xsys, condmax=None, sort=False): Returns ------- - zsys : StateSpace object + zsys : `StateSpace` object System in modal canonical form, with state z. - T : (M, M) ndarray + T : (M, M) array Coordinate transformation: z = T * x. Examples diff --git a/control/config.py b/control/config.py index c0b0804fc..c34bef58b 100644 --- a/control/config.py +++ b/control/config.py @@ -29,7 +29,7 @@ class DefaultDict(collections.UserDict): - """Map names for settings from older version to their renamed ones. + """Default parameters dictionary, with legacy warnings. If a user wants to write to an old setting, issue a warning and write to the renamed setting instead. Accessing the old setting returns the value diff --git a/control/ctrlplot.py b/control/ctrlplot.py index 942efaed2..661c2001f 100644 --- a/control/ctrlplot.py +++ b/control/ctrlplot.py @@ -122,7 +122,7 @@ # class ControlPlot(): - """A class for representing control figures. + """Return class for control platting functions. This class is used as the return type for control plotting functions. It contains the information required to access portions of the plot @@ -136,17 +136,17 @@ class ControlPlot(): Parameters ---------- - lines : array of list of `matplotlib:Line2D` + lines : array of list of `matplotlib.lines.Line2D` Array of Line2D objects for each line in the plot. Generally, the shape of the array matches the subplots shape and the value of the array is a list of Line2D objects in that subplot. Some plotting functions will return variants of this structure, as described in the individual documentation for the functions. - axes : 2D array of `matplotlib:Axes` + axes : 2D array of `matplotlib.axes.Axes` Array of Axes objects for each subplot in the plot. - figure : `matplotlib:Figure` + figure : `matplotlib.figure.Figure` Figure on which the Axes are drawn. - legend : `matplotlib:.legend.Legend` (instance or ndarray) + legend : `matplotlib.legend.Legend` (instance or ndarray) Legend object(s) for the plot. If more than one legend is included, this will be an array with each entry being either None (for no legend) or a legend object. @@ -176,6 +176,7 @@ def __setitem__(self, item, val): self.lines[item] = val shape = property(lambda self: self.lines.shape, None) def reshape(self, *args): + """Reshape lines array (legacy).""" return self.lines.reshape(*args) def set_plot_title(self, title, frame='axes'): @@ -310,11 +311,11 @@ def pole_zero_subplots( case 'empty', _: # empty grid ax_array[row, col] = fig.add_subplot(nrows, ncols, index+1) - case True, True: # continuous time grid + case True, True: # continuous-time grid ax_array[row, col], _ = sgrid( (nrows, ncols, index+1), scaling=scaling) - case True, False: # discrete time grid + case True, False: # discrete-time grid ax_array[row, col] = fig.add_subplot(nrows, ncols, index+1) zgrid(ax=ax_array[row, col], scaling=scaling) diff --git a/control/descfcn.py b/control/descfcn.py index 383df0149..9f7fdede4 100644 --- a/control/descfcn.py +++ b/control/descfcn.py @@ -49,6 +49,16 @@ def describing_function(self, A): describing function for a nonlinearity. It turns the (complex) value of the describing function for sinusoidal input of amplitude `A`. + Parameters + ---------- + A : float + Amplitude of the sinusoidal input to the nonlinearity. + + Returns + ------- + float + Value of the describing function at the given amplitude. + """ raise NotImplementedError( "describing function not implemented for this function") @@ -230,13 +240,13 @@ class is used by the `describing_function_response` Frequency response of the linear system component of the system. intersections : 1D array of 2-tuples or None A list of all amplitudes and frequencies in which - :math:`H(j\\omega) N(a) = -1`, where :math:`N(a)` is the describing + :math:`H(j\\omega) N(A) = -1`, where :math:`N(A)` is the describing function associated with `F`, or None if there are no such points. Each pair represents a potential limit cycle for the closed loop system with amplitude given by the first value of the tuple and frequency given by the second value. N_vals : complex array - Complex value of the describing function. + Complex value of the describing function, indexed by amplitude. positions : list of complex Location of the intersections in the complex plane. @@ -601,6 +611,19 @@ def _isstatic(self): return True def describing_function(self, A): + """Return the describing function for a saturation nonlinearity. + + Parameters + ---------- + A : float + Amplitude of the sinusoidal input to the nonlinearity. + + Returns + ------- + float + Value of the describing function at the given amplitude. + + """ # Check to make sure the amplitude is positive if A < 0: raise ValueError("cannot evaluate describing function for A < 0") @@ -678,6 +701,19 @@ def _isstatic(self): return False def describing_function(self, A): + """Return the describing function for a hysteresis nonlinearity. + + Parameters + ---------- + A : float + Amplitude of the sinusoidal input to the nonlinearity. + + Returns + ------- + float + Value of the describing function at the given amplitude. + + """ # Check to make sure the amplitude is positive if A < 0: raise ValueError("cannot evaluate describing function for A < 0") @@ -748,6 +784,19 @@ def _isstatic(self): return False def describing_function(self, A): + """Return the describing function for a backlash nonlinearity. + + Parameters + ---------- + A : float + Amplitude of the sinusoidal input to the nonlinearity. + + Returns + ------- + float + Value of the describing function at the given amplitude. + + """ # Check to make sure the amplitude is positive if A < 0: raise ValueError("cannot evaluate describing function for A < 0") diff --git a/control/dtime.py b/control/dtime.py index 783ba93f8..b7c9dc670 100644 --- a/control/dtime.py +++ b/control/dtime.py @@ -1,22 +1,22 @@ -# dtime.py - functions for manipulating discrete time systems +# dtime.py - functions for manipulating discrete-time systems # # Initial author: Richard M. Murray # Creation date: 6 October 2012 -"""Functions for manipulating discrete time systems.""" +"""Functions for manipulating discrete-time systems.""" from .iosys import isctime __all__ = ['sample_system', 'c2d'] -# Sample a continuous time system +# Sample a continuous-time system def sample_system(sysc, Ts, method='zoh', alpha=None, prewarp_frequency=None, name=None, copy_names=True, **kwargs): - """Convert a continuous time system to discrete time by sampling. + """Convert a continuous-time system to discrete time by sampling. Parameters ---------- - sysc : LTI (`StateSpace` or `TransferFunction`) + sysc : `StateSpace` or `TransferFunction` Continuous time system to be converted. Ts : float > 0 Sampling period. @@ -33,7 +33,7 @@ def sample_system(sysc, Ts, method='zoh', alpha=None, prewarp_frequency=None, Returns ------- - sysd : LTI of the same class (StateSpace or TransferFunction) + sysd : LTI of the same class (`StateSpace` or `TransferFunction`) Discrete time system, with sampling rate Ts. Other Parameters @@ -49,7 +49,7 @@ def sample_system(sysc, Ts, method='zoh', alpha=None, prewarp_frequency=None, available if the system is `StateSpace`. name : string, optional Set the name of the sampled system. If not specified and - if `copy_names` is False, a generic name is generated + if `copy_names` is False, a generic name 'sys[id]' is generated with a unique integer id. If `copy_names` is True, the new system name is determined by adding the prefix and suffix strings in `config.defaults['iosys.sampled_system_name_prefix']` and @@ -75,9 +75,9 @@ def sample_system(sysc, Ts, method='zoh', alpha=None, prewarp_frequency=None, """ - # Make sure we have a continuous time system + # Make sure we have a continuous-time system if not isctime(sysc): - raise ValueError("First argument must be continuous time system") + raise ValueError("First argument must be continuous-time system") return sysc.sample(Ts, method=method, alpha=alpha, prewarp_frequency=prewarp_frequency, diff --git a/control/flatsys/basis.py b/control/flatsys/basis.py index 93243cc1c..c1d295577 100644 --- a/control/flatsys/basis.py +++ b/control/flatsys/basis.py @@ -30,6 +30,18 @@ class BasisFamily: N : int Order of the basis set. + Attributes + ---------- + nvars : int or None + Number of variables represented by the basis (possibly of different + order/length). Default is None (single variable). + + coef_offset : list + Coefficient offset for each variable. + + coef_length : list + Coefficient length for each variable. + """ def __init__(self, N): """Create a basis family of order N.""" @@ -47,11 +59,39 @@ def __call__(self, i, t, var=None): return self.eval_deriv(i, 0, t, var=var) def var_ncoefs(self, var): - """Get the number of coefficients for a variable.""" + """Get the number of coefficients for a variable. + + Parameters + ---------- + var : int + Variable offset. + + Returns + ------- + int + + """ return self.N if self.nvars is None else self.coef_length[var] def eval(self, coeffs, tlist, var=None): - """Compute function values given the coefficients and time points.""" + """Compute function values given the coefficients and time points. + + Parameters + ---------- + coeffs : array + Basis function coefficient values. + tlist : array + List of times at which to evaluate the function. + var : int or None, optional + Number of independent variables represented using the basis. + If None, then basis represents a single variable. + + Returns + ------- + array + Values of the variable(s) at the times in `tlist`. + + """ if self.nvars is None and var != None: raise SystemError("multi-variable call to a scalar basis") @@ -80,6 +120,23 @@ def eval(self, coeffs, tlist, var=None): for i in range(self.var_ncoefs(var))]) for t in tlist]) - def eval_deriv(self, i, j, t, var=None): - """Evaluate the kth derivative of the ith basis function at time t.""" + def eval_deriv(self, i, k, t, var=None): + """Evaluate kth derivative of ith basis function at time t. + + Parameters + ---------- + i : int + Basis function offset. + k : int + Derivative order. + t : float + Time at which to evaluating the derivative. + var : int or None, optional + Variable offset. + + Returns + ------- + float + + """ raise NotImplementedError("Internal error; improper basis functions") diff --git a/control/flatsys/bezier.py b/control/flatsys/bezier.py index aa4fcf013..41b8d1cb3 100644 --- a/control/flatsys/bezier.py +++ b/control/flatsys/bezier.py @@ -32,7 +32,7 @@ class BezierFamily(BasisFamily): Degree of the Bezier curve. T : float - Final time (used for rescaling). + Final time (used for rescaling). Default value is 1. """ def __init__(self, N, T=1): @@ -42,7 +42,11 @@ def __init__(self, N, T=1): # Compute the kth derivative of the ith basis function at time t def eval_deriv(self, i, k, t, var=None): - """Evaluate the kth derivative of the ith basis function at time t.""" + """Evaluate kth derivative of ith basis function at time t. + + See `BasisFamily.eval_deriv` for more information. + + """ if i >= self.N: raise ValueError("Basis function index too high") elif k >= self.N: diff --git a/control/flatsys/bspline.py b/control/flatsys/bspline.py index 9b296142b..d888be168 100644 --- a/control/flatsys/bspline.py +++ b/control/flatsys/bspline.py @@ -43,7 +43,7 @@ class BSplineFamily(BasisFamily): The number of spline variables. If specified as None (default), then the spline basis describes a single variable, with no indexing. If the number of spine variables is > 0, then the spline basis is - index using the `var` keyword. + indexed using the `var` keyword. """ def __init__(self, breakpoints, degree, smoothness=None, vars=None): @@ -185,7 +185,11 @@ def __repr__(self): # Compute the kth derivative of the ith basis function at time t def eval_deriv(self, i, k, t, var=None): - """Evaluate the kth derivative of the ith basis function at time t.""" + """Evaluate kth derivative of ith basis function at time t. + + See `BasisFamily.eval_deriv` for more information. + + """ if self.nvars is None or (self.nvars == 1 and var is None): # Use same variable for all requests var = 0 diff --git a/control/flatsys/flatsys.py b/control/flatsys/flatsys.py index a9036792d..811b0d830 100644 --- a/control/flatsys/flatsys.py +++ b/control/flatsys/flatsys.py @@ -60,7 +60,7 @@ class FlatSystem(NonlinearIOSystem): ``zflag = flatsys.foward(x, u, params)`` This function computes the flag (derivatives) of the flat output. - The inputs to this function are the state 'x' and inputs 'u' (both + The inputs to this function are the state `x` and inputs `u` (both 1D arrays). The output should be a 2D array with the first dimension equal to the number of system inputs and the second dimension of the length required to represent the full system @@ -76,8 +76,10 @@ class FlatSystem(NonlinearIOSystem): `x` and inputs `u` (both 1D arrays). A flat system is also an input/output system supporting simulation, - composition, and linearization. If the update and output methods are - given, they are used in place of the flat coordinates. + composition, and linearization. In the current implementation, the + update function must be given explicitly, but the output function + defaults to the flat outputs. If the output method is given, it is + used in place of the flat outputs. """ def __init__(self, @@ -111,7 +113,6 @@ def __str__(self): + f"Reverse: {self.reverse}" def forward(self, x, u, params=None): - """Compute the flat flag given the states and input. Given the states and inputs for a system, compute the flat @@ -193,7 +194,7 @@ def flatsys(*args, updfcn=None, outfcn=None, **kwargs): ``fs.flatsys(linsys)`` - Create a flat system from a linear (StateSpace) system. + Create a flat system from a linear (`StateSpace`) system. Parameters ---------- diff --git a/control/flatsys/linflat.py b/control/flatsys/linflat.py index 8ed57cd6c..724586db6 100644 --- a/control/flatsys/linflat.py +++ b/control/flatsys/linflat.py @@ -21,8 +21,8 @@ class LinearFlatSystem(FlatSystem, StateSpace): Parameters ---------- - linsys : StateSpace - LTI StateSpace system to be converted. + linsys : `StateSpace` + LTI `StateSpace` system to be converted. inputs : int, list of str or None, optional Description of the system inputs. This can be given as an integer count or as a list of strings that name the individual signals. @@ -59,7 +59,7 @@ def __init__(self, linsys, **kwargs): # Make sure we can handle the system if (not control.isctime(linsys)): raise control.ControlNotImplemented( - "requires continuous time, linear control system") + "requires continuous-time, linear control system") elif (not control.issiso(linsys)): raise control.ControlNotImplemented( "only single input, single output systems are supported") diff --git a/control/flatsys/poly.py b/control/flatsys/poly.py index 4e925aa2c..bf13039e2 100644 --- a/control/flatsys/poly.py +++ b/control/flatsys/poly.py @@ -30,7 +30,7 @@ class PolyFamily(BasisFamily): Degree of the Bezier curve. T : float - Final time (used for rescaling). + Final time (used for rescaling). Default value is 1. """ def __init__(self, N, T=1): @@ -40,7 +40,11 @@ def __init__(self, N, T=1): # Compute the kth derivative of the ith basis function at time t def eval_deriv(self, i, k, t, var=None): - """Evaluate the kth derivative of the ith basis function at time t.""" + """Evaluate kth derivative of ith basis function at time t. + + See `BasisFamily.eval_deriv` for more information. + + """ if (i < k): return 0 * t # higher derivative than power return factorial(i)/factorial(i-k) * \ np.power(t/self.T, i-k) / np.power(self.T, k) diff --git a/control/flatsys/systraj.py b/control/flatsys/systraj.py index 41c363bbe..d8255147d 100644 --- a/control/flatsys/systraj.py +++ b/control/flatsys/systraj.py @@ -14,17 +14,17 @@ class SystemTrajectory: - """Class representing a trajectory for a flat system. + """Trajectory for a differentially flat system. - The `SystemTrajectory` class is used to represent the - trajectory of a (differentially flat) system. Used by the - `point_to_point` function to return a trajectory. + The `SystemTrajectory` class is used to represent the trajectory + of a (differentially flat) system. Used by the `point_to_point` + and `solve_flat_ocp` functions to return a trajectory. Parameters ---------- - sys : FlatSystem + sys : `FlatSystem` Flat system object associated with this trajectory. - basis : BasisFamily + basis : `BasisFamily` Family of basis vectors to use to represent the trajectory. coeffs : list of 1D arrays, optional For each flat output, define the coefficients of the basis @@ -96,18 +96,18 @@ def eval(self, tlist): return xd, ud # Return the system trajectory as a TimeResponseData object - def response(self, tlist, transpose=False, return_x=False, squeeze=None): + def response(self, timepts, transpose=False, return_x=False, squeeze=None): """Compute trajectory of a system as a TimeResponseData object. Evaluate the trajectory at a list of time points, returning the state and input vectors for the trajectory: - response = traj.response(tlist) + response = traj.response(timepts) time, yd, ud = response.time, response.outputs, response.inputs Parameters ---------- - tlist : 1D array + timepts : 1D array List of times to evaluate the trajectory. transpose : bool, optional @@ -131,7 +131,7 @@ def response(self, tlist, transpose=False, return_x=False, squeeze=None): Returns ------- - results : `control.TimeResponseData` + response : `control.TimeResponseData` Time response represented as a `TimeResponseData` object containing the following properties: @@ -157,13 +157,13 @@ def response(self, tlist, transpose=False, return_x=False, squeeze=None): """ # Compute the state and input response using the eval function sys = self.system - xout, uout = self.eval(tlist) + xout, uout = self.eval(timepts) yout = np.array([ - sys.output(tlist[i], xout[:, i], uout[:, i]) - for i in range(len(tlist))]).transpose() + sys.output(timepts[i], xout[:, i], uout[:, i]) + for i in range(len(timepts))]).transpose() return TimeResponseData( - tlist, yout, xout, uout, issiso=sys.issiso(), + timepts, yout, xout, uout, issiso=sys.issiso(), input_labels=sys.input_labels, output_labels=sys.output_labels, - state_labels=sys.state_labels, + state_labels=sys.state_labels, sysname=sys.name, transpose=transpose, return_x=return_x, squeeze=squeeze) diff --git a/control/frdata.py b/control/frdata.py index 44713b0cd..ac41ff2d2 100644 --- a/control/frdata.py +++ b/control/frdata.py @@ -30,7 +30,7 @@ class FrequencyResponseData(LTI): """FrequencyResponseData(response, omega[, smooth]) - A class for models defined by frequency response data (FRD). + Input/output model defined by frequency response data (FRD). The FrequencyResponseData (FRD) class is used to represent systems in frequency response data form. It can be created manually using the @@ -48,10 +48,10 @@ class constructor, using the `frd` factory function, or omega : iterable of real frequencies List of frequency points for which data are available. smooth : bool, optional - If True, create an interpolation function that allows the - frequency response to be computed at any frequency within the range of - frequencies give in `w`. If False (default), frequency response - can only be obtained at the frequencies specified in `w`. + If True, create an interpolation function that allows the frequency + response to be computed at any frequency within the range of + frequencies give in `omega`. If False (default), frequency response + can only be obtained at the frequencies specified in `omega`. dt : None, True or float, optional System timebase. 0 (default) indicates continuous time, True indicates discrete time with unspecified sampling time, positive @@ -113,12 +113,12 @@ class constructor, using the `frd` factory function, or See Also -------- - frd, InputOutputSystem, TransferFunction, TimeResponseData + frd, frequency_response, InputOutputSystem, TransferFunction Notes ----- - The main data members are 'omega' and 'fresp', where 'omega' is a 1D array - of frequency points and and 'fresp' is a 3D array of frequency responses, + The main data members are `omega` and `fresp`, where `omega` is a 1D array + of frequency points and and `fresp` is a 3D array of frequency responses, with the first dimension corresponding to the output index of the FRD, the second dimension corresponding to the input index, and the 3rd dimension corresponding to the frequency points in omega. For example, @@ -134,10 +134,6 @@ class constructor, using the `frd` factory function, or the imaginary axis). See `FrequencyResponseData.__call__` for a more detailed description. - A state space system is callable and returns the value of the transfer - function evaluated at a point in the complex plane. See - `StateSpace.__call__` for a more detailed description. - Subsystem response corresponding to selected input/output pairs can be created by indexing the frequency response data object:: @@ -654,7 +650,7 @@ def eval(self, omega, squeeze=None): If `squeeze` is True then single-dimensional axes are removed. """ - omega_array = np.array(omega, ndmin=1) # array-like version of omega + omega_array = np.array(omega, ndmin=1) # array_like version of omega # Make sure that we are operating on a simple list if len(omega_array.shape) > 1: @@ -735,9 +731,9 @@ def __call__(self, s=None, squeeze=None, return_magphase=None): Raises ------ ValueError - If `s` is not purely imaginary, because - `FrequencyResponseData` systems are only defined at - imaginary values (corresponding to real frequencies). + If `s` is not purely imaginary, because `FrequencyResponseData` + systems are only defined at imaginary values (corresponding to + real frequencies). """ if s is None: @@ -817,8 +813,17 @@ def freqresp(self, omega): return self.frequency_response(omega) def feedback(self, other=1, sign=-1): - """Feedback interconnection between two FRD objects.""" + """Feedback interconnection between two FRD objects. + Parameters + ---------- + other : `LTI` + System in the feedack path. + + sign : float, optional + Gain to use in feedback path. Defaults to -1. + + """ other = _convert_to_frd(other, omega=self.omega) if (self.noutputs != other.ninputs or self.ninputs != other.noutputs): @@ -1005,7 +1010,7 @@ def frd(*args, **kwargs): Parameters ---------- - sys : LTI (StateSpace or TransferFunction) + sys : `StateSpace` or `TransferFunction` A linear system that will be evaluated for frequency response data. response : array_like or LTI system Complex vector with the system response or an LTI system that can @@ -1023,7 +1028,7 @@ def frd(*args, **kwargs): Returns ------- - sys : FrequencyResponseData + sys : `FrequencyResponseData` New frequency response data system. Other Parameters @@ -1041,7 +1046,7 @@ def frd(*args, **kwargs): `config.defaults['iosys.sampled_system_name_prefix']` and `config.defaults['iosys.sampled_system_name_suffix']`, with the default being to add the suffix '$ampled'. Otherwise, a generic - name is generated with a unique integer id + name 'sys[id]' is generated with a unique integer id See Also -------- diff --git a/control/freqplot.py b/control/freqplot.py index 0027bbab4..382a5061c 100644 --- a/control/freqplot.py +++ b/control/freqplot.py @@ -66,7 +66,7 @@ class FrequencyResponseList(list): def plot(self, *args, plot_type=None, **kwargs): - """Plot a list of frequency responses. + """List of FrequencyResponseData objects with plotting capability. See `FrequencyResponseData.plot` for details. @@ -248,7 +248,7 @@ def bode_plot( a system (or list of systems) without plotting, use the `frequency_response` command. - If a discrete time model is given, the frequency response is plotted + If a discrete-time model is given, the frequency response is plotted along the upper branch of the unit circle, using the mapping ``z = exp(1j * omega * dt)`` where `omega` ranges from 0 to pi/`dt` and `dt` is the discrete timebase. If timebase not specified (`dt` = True), @@ -1119,7 +1119,7 @@ class NyquistResponseData: Number of encirclements of the -1 point by the Nyquist curve for a system evaluated along the Nyquist contour. contour : complex array - The Nyquist 'D' contour, with appropriate indendtations to avoid + The Nyquist 'D' contour, with appropriate indentations to avoid open loop poles and zeros near/on the imaginary axis. response : complex array The value of the linear system under study along the Nyquist contour. @@ -1129,7 +1129,7 @@ class NyquistResponseData: The name of the system being analyzed. return_contour : bool If true, when the object is accessed as an iterable return two - elements": `count` (number of encirlements) and `contour`. If + elements: `count` (number of encirlements) and `contour`. If false (default), then return only `count`. """ @@ -1159,6 +1159,11 @@ def __len__(self): return 2 if self.return_contour else 1 def plot(self, *args, **kwargs): + """Plot a list of Nyquist responses. + + See `nyquist_plot` for details. + + """ return nyquist_plot(self, *args, **kwargs) @@ -1249,7 +1254,7 @@ def nyquist_response( Notes ----- - If a discrete time model is given, the frequency response is computed + If a discrete-time model is given, the frequency response is computed along the upper branch of the unit circle, using the mapping ``z = exp(1j * omega * dt)`` where `omega` ranges from 0 to pi/`dt` and `dt` is the discrete timebase. If timebase not specified @@ -1556,7 +1561,7 @@ def nyquist_plot( Parameters ---------- - data : list of LTI or NyquistResponseData + data : list of `LTI` or `NyquistResponseData` List of linear input/output systems (single system is OK) or Nyquist ersponses (computed using `nyquist_response`). Nyquist curves for each system are plotted on the same graph. @@ -1639,7 +1644,7 @@ def nyquist_plot( If present, replace automatically generated label(s) with the given label(s). If sysdata is a list, strings should be specified for each system. - label_freq : int, optiona + label_freq : int, optional Label every nth frequency on the plot. If not specified, no labels are generated. legend_loc : int or str, optional @@ -1715,7 +1720,7 @@ def nyquist_plot( Notes ----- - If a discrete time model is given, the frequency response is computed + If a discrete-time model is given, the frequency response is computed along the upper branch of the unit circle, using the mapping ``z = exp(1j * omega * dt)`` where `omega` ranges from 0 to pi/`dt` and `dt` is the discrete timebase. If timebase not specified @@ -2294,7 +2299,7 @@ def singular_values_response( Returns ------- - response : FrequencyResponseData + response : `FrequencyResponseData` Frequency response with the number of outputs equal to the number of singular values in the response, and a single input. @@ -2450,7 +2455,7 @@ def singular_values_plot( Notes ----- - If `plot` = `False`, the following legacy values are returned: + If `plot` = False, the following legacy values are returned: * `mag` : ndarray (or list of ndarray if len(data) > 1)) Magnitude of the response (deprecated). * `phase` : ndarray (or list of ndarray if len(data) > 1)) @@ -2629,7 +2634,7 @@ def _determine_omega_vector(syslist, omega_in, omega_limits, omega_num, _default_frequency_range and tailored for the list of systems syslist, and omega_range_given is set to False. - If omega_in is None but omega_limits is an array-like of 2 elements, then + If omega_in is None but omega_limits is an array_like of 2 elements, then omega_out is computed with the function np.logspace on omega_num points within the interval [min, max] = [omega_limits[0], omega_limits[1]], and omega_range_given is set to True. diff --git a/control/grid.py b/control/grid.py index 2c6b31389..8d26148c0 100644 --- a/control/grid.py +++ b/control/grid.py @@ -150,7 +150,7 @@ def nogrid(dt=None, ax=None, scaling=None): if ax is None: ax = fig.gca() - # Draw the unit circle for discrete time systems + # Draw the unit circle for discrete-time systems if isdtime(dt=dt, strict=True): s = np.linspace(0, 2*pi, 100) ax.plot(np.cos(s), np.sin(s), 'k--', lw=0.5, dashes=(5, 5)) @@ -158,7 +158,7 @@ def nogrid(dt=None, ax=None, scaling=None): _final_setup(ax, scaling=scaling) return ax, fig -# Grid for discrete time system (drawn, not rendered by AxisArtist) +# Grid for discrete-time system (drawn, not rendered by AxisArtist) # TODO (at some point): think about using customized grid generator? def zgrid(zetas=None, wns=None, ax=None, scaling=None): """Draws discrete damping and frequency grid""" diff --git a/control/iosys.py b/control/iosys.py index 7336c1604..b985817a9 100644 --- a/control/iosys.py +++ b/control/iosys.py @@ -41,7 +41,12 @@ # Named signal class class NamedSignal(np.ndarray): - """Named signal class.""" + """Named signal with label-based access. + + This class modifies the `numpy.ndarray` class and allows signals to + be accessed using the signal name in addition to indices and slices. + + """ def __new__(cls, input_array, signal_labels=None, trace_labels=None): # See https://numpy.org/doc/stable/user/basics.subclassing.html obj = np.asarray(input_array).view(cls) # Cast to our class type @@ -104,14 +109,14 @@ def __getitem__(self, key): class InputOutputSystem(): - """A class for representing input/output systems. + """Base class for input/output systems. The `InputOutputSystem` class allows (possibly nonlinear) input/output systems to be represented in Python. It is used as a parent class for a set of subclasses that are used to implement specific structures and operations for different types of input/output dynamical systems. - The timebase for the system, dt, is used to specify whether the system + The timebase for the system, `dt`, is used to specify whether the system is operating in continuous or discrete time. It can have the following values: @@ -132,10 +137,10 @@ class InputOutputSystem(): based on other information provided to functions using the system. outputs : int, list of str, or None Description of the system outputs. Same format as `inputs`, with - the prefix given by output_prefix (defaults to 'y'). + the prefix given by `output_prefix` (defaults to 'y'). states : int, list of str, or None Description of the system states. Same format as `inputs`, with - the prefix given by state_prefix (defaults to 'x'). + the prefix given by `state_prefix` (defaults to 'x'). dt : None, True or float, optional System timebase. 0 (default) indicates continuous time, True indicates discrete time with unspecified sampling time, positive @@ -143,7 +148,7 @@ class InputOutputSystem(): unspecified timebase (either continuous or discrete time). name : string, optional System name (used for specifying signals). If unspecified, a generic - name is generated with a unique integer id. + name 'sys[id]' is generated with a unique integer id. params : dict, optional Parameter values for the system. Passed to the evaluation functions for the system as default values, overriding internal defaults. @@ -309,7 +314,7 @@ def repr_format(self): * 'eval' : system specific, loadable representation * 'latex' : HTML/LaTeX representation of the object - The default representation for an input/output is set to 'info'. + The default representation for an input/output is set to 'eval'. This value can be changed for an individual system by setting the `repr_format` parameter when the system is created or by setting the `repr_format` property after system creation. Set @@ -422,7 +427,7 @@ def _copy_names(self, sys, prefix="", suffix="", prefix_suffix_name=None): self.state_index = sys.state_index.copy() def copy(self, name=None, use_prefix_suffix=True): - """Make a copy of an input/output system + """Make a copy of an input/output system. A copy of the system is made, with a new name. The `name` keyword can be used to specify a specific name for the system. If no name @@ -432,6 +437,21 @@ def copy(self, name=None, use_prefix_suffix=True): Otherwise, a generic system name of the form 'sys[]' is used, where '' is based on an internal counter. + Parameters + ---------- + name : str, optional + Name of the newly created system. + + use_prefix_suffix : bool, optional + If True and `name` is None, set the name of the new system + to the name of the original system with prefix + `config.defaults['duplicate_system_name_prefix']` and + suffix `config.defaults['duplicate_system_name_suffix']`. + + Returns + ------- + `InputOutputSystem` + """ # Create a copy of the system newsys = deepcopy(self) @@ -467,11 +487,39 @@ def set_inputs(self, inputs, prefix='u'): _process_signal_list(inputs, prefix=prefix) def find_input(self, name): - """Find the index for an input given its name (None if not found)""" + """Find the index for an input given its name (None if not found). + + Parameters + ---------- + name : str + Signal name for the desired input. + + Returns + ------- + int + Index of the named input. + + """ return self.input_index.get(name, None) def find_inputs(self, name_list): - """Return list of indices matching input spec (None if not found)""" + """Return list of indices matching input spec (None if not found). + + Parameters + ---------- + name_list : str or list of str + List of signal specifications for the desired inputs. A + signal can be described by its name or by a slice-like + description of the form 'start:end` where 'start' and + 'end' are signal names. If either is omitted, it is taken + as the first or last signal, respectively. + + Returns + ------- + list of int + List of indices for the specified inputs. + + """ return self._find_signals(name_list, self.input_index) # Property for getting and setting list of input signals @@ -488,7 +536,7 @@ def set_outputs(self, outputs, prefix='y'): Description of the system outputs. This can be given as an integer count or as a list of strings that name the individual signals. If an integer count is specified, the names of the - signal will be of the form 'u[i]' (where the prefix 'u' can be + signal will be of the form 'y[i]' (where the prefix 'y' can be changed using the optional prefix parameter). prefix : string, optional If `outputs` is an integer, create the names of the states using @@ -500,11 +548,39 @@ def set_outputs(self, outputs, prefix='y'): _process_signal_list(outputs, prefix=prefix) def find_output(self, name): - """Find the index for an output given its name (None if not found)""" + """Find the index for a output given its name (None if not found). + + Parameters + ---------- + name : str + Signal name for the desired output. + + Returns + ------- + int + Index of the named output. + + """ return self.output_index.get(name, None) def find_outputs(self, name_list): - """Return list of indices matching output spec (None if not found)""" + """Return list of indices matching output spec (None if not found). + + Parameters + ---------- + name_list : str or list of str + List of signal specifications for the desired outputs. A + signal can be described by its name or by a slice-like + description of the form 'start:end` where 'start' and + 'end' are signal names. If either is omitted, it is taken + as the first or last signal, respectively. + + Returns + ------- + list of int + List of indices for the specified outputs. + + """ return self._find_signals(name_list, self.output_index) # Property for getting and setting list of output signals @@ -521,7 +597,7 @@ def set_states(self, states, prefix='x'): Description of the system states. This can be given as an integer count or as a list of strings that name the individual signals. If an integer count is specified, the names of the signal will be - of the form 'u[i]' (where the prefix 'u' can be changed using the + of the form 'x[i]' (where the prefix 'x' can be changed using the optional prefix parameter). prefix : string, optional If `states` is an integer, create the names of the states using @@ -533,11 +609,39 @@ def set_states(self, states, prefix='x'): _process_signal_list(states, prefix=prefix, allow_dot=True) def find_state(self, name): - """Find the index for a state given its name (None if not found)""" + """Find the index for a state given its name (None if not found). + + Parameters + ---------- + name : str + Signal name for the desired state. + + Returns + ------- + int + Index of the named state. + + """ return self.state_index.get(name, None) def find_states(self, name_list): - """Return list of indices matching state spec (None if not found)""" + """Return list of indices matching state spec (None if not found). + + Parameters + ---------- + name_list : str or list of str + List of signal specifications for the desired states. A + signal can be described by its name or by a slice-like + description of the form 'start:end` where 'start' and + 'end' are signal names. If either is omitted, it is taken + as the first or last signal, respectively. + + Returns + ------- + list of int + List of indices for the specified states.. + + """ return self._find_signals(name_list, self.state_index) # Property for getting and setting list of state signals @@ -607,11 +711,10 @@ def isctime(self, strict=False): Parameters ---------- - sys : Named I/O system - System to be checked. strict : bool, optional If strict is True, make sure that timebase is not None. Default is False. + """ # If no timebase is given, answer depends on strict flag if self.dt is None: @@ -676,7 +779,7 @@ def timebase(sys, strict=True): Parameters ---------- - sys : InputOutputSystem or float + sys : `InputOutputSystem` or float System whose timebase is to be determined. strict : bool, optional Whether to implement strict checking. If set to True (default), @@ -708,9 +811,9 @@ def common_timebase(dt1, dt2): Parameters ---------- - dt1, dt2 : InputOutputSystem or float - Number or system with a 'dt' attribute (e.g. TransferFunction - or StateSpace system). + dt1, dt2 : `InputOutputSystem` or float + Number or system with a 'dt' attribute (e.g. `TransferFunction` + or `StateSpace` system). Returns ------- @@ -721,7 +824,8 @@ def common_timebase(dt1, dt2): Raises ------ ValueError - when no compatible time base can be found + When no compatible time base can be found. + """ # explanation: # if either dt is None, they are compatible with anything @@ -752,10 +856,10 @@ def common_timebase(dt1, dt2): else: raise ValueError("Systems have incompatible timebases") -# Check to see if a system is a discrete time system +# Check to see if a system is a discrete-time system def isdtime(sys=None, strict=False, dt=None): """ - Check to see if a system is a discrete time system. + Check to see if a system is a discrete-time system. Parameters ---------- @@ -784,7 +888,7 @@ def isdtime(sys=None, strict=False, dt=None): return sys.isdtime(strict) -# Check to see if a system is a continuous time system +# Check to see if a system is a continuous-time system def isctime(sys=None, dt=None, strict=False): """ Check to see if a system is a continuous-time system. @@ -821,12 +925,12 @@ def iosys_repr(sys, format=None): Parameters ---------- - sys : InputOutputSystem + sys : `InputOutputSystem` System for which the representation is generated. format : str Format to use in creating the representation: - * 'info' : [outputs] + * 'info' : [outputs]> * 'eval' : system specific, loadable representation * 'latex' : HTML/LaTeX representation of the object @@ -865,7 +969,7 @@ def _process_iosys_keywords( This function processes the standard keywords used in initializing an I/O system. It first looks in the `keywords` dictionary to see if a value is specified. If not, the `defaults` dictionary is used. The - `defaults` dictionary can also be set to an InputOutputSystem object, + `defaults` dictionary can also be set to an `InputOutputSystem` object, which is useful for copy constructors that change system/signal names. If `end` is True, then generate an error if there are any remaining diff --git a/control/lti.py b/control/lti.py index e87c1f01a..df9f69ede 100644 --- a/control/lti.py +++ b/control/lti.py @@ -24,11 +24,12 @@ class LTI(InputOutputSystem): - """Parent class for linear time-invariant (LTI) system objects. + """Parent class for linear time-invariant system objects. - LTI is the parent to the StateSpace and TransferFunction child classes. It - contains the number of inputs and outputs, and the timebase (dt) for the - system. This function is not generally called directly by the user. + LTI is the parent to the `StateSpace` and `TransferFunction` child + classes. It contains the number of inputs and outputs, and the timebase + (dt) for the system. This class is not generally accessed directly by + the user. See Also -------- @@ -63,7 +64,18 @@ def damp(self): return wn, zeta, poles def feedback(self, other=1, sign=-1): - """Feedback interconnection between two LTI objects.""" + """Feedback interconnection between two input/output systems.""" + + Parameters + ---------- + other : `InputOutputSystem` + System in the feedack path. + + sign : float, optional + Gain to use in feedback path. Defaults to -1. + + """ + # Implemented in subclasses, but documented here raise NotImplementedError( "feedback not implemented for base " f"{self.__class__.__name__} objects") @@ -71,11 +83,12 @@ def feedback(self, other=1, sign=-1): def frequency_response(self, omega=None, squeeze=None): """Evaluate LTI system response at an array of frequencies. - For continuous time systems, computes the frequency response as + For continuous-time systems with transfer function G, computes + the frequency response as G(j*omega) = mag * exp(j*phase) - For discrete time systems, the response is evaluated around the + For discrete-time systems, the response is evaluated around the unit circle such that G(exp(j*omega*dt)) = mag * exp(j*phase). @@ -174,9 +187,10 @@ def bandwidth(self, dbdrop=-3): Raises ------ TypeError - if 'sys' is not an SISO LTI instance + If `sys` is not an SISO LTI instance. ValueError - if 'dbdrop' is not a negative scalar + If `dbdrop` is not a negative scalar. + """ # check if system is SISO and dbdrop is a negative scalar if not self.issiso(): @@ -215,6 +229,11 @@ def bandwidth(self, dbdrop=-3): raise Exception(result.message) def ispassive(self): + r"""Indicate if a linear time invariant (LTI) system is passive. + + See `ispassive` for details. + + """ # importing here prevents circular dependancy from control.passivity import ispassive return ispassive(self) @@ -251,7 +270,7 @@ def poles(sys): Parameters ---------- - sys : StateSpace or TransferFunction + sys : `StateSpace` or `TransferFunction` Linear system. Returns @@ -274,7 +293,7 @@ def zeros(sys): Parameters ---------- - sys : StateSpace or TransferFunction + sys : `StateSpace` or `TransferFunction` Linear system. Returns @@ -296,7 +315,7 @@ def damp(sys, doprint=True): Parameters ---------- - sys : LTI (StateSpace or TransferFunction) + sys : `StateSpace` or `TransferFunction` A linear system object. doprint : bool (optional) If True, print table with values. @@ -367,7 +386,7 @@ def evalfr(sys, x, squeeze=None): Parameters ---------- - sys : StateSpace or TransferFunction + sys : `StateSpace` or `TransferFunction` Linear system. x : complex scalar or 1D array_like Complex frequency(s). @@ -533,7 +552,7 @@ def frequency_response( else: omega_sys = omega_syslist.copy() # use common omega vector - # Add the Nyquist frequency for discrete time systems + # Add the Nyquist frequency for discrete-time systems if sys_.isdtime(strict=True): nyquistfrq = math.pi / sys_.dt if not omega_range_given: @@ -592,7 +611,7 @@ def bandwidth(sys, dbdrop=-3): Parameters ---------- - sys : StateSpace or TransferFunction + sys : `StateSpace` or `TransferFunction` Linear system for which the bandwidth should be computed. dbdrop : float, optional By how much the gain drop in dB (default = -3) that defines the @@ -608,9 +627,9 @@ def bandwidth(sys, dbdrop=-3): Raises ------ TypeError - if 'sys' is not an SISO LTI instance + If `sys` is not an SISO LTI instance. ValueError - if 'dbdrop' is not a negative scalar + If `dbdrop` is not a negative scalar. Examples -------- diff --git a/control/margins.py b/control/margins.py index 1817c7d70..d4e735b59 100644 --- a/control/margins.py +++ b/control/margins.py @@ -476,7 +476,7 @@ def margin(*args): Parameters ---------- - sys : StateSpace or TransferFunction + sys : `StateSpace` or `TransferFunction` Linear SISO system representing the loop transfer function. mag, phase, omega : sequence of array_like Input magnitude, phase (in deg.), and frequencies (rad/sec) from diff --git a/control/matlab/timeresp.py b/control/matlab/timeresp.py index f3ad29a1e..1f26ca504 100644 --- a/control/matlab/timeresp.py +++ b/control/matlab/timeresp.py @@ -20,9 +20,9 @@ def step(sys, T=None, input=0, output=None, return_x=False): Parameters ---------- - sys : StateSpace, or TransferFunction + sys : `StateSpace` or `TransferFunction` LTI system to simulate. - T : array-like or number, optional + T : array_like or number, optional Time vector, or simulation time duration if a number (time vector is autocomputed if not given). input : int @@ -68,9 +68,9 @@ def stepinfo(sysdata, T=None, yfinal=None, SettlingTimeThreshold=0.02, Parameters ---------- - sysdata : StateSpace or TransferFunction or array_like - The system data. Either LTI system to similate (StateSpace, - TransferFunction), or a time series of step response data. + sysdata : `StateSpace` or `TransferFunction` or array_like + The system data. Either LTI system to similate (`StateSpace`, + `TransferFunction`), or a time series of step response data. T : array_like or float, optional Time vector, or simulation time duration if a number (time vector is autocomputed if not given). @@ -146,9 +146,9 @@ def impulse(sys, T=None, input=0, output=None, return_x=False): Parameters ---------- - sys : StateSpace, TransferFunction + sys : `StateSpace` or `TransferFunction` LTI system to simulate. - T : array-like or number, optional + T : array_like or number, optional Time vector, or simulation time duration if a number (time vector is autocomputed if not given). input : int @@ -195,12 +195,12 @@ def initial(sys, T=None, X0=0., input=None, output=None, return_x=False): Parameters ---------- - sys : StateSpace, or TransferFunction + sys : `StateSpace` or `TransferFunction` LTI system to simulate. - T : array-like or number, optional + T : array_like or number, optional Time vector, or simulation time duration if a number (time vector is autocomputed if not given). - X0 : array-like object or number, optional + X0 : array_like object or number, optional Initial condition (default = 0). input : int This input is ignored, but present for compatibility with step @@ -248,15 +248,15 @@ def lsim(sys, U=0., T=None, X0=0.): Parameters ---------- - sys : LTI (StateSpace, or TransferFunction) + sys : `StateSpace` or `TransferFunction` LTI system to simulate. - U : array-like or number, optional + U : array_like or number, optional Input array giving input at each time `T` (default = 0). If `U` is None or 0, a special algorithm is used. This special algorithm is faster than the general algorithm, which is used otherwise. - T : array-like, optional for discrete LTI `sys` + T : array_like, optional for discrete LTI `sys` Time steps at which the input is defined; values must be evenly spaced. - X0 : array-like or number, optional + X0 : array_like or number, optional Initial condition (default = 0). Returns diff --git a/control/matlab/wrappers.py b/control/matlab/wrappers.py index a78e720d6..f11e693e5 100644 --- a/control/matlab/wrappers.py +++ b/control/matlab/wrappers.py @@ -226,11 +226,9 @@ def rlocus(*args, **kwargs): gains : array_like, optional Gains to use in computing plot of closed-loop poles. xlim : tuple or list, optional - Set limits of x axis, normally with tuple - (see `matplotlib:api/axes_api`). + Set limits of x axis (see `matplotlib.axes.Axes.set_xlim`). ylim : tuple or list, optional - Set limits of y axis, normally with tuple - (see `matplotlib:api/axes_api`). + Set limits of y axis (see `matplotlib.axes.Axes.set_ylim`). plot : bool If False, do not generate a plot. @@ -273,7 +271,7 @@ def pzmap(*args, **kwargs): Parameters ---------- - sys : LTI (StateSpace or TransferFunction) + sys : `StateSpace` or `TransferFunction` Linear system for which poles and zeros are computed. plot : bool, optional If True a graph is generated with Matplotlib, @@ -336,13 +334,13 @@ def dcgain(*args): Parameters ---------- - A, B, C, D : array-like + A, B, C, D : array_like A linear system in state space form. - Z, P, k : array-like, array-like, number + Z, P, k : array_like, array_like, number A linear system in zero, pole, gain form. - num, den : array-like + num, den : array_like A linear system in transfer function form. - sys : LTI (StateSpace or TransferFunction) + sys : `StateSpace` or `TransferFunction` A linear system object. Returns diff --git a/control/modelsimp.py b/control/modelsimp.py index c3f34dd9b..8c08defdd 100644 --- a/control/modelsimp.py +++ b/control/modelsimp.py @@ -36,7 +36,7 @@ def hankel_singular_values(sys): Parameters ---------- - sys : StateSpace + sys : `StateSpace` State space system. Returns @@ -64,7 +64,7 @@ def hankel_singular_values(sys): np.float64(0.25) """ - # TODO: implement for discrete time systems + # TODO: implement for discrete-time systems if (isdtime(sys, strict=True)): raise NotImplementedError("Function not implemented in discrete time") @@ -99,7 +99,7 @@ def model_reduction( Parameters ---------- - sys : StateSpace + sys : `StateSpace` Original system to reduce. elim_inputs, elim_outputs, elim_states : array of int or str, optional Vector of inputs, outputs, or states to eliminate. Can be specified @@ -114,7 +114,7 @@ def model_reduction( Returns ------- - rsys : StateSpace + rsys : `StateSpace` Reduced order model. Raises @@ -122,7 +122,7 @@ def model_reduction( ValueError If `method` is not either 'matchdc' or 'truncate'. NotImplementedError - If the 'matchdc' method is used for a discrete time system. + If the 'matchdc' method is used for a discrete-time system. Warns ----- @@ -214,7 +214,7 @@ def _expand_key(key): if method == 'matchdc' and A22.size > 0: if sys.isdtime(strict=True): raise NotImplementedError( - "'matchdc' not (yet) supported for discrete time systems") + "'matchdc' not (yet) supported for discrete-time systems") # if matchdc, residualize # Check if the matrix A22 is invertible @@ -268,7 +268,7 @@ def balanced_reduction(sys, orders, method='truncate', alpha=None): Parameters ---------- - sys : StateSpace + sys : `StateSpace` Original system to reduce. orders : integer or array of integer Desired order of reduced order model (if a vector, returns a vector @@ -285,7 +285,7 @@ def balanced_reduction(sys, orders, method='truncate', alpha=None): Returns ------- - rsys : StateSpace + rsys : `StateSpace` A reduced order model or a list of reduced order models if orders is a list. @@ -294,10 +294,9 @@ def balanced_reduction(sys, orders, method='truncate', alpha=None): ValueError If `method` is not 'truncate' or 'matchdc'. ImportError - if slycot routine ab09ad, ab09md, or ab09nd is not found. - + If slycot routine ab09ad, ab09md, or ab09nd is not found. ValueError - if there are more unstable modes than any value in orders + If there are more unstable modes than any value in orders. Examples -------- @@ -389,7 +388,7 @@ def minimal_realization(sys, tol=None, verbose=True): Parameters ---------- - sys : StateSpace or TransferFunction + sys : `StateSpace` or `TransferFunction` Original system. tol : real Tolerance. @@ -398,7 +397,7 @@ def minimal_realization(sys, tol=None, verbose=True): Returns ------- - rsys : StateSpace or TransferFunction + rsys : `StateSpace` or `TransferFunction` Cleaned model. """ @@ -429,7 +428,7 @@ def eigensys_realization(arg, r, m=None, n=None, dt=True, transpose=False): Calculate ERA model based on impulse-response data. - This function computes a discrete time system + This function computes a discrete-time system .. math:: @@ -449,10 +448,10 @@ def eigensys_realization(arg, r, m=None, n=None, dt=True, transpose=False): Parameters ---------- YY : array_like - Impulse response from which the StateSpace model is estimated, 1D + Impulse response from which the `StateSpace` model is estimated, 1D or 3D array. - data : TimeResponseData - Impulse response from which the StateSpace model is estimated. + data : `TimeResponseData` + Impulse response from which the `StateSpace` model is estimated. r : integer Order of model. m : integer, optional @@ -462,19 +461,19 @@ def eigensys_realization(arg, r, m=None, n=None, dt=True, transpose=False): dt : True or float, optional True indicates discrete time with unspecified sampling time and a positive float is discrete time with the specified sampling time. - It can be used to scale the StateSpace model in order to match the + It can be used to scale the `StateSpace` model in order to match the unit-area impulse response of python-control. Default is True. transpose : bool, optional Assume that input data is transposed relative to the standard - :ref:`time-series-convention`. For TimeResponseData this parameter + :ref:`time-series-convention`. For `TimeResponseData` this parameter is ignored. Default is False. Returns ------- - sys : StateSpace - A reduced order model sys=StateSpace(Ar,Br,Cr,Dr,dt). + sys : `StateSpace` + State space model of the specified order. S : array - Singular values of Hankel matrix. Can be used to choose a good r + Singular values of Hankel matrix. Can be used to choose a good `r` value. References @@ -539,7 +538,7 @@ def markov(*args, m=None, transpose=False, dt=None, truncate=False): Calculate Markov parameters [D CB CAB ...] from data. This function computes the the first `m` Markov parameters [D CB CAB - ...] for a discrete time system. + ...] for a discrete-time system. .. math:: @@ -570,7 +569,7 @@ def markov(*args, m=None, transpose=False, dt=None, truncate=False): taken as time points. U : array_like Input data, arranged in the same way as `Y`. - data : TimeResponseData + data : `TimeResponseData` Response data from which the Markov parameters where estimated. Input and output data must be 1D or 2D array. m : int, optional @@ -581,12 +580,12 @@ def markov(*args, m=None, transpose=False, dt=None, truncate=False): It can be used to scale the Markov parameters in order to match the unit-area impulse response of python-control. Default is True for array_like and dt=data.time[1]-data.time[0] for - TimeResponseData as input. + `TimeResponseData` as input. truncate : bool, optional Do not use first m equation for least squares. Default is False. transpose : bool, optional Assume that input data is transposed relative to the standard - :ref:`time-series-convention`. For TimeResponseData this parameter + :ref:`time-series-convention`. For `TimeResponseData` this parameter is ignored. Default is False. Returns diff --git a/control/nichols.py b/control/nichols.py index 270bf462a..ca17f4768 100644 --- a/control/nichols.py +++ b/control/nichols.py @@ -184,10 +184,10 @@ def nichols_grid(cl_mags=None, cl_phases=None, line_style='dotted', ax=None, Parameters ---------- - cl_mags : array-like (dB), optional + cl_mags : array_like (dB), optional Array of closed-loop magnitudes defining the iso-gain lines on a custom Nichols chart. - cl_phases : array-like (degrees), optional + cl_phases : array_like (degrees), optional Array of closed-loop phases defining the iso-phase lines on a custom Nichols chart. Must be in the range -360 < cl_phases < 0 line_style : string, optional @@ -343,9 +343,9 @@ def closed_loop_contours(Gcl_mags, Gcl_phases): Parameters ---------- - Gcl_mags : array-like + Gcl_mags : array_like Array of magnitudes of the contours - Gcl_phases : array-like + Gcl_phases : array_like Array of phases in radians of the contours Returns @@ -369,7 +369,7 @@ def m_circles(mags, phase_min=-359.75, phase_max=-0.25): Parameters ---------- - mags : array-like + mags : array_like Array of magnitudes in dB of the M-circles phase_min : degrees Minimum phase in degrees of the N-circles @@ -395,7 +395,7 @@ def n_circles(phases, mag_min=-40.0, mag_max=12.0): Parameters ---------- - phases : array-like + phases : array_like Array of phases in degrees of the N-circles mag_min : dB Minimum magnitude in dB of the N-circles diff --git a/control/nlsys.py b/control/nlsys.py index 3c184597a..93e3b43ac 100644 --- a/control/nlsys.py +++ b/control/nlsys.py @@ -4,13 +4,13 @@ # Additional features to add: # * Allow constant inputs for MIMO input_output_response (w/out ones) # * Add unit tests (and example?) for time-varying systems -# * Allow time vector for discrete time simulations to be multiples of dt -# * Check the way initial outputs for discrete time systems are handled +# * Allow time vector for discrete-time simulations to be multiples of dt +# * Check the way initial outputs for discrete-time systems are handled """This module contains the `NonlinearIOSystem` class that represents (possibly nonlinear) input/output systems. The `NonlinearIOSystem` class is a general class that defines any -continuous or discrete time dynamical system. Input/output systems +continuous- or discrete-time dynamical system. Input/output systems can be simulated and also used to compute operating points and linearizations. @@ -34,11 +34,11 @@ class NonlinearIOSystem(InputOutputSystem): - """Nonlinear I/O system. + """Nonlinear input/output system model. Creates an `InputOutputSystem` for a nonlinear system by specifying a state update function and an output function. The new - system can be a continuous or discrete time system. Nonlinear I/O + system can be a continuous or discrete-time system. Nonlinear I/O systems are usually created with the `nlsys` factory function. @@ -49,10 +49,10 @@ class NonlinearIOSystem(InputOutputSystem): ``updfcn(t, x, u, params) -> array`` - where `x` is a 1-D array with shape (nstates,), `u` is a 1-D array - with shape (ninputs,), `t` is a float representing the currrent - time, and `params` is a dict containing the values of parameters - used by the function. + where `t` is a float representing the currrent time, `x` is a 1-D + array with shape (nstates,), `u` is a 1-D array with shape + (ninputs,), and `params` is a dict containing the values of + parameters used by the function. outfcn : callable Function returning the output at the given state @@ -74,8 +74,8 @@ class NonlinearIOSystem(InputOutputSystem): operating in continuous or discrete time. It can have the following values: - * `dt` = 0: continuous time system (default) - * `dt` > 0: discrete time system with sampling period 'dt' + * `dt` = 0: continuous-time system (default) + * `dt` > 0: discrete-time system with sampling period 'dt' * `dt` = True: discrete time with unspecified sampling period * `dt` = None: no timebase specified @@ -355,7 +355,7 @@ def __truediv__(self, other): else: return NotImplemented - def _update_params(self, params, warning=False): + def _update_params(self, params): # Update the current parameter values self._current_params = self.params.copy() if params: @@ -376,15 +376,15 @@ def dynamics(self, t, x, u, params=None): """Dynamics of a differential or difference equation. Given time `t`, input `u` and state `x`, returns the value of the - right hand side of the dynamical system. If the system is continuous, - returns the time derivative:: + right hand side of the dynamical system. If the system is a + continuous-time system, returns the time derivative:: - dx/dt = f(t, x, u[, params]) + dx/dt = updfcn(t, x, u[, params]) - where `f` is the system's (possibly nonlinear) dynamics function. - If the system is discrete-time, returns the next value of `x`:: + where `updfcn` is the system's (possibly nonlinear) update function. + If the system is discrete time, returns the next value of `x`:: - x[t+dt] = f(t, x[t], u[t][, params]) + x[t+dt] = updfcn(t, x[t], u[t][, params]) where `t` is a scalar. @@ -405,6 +405,7 @@ def dynamics(self, t, x, u, params=None): Returns ------- dx/dt or x[t+dt] : ndarray + """ self._update_params(params) return self._rhs( @@ -435,9 +436,9 @@ def output(self, t, x, u, params=None): """Compute the output of the system. Given time `t`, input `u` and state `x`, returns the output of the - system: + system:: - y = g(t, x, u[, params]) + y = outfcn(t, x, u[, params]) The inputs `x` and `u` must be of the correct length. @@ -461,7 +462,26 @@ def output(self, t, x, u, params=None): t, np.asarray(x).reshape(-1), np.asarray(u).reshape(-1)) def feedback(self, other=1, sign=-1, params=None): - """Feedback interconnection between two input/output systems.""" + """Feedback interconnection between two I/O systems. + + Parameters + ---------- + other : `InputOutputSystem` + System in the feedack path. + + sign : float, optional + Gain to use in feedback path. Defaults to -1. + + params : dict, optional + Parameter values for the overall system. Passed to the + evaluation functions for the system as default values, + overriding defaults for the individual systems. + + Returns + ------- + `NonlinearIOSystem` + + """ # Convert sys2 to an I/O system if needed other = _convert_static_iosystem(other) @@ -497,7 +517,7 @@ def linearize(self, x0, u0=None, t=0, params=None, eps=1e-6, """Linearize an input/output system at a given state and input. Return the linearization of an input/output system at a given - operating point (or state and input value) as a StateSpace system. + operating point (or state and input value) as a `StateSpace` system. See `linearize` for complete documentation. """ @@ -868,13 +888,13 @@ def cxn_string(signal, gain, first): return out - def _update_params(self, params, warning=False): + def _update_params(self, params): for sys in self.syslist: local = sys.params.copy() # start with system parameters local.update(self.params) # update with global params if params: local.update(params) # update with locally passed parameters - sys._update_params(local, warning=warning) + sys._update_params(local) def _rhs(self, t, x, u): # Make sure state and input are vectors @@ -1205,10 +1225,9 @@ def _find_outputs_by_basename(self, basename): for sig, isig in sys.output_index.items() if sig == (basename)} - # TODO: change `warning` to `print_warning` or `warn_unsed`? # TODO: change to internal function? (not sure users need to see this) def check_unused_signals( - self, ignore_inputs=None, ignore_outputs=None, warning=True): + self, ignore_inputs=None, ignore_outputs=None, print_warning=True): """Check for unused subsystem inputs and outputs. Check to see if there are any unused signals and return a list of @@ -1231,7 +1250,7 @@ def check_unused_signals( If the 'sig' form is used, all subsystem outputs with that name are considered ignored. - warning : bool, optional + print_warning : bool, optional If True, print a warning listing any unused signals. Returns @@ -1290,25 +1309,25 @@ def check_unused_signals( used_ignored_inputs = set(ignore_input_map) - set(unused_inputs) used_ignored_outputs = set(ignore_output_map) - set(unused_outputs) - if warning and dropped_inputs: + if print_warning and dropped_inputs: msg = ('Unused input(s) in InterconnectedSystem: ' + '; '.join(f'{inp}={unused_inputs[inp]}' for inp in dropped_inputs)) warn(msg) - if warning and dropped_outputs: + if print_warning and dropped_outputs: msg = ('Unused output(s) in InterconnectedSystem: ' + '; '.join(f'{out} : {unused_outputs[out]}' for out in dropped_outputs)) warn(msg) - if warning and used_ignored_inputs: + if print_warning and used_ignored_inputs: msg = ('Input(s) specified as ignored is (are) used: ' + '; '.join(f'{inp} : {ignore_input_map[inp]}' for inp in used_ignored_inputs)) warn(msg) - if warning and used_ignored_outputs: + if print_warning and used_ignored_outputs: msg = ('Output(s) specified as ignored is (are) used: ' + '; '.join(f'{out}={ignore_output_map[out]}' for out in used_ignored_outputs)) @@ -1322,11 +1341,11 @@ def nlsys(updfcn, outfcn=None, **kwargs): Creates an `InputOutputSystem` for a nonlinear system by specifying a state update function and an output function. The new system can be a - continuous or discrete time system. + continuous or discrete-time system. Parameters ---------- - updfcn : callable (or StateSpace) + updfcn : callable (or `StateSpace`) Function returning the state update function ``updfcn(t, x, u, params) -> array`` @@ -1367,14 +1386,14 @@ def nlsys(updfcn, outfcn=None, **kwargs): operating in continuous or discrete time. It can have the following values: - * `dt` = 0: continuous time system (default) - * `dt` > 0: discrete time system with sampling period 'dt' + * `dt` = 0: continuous-time system (default) + * `dt` > 0: discrete-time system with sampling period 'dt' * `dt` = True: discrete time with unspecified sampling period * `dt` = None: no timebase specified name : string, optional System name (used for specifying signals). If unspecified, a - generic name is generated with a unique integer id. + generic name 'sys[id]' is generated with a unique integer id. params : dict, optional Parameter values for the system. Passed to the evaluation functions @@ -1456,15 +1475,15 @@ def input_output_response( Parameters ---------- - sys : NonlinearIOSystem or list of NonlinearIOSystem + sys : `NonlinearIOSystem` or list of `NonlinearIOSystem` I/O system(s) for which input/output response is simulated. - T : array-like + T : array_like Time steps at which the input is defined; values must be evenly spaced. - U : array-like, list, or number, optional + U : array_like, list, or number, optional Input array giving input at each time `T` (default = 0). If a list is specified, each element in the list will be treated as a portion of the input and broadcast (if necessary) to match the time vector. - X0 : array-like, list, or number, optional + X0 : array_like, list, or number, optional Initial condition (default = 0). If a list is given, each element in the list will be flattened and stacked into the initial condition. If a smaller number of elements are given that the @@ -1488,7 +1507,7 @@ def input_output_response( Returns ------- - results : TimeResponseData + results : `TimeResponseData` Time response represented as a `TimeResponseData` object containing the following properties: @@ -1533,7 +1552,7 @@ def input_output_response( TypeError If the system is not an input/output system. ValueError - If time step does not match sampling time (for discrete time systems). + If time step does not match sampling time (for discrete-time systems). Notes ----- @@ -1804,9 +1823,9 @@ def ivp_rhs(t, x): class OperatingPoint(): - """Class for representing operating point of nonlinear I/O system. + """Operating point of nonlinear I/O system. - The `OperatingPoint` class stores the operating point of a nonlinear + The OperatingPoint class stores the operating point of a nonlinear system, consisting of the state and input vectors for the system. The main use for this class is as the return object for the `find_operating_point` function and as an input to the @@ -1895,7 +1914,7 @@ def find_operating_point( and uses x0 and u0 as an initial guess to find the equilibrium point. If no equilibrium point can be found, the function returns the operating point that minimizes the state update (state derivative for - continuous time systems, state difference for discrete time systems). + continuous-time systems, state difference for discrete-time systems). More complex operating points can be found by specifying which states, inputs, or outputs should be used in computing the operating point, as @@ -1904,7 +1923,7 @@ def find_operating_point( Parameters ---------- - sys : NonlinearIOSystem + sys : `NonlinearIOSystem` I/O system for which the operating point is sought. x0 : list of initial state values Initial guess for the value of the state near the operating point. @@ -1955,7 +1974,7 @@ def find_operating_point( Returns ------- - op_point : OperatingPoint + op_point : `OperatingPoint` The solution represented as an `OperatingPoint` object. The main attributes are `states` and `inputs`, which represent the state and input arrays at the operating point. See @@ -1967,9 +1986,9 @@ def find_operating_point( Notes ----- - For continuous time systems, equilibrium points are defined as points + For continuous-time systems, equilibrium points are defined as points for which the right hand side of the differential equation is zero: - :math:`f(t, x_e, u_e) = 0`. For discrete time systems, equilibrium + :math:`f(t, x_e, u_e) = 0`. For discrete-time systems, equilibrium points are defined as points for which the right hand side of the difference equation returns the current state: :math:`f(t, x_e, u_e) = x_e`. @@ -2197,7 +2216,7 @@ def linearize(sys, xeq, ueq=None, t=0, params=None, **kw): Parameters ---------- - sys : InputOutputSystem + sys : `InputOutputSystem` The system to be linearized. xeq : array or `OperatingPoint` The state or operating point at which the linearization will be @@ -2214,7 +2233,7 @@ def linearize(sys, xeq, ueq=None, t=0, params=None, **kw): for the system as default values, overriding internal defaults. name : string, optional Set the name of the linearized system. If not specified and - if `copy_names` is False, a generic name is generated + if `copy_names` is False, a generic name 'sys[id]' is generated with a unique integer id. If `copy_names` is True, the new system name is determined by adding the prefix and suffix strings in `config.defaults['iosys.linearized_system_name_prefix']` and @@ -2226,7 +2245,7 @@ def linearize(sys, xeq, ueq=None, t=0, params=None, **kw): Returns ------- - ss_sys : StateSpace + ss_sys : `StateSpace` The linearization of the system, as a `StateSpace` object. @@ -2284,7 +2303,7 @@ def interconnect( Parameters ---------- - syslist : list of NonlinearIOSystems + syslist : list of `NonlinearIOSystem` The list of (state-based) input/output systems to be connected. connections : list of connections, optional @@ -2388,17 +2407,17 @@ def interconnect( dt : timebase, optional The timebase for the system, used to specify whether the system is - operating in continuous or discrete time. It can have the following + operating in continuous or discrete-time. It can have the following values: - * `dt` = 0: continuous time system (default) - * `dt` > 0`: discrete time system with sampling period 'dt' + * `dt` = 0: continuous-time system (default) + * `dt` > 0`: discrete-time system with sampling period 'dt' * `dt` = True: discrete time with unspecified sampling period * `dt` = None: no timebase specified name : string, optional System name (used for specifying signals). If unspecified, a generic - name is generated with a unique integer id. + name 'sys[id]' is generated with a unique integer id. Returns ------- @@ -2819,7 +2838,7 @@ def _find_output_or_input_signal(spec): if add_unused: # Get all unused signals dropped_inputs, dropped_outputs = newsys.check_unused_signals( - ignore_inputs, ignore_outputs, warning=False) + ignore_inputs, ignore_outputs, print_warning=False) # Add on any unused signals that we aren't ignoring for isys, isig in dropped_inputs: diff --git a/control/optimal.py b/control/optimal.py index 68285dec5..4f6afabf9 100644 --- a/control/optimal.py +++ b/control/optimal.py @@ -59,7 +59,7 @@ class OptimalControlProblem(): Parameters ---------- - sys : InputOutputSystem + sys : `InputOutputSystem` I/O system for which the optimal input will be computed. timepts : 1D array_like List of times at which the optimal input should be computed. @@ -79,8 +79,8 @@ class OptimalControlProblem(): trajectory_method : string, optional Method to use for carrying out the optimization. Currently supported methods are 'shooting' and 'collocation' (continuous time only). The - default value is 'shooting' for discrete time systems and - 'collocation' for continuous time systems. + default value is 'shooting' for discrete-time systems and + 'collocation' for continuous-time systems. initial_guess : (tuple of) 1D or 2D array_like Initial states and/or inputs to use as a guess for the optimal trajectory. For shooting methods, an array of inputs for each time @@ -104,7 +104,7 @@ class OptimalControlProblem(): Other Parameters ---------------- - basis : BasisFamily, optional + basis : `BasisFamily`, optional Use the given set of basis functions for the inputs instead of setting the value of the input at each point in the timepts vector. terminal_constraints : list of constraints, optional @@ -138,12 +138,13 @@ class OptimalControlProblem(): and inputs at each point along the trajectory and then adding the value of a user-defined terminal cost at the final point in the trajectory. - The `_constraint_function` method evaluates the constraint functions along - the trajectory generated by the proposed input. As in the case of the - cost function, the constraints are evaluated at the state and input along - each point on the trajectory. This information is compared against the - constraint upper and lower bounds. The constraint function is processed - in the class initializer, so that it only needs to be computed once. + The `_constraint_function` method evaluates the constraint functions + along the trajectory generated by the proposed input. As in the case + of the cost function, the constraints are evaluated at the state and + input along each time point on the trajectory. This information is + compared against the constraint upper and lower bounds. The constraint + function is processed in the class initializer, so that it only needs + to be computed once. If `basis` is specified, then the optimization is done over coefficients of the basis elements. Otherwise, the optimization is performed over the @@ -199,7 +200,7 @@ def __init__( len(self.solve_ivp_kwargs) > 1: raise TypeError( "solve_ivp method, kwargs not allowed for" - " discrete time systems") + " discrete-time systems") # Make sure there were no extraneous keywords if _kwargs_check and kwargs: @@ -305,7 +306,7 @@ def __init__( # cost = sum_k integral_cost(x[t_k], u[t_k]) # + terminal_cost(x[t_N], u[t_N]) # - # The actual calculation is a bit more complex: for continuous time + # The actual calculation is a bit more complex: for continuous-time # systems, we use a trapezoidal approximation for the integral cost. # # The initial state used for generating the simulation is stored in the @@ -531,7 +532,7 @@ def _collocation_constraint(self, coeffs): fk = fkp1 else: raise NotImplementedError( - "collocation not yet implemented for discrete time systems") + "collocation not yet implemented for discrete-time systems") # Return the value of the constraint function return self.colloc_vals.reshape(-1) @@ -763,11 +764,11 @@ def _simulate_states(self, x0, inputs): def compute_trajectory( self, x, squeeze=None, transpose=None, return_states=True, initial_guess=None, print_summary=True, **kwargs): - """Compute the optimal input at state x. + """Compute the optimal trajectory starting at state x. Parameters ---------- - x : array-like or number, optional + x : array_like or number, optional Initial state for the system. initial_guess : (tuple of) 1D or 2D array_like Initial states and/or inputs to use as a guess for the optimal @@ -842,7 +843,7 @@ def compute_mpc(self, x, squeeze=None): Parameters ---------- - x : array-like or number, optional + x : array_like or number, optional Initial state for the system. squeeze : bool, optional If True and if the system has a single output, return @@ -854,11 +855,8 @@ def compute_mpc(self, x, squeeze=None): Returns ------- input : array - Optimal input for the system at the current time. If the system - is SISO and squeeze is not True, the array is 1D (indexed by - time). If the system is not SISO or squeeze is False, the array - is 2D (indexed by the output number and time). Set to None - if the optimization failed. + Optimal input for the system at the current time, as a 1D array + (even in the SISO case). Set to None if the optimization failed. """ res = self.compute_trajectory(x, squeeze=squeeze) @@ -866,11 +864,42 @@ def compute_mpc(self, x, squeeze=None): # Create an input/output system implementing an MPC controller def create_mpc_iosystem(self, **kwargs): - """Create an I/O system implementing an MPC controller""" + """Create an I/O system implementing an MPC controller. + + For a discrete-time system, creates an input/output system taking + the current state x and returning the control u to apply at the + current time step. + + Parameters + ---------- + name : str, optional + Name for the system controller. Defaults to a generic system + name of the form 'sys[i]'. + inputs : list of str, optional + Labels for the controller inputs. Defaults to the system state + labels. + outputs : list of str, optional + Labels for the controller outputs. Defaults to the system input + labels. + states : list of str, optional + Labels for the internal controller states, which consist either + of the input values over the horizon of the controller or the + coefficients of the basis functions. Defaults to strings of + the form 'x[i]'. + + Returns + ------- + `NonlinearIOSystem` + + Notes + ----- + Only works for discrete-time systems. + + """ # Check to make sure we are in discrete time if self.system.dt == 0: raise ct.ControlNotImplemented( - "MPC for continuous time systems not implemented") + "MPC for continuous-time systems not implemented") def _update(t, x, u, params={}): coeffs = x.reshape((self.system.ninputs, -1)) @@ -912,6 +941,7 @@ class OptimalControlResult(sp.optimize.OptimizeResult): This class is a subclass of `scipy.optimize.OptimizeResult` with additional attributes associated with solving optimal control problems. + It is used as the return type for optimal control problems. Parameters ---------- @@ -998,7 +1028,7 @@ def solve_ocp( The optimal trajectory (states and inputs) is computed so as to approximately mimimize a cost function of the following form (for - continuous time systems): + continuous-time systems): J(x(.), u(.)) = \int_0^T L(x(t), u(t)) dt + V(x(T)), @@ -1013,13 +1043,13 @@ def solve_ocp( Parameters ---------- - sys : InputOutputSystem + sys : `InputOutputSystem` I/O system for which the optimal input will be computed. timepts : 1D array_like List of times at which the optimal input should be computed. - X0 : array-like or number, optional + X0 : array_like or number, optional Initial condition (default = 0). cost : callable @@ -1060,15 +1090,15 @@ def solve_ocp( 1D input of shape (ninputs,) that will be broadcast by extension of the time axis. - basis : BasisFamily, optional + basis : `BasisFamily`, optional Use the given set of basis functions for the inputs instead of setting the value of the input at each point in the timepts vector. trajectory_method : string, optional Method to use for carrying out the optimization. Currently supported methods are 'shooting' and 'collocation' (continuous time only). The - default value is 'shooting' for discrete time systems and - 'collocation' for continuous time systems. + default value is 'shooting' for discrete-time systems and + 'collocation' for continuous-time systems. log : bool, optional If True, turn on logging messages (using Python logging module). @@ -1116,7 +1146,7 @@ def solve_ocp( Notes ----- - For discrete time systems, the final value of the timepts vector + For discrete-time systems, the final value of the timepts vector specifies the final time t_N, and the trajectory cost is computed from time t_0 to t_{N-1}. Note that the input u_N does not affect the state x_N and so it should always be returned as 0. Further, if neither a @@ -1183,7 +1213,7 @@ def create_mpc_iosystem( Parameters ---------- - sys : InputOutputSystem + sys : `InputOutputSystem` I/O system for which the optimal input will be computed. timepts : 1D array_like @@ -1211,7 +1241,7 @@ def create_mpc_iosystem( Returns ------- - ctrl : InputOutputSystem + ctrl : `InputOutputSystem` An I/O system taking the current state of the model system and returning the current input to be applied that minimizes the cost function while satisfying the constraints. @@ -1227,7 +1257,7 @@ def create_mpc_iosystem( (e.g., to a file). name : string, optional System name (used for specifying signals). If unspecified, a generic - name is generated with a unique integer id. + name 'sys[id]' is generated with a unique integer id. Notes ----- @@ -1267,7 +1297,7 @@ class OptimalEstimationProblem(): Parameters ---------- - sys : InputOutputSystem + sys : `InputOutputSystem` I/O system for which the optimal input will be computed. timepts : 1D array Set up time points at which the inputs and outputs are given. @@ -1804,7 +1834,7 @@ def create_mhe_iosystem( state should be used. Default is "xhat[{i}]". These settings can also be overriden using the `outputs` keyword. measurement_labels, control_labels : str or list of str, optional - Set the name of the measurement and control signal names + Set the names of the measurement and control signal names (estimator inputs). If a single string is specified, it should be a format string using the variable `i` as an index. Otherwise, a list of strings matching the size of the system @@ -1817,7 +1847,7 @@ def create_mhe_iosystem( Returns ------- - estim : InputOutputSystem + estim : `InputOutputSystem` An I/O system taking the measured output and applied input for the model system and returning the estimated state of the system, as determined by solving the optimal estimation problem. @@ -1834,7 +1864,7 @@ def create_mhe_iosystem( # Check to make sure we are in discrete time if self.system.dt == 0: raise ct.ControlNotImplemented( - "MHE for continuous time systems not implemented") + "MHE for continuous-time systems not implemented") # Figure out the location of the disturbances self.ctrl_idx, self.dist_idx = \ @@ -2009,7 +2039,7 @@ def solve_oep( Parameters ---------- - sys : InputOutputSystem + sys : `InputOutputSystem` I/O system for which the optimal input will be computed. timepts : 1D array_like List of times at which the optimal input should be computed. @@ -2043,7 +2073,7 @@ def solve_oep( Returns ------- - res : TimeResponseData + res : `TimeResponseData` Bundle object with the estimated state and noise values. res.success : bool Boolean flag indicating whether the optimization was successful. @@ -2098,7 +2128,7 @@ def quadratic_cost(sys, Q, R, x0=0, u0=0): Parameters ---------- - sys : InputOutputSystem + sys : `InputOutputSystem` I/O system for which the cost function is being defined. Q : 2D array_like Weighting matrix for state cost. Dimensions must match system state. @@ -2151,7 +2181,7 @@ def gaussian_likelihood_cost(sys, Rv, Rw=None): Parameters ---------- - sys : InputOutputSystem + sys : `InputOutputSystem` I/O system for which the cost function is being defined. Rv : 2D array_like Covariance matrix for input (or state) disturbances. @@ -2211,7 +2241,7 @@ def state_poly_constraint(sys, A, b): Parameters ---------- - sys : InputOutputSystem + sys : `InputOutputSystem` I/O system for which the constraint is being defined. A : 2D array Constraint matrix. @@ -2248,7 +2278,7 @@ def state_range_constraint(sys, lb, ub): Parameters ---------- - sys : InputOutputSystem + sys : `InputOutputSystem` I/O system for which the constraint is being defined. lb : 1D array Lower bound for each of the states. @@ -2283,7 +2313,7 @@ def input_poly_constraint(sys, A, b): Parameters ---------- - sys : InputOutputSystem + sys : `InputOutputSystem` I/O system for which the constraint is being defined. A : 2D array Constraint matrix. @@ -2321,7 +2351,7 @@ def input_range_constraint(sys, lb, ub): Parameters ---------- - sys : InputOutputSystem + sys : `InputOutputSystem` I/O system for which the constraint is being defined. lb : 1D array Lower bound for each of the inputs. @@ -2368,7 +2398,7 @@ def output_poly_constraint(sys, A, b): Parameters ---------- - sys : InputOutputSystem + sys : `InputOutputSystem` I/O system for which the constraint is being defined. A : 2D array Constraint matrix. @@ -2409,7 +2439,7 @@ def output_range_constraint(sys, lb, ub): Parameters ---------- - sys : InputOutputSystem + sys : `InputOutputSystem` I/O system for which the constraint is being defined. lb : 1D array Lower bound for each of the outputs. @@ -2449,7 +2479,7 @@ def disturbance_range_constraint(sys, lb, ub): Parameters ---------- - sys : InputOutputSystem + sys : `InputOutputSystem` I/O system for which the constraint is being defined. lb : 1D array Lower bound for each of the disturbancs. diff --git a/control/phaseplot.py b/control/phaseplot.py index 8037ef025..159884957 100644 --- a/control/phaseplot.py +++ b/control/phaseplot.py @@ -58,7 +58,7 @@ def phase_plane_plot( Parameters ---------- - sys : NonlinearIOSystem or callable(t, x, ...) + sys : `NonlinearIOSystem` or callable(t, x, ...) I/O system or function used to generate phase plane data. If a function is given, the remaining arguments are drawn from the `params` keyword. @@ -251,7 +251,7 @@ def vectorfield( Parameters ---------- - sys : NonlinearIOSystem or callable(t, x, ...) + sys : `NonlinearIOSystem` or callable(t, x, ...) I/O system or function used to generate phase plane data. If a function is given, the remaining arguments are drawn from the `params` keyword. @@ -344,7 +344,7 @@ def streamlines( Parameters ---------- - sys : NonlinearIOSystem or callable(t, x, ...) + sys : `NonlinearIOSystem` or callable(t, x, ...) I/O system or function used to generate phase plane data. If a function is given, the remaining arguments are drawn from the `params` keyword. @@ -473,7 +473,7 @@ def equilpoints( Parameters ---------- - sys : NonlinearIOSystem or callable(t, x, ...) + sys : `NonlinearIOSystem` or callable(t, x, ...) I/O system or function used to generate phase plane data. If a function is given, the remaining arguments are drawn from the `params` keyword. @@ -558,7 +558,7 @@ def separatrices( Parameters ---------- - sys : NonlinearIOSystem or callable(t, x, ...) + sys : `NonlinearIOSystem` or callable(t, x, ...) I/O system or function used to generate phase plane data. If a function is given, the remaining arguments are drawn from the `params` keyword. @@ -732,7 +732,7 @@ def boxgrid(xvals, yvals): Parameters ---------- - xvals, yvals : 1D array-like + xvals, yvals : 1D array_like Array of points defining the points on the lower and left edges of the box. @@ -761,7 +761,7 @@ def meshgrid(xvals, yvals): Parameters ---------- - xvals, yvals : 1D array-like + xvals, yvals : 1D array_like Array of points defining the points on the lower and left edges of the box. @@ -788,7 +788,7 @@ def circlegrid(centers, radius, num): Parameters ---------- - centers : 2D array-like + centers : 2D array_like Array of points with shape (p, 2) defining centers of the circles. radius : float Radius of the points to be generated around each center. @@ -1064,7 +1064,7 @@ def phase_plot(odefun, X=None, Y=None, scale=1, X0=None, T=None, X0: ndarray of initial conditions, optional List of initial conditions from which streamlines are plotted. Each initial condition should be a pair of numbers. - T: array-like or number, optional + T: array_like or number, optional Length of time to run simulations that generate streamlines. If a single number, the same simulation time is used for all initial conditions. Otherwise, should be a list of length @@ -1079,7 +1079,7 @@ def phase_plot(odefun, X=None, Y=None, scale=1, X0=None, T=None, If a single integer N is given, draw N arrows using equally space time points. If a tuple (N, lambda) is given, draw N arrows using exponential time constant lambda - timepts : array-like, optional + timepts : array_like, optional Draw arrows at the given list times [t1, t2, ...] tfirst : bool, optional If True, call `func` with signature ``func(t, x, ...)``. diff --git a/control/pzmap.py b/control/pzmap.py index 7a72b60e5..29eaf56f6 100644 --- a/control/pzmap.py +++ b/control/pzmap.py @@ -108,20 +108,18 @@ def __iter__(self): def plot(self, *args, **kwargs): """Plot the pole/zero data. - See `pole_zero_plot` for description of arguments - and keywords. + See `pole_zero_plot` for description of arguments and keywords. """ return pole_zero_plot(self, *args, **kwargs) class PoleZeroList(list): - """List of PoleZeroData objects.""" + """List of PoleZeroData objects with plotting capability.""" def plot(self, *args, **kwargs): """Plot pole/zero data. - See `pole_zero_plot` for description of arguments - and keywords. + See `pole_zero_plot` for description of arguments and keywords. """ return pole_zero_plot(self, *args, **kwargs) @@ -133,7 +131,7 @@ def pole_zero_map(sysdata): Parameters ---------- - sysdata : LTI system (StateSpace or TransferFunction) + sysdata : `StateSpace` or `TransferFunction` Linear system for which poles and zeros are computed. Returns @@ -176,13 +174,13 @@ def pole_zero_plot( Parameters ---------- - data : List of PoleZeroData objects or LTI systems + data : List of `PoleZeroData` objects or `LTI` systems List of pole/zero response data objects generated by pzmap_response() or root_locus_map() that are to be plotted. If a list of systems is given, the poles and zeros of those systems will be plotted. grid : bool or str, optional If True plot omega-damping grid, if False show imaginary - axis for continuous time systems, unit circle for discrete time + axis for continuous-time systems, unit circle for discrete-time systems. If 'empty', do not draw any additonal lines. Default value is set by `config.defaults['pzmap.grid']` or `config.defaults['rlocus.grid']`. @@ -268,7 +266,7 @@ def pole_zero_plot( matplotlib.pyplot.gca().axis('auto') and then set the axis limits to the desired values. - Pole/zero plots that use the continuous time omega-damping grid do not + Pole/zero plots that use the continuous-time omega-damping grid do not work with the `ax` keyword argument, due to the way that axes grids are implemented. The `grid` argument must be set to False or 'empty' when using the `ax` keyword argument. diff --git a/control/rlocus.py b/control/rlocus.py index a913a84b4..aa9598216 100644 --- a/control/rlocus.py +++ b/control/rlocus.py @@ -49,7 +49,7 @@ def root_locus_map(sysdata, gains=None): Returns ------- - rldata : PoleZeroData or list of PoleZeroData + rldata : `PoleZeroData` or list of `PoleZeroData` Root locus data object(s). The loci of the root locus diagram are available in the array `rldata.loci`, indexed by the gain index and the locus index, and the gains are in the array `rldata.gains`. @@ -107,17 +107,15 @@ def root_locus_plot( Gains to use in computing plot of closed-loop poles. If not given, gains are chosen to include the main features of the root locus map. xlim : tuple or list, optional - Set limits of x axis, normally with tuple - (see `matplotlib:api/axes_api`). + Set limits of x axis (see `matplotlib.axes.Axes.set_xlim`). ylim : tuple or list, optional - Set limits of y axis, normally with tuple - (see `matplotlib:api/axes_api`). + Set limits of y axis (see `matplotlib.axes.Axes.set_ylim`). plot : bool, optional (legacy) If given, `root_locus_plot` returns the legacy return values of roots and gains. If False, just return the values with no plot. grid : bool or str, optional If True plot omega-damping grid, if False show imaginary axis - for continuous time systems, unit circle for discrete time systems. + for continuous-time systems, unit circle for discrete-time systems. If 'empty', do not draw any additonal lines. Default value is set by `config.defaults['rlocus.grid']`. initial_gain : float, optional diff --git a/control/robust.py b/control/robust.py index 89735c5c6..894b690ca 100644 --- a/control/robust.py +++ b/control/robust.py @@ -19,7 +19,7 @@ def h2syn(P, nmeas, ncon): Parameters ---------- - P : StateSpace + P : `StateSpace` Partitioned LTI plant (state-space system). nmeas : int Number of measurements (input to controller). @@ -28,13 +28,13 @@ def h2syn(P, nmeas, ncon): Returns ------- - K : StateSpace + K : `StateSpace` Controller to stabilize `P`. Raises ------ ImportError - if slycot routine sb10hd is not loaded + If slycot routine sb10hd is not loaded. See Also -------- @@ -91,7 +91,7 @@ def hinfsyn(P, nmeas, ncon): Parameters ---------- - P : StateSpace + P : `StateSpace` Partitioned LTI plant (state-space system). nmeas : int Number of measurements (input to controller). @@ -100,9 +100,9 @@ def hinfsyn(P, nmeas, ncon): Returns ------- - K : StateSpace + K : `StateSpace` Controller to stabilize `P`. - CL : StateSpace + CL : `StateSpace` Closed loop system. gam : float Infinity norm of closed loop system. @@ -116,7 +116,7 @@ def hinfsyn(P, nmeas, ncon): Raises ------ ImportError - if slycot routine sb10ad is not loaded + If slycot routine sb10ad is not loaded. See Also -------- @@ -188,15 +188,15 @@ def _size_as_needed(w, wname, n): Returns ------- - w_: processed weighting function, a StateSpace object: - - if w is None, empty StateSpace object + w_: processed weighting function, a `StateSpace` object: + - if w is None, empty `StateSpace` object - if w is scalar, w_ will be w * eye(n) - - otherwise, w as StateSpace object + - otherwise, w as `StateSpace` object Raises ------ ValueError - - if w is not None or scalar, and doesn't have n inputs + If w is not None or scalar, and does not have n inputs. See Also -------- @@ -241,14 +241,14 @@ def augw(g, w1=None, w2=None, w3=None): Returns ------- - p : StateSpace + p : `StateSpace` Plant augmented with weightings, suitable for submission to `hinfsyn` or `h2syn`. Raises ------ ValueError - If all weightings are None + If all weightings are None. See Also -------- @@ -354,9 +354,9 @@ def mixsyn(g, w1=None, w2=None, w3=None): Returns ------- - k : StateSpace + k : `StateSpace` Synthesized controller. - cl : StateSpace + cl : `StateSpace` Closed system mapping evaluation inputs to evaluation outputs. Let p be the augmented plant, with:: diff --git a/control/sisotool.py b/control/sisotool.py index 466f2b345..16ddb694f 100644 --- a/control/sisotool.py +++ b/control/sisotool.py @@ -59,10 +59,9 @@ def sisotool(sys, initial_gain=None, xlim_rlocus=None, ylim_rlocus=None, Initial gain to use for plotting root locus. Defaults to 1 (`config.defaults['sisotool.initial_gain']`). xlim_rlocus : tuple or list, optional - Control of x-axis range, normally with tuple - (see :doc:`matplotlib:api/axes_api`). + Control of x-axis range (see `matplotlib.axes.Axes.set_xlim`). ylim_rlocus : tuple or list, optional - Control of y-axis range. + Control of y-axis range (see `matplotlib.axes.Axes.set_ylim`). plotstr_rlocus : `matplotlib.pyplot.plot` format string, optional Plotting style for the root locus plot(color, linestyle, etc). rlocus_grid : boolean (default = False) @@ -386,7 +385,7 @@ def rootlocus_pid_designer(plant, gain='P', sign=+1, input_signal='r', prop = tf(1, 1, inputs='e', outputs='prop_e') integ = tf(1, [1, 0], inputs='e', outputs='int_e') deriv = tf([1, 0], [tau, 1], inputs='y', outputs='deriv') - else: # discrete-time + else: # discrete time prop = tf(1, 1, dt, inputs='e', outputs='prop_e') integ = tf([dt/2, dt/2], [1, -1], dt, inputs='e', outputs='int_e') deriv = tf([1, -1], [dt, 0], dt, inputs='y', outputs='deriv') diff --git a/control/statefbk.py b/control/statefbk.py index 1fbe8fe61..916bfce11 100644 --- a/control/statefbk.py +++ b/control/statefbk.py @@ -118,7 +118,7 @@ def place_varga(A, B, p, dtime=False, alpha=None): p : 1D array_like Desired eigenvalue locations. dtime : bool, optional - False (default) for continuous time pole placement or True + False (default) for continuous-time pole placement or True for discrete time. alpha : float, optional If `dtime` is false then place_varga will leave the eigenvalues with @@ -279,7 +279,7 @@ def lqr(*args, **kwargs): ---------- A, B : 2D array_like Dynamics and input matrices. - sys : LTI StateSpace system + sys : LTI `StateSpace` system Linear system. Q, R : 2D array State and input weight matrices. @@ -315,7 +315,7 @@ def lqr(*args, **kwargs): ----- If the first argument is an LTI object, then this object will be used to define the dynamics and input matrices. Furthermore, if the LTI - object corresponds to a discrete time system, the `dlqr` function + object corresponds to a discrete-time system, the `dlqr` function will be called. Examples @@ -328,7 +328,7 @@ def lqr(*args, **kwargs): # Process the arguments and figure out what inputs we received # - # If we were passed a discrete time system as the first arg, use dlqr() + # If we were passed a discrete-time system as the first arg, use dlqr() if isinstance(args[0], LTI) and isdtime(args[0], strict=True): # Call dlqr return dlqr(*args, **kwargs) @@ -583,7 +583,7 @@ def create_statefbk_iosystem( Parameters ---------- - sys : NonlinearIOSystem + sys : `NonlinearIOSystem` The I/O system that represents the process dynamics. If no estimator is given, the output of this system should represent the full state. @@ -621,7 +621,7 @@ def create_statefbk_iosystem( multiplied by the current and desired state to generate the error for the internal integrator states of the control law. - estimator : NonlinearIOSystem, optional + estimator : `NonlinearIOSystem`, optional If an estimator is provided, use the states of the estimator as the system inputs for the controller. @@ -646,14 +646,14 @@ def create_statefbk_iosystem( controller_type : 'linear' or 'nonlinear', optional Set the type of controller to create. The default for a linear gain is a linear controller implementing the LQR regulator. If the type - is 'nonlinear', a NonlinearIOSystem is created instead, with + is 'nonlinear', a `NonlinearIOSystem` is created instead, with the gain `K` as a parameter (allowing modifications of the gain at runtime). If the gain parameter is a tuple, then a nonlinear, gain-scheduled controller is created. Returns ------- - ctrl : NonlinearIOSystem + ctrl : `NonlinearIOSystem` Input/output system representing the controller. For the 'trajgen' design pattern (default), this system takes as inputs the desired state `x_d`, the desired input `u_d`, and either the system state @@ -668,7 +668,7 @@ def create_statefbk_iosystem( and integral) are evaluated using the scheduling variables specified by `gainsched_indices`. - clsys : NonlinearIOSystem + clsys : `NonlinearIOSystem` Input/output system representing the closed loop system. This system takes as inputs the desired trajectory (x_d, u_d) and outputs the system state `x` and the applied input `u` @@ -707,7 +707,7 @@ def create_statefbk_iosystem( as the original system. name : string, optional - System name. If unspecified, a generic name is generated + System name. If unspecified, a generic name 'sys[id]' is generated with a unique integer id. params : dict, optional @@ -1122,7 +1122,7 @@ def gram(sys, type): Parameters ---------- - sys : StateSpace + sys : `StateSpace` System description. type : String Type of desired computation. `type` is either 'c' (controllability) @@ -1137,13 +1137,13 @@ def gram(sys, type): Raises ------ ValueError - * if system is not instance of StateSpace class - * if `type` is not 'c', 'o', 'cf' or 'of' - * if system is unstable (sys.A has eigenvalues not in left half plane) + * If system is not instance of `StateSpace` class, or + * if `type` is not 'c', 'o', 'cf' or 'of', or + * if system is unstable (sys.A has eigenvalues not in left half plane). ControlSlycot - if slycot routine sb03md cannot be found - if slycot routine sb03od cannot be found + If slycot routine sb03md cannot be found or + if slycot routine sb03od cannot be found. Examples -------- diff --git a/control/statesp.py b/control/statesp.py index 33b422a89..2c0fd7f35 100644 --- a/control/statesp.py +++ b/control/statesp.py @@ -60,7 +60,7 @@ class StateSpace(NonlinearIOSystem, LTI): r"""StateSpace(A, B, C, D[, dt]) - A class for representing state-space models. + State space representation for LIT input/output systems. The StateSpace class is used to represent state-space realizations of linear time-invariant (LTI) systems: @@ -105,22 +105,22 @@ class StateSpace(NonlinearIOSystem, LTI): matrices. The class also keeps track of the number of states (i.e., the size of A). - A discrete time system is created by specifying a nonzero 'timebase', dt + A discrete-time system is created by specifying a nonzero 'timebase', dt when the system is constructed: - * `dt` = 0: continuous time system (default) - * `dt` > 0: discrete time system with sampling period 'dt' + * `dt` = 0: continuous-time system (default) + * `dt` > 0: discrete-time system with sampling period 'dt' * `dt` = True: discrete time with unspecified sampling period * `dt` = None: no timebase specified Systems must have compatible timebases in order to be combined. A - discrete time system with unspecified sampling time (`dt` = True) can + discrete-time system with unspecified sampling time (`dt` = True) can be combined with a system having a specified sampling time; the result - will be a discrete time system with the sample time of the latter + will be a discrete-time system with the sample time of the other system. Similarly, a system with timebase None can be combined with a system having any timebase; the result will have the timebase of the - latter system. The default value of dt can be changed by changing the - value of `control.config.defaults['control.default_dt']`. + other system. The default value of dt can be changed by changing the + value of `config.defaults['control.default_dt']`. A state space system is callable and returns the value of the transfer function evaluated at a point in the complex plane. See @@ -139,17 +139,17 @@ class StateSpace(NonlinearIOSystem, LTI): StateSpace instances have support for IPython HTML/LaTeX output, intended for pretty-printing in Jupyter notebooks. The HTML/LaTeX output can be - configured using `control.config.defaults['statesp.latex_num_format']` - and `control.config.defaults['statesp.latex_repr_type']`. The + configured using `config.defaults['statesp.latex_num_format']` + and `config.defaults['statesp.latex_repr_type']`. The HTML/LaTeX output is tailored for MathJax, as used in Jupyter, and may look odd when typeset by non-MathJax LaTeX systems. - `control.config.defaults['statesp.latex_num_format']` is a format string + `config.defaults['statesp.latex_num_format']` is a format string fragment, specifically the part of the format string after '{:' used to convert floating-point numbers to strings. By default it is '.3g'. - `control.config.defaults['statesp.latex_repr_type']` must either be + `config.defaults['statesp.latex_repr_type']` must either be 'partitioned' or 'separate'. If 'partitioned', the A, B, C, D matrices are shown as a single, partitioned matrix; if 'separate', the matrices are shown separately. @@ -161,7 +161,7 @@ def __init__(self, *args, **kwargs): Construct a state space object. The default constructor is StateSpace(A, B, C, D), where A, B, C, D - are matrices or equivalent objects. To create a discrete time + are matrices or equivalent objects. To create a discrete-time system, use StateSpace(A, B, C, D, dt) where `dt` is the sampling time (or True for unspecified sampling time). To call the copy constructor, call StateSpace(sys), where sys is a StateSpace @@ -859,14 +859,24 @@ def slycot_laub(self, x): return out def horner(self, x, warn_infinite=True): - """Evaluate system's transfer function at complex frequency - using Laub's or Horner's method. + """Evaluate value of transfer function using Horner's method. - Evaluates ``sys(x)`` where `x` is `s` for continuous-time systems - and `z` for discrete-time systems. + Evaluates ``sys(x)`` where `x` is a complex number `s` for + continuous-time systems and `z` for discrete-time systems. Expects + inputs and outputs to be formatted correctly. Use ``sys(x)`` for a + more user-friendly interface. - Expects inputs and outputs to be formatted correctly. Use ``sys(x)`` - for a more user-friendly interface. + Parameters + ---------- + x : complex + Complex frequency at which the transfer function is evaluated. + + warn_infinite : bool, optional + If True (default), generate a warning if `x` is a pole. + + Returns + ------- + complex Notes ----- @@ -992,7 +1002,17 @@ def zeros(self): # Feedback around a state space system def feedback(self, other=1, sign=-1): - """Feedback interconnection between two LTI systems.""" + """Feedback interconnection between two LTI objects. + + Parameters + ---------- + other : `InputOutputSystem` + System in the feedack path. + + sign : float, optional + Gain to use in feedback path. Defaults to -1. + + """ # Convert the system to state space, if possible try: other = _convert_to_statespace(other) @@ -1147,8 +1167,18 @@ def lft(self, other, nu=-1, ny=-1): return StateSpace(Ares, Bres, Cres, Dres, dt) def minreal(self, tol=0.0): - """Calculate a minimal realization, removes unobservable and - uncontrollable states""" + """Remove unobservable and uncontrollable states. + + Calculate a minimal realization for a state space system, + removing all unobservable and/or uncontrollable states. + + Parameters + ---------- + tol : float + Tolerance for determining whether states are unobservable + or uncontrollable. + + """ if self.nstates: try: from slycot import tb01pd @@ -1183,7 +1213,7 @@ def returnScipySignalLTI(self, strict=True): The timebase `ssobject.dt` cannot be None; it must be continuous (0) or discrete (True or > 0). False: - If `ssobject.dt` is None, continuous time + If `ssobject.dt` is None, continuous-time `scipy.signal.lti` objects are returned. Returns @@ -1199,7 +1229,7 @@ def returnScipySignalLTI(self, strict=True): if self.dt: kwdt = {'dt': self.dt} else: - # scipy convention for continuous time lti systems: call without + # scipy convention for continuous-time lti systems: call without # dt keyword argument kwdt = {} @@ -1220,7 +1250,19 @@ def append(self, other): """Append a second model to the present model. The second model is converted to state-space if necessary, inputs and - outputs are appended and their order is preserved""" + outputs are appended and their order is preserved + + Parameters + ---------- + other : `StateSpace` + System to be appended. + + Returns + ------- + sys : `StateSpace` + System model with `other` appended to `self`. + + """ if not isinstance(other, StateSpace): other = _convert_to_statespace(other) @@ -1265,7 +1307,7 @@ def __getitem__(self, key): def sample(self, Ts, method='zoh', alpha=None, prewarp_frequency=None, name=None, copy_names=True, **kwargs): - """Convert a continuous time system to discrete time. + """Convert a continuous-time system to discrete time. Creates a discrete-time system from a continuous-time system by sampling. Multiple methods of conversion are supported. @@ -1296,7 +1338,7 @@ def sample(self, Ts, method='zoh', alpha=None, prewarp_frequency=None, ignored otherwise. name : string, optional Set the name of the sampled system. If not specified and if - `copy_names` is False, a generic name is + `copy_names` is False, a generic name 'sys[id]' is generated with a unique integer id. If `copy_names` is True, the new system name is determined by adding the prefix and suffix strings in @@ -1309,7 +1351,7 @@ def sample(self, Ts, method='zoh', alpha=None, prewarp_frequency=None, Returns ------- - sysd : StateSpace + sysd : `StateSpace` Discrete-time system, with sampling rate Ts. Other Parameters @@ -1334,7 +1376,7 @@ def sample(self, Ts, method='zoh', alpha=None, prewarp_frequency=None, """ if not self.isctime(): - raise ValueError("System must be continuous time system") + raise ValueError("System must be continuous-time system") if prewarp_frequency is not None: if method in ('bilinear', 'tustin') or \ (method == 'gbt' and alpha == 0.5): @@ -1400,7 +1442,7 @@ def dynamics(self, t, x, u=None, params=None): dx/dt = A x + B u where A and B are the state-space matrices of the system. If the - system is discrete-time, returns the next value of `x`: + system is discrete time, returns the next value of `x`: x[t+dt] = A x[t] + B u[t] @@ -1619,7 +1661,7 @@ def ss(*args, **kwargs): x[k+1] &= A x[k] + B u[k] \\ y[k] &= C x[k] + D u[k] - The matrices can be given as 2D array-like data types. For SISO + The matrices can be given as 2D array_like data types. For SISO systems, `B` and `C` can be given as 1D arrays and D can be given as a scalar. @@ -1629,7 +1671,7 @@ def ss(*args, **kwargs): Parameters ---------- - sys : StateSpace or TransferFunction + sys : `StateSpace` or `TransferFunction` A linear system. A, B, C, D : array_like or string System, control, output, and feed forward matrices. @@ -1649,7 +1691,7 @@ def ss(*args, **kwargs): Returns ------- - out : StateSpace + out : `StateSpace` Linear input/output system. Other Parameters @@ -1664,7 +1706,7 @@ def ss(*args, **kwargs): 'u', 'y', 'x'. name : string, optional System name (used for specifying signals). If unspecified, a generic - name is generated with a unique integer id. + name 'sys[id]' is generated with a unique integer id. Raises ------ @@ -1684,11 +1726,11 @@ def ss(*args, **kwargs): Examples -------- - Create a Linear I/O system object from matrices. + Create a linear I/O system object from matrices: >>> G = ct.ss([[-1, -2], [3, -4]], [[5], [7]], [[6, 8]], [[9]]) - Convert a TransferFunction to a StateSpace object. + Convert a transfer function to a state space system: >>> sys_tf = ct.tf([2.], [1., 3]) >>> sys2 = ct.ss(sys_tf) @@ -1766,7 +1808,7 @@ def tf2io(*args, **kwargs): ``tf2io(sys)`` Convert a linear system into space space form. Always creates - a new system, even if sys is already a StateSpace object. + a new system, even if sys is already a `StateSpace` object. ``tf2io(num, den)`` @@ -1777,7 +1819,7 @@ def tf2io(*args, **kwargs): Parameters ---------- - sys : LTI (StateSpace or TransferFunction) + sys : `StateSpace` or `TransferFunction` A linear system. num : array_like, or list of list of array_like Polynomial coefficients of the numerator. @@ -1786,7 +1828,7 @@ def tf2io(*args, **kwargs): Returns ------- - out : StateSpace + out : `StateSpace` New I/O system (in state space form). Other Parameters @@ -1796,17 +1838,17 @@ def tf2io(*args, **kwargs): system. If not given, the inputs and outputs are the same as the original system. name : string, optional - System name. If unspecified, a generic name is generated + System name. If unspecified, a generic name 'sys[id]' is generated with a unique integer id. Raises ------ ValueError - if `num` and `den` have invalid or unequal dimensions, or if an + If `num` and `den` have invalid or unequal dimensions, or if an invalid number of arguments is passed in. TypeError - if `num` or `den` are of incorrect type, or if sys is not a - TransferFunction object. + If `num` or `den` are of incorrect type, or if `sys` is not a + `TransferFunction` object. See Also -------- @@ -1849,7 +1891,7 @@ def tf2ss(*args, **kwargs): Parameters ---------- - sys : LTI (StateSpace or TransferFunction) + sys : `StateSpace` or `TransferFunction` A linear system. num : array_like, or list of list of array_like Polynomial coefficients of the numerator. @@ -1858,7 +1900,7 @@ def tf2ss(*args, **kwargs): Returns ------- - out : StateSpace + out : `StateSpace` New linear system in state space form. Other Parameters @@ -1868,7 +1910,7 @@ def tf2ss(*args, **kwargs): system. If not given, the inputs and outputs are the same as the original system. name : string, optional - System name. If unspecified, a generic name is generated + System name. If unspecified, a generic name 'sys[id]' is generated with a unique integer id. method : str, optional Set the method used for computing the result. Current methods are @@ -1878,11 +1920,11 @@ def tf2ss(*args, **kwargs): Raises ------ ValueError - if `num` and `den` have invalid or unequal dimensions, or if an + If `num` and `den` have invalid or unequal dimensions, or if an invalid number of arguments is passed in. TypeError - if `num` or `den` are of incorrect type, or if sys is not a - TransferFunction object. + If `num` or `den` are of incorrect type, or if `sys` is not a + `TransferFunction` object. See Also -------- @@ -1925,7 +1967,7 @@ def ssdata(sys): Parameters ---------- - sys : LTI (StateSpace, or TransferFunction) + sys : `StateSpace` or `TransferFunction` LTI system whose data will be returned. Returns @@ -1943,7 +1985,7 @@ def linfnorm(sys, tol=1e-10): Parameters ---------- - sys : LTI (StateSpace or TransferFunction) + sys : `StateSpace` or `TransferFunction` System to evalute L-infinity norm of. tol : real scalar Tolerance on norm estimate. @@ -2016,17 +2058,17 @@ def rss(states=1, outputs=1, inputs=1, strictly_proper=False, **kwargs): indicates unspecified timebase (either continuous or discrete time). name : string, optional System name (used for specifying signals). If unspecified, a generic - name is generated with a unique integer id. + name 'sys[id]' is generated with a unique integer id. Returns ------- - sys : StateSpace + sys : `StateSpace` The randomly created linear system. Raises ------ ValueError - if any input is not a positive integer. + If any input is not a positive integer. Notes ----- @@ -2061,7 +2103,7 @@ def drss(*args, **kwargs): Create a stable, discrete-time, random state space system. - Create a stable *discrete time* random state space object. This + Create a stable *discrete-time* random state space object. This function calls `rss` using either the `dt` keyword provided by the user or `dt` = True if not specified. @@ -2084,7 +2126,7 @@ def drss(*args, **kwargs): elif dt is None: warn("drss called with unspecified timebase; " "system may be interpreted as continuous time") - kwargs['dt'] = True # force rss to generate discrete time sys + kwargs['dt'] = True # force rss to generate discrete-time sys else: dt = True kwargs['dt'] = True @@ -2125,7 +2167,7 @@ def summing_junction( by the `inputs` and `output` keywords. Default value is None. name : string, optional System name (used for specifying signals). If unspecified, a generic - name is generated with a unique integer id. + name 'sys[id]' is generated with a unique integer id. prefix : string, optional If `inputs` is an integer, create the names of the states using the given prefix (default = 'u'). The names of the input will be of the @@ -2133,7 +2175,7 @@ def summing_junction( Returns ------- - sys : static StateSpace + sys : static `StateSpace` Linear input/output system object with no states and only a direct term that implements the summing junction. diff --git a/control/stochsys.py b/control/stochsys.py index c84f9898f..08ea6efa2 100644 --- a/control/stochsys.py +++ b/control/stochsys.py @@ -38,7 +38,7 @@ def lqe(*args, **kwargs): Continuous-time linear quadratic estimator (Kalman filter). - Given the continuous time system + Given the continuous-time system .. math:: @@ -72,7 +72,7 @@ def lqe(*args, **kwargs): ---------- A, G, C : 2D array_like Dynamics, process noise (disturbance), and output matrices. - sys : LTI (StateSpace or TransferFunction) + sys : `StateSpace` or `TransferFunction` Linear I/O system, with the process noise input taken as the system input. QN, RN : 2D array_like @@ -102,7 +102,7 @@ def lqe(*args, **kwargs): ----- If the first argument is an LTI object, then this object will be used to define the dynamics, noise and output matrices. Furthermore, if the - LTI object corresponds to a discrete time system, the `dlqe` + LTI object corresponds to a discrete-time system, the `dlqe` function will be called. Examples @@ -126,7 +126,7 @@ def lqe(*args, **kwargs): # Process the arguments and figure out what inputs we received # - # If we were passed a discrete time system as the first arg, use dlqe() + # If we were passed a discrete-time system as the first arg, use dlqe() if isinstance(args[0], LTI) and isdtime(args[0], strict=True): # Call dlqe return dlqe(*args, **kwargs) @@ -261,7 +261,7 @@ def dlqe(*args, **kwargs): # If we were passed a continus time system as the first arg, raise error if isinstance(args[0], LTI) and isctime(args[0], strict=True): - raise ControlArgument("dlqr() called with a continuous time system") + raise ControlArgument("dlqr() called with a continuous-time system") # If we were passed a state space system, use that to get system matrices if isinstance(args[0], StateSpace): @@ -316,7 +316,7 @@ def create_estimator_iosystem( r"""Create an I/O system implementing a linear quadratic estimator. This function creates an input/output system that implements a - continuous time state estimator of the form + continuous-time state estimator of the form .. math:: @@ -324,7 +324,7 @@ def create_estimator_iosystem( dP/dt &= A P + P A^T + F Q_N F^T - P C^T R_N^{-1} C P \\ L &= P C^T R_N^{-1} - or a discrete time state estimator of the form + or a discrete-time state estimator of the form .. math:: @@ -344,7 +344,7 @@ def create_estimator_iosystem( Parameters ---------- - sys : StateSpace + sys : `StateSpace` The linear I/O system that represents the process dynamics. QN, RN : ndarray Disturbance and measurement noise covariance matrices. @@ -361,7 +361,7 @@ def create_estimator_iosystem( Returns ------- - estim : InputOutputSystem + estim : `InputOutputSystem` Input/output system representing the estimator. This system takes the system output y and input u and generates the estimated state xhat. @@ -409,7 +409,7 @@ def create_estimator_iosystem( `InputOutputSystem`. Overrides signal labels. name : string, optional System name (used for specifying signals). If unspecified, a generic - name is generated with a unique integer id. + name 'sys[id]' is generated with a unique integer id. Notes ----- @@ -593,7 +593,7 @@ def white_noise(T, Q, dt=0): This function generates a (multi-variable) white noise signal of specified intensity as either a sampled continous time signal or a - discrete time signal. A white noise signal along a 1D array + discrete-time signal. A white noise signal along a 1D array of linearly spaced set of times T can be computing using V = ct.white_noise(T, Q, dt) @@ -613,7 +613,7 @@ def white_noise(T, Q, dt=0): Q : 2D array_like Noise intensity matrix of dimension nxn. dt : float, optional - If 0, generate continuous time noise signal, otherwise discrete time. + If 0, generate continuous-time noise signal, otherwise discrete time. Returns ------- @@ -661,7 +661,7 @@ def correlation(T, X, Y=None, squeeze=True): tau, Rtau = correlation(T, X[, Y]) The signal X (and Y, if present) represent a continuous or - discrete time signal sampled at times T. The return value + discrete-time signal sampled at times T. The return value provides the correlation Rtau between X(t+tau) and X(t) at a set of time offets tau. diff --git a/control/sysnorm.py b/control/sysnorm.py index 9f1745ab3..603c16b81 100644 --- a/control/sysnorm.py +++ b/control/sysnorm.py @@ -110,7 +110,7 @@ def system_norm(system, p=2, tol=1e-6, print_warning=True, method=None): Notes ----- - Does not yet compute the L-infinity norm for discrete time systems + Does not yet compute the L-infinity norm for discrete-time systems with pole(s) at the origin unless Slycot is used. Examples @@ -268,9 +268,9 @@ def system_norm(system, p=2, tol=1e-6, print_warning=True, method=None): # ------------------ # Discrete time case # ------------------ - # Use inverse bilinear transformation of discrete time system + # Use inverse bilinear transformation of discrete-time system # to s-plane if no poles on |z|=1 or z=0. Allows us to use - # test for continuous time systems next. + # test for continuous-time systems next. if G.isdtime(): Ad = A Bd = B @@ -278,7 +278,7 @@ def system_norm(system, p=2, tol=1e-6, print_warning=True, method=None): Dd = D if any(np.isclose(la.eigvals(Ad), 0.0)): raise ct.ControlArgument( - "L-infinity norm computation for discrete time " + "L-infinity norm computation for discrete-time " "system with pole(s) in z=0 currently not supported " "unless Slycot installed.") diff --git a/control/tests/discrete_test.py b/control/tests/discrete_test.py index cccb53708..9dbc3eb00 100644 --- a/control/tests/discrete_test.py +++ b/control/tests/discrete_test.py @@ -1,4 +1,4 @@ -"""discrete_test.py - test discrete time classes +"""discrete_test.py - test discrete-time classes RMM, 9 Sep 2012 """ @@ -22,7 +22,7 @@ def tsys(self): class Tsys: pass T = Tsys() - # Single input, single output continuous and discrete time systems + # Single input, single output continuous and discrete-time systems sys = rss(3, 1, 1) T.siso_ss1 = StateSpace(sys.A, sys.B, sys.C, sys.D, None) T.siso_ss1c = StateSpace(sys.A, sys.B, sys.C, sys.D, 0.0) @@ -30,7 +30,7 @@ class Tsys: T.siso_ss2d = StateSpace(sys.A, sys.B, sys.C, sys.D, 0.2) T.siso_ss3d = StateSpace(sys.A, sys.B, sys.C, sys.D, True) - # Two input, two output continuous time system + # Two input, two output continuous-time system A = [[-3., 4., 2.], [-1., -3., 0.], [2., 5., 3.]] B = [[1., 4.], [-3., -3.], [-2., 1.]] C = [[4., 2., -3.], [1., 4., 3.]] @@ -38,7 +38,7 @@ class Tsys: T.mimo_ss1 = StateSpace(A, B, C, D, None) T.mimo_ss1c = StateSpace(A, B, C, D, 0) - # Two input, two output discrete time system + # Two input, two output discrete-time system T.mimo_ss1d = StateSpace(A, B, C, D, 0.1) # Same system, but with a different sampling time @@ -463,7 +463,7 @@ def test_sample_tf(self, tsys): @pytest.mark.usefixtures("legacy_plot_signature") def test_discrete_bode(self, tsys): - # Create a simple discrete time system and check the calculation + # Create a simple discrete-time system and check the calculation sys = TransferFunction([1], [1, 0.5], 1) omega = [1, 2, 3] mag_out, phase_out, omega_out = bode(sys, omega, plot=True) @@ -473,7 +473,7 @@ def test_discrete_bode(self, tsys): np.testing.assert_array_almost_equal(phase_out, np.angle(H_z)) def test_signal_names(self, tsys): - "test that signal names are preserved in conversion to discrete-time" + "test that signal names are preserved in conversion to discrete time" ssc = StateSpace(tsys.siso_ss1c, inputs='u', outputs='y', states=['a', 'b', 'c']) ssd = ssc.sample(0.1) diff --git a/control/tests/docstrings_test.py b/control/tests/docstrings_test.py index 158798782..74b3e1edb 100644 --- a/control/tests/docstrings_test.py +++ b/control/tests/docstrings_test.py @@ -117,10 +117,9 @@ def test_parameter_docs(module, prefix): _info(f"member '{objname}' is outside `control` module", 5) continue - # Skip non-top-level functions without parameter lists - if prefix != "" and inspect.getmodule(obj) != module and \ - doc is not None and doc["Parameters"] == []: - _info(f"skipping {objname} [no doc or no Paramaters]", 2) + # Skip non-top-level functions without documentation + if prefix != "" and inspect.getmodule(obj) != module and doc is None: + _info(f"skipping {objname} [no docstring]", 1) continue # If this is a class, recurse through methods @@ -142,12 +141,14 @@ def test_parameter_docs(module, prefix): _info(f"skipping {objname} [inherited, hidden, or checked]", 4) continue - # Skip non-top-level functions without parameter lists + # Don't fail on non-top-level functions without parameter lists + # TODO: may be able to delete this if prefix != "" and inspect.getmodule(obj) != module and \ doc is not None and doc["Parameters"] == [] and \ doc["Returns"] == [] and doc["Yields"] == []: - _info(f"skipping {prefix}{name} (not top-level, no params)", 2) - continue + fail_if_missing = False + else: + fail_if_missing = True _info(f"Checking function {objname} against numpydoc", 2) _check_numpydoc_style(obj, doc) @@ -192,6 +193,12 @@ def test_parameter_docs(module, prefix): # If first argument is *args, try to use docstring instead sig = _replace_var_positional_with_docstring(sig, doc) + # Skip functions whose documentation is found elsewhere + if doc["Parameters"] == [] and re.search( + r"See[\s]+`[\w.]+`[\s]+(for|and)", doc_extended): + _info("skipping {objname}; references another function", 4) + continue + # Go through each parameter and make sure it is in the docstring for argname, par in sig.parameters.items(): # Look for arguments that we can skip @@ -260,6 +267,11 @@ def test_parameter_docs(module, prefix): f"{obj} return value '{retname}' " "docstring missing space") + # Look at the exceptions + for exc in doc["Raises"]: + _check_numpydoc_param( + obj.__name__, exc, noname_ok=True, section="Raises") + @pytest.mark.parametrize("module, prefix", [ (control, ""), (control.flatsys, "flatsys."), @@ -669,6 +681,29 @@ def _check_numpydoc_style(obj, doc): if re.search(f"`{pyobj}`", text) is not None: _warn(f"{pyobj} appears in {section} for {name} with backticks") + control_classes = [ + 'InputOutputSystem', 'NonlinearIOSystem', 'StateSpace', + 'TransferFunction', 'FrequencyResponseData', 'LinearICSystem', + 'Flatsystem', 'InterconnectedSystem', 'TimeResponseData', + 'NyquistResponseData', 'PoleZeroData', 'RootLocusData', + 'ControlPlot', 'OperatingPoint', 'flatsys.Flatsystem'] + for pyobj in control_classes: + if obj.__name__ == pyobj: + continue + for section in ["Extended Summary", "Notes"]: + text = "\n".join(doc[section]) + if re.search(f"[^`]{pyobj}[^`.]", text) is not None: + _warn(f"{pyobj} in {section} for {name} w/o backticks") + + for section in [ + "Parameters", "Returns", "Additional Parameters", "Yields"]: + if section not in doc: + continue + for arg in doc[section]: + text = arg.type + "\n".join(arg.desc) + if re.search(f"(^|[^`]){pyobj}([^`.]|$)", text) is not None: + _warn(f"{pyobj} in {section} for {name} w/o backticks") + if inspect.isclass(obj): # Specialized checks for classes if doc["Returns"] != []: @@ -684,6 +719,8 @@ def _check_numpydoc_style(obj, doc): for param in doc["Parameters"] + doc["Other Parameters"]: _check_numpydoc_param(name, param, section="Parameters") + for param in doc["Attributes"]: + _check_numpydoc_param(name, param, section="Attributes") for param in doc["Returns"]: _check_numpydoc_param( name, param, empty_ok=True, noname_ok=True, section="Returns") @@ -696,7 +733,8 @@ def _check_numpydoc_style(obj, doc): def _check_numpydoc_param( name, param, empty_ok=False, noname_ok=False, section="??"): param_desc = "\n".join(param.desc) - param_name = f"{name} '{param.name}'" + param_name = f"{name} " + \ + (f" '{param.name}'" if param.name != '' else f" '{param.type}'") # Check for empty section if param.name == "" and param.type == '': @@ -780,8 +818,7 @@ def _info(str, level): print(" " * level + str) def _warn(str, level=-1): - if verbose > level: - print("WARN: " + " " * level + str) + print("WARN: " + " " * level + str) if not standalone: warnings.warn(str, stacklevel=2) @@ -831,7 +868,7 @@ def simple_function(arg1, arg2, opt1=None, **kwargs): doc_test + doc_returns + doc_ret_nospace, UserWarning, "missing space"), (doc_header + doc_returns + doc_ret_nospace, Failed, "missing Parameters section"), - (doc_header, None, ""), + (doc_header + "\nSee `other_function` for details", None, ""), (doc_header + "\n.. deprecated::", None, ""), (doc_header + "\n\n simple_function() is deprecated", UserWarning, "deprecated, but not numpydoc compliant"), diff --git a/control/tests/freqresp_test.py b/control/tests/freqresp_test.py index 3ff1a51c5..6c91a730b 100644 --- a/control/tests/freqresp_test.py +++ b/control/tests/freqresp_test.py @@ -276,7 +276,7 @@ def dsystem_type(request, dsystem_dt): @pytest.mark.parametrize("dsystem_type", ['sssiso', 'ssmimo', 'tf'], indirect=True) def test_discrete(dsystem_type): - """Test discrete time frequency response""" + """Test discrete-time frequency response""" dsys = dsystem_type # Set frequency range to just below Nyquist freq (for Bode) omega_ok = np.linspace(10e-4, 0.99, 100) * np.pi / dsys.dt diff --git a/control/tests/iosys_test.py b/control/tests/iosys_test.py index 48aa71795..42b09ff25 100644 --- a/control/tests/iosys_test.py +++ b/control/tests/iosys_test.py @@ -742,7 +742,7 @@ def test_nonsquare_bdalg(self, tsys): ct.series(*args) def test_discrete(self, tsys): - """Test discrete time functionality""" + """Test discrete-time functionality""" # Create some linear and nonlinear systems to play with linsys = ct.StateSpace( [[-1, 1], [0, -2]], [[0], [1]], [[1, 0]], [[0]], True) @@ -774,7 +774,7 @@ def test_discrete(self, tsys): np.testing.assert_allclose(ios_y, lin_y,atol=0.002,rtol=0.) def test_discrete_iosys(self, tsys): - """Create a discrete time system from scratch""" + """Create a discrete-time system from scratch""" linsys = ct.StateSpace( [[-1, 1], [0, -2]], [[0], [1]], [[1, 0]], [[0]], True) diff --git a/control/tests/lti_test.py b/control/tests/lti_test.py index 1b059ef6f..bd50b46c8 100644 --- a/control/tests/lti_test.py +++ b/control/tests/lti_test.py @@ -75,7 +75,7 @@ def test_issiso_mimo(self): assert not issiso(sys, strict=True) def test_damp(self): - # Test the continuous time case. + # Test the continuous-time case. zeta = 0.1 wn = 42 p = -wn * zeta + 1j * wn * np.sqrt(1 - zeta**2) @@ -84,7 +84,7 @@ def test_damp(self): np.testing.assert_allclose(sys.damp(), expected) np.testing.assert_allclose(damp(sys), expected) - # Also test the discrete time case. + # Also test the discrete-time case. dt = 0.001 sys_dt = c2d(sys, dt, method='matched') p_zplane = np.exp(p*dt) diff --git a/control/tests/matlab2_test.py b/control/tests/matlab2_test.py index 7f5bf945c..d4d8747e5 100644 --- a/control/tests/matlab2_test.py +++ b/control/tests/matlab2_test.py @@ -230,7 +230,7 @@ def test_check_convert_shape(self): assert isinstance(arr, np.ndarray) assert not isinstance(arr, matrix) - #Convert array-like objects to arrays + #Convert array_like objects to arrays #Input is matrix, shape (1,3), must convert to array arr = _check_convert_array(matrix("1. 2 3"), [(3,), (1,3)], 'Test: ') assert isinstance(arr, np.ndarray) diff --git a/control/tests/modelsimp_test.py b/control/tests/modelsimp_test.py index dead4eb75..7dcda6296 100644 --- a/control/tests/modelsimp_test.py +++ b/control/tests/modelsimp_test.py @@ -201,14 +201,14 @@ def testMarkovResults(self, k, m, n): # 0 for k > m-2 (see modelsimp.py). # - # Generate stable continuous time system + # Generate stable continuous-time system Hc = rss(k, 1, 1) # Choose sampling time based on fastest time constant / 10 w, _ = np.linalg.eig(Hc.A) Ts = np.min(-np.real(w)) / 10. - # Convert to a discrete time system via sampling + # Convert to a discrete-time system via sampling Hd = c2d(Hc, Ts, 'zoh') # Compute the Markov parameters from state space diff --git a/control/tests/nyquist_test.py b/control/tests/nyquist_test.py index 8d6fd8561..3b27ee27c 100644 --- a/control/tests/nyquist_test.py +++ b/control/tests/nyquist_test.py @@ -441,7 +441,7 @@ def test_nyquist_legacy(): def test_discrete_nyquist(): # TODO: add tests to make sure plots make sense - # Make sure we can handle discrete time systems with negative poles + # Make sure we can handle discrete-time systems with negative poles sys = ct.tf(1, [1, -0.1], dt=1) * ct.tf(1, [1, 0.1], dt=1) ct.nyquist_response(sys) diff --git a/control/tests/optimal_test.py b/control/tests/optimal_test.py index f746db7d5..46253b794 100644 --- a/control/tests/optimal_test.py +++ b/control/tests/optimal_test.py @@ -56,7 +56,7 @@ def test_continuous_lqr(method, npts): @pytest.mark.parametrize("method", ['shooting']) # TODO: add 'collocation' def test_finite_horizon_simple(method): - # Define a (discrete time) linear system with constraints + # Define a (discrete-time) linear system with constraints # Source: https://www.mpt3.org/UI/RegulationProblem # LTI prediction model (discrete time) @@ -216,7 +216,7 @@ def test_mpc_iosystem_aircraft(): def test_mpc_iosystem_rename(): - # Create a discrete time system (double integrator) + cost function + # Create a discrete-time system (double integrator) + cost function sys = ct.ss([[1, 1], [0, 1]], [[0], [1]], np.eye(2), 0, dt=True) cost = opt.quadratic_cost(sys, np.eye(2), np.eye(1)) timepts = np.arange(0, 5) diff --git a/control/tests/statefbk_test.py b/control/tests/statefbk_test.py index adc7d437d..ebf531546 100644 --- a/control/tests/statefbk_test.py +++ b/control/tests/statefbk_test.py @@ -574,7 +574,7 @@ def test_dare(self, stabilizing): assert np.all(sgn * (np.abs(L) - 1) > 0) def test_lqr_discrete(self): - """Test overloading of lqr operator for discrete time systems""" + """Test overloading of lqr operator for discrete-time systems""" csys = ct.rss(2, 1, 1) dsys = ct.drss(2, 1, 1) Q = np.eye(2) @@ -587,7 +587,7 @@ def test_lqr_discrete(self): np.testing.assert_almost_equal(S_csys, S_expl) np.testing.assert_almost_equal(E_csys, E_expl) - # Calling lqr() with a discrete time system should call dlqr() + # Calling lqr() with a discrete-time system should call dlqr() K_lqr, S_lqr, E_lqr = ct.lqr(dsys, Q, R) K_dlqr, S_dlqr, E_dlqr = ct.dlqr(dsys, Q, R) np.testing.assert_almost_equal(K_lqr, K_dlqr) @@ -602,7 +602,7 @@ def test_lqr_discrete(self): np.testing.assert_almost_equal(S_asys, S_expl) np.testing.assert_almost_equal(E_asys, E_expl) - # Calling dlqr() with a continuous time system should raise an error + # Calling dlqr() with a continuous-time system should raise an error with pytest.raises(ControlArgument, match="dsys must be discrete"): K, S, E = ct.dlqr(csys, Q, R) @@ -783,7 +783,7 @@ def test_statefbk_iosys_unused(self): def test_lqr_integral_continuous(self): - # Generate a continuous time system for testing + # Generate a continuous-time system for testing sys = ct.rss(4, 4, 2, strictly_proper=True) sys.C = np.eye(4) # reset output to be full state C_int = np.eye(2, 4) # integrate outputs for first two states @@ -850,7 +850,7 @@ def test_lqr_integral_continuous(self): assert abs(ctrl_tf(1e-9)[1][1]) > 1e6 def test_lqr_integral_discrete(self): - # Generate a discrete time system for testing + # Generate a discrete-time system for testing sys = ct.drss(4, 4, 2, strictly_proper=True) sys.C = np.eye(4) # reset output to be full state C_int = np.eye(2, 4) # integrate outputs for first two states @@ -885,7 +885,7 @@ def test_lqr_integral_discrete(self): "rss_fun, lqr_fun", [(ct.rss, lqr), (ct.drss, dlqr)]) def test_lqr_errors(self, rss_fun, lqr_fun): - # Generate a discrete time system for testing + # Generate a discrete-time system for testing sys = rss_fun(4, 4, 2, strictly_proper=True) with pytest.raises(ControlArgument, match="must pass an array"): diff --git a/control/tests/statesp_test.py b/control/tests/statesp_test.py index 54f347d69..3722640e4 100644 --- a/control/tests/statesp_test.py +++ b/control/tests/statesp_test.py @@ -1492,8 +1492,8 @@ def test_html_repr_testsize(editsdefaults): class TestLinfnorm: # these are simple tests; we assume ab13dd is correct # python-control specific behaviour is: - # - checking for continuous- and discrete-time - # - scaling fpeak for discrete-time + # - checking for continuous and discrete time + # - scaling fpeak for discrete time # - handling static gains # the underdamped gpeak and fpeak are found from diff --git a/control/tests/stochsys_test.py b/control/tests/stochsys_test.py index 8b846d4a0..0bbf49b57 100644 --- a/control/tests/stochsys_test.py +++ b/control/tests/stochsys_test.py @@ -88,7 +88,7 @@ def test_DLQE(method): check_DLQE(L, P, poles, G, QN, RN) def test_lqe_discrete(): - """Test overloading of lqe operator for discrete time systems""" + """Test overloading of lqe operator for discrete-time systems""" csys = ct.rss(2, 1, 1) dsys = ct.drss(2, 1, 1) Q = np.eye(1) @@ -101,7 +101,7 @@ def test_lqe_discrete(): np.testing.assert_almost_equal(S_csys, S_expl) np.testing.assert_almost_equal(E_csys, E_expl) - # Calling lqe() with a discrete time system should call dlqe() + # Calling lqe() with a discrete-time system should call dlqe() K_lqe, S_lqe, E_lqe = ct.lqe(dsys, Q, R) K_dlqe, S_dlqe, E_dlqe = ct.dlqe(dsys, Q, R) np.testing.assert_almost_equal(K_lqe, K_dlqe) @@ -116,7 +116,7 @@ def test_lqe_discrete(): np.testing.assert_almost_equal(S_asys, S_expl) np.testing.assert_almost_equal(E_asys, E_expl) - # Calling dlqe() with a continuous time system should raise an error + # Calling dlqe() with a continuous-time system should raise an error with pytest.raises(ControlArgument, match="called with a continuous"): K, S, E = ct.dlqe(csys, Q, R) diff --git a/control/tests/timeresp_test.py b/control/tests/timeresp_test.py index 1174947d8..a410bf30f 100644 --- a/control/tests/timeresp_test.py +++ b/control/tests/timeresp_test.py @@ -358,7 +358,7 @@ def test_step_response_mimo(self, tsystem): def test_step_response_return(self, tsystem): """Verify continuous and discrete time use same return conventions.""" sysc = tsystem.sys - sysd = c2d(sysc, 1) # discrete time system + sysd = c2d(sysc, 1) # discrete-time system Tvec = np.linspace(0, 10, 11) # make sure to use integer times 0..10 Tc, youtc = step_response(sysc, Tvec, input=0) Td, youtd = step_response(sysd, Tvec, input=0) @@ -530,7 +530,7 @@ def test_impulse_response_mimo(self, tsystem): @pytest.mark.parametrize("tsystem", ["siso_tf1"], indirect=True) def test_discrete_time_impulse(self, tsystem): - # discrete time impulse sampled version should match cont time + # discrete-time impulse sampled version should match cont time dt = 0.1 t = np.arange(0, 3, dt) sys = tsystem.sys @@ -539,7 +539,7 @@ def test_discrete_time_impulse(self, tsystem): impulse_response(sysdt, t)[1]) def test_discrete_time_impulse_input(self): - # discrete time impulse input, Only one active input for each trace + # discrete-time impulse input, Only one active input for each trace A = [[.5, 0.25],[.0, .5]] B = [[1., 0,],[0., 1.]] C = [[1., 0.],[0., 1.]] diff --git a/control/tests/xferfcn_test.py b/control/tests/xferfcn_test.py index b7be91187..87c852395 100644 --- a/control/tests/xferfcn_test.py +++ b/control/tests/xferfcn_test.py @@ -1205,7 +1205,7 @@ def test_printing_polynomial(self, args, outputfmt, var, dt, dtstring): assert polystr[2] == outputfmt.format(var=var) def test_printing_mimo(self): - """Print MIMO, continuous time""" + """Print MIMO, continuous-time""" sys = ss2tf(rss(4, 2, 3)) assert isinstance(str(sys), str) assert isinstance(sys._repr_html_(), str) diff --git a/control/timeplot.py b/control/timeplot.py index ae84b7c6c..c0a7c361d 100644 --- a/control/timeplot.py +++ b/control/timeplot.py @@ -52,7 +52,7 @@ def time_response_plot( Parameters ---------- - data : TimeResponseData + data : `TimeResponseData` Data to be plotted. plot_inputs : bool or str, optional Sets how and where to plot the inputs: @@ -751,7 +751,10 @@ def combine_time_responses(response_list, trace_labels=None, title=None): # Add on trace label and trace type if generate_trace_labels: - trace_labels.append(response.title) + trace_labels.append( + response.title if response.title is not None else + response.sysname if response.sysname is not None else + "unknown") trace_types.append( None if response.trace_types is None else response.trace_types[0]) diff --git a/control/timeresp.py b/control/timeresp.py index 5603866c4..5a85fba96 100644 --- a/control/timeresp.py +++ b/control/timeresp.py @@ -55,11 +55,11 @@ class TimeResponseData: - """A class for returning time responses. + """Input/output system time response data. This class maintains and manipulates the data corresponding to the temporal response of an input/output system. It is used as the return - type for time domain simulations (step response, input/output response, + type for time domain simulations (`step_response`, `input_output_response`, etc). A time response consists of a time vector, an output vector, and @@ -143,18 +143,18 @@ class TimeResponseData: performs squeeze processing. issiso : bool, optional Set to True if the system generating the data is single-input, - single-output. If passed as None (default), the input data - will be used to set the value. + single-output. If passed as None (default), the input and output + data will be used to set the value. ninputs, noutputs, nstates : int Number of inputs, outputs, and states of the underlying system. params : dict, optional If system is a nonlinear I/O system, set parameter values. ntraces : int, optional Number of independent traces represented in the input/output - response. If ntraces is 0 (default) then the data represents a + response. If `ntraces` is 0 (default) then the data represents a single trace with the trace index surpressed in the data. trace_labels : array of string, optional - Labels to use for traces (set to sysname it ntraces is 0) + Labels to use for traces (set to sysname it `ntraces` is 0). trace_types : array of string, optional Type of trace. Currently only 'step' is supported, which controls the way in which the signal is plotted. @@ -175,7 +175,7 @@ class TimeResponseData: assigning to a tuple (default = False). plot_inputs : bool, optional Whether or not to plot the inputs by default (can be overridden - in the plot() method). + in the `~TimeResponseData.plot` method). multi_trace : bool, optional If True, then 2D input array represents multiple traces. For a MIMO system, the `input` attribute should then be set to @@ -566,7 +566,7 @@ def states(self): def inputs(self): """Time response input vector. - Input(s) to the system, indexed by input (optiona), trace (optional), + Input(s) to the system, indexed by input (optional), trace (optional), and time. If a 1D vector is passed, the input corresponds to a scalar-valued input. If a 2D vector is passed, then it can either represent multiple single-input traces or a single multi-input trace. @@ -695,7 +695,7 @@ def plot(self, *args, **kwargs): """Plot the time response data objects. This method calls `time_response_plot`, passing all arguments - and keywords. + and keywords. See `time_response_plot` for details. """ return time_response_plot(self, *args, **kwargs) @@ -790,7 +790,7 @@ def _process_labels(labels, signal, length): return labels -# Helper function for checking array-like parameters +# Helper function for checking array_like parameters def _check_convert_array(in_obj, legal_shapes, err_msg_start, squeeze=False, transpose=False): @@ -925,8 +925,8 @@ def forced_response(sysdata, T=None, U=0., X0=0., transpose=False, params=None, U : array_like or float, optional Input array giving input at each time `T`. If `U` is None or 0, - `T` must be given, even for discrete time systems. In this case, - for continuous time systems, a direct calculation of the matrix + `T` must be given, even for discrete-time systems. In this case, + for continuous-time systems, a direct calculation of the matrix exponential is used, which is faster than the general interpolating algorithm used otherwise. @@ -941,7 +941,7 @@ def forced_response(sysdata, T=None, U=0., X0=0., transpose=False, params=None, compatibility with MATLAB and `scipy.signal.lsim`). interpolate : bool, default=False - If True and system is a discrete time system, the input will + If True and system is a discrete-time system, the input will be interpolated between the given time steps and the output will be given at system sampling rate. Otherwise, only return the output at the times given in `T`. No effect on continuous @@ -997,10 +997,10 @@ def forced_response(sysdata, T=None, U=0., X0=0., transpose=False, params=None, Notes ----- - For discrete time systems, the input/output response is computed + For discrete-time systems, the input/output response is computed using the `scipy.signal.dlsim` function. - For continuous time systems, the output is computed using the + For continuous-time systems, the output is computed using the matrix exponential exp(A t) and assuming linear interpolation of the inputs between time points. @@ -1080,10 +1080,10 @@ def forced_response(sysdata, T=None, U=0., X0=0., transpose=False, params=None, if U is not None: U = np.asarray(U) if T is not None: - # T must be array-like + # T must be array_like T = np.asarray(T) - # Set and/or check time vector in discrete time case + # Set and/or check time vector in discrete-time case if isdtime(sys): if T is None: if U is None or (U.ndim == 0 and U == 0.): @@ -1132,7 +1132,7 @@ def forced_response(sysdata, T=None, U=0., X0=0., transpose=False, params=None, xout[:, 0] = X0 yout = np.zeros((n_outputs, n_steps)) - # Separate out the discrete and continuous time cases + # Separate out the discrete and continuous-time cases if isctime(sys, strict=True): # Solve the differential equation, copied from scipy.signal.ltisys. @@ -1213,7 +1213,7 @@ def forced_response(sysdata, T=None, U=0., X0=0., transpose=False, params=None, # Discrete time simulation using signal processing toolbox dsys = (A, B, C, D, sys_dt) - # Use signal processing toolbox for the discrete time simulation + # Use signal processing toolbox for the discrete-time simulation # Transpose the input to match toolbox convention tout, yout, xout = sp.signal.dlsim(dsys, np.transpose(U), spT, X0) tout = tout + T[0] @@ -1332,11 +1332,11 @@ def step_response( T : array_like or float, optional Time vector, or simulation time duration if a number. If T is not provided, an attempt is made to create it automatically from the - dynamics of sys. If sys is continuous-time, the time increment dt + dynamics of sys. If sys is continuous time, the time increment dt is chosen small enough to show the fastest mode, and the simulation time period tfinal long enough to show the slowest mode, excluding poles at the origin and pole-zero cancellations. If this results in - too many time steps (>5000), dt is reduced. If sys is discrete-time, + too many time steps (>5000), dt is reduced. If sys is discrete time, only tfinal is computed, and final is reduced if it requires too many simulation steps. @@ -1358,7 +1358,7 @@ def step_response( T_num : int, optional Number of time steps to use in simulation if T is not provided as an - array (autocomputed if not given); ignored if sys is discrete-time. + array (autocomputed if not given); ignored if sys is discrete time. transpose : bool, optional If True, transpose all input and output arrays (for backward @@ -1489,9 +1489,9 @@ def step_info(sysdata, T=None, T_num=None, yfinal=None, params=None, Parameters ---------- - sysdata : StateSpace or TransferFunction or array_like - The system data. Either LTI system to simulate (StateSpace, - TransferFunction), or a time series of step response data. + sysdata : `StateSpace` or `TransferFunction` or array_like + The system data. Either LTI system to simulate (`StateSpace`, + `TransferFunction`), or a time series of step response data. T : array_like or float, optional Time vector, or simulation time duration if a number (time vector is autocomputed if not given, see `step_response` for more detail). @@ -1727,7 +1727,7 @@ def initial_response( sysdata : I/O system or list of I/O systems I/O system(s) for which initial response is computed. - sys : StateSpace or TransferFunction + sys : `StateSpace` or `TransferFunction` LTI system to simulate. T : array_like or float, optional @@ -1744,7 +1744,7 @@ def initial_response( T_num : int, optional Number of time steps to use in simulation if T is not provided as an - array (autocomputed if not given); ignored if sys is discrete-time. + array (autocomputed if not given); ignored if sys is discrete time. params : dict, optional If system is a nonlinear I/O system, set parameter values. @@ -1863,7 +1863,7 @@ def impulse_response( T_num : int, optional Number of time steps to use in simulation if T is not provided as an - array (autocomputed if not given); ignored if sys is discrete-time. + array (autocomputed if not given); ignored if sys is discrete time. transpose : bool, optional If True, transpose all input and output arrays (for backward @@ -1898,7 +1898,7 @@ def impulse_response( Notes ----- This function uses the `forced_response` function to compute the time - response. For continuous time systems, the initial condition is altered + response. For continuous-time systems, the initial condition is altered to account for the initial impulse. For discrete-time aystems, the impulse is sized so that it has unit area. The impulse response for nonlinear systems is not implemented. @@ -2017,7 +2017,7 @@ def _ideal_tfinal_and_dt(sys, is_step=True): Parameters ---------- - sys : StateSpace or TransferFunction + sys : `StateSpace` or `TransferFunction` The system whose time response is to be computed is_step : bool Scales the dc value by the magnitude of the nonzero mode since @@ -2177,7 +2177,7 @@ def _ideal_tfinal_and_dt(sys, is_step=True): def _default_time_vector(sysdata, N=None, tfinal=None, is_step=True): """Returns a time vector that has a reasonable number of points. - if system is discrete-time, N is ignored """ + if system is discrete time, N is ignored """ from .lti import LTI if isinstance(sysdata, (list, tuple)): diff --git a/control/xferfcn.py b/control/xferfcn.py index 485d98408..fc209ff49 100644 --- a/control/xferfcn.py +++ b/control/xferfcn.py @@ -48,7 +48,7 @@ class TransferFunction(LTI): """TransferFunction(num, den[, dt]) - A class for representing transfer functions. + Transfer function representation for LTI input/output systems. The TransferFunction class is used to represent systems in transfer function form. Transfer functions are usually created with the @@ -81,15 +81,15 @@ class TransferFunction(LTI): of 1D array objects (of varying length). num_list, den_list : 2D list of 1D array Numerator and denominator polynomial coefficients as 2D lists - of 1D array objects (of varying length) + of 1D array objects (of varying length). display_format : None, 'poly' or 'zpk' Display format used in printing the TransferFunction object. Default behavior is polynomial display and can be changed by changing `config.defaults['xferfcn.display_format']`. - s : TransferFunction - Represents the continuous time differential operator. - z : TransferFunction - Represents the discrete time delay operator. + s : `TransferFunction` + Represents the continuous-time differential operator. + z : `TransferFunction` + Represents the discrete-time delay operator. See Also -------- @@ -97,15 +97,15 @@ class TransferFunction(LTI): Notes ----- - The numerator and denominator polynomials are stored as 2D ndarrays - with each element containing a 1D ndarray of coefficients. These data + The numerator and denominator polynomials are stored as 2D arrays + with each element containing a 1D array of coefficients. These data structures can be retrieved using `num_array` and `den_array`. For example, >>> sys.num_array[2, 5] # doctest: +SKIP gives the numerator of the transfer function from the 6th input to the - 3rd output. (Note: a single 3D ndarray structure cannot be used because + 3rd output. (Note: a single 3D array structure cannot be used because the numerators and denominators can have different numbers of coefficients in each entry.) @@ -118,22 +118,22 @@ class TransferFunction(LTI): For legacy purposes, this list-based representation can also be obtained using `num` and `den`. - A discrete time transfer function is created by specifying a nonzero + A discrete-time transfer function is created by specifying a nonzero 'timebase' dt when the system is constructed: - * `dt` = 0: continuous time system (default) - * `dt` > 0: discrete time system with sampling period 'dt' + * `dt` = 0: continuous-time system (default) + * `dt` > 0: discrete-time system with sampling period 'dt' * `dt` = True: discrete time with unspecified sampling period * `dt` = None: no timebase specified Systems must have compatible timebases in order to be combined. A - discrete time system with unspecified sampling time (`dt` = True) can + discrete-time system with unspecified sampling time (`dt` = True) can be combined with a system having a specified sampling time; the result - will be a discrete time system with the sample time of the latter + will be a discrete-time system with the sample time of the other system. Similarly, a system with timebase None can be combined with a system having any timebase; the result will have the timebase of the - latter system. The default value of dt can be changed by changing the - value of `control.config.defaults['control.default_dt']`. + other system. The default value of dt can be changed by changing the + value of `config.defaults['control.default_dt']`. A transfer function is callable and returns the value of the transfer function evaluated at a point in the complex plane. See @@ -154,7 +154,7 @@ class TransferFunction(LTI): discrete time. These can be used to create variables that allow algebraic creation of transfer functions. For example, - >>> s = ct.TransferFunction.s + >>> s = ct.TransferFunction.s # or ct.tf('s') >>> G = (s + 1)/(s**2 + 2*s + 1) """ @@ -165,7 +165,7 @@ def __init__(self, *args, **kwargs): The default constructor is TransferFunction(num, den), where num and den are 2D arrays of arrays containing polynomial coefficients. - To create a discrete time transfer funtion, use TransferFunction(num, + To create a discrete-time transfer function, use TransferFunction(num, den, dt) where 'dt' is the sampling time (or True for unspecified sampling time). To call the copy constructor, call TransferFunction(sys), where sys is a TransferFunction object @@ -382,14 +382,24 @@ def __call__(self, x, squeeze=None, warn_infinite=True): return _process_frequency_response(self, x, out, squeeze=squeeze) def horner(self, x, warn_infinite=True): - """Evaluate system's transfer function at complex frequency - using Horner's method. + """Evaluate value of transfer function using Horner's method. + + Evaluates ``sys(x)`` where `x` is a complex number `s` for + continuous-time systems and `z` for discrete-time systems. Expects + inputs and outputs to be formatted correctly. Use ``sys(x)`` for a + more user-friendly interface. + + Parameters + ---------- + x : complex + Complex frequency at which the transfer function is evaluated. - Evaluates ``sys(x)`` where `x` is `s` for continuous-time systems - and `z` for discrete-time systems. + warn_infinite : bool, optional + If True (default), generate a warning if `x` is a pole. - Expects inputs and outputs to be formatted correctly. Use ``sys(x)`` - for a more user-friendly interface. + Returns + ------- + complex """ # Make sure the argument is a 1D array of complex numbers @@ -857,7 +867,17 @@ def zeros(self): return roots(self.num_array[0, 0]).astype(complex) def feedback(self, other=1, sign=-1): - """Feedback interconnection between two LTI objects.""" + """Feedback interconnection between two LTI objects. + + Parameters + ---------- + other : `InputOutputSystem` + System in the feedack path. + + sign : float, optional + Gain to use in feedback path. Defaults to -1. + + """ other = _convert_to_transfer_function(other) if (self.ninputs > 1 or self.noutputs > 1 or @@ -898,7 +918,14 @@ def append(self, other): return new_tf def minreal(self, tol=None): - """Remove cancelling pole/zero pairs from a transfer function.""" + """Remove cancelling pole/zero pairs from a transfer function. + + Parameters + ---------- + tol : float + Tolerance for determining whether poles and zeros overlap. + + """ # based on octave minreal # default accuracy @@ -955,7 +982,7 @@ def returnScipySignalLTI(self, strict=True): The timebase `tfobject.dt` cannot be None; it must be continuous (0) or discrete (True or > 0). False: - if `tfobject.dt` is None, continuous time + if `tfobject.dt` is None, continuous-time `scipy.signal.lti` objects are returned Returns @@ -971,7 +998,7 @@ def returnScipySignalLTI(self, strict=True): if self.dt: kwdt = {'dt': self.dt} else: - # scipy convention for continuous time lti systems: call without + # scipy convention for continuous-time lti systems: call without # dt keyword argument kwdt = {} @@ -1183,7 +1210,7 @@ def sample(self, Ts, method='zoh', alpha=None, prewarp_frequency=None, ignored otherwise. name : string, optional Set the name of the sampled system. If not specified and if - `copy_names` is False, a generic name is generated with + `copy_names` is False, a generic name 'sys[id]' is generated with a unique integer id. If `copy_names` is True, the new system name is determined by adding the prefix and suffix strings in `config.defaults['iosys.sampled_system_name_prefix']` and @@ -1196,7 +1223,7 @@ def sample(self, Ts, method='zoh', alpha=None, prewarp_frequency=None, Returns ------- - sysd : TransferFunction system + sysd : `TransferFunction` system Discrete-time system, with sample period Ts. Other Parameters @@ -1219,7 +1246,7 @@ def sample(self, Ts, method='zoh', alpha=None, prewarp_frequency=None, """ if not self.isctime(): - raise ValueError("System must be continuous time system") + raise ValueError("System must be continuous-time system") if not self.issiso(): raise ControlMIMONotImplemented("Not implemented for MIMO systems") if method == "matched": @@ -1302,7 +1329,7 @@ def _isstatic(self): #: Differentation operator (continuous time). #: - #: The `s` constant can be used to create continuous time transfer + #: The `s` constant can be used to create continuous-time transfer #: functions using algebraic expressions. #: #: Examples @@ -1315,7 +1342,7 @@ def _isstatic(self): #: Delay operator (discrete time). #: - #: The `z` constant can be used to create discrete time transfer + #: The `z` constant can be used to create discrete-time transfer #: functions using algebraic expressions. #: #: Examples @@ -1497,7 +1524,7 @@ def _convert_to_transfer_function( [[1., 1., 1.] [1., 1., 1.]]. - If `sys` is an array-like type, then it is converted to a constant-gain + If `sys` is an array_like type, then it is converted to a constant-gain transfer function. Note: no renaming of inputs and outputs is performed; this should be done @@ -1570,7 +1597,7 @@ def _convert_to_transfer_function( elif isinstance(sys, FrequencyResponseData): raise TypeError("Can't convert given FRD to TransferFunction system.") - # If this is array-like, try to create a constant feedthrough + # If this is array_like, try to create a constant feedthrough try: D = array(sys, ndmin=2) outputs, inputs = D.shape @@ -1592,7 +1619,7 @@ def tf(*args, **kwargs): ``tf(sys)`` Convert a linear system into transfer function form. Always creates - a new system, even if sys is already a TransferFunction object. + a new system, even if sys is already a `TransferFunction` object. ``tf(num, den)`` @@ -1609,7 +1636,7 @@ def tf(*args, **kwargs): ``tf(num, den, dt)`` - Create a discrete time transfer function system; dt can either be a + Create a discrete-time transfer function system; dt can either be a positive number indicating the sampling time or 'True' if no specific timebase is given. @@ -1625,9 +1652,9 @@ def tf(*args, **kwargs): Parameters ---------- - sys : LTI (StateSpace or TransferFunction) + sys : `LTI` (`StateSpace` or `TransferFunction`) A linear system that will be converted to a transfer function. - arr : 2D list of TransferFunction + arr : 2D list of `TransferFunction` 2D list of SISO transfer functions to create MIMO transfer function. num : array_like, or list of list of array_like Polynomial coefficients of the numerator. @@ -1639,13 +1666,13 @@ def tf(*args, **kwargs): number is discrete time with specified sampling time, None indicates unspecified timebase (either continuous or discrete time). display_format : None, 'poly' or 'zpk' - Set the display format used in printing the TransferFunction object. + Set the display format used in printing the `TransferFunction` object. Default behavior is polynomial display and can be changed by changing `config.defaults['xferfcn.display_format']`. Returns ------- - sys : TransferFunction + sys : `TransferFunction` The new linear system. Other Parameters @@ -1657,7 +1684,7 @@ def tf(*args, **kwargs): input_prefix, output_prefix : string, optional Set the prefix for input and output signals. Defaults = 'u', 'y'. name : string, optional - System name. If unspecified, a generic name is generated + System name. If unspecified, a generic name 'sys[id]' is generated with a unique integer id. Raises @@ -1696,7 +1723,7 @@ def tf(*args, **kwargs): >>> s = ct.tf('s') >>> G = (s + 1)/(s**2 + 2*s + 1) - >>> # Convert a StateSpace to a TransferFunction object. + >>> # Convert a state space system to a transfer function: >>> sys_ss = ct.ss([[1, -2], [3, -4]], [[5], [7]], [[6, 8]], 9) >>> sys_tf = ct.tf(sys_ss) @@ -1793,9 +1820,9 @@ def zpk(zeros, poles, gain, *args, **kwargs): `InputOutputSystem` for more information. name : string, optional System name (used for specifying signals). If unspecified, a generic - name is generated with a unique integer id. + name 'sys[id]' is generated with a unique integer id. display_format : None, 'poly' or 'zpk', optional - Set the display format used in printing the TransferFunction object. + Set the display format used in printing the `TransferFunction` object. Default behavior is polynomial display and can be changed by changing `config.defaults['xferfcn.display_format']`. @@ -1840,7 +1867,7 @@ def ss2tf(*args, **kwargs): Parameters ---------- - sys : StateSpace + sys : `StateSpace` A linear system. A : array_like or string System matrix. @@ -1855,7 +1882,7 @@ def ss2tf(*args, **kwargs): Returns ------- - out : TransferFunction + out : `TransferFunction` New linear system in transfer function form. Other Parameters @@ -1865,16 +1892,16 @@ def ss2tf(*args, **kwargs): system. If not given, the inputs and outputs are the same as the original system. name : string, optional - System name. If unspecified, a generic name is generated + System name. If unspecified, a generic name 'sys[id]' is generated with a unique integer id. Raises ------ ValueError - if matrix sizes are not self-consistent, or if an invalid number of - arguments is passed in + If matrix sizes are not self-consistent, or if an invalid number of + arguments is passed in. TypeError - if `sys` is not a StateSpace object. + If `sys` is not a `StateSpace` object. See Also -------- @@ -1924,7 +1951,7 @@ def tfdata(sys): Parameters ---------- - sys : LTI (StateSpace, or TransferFunction) + sys : `StateSpace` or `TransferFunction` LTI system whose data will be returned. Returns @@ -1940,7 +1967,7 @@ def tfdata(sys): def _clean_part(data, name=""): """ Return a valid, cleaned up numerator or denominator - for the TransferFunction class. + for the `TransferFunction` class. Parameters ---------- diff --git a/doc/Makefile b/doc/Makefile index 4821cf308..16c9ec9b5 100644 --- a/doc/Makefile +++ b/doc/Makefile @@ -28,29 +28,32 @@ help: # List of the first RST figure of each type in each file that is generated figures/flatsys-steering-compare.png: flatsys.rst + @$(SPHINXBUILD) -M doctest "$(SOURCEDIR)" "$(BUILDDIR)" figures/iosys-predprey-open.png: iosys.rst + @$(SPHINXBUILD) -M doctest "$(SOURCEDIR)" "$(BUILDDIR)" figures/timeplot-servomech-combined.png: nlsys.rst + @$(SPHINXBUILD) -M doctest "$(SOURCEDIR)" "$(BUILDDIR)" figures/steering-optimal.png: optimal.rst + @$(SPHINXBUILD) -M doctest "$(SOURCEDIR)" "$(BUILDDIR)" figures/phaseplot-dampedosc-default.png: phaseplot.rst -figures/timeplot-mimo_step-default.png: response.rst -figures/freqplot-siso_bode-default.png: response.rst -figures/pzmap-siso_ctime-default.png: response.rst -figures/rlocus-siso_ctime-default.png: response.rst -figures/ctrlplot-servomech.png: response.rst + @$(SPHINXBUILD) -M doctest "$(SOURCEDIR)" "$(BUILDDIR)" +figures/timeplot-mimo_step-default.png \ + figures/freqplot-siso_bode-default.png \ + figures/pzmap-siso_ctime-default.png \ + figures/rlocus-siso_ctime-default.png \ + figures/ctrlplot-servomech.png: response.rst + @$(SPHINXBUILD) -M doctest "$(SOURCEDIR)" "$(BUILDDIR)" + figures/stochastic-whitenoise-response.png: stochastic.rst + @$(SPHINXBUILD) -M doctest "$(SOURCEDIR)" "$(BUILDDIR)" # Other figure rules figure/classes.pdf: figure/classes.fig make -C figures classes.pdf -# Rule to run `make doctest` once for all figures -.docfigs: $(RST_FIGS) - @$(SPHINXBUILD) -M doctest "$(SOURCEDIR)" "$(BUILDDIR)" - touch $@ - # Catch-all target: route all unknown targets to Sphinx using the new # "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS). -html latexpdf doctest clean: Makefile .docfigs $(FIGS) +html latexpdf doctest clean: Makefile $(FIGS) $(RST_FIGS) @$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) distclean: clean diff --git a/doc/_templates/custom-class-template.rst b/doc/_templates/custom-class-template.rst index 53a76e905..2432f11d2 100644 --- a/doc/_templates/custom-class-template.rst +++ b/doc/_templates/custom-class-template.rst @@ -5,7 +5,7 @@ .. autoclass:: {{ objname }} :members: :show-inheritance: - :inherited-members: + :inherited-members: list dict :special-members: {% block methods %} diff --git a/doc/classes.rst b/doc/classes.rst index 7cde1e76c..febdc7093 100644 --- a/doc/classes.rst +++ b/doc/classes.rst @@ -14,6 +14,14 @@ systems (both linear time-invariant and nonlinear). They are usually created from factory functions such as :func:`tf` and :func:`ss`, so the user should normally not need to instantiate these directly. +The following figure illustrates the relationship between the classes and +some of the functions that can be used to convert objects from one class to +another: + +.. image:: figures/classes.pdf + :width: 800 + :align: center + .. autosummary:: :toctree: generated/ :template: custom-class-template.rst @@ -28,14 +36,6 @@ user should normally not need to instantiate these directly. InterconnectedSystem LinearICSystem -The following figure illustrates the relationship between the classes and -some of the functions that can be used to convert objects from one class to -another: - -.. image:: figures/classes.pdf - :width: 800 - :align: center - Response and Plotting Classes ============================= diff --git a/doc/config.rst b/doc/config.rst index d246d62c3..c3c4c8184 100644 --- a/doc/config.rst +++ b/doc/config.rst @@ -5,11 +5,11 @@ Package Configuration Parameters ================================ -The python-control package can be customized to allow for different default -values for selected parameters. This includes the ability to change the -way system names are created, set the style for various types of plots, and -determine default solvers and parameters to use in solving optimization -problems. +The python-control package can be customized to allow for different +default values for selected parameters. This includes the ability to +change the way system names are created, to set the style for various +types of plots, and to determine default solvers and parameters to use +in solving optimization problems. To set the default value of a configuration parameter, set the appropriate element of the `config.defaults` dictionary:: @@ -21,8 +21,8 @@ configuration parameters at the same time:: ct.set_defaults('module', param1=val1, param2=val2, ...] -Finally, there are also functions available to set collections of -parameters based on standard configurations: +Several functions available to set collections of parameters based on +standard configurations: .. autosummary:: @@ -38,6 +38,20 @@ parameters based on standard configurations: .. currentmodule:: control.config.defaults +Finally, the `config.defaults` object can be used as a context manager +for temporarily setting default parameters: + +.. testsetup:: + + import control as ct + +.. doctest:: + + >>> with ct.config.defaults({'iosys.repr_format': 'info'}): + ... sys = ct.rss(4, 2, 2, name='sys') + ... print(repr(sys)) + ['y[0]', 'y[1]']> + System creation parameters -------------------------- @@ -47,8 +61,8 @@ System creation parameters Default value of `dt` when constructing new I/O systems. If `dt` is not specified explicitly this value will be used. Set to None - to leave the timebase unspecified, 0 for continuous time systems, - True for discrete time systems. + to leave the timebase unspecified, 0 for continuous-time systems, + True for discrete-time systems. .. py:data:: iosys.converted_system_name_prefix :type: str @@ -150,7 +164,7 @@ System display parameters Set the default format used by :func:`iosys_repr` to create the representation of an :class:`InputOutputSystem`: - * 'info' : [outputs] + * 'info' : [outputs]> * 'eval' : system specific, loadable representation * 'latex' : latex representation of the object @@ -346,12 +360,12 @@ Plotting parameters :type: bool :value: False - If wrap_phase is False, then the phase will be unwrapped so that it - is continuously increasing or decreasing. If wrap_phase is True the - phase will be restricted to the range [-180, 180) (or [:math:`-\pi`, - :math:`\pi`) radians). If `wrap_phase` is specified as a float, the - phase will be offset by 360 degrees if it falls below the specified - value. + If wrap_phase is False, then the phase will be unwrapped so that it + is continuously increasing or decreasing. If wrap_phase is True the + phase will be restricted to the range [-180, 180) (or [:math:`-\pi`, + :math:`\pi`) radians). If `wrap_phase` is specified as a float, the + phase will be offset by 360 degrees if it falls below the specified + value. .. py:data:: nichols.grid :type: bool @@ -364,13 +378,7 @@ Plotting parameters :type: int :value: 2 - Specify the number of arrows for :func:`nyquist_plot`. If an integer is - passed. that number of equally spaced arrows will be plotted on each of - the primary segment and the mirror image. If a 1D array is passed, it - should consist of a sorted list of floats between 0 and 1, indicating - the location along the curve to plot an arrow. If a 2D array is passed, - the first row will be used to specify arrow locations for the primary - curve and the second row will be used for the mirror image. + Specify the default number of arrows for :func:`nyquist_plot`. .. py:data:: nyquist.arrow_size :type: float @@ -429,7 +437,7 @@ Plotting parameters When plotting scaled portion of the Nyquist plot in :func:`nyquist_plot`, increase/decrease the magnitude by this fraction - of the max_curve_magnitude to allow any overlaps between the primary and + of the `max_curve_magnitude` to allow any overlaps between the primary and mirror curves to be avoided. .. py:data:: nyquist.mirror_style @@ -439,7 +447,7 @@ Plotting parameters Linestyles for mirror image of the Nyquist curve in :func:`nyquist_plot`. The first element is used for unscaled portions of the Nyquist curve, the second element is used for portions that are - scaled (using max_curve_magnitude). If False then omit completely. + scaled (using `max_curve_magnitude`). If False then omit completely. .. py:data:: nyquist.primary_style :type: list of str @@ -502,30 +510,30 @@ Plotting parameters :type: float :value: 1.05 - The limits of the pole/zero plot generated by :func:`pole_zero_plot` - are set based on the location features in the plot, including the - location of poles, zeros, and local maxima of root locus curves. The - locations of local maxima are expanded by the buffer factor set by - `buffer_factor`. + The limits of the pole/zero plot generated by :func:`pole_zero_plot` + are set based on the location features in the plot, including the + location of poles, zeros, and local maxima of root locus curves. The + locations of local maxima are expanded by the buffer factor set by + `buffer_factor`. .. py:data:: pzmap.expansion_factor :type: float :value: 1.8 - The final axis limits of the pole/zero plot generated by - :func:`pole_zero_plot` are set to by the largest features in the plot - multiplied by an expansion factor set by `expansion_factor`. + The final axis limits of the pole/zero plot generated by + :func:`pole_zero_plot` are set to by the largest features in the plot + multiplied by an expansion factor set by `expansion_factor`. .. py:data:: pzmap.grid :type: bool :value: False If True plot omega-damping grid in :func:`pole_zero_plot`. If False - or None show imaginary axis for continuous time systems, unit circle for - discrete time systems. If `empty`, do not draw any additonal lines. + or None show imaginary axis for continuous-time systems, unit circle for + discrete-time systems. If `empty`, do not draw any additonal lines. Note: this setting only applies to pole/zero plots. For root locus - plots, the 'rlocus.grid' parameter value is used as teh default. + plots, the 'rlocus.grid' parameter value is used as the default. .. py:data:: pzmap.marker_size :type: float @@ -545,9 +553,9 @@ Plotting parameters :type: bool :value: True - If True plot omega-damping grid in :func:`root_locus_plot`. If False - or None show imaginary axis for continuous time systems, unit circle for - discrete time systems. If `empty`, do not draw any additonal lines. + If True, plot omega-damping grid in :func:`root_locus_plot`. If False + or None show imaginary axis for continuous-time systems, unit circle for + discrete-time systems. 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A -discrete time system with unspecified sampling time (`dt` = True) +discrete-time system with unspecified sampling time (`dt` = True) can be combined with a system having a specified sampling time; the -result will be a discrete time system with the sample time of the -latter system. Similarly, a system with timebase None can be combined +result will be a discrete-time system with the sample time of the +other system. Similarly, a system with timebase None can be combined with a system having a specified timebase; the result will have the -timebase of the latter system. For continuous time systems, the +timebase of the other system. For continuous-time systems, the :func:`sample_system` function or the :meth:`StateSpace.sample` and :meth:`TransferFunction.sample` methods can be used to create a -discrete time system from a continuous time system. See +discrete-time system from a continuous-time system. See :ref:`utility-and-conversions`. The default value of `dt` can be changed by changing the value of `config.defaults['control.default_dt']`. @@ -332,7 +332,7 @@ Functions operating on LTI systems will take into account whether a system is continuous time or discrete time when carrying out operations that depend on this difference. For example, the :func:`rss` function will place all system eigenvalues within the unit circle when called -using `dt` corresponding to a discrete time system: +using `dt` corresponding to a discrete-time system: .. testsetup:: @@ -417,7 +417,7 @@ Conversion of transfer functions to state space form is also possible: Time sampling ------------- -Continuous time systems can be converted to discrete time systems using +Continuous time systems can be converted to discrete-time systems using the :func:`sample_system` function and specifying a sampling time: .. doctest:: @@ -447,7 +447,7 @@ the :func:`sample_system` function and specifying a sampling time: D = [[-0.34680884 0.02138098] [ 0.29124186 -0.01476461]] -Note that the system name for the discrete time system is the name of +Note that the system name for the discrete-time system is the name of the original system with the string '$sampled' appended. Discrete time systems can also be created using the diff --git a/doc/matlab.rst b/doc/matlab.rst index e5429b99e..fd8368bc3 100644 --- a/doc/matlab.rst +++ b/doc/matlab.rst @@ -9,9 +9,9 @@ :no-inherited-members: :no-special-members: -.. warning:: This module is not closely maintained and some functionality - in main control package may not be be available via the MATLAB - compatibility module. +.. warning:: This module is not closely maintained and some + functionality in the main python-control package may not + be be available via the MATLAB compatibility module. Creating Linear Models ====================== diff --git a/doc/nlsys.rst b/doc/nlsys.rst index d05c7884e..017fd92f3 100644 --- a/doc/nlsys.rst +++ b/doc/nlsys.rst @@ -39,7 +39,7 @@ where `x` is a 1-D array with shape (n,), `u` is a 1-D array with shape (m,), `t` is a float representing the current time, and `params` is a dict containing the values of parameters used by the function. The dynamics of the system can be in continuous or discrete -time (use the `dt` keyword to create a discrete time system). +time (use the `dt` keyword to create a discrete-time system). The output function `outfcn` is used to specify the outputs of the system and has the same calling signature as `updfcn`. If it is not @@ -140,7 +140,7 @@ on an initial guess `x0`. The second form fixes the desired output values `y0` and uses `x0` and `u0` as an initial guess to find the equilibrium point. If no equilibrium point can be found, the function returns the operating point that minimizes the state update (state -derivative for continuous time systems, state difference for discrete +derivative for continuous-time systems, state difference for discrete time systems). More complex operating points can be found by specifying which states, diff --git a/doc/optimal.rst b/doc/optimal.rst index 468582423..d88e9cb20 100644 --- a/doc/optimal.rst +++ b/doc/optimal.rst @@ -70,7 +70,7 @@ can be on the input, the state, or combinations of input and state, depending on the form of :math:`g_i`. Furthermore, these constraints are intended to hold at all instants in time along the trajectory. -For a discrete time system, the same basic formulation applies except +For a discrete-time system, the same basic formulation applies except that the cost function is given by .. math:: diff --git a/doc/response.rst b/doc/response.rst index 574aa093f..59a024020 100644 --- a/doc/response.rst +++ b/doc/response.rst @@ -682,7 +682,7 @@ the plot title: .. image:: figures/rlocus-siso_ctime-clicked.png :align: center -Root locus diagrams are also supported for discrete time systems, in which +Root locus diagrams are also supported for discrete-time systems, in which case the grid is show inside the unit circle: .. testcode:: pzmap @@ -903,7 +903,7 @@ features: As this example illustrates, python-control plotting functions and Matplotlib plotting functions can generally be intermixed. One type of plot for which this does not currently work is pole/zero plots with a -continuous time omega-damping grid (including root locus diagrams), due to +continuous-time omega-damping grid (including root locus diagrams), due to the way that axes grids are implemented. As a workaround, the :func:`pole_zero_subplots` command can be used to create an array of subplots with different grid types, as illustrated in the following diff --git a/doc/statesp.rst b/doc/statesp.rst index f84066931..ca0ffee08 100644 --- a/doc/statesp.rst +++ b/doc/statesp.rst @@ -131,9 +131,9 @@ several forms: * ``K, S, E = ct.lqr(A, B, Q, R)`` * ``K, S, E = ct.lqr(A, B, Q, R, N)`` -If `sys` is a discrete time system, the first two forms will compute -the discrete time optimal controller. For the second two forms, the -:func:`dlqr` function can be used to compute the discrete time optimal +If `sys` is a discrete-time system, the first two forms will compute +the discrete-time optimal controller. For the second two forms, the +:func:`dlqr` function can be used to compute the discrete-time optimal controller. Additional arguments and details are given on the :func:`lqr` and :func:`dlqr` documentation pages. diff --git a/doc/stochastic.rst b/doc/stochastic.rst index ecb9f116f..02812b7ab 100644 --- a/doc/stochastic.rst +++ b/doc/stochastic.rst @@ -34,11 +34,11 @@ and correlation of random signals: correlation function :math:`r_X(\tau)`. The python-control package has variants of these functions that do -appropriate processing for continuous time models. +appropriate processing for continuous-time models. The :func:`white_noise` function generates a (multi-variable) white -noise signal of specified intensity as either a sampled continuous time -signal or a discrete time signal. A white noise signal along a 1D +noise signal of specified intensity as either a sampled continuous-time +signal or a discrete-time signal. A white noise signal along a 1D array of linearly spaced set of times `timepts` can be computing using .. code:: @@ -64,7 +64,7 @@ Y(t)\}`, where :math:`\mathbb{E}` represents expectation: tau, Rtau = ct.correlation(timepts, X[, Y]) The signal `X` (and `Y`, if present) represents a continuous or -discrete time signal sampled at regularly spaced times `timepts`. The +discrete-time signal sampled at regularly spaced times `timepts`. The return value provides the correlation :math:`R_\tau` between :math:`X(t+\tau)` and :math:`X(t)` at a set of time offsets :math:`\tau` (determined based on the spacing of entries in the @@ -144,7 +144,7 @@ measurement and process noise. This estimator is called the linear quadratic estimator (LQE) and its gains can be computed using the :func:`lqe` function. -We consider a continuous time, state space system +We consider a continuous-time, state space system .. math:: @@ -182,7 +182,7 @@ called in several forms: where `sys` is an :class:`LTI` object, and `A`, `G`, `C`, `QN`, `RN`, and `NN` are 2D arrays of appropriate dimension. If `sys` is a -discrete time system, the first two forms will compute the discrete +discrete-time system, the first two forms will compute the discrete time optimal controller. For the second two forms, the :func:`dlqr` function can be used. Additional arguments and details are given on the :func:`lqr` and :func:`dlqr` documentation pages. @@ -241,7 +241,7 @@ state :math:`x_\text{d}` and input :math:`u_\text{d}`:: Maximum Likelihood Estimation ============================= -Consider a *nonlinear* system with discrete time dynamics of the form +Consider a *nonlinear* system with discrete-time dynamics of the form .. math:: :label: eq_fusion_nlsys-oep @@ -356,7 +356,7 @@ estimate of the states over the time points can be computed using the estim = oep.compute_optimal(Y, U[, X0=x0, initial_guess=(xhat, v)]) xhat, v, w = estim.states, estim.inputs, estim.outputs -For discrete time systems, the +For discrete-time systems, the :func:`optimal.OptimalEstimationProblem.create_mhe_iosystem` method can be used to generate an input/output system that implements a moving horizon estimator. diff --git a/doc/xferfcn.rst b/doc/xferfcn.rst index 933bc8851..5382cb195 100644 --- a/doc/xferfcn.rst +++ b/doc/xferfcn.rst @@ -49,7 +49,7 @@ chapter. Input/output norms ------------------ -Continuous and discrete time signals can be represented as a normed +Continuous and discrete-time signals can be represented as a normed linear space with the appropriate choice of signal norm. For continous time signals, the three most common norms are the 2-norm and the :math:`\infty`-norm: diff --git a/examples/cds110-L6c_doubleint-rhc.ipynb b/examples/cds110-L6c_doubleint-rhc.ipynb index d9a693a27..2999ff3ef 100644 --- a/examples/cds110-L6c_doubleint-rhc.ipynb +++ b/examples/cds110-L6c_doubleint-rhc.ipynb @@ -424,7 +424,7 @@ "source": [ "## Discrete time RHC\n", "\n", - "Many receding horizon control problems are solved based on a discrete time model. We show here how to implement this for a \"double integrator\" system, which in discrete time has the form\n", + "Many receding horizon control problems are solved based on a discrete-time model. We show here how to implement this for a \"double integrator\" system, which in discrete time has the form\n", "\n", "$$\n", " x[k+1] = \\begin{bmatrix} 1 & 1 \\\\ 0 & 1 \\end{bmatrix} x[k] + \\begin{bmatrix} 0 \\\\ 1 \\end{bmatrix} \\text{clip}(u[k])\n", @@ -604,7 +604,7 @@ "id": "015dc953" }, "source": [ - "We can also implement a receding horizon controller for a discrete time system using `opt.create_mpc_iosystem`. This creates a controller that accepts the current state as the input and generates the control to apply from that state." + "We can also implement a receding horizon controller for a discrete-time system using `opt.create_mpc_iosystem`. This creates a controller that accepts the current state as the input and generates the control to apply from that state." ] }, { diff --git a/examples/cds112-L5_rhc-doubleint.ipynb b/examples/cds112-L5_rhc-doubleint.ipynb index 2a311fc46..52293b6ff 100644 --- a/examples/cds112-L5_rhc-doubleint.ipynb +++ b/examples/cds112-L5_rhc-doubleint.ipynb @@ -393,7 +393,7 @@ "source": [ "## Discrete time RHC\n", "\n", - "Many receding horizon control problems are solved based on a discrete time model. We show here how to implement this for a \"double integrator\" system, which in discrete time has the form\n", + "Many receding horizon control problems are solved based on a discrete-time model. We show here how to implement this for a \"double integrator\" system, which in discrete time has the form\n", "\n", "$$\n", " x[k+1] = \\begin{bmatrix} 1 & 1 \\\\ 0 & 1 \\end{bmatrix} x[k] + \\begin{bmatrix} 0 \\\\ 1 \\end{bmatrix} \\text{clip}(u[k])\n", @@ -571,7 +571,7 @@ "id": "015dc953", "metadata": {}, "source": [ - "We can also implement a receding horizon controller for a discrete time system using `opt.create_mpc_iosystem`. This creates a controller that accepts the current state as the input and generates the control to apply from that state." + "We can also implement a receding horizon controller for a discrete-time system using `opt.create_mpc_iosystem`. This creates a controller that accepts the current state as the input and generates the control to apply from that state." ] }, { diff --git a/examples/cds112-L8_fusion-kincar.ipynb b/examples/cds112-L8_fusion-kincar.ipynb index 20bc26c93..de4aad5d6 100644 --- a/examples/cds112-L8_fusion-kincar.ipynb +++ b/examples/cds112-L8_fusion-kincar.ipynb @@ -124,13 +124,13 @@ "outputs": [], "source": [ "#\n", - "# Create a discrete time, linear model\n", + "# Create a discrete-time, linear model\n", "#\n", "\n", "# Linearize about the starting point\n", "linsys = ct.linearize(kincar, x0, u0)\n", "\n", - "# Create a discrete time model by hand\n", + "# Create a discrete-time model by hand\n", "Ad = np.eye(linsys.nstates) + linsys.A * Ts\n", "Bd = linsys.B * Ts\n", "discsys = ct.ss(Ad, Bd, np.eye(linsys.nstates), 0, dt=Ts)\n", @@ -146,7 +146,7 @@ "\n", "We assume that we have two sensors: one with good longitudinal accuracy and the other with good lateral accuracy. For each sensor we define the map from the state space to the sensor outputs, the covariance matrix for the measurements, and a white noise signal (now in discrete time).\n", "\n", - "Note: we pass the keyword `dt` to the `white_noise` function so that the white noise is consistent with a discrete time model (so the covariance is _not_ rescaled by $\\sqrt{dt}$)." + "Note: we pass the keyword `dt` to the `white_noise` function so that the white noise is consistent with a discrete-time model (so the covariance is _not_ rescaled by $\\sqrt{dt}$)." ] }, { diff --git a/examples/cds112-L9_mhe-pvtol.ipynb b/examples/cds112-L9_mhe-pvtol.ipynb index 12793df7b..485365188 100644 --- a/examples/cds112-L9_mhe-pvtol.ipynb +++ b/examples/cds112-L9_mhe-pvtol.ipynb @@ -617,7 +617,7 @@ " )\n", " plot_state_comparison(timepts, est_mhe.states, lqr_resp.states)\n", "except:\n", - " print(\"MHE for continuous time systems not implemented\")" + " print(\"MHE for continuous-time systems not implemented\")" ] }, { @@ -627,7 +627,7 @@ "metadata": {}, "outputs": [], "source": [ - "# Create discrete time version of PVTOL\n", + "# Create discrete-time version of PVTOL\n", "Ts = 0.1\n", "print(f\"Sample time: {Ts=}\")\n", "dsys = ct.nlsys(\n", diff --git a/examples/kincar-fusion.ipynb b/examples/kincar-fusion.ipynb index 3444ac95a..7258d507a 100644 --- a/examples/kincar-fusion.ipynb +++ b/examples/kincar-fusion.ipynb @@ -188,13 +188,13 @@ ], "source": [ "#\n", - "# Create a discrete time, linear model\n", + "# Create a discrete-time, linear model\n", "#\n", "\n", "# Linearize about the starting point\n", "linsys = ct.linearize(vehicle, x0, u0)\n", "\n", - "# Create a discrete time model by hand\n", + "# Create a discrete-time model by hand\n", "Ad = np.eye(linsys.nstates) + linsys.A * Ts\n", "Bd = linsys.B * Ts\n", "discsys = ct.ss(Ad, Bd, np.eye(linsys.nstates), 0, dt=Ts)\n", diff --git a/examples/mhe-pvtol.ipynb b/examples/mhe-pvtol.ipynb index 0886f7172..a832df2aa 100644 --- a/examples/mhe-pvtol.ipynb +++ b/examples/mhe-pvtol.ipynb @@ -800,7 +800,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "MHE for continuous time systems not implemented\n" + "MHE for continuous-time systems not implemented\n" ] } ], @@ -819,7 +819,7 @@ " )\n", " plot_state_comparison(timepts, est_mhe.states, lqr_resp.states)\n", "except:\n", - " print(\"MHE for continuous time systems not implemented\")" + " print(\"MHE for continuous-time systems not implemented\")" ] }, { @@ -844,7 +844,7 @@ } ], "source": [ - "# Create discrete time version of PVTOL\n", + "# Create discrete-time version of PVTOL\n", "Ts = 0.1\n", "print(f\"Sample time: {Ts=}\")\n", "dsys = ct.NonlinearIOSystem(\n", From a6b30b1697e451a71e694d87a0c53871b189a98a Mon Sep 17 00:00:00 2001 From: Richard Murray Date: Fri, 17 Jan 2025 23:15:20 -0800 Subject: [PATCH 70/93] update MATLAB function list --- control/nichols.py | 4 ++-- doc/matlab.rst | 4 +++- doc/test_sphinxdocs.py | 17 ++++++++++++----- 3 files changed, 17 insertions(+), 8 deletions(-) diff --git a/control/nichols.py b/control/nichols.py index ca17f4768..6ce0d4c3d 100644 --- a/control/nichols.py +++ b/control/nichols.py @@ -177,9 +177,9 @@ def _inner_extents(ax): def nichols_grid(cl_mags=None, cl_phases=None, line_style='dotted', ax=None, label_cl_phases=True): - """Nichols chart grid. + """Plot Nichols chart grid. - Plots a Nichols chart grid on the current axis, or creates a new chart + Plots a Nichols chart grid on the current axes, or creates a new chart if no plot already exists. Parameters diff --git a/doc/matlab.rst b/doc/matlab.rst index fd8368bc3..42f1e6eb2 100644 --- a/doc/matlab.rst +++ b/doc/matlab.rst @@ -67,6 +67,7 @@ Time-Domain Analysis impulse initial lsim + stepinfo Frequency-Domain Analysis ========================= @@ -75,8 +76,9 @@ Frequency-Domain Analysis bode nyquist - nichols margin + nichols + ngrid freqresp evalfr diff --git a/doc/test_sphinxdocs.py b/doc/test_sphinxdocs.py index 34c148df7..bc87de14e 100644 --- a/doc/test_sphinxdocs.py +++ b/doc/test_sphinxdocs.py @@ -62,10 +62,9 @@ standalone = False control_module_list = [ - control, control.flatsys, control.optimal, control.phaseplot -] + control, control.flatsys, control.optimal, control.phaseplot] @pytest.mark.parametrize("module", control_module_list) -def test_sphinx_functions(module): +def test_sphinx_functions(module, check_legacy=True): # Look through every object in the package _info(f"Checking module {module}", 1) @@ -104,7 +103,8 @@ def test_sphinx_functions(module): case True, _, True, _: _warn(f"deprecated object" + referenced) case True, _, _, True: - _warn(f"legacy object" + referenced) + if check_legacy: + _warn(f"legacy object" + referenced) case False, False, False, False: _fail(f"{objname} not referenced in sphinx docs") @@ -160,6 +160,12 @@ def test_config_defaults(): _warn(f"Unknown params in config.rst: {config_rstdocs}") +# Test MATLAB library separately (and after config_defaults) +def test_sphinx_matlab(): + import control.matlab + test_sphinx_functions(control.matlab, check_legacy=False) + + def _check_deprecated(obj): with warnings.catch_warnings(): warnings.simplefilter('ignore') # debug via sphinx, not here @@ -193,5 +199,6 @@ def _fail(str, level=-1): for module in control_module_list: test_sphinx_functions(module) - test_config_defaults() + test_sphinx_matlab() + From e3ff9c9ba8a351a2e6710ed202a6460248555214 Mon Sep 17 00:00:00 2001 From: Richard Murray Date: Sun, 2 Feb 2025 21:14:06 -0800 Subject: [PATCH 71/93] update LTI convenience functions to allow proper documentation --- control/__init__.py | 9 --- control/frdata.py | 14 +++- control/lti.py | 123 ++++++++++++++++++++++++++-------- control/statesp.py | 2 +- control/tests/kwargs_test.py | 13 +++- control/tests/statesp_test.py | 50 +++++++------- control/xferfcn.py | 16 ++++- doc/classes.rst | 4 +- 8 files changed, 160 insertions(+), 71 deletions(-) diff --git a/control/__init__.py b/control/__init__.py index d9f5003c4..5d3d288e0 100644 --- a/control/__init__.py +++ b/control/__init__.py @@ -100,14 +100,5 @@ except ImportError: __version__ = "dev" -# patch the LTI class with function aliases for convenience functions -# this needs to be done after the fact since the modules that contain the functions -# all heavily depend on the LTI class -LTI.to_ss = ss -LTI.to_tf = tf -LTI.bode_plot = bode_plot -LTI.nyquist_plot = nyquist_plot -LTI.nichols_plot = nichols_plot - # Initialize default parameter values reset_defaults() diff --git a/control/frdata.py b/control/frdata.py index ac41ff2d2..126609983 100644 --- a/control/frdata.py +++ b/control/frdata.py @@ -845,7 +845,19 @@ def append(self, other): """Append a second model to the present model. The second model is converted to FRD if necessary, inputs and - outputs are appended and their order is preserved""" + outputs are appended and their order is preserved. + + Parameters + ---------- + other : `LTI` + System to be appended. + + Returns + ------- + sys : `FrequencyResponseData` + System model with `other` appended to `self`. + + """ other = _convert_to_frd(other, omega=self.omega, inputs=other.ninputs, outputs=other.noutputs) diff --git a/control/lti.py b/control/lti.py index df9f69ede..9a12c11fc 100644 --- a/control/lti.py +++ b/control/lti.py @@ -8,14 +8,11 @@ """ import math -from typing import Callable from warnings import warn import numpy as np from numpy import abs, real -import control - from . import config from .iosys import InputOutputSystem @@ -64,7 +61,7 @@ def damp(self): return wn, zeta, poles def feedback(self, other=1, sign=-1): - """Feedback interconnection between two input/output systems.""" + """Feedback interconnection between two input/output systems. Parameters ---------- @@ -238,30 +235,100 @@ def ispassive(self): from control.passivity import ispassive return ispassive(self) - # convenience aliases - # most function are only forward declaraed and patched in the __init__.py to avoid circular imports - - # conversions - #: Convert to :class:`StateSpace` representation; see :func:`ss` - to_ss: Callable - #: Convert to :class:`TransferFunction` representation; see :func:`tf` - to_tf: Callable - - # freq domain plotting - #: Bode plot; see :func:`bode_plot` - bode_plot: Callable - #: Nyquist plot; see :func:`nyquist_plot` - nyquist_plot: Callable - #: Nichols plot; see :func:`nichols_plot` - nichols_plot: Callable - - # time domain simulation - #: Forced response; see :func:`forced_response` - forced_response = control.timeresp.forced_response - #: Impulse response; see :func:`impulse_response` - impulse_response = control.timeresp.impulse_response - #: Step response; see :func:`step_response` - step_response = control.timeresp.step_response + # + # Convenience aliases for conversion functions + # + # Allow conversion between state space and transfer function types + # as methods. These are just pass throughs to factory functions. + # + # Note: in order for docstrings to created, these have to set these up + # as independent methods, not just assigned to ss() and tf(). + # + # Imports are done within the function to avoid circular imports. + # + def to_ss(self, *args, **kwargs): + """Convert to state space representation. + + See `ss` for details. + """ + from .statesp import ss + return ss(self, *args, **kwargs) + + def to_tf(self, *args, **kwargs): + """Convert to transfer function representation. + + See `tf` for details. + """ + from .xferfcn import tf + return tf(self, *args, **kwargs) + + # + # Convenience aliases for plotting and response functions + # + # Allow standard plots to be generated directly from the system object + # in addition to standalone plotting and response functions. + # + # Note: in order for docstrings to created, these have to set these up as + # independent methods, not just assigned to plotting/response functions. + # + # Imports are done within the function to avoid circular imports. + # + + def bode_plot(self, *args, **kwargs): + """Generate a Bode plot for the system. + + See `bode_plot` for more information. + """ + from .freqplot import bode_plot + return bode_plot(self, *args, **kwargs) + + def nichols_plot(self, *args, **kwargs): + """Generate a Nichols plot for the system. + + See `nichols_plot` for more information. + """ + from .nichols import nichols_plot + return nichols_plot(self, *args, **kwargs) + + def nyquist_plot(self, *args, **kwargs): + """Generate a Nyquist plot for the system. + + See `nyquist_plot` for more information. + """ + from .freqplot import nyquist_plot + return nyquist_plot(self, *args, **kwargs) + + def forced_response(self, *args, **kwargs): + """Generate the forced response for the system. + + See `forced_response` for more information. + """ + from .timeresp import forced_response + return forced_response(self, *args, **kwargs) + + def impulse_response(self, *args, **kwargs): + """Generate the impulse response for the system. + + See `impulse_response` for more information. + """ + from .timeresp import impulse_response + return impulse_response(self, *args, **kwargs) + + def initial_response(self, *args, **kwargs): + """Generate the initial response for the system. + + See `initial_response` for more information. + """ + from .timeresp import initial_response + return initial_response(self, *args, **kwargs) + + def step_response(self, *args, **kwargs): + """Generate the step response for the system. + + See `step_response` for more information. + """ + from .timeresp import step_response + return step_response(self, *args, **kwargs) def poles(sys): diff --git a/control/statesp.py b/control/statesp.py index 2c0fd7f35..fe3a28122 100644 --- a/control/statesp.py +++ b/control/statesp.py @@ -1250,7 +1250,7 @@ def append(self, other): """Append a second model to the present model. The second model is converted to state-space if necessary, inputs and - outputs are appended and their order is preserved + outputs are appended and their order is preserved. Parameters ---------- diff --git a/control/tests/kwargs_test.py b/control/tests/kwargs_test.py index 142c46c77..de0111e01 100644 --- a/control/tests/kwargs_test.py +++ b/control/tests/kwargs_test.py @@ -26,6 +26,7 @@ import control.tests.interconnect_test as interconnect_test import control.tests.iosys_test as iosys_test import control.tests.optimal_test as optimal_test +import control.tests.statesp_test as statesp_test import control.tests.statefbk_test as statefbk_test import control.tests.stochsys_test as stochsys_test import control.tests.timeplot_test as timeplot_test @@ -245,7 +246,7 @@ def test_response_plot_kwargs(data_fcn, plot_fcn, mimo): 'append': test_unrecognized_kwargs, 'bode': test_response_plot_kwargs, 'bode_plot': test_response_plot_kwargs, - 'LTI.bode_plot': test_response_plot_kwargs, # alias for bode_plot and tested via bode_plot + 'LTI.bode_plot': test_response_plot_kwargs, # tested via bode_plot 'combine_tf': test_unrecognized_kwargs, 'create_estimator_iosystem': stochsys_test.test_estimator_errors, 'create_statefbk_iosystem': statefbk_test.TestStatefbk.test_statefbk_errors, @@ -268,15 +269,19 @@ def test_response_plot_kwargs(data_fcn, plot_fcn, mimo): 'linearize': test_unrecognized_kwargs, 'lqe': test_unrecognized_kwargs, 'lqr': test_unrecognized_kwargs, + 'LTI.forced_response': statesp_test.test_convenience_aliases, + 'LTI.impulse_response': statesp_test.test_convenience_aliases, + 'LTI.initial_response': statesp_test.test_convenience_aliases, + 'LTI.step_response': statesp_test.test_convenience_aliases, 'negate': test_unrecognized_kwargs, 'nichols_plot': test_matplotlib_kwargs, - 'LTI.nichols_plot': test_matplotlib_kwargs, # alias for nichols_plot and tested via nichols_plot + 'LTI.nichols_plot': test_matplotlib_kwargs, # tested via nichols_plot 'nichols': test_matplotlib_kwargs, 'nlsys': test_unrecognized_kwargs, 'nyquist': test_matplotlib_kwargs, 'nyquist_response': test_response_plot_kwargs, 'nyquist_plot': test_matplotlib_kwargs, - 'LTI.nyquist_plot': test_matplotlib_kwargs, # alias for nyquist_plot and tested via nyquist_plot + 'LTI.nyquist_plot': test_matplotlib_kwargs, # tested via nyquist_plot 'phase_plane_plot': test_matplotlib_kwargs, 'parallel': test_unrecognized_kwargs, 'pole_zero_plot': test_unrecognized_kwargs, @@ -289,11 +294,13 @@ def test_response_plot_kwargs(data_fcn, plot_fcn, mimo): 'set_defaults': test_unrecognized_kwargs, 'singular_values_plot': test_matplotlib_kwargs, 'ss': test_unrecognized_kwargs, + 'LTI.to_ss': test_unrecognized_kwargs, # tested via 'ss' 'ss2io': test_unrecognized_kwargs, 'ss2tf': test_unrecognized_kwargs, 'summing_junction': interconnect_test.test_interconnect_exceptions, 'suptitle': freqplot_test.test_suptitle, 'tf': test_unrecognized_kwargs, + 'LTI.to_tf': test_unrecognized_kwargs, # tested via 'ss' 'tf2io' : test_unrecognized_kwargs, 'tf2ss' : test_unrecognized_kwargs, 'sample_system' : test_unrecognized_kwargs, diff --git a/control/tests/statesp_test.py b/control/tests/statesp_test.py index 3722640e4..9b21cdefa 100644 --- a/control/tests/statesp_test.py +++ b/control/tests/statesp_test.py @@ -18,12 +18,13 @@ import control as ct from control.config import defaults from control.dtime import sample_system -from control.lti import evalfr -from control.statesp import StateSpace, _convert_to_statespace, tf2ss, \ - _statesp_defaults, _rss_generate, linfnorm, ss, rss, drss +from control.lti import LTI, evalfr +from control.statesp import StateSpace, _convert_to_statespace, \ + _rss_generate, _statesp_defaults, drss, linfnorm, rss, ss, tf2ss from control.xferfcn import TransferFunction, ss2tf -from control.tests.conftest import assert_tf_close_coeff, editsdefaults, \ - slycotonly + +from .conftest import assert_tf_close_coeff, editsdefaults, \ + ignore_future_warning, slycotonly class TestStateSpace: @@ -1616,30 +1617,31 @@ def test_tf2ss_mimo(): def test_convenience_aliases(): sys = ct.StateSpace(1, 1, 1, 1) - # test that all the aliases point to the correct function - # .__funct__ a bound methods underlying function - assert sys.to_ss.__func__ == ct.ss - assert sys.to_tf.__func__ == ct.tf - assert sys.bode_plot.__func__ == ct.bode_plot - assert sys.nyquist_plot.__func__ == ct.nyquist_plot - assert sys.nichols_plot.__func__ == ct.nichols_plot - assert sys.forced_response.__func__ == ct.forced_response - assert sys.impulse_response.__func__ == ct.impulse_response - assert sys.step_response.__func__ == ct.step_response - assert sys.initial_response.__func__ == ct.initial_response - - # make sure the functions can be used as member function ie they support - # an instance of StateSpace as the first argument and that they at least return - # the correct type + # Make sure the functions can be used as member function: i.e. they + # support an instance of StateSpace as the first argument and that + # they at least return the correct type assert isinstance(sys.to_ss(), StateSpace) assert isinstance(sys.to_tf(), TransferFunction) assert isinstance(sys.bode_plot(), ct.ControlPlot) assert isinstance(sys.nyquist_plot(), ct.ControlPlot) assert isinstance(sys.nichols_plot(), ct.ControlPlot) - assert isinstance(sys.forced_response([0, 1], [1, 1]), (ct.TimeResponseData, ct.TimeResponseList)) - assert isinstance(sys.impulse_response(), (ct.TimeResponseData, ct.TimeResponseList)) - assert isinstance(sys.step_response(), (ct.TimeResponseData, ct.TimeResponseList)) - assert isinstance(sys.initial_response(X0=1), (ct.TimeResponseData, ct.TimeResponseList)) + assert isinstance(sys.forced_response([0, 1], [1, 1]), + (ct.TimeResponseData, ct.TimeResponseList)) + assert isinstance(sys.impulse_response(), + (ct.TimeResponseData, ct.TimeResponseList)) + assert isinstance(sys.step_response(), + (ct.TimeResponseData, ct.TimeResponseList)) + assert isinstance(sys.initial_response(X0=1), + (ct.TimeResponseData, ct.TimeResponseList)) + + # Make sure that unrecognized keywords for response functions are caught + for method in [LTI.impulse_response, LTI.initial_response, + LTI.step_response]: + with pytest.raises(TypeError, match="unexpected keyword"): + method(sys, unknown=True) + with pytest.raises(TypeError, match="unexpected keyword"): + LTI.forced_response(sys, [0, 1], [1, 1], unknown=True) + # Test LinearICSystem __call__ def test_linearic_call(): diff --git a/control/xferfcn.py b/control/xferfcn.py index fc209ff49..571212ce4 100644 --- a/control/xferfcn.py +++ b/control/xferfcn.py @@ -906,8 +906,20 @@ def feedback(self, other=1, sign=-1): def append(self, other): """Append a second model to the present model. - The second model is converted to a transfer function if necessary, - inputs and outputs are appended and their order is preserved""" + The second model is converted to FRD if necessary, inputs and + outputs are appended and their order is preserved. + + Parameters + ---------- + other : `LTI` + System to be appended. + + Returns + ------- + sys : `TransferFunction` + System model with `other` appended to `self`. + + """ other = _convert_to_transfer_function(other) new_tf = bdalg.combine_tf([ diff --git a/doc/classes.rst b/doc/classes.rst index febdc7093..0654a773a 100644 --- a/doc/classes.rst +++ b/doc/classes.rst @@ -14,9 +14,7 @@ systems (both linear time-invariant and nonlinear). They are usually created from factory functions such as :func:`tf` and :func:`ss`, so the user should normally not need to instantiate these directly. -The following figure illustrates the relationship between the classes and -some of the functions that can be used to convert objects from one class to -another: +The following figure illustrates the relationship between the classes. .. image:: figures/classes.pdf :width: 800 From 3de0ee7fc4103a25152709da054ef353cf46796b Mon Sep 17 00:00:00 2001 From: Richard Murray Date: Sat, 18 Jan 2025 22:37:10 -0800 Subject: [PATCH 72/93] add special processing for `sys` --- control/frdata.py | 12 +++++------ control/lti.py | 2 +- control/matlab/wrappers.py | 2 +- control/modelsimp.py | 10 +++++----- control/nlsys.py | 2 +- control/sisotool.py | 26 ++++++++++++------------ control/statesp.py | 12 +++++------ control/timeresp.py | 41 ++++++++++++++++++++------------------ control/xferfcn.py | 16 +++++++-------- doc/conf.py | 18 ++++++++++++++++- doc/develop.rst | 11 +++++++++- doc/iosys.rst | 2 +- doc/optimal.rst | 2 +- doc/statesp.rst | 2 +- doc/stochastic.rst | 4 ++-- 15 files changed, 95 insertions(+), 67 deletions(-) diff --git a/control/frdata.py b/control/frdata.py index 126609983..2eb113ed3 100644 --- a/control/frdata.py +++ b/control/frdata.py @@ -938,12 +938,12 @@ def to_pandas(self): def _convert_to_frd(sys, omega, inputs=1, outputs=1): """Convert a system to frequency response data form (if needed). - If sys is already an frd, and its frequency range matches or - overlaps the range given in omega then it is returned. If sys is - another LTI object or a transfer function, then it is converted to - a frequency response data at the specified omega. If sys is a - scalar, then the number of inputs and outputs can be specified - manually, as in: + If `sys` is already a frequency response data object, and its + frequency range matches or overlaps the range given in omega then + it is returned. If `sys` is another LTI object or a transfer + function, then it is converted to a frequency response data at the + specified omega. If `sys` is a scalar, then the number of inputs and + outputs can be specified manually, as in: >>> import numpy as np >>> from control.frdata import _convert_to_frd diff --git a/control/lti.py b/control/lti.py index 9a12c11fc..8e2ad0033 100644 --- a/control/lti.py +++ b/control/lti.py @@ -541,7 +541,7 @@ def frequency_response( frequency. If `squeeze` is True then single-dimensional axes are removed. - Returns a list of `FrequencyResponseData` objects if sys is + Returns a list of `FrequencyResponseData` objects if `sys` is a list of systems. Other Parameters diff --git a/control/matlab/wrappers.py b/control/matlab/wrappers.py index f11e693e5..06d57136e 100644 --- a/control/matlab/wrappers.py +++ b/control/matlab/wrappers.py @@ -29,7 +29,7 @@ def bode(*args, **kwargs): sys : LTI, or list of LTI System for which the Bode response is plotted and give. Optionally a list of systems can be entered, or several systems can be - specified (i.e. several parameters). The sys arguments may also be + specified (i.e. several parameters). The `sys` arguments may also be interspersed with format strings. A frequency argument (array_like) may also be added (see Examples). omega : array diff --git a/control/modelsimp.py b/control/modelsimp.py index 8c08defdd..300c23c85 100644 --- a/control/modelsimp.py +++ b/control/modelsimp.py @@ -255,12 +255,12 @@ def _expand_key(key): def balanced_reduction(sys, orders, method='truncate', alpha=None): - """Balanced reduced order model of sys of a given order. + """Balanced reduced order model of system of a given order. - States are eliminated based on Hankel singular value. - If sys has unstable modes, they are removed, the - balanced realization is done on the stable part, then - reinserted in accordance with the reference below. + States are eliminated based on Hankel singular value. If `sys` has + unstable modes, they are removed, the balanced realization is done on + the stable part, then reinserted in accordance with the reference + below. Reference: Hsu,C.S., and Hou,D., 1991, Reducing unstable linear control systems via real Schur transformation. diff --git a/control/nlsys.py b/control/nlsys.py index 93e3b43ac..5f7b30db6 100644 --- a/control/nlsys.py +++ b/control/nlsys.py @@ -584,7 +584,7 @@ class InterconnectedSystem(NonlinearIOSystem): """Interconnection of a set of input/output systems. This class is used to implement a system that is an interconnection of - input/output systems. The sys consists of a collection of subsystems + input/output systems. The system consists of a collection of subsystems whose inputs and outputs are connected via a connection map. The overall system inputs and outputs are subsets of the subsystem inputs and outputs. diff --git a/control/sisotool.py b/control/sisotool.py index 16ddb694f..95289354f 100644 --- a/control/sisotool.py +++ b/control/sisotool.py @@ -41,19 +41,19 @@ def sisotool(sys, initial_gain=None, xlim_rlocus=None, ylim_rlocus=None, Parameters ---------- sys : LTI object - Linear input/output systems. If sys is SISO, use the same system for - the root locus and step response. If it is desired to see a different - step response than feedback(K*sys,1), such as a disturbance response, - sys can be provided as a two-input, two-output system (e.g. by using - `bdgalg.connect' or `iosys.interconnect`). For two-input, - two-output system, sisotool inserts the negative of the selected gain - K between the first output and first input and uses the second input - and output for computing the step response. To see the disturbance - response, configure your plant to have as its second input the - disturbance input. To view the step response with a feedforward - controller, give your plant two identical inputs, and sum your - feedback controller and your feedforward controller and multiply them - into your plant's second input. It is also possible to accomodate a + Linear input/output systems. If `sys` is SISO, use the same system + for the root locus and step response. If it is desired to see a + different step response than ``feedback(K*sys, 1)``, such as a + disturbance response, `sys` can be provided as a two-input, + two-output system. For two-input, two-output system, sisotool + inserts the negative of the selected gain `K` between the first + output and first input and uses the second input and output for + computing the step response. To see the disturbance response, + configure your plant to have as its second input the disturbance + input. To view the step response with a feedforward controller, + give your plant two identical inputs, and sum your feedback + controller and your feedforward controller and multiply them into + your plant's second input. It is also possible to accomodate a system with a gain in the feedback. initial_gain : float, optional Initial gain to use for plotting root locus. Defaults to 1 diff --git a/control/statesp.py b/control/statesp.py index fe3a28122..375cb7b56 100644 --- a/control/statesp.py +++ b/control/statesp.py @@ -164,7 +164,7 @@ def __init__(self, *args, **kwargs): are matrices or equivalent objects. To create a discrete-time system, use StateSpace(A, B, C, D, dt) where `dt` is the sampling time (or True for unspecified sampling time). To call the copy - constructor, call StateSpace(sys), where sys is a StateSpace + constructor, call ``StateSpace(sys)``, where `sys` is a `StateSpace` object. See `StateSpace` and `ss` for more information. @@ -1639,7 +1639,7 @@ def ss(*args, **kwargs): ``ss(sys)`` Convert a linear system into space system form. Always creates a - new system, even if sys is already a state space system. + new system, even if `sys` is already a state space system. ``ss(A, B, C, D)`` @@ -1808,7 +1808,7 @@ def tf2io(*args, **kwargs): ``tf2io(sys)`` Convert a linear system into space space form. Always creates - a new system, even if sys is already a `StateSpace` object. + a new system, even if `sys` is already a `StateSpace` object. ``tf2io(num, den)`` @@ -2371,9 +2371,9 @@ def _f2s(f): def _convert_to_statespace(sys, use_prefix_suffix=False, method=None): """Convert a system to state space form (if needed). - If sys is already a state space, then it is returned. If sys is a - transfer function object, then it is converted to a state space and - returned. + If `sys` is already a state space object, then it is returned. If + `sys` is a transfer function object, then it is converted to a state + space and returned. Note: no renaming of inputs and outputs is performed; this should be done by the calling function. diff --git a/control/timeresp.py b/control/timeresp.py index 5a85fba96..520693135 100644 --- a/control/timeresp.py +++ b/control/timeresp.py @@ -1330,15 +1330,15 @@ def step_response( I/O system(s) for which step response is computed. T : array_like or float, optional - Time vector, or simulation time duration if a number. If T is not + Time vector, or simulation time duration if a number. If `T` is not provided, an attempt is made to create it automatically from the - dynamics of sys. If sys is continuous time, the time increment dt - is chosen small enough to show the fastest mode, and the simulation - time period tfinal long enough to show the slowest mode, excluding - poles at the origin and pole-zero cancellations. If this results in - too many time steps (>5000), dt is reduced. If sys is discrete time, - only tfinal is computed, and final is reduced if it requires too - many simulation steps. + dynamics of the system. If the system continuous time, the time + increment dt is chosen small enough to show the fastest mode, and + the simulation time period tfinal long enough to show the slowest + mode, excluding poles at the origin and pole-zero cancellations. If + this results in too many time steps (>5000), dt is reduced. If the + system is discrete time, only tfinal is computed, and final is + reduced if it requires too many simulation steps. X0 : array_like or float, optional Initial condition (default = 0). This can be used for a nonlinear @@ -1357,8 +1357,9 @@ def step_response( If system is a nonlinear I/O system, set parameter values. T_num : int, optional - Number of time steps to use in simulation if T is not provided as an - array (autocomputed if not given); ignored if sys is discrete time. + Number of time steps to use in simulation if `T` is not provided as + an array (autocomputed if not given); ignored if the system is + discrete time. transpose : bool, optional If True, transpose all input and output arrays (for backward @@ -1484,8 +1485,7 @@ def step_response( def step_info(sysdata, T=None, T_num=None, yfinal=None, params=None, SettlingTimeThreshold=0.02, RiseTimeLimits=(0.1, 0.9)): - """ - Step response characteristics (rise time, settling time, etc). + """Step response characteristics (rise time, settling time, etc). Parameters ---------- @@ -1497,8 +1497,8 @@ def step_info(sysdata, T=None, T_num=None, yfinal=None, params=None, autocomputed if not given, see `step_response` for more detail). Required, if sysdata is a time series of response data. T_num : int, optional - Number of time steps to use in simulation if T is not provided as an - array; autocomputed if not given; ignored if sysdata is a + Number of time steps to use in simulation if `T` is not provided as + an array; autocomputed if not given; ignored if sysdata is a discrete-time system or a time series or response data. yfinal : scalar or array_like, optional Steady-state response. If not given, sysdata.dcgain() is used for @@ -1586,6 +1586,7 @@ def step_info(sysdata, T=None, T_num=None, yfinal=None, params=None, Peak: 1.209 PeakTime: 4.242 SteadyStateValue: -1.0 + """ from .nlsys import NonlinearIOSystem from .statesp import StateSpace @@ -1743,9 +1744,10 @@ def initial_response( to None to not trim outputs. T_num : int, optional - Number of time steps to use in simulation if T is not provided as an - array (autocomputed if not given); ignored if sys is discrete time. - + Number of time steps to use in simulation if `T` is not provided as + an array (autocomputed if not given); ignored if the system is + discrete time. + params : dict, optional If system is a nonlinear I/O system, set parameter values. @@ -1862,8 +1864,9 @@ def impulse_response( specified, all outputs are reported. T_num : int, optional - Number of time steps to use in simulation if T is not provided as an - array (autocomputed if not given); ignored if sys is discrete time. + Number of time steps to use in simulation if `T` is not provided as + an array (autocomputed if not given); ignored if the system is + discrete time. transpose : bool, optional If True, transpose all input and output arrays (for backward diff --git a/control/xferfcn.py b/control/xferfcn.py index 571212ce4..734ba736c 100644 --- a/control/xferfcn.py +++ b/control/xferfcn.py @@ -163,13 +163,13 @@ def __init__(self, *args, **kwargs): Construct a transfer function. - The default constructor is TransferFunction(num, den), where num and - den are 2D arrays of arrays containing polynomial coefficients. - To create a discrete-time transfer function, use TransferFunction(num, - den, dt) where 'dt' is the sampling time (or True for unspecified - sampling time). To call the copy constructor, call - TransferFunction(sys), where sys is a TransferFunction object - (continuous or discrete). + The default constructor is TransferFunction(num, den), where num + and den are 2D arrays of arrays containing polynomial coefficients. + To create a discrete-time transfer function, use + ``TransferFunction(num, den, dt)`` where `dt` is the sampling time + (or True for unspecified sampling time). To call the copy + constructor, call ``TransferFunction(sys)``, where `sys` is a + TransferFunction object (continuous or discrete). See `TransferFunction` and `tf` for more information. @@ -1631,7 +1631,7 @@ def tf(*args, **kwargs): ``tf(sys)`` Convert a linear system into transfer function form. Always creates - a new system, even if sys is already a `TransferFunction` object. + a new system, even if `sys` is already a `TransferFunction` object. ``tf(num, den)`` diff --git a/doc/conf.py b/doc/conf.py index 0bbfa22f1..9a0d76c9a 100644 --- a/doc/conf.py +++ b/doc/conf.py @@ -107,6 +107,9 @@ 'python': ('https://docs.python.org/3/', None), } +# Don't generate external links to (local) keywords +intersphinx_disabled_reftypes = ["py:keyword"] + # If this is True, todo and todolist produce output, else they produce nothing. # The default is False. todo_include_todos = True @@ -220,7 +223,7 @@ def linkcode_resolve(domain, info): else: # specific version return base_url + "%s/control/%s%s" % (version, fn, linespec) -# Don't automaticall show all members of class in Methods & Attributes section +# Don't automatically show all members of class in Methods & Attributes section numpydoc_show_class_members = False # Don't create a Sphinx TOC for the lists of class methods and attributes @@ -294,3 +297,16 @@ def linkcode_resolve(domain, info): import control.phaseplot as pp ct.reset_defaults() """ + +# -- Customization for python-control ---------------------------------------- +# +# This code does custom processing of docstrings for the python-control +# package. + +def process_docstring(app, what, name, obj, options, lines): + # Loop through each line in docstring and replace `sys` with :code:`sys` + for i in range(len(lines)): + lines[i] = lines[i].replace("`sys`", ":code:`sys`") + +def setup(app): + app.connect('autodoc-process-docstring', process_docstring) diff --git a/doc/develop.rst b/doc/develop.rst index f74fb9207..16e3f926a 100644 --- a/doc/develop.rst +++ b/doc/develop.rst @@ -188,7 +188,8 @@ System-creating commands: System arguments: -* `sys` when an argument is a single input/output system (e.g. `bandwidth`). +* :code:`sys` when an argument is a single input/output system + (e.g. `bandwidth`). * `syslist` when an argument is a list of systems (e.g., `interconnect`). A single system should also be OK. @@ -253,6 +254,14 @@ guidelines: bolder type, so that it is easier to see what things you can click on to get more information. + - By default, the string \`sys\` in docstrings would normally + generate a link to the :mod:`sys` Python module. To avoid this, + `conf.py` includes code that converts \`sys\` in docstrings to + \:code\:\`sys`, which renders as :code:`sys` (code style, with no + link). In ``.rst`` files, this construction should be done + manually, since ``.rst`` files are not pre-processed as a + docstring. + * Use double backticks for inline code, such as a Python code fragments. - In principle single backticks might actually work OK given the way diff --git a/doc/iosys.rst b/doc/iosys.rst index c59a17c70..dcb9a6e2f 100644 --- a/doc/iosys.rst +++ b/doc/iosys.rst @@ -653,7 +653,7 @@ can be created with the command:: ctrl, clsys = ct.create_statefbk_iosystem(sys, K) -where `sys` is the process dynamics and `K` is the state feedback gain +where :code:`sys` is the process dynamics and `K` is the state feedback gain (e.g., from LQR). The function returns the controller `ctrl` and the closed loop systems `clsys`, both as I/O systems. The input to the controller is the vector of desired states :math:`x_\text{d}`, desired diff --git a/doc/optimal.rst b/doc/optimal.rst index d88e9cb20..3f4306177 100644 --- a/doc/optimal.rst +++ b/doc/optimal.rst @@ -131,7 +131,7 @@ by using the :func:`~optimal.solve_ocp` function:: res = opt.solve_ocp(sys, timepts, X0, cost, constraints) -The `sys` parameter should be an :class:`InputOutputSystem` and the +The :code:`sys` parameter should be an :class:`InputOutputSystem` and the `timepts` parameter should represent a time vector that gives the list of times at which the cost and constraints should be evaluated (the time points need not be uniformly spaced). diff --git a/doc/statesp.rst b/doc/statesp.rst index ca0ffee08..46fb537a0 100644 --- a/doc/statesp.rst +++ b/doc/statesp.rst @@ -131,7 +131,7 @@ several forms: * ``K, S, E = ct.lqr(A, B, Q, R)`` * ``K, S, E = ct.lqr(A, B, Q, R, N)`` -If `sys` is a discrete-time system, the first two forms will compute +If :code:`sys` is a discrete-time system, the first two forms will compute the discrete-time optimal controller. For the second two forms, the :func:`dlqr` function can be used to compute the discrete-time optimal controller. Additional arguments and details are given on the diff --git a/doc/stochastic.rst b/doc/stochastic.rst index 02812b7ab..e4071caf5 100644 --- a/doc/stochastic.rst +++ b/doc/stochastic.rst @@ -180,8 +180,8 @@ called in several forms: * ``L, P, E = lqe(A, G, C, QN, RN)`` * ``L, P, E = lqe(A, G, C, QN, RN, NN)`` -where `sys` is an :class:`LTI` object, and `A`, `G`, `C`, `QN`, `RN`, -and `NN` are 2D arrays of appropriate dimension. If `sys` is a +where :code:`sys` is an :class:`LTI` object, and `A`, `G`, `C`, `QN`, `RN`, +and `NN` are 2D arrays of appropriate dimension. If :code:`sys` is a discrete-time system, the first two forms will compute the discrete time optimal controller. For the second two forms, the :func:`dlqr` function can be used. Additional arguments and details are given on From d498ad52045b5f97dbb5c52b4214cca88fecafaf Mon Sep 17 00:00:00 2001 From: Richard Murray Date: Sat, 18 Jan 2025 22:49:29 -0800 Subject: [PATCH 73/93] fix citations and references --- control/delay.py | 16 ++++++++++------ control/modelsimp.py | 14 +++++++------- control/passivity.py | 8 +++++--- control/rlocus.py | 4 ++-- control/statesp.py | 18 ++++++++++++------ control/timeresp.py | 2 +- 6 files changed, 37 insertions(+), 25 deletions(-) diff --git a/control/delay.py b/control/delay.py index 66a6b10a4..49793ca17 100644 --- a/control/delay.py +++ b/control/delay.py @@ -26,16 +26,20 @@ def pade(T, n=1, numdeg=None): Returns ------- - num, den : array + num, den : ndarray Polynomial coefficients of the delay model, in descending powers of s. Notes ----- - Based on: - 1. Algorithm 11.3.1 in Golub and van Loan, "Matrix Computation" 3rd. - Ed. pp. 572-574 - 2. M. Vajta, "Some remarks on Padé-approximations", - 3rd TEMPUS-INTCOM Symposium + Based on [1]_ and [2]_. + + References + ---------- + .. [1] Algorithm 11.3.1 in Golub and van Loan, "Matrix Computation" 3rd. + Ed. pp. 572-574. + + .. [2] M. Vajta, "Some remarks on Padé-approximations", + 3rd TEMPUS-INTCOM Symposium. Examples -------- diff --git a/control/modelsimp.py b/control/modelsimp.py index 300c23c85..508590975 100644 --- a/control/modelsimp.py +++ b/control/modelsimp.py @@ -259,12 +259,13 @@ def balanced_reduction(sys, orders, method='truncate', alpha=None): States are eliminated based on Hankel singular value. If `sys` has unstable modes, they are removed, the balanced realization is done on - the stable part, then reinserted in accordance with the reference - below. + the stable part, then reinserted in accordance with [1]_. - Reference: Hsu,C.S., and Hou,D., 1991, - Reducing unstable linear control systems via real Schur transformation. - Electronics Letters, 27, 984-986. + References + ---------- + .. [1] C. S. Hsu and D. Hou, "Reducing unstable linear control + systems via real Schur transformation". Electronics Letters, + 27, 984-986, 1991. Parameters ---------- @@ -479,8 +480,7 @@ def eigensys_realization(arg, r, m=None, n=None, dt=True, transpose=False): References ---------- .. [1] Samet Oymak and Necmiye Ozay, Non-asymptotic Identification of - LTI Systems from a Single Trajectory. - https://arxiv.org/abs/1806.05722 + LTI Systems from a Single Trajectory. https://arxiv.org/abs/1806.05722 Examples -------- diff --git a/control/passivity.py b/control/passivity.py index f8079a70d..01aa76f42 100644 --- a/control/passivity.py +++ b/control/passivity.py @@ -293,9 +293,11 @@ def ispassive(sys, ofp_index=0, ifp_index=0): References ---------- - .. [1] McCourt, Michael J., and Panos J. Antsaklis - "Demonstrating passivity and dissipativity using computational - methods." + + .. [1] McCourt, Michael J., and Panos J. Antsaklis "Demonstrating + passivity and dissipativity using computational methods." + Technical Report of the ISIS Group at the University of Notre + Dame. ISIS-2013-008, Aug. 2013. """ return solve_passivity_LMI(sys, rho=ofp_index, nu=ifp_index) is not None diff --git a/control/rlocus.py b/control/rlocus.py index aa9598216..cb1d42aae 100644 --- a/control/rlocus.py +++ b/control/rlocus.py @@ -218,8 +218,8 @@ def _default_gains(num, den, xlim, ylim): References ---------- - Ogata, K. (2002). Modern control engineering (4th ed.). Upper - Saddle River, NJ : New Delhi: Prentice Hall.. + .. [1] Ogata, K. (2002). Modern control engineering (4th + ed.). Upper Saddle River, NJ : New Delhi: Prentice Hall.. """ # Compute the break points on the real axis for the root locus plot diff --git a/control/statesp.py b/control/statesp.py index 375cb7b56..c0378dcd5 100644 --- a/control/statesp.py +++ b/control/statesp.py @@ -1069,24 +1069,30 @@ def feedback(self, other=1, sign=-1): return StateSpace(A, B, C, D, dt) def lft(self, other, nu=-1, ny=-1): - """Return the Linear Fractional Transformation. + """Return the linear fractional transformation. A definition of the LFT operator can be found in Appendix A.7, - page 512 in the 2nd Edition, Multivariable Feedback Control by - Sigurd Skogestad. - - An alternative definition can be found here: + page 512 in [1]_. An alternative definition can be found here: https://www.mathworks.com/help/control/ref/lft.html Parameters ---------- - other : LTI + other : `StateSpace` The lower LTI system. ny : int, optional Dimension of (plant) measurement output. nu : int, optional Dimension of (plant) control input. + Returns + ------- + `StateSpace` + + References + ---------- + .. [1] S. Skogestad, Multivariable Feedback Control. Second + edition, 2005. + """ other = _convert_to_statespace(other) # maximal values for nu, ny diff --git a/control/timeresp.py b/control/timeresp.py index 520693135..6230484fe 100644 --- a/control/timeresp.py +++ b/control/timeresp.py @@ -1747,7 +1747,7 @@ def initial_response( Number of time steps to use in simulation if `T` is not provided as an array (autocomputed if not given); ignored if the system is discrete time. - + params : dict, optional If system is a nonlinear I/O system, set parameter values. From acaa417f05e8bdcec8e8c921ed72131676c29fb6 Mon Sep 17 00:00:00 2001 From: Richard Murray Date: Sun, 19 Jan 2025 13:40:15 -0800 Subject: [PATCH 74/93] regularize Returns docstring for bundle objects --- control/bdalg.py | 10 +-- control/canonical.py | 21 +++--- control/ctrlplot.py | 10 +-- control/ctrlutil.py | 2 +- control/descfcn.py | 44 +++++++----- control/dtime.py | 2 +- control/flatsys/flatsys.py | 2 +- control/flatsys/systraj.py | 43 ++++++------ control/freqplot.py | 136 ++++++++++++++++++------------------- control/lti.py | 118 +++++++++++++------------------- control/matlab/timeresp.py | 29 +++----- control/nichols.py | 30 ++++---- control/nlsys.py | 69 ++++++++++--------- control/optimal.py | 15 ++-- control/passivity.py | 1 + control/phaseplot.py | 28 ++++---- control/pzmap.py | 37 +++++----- control/rlocus.py | 25 ++++--- control/robust.py | 8 +-- control/statefbk.py | 20 +++--- control/statesp.py | 33 ++++++--- control/stochsys.py | 2 +- control/timeplot.py | 26 ++++--- control/timeresp.py | 87 +++++++++++------------- control/xferfcn.py | 37 +++++----- doc/develop.rst | 24 +++++++ 26 files changed, 423 insertions(+), 436 deletions(-) diff --git a/control/bdalg.py b/control/bdalg.py index 8b066c293..1dab35d6e 100644 --- a/control/bdalg.py +++ b/control/bdalg.py @@ -38,7 +38,7 @@ def series(*sys, **kwargs): Returns ------- - out : scalar, array, or `InputOutputSystem` + out : `InputOutputSystem` Series interconnection of the systems. Other Parameters @@ -109,7 +109,7 @@ def parallel(*sys, **kwargs): Returns ------- - out : scalar, array, or `InputOutputSystem` + out : `InputOutputSystem` Parallel interconnection of the systems. Other Parameters @@ -177,7 +177,7 @@ def negate(sys, **kwargs): Returns ------- - out : scalar, array, or `InputOutputSystem` + out : `InputOutputSystem` Negated system. Other Parameters @@ -233,7 +233,7 @@ def feedback(sys1, sys2=1, sign=-1, **kwargs): Returns ------- - out : scalar, array, or `InputOutputSystem` + out : `InputOutputSystem` Feedback interconnection of the systems. Other Parameters @@ -608,7 +608,7 @@ def split_tf(transfer_function): Returns ------- - np.ndarray + ndarray NumPy array of SISO transfer functions. Examples diff --git a/control/canonical.py b/control/canonical.py index b6de75a73..b171508db 100644 --- a/control/canonical.py +++ b/control/canonical.py @@ -275,7 +275,10 @@ def _bdschur_defective(blksizes, eigvals): ------- True iff Schur blocks are defective. - blksizes, eigvals are the 3rd and 4th results returned by mb03rd. + Notes + ----- + `blksizes`, `eigvals` are the 3rd and 4th results returned by mb03rd. + """ if any(blksizes > 2): return True @@ -327,9 +330,10 @@ def _bdschur_condmax_search(aschur, tschur, condmax): Notes ----- - Outputs as for slycot.mb03rd + Outputs as for slycot.mb03rd. + + `aschur`, `tschur` are as returned by scipy.linalg.schur. - aschur, tschur are as returned by scipy.linalg.schur. """ try: from slycot import mb03rd @@ -428,12 +432,11 @@ def bdschur(a, condmax=None, sort=None): If `sort` is 'continuous', the blocks are sorted according to associated eigenvalues. The ordering is first by real part of - eigenvalue, in descending order, then by absolute value of - imaginary part of eigenvalue, also in decreasing order. + eigenvalue, in descending order, then by absolute value of imaginary + part of eigenvalue, also in decreasing order. - If `sort` is 'discrete', the blocks are sorted as for - 'continuous', but applied to log of eigenvalues - (i.e., continuous-equivalent eigenvalues). + If `sort` is 'discrete', the blocks are sorted as for 'continuous', but + applied to log of eigenvalues (i.e., continuous-equivalent eigenvalues). Examples -------- @@ -509,7 +512,7 @@ def modal_form(xsys, condmax=None, sort=False): ------- zsys : `StateSpace` object System in modal canonical form, with state z. - T : (M, M) array + T : (M, M) ndarray Coordinate transformation: z = T * x. Examples diff --git a/control/ctrlplot.py b/control/ctrlplot.py index 661c2001f..0135d8071 100644 --- a/control/ctrlplot.py +++ b/control/ctrlplot.py @@ -239,13 +239,13 @@ def get_plot_axes(line_array): Parameters ---------- - line_array : array of list of Line2D + line_array : array of list of `matplotlib.lines.Line2D` A 2D array with elements corresponding to a list of lines appearing in an axes, matching the return type of a time response data plot. Returns ------- - axes_array : array of list of Axes + axes_array : array of list of `matplotlib.axes.Axes` A 2D array with elements corresponding to the Axes associated with the lines in `line_array`. @@ -287,7 +287,7 @@ def pole_zero_subplots( Returns ------- - ax_array : array + ax_array : ndarray 2D array of axes. """ @@ -612,8 +612,10 @@ def _add_arrows_to_line2D( Returns ------- - arrows: list of arrows + arrows : list of arrows + Notes + ----- Based on https://stackoverflow.com/questions/26911898/ """ diff --git a/control/ctrlutil.py b/control/ctrlutil.py index 3a951463f..4db22f9c6 100644 --- a/control/ctrlutil.py +++ b/control/ctrlutil.py @@ -28,7 +28,7 @@ def unwrap(angle, period=2*math.pi): Returns ------- - angle_out : array_like + angle_out : ndarray Output array, with jumps of period/2 eliminated. Examples diff --git a/control/descfcn.py b/control/descfcn.py index 9f7fdede4..31697e67b 100644 --- a/control/descfcn.py +++ b/control/descfcn.py @@ -124,7 +124,7 @@ def describing_function( Returns ------- - df : array of complex + df : ndarray of complex The (complex) value of the describing function at the given amplitudes. Raises @@ -312,15 +312,21 @@ def describing_function_response( ------- response : `DescribingFunctionResponse` object Response object that contains the result of the describing function - analysis. The following information can be retrieved from this - object: - response.intersections : 1D array of 2-tuples or None + analysis. The results can plotted using the + `~DescribingFunctionResponse.plot` method. + response.intersections : 1D ndarray of 2-tuples or None A list of all amplitudes and frequencies in which :math:`H(j\\omega) N(a) = -1`, where :math:`N(a)` is the describing function associated with `F`, or None if there are no such points. Each pair represents a potential limit cycle for the closed loop system with amplitude given by the first value of the tuple and frequency given by the second value. + response.Nvals : complex ndarray + Complex value of the describing function, indexed by amplitude. + + See Also + -------- + DescribingFunctionResponse, describing_function_plot Examples -------- @@ -456,20 +462,22 @@ def describing_function_plot( Returns ------- cplt : `ControlPlot` object - Object containing the data that were plotted: - - * cplt.lines: Array of `matplotlib.lines.Line2D` objects for - each line in the plot. The first element of the array is - a list of lines (typically only one) for the Nyquist plot - of the linear I/O system. The second element of the array - is a list of lines (typically only one) for the describing - function curve. - - * cplt.axes: 2D array of `matplotlib.axes.Axes` for the plot. - - * cplt.figure: `matplotlib.figure.Figure` containing the plot. - - See `ControlPlot` for more detailed information. + Object containing the data that were plotted. See `ControlPlot` + for more detailed information. + cplt.lines : array of `matplotlib.lines.Line2D` + Array containing information on each line in the plot. The first + element of the array is a list of lines (typically only one) for + the Nyquist plot of the linear I/O system. The second element of + the array is a list of lines (typically only one) for the + describing function curve. + cplt.axes : 2D array of `matplotlib.axes.Axes` + Axes for each subplot. + cplt.figure : `matplotlib.figure.Figure` + Figure containing the plot. + + See Also + -------- + DescribingFunctionResponse, describing_function_response Examples -------- diff --git a/control/dtime.py b/control/dtime.py index b7c9dc670..120688ec9 100644 --- a/control/dtime.py +++ b/control/dtime.py @@ -34,7 +34,7 @@ def sample_system(sysc, Ts, method='zoh', alpha=None, prewarp_frequency=None, Returns ------- sysd : LTI of the same class (`StateSpace` or `TransferFunction`) - Discrete time system, with sampling rate Ts. + Discrete time system, with sampling rate `Ts`. Other Parameters ---------------- diff --git a/control/flatsys/flatsys.py b/control/flatsys/flatsys.py index 811b0d830..502cc4bec 100644 --- a/control/flatsys/flatsys.py +++ b/control/flatsys/flatsys.py @@ -133,7 +133,7 @@ def forward(self, x, u, params=None): Returns ------- zflag : list of 1D arrays - For each flat output :math:`z_i`, zflag[i] should be an + For each flat output :math:`z_i`, `zflag[i]` should be an ndarray of length :math:`q_i` that contains the flat output and its first :math:`q_i` derivatives. diff --git a/control/flatsys/systraj.py b/control/flatsys/systraj.py index d8255147d..2c7c4c898 100644 --- a/control/flatsys/systraj.py +++ b/control/flatsys/systraj.py @@ -131,28 +131,27 @@ def response(self, timepts, transpose=False, return_x=False, squeeze=None): Returns ------- - response : `control.TimeResponseData` - Time response represented as a `TimeResponseData` object - containing the following properties: - - * time (array): Time values of the output. - - * outputs (array): Response of the system. If the system is - SISO and squeeze is not True, the array is 1D (indexed by - time). If the system is not SISO or `squeeze` is False, - the array is 3D (indexed by the output, trace, and time). - - * states (array): Time evolution of the state vector, - represented as either a 2D array indexed by state and time - (if SISO) or a 3D array indexed by state, trace, and time. - Not affected by `squeeze`. - - * inputs (array): Input(s) to the system, indexed in the same - manner as `outputs`. - - The return value of the system can also be accessed by assigning - the function to a tuple of length 2 (time, output) or of length 3 - (time, output, state) if `return_x` is True. + response : `TimeResponseData` + Time response data object representing the input/output response. + When accessed as a tuple, returns ``(time, outputs)`` or ``(time, + outputs, states`` if `return_x` is True. If the input/output + system signals are named, these names will be used as labels for + the time response. If `sys` is a list of systems, returns a + `TimeResponseList` object. Results can be plotted using the + `~TimeResponseData.plot` method. See `TimeResponseData` for more + detailed information. + response.time : array + Time values of the output. + response.outputs : array + Response of the system. If the system is SISO and `squeeze` is + not True, the array is 1D (indexed by time). If the system is not + SISO or `squeeze` is False, the array is 2D (indexed by output and + time). + response.states : array + Time evolution of the state vector, represented as a 2D array + indexed by state and time. + response.inputs : array + Input(s) to the system, indexed by input and time. """ # Compute the state and input response using the eval function diff --git a/control/freqplot.py b/control/freqplot.py index 382a5061c..1fb2811e5 100644 --- a/control/freqplot.py +++ b/control/freqplot.py @@ -133,20 +133,18 @@ def bode_plot( Returns ------- cplt : `ControlPlot` object - Object containing the data that were plotted: - - * cplt.lines: Array of `matplotlib.lines.Line2D` objects - for each line in the plot. The shape of the array matches the - subplots shape and the value of the array is a list of Line2D - objects in that subplot. - - * cplt.axes: 2D array of `matplotlib.axes.Axes` for the plot. - - * cplt.figure: `matplotlib.figure.Figure` containing the plot. - - * cplt.legend: legend object(s) contained in the plot - - See `ControlPlot` for more detailed information. + Object containing the data that were plotted. See `ControlPlot` + for more detailed information. + cplt.lines : Array of `matplotlib.lines.Line2D` objects + Array containing information on each line in the plot. The shape + of the array matches the subplots shape and the value of the array + is a list of Line2D objects in that subplot. + cplt.axes : 2D ndarray of `matplotlib.axes.Axes` + Axes for each subplot. + cplt.figure : `matplotlib.figure.Figure` + Figure containing the plot. + cplt.legend : 2D array of `matplotlib.legend.Legend` + Legend object(s) contained in the plot. Other Parameters ---------------- @@ -1128,9 +1126,9 @@ class NyquistResponseData: sysname : str The name of the system being analyzed. return_contour : bool - If true, when the object is accessed as an iterable return two - elements: `count` (number of encirlements) and `contour`. If - false (default), then return only `count`. + If True, when the object is accessed as an iterable return two + elements: `count` (number of encirclements) and `contour`. If + False (default), then return only `count`. """ def __init__( @@ -1212,7 +1210,7 @@ def nyquist_response( responses : list of `NyquistResponseData` For each system, a Nyquist response data object is returned. If `sysdata` is a single system, a single elemeent is returned (not a - list). For each response, the following information is available: + list). response.count : int Number of encirclements of the point -1 by the Nyquist curve. If multiple systems are given, an array of counts is returned. @@ -1247,9 +1245,9 @@ def nyquist_response( Number of samples to use for the frequeny range. Defaults to `config.defaults['freqplot.number_of_samples']`. warn_nyquist : bool, optional - If set to 'False', turn off warnings about frequencies above Nyquist. + If set to False, turn off warnings about frequencies above Nyquist. warn_encirclements : bool, optional - If set to 'False', turn off warnings about number of encirclements not + If set to False, turn off warnings about number of encirclements not meeting the Nyquist criterion. Notes @@ -1276,8 +1274,8 @@ def nyquist_response( image use a dashdot line style. If the legacy keyword `return_contour` is specified as True, the - response object can be iterated over to return `count, contour`. This - behavior is deprecated and will be removed in a future release. + response object can be iterated over to return ``(count, contour)``. + This behavior is deprecated and will be removed in a future release. See Also -------- @@ -1584,26 +1582,25 @@ def nyquist_plot( Returns ------- cplt : `ControlPlot` object - Object containing the data that were plotted: - - * cplt.lines: 2D array of `matplotlib.lines.Line2D` - objects for each line in the plot. The shape of the array is - given by (nsys, 4) where nsys is the number of systems or - Nyquist responses passed to the function. The second index - specifies the segment type: - - - lines[idx, 0]: unscaled portion of the primary curve - - lines[idx, 1]: scaled portion of the primary curve - - lines[idx, 2]: unscaled portion of the mirror curve - - lines[idx, 3]: scaled portion of the mirror curve - - * cplt.axes: 2D array of `matplotlib.axes.Axes` for the plot. - - * cplt.figure: `matplotlib.figure.Figure` containing the plot. - - * cplt.legend: legend object(s) contained in the plot - - See `ControlPlot` for more detailed information. + Object containing the data that were plotted. See `ControlPlot` + for more detailed information. + cplt.lines : 2D array of `matplotlib.lines.Line2D` + Array containing information on each line in the plot. The shape + of the array is given by (nsys, 4) where nsys is the number of + systems or Nyquist responses passed to the function. The second + index specifies the segment type: + + - lines[idx, 0]: unscaled portion of the primary curve + - lines[idx, 1]: scaled portion of the primary curve + - lines[idx, 2]: unscaled portion of the mirror curve + - lines[idx, 3]: scaled portion of the mirror curve + + cplt.axes : 2D array of `matplotlib.axes.Axes` + Axes for each subplot. + cplt.figure : `matplotlib.figure.Figure` + Figure containing the plot. + cplt.legend : 2D array of `matplotlib.legend.Legend` + Legend object(s) contained in the plot. Other Parameters ---------------- @@ -1688,7 +1685,7 @@ def nyquist_plot( Override the default parameters used for generating plots. Default is set by `config.defaults['ctrlplot.rcParams']`. return_contour : bool, optional - (legacy) If 'True', return the encirclement count and Nyquist + (legacy) If True, return the encirclement count and Nyquist contour used to generate the Nyquist plot. show_legend : bool, optional Force legend to be shown if True or hidden if False. If @@ -1709,9 +1706,9 @@ def nyquist_plot( centered relative to the axes. If set to 'figure', it will be centered with respect to the figure (faster execution). warn_nyquist : bool, optional - If set to 'False', turn off warnings about frequencies above Nyquist. + If set to False, turn off warnings about frequencies above Nyquist. warn_encirclements : bool, optional - If set to 'False', turn off warnings about number of encirclements not + If set to False, turn off warnings about number of encirclements not meeting the Nyquist criterion. See Also @@ -2244,19 +2241,17 @@ def gangof4_plot( Returns ------- cplt : `ControlPlot` object - Object containing the data that were plotted: - - * cplt.lines: 2x2 array of `matplotlib.lines.Line2D` - objects for each line in the plot. The value of each array - entry is a list of Line2D objects in that subplot. - - * cplt.axes: 2D array of `matplotlib.axes.Axes` for the plot. - - * cplt.figure: `matplotlib.figure.Figure` containing the plot. - - * cplt.legend: legend object(s) contained in the plot - - See `ControlPlot` for more detailed information. + Object containing the data that were plotted. See `ControlPlot` + for more detailed information. + cplt.lines : 2x2 array of `matplotlib.lines.Line2D` + Array containing information on each line in the plot. The value + of each array entry is a list of Line2D objects in that subplot. + cplt.axes : 2D array of `matplotlib.axes.Axes` + Axes for each subplot. + cplt.figure : `matplotlib.figure.Figure` + Figure containing the plot. + cplt.legend : 2D array of `matplotlib.legend.Legend` + Legend object(s) contained in the plot. """ if len(args) == 1 and isinstance(args[0], FrequencyResponseData): @@ -2390,19 +2385,18 @@ def singular_values_plot( Returns ------- cplt : `ControlPlot` object - Object containing the data that were plotted: - - * cplt.lines: 1-D array of `matplotlib.lines.Line2D` objects. - The size of the array matches the number of systems and the - value of the array is a list of Line2D objects for that system. - - * cplt.axes: 2D array of `matplotlib.axes.Axes` for the plot. - - * cplt.figure: `matplotlib.figure.Figure` containing the plot. - - * cplt.legend: legend object(s) contained in the plot - - See `ControlPlot` for more detailed information. + Object containing the data that were plotted. See `ControlPlot` + for more detailed information. + cplt.lines : array of `matplotlib.lines.Line2D` + Array containing information on each line in the plot. The size of + the array matches the number of systems and the value of the array + is a list of Line2D objects for that system. + cplt.axes : 2D array of `matplotlib.axes.Axes` + Axes for each subplot. + cplt.figure : `matplotlib.figure.Figure` + Figure containing the plot. + cplt.legend : 2D array of `matplotlib.legend.Legend` + Legend object(s) contained in the plot. Other Parameters ---------------- diff --git a/control/lti.py b/control/lti.py index 8e2ad0033..351efddec 100644 --- a/control/lti.py +++ b/control/lti.py @@ -38,6 +38,15 @@ def __init__(self, inputs=1, outputs=1, states=None, name=None, **kwargs): super().__init__( name=name, inputs=inputs, outputs=outputs, states=states, **kwargs) + # TODO: update ss, tf docstrings to use this one as the primary + def __call__(self, x, squeeze=None, warn_infinite=True): + """Evaluate system's frequency response at complex frequencies. + + See `StateSpace.__call__` and `TransferFunction.__call__` for details. + + """ + raise NotImplementedError("not implemented in subclass") + def damp(self): '''Natural frequency, damping ratio of system poles. @@ -80,51 +89,7 @@ def feedback(self, other=1, sign=-1): def frequency_response(self, omega=None, squeeze=None): """Evaluate LTI system response at an array of frequencies. - For continuous-time systems with transfer function G, computes - the frequency response as - - G(j*omega) = mag * exp(j*phase) - - For discrete-time systems, the response is evaluated around the - unit circle such that - - G(exp(j*omega*dt)) = mag * exp(j*phase). - - In general the system may be multiple input, multiple output (MIMO), - where ``m = self.ninputs`` number of inputs and ``p = self.noutputs`` - number of outputs. - - Parameters - ---------- - omega : float or 1D array_like, optional - A list, tuple, array, or scalar value of frequencies in - radians/sec at which the system will be evaluated. If None (default), - a set of frequencies is computed based on the system dynamics. - squeeze : bool, optional - If `squeeze` = True, remove single-dimensional entries from the - shape of the output even if the system is not SISO. If - `squeeze` = False, keep all indices (output, input and, if - omega is array_like, frequency) even if the system is SISO. The - default value can be set using - `config.defaults['control.squeeze_frequency_response']`. - - Returns - ------- - response : `FrequencyResponseData` - Frequency response data object representing the frequency - response. This object can be assigned to a tuple using - - mag, phase, omega = response - - where `mag` is the magnitude (absolute value, not dB or log10) - of the system frequency response, `phase` is the wrapped phase - in radians of the system frequency response, and `omega` is - the (sorted) frequencies at which the response was evaluated. - If the system is SISO and squeeze is not True, `magnitude` and - `phase` are 1D, indexed by frequency. If the system is not - SISO or squeeze is False, the array is 3D, indexed by the - output, input, and, if omega is array_like, frequency. If - `squeeze` is True then single-dimensional axes are removed. + See `frequency_response` for more detailed information. """ from .frdata import FrequencyResponseData @@ -438,6 +403,7 @@ def damp(sys, doprint=True): return wn, zeta, poles +# TODO: deprecate this function def evalfr(sys, x, squeeze=None): """Evaluate transfer function of LTI system at complex frequency. @@ -500,20 +466,30 @@ def frequency_response( Hz=None, squeeze=None): """Frequency response of an LTI system. - Computes the frequency response of an LTI system at a list of - frequencies. In general the system may be multiple input, multiple - output (MIMO), where ``m = sys.ninputs`` number of inputs and ``p = - sys.noutputs`` number of outputs. + For continuous-time systems with transfer function G, computes the + frequency response as + + G(j*omega) = mag * exp(j*phase) + + For discrete-time systems, the response is evaluated around the unit + circle such that + + G(exp(j*omega*dt)) = mag * exp(j*phase). + + In general the system may be multiple input, multiple output (MIMO), + where ``m = self.ninputs`` number of inputs and ``p = self.noutputs`` + number of outputs. Parameters ---------- sysdata : LTI system or list of LTI systems Linear system(s) for which frequency response is computed. omega : float or 1D array_like, optional - Frequencies in radians/sec at which the system should be - evaluated. Can be a single frequency or array of frequencies, which - will be sorted before evaluation. If None (default), a common set - of frequencies that works across all given systems is computed. + A list, tuple, array, or scalar value of frequencies in radians/sec + at which the system will be evaluated. Can be a single frequency + or array of frequencies, which will be sorted before evaluation. + If None (default), a common set of frequencies that works across + all given systems is computed. omega_limits : array_like of two values, optional Limits to the range of frequencies, in rad/sec. Specifying `omega` as a list of two elements is equivalent to providing @@ -526,23 +502,23 @@ def frequency_response( Returns ------- response : `FrequencyResponseData` - Frequency response data object representing the frequency response. - This object can be assigned to a tuple using - - mag, phase, omega = response - - where `mag` is the magnitude (absolute value, not dB or log10) of - the system frequency response, `phase` is the wrapped phase in - radians of the system frequency response, and `omega` is the - (sorted) frequencies at which the response was evaluated. If the - system is SISO and squeeze is not False, `magnitude` and `phase` - are 1D, indexed by frequency. If the system is not SISO or squeeze - is False, the array is 3D, indexed by the output, input, and - frequency. If `squeeze` is True then single-dimensional axes are - removed. - - Returns a list of `FrequencyResponseData` objects if `sys` is - a list of systems. + Frequency response data object representing the frequency + response. When accessed as a tuple, returns ``(magnitude, + phase, omega)``. If `sysdata` is a list of systems, returns a + `FrequencyResponseList` object. Results can be plotted using + the `~FrequencyResponseData.plot` method. See + `FrequencyResponseData` for more detailed information. + response.magnitude : array + Magnitude of the frequency response (absolute value, not dB or + log10). If the system is SISO and squeeze is not True, the + array is 1D, indexed by frequency. If the system is not SISO + or squeeze is False, the array is 3D, indexed by the output, + input, and, if omega is array_like, frequency. If `squeeze` is + True then single-dimensional axes are removed. + response.phase : array + Wrapped phase, in radians, with same shape as `magnitude`. + response.omega : array + Sorted list of frequencies at which response was evaluated. Other Parameters ---------------- @@ -560,7 +536,7 @@ def frequency_response( See Also -------- - evalfr, bode_plot + LTI.__call__, bode_plot Notes ----- diff --git a/control/matlab/timeresp.py b/control/matlab/timeresp.py index 1f26ca504..1a0e3453b 100644 --- a/control/matlab/timeresp.py +++ b/control/matlab/timeresp.py @@ -88,27 +88,18 @@ def stepinfo(sysdata, T=None, yfinal=None, SettlingTimeThreshold=0.02, Returns ------- S : dict or list of list of dict - If `sysdata` corresponds to a SISO system, S is a dictionary + If `sysdata` corresponds to a SISO system, `S` is a dictionary containing: - RiseTime: - Time from 10% to 90% of the steady-state value. - SettlingTime: - Time to enter inside a default error of 2% - SettlingMin: - Minimum value after RiseTime - SettlingMax: - Maximum value after RiseTime - Overshoot: - Percentage of the Peak relative to steady value - Undershoot: - Percentage of undershoot - Peak: - Absolute peak value - PeakTime: - time of the Peak - SteadyStateValue: - Steady-state value + - 'RiseTime': Time from 10% to 90% of the steady-state value. + - 'SettlingTime': Time to enter inside a default error of 2%. + - 'SettlingMin': Minimum value after `RiseTime`. + - 'SettlingMax': Maximum value after `RiseTime`. + - 'Overshoot': Percentage of the peak relative to steady value. + - 'Undershoot': Percentage of undershoot. + - 'Peak': Absolute peak value. + - 'PeakTime': Time that the first peak value is obtained. + - 'SteadyStateValue': Steady-state value. If `sysdata` corresponds to a MIMO system, `S` is a 2D list of dicts. To get the step response characteristics from the j-th input to the diff --git a/control/nichols.py b/control/nichols.py index 6ce0d4c3d..43eaac2ab 100644 --- a/control/nichols.py +++ b/control/nichols.py @@ -50,21 +50,18 @@ def nichols_plot( Returns ------- cplt : `ControlPlot` object - Object containing the data that were plotted: - - * cplt.lines: 1D array of `matplotlib.lines.Line2D` objects. - The size of the array matches the number of systems and the - value of the array is a list of Line2D objects for that system. - - * cplt.axes: 2D array of `matplotlib.axes.Axes` for the plot. - - * cplt.figure: `matplotlib.figure.Figure` containing the plot. - - * cplt.legend: legend object(s) contained in the plot - - See `ControlPlot` for more detailed information. - - lines : array of Line2D + Object containing the data that were plotted. See `ControlPlot` + for more detailed information. + cplt.lines : Array of `matplotlib.lines.Line2D` objects + Array containing information on each line in the plot. The shape + of the array matches the subplots shape and the value of the array + is a list of Line2D objects in that subplot. + cplt.axes : 2D ndarray of `matplotlib.axes.Axes` + Axes for each subplot. + cplt.figure : `matplotlib.figure.Figure` + Figure containing the plot. + cplt.legend : 2D array of `matplotlib.legend.Legend` + Legend object(s) contained in the plot. Other Parameters ---------------- @@ -352,6 +349,7 @@ def closed_loop_contours(Gcl_mags, Gcl_phases): ------- contours : complex array Array of complex numbers corresponding to the contours. + """ # Compute the contours in Gcl-space. Since we're given closed-loop # magnitudes and phases, this is just a case of converting them into @@ -380,6 +378,7 @@ def m_circles(mags, phase_min=-359.75, phase_max=-0.25): ------- contours : complex array Array of complex numbers corresponding to the contours. + """ # Convert magnitudes and phase range into a grid suitable for # building contours @@ -406,6 +405,7 @@ def n_circles(phases, mag_min=-40.0, mag_max=12.0): ------- contours : complex array Array of complex numbers corresponding to the contours. + """ # Convert phases and magnitude range into a grid suitable for # building contours diff --git a/control/nlsys.py b/control/nlsys.py index 5f7b30db6..59a3b16b3 100644 --- a/control/nlsys.py +++ b/control/nlsys.py @@ -456,6 +456,7 @@ def output(self, t, x, u, params=None): Returns ------- y : ndarray + """ self._update_params(params) return self._out( @@ -1507,37 +1508,35 @@ def input_output_response( Returns ------- - results : `TimeResponseData` - Time response represented as a `TimeResponseData` object - containing the following properties: - - * time (array): Time values of the output. - - * outputs (array): Response of the system. If the system is SISO and - `squeeze` is not True, the array is 1D (indexed by time). If the - system is not SISO or `squeeze` is False, the array is 2D (indexed - by output and time). - - * states (array): Time evolution of the state vector, represented as - a 2D array indexed by state and time. - - * inputs (array): Input(s) to the system, indexed by input and time. - - * params (dict): Parameters values used for the simulation. - - The return value of the system can also be accessed by assigning the - function to a tuple of length 2 (time, output) or of length 3 (time, - output, state) if `return_x` is True. If the input/output - system signals are named, these names will be used as labels for the - time response. + response : `TimeResponseData` + Time response data object representing the input/output response. + When accessed as a tuple, returns ``(time, outputs)`` or ``(time, + outputs, states`` if `return_x` is True. If the input/output system + signals are named, these names will be used as labels for the time + response. If `sys` is a list of systems, returns a `TimeResponseList` + object. Results can be plotted using the `~TimeResponseData.plot` + method. See `TimeResponseData` for more detailed information. + response.time : array + Time values of the output. + response.output : array + Response of the system. If the system is SISO and `squeeze` is not + True, the array is 1D (indexed by time). If the system is not SISO + or `squeeze` is False, the array is 2D (indexed by output and time). + response.states : array + Time evolution of the state vector, represented as a 2D array + indexed by state and time. + response.inputs : array + Input(s) to the system, indexed by input and time. + response.params : dict + Parameters values used for the simulation. Other Parameters ---------------- ignore_errors : bool, optional If False (default), errors during computation of the trajectory will raise a `RuntimeError` exception. If True, do not raise - an exception and instead set `results.success` to False and - place an error message in `results.message`. + an exception and instead set `response.success` to False and + place an error message in `response.message`. solve_ivp_method : str, optional Set the method used by `scipy.integrate.solve_ivp`. Defaults to 'RK45'. @@ -1976,13 +1975,17 @@ def find_operating_point( ------- op_point : `OperatingPoint` The solution represented as an `OperatingPoint` object. The main - attributes are `states` and `inputs`, which represent the state - and input arrays at the operating point. See - `OperatingPoint` for a description of other attributes. - - If accessed as a tuple, returns `states`, `inputs`, and optionally - `outputs` and `result` based on the `return_outputs` and - `return_result` parameters. + attributes are `states` and `inputs`, which represent the state and + input arrays at the operating point. If accessed as a tuple, returns + `states`, `inputs`, and optionally `outputs` and `result` based on the + `return_outputs` and `return_result` parameters. See `OperatingPoint` + for a description of other attributes. + op_point.states : array + State vector at the operating point. + op_point.inputs : array + Input vector at the operating point. + op_point.outputs : array, optional + Output vector at the operating point. Notes ----- @@ -2421,7 +2424,7 @@ def interconnect( Returns ------- - sys : InterconnectedSystem + sys : `InterconnectedSystem` `NonlinearIOSystem` consisting of the interconnected subsystems. Other Parameters diff --git a/control/optimal.py b/control/optimal.py index 4f6afabf9..da1d67623 100644 --- a/control/optimal.py +++ b/control/optimal.py @@ -793,10 +793,9 @@ def compute_trajectory( print_summary : bool, optional If True (default), print a short summary of the computation. - Returns ------- - res : OptimalControlResult + res : `OptimalControlResult` Bundle object with the results of the optimal control problem. res.success : bool Boolean flag indicating whether the optimization was successful. @@ -808,7 +807,7 @@ def compute_trajectory( If the system is not SISO or squeeze is False, the array is 2D (indexed by the output number and time). res.states : array - Time evolution of the state vector (if return_states=True). + Time evolution of the state vector. """ # Allow 'return_x` as a synonym for 'return_states' @@ -1121,23 +1120,19 @@ def solve_ocp( Returns ------- - res : OptimalControlResult + res : `OptimalControlResult` Bundle object with the results of the optimal control problem. - res.success : bool Boolean flag indicating whether the optimization was successful. - res.time : array Time values of the input. - res.inputs : array Optimal inputs for the system. If the system is SISO and squeeze is not True, the array is 1D (indexed by time). If the system is not SISO or squeeze is False, the array is 2D (indexed by the output number and time). - res.states : array - Time evolution of the state vector (if return_states=True). + Time evolution of the state vector. Other Parameters ---------------- @@ -1753,7 +1748,7 @@ def compute_estimate( Returns ------- - res : OptimalEstimationResult + res : `OptimalEstimationResult` Bundle object with the results of the optimal estimation problem. res.success : bool Boolean flag indicating whether the optimization was successful. diff --git a/control/passivity.py b/control/passivity.py index 01aa76f42..6a0d5ecbd 100644 --- a/control/passivity.py +++ b/control/passivity.py @@ -200,6 +200,7 @@ def get_output_fb_index(sys): ------- float The OFP index. + """ sol = solve_passivity_LMI(sys, nu=0.0) if sol is None: diff --git a/control/phaseplot.py b/control/phaseplot.py index 159884957..54635dc0b 100644 --- a/control/phaseplot.py +++ b/control/phaseplot.py @@ -97,21 +97,19 @@ def phase_plane_plot( Returns ------- cplt : `ControlPlot` object - Object containing the data that were plotted: + Object containing the data that were plotted. See `ControlPlot` + for more detailed information. + cplt.lines : array of list of `matplotlib.lines.Line2D` + Array of list of `matplotlib.artist.Artist` objects: - * cplt.lines: array of list of `matplotlib.artist.Artist` - objects: - - - lines[0] = list of Line2D objects (streamlines, separatrices). - - lines[1] = Quiver object (vector field arrows). - - lines[2] = list of Line2D objects (equilibrium points). - - * cplt.axes: 2D array of `matplotlib.axes.Axes` for the plot. - - * cplt.figure: `matplotlib.figure.Figure` containing the plot. - - See `ControlPlot` for more detailed information. + - lines[0] = list of Line2D objects (streamlines, separatrices). + - lines[1] = Quiver object (vector field arrows). + - lines[2] = list of Line2D objects (equilibrium points). + cplt.axes : 2D array of `matplotlib.axes.Axes` + Axes for each subplot. + cplt.figure : `matplotlib.figure.Figure` + Figure containing the plot. Other Parameters ---------------- @@ -1317,6 +1315,8 @@ def box_grid(xlimp, ylimp): # TODO: rename to something more useful (or remove??) def _find(condition): """Returns indices where ravel(a) is true. - Private implementation of deprecated matplotlib.mlab.find + + Private implementation of deprecated `matplotlib.mlab.find`. + """ return np.nonzero(np.ravel(condition))[0] diff --git a/control/pzmap.py b/control/pzmap.py index 29eaf56f6..3ef974d98 100644 --- a/control/pzmap.py +++ b/control/pzmap.py @@ -136,7 +136,7 @@ def pole_zero_map(sysdata): Returns ------- - pzmap_data : PoleZeroMap + pzmap_data : `PoleZeroMap` Pole/zero map containing the poles and zeros of the system. Use ``pzmap_data.plot()`` or ``pole_zero_plot(pzmap_data)`` to plot the pole/zero map. @@ -193,25 +193,22 @@ def pole_zero_plot( Returns ------- cplt : `ControlPlot` object - Object containing the data that were plotted: - - * cplt.lines: Array of `matplotlib.lines.Line2D` objects - for each set of markers in the plot. The shape of the array is - given by (nsys, 2) where nsys is the number of systems or - responses passed to the function. The second index specifies - the pzmap object type: - - - lines[idx, 0]: poles - - lines[idx, 1]: zeros - - * cplt.axes: 2D array of `matplotlib.axes.Axes` for the plot. - - * cplt.figure: `matplotlib.figure.Figure` containing the plot. - - * cplt.legend: legend object(s) contained in the plot - - See `ControlPlot` for more detailed information. - + Object containing the data that were plotted. See `ControlPlot` + for more detailed information. + cplt.lines : array of list of `matplotlib.lines.Line2D` + The shape of the array is given by (nsys, 2) where nsys is the number + of systems or responses passed to the function. The second index + specifies the pzmap object type: + + - lines[idx, 0]: poles + - lines[idx, 1]: zeros + + cplt.axes : 2D array of `matplotlib.axes.Axes` + Axes for each subplot. + cplt.figure : `matplotlib.figure.Figure` + Figure containing the plot. + cplt.legend : 2D array of `matplotlib.legend.Legend` + Legend object(s) contained in the plot. poles, zeros : list of arrays (legacy) If the `plot` keyword is given, the system poles and zeros are returned. diff --git a/control/rlocus.py b/control/rlocus.py index cb1d42aae..c008d5240 100644 --- a/control/rlocus.py +++ b/control/rlocus.py @@ -127,24 +127,23 @@ def root_locus_plot( Returns ------- cplt : `ControlPlot` object - Object containing the data that were plotted: - - * cplt.lines: Array of `matplotlib.lines.Line2D` objects - for each set of markers in the plot. The shape of the array is - given by (nsys, 3) where nsys is the number of systems or - responses passed to the function. The second index specifies - the object type: + Object containing the data that were plotted. See `ControlPlot` + for more detailed information. + cplt.lines : array of list of `matplotlib.lines.Line2D` + The shape of the array is given by (nsys, 3) where nsys is the number + of systems or responses passed to the function. The second index + specifies the object type: - lines[idx, 0]: poles - lines[idx, 1]: zeros - lines[idx, 2]: loci - * cplt.axes: 2D array of `matplotlib.axes.Axes` for the plot. - - * cplt.figure: `matplotlib.figure.Figure` containing the plot. - - See `ControlPlot` for more detailed information. - + cplt.axes : 2D array of `matplotlib.axes.Axes` + Axes for each subplot. + cplt.figure : `matplotlib.figure.Figure` + Figure containing the plot. + cplt.legend : 2D array of `matplotlib.legend.Legend` + Legend object(s) contained in the plot. roots, gains : ndarray (legacy) If the `plot` keyword is given, returns the closed-loop root locations, arranged such that each row corresponds to a gain, diff --git a/control/robust.py b/control/robust.py index 894b690ca..d9f8cca11 100644 --- a/control/robust.py +++ b/control/robust.py @@ -365,13 +365,11 @@ def mixsyn(g, w1=None, w2=None, w3=None): [y] [p21 g] [u] then cl is the system from w -> z with u = -k*y. - info : tuple Two-tuple (`gamma`, `rcond`) containing additional information: - - * `gamma` (scalar): H-infinity norm of cl. - * `rcond` (array): Estimates of reciprocal condition numbers computed - during synthesis. See hinfsyn for details. + - `gamma` (scalar): H-infinity norm of cl. + - `rcond` (array): Estimates of reciprocal condition numbers + computed during synthesis. See hinfsyn for details. See Also -------- diff --git a/control/statefbk.py b/control/statefbk.py index 916bfce11..5ad76f58f 100644 --- a/control/statefbk.py +++ b/control/statefbk.py @@ -60,7 +60,7 @@ def place(A, B, p): Returns ------- - K : 2D array (or matrix) + K : 2D array Gain such that A - B K has eigenvalues given in p. Notes @@ -131,7 +131,7 @@ def place_varga(A, B, p, dtime=False, alpha=None): Returns ------- - K : 2D array (or matrix) + K : 2D array Gain such that A - B K has eigenvalues given in p. See Also @@ -223,7 +223,7 @@ def place_acker(A, B, poles): Returns ------- - K : 2D array (or matrix) + K : 2D array Gains such that A - B K has given eigenvalues. See Also @@ -300,9 +300,9 @@ def lqr(*args, **kwargs): Returns ------- - K : 2D array (or matrix) + K : 2D array State feedback gains. - S : 2D array (or matrix) + S : 2D array Solution to Riccati equation. E : 1D array Eigenvalues of the closed loop system. @@ -447,9 +447,9 @@ def dlqr(*args, **kwargs): Returns ------- - K : 2D array (or matrix) + K : 2D array State feedback gains. - S : 2D array (or matrix) + S : 2D array Solution to Riccati equation. E : 1D array Eigenvalues of the closed loop system. @@ -1043,7 +1043,7 @@ def ctrb(A, B, t=None): Returns ------- - C : 2D array (or matrix) + C : 2D array Controllability matrix. Examples @@ -1086,7 +1086,7 @@ def obsv(A, C, t=None): Returns ------- - O : 2D array (or matrix) + O : 2D array Observability matrix. Examples @@ -1131,7 +1131,7 @@ def gram(sys, type): Returns ------- - gram : 2D array (or matrix) + gram : 2D array Gramian of system. Raises diff --git a/control/statesp.py b/control/statesp.py index c0378dcd5..ade6f8729 100644 --- a/control/statesp.py +++ b/control/statesp.py @@ -408,7 +408,7 @@ def _repr_html_(self): Returns ------- - s : string + s : str HTML/LaTeX representation of model, or None if either matrix dimension is greater than statesp.latex_maxsize. @@ -434,7 +434,9 @@ def _latex_partitioned_stateless(self): Returns ------- - s : string with LaTeX representation of model + s : str + LaTeX representation of model. + """ # Apply NumPy formatting with np.printoptions(threshold=sys.maxsize): @@ -466,7 +468,9 @@ def _latex_partitioned(self): Returns ------- - s : string with LaTeX representation of model + s : str + LaTeX representation of model. + """ if self.nstates == 0: return self._latex_partitioned_stateless() @@ -507,7 +511,9 @@ def _latex_separate(self): Returns ------- - s : string with LaTeX representation of model + s : str + LaTeX representation of model. + """ lines = [ r'$$', @@ -1228,6 +1234,7 @@ def returnScipySignalLTI(self, strict=True): Continuous time (inheriting from `scipy.signal.lti`) or discrete time (inheriting from `scipy.signal.dlti`) SISO objects. + """ if strict and self.dt is None: raise ValueError("with strict=True, dt cannot be None") @@ -1358,7 +1365,7 @@ def sample(self, Ts, method='zoh', alpha=None, prewarp_frequency=None, Returns ------- sysd : `StateSpace` - Discrete-time system, with sampling rate Ts. + Discrete-time system, with sampling rate `Ts`. Other Parameters ---------------- @@ -1518,6 +1525,7 @@ def output(self, t, x, u=None, params=None): Returns ------- y : ndarray + """ if params is not None: warn("params keyword ignored for StateSpace object") @@ -1978,8 +1986,9 @@ def ssdata(sys): Returns ------- - A, B, C, D : list of matrices + A, B, C, D : ndarray State space data for the system. + """ ss = _convert_to_statespace(sys) return ss.A, ss.B, ss.C, ss.D @@ -2003,15 +2012,17 @@ def linfnorm(sys, tol=1e-10): fpeak : non-negative scalar Frequency, in rad/s, at which gpeak occurs. + See Also + -------- + slycot.ab13dd + + Notes + ----- For stable systems, the L-infinity and H-infinity norms are equal; for unstable systems, the H-infinity norm is infinite, while the L-infinity norm is finite if the system has no poles on the imaginary axis. - See Also - -------- - slycot.ab13dd - """ if ab13dd is None: raise ControlSlycot("Can't find slycot module 'ab13dd'") @@ -2181,7 +2192,7 @@ def summing_junction( Returns ------- - sys : static `StateSpace` + sys : `StateSpace` Linear input/output system object with no states and only a direct term that implements the summing junction. diff --git a/control/stochsys.py b/control/stochsys.py index 08ea6efa2..d701b50f6 100644 --- a/control/stochsys.py +++ b/control/stochsys.py @@ -89,7 +89,7 @@ def lqe(*args, **kwargs): L : 2D array Kalman estimator gain. P : 2D array - Solution to Riccati equation. + Solution to Riccati equation: .. math:: diff --git a/control/timeplot.py b/control/timeplot.py index c0a7c361d..f0a3efee4 100644 --- a/control/timeplot.py +++ b/control/timeplot.py @@ -91,20 +91,18 @@ def time_response_plot( Returns ------- cplt : `ControlPlot` object - Object containing the data that were plotted: - - * cplt.lines: Array of `matplotlib.lines.Line2D` objects - for each line in the plot. The shape of the array matches the - subplots shape and the value of the array is a list of Line2D - objects in that subplot. - - * cplt.axes: 2D array of `matplotlib.axes.Axes` for the plot. - - * cplt.figure: `matplotlib.figure.Figure` containing the plot. - - * cplt.legend: legend object(s) contained in the plot. - - See `ControlPlot` for more detailed information. + Object containing the data that were plotted. See `ControlPlot` + for more detailed information. + cplt.lines : 2D array of `matplotlib.lines.Line2D` + Array containing information on each line in the plot. The shape + of the array matches the subplots shape and the value of the array + is a list of Line2D objects in that subplot. + cplt.axes : 2D array of `matplotlib.axes.Axes` + Axes for each subplot. + cplt.figure : `matplotlib.figure.Figure` + Figure containing the plot. + cplt.legend : 2D array of `matplotlib.legend.Legend` + Legend object(s) contained in the plot. Other Parameters ---------------- diff --git a/control/timeresp.py b/control/timeresp.py index 6230484fe..6c82e09ee 100644 --- a/control/timeresp.py +++ b/control/timeresp.py @@ -837,7 +837,6 @@ def _check_convert_array(in_obj, legal_shapes, err_msg_start, squeeze=False, Returns ------- - out_array : array The checked and converted contents of `in_obj`. @@ -950,13 +949,11 @@ def forced_response(sysdata, T=None, U=0., X0=0., transpose=False, params=None, return_x : bool, default=None Used if the time response data is assigned to a tuple: - * If False, return only the time and output vectors. - - * If True, also return the the state vector. - - * If None, determine the returned variables by - `config.defaults['forced_response.return_x']`, which was True - before version 0.9 and is False since then. + * If False, return only the time and output vectors. + * If True, also return the the state vector. + * If None, determine the returned variables by + `config.defaults['forced_response.return_x']`, which was True + before version 0.9 and is False since then. squeeze : bool, optional By default, if a system is single-input, single-output (SISO) then @@ -970,30 +967,32 @@ def forced_response(sysdata, T=None, U=0., X0=0., transpose=False, params=None, Returns ------- - results : `TimeResponseData` or `TimeResponseList` - Time response represented as a `TimeResponseData` object or - list of `TimeResponseData` objects containing the following - properties: - - * time (array): Time values of the output. - - * outputs (array): Response of the system. If the system is SISO and - `squeeze` is not True, the array is 1D (indexed by time). If the - system is not SISO or `squeeze` is False, the array is 2D (indexed - by output and time). - - * states (array): Time evolution of the state vector, represented as - a 2D array indexed by state and time. - - * inputs (array): Input(s) to the system, indexed by input and time. - - The `~TimeResponseData.plot` method can be used to create a plot of - the time response(s) (see `time_response_plot` for more - information). + resp : `TimeResponseData` or `TimeResponseList` + Input/output response data object. When accessed as a tuple, + returns ``(time, outputs)`` (default) or ``(time, outputs, states)`` + if `return_x` is True. The `~TimeResponseData.plot` method can + be used to create a plot of the time response(s) (see + `time_response_plot` for more information). If `sysdata` is a list + of systems, a `TimeResponseList` object is returned, which acts as + a list of `TimeResponseData` objects with a `~TimeResponseList.plot` + method that will plot responses as multiple traces. See + `time_response_plot` for additional information. + resp.time : array + Time values of the output. + resp.outputs : array + Response of the system. If the system is SISO and `squeeze` is not + True, the array is 1D (indexed by time). If the system is not SISO or + `squeeze` is False, the array is 2D (indexed by output and time). + resp.states : array + Time evolution of the state vector, represented as a 2D array indexed by + state and time. + resp.inputs : array + Input(s) to the system, indexed by input and time. See Also -------- - step_response, initial_response, impulse_response, input_output_response + impulse_response, initial_response, input_output_response, \ + step_response, time_response_plot Notes ----- @@ -1515,33 +1514,23 @@ def step_info(sysdata, T=None, T_num=None, yfinal=None, params=None, Returns ------- S : dict or list of list of dict - If `sysdata` corresponds to a SISO system, S is a dictionary + If `sysdata` corresponds to a SISO system, `S` is a dictionary containing: - RiseTime: - Time from 10% to 90% of the steady-state value. - SettlingTime: - Time to enter inside a default error of 2% - SettlingMin: - Minimum value after RiseTime - SettlingMax: - Maximum value after RiseTime - Overshoot: - Percentage of the Peak relative to steady value - Undershoot: - Percentage of undershoot - Peak: - Absolute peak value - PeakTime: - time of the Peak - SteadyStateValue: - Steady-state value + - 'RiseTime': Time from 10% to 90% of the steady-state value. + - 'SettlingTime': Time to enter inside a default error of 2%. + - 'SettlingMin': Minimum value after `RiseTime`. + - 'SettlingMax': Maximum value after `RiseTime`. + - 'Overshoot': Percentage of the peak relative to steady value. + - 'Undershoot': Percentage of undershoot. + - 'Peak': Absolute peak value. + - 'PeakTime': Time that the first peak value is obtained. + - 'SteadyStateValue': Steady-state value. If `sysdata` corresponds to a MIMO system, `S` is a 2D list of dicts. To get the step response characteristics from the j-th input to the i-th output, access ``S[i][j]``. - See Also -------- step_response, forced_response, initial_response, impulse_response diff --git a/control/xferfcn.py b/control/xferfcn.py index 734ba736c..eaed0c874 100644 --- a/control/xferfcn.py +++ b/control/xferfcn.py @@ -1026,8 +1026,7 @@ def returnScipySignalLTI(self, strict=True): return out def _common_den(self, imag_tol=None, allow_nonproper=False): - """ - Compute MIMO common denominators; return them and adjusted numerators. + """Compute MIMO common denominators; return them and adjusted numerators. This function computes the denominators per input containing all the poles of sys.den, and reports it as the array den. The @@ -1047,23 +1046,21 @@ def _common_den(self, imag_tol=None, allow_nonproper=False): Returns ------- num: array - n by n by kd where n = max(sys.noutputs,sys.ninputs) - kd = max(denorder)+1 - Multi-dimensional array of numerator coefficients. num[i,j] - gives the numerator coefficient array for the ith output and jth - input; padded for use in td04ad ('C' option); matches the - denorder order; highest coefficient starts on the left. - If allow_nonproper=True and the order of a numerator exceeds the - order of the common denominator, num will be returned as None - + Multi-dimensional array of numerator coefficients with shape + (n, n, kd) array, where n = max(sys.noutputs, sys.ninputs), kd + = max(denorder) + 1. `num[i,j]` gives the numerator coefficient + array for the ith output and jth input; padded for use in + td04ad ('C' option); matches the denorder order; highest + coefficient starts on the left. If `allow_nonproper` = True + and the order of a numerator exceeds the order of the common + denominator, `num` will be returned as None. den: array - sys.ninputs by kd Multi-dimensional array of coefficients for common denominator - polynomial, one row per input. The array is prepared for use in - slycot td04ad, the first element is the highest-order polynomial - coefficient of s, matching the order in denorder. If denorder < - number of columns in den, the den is padded with zeros. - + polynomial with shape (sys.ninputs, kd) (one row per + input). The array is prepared for use in slycot td04ad, the + first element is the highest-order polynomial coefficient of + `s`, matching the order in denorder. If denorder < number of + columns in den, the den is padded with zeros. denorder: array of int, orders of den, one per input Examples @@ -1649,7 +1646,7 @@ def tf(*args, **kwargs): ``tf(num, den, dt)`` Create a discrete-time transfer function system; dt can either be a - positive number indicating the sampling time or 'True' if no + positive number indicating the sampling time or True if no specific timebase is given. ``tf([[G11, ..., G1m], ..., [Gp1, ..., Gpm]][, dt])`` @@ -1968,8 +1965,9 @@ def tfdata(sys): Returns ------- - (num, den): numerator and denominator arrays + num, den : numerator and denominator arrays Transfer function coefficients (SISO only). + """ tf = _convert_to_transfer_function(sys) @@ -1988,6 +1986,7 @@ def _clean_part(data, name=""): Returns ------- data: list of lists of ndarrays, with int converted to float + """ valid_types = (int, float, complex, np.number) valid_collection = (list, tuple, ndarray) diff --git a/doc/develop.rst b/doc/develop.rst index 16e3f926a..61b2949e4 100644 --- a/doc/develop.rst +++ b/doc/develop.rst @@ -364,6 +364,30 @@ Follow numpydoc format with the following additional details: form that can be checked by running `make doctest` in the `doc` directory. This is also part of the CI checks. +For functions that return a named tuple, bundle object, or class +instance, the return documentation should include the primary elements +of the return value:: + + Returns + ------- + resp : `TimeResponseData` + Input/output response data object. When accessed as a tuple, returns + ``time, outputs`` (default) or ``time, outputs, states`` if + `return_states` is True. The `~TimeResponseData.plot` method can be + used to create a plot of the time response(s) (see `time_response_plot` + for more information). + resp.time : array + Time values of the output. + resp.outputs : array + Response of the system. If the system is SISO and `squeeze` is not + True, the array is 1D (indexed by time). If the system is not SISO or + `squeeze` is False, the array is 2D (indexed by output and time). + resp.states : array + Time evolution of the state vector, represented as a 2D array indexed by + state and time. + resp.inputs : array + Input(s) to the system, indexed by input and time. + Class docstrings ---------------- From 6c8da96f40a2c07082d9674f59471e61bacee49c Mon Sep 17 00:00:00 2001 From: Richard Murray Date: Sun, 19 Jan 2025 17:35:33 -0800 Subject: [PATCH 75/93] update docstrings for LTI.__call__: clearer language + consistency --- control/frdata.py | 89 +++++++++++++++++++++---------------------- control/lti.py | 7 ++-- control/statesp.py | 94 ++++++++++++++++++++++++---------------------- control/xferfcn.py | 82 ++++++++++++++++++---------------------- 4 files changed, 135 insertions(+), 137 deletions(-) diff --git a/control/frdata.py b/control/frdata.py index 2eb113ed3..03dc3b8f9 100644 --- a/control/frdata.py +++ b/control/frdata.py @@ -679,37 +679,38 @@ def eval(self, omega, squeeze=None): return _process_frequency_response(self, omega, out, squeeze=squeeze) - def __call__(self, s=None, squeeze=None, return_magphase=None): - """Evaluate system's transfer function at complex frequencies. - - Returns the complex frequency response ``sys(s)`` of system `sys` - with ``m = sys.ninputs`` number of inputs and ``p = sys.noutputs`` - number of outputs. - - To evaluate at a frequency omega in radians per second, enter - ``s = omega * 1j`` or use ``sys.eval(omega)``. - - For a frequency response data object, the argument must be an - imaginary number (since only the frequency response is defined). - - If `s` is not given, this function creates a copy of a frequency + def __call__(self, x=None, squeeze=None, return_magphase=None): + """Evaluate system transfer function at point in complex plane. + + Returns the value of the system's transfer function at a point `x` + in the complex plane, where `x` is `s` for continuous-time systems + and `z` for discrete-time systems. For a frequency response data + object, the argument should be an imaginary number (since only the + frequency response is defined) and only the imaginary component of + `x` will be used. + + By default, a (complex) scalar will be returned for SISO systems + and a p x m array will be return for MIMO systems with m inputs and + p outputs. This can be changed using the `squeeze` keyword. + + To evaluate at a frequency `omega` in radians per second, enter ``x + = omega * 1j`` for continuous-time systems, ``x = exp(1j * omega * + dt)`` for discrete-time systems, or use the + `~LTI.frequency_response` method. + + If `x` is not given, this function creates a copy of a frequency response data object with a different set of output settings. Parameters ---------- - s : complex scalar or 1D array_like - Complex frequencies. If not specified, return a copy of the - frequency response data object with updated settings for output - processing (`squeeze`, `return_magphase`). - + x : complex scalar or 1D array_like + Imaginary value(s) at which frequency response will be evaluated. + The real component of `x` is ignored. If not specified, return + a copy of the frequency response data object with updated + settings for output processing (`squeeze`, `return_magphase`). squeeze : bool, optional - If `squeeze` = True, remove single-dimensional entries from the - shape of the output even if the system is not SISO. If - `squeeze` = False, keep all indices (output, input and, if - omega is array_like, frequency) even if the system is SISO. The - default value can be set using - `config.defaults['control.squeeze_frequency_response']`. - + Squeeze output, as described below. Default value can be set + using `config.defaults['control.squeeze_frequency_response']`. return_magphase : bool, optional If True, then a frequency response data object will enumerate as a tuple of the form ``(mag, phase, omega)`` where @@ -721,12 +722,12 @@ def __call__(self, s=None, squeeze=None, return_magphase=None): Returns ------- fresp : complex ndarray - The frequency response of the system. If the system is SISO and - squeeze is not True, the shape of the array matches the shape of - omega. If the system is not SISO or squeeze is False, the first - two dimensions of the array are indices for the output and input - and the remaining dimensions match omega. If `squeeze` is True - then single-dimensional axes are removed. + The value of the system transfer function at `x`. If the system + is SISO and `squeeze` is not True, the shape of the array matches + the shape of `x`. If the system is not SISO or `squeeze` is + False, the first two dimensions of the array are indices for the + output and input and the remaining dimensions match `x`. If + `squeeze` is True then single-dimensional axes are removed. Raises ------ @@ -736,7 +737,7 @@ def __call__(self, s=None, squeeze=None, return_magphase=None): real frequencies). """ - if s is None: + if x is None: # Create a copy of the response with new keywords response = copy(self) @@ -748,18 +749,18 @@ def __call__(self, s=None, squeeze=None, return_magphase=None): return response # Make sure that we are operating on a simple list - if len(np.atleast_1d(s).shape) > 1: + if len(np.atleast_1d(x).shape) > 1: raise ValueError("input list must be 1D") - if any(abs(np.atleast_1d(s).real) > 0): + if any(abs(np.atleast_1d(x).real) > 0): raise ValueError("__call__: FRD systems can only accept " "purely imaginary frequencies") # need to preserve array or scalar status - if hasattr(s, '__len__'): - return self.eval(np.asarray(s).imag, squeeze=squeeze) + if hasattr(x, '__len__'): + return self.eval(np.asarray(x).imag, squeeze=squeeze) else: - return self.eval(complex(s).imag, squeeze=squeeze) + return self.eval(complex(x).imag, squeeze=squeeze) # Implement iter to allow assigning to a tuple def __iter__(self): @@ -800,7 +801,7 @@ def freqresp(self, omega): """(deprecated) Evaluate transfer function at complex frequencies. .. deprecated::0.9.0 - Method has been given the more pythonic name + Method has been given the more Pythonic name `FrequencyResponseData.frequency_response`. Or use `freqresp` in the MATLAB compatibility module. @@ -818,7 +819,7 @@ def feedback(self, other=1, sign=-1): Parameters ---------- other : `LTI` - System in the feedack path. + System in the feedback path. sign : float, optional Gain to use in feedback path. Defaults to -1. @@ -928,8 +929,8 @@ def to_pandas(self): # Note: This class was initially given the name "FRD", but this caused # problems with documentation on MacOS platforms, since files were generated # for control.frd and control.FRD, which are not differentiated on most MacOS -# filesystems, which are case insensitive. Renaming the FRD class to be -# FrequenceResponseData and then assigning FRD to point to the same object +# file systems, which are case insensitive. Renaming the FRD class to be +# FrequencyResponseData and then assigning FRD to point to the same object # fixes this problem. # FRD = FrequencyResponseData @@ -1026,7 +1027,7 @@ def frd(*args, **kwargs): A linear system that will be evaluated for frequency response data. response : array_like or LTI system Complex vector with the system response or an LTI system that can - be used to copmute the frequency response at a list of frequencies. + be used to compute the frequency response at a list of frequencies. omega : array_like Vector of frequencies at which the response is evaluated. dt : float, True, or None @@ -1057,7 +1058,7 @@ def frd(*args, **kwargs): by adding the prefix and suffix strings in `config.defaults['iosys.sampled_system_name_prefix']` and `config.defaults['iosys.sampled_system_name_suffix']`, with the - default being to add the suffix '$ampled'. Otherwise, a generic + default being to add the suffix '$sampled'. Otherwise, a generic name 'sys[id]' is generated with a unique integer id See Also diff --git a/control/lti.py b/control/lti.py index 351efddec..f87b125c3 100644 --- a/control/lti.py +++ b/control/lti.py @@ -40,7 +40,7 @@ def __init__(self, inputs=1, outputs=1, states=None, name=None, **kwargs): # TODO: update ss, tf docstrings to use this one as the primary def __call__(self, x, squeeze=None, warn_infinite=True): - """Evaluate system's frequency response at complex frequencies. + """Evaluate system transfer function at point in complex plane. See `StateSpace.__call__` and `TransferFunction.__call__` for details. @@ -443,7 +443,8 @@ def evalfr(sys, x, squeeze=None): See Also -------- - frequency_response, bode_plot + StateSpace.__call__, TransferFunction.__call__, frequency_response, \ + bode_plot Notes ----- @@ -455,8 +456,6 @@ def evalfr(sys, x, squeeze=None): >>> G = ct.ss([[-1, -2], [3, -4]], [[5], [7]], [[6, 8]], [[9]]) >>> fresp = ct.evalfr(G, 1j) # evaluate at s = 1j - .. todo:: Add example with MIMO system - """ return sys(x, squeeze=squeeze) diff --git a/control/statesp.py b/control/statesp.py index ade6f8729..07173c5d8 100644 --- a/control/statesp.py +++ b/control/statesp.py @@ -768,39 +768,45 @@ def __pow__(self, other): return self * (self**(other - 1)) def __call__(self, x, squeeze=None, warn_infinite=True): - """Evaluate system's frequency response at complex frequencies. + """Evaluate system transfer function at point in complex plane. - Returns the complex frequency response ``sys(x)`` where `x` is `s` for - continuous-time systems and `z` for discrete-time systems. + Returns the value of the system's transfer function at a point `x` + in the complex plane, where `x` is `s` for continuous-time systems + and `z` for discrete-time systems. - To evaluate at a frequency omega in radians per second, enter - ``x = omega * 1j``, for continuous-time systems, or - ``x = exp(1j * omega * dt)`` for discrete-time systems. Or use - `StateSpace.frequency_response`. + By default, a (complex) scalar will be returned for SISO systems + and a p x m array will be return for MIMO systems with m inputs and + p outputs. This can be changed using the `squeeze` keyword. + + To evaluate at a frequency `omega` in radians per second, + enter ``x = omega * 1j`` for continuous-time systems, + ``x = exp(1j * omega * dt)`` for discrete-time systems, or + use the `~LTI.frequency_response` method. Parameters ---------- x : complex or complex 1D array_like - Complex frequencies + Complex value(s) at which transfer function will be evaluated. squeeze : bool, optional - If `squeeze` = True, remove single-dimensional entries from the - shape of the output even if the system is not SISO. If - `squeeze` = False, keep all indices (output, input and, if - omega is array_like, frequency) even if the system is SISO. The - default value can be set using - `config.defaults['control.squeeze_frequency_response']`. - warn_infinite : bool, optional - If set to False, don't warn if frequency response is infinite. + Squeeze output, as described below. Default value can be set + using `config.defaults['control.squeeze_frequency_response']`. + warn_infinite : bool, optional If set to False, turn off + divide by zero warning. Returns ------- fresp : complex ndarray - The frequency response of the system. If the system is SISO and - squeeze is not True, the shape of the array matches the shape of - omega. If the system is not SISO or squeeze is False, the first - two dimensions of the array are indices for the output and input - and the remaining dimensions match omega. If `squeeze` is True - then single-dimensional axes are removed. + The value of the system transfer function at `x`. If the system + is SISO and `squeeze` is not True, the shape of the array matches + the shape of `x`. If the system is not SISO or `squeeze` is + False, the first two dimensions of the array are indices for the + output and input and the remaining dimensions match `x`. If + `squeeze` is True then single-dimensional axes are removed. + + Examples + -------- + >>> G = ct.ss([[-1, -2], [3, -4]], [[5], [7]], [[6, 8]], [[9]]) + >>> fresp = G(1j) # evaluate at s = 1j """ # Use Slycot if available @@ -926,7 +932,7 @@ def horner(self, x, warn_infinite=True): xr = solve(x_idx * eye(self.nstates) - self.A, self.B) out[:, :, idx] = self.C @ xr + self.D except LinAlgError: - # Issue a warning messsage, for consistency with xferfcn + # Issue a warning message, for consistency with xferfcn if warn_infinite: warn("singular matrix in frequency response", RuntimeWarning) @@ -944,7 +950,7 @@ def freqresp(self, omega): """(deprecated) Evaluate transfer function at complex frequencies. .. deprecated::0.9.0 - Method has been given the more pythonic name + Method has been given the more Pythonic name `StateSpace.frequency_response`. Or use `freqresp` in the MATLAB compatibility module. """ @@ -978,11 +984,11 @@ def zeros(self): if nu == 0: return np.array([]) else: - # Use SciPy generalized eigenvalue fucntion + # Use SciPy generalized eigenvalue function return sp.linalg.eigvals(out[8][0:nu, 0:nu], out[9][0:nu, 0:nu]).astype(complex) - except ImportError: # Slycot unavailable. Fall back to scipy. + except ImportError: # Slycot unavailable. Fall back to SciPy. if self.C.shape[0] != self.D.shape[1]: raise NotImplementedError( "StateSpace.zero only supports systems with the same " @@ -1013,7 +1019,7 @@ def feedback(self, other=1, sign=-1): Parameters ---------- other : `InputOutputSystem` - System in the feedack path. + System in the feedback path. sign : float, optional Gain to use in feedback path. Defaults to -1. @@ -1136,7 +1142,7 @@ def lft(self, other, nu=-1, ny=-1): # well-posed check F = np.block([[np.eye(ny), -D22], [-Dbar11, np.eye(nu)]]) if matrix_rank(F) != ny + nu: - raise ValueError("lft not well-posed to working precision.") + raise ValueError("LFT not well-posed to working precision.") # solve for the resulting ss by solving for [y, u] using [x, # xbar] and [w1, w2]. @@ -1242,7 +1248,7 @@ def returnScipySignalLTI(self, strict=True): if self.dt: kwdt = {'dt': self.dt} else: - # scipy convention for continuous-time lti systems: call without + # SciPy convention for continuous-time LTI systems: call without # dt keyword argument kwdt = {} @@ -1371,7 +1377,7 @@ def sample(self, Ts, method='zoh', alpha=None, prewarp_frequency=None, ---------------- inputs : int, list of str or None, optional Description of the system inputs. If not specified, the - origional system inputs are used. See `InputOutputSystem` for + original system inputs are used. See `InputOutputSystem` for more information. outputs : int, list of str or None, optional Description of the system outputs. Same format as `inputs`. @@ -1464,7 +1470,7 @@ def dynamics(self, t, x, u=None, params=None): The first argument `t` is ignored because `StateSpace` systems are time-invariant. It is included so that the dynamics can be passed to numerical integrators, such as `scipy.integrate.solve_ivp` - and for consistency with `IOSystem` systems. + and for consistency with `InputOutputSystem` models. Parameters ---------- @@ -1508,8 +1514,8 @@ def output(self, t, x, u=None, params=None): The first argument `t` is ignored because `StateSpace` systems are time-invariant. It is included so that the dynamics can be passed - to most numerical integrators, such as scipy's `integrate.solve_ivp` - and for consistency with `IOSystem` systems. + to most numerical integrators, such as SciPy's `integrate.solve_ivp` + and for consistency with `InputOutputSystem` models. The inputs `x` and `u` must be of the correct length for the system. @@ -1543,7 +1549,7 @@ def output(self, t, x, u=None, params=None): return (self.C @ x).reshape((-1,)) \ + (self.D @ u).reshape((-1,)) # return as row vector - # convenience aliase, import needs to go over the submodule to avoid circular imports + # convenience alias, import needs to go over the submodule to avoid circular imports initial_response = control.timeresp.initial_response @@ -1566,7 +1572,7 @@ def __init__(self, io_sys, ss_sys=None, connection_type=None): # Because this is a "hybrid" object, the initialization proceeds in # stages. We first create an empty InputOutputSystem of the # appropriate size, then copy over the elements of the - # InterconnectedIOSystem class. From there we compute the + # InterconnectedSystem class. From there we compute the # linearization of the system (if needed) and then populate the # StateSpace parameters. # @@ -1587,7 +1593,7 @@ def __init__(self, io_sys, ss_sys=None, connection_type=None): self.params = io_sys.params self.connection_type = connection_type - # If we didnt' get a state space system, linearize the full system + # If we didn't' get a state space system, linearize the full system if ss_sys is None: ss_sys = self.linearize(0, 0) @@ -1631,7 +1637,7 @@ def _repr_html_(self): cfg=config.defaults['statesp.latex_repr_type'])) # The following text needs to be replicated from StateSpace in order for - # this entry to show up properly in sphinx doccumentation (not sure why, + # this entry to show up properly in sphinx documentation (not sure why, # but it was the only way to get it to work). # #: Deprecated attribute; use `nstates` instead. @@ -2001,7 +2007,7 @@ def linfnorm(sys, tol=1e-10): Parameters ---------- sys : `StateSpace` or `TransferFunction` - System to evalute L-infinity norm of. + System to evaluate L-infinity norm of. tol : real scalar Tolerance on norm estimate. @@ -2038,7 +2044,7 @@ def linfnorm(sys, tol=1e-10): # ab13dd doesn't accept empty A, B, C, D; # static gain case is easy enough to compute gpeak = scipy.linalg.svdvals(d)[0] - # max svd is constant with freq; arbitrarily choose 0 as peak + # max SVD is constant with freq; arbitrarily choose 0 as peak fpeak = 0 return gpeak, fpeak @@ -2090,10 +2096,10 @@ def rss(states=1, outputs=1, inputs=1, strictly_proper=False, **kwargs): Notes ----- If the number of states, inputs, or outputs is not specified, then the - missing numbers are assumed to be 1. If dt is not specified or is given - as 0 or None, the poles of the returned system will always have a - negative real part. If dt is True or a postive float, the poles of the - returned system will have magnitude less than 1. + missing numbers are assumed to be 1. If `dt` is not specified or is + given as 0 or None, the poles of the returned system will always have a + negative real part. If `dt` is True or a positive float, the poles of + the returned system will have magnitude less than 1. """ # Process keyword arguments @@ -2432,7 +2438,7 @@ def _convert_to_statespace(sys, use_prefix_suffix=False, method=None): ssout[3][:sys.noutputs, :states], ssout[4], sys.dt) elif method in [None, 'scipy']: - # Scipy tf->ss can't handle MIMO, but SISO is OK + # SciPy tf->ss can't handle MIMO, but SISO is OK maxn = max(max(len(n) for n in nrow) for nrow in sys.num) maxd = max(max(len(d) for d in drow) diff --git a/control/xferfcn.py b/control/xferfcn.py index eaed0c874..13183b4ea 100644 --- a/control/xferfcn.py +++ b/control/xferfcn.py @@ -309,12 +309,12 @@ def __init__(self, *args, **kwargs): #: :meta hide-value: noutputs = 1 - #: Numerator polynomial coefficients as a 2D array of 1D coefficents. + #: Numerator polynomial coefficients as a 2D array of 1D coefficients. #: #: :meta hide-value: num_array = None - #: Denominator polynomial coefficients as a 2D array of 1D coefficents. + #: Denominator polynomial coefficients as a 2D array of 1D coefficients. #: #: :meta hide-value: den_array = None @@ -334,48 +334,40 @@ def den_list(self): num, den = num_list, den_list def __call__(self, x, squeeze=None, warn_infinite=True): - """Evaluate system's transfer function at complex frequencies. + """Evaluate system transfer function at point in complex plane. - Returns the complex frequency response ``sys(x)`` where `x` is `s` - for continuous-time systems and `z` for discrete-time systems. + Returns the value of the system's transfer function at a point `x` + in the complex plane, where `x` is `s` for continuous-time systems + and `z` for discrete-time systems. - In general the system may be multiple input, multiple output - (MIMO), where ``m = self.ninputs`` number of inputs and ``p = - self.noutputs`` number of outputs. + By default, a (complex) scalar will be returned for SISO systems + and a p x m array will be return for MIMO systems with m inputs and + p outputs. This can be changed using the `squeeze` keyword. - To evaluate at a frequency omega in radians per second, enter - ``x = omega * 1j``, for continuous-time systems, or - ``x = exp(1j * omega * dt)`` for discrete-time systems. Or use - `TransferFunction.frequency_response`. + To evaluate at a frequency `omega` in radians per second, + enter ``x = omega * 1j`` for continuous-time systems, + ``x = exp(1j * omega * dt)`` for discrete-time systems, or + use the `~LTI.frequency_response` method. Parameters ---------- x : complex or complex 1D array_like - Complex frequencies + Complex value(s) at which transfer function will be evaluated. squeeze : bool, optional - If `squeeze` = True, remove single-dimensional entries from the - shape of the output even if the system is not SISO. If - `squeeze` = False, keep all indices (output, input and, if - omega is array_like, frequency) even if the system is SISO. The - default value can be set using - `config.defaults['control.squeeze_frequency_response']`. If - True and the system is single-input single-output (SISO), - return a 1D array rather than a 3D array. Default value - (True) set by - `config.defaults['control.squeeze_frequency_response']`. - warn_infinite : bool, optional If set to False, turn off - divide by zero warning. + Squeeze output, as described below. Default value can be set + using `config.defaults['control.squeeze_frequency_response']`. + warn_infinite : bool, optional + If set to False, turn off divide by zero warning. Returns ------- fresp : complex ndarray - The frequency response of the system. If the system is SISO - and squeeze is not True, the shape of the array matches the - shape of omega. If the system is not SISO or squeeze is - False, the first two dimensions of the array are indices - for the output and input and the remaining dimensions match - omega. If `squeeze` is True then single-dimensional axes - are removed. + The value of the system transfer function at `x`. If the system + is SISO and `squeeze` is not True, the shape of the array matches + the shape of `x`. If the system is not SISO or `squeeze` is + False, the first two dimensions of the array are indices for the + output and input and the remaining dimensions match `x`. If + `squeeze` is True then single-dimensional axes are removed. """ out = self.horner(x, warn_infinite=warn_infinite) @@ -817,7 +809,7 @@ def __getitem__(self, key): inpdx, inputs = _process_subsys_index( indices[1], self.input_labels, slice_to_list=True) - # Construct the transfer function for the subsyste + # Construct the transfer function for the subsystem num = _create_poly_array((len(outputs), len(inputs))) den = _create_poly_array(num.shape) for row, i in enumerate(outdx): @@ -838,7 +830,7 @@ def freqresp(self, omega): """Evaluate transfer function at complex frequencies. .. deprecated::0.9.0 - Method has been given the more pythonic name + Method has been given the more Pythonic name `TransferFunction.frequency_response`. Or use `freqresp` in the MATLAB compatibility module. """ @@ -872,7 +864,7 @@ def feedback(self, other=1, sign=-1): Parameters ---------- other : `InputOutputSystem` - System in the feedack path. + System in the feedback path. sign : float, optional Gain to use in feedback path. Defaults to -1. @@ -930,7 +922,7 @@ def append(self, other): return new_tf def minreal(self, tol=None): - """Remove cancelling pole/zero pairs from a transfer function. + """Remove canceling pole/zero pairs from a transfer function. Parameters ---------- @@ -1010,7 +1002,7 @@ def returnScipySignalLTI(self, strict=True): if self.dt: kwdt = {'dt': self.dt} else: - # scipy convention for continuous-time lti systems: call without + # scipy convention for continuous-time LTI systems: call without # dt keyword argument kwdt = {} @@ -1162,7 +1154,7 @@ def _common_den(self, imag_tol=None, allow_nonproper=False): numpoly = poleset[i][j][2] * np.atleast_1d(poly(nwzeros)) # td04ad expects a proper transfer function. If the - # numerater has a higher order than the denominator, the + # numerator has a higher order than the denominator, the # padding will fail if len(numpoly) > maxindex + 1: if allow_nonproper: @@ -1286,7 +1278,7 @@ def sample(self, Ts, method='zoh', alpha=None, prewarp_frequency=None, def dcgain(self, warn_infinite=False): """Return the zero-frequency ("DC") gain. - For a continous-time transfer function G(s), the DC gain is G(0) + For a continuous-time transfer function G(s), the DC gain is G(0) For a discrete-time transfer function G(z), the DC gain is G(1) Parameters @@ -1336,7 +1328,7 @@ def _isstatic(self): # of the class attributes are set at the bottom of the file to avoid # problems with recursive calls. - #: Differentation operator (continuous time). + #: Differentiation operator (continuous time). #: #: The `s` constant can be used to create continuous-time transfer #: functions using algebraic expressions. @@ -1570,7 +1562,7 @@ def _convert_to_transfer_function( try: # Use Slycot to make the transformation - # Make sure to convert system matrices to numpy arrays + # Make sure to convert system matrices to NumPy arrays from slycot import tb04ad tfout = tb04ad( sys.nstates, sys.ninputs, sys.noutputs, array(sys.A), @@ -1651,7 +1643,7 @@ def tf(*args, **kwargs): ``tf([[G11, ..., G1m], ..., [Gp1, ..., Gpm]][, dt])`` - Create a pxm MIMO system from SISO transfer functions Gij. See + Create a p x m MIMO system from SISO transfer functions Gij. See `combine_tf` for more details. ``tf('s')`` or ``tf('z')`` @@ -1709,7 +1701,7 @@ def tf(*args, **kwargs): Notes ----- - MIMO transfer functions are created by passing a 2D array of coeffients: + MIMO transfer functions are created by passing a 2D array of coefficients: ``num[i][j]`` contains the polynomial coefficients of the numerator for the transfer function from the (j+1)st input to the (i+1)st output, and ``den[i][j]`` works the same way. @@ -1781,7 +1773,7 @@ def tf(*args, **kwargs): # # Process the numerator and denominator arguments # - # If we got through to here, we have two argume nts (num, den) and + # If we got through to here, we have two arguments (num, den) and # the keywords (including dt). The only thing left to do is look # for some special cases, like having a common denominator. # @@ -2045,7 +2037,7 @@ def _clean_part(data, name=""): # a method instead of a property/attribute. TransferFunction.s = TransferFunction([1, 0], [1], 0, name='s') -TransferFunction.s.__doc__ = "Differentation operator (continuous time)." +TransferFunction.s.__doc__ = "Differentiation operator (continuous time)." TransferFunction.z = TransferFunction([1, 0], [1], True, name='z') TransferFunction.z.__doc__ = "Delay operator (discrete time)." From 59e73146f1bdb6df31f650b2df025daf198cf5a1 Mon Sep 17 00:00:00 2001 From: Richard Murray Date: Sun, 19 Jan 2025 22:03:23 -0800 Subject: [PATCH 76/93] address first round of TODO items + add stochastic system examples --- control/lti.py | 19 +++----- control/matlab/wrappers.py | 10 ----- doc/examples.rst | 2 - doc/examples/kalman-pvtol.ipynb | 1 + doc/stochastic.rst | 60 ++++++++++++++++++++------ examples/cds110-L4b_lqr-tracking.ipynb | 16 ++++++- examples/cds112-L7_kalman-pvtol.ipynb | 16 ++++++- examples/cds112-L9_mhe-pvtol.ipynb | 27 ++++++------ examples/stochresp.ipynb | 2 +- 9 files changed, 100 insertions(+), 53 deletions(-) create mode 120000 doc/examples/kalman-pvtol.ipynb diff --git a/control/lti.py b/control/lti.py index f87b125c3..2b6b41be5 100644 --- a/control/lti.py +++ b/control/lti.py @@ -558,19 +558,12 @@ def frequency_response( >>> G = ct.ss([[-1, -2], [3, -4]], [[5], [7]], [[6, 8]], [[9]]) >>> mag, phase, omega = ct.frequency_response(G, [0.1, 1., 10.]) - .. todo:: - Add example with MIMO system - - #>>> sys = rss(3, 2, 2) - #>>> mag, phase, omega = freqresp(sys, [0.1, 1., 10.]) - #>>> mag[0, 1, :] - #array([ 55.43747231, 42.47766549, 1.97225895]) - #>>> phase[1, 0, :] - #array([-0.12611087, -1.14294316, 2.5764547 ]) - #>>> # This is the magnitude of the frequency response from the 2nd - #>>> # input to the 1st output, and the phase (in radians) of the - #>>> # frequency response from the 1st input to the 2nd output, for - #>>> # s = 0.1i, i, 10i. + >>> sys = ct.rss(3, 2, 2) + >>> mag, phase, omega = ct.frequency_response(sys, [0.1, 1., 10.]) + >>> mag[0, 1, :] # Magnitude of second input to first output + array([..., ..., ...]) + >>> phase[1, 0, :] # Phase of first input to second output + array([..., ..., ...]) """ from .frdata import FrequencyResponseData diff --git a/control/matlab/wrappers.py b/control/matlab/wrappers.py index 06d57136e..e9b4fd52d 100644 --- a/control/matlab/wrappers.py +++ b/control/matlab/wrappers.py @@ -54,16 +54,6 @@ def bode(*args, **kwargs): >>> sys = ss([[1, -2], [3, -4]], [[5], [7]], [[6, 8]], 9) >>> mag, phase, omega = bode(sys) - - .. todo:: - - Document these use cases - - * >>> bode(sys, w) # doctest: +SKIP - * >>> bode(sys1, sys2, ..., sysN) # doctest: +SKIP - * >>> bode(sys1, sys2, ..., sysN, w) # doctest: +SKIP - * >>> bode(sys1, 'plotstyle1', ..., sysN, 'plotstyleN') # doctest: +SKIP - >>> bode(sys, w) # one system, freq vector # doctest: +SKIP >>> bode(sys1, sys2, ..., sysN) # several systems # doctest: +SKIP >>> bode(sys1, sys2, ..., sysN, w) # doctest: +SKIP diff --git a/doc/examples.rst b/doc/examples.rst index 5853a7f9b..2937fecab 100644 --- a/doc/examples.rst +++ b/doc/examples.rst @@ -56,8 +56,6 @@ online sources. examples/cruise examples/describing_functions examples/interconnect_tutorial - examples/kincar-fusion - examples/mhe-pvtol examples/mpc_aircraft examples/pvtol-lqr-nested examples/pvtol-outputfbk diff --git a/doc/examples/kalman-pvtol.ipynb b/doc/examples/kalman-pvtol.ipynb new file mode 120000 index 000000000..e4018097a --- /dev/null +++ b/doc/examples/kalman-pvtol.ipynb @@ -0,0 +1 @@ +../../examples/cds112-L7_kalman-pvtol.ipynb \ No newline at end of file diff --git a/doc/stochastic.rst b/doc/stochastic.rst index e4071caf5..9169b967a 100644 --- a/doc/stochastic.rst +++ b/doc/stochastic.rst @@ -187,33 +187,49 @@ time optimal controller. For the second two forms, the :func:`dlqr` function can be used. Additional arguments and details are given on the :func:`lqr` and :func:`dlqr` documentation pages. -.. todo:: - Convert the following code to testcode +.. testsetup:: kalman + + sys = ct.rss(2, 2, 2) + Qu = np.eye(2) + Qv = np.eye(2) + Qw = np.eye(2) + Qx = np.eye(2) + + timepts = np.linspace(0, 10) + U = ct.white_noise(timepts, Qv) + Y = ct.white_noise(timepts, Qw) + + X0 = np.zeros(2) + P0 = np.eye(2) The :func:`create_estimator_iosystem` function can be used to create an I/O system implementing a Kalman filter, including integration of the Riccati ODE. The command has the form -.. code:: +.. testcode:: kalman estim = ct.create_estimator_iosystem(sys, Qv, Qw) The input to the estimator is the measured outputs `Y` and the system input `U`. To run the estimator on a noisy signal, use the command -.. code:: +.. testcode:: kalman - resp = ct.input_output_response(est, timepts, [Y, U], [X0, P0]) + resp = ct.input_output_response(estim, timepts, [Y, U], [X0, P0]) If desired, the :func:`correct` parameter can be set to False -to allow prediction with no additional sensor information:: +to allow prediction with no additional sensor information: + +.. testcode:: kalman resp = ct.input_output_response( - estim, timepts, 0, [X0, P0], param={'correct': False}) + estim, timepts, 0, [X0, P0], params={'correct': False}) The :func:`create_estimator_iosystem` and :func:`create_statefbk_iosystem` functions can be used to combine an -estimator with a state feedback controller:: +estimator with a state feedback controller: + +.. testcode:: kalman K, _, _ = ct.lqr(sys, Qx, Qu) estim = ct.create_estimator_iosystem(sys, Qv, Qw, P0) @@ -229,13 +245,19 @@ estimated state :math:`\hat x` (output of `estim`): The closed loop controller `clsys` includes both the state feedback and the estimator dynamics and takes as its input the desired -state :math:`x_\text{d}` and input :math:`u_\text{d}`:: +state :math:`x_\text{d}` and input :math:`u_\text{d}`: + +.. testcode:: kalman + :hide: + + Xd = np.zeros((2, timepts.size)) + Ud = np.zeros((2, timepts.size)) + +.. testcode:: kalman resp = ct.input_output_response( clsys, timepts, [Xd, Ud], [X0, np.zeros_like(X0), P0]) -.. todo:: - Add example (with plots?) Maximum Likelihood Estimation @@ -374,5 +396,17 @@ problems: optimal.gaussian_likelihood_cost optimal.disturbance_range_constraint -.. todo:: - Add example: LQE vs MHE (from CDS 112) +Examples +======== + +The following examples illustrate the use of tools from the stochastic +systems module. Background information for these examples can be +found in the FBS2e supplement on `Optimization-Based Control +`_). + +.. toctree:: + :maxdepth: 1 + + Kalman filter (kinematic car) + (Extended) Kalman filtering (PVTOL) + Moving horizon estimation (PVTOL) diff --git a/examples/cds110-L4b_lqr-tracking.ipynb b/examples/cds110-L4b_lqr-tracking.ipynb index a4b1a0711..f438c692a 100644 --- a/examples/cds110-L4b_lqr-tracking.ipynb +++ b/examples/cds110-L4b_lqr-tracking.ipynb @@ -907,8 +907,22 @@ } ], "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, "language_info": { - "name": "python" + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.1" } }, "nbformat": 4, diff --git a/examples/cds112-L7_kalman-pvtol.ipynb b/examples/cds112-L7_kalman-pvtol.ipynb index 435c7fce6..62270a2d8 100644 --- a/examples/cds112-L7_kalman-pvtol.ipynb +++ b/examples/cds112-L7_kalman-pvtol.ipynb @@ -416,8 +416,22 @@ } ], "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, "language_info": { - "name": "python" + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.1" } }, "nbformat": 4, diff --git a/examples/cds112-L9_mhe-pvtol.ipynb b/examples/cds112-L9_mhe-pvtol.ipynb index 485365188..be15c4bfa 100644 --- a/examples/cds112-L9_mhe-pvtol.ipynb +++ b/examples/cds112-L9_mhe-pvtol.ipynb @@ -28,17 +28,6 @@ "import control.flatsys as fs" ] }, - { - "cell_type": "code", - "execution_count": null, - "id": "1a4e4084", - "metadata": {}, - "outputs": [], - "source": [ - "# Import the new MHE routines (to be moved to python-control)\n", - "# import ctrlutil as opt_" - ] - }, { "cell_type": "markdown", "id": "d72a155b", @@ -749,8 +738,22 @@ } ], "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, "language_info": { - "name": "python" + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.1" } }, "nbformat": 4, diff --git a/examples/stochresp.ipynb b/examples/stochresp.ipynb index 74d744b0f..dda6bb501 100644 --- a/examples/stochresp.ipynb +++ b/examples/stochresp.ipynb @@ -284,7 +284,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.1" + "version": "3.13.1" } }, "nbformat": 4, From bb4ff1e50d293f224e9ad1a7a8ad3b9820187c7f Mon Sep 17 00:00:00 2001 From: Richard Murray Date: Sun, 19 Jan 2025 22:23:05 -0800 Subject: [PATCH 77/93] address remaining @sawyerbfuller review comments --- control/lti.py | 43 +++++++++++++++++++++++++++++++++++++++---- control/statesp.py | 2 +- doc/xferfcn.rst | 1 + 3 files changed, 41 insertions(+), 5 deletions(-) diff --git a/control/lti.py b/control/lti.py index 2b6b41be5..f32c3d23b 100644 --- a/control/lti.py +++ b/control/lti.py @@ -42,7 +42,43 @@ def __init__(self, inputs=1, outputs=1, states=None, name=None, **kwargs): def __call__(self, x, squeeze=None, warn_infinite=True): """Evaluate system transfer function at point in complex plane. - See `StateSpace.__call__` and `TransferFunction.__call__` for details. + Returns the value of the system's transfer function at a point `x` + in the complex plane, where `x` is `s` for continuous-time systems + and `z` for discrete-time systems. + + By default, a (complex) scalar will be returned for SISO systems + and a p x m array will be return for MIMO systems with m inputs and + p outputs. This can be changed using the `squeeze` keyword. + + To evaluate at a frequency `omega` in radians per second, + enter ``x = omega * 1j`` for continuous-time systems, + ``x = exp(1j * omega * dt)`` for discrete-time systems, or + use the `~LTI.frequency_response` method. + + Parameters + ---------- + x : complex or complex 1D array_like + Complex value(s) at which transfer function will be evaluated. + squeeze : bool, optional + Squeeze output, as described below. Default value can be set + using `config.defaults['control.squeeze_frequency_response']`. + warn_infinite : bool, optional + If set to False, turn off divide by zero warning. + + Returns + ------- + fresp : complex ndarray + The value of the system transfer function at `x`. If the system + is SISO and `squeeze` is not True, the shape of the array matches + the shape of `x`. If the system is not SISO or `squeeze` is + False, the first two dimensions of the array are indices for the + output and input and the remaining dimensions match `x`. If + `squeeze` is True then single-dimensional axes are removed. + + Notes + ----- + See `FrequencyResponseData`.__call__`, `StateSpace.__call__`, + `TransferFunction.__call__` for class-specific details. """ raise NotImplementedError("not implemented in subclass") @@ -117,7 +153,7 @@ def frequency_response(self, omega=None, squeeze=None): def dcgain(self): """Return the zero-frequency (DC) gain.""" - return NotImplemented + raise NotImplementedError("dcgain not defined for subclass") def _dcgain(self, warn_infinite): zeroresp = self(0 if self.isctime() else 1, @@ -443,8 +479,7 @@ def evalfr(sys, x, squeeze=None): See Also -------- - StateSpace.__call__, TransferFunction.__call__, frequency_response, \ - bode_plot + LTI.__call__, frequency_response, bode_plot Notes ----- diff --git a/control/statesp.py b/control/statesp.py index 07173c5d8..1bb50aedd 100644 --- a/control/statesp.py +++ b/control/statesp.py @@ -1735,7 +1735,7 @@ def ss(*args, **kwargs): See Also -------- - tf, ss2tf, tf2ss, zpk + StateSpace, nlsys, tf, ss2tf, tf2ss, zpk Notes ----- diff --git a/doc/xferfcn.rst b/doc/xferfcn.rst index 5382cb195..dbba9c628 100644 --- a/doc/xferfcn.rst +++ b/doc/xferfcn.rst @@ -38,6 +38,7 @@ domain properties of a linear systems: frequency_response phase_crossover_frequencies singular_values_response + stability_margins tfdata These functions work on both state space and transfer function models. From 1f8d2e683255d7b9f3c0e648a0f42c9461aedc95 Mon Sep 17 00:00:00 2001 From: Richard Murray Date: Mon, 20 Jan 2025 10:54:48 -0800 Subject: [PATCH 78/93] fix up docstrings, unit test for append() methods after rebase --- control/statesp.py | 2 +- control/tests/statesp_test.py | 1 + control/xferfcn.py | 6 +++--- 3 files changed, 5 insertions(+), 4 deletions(-) diff --git a/control/statesp.py b/control/statesp.py index 1bb50aedd..b88d5ed0c 100644 --- a/control/statesp.py +++ b/control/statesp.py @@ -1273,7 +1273,7 @@ def append(self, other): Parameters ---------- - other : `StateSpace` + other : `StateSpace` or `TransferFunction` System to be appended. Returns diff --git a/control/tests/statesp_test.py b/control/tests/statesp_test.py index 9b21cdefa..b0ddea616 100644 --- a/control/tests/statesp_test.py +++ b/control/tests/statesp_test.py @@ -8,6 +8,7 @@ """ import operator +import platform import numpy as np import pytest diff --git a/control/xferfcn.py b/control/xferfcn.py index 13183b4ea..07c4a3fe2 100644 --- a/control/xferfcn.py +++ b/control/xferfcn.py @@ -898,12 +898,12 @@ def feedback(self, other=1, sign=-1): def append(self, other): """Append a second model to the present model. - The second model is converted to FRD if necessary, inputs and - outputs are appended and their order is preserved. + The second model is converted to a transfer function if necessary, + inputs and outputs are appended and their order is preserved. Parameters ---------- - other : `LTI` + other : `StateSpace` or `TransferFunction` System to be appended. Returns From 84079e0d4ea4317ca45afadda327fcf5bbbafe5a Mon Sep 17 00:00:00 2001 From: Richard Murray Date: Mon, 20 Jan 2025 18:12:50 -0800 Subject: [PATCH 79/93] update kalman-pvtol.ipynb to fully evaluated notebook (for RTD) --- doc/examples/kalman-pvtol.ipynb | 613 +++++++++++++++++++++++++++++++- 1 file changed, 612 insertions(+), 1 deletion(-) mode change 120000 => 100644 doc/examples/kalman-pvtol.ipynb diff --git a/doc/examples/kalman-pvtol.ipynb b/doc/examples/kalman-pvtol.ipynb deleted file mode 120000 index e4018097a..000000000 --- a/doc/examples/kalman-pvtol.ipynb +++ /dev/null @@ -1 +0,0 @@ -../../examples/cds112-L7_kalman-pvtol.ipynb \ No newline at end of file diff --git a/doc/examples/kalman-pvtol.ipynb b/doc/examples/kalman-pvtol.ipynb new file mode 100644 index 000000000..150000c20 --- /dev/null +++ b/doc/examples/kalman-pvtol.ipynb @@ -0,0 +1,612 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "c017196f", + "metadata": {}, + "source": [ + "# Extended Kalman filter example (PVTOL)\n", + "\n", + "This notebook illustrates the implementation of an extended Kalman filter and the use of the estimated state for LQR feedback." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "544525ab", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib.patches as patches\n", + "import control as ct" + ] + }, + { + "cell_type": "markdown", + "id": "859834cf", + "metadata": {}, + "source": [ + "## System definition\n", + "The dynamics of the system\n", + "with disturbances on the $x$ and $y$ variables is given by\n", + "$$\n", + " \\begin{aligned}\n", + " m \\ddot x &= F_1 \\cos\\theta - F_2 \\sin\\theta - c \\dot x + d_x, \\\\\n", + " m \\ddot y &= F_1 \\sin\\theta + F_2 \\cos\\theta - c \\dot y - m g + d_y, \\\\\n", + " J \\ddot \\theta &= r F_1.\n", + " \\end{aligned}\n", + "$$\n", + "The measured values of the system are the position and orientation,\n", + "with added noise $n_x$, $n_y$, and $n_\\theta$:\n", + "$$\n", + " \\vec y = \\begin{bmatrix} x \\\\ y \\\\ \\theta \\end{bmatrix} + \n", + " \\begin{bmatrix} n_x \\\\ n_y \\\\ n_z \\end{bmatrix}.\n", + "$$\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "ffafed74", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + ": pvtol\n", + "Inputs (2): ['F1', 'F2']\n", + "Outputs (6): ['x0', 'x1', 'x2', 'x3', 'x4', 'x5']\n", + "States (6): ['x0', 'x1', 'x2', 'x3', 'x4', 'x5']\n", + "Parameters: ['m', 'J', 'r', 'g', 'c']\n", + "\n", + "Update: \n", + "Output: \n", + "\n", + "Forward: \n", + "Reverse: \n", + "\n", + ": pvtol_noisy\n", + "Inputs (7): ['F1', 'F2', 'Dx', 'Dy', 'Nx', 'Ny', 'Nth']\n", + "Outputs (6): ['x0', 'x1', 'x2', 'x3', 'x4', 'x5']\n", + "States (6): ['x0', 'x1', 'x2', 'x3', 'x4', 'x5']\n", + "\n", + "Update: \n", + "Output: \n" + ] + } + ], + "source": [ + "# pvtol = nominal system (no disturbances or noise)\n", + "# noisy_pvtol = pvtol w/ process disturbances and sensor noise\n", + "from pvtol import pvtol, pvtol_noisy, plot_results\n", + "\n", + "# Find the equilibrium point corresponding to the origin\n", + "xe, ue = ct.find_eqpt(\n", + " pvtol, np.zeros(pvtol.nstates),\n", + " np.zeros(pvtol.ninputs), [0, 0, 0, 0, 0, 0],\n", + " iu=range(2, pvtol.ninputs), iy=[0, 1])\n", + "\n", + "x0, u0 = ct.find_eqpt(\n", + " pvtol, np.zeros(pvtol.nstates),\n", + " np.zeros(pvtol.ninputs), np.array([2, 1, 0, 0, 0, 0]),\n", + " iu=range(2, pvtol.ninputs), iy=[0, 1])\n", + "\n", + "# Extract the linearization for use in LQR design\n", + "pvtol_lin = pvtol.linearize(xe, ue)\n", + "A, B = pvtol_lin.A, pvtol_lin.B\n", + "\n", + "print(pvtol, \"\\n\")\n", + "print(pvtol_noisy)" + ] + }, + { + "cell_type": "markdown", + "id": "2b63bf5b", + "metadata": {}, + "source": [ + "We now define the properties of the noise and disturbances. To make things (a bit more) interesting, we include some cross terms between the noise in $\\theta$ and the noise in $x$ and $y$:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "1e1ee7c9", + "metadata": {}, + "outputs": [], + "source": [ + "# Disturbance and noise intensities\n", + "Qv = np.diag([1e-2, 1e-2])\n", + "Qw = np.array([[2e-4, 0, 1e-5], [0, 2e-4, 1e-5], [1e-5, 1e-5, 1e-4]])\n", + "Qwinv = np.linalg.inv(Qw)\n", + "\n", + "# Initial state covariance\n", + "P0 = np.eye(pvtol.nstates)" + ] + }, + { + "cell_type": "markdown", + "id": "e4c52c73", + "metadata": {}, + "source": [ + "## Control system design\n", + "\n", + "To design the control system, we first construct an estimator for the state (given the commanded inputs and measured outputs. Since this is a nonlinear system, we use the update law for the nominal system to compute the state update. We also make use of the linearization around the current state for the covariance update (using the function `pvtol.A(x, u)`, which is defined in `pvtol.py`, making this an extended Kalman filter)." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "3647bf15", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + ": sys[1]\n", + "Inputs (8): ['x0', 'x1', 'x2', 'x3', 'x4', 'x5', 'F1', 'F2']\n", + "Outputs (6): ['xh0', 'xh1', 'xh2', 'xh3', 'xh4', 'xh5']\n", + "States (42): ['x[0]', 'x[1]', 'x[2]', 'x[3]', 'x[4]', 'x[5]', 'x[6]', 'x[7]', 'x[8]', 'x[9]', 'x[10]', 'x[11]', 'x[12]', 'x[13]', 'x[14]', 'x[15]', 'x[16]', 'x[17]', 'x[18]', 'x[19]', 'x[20]', 'x[21]', 'x[22]', 'x[23]', 'x[24]', 'x[25]', 'x[26]', 'x[27]', 'x[28]', 'x[29]', 'x[30]', 'x[31]', 'x[32]', 'x[33]', 'x[34]', 'x[35]', 'x[36]', 'x[37]', 'x[38]', 'x[39]', 'x[40]', 'x[41]']\n", + "\n", + "Update: \n", + "Output: \n" + ] + } + ], + "source": [ + "# Define the disturbance input and measured output matrices\n", + "F = np.array([[0, 0], [0, 0], [0, 0], [1/pvtol.params['m'], 0], [0, 1/pvtol.params['m']], [0, 0]])\n", + "C = np.eye(3, 6)\n", + "\n", + "# Estimator update law\n", + "def estimator_update(t, x, u, params):\n", + " # Extract the states of the estimator\n", + " xhat = x[0:pvtol.nstates]\n", + " P = x[pvtol.nstates:].reshape(pvtol.nstates, pvtol.nstates)\n", + "\n", + " # Extract the inputs to the estimator\n", + " y = u[0:3] # just grab the first three outputs\n", + " u = u[6:8] # get the inputs that were applied as well\n", + "\n", + " # Compute the linearization at the current state\n", + " A = pvtol.A(xhat, u) # A matrix depends on current state\n", + " # A = pvtol.A(xe, ue) # Fixed A matrix (for testing/comparison)\n", + " \n", + " # Compute the optimal again\n", + " L = P @ C.T @ Qwinv\n", + "\n", + " # Update the state estimate\n", + " xhatdot = pvtol.updfcn(t, xhat, u, params) - L @ (C @ xhat - y)\n", + "\n", + " # Update the covariance\n", + " Pdot = A @ P + P @ A.T - P @ C.T @ Qwinv @ C @ P + F @ Qv @ F.T\n", + "\n", + " # Return the derivative\n", + " return np.hstack([xhatdot, Pdot.reshape(-1)])\n", + "\n", + "def estimator_output(t, x, u, params):\n", + " # Return the estimator states\n", + " return x[0:pvtol.nstates]\n", + "\n", + "estimator = ct.NonlinearIOSystem(\n", + " estimator_update, estimator_output,\n", + " states=pvtol.nstates + pvtol.nstates**2,\n", + " inputs= pvtol_noisy.output_labels \\\n", + " + pvtol_noisy.input_labels[0:pvtol.ninputs],\n", + " outputs=[f'xh{i}' for i in range(pvtol.nstates)],\n", + ")\n", + "print(estimator)" + ] + }, + { + "cell_type": "markdown", + "id": "ba3d2640", + "metadata": {}, + "source": [ + "For the controller, we will use an LQR feedback with physically motivated weights (see OBC, Example 3.5):" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "9787db61", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + ": sys[2]\n", + "Inputs (14): ['xd[0]', 'xd[1]', 'xd[2]', 'xd[3]', 'xd[4]', 'xd[5]', 'ud[0]', 'ud[1]', 'xh0', 'xh1', 'xh2', 'xh3', 'xh4', 'xh5']\n", + "Outputs (2): ['F1', 'F2']\n", + "States (0): []\n", + "\n", + "A = []\n", + "\n", + "B = []\n", + "\n", + "C = []\n", + "\n", + "D = [[-3.16227766e+00 -1.31948922e-07 8.67680175e+00 -2.35855555e+00\n", + " -6.98881821e-08 1.91220852e+00 1.00000000e+00 0.00000000e+00\n", + " 3.16227766e+00 1.31948922e-07 -8.67680175e+00 2.35855555e+00\n", + " 6.98881821e-08 -1.91220852e+00]\n", + " [-1.31948921e-06 3.16227766e+00 -2.32324826e-07 -2.36396240e-06\n", + " 4.97998224e+00 7.90913276e-08 0.00000000e+00 1.00000000e+00\n", + " 1.31948921e-06 -3.16227766e+00 2.32324826e-07 2.36396240e-06\n", + " -4.97998224e+00 -7.90913276e-08]] \n", + "\n", + ": sys[3]\n", + "Inputs (13): ['xd[0]', 'xd[1]', 'xd[2]', 'xd[3]', 'xd[4]', 'xd[5]', 'ud[0]', 'ud[1]', 'Dx', 'Dy', 'Nx', 'Ny', 'Nth']\n", + "Outputs (14): ['x0', 'x1', 'x2', 'x3', 'x4', 'x5', 'F1', 'F2', 'xh0', 'xh1', 'xh2', 'xh3', 'xh4', 'xh5']\n", + "States (48): ['pvtol_noisy_x0', 'pvtol_noisy_x1', 'pvtol_noisy_x2', 'pvtol_noisy_x3', 'pvtol_noisy_x4', 'pvtol_noisy_x5', 'sys[1]_x[0]', 'sys[1]_x[1]', 'sys[1]_x[2]', 'sys[1]_x[3]', 'sys[1]_x[4]', 'sys[1]_x[5]', 'sys[1]_x[6]', 'sys[1]_x[7]', 'sys[1]_x[8]', 'sys[1]_x[9]', 'sys[1]_x[10]', 'sys[1]_x[11]', 'sys[1]_x[12]', 'sys[1]_x[13]', 'sys[1]_x[14]', 'sys[1]_x[15]', 'sys[1]_x[16]', 'sys[1]_x[17]', 'sys[1]_x[18]', 'sys[1]_x[19]', 'sys[1]_x[20]', 'sys[1]_x[21]', 'sys[1]_x[22]', 'sys[1]_x[23]', 'sys[1]_x[24]', 'sys[1]_x[25]', 'sys[1]_x[26]', 'sys[1]_x[27]', 'sys[1]_x[28]', 'sys[1]_x[29]', 'sys[1]_x[30]', 'sys[1]_x[31]', 'sys[1]_x[32]', 'sys[1]_x[33]', 'sys[1]_x[34]', 'sys[1]_x[35]', 'sys[1]_x[36]', 'sys[1]_x[37]', 'sys[1]_x[38]', 'sys[1]_x[39]', 'sys[1]_x[40]', 'sys[1]_x[41]']\n", + "\n", + "Subsystems (3):\n", + " * ['x0', 'x1', 'x2', 'x3', 'x4', 'x5']>\n", + " * ['F1',\n", + " 'F2']>\n", + " * ['xh0', 'xh1', 'xh2', 'xh3', 'xh4', 'xh5']>\n", + "\n", + "Connections:\n", + " * pvtol_noisy.F1 <- sys[2].F1\n", + " * pvtol_noisy.F2 <- sys[2].F2\n", + " * pvtol_noisy.Dx <- Dx\n", + " * pvtol_noisy.Dy <- Dy\n", + " * pvtol_noisy.Nx <- Nx\n", + " * pvtol_noisy.Ny <- Ny\n", + " * pvtol_noisy.Nth <- Nth\n", + " * sys[2].xd[0] <- xd[0]\n", + " * sys[2].xd[1] <- xd[1]\n", + " * sys[2].xd[2] <- xd[2]\n", + " * sys[2].xd[3] <- xd[3]\n", + " * sys[2].xd[4] <- xd[4]\n", + " * sys[2].xd[5] <- xd[5]\n", + " * sys[2].ud[0] <- ud[0]\n", + " * sys[2].ud[1] <- ud[1]\n", + " * sys[2].xh0 <- sys[1].xh0\n", + " * sys[2].xh1 <- sys[1].xh1\n", + " * sys[2].xh2 <- sys[1].xh2\n", + " * sys[2].xh3 <- sys[1].xh3\n", + " * sys[2].xh4 <- sys[1].xh4\n", + " * sys[2].xh5 <- sys[1].xh5\n", + " * sys[1].x0 <- pvtol_noisy.x0\n", + " * sys[1].x1 <- pvtol_noisy.x1\n", + " * sys[1].x2 <- pvtol_noisy.x2\n", + " * sys[1].x3 <- pvtol_noisy.x3\n", + " * sys[1].x4 <- pvtol_noisy.x4\n", + " * sys[1].x5 <- pvtol_noisy.x5\n", + " * sys[1].F1 <- sys[2].F1\n", + " * sys[1].F2 <- sys[2].F2\n", + "\n", + "Outputs:\n", + " * x0 <- pvtol_noisy.x0\n", + " * x1 <- pvtol_noisy.x1\n", + " * x2 <- pvtol_noisy.x2\n", + " * x3 <- pvtol_noisy.x3\n", + " * x4 <- pvtol_noisy.x4\n", + " * x5 <- pvtol_noisy.x5\n", + " * F1 <- sys[2].F1\n", + " * F2 <- sys[2].F2\n", + " * xh0 <- sys[1].xh0\n", + " * xh1 <- sys[1].xh1\n", + " * xh2 <- sys[1].xh2\n", + " * xh3 <- sys[1].xh3\n", + " * xh4 <- sys[1].xh4\n", + " * xh5 <- sys[1].xh5\n", + "\n" + ] + } + ], + "source": [ + "#\n", + "# LQR design w/ physically motivated weighting\n", + "#\n", + "# Shoot for 1 cm error in x, 10 cm error in y. Try to keep the angle\n", + "# less than 5 degrees in making the adjustments. Penalize side forces\n", + "# due to loss in efficiency.\n", + "#\n", + "\n", + "Qx = np.diag([100, 10, (180/np.pi) / 5, 0, 0, 0])\n", + "Qu = np.diag([10, 1])\n", + "K, _, _ = ct.lqr(A, B, Qx, Qu)\n", + "\n", + "#\n", + "# Control system construction: combine LQR w/ EKF\n", + "#\n", + "# Use the linearization around the origin to design the optimal gains\n", + "# to see how they compare to the final value of P for the EKF\n", + "#\n", + "\n", + "# Construct the state feedback controller with estimated state as input\n", + "statefbk, _ = ct.create_statefbk_iosystem(pvtol, K, estimator=estimator)\n", + "print(statefbk, \"\\n\")\n", + "\n", + "# Reconstruct the control system with the noisy version of the process\n", + "# Create a closed loop system around the controller\n", + "clsys = ct.interconnect(\n", + " [pvtol_noisy, statefbk, estimator],\n", + " inplist = statefbk.input_labels[0:pvtol.ninputs + pvtol.nstates] + \\\n", + " pvtol_noisy.input_labels[pvtol.ninputs:],\n", + " inputs = statefbk.input_labels[0:pvtol.ninputs + pvtol.nstates] + \\\n", + " pvtol_noisy.input_labels[pvtol.ninputs:],\n", + " outlist = pvtol.output_labels + statefbk.output_labels + estimator.output_labels,\n", + " outputs = pvtol.output_labels + statefbk.output_labels + estimator.output_labels\n", + ")\n", + "print(clsys)" + ] + }, + { + "cell_type": "markdown", + "id": "5f527f16", + "metadata": {}, + "source": [ + "Note that we have to construct the closed loop system manually since we need to allow the disturbance and noise inputs to be sent to the closed loop system and `create_statefbk_iosystem` does not support this (to be fixed in an upcoming release)." + ] + }, + { + "cell_type": "markdown", + "id": "7bf558a0", + "metadata": {}, + "source": [ + "## Simulations\n", + "\n", + "Finally, we can simulate the system to see how it all works. We start by creating the noise for the system:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "c2583a0e", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "

" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Create the time vector for the simulation\n", + "Tf = 10\n", + "timepts = np.linspace(0, Tf, 1000)\n", + "\n", + "# Create representative process disturbance and sensor noise vectors\n", + "np.random.seed(117) # avoid figures changing from run to run\n", + "V = ct.white_noise(timepts, Qv) # smaller disturbances and noise then design\n", + "W = ct.white_noise(timepts, Qw)\n", + "plt.plot(timepts, V[0], label=\"V[0]\")\n", + "plt.plot(timepts, W[0], label=\"W[0]\")\n", + "plt.legend();" + ] + }, + { + "cell_type": "markdown", + "id": "4d944709", + "metadata": {}, + "source": [ + "### LQR with EKF\n", + "\n", + "We can now feed the desired trajectory plus the noise and disturbances into the system and see how well the controller with a state estimator does in holding the system at an equilibrium point:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "ad7a9750", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Put together the input for the system\n", + "U = [xe, ue, V, W]\n", + "X0 = [x0, xe, P0.reshape(-1)]\n", + "\n", + "# Initial condition response\n", + "resp = ct.input_output_response(clsys, timepts, U, X0)\n", + "\n", + "# Plot the response\n", + "plot_results(timepts, resp.states, resp.outputs[pvtol.nstates:])" + ] + }, + { + "cell_type": "markdown", + "id": "86f10064", + "metadata": {}, + "source": [ + "To see how well the estimtator did, we can compare the estimated position with the actual position:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "c5f24119", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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//PC45bZu3UqLFi1s01q0aEEgEGDHjh0xl/F6vRQWFto+tYHb7cbpPBE4kYICF34/pKa2IiOjFRAC5uHQMztmZoLbfRZu9yC9jdv44gtVj+FWC4UgOblWmioIgiAIDZoGIY5uueUWfvvtNz766KM9lnVEpI42AqEjpxuMGjWK7Ozs8KdNmzYxy9UERkx2IAAFBZCS4qBt2zOA84G24XKZmZCfDy6XEnrl5dvo1w86dDAtR6EQeDy11lRBEARBaLDUe3F06623MmXKFGbOnEnr1q0rLNuyZUu2bt1qm5aXl4fb7aZp06Yxlxk5ciQFBQXhz8aNG2us7VZ8Ph9+/1hgLFlZQbZuhYwM6NTpCeAU4MBw2bZt4e+/ISlJiaN27bYxfvzLbNs2gIULVeD2I4+Ay2XUXStNFgRBEIQGSb0NyNY0jVtvvZXJkycza9YsOnTosMdl+vbty5dffmmbNn36dHr16kVSUlLMZZKTk0neB/6psrIyAoGbAGjU6FrWr3dx6KFQVtYcuN5W9ogj1Hd6ej+Ki4tJTta45ZZbAHj++WuAlSxYYFqOioogjvYTBEEQBCGCems5uvnmm/nggw8YP348mZmZbN26la1bt1JWVhYuM3LkSK666qrw/xtuuIH169czYsQIVq5cydtvv81bb73FnXfeWRebYMOa56hRI/WdnU042aOVzp2NcqcDL1FWtiU8b8uWPwEVP2VYjB5/XLr0C4IgCEJlqbfiaOzYsRQUFDBgwABatWoV/kyYMCFcJjc3lw0bNoT/d+jQgalTpzJr1iy6d+/OY489xgsvvJBQ3fjB7GWWlWUXR0ZYVFaW+laWIS/Fxesialhg+zd6NIwfX/PtFARBEISGSL12q+2JcePGRU3r378/ixcvroUW7R3W7WncWKmgeJaj1FT1rTyByRxwwEm0a6exY0dfAoE1bN6cEi6bna2CuyXfkSAIgiBUjnprOWrIRFqOIkOeDHFkCJ5DD72Mb7/9lt69X2Tz5m+AE8NlmzVT35XQkoIgCIIgIOIoYYi0HLlckJamuvUbbjTDrWb0QgsG1Xfz5kfSrFkzUlKIwsiJKeJIEARBECqHiKMEpFEjSE833GZm77RIDHHk1I+iYVGCbUAxoNxqIG41QRAEQagsIo4ShIyMDDp2/JJmzb6kSRMnaWlgdLy78MLYyxjiyLAoKcvRqUBL4EdAxJEgCIIgVJV6G5Dd0PB4PPz++xmEQvDVV8py9Pffal56uvqOTOIdCKhvu+XICFBaDxB2tYlbTRAEQRAqh4ijBMJwizVurOKNNm9W/w8+OHb5SLeaEkLt9LnrbfNEHAmCIAhC5RC3WoJQXl7Ou+++y7vvvstxx2mMHg3vvQc//QR9+yq32Iknmj3ZIFocud1gFUcbN5rzvF71feaZ8P77tb89giAIglBfEXGUIBQXFzN06FCGDh1KWhqcdBJ07Ah9+qj5Dgc89JA903WkOFK017/X07q1Oa+8XH1/9RW89VbtbYcgCIIg1HdEHCUIlUlqGUmkOFJVxHarffqpGZQtwdmCIAiCEB8RRwmIIzLyOg6xLUeGONqCz+cLB3GvXg1//KF+G+JI06C42ByDTRAEQRAEEUcJQ1UtRy++CE8/rX4bAkhVkQOcANxBIBCwCSdDBBniyOmEzEy46KK9aLggCIIgNDCkt1o95ZZbYMUK9dtuOXIAswHV4806b/du9R0K2a1Fy5bVYkMFQRAEoZ4hlqMEoToxR4bwiddd//PPP+fzz48HPqZDB7s4KiqyrrvKqxYEQRCEBouIowSjsvFGEC2Ohg2zz1++fDlbt84DhtG9+06bOCosNMuJOBIEQRAEE3GrJQjZ2dl8/PHHVVrG0FGGOGrdGnr3hgUL1H+/36+XLGH9+tHs3v0YoAK5xXIkCIIgCLERcZQgpKSkcPHFF1dpmUjLUeTv//znPxx2WDcuvfR8li9/ga1b7wAaRVmOpGu/IAiCIJiIW60eE0scuVz2MhdddA5du3bF6y1k5sxXAHGrCYIgCEJFiDhKEMrKyvjss8+YOHFipZcx3GrWMCVnxBF1Op3ceeedAKxY8SGg3GoijgRBEAQhNiKOEoT8/HwuvPDCKrnW9uRWMzjnnHNwu92Ul28AtklvNUEQBEGoABFH9ZjKuNUAGjVqxNy5c7n44u1AC1atso/RJuJIEARBEEwkIDtBMPIcVaUrf2RvtcjfVvr06UPr1ub/UaOs6670KgVBEAShwSOWo3pMZS1HBh6P+bugwPxtiKPp0+G992qufYIgCIJQHxFxlCBUx3JU2Zgjg59+ehQ4HJgVsW71fcEFMGRIpVcvCIIgCA0SEUf1mFhutYosR/n5q4DlwPe26UaeI2uQtiAIgiDsr4g4SjCqYzmyLtKiRfzynToN1H/ZxZHEHAmCIAiCiQRkJwiNGzfm7bffxlmRXyyCWG61F1+EDz4Any+6fJcuhjj6FSgCMgG7OLLGJQmCIAjC/oiIowQhPT2dq6++ukrLxHKrpaVB+/bw11/R5Vu2bAccBPwDzAFOB+ziKDu7Sk0QBEEQhAaHuNXqMbEsRxDfTaasQob16IeY5UUcCYIgCPs7Io4ShLKyMr755humT59e6WXiiaN4A8naxZEZdxQKmcs0b17p1QuCIAhCg0TcaglCXl4egwcPJjU1ldLS0kotUz3L0Ynk5LQjL68nEARcaBqsWaPKtGxZjcYLgiAIQgNCLEcJglaNLmOxBp5VdcUur7r55/Dxx+uAtwCz3/+SJeo7GKxyMwRBEAShQSHiKMGoTlf+yuoqo3xKin26psGGDdCokYgjQRAEQRBxlCBUx3IUr9d/xZYjQxwFgb8BFW+0cSO0ayfiSBAEQRBEHCUY1Rl4trJuNUNMbdmyGmgK9AKCaBps2iTiSBAEQRBAxFHCsC8sR0b59u07AA5gNzALTYPNm6FtWxFHgiAIgiDiKMGoTsxRJHtyq6WmuoFL9akfomlQXg4ZGSKOBEEQBEG68icITZs25cUXXyQpKanSy8Rzq/XpA7GyARjiSHXpPxMYC8whFIJAQE2PlyNJEARBEPYXRBwlCNnZ2dxyyy01UtdHH8W2ABmWJqW/jkW51v4mFNpOMNic5GSxHAmCIAiCuNUaIC5X7AFk+/aFp582xFE2Dkd7ADRtOcGgWkbEkSAIgrC/I+IoQSgrK2P27NnMmzev1taRkgJ3322II3C5uupzRBwJgiAIgoG41RKEzZs3M2DAALKysigoKKjVdRniKDX1CoqK+gLHizgSBEEQBB0RRwlCdbryVxdDHKWlXUxRkfot4kgQBEEQFOJWSzCq0pXfXKZq5Y1ea9ZUACKOBEEQBEEh4ihB2JeWIwMljnYC7+P1rgyLo6lT4c8/93lzBEEQBCEhqNfiaM6cOZx55pkccMABOBwOPv/88wrLz5o1C4fDEfX5M4GUQHUsR9VFiaMbgKsoL58SFkennw5XXbXPmiEIgiAICUW9FkclJSUceeSRvPTSS1VabtWqVeTm5oY/hxxySC21sPLUheVI6bDuAAQCy0lKMt1qhutNEARBEPY36nVA9qBBgxg0aFCVl8vJyaFRo0Y136AaYF9ajtSqVHf+YPAPW4ZsEUeCIAjC/kq9FkfVpUePHpSXl9OlSxceeOABTjzxxLpuEs2aNeOpp54iOTl5H6+5MwCh0F94PBrBoBJnIo4EQRCE/ZX9Shy1atWK119/nZ49e+L1enn//fcZOHAgs2bN4oQTToi5jNfrxev1hv8XFhbWStuaNm3KPffcUyt1x6NbN1i/vj3Ku1pCcfE2gsGWgIgjQRAEYf9lvxJHnTp1olOnTuH/ffv2ZePGjTz77LNxxdGoUaN45JFH9lUTq0V1PHGaBm+8AV9+mQy0Bdaxdesa/H4RR4IgCML+Tb0OyK4J+vTpw+rVq+POHzlyJAUFBeHPxo0ba6UdZWVlLFy4kKVLl9ZK/bFo2dL4dTAAublr2LVLTZF8R4IgCML+yn5lOYrFkiVLaNWqVdz5ycnJ+yQOaO3atfTu3ZtmzZqxffv2Wl8fgLnZdwE30r9/n7AoMjJnC4IgCML+RpXE0ZQpU6q8glNOOYXU1NQqL1cZiouLWbNmTfj/2rVrWbp0KU2aNKFt27aMHDmSzZs389577wEwevRo2rdvT9euXfH5fHzwwQdMnDiRiRMn1kr7qkJddOXv1AmaN4ft208FoE0bc56II0EQBGF/pUri6JxzzqlS5Q6Hg9WrV3PQQQdVabnKsnDhQltPsxEjRgAwZMgQxo0bR25uLhs2bAjP9/l83HnnnWzevJnU1FS6du3K119/zeDBg2ulfYlOZiZs22YOI+LxmPPy8uD772HgwLppmyAIgiDUFVV2q23dupWcnJxKlc3MzKxyg6rCgAEDKrS4jBs3zvb/7rvv5u67767VNlUXYzv2ZZ4jtT7j1xyeeWYacC1wEPn5cPLJKnBbEARBEPYnqiSOhgwZUiUX2RVXXEFWVlaVGyVUjZrRU08yevQ0YB3wYU1UKAiCIAj1kir1VnvnnXeqZA0aO3YszZo1q3Kj9kfqynJkMkpf93iczvV11AZBEARBqHv2qrdaeXk5v/32G3l5eYSMcSd0zjrrrL1qmFA5nE7o0aMmaurBMcccy88/z8Pt/haf7/qaqFQQBEEQ6h3VFkfffvstV111FTt27Iia53A4CEqinCqRk5PDQw89RHp6epWWq8ndfOKJA/n553k4HD8DIo4EQRCE/ZNqi6NbbrmFCy+8kIceeogWLVrUZJv2S1q0aFHnmbgPP/xwAEKhlXXaDkEQBEGoS6qdITsvL48RI0aIMGpAdOmiBqENBFYC0k1NEARB2D+ptji64IILmDVrVg02Zf+mvLyclStXVjiUSW1z2GGHAG6czkwgv87aIQiCIAh1iUOrZmrm0tJSLrzwQpo3b84RRxxBUlKSbf7w4cNrpIGJRmFhIdnZ2RQUFNRomoJly5bRvXt3WrVqxZYtW2qs3spgdJDTNHA4iunYMYO//zanCYIgCEJ9pyrP72rHHI0fP55p06aRmprKrFmzbF3QHQ5HgxVHtUVdDB8Sm4xwxmxBEARB2B+ptjh64IEHePTRR7n33ntxytO0xqi7PEfWNgD4gaQ9lBQEQRCEhke1VY3P5+Piiy8WYVRDJI7lqJBNm84AWgJ+7r+/rtsjCIIgCPuWaiubIUOGMGHChJpsi0AiWI4yKC//CdgFLOXJJ+u4OYIgCIKwj6m2Wy0YDPLf//6XadOm0a1bt6iA7Oeee26vG7c/EdNyNGECnHsueDz7sCVOPJ5jKC//BlgI9N6H6xYEQRCEuqfa4uj333+nhz5uxR9//GGbV/fWj/qLbd9dcgl8/jmcffY+bYPb3RX4Bvgzat7RR8Onn0K7dvu0SYIgCIKwz6i2OJo5c2ZNtmO/JycnhzvuuIPs7Gz7jDqI6XK5DtN/RWfK3rQJtm8XcSQIgiA0XPZq4Fmh5mjdujXPPvts9Iw6EEdOZ2f9V7Q48vuhtHTftkcQBEEQ9iVVevL+9ttvhEKhSpdfvnw5gUCgyo0SLNSJODIsR5uAIts8vx/KyvZ5kwRBEARhn1GlJ2+PHj3YuXNnpcv37duXDRs2VLlR+yNer5f169ezefNm+4w6iN/StCbAkcBAYDcAxcUQCIg4EgRBEBo+VXKraZrGgw8+SFpaWqXK+3y+ajVqf+S3337j6KOPpm3btqxfv96csY8sRykp5u9gEGCpbf6//63iwkUcCYIgCA2dKomjE044gVWrVlW6fN++fUlNTa1yowQL+0gcNWpk/lbiyE5REeTnS8yRIAiC0PCpkjiaNWtWLTVDMPIcOayjwMI+E0fWTnJ2ceQFkvH7lWsNxHIkCIIgNGxk7I9ExVAodWA5UjH3C4HWQA9CIRVvVFio5os4EgRBEBoy0pU/wQhbjgxxtI8Csq2WIyWOcoDNwDaKi/0EAkkUFKj5Io4EQRCEhoxYjhKEqOFDjBQI+0AcHXkkXH65+t2mDXTvDtAGSAcCLF++xmY5+uor+OGHWm+WIAiCINQJ1bYcbdy4kTZt2tRkWwQsliNDHMUac62GWbrU/P3nn0qPpaU5gG7AT4wdu4RAoHNYHC1YAAMH7pOmCYIgCMI+p9qWo8MOO4wHH3yQkpKSmmzPfktOTg433HADl112mZpgiKMqJN0EVFeydevU70GDYPToKi2elgapqfDddwBHATB//mL8ftNyJAiCIAgNmWqLoxkzZjB9+nQOOeQQ3nnnnZps035Jhw4dGDt2LI8//riaYMQcxepXXxHDh0OHDur3t99CNY/NwIEAPQHYsmWxza0mCIIgCA2ZaoujY489ll9++YWnnnqKhx56iB49ekhX/5qkupajTZvs/6u6vA1lOSorW4zXq4UDsqFORjURBEEQhH3CXj/irrrqKv766y/OPPNMTj/9dM4991zWrFlTE23br/D7/ezYsYP8/Hw1obriyO+3/9+rwKAutG/fD7iCkpJSm+XILf0cBUEQhAZKjbz/a5rGqaeeyr///W+mTJnC4Ycfzh133EFRUdGeFxYAWLBgAc2bN+foo49WExJCHCVxyy1zcLleoqwsXcSRIAiCsF9Q7Ufcq6++yoIFC1iwYAErV67E5XLRrVs3br75Zrp3786HH35Ily5dmDx5Mr169arJNjdI4nblr1NxpHquJSdDSQmUl5vTRRwJgiAIDZVqP+KeeOIJ+vTpw5AhQ+jTpw+9evUiOTk5PP+aa67hySefZOjQofzxxx810tj9CiMQe8sWpVAqK3IMUWWwVzFHatUeTwm7d28FOoanizgSBEEQGip7ledoT1x77bU8+OCD1V3FfkXU2GqGyJkxQ32HQpWLgo4UR3tpOVq3bj67dx8PdAD+xuVSuk3EkSAIgtBQqdU+Rzk5OfwgqZSrjjGYGcBnn6lvq0+rIiLdantpOWre/GBAA9YCpeFhRtxu2Lix8s0SBEEQhPpCrYojh8NB//79a3MVDYZwzJHXCy5XtAWotLRyFdWwOMrKysHlaoYSSH+SlaWmu1zQti3ceedeVS8IgiAICYdkq0kwHF6v+hGZ/LG64mgv3WpOJyQnd9X/LbdZjgBWr96r6gVBEAQh4RBxlCDk5ORw1VVXcc5hh6kJ8SxHDgf880/8igxxNHWq+q6B3mppaV30f8tJT1e/XC71LdkaBEEQhIaGiKMEoVOnTrz77rs8baQ9qMittnVr/IoMcXT66eq7BnqrZWaaliPDrWY0wZo1WxAEQRAaAiKOEo28PPVtFUeZmUocGUKnoq5ihlvOoAYsRxkZpjgyLEfFxepbxlsTBEEQGhq1Io6cTicnnXQSixYtqo3qGyTBYJCysjLKV640JoTnhY44As2ahdHwacUiUhztpeXI7YasrCOAq4Fb8Xg0pk0z54s4EgRBEBoatSKO3n77bfr378/w4cNro/oGyY8//khaWhrtFi4kCZj3228AbAfaL17MyXfdpQQSRLvcAHJzlY+rBsXR9Olw1VWQnNwUeBu4ndRUh81wVVZW7eoFQRAEISGpFXE0dOhQHn74YebNm1cb1TdINN1SlAcEgJlLlgDwCrCxvJwffv+dn9q3V4V9PvXdtSt89536fcABcOKJ0WIo0q32+++wc2el2nTKKWroEKuhKjnZ7tWryIglCIIgCPWRaosjGVS2hrFkv3YDwwcOBOBvS5GFRlC2IY5WrABrks2lS8HjsdcbKY66dYOhQ6vRtCCwnJ0754k4EgRBEBo01RZH/fr1Y2tFvaaEKmEdePZkICUUYh7wHnBxhw4AbDEKGOIIICPDWokK3rYSy60WGSg0cyZs2xa3bUoATQEOZ8GCm0hKipwnCIIgCA2HaoujXr16ccwxx/Dnn3/api9ZsoTBgwfvdcMqw5w5czjzzDM54IADcDgcfP7553tcZvbs2fTs2ZOUlBQOOuggXn311dpvaBW51OFgzOTJHA98DPRo1QqAXKPAli1w5JHqt9F9zMAqliB2b7V588CIXwI46SS45Za47UlJATgagPXr/8DnKw7PE3EkCIIgNDSqLY7efPNNrrnmGo4//nh+/PFH/vrrLy666CJ69epFcnJyTbYxLiUlJRx55JG89NJLlSq/du1aBg8eTL9+/ViyZAn33Xcfw4cPZ+LEibXc0j0TsARZT9A07v7mGwB+AA5o3BiwWI42bQJDlEaKo8pYjoJBePzxyAbEbduZZwIcSOvWrQmFQixf/lN4nogjQRAEoaGxV2OrP/zww3g8Hk455RSCwSCnnXYaCxYs4Kijjqqp9lXIoEGDGDRoUKXLv/rqq7Rt25bRo0cD0LlzZxYuXMizzz7L+eefX0utrBwhi4gZAOj5rTkU6NuxIy/l5HCYkQMpP990rUVahjIz4YMP4IorYs83iIwZixyuxMKQIXDUUfDii//izTffZPbsL4BTACWOQiFbyJQgCIIg1Guq/UjLzc1l+PDhPPbYY3Tp0oWkpCQuueSSfSaMqsNPP/3Eqaeeapt22mmnsXDhQvyRY5LpeL1eCgsLbZ/aoHnz5hx44IEcfPDBnGGZ3gg4uE0bbna7OQA4Fhj65ZeEpZQ1/giUW80alB2vK3/kchV0+Xc4VBz3OeecA8D3338OegucTiWQjPRMgiAIglDfqbY4Ouigg5g7dy6ffvopixYtYtKkSdx00008/fTTNdm+GmXr1q20aNHCNq1FixYEAgF27NgRc5lRo0aRnZ0d/rRp06ZW2nbUUUexadMmVq9eTWegtT59AEBaGtsKCrjE4eAn4N1//mGZsWCkyMnMxBYxHc9yFLlcBZYjg4EDB5Kens62bZuBPwCzW79h1BIEQRCE+k61xdE777zDkiVLOF0fw+u0005j5syZjBkzhptuuqnGGljTOBwO23+jl1jkdIORI0dSUFAQ/mzcuLHW2wiwBFgBHAyQlsaWkhI6WYTdj8YPQ+SoqGkljqyWI02D116D3bvtK/D5lCAyRFElxFFKSgpHH320/u8XwIw5ktgjQRAEoaFQbXF0ySWXRE076qijmD9/PrNmzdqbNtUaLVu2jEo/kJeXh9vtpmnTpjGXSU5OJisry/bZFzQDOht/0tL4A/BqGkM7dQJgjjHPEEdGEHxGht1yFArBDTeoOCQrPp/K8tinj1muEjzwwAN88ME04CJAudyg4uHeBEEQBKE+UeNhtO3bt0/YzNh9+/ZlxowZtmnTp0+nV69eJFkFRR0wZcoUHA4HDoeD0siZaWlcCXxxzDFcffjhAHwGfAumODJ6rWVmsmLLFsYAi8Hssh9pGfL54KefYOHC2PPjcNJJJ3HssacC2YA53JuII0EQBKGhUCt9jBrrXc9rm+LiYpYuXcrSpUsB1VV/6dKlbNiwAVAusauuuipc/oYbbmD9+vWMGDGClStX8vbbb/PWW29x55137pP2VoS1t1qUgy8tTX273Rzdpg3ZwOFACzDFke5yKwmFOP7WW7kNOB4IR1JFWoZ8PsjONv9bxdF775miKQbWsC1jbDXprSYIgiA0FOr1I23hwoX06NGDHj16ADBixAh69OjBQw89BKgedYZQAujQoQNTp05l1qxZdO/enccee4wXXnihzrvxgz1DdpQ4MqxCLhcpaWlsQUX8HAIs3LiRDevXh4N+vly6lHy9m/4bQHa3bkwDcmPFHFnFkVU8DRkCN94Yt63z5s3gzDPvBRaHxVElDU+CIAiCkPDUa2fIgAEDbKIiknHjxkVN69+/P4sXL67FVlWPCsVRaqr6drnA40G3I3El8MFHH/HEpEnc11lFKM1btw6A24DLgQs3buQz4PFffuF+a50ViSMwg4li8MYbb/Dll58C2Xi9KnXD9u1QUGCvUhAEQRDqI/XactSQqFAcGfFQujgyMAK2l3u9kJsLPXuySC/bS593RrNmALy9YIHNdUcwGN+ttgf6GEHceo81gMceg1deqXQVgiAIgpCw7JXl6Pvvv+f7778nLy/P/uAF3n777b1q2P5GhTFHRrSzy2UbO+0Y/Xs88ExBATmff87Sk04CTHF0wYEHcuvq1fyzaxeLFy8OT8fhAGvPu0hxVIHl6JhjjDX/DGiAg927o1MnCYIgCEJ9pNqWo0ceeYRTTz2V77//nh07dpCfn2/7CNWnsuKoP9BW/z28vJyVGzdSVlZGZkYGh+jT01NT6af/jupFaO1iVsmu/KBSNrjdbmAboGK6Skok7kgQBEFoGFTbcvTqq68ybtw4rrzyyppsz35LTk4OzZo1Iy0tDYcliBywiyPLwLJu4E3gVOBL4K3Wrfnmm29YMmcOzlGjVCGPh2NRY7XNnz+f/zMW9vvtXcwilY2mqcSRjRpFtTU1NZVu3brpsVu/AO0oLq6SvhIEQRCEhKXaliOfz8exxx5bk23Zr+nfvz/bt29n/fr1RCWbjiOOQA3/2g7wAbMWL+Zf//oXI0eMMAvo4giUOArj99sFUaQ4WrAAKkjJEBl3VFIi4kgQBEFoGFRbHA0bNozx48fXZFuEeBjiyO22udUMTgQaAz2OOEJNsA4fkpxMb8DlcLBp0ybCg5/4fBAImOVCIWUt+uijSjXJjDtaF65OxJEgCILQEKi2W628vJzXX3+d7777jm7dukVlmH7uuef2unGCjrW3WoTlCOA/wHlA27Zt7eUBPB4ygHfPPJNODz5Iq9691XS/3y6OjHHWLrusUk0aPHgwc+f+Qb9+XWxVCIIgCEJ9p9ri6LfffqN79+4A/PHHH7Z58QZxFeIzYcIELrnkElwuFwGAdu2UGPrnn7gB2Qbt9E+4nFUc6b8v79IFevUyp/v96mMQCtn/74FmzZrRpUsz2zSxHAmCIAgNgWqLo5kzZ9ZkO/Z7/LowCQaD8MILcPzxYAzua4gehyOm5SiMVUQZGEHXkcol0nJURXFkrdpahSAIgiDUd/Yqz9Hu3bt56623WLlyJQ6Hgy5dunDNNdeQLWmSq4wtT9Stt6pvIzGkIXpCIbs4cjrtisQqopYuBd2yB6AFg0yeNInxwFigeSy3WhUTFf049wccvIBGV+AJEUeCIAhCg6DaAdkLFy6kY8eOPP/88+zatYsdO3bw3HPP0bFjx4QcniPRiTkMiqE2DEuQpkFKCvz9t/pvDbwGe96iCNemIxRi1KhRTAQuBL4pLbWLo8JC+Ouv6Db4/XDHHaZQs1D89SQ0vgC+szVXEARBEOoz1RZHt99+O2eddRbr1q1j0qRJTJ48mbVr13LGGWdw22231WAT9w8McWSL1zIEiTHN+H/QQXDAAXDGGfZKrOIoJ8c+LxDgsccewwPMBgaXlPDx1q3m/Px8OO646Ib98gs89xwMGxY167DSUv3XnzRiJ0llhRVuoyAIgiDUB/bKcnTPPffomZIVbrebu+++m4ULF9ZI4/YnIodfAaKtNdb/mzerAc2sWMVRy5aqvCGs/H7+9a9/8V1GBk49WOiJTZvQInoZRtFPz68dYziYQ7Kz9BOokBU0447JMcSVIAiCINQzqi2OsrKy2BCZyRnYuHEjmRUFDQuVpyJxBKZbLTlZfbsrCCHT44n6OZ3s/PlnUoA/ysuZk5JS7ealuJx01H+vBJoXrKl2XYIgCIKQKFRbHF188cVce+21TJgwgY0bN7Jp0yY+/vhjhg0bxqWXXlqTbdwvaN68OVlZWbRq1cqcOHUqzJpl/o8njgyB44rKrW1i9EQLhWiUk8OFqIOfnJZW7TY7tRCH6b//BDRHtU8nQRAEQUgYqt1b7dlnn8XhcHDVVVcR0AN7k5KSuPHGG3nqqadqrIH7C2eeeSYFBQX2iZ07q49BPHF0/PHw559RQdiAOc3rVd/BIKSk8Biw2uPh2dJSPiXGYLexCATs1qlgkMNQ47qtBEKRWnvTJmjdujI1C4IgCELCUG1x5PF4GDNmDKNGjeLvv/9G0zQOPvhg0vbCEiHsgUhxZLjThgyBCy+seNmyMli+XH0nJdHO6eSnrCzo0EGNo1YZvF6bOHJoIboCKUAACDkslqv8fGjTRnVhk6SggiAIQj1ir/0gaWlpHHHEEXTr1k2EUW0Tz3JUUayRQUkJHH64+u10qszZpaW8lp/P2cA3lRlTrbzc/j8U4hKgCJU7yeZWM3ImVXVMkR07YNu2qi0jCIIgCDVIlSxHI0aM4LHHHiM9PZ0R1pHfYyBjq1WNcePGcc0115CSkkJpuIt8BPHE0Z56nIESRwYul1qmpISVPh9TgCmXXspioEdFdRiuOYNQiGRr82LFHAWDlRNvBt27w65dEG8fCIIgCEItUyVxtGTJkvAwF0uWLIlbTsZWqzplZWVomoY3UoBYiezu73IpK1BlLUcGhuVI07i1UyfGbtiADzgNmAt0ileHtW1eL4wfb29eLENkVS1HmzdXrbwgCIIg1DBVEkfW8dRkbLWaJWYSyMrg8VTOcmS1xBiWI6Bj8+b8DZxxxBEs+/13TgHmAW1i1VFergSa0wlffQXFxQC8AbwInOkv5b4SSE/HFHJVFUeCIAiCUMdUO+Zow4YNsYe80OcJVSPevowoFD3N46nYcmSIrVhuNYDUVFoDM6ZM4TBgI3AJEHMkEK9XLbt0qU2QlQC/A4tDAS64QJ9oDE1iHaJEEARBEOoB1RZHHTp0YPv27VHTd+7cSYcOHfaqUfsjeyWOqhpz5HSqDNoQzpHUvEULvgEygJ+A1bHq2L1bfXu9tnHduuvfywkwfz689RYUF+gWI7EcCYIgCPWManfl1zQtpguouLiYlL3Iury/EnP4kEhqUhxNmgTz5sEaPau1y0V7t5uHAgF2ESfu6MQTw2Wt6zxS/95IEAp3M2xYI7q8H6QvVF0cORyxt1MQBEEQ9hFVFkdGLzWHw8GDDz5o674fDAb55Zdf6N69e401cH+jwpijWAKqsm41q+BwOKBtW/V58kk1TQ/SvisQsLnUfgS6AVnWOgMBm+hpDLQD1gOwDOiPvzzCcjRnjhrYtqIs3oIgCIKQAFRZHBm91DRN4/fff8djca94PB6OPPJI7rzzzppr4X5C06ZNSUtLIzs7O36hWBaVV1+Frl2rv2J9ENpwD7aysrCv9TZgDKoX263AqUASKPfaoEG2arpjiKOlQH+KCyPEUf/+8OWXcMYZFbdHLEeCIAhCHVNlcWT0Urv66qt54YUXZJDZGuLyyy/n8ssvr7hQLNFw2mmVW0HnzrByZfR0w5JjiCMLJ6DE0TT9c2Z6OpMPOwzX0qVR1XQHvgCU5QhKI8URxLZ8CYIgCEKCUe0kkI0aNeLhhx+OW1aSQNYw774L/fpVf/kVK+CTT+Dii+3TrW6u66+H0aPD3f7PBYaihFEu8GVJCReuXcsrq1bRMqL6o4COePibAwAoKYjRW60yLjXJkSUIgiDUMdVOArk0hvXAQJJA1gJXXbX3dcQK3LYKlieegOHDwz3ZHMA7+qyPU1K4tLycybt2se3zz/kR+2C1ZwGH0oHOPA5AaXEMy5GII0EQBKEeIEkgE4TXX3+dm2++maysLHbu3FlzFXfrZv6OJY6cEdkcWrSAjz6CSy+1Tb4kPZ1W337LxaeeSi+gDIgcSS+IKX7KiiziyLAeRa5LEARBEBIQeVolCIWFhQQCAYqKimq24muvNcXJnixHBnG63/fv35/N/fszRtOihBGAhgMIAkWMmHCMWZcx7EhlxJFYjgRBEIQ6ptriqKyszDZA6vr16xk9ejTTpk2rkYbtbxh5jmrcJelwmAKocePo+bHEUQWB066kJCgoAGBbr15YZdT77AKySEkZaU60iqPK5DwScSQIgiDUMdUWR2effTbvvfceALt37+aYY47hf//7H+eccw5jx46tsQbuL1QqQ/becvTRsGqVfVosa05FIka3Pq0Cev/2G+cAu/VZWSQBpTidlkGJAwFTHOnxagBMmwZ33VWl5guCIAjCvqDa4mjx4sX003tPffbZZ7Ro0YL169fz3nvv8cILL9RYA/cX9ok4Ajj0UPv/WJajiqw3esLJ5cBmn4+vACMBQQ89RNvrXWpalIJBM4WAVRytXWtm5xYEQRCEBKLa4qi0tDSc42j69Omcd955OJ1O+vTpw/r162usgfsLteZW2xOxxNEll8DUqbHL65aj84A5nTrhAqYClwEnsZ40IBgs5S+jfDAIAweq39Zu/WVlsQelNbb/xReruiWCIAiCUCNUWxwdfPDBfP7552zcuJFp06Zx6qmnApCXl0dWVtYelhYi2WeWo0hiiaPk5KgM2GHRYil/XGoq/wNcwEeoTNpG37gFRiGri85qOSotrVgcPfVUZbdAEARBEGqUaoujhx56iDvvvJP27dtz9NFH07dvX0BZkXr06FFjDdxfaNy4MR6Ph8axgqZrk4p6kO3eHT3NKuKCQf4P+AxIBuZgnlA/WcqEsYqjPVmOJJu2IAiCUEdUWxxdcMEFbNiwgYULFzJ9+vTw9IEDB/L888/XSOP2J2666Sa8Xi+5ubn7dsUVJWa0DCrM0Uerb6vA0YXPOcDb+qQocWQVQJURRwaR4mjJktjlBEEQBKGGqfLYalZSUlL44YcfePnll3E4HHTu3Jlrr7224sFThcSiInFkzJszB/r0Ub9jiCNateKi3FzSgN7AdUceyfHLlqEBDqvl6Lrr4K+/4L//VW61inrFWcXRX3/BUUfJgLSCIAjCPqHalqOFCxfSsWNHnn/+eXbt2sWOHTt4/vnn6dixI4sXL67JNgq1iTWDdiSGyy0lxUwgaVh7LrgAbrgh/Dv3349xDnAgMPXii7kPNbxIyB8hgPT0D1Vyq5WXx26fkSJAEARBEGqQaouj22+/nbPOOot169YxadIkJk+ezNq1aznjjDO47bbbarCJ+wcvvvgiKSkptGnTZt+u+NBDq2aRMSxHn34KxnFu0oTyTkcCEMxpyY7Fi7kVWAcEfRHi6JBD1He8gGyDUAh+/VUJo1jxR6tWKdEmCIIgCDXMXlmO7rnnHtxu0zPndru5++67WbhwYY00bn9i586deL3emh1XrTaIFDT/+Q9cdx0OlzqVgod05qrp03kJ6ABc+sxTBJs0ge7dVfkOHVQs06ef7tlydMwx8MILpjiyiqS8vBrcKEEQBEEwqbY4ysrKYsOGDVHTN27cGM5/JFSeOuvKXxk8HvO3NeYI4OGH4cADcbhVfFLosC4MsQwrM/Hn+by+axc0b64mZGQolxpUzq3m9Zq/rTFK1dlfeXmweXPVlxMEQRD2K6otji6++GKuvfZaJkyYwMaNG9m0aRMff/wxw4YN49KIEd2FPZOw4mjRIntcUqQ40jEsR1rnLpwbCGAd4vZRoLhUF0RW609leqs5HObvispXhr59oXXrvatDEARBaPBUWxw9++yznHfeeVx11VW0b9+edu3aMXToUC644AKefvrpmmxjhbzyyit06NCBlJQUevbsydy5c+OWnTVrFg6HI+rz559/7rP2xsMQR/s8Q/aeOOoo+3AicQSKIY7o1AkPcIQ+PQfYCjSZN497I5evTG81p9NcxvieMcOcX1FAeST7Ok2CIAiCUC+ptjjyeDyMGTOG/Px8li5dypIlS9i1axfPP/88ycnJNdnGuEyYMIHbbruN+++/nyVLltCvXz8GDRoU091nZdWqVeTm5oY/hxhBwnVIqL4kPYxjOTK6/TsbZUHr1hhpQAcCSUB7RwrDwS6OrL9374b5800hZsxzucDns0879VT4Sc+k9Pvv6nv1atiTyE004SkIgiAkJFUWR6Wlpdx8880ceOCB5OTkMGzYMFq1akW3bt1IsyYN3Ac899xzXHvttQwbNozOnTszevRo2rRpw9ixYytcLicnh5YtW4Y/ropy/exjEs5yFEkcy1FIP5VcyW445JCwOCoA/gRGuQ7kAECLJ47uuguOOy5aHIVCpiCzljcEk0GXLtC5c8VtT/R9KwiCICQEVRZHDz/8MOPGjeP000/nkksuYcaMGdx444210bYK8fl8LFq0KDymm8Gpp57K/PnzK1y2R48etGrVioEDBzJz5swKy3q9XgoLC22f2iAzMxOXy0VGRkat1F9jpKfHnKw5YoujJcBBQHeH6tXo+2IqX6CGHLGJnfx89R0pYMrKoi1HEO2Sq0w8kogjQRAEoRJUWRxNmjSJt956i9dff50XXniBr7/+ms8//5xgRfEjtcCOHTsIBoO0aNHCNr1FixZs3bo15jKtWrXi9ddfZ+LEiUyaNIlOnToxcOBA5syZE3c9o0aNIjs7O/yprTxE9957L4FAIG7bE4YvvoCVK6MmhxzK+ubwJMGhh9INlQQyF9gGJGlK4Ewuzucc4FagxOqiKymJvb7S0j2Lo23bqrctgiAIgo2iIjjyyLpuRd1TZXG0ceNG+vXrF/5/9NFH43a72bJlS402rLJEuqE0TYvrmurUqRPXXXcdRx11FH379uWVV17h9NNP59lnn41b/8iRIykoKAh/Nm7cWKPtr3e0aAGHHRY1OaRbjnC74ZpryAAeB94H0oBPs4cBcB4q/9FW4LGiIrOXnqX7v42SElMcFRXBggXqt1UotWxZ8TAoBmI5EgRBqJC1a+G33+q6FXVPlcVRMBjEY817g0r+GNjbbtZVpFmzZrhcrihLS15eXpQ1qSL69OnD6tWr485PTk4mKyvL9hGi0bCIo8aNAbgPuALIBF5rfC8XMQEP8IS+zNPl5WbPRkMcRQqY0lIz5uj++80BcCMtlSKOBEEQhBqiygPPaprG0KFDbT3SysvLueGGG0i3xKNMmjSpZloYB4/HQ8+ePZkxYwbnnntuePqMGTM4++yzK13PkiVLaNWqVW00sUo899xzPPTQQ7Ru3TohUgtUFXeyLk7ccU4ph4OAfrpdAqwF7kfFsA0ZMoRWRmLIWOLIsBytX29OjwzIFnEkCIKw1yRqyr19TZXF0ZAhQ6KmXXHFFTXSmKoyYsQIrrzySnr16kXfvn15/fXX2bBhAzfoA6KOHDmSzZs3854+2Ono0aNp3749Xbt2xefz8cEHHzBx4kQmTpxYJ+23kpubS0lJSZ25J/eWgw+1WI4A7r0X71NP8RPwN0qX+PXUkA5gJPClw8HPPh/jbrmFkfEEodWtZh1axRBTBiKOBEEQ9hoRR4oqi6N33nmnNtpRLS6++GJ27tzJo48+Sm5uLocffjhTp06lXbt2gBIc1pxHPp+PO++8k82bN5OamkrXrl35+uuvGTx4cF1tQph6k+coDuEkkIY4GjWKgV8PY97vBwOQtqmIgyynmwO4AfgZGDdpEvc2b45j9+6KLUc7dpjTy8vt5ZwWD3H79vDOO3DiiRGNFHEkCIJQESKOFFUWR4nGTTfdxE033RRz3rhx42z/7777bu6+++590Kr9EMNyk2QOHFLs7EhjZwb5oWJKS5eF3WoGF2oakzIyeKK4GEenTvDjj/bhRRo3tsccWYO2I8WR1XK0fj2MGyfiSBAEoYoY4kjT9u9bZrUzZAs1S8IOH1JZjHZbYo6WLYOcUEv935IocZQGfNG/P4cDHHigmmiNJWrc2O5Ws1KROAKVLfuff2K3URAEQYiJIY72cXaehEPEUYKQsAPPVpWIgOxWGD0Ho8URAF9/rb79fsa7XMy1xhI1aWJ3q1mJjDlyRpzKO3dCx472OKVIXn4ZLrss/nxBEIT9DONRVM8jPfYaEUcJghFzVG8tR8aVFCGOWmL0BFwSDsgGuBv74MRzN27kqmCQazSNcFIIw60WSxxF5kWKtBwZYslqYYrct6+8Ah99BHfeGXubBEEQ9jNEHClEHCUIqampOBwOUlJS6rop1cO4kiIsOC3C4mg5Pkzr2DPcZSt35AUX0MjhYA3wsTExNVW51WINdhspjoz13nGH/b/1Co8UR8b///0v5iYJglA5du6MNuYK9RMRRwoRRwnCU089RSgUSvzhQ+JhXEkRAiSbxjQCwE8JGyxzzHKXpUwi6+67uUNPsPkg8BNAcrI9INtKPMuR4aYz2lGROIp0xQlCfSMQUB0P6tgtf9dd8OGHddoEoYYwbpkijgShJohzc9ZwoZI//ISLQ2KWCbpUxvWbW7WiFbAOOBZ4Zs0aFYsVa7DfeEkgDcubIXysUYUijoSGxu7dMGtWnUfPlpVF95EQ6g8zZ0JurvptnEoSkC0INUGc14wQTs4BoA8h0mKWCbhUtvWsNm1YABhpRu9esoTzAd5+O3qheOLIiHkScSTsDySIDyQQkIdpfeakk+DGG9Vv4ziK5UhICJ555hkaN25M796967op1ePII+H556Mmaxb3mTUg24phOeKAAzgQGAf8F8hKSmJEvPV5vfb/hjgyBFBl3Gr1NfhdEAxEHNU//H6oYDzPuiKyC7+IIyEhWLt2Lbt37+bvv/+u66ZUj6QkuO22qMmfcBFvua4E3mIHj2O93m7hRQBCbl0cWca4uwvYdsYZHB9vfZGWI8MKZAieWLZhsRwJDQ3jCVbHyiQYrPMm1B9eeAEOPbSuWxGFiCM78nRIEOp9EsgYTJ8OaziEexq9DdxCIeP4yzJ/MUcBFstRxADAKZoGb74JwEeogWrD7MlyZARxBwJEcc896lvE0f5NrED/+kaCRM8GAnXehPrD7t113YKYROrs/f14ytMhQWgwSSAtnHSS+k5LcwPHADAXyKM5AOWo4Gm/U8UckZ5uLnz22TB0KCQlUQzcAjwJjDfmRz7YIvMcGZYlazlDOI3XaxFxtP/y22/g8dR1K/YeQ/zXsdlG3GpVIMHvOxKQrUjso7Qf0RAtR8Y9IDUV4AQApnuyaEEeYIqjsqD+kEpONheePFkJpKQkMlCD1AJchYpJiiIylYDx0LBajox5hghL8JuUUIts2VLXLagZEuQ1f39zq/n98OWXlSh42WUwcaJ9WoLed8StZicxj9J+SEO0HBlaRImj/gB868kGPRmkFyWGCsp0cWS8yb/xRtRYbY+ghFEQuA54L3JlhuCJtBg1IHGkaWZ3W2EvaSjXm/VJ1rUrzJlTJ82oS8vRp5/CO+/s23V++y2cdVYlCn70kbqfWUnQ+4641ewk5lHaD2mI4shAiaM+gJvi4o1cffVGwLQcFfsixFGvXubCSaqHm9vjYRxwJRAAhgIzrSvxeJSlqahI/TdE0vGWkG6rODr/fFiyZO83bh/yzTdwwAF13QohobC61VasgBkzYhZbvRo2bIg5K4qLLoLvv696M4yH6aefwquvVm35veHSS+Gaa/bd+qCK2jpiSKXKiqN334WDDqrCegxWrqxWPJ1YjuyIOEoQXHrMjDvyQmoAqE1KB44EYOfOnwFTHBV6dXea4VazuhaN/ZGZiQPlUhuKsj3dYl2J16tUWHGxWj7WzcEqjtavV0OTGNSDXoI7dtR1C4SEI/JJlhQ7Xcahh8JRR1Wuyk8/hXHjqt4MoylDhpg5cxokDgfJOyPcspqmknHGIt64j3vgq69g7doYM15+ueKMm126wIsvVmodViLF0f7kJo2FiKME4dVXX0XTtPo7fEgFmFqnDwCFhasA063m1ZLUC7BhObLePIybfWammvXOO/x3zBiagZ5cUsfnU9mxCwqU+LGKI+Oqt4qjSPF0+ukVb8TSpRXP3wc0oHC0umdfW2qXLq2dAxgZkF1BkHlVOklVtalWt9r+8FD1lObbJyxfroZxiUU8y5HXa39BiyCu5eaWW2Du3IobWEG9Vs47D045Rf2ukuVI0+CLLyq1jvqKiCNhHzISyKN79wcB03LkJVm9CFUkjjIy1PfQoTTv1IklwB3Wqr1eJY7KyuDAA+2xRoYQMq7+1NRocRTnjRuAXbugRw91t8jNVQEHdYCIo3rM+vU1Wt2WLfopXEnLEdSuaLGKo1jZM/aWkSPhl19iz0uIiISKdm48cXT++RX6yWNt12+/6T/2tJMrOA82bFD3kubNVb+X776zr69S4mjTJjjnnATZ+bWDiCOh1jEf6gcCzWnbVv0L4saBhjPZo9IWGW41qziyuNWs01oDTfS/m4F/rVjB+d99x1NAqG1b+5Vt5ERKSYGcHCXCIsVRRd26jbuFpqlEl4MG7XGbawMRR4LB0QduYsoF75kPSeN8r6H0BNWxHMVLudSmzd4nhH76qRDPP1cLD+Jg0FQHe+Loo+E91RWkSrsnUhwZO/e336LHjdS0sAqMpTuOPFL/sSdx5PHw3HOxNdu8eeo70k1f6YDssjLI1y1nkcl4GxAijhKEUaNGkZOTw4nxTLP1GIcD8vLM///3fwBmEsfkZF2/VMZyBFE3mxHANGDSmjWMBP6zYwe2e4JxAQeDKmg7EKiaODIIBuu0p4mIoxpkX7/x1vDBu48nOX/KkGhfVkUW0CpQ1eZW1JV/0yZYsKCChUtK0BYvqdD4EsLFWaueCf9//XWYP1/93qtDOXOm6VeKxaZN5u8FC+Dmm4Eq7p94lqNY95xff4U+fSAQqNByU1KwZ8vRHXfYm2+QkxN7kUpbji691FRp8+fDwIEVt8VKJd19iYCIowRh1apVbN++nRUrVtR1U2qF5s2NX0sZOPBErBFDycnY3WrWO09EzBEQdbMZixJIR7ZsCcBjS5ZwKLDOKGBYjoJBtTK/P7ZbLd7dwGjPokXw8cfxN7KK+P3RL44VsV+Jo+3b7Yo6HvfdV72uUcaTYF+JpBo+eA49HUZUPi+rOJoyJfbTsRbYU1f+Cjd/1iz+Oes2UlLMSStWwP/+Zy92sN6RA+D66+Gmm6rXVht7etlp00b5ngz0F63w/o/Eeg8x/IDxArJjCVmjt6HPV6E4+md1xeIo5FJ1x9rv8Y5TpQOyrWbA8ePhhx8qbIuNjIx6k49ExFGC0JC78tsv0ExmzZqFsvWorhgpKTB6NGzZWXm3mpUmwP+AJbffzt36tH+A+4wCXi9s3qy6fqSkxBZHc+dG38Qi0YcyqSluuw2ysytfvsGJo6VLzdQLkRx5JBxyyJ7r2LHDNPFXh3raX9lJhA+ktFR9Wx+4Z58Nd99NddibgOxYfPopfPih+f/eey2H3u+npCBg8xT9979w5532Oo7eNNkWiGy8WOzVrdN4IauokvPOU64kiL9jIpPPFhUpCxDEtxzF6plsRM3vQRz5SgKcd16MGfp2BJ3xxZFx6zPCnYzbXqUsR7Nnq04vBtWxBBUXV32ZOkDEUYLRkDJkG9g3qSOnnHIKqjO+So6WnKx6nn74aQVutWOOMafFSXfgSE3laeBX3ZKwU18LXi/8+9+qUDxxVBHGXaKGu/uvWhUxITfX/pba0LnrrvjxHrm5lTOr+Xzm3dzvr/rNel91rapty5Gx3ZEP+WqutzputYoe5pMnwxVXmP+fftqic/x+HNgXVrnRYvDjj+Gfkbq6uLgah9MQR3uKnTH2s75j4u4fa3yigdvNJkdrfnt/GTRrZt7fYlmtLHnaKtJrZcWB2P1CdCu5YTmKhd+vOuw2bRpunq3JFYqjAQPUi6ZBVYSOUXE9ecaJOEoQQvX0DbY6/NsQKnwEFvN0k1YVWI6OOca8euPFVeh2+V6nnsqbwFT0wMm5c2HqVLNMrJijijCOTcykI9UnSuM9/DDW18Fdu2IPDddgCAb3PqDT77cn2KmsKc44l+rpdRcWR8a2G+IoUh04neBw0J6qnbt7PNcuvxy+/jr8tzoZssMP/0AAp2ZfOK44siiGSO2cman0dpUw7jWGZciK9dwwLsQIcRRuTqSysOJ205rNaF9PhZ07zXXqlXw+2aKCjI3ag+XIEQjg9VrW/8cfsHBheDsCFbjGfD61fw29VyVxFElVXkYiBGaiI+IowWiIlqNIBg0aBKSgooJ+D4dFbC+IbznSXG5WrtSnxUuUmZqqkkU2acK1gOEk840ZwyKjzN5YjqoSIFQJDHP2b7/BmjXYg86Ba69VYSMGNXVqVGZYMUunmWrz0EPQs2cFBUKhve/37febx+e33yr/hK7oYVZZquLP2VeWo1jiCGjDxkrX7cYPmkYwqJLVB35fCcceay80fjy89Vb4714NH+L349QqaTmy7HNDVzdhJzlsAyxd3SvD//2fGvsMTLekFatwj7xf6O0IC4jIQB3rztAvdM2pX/AR5/xN15Sbky2Wo9jiRB+DMxiwXz7HHw+9e4fFUcgbiGqGdVPS0sxNMt41qzV8SCUsR36/PhhBZEqVBEfEUYKw/8QcQXp6OmD0EPkinOz10adj+P/1K/f72W66dNGnxRNHhxyigpcs88uB89asoT8QHiykuuKoGin5K8IQR6+9psd5R4ij4mL7Pbsmnq8FBSoN1J4eZFu2VNyJpzJ88AEsXgyXXBKnQCi09/vUajnaU8xY5Lph78SR06k2sA6IshwZJ0occRRwKqvs66/rKZcWLIg7DpsfD6esGEN5ueqD4N+2K3Z6dst1FtlbzeUCTj7Zru4jqIzlKOq2GOM++QvHsJ52RlWAMvKuWxd31YrXXzeDi2NZjrxmj9rI89S4FsPbHJnkKYY4Cn8bNzxd0LZqpupOSoJdG+K71YqKzFgzzR+wN7GJSmySu1bVHfIHbc2x4vMpcRQrht/a9EpdGhWJI02D0aN5642Qys5u7MN6kiVUxFGCYIijhmg5ir1JZ+vfZpZVI2O27YrWb8C7i91R06JIS1ODLFkekk7A6/NRAhwFHDlmDC+vW0eoKmLUeJDWcHY7w0BWXq7fLyLEkdcbe9zcvdHR8Z6hVj7+WD0rYr1MWzHypcTDiNucMCFOAUMcXXNN3JvsHrfVajmqytA7kfmBqktlkzvW0HW9cqUKfQsHZO/JcqRvn18XR9dfr4KdteOOg/79464np3B1uGotEKC80GeLwwVs+9ua5wigmXOXGqBt+fK46wiX9/tx2pNvhHuuWfWJdSEjJRpAO9aToqcGMTa/Y0fo3DnuqhVpaeGfuX+XMn483GHNLGuxHGm+CHGki9MocRQMqgvHehyMk9iaGRvQ9B3aoql5kft3xbccZWWZxz3gDVqrCvfPv+ZSJfKCvqC9fRbiWY726FaLdTFW5FYrLYXbbydtrX4ORPasTHBEHCUI77//PoFAgI0bK2/+rm8cd5yK51OcCZwB3IRhKg4Rw+ysX7l+rQJxdNJJ6jvyDQ3wAJ9pGl31/79t3Mgta9fyUFUaXkviyPoiGQigoiQt6/P5YsccVel5vm2b7a9RX0WbcumlapioYDB+uVBIWfIr6pW7Ry9kMKga9M47sGxZ3PVUSHUtRzU11kVVxdVeWoi7dIFbD5/J1YxTEyItR5EHzHgQu8xrxuGA0lAKFeFNymTkSPU75Auyc5ufZ+7fbS9kMTlEutVynLql6fffSccUvp98Av/8E7Eyvx9HhFvN2E1R55A+I14P/LCg0yoefgyw+e7OPq2Myy+H556zzLcos7Kd+v6NeEOJ6VZLT4fPPzfr0eeFt1GvN7hzNwAtmpk7Lqms4pgjly4iQ151IYe3sUULNb3MsBypHWHsj4ULzWs/0nJk3E736FaLsUO1isSRXlGb9T8ygJmmCTnyHB07VpmZMde7YkXde99EHCUITqcTl8sVHoC2IWHcT+bMsY72nQN8CVxDVL7ZGDFHfiy230hxZLxGxhBHANnAt8DtAwYwTB9DbRQwGZTpP5IffjAzzEHNuGBiYGxGWBwZr8t63EEsy5GDUOU1WigELVvaBtUyXoYrW4fN23DllfDkk4Bp6KnIWLPHdVTCrRYIoALV4700+HzVsxzVlDiq6h28BgLAW3stvSaNnWw8uCK3R38QJ7nM9TqdUBpMJpLSUhg8WK8uKZM3xvrRcKAVFZOEn8dfbqxijQwqcKuluvQT7aOPGMCs8PSLLzatMxW51YzToqBADcAaZg/7r8LDWVio0huEG2mKozRMM2n4vLXGHBknfEQgf1y32p9/msvq08KuUP2YuENqI5s3Ni8Uj7dIiSuLW+3BB82qDHEU9Kllwtbdxo0BaBTcpZrpt1iO8vLo3RveflsVjbQcRQZkq+ZquLfrbz7jxsEzz8ROu1GRW03fJ+nlO7mad8x8SJE3hptuUvcWfX+XlkLXrtQ5Io6EWscQR05n7De+u+6Cq69Wv7uxzJ7fRr9yA5oralqYyGFH9B46VloDz112GW/cdx9D0tNJBg4GOPVUe12BgMr4etxx5rQ93JD/+gs++0z9Xriw8jnOoixHxnp0c7vPZ7+PNFs5lxCuyj/PrV3cMdcVMalCbOLogw/ghRcA841+r571lQjIDgaBRx8ND9sQxd5ajvZSrHz1ZSXFUbXMfnaM/KNBzXJuG9uhP3BzNwVZc/nD8Mortulup10cGeMaGuTnqxQ233yjL+bJxIN6WGll5SThtzcCwO2moECd75GWozSXaXXJwm7+MXZBWBz5/bjiiKPSUjjzTMuMKD+bnVhu6DDLl5sxUNu22cRRKmXh8mFrldWtVlhkm6YFI8RRZKyR5cLRylWbIy1HBs6Q2Wi3rxQaNbKJo8cft5TV3WonT7ubp7iHQw9VusK4951T/pFqhi6e3KuWQ79+ttUa4igy5sjqVhvI9/Q9/wCuuAJCN92s8mXFEEKOcksweST6vnCHvASw3LMthW2JPK+/HlC72OOp+05tIo4ShCeeeII2bdpwtvXNZr9gO8HgHSxZMhDQ+J1ugMVQoF+5NkEQGUEYaTkyfhsxPMZV5nCA281bKSnMA44ASE+nHLgO3bkXyxYf64FmmXbrrXDhher3f/8LX35ZweZaiBJHxkaOHQvvvBPlVsvIVcGjwaBy9UfFgEQSQ0UZm1dZy1HU7tD3pfEA2SvDS2UtR5b1RlEHbrUZM8zmvPdeFS1He7HDLr20gvr0J9977wQ5ePyjKtjYMt1qOXI4osXRGWcoq5FHj93xejLDgihU7gsLJdasMTNauN38618qmaCR58jIZZTqNAVAJnaLQ1QqoEAgKs+RcVpEaSFddPQILkSLMcKZ9byOOh2s51DLlvD77+G/aZSG2xPWRJaVa0XFtsZrwRB38zQsXmTfqBjiKFSifjtC+ryIi8qIDwIVaO1NSueaKyp2qwHcwkuA7pHSG98+sAZatKDlM3fSkTW4tm8NW3yM/WG41YxrOFZXfkOEffihRjCgz4iXsBV49131vWYNHH64sWGqrUlBLz4sQ6VYDpKlwyObpi5j6VJTHNU1Io4ShGXLlrFp0yYWLVq058INiGOP9fDKK6+wdOkPoHe4/+UXwoPTGldu+AK1TAtjuKNcEdYlfTiRcH9yTYOkJFxlZfTQi2ktW3JP9+68CXwHsd9MY92l/H7Vg6d5c6ZNN2+6VUmhFFccPfUUXHNNlFtNC5lBoAMGwEEH7WEFMR7Exj27Wm41CN9Ba1scafqDLFx/ReKoKm61/Hx1B6+OOAoG4YEHmDePcABx3GEkIqkBy5GBTRREuNWMXky0U723wuLIaW5nLMuRYe3MRiluzeEMiyOb5Wj3bpYu1Rd65RWa/Gm6nydPVlmwT2A2aU5TAOyN5SjqctT9SD1C5n3Suj/iiaOLL4ay71QCyVhhMumYE8PiyGo5KrJbTbRgiKe5l7RnH1MTIs8nS28GrVQXR8HojfLiUfFB06eznC4k4acolE7eZl/MU9MqjoJYNlAvnKUVqMhtoCvLcezeFV6fsT8My5FBLMtRASpfWCN2h89x77bd0Q0ytlFfdtkySwx+WByV28MiLAfJ6rnMz/MzdiwE1qwTcSSY7G+91Xr2VKNHzJuXzbnnnqtPVa8fthuj0wkeD8VJjc2F47nVrCtyudSyK1aYCSB1cWR94jtcLjTdvH4V8PO8edH1x3qg+XzKhx7RxdmIMa4MVnG0bRsEyu2KJdJyZDXl//abShJZITFcR1bL0Zo1KhY6chENB+k7N9jKR1LrliP9WNa45ejyy5Xbtjq91XbvhieeIOALhcWCk0ouXxN5lYyqrOIownLk9ukPZf3hHCqPbTkK9wyNaJ4hjhxayCaOPIY4KigI95QC6FMaPa7WbAbQJ2Bmso4UR7EsR5G91fZkOYqHcViTKbcJtC8/KSX1QZUhMjMj+phZrVvGA9twhwGECovMDhOY16LxwhIVcxRDHDm95VEbVe5IRfMFYP58urASN0FCKel48FlDBcNYzzebONLXmxUqCA+11J/ZNJ05MUocGZYjgI6sCf+2hlYa62nJVjO+alP8eAE95CmcdbuszNwn7qDXJo6K8mO/mXnw4fdD634d6OpcGbPMvkTEUYKwP+U5AhWbYwzsPHToUH3qeMAbvr9s3apP9nrxuiyvOpGvFYY4iswt4nSq/rzGqLehkBJH1n3tcnH7CSfQGNgKnDF0KLnJEcGq8cRRjA2rStJna0D2p5/CxE/sD4goy5FFHFXqGRvDOmIVRw88oHrRWzHW12ibGtukupajSo0qEAqFd9aGDRHzqmM5qow4Mp441bEc6Tsv6AuGhYOLSi5fg0H9Iett2zhg+gPQ49ctIPoBWrM8OubI5lbLzYXy8vAlkaH3LHNiEUdWhVJaSrDEFB3lwdjWumZppjjYkzjylUb0Vtu2jRN+eca6WSa62Seexc6oexE9meXtE7MNYSuYBas4KihQu1XzWi7kwiJbqo2QIY6MZlTgVjPEUXhjrOLImaZyErVubZvmwRdz3GXr+WacB9fxOnykYo0yLZajETxPzswJFVqO1nAIXV1/chgreWyt8okGAqY4asE2M21BBeLIuJcZYZ9bt8L2raqtB//wOtdi+s8Kd8UWR0n4w6dzlnsPeUT2ASKOEoSGbDnaEwMHDqRlywOBXcBX4QerNZOzzQ0U+RA0rszI/EiR+7Jdu+h4JZeLDs2asQI4HNiZn881fr/91lsFcVRdtxrA7p2WB2dKSjgge9481QQtVLPiKJ63ECCkvxHHe1E3OvPFa4eRQeAdhvIZ58cuFAqFG3TpZRFWqkjLEcD06Spq2Ip1bLXI4IlYGHdv67655x5ivqZHYmQf9pviKByLsyeMnV3TbjVjO/SdZ3Sb927Vey2VR7vVHA4oRX86HnAADB8e3mUpqHocWsjctnK7Qgk/7IHyYOyhfKz7JYtCvuJ00nTXlfWYLlgAY/4XsLvV3n2Xc+arAXOjxJF+c4h3hI26u7KCbiGVHqK01C6OYh0zqzjq1Quuuw5CZZaVFxfb85Dp43NokRbBGOLI+O3wRVuOfM5U5Qq11F3qVJajnTujty+W5cgqPDK1Qvsg3RCO6YplOQJI8+ZzEZ8wOF+NDOz3myLsEFaHz9mxD8cXR5EpQrZsgR3bzGOabdn/joAq/PDD9joMyxHEHyFqXyLiKEHY3yxHVlwuFxdeeKX+791wh5K44igehoVIVWrvGldWBoMG2UzjQLgLXUtgApCSnMy3Ph9TrWXiiaMYXe/2aDn65x+KilQZ42Zl3Ctdjmhx5PerfELffw9E9pDZE3sjjvwhW/lInn8+qmobxlvvUN7lfCbFLhQK2R4UxcVqfWvXmgLAqH/degecdpo1UZbZ4EjLUUUnS2Rq41BIRdHvKaMlmJYjbyAsjpKpuPdUmBq0HMWMOdL3oxE7U7xhl61927bae6vZXDJ//x0WR6noLiCr5SjiJHAVmP5cWy8kC86Q+YaQTQGnM5UDUBe0cY4VFkLfvspioFn3i+XEjBJHRlBwnNtlrEOfnm66C2HPliNQsdo2y1GEONLiWI6250a71UxxFG05CjndShxZtr8slBJXdEfGHOWwzXYsXYSiksmCOg+slqPUVDNurmBrmc0SZxVHb/DvsHBtw8a4Yxf6ytX+MIZv2bED8nJjn+sOvXfeo4/ap3vwmT3oPHVvJBBxlCA0ZMtRZTZpyJAh+q+pvPGGerLm55vz9/hM0TTVBdYgUhwZQdu6ydmWE0n/3QW45Xxl5VhgrbuKbrW4liNNg44dOThrG8OGRVuO3JYbn5acTCAQkVQ2MreKUXbqN/Cvf0WvL4Y4sgZkx9qscChOIGQrb7BNFz16Qt64hpBYo01EYbEcOdBYvlzdtK2B5qkfvgnAy2MrEXNkHI+K4lIiLUfGwarMgLVlZvbhfW05sr47VWQ5yqCYnTShMeriMcSR9aFaXq7aXe7QrwlLt3FDHFljjiItR558M7FotDhSFbmD5n5phjoZmqBElbHLV6xQzU/Cb4/diiGO8mnE24M/q7DHFKgXqliCPpZbLWTZj5HiKCkpwnJUUmwztxjiiAhxdMbgGJaj8oiYI0sDQy63cqtZVN223R6b6G5OXvhtw3ocW7GVbbS0C12wpw83JuENn/pGbzDj3N2+we7CCgRiu4vP4CvmNjojajqojN0rVsDtt6v/ZWWmWy2Sv1cFYj4TPPgI+NR+FcuRsF9QGXF05JGHAWcBtwDqRmH1dFQ5OXUstxqYIsl4GkSIqIcvvZTzGzXibusysZRZ1B1Y1deqcFV8caSblNwEWL8+hjhyWDYyWbXT2G6nM0ZuFeC77+Cj09+HadOi16cXHHxaMPyWVmnLURxxZLxh+v3qBhZPtO4xOzFQXhbCX6QKOgnx/vvWFalj12T0g4BFEEQeU6s4ihvFi+miiBRHugIfcmNa1DJR6DujRd7vTOEsIIblaMIEePHF6GWrYjlatgweeyzm4hDbcrTgR9NylO9sGn5guvVrySo+xoxRD6K85DZqgsXUabjVCJniyMi6bOAoMQPK/CTxBsPUYLWYD1V3yKyzhT4obFPUQTAO0//+R7iNxnL3328RHlisqgQpS25ksRxp4a/ILv2xdv/3mMlezWB6U3HGFEfl5jY4iorMewemizsyINvY39YucQ7DcuSvwHJkucFt2Oqxie4VdAlnRYzVASC8Tp1dpdHZz5Pxhm95xrVrrCOToijLkZMQIYddHuygGdPXd4qqG9QLQ0mJEnL/4hsWPTuTTetjn+vLl5rtXeMy60vCH05r4BZxJBh88MEH5ObmsjTcT7Zh8PnnsW9WkahwkS+A0cABgD2Pj/FM+de/VNJFGwcfHF1hpOXIwHi4GqZna7nsbDICAT7r1k1FZERkw7VRVmZ7qCThh2XL+HDRYXa32q+/mg0uN4VAdrYpjgwBYrUchTzq7c94kDidpnXxoI4OBqrEA+TmRt8cw+g7be3fQb74wtaEPVqOjNQJ8USO36+eFcZxGTwYVq0ylzduwBWxeWOI335VK3ARtAdx68fJUaoeMhWKo8iBgSPU6apV0KyZ/sc41kZDdRPXij8qIVr0ndFt7RccpQ9jHGU5GjYMhg+PXrYqlqP//hceesg2KbBqTdgNEstyVFZgiqNQcipFqLgT49z4npPDAsZod3mSHptieVgbliMtYFrHQpYAbJKScJZZHvxoDOOtsPAx1hdLHEVajsJV4g+LoyefhIB+7rnddnFU7G4UZTmyut4NbBEKMfrt7ynmSN9MrEE/juIiu0VGF3Dr19nSSof3g60rqW4xcsaIOcLlihJH22lOazYB0IF/aMbO8Hkay6LTDvv4fnkF0ZYjDz7bO0RSkinsrS5HY76LoL2HMLCbRvYu+RYC5QFCIZjI+XzDYJ5ZdBKffhz7msrborY1O0ujY9C8mSfhZ8a3al5SUt17UEQcJQjp6em0bNmSxo0b77lwPeLssyuRjycOVnFk3DumTTOz+AJw1FFw443RC7vd8QdgAtONYk3bnZ2tYgv0uKTSbdu44447GKP3BLFRWmoTR6mUEcxXpnvbzf+YY1SsDIQfrh58NGpkiqNwmh5LzJGWZBdHDgc4LFaHzqiurs2axb5hzpwJTz2hprsIhtdRWcuRIY7iWY4CAbs4+uYblXy4VStTN6ZEv8CG2b1bicTy3aY4sj/31M3ReBDHSvgXbnCk5Sgi6KugAFqSC//3f+FjXVKkL1PBQydc1wLdyarvDJ/D7C0Zthzdfz8sWRI/GFxv4ysvBtXBjFL4FmIcmOSuh4QDb237Qt9Wox1ZzmJ8zlQKUe5jq3A2xAmoczC3KCNcR2RAthY0A7Jt7qWUlLBgBauLymlbX1LIXKaR/vCNtBwl4cONHzcBe6CxniogI8PUEW4C6mFdVGTbx598ErWr7CE3F1wQNb8yMUdJSeBY+0/4v6O02G450sXRunV2y9EUY0Bty1uFw3CrGZYjyzzN5VbLWsTRbPpzIjMBmMZptnY5CeF1262cB2AGSpeRQsAffQ4mW3oBl5erTTGOrzWXEZhutUJ3E1sd+TSOL4686h5jPcfKSmJfU7vyVEM85fZejB79XABITtr7jgt7i4gjIcHQgB+At+K61UIhYLXKFk1mZmz3mctlWh8c8OabEfMNcWSJOaJRI3Xz1R9OE8eP57nnnmPkq6/yd2T9EZajvvxE8OtvgRgB2Ub9elLKZLw2cRQuZrMcpYRXY2yD1d1g3MycztiWoyefhLffjBZH1rHVKrQc7SEg2+9XL9JWL1FpqdIa27er9cQIfQjTuLG60QfLzJiYoiJoy3pcBMK3aofhvohhOfL7IVjuC++X7VtiiyNNg/OZqIY+0cXRM0/pDd++Pbz+mAwfDkcfjXVn+DXzARG2Qjz5pPJXxRNH+nYsW6Lv9IULVRzJgQfCu++aIrS8XClbUEmoLDs48gFubZMhjjIdxZQ7UsKWI6vbz7AKGe02BBQrV5IWLLKVCQVMt9rG1ZaTIDUVzWLiM8SUUTYcqB4qjTrBjQencY49yGPcw9M2yxEQjjuxiiMXQYpd2Wp/WFIP7N4dLZxtRrc//iAyejtKHDmdtrHVQBdH69eaRUqKbSe09VoEKnSXunxl+ncsy5HuVrO8Ua2kMx1Q6460crkIUpoc/wW6zJFG0G9vSygtnecYQWqesjCVlqrwKaPueJajAldT2/SKLEdBbyDqfhLPou0tCXAMP5PnaxQ1rxcLAUh2733Hhb1FxFGC8MQTT3DIIYdw1VVX1XVT6pi5wEDc7tvYscO8aK33nmCQ2K40KxFutZ9+ipifkaHmR7jVKC4Or+yKO+7gxBNPpMzr5cHI+ktLbTe5cQzF86walPW67y5Wab4NPB6bO8CDL2b8r/VmEvSkcDi/h81nXi98Ptm8yRtv2oFA7JuQ12s+8F0Eo4ZGqG7MkYYDTVO7KFIcGUJqx449W46MbUgO2S1H62nPXTwTJXhjWY5KikK4CBHSu1VvWhvbrQaWgUX1Yx1+GOsxR2HLxezZ0KOHueDPPwNwyy2YliNiWI5AnUt7EEfhY3X55Wo9W7bAhx+SlgYrV6LGUzDyIFx/Pf/M3Rz+azyYbEJO3+kplBPESYZWTLnDdKt5LEIgO8kUAEn4w2UABm79IFwP2MXRlnWWbUxNxWFxVbVpVBzerg204TLUwLQpodIodWxYjgxxlE0BnVhlizlS89U+tFuOgqzZ4CGYnIpWUBh2MVtfoJL0h721C/yuTSVRcWFRcWJud9S0pCRwrVrBIKbyNYNxlES41fTjGba4VCCODIHvDETHHOFyoQXslqMS0sPna6SQcxEk5PZwegslIvx6QPxy4GtgMg5KImLEnKUlnMWXdJkxGoA7l1xGx1/Gh8WRdds1TcPn09jCbD7yF/IlMA6YjxJH8XonBn3BqFC/eC8cIX+AVhZrl8FqDuYglLUu2VXVINOaR8RRgvDzzz+zZs0a5lWmS3EDRQ3a3I+cnM4EAsUsXXpP+CYYp6dvfCLcalHPrJYt1c3O6lYzLEfhUbThv//9LwATUYObbAX1/h7hVluC+UA9Zv0n8MEH5jqTk81ecihx5PFEb4e1K3/IlcTvdOO0laMBlU3cYckFM5rbaeHIs3W7tRIpjqpqOaooINvQHla3Gpji6Ntv1Xhz8cSRMa6wk1D4YWx1qxldvq3Eshw59FgOLRBC08ClxbccxRVH5eb62bZN+QaXLlXrOeWUsPXj5Ze1cFlfyHxA2N7sKyGO0gOWt3Q9YKb8bxVf8s8/2B6SoUCQc0/MZ9Ag9d8QR7agXIvlyEsKqVoJ5VqKaRWykJNmihoPPps42oRKQmhYjv5eY4qjcJA2QGqqLeYo26kOWhJ+2rCJc5mslgmVRiVrjXar+WnPOltvtUbkE7RYjh5/nPC4a9/NdrPdm0Vot+mOKSgwe8y9w9Wsxv7SlE4JKZSHRQRAP+bad4zbHWWhaVm8Bmf+TmZwChtoS6iwmHIt2nIUSxyVOiPShegcuOlX9cOqItxubN1SUTmokvHhImBr1394WO0nh5MCr7q4HqcJbVE52s4Ahmk7OWPph1wGfAC80vMCHgQGAbf8/B6vvPIKx2//iIwpo9nBfxiOKcAKgbSkbKZOTWIhz/NE+QrOAq4GXsS0HK0CPsRujwt6A1FW5niWIzeBmCLLKgrFciSEach5jiqLSj3v4Mwzn8XhcJCb+xrff/89YH/eVWpXWdxqUcv88Qe8/bZ6ekdajv75x3ajOuywngw+5hh8QG+gI+otKtJyFOtiN3L9aEn2h4QRHBlthjZvCO6Nyqy+U1Mm9MLC6J4qHZ1r8fsrbzkaM8Y0SkSKo1271Ka//LL6HwzGD8gOD9EQYTkyhNTnn6vvWOIoGFTDlkB8caRu1nu2HGUfoB5Cf/wW5MADwR2KbTkKBCwupTjiqLPzLyWYrf7b774L/0ylzEwCGTRPpqpajtL9u6NmbftHiY38fPhjpemKcmohmrCLgt2qzqe4l2Zsj2s5KiMFj+ajjFR20IxImiSboqZJhs8moIyYIeN4+MpDnMCc6G1MScHlNevJsogjgJP5nnJgWWA7xQ77NWG41XJzVY4dD76wOHITJI0S8mlCsExd7EbskHF+B3Gxy9GUYJ5pGjIsR+9yFRfyKQdHOMCT8ZFCuW0sudHcbiujxbAc5eSvwt/pCIK48eEhtLuQ7+eZdXRYEBHsZLkQgo7Y1hUDf0m0OAqPiQd4+ZYRQBLPU2wRRw/zqLqWXS4Kfaot09AwxujOAbJwkR8o5yPgSuDmRZ/xOPAt8GPpLm6++WYOBU76exk7mcCLwCZ+J4VyJgHlwSLQz68DSac9cDLQHzXemp8kdgJXANkoUdYUePLN41ixYrFtOzMoppBM8oFfganAMGAtC0nCz24akQ/hu9oOUknVk5gmu8VyJOg05DxHlcUIUejWbTCnn34tABMnTgTsD+lK5dGLcKvZnlldu6qgl+Rke8xReroaOXP5cjj9dAAeehDGjxzJUag3pVLgr4MOghtugI0bKQZyMYddsBIObnbbxVEyXg5cN49/f9jf3mTLQ89VqNw97nJV75E/v8YwIgKnnM64OUnKy82HimE5eu45PQQDfXgEyz5p2hQ6djRjszSL5WjFCvj6azXdsBy5XOq+/tdf0L69mpeUn0db1ocHMb13+4i4+wR0txpmDz6rONIiroNu/GavyNL4zRtD5ObGtxx5vdHiKCwodYF7uGtlzGWNZH6N2B0WR66A+XCzvtlvzHWFM4vz+eewahXvvqtXoYujtIA9vgMIW3B274ZNudFxOh7N6FVUyPW8ZjveWplpOSojNVxfHjlR65mQd2L4d5JmWo5W0Dm8P6xJIO/kf+QCW9nFD6i+pOUeDx6vea5nYRdHJUA/YGhoHcssImEHUMJmHuMBerGAIG6OYx4Hstm0FugCJVSq52zKANAYiHpBCuIiT2tOKG9H+Hor3B0iGS/5NLa5EK1EiqMo3G6asYPOrAhPCpX7wj1Gy0glk2L8TtNydMivH9rrsNyU4rmeDJyBcr4HXgUcbtOtlgf8HwDn8jxQzt30poj7gR+BJ4HVXMfosl2sLV3EYJrxB7tpBpwEHE4Sk+nFBfTkMpRYatGsI0OA0/VzA2ATsMHvo3VODjOAELP5gGd4Q6/H4TiaQzmNlziRp/s+zwzgBmA9RcxgDCfo9RSh3Hm7gJ0F63jggRN4hq0EgduADK7hUMppgoNjgNOBt4AMUnETYKHnWB5t3px2QDfgFH7mNZ7iRyDZJZajveaVV16hQ4cOpKSk0LNnT+bOnVth+dmzZ9OzZ09SUlI46KCDePXVV/dRSytGLEemRklKghNOUHlkfvjhB6ZMgUmWJMs2i0s8MbkntxpA796qu5dR7m/9rbOwEG67DYDSXeVkp6UxH5gJ/Prrr9zcR43ZtGXmTNqgEg88z4qopL1GktygK1Ic+ei6/BMO2jTHvv1WcVSsHqIp5er70tk30IF1tvIOp8PmVrv1VnNeLLdaUZHZs9lqOXr2WWutuhvT0lvt4ovhDEvuN59Pj8lwqZCc9XpP4r5/vs1dPBPuXt27eCaR+HzKCnQxH9OCvPCDPzUpaElpEIg6rsOMIRKM6RarnVN3N7o1P148UQLH57O41fTlw/tar6eNpm9EZNdvfeOyKTBTMQTM+q3iaPIXTrOn0LnnwnXXMXQoTHtjQ3hnZ8SwHBmiJj8ffMFocZSmmW3qwRJ74L5FHBkCYI7nZLbTnIpwBU1xVEJ6WBwdixqexUmAsahz+3KWMRA4B2i/ZAm/FqwK15OhFenlS3kPOA70kFpodNZtnMgUxgB9gPNZzq88QQvX1bwJLGUVb6DRFGVKNPalMTxJRobqpv4Ng9W24mQHzXDMncN/uQeA4t0BIMAKSuOm42yeUU7AXYE4SkoiBS8rULmEbuYljsr7Fk1/qTGGWnGmRvcwGMS3cOWVEeLIDFrWgHXAK8CNQF+gBcoacyMwrmgjjmCAopJSOgMv6MtdDLRDiconUYLzfqCYxTxRuoNiLuYbdlCCnx2oLizz8FNKGmfRmQ+BbcADI5YwDriJASzGwTF654LGDidfPv44JwM/A7+jLOI/AJr2K6nM4iV+55M0c7vy2M0WlobPPg/KatQNaJLZDq+3hHI0gsAY4DO2sg0/oOEEGrk8nEsarchRL0AuN008Hjbp6wcopJB+wNhfbsdX2UEqa4l6LY4mTJjAbbfdxv3338+SJUvo168fgwYNYkPUKJaKtWvXMnjwYPr168eSJUu47777GD58eNg6kQjsz5YjY2gsjweOPLIvAH/99Rdnn50f3VttT1TkVgNGj4bkaVPU2FKGOBqhWzqCQfB4KCVVWW5CIZKBAUDv3r1x6kHSb65YwW69vi/J4w5gu2Ud/pXqpp/84w+2dQ92T+fYhcZtUONcJgEaLot7zKE3ONlr7+5qJcXhtQVkv/SSOS+WOCosNAWbVRzddZe5nBFDYcQclZdHJyVeu1YdK5fLbsXTvH5SKQvX63BGnMs+H/4Nyqz0MZeq7dN0K4HlJpyEP37XfeOYWnpMhRMPaurhEOlW83oriDkyxFFofVS9AJSVUehubLMcuYOmMLO6YwK4Yw6IetJtR/DeM8qfGcutZrhL8/PBH7KLo8bkk6qZgdQhnFGWo2BSss06stB5dEzLkVqXfk4ETHFUShqF/IWHMxjKErx4cBMKP+KdQBMgC9jm9zNq+3yWACOAZd71DAfWcTxDgGWoB+Y3pHDLuFXM4t/cBmFn15fA18HlXAdcCtwF+NnEbMDFBs4Czpv3JeOB+fPPJY8hLAu33sEOmrFx1ve8gXJz/7X6InoBb/AGRwMFwABm8gvwGcpB1CilHK/TtJxEornslp6XuJVz8l4nlGRajgAcqXEE1gcf2MWRxWI2FegA3IyyFP0MRiYkzgJ6ZzaHQIBn589nF3AI8M477/M/sGUvaqp/nMaYeDQGbuE2x8k8CzwCTKAVQTJt7ve/1iYxgJkM4V2OcHuY/8UXLAHWdOhI93//G4CPUcflE+BWlOhZhpfvWc/G7UvCdR2e3ZtDuZi5KAv6ZhqzA3XM/++Mrxky5H1uoBEacB8qzqmJfhaFgN1BH2tpzWbWUMZGgq4krm/VipOAI4AOqWYMXHZqNp7IAcb3MfVaHD333HNce+21DBs2jM6dOzN69GjatGnD2LFjY5Z/9dVXadu2LaNHj6Zz584MGzaMa665hmftr851gliO7JajJk2a4fF0pmfPAWDJnQEWcfT22+YgX7Eqq8By9NlnFgODUa5bNxg4UP12uykmA1dZcZQa0/RAh4eAggceYHhGBwCeB7oDGwB8Ppq9as9ybNDRYeZPUb7+88mmIKZ7LM0f7YYxSMZriznyeFT+QIgWRyUl6v4dSxxZMW6sRsBpWVl4gPcwxxxjWo5sdfj9NrEQJfRHjKDpEQfYJhkxLhkplnirGJYjS6Xq22LhMdqcpPkpIZ1nn/QxZoy5SGUsR5mavpGR4gjI87RR4sgI3g7FthyN4HmSrPFf+rrSKWHrX+o4xhJHxvErLgZfwH5LbsIuUkPmtgZwR4uj5DRchMJZsbeGcviYS1hOl6h1qbHXQnwd8LKIH3gP+A+r+Y778PE1GTh5gZvx4KMl8DnwFwexE5jucNMiKYmCkI++wEvAIQR5ESggGH6Y7ALOo5xZvANsJZMsHuFgHgUOBpIsvf2KgF6oF48t9OVL4OeCPC4HNmz4nDLm0B3CNtPtNOWa5cv4N8pCVVD6RdjhuhVIBWZyEjuAC1EPaI9zG2twEu81YwPYx1LUCSUZlqNktgE/FPxui/obDRyNsux8PHcuVwEnAlcFd4bLnUAGHVqPoB2NuAN4DngZ1Sf3C6BbVlMIBhnauQvrgb+AoUOv4EBUXM9JwD+ol64dQGfu546sFsBKHI4XOcJ1EHeg7kWHk0opabbzY/vuJLYdNoAhdzSnPLkRjqJiugNN/lkdLnMgKpj7QpTl6h/gdqA3B9C5k5knquexKZTzNMfr+9lncVVqAejW7QraoZEM9OV0pgKT6UNTxmM4dJfyF58xnn+YTciZRE5qKmegLEdry9RbmANo0aJurUZQj8WRz+dj0aJFnHrqqbbpp556KvONIcMj+Omnn6LKn3baaSxcuBB/nDEfvF4vhYWFtk9tIDFHdnHkckHbtstYtGgmKgzaJPxAvvpq6N49dmV7cKtt2mT+3pFvGWfN6FWmi6OO2+ZHWSK0ErMLV1ZWFldnHMKzNKYjsAW4Cgi+/jotvn0v5gMqxWle+EbumkyKosTRVlqQGoh9vpUCOwOz2LZtQ/jh6vOpAeYhWhwZCTX/U3QHKZSxcmXF4siaIdtqOTIsI/cW3c+YZf3tliN/wN4tOPJc/vPPqPWFxVGq3XK0R2JZjlDiaP4cPy+8ABs3mmPbhuvUNzpSHKVo+jG1utX0gYw3e9rTjB0E9eOeFPSyi8bMcQ5Q27vafNDY0DQchHATDA8ImxqIzlVktEXToKisYrdaEJf9PCkrJ+BR1oQsCikH8svnUIqbN7gOUG/2HwGPAynk4eA/nA0s5SOGALP0btUeunIlGUzjV9Io5QzgbOA2cmkB9NECbNOvBS9wRMqBdNK83ICyKnVBPVA0sGRUeogi8niapTwIrAbOSruA41BXdUsMsQoQ4gygG2Z+nXTUw1JlEBvD07zIfP2kOwdoxL8ZjovmdKU5yvpRBhwPZAIzgKl5/+IU3yoaoYTAJyhLVhHKwtFpy2YeQVk3rr56PKehLGULigqBObzHo7QERq/6hCF6u34H7kaNwXg/cOlXX/E+MAv4TivDyBwSwklS2v943nESz6JEx03Ar9zODYzl6+ItfFR2F9fPmMZ/gDzLsX8ZmI6yPBlXUmuO4r5mrYEWpKdD0Gm68EI4KSE9HFwPUFzmIiVF3Q79SakEiioYd1DnQJSIu50TOLDdYLP+pGQ20Db83xrHFfIFuOMO82Vhiz7KQWPycXAyPwAPHHEjR3I4TYCH+ZWgKwncbq4C/gu8dMwpvME5BIGv7rtnj+2sbSqOHEtgduzYQTAYpEWLFrbpLVq0YOvWrTGX2bp1a8zygUCAHTt20KpVq6hlRo0axSOPPFJzDY/De++9R15eHjNnzuSss1S8zYcffsh9993H+vXrOfzww7nlllu44YYbALjuuuvw+/2MGzcOgLfffpunn36aVatWcfDBB/PAAw8wdOhQAK688krS0tJ47bXXABWn9dprr7Fs2TLatGnDM888wyWXXALARRddRKtWrRijv3o///zzfPTRR/z666/k5OTw2muvce655wJw9tln06lTp3B396eeeoqvv/6auXPnkpWVxQcffMB5551HIBDgtNNO4+ijj+Yxfcyo//znP8ybN48ZM2aQnJzMp59+yvDhlwHFfPttfzyeU9my5X5979wL/AF8BYDfP4Wrr76anTt30rdvXy644ALuuOMOAEaMGMGGDRv47Hflxf6kvBy4nVmzNvPwwz249tprueWWW/RQkpuYMKGAl559lsbA2G0lPPHbn2wCOr/8Mv8ime9+vYLvbm7NNXpLnjnuLNLSGtOT/0NjDCvffZf0olKeROMb1BvuKmAK8A6QzxY+RAUiLkHdeC5ylOsjc4GHN5gFFHAld21aw1sod8BPgEaQu0MFwEmcBJyAill4GXUj/y0wll8efoyHUUZ2uATYwZlnplFaegqlJHEWsJJ7CJamAr8zi+dIYTFPPDGT1o3P4QA2soVLUOGS9xIgxI/AvK0zcTo/ZeFCCAQmA9dzFnA4ZcDfzAg8z4+7yyjc8j0qJP0TnstbwUOuQyF4M7CR84tW8izKwsBZZ/HvTZsoRXUxBpU/5XFCrAFW5I0CPqczEGIWJQE/2cAbetlX9Xr+8Ptpd+utPHnuuVyuz+vOVmA6Q8inFC9ZbGbXrkdp23YhAwa04pJLXuIVfuU14Ny1a+kIjOVbvgYunbWbDcBM8mgBvFtSwjn6/h2cnU2P7du5o2Qp+axhwfp2/AyM3z2VZ8ggx30PId95jDn0UE5CWT+M/IP3A4u35AJncQ5wLsUMAf7aPJnfUaLDGL/vQgp5GwdPTD2Zn0p2cjVqhMFcIJV5hHwDw+dLFzYrgaH/f2JNCU8XlVAIdGSr6jRQ+i+gCZ/RlDcBaxRVFk/gYB0uiOrj6GM5/wcks4BTaMuDKOvMtxap09Tl4oJgkEI6sYZyrivbxIuo2KQFQE992y8F1tGFnbQFFlDOU5wFPAWUhXbRBGgP/I/mXE0eTuBXzuU8JjOJnaQBfVMa8WL5blqDnmfstvC2nAk8Cozkdb7AwXaWsx3VQ2soyhKSAhRjdjnXUC8vFxPNIqA5sOudy8PTrl30I3AixRZ70QbgMpT77iKUwHsT2A0cBDQDFuLkLkJMBkZRzl9/ncV7rj84KajaB7CdAn7mL/hL5dH6Tu9y9hlw6fXXMwZlOQK4AGiLEizLeJB/gn5gFD7fT7wT/JvrgEEpmWwvL+Fg/uFb7uAjxwQcWoitW39n167pTJo0m79Kd/FufhmXocTtKagYsf/o63kQ1atsGkoYnIeLSV9ew++omCetLA84m3ROYho/MIsy9OQEdNztA4ZxLds5Hjh9cBFnTYUS/qGEn/gAmLrpR9aoYXQZTjG/Fc/i13VurkdZ0vxb/qY3zfgMCMyZw6X6IOB1hlZP2bx5swZo8+fPt01//PHHtU6dOsVc5pBDDtGefPJJ27Qff/xRA7Tc3NyYy5SXl2sFBQXhz8aNGzVAKygoqJkNEcLk5moaaNrnn2va779rWvv26j9s0OBT/bem3X57JSo77TRNO/lkTdPUMpdeap9t1KVpmvbTPZM1DbR/X7BTG8OtmgZa8byl2u90VYUuuyy8AGja5ZdrWl/mqWmffKLNP+B8rYRUTQPtE9AWGJWD9hUDtJDlvwbawvR+4d8nM10LgdaaK7SuyU20hZZyVzgP01D39PDnINCW6vOP4sqo+YB2990jNdC0k5kerutdrtR0+6R2DD9poGn3d/xIm8Yp1qZpKZSG/7yaPFzr1cuyr0DbSWMNNG0TB2gaaBdcYM5/r9nt2vwMo75PNEBbCFoItKOOKtHOS2ukfagXLgXtBtD+0P/feei9GrQOb0Nbh0O7EbRhoF0JWl64gSnqoH3/fXjFczlOA00rIl2by3HaFbwXPndeeUXTXn5Z02aj7/PBgzUNtLcZajsmxaSp3x07mtMfeUTT5szRXmlyvzaKe7Sy08/TNNC+yLxM+5BLtVNc32t5NLPVY/0Eju2npVOkaaB9hlp2drsrbWXKXanaatQ6Hz/zZ+2ZHh/a5n/HSdrVrb4J/5/AhdpdPK0Vg/YsaDfSRBtOK7U+nNrGGOcDoKWCdpkrQ7uWN7SJnKstdTg1J8naSaDdwnHamIsv1np27qwBWgfaaS9yvqaBtgu0p/FoE0D7hQxt1wGdNQ20rxmkTTrwlrjbroE2mK/Cf134w9NHZz8U/v03HcK/c2kR/u0FrZ87JWI7XNoJ6VdoP4G2xrKekaRpjTlaewC0dhHbfS5o4/Tz5yfQHKBl6d+A5omxr46I+N+aE7TeEdOagHa2Xn9BxHb/4Toi/LskRV0v87NPsxxTNHCE62rnPEy75dAjtdNA+xLsN6iIzxW8p+Uf1kcDdX98Of0uTQNtwf+9r73Lldoo7tFA03b2OEnTQOvdW9MGDNC0hx7StE053TXfYYdXeMysn3Fcpd10k9mW9y75WgNN68DfmgbaYrqr886VpF3d9RcNNK2ATFX+uus0DbS1tNM8lKt70NEvaaBpT3KvVkqKNrHZvzVt0CBNA+19Ltd23ThSm87Javnx4/f+gRKDgoICrbLP73rrVmvWrBkulyvKSpSXlxdlHTJo2bJlzPJut5umTZvGXCY5OZmsrCzbR6gdIt1qynr+F+r97ArQzf8xBl2PpjK91Qz0cnk7XeHcLwHc4UBM2yCSqAy8Ow3Tf7du+Bwe0vS36wtRMRQGY9nAYcATKFP+W1xDssM+KOf3wCY+YLl3FxejgkjnA+NDq4gkBcKp+67ocSwez785nwzGoNwMAM899wyw0uZ+aRQOHbcMPRLwReVIchJiC+rNcoj3tagkkAY/UMp6rHGo85levJCQpxj17q7ez6eiXAKLF7/NpNLdPIRyCV6MsgbpHejp0rQNcBdnAY1IYoOmMRb1Vp4HGP2ECsv1A2lxq4VjjvTeOh7UWGGj+T9alK1j8IuDOMFI/Bc5erpOuKu/NebI7YZ+/cjTmtOc7eGA7GzfdoK4KAh6+ZVSNqOCZ6eg3vwNQiFw8KPuEsrnb2BbeR6lmLldyp1pZOnRMMGQQ3XrttCEXRTkmrYfF0GchLgMuBMYyy5eIJdy4AEeZ94VjxOJGygHzkltzq28yHlMpqsnhb48zXdALzoy/IQTWPj22yyiC0MYQis0cmnJLM7hdkK6lcSDSx8fzIMvKng8EmsmcSMeCrAN1VEaDjCGlvoAtap++Cq9KZ+hgpnV2TSDS/tdQB/sjvaraUxHXuAxYC3q2nkcZWF9FhiCsgr1ATajrD4+lHV2PfA0zTkF1V39f/+bwXCUZap/i3bAhxzGffwKXNfjSdrp69yFihn6E/N61FDH//HgViajAq9fa3cQcDW/aJvD7T0Kw03WgivbHsrQpv/mtIJefIuyeAGMv/c3W+JKg1TKcLjVvkxLU8liAZweN36SzJ51+hlWrA8J53bDzrwgSX/+EVVnPEI4sT4WNT21geG28+pXZSgpmfwdQZqll4VTOxgk4Q+fBxvWqnO7hHRSKSfgTAr3wnmGu3Ame8xzo1L5WmqXeiuOPB4PPXv2ZMaMGbbpM2bM4Nhjj425TN++faPKT58+nV69epG0pyHEhVrH6K1mF0eHoPqleIGngejEhJqmD8xqDXHZQ281G7oqC+KigGwAQk5XeKBPIgR1QQHhrtKhgw7GbxmI1EoI+IWt/AU8AHQC/scMJvo3h039jdjNy5ZlPkHFXyQBITS6kwr8zAuk8AUqr4gxjm+7pho+32u8SAbDgcmooPDx4z8HOtvEkRJzaq3hwWNLvBHxPRptD3qCA4FDgXWooQRUf5b/4gd2owGvcgO7OQTYvn2RXu8YPiify/G7fgLeBTQGuhthdoS7muY4+RsVR/IlSugdqM/NTAUYzhfAO/TkCFy0Q7ln3oFwusIr8fPxxx/HjDlK0mOOPPjw+eD/eIFOi8bT/s9vzU0MBm3LGDiNI2KNOdLPi61BQxypE+947wy+YiWLuJAzKKU1ykV0NnA5KnAW4J+yckq4nEHABczkYOCibdNIR/XOKQNKHek0YRdvAn9tmkEwYFf+TdhFGqVoKLfiY8zhaUYyBfWAvRI4gRwWAMvpSs419+uteIhzuZ98lBgtBHpn5IQfPlqShwJOwoGelycQAK8XjRRCqLHGykkhm4JwkHkIZzjHkwdfVNoB0uwDonrDkhasST3TNfPYWcVRJEkhP+fr2/0xACfS+rCMqHLZGckEPRnhtfRFufZuwrxW/tdYiUYjcMKNEkstgQvIYDowFjjm6IEMQwnd1/peAlxGud7GJs1OY7XTw0bUHakXyk1obFkRKk/Rx2znPJSLbcSqRcA47itawWqg5JJraQwM5i1gK8/17E+jdAc7ttnFelHmAbbYIYPXuT4sjtLTTXHkSnZTSBbKYWmez7t3W3LdRiSR5c472fqSkvNn8GXUui66xMUDDwCvvw6Y4sgQusbxDSSnE9iez8KQZdgd/d6blWomdN21Xe94gDpWQYs4CuDGYRVHAfv+qAvqrTgCFV/y5ptv8vbbb7Ny5Upuv/12NmzYEI7LGTlypG2sshtuuIH169czYsQIVq5cydtvv81bb73FnXfeWVebIFiwWo6cTiN21gHhkc3GABOjLEdG0kEbVbEc6Xe3IKblKORw8zvdeOOgJ6NW8NNPcEDXJrhdGkWlLtsbshUnMIYTeROVYTYIrGQj//Fu4k5U75pVfMvngAMHk1oN4Ch92d7AlWnH8RotuZtZ3IoZp2Rw3nc34cFLptN8oN8GdO+uEli6COpJ/GADu3DpnYMNcVS008cSdqA6CR8IHM2f/4wCVGyFA4euJUYB9+ABOrIbuJFSVBxCcnIPYCOwNNyGLl26APN5KaOTJWQznfvc5sMyFfWW3deYm2yKlRwc/EAW64DxmA+0nai3/uuvv54NRnIlDGtKECcaZY50kvAbQ6bh8UX0PguYiTFjYhFdY193MWjQH3xe8jyvsyBsOfoH+IRFBCkJd3d3owTPaZhdsNNwkkxPS/o9y2r0fVCqqd5FtwEfLnuAF1Y+y2xUdF0BsJHteNhFEBVcvCwsvVTg/9HAXPL4EfVGrhInvgs8QmM60AgltDOAZm3TyXLr4sjt4Q+OwEHIFEc+H16Sw+LIS7IShTpBXLj8SiDGFEc33WT76yWZXr3g8MPtxawB5iXEHmoDwBmM7rFkZEW30rhVMn53/K76PmcyLze6nx84kYmcF70ei2hICpk3l5KAuq4NC/IOR3NCOGmNis1ZALZr8qbul1IAHE9jWurT0tMzufPOOxnX8SA6AunHqJ3h1wOWSXKTlRqIsmQGnJ64HRMcbnVfa90asprqlqPkJEYyite43rZNO3YoceRwxMik37gxWpZ6GSwgm5Ilf9lmp2e51Agwp5wCRFuOjIDs7cefxwWhCbQri7Z0Z6aY6zSuOeOYhxzusDgK4rKLI7Ec7R0XX3wxo0eP5tFHH6V79+7MmTOHqVOn0q6dMn7m5ubach516NCBqVOnMmvWLLp3785jjz3GCy+8wPl1HfglAKY4is6hcxoqZRrAMNatUy6S4mJ1T//H7BlviqA9dOW3YuQUsoojYwiAXa4cc8wNC8uXq6HY8vOJK44AVtCHw1AupNlAH7oDKrjyP8B3qNTTpzU6noPd9rfo07L6kEOIW3iJSNYldwKgCytIC9kFgKEbNvIN3VHi4j9MIUgHzgIKW98KFOLWfGQQQjkJtmCk77sX1eW63OEgGHSgQjetNGMkHqYA5eVOVLjoKu5ynsILaa1YvHgx0NeWq2gBvRgeKGM0KrfNclQIuEGaRRwl4ccRa7gQlLWlsLCQIW+8EX6kOTHHAfMlKbeaYV10795hq2PzxtiWI1A9kD4HXgMmAC/+8wnfftudrcGFpKGBPpbbSiCbNHK4nzKUhdAL/IayOPTU62viTKIlr1ECvEgLZgLzmvTiJ2C4XqY4lEY5cB7gJpVcVjEA5dZpBPSjDBe/40b1OEvBRSqpHIySQLei7HYOlBUmPV1tHdhdWQCOjHQyXfoQKOEhbRwMuca0HPnwEMJJOiWUkxLORJ2fcyhBXDh1t1oy3mhxFIEPD9nZMGCAfXplLUeuWOLowGjLER4P5aH412CJK4tQCAbyAxcwkaUdzrXNzw3Lb/AETJdfiU/VaWS83hZqjuaI/cgMJqfS9ey3KQQeoxv/oHrYffXVSp555hkuysxUD1t9TJ0S0nE4wOl2kZ6sxNFcjocLL1TrdCSZ1swIDMvRoYfC0OtMt5qXlHACSmM8Or9frbKsLIY4ysjAmazK+/DgOPQQ5eoysN6QgZCeFDPSrVbadyBHYR86JIylp2+kOLJajoK4cKZ4TPe2WI72nptuuol169bh9XpZtGgRJ5xwQnjeuHHjmDVrlq18//79Wbx4MV6vl7Vr14atTELdY1yLxoge5vXhQFmNjgZ2M29ef7799luuuQYmTLCLo3D39BhuNa9XTYrqwm5YjjSnGXOkqcbscMZOpgdKHO3eDT4t+sasoXqtzT3wa45HjUH0K5BEGs+4MzgT02pyF3DdET3QgvY3Rb87BTeBmAkRy9wq0uFY5uMAgkmmVWbrVsjMtOeTMTb0S+CPTUto1GgMzTK8dCIJ1aH5DpV/ZcBgRmFEejl1gfossI1pwBIygW08gIt07APTHqm14HJ3MsmLFultNtvdi0U4UW6H/6K6J1tJ9djzHMXaZjfwBh7SkpKYtXYto/XpLoIk4SeEg3JSbW/c5Zt3hn8HcbLubzPmaMKBt4cFloaKTTkXFXtyCbCSBUCQZo5+XEFjHPrGngWM4irSuQaXvpWxbqShkLKwOFA9jgYArbQgfYA79DKFwTRSURmTD+AwkmyuKGX1OU1PG5gEpOOmjDI9pzQ0IpVeHMud6AOWJpvXUaRbRsvMIsWncjpYx/tLTjfFkZdkklOc9GIR3VkWjodaf/DJ6u1eF4jJDh/eQIQ4ingD8ZLM88/b3lGAqliO7NdD8+bQpG20OHJ4PBSWxxdHZe5MW9NCmnlunckUBvFN+H+S3xRHBeXqWBgvP0X+lJiuLgCXtwx/yI0TJaZSUa90BxygO44z9cgkXRwVk0FWFjiS3CS7g7gJ8Ob/t3feYVJU2f9+q3NPYJjAMGQGEYmigqiIigkDsmKOrOmLYU3gursqZlYxrK5rQnHVDbo/cVUM6xow4SqC6IIgIuoayEFF0sTurt8ft8Kt6jAzpBnG8z7PPDNTXVV963b1vZ8659xz+D94WtVs05fo+wlYlqNgEGVmR7nVPPto1rBYTHmL0x4I8vIccVRPmEAAkmEt0aU+IIMqt4Qrum3LkTGgPwPwxTLZHa6JIyefVxa3WiAWoaKNWI4EIQ37OxgIqB9v9vgwapHpLwCTo456hkWLlNnYzuEDmvAJhUgZAR58UP1rmu5+/ocS282kW45qk+pLuzKlBfdrYquyUpVny2Y5qkEJoneXz7LOrUTQf5jJbYlNPAnsCRxc1JGzgfNnP8ojG/7Hmyhz/Tqg2oxnzRZdnFTujr58Rm2sjVPqAKBmU4J4HCoZykqURWQ2fcjnj0SAjiXtKCs7hXZFdRSTAm6hTZs/8CtgmFFt9QkYRkDrq3JGAN0IgpalWY//Cpr1hFK18KFa4FvgJrxtkFg46UyiQZJZM2T3IMjd1oD7G1SW5g9YT5h66gnzSmIem3DdoKsXKWGRBD4nTJj5zAWq2cB1P/yLKdZ+BjAhEGAgyk42COhGP2AWu4buZleAGlcJJgn6YmrSKfzkPdpbQca2BSaS9Ea4q+zUqiDnEuZSj11HDUYYeTzMAexhpTkMA/ewB/3pzfGowOO7OJV9OZUQSmhEo27snt9ylOzSnYBpJfnU7hcjHFKTkeVWM4LutGC3u96IkCTIfVwKQDxYx8aq3JajbrtGGTAgXRzFNXGUy3IUSHjF0Zo1UNo1g5iKRlXZmCz4xVFSW4e0jM6sp63zf+qHdc7fS1ercy6iLx1ZzowZ6YJTx7ak6bXVnGt/9lmVD0sTRzffDAXtYuQFqtMq1fs/O52AZTnSxZEtcmwMsxHiaMMGjIhmOTIgpYsju/HWDbXf8MwB2XndvKVqHut2I1x+ufqniZajiC1OxXIkCC5+y5EeW/Tii6AcDf8AXgEe4fPPVYJCPUejbjnauCnAxRerf03TFVv+hxJ7GjYxHHFUXacas7xesxxp4ujjj13LkT0wj+FvbLDWrsRRuWrK8ko5P3I6+1rHdaQrU1ErXHYHHuuyJ3OBH+o2c/+mrzkMZR8rBW5ZPZnV1KYJhSQB5pSrdS2l/EBdtA2pkDs4HjjlDBWEaWUt7g+UUUM+p7IKmDPpYYqLe1FW6AZkO/3mG5SyPcDZT4G65ShEQpXWsD6Qdu0an9A0SNKpwp4LE4PzgaNR7qw/AnUkCFPPYoL8K/UeU3gY5bhUAc0vA32A/tSyD5vYCzidt/my5ktPiYbjCtswD5V47yPgKC4G9qEqFSNGDT+tcpVgkuyxZjp3WiHpBVaGnkiymo2olU6gJopnrb93pQ99GMw/UXlzHs3vQpJe7IJrGu1GmDGM4o9GV/az2qGXAYlG3TnNP5Enu7trvEy9NEMoBNOmwW23qWsKuhNz1ErqV0+YJEF+zd0sNzrxefF+1PlXq/ksR2eepybPNMtRqnFutUzEyzKIo0gk52dRH4h5rMUJTXjs0tPbuDaffeD8/fUy95wrrRihRDL7PZ1IGqQwPCLH6cqyMujZ0+NWu+wyCJe1pU1yXZo4yhUGEAini6M0y1EGcZTmVguHlTDGtRxF2xe5r/ssR332iHLKKeniqKAoSIAk74+ZzKdzqhk5+wZV3Bs8Znr7/Z2Yo4Av5igWcQcUsRwJgos9iBqG360G3ew1tOQDRwIGiQQsXPgOmze7pvCY/eATDGIEvW4128qRzXKUSOCKo3r1pf12s/ZUpImjaFSzHJmqeOLnzOBOapxF83cBD41+kN7RnsxAJSI4ifEcqr13tGY9pwOHttMXJys3z/LEcv5HnSOOpqOSDF5EiF9u+JCKWCEP8jaLwzESWn2odt995Igjp1+oIUw9xUBeMEV+PpQU1DniyBmMtUHJxMi6lD+IW17EJkSCcKLGVav+2mo5CJC04mXs82ceHA2rNx5HZWTOAx7iezZxCSOpIUWKOuqAP/JH4FOW8xVxvkQNdm3BceQECDJKO7eZ71Vn9tO7LY70JegGZoOWI4AOrKQWeBd4E7iheg3DgBNQ90MVeZyFStR4FscxkpOxCzYEwlo6CYsQCYIkedRUaUlTBDwFZHW3mt3+lHX/pLr3cM6ju9UIhWDuXJg/n1qijttGxxZHAD1D3/G3g/+S04oCcMY56j384qh3zSfO340WR7aYi8fht7/1vGTEojnFUcoIei1HKbdBsXx1TXt3XU3tzbfTbc1Hzms/bk4/Z65rXrsWzECQeq3wbNBvALJcU/P/Z91rxcUU1qeLo1GjSOPHQpWdOmCNa0VFOOIoFPOKI8NwLzijOPrkE7joIsyQ13IU6FDh7uOLOSIaVdalDOLIJMD3J1xI/8ExsmTScb7TtlstFfS61YIxrb9FHAlCOrZbTSeacR4yefXVi/j3v6/ybK2pAUIhj3vANN26YmmWI2sOr6/HWcpvi6PlP2kzttaoaBQ6dIDvvqvn05olVAIf8Si/px577WMACMViGKEAEdSyYf8gHqnZgAH8oc9h3FmwCx+hLCIvAYcXnM7AcIqitqqBv6A9E4FHqOOnn+awumYj/2ENQ77/kjdS7kX9+KNqny4wotQ6g2PeB2/SLzmf4jx3mzN5+JSjv0i9v7Cq7lYLkSCoWY6y1kfLQND0Wo4yrdRJAQk2YwLlqIDuKmARtVTxDMswCTgTzDKuAM7gR8ZRze9Q1ph1KFFyLV05qeJG9KQfyTJtYsAVF9VmlA6s8pRGCZByPksTtaT7cKAjl6GH7y9kOYNQqxUPAyanqpmPWj32DUocBLDjiTZ7PrNAOOjEdfxgLdEOkiRI0mmb7t7zxxzZ+zhxPUWuVcDULI36DK671f7OmXxJT97mYOoJu6uU6oN06RbIKmAdopktRzqNFkd2mgDDgNtv97wUjEc45bTsbijTMLJajuIF6u8fguVE27XxqH2/+D366MziKIXBt+GePPYYmEaABCFHkKaJI+uL1q6bdT1t25JX91OaOOrTJ/067jjrMwACKfXdOPRQslqO/G410/Q9cHTtCqGQRxwFAkBFBnFk/7YEqn1f2fd/OKZKlJSVpbdZZ561GGWdlc8/EQh70qgEdHEkbjVBSCcQSB9U7AdH73y7kJ9++pxZs+5H1btWfPstVkB2I8WRNeHX18NGCqklQlV9mDx/gXftzZPJOmbMOI6JE+M8+OMjJIAAYY4kgJ6Gz4iEMbSL8Yuj0mUqniROipOC+QxCufmOAS77xUh6mEmnun0e3TkBOJEyhg37O48eMoY9rMnzXwk3QOtT6ggEVnmeFGPUuAVqH53MPXMPpGfXDJYj7YINM5Vm3o9Rw9B93YFXtxyFqSeQSjr+0KbUCQyYSQry3TxMtsVk7R6H8Rq78zDKDdkTHIkyARXUPpFSCjmW/YwIA7iZW+gJXMRgcOwudaN+7STrWwZUU8unq5ayAXgDFf81fsN6z3TvDzzV60oFSFHaYQaHoBIMXm2dZyX3Osu4NwPnk2Khds7jMbgZVUpmKFClBSTHUbEnc4oOU/0QCTnvfTl/4kheIUSCAClnkjYJUN5e/Z0gTETzijmJAK0ndU9cimU5+vBDXMsAMGT/CCecrM53HRPpz6ccE3qVOiKeOJgOHTK4aWysUkS2OEoTCBq6lQUgFQxxVP67rHcyW1nk5RBR0Shduma/10yMrDFH0TzVONO02qv58vXrDQTUGJRJHF1WPpUe9WoZe8oIkiDkHJsmDO1J3+6U4mJiNetUsk+KyEV9UN0LgUiYjz+GAQNwY47aqM/4mmus9mriKB6Hhx+GzeOvc09mW5zibkC2YaA+WP2itX39lqPDjgg511Je7j3Uj4HJPzkZwM3FZOBzq2liVCxHgpBO+/bpA6ptOYrF9K396dLll6hn9zGofDvWU1cw6InV0cWR/6HETLlutQRhduVLNiXjxGKeh23PSJdKpdh110pMU32J48DhPMILBNHtD0YkzPPtL3D+z+aKCSbrCCerqdMmi2NOjEMi4WiyftzBM8AFDKRfvzM5qEdfXqaIj3sfzN3FKjZqEXAOK/jii4NJaHXI44EajhuprRypqyY/XJceczR3LgDraOsWRMVgMHMAiFHL2GU3uNejxYU5k6WdK6iJbrWiAqsv4/ATbbnt5DncsN8ojuFzLkQt/z8enNxJBaEI7wNnUkAXbmFarAMER3E69QS4jzkoy9LtXMSB594MKAk9DLiL1SxkCu1QFp95wBfLCjxhsP5kd99oa+wS1LFq9cm8Ddjr4YYA0I62MSV48oG3gbEoK9HXwLOYXIe7Wi9QoCb9ekLEqSZIkrUF6tVg1BVHGylkA20IkiQWdi1HF18a4LG/uq22XdLgZqK2BbkdfAtuzFEwiEcc9R8UZbfeAatNYeqIkgxGqNPcaqAWX+niqIaoq7BtIZPBcrQ2ryurI52d//3fh1XDTmRW+ID0gOR99iErkYh+CQ7XoT5zDK84SmixUpG49j7RqDKFlpVhVlTwmVY0OhRSL2cSR6GCGKa1PUnQ44JME4b+Sb+4mFjVOrrzLd/SPfs16icrKmIvOyGalb0/1bMXoOIgwWs5GjAAysuh693jmGoJFLvDisu9bjVPSmzdYmSazhOq3QfhvLCz3yefQA/Xa5uTGuuRJS+1yeNWI665kMVyJAheTFNZdu0B1Y5D8f8PKg6pe/e7ycsrB75CxSIl1AARCnkGskTCMkPjHZ9SKZjxjiuOAJbSlepq9cAUiTzNAOBQ4J+pFFhV5GOxGBMn3k3btg9QbHRgEhCiNC0LbSAapiq/HR93GQ1kz4kUTNUTSVZ7Kl3bg0XAEmC22X0dxSqmKBamLRsozW9L0BpkIkCUANXVn/Nv/sSpqGzXkVSKtxZc77aurg5qayku9LnVLNZR7Blgb+Am5+8uNd5kcTZ+ceSXRosrj0g7Zj0q58/496dBStlYDAPqSfC3969k8uTLSVBHT+BW4E79YENlQ4pSy97MIWik+D7cge58R1JfMUQeJSV5rKSCMlT5ht6ECDg1xJWba//ECNUXwBTgAx4DLqeKD1gHrMYNpogSpLz8EvYBHqKSSZ0uYjYQZCl71Ux19utpnas7riBKaT0TLVE39CYKHHFk55MJRly32mbyue7GECESFMRccUQwCIMHs6TfUc45/eLI6QfdbRHOLI70iG77fgsEoC7lFUf5+a7rsxeL2b9Is4/Zy0ItVe+3ntg5xNi4Md1aErKWlGvvteLsa+Cpp8hKNJpRHP3eSh5rEvC41b4PuY8vaeKotlbFK773HvMZ6LzUoYOlETKsorRdc6DanSDELUzgAX6VLo6GDoVjj3X/b9uWgnVLKGI9y5188Rm46y73b/2JzRIsgYj7WamLVhf89NPePFOO6y7sChvQxqWLL1ZJ3AoLM5r8DEP7bOxODwQcUeYhlz8VyEtu9LZLtw6K5UgQMmN/L2Mx9TRkjwcFBe537phjoLa2lBNPfBe1+Pkz4G2Ki9UJUppb7f333XPr37sffoD/vKf+1l1o1dVQV/c31q49hU+Bt4CT6+pQRQHUAfE4mOavOMa8jctRg0bIF4dhRMJEo67wuvSKLNm0M4kj+8lbE0dz2h7Oo5ynlmzHw+RRTbRt3BmodgGm0JFQqA1rWcJU4EvUUva16xc5Vc1rgLqaGoz6eor5MS3304+UECRJkRVefoyVrBKgNpjZxZFmOfLNI88d7E1meR0qQPo+4MUli2jXbpZ1WIrb+J5Fy2cQjcY4klEsRrmunGmhpMQ5TwdW8RfOoaxqKZuipfipR7lIa6e+QE9UYPtMCjiAafwZVdrlf8AznMn4vIf5DpgI/I93gHtJciRlwPW8wSEoQRcgRWXlBF4GhhGnZxsVcJEiTCpLHM0KK9lgIuw+IR93htp3EwXkUaViT4Lqcw+FDOd+qCKPSDxIiAR5UU0cBQJQWspLF/3bOadfHNlu48ZYjnS/XD1hKipg0CDoukuYFEHsyky6OPqSXnxlagsKfvrJc936HGkYmjgqKEgXG+GwdwIGUsWlrl89E5FI2jy+oscw91p9lqN3D76BmjPPAyAa13IG2eIokcCvtmbNyu5WK4+41xsIKXH0e67jEh5I1xft28Pzz7v/FxYSrq9mBR2dBI4Z0QWVXt/zvPOgutqzmAXcxLYnneQNRThmtPUePuHiuDfLyqBvXzW4ZfGHOn1gC6xs7vMTToD99vNs0nfNS25UAzCWNUksR4LQMPqXfd0693tTUOB+Z8eMUQHDBQW7YZemhOeV+ywYxNSSvem5kPTvnR4zo29fv76K9et/DcA5qDSJFYaBiiQZz+rVq4nF1Hnrg6pxekBlieVsCURVHIhdT3TYIRkG+bvuIpisI5Ks9roZIhEIBAikEs75x/d7nTdDRzqWI4DCdt6BbDeiDB/+PpcAv0UFdz8dCnFu34lO3M2fw2Eq//1vTqut5RpKKUk+5YkgWUcxIZL8ZAVP6tQGMk/+ThD1RvVEaNS5PreUEfAIv7+AE5tVCAwp68jYcyqs60wyk2oioThPPPEKh7CbZ6CqHnKQM6imtSEMffjMs62esBrrtaBVJRzyOA8lhMpRrrw/J87mS1SG6i4MRRUFUcHg37Cauag6bwFSFBTkUWpdd9zqEpNA2oRvFxC1A1ITEbf/yndVck8V41SWIzsBoBE0PJajcDxEkKRXHNkBrZomd6yslhTOJI6wAnE94ui22+A3v3FmsHrCfPUVTJ8O+x4YYfA+Qce9rYsj8M1lOcQRhuGKIx27koG1sy5CPLEomfC51e7kSl767X+c/00M3ngD3n5beaHufTgKlcoHZFuOAgFccVRf7078FhUVmcVRXxbyWT+V1fqjjyCvIOCJo2rAeOI8AOWMN5o8Gbp3d4XFgAHua4YBsRilpTB6tHZMWqZbRVEv31KyiDcLuEM8nrHxXbpofRDKEUwGymw1c6Znky5SY6nNaokfsHw5YjkShMagJ4TUyc/35iZLJGyLz1EoU8UPVFWBGQxRn8x8e/vFkR6QbbNpU4Dy8uspLh7OFOAWYHlBAaoQyEDWrl3rxj9ZX2r9adceIANRr+Uo47K7zp0JJjNYjkIhSKXIW7/KOX8q5cY/GFaAbV5pPO1Jt127/tyHKtV7DHBSXh559VYjCgp4NpFgRU0NT5kmvwHWmKfRB5Wb6Q3coMklwEuEmAa8jyot8tTm+bwBvI4BzgRpupYjSxyFP57t9nk4j7pUiHWomJ9zrO2XolaRPX/IiRx+0EEA1JDgRPL49QnPcPTRwylCU7aoKuAA746bxhWxBz2vhcPwOX2o1vrRthzZ+WFA5e/xx7XUEaGqLsjhwIPAYH6NKgqygv8AR3EkdVzHRpQ4sj//MPXkxbInpvnJSjK4wBJanrIQ3bsDXrdaMmgvgfeKo0iecqvFGxBHNvPZHXDvb7tIqWp0yD3cvjmvuEKZaK0ZrJ4w+flWnJ9VDdo24BQUeGOOEgncmW+99/PyW45SgQziqFMnq29SaZYjIx5L318ng1tN1zamEWCPPZR7qbjY+u7Yle3baO6oHJYjyCyOFtGXwhL1ZqWlQFBZjmwhkysY3Tkp6YHpHi680DlRG9bDaael7RIOq1RVjvjIlijJzYmiKCvj+Xu+Jc3Mm8VydPPN8Nbbqg+MTL7MRnIY03mi7ySV2RPo2BFXHMXjcMcdWQXejkLEkdAisb+XfottQQEMGWKtlvGIoyOB7wmHn2LjRrhy0ib+NT3zAKEHEW/Y4E4eKmD7RWA9q1bF6NfvUoYPf9t9pgoEgQuAP9O/f39ncrSDat3l1e7TYzAcIBqFpD2PWIPhpdzLOTzG6r4HQzhMqHYTAUxq/eJII0GIZFJtjsWgorNlYcjP8wxkJgYhwzdbxmJuEdaePXnGNJnYrRuXoUpmhGjDV8ADwNPAM1a2nReBX5DgeJSouR14esN7HA4cgYlad6UsJntZf9uTowkM4kp+AdSHY9SlQhSBU4XpUlQuqABQ+tY/nerf7Qy4jAK6d+5JPA5tncxRCrtUyiF3HsUnQy9i8wVXOK/ZE/FPWtZj23LkzwWTSRzpk5/7egeGAf04hCpudixHts4NU0887t5rfsuRbRWwc2iFEpq50pqsbHEUIuGIo1CYjG61eCS3OLLnxRcYzVdfmk4cXH6xZrWMaCvJ7HwNtqKwTuaZsH3iyG858oizXJYjVN0wp612X1k7mbE4lZXelBEZLUefuLmS/JYjE+/SfTPDNGeXJgmUFrtv7xNHmVbMZnKrxeMqIXTnzkAwSF5hiCOPVK81KI6sm6hNURbXlHcFChtpkzNFhqOJsgkLS4zrVLXrlr5fFnEUiSgdmySQZl1rCm9yGBvyKhzLkfOeANddp0RTg2a37YuII6FF4vjOfeNAfj68/rrKxO8VRzGgxHn4eIbnucopI7oStKdcXRwNGQILCdAb+PFHAzgW6M/ixavo2NEbAL5uvffrEolYq4MK1Je6oEiN0DXEnInFtvLYbjV7dqkij79wDu9c/xaEw4SrN5AgSCqgDUgZxFEqpcYs52ke1KDi29cu4PjsLr+FW26BWIywLY4qKykFri0q4k/Ac8AR/ImHUWUsKoEXUDEOpcBuxNiFCAYqM/WRQJdAgPPjbbHXaG1CZX3+DXDpp58yAtgN+C9/4FMgGc2nPhUkgMpxvgi4F5zpN/L9Svjd7wAImmq5ejw/gGHA3VzBJNxcVqmQO1l+8QXkl7sfkn2/rNPcgfX2EveId7D3iyOVTNHI+rq9JB7SLUdtChoWRykCVPI1yw46w32xs1q5VWUot9q+zCJlJfSMRAymTlPXaluOgiTp3K6WwlJLpVgTyJFHgmV48xgNIhFXaNjLtgHvMnt/MitrYk3p12+5eDO51QoLrcs4/HA1c06bZqe0B7wWWdO0qrFnoar/EF59FTq1cYN1/QIBgN1391yLfx73iMUMYiKwXpUJibXRYq9iMY84KvZ5lLMFZBsG3HOP9RUMBhn7qzBD1NeiYXFk7ZBR7nzzDbz1VgMn8OJoomziaMQIp36bTUatlSPmKBhU97KdXbshliyBk09O3x4IAP/v/6n7BVzLUSymCuk1M1tuFxOEHYD/4aGoyB0rveJIobTHWn5kHZvYhCojeiFQwB57/B8rVuzN2rVHokJ7TwLyWcwTvncdw8qV7dlnHzcUAtIHRsvdT6iN+lLX1Lt5ceyJJRQ0iUahPmEda80ua2nnXl8kQrh6A7VGjHvyJvDYsMfg1VcziiPTdAWXLo4M30AWTVaxmTz+1u92TrgG+MtfiNRa4qhHD5yAKYvO1HA+cL71/7VW+08DOsUOoKRmBf1Y6PbAH/7AurseY8pytUx4+hq1zN6PQYizSJAygtSl1PWM9u1TS8QpUwF2bTUljgBmsy8fMoSruQ1QlqPx493PwBPIaaGLo4vHhSkpgXXRdMvRL/krx/ICJ/CcVwxYr9vGhKmczL84xnktQMp52zD1yi1g4b9PbCtWigDfUkkobgmba65xxHJ1oIDdkmoV4FfGCc6xoQLXchTNU5ajNqzjzj+XKJOf9bn37w/vvJPWDdbpLbUUbpo48hCPQzTqWI7y8lxx9L//Wfdx6dGwbFnaoXZ8PihxlNSyuet9tfisW9n1yhMIFKJMuvY+kYZjjvTb37Sy57tkEDSWOLI/Q8dyVFWF7bsuLla1GxcscN7GYzl659i74QWfuAgGIRTi4otV7HWjPU+ZBEr37hktPblwPrpDDvElabMIhVSUtv7W2cRRFsuNLY4aaznq0sUds889VwnXv/7VOr2eosE+XzNbjGxaRisEIQv6F/edd+CPf3T/t8WRPhCq/dsxlis5hj3BedrfxLx597BmzS8577zpQBDYFzzC6HjgeeBWli0z6NjRG7Cd6akxFoNwkRJHVbVqhNZLPoRCalD1u9W+p8xtbzhMqGoDNcR5rc1J7kVmEEexmOtWy2Y5MjGI1G/2Zh+OxYjVb+Q99off/16dQJuALuYBz3vpk8AXeXuqJHG+C7dddxUVsArogIr8+jXKPfcG0IvXuQGV8sguzOm02UJfIg9qdV5AE0fDhyvXyDhUv6RCUe65x7pWE/cJt1Mnx2qii6MevSwXp89ylCDE3/llWokOmyRBJ2v3qUxlGV3cNmputYhR71ldbd8nE6yQc1sI231aXGa14xhXbNUE061ftvo2w2Gq6sKO5Siwfh2OWaOBiUS3HHm+TLnEUaYAphEj4LHHHHEUibg5itq186bH8bNRMwKlUllijoDd/nI1gcL02mlZxdFzz6nf0Wh6/Tbt1tdXrdoYP63z7OeII83FaHdx//7qdyjk/V58NmK8e6xNIAChEO3awUUXNSFJfBMSpubCsRo+/LCVDbdhMt5CDViOpnEcqcLcSSt1bNH26KPwl7+ov9MuudGBWjsGEUdCi0b/Ah10kHcQDoVg9Wp3jNQJkMcoBqMKRswAfk/HjvtSXn4HK1bYK9vOQK3nGo+qfPUsWO6kTZuUu0AvjzGZi9LeJxaDSFvLcpRUg7492X5LN+o7dsvoVrPFUSCAcqtVracKlXjSLU/vHST+dH+IZ5/VLEd6vSnfypFIoorN5Lv9F42yx66biJfmqUb7xNEAPvVdmTpwE/k80nWi4yZ8gF85F573/Xd0ZikVFXARsAL4N/AH4FfA8F+MZjEHW2czqbX65/PjroY//EGdZ/Ro/sHp3j4NK3GUV6D64e23Ybfd4GVGAvDVMneyrK7GvUmWLXPmdT3mKFv9KdttZlee95MkmMkoBShxZGvT/Ei9J0bmA/bjeJ7lQauvlqFcZ51RVpX8NumrDWpDriiIJa0J2hJHRl4e4TBE81VAdkPiKJtbzYP1KB8MQlqSmkziyAo2sQVhKKSydv/zmv+m7+sjzXKkudUaU7w3Fc0SkH3ccXYyMs848X9jDUaM0PbLIDyMdQ2Io2CQv/0NXnjBPSaRcMXRBTzkfARplqOtiMXJRWP005bEMDfVrRYIwGk8ldnd2ch2nXUWXHBB5n1bijgSt5rQosn1YJzJZG1/0adxHMWsQ93iBwIHct55E5g2zVkgAXRChRhnJhbzWo5usDLuLl3q3SdWrGZQvdxEt25Q+d23vF+ixly7OGhGcRSJEEjUU0VcWcH05XgafQcEnezhHstRQUHayhHbcuQMfLEYAyo3gx13Eos5k+D/6EEl33hXUVl8F+hBpMAtHeFMZtEowZoqltKVsyoyB75722RSb2UmDsQi7jVOm8Znxt88x8VCCWpJEstzP/xUyu3fD+a6g7JHHOHO67rlyKk/lSXmKJs4ShHIOv4HSLklberrPYokRZBpHE/cyjNkxxzls5nZs4F/eoOpwSuO4nZyPMNQwT2F6t6x3WqBdT+6eZ4yTCT6RBSJwGscwDEd/+vNRR3TLEe33orjp4Scy6jtjzQYhJ8oZlVFeqoHP7o48luO/h+nEe/VhYczHRgOQ309bcpyuNWsJ4WAZj0uLYFVPjdbGuefz2MzetDDL46SSae44267KVFuU1/viqMpXMBx2cTRFqzi2kaGI049NaNns+nvfeihKt9RBuxbLlVS1uj30FMzgWs9yoi41QShYXINGrnGoK/ZhY8Z7NkWDjdt3IpGVTzN93h9Bp3d6gfE4xArUSOs/YQeyItz003ue0ajcBn38udLP3FmLtuyYVuOQFmcliwhvRq2RShsOJs94qioyGM5MjEI11dRRZ4bUB6JWKW5Q27DLWqIeYKNdUzDIC8vvZSGnpKgojzL46oWsGkYUJewEu7Fwp4BMG0Zc1JZjsIx95p0caTngrJLwmiHApnFkW05SliWC/t8hWh+Hx8FmbuFACm30GZBQcal07aQrCdMG9YzPnS/CtS1r123HIXdN9qvr2XRMwzo3RveeAOAWEGICHUYG9Y32nIUDsPJPM28p7/07GPo4igWU4EhNjnEkf/WbEyuPo84Mg267eLeFwnCfFRwcOYDu6padkXtc1goLL+1XyPqFt+MAT2nn855PJYecwRZLRd1dV63Wtu26pADDtB22kJx1BgaI6B22UWlRdrq815wge/CXOzuqRp0gDLdN4I771SJtxtkxAgV2N8CEHEktGi2RBydcUbm7eFwwxbbf/7T/TsaVT7yQ0o+Yc7UrzPuH4tBQYmaBCPUMZpp9PvwcSfmMRhUumQDRfzUdXfo3p05f3zPWV7sF0fuxvQLDAVNZ7MnILuoKG3liO1W62RXJAgG1Yxhn1MzidQSzVodPYDpEUeO5Ug7/roL1mQ61NMmA5NEUn2YwXjE88GmJaCzxVHUHZ5MM7M48mNbTXrvm91yVB/yWvr8ZTZ09PulshL+/Gf194hDU1x+OfD11zB/fsZj7RImCUJspA2JUMx7Uu3kqXBULY8GikOWWMvPV/1kmS+U5SiJGQ67/qAGLEeqUGgwLfGikasorHWCK65If8nf9Ewxv350cVRSAqX33QS3Z7fYOtiCLVNuMBtLHBkGxKwVmhgGHTrAKZb33MwQcwQqt04vVZLMK46yUFvrFUfFxeordcgh2k5b6lbbVqajLaCpb+3x+peXN+qYgoKshigvr73W+CJt2xkRR0KLZkvcak88QdoyXGic5ejEE90ca9Go+lJHe3TigX9XZtw/FnNzlLSJ1LKo12jo1895H8Nwx9xwWG3Ye9z+zJrlvu6sWMohjl5jBEa5CuwdONDKmaeLI1+eo0hCudUcK1copCxHYc2tZm1/i0Pc9/Z3nOENbs0kjgrWfpOxb3S3mmGm3ILk8Yjng713sncyMRJqKX8k6o7aqZRrYUoTR8cfr4IYcI0eJ5/flqqgZY2xr9lqj53R3BZHo3meo9t6M/kCXPwrr3g44wxVrQGgMN+KOaqsVLmKNHONnePGxsl5ZZ8rQ4ZTIxR0PwM7FswxTVm7W/WzjMJCd0ZrwHJk47fwpNpXeNukY3WiXs7LxiMmMpw3E6edBkdYZfXy4sDee8Nvf9vwgfbN25A4sgKy9Rxh0ahbji2bOFq+3L3dPZnCc1iOTuZpfvjHa0DmMcYOyG4yO5E4yqDtWyUijoQWzZa61TKJqsZYjvTzOquRImrpaSZiMbfu29RZ3RzR46/lab+/jW3R8VuOzjkHr+/i1VcBuIUJhKJq+9NPWw9XuSxHllttl12sDcGgcs/svbfbcIDVq7m3yx9cq4ZvQsZyq0VRyaFunJhueeIbnziyL95qX7KwiPW7DHIm0lC+13LUoUu6Wy0WqGO3AW6wbirlrvpqh5s4rm1bVGdYQQyO1aS42F0B5iuyWR/2lnv5ml14u8ZbAwqUUNbvF889lWOJtb8MWFZxpJ08ENbE0b33qt/+z8LuV/0NGrAc2XhEzPHHU3fMCdkOzxnVe8EF8PHH7seXa5WazeWXO7dxxi901rJpRxyhBdhlwbIc5XqIypTnyE8gkLltOnV1sJje1B+sor0zFlvd0pijRuxz2GEqPcC2pqkhPtmqF7Q2JCBbaNFsqTjK9NqWiqNcVvKrrrLy0Zkm+lSm14bLdB4nu3YAJ/6nmjiPPQasNNyGWI/cYerTr8neUFTklEOwiSSqOHhkPsVH+fa14wjsBkQiLFgABQfEYAFpI54BHnHkrPjSn+a/1lyObdoosTJvntPZi95YwdKVIRJWhoJooddylHZh9fXqqS3uvkcq5WY67sMiZ/tXX3kPdcJlBg7k091O4MAFD6ZZjuwM1HqSR2+Mios+cTv34g8/pAcjaeYa/2R/8y0hJk/QLjnD7JKKRKkmzouM4hc9e6qN/kR49nH6G2SYoWxtY9c3PfdcGKyH33Xt6gQp57IcZcIwYK+91N/LlqlM9VvDRx9lOYfdn//8ZxYVYqFZjjyN9NCw9PD0Q5bSGzfdpKyC7durZmUUdVvoVmuMgBsxAlatavKpG2RL3WqtXRy18ssTdnZyfQH1MUhLGQN48yHp+zfmoc4flpNrrDv88Iaf5uxBNEMOPnV91mqko4/zPSFrI3aY+vQH+jorcWJBgcdyZGIQTWympLO2Ws0+l1+pRaMUFblZvtNGSp/lKK1zOnXyiqNvvoEZM9TfVsyIGc8jGI84m2N+cZStgzUBZmuRU3iK33Cns91vuXD6qLKSfd+4xXt+qw9SoXRx9Nhj6sePPi87TS4pSZ8Zc4ijsoqQ/vZplqMXx7/NG13OpZo4R4zSRKf/xsrkz8igbuym2IXcH33Ut1oolXL2aao40unUaesnyEGD8CTQTOOFFzIm+XTIZDmyArltsrnVdDy3fRbLWadOyppoGOp3RrbQctS2rfLQNgf9+jVt/9buTrMRcSS0aHKNi/qA+Mtfqt/2IJehNmOa5WjChMznzeRW21IaZTmygnryYtagbM9cVkPMvn2Zz+7pK6e0RzhDK6oaJEkkUeWtfWJflN814xc7/tnO8FqOnA6296+s9IqjkhI1Ey9f7okr0VeVxdpE4MADYT/LlZVNHGlteeMNWLQInuYUFqKy8mWy9ujzeqTAukafqEiGVduTBDnqKGV9Oucc9eNHT+7Y2Cds/XPamN/euU7n3vM9encZM5xjjo9QTZxwvtXm11+H0735n5wG2JN3t24ZVxRlqznqkEpRWqpcZBnFTQuoiN5orOWgznX88ENaAp2MS/l9ePphawqebqE4Km7r/RrtSHbZpRH3jIaII0FoAeRlXkSVxp57Zn9NXxmqj1v2ahY/2YwsTeX552HAgMznCYXUgGwYuJOevfTH91hvLFzICrOjR+sA6qI//1ztY13Y+mNOJ0y9Ekd65/nXYNvixn5vy3qVLo4MVTnCFkd22+yL6tHDHdUvu8w9rmNHCIXYYw8VnqM/zAdiEbVx5kxvm3R8I3CnTmpVu06mOF3PvG4LQLvN1rXV5bVV+xIkFsONy8pARstRJrTZZehQeP999fctl6yCPn0A7TJ9Ym3PPVU25d2HxAnYrsTDD89+49kBRN9+66Zv1mhwbjdNQiF46KEsr597bualai2Rxx+HPfd0hWtJSUbrZ0N4bretEUeBwHZLAtlSEHEkCC2Axoij//s/sMM0/OTnq9WhoOYv/YudzSKULeZo+PCG26Jz7LFqrMwkjmyLkmfCtSe9uG/VWi7sLHXWvj+e/WvyqCKa3OztPL/lyK8s7CW5vonEQOmoNHFkX0xlpcqKWVkJf/pTWvPmzlW6a6+9VP9dwV140xfj7Ri7HdsiV4x9Dt9kV5evlhklCTb4xLwl4igUUgLJt9m997LUkArkxxtcTg40uESsQStAQ36U3r0zL1VriQwdCqHQtgnIdg5oghnFTzjcdHF0880wceKWv+cOJltR8NaGBGQLLZrGiCP9S+r/wqZSXm9EMKjG/pdeSp/spk5Vv+0J0S9q9twzc3HPhshmgYrFsoijkpL07IYNYZlM2g7tS4yfqORbyN/Vfd1vOfJPwnZ8S9pTt9o1TRzZ57PXMzfC9xgKwR+5grv9FjC9YyoqVArzbZlIzzfZJfLbqt9WId9c6G61nOLo6KNJvPwqLPYKcPv811/vLoN3Tup/BI83Uhw14PbKeU11ddstSWGDHHAA7L//djl1TnHUCBvANgsuvu8+5e5sCtddt43eXNiWiOVIaNE0VRz50Y0GVrFtCguVpUmfk/faC04+Wf1tLxLSFnQRizXexecnU0A25LAcQe5gq0xYLrniDjHC++zFbqtn5HarNWA5+pVVQg3D8Ioj//nsdjZiUs86J9sdM3gwXH11zp2vumoLljP7LEfxChWdnCSY24Oy226cf74rmnM+KR96KOvfVymAM4mjm27SkpNmy27dWHHUgOUo5zWFw833yP/uuzBp0nY5dTZXz9k8ztMDcltlbrghe/xhk+nTZ8sHCqFFIeJIaNE0ZpzJ+dRoev8OBt2AWV2s6BNKJstR27Zb7msfMED99tcXymo52hK0Y0OVXTE2bswdkO2fhO0neqtBDzyg/jWsXc/lMZZcMzndcmR/QI20HOV84cknVXEo/fw+Jk1S9UazceONGR7EffmCegx0xVEmK8tC+jL1d/+Fjh0pKHBFc0OaIku94HTsG2w7WY62JmRmZyXbZ/NXzmZ1QY6gMtQ9Y7tBufzyxiWoFLZLzqWWhLjVhBbNtnCr2ZimmodtzaDP5/okac9d9mS3teKoXTtVesCvH6JRX3u35olTr+NgFyVtTEC2zaBBqhM8CXHc2Kh/cAZ3XQo88Qf1gt05TRBHd9wBo0dneMFWqbpazdHZuUTKDTf4NmzcmJ6TyLKSZbMc9Wch93VO396Q66XRyfFyWY4aszRSxFEaufq8SYaye+7Z2qb8LNiasKydBRFHQotmW7rVclmO9C+7P3dOJKLms61ZpZFpzrv9diuBJMCnn25dRj3d6mRfQK6A7Gw1kXydWVysypU4/eO3HDUmGZRFnz7Owi0vmcRRjriYJk12mSrH5uer6zCyC4lM2xsSPZksRxknEU/NCo2RI11hm4utDchuhWwzcSQIFiKOhBaNIx5ykG3w69zZ61EpKfFWI8gmjoYMcQqhO/ttjeUoG3aSPqDpmdj86JYjWxzpKs9vOTr//MzL73yd2a7coJ2enM4vjmw30NYE+frKjXjOvz2wruGpp2CPPXLu4mGbiyP/jeu5IbJw9dW5M0Zne89WTmvP1izseEQcCS2W6urGhWBkc6stWuQOmkuXqlw5r73mutWyxRwZBhx6qPu/LY4uuSSL5aMlkCmY258YCLzFNTNdTEOzTDZxtDW5XZpoOTrjDFi5csvfzv6ws+W5gswCo7ExR/p+GYWKbc3akuyit97a4C4/R7eaXaswE2I5ErYEEUdCiyVXvUmdbIOf7lGxC3zrbrVgUE1oqVTup+1wWD3sFxQ07uG+WdAtR9XV6rfuVvNl3c5KQzOJL6Fic1iOhg6F557b8rdrjHrYGnHU0HkwjJ+neWc7svvuKq5PELYVIo6EnxWFhV5v08iRKudRrrnq5JOzFyZtMejiaOxY2HvvzPs11jdkk00RbEtxlMkftT3dalsojhrbdXqXNcYtLGwbshni7EK5gtAURBwJOz1NMZtPnOidd198seEH+a0NB9oh6OIoL09bm2xhX2BDndXQ60OGeP+3A4i3JugjP18todZNhdsriGTq1PQqxRqrVqk8lJncNE0VR8mkuHSam0RC4pGELUPEkbDT05TBL1tuxZ3ey6GLo62hIcvRIYe4nVVdvW3qSAUC3iXUhYWwadPWnzcTdtKiLLRvD+vWeTNj2zRWV9q/ZVJufn4udcCEbY+II2GnR/fobOmT+k4vjhpKINnYC2xKB8ZijbdINYUlS+Cnn7bd+ZpItsVgDYmdn0vNKUH4OSDiSNipefNNlb9wa9npxVFlJXzyydafp7ExR419fUto27bB5erNQWMtQSKOBGHnR8SRsFNzyCFbf44//xl2yV1hoOXz5JPuKrVMbA/L0bY4bidCxJEg/HwQcSS0KrZkYjrvvG3fjh1OXt62KXgpgTJZaWxgvogjQdj5EXEkCD8HtrflqJXTFLdrc3fh88837/sLQmtAxJEgCC49emzZcc2tCFoQzd0VLTZRqSDsRIgNXWhVNPfE1GJprOnj3nth7Vr3/8Z2qHS8g3SFIOz8iOVIEASXaLRxBe2ErIg4EoSdn53WcrRu3TrGjBlDUVERRUVFjBkzhp8ayI1y9tlnYxiG52fffffdMQ0WhJ0RsRwJgvAzZKe1HJ1++uksW7aMV199FYDzzz+fMWPG8NJLL+U87sgjj+Txxx93/o9sSWVsocUiGXGF5mTqVNh//+ZuhSAIW8tOKY4WLVrEq6++yqxZs9hnn30AeOSRR9hvv/1YvHgxu+22W9Zjo9EoFRUVO6qpwg7kww+9RWUFjS3NcimWoybRQHUSQRB2EnZKt9oHH3xAUVGRI4wA9t13X4qKipg5c2bOY9955x3Ky8vp1asXY8eOZc2aNTn3r62tZcOGDZ4foWWy995bvthKyEJop3x+EgRB2Cp2SnG0atUqysvL07aXl5ezatWqrMcdddRRPPnkk7z11lvcddddzJkzh0MOOYTa2tqsx0yaNMmJayoqKqJLly7b5BoEYYeyJZaj2bOVn0gQBOFnRosSRzfeeGNawLT/56OPPgLAyGDGN00z43abU045hZEjR9K/f39GjRrFK6+8whdffMHLL7+c9Zirr76a9evXOz9Lly7d+gsVhB3N6NEwZEjTjhkyBDp2bNy+4lYTBKEV0aJs5pdccgmnnnpqzn26d+/O/PnzWb16ddpra9eupX379o1+vw4dOtCtWze+/PLLrPtEo1GisrRZ2Nk5/HD1s70QcSQIQiuiRYmjsrIyysrKGtxvv/32Y/369Xz44YcMsZ6GZ8+ezfr16xk6dGij3++HH35g6dKldOjQYYvbLAgCIo4EQWhVtCi3WmPp06cPRx55JGPHjmXWrFnMmjWLsWPHcswxx3hWqvXu3Ztp06YBsGnTJq688ko++OADvv32W9555x1GjRpFWVkZxx13XHNdiiAIgiAILYydUhwBPPnkkwwYMIARI0YwYsQIdt99d/7+97979lm8eDHr168HIBgMsmDBAo499lh69erFWWedRa9evfjggw8oLCxsjksQBEEQBKEF0qLcak2hpKSEJ554Iuc+prZCJx6P89prr23vZgnCz5McucUEQRB2NnZacSQIQgth40bIy2vuVgiCIGwzRBwJgrB1FBQ0dwsEQRC2KTttzJEgCIIgCML2QMSRIAiCIAiChogjQRAEQRAEDRFHgiAIgiAIGiKOBEEQBEEQNEQcCYIgCIIgaIg4EgRBEARB0BBxJAiCIAiCoCHiSBAEQRAEQUPEkSAIgiAIgoaII0EQBEEQBA0RR4IgCIIgCBoijgRBEARBEDRCzd2AnQ3TNAHYsGFDM7dEEARBEITGYs/b9jyeCxFHTWTjxo0AdOnSpZlbIgiCIAhCU9m4cSNFRUU59zHMxkgowSGVSrFixQoKCwsxDGObnnvDhg106dKFpUuX0qZNm216bsFF+nnHIP28Y5B+3jFIP+8Ytmc/m6bJxo0b6dixI4FA7qgisRw1kUAgQOfOnbfre7Rp00a+fDsA6ecdg/TzjkH6eccg/bxj2F793JDFyEYCsgVBEARBEDREHAmCIAiCIGiIOGpBRKNRbrjhBqLRaHM3pVUj/bxjkH7eMUg/7xikn3cMLaWfJSBbEARBEARBQyxHgiAIgiAIGiKOBEEQBEEQNEQcCYIgCIIgaIg4EgRBEARB0BBx1EJ48MEHqaysJBaLMWjQIP7zn/80d5NaFZMmTWLvvfemsLCQ8vJyRo8ezeLFi5u7Wa2eSZMmYRgG48aNa+6mtEqWL1/OmWeeSWlpKXl5eeyxxx58/PHHzd2sVkUikeDaa6+lsrKSeDxOjx49uPnmm0mlUs3dtJ2ad999l1GjRtGxY0cMw+D555/3vG6aJjfeeCMdO3YkHo8zfPhwFi5cuMPaJ+KoBTB16lTGjRvHhAkTmDt3LgcccABHHXUUS5Ysae6mtRpmzJjBxRdfzKxZs5g+fTqJRIIRI0awefPm5m5aq2XOnDlMmTKF3Xffvbmb0ipZt24d+++/P+FwmFdeeYXPPvuMu+66i7Zt2zZ301oVt99+Ow899BD3338/ixYt4o477uDOO+/kvvvua+6m7dRs3ryZgQMHcv/992d8/Y477uDuu+/m/vvvZ86cOVRUVHD44Yc79U23O6bQ7AwZMsS88MILPdt69+5tXnXVVc3UotbPmjVrTMCcMWNGczelVbJx40Zz1113NadPn24edNBB5uWXX97cTWp1/O53vzOHDRvW3M1o9YwcOdI899xzPduOP/5488wzz2ymFrU+AHPatGnO/6lUyqyoqDBvu+02Z1tNTY1ZVFRkPvTQQzukTWI5ambq6ur4+OOPGTFihGf7iBEjmDlzZjO1qvWzfv16AEpKSpq5Ja2Tiy++mJEjR3LYYYc1d1NaLS+++CKDBw/mpJNOory8nD333JNHHnmkuZvV6hg2bBhvvvkmX3zxBQCffPIJ7733HkcffXQzt6z18s0337Bq1SrPvBiNRjnooIN22LwohWebme+//55kMkn79u0929u3b8+qVauaqVWtG9M0ueKKKxg2bBj9+/dv7ua0Op566in++9//MmfOnOZuSqvm66+/ZvLkyVxxxRVcc801fPjhh1x22WVEo1F++ctfNnfzWg2/+93vWL9+Pb179yYYDJJMJrnllls47bTTmrtprRZ77ss0L3733Xc7pA0ijloIhmF4/jdNM22bsG245JJLmD9/Pu+9915zN6XVsXTpUi6//HJef/11YrFYczenVZNKpRg8eDC33norAHvuuScLFy5k8uTJIo62IVOnTuWJJ57gH//4B/369WPevHmMGzeOjh07ctZZZzV381o1zTkvijhqZsrKyggGg2lWojVr1qSpZmHrufTSS3nxxRd599136dy5c3M3p9Xx8ccfs2bNGgYNGuRsSyaTvPvuu9x///3U1tYSDAabsYWthw4dOtC3b1/Ptj59+vDss882U4taJ7/5zW+46qqrOPXUUwEYMGAA3333HZMmTRJxtJ2oqKgAlAWpQ4cOzvYdOS9KzFEzE4lEGDRoENOnT/dsnz59OkOHDm2mVrU+TNPkkksu4bnnnuOtt96isrKyuZvUKjn00ENZsGAB8+bNc34GDx7MGWecwbx580QYbUP233//tHQUX3zxBd26dWumFrVOqqqqCAS8U2UwGJSl/NuRyspKKioqPPNiXV0dM2bM2GHzoliOWgBXXHEFY8aMYfDgwey3335MmTKFJUuWcOGFFzZ301oNF198Mf/4xz944YUXKCwsdCx1RUVFxOPxZm5d66GwsDAtjis/P5/S0lKJ79rGjB8/nqFDh3Lrrbdy8skn8+GHHzJlyhSmTJnS3E1rVYwaNYpbbrmFrl270q9fP+bOncvdd9/Nueee29xN26nZtGkTX331lfP/N998w7x58ygpKaFr166MGzeOW2+9lV133ZVdd92VW2+9lby8PE4//fQd08AdsiZOaJAHHnjA7NatmxmJRMy99tpLlphvY4CMP48//nhzN63VI0v5tx8vvfSS2b9/fzMajZq9e/c2p0yZ0txNanVs2LDBvPzyy82uXbuasVjM7NGjhzlhwgSztra2uZu2U/P2229nHJPPOuss0zTVcv4bbrjBrKioMKPRqHnggQeaCxYs2GHtM0zTNHeMDBMEQRAEQWj5SMyRIAiCIAiChogjQRAEQRAEDRFHgiAIgiAIGiKOBEEQBEEQNEQcCYIgCIIgaIg4EgRBEARB0BBxJAiCIAiCoCHiSBAEQRAEQUPEkSAIgiAIgoaII0EQdiqGDx/OuHHjmrsZWRk+fDiGYWAYBvPmzWvUMWeffbZzzPPPP79d2ycIQsOIOBIEocVgC4RsP2effTbPPfccEydObJb2jRs3jtGjRze439ixY1m5cmWji+3+6U9/YuXKlVvZOkEQthWh5m6AIAiCjS4Qpk6dyvXXX8/ixYudbfF4nKKiouZoGgBz5sxh5MiRDe6Xl5dHRUVFo89bVFTUrNclCIIXsRwJgtBiqKiocH6KioowDCNtm9+tNnz4cC699FLGjRtHcXEx7du3Z8qUKWzevJlzzjmHwsJCdtllF1555RXnGNM0ueOOO+jRowfxeJyBAwfyzDPPZG1XfX09kUiEmTNnMmHCBAzDYJ999mnStT3zzDMMGDCAeDxOaWkphx12GJs3b25yHwmCsP0RcSQIwk7PX//6V8rKyvjwww+59NJLueiiizjppJMYOnQo//3vfzniiCMYM2YMVVVVAFx77bU8/vjjTJ48mYULFzJ+/HjOPPNMZsyYkfH8wWCQ9957D4B58+axcuVKXnvttUa3b+XKlZx22mmce+65LFq0iHfeeYfjjz8e0zS3/uIFQdjmiFtNEISdnoEDB3LttdcCcPXVV3PbbbdRVlbG2LFjAbj++uuZPHky8+fPZ8CAAdx999289dZb7LfffgD06NGD9957j4cffpiDDjoo7fyBQIAVK1ZQWlrKwIEDm9y+lStXkkgkOP744+nWrRsAAwYM2NLLFQRhOyPiSBCEnZ7dd9/d+TsYDFJaWuoRH+3btwdgzZo1fPbZZ9TU1HD44Yd7zlFXV8eee+6Z9T3mzp27RcIIlHg79NBDGTBgAEcccQQjRozgxBNPpLi4eIvOJwjC9kXEkSAIOz3hcNjzv2EYnm2GYQCQSqVIpVIAvPzyy3Tq1MlzXDQazfoe8+bN22JxFAwGmT59OjNnzuT111/nvvvuY8KECcyePZvKysotOqcgCNsPiTkSBOFnRd++fYlGoyxZsoSePXt6frp06ZL1uAULFngsVE3FMAz2339/brrpJubOnUskEmHatGlbfD5BELYfYjkSBOFnRWFhIVdeeSXjx48nlUoxbNgwNmzYwMyZMykoKOCss87KeFwqlWL+/PmsWLGC/Pz8Ji29nz17Nm+++SYjRoygvLyc2bNns3btWvr06bOtLksQhG2IWI4EQfjZMXHiRK6//nomTZpEnz59OOKII3jppZdyurh+//vfM3XqVDp16sTNN9/cpPdr06YN7777LkcffTS9evXi2muv5a677uKoo47a2ksRBGE7YJiyllQQBGGbMXz4cPbYYw/uueeeJh9rGAbTpk1rVBZuQRC2H2I5EgRB2MY8+OCDFBQUsGDBgkbtf+GFF1JQULCdWyUIQmMRy5EgCMI2ZPny5VRXVwPQtWtXIpFIg8esWbOGDRs2ANChQwfy8/O3axsFQciNiCNBEARBEAQNcasJgiAIgiBoiDgSBEEQBEHQEHEkCIIgCIKgIeJIEARBEARBQ8SRIAiCIAiChogjQRAEQRAEDRFHgiAIgiAIGiKOBEEQBEEQNEQcCYIgCIIgaIg4EgRBEARB0Pj/40Sof14AOoUAAAAASUVORK5CYII=", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Response of the first two states, including internal estimates\n", + "h1, = plt.plot(resp.time, resp.outputs[0], 'b-', linewidth=0.75)\n", + "h2, = plt.plot(resp.time, resp.outputs[1], 'r-', linewidth=0.75)\n", + "\n", + "# Add on the internal estimator states\n", + "xh0 = clsys.find_output('xh0')\n", + "xh1 = clsys.find_output('xh1')\n", + "h3, = plt.plot(resp.time, resp.outputs[xh0], 'k--')\n", + "h4, = plt.plot(resp.time, resp.outputs[xh1], 'k--')\n", + "\n", + "plt.plot([0, 10], [0, 0], 'k--', linewidth=0.5)\n", + "plt.ylabel(r\"Position $x$, $y$ [m]\")\n", + "plt.xlabel(r\"Time $t$ [s]\")\n", + "plt.legend(\n", + " [h1, h2, h3, h4], ['$x$', '$y$', r'$\\hat{x}$', r'$\\hat{y}$'], \n", + " loc='upper right', frameon=False, ncol=2);" + ] + }, + { + "cell_type": "markdown", + "id": "7139202f", + "metadata": {}, + "source": [ + "Note the rapid convergence of the estimate to the proper value, since we are directly measuring the position variables. If we look at the full set of states, we see that other variables have different convergence properties:" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "78a61e74", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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7+5tf9tqRKOzMrZGqdYCoqF8aWbNmDXfv3uWPP/6gcuXKLF++3N0iFQu7d+8mOjraYlnv3r05cOAAWq3W5jZqtZq0tDSLlyM8rNyFAlETTeBeNBoNBw8etDrvo6Oj2bVrl93tvvnmG86ePcsHH3zg0HEKOk8eVcaa3yfLlQHwlrTC4u0gr7zyCvv37+d///ufu0VxC+VGsZNlmXHjxtG5c2eaNWtmd9yECRNITU01vy5dsh0rJ2VZ7C4agvhSm23VGK1aQ1XS2HgiybVfQFDkDBgwwNwuaenSpTz11FNulqh4SEpKIjg42GJZcHAwOp2OlJQUm9s4+gBkqwJxZe4WVmSBmyntIQspKSno9Xqb531Sku1r95kzZxg/fjzLly9HpXLM2eXwPMlCozM+9LRVZCcT3cMbtWw8XlVEH/L8KK8P6DkpN4rdmDFjOHbsGCtWrMhznJeXF35+fhYv2xjvWP8YmjFLP4i7cnbniZpSMnfUOjvbCQQlDylXaIGcZXLOvdyEow9AJlbpskMfRD2u0k9ZsYjYOu9tnfN6vZ4hQ4YwefJkGjRo4PD+nZknF1Lu0eDdvwDIwBOA97XPABK3smo/VpGEYpcf5fUBPSflQrF7+eWXWbNmDVu3bqVGjRqu2WkuS8Qy/QPm9zmzYwWCkk5ISIiVlSI5ORmVSkVAQIDNbZx9ADom1+Falktpvdd4LngP4YL3EItyKJ+tP1Xo7yIoesqCRSQwMBClUmnzvM9txQO4c+cOBw4cYMyYMahUKlQqFVOmTOHo0aOoVCq2bNli8ziOzxOYv/2s+X1IVqkTU+H7W7JJsRPWbkH+lGnFTpZlxowZw+rVq9myZQsRERFFdqxpuif5S98OgDCh2JUqVqxYgbe3N1euXDEvGzlyJC1atDDHZZZloqKi2LRpk8WyjRs30rZtWzw8PFxyDBmJYBuWuqeUm83v5247S8pdEUNU0ikLFhFPT08iIyOtzvtNmzbRsaN1co+fnx/Hjx/nyJEj5teoUaNo2LAhR44coX379oWWKaehsIl0EYAkuQoAN02KnXDFChygTCt2L730EsuWLeOHH37A19eXpKQkkpKSyMjIKPS+u9S3tmRckoMAqCYZlYF75dkdK8ugueeel5OZK0888QQNGzY0Z8JNnjyZDRs28Ndff+Hv718Uv06RcvfuXfPNB4zlTI4cOWIu4zBhwgSGDRtmHj9q1CguXrzIuHHjiIuLY8mSJSxevJg33nij0LLIOUzbIzSvW60PlCwVZ51eZB0Jiodx48axaNEilixZQlxcHK+99hoJCQmMGjUKsJwnCoWCZs2aWbyCgoLw9vamWbNmVKxYsdDymFzAdaSreElGS3aibLzPmFyxVYUr1iFmzZpFREQEPj4+PPzww+XiAT0nZbrcybx58wDo3r27xfJvvvmGZ599tlD7bhVeBc4aLREmTE9VAZIx82no4r2sHt2pUMcptWjT4ZMw9xx74lXwdPxCK0kSH3/8MY8++ihhYWHMmjWLHTt2UL169SIUsug4cOAAPXr0MH8eN24cAM888wxLly4lMTHRolZXREQE69at47XXXuOrr74iLCyM2bNnu6bUSQ4l+x+DddKSP4WvKSkQFITBgwdz48YNpkyZQmJiIs2aNWPdunXUqlULwGqeFDWmO0lXxTHzsmQqA3BLrgSIGDtHmDhxIqtWreLbb7+lUqVKDBw4kMmTJzNjxgx3i1ZslGnFTi6GmiPNqvuB0WrODYzxE1UxKnaHEm5z4moqTcNKn9WnvPHggw/SpEkTJk+ezMaNG2natKm7RSow3bt3z/PcX7p0qdWybt26cejQoSKUCjLxIl32wkfKdreGSTeK9JgC11GjRg0mTpzI6NGjzct27dpFr169iIuLMytEpYnRo0dbfJ+c2JonOZk0aRKTJk1ymSwmV6zJDfuNrjcmde+myWInXLF5sn//fj799FP2799PmzZtAGPdwaVLlwrFTuA4LWtU5pW69Zi95T+zxS6nubzf7J1cmNbPXeK5Dw8fo+XMXcd2kg0bNnDq1CmbJRAEruOkXIu2UnYph9yK3e0MDSH+3rk3K7vIstG67Q48fCwDu/KhQ4cO7N+/3/xZlmXGjh3L2LFjS6VSV9KQspS4qlken1NyTfO6u7Kxg0slqfBhRGWZ6dOn07NnT7NSB1CtWjW7ZZvKKkKxKzBZFhFJ4qWeRsUuOSvQtaXiHD0Uh9lqaO1G+dyMJDnlDnUnhw4d4rHHHmPBggX8+OOPvPfee6xatcrdYpUJclsOb8uW50S1XAkV7/92gp9GRRW1WCWHUhSy0KFDBwsr1vfff09CQgITJkwoAuHKHyYd208yKvppcvYD6l2yFDsyi10utz18OPngoVarWbt2LdOnT7dYnpGRUSpjpQuDUOxcgJdKCcAJuTb3ZC8qSmqGKLeUb8WulHDhwgX69evH+PHjGTp0KE2aNKFdu3YcPHiQyMhId4tXZjDFop6Tw4DD5uVVuIsSPXqMc+jwpVvuEE/gAB06dODtt9/m7t27KBQKJk6cyEcffYSvr6+7RSsTmFQYP7IUO7IVu3tZFruKuMFi566HDycfPA4dOkRGRgavv/46b731lnm5Vqu1iDkuDwjFzoUYUPCy9mWWeE6nteIMoq9fyebmzZvExMQwYMAAJk6cCEBkZCT9+/fnnXfeseo1LHAeKVfBx//TDcQTLT/pu7PW8x2UkkxV7nA9K0jcXkHkMkspCllo27YtSqWSQ4cOsXnzZgICAhg+fHgRCVf+MJ37fpIxoSgth3X7DsIVmx+nT5/G29ub48ePWywfMGAAnTqVryRGodi5mB2GFmTIngRKadSRErMsFIKSSNWqVYmLi7Na/vvvv7tBmrJJBQ/jJcZksbuDD5N0zwLGgPBqpBEopXI9q3ixvR7MZZZSFLLg7e1Ny5YtWb16NV9//TVr165FoSjTFbPcgi2L3V2zxc4Nrlh3PXw4+OBhCvdIS0sjKCiIevXqmdclJCRw6tQp12T4lyKEYldQzLFDlnciLSrOymE0ky6YFbtDCbdoU7NK8csoELiZSl5Ku+tSZH+qSWnG8kBZ0ylTa+CLjfFU9FIxqlvdYpJS4CgdOnRg9uzZPPjgg9x///3uFqdMIUmgRE8lyai83cmKsftf97ps334BAF93WOxK8MOHLMucT7mHQZYJCAggLS3Noi3cxx9/TN++fWnSpImbJS1exONWEXBeDgEgQjI2yH5k7i53iiMQlEhMVro3VCstlv/flv+Y9tep8l3gu4TSqlUrVCoVn3/+ubtFKXNISFTG2DLMIEukYlSm3u7TiO4tjA85bomxK+HcVetI1+jp1LU7mZmZTJs2jQsXLvDJJ5+wZs0acz3b8oRQ7IqAc3IokK3YCQTlHVtV9XYZjLUCjXW7rEc0/WBD0QolcJrly5czevRoGjZs6G5RyhySlF2AOJWK6FHy2aAWAGhVRutdRUmNAoPbZCzJBAcHs3TpUubNm0eTJk3YtWsXO3fuJDw83N2iFTvCFVtg7BeAPW8wKnZ1FEl2xwgE5Z2l+t6M9/gRT0kv4lFLMAaDgevXr7N48WLi4+PNfWIFrkUCmkvnATBkhfgMiqwBgEaZ7Qp1S5xdKWHw4MEMHjzY3WK4HWGxKyw2svhyu2LLC8XR6aOkUJ6+a6HI43fKxMv8/mPVEptjDAbxO7ub2NhYQkNDWbZsGatXry53NcGKC0mCdop4ADwwhiGYkolkpRca2RivWokMtHphtYO8zCvlG6HYFQHns1yxwdLtchET4eHhAUB6upsq6LsB03c1fXdB3sh2yv78a6gNQEU7QeH95+wUyp2b6d69OwaDgZMnT9K+fXt3i1NmUUgSnpJRodtsMNbQNCUBJKepzUWKK0oZ/HTgknuEFJQKhCu2CEijIimyH4FSGrWla5yQa7tbpCJFqVRSuXJlkpOTAfDx8Smz9chkWSY9PZ3k5GQqV66MUmk/61OQP1/rHmS25xxzAdbcnLiaxtXUDGpU8WHt0atUr1JBZJgLyiYSeKIF4ITBskXb+hNJvONZgarSXXzJIPG2cMcK7CMUu4Jip9yJiQtySJZil1TmFTuAkBCj+9mk3JV1KleubP7OgrzI29p2HaNbL0p5kqx7mk3+vZLKyyuMHSvKZe9lQZlHkmUGKHcDkI51v2RTXTtfKR25jDghDQaZa2mZ+FbwoJJXAdSRsvEzuByh2BURF+Vg2nKaWlL5SKCQJInQ0FCCgoLQavO4Q5cBPDw8hKXOSey5Ys8ashMm6kpXOCtXtznufMq9IpFLICgpSOpU8/tKNkJ4TJ0oTAWMi5LiiiFOuavmetarRY3KhdrXrXQNof62Lf/2cPR7GgwyF27cIyKwYqnwRgnFroi4LAcCECbdcLMkxYtSqRRKj8BhkqnCIUM92ij+o7fiAHP1thW7UnAtdZjylHhTnr5rYVm19zxvZxnqDhvqWa03Wez8pXt55SQVipzx0hUqOKckFQS1znVJINfvqJ1W7DQaDUC+96wpf5xk6a4LvNm7IS/1sP7blDSEYudiPnyoKQ80CeHLz7YCECrddLNEAoEbceAOtErfjTaK/3jLYyXz9P2RbeR0lQX9oLhvmiUBkWTkOJ45YhEOyNZ1AlNzWOyKajoUd7y0TqNG1hm/d2am83GDeoOMrNOYPzuzD4PBQNK1ZM7e1BKYqaeanXP0wIWbLN11AYDPN8QLxa5skzW1bJz0If7eJMpVAQiTUoyjc7Q5EQjKG3ndiLboW0PWNbW/YjdrDGWzYbdIMhLkhadkVHDuyBXIHbutkLItdn7SPdJcfGyNzoCnyvhAVdTx0nqDzM17Gip5q8jQ6EnX6AHwzHD+YccgyyTnSCRxdh9xSXf5aHsKTeIyWDbSdsb3c9/sd1oudyMUuyLiSpYrtnqWYrc1PpmejYLdKZJA4Abyty1co6rZHRulOGml2I376SjDomrZ2bp0IZKMBPbwzKpdp7FxW1ZIkkWMXaoLTXbv/nacZXsS2DyuG/WCKhV5vPSE1cfYd97oyXqgSTCbThrnwt+vd3d6X/c0Ol74daf5s7P76P/d3+hkzPKka3RU8FBaPHBpXFgzsLgMPEKxczVZf7SkLIudn5RBRTI4djlVKHaCckvO5Im+zUNYd9wyqWihrh/zPGfxpGorn+ie4k6WdQKMF92yotiJJCOBLTI0erMrVoO1S1DG0mLnytjFZXsSAJi37SxfPN7SvDx3vLQsy7yx6hhhlb15PbrgLeX+u6Hhyh2jlS5NK5nfe3tbZwLnhxateXsAT08vFArHFSddjp/x0s10uny2lV6Ng1j0TDunZcmPW/c09Ju9g77NQ3n3wSYu339OhGJXUPIpd3KPCqTJPvhJ6YRIN9GLIqsCAS90rcPEvo2pPf5Pi+XHDHXM70eq1vGl7tHiFq1YEUlGgpx89OfJbIudbH1bVkqSOcbOn3sY3BB0euJqGr8cugxQKMXOleT+FU5cTaN5Dcc6o5y+dsfi84/7jQru5rjCW9N1egMqpWWs8He7L3I1NZNFO88XuWInOk8UIaY4u1DpJjqh2AnKI1k3oDE96jGycwSv3l/f5rArVONXvdEF+4RyC7kv2ZKdByiBoDDMnTuXiIgIvL29iYyMZMeOHXbHrl69mgceeIBq1arh5+dHVFQUGzZscIkcG04k4SXZt9h9O/y+HBa79CJJJspdG2/232fMHS7SNTp+O3ylSI5TqH3l2pUzCm+fmbEuk8OEWqdn0poT1HvnL76OPWuxLvf3ztTqzV11vo49y4p9CS6TQyh2RUTL8Mpmd2yodIPb6WXb7SIQ5EWtgIq8+2ATKmYVIVXZcJe8rX0BtexBsHSb2uWk/qPAfaxcuZKxY8fyzjvvcPjwYbp06UJMTAwJCbZvsLGxsTzwwAOsW7eOgwcP0qNHD/r378/hw4cLLYskSXnG2EXVDTDH2PmSzqKd51Hr9FbjXEVcYhozNp3mrZ+PATBh9XEW7Txf4P39eyWVbp9v5eDFm65VSguxr9y2Flc8PE7fEG/OoP1k3Sku3UwnPumO1bjUDC2N3lvPwHm7uHwrnU/WnWLC6uMuc7ELxa6ImDqwudliF8JNl2rjAkFpZ92rXayWafDguBwBQGvpP4t1ZaXSvqDkMGPGDEaMGMHIkSNp3LgxM2fOJDw8nHnz5tkcP3PmTN566y3atWtH/fr1+eSTT6hfvz5r1661ewy1Wk1aWprFyxZKScozxg4gsFoQYIyxA/hu10WHv6tD5JhiqRmWhojfj1y1HJqPArLzTAqv/niYW/eMpUge/L+dXLyRzqB5u/PcTm+QSUx1vL/6wQTLcmI58xL+PJbId7svOLwvR9HmkUzx62HL36nLZ1vpPTOWlLtqC8Vxx5nrABy9dNucFQzWymZBEYpdgbFf7gQgyM+Lq1mZsXUUicUllEBQwrB9pWoQ7It/BesbmKkwazvFKYvlY34ovFVEIDCh0Wg4ePAg0dHRFsujo6PZtWuXQ/swGAzcuXOHqlWr2h0zdepU/P39za/w8HCb4xRS3lmxAJmKSoAxxg5kLt8q+g4U9sgvZvzpxXv5/chVPl4X59R+X/z+AFFTt7D1VP5xbompGQxfesDu+pd+OMT7v5/g3PW7Dh075638/d//tTtu11n7TQc0dqyoF29Y/q1y/nw5nReuip0Uip2LaR1eGTCmp5+UjZl8daWreWwhEJQHrB+ANo3rimeuAONYQwsAYpT7LQq2CgSuJCUlBb1eT3CwZaWC4OBgkpIcCwP44osvuHfvHo8//rjdMRMmTCA1NdX8unTpks1xCkW2xU4t27bYpSuNip2npMcbjcusOyZWH75C7fF/Mn/72Xydko4e+8ota+tbXrqLKXFhyT/5u30PXbxttWzOlv+slt1yMAwq53f+brfRGnrk0m2numM4Wholp8Vz2OJ95veuSrIUWbEuYtf4nly9nUGz6saMHIUEV+UAoPy1FRMIHCHI15vezUJYezT7wecfQzOuyZUJlm7TQ3GEDQbrsgMJN9KpGeBjtVwgcJbcNcUcrTO2YsUKJk2axO+//05QUJDdcV5eXnh5eeW7P4Uk4SHlbbFTSxXQyQpUkqFIM2On/XWKV3rm3V3B0WPbCqFwVuq0TC2ZWj3KrL+LWmfg2W/2UbWip9XYjSevAdmuzsLy8Ff/ODVep3ew92yO3+9qanaBZVf9SYViV2iMJ1tY5QqEVc6uei1JkjnGLlBKE9YHQfkknytV7hwKAwp+1XdmlOoPBiljbSp2Ry7fFoqdoFAEBgaiVCqtrHPJyclWVrzcrFy5khEjRrBq1Sp69erlEnksXbG2LXYKhcQ9vPEnnYpSZpFGnc62YfnKiaOK3Y27Gv5Ltk4eyI+cynWLSRst1sU0C+H0Nfvu1Us30xmawwpmYs6WM2yLv873I9pTwbPgpYbyUvsdrT1ssGPY0wtXrJtx4IZ1C1/UWTWJqnG7GIQSCEoodq54tpb+nlX2pIviOKqsm11Ovt11gbtq6+UCgaN4enoSGRnJpk2bLJZv2rSJjh072t1uxYoVPPvss/zwww/069fPZfIoFBK+GN2WGdi28EmSRDrGIr4+ZLokg7Kg88hRl+GZ5Lv0muFYWRFdDjdmXvpRUlre/WBXHbB0d5suPdM3nubAxVv8fNC2O9xRFLmuZZlaPUcu3TaXLrHF5VvpLN55zvzZnmIsYuxKOMY/vsR1KgMQLN3KM5tGICib5H2hsuX2OiWHkyr7UEHS0EiyziY/ePEWk9eccJmEgvLJuHHjWLRoEUuWLCEuLo7XXnuNhIQERo0aBRjj44YNG2Yev2LFCoYNG8YXX3xBhw4dSEpKIikpidTU1ELLopAkgqRbACTLlW2OkYB02aj0+aDmnrrw5U5eW3mkQNsVJhTMnkL6+cZ483uTJf+v49aJh/kplflZG9/7/QS/HLxsvcJBc9uSf87T7fOt5uzdF74/yMNf/cPcbf+hteOKffXHI6RlZivRn9hJKslLOXQGodgVESat/ppcBYAg6TbtPt4sLA2Ccorti2brmpWtlskoOGhoAMB9inir9QCrDl7m9yOuKZgqKJ8MHjyYmTNnMmXKFFq1akVsbCzr1q2jVi1j0ltiYqJFTbsFCxag0+l46aWXCA0NNb9effXVQsuikKCaZFQQk+XKPNgilE2vdc01RiI9y5rnI6lZc/RqoY0Fm7Ji0hyRLyeuUkBysiRHnTzTA9//lh+yGueKBIPXVx21WuZoFbstp5K5eCOd2X+fASD2tDGeb/rG0w4f315Ch6t+VhFjV2DyLndiWpyt2N3idrqWraeS6d8yrDgEFAhKPEPuqwlA7OkUNsdl32T2GRrRU3mEt1Q/skQfY3PbV388wkOtqheLnIKyyejRoxk9erTNdUuXLrX4vG3btiKVxQeji/EuFejfMoz6wb4W6yUJsyu2AmrAGMMW4u98j1VnkGUZhSRZuAmLInHDWOdNznpvn6Jo4uRoNqvFNjrXC+KqrNgSb7HTaDTEx8ej05UuS5fJYmcyqwdnmdnd0eNPIHAb+ZzvKqWCYVG1aRhSyWL5HoOxl6K3pKWulLdlLjktk+/3XBTWcEGpJme5E1vTRpIgI8sVW1HKO87MlWRqDVYtMV0V5L/6kO25LUlw467a5jpXdWewdUxnjvPLoctsOOHaDjllvvNEeno6I0aMwMfHh6ZNm5pN4q+88grTpk1zs3T5YzJdJ2dZ7IKl24BQ7ATllHziVx5oEmLx+YhcF71s3KaL4nie2w7+eg/v/fYvH/wu4u4EpRMJCU8p76xYiWxXrMliVxxsirN219q7ja3/N5EZG22HT5i4cOOezeWWVjOJ55butznOVVat/Lh/xvZ8x7z4/UGXHrPMZ8VOmDCBo0ePsm3bNry9s03NvXr1YuXKlW6ULDe2b1hWMXZkWexE/oSgGHCmufm2bduQJMnqderUKbvbuJpW4ZXZPC5nTJHEdN1gACZ5fIcC2xPn6u0MzqcYbxSbbdyABILSQnZLMRW2ko4UCnJkxRafYmfLimRPuRq17FC+yQuZ2vxvgpIExy7bTkpxlfJjdcxc9/Jz120roEVJmW8p9ttvvzFnzhw6d+5skTnXpEkTzp4960bJssjn5DLH2JGdPAFw/ErhM6gEgrxwtrm5ifj4eBITE82v+vXru0Aax69U9YIsY4r+NrQ2v28p2Z7zHadtKZhYAkEJwyu/OnaSlJ0VKxkVO0frphWGV388YrWsqK1muZM1cuKswlUMP5HLKPNZsdevX7dZ0fvevXsOVQZ3N5Ik0bleoDnGLkQyNiteuuuC+4QSlAucbW5uIigoiJCQEPNLqSx4EU9rnJ+zp+Vw4gzG3pqd83HHCgSlHbPFTraf02jOiqX4YuxsYcuukaktfPkVE7mtZ4Vh4NxdjP/lmMv2V5SU+Tp27dq1488//zR/NilzCxcuJCoqyl1iOcWyke2RfI2xQ5Wle3ihcbNEgrJOYZqbt27dmtDQUO6//362bt2a51i1Wk1aWprFqyhYpn8AgM5K+025TRRVULVAUByYOk9oUdlJnpDMyRMmV+zHf9quh1bUJN/JtFLk7vt4s8v272rbzY/78y9KXBLsRWW+3MnUqVPp06cPJ0+eRKfTMWvWLE6cOMHu3bvZvj3/oMZiI5+zoWHtmqjjPfCStFSTUrksVysmwQTlkYI0Nw8NDeXrr78mMjIStVrN999/z/3338+2bdvo2rWrzW2mTp3K5MmT8xfISWXrn/E9+fnAZb7cbKwJFWtoDkAb6QwVyeAeFfLaXCAotXhKphg7e65YuGcqd5Llil1z9Cqzn2xtc3xR8uj83VTwUDI+phGPta2Bj6fKogBvYbl5r/iNICVAryv7FruOHTvyzz//kJ6eTt26ddm4cSPBwcHs3r2byMhId4uHw7FDkmR2x5oSKASCosaZ5uYNGzbk+eefp02bNkRFRTF37lz69evH9OnT7e5/woQJpKamml+XLuXzROzg43D1yhWoF5Rd+uSSHMxFQxAekp72CvdYJwSC4sAjy2KntmNvUUiSud1YRTe7YgEytHo+WHOCD/+II0PjOjcswN7zN126P0eYszXvpI/iwFVehxJrsQNo3rw53377rbvFKBQyxgSKcK4bEyiEt0hQhBSmuXlOOnTowLJly+yu9/LywsvLdk9LS5w/4eVc2/xjaEotRTLtFPFsMbRxen8CQUlHRqaqZGxsn4mnzVnTo1EQh04Xf7mT/FixL4EV+/JOzCoNqHXuL1lR5rNilUolycnJVstv3Ljh4qDuoid3keIUO4UXBYLCUtDm5rk5fPgwoaGhrhbPIXI/tJ6Sjd0p6klX896uqAQSCIqYKvob5vdpso/NMYPbhpvLnRRngWJB8eGqbOMSa7GzZ5JUq9V4enoWszQ2cNBkKsuyuZadSbG7dU9DYCVHrB0CgfOMGzeOoUOH0rZtW6Kiovj666+tmptfuXKF7777DoCZM2dSu3ZtmjZtikajYdmyZfzyyy/88ssvLpSq4BEsZ2VjC776ko3G3QJBGaCW7oL5/R1sK3ZKhURa1jo/0otDLEEx46oYuxKn2M2ePRswxggtWrSISpWy4230ej2xsbE0atTIXeI5jQwkylUBCJNSgJJh8hWUXQYPHsyNGzeYMmUKiYmJNGvWLM/m5hqNhjfeeIMrV65QoUIFmjZtyp9//knfvn0LL0wBLlS5tzhuiEAvS9RWXCOUGyQSYHO7O5k6TlxNpWmYfwEEFQjcRwbZxgoZBdUrWycJSUCqXBEAf6n4i+cKih5XJfaXOMXuyy+/BIyWrvnz51u4XT09Palduzbz5893l3hO07tpCBv+NWbCVheKnaCYcKa5+VtvvcVbb71VtAI5UUsgt7U+jUocluvTVjpNV+UxVup72N223+ydXJjWr8BiCgTuQCkbEydOGcKZ1L8JLcMrW42RJEgjS7GjcIpdhkZPBc/SFdJUHiizWbHnz5/n/PnzdOvWjaNHj5o/nz9/nvj4eDZs2ED79u0d3l9sbCz9+/cnLCwMSZL47bffXCtwPjes/i1CuSIHAtmKXV5VtQUCgTWx+hYAdFMcdbMkRU+6RkdaptbdYgiKEYXBVMNOyRP31bQ5RpIks8XOS9IWuC7qV1v/o/H769no4gb2gsJT5pMntm7dSpUqVQq9n3v37tGyZUvmzJnjAqly4thfQJIks2IXzC1U6EpF5wyBwDW45kq13WBU7Dor/kWJa0srlCRkWabJ+xtoMWmjSyv5C0o2StmoyOtQoczjyf8u3uhl4/qCWu0+3xAPwITVoptLSaPMxtjl5PLly6xZs4aEhAQ0GsunkxkzZji0j5iYGGJiYopCPIfp2rox6pMqvCQdIdJN/vkvhVY2TO0CgcB2nMlxuQ435UpUle7STXG0zJY9yfnEfvlWulX/XEHZRGXuE6tCmceDv4yCNCpShbv4S/dIlgtu/LBlYGgp/cdjyu2s0PfkhBxR4H0LCoaresWWWMXu77//ZsCAAURERBAfH0+zZs24cOECsizTpk3RXdTVajVqdXY5Ele0SvpkUEsun6hGXSmRmlIyn2+I56Ue9Qq9X4GgLGIrY9yAglX6bryo+pPhyr/KrGKXM77QHR3Sdp+9gUop0a521eI/eDnG7IqVlfmGo6bKFaki3cWfu04d48d9Caz7N9v9mtswGEgqX3vOIFi6zdOqv3lP+yzf66MROItMXekqGlRckh2vHQrlwBU7YcIEXn/9df7991+8vb355ZdfuHTpEt26deOxxx4rsuNOnToVf39/8ys8PDyfLfJ3q3qplJyXjT1jIyQR1yAoRxRAO+lUL4AxNh58vtNFo5clOitPEEzxV6YvDtxZiy8tU8uTC/fw2PzdaESCV7Giykqe0KHKN1QnlYJlxo5ffZzY09fNnxU5jiNh4DOPBQRLt83LPvRYyl+e4/FzUoEsz4SRwjrPifzt9SY7vF7jC4+5ToWOlNnkCRNxcXE888wzAKhUKjIyMqhUqRJTpkzh008/LbLjOtwqyck/wG0Po2JnKnkiEJQrnIgrlSSJN3o3ZP87vSyWX6Ea/2a5h7oqj7lUPFej1RuYtOYEm09ec2o7d1jpTNzJ0etTqxeKXXGixBhjp3XAiWYueZIVY1fQNlQ5LXZjVb/QU3kEgyzRT/0xZw3G4uSNFQkc834B36y6eR7o8KRkJPbUkJJ5WbmaLZ7jWOc5gaHKjWaXtjsI4hY/eH5ME8VFdLJRtRqk3MmnHgsdlqvMK3YVK1Y0u0TDwsI4e/aseV1KStEpR15eXvj5+Vm8XEHjRo0BCJXKpqVBIHA11XytXbJ/640u2H6KvcUtjlP8dOASS3ddYOR3B5zazlUXdmf5L/kuk9accLscxc3cuXOJiIjA29ubyMhIduzYkef47du3ExkZibe3N3Xq1HFZ6S2FnJ0Vmx9puSx2Bf1TmSyDvqTzP+UaAL7UDeKEHMEY7SsWY497jyTeaxjHvUaw1+slOir+zXf/gaRSU7qGhGsfEu5XHGSux0y2er7O6x4/U0eRRBPFRT70WEqs11jGKH/lPimOx5TbGKn8k3rFUNi8Euks9/yE2oprJBiq0UU9ixc1Y9HLEo8qY1nh+ZFDveLLbB07Ex06dOCff/6hSZMm9OvXj9dff53jx4+zevVqOnTo4G7xsnHQEuFTzVgcNky6kc9IgUBg4ovHWvL6quwSJ38a2jOOn+mo+Bc/7plvciWNpNTCt3z65dAVxscUTzH2AXN2kp6jkXt5UOtWrlzJ2LFjmTt3Lp06dWLBggXExMRw8uRJata0Ljly/vx5+vbty/PPP8+yZcv4559/GD16NNWqVWPQoEGFkkUl60By0mJnUuwKeExFllmnj3IfnpKeS4Zq/J9+IABxci1qZ/7AYOVWPvVYCICXZFQ+vdEyWfUtD2g+w14oUiMpgd8938NLMlr31LIH/8q1+VbXm3WG+9AVQPXwRs1k1bcMVm0zL9traMQafUcqoOZF1R+ESTd5w2OVxXYT5R9Yb2jHYUM90vEm3lCDQ3IDDC6za8lM81hEfcUVEuWqDNG+SyIBJBoCeFE7jhkec2mnOM2fXhP5VPcEv+o7o7ejwJd5i92MGTPM9eomTZrEAw88wMqVK6lVqxaLFy92eD93797lyJEjHDlyBDBOziNHjlhU3i8OakfUByAUodgJyiMFK/EzKLIGlX08zJ/PytU5baiOp6Snl+Kgq4RzOQW9Pufcbv72s/YHuoDzKfdYe/QqsixbKHW55dDpDaRlapm05gSHE/K3OpQWZsyYwYgRIxg5ciSNGzdm5syZhIeHM2/ePJvj58+fT82aNZk5cyaNGzdm5MiRDB8+nOnTpxdalqqy8XfVO3BLTsV5V+w//1l7uUwxdj0VhwFYqe9O7nm6Ut+DgerJJGVl307WDkUte1BfcYUoxUm7xxut+t2s1IGx7l6k4gyzPefwu+d7VCQjX5lzUoFMlnt+wmDVNvSyxLe6B4hRT2Ww5n2W63uxSN+PTurZjNWMZq+hEQmGauzSNyFW3xyFJNNXuY93PH7gY48l/Ow1hd1eY3hNtcolsbpDlFt4ULkHraxktOZVLsvVzOs2GyIZoPmIOEM41aRUpnssYKPnW/RT7LFpySzzvWLr1Kljfu/j48PcuXMLtJ8DBw7Qo0d2pfpx48YB8Mwzz1hV4C9KJP8aAIRIN11umhYISiwueALNXf5hnaE9DRSriVHuY7Whq9V4jc6AJIGHssQ+t6LW6fli42m6N6xGx7qB5uVyMdrKekzfBsDLKw5br8wSY962s3y5+TT1gypx4moaS3ddsNnZ465ah0ICH88Se0uxQKPRcPDgQcaPH2+xPDo6ml27dtncZvfu3URHW2aJ9u7dm8WLF6PVavHw8LDaxtEqCy8ojK7Qyg4kKpgsdn5OWOyeWmQdupB422hVriMlAnBMrmM1BuCwXJ8O6q/MnyOkJIapNvGMciO7DU1zjZYZrlzPAOVuDLLEKO1YPNFhQKKPcj/dFUdoqrjIEOXfLNQ/6IDk4MddFntOJ1JxhttyRf6nHWvjuKDBg98MnflN09lieUNdAk8otxIgpVEBNfcpThEs3eZV1a+8pPydTYZIjhjqoURPLSmZVCrynxxGnKEWx7Nier3Qosa6R31vxX4mqZYC8JluMIfl+lZjLsihPKz5kGeUG/ifai11FYl85Tmbk4ZaTNc9xhZDa0wKdZl3xdapU4f9+/cTEGDZF/L27du0adOGc+fOObSf7t27Fzi41KX4hWGQJbwkHQHccbc0AkHx4sKi3Bv1bRmrWk0PxRGqksZNLONgP/rzJN/tvkjPRkEsHNY2z4Kv7uK7XRf5OvYcX8ees1CUSsKlCmDX2RRimofy6fpTAJy4ar/sk1qnp9kHGwA490lfFCXw985NSkoKer2e4GDLchTBwcEkJdmuXJCUlGRzvE6nIyUlhdDQUKttpk6dyuTJkx2WS3bAsm1tsXN49xZo9Aa8UVNXugrAGUMNh7b7Xv8Aw1SbeEBxgDBSuEr2g8lw5Xre9/gegMX6GDYa2pnXrTN04HHlVj7zWMgzqo0s0cfYdUma8ELDIs8vaKc4TZrsw3DNmxySGzj1PePlmkzWPWP+7IGOaMUBhqk20l5xihjlfmKU+21umyl7YECBj6TmnCGEg4YG7DA0Z5uhJX2V+/hItQSVZGCNPopFevu9tdV48rW+Pz/o72eE8i9GqtbRRHGRJZ7TOWioz2zdI+wzNCz7rtgLFy6g11unCavVaq5cueIGiezh4EVM6cF1jM3JQ0WcnUDgMB893Mzi80m5NkcNdVBJBiZ6/GA1/rvdFwHYciqZFfuKN+TCUS7csF2q4mRi4etmanQGzly7Y/VAazDIfPPPeY5fTiUxNW9X2P+WH2LrqWSHjnflVva+Slsf7NylRWRZzrPciK3xtpabcLjKgml/DshsHWNXcGWgt2I/KsnAFTmARByrXXhGrsEufROUkswQ1d/m5VVI42XVrwDM0g3kE90Qq21/13fiplyJGlIKg5Sx+R7rXdUy7lPEkyb78JjmfaeVOltoUfGnoQODNe/TRz2NObqHWK3vzK/6TszQPspiXQyx+uZkyh54S1p8JKPFtY4iicdUscz2/Ipj3i8wzWMRKsnAL/rOvKYdjeyAOnUXH2bpB9FFPZP5uv5kyJ5EKs7wreenxHkPp/rp7wv9/aAEWuzWrFljfr9hwwb8/f3Nn/V6PX///Te1a9d2g2S5KIBmnSgHECzdFgkUgnJE4Z9AY5qH0rFuALvOZs+b3/SdaKk4x6PKWGbpBtotBLr99HWe7lCr0DLkh05v4M/jibSPCCDE37vA+3ls/u4CbSfLMp+uj6dWgA9/Hktk538pzBzciodbVzeP+eXQZSavNcZFeanyvwk9t9S2FSM3X24+Y36/8WQSfZuH8uP+S3SqG0CdapWc/CbFQ2BgIEql0so6l5ycbGWVMxESEmJzvEqlsvIsmfDy8sLLyzq72x7FabEDeFC5B4CVuh44Ewf7vf4BOipP8qgylhm6x1Bi4GOPJVSR7nLKEM5s3SM2FR01nnyvf4BXVb/ytupH1uqjyMD2fOmmOMpQ1WYMssQr2peIl2330C0Mp+SanNLZ3m8FMgmUUlEgky5701RxgfsUp+ij2EcdRRI3ZF+W6GKYqx/gkFKXk9v4Mk33JIt1fRil+oNBylgqS/dQexa+jSqUQMXu4YcfBoxPQKY6diY8PDyoXbs2X3zxhRskKzxX5QBacVZY7ATlkMK55/wrWMYv/aLvygdZLp83VKt4VTvG5nbpmqKpa5Wh0aNSSvxv2UHa1KqCh0LBx+viqOSl4t/JvfPd3lHP9BurjjL9sZb5jjty6bZVssUn6+IsFLv4pOwQEFda1tYevWp+/+qPR7iWlskn64wuXFvxeCUBT09PIiMj2bRpEwMHDjQv37RpEw899JDNbaKioli7dq3Fso0bN9K2bVub8XUFwwHFTnauQLEuj5qETRUXANhtaOLQvkz8bWjDTbkSIdItlnh8Tndldub6+9pn83SxztU9xCDlDmpIKTyp3MoSvXXLzwpkMjkrdm2Jvg/bDK2dks8VZODNJTlb6dxmaMU2Qys+YzBVuEMqlQqdWXudKnyoG8qHuqepSCZfhkUWVmygBLpiDQYDBoOBmjVrkpycbP5sMBhQq9XEx8fz4IOOBV0WC07EDl2VjU91odKNkhH3JxCUEnLHbaVRkbGa0QD0UBy2W939WpraatmWU9c4c63gca5Xb2fQ+P311H/nLzbHJfPZ+ni2nTa6Le+qXatI/nzQsRpcqRnWRWOT71h+9+KKfTtwoXRkzo4bN45FixaxZMkS4uLieO2110hISGDUqFGA0Y06bNgw8/hRo0Zx8eJFxo0bR1xcHEuWLGHx4sW88cYbhZIjZ39QgwOK3Q3ZGFNalTtIGPK12L39y3Gby/25S1hWXdVTTlrDNHgwNcvValLqbsvGOblPbpzntmo8+UpnVJ5fUP1hs+Dx26ofqa24xhU5gFm6wpWScT0St/BzYbkU4z7vUQGtVHBrf05KnGK3d+9e/vrrL86fP09goDEo87vvviMiIoKgoCBeeOEFiyyj0kRilmIXJt0oMUHSAkGR4qIT3VZj9DWGjqTJFfCTMmgunbe5XW6X4/HLqQxfeoAHvsw/vscetpStnF9z9t9nrNYXhrPXszMlUzO0/LgvgSe+3k3CjfTs4zuwH4ULE1jyorRc2gYPHszMmTOZMmUKrVq1IjY2lnXr1lGrltF1n5iYaFEWKyIignXr1rFt2zZatWrFhx9+yOzZswtdwy6nwu2fT0H8vs1DSMEfgyzhIempyp18Y+x+OWT74aCJwhiLmmCoxh18nJQaVum7sdeQXWfxKc1EfjN0zmOLHDLpu5IoVyVEusU7qmWYzprW0hm+85jKs6qNALytfaFAspVWXJU8UeJcsR988AE9evQgJsZonj1+/DgjRozg2WefpXHjxnz++eeEhYUxadIk9wpagMtXtsXuZqm5+AkELqGQSoWtzFYDCnYZmhnLKCiPcERn3V8293UyvhCWOhO2vknO48zYdJqXetS1GnPjrprYM9eJaWadPZkXE1YfZ+ULHfjzeCJjfsguTfL6qiNMf6wlnnnEy33850n+170eVSt6FnldPBMGV3UyLwZGjx7N6NGjba6zVQ6rW7duHDp0qMjkqehbOc/1s55oTf3jSdzAl2qkESzdKvCzUxPJqNjFyQWNQZV4SjMRHzKzCoU7Psc1ePCR9mm+8pzNM6pNNJCucJcK3K84hEIyfqH5ugfZaWheQNlKJ2VWsTt69CgfffSR+fOPP/5I+/btWbjQWP06PDycDz74oAQods6TmMMVu/boVYv4F4FAYB971qZN+kj6KPcTo9jHTB61Wp/7QqlygTvSliiOZCY+uXAPp6/d5eilVKeOt+/8TZq8v4EMraW7+b/ku3T7fBsA3zzbzsaWsHDHeS7dzGD+UNfE7jjC3w5m0wqyWaHrQbTyAAdqv0BeqoypNmOyXIVqUhpB0u0CGwlM8XX/GmoXcA+gQ0UaBUuQ+dPQgaraNCarviVKmV3s+A99B5bo+rgkA7a04SpPXolzxd66dcsiK2n79u306dPH/Lldu3b5powXL87H2AVzi3Eri+6pTyAoObioLpOdabbJYOwd21BxmbdUP1qtz13JPa+adjfvaRyKfbVV2sKRC/Lpa0aX6rrjifkPzkVupQ7gVnp2bFJeiuXxK84pkq7k69izLqumX5Y5ETmFGNViHurimAJuirMLII0rtzLQ5pEgYQtPtHRUGHsDm4rwuoPv9dE8opnMVn1L1uo70E/9CWO0r5RLpQ7KcEux4OBgzp83xstoNBoOHTpEVFSUef2dO3dcmIFUCArwB0jBH62sRCUZHGoILBCUHVzvigVIoxKHDUYX7IvKtVZdXfKy2N1V67h00xintunkNdp8uIn3frdsbj5jYzxPfL0bTT5ZpIcv3Xboe0DRxKDtPW+/NZIsy+yy0VKqoKRmKZR6g8yszXnHE36y7hTL9lx02bHLKh8NbMGed6KpWtG6u4EtbmQV5a4i3aH3zFirzhLbT1+n07Qt7Dpr++8+RvUrIdItrsv+Nrs4FCdH5Ho8p32bl7WvcEKu7VZZ3I2rnoFKnGLXp08fxo8fz44dO5gwYQI+Pj506dLFvP7YsWPUrWsdv1IaMKDgGsY6NaKWnaBc4KIn0H4t7MelPa95HQClJNNXsc9iXe4LZU4Fsf3Hm+ny2VYupNzjs6wOC8v2WBY0nr3lP/acu2lhZbPlis2t+OX1tYsicWp7/PU81w+x0VKqoAz4aicAK/Yl8OXm0/mO/2DNCZcduyzjTNbyLdkXgKqSMWZ0Xy7F/pkl+7hyO4MhC63/7v7cZbhyPQCf6p6w2SpL4B5cFZ9a4hS7jz76CKVSSbdu3Vi4cCELFy7E0zP7xFuyZIlVvz634mRQeM4ECoGg3FDI5Iku9avx5yu2M+5S8OcHnbEf9Fees6lApnnd+ZR7XM9R9kOlzJbjXlbj+3/sWDVyosnh6pIcsD7mvjxbKn6yQ/twBl0xujsvZmXjrv/XdustQdFz06TYkd2pRJZlvtt9gaP5WI/7K3dTScokzlCTn/XWvZZLC4GVyp5C6u2Zd4s1RylxyRPVqlVjx44dpKamUqlSJZRKyy+6atUqKlUqmdXMHSFnAoVAIHCcpmH+dtfN0Q1kiGorAI2kSxbNuLt+tpW4D41xurbi42wpWQcu3OSnA7ZjeR3RUedts8xAnbQ222pVFBa7vGKs0m3E5xWWpNRMpyxMiakZhPpXcLkc5RVTf2STxQ5g3fEk3v89P+uozKuqXwBYq+9AYUMk3MmSZ9ux4UQSX20tnmzvnEQEVuR8imMFop1hQMswl+ynxFnsTPj7+1spdQBVq1a1sOC5j4JdnXPWshMIyj6u1WI61rXduukqgezQG3vKdlcesVhnK/EgJ7YUtUfn7+anA9b1v1IztAVKBvhhb9H2rNXmEQN4O926AGxh6TD1b2JP5+3+zcmXm/J32Qocx2Sxq5JDsXOklE8PxRGqSUYr33ZDqyKRrbhw1Oo9sHV1Pn+0BZ3q2b52lEVKrGJXVrkqGxstC8VOUL5wjWVg+cj2VPaxnTz1l6E9AN0Ux1xyrNzcuKum5eSNfL4h3qntTl5Ns/hcFE7Tq6mZ+Q9yIyIx1rVku2Kzlbn8ZpiEgTdVPwFwyVCtwIkKs55oVaDt3IV/BQ8eaxvO1IEtXLbP+xsFuWxfRYFQ7AqNczcs4YoVCAqOJElU8LAdhxJrMF64m0oX8EJjsS75TibDl+5nS5x1jbWNJ5I4k3zXanludp0t2Jx9cdkBi8+yLBc25FBQjqkV4MNNjIpdNSkV06NCfudUYynB3G3iYc2UAh07yNeL/i1c4y4sLI7OIVMNTFfOuTd6N8xz/fyn29C6ZmXXHdBJhGJXjPz2Uidz8kSYdIO4xLR8thAISjnF2DvvshxIolwVD0lPO4WlVW3K2pNsOZXM9zZKb2zNJ6PUREFvDHcyLfvH3krX8ncOBVOWZX46cIkjTpRMKW0IPdZ1bB7XjY5tIzHIEn5SOoFZCRT5uSZNdeu26FtxA/vxqnnh660qUQ8ljlxeXC1vyxr+eHso2fFWD7uFwaPqBrosXq4gCMWuoBTghtU0zM9ssasmpbLl35JUaFkgKEJceHW13/NUYofeWLe/ay537B/HnC8KbL33gn0HW1tduZ1hfv/Pfzd46+djPPzVPwWUrOQjPLGuw0OpwLtCRW5ldXwIkIwFqPObYp0UxhqN/xSibp2M7QQkd+CoGEUlbXhVH3qUUJesUOyKEQm4iS+ZsjFG6Mcte9E5WTFcICjv5HVB325oCUA3xdFikiZ/buWTvPD0YtfVmBOUDyTgpmyZGZuXAqNCx30KY63GXYZmRSydNfWC3FfJwnS9cFVXh9zYCg1xt+4rFLvC4sRf0GhpkEg0JVBwk2/+uVA0cgkEJQLXX0xf7FrH7rqdhmboZYmGisuE4MI4Vtn9F+vSjPjpXIyEOc4ukPxbxrWQzlFRUnND9uWUHF7w4zownf96tQv/92Rri2VTHnJNdws/b8sKbY5cXUwWxqJK4Dnwbi/rY1KsUShWCMWuwDj/VzPdGHImUHy8Ls6VQglcQKZWz5qjV7l5T5P/YIGDuO7W3i6iqt11qVTiiGxsMdZV6drsWKGcGHuMOqJICIoWCcncL9ZsscvjBG2bFXO639AI2c5tv2mYn/l9gIOtzWzRONSP/rniy2oHVCzw/nISEWi5n4daGY/TKMTX7jam38WRPtCOkHsvFb1KXDlgodgVJ6Ynh0RELbuSzOcb4nllxWGGLNzjblEEBWC73uiO/cxjIU2l826WpvQhYcADHdXJTirx4x6LPT7ntPcz7PMaTT+F5dyoQCbdFYfxxtjlo70UR03pWrHKXZ6QpBwlT6T8k/BaKoxFfI8Y7Lfj9FBmqwObx3VjbK/6VmMKqhqFVXZNcercrQUbhfixb+L9rH3ZdlcayI6NzS1794bViG4S7BK5rI7pZvN+yVM1Sx3O/wGvipInJZq1R68CcCop/4Kfgnxwgz9iu6EF4/gZgMke3/KoZlKh93k25S4Ltp8r9H5KOk2kC/zg+TGVJWNV/R90PTgs1+dzj6/NYxSSzCyPOfQ0HGaprjdeaPjZy375jE+0T3KL/xW57OUJlUIyZ7YGmLJi81AmWpkUuyxrti1C/LzN76tU9GRsrwZsOnmNE1ddU73hzMcxnLiaVqgkocciw/lknTFW0HRpCcohty0Udix2s55ozdXbGZxPucfr0Q0ZtexggeXKjbut+8JiV8x4KhVCsSvhlIUMvrlz5xIREYG3tzeRkZHs2LEjz/Hbt28nMjISb29v6tSpw/z5810rUBE/webc/VG5Lr/ouwDQVnGaF5VrC73/kqrUVSSDCarlvKr8hcKeuS8q17LOa6JZqQMYotpqodTN0j3CKUM4KsnAIOUO1nq9m6dSBzDRYwU1MkTIiSsZ3imCG2aLXd4PoCHcoLp0A70sccxgPz711V716d8yjEXD2rpUVhMeSgWtwivzQf8mBd6HM23sTGS7Yi2X+1fwoHGoH5vGdaNPs5Ay1ZlCKHYFpYCWiKMfROdoK3YTMFa0F5Qc3Bn06gpWrlzJ2LFjeeeddzh8+DBdunQhJiaGhATbba3Onz9P37596dKlC4cPH2bixIm88sor/PLLLy6QxvU/pq1yJz+P6pjjk8Tr2v8Rm1X65FnVBiTKTvb5fVIcf3u+zrce0zjhPYIXVX/ymscvXPB+irdUP+LYby7zrup7Yj1fpYV0lpeUvzHBY4XFiNtyRdRytlNntOYVvtQ9ysOaKbyqGU2cITsI/19DbV7UjGWnvilnDNV5Q/siC3T9zOvvv/59Yb+2IAdVKnriH2h0Swbk44rtrDSWOTkm1yUd+9YtX28V//dka3rlcE8WxbXwuU4RBdquRhVLd67j5U5su2Jz839PtmFCTKMCSGbjmJJ7DQTCFVvMVPBUWlnsVh28zKhu9mMfyhsZGj3eHgo3ximUbs1uxowZjBgxgpEjRwIwc+ZMNmzYwLx585g6darV+Pnz51OzZk1mzpwJQOPGjTlw4ADTp09n0KBBLpLKdX/L+kGV6FI/kEytnv0XbgEQXtU6hudl7cscVIwiVLpJN8VRthlaW42xxdZT1t0p3EUFMqmImkeV27mHNw8oDtJVeRyAuljX5hutWkOIdJOd+mb4SGquy/78a4jgCtUAeFu1gjaKM5ww1Ga4aj0Aa7zes9hHf/VH3KYil+RgmknnaKk4x4/6HugxlnXIxIvfDZ3ZpmnF86o/2aFvwV65MQAbDPdZ7OsnfXeGKTdyLXgYxV9ko2yTpjC6Yk1txexdLptKFwDYZ8i7W4ItnC0RktOdO6JzBIt3nuelHq65t4X6exfI8O9ouZOqFT15sVtdpv51Ks9xDhVFdrMzVih2haUAZ5rJYldFuos3aqb9dUoodlmcSkqjz8wdPBpZg+mPtSz248uyTMrd0psNq9FoOHjwIOPHj7dYHh0dza5du2xus3v3bqKjoy2W9e7dm8WLF6PVavHwsO7NqlarUauzLc1pacXXRUWSJL4f0Z4rtzPoNG2LcZmNC2kqlVih78lQ1WZiFPsdVuyeW7rfpfIWlImq5byg+tPu+i36VtSQrvOjvie35YrM8DS6zx9R7uQR5U6LsVv1LYlSnMRbMtbUa6+wvnn9rW/NKO1raHPcFv6V6/Cv3rb7LpVKTNcNzvM7nJWr84HuOR7zqpHnOIHzpElZip2Ud+eJ+tJlAM7Ief8NbD1I63PVCMkvs3Tp8OxODO/0bczgduHUt1HDzr+CB6kZedd3tCmjg+O+eKwlr68y1rI0lzspO0b7fBGuWDdwBx/uyEYLg8iMtWT+NmOQ788HL7vl+It3lu4sypSUFPR6PcHBltlewcHBJCUl2dwmKSnJ5nidTkdKSorNbaZOnYq/v7/5FR5upzZW88cgagxUqeX8l8kHRy7yfxo6APCgcjf+5N8PtjiQMNBYumjVzzYnXRTH8lTqWmZ+zXDtW0RrPmeJPobVhq7UzvyBL7SP2hzfQ3nUrNSZSJe9aJf5FcM1b/CyZgwv5lLqyjK3bt1i6NCh5vN36NCh3L592+54rVbL22+/TfPmzalYsSJhYWEMGzaMq1evFp/QudiZdegq3EWBwa6Nob7iCgD/GarnuT9bSltuxS7E374rt0v9QBqFZJdMUSgkGgT72lQYm1d3vqWZhGSxr9ziTuxrdKN+MrA5gyJr5NjOSJCfl3mZbxGXKHF3zcvyMYuLhMK56xLlqvhKVwiVbnBOLhlNlQXw5abT7hbBJeS+mBobz9u/2tgab2u5iQkTJjBu3Djz57S0NNvKXYeiy4bMKZq9r7bH0Jg4Q00aKxKY6zGL4do3UVPwGl2FoSIZvKT6nQcUB80327/07XhDO4p7ZLuSfcjkE9Vi8+eThlrsMjRhrT6KU3LNPOX/P/1AvCQtjaQEZuoGUUNKoa9yLz0UR4iTa/KrvjMV0NBJ8S9f6R7iOlXYYqhSdF86C3ff6HIzZMgQLl++zPr1Rnf0Cy+8wNChQ1m71naiTXp6OocOHeK9996jZcuW3Lp1i7FjxzJgwAAOHDhQnKKbMbUUU0gyVbhj80HHj7sES7cB+K8A9xl9Du3p/kZBTM6j0HBxhM7kdYQXutZlcLua+Few9DCYYnIDK3mx8oUOpGZo6Vw/sAildD9CsSs0BTuZE+UAGnDFbLHL78ZbXnBVdFtquhZPlYIKntbtXsoygYGBKJVKK+tccnKylVXOREhIiM3xKpWKgADbmWJeXl54eXnZXFdc2O8ZmxOJd7TDWe75CZ2UJ5jLLEZo3yxy2SyReUr5Nx97LLFaE6PcT4xyP6v1nXlDO4qa0jXeVv1IuOI6GbInUer/4zb2i69aI1m4R/+V67A+V9wbwBJ9TEG+SIEpSQlJcXFxrF+/nj179tC+fXsAFi5cSFRUFPHx8TRsaB2L5u/vz6ZNmyyW/d///R/33XcfCQkJ1KxZs1hkz4kOFbflilSW7lFVumNTea4nGc16V+Wq3MXH6WPktNgtztHwfvub3en2+Tan92dCtnGlr+brxfU7xvAOSbJxzkj5PyDkVupM+zLRvk7xZb66qiByQRCuWDdQvXKF7AQKjJmxWr3rToKz1++yYPtZMjR6l+3T1dxT69DojEEP567f5dx1azdZpta2/Ak30hmxdD/7zt+0uf6uWkfLKRtpOXmjzfVHLt3m2OXbNtc5piyUXDw9PYmMjLS6CW3atImOHTva3CYqKspq/MaNG2nbtq3N+LqSgmTnfW4OyQ14TTsagPuVh4u1aHEV0pjjMdtCqdusb81CXV+LcY8od3LO+2m2eb1OjNIY4zdH97CTSp3AEXbv3o2/v79ZqQPo0KED/v7+duNQbZGamookSVSuXNnuGLVaTVpamsXLlZi6TwRKqeb6bjlx1A0Lti1uBjt9uMKrOK8k5sUjbaqzd8L9eY7JLZ3jWbGuxZZSanXMXAfd+kZ3F0uRN0KxKyiF0MYreaks2ooBaPWui+y8/4vtTP3rFF9uLpluxXtqHU0/2EDXz7ai1unp+cV2en6xnUyt3uJnff472y6Ol344xN+nknl8wW6b6+OzCgtrbPym6RodD3/1DwPm/INaZ6043lHrCvCNShbjxo1j0aJFLFmyhLi4OF577TUSEhIYNWoUYHSjDhs2zDx+1KhRXLx4kXHjxhEXF8eSJUtYvHgxb7zxhru+gtPkZ+1eb7iPNfooAGZ5fIUnzgduO0tr6QyHvUfxoHIvANdlf57XjGOk9k0+1j1N7cwf6Kf+2G43gK/0DxW5jMVFSXpeSkpKIigoyGp5UFCQ3TjU3GRmZjJ+/HiGDBmCn5+f3XEOx6IWAF8vFdepDNjvF1tPylLs5PwVO1vo7dznClJPLie5dxsRUNGhfTqTbfpYZA0qeal44r7it6bmljN3K7SiRih2bsAgyyRi7HdpcsXqXGixM3Hggm2LljtISs1k/vaz3E7XmCuZJ6VlcjczW5G6l0up2nEmhbs5lv2XfIfnvzvA8SsF71WZlpG9v+kb4tHoDBgMMompGUz9q2wUUR08eDAzZ85kypQptGrVitjYWNatW0etWsYEhsTERIuadhEREaxbt45t27bRqlUrPvzwQ2bPnu3CUidFhJP3lve1z5Iq+1BPcZW/PMdTGdd3FgkglVBu8IxyA796fWBevknfhg7qOWwyWBZ/PSFHMEgziZGa15mje4ibciWOGurQIvNr3F+/vnQxadIkJEnK82WKh7P1IOBoOIxWq+WJJ57AYDAwd+7cPMdOmDCB1NRU8+vSpUsF+3I2MMgy12VjEkI1yfY1sX6WYnemgIrd548aKxOYEhOKCn+f/D0DdapVsnhAyM+28vljLTny/gNU8y18yMj8p9vQsa7RGDO0g2sTwao48N2dRcTYFZYCPIoaZNmqlp22kLnYti5KJSishacX7+W/5LvsPXeDMT2zexDmJ+Pj83ez5Nl27D6Xwmsrjzp0rLz+JDnN6At3nCcpTY1Cgt+PuC+7rSgYPXo0o0ePtrlu6dKlVsu6devGoUOHiliqosORWXgbX1bru/CcagN1FYkc8X6RD7VPkSgHsM7Q3sG92GeQIpYvPK07dnRXf8EFOdTGFkb0KNlsiGSzITLf8iEC+4wZM4YnnngizzG1a9fm2LFjXLtm3cf2+vXrduNQTWi1Wh5//HHOnz/Pli1b8rTWQdHGouoMstliF5SVIJGbhgqjIumQK9bGsq4NqhH/UR+8VK6NVW5dszK7zhrvfY+0qc7gdvYtmTWr+tClfiBv9XFeuVQpC2+7+mFkezrWC6RHoyDOXb9Ho5D8wyNs3YPCq1bg0s0Mq+Uju9Th8w3xhZYzJ0KxcwMycEk2ugJqSsko0XPpZjqBlQp2AVDr9Az4v39oUcOfz91Q+80R/ks2xtBtjb/Oq70amJfnjOGQJMlK0TuZmEaHqX8X+thXb2fQtUE1coeMmPrCCkofOd0djj7EfKuP5jnVBvPn9zyWA3BFDqCLehaGAjoxBip22FTqPtU+kadSJ3AdgYGBBAbmn+0YFRVFamoq+/bt4777jIkle/fuJTU11W4cKmQrdWfOnGHr1q12E4uKi7DKFUi+VRmAatItq/XVuU6odBOdrOBfuXa++/Oxk2hmT6l75f76zP77jMPy5uTlnvXx9fbg/kZB1A+2VpQksud0ZK0qfDzQ2EXGVvhMUTKycwRRWZY6L5WSxqG2Ffmn2tdk+d5sL4gtJfmbZ+/j0/WneKVnffrPMdaZfKFrHbxUrnecCsWuwBTcHibLcEmuxh25Ar5SBvWlK7z9yzHWv9q1QLEL2+KvE3/tDvHX7vBYW/tPPiarnsFgtFspcxxrz7kb1ArwIdTfuoK/q1HmeJzR5dC0MuwkS+SFVm/AI8dTmVZvsKq91GvGdgC6N6xGnUDrYpnOoNMbXPIUKCg8BYnbuiCHUjvzByKleFZ6fohKMlrKq0tG9+k3TmeLynyiWswQ1Rbzkve0zxJraMFFORjhTi15NG7cmD59+vD888+zYMECwFju5MEHH7TIiG3UqBFTp05l4MCB6HQ6Hn30UQ4dOsQff/yBXq83x+NVrVoVT8/iL6GzcFgkX800lmsxJeHlJFJhjLH+V65Nho1WYmN71adxqJ/5mlnZx7nv8GzH2gVW7Lw9lIUuyl/UcZtRdQJ490HH+tp+PLA5w6Jq03tmrN0x9YIqsbCI+vDmRtyhCk3BXLEyCv41GHvmNVec4/S1u3T5bCuvrTxSKAlyJhTkjEH4bP0pOn+6lZv3NPSZFUv36VvR6g3o9Ab2nrvBE1/vIWrqFnOK9pS1J3lgxnbSNa5PJsipUF6+lW2a7v75VqdTxCesPm5+H590hzYfbuKx+dm/waWb6eb32+Kvs+Qf5zIiF8aeY+S3+9HoDLz/+780m7SBy7fS899QUKw4e94clBvSQr2IBpnfmnvKfuDxPR0UJx3ehx/36KfYa1bq4gzhNM5cwvf6aC7KIQilruSyfPlymjdvTnR0NNHR0bRo0YLvv7fsZxsfH09qqjF27fLly6xZs4bLly/TqlUrQkNDzS9nMmldSb0gX85lWYPrK6wLujdRXATgmJ3knLG9GtC7aQgPtgjjoVbOx+BVrejJ3onZmayuKO8RllUAOapuAK/1akBARU/GPZDt4bGw0pekWCNy19WU8pTP5M4d0DKsSMqcCYudGzD9wY/LEURxkhbSOVbRnSu3M/j18BW+HNzKqf3ZK9GR87yam9XR4cHZO7iamgnAuJ+OsuFEEh1y1PY5lXSHxqF+ZgXo18NXeKp94YJFc5vPVcpseXMqolq9zBYn+3T+fPAyUx5qyus/HeWvf60z2mJm7XBSWks+XmdMqFh79Crf7TZeKL+OPcezHWuz8eQ1wipXYEBLUWDaHRT2cmhqiP6i9jWOKF7AS9Lxo+dHDNe8wRZDmzy3XeAxg97K7Kzt3fomPKWdWGBXblnH3b0zc1O1alWWLVuW55icikrt2rXdWpfMHqY2YdWkNPy4RxrZ2ZeNJaNr8JScnRXaPqIqe+2UiSoIwX72O1EUhJ9GRfHzwcsM7VCLgEpevHJ/PQvFpzizqwtzrPw2XTOmMzfuqQn1r2C3bFdhEFchN2BqRnw060mqo+IEzrp2Z2w6TZ+ZscQn3bGwgOWHSakDo7Ki0RmIPX3dvCx32RU7ZYwsuHQznZhZOyz2k5OdZyzbUk3PI1A0vQC19zQ6g02lDrDIqi0Mvx6+Yn7/3e6L9PxiO9P+OsWyPRddsn9B4SjMLTcDb+bpB5g/L/Gczkil/XZeVUmzUOrAqByWdqWuR8NqbHqtq811IzpHFGrf/9moUykoPOl4c0M2Wn9qSDmvvzLNFMaH85OGWjmWlmxqVPFhbK8GBGTFm+e2ZhXn44Gzip0zdfY8VQpz2FPOVmeuonRfidxJIZ7eTJvGGlqQKXtQV5FIXcnxIP7jl1OZ/fcZTiXdoffMWLtxeUcv3TYXAXZWNvsLrOny2VbiEtMYtmSfzfVjc7mXN560zkgrDE8t2uvS/dli53+2e6YqS1KBrnJGXn0jnWWmbhCPqCeRIRvjjN71WM4F7yGc8HqO1Z7v00dhPLclDExQ/WCx7QfaZywsJaUVT5XCZvxok1A/3uxt3Y3BFs91qs2pD/vQMFdA/Olrri8tIzBySa4GWCp2NaQUAqQ7aGQlcTksdiXR6ugMJbk7k3VbRse269sslOGdIpgzpLXLZBGKXWEpYLkTgDv4cFQ2Wu3aKBwPQr1xT23xOS/lYrsdK5o9ZGD1Iet4jcJwJ7Noi/6a6uK5A2espQLX4tpfXuKQ3ICu6i85kcPCUVFS00bxH/M9ZzJE+Tc7vMbymCoWvSzxrOZNamf+wLf63i6VxJ3YOp3/fKUznk4kDHl7KFn0jGWQ+OA8kroEheOyWbHLfvhsKRlDb+LkWubewv+M71ni4tJKMlWcTCapE1iRtrWq0KtxsFNJkAqFxPv9m/BgC9eF9AjFzg3knFyHDMaabm0kxxW73DF1SWmZdkZmK5GO8tvhK4z7KbtenD0Xp0BQkpCRqVqx8JmJ16lCP81UvtQO4p5s6SL5xGOx+eb5lvZFthlc94RdEpCQLK4tDYIr8eHDzbKK+2aPC/HztmvBM8XS1ahSgVbhlc3Lh0XVLgqRBeRU7LIf4htkJVPkdMNWr1zB6ftBSaM4HqPnP92GzvUCed/BjFgTCoXEqlFR5ocaR1qPFRVCsSswhXDF5tj2qGS8QDpjscttoHtjlf3CvT/tv8SP+xLsrs/N0l0XLD7vOnuDK7eNmauZWj3r/00iMTWDkd/u5/cjV2zsoXyRu7SKoPjIPQ+2v9mdDWNtx4g5yyz9IJqqv6F25g/Uz/yOy3J2fbT3tc/wi8E1xyksJ6e41lqY09Lw9dC25ir7kiRRvXIFfDyVbH+rOy/1qJfnfiRJ4rsR9+X47FIxBTkwKXY1pewQlwgpEcCcNftYpDHJorRfrYrjPOrTLJRlI9sTVIDEkJLiKhZZsYXG+T9k9wZBrDxwiTB/b/7NMKZyN1RcNmc1/XsllWbV/bl44x4qpYLqlS1ryzmTYfb3qWT+djLTNDenEtPwVCqY9fdplu3JVhI3xyXbTJP/bP0prqWpmf5YixJzohcVu8/dcLcIAgAZfL09aBji+vY8WlT0V39ES8U5dhqaoXPzZfOTgc2Z+Otxmob54ePpOlkkydIVmzvMYNub3THIcp5dCHJO95zWv8L2FhXYx1gvEWpJ2df52pLR03JBDgFg6iPGkj6l3GBX5u8nrkIodm7g/f5NaFrdj+gmIUR/uZ3zhmAiFNdopfiPWENLfjpwidqBFen2+TYAzn3S1+LCWNzn9ohvD9hd9/NB63g8U2mVcyl3aRbmX2RyuQIvNERISZySwwEJLzS0lM6SjhcqDMTJNc0xKoKShZ+3B/WCKqHTGwrctcVRbuHHNkOrIj1GbhqF+HIqyTrpYFBkdXo1DqKKC1zPuVHmoYx55IqzG9WtLvO3n+WRNtVZfchova8d4GNenzNQX+h1RYdJsaspJSNhQEYiIkuxO5er68nA1tU5cum2Q22xBKUXodi5gYpeKnPMyaDIGhza14AIrtFOEU+soSV6g8y1HHFzBllGgUTs6etU9FKVKLdGbjdwzov54YTbHE64XaD9+nOXNHyQiyxaQGaEch2vqH7FXzIWHD5pqGUu6mlCL0vslxvxiXYIx+TCVUoXuBaFQmLj2K7IWCohFTyUBepiUtL4bvh93PeJdTs9CalAbqIaVSrQMNjXrgVfkiwtIvkpY2/2bkjvpsE0q+7Po5E12HkmhSfvy87ArOipok5gRTR6A0G+rq13JsjmqhyAQZbwkrQEcAcJGV8pA70smVtXmv6uQzvUon5QJZrVcP0Dd40qPvkPEhQL5SLGbu7cuURERODt7U1kZCQ7dhSuaC3gMpv2oDY12GNoDEAXhbGLgt4gW7gxjl5OJSk1k2FL9jFo3i67BYlLAhET1jm9TU3pGgs8ZrDQ4wuiFCfopDjOPq/RnPd+mgveQ+iuOIwXGottKpJBQSNG6kpX+NvzDd7zWG5W6gArpQ5AKcl0UMSxxus9/vF6mdWe7yNhAGR6KA5TGVHGwZ0oFJKVy/CvV7swtld9iwD/v1/vZnZHlRbsKW/2FK5+zfPuSRvi502TsLyb1ivsuFJtoVRItK5ZBQ+lgo51jU3ac5ZLUSgkNo3rxrY3uovs8SJEh4oUjIpasHTT7Ia9IgeiwTI0QaGQ6FgvED9v14UsLB/ZnkfaVGd8n0Yu22dZwJ1u7zJvsVu5ciVjx45l7ty5dOrUiQULFhATE8PJkyepWbNm/jvIDxdcrw4YjDegRlICSvToDbLFbgfN22WRYTZi6f7CH7QEUIl0hij/ZqLHCvOyB5QHrcYt9fzc7j5W6Hrwnu45vNFwl/yfGJtKF1ju+TGVpXsAJBiqMVr7Kl0U//KYchuVpbuM1LyBv3SPHYbmfKJazOMqY6/Z6tINqks3OOQ1iiqSseDqNn1LMDwOinLxjFQqqB1YkbG9GrA1PtsyVbdaJepWq2TRgq60Yk/hGti6On8eT7S7nSRZXq5+ejGKxNQMXv3xiHE91kpyYTHuTyh1RU2SXIUg6TYh0k0CJGP5J1N8HRTtX6BTvUA61QvMf6CLCfUXVmB7lHnFbsaMGYwYMYKRI0cCMHPmTDZs2MC8efOYOnWq1Xi1Wo1anV0nLi2t6GukXZCDuSd7UVFSEyElojfUtHK3Hrl02/z+Xj7dGapxmxkec+moOMEr2pf509Ce/Kb2w4qdBEiprNJ3I0S6RSUyOCQ3yHObgtBWOsUTqm1ckQN5XLmNUMl2O5UrcgDX5cq0UpzNc39PqrbypGorellCKcmcM4QQL4fznT6a3YamFmM/Ui3maVW2a+tT7RPM0/cHJP7V12Gevj8VUBsbZmc9bb2le5G3dC/QXXGUsaqfaaU4Z1bqALS+1cGgA4WIwytpdG9Qjbf7NMrXSuVKPFUKu0XBY5qFFLh8UFSdAHOiTl6GtF6Ng9gcl4ynUoEmVxeZ3IHn90VUBTArdlDyWn8JHOOaXBU4T4h0i/Cssic54+tKsJPHaTaP60q6Rm/uTiGwpkwrdhqNhoMHDzJ+/HiL5dHR0XYbN0+dOpXJkycXh3iA0VwroyBOrkVb6TRNpIucvd6UuETnXHxVSWOx53RaK/6zWP6V52ya6gbwme4Jq23qSZf5QPUdKgxEKY3Nz9/zWG4xZpEuho90Q538VtZ4oeFLj7n0VVp3p/hG15tPs+T71GMh9aQrvKgdZ07j90LDcOV6/qf6nb2GxlyXK5NGRR5U7jbXFVNKRk2sjiKJOiQRo9yPRlYyRTcMNR48p9xgdrX+ZwjjYc0UGxY+yajUWSGxzdCKbZpWtJFO01+5m+uyP8v0DzCqexseUAmlriQiSRL/6148cZGh/t58MrA51++qeevnYzbHzHs6ktrj7bcqywt9Dr9OXpmBXw9ty66zN6hZ1Yeun2+1WJfvvb0M3fzLG0lyFQBCpJs0yuoR+59sXbGgLFAvqHQkfvRoFMTUv07hX8H1mfr5UaYVu5SUFPR6PcHBwRbLg4ODSUqy/eQ8YcIExo0bZ/6clpZGeLiNqukPTIZub4NX4U4yU027fw21aas4TZTiJGsud2LUMmuXZF48rdxspdSZGK1aQ6yhBecMobzn8T39lXsc3u9I1V/sNzRkg+G+/AfbIUJKZKvX61bLDxnq8ZzmLVKpZF72qnaM1Tg1nszTD7Do5wkwTfck9aXLRsVPtZ4W0jlOyrVoLp0jQnENT0nPRx7fWGyzS9+El7UvO+S2tcUhuQGHdNmWzNJePkDgGnZPuB9wXdeWb55rZ/HZ18uxS7VCIdG5fqBNq2F+MXNCryu9JMlG62uodJOmWT1iTxhqm9eLMiHFT4NgX2Lf7EFApeJ/8C/Tip0J6x5ust0T3cvLCy8vB0y8FaoYX4XEpBhsMLTjWTbSX7mbz3WDuYlj7qOHFTuZ6TnXYtkZQ3We077JZTmIhR5f8IDyID96fpTnfk4YahEgpREi3WKXvgmrDV2Y7rEAgPc8lrFJ3dbpJucBpPK+x/c8pMy2jq7SdeVD3VC80ZBMZQp7OzkjGwtvjtOOzrFUZrByG8OVf9EwqwL7Bn1b3tU+x3UK/zfLSWnvvVieqezjwfY3etByykaHt9n0Wlce+DK20Meu6KmkZkBF4hKzQz1yuk97NAyyGN8qvDJd6gdS2cE2R54qBX+83Bm9Qeahr/4B8nfHeSoVeHlkz/FKDiqTAvdzOus6+KjSeG7qZcmiR6zAPdQMcE+mcJmeuYGBgSiVSivrXHJyspUVz12YWrzsMTQmzhBOY8UlRqrW2XSd5kSBgZkeXzFAudti+WD1exyQG6DHWET0Xe1zNhMSAC4aghin/R8yEiflWgRKadSTrrDN0BKQWKuPYr/X/6ghpXDO++msmLQBNveVmyjFCVZ4fmyx7C3t8/yk7wFQxE3TJVbqe7BS34PK3CFQSuW/rAufq8lZ3kFQuvjxhQ5U8nbuElgzwIc/Xu7Mg/+3s0DH7FQvgH/+u8Hj7cLZe84yvlRv4yFh7lNtWP9vEiO71KGCp/3CwAC1Ay1vIs2qW5a08FTZfjB7t19jlu25yJt9GuLtoeSnF6PQG2QqCsWu1LDN0Iobsi8BkjGE55wcRiYiBq28UqZnrqenJ5GRkWzatImBAweal2/atImHHnrIjZJlI5v/VzBb9wjzPGfxuHIbM3WDrFLVc/Kq6hcrpe5R9fsckC1Tzq9RlWaZi3hCuZX2ijg+0j3NxRzZUjm5LFczx7WB0QX6k747I1V/AfC2x4+0UJxlru4hjst1rL5JG+kMOpSMV62gY1bMHsAPup68qxvutMXPFdzGl9ty0cVkiADe0kujED+nLa4KSaJZdX+8VArUdpIk8mLB0LbsO3+DTvUCGfiVZZyvrfZ0fZuH0jefMiZ/vNyZ5DuZdmOPPnq4GV/HnmPygKb8dviq1fqRXeowskv2fDYlVQhKD1pUHDbUo5fyMACHDXm3fBOUbcq0Ygcwbtw4hg4dStu2bYmKiuLrr78mISGBUaNGuVs0wDJGa6OhLYlyVUKlm/RR7GONoZPNbZ5QbuFV1a8AHDHUYaTmTVLww55b8y4+LNL3Y5G+n9Py/Z9uIOHSdXorjd0nYpT7iVEay61s0rfhVe0YDEj87DmZZooLFtueMVRnoGZygePZXMmzHWtb9cEVCJyNPTLFqXkqbSt2tso++Hmr+GigsYZeJS8VPRsFZx3bWWltY7TM2S84+3SHWjyd1fP1mY61+PnQpXyVRUHpwxRnB7DDULpqNgpcS5kvvjV48GBmzpzJlClTaNWqFbGxsaxbt45atWq5WzQAalbNVnr0KFmh6wnA06rNVmOjFCdY7/k20zwWmZc9oxmfVZyyaIJjU6nEi9pxRGbOY4WuB7fk7ESHB5SHOOk9nFPez1kpdX/q76Of5pMiUerWjLGt8OZFnWqFc/22zFFHUFB6GZ2VJftuv8YF2t5U5s2enS/Yz5utb3Q3f57xeEuOTerNgJZhVmNzK3bF0eapso8nsW/2YEJMwb6/oOQSJ2ff0/4qRLKboPRT5hU7gNGjR3PhwgXUajUHDx6ka9eu7hbJTDVfL169v77584/6HmhlJfcp4qkj5XSbyMz2+D8aKS6ZlzyinmSRUVqU3MCfCbrn6aaeYdfM/6H2KXqrp9FL/Rkvacfm6UouDAWptWWw4ebKi9yB4ytf6GA1pmkx1kcTuIY3ezdk1/ieFq5HZ3DEwpczCy6qboD9feU4j78eGsn4GGMYhU8+sXSFRWRIlh1e7pl9Lf5Z35WvdAPoq/4EXdl3xgnyoFwodiWdnAVUk6nCIdmo6H3nOQ2Q8UTLFs/XqZZVUTxT9uBlzZgiKSCcH2lUYqBmCrUzf2CY5m30svEm8af+Phbr+xIv1yyyRAUTBbkvOaLXnf4oxvxelmXG9Mi+aNo65rfDxVNxaUOSJMIqV8hzTGEr2jt6eppq7MU0CyG6aQjdGlTjpxejiH2rR6GOLyg/PNIm+1qrxpPPdU9wUq7tPoEEJQKh2JVA1uqjAKghpXDB+ylOez9DHYUxs/cPfXsaqb9lraGjO0UEINbQkrrq5dTO/IGXtGMprkpYBVHsqlfJ+2YOllmDMpbxUrZqgAWKxIlyi6NJF3kN69s8lH/G9+SrIW0Ao9J5X0RVcV4VA7du3WLo0KH4+/vj7+/P0KFDuX37tsPbv/jii0iSxMyZM4tMRkewdx6KbP3yjVDsSgC55+Yy/QNM0I6wGpckV2GM9pViksq9vNDVvqvMWVfsyM4RdG9Yzam6XLn/JkrhvhLkYHA7+zfOCh7ZrtQq+dSdq165AgoX92cV5M+QIUM4cuQI69evZ/369Rw5coShQx3rsPPbb7+xd+9ewsKs4yaLG1tqXfV8LNKCso9Q7EooK/T384zmbfPn2bqH6aCeQ3mpD59XnFFeOlbuAPR+zUN598EmeKmUNn+5/llB7U1CLePl5Kx/OY+Zc0xBEjgEpQNHZtjbMQ3N7yv7WMaSqpQKdrzVg+1vds+39pyg+ImLi2P9+vUsWrSIqKgooqKiWLhwIX/88Qfx8fF5bnvlyhXGjBnD8uXL8fDIP4ZYrVaTlpZm8XIltgx2kgQtatjPkhaUfUSEZYnAtjl9u6EltTN/KGZZSga2iqmufKEDAZW80Bns1w+b2Lcxw5Zk96N998Ec2X827thd6gfy6v31qWHLVZvjzyJJEkuebcfyvRcZ0r4mof7iqbiskns2ju1Vn5mbz1gs81Ip2TW+Jx//GcfwzhFW+wiv6v4SPwLb7N69G39/f9q3b29e1qFDB/z9/dm1axcNGza0uZ3BYGDo0KG8+eabNG3a1KFjFXXvcU+l9XVSluHxtuFsPJHE1vjrRXZsQclFWOxKAMXVlSqylnU7ramPNGdprr6UD7dy3sXQt7ntoscm5gxpzcbXbGcjj+5el99f6oQyh0tqaIdaNKtuaUVrXyeAekGV8nTFqnK5tXIqYLbi5JqF+VMvqBLeHpaWFVm2vsGH+HvzenRDodQJAAirXIGvnmpjc14JSi5JSUkEBQVZLQ8KCrLbQxzg008/RaVS8corjofDTJgwgdTUVPPr0qVL+W/kBDUDfHiqvXVYgFIh8VT7klHSS1D8CMWuBOBkJY4CsWBoJPOfjrRa/lhkDavYsw/6N+XI+w84tf+84tdqBfjwYIswGgTbrtMV4u9Ny/DKxH/Yh3f7NeanF6Pw9fbgj5e7cHJKb57tWNui3EherlilQuKVrBIAP4+Kslg3KCuDLLJWFba/2Z0Vz3ewyEgG6FDHWOTzsbZFm9krKDkceLcX29/sbv5cPoIdyh6TJk1CkqQ8XwcOGAut2yr5klcP8YMHDzJr1iyWLl3qVLkYLy8v/Pz8LF6u5uOBohixwBLhii0B5OVatMeLXesgA1/HnrNYfurDPgxdvJdO9QIt3Ee1Anyo5utF+4iq7D2f3aNSIUnUCrAs3uuhUlDJS0VlHw9up2sdkkepsP+MsOrFKJvLv3isJdtPX+eJrEB0lVJhVV/Mx1PFpAGWbo/8LqvjohsyLtranfJ2TEPui6hKVN0A/Ct4WH1vgIXD2rLr7A26NajGgQu38jmSoCwQWMlLZKKWAcaMGcMTT+TdY7t27docO3aMa9euWa27fv263R7iO3bsIDk5mZo1s61jer2e119/nZkzZ3LhwoVCyV4UiHyv8otQ7EoAWr3zJjuFQuKt3g2tFDtvDyWrRhlLofx1PIn4a8am0KbsvNyWNUkyFkn+7NEWvPXzMQA8lMYrwoCWYXy3+6JD8tgI9eDCtH55PgUPiqzBoEjnLWN5XbC8POwHq3uplPRplrfL2Nfbg95NjWNku/0FBAJBSSMwMJDAQOuWbrmJiooiNTWVffv2cd99xlqUe/fuJTU1lY4dbZeRGjp0KL169bJY1rt3b4YOHcpzzz1XeOGLgOIK8RGUPIQrtgSg1TtvsZPl/CvI//hCBx5uFcZng1oQ7Gcsumpl/craR9sccUIeWda3V3J0xDBhL/4udzmQfRPvt9i/a7He5yv31+eR1tVp6cJssFpVC9eGTFB6WT4yO7Be3CDLFo0bN6ZPnz48//zz7Nmzhz179vD888/z4IMPWiRONGrUiF9/NfbkDggIoFmzZhYvDw8PQkJC7CZbCATuQih2JQBdgRQ7491mx1s9iMmyQjUItmwvVqWiJzOfaM3j7cLNy8Kr+nB/I+vA4TrVKjEsqhYv96xnrquV0z31Tt/G/P5SJ2Y+0dqmPLldsUF+havenxe2dMVxDzRgxuBWLlUkawb4sHxke9a90sVl+yxqClJ49dlnn7WKRerQwbqFWnmiflDxtOoTuIfly5fTvHlzoqOjiY6OpkWLFnz//fcWY+Lj40lNTXWThIVHuGLLL8IVWwIY0LI6MzefoUv9QH47cjX/DcDsUgyv6sO8pyO5eOOe2SqXH3OGtOHwpVu0q13VYvmUh5rZ3SameQg1qhhLOEyIacTUv05ZrLflii0q/CsUTQ9aW+TsPlEaGDJkCJcvX2b9+vUAvPDCCwwdOpS1a9fmuV2fPn345ptvzJ89PfMurFuWyf1wIAx2ZY+qVauybNmyPMfk112kJMXVHf0gmpaTNwKOd0URlF2EYlcC8PfxYO/E+1EpFRaKXUVPJfc0eqvxTcP8aF3TssSCrUQAe1TwVNKxrmMKy+ZxXbmVrjUrdQAvdqtrpdgVZ/X8wEpezH6yNa+sOAxgVRalvGIqvLpnzx5zja6FCxcSFRVFfHx8ni4jLy8vQkLyjj/MiVqtRq1Wmz+7uvCqO7G6MYobpaCEk/NhV5ytAuGKLSGobJi8OtcP5N1+2QV2H24Vxp+vdOa3l4qv60G9IF8ryx7AiuctXXU5a8RNf6xlkcs1oGUYB97txZu9G7Lk2Xb5b1AOyK/wal5s27aNoKAgGjRowPPPP09ycnKe46dOnWp29/r7+xMeHp7n+NJGxRxJRnkl5AgEAkFJQyh2JYzoJpbp9jnLf/h6e9A0zB+P4vR72iGqbgBxU/qYP+c02HWpXzzuy8BKXrzUox5BvkUXz1eaKGjh1ZiYGJYvX86WLVv44osv2L9/Pz179rSwyOWmqAuvugNTt5O2tatS0UvFshHtWT6yPV42uqAIBAJBSUVcsUoYXz3VxmrZZ4Na0LZWFV57oIEbJLJPzj6Y9XIEm+fllDWVUhE4TlEWXgUYPHgw/fr1o1mzZvTv35+//vqL06dP8+eff9rdpjgKrxY3G8Z25dX76/Phw8ZY0871A0tdjKWg/PJsx9oAjI9p5F5BBG5HxNiVMGxZ4x5vF26R2VqS+OV/HTl5NZXuDXJYivLQ3Va+GMXkNSd478EmRS9cGaEoC6/aIjQ0lFq1anHmzJn8B5chIgIrlriHJ4HAUT7o34SXetSjmq8otl3eEYpdCSavnqglhchaVYisVYVb9zQOjW9Tswq/j+lcxFKVLYqy8Kotbty4waVLlwgNDS2wzGUJkTshKA1IkmSh1IlyJ+UX4YotwVT2Kb6yHoXFxyvbLevnXXrkLksUpPDq3bt3eeONN9i9ezcXLlxg27Zt9O/fn8DAQAYOHOiur1KiqJhHH2SBoKTirRJJP+UVccUqgcx+sjU/7b/Em71LT0VzL5WSrW90xyDLeIssQrexfPlyXnnlFaKjowEYMGAAc+bMsRiTs/CqUqnk+PHjfPfdd9y+fZvQ0FB69OjBypUr8fX1LXb5SyKDIquzOe5asSUFCQSuoEOdAB5sEUr9IDGPyxuSLKoZ5klaWhr+/v6kpqaWiQBxQdFTHs+Z8vidBYWjPJ4z5fE7CwpHQc4Z4YoVCAQCgUAgKCMIxU4gEAgEAoGgjCAUO4FAIBAIBIIyglDsBAKBQCAQCMoIQrETCAQCgUAgKCOIcif5YEoaTktLc7MkgtKC6VwpTwnnYp4InEXME4EgfwoyT4Rilw937twBIDy8ZLb0EpRc7ty5g7+/v7vFKBbEPBEUFDFPBIL8cWaeiDp2+WAwGLh69Sq+vr4WjdTT0tIIDw/n0qVL5bYeUXn/Dex9f1mWuXPnDmFhYSgU5SPaQcwT25T37w9inuREzBPbiO9v//sXZJ4Ii10+KBQKatSoYXe9n59fuTwRc1LefwNb37+8WCBMiHmSN+X9+4OYJyDmSX6I72/7+zs7T8rHY5JAIBAIBAJBOUAodgKBQCAQCARlBKHYFRAvLy8++OADvLy83C2K2yjvv0F5//6OUN5/o/L+/UH8Bo5Q3n8j8f1d+/1F8oRAIBAIBAJBGUFY7AQCgUAgEAjKCEKxEwgEAoFAICgjCMVOIBAIBAKBoIwgFDuBQCAQCASCMoJQ7AQCgUAgEAjKCEKxKyBz584lIiICb29vIiMj2bFjh7tFKhYmTZqEJEkWr5CQEHeLVWTExsbSv39/wsLCkCSJ3377zWK9LMtMmjSJsLAwKlSoQPfu3Tlx4oR7hC2BiHki5gmIeZIfYp6IeQKumydCsSsAK1euZOzYsbzzzjscPnyYLl26EBMTQ0JCgrtFKxaaNm1KYmKi+XX8+HF3i1Rk3Lt3j5YtWzJnzhyb6z/77DNmzJjBnDlz2L9/PyEhITzwwAPmZt/lGTFPxDwxIeaJfcQ8EfPEhMvmiSxwmvvuu08eNWqUxbJGjRrJ48ePd5NExccHH3wgt2zZ0t1iuAVA/vXXX82fDQaDHBISIk+bNs28LDMzU/b395fnz5/vBglLFmKetHS3GG5BzBPnEPOkpbvFcAtFOU+Exc5JNBoNBw8eJDo62mJ5dHQ0u3btcpNUxcuZM2cICwsjIiKCJ554gnPnzrlbJLdw/vx5kpKSLM4FLy8vunXrVm7OBXuIeSLmiQkxT+wj5omYJyZcOU+EYuckKSkp6PV6goODLZYHBweTlJTkJqmKj/bt2/Pdd9+xYcMGFi5cSFJSEh07duTGjRvuFq3YMf29y+u5kBdinoh5YkLME/uIeSLmiQlXzhOVy6QqZ0iSZPFZlmWrZWWRmJgY8/vmzZsTFRVF3bp1+fbbbxk3bpwbJXMf5fVccITy+tuIeWJNeT0XHKG8/jZinljjinNBWOycJDAwEKVSaaVBJycnW2na5YGKFSvSvHlzzpw5425Rih1T9pY4F6wR88QSMU/EPLGFmCeWiHnimnkiFDsn8fT0JDIykk2bNlks37RpEx07dnSTVO5DrVYTFxdHaGiou0UpdiIiIggJCbE4FzQaDdu3by+X50JOxDyxRMwTMU9sIeaJJWKeuGieFD63o/zx448/yh4eHvLixYvlkydPymPHjpUrVqwoX7hwwd2iFTmvv/66vG3bNvncuXPynj175AcffFD29fUts9/9zp078uHDh+XDhw/LgDxjxgz58OHD8sWLF2VZluVp06bJ/v7+8urVq+Xjx4/LTz75pBwaGiqnpaW5WXL3I+aJmCdinuSPmCdinrh6ngjFroB89dVXcq1atWRPT0+5TZs28vbt290tUrEwePBgOTQ0VPbw8JDDwsLkRx55RD5x4oS7xSoytm7dKgNWr2eeeUaWZWOK+gcffCCHhITIXl5ecteuXeXjx4+7V+gShJgnYp7Ispgn+SHmiZgnsuy6eSLJsiwXyn4oEAgEAoFAICgRiBg7gUAgEAgEgjKCUOwEAoFAIBAIyghCsRMIBAKBQCAoIwjFTiAQCAQCgaCMIBQ7gUAgEAgEgjKCUOwEAoFAIBAIyghCsRMIBAKBQCAoIwjFTiAQCAQCgaCMIBQ7gUAgEAgEgjKCUOwEAoFAIBAIyghCsRMIBAKBQCAoIwjFTiAQCAQCgaCMIBQ7gUAgEAgEgjKCUOwEAoFAIBAIyghCsRMIBAKBQCAoIwjFTiAQCAQCgaCMoHK3ACUdg8HA1atX8fX1RZIkd4sjKAXIssydO3cICwtDoSgfz05ingicRcwTMU8E+VOQeSIUu3y4evUq4eHh7hZDUAq5dOkSNWrUcLcYxYKYJ4KCIuaJQJA/zswTodjlg6+vL2D8Uf38/NwsjaA0kJaWRnh4uPnccQdz587l888/JzExkaZNmzJz5ky6dOlic+zq1auZN28eR44cQa1W07RpUyZNmkTv3r0dPp6YJwJnKQnzpLgR80TgLAWZJ0KxyweTudzPz09MRIFTuMvVsnLlSsaOHcvcuXPp1KkTCxYsICYmhpMnT1KzZk2r8bGxsTzwwAN88sknVK5cmW+++Yb+/fuzd+9eWrdu7dAxxTwRFJTy5JIU80RQUJyZJ5Isy3IRylLqSUtLw9/fn9TUVDERBQ7h7nOmffv2tGnThnnz5pmXNW7cmIcffpipU6c6tI+mTZsyePBg3n//fYfGu/s7C0of5fGcKY/fWVA4CnLOlI+IVYGgnKDRaDh48CDR0dEWy6Ojo9m1a5dD+zAYDNy5c4eqVavaHaNWq0lLS7N4CQQCgcD9CMVOIChDpKSkoNfrCQ4OtlgeHBxMUlKSQ/v44osvuHfvHo8//rjdMVOnTsXf39/8EgHhAoFAUDIQMXYCQRkkdzyGLMsOxWisWLGCSZMm8fvvvxMUFGR33IQJExg3bpz5synAVyAQCFyFwWBAo9G4W4wixcPDA6VS6dJ9CsWuNGEwwPltENYaKlRxtzSCEkhgYCBKpdLKOpecnGxlxcvNypUrGTFiBKtWraJXr155jvXy8sLLy8txwVLOgDYdQls6vo1AUN64tB8qVYMqtd0tidvRaDScP38eg8HgblGKnMqVKxMSEuKyRCKh2JUmDn8Pa18Bn0DoPBbaDgfPiu6WSlCC8PT0JDIykk2bNjFw4EDz8k2bNvHQQw/Z3W7FihUMHz6cFStW0K9fP9cLNqet8f+3zoOP/dg9gaCkMnXqVFavXs2pU6eoUKECHTt25NNPP6Vhw4auOUDKGVic9UA1KdU1+yylyLJMYmIiSqWS8PDwMlvAWpZl0tPTSU5OBiA0NNQl+xWKXWkibq3x//QU2Piu8X3Hl90nj6BEMm7cOIYOHUrbtm2Jiori66+/JiEhgVGjRgFGN+qVK1f47rvvAKNSN2zYMGbNmkWHDh3M1r4KFSrg7+/vWuHSrgjFTlAq2b59Oy+99BLt2rVDp9PxzjvvEB0dzcmTJ6lY0QUP2IlHC7+PMoJOpyM9PZ2wsDB8fHzcLU6RUqFCBcDoVQkKCnKJW1YodqWJO4mWn6+dcI8cghLN4MGDuXHjBlOmTCExMZFmzZqxbt06atWqBUBiYiIJCQnm8QsWLECn0/HSSy/x0ksvmZc/88wzLF26tPACiYpKgjLA+vXrLT5/8803BAUFcfDgQbp27WpzG7VajVqtNn/OM3tcLTLLTej1esDogSgPmJRXrVYrFLtyhSyju3He8g92dAVEvQQhzd0llaCEMnr0aEaPHm1zXW5lbdu2bUUrjJwzRqb8FKMVlG1SU43u0rzKAk2dOpXJkyc7tsM/XnOFWGWK8lK82tXfs2w6rssiGbdQ6e5ZL5/fGbQZxS+PQOAoctkPfhaUL2RZZty4cXTu3JlmzZrZHTdhwgRSU1PNr0uXLjl6ABdJKiiPCMWulJCRcgGA67Ifh548Yrlu5zzrDQSCkoJQ7ARljDFjxnDs2DFWrFiR5zgvLy9z+zCn2ogZ9C6QUlBeEYpdKSF5x7cAqCRo3aA26pcOmddd2/OjeMITlFzEuSkoQ7z88susWbOGrVu3UqNGjaI5iL5s124rDyQkJFCpUiWOHz9e7McWil0pQbqyz/jGsyKSJOFVrS6HB24HoLY6nsRTe90onUCQB8JiJygDyLLMmDFjWL16NVu2bCEiIqIIDyYsdqWdsLAwjhw54rpyOE4gkidKCZkZxji6xDbjMJUmbt2yFWf+bEx9TRzHjx0gtHEH9wmYRaZWz+Kd57l8K4NKhjSiGtWkSXg1Qvy93S2awF3kVOzKSTC0oOzx0ksv8cMPP/D777/j6+trLgvk7+9vLlnhMgw61+5PUOyoVCrq1avnnmO75agCp7h6K51wwxWQoHbT9hbrFIH14WocdxOOuUm6bLQ6PbO+/pru15bykuKUceG/8Lx+Ai+9MIpW4ZXdKp/ATQiLnaAMMG+eMZa5e/fuFsu/+eYbnn32WdcerBx0WxAUHcIVWwq4sfX/qCAZYy58QhtbrAsMbwDAI/dWwiR/2PJRscsHQMJePD6qytvXx9PepNRlsVA5laoL27Lx7w3IIii4/CHKnQjKALIs23y5XKkDYbETFAqh2JUGLu3Lfq+yLNjo36Kv5djYz4tBoFzIMpotn+Q5pKbiOtE7HmftB/24fCu9mASzT2q6Fo1OPBUXCxaKnUikEAjyRcTYlXpmzZpFREQEPj4+PPzww+a6h8WBUOxKAbfvGSuXxzccZb0yrA0631yZWXqt8wc5sgLObS+AdMCVQ3he2AbAYWUL1H1nGXsdTkqF92+SUje7Z+kA5W6uz+pBcqp7lLuDF2/R9qNNtJyykZhZsZxPsVEbUOBaRFasQOAcwrNRqpk4cSJz5szh22+/ZefOnRw+fNjxQtUuQCh2JZwMjR7vTGOD4MCIltYDJAnVw3Msl22e5FiMxu0EWDsWDiyB30bBdwPgfKzTMqrP7QTgb31rrj70E173PZu9UqEkcOhSeOYP86LWnGL+nGmcvX7X6WMVFJ3ewKfrTzFo3i5S7hrd2mev3+OhOTtZ/29iPlsLCodQ7AQCpxCu2FLL/v37+fTTT1m5ciVdu3alTZs2vPjii/zxxx/5b+wihGJXGLQZRW6NOHbpFnWlKwBUrdnY9qCIblwIfzj78+45MKWKUWHLi1+eh4PfWLayOfSd0zImHvoTgJPerYluGmxHxi7w/k0MSi8AYtR/MfLbA2Rqi+fJ9JN1p5i37SwA7/v9ySn/V7jgPYSt8gjeW7aFaX+dQhaWpaIhpytW/MYCgW0qhWS/FwlHpZbp06fTs2dP2rRpY15WrVo1UlJSik0GodgVlLvJMLUG/DC4SA8TfyaOqtJdDCiQqtlR7BQKajz7DTuUlhmz/PEaXNpvexuDAS7tsV5+85xT8snqu4TdPghAk66P4KHM45RSKFEMWQlAO8Vpgm/uZ8k/5506XkH46cAl83EWtr7AcM1yvNXGSRYg3WGt1zus2n6I1YeuFLks5RIRYycQ5I+Xb/Z7YbGzQJZl0jU6t7yceeBXq9WsXbuWgQMHWizPyMjA39/f1T+LXUS5k4Jy/Gfj5DuzgbRMLX7eHkVznDN/A3DdvznBHvZrwamUCuROY7m5/XmqSjlcnEui4YNb1htsmWL5ObQlJB6FKweN2bWBDWH0blAo7cumUyNNrY4ncEkOIuo+B+ro1e5sfvuj50fM3XyMCZuqoQysz+hnhxFWpWL++3CCdccTefsXYymYaWE7eCDOuv1aiHSLMarfeOe3KtwXUZXwqj4ulaHcY2GxE5YIgcAmORMmRIydBRlaPU3e3+CWY5+c0hsfT8dUpUOHDpGRkcHrr7/OW2+9ZV6u1Wrp0aNHUYloRamz2M2dO5eIiAi8vb2JjIxkx44deY7fvn07kZGReHt7U6dOHebPn+8iSbK1+Pd++9dF+8x1BFlGe/MiAFKYjfi6XLTv2pueym9omfk1CRWbZ+3EYG2FW/Uc7PzS+L7JwzDxKgzPNWlS4uHEr3kJB193N3/cETIMHy8HlFulB4w5aP44WrWGqR6L+Sh1PGGzwtBcOZr/PhwgU6vn0/WnGPPDIWQZeoZLDL69yLiySgS8f9OY3DFkFQBPq7ZQQXubyWtPuOT4ghyUB1esLEPCXjjwDVyPNy/O1OqLLdxAUMrJaaUTFrtSyenTp/H29ub48eMcOXLE/Kpbty6dOnUqNjlKlcVu5cqVjB07lrlz59KpUycWLFhATEwMJ0+epGbNmlbjz58/T9++fXn++edZtmwZ//zzD6NHj6ZatWoMGjSoULLcuJNBQNb7LXHJhdqXPS7eSKey7joooWpIrXzHe6mUTB3YnP8tP8TA269w0ON544pD30OvD4zvr52AE6uzN+o1CTyzrGQhzSEpR1+7X0YYl1XL1RLFYICvu0HySQD2GRoS0PV5x79YYD0YsRnDL8+juG3pir363fOEvbkHzxtx4F8DvJ03X2v1Bp5etJcDF42Wyj6NKjOXaUjXs7KFn/4l2xJZrxcE1MPjxn+85rGa9+OeZf2/SfRpFmJn7wKnKWMWu3tqHfc0OoJ8syzoGbfgj3HZ88q7Mow9TqpcgQdmbCf5jpq9E+8n2E90XxHkQc6EN1HuxIIKHkpOTunttmM7SlpaGkFBQRYdJxISEjh16lShdQ5nKFUWuxkzZjBixAhGjhxJ48aNmTlzJuHh4eaK4LmZP38+NWvWZObMmTRu3JiRI0cyfPhwpk+fbvcYarWatLQ0i5ctPA4vNb+/o9aRfCezUN/NFkcv36apdAEAVZBj/eZimofStUE1bugrMs07KyniQg6r5vbPzG/lAXM4cq8KV24b25UxaDEMXABjc1gg175qfZBrxyHJ6N68IfsyQppCtwbVHP5eAIS3QzH2CLx5Fl47ydnI9wBj39sdU3rBvI4wrSbXj210arenL17ioclLOXbRqGy//2AT5gb9juJCLKgqwHPrIaBu9gYKBXQbD8Aj3gdQYGDUsoPEzNrB4wt289EfJ7lxV+3cdxNYUsYsdk8u3MN9H//NrrMpoLkHn9a2fFjKvE36l234v8WLmauewCBFLEv+Pirca4K8sXDFlv4HIFciSRI+niq3vCQn2iAGBgaSlpZmEZf38ccf07dvX5o0aVIUP41NSo3FTqPRcPDgQcaPH2+xPDo6ml27dtncZvfu3URHR1ss6927N4sXL0ar1eLhYe06nDp1qkP1Zm7pvfEzf5K5kJKe/QTvIi5d+I+HFJeMH2pGObzdJwObMXDuLjamVme8F5D0L+h1oFQZkz4AtW9N/nesEVtO/QPA421r8ECTEHq1GGw8kWu0g8v7IWE33DwPVXM0vE7YC8A9ZWXa3ZvDw21C8HbiqcaCioEA1O3/BikJmwm8vpv7Fdmu2mqrH+P1Pd8xaVhffE1xjOq78OOTxtIsXV7nWOij/Lt7AzXVp+mc/APrFIA36FQ+qLZowZBlqXvsG6hl43ds8hD89SaVMm4y3G8/i9LaE5doVOj3nb/Jop3nefK+cLrWr0bjUD/OXr/LgYu3WLU/gZlPtKFTvQCnJn+5w0KZK92K3bHLtzl2ORUVOj5atJI/BnrZfDr2UV/nXfXboIC2nqfh6Hw4CvSaDC2fBF872eOC8ok2E+7kKLskXLGlkp49e5KZmcm0adN48skn+eGHH1izZg379u3Lf2MXUmosdikpKej1eoKDLS+IwcHB5mbMuUlKSrI5XqfT2U09njBhAqmpqebXpUuXbI7zGjTX/F6BTMJN1xfcbXN6FgD3vEPNCpAj1Kjiwyv31+e8HEIG3qDLgBtn4PYlSDAqwf1vjGHLqWwX8k8HLvP8dweYm1UShJGboU5WsOee7O+KNhP+ehOAtdpIDCh4LDK8EN8ym8AnvrK5/Iurw/CdFkjS5/eh/aIZTK2eXW9vxxe0+CmKIZcm0Tn5B4vtVLr0bKWucX9o0Mf2gVWe0P5/ALyrmcXfNb+hEul4KLOVtRX7LvG/5YfoPn0bI749wNJtJ5ilmcTvv/5ge5+CbMqIKzY1XcuXm04DMM1jEeu8JqJY9zoA1+QqfFR9Hk9q3uG8IQ+lbfMHsLCnWywyp5LSuFQE1ymBCzj5m+VnTfHV+BS4juDgYJYuXcq8efNo0qQJu3btYufOnYSHu+Ye6SilRrEzkdsyIstyntYSW+NtLTfh5eWFn5+fxcsWITUbmN8rMRSJYuebcRkAfeX84+tyM6BFGCqlkn8NWbGHiUfNfWQvK6tzxhBG53qB/PK/KF7umR0P8PmGeLaaFL42Q43/XzAWIMagh7nZma8rtV1pU7MyHepUdVo+mwTUhdF7jRaNl/YhD7KswxdyLx6PO7YVbRNpnsHIjywCRQ5jdEhz6DcD8rKqtRpifls3eRP/eo8kvs2vHHutCQ80sb5Rf+yxhE7KE3ygm42kc70bvkyR02JXSl2xGp2BllM2sjX+OgoMPKq0LOT9lGYCi876s9vQlD6aT1mtz87+vqvKNT/SLhvrTP63uThEB+Dk1TT6zNxBl8+2kppegM40gqIlt4Vu47vukUNQaAYPHkxCQgLp6en88ccf1K1bN/+NXEypccUGBgaiVCqtrHPJyclWVjkTISEhNserVCoCAgJsbuMwUrZOrMDAZRcrdpkaHWGGqyCB3GtK/hvkwt/Hg0cjwzl2qC7tFKdRn/0Hz1N/IAErM6Pw8lAx64lWBFTyIrJWVV6PbsiE1cdZsS+BD/84SWTtKvjVyro5JcdB/HpjbN0tY7LD97peHJbrs/T++q51QwY1goHGzGWpWkNo2IdbSx6jStI/5iFpsg8TtSPYbGjD3No7CQuPoEF4CJkXD+D3wERjwkWLx4xKhEFvdEHnR+VwaD8K9mZnTSv+v73zjo+iTv/4e7ambwghjRICKL0jzQJYKFIsJyfqYUc5Tv0hlhM9FT0V9dSznWLHflwBTkUpForSmyCE0AklIQmkl63z+2M2m4Rs+m425Xm/XvtiZnbKs2G/O595vk/Z/S8idv+L97qMgfufJTM7l91ndQx3bSfk+59B0RF206dgDPbd52+JNGMvXSkflau32FmpPENwSE3wLF87tCsXX/IxbH4R+l1Pjqkzg19ZyyDdAd5LWEZY1k5tx5/ma8k7VbFxASR/DVNerxgXWg++K9ddZfqHmzyJHC/8rh9RoaaqDhMaC905YUHuxDRBqA/NRtiZTCYGDx7MqlWrKhT/W7VqFVdddZXXY0aMGMHXX39dYdvKlSsZMmSI1/i6OlGuvpvODx67E8cO0E3Jx46eiMR+9TrHo1f24Om9A8DxHeZdZR0lPnKO5+7RXWkbZq6w/+zLz2PV3tMczirkmW/28sLv+qFEdtJaj31ZVoh5jyuRpx038+Lv+jG6e0y9bKs1plDazPyWopwM7Ckr+TKnDx3iY7gp1MwbXaJQlGs9u4b0m1rxWEWpnagrZcIL2mv7p/DVPWXbD/8Eb4+kHXBp+f0vut97zJ5QkRZQoPi/2094lu/vY4UDZe8d6HwTP066lJPZxVx0XrmQiSu1RKUOwNDzElh3wMTonIFsmXgcZdn9cHIrFJ2FEC8e70M/wfI/a8sfjIUH9mmlguqBzeHin1vKPN27TuQCWkPybjGH+fP4HvU6r+BDlGY3eSY0YZrVt2nOnDm8//77fPjhhyQnJ3P//feTmprKzJkzAS0+7uabb/bsP3PmTI4dO8acOXNITk7mww8/5IMPPuDBBx9suDFKmbDzx1Rs/kGtK0SqoTNKPT1C4UFG7vjD9ErbLxvQjf+77LxK22Mjgnj9hgGAFnP32cZjEFLZs3mD7TEu7d2e31/QeHEDIZExWIb9gZnjBjCpXwIjuvoxYWHQdK3G3RNnof0Q7/t0GApjZLqkVjRzj53V4SQtR5tuX3TXcCbHZGpvDL4NHs/ivJvfJCk6tKKoO4dHJmjiKavAxh+T+5V5aF5M0loTnoP641/LVoqy4K/RsLbqbP6qWP5bOuf/5Tsy8620CzdXev+bXadQVZVVe0/zQ/LpOp9f8BGSLCH4kGYl7K6//npeffVVnn76aQYMGMDatWv59ttvSUzUYtDS0tJITU317J+UlMS3337L6tWrGTBgAH/96195/fXXfVNP5hyPXUa+1aeFSF2ndgKQHta7Qefp0bk9GVe86Vn/rv+bvHr9AHQ676JoZNdoHhqnlVaZ/90+svrNrPD+Q/a7yFfCPPu0aHR6mPGDVsB54stw47+gz+9g4B/ctfCa1fAJHEfKxaM1wxi7TzccI9/qIDbCzAUJZq0XM0DHYZoXrRbfg94JFm4cpsW7Lt97mqKOF5e9+d87K+x75vAOlJNaZvgxytVT/PGvcGqnNkX7+iDY+z+v13K6VJb/lk5abjGvfr/fs/3Oi5L44s6KbQePny0mae63zPhkK3d8vJULn/+R/afza/w8go+xdKi8zS6xu0L9aDZTsaXMmjWLWbNmeX1v4cKFlbaNGjWK7du3+96Qch47S5COkhI7J7KL6BYTXs1BtScoR/tBtrdtuICKuXA6apd+KMXZTOgyusb9776kC1/tPEXK6XzeyuzHE7M2UWKO4qZ//Mi2knD+NKarzz5ns8AUChe4b77nB6ZIZrPlwPdlU4pAc5uKzSux88yyZAAm9IlHd2C55l0xhWtlcurAE5N68cUm7cHz5aD7eJwftTf2fcO+Lx7mf1G3kxQdStJXd9HWrRW/sI9hrvHLspO8O6ps+V83U3LPrwTt+RckXgidtcr2C9Yc4m8ryrpfAPxpTFfuuqQLiqJw9PmJANy+cEuFzHiAkznF3PHqv5l26VD+dEWfOn0+oSF4GRf7l0PvqxvdEqH5Iy6H+lLuKf0F4/vsMN9F5jHfBbyGF2kN6YNiz69hz9qhxPeHWog60PrO/nmCJigXbUnl08PBXLswhW154cRFBPGnMd1qOIMguDm4KtAWNIi5/y3rxDJrTFfY/rG2MnQGmOrWUzjIqOfF32nxsh/8WsTQkrLyPj32v8OS1Zv5dvHHXKDTHuo+dVzOl85L+dE5oOpzvtkffnoWPvsdFOcA8K+tFTPHEyxBPDSuR6XQhWsHta90vmt1a1lnvp+ktfez7deddfp8QgPwFq6ga3Z+F6GJIMKuIbgDXkfaNxKqWDHt/8Y351VV2jm1zLuoDr4RdnVl9PkxDOoUSaHNyeNLf2NvWh6KAvOm9K51Q2RBqBQ71IymYtNzS1i2W8smvbxnLDH6Ijji7uLS/4Z6nXNc77Kp1QzaMN76vGd9Y9C9LDT9zbN+8Z0vkkcot9sfpnvJQhY6yoqtP2O/qeKJHcX8tOxLOj+yjGNnKsb7zhnrxeuvqkzadR9Hg27kkTEJLJx2HkcsM3nFpGWFX6nfzOAlo/h17dJ6fU6hjngTdtJWTKgnIuwaQrnpWABbsW8SKPJOHyUYKy5VoX1i5SSHxkCnU/jglgu4fkhHTAYdiW1DWHjbUOmh2kx46623SEpKIigoiMGDB7Nu3bpq91+zZg2DBw8mKCiILl26sGDBgmr3rzXOc2umNR9h92+356tfBwvvTh/sjhVUoV1PaFe/By5LiJEr+2pjKMxsYJ/aif84L/G6b+fOXRndXWvVZ8XEPMetdC75gs4lX/C+cyLP2m+ssP+Y3x7BiCakLzm/Hd/PuYRDz13JdYPLxW+dOQSH10DWfo83deapvzA6ZzGKtXL7xKJVL7D7RC6qtQAW3w1f3gjZR+v12YVqKBV2sX3Ltlkl1lGoH+J6aQiuijetYptvCn9mJ68mAtir60af0FCfnLM+tAk18cJ1/Xj+d9qPjbTNah4sWrSI2bNn89Zbb3HhhRfyzjvvMGHCBPbu3UunTp0q7X/kyBGuvPJKZsyYwWeffcYvv/zCrFmzaNeuXcMTjZpxtt+6A1p3mhuGdtKSjX51x7p1vbSao2rm5akDuKJXGqPOj8GgV1j8QyhsqVjwmDu04sWv3zCQrUfP8vjSPZzMKWZwYhuS0/Iosjn5wHklBQRToAbzhklL6DgQdDMX6j7j4XHdK8fBupzwyVWQe06R72M/ay8vjNDvZd7bf6GvsaxcEvuXw6wN0K4VJFA1Fm5P9sEzxbRNmkSbI99orRMFoR6IsPMhJTbf3MRyz5zS/g2qHAMTCETQNS9eeeUV7rjjDu68U0v4ePXVV1mxYgVvv/028+fPr7T/ggUL6NSpE6+++ioAPXv2ZOvWrbz00ktVCjur1YrVavWs5+VV9vagqrDz88rbmgH7T+ez+ehZAC6OLoRvH9IEDcCA+k3DlhJs0nPNwDIv2q0TR8NEra4cOz4DhxU6XgBARJCRS3vEMubPMRXGYV6JnWCjnotfCCE9r5hZrq/oqdMSM365cAekn4FffoTOF0PiSAiPh3/fUlnUncvDR+DMQfj5VUhZBsC88qIOQHWibv8UZdwzDfo7COVwe+wKbC52HC5mqoJ47Jo52dnZvP7669x1113Ex8c36rVlKtaHWO2+EXYlOVqdLMVb4VJBqAabzca2bdsYO3Zshe1jx45l/fr1Xo/ZsGFDpf3HjRvH1q1bsdu9e6Hnz5+PxWLxvLz2QvT6QNA8hN3MT7VyI1d0cNLhk+Gw+V3tjcQLtRZ1/mLgH+CCOyptPvfhKiLIiFGv48cHR3HVgPZk3vQDRLm7U6x7Gb7+P60cyrcPwtsj4bX+FcvOABhD4Z6tZes3/FMrltxxKNzwBSXTv61kxwM2rfxR2vZlZRtdTphn0V7rXoaCDPj0Wvjltfr9DVojLi2eTkVHtlPrCoKXqXGh+XDfffexZcsW/vjHPzb6tUXY+ZCSKm6CdWXoiY8AMAW3opIigk/IysrC6XRWarMXGxtbqb1eKenp6V73dzgcZGVleT1m7ty55Obmel7Hj3v3BOXoG9i6LwCcyC7icFYhAM+ZP674ZvcrA2BR1YSYDLw2bSCXdI+BmdXEUXoTCZ2GQfR5WjHuebnQfUKFt4O6XojT3VbwBccN9C15n7UuLas3wXoYx3uXa9OFH5X7m/zwNLx0Hhz6AVY9Ac91AHdNPqEa3B47FwqFqibsTp7OqO4IoQnz1VdfUVBQwDfffENkZCSff/55zQf5EBF2PsTqi6nYorOeRX1Yu4afT2iVnOvhUVW12il1b/t7216K2WwmIiKiwssb/+t8TneOZjAV+80uLRN2RGI47U5+X/ZG4oVaV5KmiikURs+t+v24vjD3hLZPux616pyi//0nMPMX7nzkNX58bAoJHRJZ6hwJgOHkFq1zxvGNVZ/Alg+fXA2u5t19xO+4hZ0THcVovXtzvIU3CM2CKVOmsGTJEkCrr3vTTTfVcIRvEWHnQ6wOl+eGWG/OHPQs5va7tWHnElod0dHR6PX6St65jIyMSl65UuLi4rzubzAYaNu2YR63UEubc7Y0bWFnc7h4/rt9ADxsc9eZMwTDI6lw27cQZAmgdbVg4B/cCwrcvQ6ezIFZm2DiKzDtSzCHw+hH4E+boMPgms8X2hbi+tA2zEy7cDP//eNIfuj2OKud/bX3nTbtX0snmPRq2XGDby1btuZVL/4Ej7BTUShxCzu901rdEYJQJSLsfIgdPQXWBnrt3MLuZ2dvYiKb+E1EaHKYTCYGDx7MqlUVCwOvWrWKkSNHej1mxIgRlfZfuXIlQ4YMwWisX+P5UtqGBTXo+MamtJ1WV+UkA7PdyRJDbmv6gq4USweYvRvu/AHi+2lxjjE9tLi9yIb3djbodfzfhL7cav8zPzvLtTu8cZEm5ubsg5u/gol/h4cOaZ5BgH3LvJ5PcFM6FavqKMbd09dRuYewINQGEXY+xIiDvJKGCTtnhlZ1/ogaT5yled0UhabBnDlzeP/99/nwww9JTk7m/vvvJzU1lZkztcD3uXPncvPNN3v2nzlzJseOHWPOnDkkJyfz4Ycf8sEHH/Dggw822Jaoc4VdE5+KPZSplZi42JBctvHiBwJkTT2J7FQ7b1w96RYTzmvTBvCA/Y+scg5ihm0Ou+3tNREZEQ9dRmmdeUKj4ZKHIH4AWBouKls05WLsSlTNY4fNN3VRhcCQmppKWFgYu3fvrnlnHyPlTnyICQd5xXbaRwbX+xzWrCOEACeUeNqENMxbIrROrr/+es6cOcPTTz9NWloaffr04dtvvyUxMRGAtLQ0UlNTPfsnJSXx7bffcv/99/OPf/yDhIQEXn/99YbXsMObx65pC7vS3qkPm/4NTjRhEhodWKOaIFcNaI9RfzkzPtcy91e9+TNb/3I50WHmijv2vU57CdXjfuBxoXg8dop47Jo1CQkJ7Ny502vtUH8jws6HGHCSV9ywzFhHvlbqxBbUVurHCfVm1qxZzJo1y+t7CxcurLRt1KhRbN++3ed2tA1vPl5nVVX5MTmDSboNhDjdNcS6XhZYo5owV/aNZ/61fZm7WPNIDHnme968cSCT+iUE2LJmSLkYu9LkCYOzJJAWCQ3EYDDQrVtg+qrLVKwPMeIgt4HCjqIzAOhCm1+ZCEE4l+BzY/Sa8FRsWm4JRVYrfzb+U9tgCofEEYE1qolzw9BOXDWgTMjd88UOJr/hvYuFUDWquy+sCx1WVRszBtUWSJOEZowIOx9iVBoeY6cv0cqdmMJF2AktAOXcn5imK+xSTuczQ7+MjormNeferdUfIAAw/9q+9OtQllyy+2Qun2w4yrZjZ7n70628/sOBhlcLaOE4HKXCTsHunkgzqHb5uzVjXnvtNZKSkggJCeHqq68mNze30a4tws6HGBs6FauqmG3ZAJgt3ktTCEKzohmFE9h3LeGRUm9dbB8IjwusQc2EEJOB//3pQm4ekejZ9sT/9vC7tzewYs9pXlm1n5s/3BxAC5s+dod233ChYEPz2JmwU2KX+n/NkUcffZQ333yTjz/+mJ9//pkdO3bw1FNPNdr1Rdj5EC0rtgHCzpqPQdWOD4uSm4rQAjjXY9dEHRCqy8XYPQ+Xbfj9J1XvLFRCURSevqoPj0/q5fX9Sf0at1dmc8PuKGspphi05AkTdvIbcj8RAsKWLVt44YUXWLRoEZdccgmDBg3i7rvv5ptvvmk0GyR5woc0OMauUJsCKlTNRLc5t7CrIDRDmslU7M/bd3Oxe7mwy5WEtu0aUHuaKzcN60RWgZV/bTlOsd3JK7/vT0SQkZHdJLO4OjzCTtHxr1mjYIFWZSGzxEGM96YuQhPlpZde4tJLL2XQoEGebe3atauyPaM/EI+dD9GmYhsQY5evtTI6rbYhJsJcw86C0AyoJOyaGKoKCy7i4m8u8WwKnfZ+AA1q3gQZ9fx5fA+2PX4Fe58ez/g+8S1K1K1du5bJkyeTkJCAoigsXbrUJ+d1OLX7hl6vJywkFBCPXXPEarXy9ddfc80111TYXlxcjMXSeEXOm/ivbhnZ2dlMnz4di8WCxWJh+vTp5OTkVHvMrbfeiqIoFV7Dhw/3m40N9tjllQq7KOIimk+ZCEGoknNj7JpaMPiZg5BeVkA0rf1YreeqIHihsLCQ/v378+abb/r0vKXJEyg6cE/F6hWV/CIpeQJovxu2wsC86vCbtX37doqLi3nggQcICwvzvB566CG6d+/uxz9QRZrNVOyNN97IiRMnWL5ca/Nz1113MX36dL7++utqjxs/fjwfffSRZ91kMvnNRqPioMBaf2FnzT6BGUinDX1F2AktgnOTJ5qYsFvxaIXV4L5XBcgQoTkwYcIEJkyYUOv9rVYrVmtZz9e8vDyv+7lc7iQJRQf6sntUYVFh/QxtadiL4LkA1Ud89FStH/b2799PUFBQpW4TU6ZM4cILL/SHdV5pFsIuOTmZ5cuXs3HjRoYNGwbAe++9x4gRI0hJSalWCZvNZuLiGicRIRRrg3rFFp89iRnI1rUlzNws/msEoXqa8lSsquI8+gt6YJOrB8X9bmb0sJsCbZXQgpg/f36tsiHV8sLOUBaGk1cgbcWaE3l5ecTExFQoTJyamsq+fft80smntjQL9bBhwwYsFotH1AEMHz4ci8XC+vXrqxV2q1evJiYmhsjISEaNGsWzzz5LTExMlfvX9gnLG710xyi0Omu9/7nY8rXixA5zZL3PIQhNinOEncPpbDo/OmcPo7drHpFbbH9m33XXBtggoaUxd+5c5syZ41nPy8ujY8fKfXNdrtLkCQV0Blzo0OEiLz+/0Wxt0hhDNM9ZoK5dS6Kjo8nLy0NVVU/nqGeffZYrr7ySXr28Z4z7gybzG1sd6enpXsVYTEwM6enpVR43YcIEpk6dSmJiIkeOHOHxxx/n0ksvZdu2bZjN3pMTavuEVRWFxdaad6oCV1GOthAcWe9zCEKT4hxhV2Rz0GSS/E7tAGCHqxvXDD0vwMYILRGz2VzlvaY8pZ0nUHSgKFgNYQQ78iguyPazhc0ERWkWsa+XXnopJSUlPP/889xwww188cUXfPXVV2ze3Lh1HAM6TzJv3rxKyQ3nvrZu1aq/e+ubWl4Ve+P6669n4sSJ9OnTh8mTJ/Pdd9+xf/9+li1bVuUxc+fOJTc31/M6fvx4nT6Tzlb/JyzFmqMtBEXW+xyC0KQ4Z3wWNiBUweec1Hrj7nIlMSRRygsJgaM0xk5xPwg5jOEA2PJF2DUnYmNjWbhwIW+//Ta9evVi/fr1/Pzzz169tP4koB67e+65h2nTplW7T+fOndm1axenT5+u9F5mZiaxsbXv0BAfH09iYiIHDhyocp/aPmFVhcNuw+F0YdDXXTPr3cJOFyI3GaGF4MVj12RI+xWA39QkpkXXfrpFEHyN6tSEneoeLy5TBBSfxFaYE0CrhPpw/fXXc/311wfUhoAKu+joaKKja65xNGLECHJzc9m8eTNDhw4FYNOmTeTm5jJy5MhaX+/MmTMcP36c+Hj/VUHX4aLQ6sQSUndhZ7Jr8XymMBF2QgvhXGHXwF7KPkNVUdN3oQB7XJ3pFNX0p3mEwFNQUMDBgwc960eOHGHnzp1ERUXRqVOnep9XdZXGZrvHS5AFcsFVklN/Y4VWSxNOWSujZ8+ejB8/nhkzZrBx40Y2btzIjBkzmDRpUoXEiR49erBkyRJAG4APPvggGzZs4OjRo6xevZrJkycTHR1dqXhgvZnwt0qbDDgpqI9XQlUJdmhNgk3h7RpqmSA0Dc4RdjnFtgAZcg5nDqFY87CqRk4aE4kO818ZJKHlsHXrVgYOHMjAgQMBmDNnDgMHDuSJJ55o0HlV1Z0Vq9PGiy4kEgClpPEaxwsth2aRPAHw+eefc9999zF27FhAqwtzbpHIlJQUcnO1gaDX69m9ezeffPIJOTk5xMfHM2bMGBYtWkR4eLhvjOoyqtImneKioD5eCWseRnef2CBL7aeXBaFpUzHG7nRe0yi46vxtMXrgN7UzSbFtqo3VFYRSRo8ejeqHItulWbGlD0JGt7Az2PPrHdojtF6ajbCLioris88+q3af8gMuODiYFStW+NcoLzW69LjqV8uuQOsTW6AGEWFpMnmDgtAwzhFMaTlNoC6XquJa/w/0wH+dl3DnxUmBtkho7ZSvY0dZOE4EhWQX2WkXLi0mhdojjwENwZfCrlATdlmqhTYhMi0ktBDOGSNpuU3AY5dzDKMtB5uq59/OUUzs67+YW0GoDS61orDTuUteRVDUtBKOhGaBCLuGUIWwq1dJh8IMAM4QQVSoCDuhhXDOGDlTYKXYVv8i3r4ga9v/ANivduSlaUNkGlYIOOo5U7EEaQ3jw5UiSuyuAFkVePwx7d0U8fXnFGHXEHT6Spv01C/Gzp6nCbss1UKkeOyElsI5wk4Bjp0NbP/LnMNabcxDaoJ464QmgXpOHbtSYRdBEVZHYB+EAoFer91bbbYmkmzlZ4qKtBAVo9Hok/M1mxi7JokPp2KtOekYgbNYiAiS/xahheDFG3Y0q5AecYGLIw3J3AmAq+dVEpQuNAnOzYr1CDulsFV67AwGAyEhIWRmZmI0GtHpWuY4VVWVoqIiMjIyiIyM9AjahiIKoiF4EXa6ego7W55WgLnAIBl6QguiksdO5fjZ4gAZAy67jTjbcVAguMvwgNkhCBWowmNnoZBTrdBjpygK8fHxHDlyhGPHjgXaHL8TGRlJXFycz84nwq4hKJXVdaJyul7CTs3XpmKt5qgGmyUITQYvwu5EduAyY3fs2cNgRcWqGhnSu3vNBwhCI3Bu8gRhWsmrWCWbI63QYwdgMpk477zzWvx0rNFo9JmnrhQRdg3Bi8fuTdMbPFry+7qfqygLAFtQzZ04BKHZ4GWMnMwJnMcuav0zAJgVO+bw4IDZIQgVcCdPeDx2IW0BCFNKsNqaQCZ5gNDpdAQFBQXajGZHy5y4biy8JE8AUJhV51MZi7VyJ64Q6TohtCSUSmsnsgMk7JwOkjK+ByDPFBMYGwTBC6XJE557iqmsxZ2zJLDJRkLzQ4RdQ6giFs5Rj4Fotp4FQBcmwk5oQXiZij2ZXRyYMgb7l3sWfxn+buNfXxCqQnV77EqTBPQmnO7bs8NaECirhGaKCLuGoPM+k11n17m9BLNTG7z6CGknJtSf7Oxspk+fjsViwWKxMH36dHJycqrc32638+c//5m+ffsSGhpKQkICN998M6dOnfKNQV4efvKtDvKKG7/oam7yjwAscoymXZf+jX59QagK9dwYO0XBptNCBVwlTaBbi9CsaJCws9lspKSk4HC00srYOu81Z2xWa93OU3QGALuqJyS8bUOtEloxN954Izt37mT58uUsX76cnTt3Mn369Cr3LyoqYvv27Tz++ONs376dxYsXs3//fqZMmeIbg84RduFmbarpRABai1l2fQDAL64+DOrUptGvLwhV4p6K1ZUL77HptNgy1SZTsULdqFfyRFFREffeey8ff/wxAPv376dLly7cd999JCQk8Mgjj/jUyCaL3ruws9vqKOyKtWnYHEJpEyY9AYX6kZyczPLly9m4cSPDhg0D4L333mPEiBGkpKTQvXvlLFCLxcKqVasqbHvjjTcYOnQoqampdOrUyeu1rFYr1nIPMHl5ebWysU2IEYohPbeE3gmW2n60hnPmkGcxceAYdDopKSQ0IdxTseXjtu2lHjsRdkIdqZfHbu7cufz666+sXr26QsbK5ZdfzqJFi3xmXJOniuQJu72OU7FFbmGnhhMV6pvK00LrY8OGDVgsFo+oAxg+fDgWi4X169fX+jy5ubkoikJkZGSV+8yfP98z3WuxWOjYsWOtzl3aVeVUY/eMPbHFsxjf6fzGvbYg1IDics96lSuh5dC7s7ZtMhUr1I16CbulS5fy5ptvctFFF1UopturVy8OHTpUzZGtA7u1jnV33B67bMKknZhQb9LT04mJqZztGRMTQ3p6eq3OUVJSwiOPPMKNN95IRETV3SHmzp1Lbm6u53X8+PFanb9NiDZJkNbYJU9OaG3E3nNcSY/48Ma9tiDUhMdjVzaJ5hF2dvHYCXWjXsIuMzPT6w2ksLBQuiYAqsuO3VmHopIej10YUSLshHOYN28eiqJU+9q6VRMu3safqqq1Gpd2u51p06bhcrl46623qt3XbDYTERFR4VUb2oRoHun0RvbYnd2veSx/dXWle6wIO6FpoXiyYss8dk6DJuwUu3jshLpRrxi7Cy64gGXLlnHvvfcCZTeT0nieVsXwWZC+G4bOgH/dDIAJB0VWJ5aQ2ulme8EZjEC2Gk6bUBF2QkXuuecepk2bVu0+nTt3ZteuXZw+fbrSe5mZmcTGVp9tbbfb+f3vf8+RI0f48ccfay3U6kqpsDuV24geO3sJEbkpAPyqdiPULHXZhSaGq7LHzuUWdjjqGLNdjtxiOwdO5zOoUxuJK21F1OsXbv78+YwfP569e/ficDh47bXX2LNnDxs2bGDNmjW+trFpM35+2XL7IXByKwYcFNgcWEJqFy9ny8/CCOQQRrjcdIRziI6OJjq65o4kI0aMIDc3l82bNzN06FAANm3aRG5uLiNHjqzyuFJRd+DAAX766SfatvVfZnZEkDYmMvLrf7OqM+m7MeAgS43g3msubbzrCkIt8eaxQ68l0imOuj8E/XYylw9/OULynl/pZd/LLxffyP9NkBI/rYV6TcWOHDmSX375haKiIrp27crKlSuJjY1lw4YNDB482Nc2Nh/cWbJGnBTWoV+so0Ard1JitMhTlVBvevbsyfjx45kxYwYbN25k48aNzJgxg0mTJlXIiO3RowdLliwBwOFwcN1117F161Y+//xznE4n6enppKen+6VHY3iQduPKakRh53InTux0dWVwkvRiFpoepcIOfdmDvWpwCztn3cZKXomdue8uZtpvd/Gdch8vmxYwYMtDPrNVaPrU2z3Ut29fT7kTwY1b2Jlw1EnYqe4YO5sp0h9WCa2Izz//nPvuu4+xY8cCMGXKFN58880K+6SkpJCbmwvAiRMn+OqrrwAYMGBAhf1++uknRo8e7VP7woO0n5y8EgdWhxOzwbfNr71ReHgz4cBvynmMaRta4/6C0Ngopb1iywk7DFrFCaWOU7E/7k3nVfUFuurSPNuGO7dxKussCdHyYNMaqJew0+v1pKWlVUqgOHPmDDExMTidTp8Y1+zQa/FxBhwUWuvwN3BnxTrMUjRVaBhRUVF89tln1e5Tvp1X586dG7W9V4hRh0Gn4HCpnCmwkRAZ7P+LntQSS3Lb9EMvHnGhCeJtKlZxCztdXT12KWvLRN3k18la9hTRrjMc3vYDCeOmVtjX6VJlTLRA6jUVW9WNwGq1YjL5J/j/2WefZeTIkYSEhFRbX6s8qqoyb948EhISCA4OZvTo0ezZs8cv9gGeThRGxUlBHTx2+pJsAFxBIuyElo2CQtsw7Tciq6ARpmPTfiW8SCvFEtuz6jhDQQgkZcKuzNeiGLWpWJ2rbuMkOGM7AMfixsLgWzjdVqtrmbP3hwr7bTp8holPfMAL3+ysr9lCE6VOHrvXX38d0LJg33//fcLCwjzvOZ1O1q5dS48ePXxroRubzcbUqVMZMWIEH3zwQa2OefHFF3nllVdYuHAh559/Ps888wxXXHEFKSkphIf7oeSBJ8bOQZGt9sLOaM0BQAkRN7nQ0lGJDjNzOs/KmQLfx/Cdi+3fMzABh11xDO6R5PfrCUJ90HkVdpo3u64eu6j8/dpCnJYsEd33CvjxWxKzN3K20EaUu/LC4ZULWG54ka2bzyfj4rXEWBrBey40CnUSdn//+98BzRO2YMEC9Poyt7HJZKJz584sWLDAtxa6eeqppwBYuHBhrfZXVZVXX32Vxx57jGuvvRaAjz/+mNjYWL744gvuvvtu3xvpnoo11iXGzuXE7NDaMelDpU+s0MJRVU9mbF6J3e+X02cfBGC9qzdTOzRiCzNBqAOlHjtduRg7nVvYGevgsXO6VNraToIOwjr0BCB20CScPz5AX90Rvl6/jslXXIaqqvQ5vRSAIbr9fLNtC5MuvcRHn0YINHUSdkeOHAFgzJgxLF68mDZtmu7U4ZEjR0hPT/cEkYNWVHXUqFGsX7++SmFX3x6YQAVhV1DbGLuSXBS0qW1jmAg7oeUT5k6gqEu4Qr04vhm9+4bpuvyvjZKoIQj1Qeclxk5n1GLs9K7ae7bPFFiJV7QqC5bYztrGsBhSo0eRlPUTuh2fwhWXsf/YSXq6DoI7vC7nt5Ugwq7FUK8Yu59++qlJizrA00Lp3MKssbGx1bZXqm8PTMCTql6ncifFWnxdoWomIiyk9tcShGaJSpi7VmNdMsfrRfouz2KX9tUXaBaEQKKjclas3uz22Km199ilZefTDi3j3dCm7N5luegOAEYWruJkVjb7Nn6HQSnrjtQ9a1XjxLwKjUK9y52UlklITU2tVO/qlVdeqdU55s2b55lirYotW7YwZMiQ+ppZqZVSTe2V5s6dy5w5czzreXl5tRd3bo+dSXGQU9sYO6vmEcwllIjg2hU0FoRmi1om7ApK/CvsXNnH0QEfO67g0rby0CQ0XRRVE1kVhJ2pdCq29h67nNOp6BQVOwaMIWVFzaP6XUnWV+2IdmWybvknxB/8FwDpcWOITl/LBbp9/LLjZ6IvvswXH0cIMPUSdj/88ANTpkwhKSmJlJQU+vTpw9GjR1FVlUGDBtX6PLVtlVQf4uLiAM1zFx8f79mekZFRbXsls9mM2Wyu1zUrljup5U2rRHu6yldDsIiwE1oBZVOx/i2LlHf6MJHASWIbp6yKINQTnZcYO0M9PHZFmccAyDG0o52u3IScTk9al+uIPvg2Uw4+AYAdA9FTX2X/x/fQK28d1v0/ggi7FkG9pmLnzp3LAw88wG+//UZQUBD//e9/OX78OKNGjWLq1Kk1n8BNdHQ0PXr0qPYVFBRUHxNJSkoiLi6OVatWebbZbDbWrFlTbXulBqErPxVb2xg7zWOXRwiRIuyEls7JrcSpmTxp+BhT3lG/Xirr+AEAXJZOUqtLaNJ4S54wmLVi2iZsOF21qzXpzD0FQIG5svOi0+V34VLLxkFmnxkY2nbGlnABAKGZO+pnvNDkqJewS05O5pZbbgHAYDBQXFxMWFgYTz/9NC+88IJPDSwlNTWVnTt3kpqaitPpZOfOnezcuZOCggLPPuVbJSmKwuzZs3nuuedYsmQJv/32G7feeishISHceOONfrGxQlZsbadi3R67PDW01r1lBaHZsm0hE5P/zG2GFcw8fK/fLlNSUkx76yEAJoyRoHChaaN3x9jpy5U7MZg0p0YQdmwOl9fjzqW0i5G3YveWuC4cG3A/NsXEmfDuJFz1JACR52uOjqTivbictbuO0LSp11RsaGioJ3M0ISGBQ4cO0bt3bwCysrJ8Z105nnjiiQotzAYOHAhUbHtUvlUSwMMPP0xxcTGzZs0iOzubYcOGsXLlSv/UsIN6lTtRS3JQgHyCiQz2T3FnQWhKROdpRcIjnf75rQDI3P0jHRUbZ4hg0KBhfruOIPgCbzF2xiAtLtSMDavDSbCp5qxuxVPsPtLr+0nXPAlXPU7bctO0HXqNwPE/HTFKNgcO7+e88/xTi1ZoPOol7IYPH84vv/xCr169mDhxIg888AC7d+9m8eLFDB8+3Nc2Alr9uppq2J3bEUNRFObNm8e8efP8YlMl3IPSVIdyJ/bCXEy4PXYyFSsIPqHk4FoAfjUP5VJdvSYmBKHRKPXY6cqV5DGYNGEXpNix1tJjp7e6HRvBkVXvdM54MASFccTUhST7QVJ/Xd0gYZdfYifIqMeob/iYsxZko9PpMIZI/cm6Uq+//iuvvMKwYdpT8Lx587jiiitYtGgRiYmJte4K0SIpTZ5Qal/uxFaoPWEVKSEEGeUGJAi+oCRbizWyRiQG2BJBqJmy5IlyszaG0qlYGyX22jkKjDZ3sfuQupUjK47T4uzUA9/X6bjyfLfyW44/N5h1z00kOfU0ucX1L0C+d91i9H/rgvJCZ7Z//jg0Yj/rurLr6zfZP38k2z64j7zMk4E2B6inx65Lly6e5ZCQEN566y2fGdSs0dW9pZjDLexsxohqy7AIQkuhqG1fQs7s9us17HmnAWgT096v1xEEX6BD88jpy03F4i5QbKb2HrvSLkbG0Lq1p0wYeT0s+pILSn7hYNoZusXXrVh+dn4xXdY/QnfdMXo5j8GH57NDPZ+wW//FeUl1a+WnupyE/vSEp87eoAOvs/PNQ/S49U2CwptW283k1Yvot+0xbeX4Hs7+Yyn7Ln+dHhddHVC76uUi6tKlC2fOnKm0PScnp4Loa3V4esU6a11V31WcA4DDFOEvqwShSaHzwTRNdaiqirlEi9+Lb9/Jr9cSBF/gmYot16YTg1buJAgbxbW8nwQ58wEw11EARXa/hLP6aCxKEbtX/6dOx5bYHGx56za6c6zC9oHKfk6tfL1O5wLYt/E7El3HyVeD+SnudhyqjgFnlmF4qSu7548m49jeOp/TH6Qd3UeH1bMB+M3Qm6NKe6LIpdOqu0g/ti+gttXrF/bo0aM4nZVdw1arlZMnm4YrMiC4hZ0BByV2F47aZBi5hZ3TLHEEQutAp6t3XfRacSq3hDaq5gmPby9TsYJveeutt0hKSiIoKIjBgwezbt26Bp+z1GOn05eLs3Z77HSKSom1uMZzuFwqYS6tSkRQRB3bU+r0ZCdNBsBy8H91OnTT8s8YW/wdLhROjXuXzCmfkWXWHqiiMzfWzQ6gaNNCAPZGXcGYmX9n28i3OUp7DIqLvtYdhHx0GYd/eD+g07M2u5OTn80knCKSDT3o9uAPxDy0mT3G3oQoVlKX/S1gtkEdp2K/+uorz/KKFSuwWMrEiNPp5Icffqh3QeEWQWnnCbSnqyK7k4gavBO60mDXoKbdok0QfEV5r4TDWuwpxOorHvnPr3zgbqtkssTXsLcg1J5FixYxe/Zs3nrrLS688ELeeecdJkyYwN69e+nUqf7eYa8eO2OoZ9FeXHDuIZXIL3EQoRQCEGqJrmHvysRfPB0OfsRIx2bSMrKIj6ndOYL3/BOA3R1vov+I6wHIiOoCC0fSzX6AwqIiQkNq7vySsvFbnKv/Rv/inaCA5eI7ARg2bhrq2Os5mLwT639n0tu5j7B1D5C97imOtruUjlc9SnSH7nX+vA3h5yULuNSxAytG2v7hA4KCtN8w+4UPwurb6JmxjJKifIJC/FSBowbq5LG7+uqrufrqq1EUhVtuucWzfvXVVzNt2jRWrVrFyy+/7C9bmz6lU7GKNkhrk0BhsLuDXYPFYye0DsoXYbXt+q9Pz21zuNh18Bgm9xgktJ1Pzy+0bl555RXuuOMO7rzzTnr27Mmrr75Kx44defvttxt0Xr1aGmNXLnlCb8Du9r04SvJrPEdOsY1INAFoDKt7LFpIp0Gc1sUQpNg5tmt1rY45eeokA0o2A9B+9J2e7TGJvcgmArNiJ3n72hrPs+3rBXT77kZ6lWzHoLjYHHYpPQaN8ryvKArdeg0k8cG1/NTmOqyqkTbkMTBzKaHvX8SJvWWewfzs0/z692vIeiqJnV9r8f9Ou5Uj21ZitxZVurbLYWfvN2/y69+vYdMXT6O6ymYjc9OPkLLqQ3LSjni2ZWWk03/P8wDs7z6TmM59PO/1vfgqTiqxhFPM3lVl5dlqYu+6xex8eQo5p4/X+pjqqJOwc7lcuFwuOnXqREZGhmfd5XJhtVpJSUlh0qRJPjGsWeJOngjS1V7Y6R0lABgDpOwFobHR6cq8Eq70PT49d3puCbGKNg3rNFs801mC0FBsNhvbtm1j7NixFbaPHTuW9evXez3GarWSl5dX4eUNfWnyhLHiJFqJTvMEOUpq9tjlFBQRqrjbj1VRx65aFIXTlv7adQ+XCSWH08WXS7/iH/94mV2pFWPrD675ApPi5JixC9FdB1Y414kI7VydV83gv+88TX6x99ZoNmsJ3bY9jV5R2RI2hpRrVjL4fu9xfmHBZsb83wcU3reP9YP/zmnaEIyNwq8eBpeLnNOp5L4xhv65PxKtnqXX1sc5snMNx14YTtLXUzn9whDSD/3qOd+Z08fZ87ex9Nr6GP1zf2TY/pc58MLF7PjHLaT87TIsCwbQ/Zf7Mb0zjO1fzsNuK+HIl3Noq+SRqu9E7+ser2CfXq/nWOJ1AIT+9lmt/uzFBbm0+fFhBuSvIXnp87U6pibqJOw2bdrEd999x5EjR4iO1ty0n3zyCUlJScTExHDXXXd5Che3StxPW2a3t6A2tewMLk3YmUPC/GeXIDQlygk7u48L3Z/MKaavoj1d6yM7+vbkQqsmKysLp9NZqdd4bGws6enpXo+ZP38+FovF8+rY0ft3MtWQyBFdoqfbRCk2pVTYVfY0nUthbjnRFVTPGaAOWtmTsNOb2XYsm9N5JXzwyUdcu+M2/pT5NK4PxnEsLdOzu+nkJgCy2l9e6VTtr/4rOYqFaCWP36W9zPr3H/B6yeT1X2OhkCwiGfh//6J7/2Ho9dUXY45qG83IybdTeOMySlQj3Ut+Zd8/fo9twRg6uE6STjQniMGkOEhaOoUujsPax3OdpN0no/jtb+PY/o9bUN6+kL7W7ZSoRjYHX4Rd1XO+dQ8DM5fSvXCr9tnUCEKwMijl71ifS+KC7GUAFI37Ozpj5b7y3cbOxK7q6W5PJjV5C6Bl+v628iO2LnyIfWv/Q25GKqm/beDY9hUkf3wv8Womp4ihzw3PVvu5a0udhN2TTz7Jrl27POu7d+/mjjvu4PLLL+eRRx7h66+/Zv78+T4xrFlSWqDYLeyKavLYuZwYVRsAwSLshNaCUvazU2z0bWypa98yXjYt0FacNp+eWxCASmWpVFWtslTV3Llzyc3N9byOH/c+1Xbe49tIemIX0bEdKmy36zWh57LW7LErztOEXaESWuHhqS50GXEVAEOcvzL4o87EvhLL3cfmYFa0e9kA5QAHlr0KaJ87rkDLUA3rWrkxQVSXgYTN3sThLn8A4LKsz1m7vLInzr5Di9E7FH0pBmPdui91Ob83v7S/A4AeZ1YRo2ZxkhhKbvof+hmryFG1+6pLVfhl0EvsMA1Cr6j0KdzIoMylRJHLYV0i6dOWM/TPy0i9aQ1rEu9jq3kYmWokP5//ZyyPH+WnHvPIIYwwNIG9NvoGegytLGYBYhI6sStUa9OW/tM7FGZn8NvLE+mzfjZDjr5Ljx/vwPJWXzr9ZzyJX/2eQZlaskr6Jc8THhFZp89fFXVKnvj111955plnPOv//Oc/GTZsGO+99x4AHTt25Mknn2y8Tg9NjdLkCY/HrgZhZy97CgsJlalYoZVQLoalWPHhVKm1gAu3lOs/Gz/Ad+cWWj3R0dHo9fpK3rmMjIxKXrxSzGYzZnNlr05tcbinYl3Wwhr3tRdowq5YH05oDftWRVhCDw5FjaLr2TUVtp+OuYjM2Avps/sFYtK1906lnSIJrQpGYr+LvZ7PYImny83/YM8/ztI781u6b3yIolFXEhKsJVOcPLyXAbk/ggJtRt5aL5svvOVpfvjITlTWNk7HjaLP5PvoHKvNKO64bAElmz7CMPAGLrx8KurkO0nZs43M718novAIOdFDGHzT04SGafffruf3pev5fQEty/ginSbYx0y7n8z037Fx/Vfow2O48NLrqrVJN+RWWLuOoRn/xvnqf+irqJSoRvYGDyKm5AgdyMCqGrFjwISNTR3v4OJLf1evz++NOgm77OzsCl/gNWvWMH78eM/6BRdcUOUTSatAd07yRE1Fiu1lKeyh4rETWgvlhJ3dVv/q9JX44WnPYqGxLaGTXvHduYVWj8lkYvDgwaxatYprrrnGs33VqlVcddVVfrmmo9RjZ6+53Im9QIstLTE0zEkQO+kxnJ+s88T9pcWNJv7upeiO7oHdL9Ddvo/8gnyO7VpLe+CkPoH2lphqz9ljxodkzu9FrHqWn5e9z0XX3QfAqaVP0l5xsSvoAvqVS5aoC0FmM5fN9D7WB14yGS6Z7FlXFIXufYbQvc8nNZ5Xp6vohW0X14l2195TK5v6XnI1x9f9hY5qGnpF5ZiSQP6ENxg0TPPypaWdICQkHHNwCDaHg4tDfFsZoE7CLjY2liNHjtCxY0dsNhvbt2/nqaee8ryfn5+P0diK+50atCezYFWLmyusKcbO7bErUs1EhLTiv5vQunCViTm73UfTpS4XbH7Hs7ry8hVcYxYvuOBb5syZw/Tp0xkyZAgjRozg3XffJTU1lZkzZ/rlei53kWLVVnOMnatIE3Z2Y8OK3Yd1GYZ614+4XCq6qM7EB0WCotCuc2/O0Ia2SjbJO9ZgO7IBgCxLf2rq76I3BXO0y020O/QGEQeWAvdx8vBeBueuAgVCxj3RIJubGgaDAfUPS1m37TuizhtGj37D0ZcrfRYfX27K3eT7e3+dhN348eN55JFHeOGFF1i6dCkhISFcfHGZC3bXrl107drV50Y2G6K01imRzjPEcrbmrFj3YC3CTESQCDuhleAsL+x85LHLP+VZXOvsS6e4utfxEoSauP766zlz5gxPP/00aWlp9OnTh2+//ZbERP8Uwna5+8WWD9upkmJN2DnMkQ2+rpIwkEpRg4rCsYiBtM37keKUn4g+oyUXKIkja3XOmMGT4dAbdC35DZvVyrEVb9BeUdkdNJi+Ay9psM1NjU5de9Cpa4+AXLtOyRPPPPMMer2eUaNG8d577/Hee+9hMpUFO3744YeVUsFbFcFlgeAPGf9Vs7Bzu9dLMGEJFmEntFDOn1Bx3VU2Lhy+8tidPexZfNZxE4M6RfrmvIJwDrNmzeLo0aNYrVa2bdvGJZf4T5Sobo8d9pIa91Xcxe7V+pQ6qSX2jhcB0CZtLefZUwBIGOA9ieBcOvUYQjbhhCpWUjYvp+fprwFQL5jhH2NbMXUSdu3atWPdunVkZ2eTnZ1dIc4A4N///jdPPvmkTw1sdkRoTulC1VxjuRN7iRYQW6yKx07wDdnZ2UyfPt1TXmH69Onk5OTU+vi7774bRVF49dVXfWfUtC8qrpeLsXM4fCTsclIBWOPsx5ChF1WZpSgIzQnVoCUZKI6aY+wMttIuRpF+sye2nybiejj3Y1YcZClRRHfqWatjFZ2eo+GDAej7w820IZ/TSjS9R031m72tlXo1bSzfSqw8UVF1r3bd4hhyG/z4DGbsFNWQPFFcmIsRbSo2LMi//TOF1sGNN97IiRMnWL58OQB33XUX06dP5+uvv67x2KVLl7Jp0yYSEhJ8a5TunOdHl++nYtUzR1CAE2o7+neM9Mk5/YnT6fTdNHQTxWg01liPTKgBo+ax09VC2BlLuxiF+K89ZeJ5fTmu70RHp/YgdThuAtF1eIgKGnYbfL8aAIeq4/TFzxBrkHufr5G/qK9xPy1ZlMIay52UFOQQARQrIeh14mEQGkZycjLLly9n48aNDBs2DID33nuPESNGkJKSQvfuVfdTPHnyJPfccw8rVqxg4sSJ/jW03FSsr8SN7cgvmIFDagKPDKgplDtwqKpKenp6nbyozZnIyEji4uLEg1pfTJrHrjbCLsihCTtDqP+EnaLTETLtAwq/uIp8nYW+0/5ap+N7XnQ1B03/pCB1F50vvoF+sfXvrytUjQg7X+OOs7NQWGOMnbVIG4g2fc0NkgWhJjZs2IDFYvGIOoDhw4djsVhYv359lcLO5XIxffp0HnroIXr37l2ra1mt1gpdZqpqleQVZ/kYOx90qrHmY0jfDsBRy1BMhjpFmDQqpaIuJiaGkJCQFit4VFWlqKiIjIwMAOLj4wNsUfNE5xZ2emfNMXbBTq2frDncvzNnbc8bCo/sJ8QQhKKvewhRt6ETYOiEmncU6o0IO1/j9thFKEUU2mqIsXMLO7sIO8EHpKenExNTuZ5UTExMlS2PAF544QUMBgP33Xdfra81f/78CqWO6kS5qVinwwceu/Td6J1W0tU2KO0Ck4VWG5xOp0fUtW3bNtDm+J3gYG0aMSMjg5iYGJmWrQc6k/Y3NDir99i5XCphrgLQQVB4I3y3zOGVs2aFJkPTfbRtrrh79NXGY+cs1oSdwyjFiYWqmTdvHoqiVPvautVdesCLB6i6lkfbtm3jtddeY+HChXXyHtW2VZJXyk3F+kLYudJ/A+A3V2c6tq1vzX3/UzrtHBLSeh7kSj9rS48n9Bd6k/Z9Lu0pXhX5VgcWRUvGC4mUUj+tnWYj7J599llGjhxJSEgIkZGRtTrm1ltvrXQDHD68ck87nxKs2WZRaiHsSjRh5xJhJ1TDPffcQ3JycrWvPn36EBcXx+nTpysdn5mZWWXLo3Xr1pGRkUGnTp0wGAwYDAaOHTvGAw88QOfOnau0yWw2ExERUeFVa5y+9dj9uEmbhk1VY+kY1fRFU0udfvVGa/qs/kBv1r7PJlf1IQt5xXYsaMLOFCpJjK2dZjMVa7PZmDp1KiNGjOCDDz6o9XHjx4/no48+8qyXr7vnF8pNxRaVVF/KQXU3dlZNLUfYSbaf74mOjiY6uuan8BEjRpCbm8vmzZsZOnQoAJs2bSI3N5eRI70XEZ0+fTqXX16xDtW4ceOYPn06t912W8ON90Y5j53L2fDvSkbGaTBArhrK+RE+7D0rCAHGEKR57Ixq9R67vIJCOipu8ed2Lgitl2Yj7ErjeRYuXFin48xmM3FxcbXev0FB4VBhUOnt+dXuqtjc77eA1keS7Rd4evbsyfjx45kxYwbvvKO117rrrruYNGlShcSJHj16MH/+fK655hratm1bKd7LaDQSFxdXbRZtgygn5lSnQ2tdVM+s8LwSOxHuKagcwhjZteXHrgmtB6Nb2JnV6j12hblnAHChoDN7L0cmtB6ajbCrL6tXryYmJobIyEhGjRrFs88+6zXAvJQGBYUD6I2oxhAUexFGe161Ny2dTbsh6YKav7CTbL+mweeff859993n6QAzZcoU3nzzzQr7pKSkkJubGwjzNMp57PQ4KbQ5CK9nge6f9mXQFs3z/dTvL4RQP3vkBQBSU1Pp1asXGzZsoG/fvoE2p8VSXtg5XWqVZbGK8zRhV6SEEnZu3Uih1dGihd2ECROYOnUqiYmJHDlyhMcff5xLL72Ubdu2YTabvR4zd+5c5syZ41nPy8ujY8eOdbuwOQLsRYRRXO1Ny+DQbkiG4IY1bQ40ku3XdLL9oqKi+Oyzz6rdR1XVat8/evSoDy3yZkBZtrgBJ3kl9Rd2qWeKGK47qa208U+/TqEyCQkJ7Ny5k06dpA6ZPzG5hV0wNqwOJyEm77dsW4Em7Ir1YbScwB6hvgRU2tcl268+XH/99UycOJE+ffowefJkvvvuO/bv38+yZcuqPKZBQeGek2hDK5QSiqopeWJ0aB47YzMXdpLtJ9QXA07yiuv/Nyw8c4JYJQcXOogTz1FjYTAY6Natm/9jlls5pcIuSLFSXM29xF6YDUCJoXnfSwTfEFCP3T333MO0adOq3ae6zLy6Eh8fT2JiIgcOHPDZOb2huFPUQ5USCqwOvOcjgslZpP0b2jIGY0udfvVGa/qs/sSAi5yi+gu7/MNbAMgLSyLS1HRLnQhCfdC5s2KDsZFjr1rYuQrPAmAzSnydEGBhV9tsP19x5swZjh8/7v+4KJMWMxdKSbUlT4JUTdgFhUb61x5BaKIYcHCmsH7dJ/al59GuIBkMUBzdj0jfmiYIgcdYKuyspFfTe9xZmAWAy12VQWjdNJsoy9TUVHbu3ElqaipOp5OdO3eyc+dOCgoKPPv06NGDJUuWAFBQUMCDDz7Ihg0bOHr0KKtXr2by5MlER0dzzTXX+NdYt+cgxO2xq4oQVasmHhIe6V97hFqTmppKWFgYu3fvDrQprQI9LrLy6yfsvt97mu6KVhi5bbfBvjRL8EKHDh146623Kmxbv349ISEhHDt2LEBWtXCMWjyvXlGxWqsueWIocHeWCW9ayVxCYGg2yRNPPPEEH3/8sWd94MCBAPz000+MHj0aqJjtp9fr2b17N5988gk5OTnEx8czZswYFi1aRHi4n7NQ3TF2YRRTZK3Cfe6wYkQTfSLsmg4SFN64GHCSVVB9vceq2HYojXv02lSsqV03X5rVaKiqSnE1U2z+JNior1NIwfDhw9myZYtnXVVVZs+ezezZs0lMlMQVv2Asi1u2FhdWuVtQiZalb2jTwe8mCU2fZiPsFi5cWGMNu/LZfsHBwaxYscLPVlVBqccOK4VVuM/txXmU5gGGR0Q2jl1CjZQGhQuNg0FxklXg3WO3PTWbD34+wnPX9MUSXDFrNjPfinpkLZTG7rdJ8rOl/qHY7qTXE4H5ndr79Lgqsyy9MXz48Aq/wZ9++impqanMnTvXD9YJAOiNONBjwImtuMDrLqqqYrFngAKh0XWs4CC0SJrNVGyzwt1JIkwprnIqtjBPy2IqUs2EBXsvvSIILZ2qPHZfbErl2rfWs2xXGk/877dK7//jp4NEUK4xejs/FVMWPAwfPpzk5GQKCgooKiri0Ucf5ZlnnvH/DEgrx6po9wdbFR67fKuDdqqWPGGJEc+p0Iw8ds0Kt7ALoaTKqdjC/BwigSKCCNGLvhZaONHdISul0mY9rkoeu2NnCnl0SVmM487jOZWOW3cgk+cNK7WVnpOhmWYpBxv17H16XMCuXReGDBmCXq9n+/btfP/997Rt25bbb7/dT9YJpdiVIFCLqvTYpecUk6hojgJzlEzFCuKx8w/uqdiwapInigu0WMAiXeup/dZU+fLLLwkKCuLkyZOebXfeeSf9+vULbIeGlsQtX3ndbMRZKSv2v9tOVFg/fraIonIhDUU2B4ezCrlAt1/bYK++j2ZTRlEUQkyGgLzqWrInKCiI/v37s3jxYl566SVeeeUVdNLlwO/Y9W6PndW7xy4zIw2z4i4ZJMkTAiLs/IO5zGNXVbkTa0GO9m8LFXaqqlJkcwTkVVNnhXOZNm0a3bt3Z/78+YDWl3jFihV89913WCxSF8onhHvv16zHSVZ+2VSsqqp8ulHLsPzLxJ7EW4JwqbB2f5Znn33p+VT4L75otj8sFrwwfPhwXn/9dS6//HIuu+yyQJvTKnDoggCwl3gXdgUZ2njJ1UWCQQpGCzIV6x9MZZ0nqkqesBXlaf+2UGHXnILCFUXh2Wef5brrriMhIYHXXnuNdevW0b59ez9aKYAWY1dsd1JodRBqNpCZbyW7yI6iwPQRiWQV2Fiw5hAzP9vGi9f14/dDOrLnZC6geoLKm2viRHNkwIABGAwG/va3vwXalFaDU6+VPHFYi7y+X3xW83AXmGOQx1ABxGPnH0qFnVJCYRUxdvZiTdjZjdLZrykwadIkevXqxVNPPcWSJUvo3bt3oE1qgVSe+jMq2vg4406gOJihxRF1igrBbNAztndZ35aH/7OL1DNFrNmfRTjFmqgDCGn5/YmbCp9//jmzZs2ie3dJVmksXAZN2LmqEHauHC2ExBpcVY8jobUhHjt/UNpSrJqpWKdb2DkNLbMNUnMKCgdYsWIF+/btw+l0EhsrP5B+4fcfw79urrDJpNPmVDMLrHRqG0Jyej4APeK0TMveCRF0jArm+FktA/aFFfv4Pvk0iUqe+wRhYAxqpA/QOnG5XGRmZvLBBx+QkpLiKQIvNA6qQft+O6sQdrqCNABcYRJfJ2iIsPMH5rKWYlUlT7hKtBuYamqZHrvSoPDmwPbt25k6dSrvvPMO//znP3n88cf597//HWizWh6dL660yeTx2FlRVZXvdms3qR5xWv9ks0HPN/dezL1f7mDt/kyW7dLej0IbP4RENYLhrZu1a9dy6aWX0qNHDxYvXixxp42Nu/sENu8xdsHFpwHQR0pGrKDRPO68zY1Sj51STJGtiqryNm3KqaUKu+bC0aNHmThxIo888gjTp0+nV69eXHDBBWzbto3Bg6VNlU/RVfakGnABkFVgY9uxbLYe08o2XHJ+O88+lmAjD1xxPmv3Z3q2fXhdJ/gamYZtBEaPHo3L5Qq0Ga2X0nuE3buwC7dr4yK4rQg7QUNi7PyBJ3nCWuVUrGLVPA5KkBT3DBRnz55lwoQJTJkyhUcffRSAwYMHM3nyZB577LEAW9cCUSr/3BjKeewWrj8KwLjesQxObKPt8Osi2PIB/TtGctclXQB44IrzaVNwSHs/WmK9hJaN6p4BMtgr17Ersjlo6zoDQESMtEEUNMRj5w/cHjuzYqe4xHuNLZ17kOqDIhrNLKEiUVFRJCcnV9r+v//9LwDWtAIULx47VRN2u0/msnKvNqU06vwY7c0vb4SUZdpyhyE8emV/Hr2yp7a+5LD2b7S0fxNaNjr3PcLoqOyxS88tIU7Ruk6EtJV2YoKGeOz8QfnpVZv3auF6t1vdHCLxKkIrwYvHTufObC0VdRFBBq7qEQYvdikTdQCH15Qtqyr8+oW2LKVOhBaOPlgTdiZH5XvJ6TPZWBR3UkWEJE8IGiLs/IHBhKrXCkXq7IW4XJUL5hqdmrALChdhJ7QSvMTY6dSKoQrXDupA6LEfoOhMxR2PrC1bzj5Sthzf35cWCkKTwxCs3SOCXJU9djkZqYC7n6xZZn8EDRF2/sLttQumhGJ75QSKILewCwlr06hmCULA8OaxU51A2YNPTIQZDq+ufOzZQ2XLP80vW44+z3f2CUITxBRStbArztKKE+cZo5ttv2TB94iw8xel/WIpIb+koleixO4kCq0HaVhb762WBKHF4UXYAVzZK8azfOPQTrDzs7I3O1yg/Xv2MDjcrcfSd/nLQkFocpjCNGEXRhFWR0Ungb20OHFQTKXjhNaLCDs/obgzmUKUErKLbBXeO5tfTFu3sAuNSmh02wQhIFThUXjuqu7MGt2VT24fSqSaV/bG5Nfg9nJt6fYsBpcT8rVadkz70o/GCkLTwByiTbFqBe8rCjsl/xQADilOLJRDhJ2/cE/FhlNUSdjlnklHr6i4UFBC23k7WhBaDZFBOh4e30OrXbflg7I3Bk7X4vIi3WUczh6GE1ugJBcMwdB1TGAMFoRGxOBOntBaVFac/TEVaUlHikX6WgtliLDzF2GaazxGySGnyF7hraKz2lNWrmIBvVScEVo5LvfNqvAMrH5OW+58cVmyRb/rtX+LzmrCDuC8y8sq8gtCS8ZTF7VyJ6MwqybsgqKkOLFQhqgKf+FudWShsJLHrjhbE3Z5hjZI6oTQ6nG6b1bZR8u2ucpNOQW7R0lxdpmwi+nVKKYJQsBxh/WEKSUUlpTdS0rsTqJcZ0AHYe0SA2Wd0ARpFh67o0ePcscdd5CUlERwcDBdu3blySefxGazVXucqqrMmzePhIQEgoODGT16NHv27Gkco92p5+FKUSWPnS1Xe8oqNkU3ji2C0BTRuZ8rSz12R8rVqutzbdlyqbDLTIG0ndpyO+k4EUiys7N56qmnSEtLC7Qpjcazzz7LyJEjCQkJITIysvEuXK4uanFhWQzqyZzisuLE0VKcWCijWQi7ffv24XK5eOedd9izZw9///vfWbBggacNVFW8+OKLvPLKK7z55pts2bKFuLg4rrjiCvLz8/1vdKmwo5jswooC1JWnCTt7kAg7wbdkZ2czffp0LBYLFouF6dOnk5OTU+NxycnJTJkyBYvFQnh4OMOHDyc1NdW/xnqEnfvB50y5kiaDbytbDta835zeXbat2xX+tU2olvvuu48tW7bwxz/+MdCmNBo2m42pU6c2/mc2BuNy36qt5YRdalY+MeQAoERIEp5QRrMQduPHj+ejjz5i7NixdOnShSlTpvDggw+yePHiKo9RVZVXX32Vxx57jGuvvZY+ffrw8ccfU1RUxBdffOF/o91tYMKUYrLP8djpCjVhp4ZJinpTo7l7Im688UZ27tzJ8uXLWb58OTt37mT69OnVHnPo0CEuuugievTowerVq/n11195/PHHCQoK8q+x53rsSnK0f8c+UzH2NPicgIXe13rGl9D4fPXVVxQUFPDNN98QGRnJ559/HmiTGoWnnnqK+++/n759+9b6GKvVSl5eXoVXnVEUShQtntReVHZ8VvpxDIoLJzoIi637eYUWS7ONscvNzSUqKqrK948cOUJ6ejpjx471bDObzYwaNYr169dz9913ez3OarVitVo96/UaiOCJiwiniJxzYuxMJVkA6MOlhl1T47777iM7O5sdO3awdOnSQJtTJ5KTk1m+fDkbN25k2LBhALz33nuMGDGClJQUunf3Pn352GOPceWVV/Liiy96tnXp0sX/BnuEnTuezuoea2HnjIvY3hDStqwbRceh/rdNqJIpU6YwZcoUABYuXBhYY5o48+fP56mnnmrweaz6YEIchdiKy+5HeaePAVBobEuEl64uQuulWXjszuXQoUO88cYbzJw5s8p90tPTAYiNrfgkExsb63nPG/Pnz/dMY1ksFjp2rGfsgrnMY3f2HGEXZsvUdmkjtYeaEs3dE7FhwwYsFotH1AEMHz4ci8XC+vXrvR7jcrlYtmwZ559/PuPGjSMmJoZhw4bVKGrr7YlQyt2ASoWd0+3RtrpDJNwPRR7MYXDJQ2XriRfW7lqCEGDmzp1Lbm6u53X8+PF6nceu1wreO4vLwoiKTh8EoCRM4uuEigRU2M2bNw9FUap9bd26tcIxp06dYvz48UydOpU777yzxmso5xRFVVW10rby+GoglnnsKsfYxTi1qdiQmEbwigi1ZsqUKSxZsgTQPBE33XRTgC2qG+np6cTEVJ7ej4mJqfJhJiMjg4KCAp5//nnGjx/PypUrueaaa7j22mtZs2aN12OgIQ9A5fom643av6VTsVZ3k/NzhR1UTJaI6VnLawlC9dTnHlQXzGYzERERFV71wa4PAcDp9mq7XCo6d5s9Y8z59bZPaJkEdCr2nnvuYdq0adXu07lzZ8/yqVOnGDNmDCNGjODdd9+t9ri4OG06Jz09nfj4Ms9YRkZGJS9eecxmM2azuRbW10C5GLszBWXCrqDETgxaJlNkXOeGX0do8cybN6/G6ZwtW7QyIN4eWqp7mHG5XABcddVV3H///QAMGDCA9evXs2DBAkaNGuX1uLlz5zJnzhzPel5eXt292+fG2FXlsQNIGg0j74Po88sEoRAwUlNT6dWrFxs2bKhTzFlTo673oEDhNGoeO0q0h5/T+SW0d50EPUS0lwxxoSIBFXbR0dFER9cuM/TkyZOMGTOGwYMH89FHH6HTVe9sTEpKIi4ujlWrVjFw4EBAy2pas2YNL7zwQoNtrxH3VGwEReRbHZTYnQQZ9WRlZdBZ0W6mIZES8CrUTG1vPrt27eL06dOV3svMzKzyYSY6OhqDwUCvXhXrwvXs2ZOff/65yuv55AGoNC6oNsJOp4Oxf23Y9QSfkZCQwM6dO+nUqVOgTWkQdbkHBRKXUSt5otoKATiSVUiikgGAPrprwOwSmibNInni1KlTjB49mk6dOvHSSy+RmZnpea/UMwfQo0cP5s+fzzXXXIOiKMyePZvnnnuO8847j/POO4/nnnuOkJAQbrzxRv8bXRpjRzGgkplvpWNUCNmZ6XQGigki2OjnrEOhzjRFT0Rtbz4jRowgNzeXzZs3M3SolmCwadMmcnNzGTlypNdjTCYTF1xwASkpKRW279+/n8REPxQ9VctNxerKTcW6nGDXblqlY0douhgMBrp16xZoMxqV1NRUzp49S2pqKk6nk507dwLQrVs3wsLCqj+4gajuWnaKTfPYHcosZKLiDq+IkpAeoSLNQtitXLmSgwcPcvDgQTp0qNg6RS13o0hJSSE3N9ez/vDDD1NcXMysWbPIzs5m2LBhrFy5kvBwLx4BX+P2OugUlVBKyCzQhF1+tuZRyddHIA2Rmh7N2RPRs2dPxo8fz4wZM3jnnXcAuOuuu5g0aVKFjNjyD0AADz30ENdffz2XXHIJY8aMYfny5Xz99desXr3avwaXT56wlqstafbvTVIQ6sMTTzzBxx9/7FkvnQn66aefGD16tH8v7h4TOrsm7E6kpROluGNS23T277WFZkezyIq99dZbUVXV66s8qqpy6623etYVRWHevHmkpaVRUlLCmjVr6NOnT+MYbQz23LjCKCYrXyuhUpSreRtLDJGNY4dQJ0o9ESaTKdCm1IvPP/+cvn37MnbsWMaOHUu/fv349NNPK+xz7gPQNddcw4IFC3jxxRfp27cv77//Pv/973+56KKL/GBheY9duRi70lImih4MPohxFXxOhw4deOuttypsW79+PSEhIRw7dixAVjUeCxcu9HoP8ruoAxST5igwuL3ahekHACgxRXkPXRBaNc3CY9csURRtwBVnE64UkVmgCTtHnhYXYTdLl1jB90RFRfHZZ59Vu8+5D0QAt99+O7fffru/zPKOvlwdu//9SVtWnVXv3xJRVbAXBebaxhDtd6qWDB8+3JOkA9r3aPbs2cyePds/0/aCB12QW9g5NWGnnjkCgCMyKWA2CU0XEXb+xByhCTuKyXR77PT5JwCwhUoLmKbCl19+yW233cahQ4do3749AHfeeSebN29m3bp1WCyWAFvYQlG1JCLWv1623Noq6NuL4LkA/RY8egpMobXeffjw4RUKEn/66aekpqYyd+5cPxgnlCcoVIs7NTgKKbQ6iCxOBSMY20nihFCZZjEV22wpV6Q4y+2xCypyt6qydKjqqJaBqoKtMDAvLx6p6pg2bRrdu3dn/vz5gNY6aMWKFXz33Xci6vxJ2q/av0fXlQmMyxtepV/wD8OHDyc5OZmCggKKiop49NFHeeaZZxonZrmVExym/Q4ZHUUcyCigq+4UAOY4qekoVEY8dv7EXcsunCKPxy7CqmUyGaKaX3B+nWhGnghFUXj22We57rrrSEhI4LXXXmPdunUe753QCLjLONTl/61FYAzRvq+BunYdGDJkCHq9nu3bt/P999/Ttm3bxp++b6WEuIVdqFLC1qNnuUBxf2eipTixUBkRdv7EHQQ+QHeIZXlWVFUlypEBCoTHSmxEU2LSpEn06tWLp556ipUrV9K7d+9Am9Qy6TkZkr+G7lfCkbXgLt9Q1nWilWXEKkqzEbNBQUH079+fxYsX8+677/L111/XWE9U8A364LLyWVuOnuU6xV2vsq1MxQqVEWHnT45vBuAuwzLezb6NzLwS4skCoG37Fj4gm5EnAmDFihXs27cPp9NZbWcSoYFc9Q/oPhF6TISzh+Fdd2eLAndNLpNM6zVlhg8fzuuvv86kSZO47LLLAm1O68FcNvuz7cAJInVuD3eEzCoIlZHHLX8y5DbPYlaBjYPHjhGk2HGhYIxs4QOy1BMRiFcdMv0Atm/fztSpU3nnnXcYN24cjz/+uJ/+KAJBFhhwgxamEFeuAHRpuZPW5rFrZgwYMACDwcDf/va3QJvSugjWqihEKoWE2zXngMMQ6gn3EYTyiLDzJxfMAKAEI6CSnLwHgFxdG6nV1UQ4evQoEydO5JFHHmH69Ok8/fTT/Pe//2Xbtm2BNq3lU9pSrDzNZFqytfL5558za9asCgWvhUYgJAqACKWI9oom7NTw+OqOEFoxIuz8SUhbAIKwE4yV4pQfACgMlgHZFDh79iwTJkxgypQpPProowAMHjyYyZMn89hjjwXYulaKSTx2TQ2Xy8Xp06d57rnnSElJ4amnJHO50QmK9Cx2V1IBMLT0WR+h3kiMnT8xhYIhCBwltFXyucf1OQDRzowAGyaAVsw3OTm50vb//e9/AbBGADxTTkLTYe3atVx66aX06NGDxYsXSwmgQKA34DBFYLDl0VN3HAAlQmqhCt4RYedPFEXz2uWdpC1lLZyCSjIDaJQgNFFietU5PlLwP6NHj8blcgXajFaPEhYDZ/MYqrgfRkXYCVUgU7H+JjwOgPN1JzybXOOeD5Q1gtC0uOKvZcvDZwXODkFo4ug7DQegk87tGJAYO6EKRNj5m0itEPEkyxHPJt3wmYGyRhCaFqHRZcvR5wXODkFo6kR2rLguHjuhCkTY+Zt2WsuXUUWrtPW23WS6SRBK0ZWLBrF0rHo/QWjtnJtYFC2ZyYJ3RNj5m3bntHxp2y0wdghCU8ReXLbsLukgCIIXki6uuB4l3YsE74iw8zedRlRcH3lvYOxoBFpTgHVr+qx+xVFStmwMDpwdjUhr+u60ps/qd+L7ly2bwr3XgRQEJCvW/4THwY3/gi9+r3nrzhV6LQCTyYROp+PUqVO0a9cOk8mE0kKnm1VVxWazkZmZiU6nw2QyBdqk5k3PyfDdw9B+cKAt8TsyToQGM/4FWP5nGPGnQFsiNGFE2DUG54+DBw9ode1a4FOWTqcjKSmJtLQ0Tp0KUH/YRiYkJIROnTpJE/SGEpEADx1qFYWJZZwIDWb4TOh9NYTGBNoSoQkjwq6xCGvZA9FkMtGpUyccDgdOpzPQ5vgVvV6PwWBosd6WRqd8ZmwLR8aJ0GDcJbQEoSpE2Ak+Q1EUjEYjRqMx0KYIQpNFxokgCP6kWfjHjx49yh133EFSUhLBwcF07dqVJ598EpvNVu1xt956K4qiVHgNHz68kawWBEEQBEFoXJqFx27fvn24XC7eeecdunXrxm+//caMGTMoLCzkpZdeqvbY8ePH89FHH3nWJYhXEARBEISWSrMQduPHj2f8+PGe9S5dupCSksLbb79do7Azm83ExdU+JsFqtWK1Wj3reXl5dTdYEARBEAQhADQLYeeN3NxcoqJqLmi6evVqYmJiiIyMZNSoUTz77LPExFSdyDB//nyeeuqpSttF4Am1pfS7oqpqgC1pPEo/q4wTobbIOBGEmqnPOFHUZjiqDh06xKBBg3j55Ze58847q9xv0aJFhIWFkZiYyJEjR3j88cdxOBxs27YNs9ns9ZhzPXYnT56kV69ePv8MQsvn+PHjdOjQIdBmNAonTpygY0dpCSbUHRknglAzdRknARV28+bN8+odK8+WLVsYMmSIZ/3UqVOMGjWKUaNG8f7779fpemlpaSQmJvLPf/6Ta6+9tlbHuFwuTp06RXh4eIW0/by8PDp27Mjx48eJiIiokx0thdb+N6jq86uqSn5+PgkJCa2mfpeME++09s8PMk7KI+PEO/L5q/789RknAZ2Kveeee5g2bVq1+3Tu3NmzfOrUKcaMGcOIESN4991363y9+Ph4EhMTOXDgQK2P0el01arkiIiIVvlFLE9r/xt4+/wWiyVA1gQGGSfV09o/P8g4ARknNSGf3/vnr+s4Caiwi46OJjq6dsVJT548yZgxYxg8eDAfffRRvZ7wzpw5w/Hjx4mPj6/zsYIgCIIgCE2dZuH/PnXqFKNHj6Zjx4689NJLZGZmkp6eTnp6eoX9evTowZIlSwAoKCjgwQcfZMOGDRw9epTVq1czefJkoqOjueaaawLxMQRBEARBEPxKs8iKXblyJQcPHuTgwYOV3NjlQwRTUlLIzc0FtHY2u3fv5pNPPiEnJ4f4+HjGjBnDokWLCA8Pb7BNZrOZJ598ssokjNZAa/8btPbPXxta+9+otX9+kL9BbWjtfyP5/L79/M0yK1YQBEEQBEGoTLOYihUEQRAEQRBqRoSdIAiCIAhCC0GEnSAIgiAIQgtBhJ0gCIIgCEILQYSdIAiCIAhCC0GEXT156623SEpKIigoiMGDB7Nu3bpAm9QozJs3D0VRKrzi4uICbZbfWLt2LZMnTyYhIQFFUVi6dGmF91VVZd68eSQkJBAcHMzo0aPZs2dPYIxtgsg4kXECMk5qQsaJjBPw3TgRYVcPFi1axOzZs3nsscfYsWMHF198MRMmTCA1NTXQpjUKvXv3Ji0tzfPavXt3oE3yG4WFhfTv358333zT6/svvvgir7zyCm+++SZbtmwhLi6OK664gvz8/Ea2tOkh40TGSSkyTqpGxomMk1J8Nk5Uoc4MHTpUnTlzZoVtPXr0UB955JEAWdR4PPnkk2r//v0DbUZAANQlS5Z41l0ulxoXF6c+//zznm0lJSWqxWJRFyxYEAALmxYyTvoH2oyAIOOkbsg46R9oMwKCP8eJeOzqiM1mY9u2bYwdO7bC9rFjx7J+/foAWdW4HDhwgISEBJKSkpg2bRqHDx8OtEkB4ciRI6Snp1f4LpjNZkaNGtVqvgtVIeNExkkpMk6qRsaJjJNSfDlORNjVkaysLJxOJ7GxsRW2x8bGVupd2xIZNmwYn3zyCStWrOC9994jPT2dkSNHcubMmUCb1uiU/n+31u9Cdcg4kXFSioyTqpFxIuOkFF+Ok2bRK7YpoihKhXVVVStta4lMmDDBs9y3b19GjBhB165d+fjjj5kzZ04ALQscrfW7UBta699GxkllWut3oTa01r+NjJPK+OK7IB67OhIdHY1er6+koDMyMiop7dZAaGgoffv25cCBA4E2pdEpzd6S70JlZJxURMaJjBNvyDipiIwT34wTEXZ1xGQyMXjwYFatWlVh+6pVqxg5cmSArAocVquV5ORk4uPjA21Ko5OUlERcXFyF74LNZmPNmjWt8rtQHhknFZFxIuPEGzJOKiLjxEfjpOG5Ha2Pf/7zn6rRaFQ/+OADde/evers2bPV0NBQ9ejRo4E2ze888MAD6urVq9XDhw+rGzduVCdNmqSGh4e32M+en5+v7tixQ92xY4cKqK+88oq6Y8cO9dixY6qqqurzzz+vWiwWdfHixeru3bvVG264QY2Pj1fz8vICbHngkXEi40TGSc3IOJFx4utxIsKunvzjH/9QExMTVZPJpA4aNEhds2ZNoE1qFK6//no1Pj5eNRqNakJCgnrttdeqe/bsCbRZfuOnn35SgUqvW265RVVVLUX9ySefVOPi4lSz2axecskl6u7duwNrdBNCxomME1WVcVITMk5knKiq78aJoqqq2iD/oSAIgiAIgtAkkBg7QRAEQRCEFoIIO0EQBEEQhBaCCDtBEARBEIQWggg7QRAEQRCEFoIIO0EQBEEQhBaCCDtBEARBEIQWggg7QRAEQRCEFoIIO0EQBEEQhBaCCDtBEARBEIQWggi7Fs7o0aOZPXt2oM2oktGjR6MoCoqisHPnzlodc+utt3qOWbp0qV/tE1oHMk4EoXpkjDQfRNg1Y0q/kFW9br31VhYvXsxf//rXgNg3e/Zsrr766hr3mzFjBmlpafTp06dW533ttddIS0troHVCa0HGiSBUj4yRloUh0AYI9af8F3LRokU88cQTpKSkeLYFBwdjsVgCYRoAW7ZsYeLEiTXuFxISQlxcXK3Pa7FYAvq5hOaFjBNBqB4ZIy0L8dg1Y+Li4jwvi8WCoiiVtp3rPh89ejT33nsvs2fPpk2bNsTGxvLuu+9SWFjIbbfdRnh4OF27duW7777zHKOqKi+++CJdunQhODiY/v3785///KdKu+x2OyaTifXr1/PYY4+hKArDhg2r02f7z3/+Q9++fQkODqZt27ZcfvnlFBYW1vlvJAgyTgShemSMtCxE2LVCPv74Y6Kjo9m8eTP33nsvf/zjH5k6dSojR45k+/btjBs3junTp1NUVATAX/7yFz766CPefvtt9uzZw/33388f/vAH1qxZ4/X8er2en3/+GYCdO3eSlpbGihUram1fWloaN9xwA7fffjvJycmsXr2aa6+9FlVVG/7hBaGWyDgRhOqRMdJEUYUWwUcffaRaLJZK20eNGqX+3//9X4X1iy66yLPucDjU0NBQdfr06Z5taWlpKqBu2LBBLSgoUIOCgtT169dXOO8dd9yh3nDDDVXas2TJErVt27Y12n2ufaqqqtu2bVMB9ejRo9UeC6hLliyp8RqCUIqME0GoHhkjzR+JsWuF9OvXz7Os1+tp27Ytffv29WyLjY0FICMjg71791JSUsIVV1xR4Rw2m42BAwdWeY0dO3bQv3//etnXv39/LrvsMvr27cu4ceMYO3Ys1113HW3atKnX+QShPsg4EYTqkTHSNBFh1woxGo0V1hVFqbBNURQAXC4XLpcLgGXLltG+ffsKx5nN5iqvsXPnznoPRr1ez6pVq1i/fj0rV67kjTfe4LHHHmPTpk0kJSXV65yCUFdknAhC9cgYaZpIjJ1QLb169cJsNpOamkq3bt0qvDp27Fjlcbt3767wNFdXFEXhwgsv5KmnnmLHjh2YTCaWLFlS7/MJgj+RcSII1SNjpPEQj51QLeHh4Tz44IPcf//9uFwuLrroIvLy8li/fj1hYWHccsstXo9zuVzs2rWLU6dOERoaWqeU8k2bNvHDDz8wduxYYmJi2LRpE5mZmfTs2dNXH0sQfIqME0GoHhkjjYd47IQa+etf/8oTTzzB/Pnz6dmzJ+PGjePrr7+u1pX9zDPPsGjRItq3b8/TTz9dp+tFRESwdu1arrzySs4//3z+8pe/8PLLLzNhwoSGfhRB8BsyTgShemSMNA6Kqrb0vF+hKTN69GgGDBjAq6++WudjFUVhyZIltapILgjNGRknglA9MkbKEI+dEHDeeustwsLC2L17d632nzlzJmFhYX62ShCaFjJOBKF6ZIxoiMdOCCgnT56kuLgYgE6dOmEymWo8JiMjg7y8PADi4+MJDQ31q42CEGhknAhC9cgYKUOEnSAIgiAIQgtBpmIFQRAEQRBaCCLsBEEQBEEQWggi7ARBEARBEFoIIuwEQRAEQRBaCCLsBEEQBEEQWggi7ARBEARBEFoIIuwEQRAEQRBaCCLsBEEQBEEQWggi7ARBEARBEFoI/w9EA9iL24j1RQAAAABJRU5ErkJggg==", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, axs = plt.subplots(2, 3)\n", + "var = ['x', 'y', r'\\theta', r'\\dot x', r'\\dot y', r'\\dot \\theta']\n", + "for i in [0, 1]:\n", + " for j in [0, 1, 2]:\n", + " k = i * 3 + j\n", + " axs[i, j].plot(resp.time, resp.outputs[k], label=f'${var[k]}$')\n", + " axs[i, j].plot(resp.time, resp.outputs[xh0+k], label=f'$\\\\hat {var[k]}$')\n", + " axs[i, j].legend()\n", + " if i == 1:\n", + " axs[i, j].set_xlabel(\"Time $t$ [s]\")\n", + " if j == 0:\n", + " axs[i, j].set_ylabel(\"State\")\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "id": "2039578e", + "metadata": {}, + "source": [ + "Note the lag in tracking changes in the $\\dot x$ and $\\dot y$ states (varies from simulation to simulation, depending on the specific noise signal)." + ] + }, + { + "cell_type": "markdown", + "id": "0c0d5c99", + "metadata": {}, + "source": [ + "### Full state feedback\n", + "\n", + "To see how the inclusion of the estimator affects the system performance, we compare it with the case where we are able to directly measure the state of the system." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "3b6a1f1c", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/murray/Library/CloudStorage/Dropbox/macosx/src/python-control/murrayrm/control/statefbk.py:788: UserWarning: cannot verify system output is system state\n", + " warnings.warn(\"cannot verify system output is system state\")\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Compute the full state feedback solution\n", + "lqr_ctrl, _ = ct.create_statefbk_iosystem(pvtol, K)\n", + "\n", + "lqr_clsys = ct.interconnect(\n", + " [pvtol_noisy, lqr_ctrl],\n", + " inplist = lqr_ctrl.input_labels[0:pvtol.ninputs + pvtol.nstates] + \\\n", + " pvtol_noisy.input_labels[pvtol.ninputs:],\n", + " inputs = lqr_ctrl.input_labels[0:pvtol.ninputs + pvtol.nstates] + \\\n", + " pvtol_noisy.input_labels[pvtol.ninputs:],\n", + " outlist = pvtol.output_labels + lqr_ctrl.output_labels,\n", + " outputs = pvtol.output_labels + lqr_ctrl.output_labels\n", + ")\n", + "\n", + "# Put together the input for the system (turn off sensor noise)\n", + "U = [xe, ue, V, W*0]\n", + "\n", + "# Run a simulation with full state feedback\n", + "lqr_resp = ct.input_output_response(lqr_clsys, timepts, U, x0)\n", + "\n", + "# Compare the results\n", + "plt.plot(resp.states[0], resp.states[1], 'b-', label=\"Extended KF\")\n", + "plt.plot(lqr_resp.states[0], lqr_resp.states[1], 'r-', label=\"Full state\")\n", + "\n", + "plt.xlabel('$x$ [m]')\n", + "plt.ylabel('$y$ [m]')\n", + "plt.axis('equal')\n", + "plt.legend(frameon=False);" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.1" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From e82670e054db9c2889af972ee116224ca0f7e248 Mon Sep 17 00:00:00 2001 From: Richard Murray Date: Tue, 21 Jan 2025 12:23:11 -0800 Subject: [PATCH 80/93] add examples for create_{statefbk,estimator}_iosystem + related fixes --- control/mateqn.py | 6 +- control/nlsys.py | 6 +- control/statefbk.py | 8 +- control/statesp.py | 2 +- control/stochsys.py | 17 +- control/tests/response_test.py | 2 +- doc/iosys.rst | 288 ++++++++++++++++++++++++++++++++- doc/nlsys.rst | 2 + examples/repr_gallery.ipynb | 21 ++- 9 files changed, 321 insertions(+), 31 deletions(-) diff --git a/control/mateqn.py b/control/mateqn.py index 870490bb2..3f43ceea0 100644 --- a/control/mateqn.py +++ b/control/mateqn.py @@ -606,7 +606,9 @@ def _slycot_or_scipy(method): # Utility function to check matrix dimensions def _check_shape(M, n, m, square=False, symmetric=False, name="??"): - if square and M.shape[0] != M.shape[1]: + M = np.atleast_2d(M) + + if (square or symmetric) and M.shape[0] != M.shape[1]: raise ControlDimension("%s must be a square matrix" % name) if symmetric and not _is_symmetric(M): @@ -617,6 +619,8 @@ def _check_shape(M, n, m, square=False, symmetric=False, name="??"): f"Incompatible dimensions of {name} matrix; " f"expected ({n}, {m}) but found {M.shape}") + return M + # Utility function to check if a matrix is symmetric def _is_symmetric(M): diff --git a/control/nlsys.py b/control/nlsys.py index 59a3b16b3..f96d907bf 100644 --- a/control/nlsys.py +++ b/control/nlsys.py @@ -873,7 +873,7 @@ def cxn_string(signal, gain, first): cxn, width=78, initial_indent=" * ", subsequent_indent=" ")) + "\n" - out += "\nOutputs:\n" + out += "\nOutputs:" for i in range(len(self.output_labels)): first = True cxn = f"{self.output_labels[i]} <- " @@ -883,9 +883,9 @@ def cxn_string(signal, gain, first): cxn += cxn_string( output_list[j], self.output_map[i, j], first) first = False - out += "\n".join(textwrap.wrap( + out += "\n" + "\n".join(textwrap.wrap( cxn, width=78, initial_indent=" * ", - subsequent_indent=" ")) + "\n" + subsequent_indent=" ")) return out diff --git a/control/statefbk.py b/control/statefbk.py index 5ad76f58f..fa1c7e479 100644 --- a/control/statefbk.py +++ b/control/statefbk.py @@ -783,12 +783,12 @@ def create_statefbk_iosystem( if integral_action is not None: if not isinstance(integral_action, np.ndarray): raise ControlArgument("Integral action must pass an array") - elif integral_action.shape[1] != sys_nstates: + + C = np.atleast_2d(integral_action) + if C.shape[1] != sys_nstates: raise ControlArgument( "Integral gain size must match system state size") - else: - nintegrators = integral_action.shape[0] - C = integral_action + nintegrators = C.shape[0] else: # Create a C matrix with no outputs, just in case update gets called C = np.zeros((0, sys_nstates)) diff --git a/control/statesp.py b/control/statesp.py index b88d5ed0c..798c17d8c 100644 --- a/control/statesp.py +++ b/control/statesp.py @@ -1608,7 +1608,7 @@ def __call__(self, *args, **kwargs): return StateSpace.__call__(self, *args, **kwargs) def __str__(self): - string = InterconnectedSystem.__str__(self) + "\n" + string = InterconnectedSystem.__str__(self) + "\n\n" string += "\n\n".join([ "{} = {}".format(Mvar, "\n ".join(str(M).splitlines())) diff --git a/control/stochsys.py b/control/stochsys.py index d701b50f6..762a441f9 100644 --- a/control/stochsys.py +++ b/control/stochsys.py @@ -321,7 +321,7 @@ def create_estimator_iosystem( .. math:: d \hat{x}/dt &= A \hat{x} + B u - L (C \hat{x} - y) \\ - dP/dt &= A P + P A^T + F Q_N F^T - P C^T R_N^{-1} C P \\ + dP/dt &= A P + P A^T + G Q_N G^T - P C^T R_N^{-1} C P \\ L &= P C^T R_N^{-1} or a discrete-time state estimator of the form @@ -329,7 +329,7 @@ def create_estimator_iosystem( .. math:: \hat{x}[k+1] &= A \hat{x}[k] + B u[k] - L (C \hat{x}[k] - y[k]) \\ - P[k+1] &= A P A^T + F Q_N F^T - A P C^T R_e^{-1} C P A \\ + P[k+1] &= A P A^T + G Q_N G^T - A P C^T R_e^{-1} C P A \\ L &= A P C^T R_e^{-1} where :math:`R_e = R_N + C P C^T`. It can be called in the form:: @@ -353,7 +353,7 @@ def create_estimator_iosystem( state covariance. G : ndarray, optional Disturbance matrix describing how the disturbances enters the - dynamics. Defaults to sys.B. + dynamics. Defaults to `sys.B`. C : ndarray, optional If the system has full state output, define the measured values to be used by the estimator. Otherwise, use the system output as the @@ -430,6 +430,11 @@ def create_estimator_iosystem( resp = ct.input_output_response( est, T, 0, [X0, P0], param={'correct': False) + References + ---------- + .. [1] R. M. Murray, `Optimization-Based Control + >> print(estim) + : estim + Inputs (2): ['y[0]', 'u[0]'] + Outputs (2): ['xhat[0]', 'xhat[1]'] + States (6): ['xhat[0]', 'xhat[1]', 'P[0,0]', 'P[0,1]', 'P[1,0]', 'P[1,1]'] + + Update: ._estim_update at 0x...> + Output: ._estim_output at 0x...> + +The estimator is a nonlinear system with states consisting of the +estimates of the process states (:math:`\hat x`) and the entries of +the covariance of the state error (:math:`P`). The estimator dynamics +are given by + +.. math:: + + \dot {\hat x} &= A \hat x + B u - L (C \hat x - y), \\ + \dot P &= A P + P A^\mathsf{T} + - P C^\mathsf{T} Q_w^{-1} C P + F Q_v F^\mathsf{T}, + +where :math:`L` is the estimator gain and :math:`F` is the mapping +from disturbance signals to the state dynamics (see +`create_estimator_iosystem` and `Optimization-Based Control +`_, Chapter 6 [Kalman Filtering] for more +detailed information). + +We can now create the entire closed loop system using the estimated state: + +.. testcode:: statefbk + + # Estimation-based controller + ctrl, clsys = ct.create_statefbk_iosystem( + sys, K, estimator=estim, name='ctrl') + +The resulting controller is given by + +.. doctest:: statefbk + + >>> print(ctrl) + : ctrl + Inputs (5): ['xd[0]', 'xd[1]', 'ud[0]', 'xhat[0]', 'xhat[1]'] + Outputs (1): ['u[0]'] + States (0): [] + + A = [] + + B = [] + + C = [] + + D = [[ 1. 1.73205081 1. -1. -1.73205081]] + +Note that controller input signals have automatically been named to +match the estimator output signals. The full closed loop system is +given by + +.. doctest:: statefbk + + >>> print(clsys) + : sys_ctrl + Inputs (3): ['xd[0]', 'xd[1]', 'ud[0]'] + Outputs (2): ['y[0]', 'u[0]'] + States (8): ['sys_x[0]', 'sys_x[1]', 'estim_xhat[0]', 'estim_xhat[1]', 'estim_P[0,0]', 'estim_P[0,1]', 'estim_P[1,0]', 'estim_P[1,1]'] + + Subsystems (3): + * ['y[0]']> + * + ['u[0]']> + * ['xhat[0]', 'xhat[1]']> + + Connections: + * sys.u[0] <- ctrl.u[0] + * ctrl.xd[0] <- xd[0] + * ctrl.xd[1] <- xd[1] + * ctrl.ud[0] <- ud[0] + * ctrl.xhat[0] <- estim.xhat[0] + * ctrl.xhat[1] <- estim.xhat[1] + * estim.y[0] <- sys.y[0] + * estim.u[0] <- ctrl.u[0] + + Outputs: + * y[0] <- sys.y[0] + * u[0] <- ctrl.u[0] + +We see that the state of the full closed loop system consists of the +process states as well as the estimated states and the entries of the +covariance matrix. Adding integral action ---------------------- @@ -731,6 +854,91 @@ must match the number of additional columns in the `K` matrix. If an estimator is specified, :math:`\hat x` will be used in place of :math:`x`. +As an example, consider the servo-mechanism model `servomech` +described in the :ref:`sec-nonlinear-models`. We construct a state +space controller by linearizing the system around an equilibrium +point, augmenting the model with an integrator, and computing a state +feedback that optimizes a quadratic cost function: + +.. testsetup:: integral_action + + import numpy as np + import control as ct + + # Parameter values + servomech_params = { + 'J': 100, # Moment of inertia of the motor + 'b': 10, # Angular damping of the arm + 'k': 1, # Spring constant + 'r': 1, # Location of spring contact on arm + 'l': 2, # Distance to the read head + 'eps': 0.01, # Magnitude of velocity-dependent perturbation + } + + # State derivative + def servomech_update(t, x, u, params): + # Extract the configuration and velocity variables from the state vector + theta = x[0] # Angular position of the disk drive arm + thetadot = x[1] # Angular velocity of the disk drive arm + tau = u[0] # Torque applied at the base of the arm + + # Get the parameter values + J, b, k, r = map(params.get, ['J', 'b', 'k', 'r']) + + # Compute the angular acceleration + dthetadot = 1/J * ( + -b * thetadot - k * r * np.sin(theta) + tau) + + # Return the state update law + return np.array([thetadot, dthetadot]) + +.. testcode:: integral_action + + # System dynamics (with full state output) + servomech = ct.nlsys( + servomech_update, None, name='servomech', + params=servomech_params, states=['theta', 'thdot'], + outputs=['theta', 'thdot'], inputs=['tau']) + + # Find operating point with output angle pi/4 + xeq, ueq = ct.find_operating_point( + servomech, [0, 0], 0, y0=[np.pi/4, 0], iy=0) + + # Compute linearization and augment with an integrator on angle + A, B, _, _ = ct.ssdata(servomech.linearize(xeq, ueq)) + C = np.array([[1, 0]]) # theta + A_aug = np.block([ + [A, np.zeros((2, 1))], + [C, np.zeros((1, 1))] + ]) + B_aug = np.block([[B], [0]]) + + # Compute LQR controller + K, _, _ = ct.lqr(A_aug, B_aug, np.diag([1, 1, 0.1]), 1) + + # Create controller with integral action + ctrl, _ = ct.create_statefbk_iosystem( + servomech, K, integral_action=C, name='ctrl') + +The resulting controller now has internal dynamics corresponding to +the integral action: + +.. doctest:: integral_action + + >>> print(ctrl) + : ctrl + Inputs (5): ['xd[0]', 'xd[1]', 'ud[0]', 'theta', 'thdot'] + Outputs (1): ['tau'] + States (1): ['x[0]'] + + A = [[0.]] + + B = [[-1. 0. 0. 1. 0.]] + + C = [[-0.31622777]] + + D = [[ 3.76244547 19.21453568 1. -3.76244547 -19.21453568]] + Adding gain scheduling ---------------------- @@ -758,8 +966,74 @@ where :math:`\mu` represents the scheduling variables. See scheduled controller (in the alternative formulation section at the bottom of the file). +As an example, consider the following simple model of a mobile robot +("unicycle" model), which has dynamics given by + +.. math:: + + \frac{dx}{dt} &= v \cos\theta \\ + \frac{dy}{dt} &= v \sin\theta \\ + \frac{d\theta}{dt} &= \omega + +where :math:`x`, :math:`y` is the position of the robot in the plane, +:math:`\theta` is the angle with respect to the :math:`x` axis, +:math:`v` is the commanded velocity, and :math:`\omega` is the +commanded angular rate. + +We define the nonlinear dynamics as follows: + +.. testsetup:: gainsched + + import itertools + import numpy as np + import control as ct + +.. testcode:: gainsched + + def unicycle_update(t, x, u, params): + return np.array([u[0] * np.cos(x[2]), u[0] * np.sin(x[2]), u[1]]) + + unicycle = ct.nlsys( + unicycle_update, None, name='unicycle', states=3, + inputs=['v', 'omega'], outputs=['x', 'y', 'theta']) + +We construct a gain-scheduled controller by linearizing the dynamics +about a range of different speeds :math:`v` and angles :math:`\theta`: + +.. testcode:: gainsched + + # Speeds and angles at which to compute the gains + speeds = [1, 5, 10] + angles = np.linspace(0, np.pi/2, 4) + points = list(itertools.product(speeds, angles)) + + # Gains for each speed (using LQR controller) + Q = np.identity(unicycle.nstates) + R = np.identity(unicycle.ninputs) + gains = [np.array(ct.lqr(unicycle.linearize( + [0, 0, angle], [speed, 0]), Q, R)[0]) for speed, angle in points] + + # Create gain scheduled controller + ctrl, clsys = ct.create_statefbk_iosystem( + unicycle, (gains, points), gainsched_indices=['v_d', 'th_d'], name='ctrl', + inputs=['x_d', 'y_d', 'th_d', 'v_d', 'omega_d', 'x', 'y', 'theta']) + +The resulting controller has the following structure: + +.. doctest:: gainsched + + >>> print(ctrl) + : ctrl + Inputs (8): ['x_d', 'y_d', 'th_d', 'v_d', 'omega_d', 'x', 'y', 'theta'] + Outputs (2): ['v', 'omega'] + States (0): [] + + Update: ._control_update at 0x...> + Output: ._control_output at 0x...> + +This is a static, nonlinear controller, with the gains scheduled based +on the values of :math:`v_\text{d}` (index 3) and +:math:`\theta_\text{d}` (index 2). + Integral action and state estimation can also be used with gain scheduled controllers. - -.. todo:: - Add example? diff --git a/doc/nlsys.rst b/doc/nlsys.rst index 017fd92f3..dbe693941 100644 --- a/doc/nlsys.rst +++ b/doc/nlsys.rst @@ -23,6 +23,8 @@ Discrete time systems are also supported and have dynamics of the form y[t] &= h(t, x[t], u[t], \theta). +.. _sec-nonlinear-models: + Creating nonlinear models ------------------------- diff --git a/examples/repr_gallery.ipynb b/examples/repr_gallery.ipynb index e0d33c147..56962210e 100644 --- a/examples/repr_gallery.ipynb +++ b/examples/repr_gallery.ipynb @@ -413,8 +413,8 @@ "States (2): ['x[0]', 'x[1]']\n", "Parameters: ['a', 'b']\n", "\n", - "Update: \n", - "Output: \n", + "Update: \n", + "Output: \n", "----\n", "\n", "NonlinearIOSystem: sys_dnl, dt=0.1:\n", @@ -425,8 +425,8 @@ "States (2): ['x[0]', 'x[1]']\n", "dt = 0.1\n", "\n", - "Update: \n", - "Output: \n", + "Update: \n", + "Output: \n", "----\n", "\n", "InterconnectedSystem: sys_ic, dt=0:\n", @@ -449,7 +449,6 @@ "Outputs:\n", " * y[0] <- proc_nl.y[0]\n", " * y[1] <- proc_nl.y[1]\n", - "\n", "----\n", "\n", "LinearICSystem: sys_ic, dt=0:\n", @@ -496,8 +495,8 @@ "Update: ['y[0]']>>\n", "Output: ['y[0]']>>\n", "\n", - "Forward: \n", - "Reverse: \n", + "Forward: \n", + "Reverse: \n", "----\n", "\n", "FlatSystem: sys_fsnl, dt=0:\n", @@ -507,11 +506,11 @@ "Outputs (1): ['y[0]']\n", "States (2): ['x[0]', 'x[1]']\n", "\n", - "Update: \n", - "Output: \n", + "Update: \n", + "Output: \n", "\n", - "Forward: \n", - "Reverse: \n", + "Forward: \n", + "Reverse: \n", "----\n", "\n", "============================================================================\n", From 6e8195a54a2c8722af8432fd25847e605abe775e Mon Sep 17 00:00:00 2001 From: Richard Murray Date: Sun, 2 Feb 2025 21:25:15 -0800 Subject: [PATCH 81/93] address doc-related TODOs; rename optimal_{ocp,oep} --- control/flatsys/__init__.py | 5 +++-- control/flatsys/flatsys.py | 6 +++++- control/flatsys/systraj.py | 2 +- control/lti.py | 1 - control/optimal.py | 29 ++++++++++++++----------- control/statesp.py | 29 +------------------------ control/stochsys.py | 2 +- control/tests/docstrings_test.py | 5 +++-- control/tests/flatsys_test.py | 30 +++++++++++++------------- control/tests/kwargs_test.py | 4 ++++ control/tests/optimal_test.py | 36 ++++++++++++++++---------------- control/xferfcn.py | 29 +------------------------ doc/config.rst | 17 ++++++++------- doc/develop.rst | 3 +-- doc/flatsys.rst | 10 ++++----- doc/functions.rst | 6 +++--- doc/optimal.rst | 35 ++++++++++++++++--------------- doc/test_sphinxdocs.py | 3 +++ 18 files changed, 108 insertions(+), 144 deletions(-) diff --git a/control/flatsys/__init__.py b/control/flatsys/__init__.py index 7de3f88ea..f937eb129 100644 --- a/control/flatsys/__init__.py +++ b/control/flatsys/__init__.py @@ -16,7 +16,7 @@ using the `BasisFamily` class. The resulting trajectory is return as a `SystemTrajectory` object and can be evaluated using the `SystemTrajectory.eval` member function. Alternatively, the -`solve_flat_ocp` function can be used to solve an optimal control +`solve_flat_optimal` function can be used to solve an optimal control problem with trajectory and final costs or constraints. The docstring examples assume that the following import commands:: @@ -29,9 +29,9 @@ # Basis function families from .basis import BasisFamily as BasisFamily -from .poly import PolyFamily as PolyFamily from .bezier import BezierFamily as BezierFamily from .bspline import BSplineFamily as BSplineFamily +from .poly import PolyFamily as PolyFamily # Classes from .systraj import SystemTrajectory as SystemTrajectory @@ -41,4 +41,5 @@ # Package functions from .flatsys import point_to_point as point_to_point +from .flatsys import solve_flat_optimal as solve_flat_optimal from .flatsys import solve_flat_ocp as solve_flat_ocp diff --git a/control/flatsys/flatsys.py b/control/flatsys/flatsys.py index 502cc4bec..be2f137fd 100644 --- a/control/flatsys/flatsys.py +++ b/control/flatsys/flatsys.py @@ -656,7 +656,7 @@ def traj_const(null_coeffs): # Solve a point to point trajectory generation problem for a flat system -def solve_flat_ocp( +def solve_flat_optimal( sys, timepts, x0=0, u0=0, trajectory_cost=None, basis=None, terminal_cost=None, trajectory_constraints=None, initial_guess=None, params=None, **kwargs): @@ -996,3 +996,7 @@ def traj_const(null_coeffs): # Return a function that computes inputs and states as a function of time return systraj + + +# Convenience aliases +solve_flat_ocp = solve_flat_optimal diff --git a/control/flatsys/systraj.py b/control/flatsys/systraj.py index 2c7c4c898..37fff29ef 100644 --- a/control/flatsys/systraj.py +++ b/control/flatsys/systraj.py @@ -18,7 +18,7 @@ class SystemTrajectory: The `SystemTrajectory` class is used to represent the trajectory of a (differentially flat) system. Used by the `point_to_point` - and `solve_flat_ocp` functions to return a trajectory. + and `solve_flat_optimal` functions to return a trajectory. Parameters ---------- diff --git a/control/lti.py b/control/lti.py index f32c3d23b..6272610cd 100644 --- a/control/lti.py +++ b/control/lti.py @@ -38,7 +38,6 @@ def __init__(self, inputs=1, outputs=1, states=None, name=None, **kwargs): super().__init__( name=name, inputs=inputs, outputs=outputs, states=states, **kwargs) - # TODO: update ss, tf docstrings to use this one as the primary def __call__(self, x, squeeze=None, warn_infinite=True): """Evaluate system transfer function at point in complex plane. diff --git a/control/optimal.py b/control/optimal.py index da1d67623..a9ebc7449 100644 --- a/control/optimal.py +++ b/control/optimal.py @@ -7,13 +7,14 @@ This module provides support for optimization-based controllers for nonlinear systems with state and input constraints. An optimal -control problem can be solved using the `solve_ocp` function or set up -using the `OptimalControlProblem` class and then solved using the -`~OptimalControlProblem.compute_trajectory` method. Utility functions -are available to define common cost functions and input/state -constraints. Optimal estimation problems can be solved using the -`solve_oep` function or by using the `OptimalEstimationProblem` class -and the `~OptimalEstimationProblem.compute_estimate` method.. +control problem can be solved using the `solve_optimal_trajectory` +function or set up using the `OptimalControlProblem` class and then +solved using the `~OptimalControlProblem.compute_trajectory` method. +Utility functions are available to define common cost functions and +input/state constraints. Optimal estimation problems can be solved +using the `solve_optimal_estimate` function or by using the +`OptimalEstimationProblem` class and the +`~OptimalEstimationProblem.compute_estimate` method.. The docstring examples assume the following import commands:: @@ -760,7 +761,6 @@ def _simulate_states(self, x0, inputs): # # Compute the optimal trajectory from the current state - # TODO: update docstring to refer to primary function (?) def compute_trajectory( self, x, squeeze=None, transpose=None, return_states=True, initial_guess=None, print_summary=True, **kwargs): @@ -1017,7 +1017,7 @@ def __init__( # Compute the input for a nonlinear, (constrained) optimal control problem -def solve_ocp( +def solve_optimal_trajectory( sys, timepts, X0, cost, trajectory_constraints=None, terminal_cost=None, terminal_constraints=None, initial_guess=None, basis=None, squeeze=None, transpose=None, return_states=True, @@ -1220,7 +1220,7 @@ def create_mpc_iosystem( constraints : list of tuples, optional List of constraints that should hold at each point in the time - vector. See `solve_ocp` for more details. + vector. See `solve_optimal_trajectory` for more details. terminal_cost : callable, optional Function that returns the terminal cost given the final state @@ -2020,7 +2020,7 @@ def __init__( # Compute the finite horizon estimate for a nonlinear system -def solve_oep( +def solve_optimal_estimate( sys, timepts, Y, U, trajectory_cost, X0=None, trajectory_constraints=None, initial_guess=None, squeeze=None, print_summary=True, **kwargs): @@ -2047,7 +2047,7 @@ def solve_oep( Mean value of the initial condition (defaults to 0). trajectory_constraints : list of tuples, optional List of constraints that should hold at each point in the time - vector. See `solve_ocp` for more information. + vector. See `solve_optimal_trajectory` for more information. control_indices : int, slice, or list of int or string, optional Specify the indices in the system input vector that correspond to the control inputs. For more information on possible values, see @@ -2544,3 +2544,8 @@ def _process_constraints(clist, name): constraint.lb, constraint.ub)) return constraint_list + + +# Convenience aliases +solve_ocp = solve_optimal_trajectory +solve_oep = solve_optimal_estimate diff --git a/control/statesp.py b/control/statesp.py index 798c17d8c..b007e338f 100644 --- a/control/statesp.py +++ b/control/statesp.py @@ -774,34 +774,7 @@ def __call__(self, x, squeeze=None, warn_infinite=True): in the complex plane, where `x` is `s` for continuous-time systems and `z` for discrete-time systems. - By default, a (complex) scalar will be returned for SISO systems - and a p x m array will be return for MIMO systems with m inputs and - p outputs. This can be changed using the `squeeze` keyword. - - To evaluate at a frequency `omega` in radians per second, - enter ``x = omega * 1j`` for continuous-time systems, - ``x = exp(1j * omega * dt)`` for discrete-time systems, or - use the `~LTI.frequency_response` method. - - Parameters - ---------- - x : complex or complex 1D array_like - Complex value(s) at which transfer function will be evaluated. - squeeze : bool, optional - Squeeze output, as described below. Default value can be set - using `config.defaults['control.squeeze_frequency_response']`. - warn_infinite : bool, optional If set to False, turn off - divide by zero warning. - - Returns - ------- - fresp : complex ndarray - The value of the system transfer function at `x`. If the system - is SISO and `squeeze` is not True, the shape of the array matches - the shape of `x`. If the system is not SISO or `squeeze` is - False, the first two dimensions of the array are indices for the - output and input and the remaining dimensions match `x`. If - `squeeze` is True then single-dimensional axes are removed. + See `LTI.__call__` for details. Examples -------- diff --git a/control/stochsys.py b/control/stochsys.py index 762a441f9..6834df0c4 100644 --- a/control/stochsys.py +++ b/control/stochsys.py @@ -433,7 +433,7 @@ def create_estimator_iosystem( References ---------- .. [1] R. M. Murray, `Optimization-Based Control - `_, 2013. """ diff --git a/control/tests/docstrings_test.py b/control/tests/docstrings_test.py index 74b3e1edb..d1747486e 100644 --- a/control/tests/docstrings_test.py +++ b/control/tests/docstrings_test.py @@ -48,7 +48,8 @@ keyword_skiplist = { control.input_output_response: ['method'], control.nyquist_plot: ['color'], # separate check - control.optimal.solve_ocp: ['method', 'return_x'], # deprecated + control.optimal.solve_optimal_trajectory: + ['method', 'return_x'], # deprecated control.sisotool: ['kvect'], # deprecated control.nyquist_response: ['return_contour'], # deprecated control.create_estimator_iosystem: ['state_labels'], # deprecated @@ -61,7 +62,7 @@ control.flatsys.point_to_point: [ 'method', 'options', # minimize_kwargs ], - control.flatsys.solve_flat_ocp: [ + control.flatsys.solve_flat_optimal: [ 'method', 'options', # minimize_kwargs ], control.optimal.OptimalControlProblem: [ diff --git a/control/tests/flatsys_test.py b/control/tests/flatsys_test.py index 772dea7b0..10c512bca 100644 --- a/control/tests/flatsys_test.py +++ b/control/tests/flatsys_test.py @@ -198,7 +198,7 @@ def test_kinematic_car_ocp( with warnings.catch_warnings(): warnings.filterwarnings( 'ignore', message="unable to solve", category=UserWarning) - traj_ocp = fs.solve_flat_ocp( + traj_ocp = fs.solve_flat_optimal( vehicle_flat, timepts, x0, u0, trajectory_cost=traj_cost, trajectory_constraints=input_constraints, @@ -384,7 +384,7 @@ def test_flat_solve_ocp(self, basis): terminal_cost = opt.quadratic_cost( flat_sys, 1e3, 1e3, x0=xf, u0=uf) - traj_cost = fs.solve_flat_ocp( + traj_cost = fs.solve_flat_optimal( flat_sys, timepts, x0, u0, terminal_cost=terminal_cost, basis=basis) @@ -398,7 +398,7 @@ def test_flat_solve_ocp(self, basis): # Solve with trajectory and terminal cost functions trajectory_cost = opt.quadratic_cost(flat_sys, 0, 1, x0=xf, u0=uf) - traj_cost = fs.solve_flat_ocp( + traj_cost = fs.solve_flat_optimal( flat_sys, timepts, x0, u0, terminal_cost=terminal_cost, trajectory_cost=trajectory_cost, basis=basis) @@ -421,7 +421,7 @@ def test_flat_solve_ocp(self, basis): assert np.any(x_cost[0, :] < lb[0]) or np.any(x_cost[0, :] > ub[0]) \ or np.any(x_cost[1, :] < lb[1]) or np.any(x_cost[1, :] > ub[1]) - traj_const = fs.solve_flat_ocp( + traj_const = fs.solve_flat_optimal( flat_sys, timepts, x0, u0, terminal_cost=terminal_cost, trajectory_cost=trajectory_cost, trajectory_constraints=constraints, basis=basis, @@ -444,7 +444,7 @@ def test_flat_solve_ocp(self, basis): # Use alternative keywords as well nl_constraints = [ (sp.optimize.NonlinearConstraint, lambda x, u: x, lb, ub)] - traj_nlconst = fs.solve_flat_ocp( + traj_nlconst = fs.solve_flat_optimal( flat_sys, timepts, x0, u0, trajectory_cost=trajectory_cost, terminal_cost=terminal_cost, trajectory_constraints=nl_constraints, basis=basis, @@ -668,7 +668,7 @@ def test_solve_flat_ocp_errors(self): # Solving without basis specified should be OK (may generate warning) with warnings.catch_warnings(): warnings.simplefilter("ignore") - traj = fs.solve_flat_ocp(flat_sys, timepts, x0, u0, cost_fcn) + traj = fs.solve_flat_optimal(flat_sys, timepts, x0, u0, cost_fcn) x, u = traj.eval(timepts) np.testing.assert_array_almost_equal(x0, x[:, 0]) if not traj.success: @@ -681,40 +681,40 @@ def test_solve_flat_ocp_errors(self): # Solving without a cost function generates an error with pytest.raises(TypeError, match="cost required"): - traj = fs.solve_flat_ocp(flat_sys, timepts, x0, u0) + traj = fs.solve_flat_optimal(flat_sys, timepts, x0, u0) # Try to optimize with insufficient degrees of freedom with pytest.raises(ValueError, match="basis set is too small"): - traj = fs.solve_flat_ocp( + traj = fs.solve_flat_optimal( flat_sys, timepts, x0, u0, trajectory_cost=cost_fcn, basis=fs.PolyFamily(2)) # Solve with the errors in the various input arguments with pytest.raises(ValueError, match="Initial state: Wrong shape"): - traj = fs.solve_flat_ocp( + traj = fs.solve_flat_optimal( flat_sys, timepts, np.zeros(3), u0, cost_fcn) with pytest.raises(ValueError, match="Initial input: Wrong shape"): - traj = fs.solve_flat_ocp( + traj = fs.solve_flat_optimal( flat_sys, timepts, x0, np.zeros(3), cost_fcn) # Constraint that isn't a constraint with pytest.raises(TypeError, match="must be a list"): - traj = fs.solve_flat_ocp( + traj = fs.solve_flat_optimal( flat_sys, timepts, x0, u0, cost_fcn, trajectory_constraints=np.eye(2), basis=fs.PolyFamily(8)) # Unknown constraint type with pytest.raises(TypeError, match="unknown constraint type"): - traj = fs.solve_flat_ocp( + traj = fs.solve_flat_optimal( flat_sys, timepts, x0, u0, cost_fcn, trajectory_constraints=[(None, 0, 0, 0)], basis=fs.PolyFamily(8)) # Method arguments, parameters - traj_method = fs.solve_flat_ocp( + traj_method = fs.solve_flat_optimal( flat_sys, timepts, x0, u0, trajectory_cost=cost_fcn, basis=fs.PolyFamily(6), minimize_method='slsqp') - traj_kwarg = fs.solve_flat_ocp( + traj_kwarg = fs.solve_flat_optimal( flat_sys, timepts, x0, u0, trajectory_cost=cost_fcn, basis=fs.PolyFamily(6), minimize_kwargs={'method': 'slsqp'}) np.testing.assert_allclose( @@ -723,7 +723,7 @@ def test_solve_flat_ocp_errors(self): # Unrecognized keywords with pytest.raises(TypeError, match="unrecognized keyword"): - traj_method = fs.solve_flat_ocp( + traj_method = fs.solve_flat_optimal( flat_sys, timepts, x0, u0, cost_fcn, solve_ivp_method=None) @pytest.mark.parametrize( diff --git a/control/tests/kwargs_test.py b/control/tests/kwargs_test.py index de0111e01..2e4919004 100644 --- a/control/tests/kwargs_test.py +++ b/control/tests/kwargs_test.py @@ -308,11 +308,15 @@ def test_response_plot_kwargs(data_fcn, plot_fcn, mimo): 'zpk': test_unrecognized_kwargs, 'flatsys.point_to_point': flatsys_test.TestFlatSys.test_point_to_point_errors, + 'flatsys.solve_flat_optimal': + flatsys_test.TestFlatSys.test_solve_flat_ocp_errors, 'flatsys.solve_flat_ocp': flatsys_test.TestFlatSys.test_solve_flat_ocp_errors, 'flatsys.FlatSystem.__init__': test_unrecognized_kwargs, 'optimal.create_mpc_iosystem': optimal_test.test_mpc_iosystem_rename, + 'optimal.solve_optimal_trajectory': optimal_test.test_ocp_argument_errors, 'optimal.solve_ocp': optimal_test.test_ocp_argument_errors, + 'optimal.solve_optimal_estimate': optimal_test.test_oep_argument_errors, 'optimal.solve_oep': optimal_test.test_oep_argument_errors, 'ControlPlot.set_plot_title': freqplot_test.test_suptitle, 'FrequencyResponseData.__init__': diff --git a/control/tests/optimal_test.py b/control/tests/optimal_test.py index 46253b794..4ea436515 100644 --- a/control/tests/optimal_test.py +++ b/control/tests/optimal_test.py @@ -42,7 +42,7 @@ def test_continuous_lqr(method, npts): Tf = 10 timepts = np.linspace(0, Tf, npts) - res = opt.solve_ocp( + res = opt.solve_optimal_trajectory( sys, timepts, x0, traj_cost, constraints, terminal_cost=term_cost, trajectory_method=method ) @@ -77,7 +77,7 @@ def test_finite_horizon_simple(method): x0 = [4, 0] # Retrieve the full open-loop predictions - res = opt.solve_ocp( + res = opt.solve_optimal_trajectory( sys, time, x0, cost, constraints, squeeze=True, trajectory_method=method, terminal_cost=cost) # include to match MPT3 formulation @@ -152,7 +152,7 @@ def test_discrete_lqr(): ] # Re-solve - res2 = opt.solve_ocp( + res2 = opt.solve_optimal_trajectory( sys, time, x0, integral_cost, trajectory_constraints, terminal_cost=terminal_cost, initial_guess=lqr_u) @@ -379,7 +379,7 @@ def test_terminal_constraints(sys_args): np.testing.assert_almost_equal(res.states, x1, decimal=4) # Re-run using a basis function and see if we get the same answer - res = opt.solve_ocp( + res = opt.solve_optimal_trajectory( sys, time, x0, cost, terminal_constraints=final_point, basis=flat.BezierFamily(8, Tf)) @@ -448,7 +448,7 @@ def test_optimal_logging(capsys): # Solve it, with logging turned on (with warning due to mixed constraints) with pytest.warns(sp.optimize.OptimizeWarning, match="Equality and inequality .* same element"): - res = opt.solve_ocp( + res = opt.solve_optimal_trajectory( sys, time, x0, cost, input_constraint, terminal_cost=cost, terminal_constraints=state_constraint, log=True) @@ -513,21 +513,21 @@ def test_ocp_argument_errors(): # Trajectory constraints not in the right form with pytest.raises(TypeError, match="constraints must be a list"): - res = opt.solve_ocp(sys, time, x0, cost, np.eye(2)) + res = opt.solve_optimal_trajectory(sys, time, x0, cost, np.eye(2)) # Terminal constraints not in the right form with pytest.raises(TypeError, match="constraints must be a list"): - res = opt.solve_ocp( + res = opt.solve_optimal_trajectory( sys, time, x0, cost, constraints, terminal_constraints=np.eye(2)) # Initial guess in the wrong shape with pytest.raises(ValueError, match="initial guess is the wrong shape"): - res = opt.solve_ocp( + res = opt.solve_optimal_trajectory( sys, time, x0, cost, constraints, initial_guess=np.zeros((4,1,1))) # Unrecognized arguments with pytest.raises(TypeError, match="unrecognized keyword"): - res = opt.solve_ocp( + res = opt.solve_optimal_trajectory( sys, time, x0, cost, constraints, terminal_constraint=None) with pytest.raises(TypeError, match="unrecognized keyword"): @@ -541,21 +541,21 @@ def test_ocp_argument_errors(): # Unrecognized trajectory constraint type constraints = [(None, np.eye(3), [0, 0, 0], [0, 0, 0])] with pytest.raises(TypeError, match="unknown trajectory constraint type"): - res = opt.solve_ocp( + res = opt.solve_optimal_trajectory( sys, time, x0, cost, trajectory_constraints=constraints) # Unrecognized terminal constraint type with pytest.raises(TypeError, match="unknown terminal constraint type"): - res = opt.solve_ocp( + res = opt.solve_optimal_trajectory( sys, time, x0, cost, terminal_constraints=constraints) # Discrete time system checks: solve_ivp keywords not allowed sys = ct.rss(2, 1, 1, dt=True) with pytest.raises(TypeError, match="solve_ivp method, kwargs not allowed"): - res = opt.solve_ocp( + res = opt.solve_optimal_trajectory( sys, time, x0, cost, solve_ivp_method='LSODA') with pytest.raises(TypeError, match="solve_ivp method, kwargs not allowed"): - res = opt.solve_ocp( + res = opt.solve_optimal_trajectory( sys, time, x0, cost, solve_ivp_kwargs={'eps': 0.1}) @@ -583,7 +583,7 @@ def test_optimal_basis_simple(basis): x0 = [4, 0] # Basic optimal control problem - res1 = opt.solve_ocp( + res1 = opt.solve_optimal_trajectory( sys, time, x0, cost, constraints, terminal_cost=cost, basis=basis, return_x=True) assert res1.success @@ -594,14 +594,14 @@ def test_optimal_basis_simple(basis): np.testing.assert_array_less(np.abs(res1.inputs[0]), 1 + 1e-6) # Pass an initial guess and rerun - res2 = opt.solve_ocp( + res2 = opt.solve_optimal_trajectory( sys, time, x0, cost, constraints, initial_guess=0.99*res1.inputs, terminal_cost=cost, basis=basis, return_x=True) assert res2.success np.testing.assert_allclose(res2.inputs, res1.inputs, atol=0.01, rtol=0.01) # Run with logging turned on for code coverage - res3 = opt.solve_ocp( + res3 = opt.solve_optimal_trajectory( sys, time, x0, cost, constraints, terminal_cost=cost, basis=basis, return_x=True, log=True) assert res3.success @@ -748,7 +748,7 @@ def vehicle_output(t, x, u, params): with warnings.catch_warnings(): warnings.filterwarnings( 'ignore', message="unable to solve", category=UserWarning) - result = opt.solve_ocp( + result = opt.solve_optimal_trajectory( vehicle, timepts, x0, traj_cost, constraints, terminal_cost=term_cost, initial_guess=initial_guess, trajectory_method=method, @@ -794,7 +794,7 @@ def test_oep_argument_errors(): # Unrecognized arguments with pytest.raises(TypeError, match="unrecognized keyword"): - res = opt.solve_oep(sys, timepts, Y, U, cost, unknown=True) + res = opt.solve_optimal_estimate(sys, timepts, Y, U, cost, unknown=True) with pytest.raises(TypeError, match="unrecognized keyword"): oep = opt.OptimalEstimationProblem(sys, timepts, cost, unknown=True) diff --git a/control/xferfcn.py b/control/xferfcn.py index 07c4a3fe2..9c40e66d8 100644 --- a/control/xferfcn.py +++ b/control/xferfcn.py @@ -340,34 +340,7 @@ def __call__(self, x, squeeze=None, warn_infinite=True): in the complex plane, where `x` is `s` for continuous-time systems and `z` for discrete-time systems. - By default, a (complex) scalar will be returned for SISO systems - and a p x m array will be return for MIMO systems with m inputs and - p outputs. This can be changed using the `squeeze` keyword. - - To evaluate at a frequency `omega` in radians per second, - enter ``x = omega * 1j`` for continuous-time systems, - ``x = exp(1j * omega * dt)`` for discrete-time systems, or - use the `~LTI.frequency_response` method. - - Parameters - ---------- - x : complex or complex 1D array_like - Complex value(s) at which transfer function will be evaluated. - squeeze : bool, optional - Squeeze output, as described below. Default value can be set - using `config.defaults['control.squeeze_frequency_response']`. - warn_infinite : bool, optional - If set to False, turn off divide by zero warning. - - Returns - ------- - fresp : complex ndarray - The value of the system transfer function at `x`. If the system - is SISO and `squeeze` is not True, the shape of the array matches - the shape of `x`. If the system is not SISO or `squeeze` is - False, the first two dimensions of the array are indices for the - output and input and the remaining dimensions match `x`. If - `squeeze` is True then single-dimensional axes are removed. + See `LTI.__call__` for details. """ out = self.horner(x, warn_infinite=warn_infinite) diff --git a/doc/config.rst b/doc/config.rst index c3c4c8184..fc760a93d 100644 --- a/doc/config.rst +++ b/doc/config.rst @@ -620,34 +620,35 @@ Optimization parameters :value: None Set the method used by :func:`scipy.optimize.minimize` when called in - :func:`solve_ocp` and :func:`solve_oep`. + :func:`solve_optimal_trajectory` and :func:`solve_optimal_estimate`. .. py:data:: optimal.minimize_options :type: dict :value: {} Set the value of the options keyword used by - :func:`scipy.optimize.minimize` when called in :func:`solve_ocp` and - :func:`solve_oep`. + :func:`scipy.optimize.minimize` when called in + :func:`solve_optimal_trajectory` and :func:`solve_optimal_estimate`. .. py:data:: optimal.minimize_kwargs :type: dict :value: {} - Set the keyword arguments passed to :func:`scipy.optimize.minimize` when - called in :func:`solve_ocp` and :func:`solve_oep`. + Set the keyword arguments passed to :func:`scipy.optimize.minimize` + when called in :func:`solve_optimal_trajectory` and + :func:`solve_optimal_estimate`. .. py:data:: optimal.solve_ivp_method :type: str :value: None Set the method used by :func:`scipy.integrate.solve_ivp` when called in - :func:`solve_ocp` and :func:`solve_oep`. + :func:`solve_optimal_trajectory` and :func:`solve_optimal_estimate`. .. py:data:: optimal.solve_ivp_options :type: dict :value: {} Set the value of the options keyword used by - :func:`scipy.integrate.solve_ivp` when called in :func:`solve_ocp` and - :func:`solve_oep`. + :func:`scipy.integrate.solve_ivp` when called in + :func:`solve_optimal_trajectory` and :func:`solve_optimal_estimate`. diff --git a/doc/develop.rst b/doc/develop.rst index 61b2949e4..2dce6e243 100644 --- a/doc/develop.rst +++ b/doc/develop.rst @@ -163,8 +163,7 @@ general patterns are emerging: * Use longer description parameter names that describe the action or role (e.g., `trajectory_constraints` and `print_summary` in - `optimal.solve_ocp` (which probably should be named - `optimal.`find_optimal_trajectory`...). + `optimal.solve_optimal_trajectory`. System-creating commands: diff --git a/doc/flatsys.rst b/doc/flatsys.rst index 649d151a9..4440906a8 100644 --- a/doc/flatsys.rst +++ b/doc/flatsys.rst @@ -196,13 +196,13 @@ a `TimeResponseData` object. The :func:`~flatsys.point_to_point` function also allows the specification of a cost function and/or constraints, in the same -format as :func:`optimal.solve_ocp`. +format as :func:`optimal.solve_optimal_trajectory`. -The :func:`~flatsys.solve_flat_ocp` function can be used to solve an +The :func:`~flatsys.solve_flat_optimal` function can be used to solve an optimal control problem for a differentially flat system without a final state constraint:: - traj = fs.solve_flat_ocp( + traj = fs.solve_flat_optimal( sys, timepts, x0, u0, cost, basis=basis) The `cost` parameter is a function with call signature @@ -336,7 +336,7 @@ cost: # Solve the optimal control problem, evaluating cost at timepts bspline = fs.BSplineFamily([0, Tf/2, Tf], 4) - traj = fs.solve_flat_ocp( + traj = fs.solve_flat_optimal( vehicle_flat, evalpts, x0, u0, traj_cost, terminal_cost=term_cost, initial_guess=initial_guess, basis=bspline) @@ -385,4 +385,4 @@ compute trajectories: flatsys.flatsys flatsys.point_to_point - flatsys.solve_flat_ocp + flatsys.solve_flat_optimal diff --git a/doc/functions.rst b/doc/functions.rst index 033e850ae..2201d8c82 100644 --- a/doc/functions.rst +++ b/doc/functions.rst @@ -247,7 +247,7 @@ Differentially flat systems flatsys.flatsys flatsys.point_to_point - flatsys.solve_flat_ocp + flatsys.solve_flat_optimal Optimal control @@ -271,8 +271,8 @@ Optimal control optimal.output_poly_constraint optimal.output_range_constraint optimal.quadratic_cost - optimal.solve_ocp - optimal.solve_oep + optimal.solve_optimal_trajectory + optimal.solve_optimal_estimate optimal.state_poly_constraint optimal.state_range_constraint diff --git a/doc/optimal.rst b/doc/optimal.rst index 3f4306177..67a50e2da 100644 --- a/doc/optimal.rst +++ b/doc/optimal.rst @@ -127,9 +127,9 @@ state and/or input, along the trajectory and/or at the terminal time. The optimal control module operates by converting the optimal control problem into a standard optimization problem that can be solved by :func:`scipy.optimize.minimize`. The optimal control problem can be solved -by using the :func:`~optimal.solve_ocp` function:: +by using the :func:`~optimal.solve_optimal_trajectory` function:: - res = opt.solve_ocp(sys, timepts, X0, cost, constraints) + res = opt.solve_optimal_trajectory(sys, timepts, X0, cost, constraints) The :code:`sys` parameter should be an :class:`InputOutputSystem` and the `timepts` parameter should represent a time vector that gives the list @@ -169,8 +169,8 @@ points on the trajectory. The `terminal_constraint` parameter can be used to specify a constraint that only holds at the final point of the trajectory. -The return value for :func:`~optimal.solve_ocp` is a bundle object -that has the following elements: +The return value for :func:`~optimal.solve_optimal_trajectory` is a +bundle object that has the following elements: * `res.success`: True if the optimization was successfully solved * `res.inputs`: optimal input @@ -274,7 +274,7 @@ Finally, we solve for the optimal inputs: .. testcode:: optimal timepts = np.linspace(0, Tf, 10, endpoint=True) - result = opt.solve_ocp( + result = opt.solve_optimal_trajectory( vehicle, timepts, x0, traj_cost, constraints, terminal_cost=term_cost, initial_guess=u0) @@ -355,16 +355,17 @@ solutions do not seem close to optimal, here are a few things to try: * Use a smooth basis: as an alternative to parameterizing the optimal control inputs using the value of the control at the listed time points, you can specify a set of basis functions using the `basis` - keyword in :func:`~optimal.solve_ocp` and then parameterize the controller by - linear combination of the basis functions. The :ref:`flatsys - sub-package ` defines several sets of basis - functions that can be used. - -* Tweak the optimizer: by using the `minimize_method`, `minimize_options`, - and `minimize_kwargs` keywords in :func:`~optimal.solve_ocp`, you can - choose the SciPy optimization function that you use and set many - parameters. See :func:`scipy.optimize.minimize` for more information on - the optimizers that are available and the options and keywords that they + keyword in :func:`~optimal.solve_optimal_trajectory` and then + parameterize the controller by linear combination of the basis + functions. The :ref:`flatsys sub-package ` defines + several sets of basis functions that can be used. + +* Tweak the optimizer: by using the `minimize_method`, + `minimize_options`, and `minimize_kwargs` keywords in + :func:`~optimal.solve_optimal_trajectory`, you can choose the SciPy + optimization function that you use and set many parameters. See + :func:`scipy.optimize.minimize` for more information on the + optimizers that are available and the options and keywords that they accept. * Walk before you run: try setting up a simpler version of the optimization, @@ -401,7 +402,7 @@ The following classes and functions are defined in the optimal.output_poly_constraint optimal.output_range_constraint optimal.quadratic_cost - optimal.solve_ocp - optimal.solve_oep + optimal.solve_optimal_trajectory + optimal.solve_optimal_estimate optimal.state_poly_constraint optimal.state_range_constraint diff --git a/doc/test_sphinxdocs.py b/doc/test_sphinxdocs.py index bc87de14e..1a49f357c 100644 --- a/doc/test_sphinxdocs.py +++ b/doc/test_sphinxdocs.py @@ -41,6 +41,9 @@ 'rlocus', # root_locus_plot 'rlocus', # root_locus_plot 'root_locus', # root_locus_plot + 'solve_ocp', # solve_optimal_trajectory + 'solve_oep', # solve_optimal_estimate + 'solve_flat_ocp', # solve_flat_optimal ] # Functons that we can skip From 23cbde919e4dddaae6ceebda9af360fe42ed7e5e Mon Sep 17 00:00:00 2001 From: Richard Murray Date: Tue, 21 Jan 2025 22:06:10 -0800 Subject: [PATCH 82/93] spell check --- control/bdalg.py | 19 +++++++------ control/canonical.py | 2 +- control/config.py | 16 +++++------ control/ctrlplot.py | 8 +++--- control/delay.py | 2 +- control/descfcn.py | 4 +-- control/dtime.py | 4 ++- control/exception.py | 4 +-- control/flatsys/__init__.py | 4 +-- control/flatsys/bspline.py | 2 +- control/flatsys/flatsys.py | 26 +++++++++--------- control/flatsys/poly.py | 4 +-- control/flatsys/systraj.py | 6 ++-- control/frdata.py | 2 +- control/freqplot.py | 52 +++++++++++++++++------------------ control/grid.py | 12 ++++---- control/iosys.py | 6 ++-- control/lti.py | 27 ++++++++---------- control/margins.py | 2 +- control/mateqn.py | 34 +++++++++++------------ control/matlab/__init__.py | 4 +-- control/matlab/timeresp.py | 24 ++++++++-------- control/matlab/wrappers.py | 8 +++--- control/modelsimp.py | 20 +++++++------- control/nichols.py | 2 +- control/nlsys.py | 34 +++++++++++------------ control/optimal.py | 46 +++++++++++++++---------------- control/passivity.py | 27 +++++++++--------- control/phaseplot.py | 8 +++--- control/pzmap.py | 8 +++--- control/rlocus.py | 13 +++++---- control/robust.py | 6 ++-- control/sisotool.py | 12 ++++---- control/statefbk.py | 16 +++++------ control/statesp.py | 8 +++--- control/stochsys.py | 20 +++++++------- control/tests/matlab2_test.py | 3 +- control/timeplot.py | 12 ++++---- control/timeresp.py | 38 ++++++++++++------------- control/xferfcn.py | 4 +-- doc/config.rst | 2 +- doc/develop.rst | 15 +++++----- doc/flatsys.rst | 6 ++-- doc/nonlinear.rst | 2 +- doc/optimal.rst | 2 +- 45 files changed, 288 insertions(+), 288 deletions(-) diff --git a/control/bdalg.py b/control/bdalg.py index 1dab35d6e..48e0f6365 100644 --- a/control/bdalg.py +++ b/control/bdalg.py @@ -496,7 +496,7 @@ def combine_tf(tf_array, **kwargs): Raises ------ ValueError - If timesteps of transfer functions do not match. + If timebase of transfer functions do not match. ValueError If `tf_array` has incorrect dimensions. ValueError @@ -548,7 +548,7 @@ def combine_tf(tf_array, **kwargs): dt_set = set(dt_list) dt_set.discard(None) if len(dt_set) > 1: - raise ValueError("Timesteps of transfer functions are " + raise ValueError("Time steps of transfer functions are " f"mismatched: {dt_set}") elif len(dt_set) == 0: dt = None @@ -665,11 +665,11 @@ def _ensure_tf(arraylike_or_tf, dt=None): arraylike_or_tf : `TransferFunction` or array_like Array-like or transfer function. dt : None, True or float, optional - System timebase. 0 (default) indicates continuous - time, True indicates discrete time with unspecified sampling - time, positive number is discrete time with specified - sampling time, None indicates unspecified timebase (either - continuous or discrete time). If None, timestep is not validated. + System timebase. 0 (default) indicates continuous time, True + indicates discrete time with unspecified sampling time, positive + number is discrete time with specified sampling time, None + indicates unspecified timebase (either continuous or discrete + time). If None, timebase is not validated. Returns ------- @@ -681,11 +681,12 @@ def _ensure_tf(arraylike_or_tf, dt=None): ValueError If input cannot be converted to a transfer function. ValueError - If the timesteps do not match. + If the timebases do not match. + """ # If the input is already a transfer function, return it right away if isinstance(arraylike_or_tf, tf.TransferFunction): - # If timesteps don't match, raise an exception + # If timebases don't match, raise an exception if (dt is not None) and (arraylike_or_tf.dt != dt): raise ValueError( f"`arraylike_or_tf.dt={arraylike_or_tf.dt}` does not match " diff --git a/control/canonical.py b/control/canonical.py index b171508db..48fda7f5a 100644 --- a/control/canonical.py +++ b/control/canonical.py @@ -58,7 +58,7 @@ def canonical_form(xsys, form='reachable'): """ - # Call the appropriate tranformation function + # Call the appropriate transformation function if form == 'reachable': return reachable_form(xsys) elif form == 'observable': diff --git a/control/config.py b/control/config.py index c34bef58b..bda59da15 100644 --- a/control/config.py +++ b/control/config.py @@ -1,7 +1,7 @@ # config.py - package defaults # RMM, 4 Nov 2012 # -# TODO: add ability to read/write configuration files (ala Matplotlib) +# TODO: add ability to read/write configuration files (ala matplotlib) """Functions to access default parameter values. @@ -133,7 +133,7 @@ def set_defaults(module, **keywords): defaults[module + '.' + key] = val -# TODO: allow individual modules and individaul parameters to be reset +# TODO: allow individual modules and individual parameters to be reset def reset_defaults(): """Reset configuration values to their default (initial) values. @@ -207,7 +207,7 @@ def _get_param(module, param, argval=None, defval=None, pop=False, last=False): module : str Name of the module whose parameters are being requested. param : str - Name of the parameter value to be determeind. + Name of the parameter value to be determined. argval : object or dict Value of the parameter as passed to the function. This can either be an object or a dictionary (i.e. the keyword list from the function @@ -219,11 +219,11 @@ def _get_param(module, param, argval=None, defval=None, pop=False, last=False): None. pop : bool, optional If True and if argval is a dict, then pop the remove the parameter - entry from the argval dict after retreiving it. This allows the use + entry from the argval dict after retrieving it. This allows the use of a keyword argument list to be passed through to other functions internal to the function being called. last : bool, optional - If True, check to make sure dictionary is empy after processing. + If True, check to make sure dictionary is empty after processing. """ @@ -270,7 +270,7 @@ def use_matlab_defaults(): def use_fbs_defaults(): """Use Feedback Systems (FBS) compatible settings. - The following conventions fomr `Feedback Systems `_ + The following conventions from `Feedback Systems `_ are used: * Bode plots plot gain in powers of ten, phase in degrees, @@ -352,7 +352,7 @@ def use_legacy_defaults(version): # switched to 'array' as default for state space objects warnings.warn("NumPy matrix class no longer supported") - # switched to 0 (=continuous) as default timestep + # switched to 0 (=continuous) as default timebase set_defaults('control', default_dt=None) # changed iosys naming conventions @@ -388,7 +388,7 @@ def _process_legacy_keyword(kwargs, oldkey, newkey, newval, warn_oldkey=True): Parameters ---------- kwargs : dict - Dictionary of keword arguments (from function call). + Dictionary of keyword arguments (from function call). oldkey : str Old (legacy) parameter name. newkey : str diff --git a/control/ctrlplot.py b/control/ctrlplot.py index 0135d8071..9abfbe5c1 100644 --- a/control/ctrlplot.py +++ b/control/ctrlplot.py @@ -350,7 +350,7 @@ def _process_ax_keyword( current figure and axes are returned. Otherwise a new figure is created with axes of the desired shape. - If `create_axes` is False and a new/empty figure is returned, then axs + If `create_axes` is False and a new/empty figure is returned, then `axs` is an array of the proper shape but None for each element. This allows the calling function to do the actual axis creation (needed for curvilinear grids that use the AxisArtist module). @@ -630,13 +630,13 @@ def _add_arrows_to_line2D( color = line.get_color() use_multicolor_lines = isinstance(color, np.ndarray) if use_multicolor_lines: - raise NotImplementedError("multicolor lines not supported") + raise NotImplementedError("multi-color lines not supported") else: arrow_kw['color'] = color linewidth = line.get_linewidth() if isinstance(linewidth, np.ndarray): - raise NotImplementedError("multiwidth lines not supported") + raise NotImplementedError("multi-width lines not supported") else: arrow_kw['linewidth'] = linewidth @@ -717,7 +717,7 @@ def _get_color( """Get color to use for plotting line. This function returns the color to be used for the line to be drawn (or - None if the detault color cycle for the axes should be used). + None if the default color cycle for the axes should be used). Parameters ---------- diff --git a/control/delay.py b/control/delay.py index 49793ca17..550a779af 100644 --- a/control/delay.py +++ b/control/delay.py @@ -73,7 +73,7 @@ def pade(T, n=1, numdeg=None): num[-1] = 1. cn = 1. for k in range(1, numdeg+1): - # derived from Gloub and van Loan eq. for Dpq(z) on p. 572 + # derived from Golub and van Loan eq. for Dpq(z) on p. 572 # this accumulative style follows Alg 11.3.1 cn *= -T * (numdeg - k + 1)/(numdeg + n - k + 1)/k num[numdeg-k] = cn diff --git a/control/descfcn.py b/control/descfcn.py index 31697e67b..e9472a477 100644 --- a/control/descfcn.py +++ b/control/descfcn.py @@ -34,7 +34,7 @@ class DescribingFunctionNonlinearity(): """ def __init__(self): - """Initailize a describing function nonlinearity (optional).""" + """Initialize a describing function nonlinearity (optional).""" pass def __call__(self, A): @@ -208,7 +208,7 @@ def describing_function( # Evaluate the function along a sinusoid F_eval = np.array([F(x) for x in a*sin_theta]).squeeze() - # Compute the prjections onto sine and cosine + # Compute the projections onto sine and cosine df_real = (F_eval @ sin_theta) * scale # = M_1 \cos\phi / a df_imag = (F_eval @ cos_theta) * scale # = M_1 \sin\phi / a diff --git a/control/dtime.py b/control/dtime.py index 120688ec9..481615671 100644 --- a/control/dtime.py +++ b/control/dtime.py @@ -55,7 +55,7 @@ def sample_system(sysc, Ts, method='zoh', alpha=None, prewarp_frequency=None, `config.defaults['iosys.sampled_system_name_prefix']` and `config.defaults['iosys.sampled_system_name_suffix']`, with the default being to add the suffix '$sampled'. - copy_names : bool, Optional + copy_names : bool, optional If True, copy the names of the input signals, output signals, and states to the sampled system. @@ -83,4 +83,6 @@ def sample_system(sysc, Ts, method='zoh', alpha=None, prewarp_frequency=None, method=method, alpha=alpha, prewarp_frequency=prewarp_frequency, name=name, copy_names=copy_names, **kwargs) + +# Convenience aliases c2d = sample_system diff --git a/control/exception.py b/control/exception.py index 31c09769a..69b140203 100644 --- a/control/exception.py +++ b/control/exception.py @@ -29,10 +29,10 @@ class ControlNotImplemented(NotImplementedError): """Functionality is not yet implemented.""" pass -# Utility function to see if slycot is installed +# Utility function to see if Slycot is installed slycot_installed = None def slycot_check(): - """Return True if slycot is installed, otherwise False.""" + """Return True if Slycot is installed, otherwise False.""" global slycot_installed if slycot_installed is None: try: diff --git a/control/flatsys/__init__.py b/control/flatsys/__init__.py index f937eb129..ce9650e9a 100644 --- a/control/flatsys/__init__.py +++ b/control/flatsys/__init__.py @@ -3,9 +3,9 @@ # Author: Richard M. Murray # Date: 1 Jul 2019 -r"""Flat systems sub-package. +r"""Flat systems subpackage. -This sub-package contains a set of classes and functions to compute +This subpackage contains a set of classes and functions to compute trajectories for differentially flat systems. A differentially flat system is defined by creating an object using the diff --git a/control/flatsys/bspline.py b/control/flatsys/bspline.py index d888be168..f8247f04e 100644 --- a/control/flatsys/bspline.py +++ b/control/flatsys/bspline.py @@ -125,7 +125,7 @@ def process_spline_parameters( smoothness, nvars, (int), name='smoothness', minimum=0, default=[d - 1 for d in degree]) - # Make sure degree is sufficent for the level of smoothness + # Make sure degree is sufficient for the level of smoothness if any([degree[i] - smoothness[i] < 1 for i in range(nvars)]): raise ValueError("degree must be greater than smoothness") diff --git a/control/flatsys/flatsys.py b/control/flatsys/flatsys.py index be2f137fd..b57f9bd7b 100644 --- a/control/flatsys/flatsys.py +++ b/control/flatsys/flatsys.py @@ -57,7 +57,7 @@ class FlatSystem(NonlinearIOSystem): ----- The class must implement two functions: - ``zflag = flatsys.foward(x, u, params)`` + ``zflag = flatsys.forward(x, u, params)`` This function computes the flag (derivatives) of the flat output. The inputs to this function are the state `x` and inputs `u` (both @@ -84,11 +84,11 @@ class FlatSystem(NonlinearIOSystem): """ def __init__(self, forward, reverse, # flat system - updfcn=None, outfcn=None, # nonlinar I/O system + updfcn=None, outfcn=None, # nonlinear I/O system **kwargs): # I/O system """Create a differentially flat I/O system. - The FlatIOSystem constructor is used to create an input/output system + The `FlatSystem` constructor is used to create an input/output system object that also represents a differentially flat system. """ @@ -186,7 +186,7 @@ def flatsys(*args, updfcn=None, outfcn=None, **kwargs): ``fs.flatsys(forward, reverse)`` - Create a flat system with mapings to/from flat flag. + Create a flat system with mappings to/from flat flag. ``fs.flatsys(forward, reverse, updfcn[, outfcn])`` @@ -207,20 +207,20 @@ def flatsys(*args, updfcn=None, outfcn=None, **kwargs): updfcn : callable, optional Function returning the state update function - ``updfcn(t, x, u[, param]) -> array`` + ``updfcn(t, x, u[, params]) -> array`` where `x` is a 1-D array with shape (nstates,), `u` is a 1-D array - with shape (ninputs,), `t` is a float representing the currrent - time, and `param` is an optional dict containing the values of + with shape (ninputs,), `t` is a float representing the current + time, and `params` is an optional dict containing the values of parameters used by the function. If not specified, the state space update will be computed using the flat system coordinates. outfcn : callable, optional Function returning the output at the given state - ``outfcn(t, x, u[, param]) -> array`` + ``outfcn(t, x, u[, params]) -> array`` - where the arguments are the same as for `upfcn`. If not + where the arguments are the same as for `updfcn`. If not specified, the output will be the flat outputs. inputs : int, list of str, or None @@ -339,7 +339,7 @@ def point_to_point( define a function `~FlatSystem.forward` that takes the system state and produces the flag of flat outputs and a function `~FlatSystem.reverse` that takes the flag of the flat output and - prodes the state and input. + produces the state and input. timepts : float or 1D array_like The list of points for evaluating cost and constraints, as well as @@ -516,7 +516,7 @@ def point_to_point( warnings.warn("basis too small; solution may not exist") if cost is not None or trajectory_constraints is not None: - # Make sure that we have enough timepoints to evaluate + # Make sure that we have enough time points to evaluate if timepts.size < 3: raise ControlArgument( "There must be at least three time points if trajectory" @@ -662,7 +662,7 @@ def solve_flat_optimal( initial_guess=None, params=None, **kwargs): """Compute trajectory between an initial and final conditions. - Compute an optimial trajectory for a differentially flat system starting + Compute an optimal trajectory for a differentially flat system starting from an initial state and input value. Parameters @@ -672,7 +672,7 @@ def solve_flat_optimal( define a function `~FlatSystem.forward` that takes the system state and produces the flag of flat outputs and a function `~FlatSystem.reverse` that takes the flag of the flat output and - prodes the state and input. + produces the state and input. timepts : float or 1D array_like The list of points for evaluating cost and constraints, as well as diff --git a/control/flatsys/poly.py b/control/flatsys/poly.py index bf13039e2..8902bc795 100644 --- a/control/flatsys/poly.py +++ b/control/flatsys/poly.py @@ -1,7 +1,7 @@ # poly.m - simple set of polynomial basis functions # RMM, 10 Nov 2012 # -# TODO: rename this as taylor.m +# TODO: rename this as taylor.m? """Simple set of polynomial basis functions. @@ -27,7 +27,7 @@ class PolyFamily(BasisFamily): Parameters ---------- N : int - Degree of the Bezier curve. + Degree of the polynomial. T : float Final time (used for rescaling). Default value is 1. diff --git a/control/flatsys/systraj.py b/control/flatsys/systraj.py index 37fff29ef..2de778d88 100644 --- a/control/flatsys/systraj.py +++ b/control/flatsys/systraj.py @@ -3,7 +3,7 @@ """SystemTrajectory class. -The SystemTrajetory class is used to store a feasible trajectory for +The SystemTrajectory class is used to store a feasible trajectory for the state and input of a (nonlinear) control system. """ @@ -30,7 +30,7 @@ class SystemTrajectory: For each flat output, define the coefficients of the basis functions used to represent the trajectory. Defaults to an empty list. - flaglen : list of ints, optional + flaglen : list of int, optional For each flat output, the number of derivatives of the flat output used to define the trajectory. Defaults to an empty list. @@ -40,7 +40,7 @@ class SystemTrajectory: """ def __init__(self, sys, basis, coeffs=[], flaglen=[], params=None): - """Initilize a system trajectory object.""" + """Initialize a system trajectory object.""" self.nstates = sys.nstates self.ninputs = sys.ninputs self.system = sys diff --git a/control/frdata.py b/control/frdata.py index 03dc3b8f9..6374c2075 100644 --- a/control/frdata.py +++ b/control/frdata.py @@ -999,7 +999,7 @@ def _convert_to_frd(sys, omega, inputs=1, outputs=1): except Exception: pass - raise TypeError('''Can't convert given type "%s" to FRD system.''' % + raise TypeError("Can't convert given type '%s' to FRD system." % sys.__class__) diff --git a/control/freqplot.py b/control/freqplot.py index 1fb2811e5..057426d7b 100644 --- a/control/freqplot.py +++ b/control/freqplot.py @@ -107,9 +107,9 @@ def bode_plot( data : list of `FrequencyResponseData` or `LTI` List of LTI systems or `FrequencyResponseData` objects. A single system or frequency response can also be passed. - omega : array_like, optoinal + omega : array_like, optional Set of frequencies in rad/sec to plot over. If not specified, this - will be determined from the proporties of the systems. Ignored if + will be determined from the properties of the systems. Ignored if `data` is not a list of systems. *fmt : `matplotlib.pyplot.plot` format string, optional Passed to `matplotlib` as the format string for all lines in the plot. @@ -186,7 +186,7 @@ def bode_plot( elements is equivalent to providing `omega_limits`. Ignored if data is not a list of systems. omega_num : int - Number of samples to use for the frequeny range. Defaults to + Number of samples to use for the frequency range. Defaults to `config.defaults['freqplot.number_of_samples']`. Ignored if data is not a list of systems. overlay_inputs, overlay_outputs : bool, optional @@ -1014,7 +1014,7 @@ def gen_zero_centered_series(val_min, val_max, period): # Create legends # # Legends can be placed manually by passing a legend_map array that - # matches the shape of the suplots, with each item being a string + # matches the shape of the sublots, with each item being a string # indicating the location of the legend for that axes (or None for no # legend). # @@ -1059,7 +1059,7 @@ def gen_zero_centered_series(val_min, val_max, period): legend_array = None # - # Legacy return pocessing + # Legacy return processing # if plot is True: # legacy usage; remove in future release # Process the data to match what we were sent @@ -1190,7 +1190,7 @@ def nyquist_response( Computes a Nyquist contour for the system over a (optional) frequency range and evaluates the number of net encirclements. The curve is - computed by evaluating the Nyqist segment along the positive imaginary + computed by evaluating the Nyquist segment along the positive imaginary axis, with a mirror image generated to reflect the negative imaginary axis. Poles on or near the imaginary axis are avoided using a small indentation. The portion of the Nyquist contour at infinity is not @@ -1209,7 +1209,7 @@ def nyquist_response( ------- responses : list of `NyquistResponseData` For each system, a Nyquist response data object is returned. If - `sysdata` is a single system, a single elemeent is returned (not a + `sysdata` is a single system, a single element is returned (not a list). response.count : int Number of encirclements of the point -1 by the Nyquist curve. If @@ -1242,7 +1242,7 @@ def nyquist_response( in Hz otherwise in rad/s. Specifying `omega` as a list of two elements is equivalent to providing `omega_limits`. omega_num : int, optional - Number of samples to use for the frequeny range. Defaults to + Number of samples to use for the frequency range. Defaults to `config.defaults['freqplot.number_of_samples']`. warn_nyquist : bool, optional If set to False, turn off warnings about frequencies above Nyquist. @@ -1267,7 +1267,7 @@ def nyquist_response( to 'none' will turn off indentation. For those portions of the Nyquist plot in which the contour is indented - to avoid poles, resuling in a scaling of the Nyquist plot, the line + to avoid poles, resulting in a scaling of the Nyquist plot, the line styles are according to the settings of the `primary_style` and `mirror_style` keywords. By default the scaled portions of the primary curve use a dotted line style and the scaled portion of the mirror @@ -1440,7 +1440,7 @@ def nyquist_response( splane_contour[last_point:])) # Indent points that are too close to a pole - if len(splane_poles) > 0: # accomodate no splane poles if dtime sys + if len(splane_poles) > 0: # accommodate no splane poles if dtime sys for i, s in enumerate(splane_contour): # Find the nearest pole p = splane_poles[(np.abs(splane_poles - s)).argmin()] @@ -1496,10 +1496,10 @@ def nyquist_response( " frequency range that does not include zero.") # - # Make sure that the enciriclements match the Nyquist criterion + # Make sure that the encirclements match the Nyquist criterion # # If the user specifies the frequency points to use, it is possible - # to miss enciriclements, so we check here to make sure that the + # to miss encirclements, so we check here to make sure that the # Nyquist criterion is actually satisfied. # if isinstance(sys, (StateSpace, TransferFunction)): @@ -1550,7 +1550,7 @@ def nyquist_plot( """Nyquist plot for a system. Generates a Nyquist plot for the system over a (optional) frequency - range. The curve is computed by evaluating the Nyqist segment along + range. The curve is computed by evaluating the Nyquist segment along the positive imaginary axis, with a mirror image generated to reflect the negative imaginary axis. Poles on or near the imaginary axis are avoided using a small indentation. The portion of the Nyquist contour @@ -1561,7 +1561,7 @@ def nyquist_plot( ---------- data : list of `LTI` or `NyquistResponseData` List of linear input/output systems (single system is OK) or - Nyquist ersponses (computed using `nyquist_response`). + Nyquist responses (computed using `nyquist_response`). Nyquist curves for each system are plotted on the same graph. omega : array_like, optional Set of frequencies to be evaluated, in rad/sec. Specifying @@ -1669,7 +1669,7 @@ def nyquist_plot( in Hz otherwise in rad/s. Specifying `omega` as a list of two elements is equivalent to providing `omega_limits`. omega_num : int, optional - Number of samples to use for the frequeny range. Defaults to + Number of samples to use for the frequency range. Defaults to `config.defaults['freqplot.number_of_samples']`. Ignored if data is not a list of systems. plot : bool, optional @@ -1733,7 +1733,7 @@ def nyquist_plot( the exact contour used for evaluation is returned. For those portions of the Nyquist plot in which the contour is indented - to avoid poles, resuling in a scaling of the Nyquist plot, the line + to avoid poles, resulting in a scaling of the Nyquist plot, the line styles are according to the settings of the `primary_style` and `mirror_style` keywords. By default the scaled portions of the primary curve use a dotted line style and the scaled portion of the mirror @@ -1848,7 +1848,7 @@ def _parse_linestyle(style_name, allow_false=False): contours = [response.contour for response in nyquist_responses] if plot is False: - # Make sure we used all of the keywrods + # Make sure we used all of the keywords if kwargs: raise TypeError("unrecognized keywords: ", str(kwargs)) @@ -2054,7 +2054,7 @@ def _parse_linestyle(style_name, allow_false=False): title, fig=fig, rcParams=rcParams, frame=title_frame, use_existing=False) - # Legacy return pocessing + # Legacy return processing if plot is True or return_contour is not None: if len(data) == 1: counts, contours = counts[0], contours[0] @@ -2080,7 +2080,7 @@ def _compute_curve_offset(resp, mask, max_offset): offset = np.zeros(resp.size) arclen = np.zeros(resp.size) - # Walk through the response and keep track of each continous component + # Walk through the response and keep track of each continuous component i, nsegs = 0, 0 while i < resp.size: # Skip the regular segment @@ -2144,7 +2144,7 @@ def gangof4_response( elements is equivalent to providing `omega_limits`. Ignored if data is not a list of systems. omega_num : int - Number of samples to use for the frequeny range. Defaults to + Number of samples to use for the frequency range. Defaults to `config.defaults['freqplot.number_of_samples']`. Ignored if data is not a list of systems. Hz : bool, optional @@ -2170,7 +2170,7 @@ def gangof4_response( raise ControlMIMONotImplemented( "Gang of four is currently only implemented for SISO systems.") - # Compute the senstivity functions + # Compute the sensitivity functions L = P * C S = feedback(1, L) T = L * S @@ -2231,7 +2231,7 @@ def gangof4_plot( elements is equivalent to providing `omega_limits`. Ignored if data is not a list of systems. omega_num : int - Number of samples to use for the frequeny range. Defaults to + Number of samples to use for the frequency range. Defaults to `config.defaults['freqplot.number_of_samples']`. Ignored if data is not a list of systems. Hz : bool, optional @@ -2305,7 +2305,7 @@ def singular_values_response( in Hz otherwise in rad/s. Specifying `omega` as a list of two elements is equivalent to providing `omega_limits`. omega_num : int, optional - Number of samples to use for the frequeny range. Defaults to + Number of samples to use for the frequency range. Defaults to `config.defaults['freqplot.number_of_samples']`. See Also @@ -2421,7 +2421,7 @@ def singular_values_plot( in Hz otherwise in rad/s. Specifying `omega` as a list of two elements is equivalent to providing `omega_limits`. omega_num : int, optional - Number of samples to use for the frequeny range. Defaults to + Number of samples to use for the frequency range. Defaults to `config.defaults['freqplot.number_of_samples']`. Ignored if data is not a list of systems. plot : bool, optional @@ -2779,7 +2779,7 @@ def _default_frequency_range(syslist, Hz=None, number_of_samples=None, (np.abs(sys.poles()), np.abs(sys.zeros()))) # Get rid of poles and zeros on the real axis (imag==0) # * origin and real < 0 - # * at 1.: would result in omega=0. (logaritmic plot!) + # * at 1.: would result in omega=0. (logarithmic plot!) toreplace = np.isclose(features_.imag, 0.0) & ( (features_.real <= 0.) | (np.abs(features_.real - 1.0) < 1.e-10)) @@ -2830,7 +2830,7 @@ def _default_frequency_range(syslist, Hz=None, number_of_samples=None, # def get_pow1000(num): - """Determine exponent for which significand of a number is within the + """Determine exponent for which significance of a number is within the range [1, 1000). """ # Based on algorithm from http://www.mail-archive.com/ diff --git a/control/grid.py b/control/grid.py index 8d26148c0..a3e7f36e5 100644 --- a/control/grid.py +++ b/control/grid.py @@ -23,7 +23,7 @@ class FormatterDMS(): - '''Transforms angle ticks to damping ratios''' + """Transforms angle ticks to damping ratios.""" def __call__(self, direction, factor, values): angles_deg = np.asarray(values)/factor damping_ratios = np.cos((180-angles_deg) * np.pi/180) @@ -32,10 +32,10 @@ def __call__(self, direction, factor, values): class ModifiedExtremeFinderCycle(angle_helper.ExtremeFinderCycle): - '''Changed to allow only left hand-side polar grid + """Changed to allow only left hand-side polar grid. https://matplotlib.org/_modules/mpl_toolkits/axisartist/angle_helper.html#ExtremeFinderCycle.__call__ - ''' + """ def __call__(self, transform_xy, x1, y1, x2, y2): x, y = np.meshgrid( np.linspace(x1, x2, self.nx), np.linspace(y1, y2, self.ny)) @@ -176,11 +176,11 @@ def zgrid(zetas=None, wns=None, ax=None, scaling=None): x = linspace(0, sqrt(1-zeta**2), 200) ang = pi*x mag = exp(-pi*factor*x) - # Draw upper part in retangular coordinates + # Draw upper part in rectangular coordinates xret = mag*cos(ang) yret = mag*sin(ang) ax.plot(xret, yret, ':', color='grey', lw=0.75) - # Draw lower part in retangular coordinates + # Draw lower part in rectangular coordinates xret = mag*cos(-ang) yret = mag*sin(-ang) ax.plot(xret, yret, ':', color='grey', lw=0.75) @@ -199,7 +199,7 @@ def zgrid(zetas=None, wns=None, ax=None, scaling=None): x = linspace(-pi/2, pi/2, 200) ang = pi*a*sin(x) mag = exp(-pi*a*cos(x)) - # Draw in retangular coordinates + # Draw in rectangular coordinates xret = mag*cos(ang) yret = mag*sin(ang) ax.plot(xret, yret, ':', color='grey', lw=0.75) diff --git a/control/iosys.py b/control/iosys.py index b985817a9..bbd3bb4c1 100644 --- a/control/iosys.py +++ b/control/iosys.py @@ -177,7 +177,7 @@ class InputOutputSystem(): String representation format. See `control.iosys_repr`. """ - # Allow NDarray * IOSystem to give IOSystem._rmul_() priority + # Allow ndarray * IOSystem to give IOSystem._rmul_() priority # https://docs.scipy.org/doc/numpy/reference/arrays.classes.html __array_priority__ = 20 @@ -797,7 +797,7 @@ def timebase(sys, strict=True): elif not isinstance(sys, InputOutputSystem): raise ValueError("Timebase not defined") - # Return the sample time, with converstion to float if strict is false + # Return the sample time, with conversion to float if strict is false if sys.dt == None: return None elif strict: @@ -831,7 +831,7 @@ def common_timebase(dt1, dt2): # if either dt is None, they are compatible with anything # if either dt is True (discrete with unspecified time base), # use the timebase of the other, if it is also discrete - # otherwise both dts must be equal + # otherwise both dt's must be equal if hasattr(dt1, 'dt'): dt1 = dt1.dt if hasattr(dt2, 'dt'): diff --git a/control/lti.py b/control/lti.py index 6272610cd..a4fde0d82 100644 --- a/control/lti.py +++ b/control/lti.py @@ -34,7 +34,7 @@ class LTI(InputOutputSystem): """ def __init__(self, inputs=1, outputs=1, states=None, name=None, **kwargs): - """Assign the LTI object's numbers of inputs and ouputs.""" + """Assign the LTI object's numbers of inputs and outputs.""" super().__init__( name=name, inputs=inputs, outputs=outputs, states=states, **kwargs) @@ -83,7 +83,7 @@ def __call__(self, x, squeeze=None, warn_infinite=True): raise NotImplementedError("not implemented in subclass") def damp(self): - '''Natural frequency, damping ratio of system poles. + """Natural frequency, damping ratio of system poles. Returns ------- @@ -93,7 +93,7 @@ def damp(self): Damping ratio for each system pole. poles : array System pole locations. - ''' + """ poles = self.poles() if self.isdtime(strict=True): @@ -110,16 +110,13 @@ def feedback(self, other=1, sign=-1): Parameters ---------- other : `InputOutputSystem` - System in the feedack path. + System in the feedback path. sign : float, optional Gain to use in feedback path. Defaults to -1. """ - # Implemented in subclasses, but documented here - raise NotImplementedError( - "feedback not implemented for base " - f"{self.__class__.__name__} objects") + raise NotImplementedError("feedback not implemented in subclass") def frequency_response(self, omega=None, squeeze=None): """Evaluate LTI system response at an array of frequencies. @@ -166,7 +163,7 @@ def bandwidth(self, dbdrop=-3): """Evaluate bandwidth of an LTI system for a given dB drop. Evaluate the first frequency that the response magnitude is lower than - DC gain by dbdrop dB. + DC gain by `dbdrop` dB. Parameters ---------- @@ -178,7 +175,7 @@ def bandwidth(self, dbdrop=-3): ------- bandwidth : ndarray The first frequency (rad/time-unit) where the gain drops below - dbdrop of the dc gain of the system, or nan if the system has + `dbdrop` of the dc gain of the system, or nan if the system has infinite dc gain, inf if the gain does not drop for all frequency. Raises @@ -231,7 +228,7 @@ def ispassive(self): See `ispassive` for details. """ - # importing here prevents circular dependancy + # importing here prevents circular dependency from control.passivity import ispassive return ispassive(self) @@ -676,7 +673,7 @@ def dcgain(sys): def bandwidth(sys, dbdrop=-3): - """Find first freqency where the gain drop by dbdrop of the system. + """Find first frequency where gain drops by 3 dB. Parameters ---------- @@ -689,9 +686,9 @@ def bandwidth(sys, dbdrop=-3): Returns ------- bandwidth : ndarray - The first frequency (rad/time-unit) where the gain drops below dbdrop - of the dc gain of the system, or nan if the system has infinite dc - gain, inf if the gain does not drop for all frequency. + The first frequency where the gain drops below `dbdrop` of the zero + frequency (DC) gain of the system, or nan if the system has infinite + zero frequency gain, inf if the gain does not drop for any frequency. Raises ------ diff --git a/control/margins.py b/control/margins.py index d4e735b59..d7c7992be 100644 --- a/control/margins.py +++ b/control/margins.py @@ -235,7 +235,7 @@ def stability_margins(sysdata, returnall=False, epsw=0.0, method='best'): interpolation of a `FrequencyResponseData` representation of the system if passed a `LTI` system. * 'best': use the 'poly' method if possible, reverting to 'frd' if - it is detected that numerical inaccuracy is likey to arise in the + it is detected that numerical inaccuracy is likely to arise in the 'poly' method for for discrete-time systems. Returns diff --git a/control/mateqn.py b/control/mateqn.py index 3f43ceea0..8efa2531c 100644 --- a/control/mateqn.py +++ b/control/mateqn.py @@ -20,7 +20,7 @@ from .exception import ControlArgument, ControlDimension, ControlSlycot, \ slycot_check -# Make sure we have access to the right slycot routines +# Make sure we have access to the right Slycot routines try: from slycot.exceptions import SlycotResultWarning except ImportError: @@ -108,9 +108,9 @@ def lyap(A, Q, C=None, E=None, method=None): method = _slycot_or_scipy(method) if method == 'slycot': if sb03md is None: - raise ControlSlycot("Can't find slycot module 'sb03md'") + raise ControlSlycot("Can't find Slycot module 'sb03md'") if sb04md is None: - raise ControlSlycot("Can't find slycot module 'sb04md'") + raise ControlSlycot("Can't find Slycot module 'sb04md'") # Reshape input arrays A = np.array(A, ndmin=2) @@ -165,12 +165,12 @@ def lyap(A, Q, C=None, E=None, method=None): raise ControlArgument( "method='scipy' not valid for generalized Lyapunov equation") - # Make sure we have access to the write slicot routine + # Make sure we have access to the write Slycot routine try: from slycot import sg03ad except ImportError: - raise ControlSlycot("Can't find slycot module 'sg03ad'") + raise ControlSlycot("Can't find Slycot module 'sg03ad'") # Solve the generalized Lyapunov equation by calling Slycot # function sg03ad @@ -236,11 +236,11 @@ def dlyap(A, Q, C=None, E=None, method=None): if method == 'slycot': # Make sure we have access to the right slycot routines if sb03md is None: - raise ControlSlycot("Can't find slycot module 'sb03md'") + raise ControlSlycot("Can't find Slycot module 'sb03md'") if sb04qd is None: - raise ControlSlycot("Can't find slycot module 'sb04qd'") + raise ControlSlycot("Can't find Slycot module 'sb04qd'") if sg03ad is None: - raise ControlSlycot("Can't find slycot module 'sg03ad'") + raise ControlSlycot("Can't find Slycot module 'sg03ad'") # Reshape input arrays A = np.array(A, ndmin=2) @@ -356,7 +356,7 @@ def care(A, B, Q, R=None, S=None, E=None, stabilizing=True, method=None, Returns ------- X : 2D array (or matrix) - Solution to the Ricatti equation. + Solution to the Riccati equation. L : 1D array Closed loop eigenvalues. G : 2D array (or matrix) @@ -399,16 +399,16 @@ def care(A, B, Q, R=None, S=None, E=None, stabilizing=True, method=None, E, _ = np.linalg.eig(A - B @ K) return X, E, K - # Make sure we can import required slycot routines + # Make sure we can import required Slycot routines try: from slycot import sb02md except ImportError: - raise ControlSlycot("Can't find slycot module 'sb02md'") + raise ControlSlycot("Can't find Slycot module 'sb02md'") try: from slycot import sb02mt except ImportError: - raise ControlSlycot("Can't find slycot module 'sb02mt'") + raise ControlSlycot("Can't find Slycot module 'sb02mt'") # Solve the standard algebraic Riccati equation by calling Slycot # functions sb02mt and sb02md @@ -445,11 +445,11 @@ def care(A, B, Q, R=None, S=None, E=None, stabilizing=True, method=None, eigs, _ = sp.linalg.eig(A - B @ K, E) return X, eigs, K - # Make sure we can find the required slycot routine + # Make sure we can find the required Slycot routine try: from slycot import sg02ad except ImportError: - raise ControlSlycot("Can't find slycot module 'sg02ad'") + raise ControlSlycot("Can't find Slycot module 'sg02ad'") # Solve the generalized algebraic Riccati equation by calling the # Slycot function sg02ad @@ -512,7 +512,7 @@ def dare(A, B, Q, R, S=None, E=None, stabilizing=True, method=None, Returns ------- X : 2D array (or matrix) - Solution to the Ricatti equation. + Solution to the Riccati equation. L : 1D array Closed loop eigenvalues. G : 2D array (or matrix) @@ -564,11 +564,11 @@ def dare(A, B, Q, R, S=None, E=None, stabilizing=True, method=None, return X, L, G - # Make sure we can import required slycot routine + # Make sure we can import required Slycot routine try: from slycot import sg02ad except ImportError: - raise ControlSlycot("Can't find slycot module 'sg02ad'") + raise ControlSlycot("Can't find Slycot module 'sg02ad'") # Initialize optional matrices S = np.zeros((n, m)) if S is None else np.array(S, ndmin=2) diff --git a/control/matlab/__init__.py b/control/matlab/__init__.py index 8080b4cce..6414c9131 100644 --- a/control/matlab/__init__.py +++ b/control/matlab/__init__.py @@ -2,9 +2,9 @@ # Creation date: 29 May 09 # Pre-2014 revisions: Kevin K. Chen, Dec 2010 -"""MATLAB compatibility sub-package. +"""MATLAB compatibility subpackage. -This sub-package contains a number of functions that emulate some of +This subpackage contains a number of functions that emulate some of the functionality of MATLAB. The intent of these functions is to provide a simple interface to the python control systems library (python-control) for people who are familiar with the MATLAB Control diff --git a/control/matlab/timeresp.py b/control/matlab/timeresp.py index 1a0e3453b..a0554ec9b 100644 --- a/control/matlab/timeresp.py +++ b/control/matlab/timeresp.py @@ -10,7 +10,7 @@ __all__ = ['step', 'stepinfo', 'impulse', 'initial', 'lsim'] def step(sys, T=None, input=0, output=None, return_x=False): - '''Step response of a linear system. + """Step response of a linear system. If the system has multiple inputs or outputs (MIMO), one input has to be selected for the simulation. Optionally, one output may be @@ -52,7 +52,7 @@ def step(sys, T=None, input=0, output=None, return_x=False): >>> G = rss(4) >>> yout, T = step(G) - ''' + """ from ..timeresp import step_response # Switch output argument order and transpose outputs @@ -69,7 +69,7 @@ def stepinfo(sysdata, T=None, yfinal=None, SettlingTimeThreshold=0.02, Parameters ---------- sysdata : `StateSpace` or `TransferFunction` or array_like - The system data. Either LTI system to similate (`StateSpace`, + The system data. Either LTI system to simulate (`StateSpace`, `TransferFunction`), or a time series of step response data. T : array_like or float, optional Time vector, or simulation time duration if a number (time vector is @@ -82,7 +82,7 @@ def stepinfo(sysdata, T=None, yfinal=None, SettlingTimeThreshold=0.02, (noutputs, ninputs) array_like for MIMO systems. SettlingTimeThreshold : float, optional Defines the error to compute settling time (default = 0.02). - RiseTimeLimits : tuple (lower_threshold, upper_theshold) + RiseTimeLimits : tuple (lower_threshold, upper_threshold) Defines the lower and upper threshold for RiseTime computation. Returns @@ -102,8 +102,8 @@ def stepinfo(sysdata, T=None, yfinal=None, SettlingTimeThreshold=0.02, - 'SteadyStateValue': Steady-state value. If `sysdata` corresponds to a MIMO system, `S` is a 2D list of dicts. - To get the step response characteristics from the j-th input to the - i-th output, access ``S[i][j]``. + To get the step response characteristics from the jth input to the + ith output, access ``S[i][j]``. See Also -------- @@ -127,7 +127,7 @@ def stepinfo(sysdata, T=None, yfinal=None, SettlingTimeThreshold=0.02, return S def impulse(sys, T=None, input=0, output=None, return_x=False): - '''Impulse response of a linear system. + """Impulse response of a linear system. If the system has multiple inputs or outputs (MIMO), one input has to be selected for the simulation. Optionally, one output may be @@ -169,7 +169,7 @@ def impulse(sys, T=None, input=0, output=None, return_x=False): >>> G = rss() >>> yout, T = impulse(G) - ''' + """ from ..timeresp import impulse_response # Switch output argument order and transpose outputs @@ -178,7 +178,7 @@ def impulse(sys, T=None, input=0, output=None, return_x=False): return (out[1], out[0], out[2]) if return_x else (out[1], out[0]) def initial(sys, T=None, X0=0., input=None, output=None, return_x=False): - '''Initial condition response of a linear system. + """Initial condition response of a linear system. If the system has multiple outputs (?IMO), optionally, one output may be selected. If no selection is made for the output, all @@ -221,7 +221,7 @@ def initial(sys, T=None, X0=0., input=None, output=None, return_x=False): >>> G = rss(4) >>> yout, T = initial(G) - ''' + """ from ..timeresp import initial_response # Switch output argument order and transpose outputs @@ -231,7 +231,7 @@ def initial(sys, T=None, X0=0., input=None, output=None, return_x=False): def lsim(sys, U=0., T=None, X0=0.): - '''Simulate the output of a linear system. + """Simulate the output of a linear system. As a convenience for parameters `U` and `X0`, numbers (scalars) are converted to constant arrays with the correct shape. The correct @@ -271,7 +271,7 @@ def lsim(sys, U=0., T=None, X0=0.): >>> T = np.linspace(0,10) >>> yout, T, xout = lsim(G, T=T) - ''' + """ from ..timeresp import forced_response # Switch output argument order and transpose outputs (and always return x) diff --git a/control/matlab/wrappers.py b/control/matlab/wrappers.py index e9b4fd52d..e244479ad 100644 --- a/control/matlab/wrappers.py +++ b/control/matlab/wrappers.py @@ -193,7 +193,7 @@ def _parse_freqplot_args(*args): if len(syslist) == 0: raise ControlArgument("no systems specified") elif len(syslist) == 1: - # If only one system given, retun just that system (not a list) + # If only one system given, return just that system (not a list) syslist = syslist[0] return syslist, omega, plotstyle, other @@ -264,7 +264,7 @@ def pzmap(*args, **kwargs): sys : `StateSpace` or `TransferFunction` Linear system for which poles and zeros are computed. plot : bool, optional - If True a graph is generated with Matplotlib, + If True a graph is generated with matplotlib, otherwise the poles and zeros are only computed and returned. grid : boolean (default = False) If True, plot omega-damping grid. @@ -308,7 +308,7 @@ def ngrid(): def dcgain(*args): - '''dcgain(sys) \ + """dcgain(sys) \ dcgain(num, den) \ dcgain(Z, P, k) \ dcgain(A, B, C, D) @@ -348,7 +348,7 @@ def dcgain(*args): computes: .. math:: gain = - C \\cdot A^{-1} \\cdot B + D - ''' + """ #Convert the parameters to state space form if len(args) == 4: A, B, C, D = args diff --git a/control/modelsimp.py b/control/modelsimp.py index 508590975..307ab59bc 100644 --- a/control/modelsimp.py +++ b/control/modelsimp.py @@ -53,7 +53,7 @@ def hankel_singular_values(sys): The Hankel singular values are the singular values of the Hankel operator. In practice, we compute the square root of the eigenvalues of the matrix formed by taking the product of the observability and controllability - gramians. There are other (more efficient) methods based on solving the + Gramians. There are other (more efficient) methods based on solving the Lyapunov equation in a particular way (more details soon). Examples @@ -295,7 +295,7 @@ def balanced_reduction(sys, orders, method='truncate', alpha=None): ValueError If `method` is not 'truncate' or 'matchdc'. ImportError - If slycot routine ab09ad, ab09md, or ab09nd is not found. + If Slycot routine ab09ad, ab09md, or ab09nd is not found. ValueError If there are more unstable modes than any value in orders. @@ -314,16 +314,16 @@ def balanced_reduction(sys, orders, method='truncate', alpha=None): from slycot import ab09ad, ab09md except ImportError: raise ControlSlycot( - "can't find slycot subroutine ab09md or ab09ad") + "can't find Slycot subroutine ab09md or ab09ad") elif method == 'matchdc': try: from slycot import ab09nd except ImportError: - raise ControlSlycot("can't find slycot subroutine ab09nd") + raise ControlSlycot("can't find Slycot subroutine ab09nd") # Check for ss system object, need a utility for this? - # TODO: Check for continous or discrete, only continuous supported for now + # TODO: Check for continuous or discrete, only continuous supported for now # if isCont(): # dico = 'C' # elif isDisc(): @@ -380,12 +380,12 @@ def balanced_reduction(sys, orders, method='truncate', alpha=None): def minimal_realization(sys, tol=None, verbose=True): - """ Eliminate uncontrollable or unobservable states. + """Eliminate uncontrollable or unobservable states. Eliminates uncontrollable or unobservable states in state-space - models or cancelling pole-zero pairs in transfer functions. The - output sysr has minimal order and the same response - characteristics as the original model sys. + models or canceling pole-zero pairs in transfer functions. The + output `sysr` has minimal order and the same response + characteristics as the original model `sys`. Parameters ---------- @@ -647,7 +647,7 @@ def markov(*args, m=None, transpose=False, dt=None, truncate=False): # Make sure the number of time points match if Umat.shape[1] != Ymat.shape[1]: raise ControlDimension( - "Input and output data are of differnent lengths") + "Input and output data are of different lengths") l = Umat.shape[1] # If number of desired parameters was not given, set to size of input data diff --git a/control/nichols.py b/control/nichols.py index 43eaac2ab..a88dbdfb8 100644 --- a/control/nichols.py +++ b/control/nichols.py @@ -193,7 +193,7 @@ def nichols_grid(cl_mags=None, cl_phases=None, line_style='dotted', ax=None, ax : matplotlib.axes.Axes, optional Axes to add grid to. If None, use `matplotlib.pyplot.gca`. label_cl_phases : bool, optional - If True, closed-loop phase lines will be labelled. + If True, closed-loop phase lines will be labeled. Returns ------- diff --git a/control/nlsys.py b/control/nlsys.py index f96d907bf..bae79d9a4 100644 --- a/control/nlsys.py +++ b/control/nlsys.py @@ -49,7 +49,7 @@ class NonlinearIOSystem(InputOutputSystem): ``updfcn(t, x, u, params) -> array`` - where `t` is a float representing the currrent time, `x` is a 1-D + where `t` is a float representing the current time, `x` is a 1-D array with shape (nstates,), `u` is a 1-D array with shape (ninputs,), and `params` is a dict containing the values of parameters used by the function. @@ -59,7 +59,7 @@ class NonlinearIOSystem(InputOutputSystem): `outfcn(t, x, u, params) -> array` - where the arguments are the same as for `upfcn`. + where the arguments are the same as for `updfcn`. inputs, outputs, states : int, list of str or None, optional Description of the system inputs, outputs, and states. See @@ -96,7 +96,7 @@ class NonlinearIOSystem(InputOutputSystem): Notes ----- - The `InputOuputSystem` class (and its subclasses) makes use of two + The `InputOutputSystem` class (and its subclasses) makes use of two special methods for implementing much of the work of the class: * _rhs(t, x, u): compute the right hand side of the differential or @@ -312,7 +312,7 @@ def __sub__(self, other): # Make sure number of input and outputs match if self.ninputs != other.ninputs or self.noutputs != other.noutputs: raise ValueError( - "can't substract systems with incompatible numbers of " + "can't subtract systems with incompatible numbers of " "inputs or outputs") ninputs = self.ninputs noutputs = self.noutputs @@ -339,7 +339,7 @@ def __neg__(self): if self.ninputs is None or self.noutputs is None: raise ValueError("Can't determine number of inputs or outputs") - # Create a new selftem to hold the negation + # Create a new system to hold the negation inplist = [(0, i) for i in range(self.ninputs)] outlist = [(0, i, -1) for i in range(self.noutputs)] newsys = InterconnectedSystem( @@ -468,7 +468,7 @@ def feedback(self, other=1, sign=-1, params=None): Parameters ---------- other : `InputOutputSystem` - System in the feedack path. + System in the feedback path. sign : float, optional Gain to use in feedback path. Defaults to -1. @@ -502,7 +502,7 @@ def feedback(self, other=1, sign=-1, params=None): (self, other), inplist=inplist, outlist=outlist, params=params, dt=dt) - # Set up the connecton map manually + # Set up the connection map manually newsys.set_connect_map(np.block( [[np.zeros((self.ninputs, self.noutputs)), sign * np.eye(self.ninputs, other.noutputs)], @@ -595,7 +595,7 @@ class InterconnectedSystem(NonlinearIOSystem): Parameters ---------- - syslist : list of NonlinearIOsystem + syslist : list of `NonlinearIOSystem` List of state space systems to interconnect. connections : list of connections Description of the internal connections between the subsystem. See @@ -630,7 +630,7 @@ class InterconnectedSystem(NonlinearIOSystem): input_labels, output_labels, state_labels : list of str Names for the input, output, and state variables. input_offset, output_offset, state_offset : list of int - Offset to the subsystem inputs, outputs, and states in the overal + Offset to the subsystem inputs, outputs, and states in the overall system input, output, and state arrays. syslist_index : dict Index of the subsystem with key given by the name of the subsystem. @@ -1352,7 +1352,7 @@ def nlsys(updfcn, outfcn=None, **kwargs): ``updfcn(t, x, u, params) -> array`` where `x` is a 1-D array with shape (nstates,), `u` is a 1-D array - with shape (ninputs,), `t` is a float representing the currrent + with shape (ninputs,), `t` is a float representing the current time, and `params` is a dict containing the values of parameters used by the function. @@ -1365,7 +1365,7 @@ def nlsys(updfcn, outfcn=None, **kwargs): ``outfcn(t, x, u, params) -> array`` - where the arguments are the same as for `upfcn`. + where the arguments are the same as for `updfcn`. inputs : int, list of str or None, optional Description of the system inputs. This can be given as an integer @@ -1637,7 +1637,7 @@ def input_output_response( if isinstance(U, (tuple, list)) and len(U) != ntimepts: U_elements = [] for i, u in enumerate(U): - u = np.array(u) # convert everyting to an array + u = np.array(u) # convert everything to an array # Process this input if u.ndim == 0 or (u.ndim == 1 and u.shape[0] != T.shape[0]): # Broadcast array to the length of the time input @@ -2226,7 +2226,7 @@ def linearize(sys, xeq, ueq=None, t=0, params=None, **kw): evaluated (does not need to be an equilibrium state). ueq : array, optional The input at which the linearization will be evaluated (does not need - to correspond to an equlibrium state). Can be omitted if `xeq` is + to correspond to an equilibrium state). Can be omitted if `xeq` is an `OperatingPoint`. Defaults to 0. t : float, optional The time at which the linearization will be computed (for time-varying @@ -2339,9 +2339,9 @@ def interconnect( subsystem are used. If systems and signals are given names, then the form 'sys.sig', ('sys', 'sig') or ('sys', 'sig', gain) are also recognized, and the special form '-sys.sig' can be used to specify - a signal with gain -1. Lists, slices, and base namess can also be + a signal with gain -1. Lists, slices, and base names can also be used, as long as the number of elements for each output spec - mataches the input spec. + matches the input spec. If omitted, the `interconnect` function will attempt to create the interconnection map by connecting all signals with the same base @@ -2680,7 +2680,7 @@ def interconnect( new_connection.append((isys, isig, gain)) if len(new_connections) == 0: - # First time we have seen this signal => initalize + # First time we have seen this signal => initialize for cnx in new_connection: new_connections.append([cnx]) if inplist_none: @@ -2945,7 +2945,7 @@ def _convert_static_iosystem(sys): def connection_table(sys, show_names=False, column_width=32): """Print table of connections inside interconnected system. - Intended primarily for `InterconnectedSystems` that have been + Intended primarily for `InterconnectedSystem`'s that have been connected implicitly using signal names. Parameters diff --git a/control/optimal.py b/control/optimal.py index a9ebc7449..31a9998a9 100644 --- a/control/optimal.py +++ b/control/optimal.py @@ -290,7 +290,7 @@ def __init__( # Log information if log: - logging.info("New optimal control problem initailized") + logging.info("New optimal control problem initialized") # # Cost function @@ -547,7 +547,7 @@ def _collocation_constraint(self, coeffs): # # The functions below are used to process the initial guess, which can # either consist of an input only (for shooting methods) or an input - # and/or state trajectory (for collocaiton methods). + # and/or state trajectory (for collocation methods). # # Note: The initial input guess is passed as the inputs at the given time # vector. If a basis is specified, this is converted to coefficient @@ -701,7 +701,7 @@ def _print_statistics(self, reset=True): # Compute the states and inputs from the coefficient vector # # These internal functions return the states and inputs at the - # collocation points given the ceofficient (optimizer state) vector. + # collocation points given the coefficient (optimizer state) vector. # They keep track of whether a shooting method is being used or not and # simulate the dynamics if needed. # @@ -736,7 +736,7 @@ def _compute_states_inputs(self, coeffs): return states, inputs - # Simulate the system dynamis to retrieve the state + # Simulate the system dynamics to retrieve the state def _simulate_states(self, x0, inputs): if self.log: logging.debug( @@ -817,13 +817,13 @@ def compute_trajectory( # Store the initial state (for use in _constraint_function) self.x = x - # Allow the initial guess to be overriden + # Allow the initial guess to be overridden if initial_guess is None: initial_guess = self.initial_guess else: initial_guess = self._process_initial_guess(initial_guess) - # Call ScipPy optimizer + # Call SciPy optimizer res = sp.optimize.minimize( self._cost_function, initial_guess, constraints=self.constraints, **self.minimize_kwargs) @@ -903,7 +903,7 @@ def create_mpc_iosystem(self, **kwargs): def _update(t, x, u, params={}): coeffs = x.reshape((self.system.ninputs, -1)) if self.basis: - # Keep the coeffecients unchanged + # Keep the coefficients unchanged # TODO: could compute input vector, shift, and re-project (?) self.initial_guess = coeffs else: @@ -971,7 +971,7 @@ class OptimalControlResult(sp.optimize.OptimizeResult): eqconst_evaluations : int Number of system simulations and evaluations of the cost function, (inequality) constraint function, and equality constraint function - performed during the optimzation. + performed during the optimization. cost_process_time, constraint_process_time, eqconst_process_time : float If logging was enabled, the amount of time spent evaluating the cost and constraint functions. @@ -1026,7 +1026,7 @@ def solve_optimal_trajectory( r"""Compute the solution to an optimal control problem. The optimal trajectory (states and inputs) is computed so as to - approximately mimimize a cost function of the following form (for + approximately minimize a cost function of the following form (for continuous-time systems): J(x(.), u(.)) = \int_0^T L(x(t), u(t)) dt + V(x(T)), @@ -1257,7 +1257,7 @@ def create_mpc_iosystem( Notes ----- Additional keyword parameters can be used to fine-tune the behavior of - the underlying optimization and integrations functions. See + the underlying optimization and integration functions. See `OptimalControlProblem` for more information. """ @@ -1299,7 +1299,7 @@ class OptimalEstimationProblem(): integral_cost : callable Function that returns the integral cost given the estimated state, system inputs, and output error. Called as integral_cost(xhat, u, - v, w) where xhat is the estimated state, u is the appplied input to + v, w) where xhat is the estimated state, u is the applied input to the system, v is the estimated disturbance input, and w is the difference between the measured and the estimated output. trajectory_constraints : list of constraints, optional @@ -1325,8 +1325,8 @@ class OptimalEstimationProblem(): Specify the indices in the system input vector that correspond to the process disturbances. If value is an integer `m`, the last `m` system inputs are used. Otherwise, the value should be a slice or - a list of indices, as describedf for `control_indices`. If not - specified, defaults to the complement of the control indicies (see + a list of indices, as described for `control_indices`. If not + specified, defaults to the complement of the control indices (see also notes below). Attributes @@ -1792,7 +1792,7 @@ def compute_estimate( # Process the initial guess initial_guess = self._process_initial_guess(initial_guess) - # Call ScipPy optimizer + # Call SciPy optimizer res = sp.optimize.minimize( self._cost_function, initial_guess, constraints=self.constraints, **self.minimize_kwargs) @@ -1827,7 +1827,7 @@ def create_mhe_iosystem( should be a format string using the variable `i` as an index. Otherwise, a list of strings matching the size of the estimated state should be used. Default is "xhat[{i}]". These settings - can also be overriden using the `outputs` keyword. + can also be overridden using the `outputs` keyword. measurement_labels, control_labels : str or list of str, optional Set the names of the measurement and control signal names (estimator inputs). If a single string is specified, it should @@ -1835,7 +1835,7 @@ def create_mhe_iosystem( Otherwise, a list of strings matching the size of the system inputs and outputs should be used. Default is the signal names for the system outputs and control inputs. These settings can - also be overriden using the `inputs` keyword. + also be overridden using the `inputs` keyword. **kwargs, optional Additional keyword arguments to set system, input, and output signal names; see `InputOutputSystem`. @@ -1936,7 +1936,7 @@ def _mhe_output(t, xvec, uvec, params={}): # Optimal estimation result class OptimalEstimationResult(sp.optimize.OptimizeResult): - """Result from solving an optimal estimationproblem. + """Result from solving an optimal estimation problem. This class is a subclass of `scipy.optimize.OptimizeResult` with additional attributes associated with solving optimal estimation @@ -1963,7 +1963,7 @@ class OptimalEstimationResult(sp.optimize.OptimizeResult): inputs : ndarray The disturbances associated with the estimated state trajectory. outputs : - The error between measured outputs and estiamted outputs. + The error between measured outputs and estimated outputs. success : bool Whether or not the optimizer exited successful. problem : OptimalControlProblem @@ -1973,7 +1973,7 @@ class OptimalEstimationResult(sp.optimize.OptimizeResult): system_simulations, {cost, constraint, eqconst}_evaluations : int Number of system simulations and evaluations of the cost function, (inequality) constraint function, and equality constraint function - performed during the optimzation. + performed during the optimization. cost_process_time, constraint_process_time, eqconst_process_time : float If logging was enabled, the amount of time spent evaluating the cost and constraint functions. @@ -2030,7 +2030,7 @@ def solve_optimal_estimate( This function computes the maximum likelihood estimate of a system state given the input and output over a fixed horizon. The likelihood is evaluated according to a cost function whose value is minimized - to compute the maximum likelhood estimate. + to compute the maximum likelihood estimate. Parameters ---------- @@ -2110,7 +2110,7 @@ def solve_optimal_estimate( # # Since a quadratic function is common as a cost function, we provide a # function that will take a Q and R matrix and return a callable that -# evaluates to associted quadratic cost. This is compatible with the way that +# evaluates to associated quadratic cost. This is compatible with the way that # the `_cost_function` evaluates the cost at each point in the trajectory. # def quadratic_cost(sys, Q, R, x0=0, u0=0): @@ -2477,9 +2477,9 @@ def disturbance_range_constraint(sys, lb, ub): sys : `InputOutputSystem` I/O system for which the constraint is being defined. lb : 1D array - Lower bound for each of the disturbancs. + Lower bound for each of the disturbance. ub : 1D array - Upper bound for each of the disturbances. + Upper bound for each of the disturbance. Returns ------- diff --git a/control/passivity.py b/control/passivity.py index 6a0d5ecbd..605d8c726 100644 --- a/control/passivity.py +++ b/control/passivity.py @@ -21,7 +21,7 @@ def solve_passivity_LMI(sys, rho=None, nu=None): - """Compute passivity indices and/or solves feasiblity via a LMI. + """Compute passivity indices and/or solves feasibility via a LMI. Constructs a linear matrix inequality (LMI) such that if a solution exists and the last element of the solution is positive, the system @@ -32,16 +32,6 @@ def solve_passivity_LMI(sys, rho=None, nu=None): is either the output or input passivity index, for `rho` = None and `nu` = None, respectively. - The sources for the algorithm are: - - McCourt, Michael J., and Panos J. Antsaklis - "Demonstrating passivity and dissipativity using computational - methods." - - Nicholas Kottenstette and Panos J. Antsaklis - "Relationships Between Positive Real, Passive Dissipative, & Positive - Systems", equation 36. - Parameters ---------- sys : LTI @@ -56,6 +46,15 @@ def solve_passivity_LMI(sys, rho=None, nu=None): solution : ndarray The LMI solution. + References + ---------- + .. [1] McCourt, Michael J., and Panos J. Antsaklis, "Demonstrating + passivity and dissipativity using computational methods." + + .. [2] Nicholas Kottenstette and Panos J. Antsaklis, + "Relationships Between Positive Real, Passive Dissipative, & + Positive Systems", equation 36. + """ if cvx is None: raise ModuleNotFoundError("cvxopt required for passivity module") @@ -101,7 +100,7 @@ def make_P_basis_matrices(n, rho, nu): """Make list of matrix constraints for passivity LMI. Utility function to make basis matrices for a LMI from a - symmetric matrix P of size n by n representing a parametrized + symmetric matrix P of size n by n representing a parameterized symbolic matrix. """ @@ -125,7 +124,7 @@ def P_pos_def_constraint(n): """Make a list of matrix constraints for P >= 0. Utility function to make basis matrices for a LMI that ensures - parametrized symbolic matrix of size n by n is positive definite + parameterized symbolic matrix of size n by n is positive definite """ matrix_list = [] for i in range(0, n): @@ -141,7 +140,7 @@ def P_pos_def_constraint(n): n = sys.nstates - # coefficents for passivity indices and feasibility matrix + # coefficients for passivity indices and feasibility matrix sys_matrix_list = make_P_basis_matrices(n, rho, nu) # get constants for numerical values of rho and nu diff --git a/control/phaseplot.py b/control/phaseplot.py index 54635dc0b..e9225b904 100644 --- a/control/phaseplot.py +++ b/control/phaseplot.py @@ -584,11 +584,11 @@ def separatrices( a dict of parameters and values. For a callable, `params` should be dict with key 'args' and value given by a tuple (passed to callable). color : matplotlib color spec, optional - Plot the separatrics in the given color. If a single color + Plot the separatrices in the given color. If a single color specification is given, this is used for both stable and unstable separatrices. If a tuple is given, the first element is used as the color specification for stable separatrices and the second - elmeent for unstable separatrices. + element for unstable separatrices. ax : matplotlib.axes.Axes Use the given axes for the plot, otherwise use the current axes. @@ -607,7 +607,7 @@ def separatrices( Notes ----- The value of `config.defaults['separatrices_radius']` is used to set the - offset from the equlibrium point to the starting point of the separatix + offset from the equilibrium point to the starting point of the separatix traces, in the direction of the eigenvectors evaluated at that equilibrium point. @@ -974,7 +974,7 @@ def _parse_arrow_keywords(kwargs): def _create_trajectory( sys, revsys, timepts, X0, params, dir, suppress_warnings=False, gridtype=None, gridspec=None, xlim=None, ylim=None): - # Comput ethe forward trajectory + # Compute the forward trajectory if dir == 'forward' or dir == 'both': fwdresp = input_output_response( sys, timepts, X0=X0, params=params, ignore_errors=True) diff --git a/control/pzmap.py b/control/pzmap.py index 3ef974d98..8fd224535 100644 --- a/control/pzmap.py +++ b/control/pzmap.py @@ -181,11 +181,11 @@ def pole_zero_plot( grid : bool or str, optional If True plot omega-damping grid, if False show imaginary axis for continuous-time systems, unit circle for discrete-time - systems. If 'empty', do not draw any additonal lines. Default + systems. If 'empty', do not draw any additional lines. Default value is set by `config.defaults['pzmap.grid']` or `config.defaults['rlocus.grid']`. plot : bool, optional - (legacy) If True a graph is generated with Matplotlib, + (legacy) If True a graph is generated with matplotlib, otherwise the poles and zeros are only computed and returned. If this argument is present, the legacy value of poles and zeros is returned. @@ -476,7 +476,7 @@ def pole_zero_plot( title, fig, rcParams=rcParams, frame='figure', use_existing=False) - # Add dispather to handle choosing a point on the diagram + # Add dispatcher to handle choosing a point on the diagram if interactive: if len(pzmap_responses) > 1: raise NotImplementedError( @@ -560,7 +560,7 @@ def _mark_root_locus_gain(ax, sys, K): line.remove() del line - # Visualise clicked point, displaying all roots + # Visualize clicked point, displaying all roots # TODO: allow marker parameters to be set nump, denp = _systopoly1d(sys) root_array = _RLFindRoots(nump, denp, K.real) diff --git a/control/rlocus.py b/control/rlocus.py index c008d5240..9773abe7b 100644 --- a/control/rlocus.py +++ b/control/rlocus.py @@ -5,10 +5,11 @@ # # RMM, 17 June 2010: modified to be a standalone piece of code # -# RMM, 2 April 2011: modified to work with new LTI structure (see ChangeLog) +# RMM, 2 April 2011: modified to work with new LTI structure # -# Sawyer B. Fuller (minster@uw.edu) 21 May 2020: -# * added compatibility with discrete-time systems. +# Sawyer B. Fuller (minster@uw.edu) 21 May 2020: added compatibility +# with discrete-time systems. +# """Code for computing a root locus plot.""" @@ -116,7 +117,7 @@ def root_locus_plot( grid : bool or str, optional If True plot omega-damping grid, if False show imaginary axis for continuous-time systems, unit circle for discrete-time systems. - If 'empty', do not draw any additonal lines. Default value is set + If 'empty', do not draw any additional lines. Default value is set by `config.defaults['rlocus.grid']`. initial_gain : float, optional Mark the point on the root locus diagram corresponding to the @@ -192,7 +193,7 @@ def root_locus_plot( # Process `plot` keyword # # See bode_plot for a description of how this keyword is handled to - # support legacy implementatoins of root_locus. + # support legacy implementations of root_locus. # if plot is not None: warnings.warn( @@ -394,7 +395,7 @@ def _k_max(num, den, real_break_points, k_break_points): def _systopoly1d(sys): - """Extract numerator and denominator polynomails for a system""" + """Extract numerator and denominator polynomials for a system""" # Allow inputs from the signal processing toolbox if (isinstance(sys, scipy.signal.lti)): nump = sys.num diff --git a/control/robust.py b/control/robust.py index d9f8cca11..55d453154 100644 --- a/control/robust.py +++ b/control/robust.py @@ -34,7 +34,7 @@ def h2syn(P, nmeas, ncon): Raises ------ ImportError - If slycot routine sb10hd is not loaded. + If Slycot routine sb10hd is not loaded. See Also -------- @@ -42,7 +42,7 @@ def h2syn(P, nmeas, ncon): Examples -------- - >>> # Unstable first order SISI system + >>> # Unstable first order SISO system >>> G = ct.tf([1], [1, -1], inputs=['u'], outputs=['y']) >>> all(G.poles() < 0) # Is G stable? False @@ -124,7 +124,7 @@ def hinfsyn(P, nmeas, ncon): Examples -------- - >>> # Unstable first order SISI system + >>> # Unstable first order SISO system >>> G = ct.tf([1], [1,-1], inputs=['u'], outputs=['y']) >>> all(G.poles() < 0) False diff --git a/control/sisotool.py b/control/sisotool.py index 95289354f..78be86b16 100644 --- a/control/sisotool.py +++ b/control/sisotool.py @@ -53,7 +53,7 @@ def sisotool(sys, initial_gain=None, xlim_rlocus=None, ylim_rlocus=None, input. To view the step response with a feedforward controller, give your plant two identical inputs, and sum your feedback controller and your feedforward controller and multiply them into - your plant's second input. It is also possible to accomodate a + your plant's second input. It is also possible to accommodate a system with a gain in the feedback. initial_gain : float, optional Initial gain to use for plotting root locus. Defaults to 1 @@ -85,7 +85,7 @@ def sisotool(sys, initial_gain=None, xlim_rlocus=None, ylim_rlocus=None, margins_bode : boolean If True, plot gain and phase margin in the bode plot. tvect : list or ndarray, optional - List of timesteps to use for closed loop step response. + List of time steps to use for closed loop step response. Examples -------- @@ -192,7 +192,7 @@ def _SisotoolUpdate(sys, fig, K, bode_plot_params, tvect=None): sys_loop = sys if sys.issiso() else sys[0,0] - # Update the bodeplot + # Update the Bode plot bode_plot_params['data'] = frequency_response(sys_loop*K.real) bode_plot(**bode_plot_params, title=False) @@ -276,7 +276,7 @@ def rootlocus_pid_designer(plant, gain='P', sign=+1, input_signal='r', different gain, e.g. 'I', make sure to add your chosen `deltaK` to the previous gain you you were tuning. - Example: to examine the effect of varying `Kp` starting from an intial + Example: to examine the effect of varying `Kp` starting from an initial value of 10, use the arguments ``gain='P', Kp0=10`` and try varying values of `deltaK`. Suppose a `deltaK` of 5 gives satisfactory performance. Then, to tune the derivative gain, add your selected `deltaK` to `Kp0` in the @@ -293,7 +293,7 @@ def rootlocus_pid_designer(plant, gain='P', sign=+1, input_signal='r', deactivate magnification mode when you are done by clicking the magnifying glass. Otherwise you will not be able to be able to choose a gain on the root locus plot. When you are done, ``%matplotlib inline`` returns to - inline, non-interactive ploting. + inline, non-interactive plotting. By default, all three PID terms are in the forward path C_f in the diagram shown below, that is, @@ -339,7 +339,7 @@ def rootlocus_pid_designer(plant, gain='P', sign=+1, input_signal='r', Initial values for proportional, integral, and derivative gains, respectively. deltaK : float, optional - Perturbation value for gain specified by the `gain` keywoard. + Perturbation value for gain specified by the `gain` keyword. tau : float, optional The time constant associated with the pole in the continuous-time derivative term. This is required to make the derivative transfer diff --git a/control/statefbk.py b/control/statefbk.py index fa1c7e479..e4f0bac27 100644 --- a/control/statefbk.py +++ b/control/statefbk.py @@ -19,7 +19,7 @@ from .nlsys import NonlinearIOSystem, interconnect from .statesp import StateSpace, _ssmatrix, ss -# Make sure we have access to the right slycot routines +# Make sure we have access to the right Slycot routines try: from slycot import sb03md57 @@ -140,7 +140,7 @@ def place_varga(A, B, p, dtime=False, alpha=None): Notes ----- - This function is a wrapper for the slycot function sb01bd, which + This function is a wrapper for the Slycot function sb01bd, which implements the pole placement algorithm of Varga [1]_. In contrast to the algorithm used by `place`, the Varga algorithm can place multiple poles at the same location. The placement, however, may @@ -159,7 +159,7 @@ def place_varga(A, B, p, dtime=False, alpha=None): """ - # Make sure that SLICOT is installed + # Make sure that Slycot is installed try: from slycot import sb01bd except ImportError: @@ -184,21 +184,21 @@ def place_varga(A, B, p, dtime=False, alpha=None): # (if DICO='C') or with modulus less than alpha # (if DICO = 'D'). if dtime: - # For discrete time, slycot only cares about modulus, so just make + # For discrete time, Slycot only cares about modulus, so just make # alpha the smallest it can be. alpha = 0.0 else: # Choosing alpha=min_eig is insufficient and can lead to an # error or not having all the eigenvalues placed that we wanted. # Evidently, what python thinks are the eigs is not precisely - # the same as what slicot thinks are the eigs. So we need some + # the same as what Slycot thinks are the eigs. So we need some # numerical breathing room. The following is pretty heuristic, # but does the trick alpha = -2*abs(min(system_eigs.real)) elif dtime and alpha < 0.0: raise ValueError("Discrete time systems require alpha > 0") - # Call SLICOT routine to place the eigenvalues + # Call Slycot routine to place the eigenvalues A_z, w, nfp, nap, nup, F, Z = \ sb01bd(B_mat.shape[0], B_mat.shape[1], len(placed_eigs), alpha, A_mat, B_mat, placed_eigs, DICO) @@ -472,7 +472,7 @@ def dlqr(*args, **kwargs): if (len(args) < 3): raise ControlArgument("not enough input arguments") - # If we were passed a continus time system as the first arg, raise error + # If we were passed a continues time system as the first arg, raise error if isinstance(args[0], LTI) and isctime(args[0], strict=True): raise ControlArgument("dsys must be discrete time (dt != 0)") @@ -1197,7 +1197,7 @@ def gram(sys, type): return gram elif type == 'cf' or type == 'of': - # Compute cholesky factored gramian from slycot routine sb03od + # Compute Cholesky factored Gramian from Slycot routine sb03od if sb03od is None: raise ControlSlycot("can't find slycot module 'sb03od'") tra = 'N' diff --git a/control/statesp.py b/control/statesp.py index b007e338f..2908d1d6d 100644 --- a/control/statesp.py +++ b/control/statesp.py @@ -1182,7 +1182,7 @@ def minreal(self, tol=0.0): return StateSpace(A[:nr, :nr], B[:nr, :self.ninputs], C[:self.noutputs, :nr], self.D, self.dt) except ImportError: - raise TypeError("minreal requires slycot tb01pd") + raise TypeError("minreal requires Slycot tb01pd") else: return StateSpace(self) @@ -1312,10 +1312,10 @@ def sample(self, Ts, method='zoh', alpha=None, prewarp_frequency=None, Method to use for sampling: * 'gbt': generalized bilinear transformation - * 'backward_diff': Backwards differencing ('gbt' with alpha=1.0) + * 'backward_diff': Backwards difference ('gbt' with alpha=1.0) * 'bilinear' (or 'tustin'): Tustin's approximation ('gbt' with alpha=0.5) - * 'euler': Euler (or forward differencing) method ('gbt' with + * 'euler': Euler (or forward difference) method ('gbt' with alpha=0) * 'zoh': zero-order hold (default) alpha : float within [0, 1] @@ -2346,7 +2346,7 @@ def _f2s(f): """ fmt = "{:" + config.defaults['statesp.latex_num_format'] + "}" sraw = fmt.format(f) - # significand-exponent + # significant-exponent se = sraw.lower().split('e') # whole-fraction wf = se[0].split('.') diff --git a/control/stochsys.py b/control/stochsys.py index 6834df0c4..cd01fe291 100644 --- a/control/stochsys.py +++ b/control/stochsys.py @@ -259,7 +259,7 @@ def dlqe(*args, **kwargs): if (len(args) < 3): raise ControlArgument("not enough input arguments") - # If we were passed a continus time system as the first arg, raise error + # If we were passed a continuous time system as the first arg, raise error if isinstance(args[0], LTI) and isctime(args[0], strict=True): raise ControlArgument("dlqr() called with a continuous-time system") @@ -292,7 +292,7 @@ def dlqe(*args, **kwargs): # NG = G @ NN if len(args) > index + 2: # NN = np.array(args[index+2], ndmin=2, dtype=float) - raise ControlNotImplemented("cross-covariance not yet implememented") + raise ControlNotImplemented("cross-covariance not yet implemented") # Check dimensions of G (needed before calling care()) _check_shape(QN, G.shape[1], G.shape[1], name="QN") @@ -402,7 +402,7 @@ def create_estimator_iosystem( string using the variable `i` as an index. Otherwise, a list of strings matching the size of the system inputs and outputs should be used. Default is the signal names for the system measurements and - known control inputs. These settings can also be overriden using the + known control inputs. These settings can also be overridden using the `inputs` keyword. inputs, outputs, states : int or list of str, optional Set the names of the inputs, outputs, and states, as described in @@ -428,12 +428,12 @@ def create_estimator_iosystem( prediction with no additional measurement information:: resp = ct.input_output_response( - est, T, 0, [X0, P0], param={'correct': False) + est, T, 0, [X0, P0], params={'correct': False) References ---------- .. [1] R. M. Murray, `Optimization-Based Control - `_, 2013. + `_, 2023. """ @@ -489,7 +489,7 @@ def create_estimator_iosystem( # Initialize the covariance matrix if P0 is None: - # Initalize P0 to the steady state value + # Initialize P0 to the steady state value _, P0, _ = lqe(A, G, C, QN, RN) P0 = _check_shape(P0, sys.nstates, sys.nstates, symmetric=True, name='P0') @@ -603,7 +603,7 @@ def white_noise(T, Q, dt=0): """Generate a white noise signal with specified intensity. This function generates a (multi-variable) white noise signal of - specified intensity as either a sampled continous time signal or a + specified intensity as either a sampled continuous time signal or a discrete-time signal. A white noise signal along a 1D array of linearly spaced set of times T can be computing using @@ -672,9 +672,9 @@ def correlation(T, X, Y=None, squeeze=True): tau, Rtau = correlation(T, X[, Y]) The signal X (and Y, if present) represent a continuous or - discrete-time signal sampled at times T. The return value - provides the correlation Rtau between X(t+tau) and X(t) at a set - of time offets tau. + discrete-time signal sampled at times T. The return value provides the + correlation Rtau between X(t+tau) and X(t) at a set of time offsets + tau. Parameters ---------- diff --git a/control/tests/matlab2_test.py b/control/tests/matlab2_test.py index d4d8747e5..4d135e33e 100644 --- a/control/tests/matlab2_test.py +++ b/control/tests/matlab2_test.py @@ -153,7 +153,8 @@ def test_impulse(self, SISO_mats, mplcleanup): t, y = impulse(sys, T) plot(t, y, label='t=0..2') - #Test system with direct feed-though, the function should print a warning. + # Test system with direct feedthough, the function should + # print a warning. D = [[0.5]] sys_ft = ss(A, B, C, D) with pytest.warns(UserWarning, match="has direct feedthrough"): diff --git a/control/timeplot.py b/control/timeplot.py index f0a3efee4..eda6fd1ea 100644 --- a/control/timeplot.py +++ b/control/timeplot.py @@ -5,7 +5,7 @@ This module contains routines for plotting out time responses. These functions can be called either as standalone functions or access from -the TimeDataResponse class. +the TimeResponseData class. """ @@ -115,7 +115,7 @@ def time_response_plot( figure with the correct number and shape of axes, a new figure is created. The shape of the array must match the shape of the plotted data. - input_props : array of dicts + input_props : array of dict List of line properties to use when plotting combined inputs. The default values are set by `config.defaults['timeplot.input_props']`. label : str or array_like of str, optional @@ -131,7 +131,7 @@ def time_response_plot( legend_loc : int or str, optional Include a legend in the given location. Default is 'center right', with no legend for a single response. Use False to suppress legend. - output_props : array of dicts, optional + output_props : array of dict, optional List of line properties to use when plotting combined outputs. The default values are set by `config.defaults['timeplot.output_props']`. rcParams : dict @@ -151,7 +151,7 @@ def time_response_plot( Set the title of the plot. Defaults to plot type and system name(s). trace_labels : list of str, optional Replace the default trace labels with the given labels. - trace_props : array of dicts + trace_props : array of dict List of line properties to use when plotting multiple traces. The default values are set by `config.defaults['timeplot.trace_props']`. @@ -562,7 +562,7 @@ def _make_line_label(signal_index, signal_labels, trace_index): # Create legends # # Legends can be placed manually by passing a legend_map array that - # matches the shape of the suplots, with each item being a string + # matches the shape of the subplots, with each item being a string # indicating the location of the legend for that axes (or None for no # legend). # @@ -677,7 +677,7 @@ def combine_time_responses(response_list, trace_labels=None, title=None): Parameters ---------- response_list : list of `TimeResponseData` objects - Reponses to be combined. + Responses to be combined. trace_labels : list of str, optional List of labels for each trace. If not specified, trace names are taken from the input data or set to None. diff --git a/control/timeresp.py b/control/timeresp.py index 6c82e09ee..5104a9895 100644 --- a/control/timeresp.py +++ b/control/timeresp.py @@ -102,7 +102,7 @@ class TimeResponseData: a multi-input system) or trace and time (for a single-input, multi-trace response), or a 3D array indexed by input, trace, and time. - title : str, optonal + title : str, optional Title of the data set (used as figure title in plotting). squeeze : bool, optional By default, if a system is single-input, single-output (SISO) then @@ -152,7 +152,7 @@ class TimeResponseData: ntraces : int, optional Number of independent traces represented in the input/output response. If `ntraces` is 0 (default) then the data represents a - single trace with the trace index surpressed in the data. + single trace with the trace index suppressed in the data. trace_labels : array of string, optional Labels to use for traces (set to sysname it `ntraces` is 0). trace_types : array of string, optional @@ -229,7 +229,7 @@ class TimeResponseData: The default settings for `return_x`, `squeeze` and `transpose` can be changed by calling the class instance and passing new values:: - response(tranpose=True).input + response(transpose=True).input See `TimeResponseData.__call__` for more information. @@ -369,7 +369,7 @@ def __init__( self.u = np.array(inputs) self.plot_inputs = plot_inputs - # Make sure the shape is OK and figure out the nuumber of inputs + # Make sure the shape is OK and figure out the number of inputs if multi_trace and self.u.ndim == 3 and \ self.u.shape[1] == self.ntraces: self.ninputs = self.u.shape[0] @@ -1203,7 +1203,7 @@ def forced_response(sysdata, T=None, U=0., X0=0., transpose=False, params=None, # https://github.com/scipyscipy/blob/v1.6.1/scipy/signal/ltisys.py#L3462 scipy_out_samples = int(np.floor(spT[-1] / sys_dt)) + 1 if scipy_out_samples < n_steps: - # parantheses: order of evaluation is important + # parentheses: order of evaluation is important spT[-1] = spT[-1] * (n_steps / (spT[-1] / sys_dt + 1)) else: @@ -1357,7 +1357,7 @@ def step_response( T_num : int, optional Number of time steps to use in simulation if `T` is not provided as - an array (autocomputed if not given); ignored if the system is + an array (auto-computed if not given); ignored if the system is discrete time. transpose : bool, optional @@ -1493,11 +1493,11 @@ def step_info(sysdata, T=None, T_num=None, yfinal=None, params=None, `TransferFunction`), or a time series of step response data. T : array_like or float, optional Time vector, or simulation time duration if a number (time vector is - autocomputed if not given, see `step_response` for more detail). + auto-computed if not given, see `step_response` for more detail). Required, if sysdata is a time series of response data. T_num : int, optional Number of time steps to use in simulation if `T` is not provided as - an array; autocomputed if not given; ignored if sysdata is a + an array; auto-computed if not given; ignored if sysdata is a discrete-time system or a time series or response data. yfinal : scalar or array_like, optional Steady-state response. If not given, sysdata.dcgain() is used for @@ -1508,7 +1508,7 @@ def step_info(sysdata, T=None, T_num=None, yfinal=None, params=None, If system is a nonlinear I/O system, set parameter values. SettlingTimeThreshold : float, optional Defines the error to compute settling time (default = 0.02). - RiseTimeLimits : tuple (lower_threshold, upper_theshold) + RiseTimeLimits : tuple (lower_threshold, upper_threshold) Defines the lower and upper threshold for RiseTime computation. Returns @@ -1528,8 +1528,8 @@ def step_info(sysdata, T=None, T_num=None, yfinal=None, params=None, - 'SteadyStateValue': Steady-state value. If `sysdata` corresponds to a MIMO system, `S` is a 2D list of dicts. - To get the step response characteristics from the j-th input to the - i-th output, access ``S[i][j]``. + To get the step response characteristics from the jth input to the + ith output, access ``S[i][j]``. See Also -------- @@ -1722,7 +1722,7 @@ def initial_response( T : array_like or float, optional Time vector, or simulation time duration if a number (time vector is - autocomputed if not given; see `step_response` for more detail). + auto-computed if not given; see `step_response` for more detail). X0 : array_like or float, optional Initial condition (default = 0). Numbers are converted to constant @@ -1734,7 +1734,7 @@ def initial_response( T_num : int, optional Number of time steps to use in simulation if `T` is not provided as - an array (autocomputed if not given); ignored if the system is + an array (auto-computed if not given); ignored if the system is discrete time. params : dict, optional @@ -1837,11 +1837,11 @@ def impulse_response( Parameters ---------- sysdata : I/O system or list of I/O systems - I/O system(s) for which impluse response is computed. + I/O system(s) for which impulse response is computed. T : array_like or float, optional Time vector, or simulation time duration if a scalar (time vector is - autocomputed if not given; see `step_response` for more detail). + auto-computed if not given; see `step_response` for more detail). input : int, optional Only compute the impulse response for the listed input. If not @@ -1854,7 +1854,7 @@ def impulse_response( T_num : int, optional Number of time steps to use in simulation if `T` is not provided as - an array (autocomputed if not given); ignored if the system is + an array (auto-computed if not given); ignored if the system is discrete time. transpose : bool, optional @@ -1891,7 +1891,7 @@ def impulse_response( ----- This function uses the `forced_response` function to compute the time response. For continuous-time systems, the initial condition is altered - to account for the initial impulse. For discrete-time aystems, the + to account for the initial impulse. For discrete-time systems, the impulse is sized so that it has unit area. The impulse response for nonlinear systems is not implemented. @@ -1971,7 +1971,7 @@ def impulse_response( U = np.zeros((sys.ninputs, T.size)) U[i, 0] = 1./sys.dt # unit area impulse - # Simulate the impulse response fo this input + # Simulate the impulse response for this input response = forced_response(sys, T, U, X0) # Store the output (and states) @@ -2077,7 +2077,7 @@ def _ideal_tfinal_and_dt(sys, is_step=True): # zero - negligible effect on tfinal m_z = np.abs(p) < sqrt_eps p = p[~m_z] - # Negative reals- treated as oscillary mode + # Negative reals- treated as oscillatory mode m_nr = (p.real < 0) & (np.abs(p.imag) < sqrt_eps) p_nr, p = p[m_nr], p[~m_nr] if p_nr.size > 0: diff --git a/control/xferfcn.py b/control/xferfcn.py index 9c40e66d8..2a9572954 100644 --- a/control/xferfcn.py +++ b/control/xferfcn.py @@ -1165,10 +1165,10 @@ def sample(self, Ts, method='zoh', alpha=None, prewarp_frequency=None, Method to use for sampling: * 'gbt': generalized bilinear transformation - * 'backward_diff': Backwards differencing ('gbt' with alpha=1.0) + * 'backward_diff': Backwards difference ('gbt' with alpha=1.0) * 'bilinear' (or 'tustin'): Tustin's approximation ('gbt' with alpha=0.5) - * 'euler': Euler (or forward differencing) method ('gbt' with + * 'euler': Euler (or forward difference) method ('gbt' with alpha=0) * 'matched': pole-zero match method * 'zoh': zero-order hold (default) diff --git a/doc/config.rst b/doc/config.rst index fc760a93d..c49ee0802 100644 --- a/doc/config.rst +++ b/doc/config.rst @@ -271,7 +271,7 @@ Plotting parameters :type: dict :value: {'axes.labelsize': 'small', 'axes.titlesize': 'small', 'figure.titlesize': 'medium', 'legend.fontsize': 'x-small', 'xtick.labelsize': 'small', 'ytick.labelsize': 'small'} - Overrides the default Matplotlib parameters used for generating + Overrides the default matplotlib parameters used for generating plots. This dictionary can also be accessed as `ct.rcParams`. .. py:data:: freqplot.dB diff --git a/doc/develop.rst b/doc/develop.rst index 2dce6e243..f461ff0eb 100644 --- a/doc/develop.rst +++ b/doc/develop.rst @@ -49,11 +49,10 @@ GitHub repository file and directory layout: + optimal.py - optimal control module - + **flatsys/** - flat systems sub-package + + **flatsys/** - flat systems subpackage - - __init__.py, basis.py, bezier.py, bspline.py, - flatsys.py, linflat.py, poly.py, systraj.py - - sub-package files + - __init__.py, basis.py, bezier.py, bspline.py, flatsys.py, + linflat.py, poly.py, systraj.py - subpackage files + **matlab/** - MATLAB compatibility subpackage @@ -439,7 +438,7 @@ the `include` directive (see `nlsys.py` for an example). Sphinx files guidelines: * Each file should declare the `currentmodule` at or near the top of - the file. Except for sub-packages (`control.flatsys`) and modules + the file. Except for subpackages (`control.flatsys`) and modules that need to be imported separately (`control.optimal`), `currentmodule` should be set to control. @@ -482,10 +481,10 @@ The reference manual should provide a fairly comprehensive description of every class, function and configuration variable in the package. -Modules and sub-packages ------------------------- +Modules and subpackages +----------------------- -When documenting (independent) modules and sub-packages (refered to +When documenting (independent) modules and subpackages (refered to here collectively as modules), use the following guidelines for documentatation: diff --git a/doc/flatsys.rst b/doc/flatsys.rst index 4440906a8..cb17c2901 100644 --- a/doc/flatsys.rst +++ b/doc/flatsys.rst @@ -5,9 +5,9 @@ Differentially Flat Systems =========================== -The `flatsys` sub-package contains a set of classes and functions to +The `flatsys` subpackage contains a set of classes and functions to compute trajectories for differentially flat systems. The objects in -this sub-package must be explictly imported:: +this subpackage must be explictly imported:: import control as ct import control.flatsys as fs @@ -364,7 +364,7 @@ The results of the two approaches can be shown using the Sub-package classes and functions --------------------------------- -The flat systems sub-package `flatsys` utilizes a number of classes to +The flat systems subpackage `flatsys` utilizes a number of classes to define the flatsystem, the basis functions, and the system trajetory: .. autosummary:: diff --git a/doc/nonlinear.rst b/doc/nonlinear.rst index 63be7e4f3..b0ea3058f 100644 --- a/doc/nonlinear.rst +++ b/doc/nonlinear.rst @@ -8,7 +8,7 @@ The Python Control Systems Library contains a variety of tools for modeling, analyzing, and designing nonlinear feedback systems, including support for simulation and optimization. This chapter describes the primary functionality available, both in the core -python-control package and in specalized modules and sub-packages. +python-control package and in specalized modules and subpackages. .. include:: nlsys.rst diff --git a/doc/optimal.rst b/doc/optimal.rst index 67a50e2da..aa2420f47 100644 --- a/doc/optimal.rst +++ b/doc/optimal.rst @@ -357,7 +357,7 @@ solutions do not seem close to optimal, here are a few things to try: points, you can specify a set of basis functions using the `basis` keyword in :func:`~optimal.solve_optimal_trajectory` and then parameterize the controller by linear combination of the basis - functions. The :ref:`flatsys sub-package ` defines + functions. The :ref:`flatsys subpackage ` defines several sets of basis functions that can be used. * Tweak the optimizer: by using the `minimize_method`, From fe0cd47f8af6ccbb31e82498f4c1f8e451c22f84 Mon Sep 17 00:00:00 2001 From: Richard Murray Date: Fri, 24 Jan 2025 15:12:02 -0800 Subject: [PATCH 83/93] make frdata/xferfcn parameters and attributes more consistent --- control/config.py | 4 + control/descfcn.py | 10 +- control/frdata.py | 275 +++++++++++++++++-------------- control/freqplot.py | 19 ++- control/iosys.py | 3 +- control/rlocus.py | 1 - control/tests/config_test.py | 6 + control/tests/docstrings_test.py | 51 +++--- control/tests/frd_test.py | 88 +++++----- control/tests/freqresp_test.py | 2 +- control/tests/iosys_test.py | 4 +- control/tests/lti_test.py | 6 +- control/tests/matlab_test.py | 2 +- doc/config.rst | 7 + doc/develop.rst | 96 +++++++++++ doc/linear.rst | 16 +- 16 files changed, 370 insertions(+), 220 deletions(-) diff --git a/control/config.py b/control/config.py index bda59da15..c49913145 100644 --- a/control/config.py +++ b/control/config.py @@ -256,6 +256,7 @@ def use_matlab_defaults(): The following conventions are used: * Bode plots plot gain in dB, phase in degrees, frequency in rad/sec, with grids + * Frequency plots use the label "Magnitude" for the system gain. Examples -------- @@ -264,6 +265,7 @@ def use_matlab_defaults(): """ set_defaults('freqplot', dB=True, deg=True, Hz=False, grid=True) + set_defaults('freqplot', magnitude_label="Magnitude") # Set defaults to match FBS (Astrom and Murray) @@ -275,6 +277,7 @@ def use_fbs_defaults(): * Bode plots plot gain in powers of ten, phase in degrees, frequency in rad/sec, no grid + * Frequency plots use the label "Gain" for the system gain. * Nyquist plots use dashed lines for mirror image of Nyquist curve Examples @@ -284,6 +287,7 @@ def use_fbs_defaults(): """ set_defaults('freqplot', dB=False, deg=True, Hz=False, grid=False) + set_defaults('freqplot', magnitude_label="Gain") set_defaults('nyquist', mirror_style='--') diff --git a/control/descfcn.py b/control/descfcn.py index e9472a477..3c044b7e3 100644 --- a/control/descfcn.py +++ b/control/descfcn.py @@ -336,7 +336,7 @@ def describing_function_response( >>> response = ct.describing_function_response(H_simple, F_saturation, amp) >>> response.intersections # doctest: +SKIP [(3.343844998258643, 1.4142293090899216)] - >>> lines = response.plot() + >>> cplt = response.plot() """ # Decide whether to turn on warnings or not @@ -484,7 +484,7 @@ def describing_function_plot( >>> H_simple = ct.tf([8], [1, 2, 2, 1]) >>> F_saturation = ct.saturation_nonlinearity(1) >>> amp = np.linspace(1, 4, 10) - >>> lines = ct.describing_function_plot(H_simple, F_saturation, amp) + >>> cplt = ct.describing_function_plot(H_simple, F_saturation, amp) """ # Process keywords @@ -521,8 +521,8 @@ def describing_function_plot( lines = np.empty(2, dtype=object) # Plot the Nyquist response - cfig = dfresp.response.plot(**kwargs) - lines[0] = cfig.lines[0] # Return Nyquist lines for first system + cplt = dfresp.response.plot(**kwargs) + lines[0] = cplt.lines[0] # Return Nyquist lines for first system # Add the describing function curve to the plot lines[1] = plt.plot(dfresp.N_vals.real, dfresp.N_vals.imag) @@ -533,7 +533,7 @@ def describing_function_plot( # Add labels to the intersection points plt.text(pos.real, pos.imag, point_label % (a, omega)) - return ControlPlot(lines, cfig.axes, cfig.figure) + return ControlPlot(lines, cplt.axes, cplt.figure) # Utility function to figure out whether two line segments intersection diff --git a/control/frdata.py b/control/frdata.py index 6374c2075..96d2cd5b6 100644 --- a/control/frdata.py +++ b/control/frdata.py @@ -28,7 +28,7 @@ class FrequencyResponseData(LTI): - """FrequencyResponseData(response, omega[, smooth]) + """FrequencyResponseData(frdata, omega[, smooth]) Input/output model defined by frequency response data (FRD). @@ -39,14 +39,15 @@ class constructor, using the `frd` factory function, or Parameters ---------- - response : 1D or 3D complex array_like + frdata : 1D or 3D complex array_like The frequency response at each frequency point. If 1D, the system is assumed to be SISO. If 3D, the system is MIMO, with the first dimension corresponding to the output index of the FRD, the second dimension corresponding to the input index, and the 3rd dimension - corresponding to the frequency points in omega + corresponding to the frequency points in `omega`. When accessed as an + attribute, `frdata` is always stored as a 3D array. omega : iterable of real frequencies - List of frequency points for which data are available. + List of monotonically increasing frequency points for the response. smooth : bool, optional If True, create an interpolation function that allows the frequency response to be computed at any frequency within the range of @@ -74,9 +75,15 @@ class constructor, using the `frd` factory function, or Attributes ---------- - fresp : 3D array - Frequency response, indexed by output index, input index, and - frequency point. + complex : array + Complex frequency response, indexed by output index, input index, and + frequency point, with squeeze processing. + magnitude : array + Magnitude of the frequency response, indexed by output index, input + index, and frequency point, with squeeze processing. + phase : array + Phase of the frequency response, indexed by output index, input index, + and frequency point, with squeeze processing. frequency : 1D array Array of frequency points for which data are available. ninputs, noutputs : int @@ -89,10 +96,6 @@ class constructor, using the `frd` factory function, or System name. For data generated using `frequency_response`, stores the name of the system that created the data. - magnitude : array - Magnitude of the frequency response, indexed by frequency. - phase : array - Phase of the frequency response, indexed by frequency. Other Parameters ---------------- @@ -117,11 +120,11 @@ class constructor, using the `frd` factory function, or Notes ----- - The main data members are `omega` and `fresp`, where `omega` is a 1D array - of frequency points and and `fresp` is a 3D array of frequency responses, - with the first dimension corresponding to the output index of the FRD, the - second dimension corresponding to the input index, and the 3rd dimension - corresponding to the frequency points in omega. For example, + The main data members are `omega` and `frdata`, where `omega` is a 1D + array of frequency points and and `frdata` is a 3D array of frequency + responses, with the first dimension corresponding to the output index of + the FRD, the second dimension corresponding to the input index, and the + 3rd dimension corresponding to the frequency points in omega. For example, >>> frdata[2,5,:] = numpy.array([1., 0.8-0.2j, 0.2-0.8j]) # doctest: +SKIP @@ -182,18 +185,19 @@ class constructor, using the `frd` factory function, or _epsw = 1e-8 #: Bound for exact frequency match def __init__(self, *args, **kwargs): - """FrequencyResponseData(d, w[, dt]) + """FrequencyResponseData(response, omega[, dt]) Construct a frequency response data (FRD) object. - The default constructor is `FrequencyResponseData(d, w)`, where `w` - is an iterable of frequency points and `d` is the matching - frequency data. If `d` is a single list, 1D array, or tuple, a - SISO system description is assumed. `d` can also be a 2D array, in - which case a MIMO response is created. To call the copy - constructor, call `FrequencyResponseData(sys)`, where `sys` is a - FRD object. The timebase for the frequency response can be - provided using an optional third argument or the `dt` keyword. + The default constructor is `FrequencyResponseData(response, omega)`, + where `omega` is an iterable of frequency points and `response` is + the matching frequency data. If `response` is a single list, 1D + array, or tuple, a SISO system description is assumed. `response` + can also be a 2D array, in which case a MIMO response is created. + To call the copy constructor, call `FrequencyResponseData(sys)`, + where `sys` is a FRD object. The timebase for the frequency + response can be provided using an optional third argument or the + `dt` keyword. To construct frequency response data for an existing LTI object, other than an FRD, call `FrequencyResponseData(sys, omega)`. This @@ -228,10 +232,10 @@ def __init__(self, *args, **kwargs): # calculate frequency response at specified points if otherlti.isctime(): s = 1j * self.omega - self.fresp = otherlti(s, squeeze=False) + self.frdata = otherlti(s, squeeze=False) else: z = np.exp(1j * self.omega * otherlti.dt) - self.fresp = otherlti(z, squeeze=False) + self.frdata = otherlti(z, squeeze=False) arg_dt = otherlti.dt # Copy over signal and system names, if not specified @@ -244,12 +248,12 @@ def __init__(self, *args, **kwargs): else: # The user provided a response and a freq vector - self.fresp = array(args[0], dtype=complex, ndmin=1) - if self.fresp.ndim == 1: - self.fresp = self.fresp.reshape(1, 1, -1) + self.frdata = array(args[0], dtype=complex, ndmin=1) + if self.frdata.ndim == 1: + self.frdata = self.frdata.reshape(1, 1, -1) self.omega = array(args[1], dtype=float, ndmin=1) - if self.fresp.ndim != 3 or self.omega.ndim != 1 or \ - self.fresp.shape[-1] != self.omega.shape[-1]: + if self.frdata.ndim != 3 or self.omega.ndim != 1 or \ + self.frdata.shape[-1] != self.omega.shape[-1]: raise TypeError( "The frequency data constructor needs a 1-d or 3-d" " response data array and a matching frequency vector" @@ -263,7 +267,7 @@ def __init__(self, *args, **kwargs): "The one-argument constructor can only take in" " an FRD object. Received %s." % type(args[0])) self.omega = args[0].omega - self.fresp = args[0].fresp + self.frdata = args[0].frdata arg_dt = args[0].dt # Copy over signal and system names, if not specified @@ -299,9 +303,9 @@ def __init__(self, *args, **kwargs): raise ValueError("unknown squeeze value") defaults = { - 'inputs': self.fresp.shape[1] if not getattr( + 'inputs': self.frdata.shape[1] if not getattr( self, 'input_index', None) else self.input_labels, - 'outputs': self.fresp.shape[0] if not getattr( + 'outputs': self.frdata.shape[0] if not getattr( self, 'output_index', None) else self.output_labels, 'name': getattr(self, 'name', None)} if arg_dt is not None: @@ -322,14 +326,14 @@ def __init__(self, *args, **kwargs): raise ValueError("can't smooth with only 1 frequency") degree = 3 if self.omega.size > 3 else self.omega.size - 1 - self._ifunc = empty((self.fresp.shape[0], self.fresp.shape[1]), + self._ifunc = empty((self.frdata.shape[0], self.frdata.shape[1]), dtype=tuple) - for i in range(self.fresp.shape[0]): - for j in range(self.fresp.shape[1]): + for i in range(self.frdata.shape[0]): + for j in range(self.frdata.shape[1]): self._ifunc[i, j], u = splprep( - u=self.omega, x=[real(self.fresp[i, j, :]), - imag(self.fresp[i, j, :])], - w=1.0/(absolute(self.fresp[i, j, :]) + 0.001), + u=self.omega, x=[real(self.frdata[i, j, :]), + imag(self.frdata[i, j, :])], + w=1.0/(absolute(self.frdata[i, j, :]) + 0.001), s=0.0, k=degree) else: self._ifunc = None @@ -357,8 +361,10 @@ def magnitude(self): :type: 1D, 2D, or 3D array """ + frdata = _process_frequency_response( + self, self.omega, self.frdata, squeeze=self.squeeze) return NamedSignal( - np.abs(self.fresp), self.output_labels, self.input_labels) + np.abs(frdata), self.output_labels, self.input_labels) @property def phase(self): @@ -376,8 +382,10 @@ def phase(self): :type: 1D, 2D, or 3D array """ + frdata = _process_frequency_response( + self, self.omega, self.frdata, squeeze=self.squeeze) return NamedSignal( - np.angle(self.fresp), self.output_labels, self.input_labels) + np.angle(frdata), self.output_labels, self.input_labels) @property def frequency(self): @@ -389,7 +397,7 @@ def frequency(self): return self.omega @property - def response(self): + def complex(self): """Complex value of the frequency response. Value of the frequency response as a complex number, indexed by @@ -404,8 +412,20 @@ def response(self): :type: 1D, 2D, or 3D array """ + frdata = _process_frequency_response( + self, self.omega, self.frdata, squeeze=self.squeeze) return NamedSignal( - self.fresp, self.output_labels, self.input_labels) + frdata, self.output_labels, self.input_labels) + + @property + def response(self): + warn("response property is deprecated; use complex", FutureWarning) + return self.complex + + @property + def fresp(self): + warn("fresp attribute is deprecated; use frdata", FutureWarning) + return self.frdata def __str__(self): @@ -426,15 +446,15 @@ def __str__(self): outstr.extend( [sp + '%12.3f %10.4g%+10.4gj' % (w, re, im) for w, re, im in zip(self.omega, - real(self.fresp[j, i, :]), - imag(self.fresp[j, i, :]))]) + real(self.frdata[j, i, :]), + imag(self.frdata[j, i, :]))]) return '\n'.join(outstr) def _repr_eval_(self): # Loadable format out = "FrequencyResponseData(\n{d},\n{w}{smooth}".format( - d=repr(self.fresp), w=repr(self.omega), + d=repr(self.frdata), w=repr(self.omega), smooth=(self._ifunc and ", smooth=True") or "") out += self._dt_repr() @@ -447,7 +467,7 @@ def _repr_eval_(self): def __neg__(self): """Negate a transfer function.""" - return FRD(-self.fresp, self.omega) + return FRD(-self.frdata, self.omega) def __add__(self, other): """Add two LTI objects (parallel connection).""" @@ -484,7 +504,7 @@ def __add__(self, other): "The first summand has %i output(s), but the " \ "second has %i." % (self.noutputs, other.noutputs)) - return FRD(self.fresp + other.fresp, other.omega) + return FRD(self.frdata + other.frdata, other.omega) def __radd__(self, other): """Right add two LTI objects (parallel connection).""" @@ -506,7 +526,7 @@ def __mul__(self, other): # Convert the second argument to a transfer function. if isinstance(other, (int, float, complex, np.number)): - return FRD(self.fresp * other, self.omega, + return FRD(self.frdata * other, self.omega, smooth=(self._ifunc is not None)) else: other = _convert_to_frd(other, omega=self.omega) @@ -526,11 +546,11 @@ def __mul__(self, other): inputs = other.ninputs outputs = self.noutputs - fresp = empty((outputs, inputs, len(self.omega)), - dtype=self.fresp.dtype) + frdata = empty((outputs, inputs, len(self.omega)), + dtype=self.frdata.dtype) for i in range(len(self.omega)): - fresp[:, :, i] = self.fresp[:, :, i] @ other.fresp[:, :, i] - return FRD(fresp, self.omega, + frdata[:, :, i] = self.frdata[:, :, i] @ other.frdata[:, :, i] + return FRD(frdata, self.omega, smooth=(self._ifunc is not None) and (other._ifunc is not None)) @@ -539,7 +559,7 @@ def __rmul__(self, other): # Convert the second argument to an frd function. if isinstance(other, (int, float, complex, np.number)): - return FRD(self.fresp * other, self.omega, + return FRD(self.frdata * other, self.omega, smooth=(self._ifunc is not None)) else: other = _convert_to_frd(other, omega=self.omega) @@ -560,11 +580,11 @@ def __rmul__(self, other): inputs = self.ninputs outputs = other.noutputs - fresp = empty((outputs, inputs, len(self.omega)), - dtype=self.fresp.dtype) + frdata = empty((outputs, inputs, len(self.omega)), + dtype=self.frdata.dtype) for i in range(len(self.omega)): - fresp[:, :, i] = other.fresp[:, :, i] @ self.fresp[:, :, i] - return FRD(fresp, self.omega, + frdata[:, :, i] = other.frdata[:, :, i] @ self.frdata[:, :, i] + return FRD(frdata, self.omega, smooth=(self._ifunc is not None) and (other._ifunc is not None)) @@ -573,7 +593,7 @@ def __truediv__(self, other): """Divide two LTI objects.""" if isinstance(other, (int, float, complex, np.number)): - return FRD(self.fresp * (1/other), self.omega, + return FRD(self.frdata * (1/other), self.omega, smooth=(self._ifunc is not None)) else: other = _convert_to_frd(other, omega=self.omega) @@ -582,7 +602,7 @@ def __truediv__(self, other): # FRD.__truediv__ is currently only implemented for SISO systems return NotImplemented - return FRD(self.fresp/other.fresp, self.omega, + return FRD(self.frdata/other.frdata, self.omega, smooth=(self._ifunc is not None) and (other._ifunc is not None)) @@ -590,7 +610,7 @@ def __truediv__(self, other): def __rtruediv__(self, other): """Right divide two LTI objects.""" if isinstance(other, (int, float, complex, np.number)): - return FRD(other / self.fresp, self.omega, + return FRD(other / self.frdata, self.omega, smooth=(self._ifunc is not None)) else: other = _convert_to_frd(other, omega=self.omega) @@ -605,12 +625,12 @@ def __pow__(self, other): if not type(other) == int: raise ValueError("Exponent must be an integer") if other == 0: - return FRD(ones(self.fresp.shape), self.omega, + return FRD(ones(self.frdata.shape), self.omega, smooth=(self._ifunc is not None)) # unity if other > 0: return self * (self**(other-1)) if other < 0: - return (FRD(ones(self.fresp.shape), self.omega) / self) * \ + return (FRD(ones(self.frdata.shape), self.omega) / self) * \ (self**(other+1)) # Define the `eval` function to evaluate an FRD at a given (real) @@ -621,36 +641,36 @@ def __pow__(self, other): # update Sawyer B. Fuller 2020.08.14: __call__ added to provide a uniform # interface to systems in general and the lti.frequency_response method def eval(self, omega, squeeze=None): - """Evaluate a transfer function at angular frequency omega. + """Evaluate a transfer function at a frequency point. Note that a "normal" FRD only returns values for which there is an - entry in the omega vector. An interpolating FRD can return + entry in the `omega` vector. An interpolating FRD can return intermediate values. Parameters ---------- omega : float or 1D array_like - Frequencies in radians per second. + Frequency(s) for evaluation, in radians per second. squeeze : bool, optional - If `squeeze` = True, remove single-dimensional entries from - the shape of the output even if the system is not SISO. If - `squeeze` = False, keep all indices (output, input and, if omega - is array_like, frequency) even if the system is SISO. The - default value can be set using + If `squeeze` = True, remove single-dimensional entries from the + shape of the output even if the system is not SISO. If + `squeeze` = False, keep all indices (output, input and, if + `omega` is array_like, frequency) even if the system is + SISO. The default value can be set using `config.defaults['control.squeeze_frequency_response']`. Returns ------- - fresp : complex ndarray + frdata : complex ndarray The frequency response of the system. If the system is SISO and `squeeze` is not True, the shape of the array matches the - shape of omega. If the system is not SISO or `squeeze` is + shape of `omega`. If the system is not SISO or `squeeze` is False, the first two dimensions of the array are indices for - the output and input and the remaining dimensions match omega. + the output and input and the remaining dimensions match `omega`. If `squeeze` is True then single-dimensional axes are removed. """ - omega_array = np.array(omega, ndmin=1) # array_like version of omega + omega_array = np.array(omega, ndmin=1) # array of frequencies # Make sure that we are operating on a simple list if len(omega_array.shape) > 1: @@ -658,16 +678,16 @@ def eval(self, omega, squeeze=None): # Make sure that frequencies are all real-valued if any(omega_array.imag > 0): - raise ValueError("FRD.eval can only accept real-valued omega") + raise ValueError("eval can only accept real-valued frequencies") if self._ifunc is None: elements = np.isin(self.omega, omega) # binary array if sum(elements) < len(omega_array): raise ValueError( - "not all frequencies omega are in frequency list of FRD " + "not all frequencies are in frequency list of FRD " "system. Try an interpolating FRD for additional points.") else: - out = self.fresp[:, :, elements] + out = self.frdata[:, :, elements] else: out = empty((self.noutputs, self.ninputs, len(omega_array)), dtype=complex) @@ -712,16 +732,16 @@ def __call__(self, x=None, squeeze=None, return_magphase=None): Squeeze output, as described below. Default value can be set using `config.defaults['control.squeeze_frequency_response']`. return_magphase : bool, optional - If True, then a frequency response data object will - enumerate as a tuple of the form ``(mag, phase, omega)`` where - where `mag` is the magnitude (absolute value, not dB or log10) - of the system frequency response, `phase` is the wrapped phase - in radians of the system frequency response, and `omega` is + (`x` = None only) If True, then a frequency response data object + will enumerate as a tuple of the form ``(mag, phase, omega)`` + where where `mag` is the magnitude (absolute value, not dB or + log10) of the system frequency response, `phase` is the wrapped + phase in radians of the system frequency response, and `omega` is the (sorted) frequencies at which the response was evaluated. Returns ------- - fresp : complex ndarray + frdata : complex ndarray The value of the system transfer function at `x`. If the system is SISO and `squeeze` is not True, the shape of the array matches the shape of `x`. If the system is not SISO or `squeeze` is @@ -748,6 +768,9 @@ def __call__(self, x=None, squeeze=None, return_magphase=None): return response + if return_magphase is not None: + raise ValueError("return_magphase not allowed when x != None") + # Make sure that we are operating on a simple list if len(np.atleast_1d(x).shape) > 1: raise ValueError("input list must be 1D") @@ -764,14 +787,14 @@ def __call__(self, x=None, squeeze=None, return_magphase=None): # Implement iter to allow assigning to a tuple def __iter__(self): - fresp = _process_frequency_response( - self, self.omega, self.fresp, squeeze=self.squeeze) + frdata = _process_frequency_response( + self, self.omega, self.frdata, squeeze=self.squeeze) if self._return_singvals: # Legacy processing for singular values - return iter((self.fresp[:, 0, :], self.omega)) + return iter((self.frdata[:, 0, :], self.omega)) elif not self.return_magphase: - return iter((self.omega, fresp)) - return iter((np.abs(fresp), np.angle(fresp), self.omega)) + return iter((self.omega, frdata)) + return iter((np.abs(frdata), np.angle(frdata), self.omega)) def __getitem__(self, key): if not isinstance(key, Iterable) or len(key) != 2: @@ -780,7 +803,7 @@ def __getitem__(self, key): # Convert signal names to integer offsets (via NamedSignal object) iomap = NamedSignal( - self.fresp[:, :, 0], self.output_labels, self.input_labels) + self.frdata[:, :, 0], self.output_labels, self.input_labels) indices = iomap._parse_key(key, level=1) # ignore index checks outdx, outputs = _process_subsys_index(indices[0], self.output_labels) inpdx, inputs = _process_subsys_index(indices[1], self.input_labels) @@ -790,7 +813,7 @@ def __getitem__(self, key): self.name + config.defaults['iosys.indexed_system_name_suffix'] return FrequencyResponseData( - self.fresp[outdx, :][:, inpdx], self.omega, self.dt, + self.frdata[outdx, :][:, inpdx], self.omega, self.dt, inputs=inputs, outputs=outputs, name=sysname) # Implement (thin) len to emulate legacy testing interface @@ -834,13 +857,13 @@ def feedback(self, other=1, sign=-1): # TODO: handle omega re-mapping # reorder array axes in order to leverage numpy broadcasting - myfresp = np.moveaxis(self.fresp, 2, 0) - otherfresp = np.moveaxis(other.fresp, 2, 0) - I_AB = eye(self.ninputs)[np.newaxis, :, :] + otherfresp @ myfresp - resfresp = (myfresp @ linalg.inv(I_AB)) - fresp = np.moveaxis(resfresp, 0, 2) + myfrdata = np.moveaxis(self.frdata, 2, 0) + otherfrdata = np.moveaxis(other.frdata, 2, 0) + I_AB = eye(self.ninputs)[np.newaxis, :, :] + otherfrdata @ myfrdata + resfrdata = (myfrdata @ linalg.inv(I_AB)) + frdata = np.moveaxis(resfrdata, 0, 2) - return FRD(fresp, other.omega, smooth=(self._ifunc is not None)) + return FRD(frdata, other.omega, smooth=(self._ifunc is not None)) def append(self, other): """Append a second model to the present model. @@ -864,15 +887,15 @@ def append(self, other): # TODO: handle omega re-mapping - new_fresp = np.zeros( + new_frdata = np.zeros( (self.noutputs + other.noutputs, self.ninputs + other.ninputs, self.omega.shape[-1]), dtype=complex) - new_fresp[:self.noutputs, :self.ninputs, :] = np.reshape( - self.fresp, (self.noutputs, self.ninputs, -1)) - new_fresp[self.noutputs:, self.ninputs:, :] = np.reshape( - other.fresp, (other.noutputs, other.ninputs, -1)) + new_frdata[:self.noutputs, :self.ninputs, :] = np.reshape( + self.frdata, (self.noutputs, self.ninputs, -1)) + new_frdata[self.noutputs:, self.ninputs:, :] = np.reshape( + other.frdata, (other.noutputs, other.ninputs, -1)) - return FRD(new_fresp, self.omega, smooth=(self._ifunc is not None)) + return FRD(new_frdata, self.omega, smooth=(self._ifunc is not None)) # Plotting interface def plot(self, plot_type=None, *args, **kwargs): @@ -916,7 +939,7 @@ def to_pandas(self): # Create a dict for setting up the data frame data = {'omega': self.omega} data.update( - {'H_{%s, %s}' % (out, inp): self.fresp[i, j] \ + {'H_{%s, %s}' % (out, inp): self.frdata[i, j] \ for i, out in enumerate(self.output_labels) \ for j, inp in enumerate(self.input_labels)}) @@ -939,11 +962,11 @@ def to_pandas(self): def _convert_to_frd(sys, omega, inputs=1, outputs=1): """Convert a system to frequency response data form (if needed). - If `sys` is already a frequency response data object, and its - frequency range matches or overlaps the range given in omega then - it is returned. If `sys` is another LTI object or a transfer - function, then it is converted to a frequency response data at the - specified omega. If `sys` is a scalar, then the number of inputs and + If `sys` is already a frequency response data object, and its frequency + range matches or overlaps the range given in `omega` then it is + returned. If `sys` is another LTI object or a transfer function, then + it is converted to a frequency response data system at the specified + values in `omega`. If `sys` is a scalar, then the number of inputs and outputs can be specified manually, as in: >>> import numpy as np @@ -976,26 +999,26 @@ def _convert_to_frd(sys, omega, inputs=1, outputs=1): elif isinstance(sys, LTI): omega = np.sort(omega) if sys.isctime(): - fresp = sys(1j * omega) + frdata = sys(1j * omega) else: - fresp = sys(np.exp(1j * omega * sys.dt)) - if len(fresp.shape) == 1: - fresp = fresp[np.newaxis, np.newaxis, :] - return FRD(fresp, omega, smooth=True) + frdata = sys(np.exp(1j * omega * sys.dt)) + if len(frdata.shape) == 1: + frdata = frdata[np.newaxis, np.newaxis, :] + return FRD(frdata, omega, smooth=True) elif isinstance(sys, (int, float, complex, np.number)): - fresp = ones((outputs, inputs, len(omega)), dtype=float)*sys - return FRD(fresp, omega, smooth=True) + frdata = ones((outputs, inputs, len(omega)), dtype=float)*sys + return FRD(frdata, omega, smooth=True) # try converting constant matrices try: sys = array(sys) outputs, inputs = sys.shape - fresp = empty((outputs, inputs, len(omega)), dtype=float) + frdata = empty((outputs, inputs, len(omega)), dtype=float) for i in range(outputs): for j in range(inputs): - fresp[i, j, :] = sys[i, j] - return FRD(fresp, omega, smooth=True) + frdata[i, j, :] = sys[i, j] + return FRD(frdata, omega, smooth=True) except Exception: pass @@ -1004,14 +1027,14 @@ def _convert_to_frd(sys, omega, inputs=1, outputs=1): def frd(*args, **kwargs): - """frd(response, omega[, dt]) + """frd(frdata, omega[, dt]) Construct a frequency response data (FRD) model. A frequency response data model stores the (measured) frequency response of a system. This factory function can be called in different ways: - ``frd(response, omega)`` + ``frd(frdata, omega)`` Create an frd model with the given response data, in the form of complex response vector, at matching frequencies `omega` [in rad/s]. @@ -1023,11 +1046,11 @@ def frd(*args, **kwargs): Parameters ---------- - sys : `StateSpace` or `TransferFunction` - A linear system that will be evaluated for frequency response data. - response : array_like or LTI system + frdata : array_like or LTI system Complex vector with the system response or an LTI system that can be used to compute the frequency response at a list of frequencies. + sys : `StateSpace` or `TransferFunction` + A linear system that will be evaluated for frequency response data. omega : array_like Vector of frequencies at which the response is evaluated. dt : float, True, or None diff --git a/control/freqplot.py b/control/freqplot.py index 057426d7b..58b11416c 100644 --- a/control/freqplot.py +++ b/control/freqplot.py @@ -50,6 +50,7 @@ 'freqplot.grid': True, # Turn on grid for gain and phase 'freqplot.wrap_phase': False, # Wrap the phase plot at a given value 'freqplot.freq_label': "Frequency [{units}]", + 'freqplot.magnitude_label': "Magnitude", 'freqplot.share_magnitude': 'row', 'freqplot.share_phase': 'row', 'freqplot.share_frequency': 'col', @@ -289,7 +290,9 @@ def bode_plot( freq_label = config._get_param( 'freqplot', 'freq_label', kwargs, _freqplot_defaults, pop=True) if magnitude_label is None: - magnitude_label = "Magnitude [dB]" if dB else "Magnitude" + magnitude_label = config._get_param( + 'freqplot', 'magnitude_label', kwargs, + _freqplot_defaults, pop=True) + (" [dB]" if dB else "") if phase_label is None: phase_label = "Phase [deg]" if deg else "Phase [rad]" @@ -406,8 +409,8 @@ def bode_plot( phase = phase.reshape(response.magnitude.shape) # Save the data for later use (legacy return values) - mag_data.append(response.magnitude) - phase_data.append(phase) + mag_data.append(response.magnitude.reshape(noutputs, ninputs, -1)) + phase_data.append(phase.reshape(noutputs, ninputs, -1)) omega_data.append(response.omega) # @@ -1286,7 +1289,7 @@ def nyquist_response( >>> G = ct.zpk([], [-1, -2, -3], gain=100) >>> response = ct.nyquist_response(G) >>> count = response.count - >>> lines = response.plot() + >>> cplt = response.plot() """ # Create unified list of keyword arguments @@ -2162,7 +2165,7 @@ def gangof4_response( >>> P = ct.tf([1], [1, 1]) >>> C = ct.tf([2], [1]) >>> response = ct.gangof4_response(P, C) - >>> lines = response.plot() + >>> cplt = response.plot() """ if not P.issiso() or not C.issiso(): @@ -2336,7 +2339,7 @@ def singular_values_response( svd_responses = [] for response in responses: # Compute the singular values (permute indices to make things work) - fresp_permuted = response.fresp.transpose((2, 0, 1)) + fresp_permuted = response.frdata.transpose((2, 0, 1)) sigma = np.linalg.svd(fresp_permuted, compute_uv=False).transpose() sigma_fresp = sigma.reshape(sigma.shape[0], 1, sigma.shape[1]) @@ -2503,12 +2506,12 @@ def singular_values_plot( "deprecated; use singular_values_response()", FutureWarning) # Warn the user if we got past something that is not real-valued - if any([not np.allclose(np.imag(response.fresp[:, 0, :]), 0) + if any([not np.allclose(np.imag(response.frdata[:, 0, :]), 0) for response in responses]): warnings.warn("data has non-zero imaginary component") # Extract the data we need for plotting - sigmas = [np.real(response.fresp[:, 0, :]) for response in responses] + sigmas = [np.real(response.frdata[:, 0, :]) for response in responses] omegas = [response.omega for response in responses] # Legacy processing for no plotting case diff --git a/control/iosys.py b/control/iosys.py index bbd3bb4c1..7e129c70b 100644 --- a/control/iosys.py +++ b/control/iosys.py @@ -70,7 +70,8 @@ def _parse_key(self, key, labels=None, level=0): if isinstance(key, str): key = labels.index(item := key) if level == 0 and len(self.data_shape) < 2: - raise ControlIndexError + # This is the only signal => use it + return () elif isinstance(key, list): keylist = [] for item in key: # use for loop to save item for error diff --git a/control/rlocus.py b/control/rlocus.py index 9773abe7b..355731ce8 100644 --- a/control/rlocus.py +++ b/control/rlocus.py @@ -9,7 +9,6 @@ # # Sawyer B. Fuller (minster@uw.edu) 21 May 2020: added compatibility # with discrete-time systems. -# """Code for computing a root locus plot.""" diff --git a/control/tests/config_test.py b/control/tests/config_test.py index c214526cd..646a20a16 100644 --- a/control/tests/config_test.py +++ b/control/tests/config_test.py @@ -108,6 +108,9 @@ def test_fbs_bode(self, mplcleanup): np.testing.assert_almost_equal(mag_x[0], 0.001, decimal=6) np.testing.assert_almost_equal(mag_y[0], 10, decimal=3) + # Make sure x-axis label is Gain + assert mag_axis.get_ylabel() == "Gain" + # Get the phase line phase_axis = plt.gcf().axes[1] phase_line = phase_axis.get_lines() @@ -153,6 +156,9 @@ def test_matlab_bode(self, mplcleanup): np.testing.assert_almost_equal(mag_x[0], 0.001, decimal=6) np.testing.assert_almost_equal(mag_y[0], 20*log10(10), decimal=3) + # Make sure x-axis label is Gain + assert mag_axis.get_ylabel() == "Magnitude [dB]" + # Get the phase line phase_axis = plt.gcf().axes[1] phase_line = phase_axis.get_lines() diff --git a/control/tests/docstrings_test.py b/control/tests/docstrings_test.py index d1747486e..3b7de8b8c 100644 --- a/control/tests/docstrings_test.py +++ b/control/tests/docstrings_test.py @@ -340,7 +340,7 @@ def test_deprecated_functions(module, prefix): fs = control.flatsys # Dictionary of factory functions associated with primary classes -class_factory_function = { +iosys_class_factory_function = { fs.FlatSystem: fs.flatsys, ct.FrequencyResponseData: ct.frd, ct.InterconnectedSystem: ct.interconnect, @@ -357,9 +357,9 @@ def test_deprecated_functions(module, prefix): # arguments should be documented in the factory functions and do not need # to be duplicated in the class documentation (=> don't list here). # -class_args = { +iosys_class_args = { fs.FlatSystem: ['forward', 'reverse'], - ct.FrequencyResponseData: ['response', 'omega', 'dt'], + ct.FrequencyResponseData: ['frdata', 'omega', 'dt'], ct.NonlinearIOSystem: [ 'updfcn', 'outfcn', 'inputs', 'outputs', 'states', 'params', 'dt'], ct.StateSpace: ['A', 'B', 'C', 'D', 'dt'], @@ -376,16 +376,16 @@ def test_deprecated_functions(module, prefix): # in the class docstring. # # Attributes that are part of all I/O system classes should be listed in -# `std_class_attributes`. Attributes that are not commonly needed are +# `std_iosys_class_attributes`. Attributes that are not commonly needed are # defined as part of a parent class can just be documented there, and # should be listed in `iosys_parent_attributes` (these will be searched # using the MRO). -std_class_attributes = [ +std_iosys_class_attributes = [ 'ninputs', 'noutputs', 'input_labels', 'output_labels', 'name', 'shape'] # List of attributes defined for specific I/O systems -class_attributes = { +iosys_class_attributes = { fs.FlatSystem: [], ct.FrequencyResponseData: [], ct.NonlinearIOSystem: ['nstates', 'state_labels'], @@ -428,8 +428,8 @@ def test_deprecated_functions(module, prefix): @pytest.mark.parametrize( "cls, fcn, args", - [(cls, class_factory_function[cls], class_args[cls]) - for cls in class_args.keys()]) + [(cls, iosys_class_factory_function[cls], iosys_class_args[cls]) + for cls in iosys_class_args.keys()]) def test_iosys_primary_classes(cls, fcn, args): docstring = inspect.getdoc(cls) with warnings.catch_warnings(): @@ -438,7 +438,8 @@ def test_iosys_primary_classes(cls, fcn, args): _check_numpydoc_style(cls, doc) # Make sure the typical arguments are there - for argname in args + std_class_attributes + class_attributes[cls]: + for argname in args + std_iosys_class_attributes + \ + iosys_class_attributes[cls]: _check_parameter_docs(cls.__name__, argname, docstring) # Make sure we reference the factory function @@ -473,9 +474,9 @@ def test_iosys_primary_classes(cls, fcn, args): f"'{argname}'") -@pytest.mark.parametrize("cls", class_args.keys()) +@pytest.mark.parametrize("cls", iosys_class_args.keys()) def test_iosys_attribute_lists(cls, ignore_future_warning): - fcn = class_factory_function[cls] + fcn = iosys_class_factory_function[cls] # Create a system that we can scan for attributes sys = ct.rss(2, 1, 1) @@ -487,6 +488,7 @@ def test_iosys_attribute_lists(cls, ignore_future_warning): case ct.frd: sys = ct.frd(sys, [0.1, 1, 10]) ignore_args = ['state_labels'] + ignore_args += ['fresp', 'response'] # deprecated case ct.interconnect: sys = ct.nlsys(sys, name='sys') sys = ct.interconnect([sys], inplist='sys.u', outlist='sys.y') @@ -497,16 +499,19 @@ def test_iosys_attribute_lists(cls, ignore_future_warning): sys = fs.flatsys(sys.forward, sys.reverse) docstring = inspect.getdoc(cls) - for name, obj in inspect.getmembers(sys): + for name, value in inspect.getmembers(sys): + if name.startswith('_') or name in ignore_args or \ + inspect.ismethod(value): + # Skip hidden and ignored attributes; methods checked elsewhere + continue + + # Get the object associated with this attribute + obj = getattr(cls, name, getattr(sys, name)) if getattr(obj, '__module__', None): objname = ".".join([obj.__module__.removeprefix("control."), name]) else: objname = name - if name.startswith('_') or inspect.ismethod(obj) or name in ignore_args: - # Skip hidden variables; class methods are checked elsewhere - continue - # Try to find documentation in primary class if _check_parameter_docs( cls.__name__, name, docstring, fail_if_missing=False): @@ -583,15 +588,15 @@ def test_iosys_factory_functions(fcn): doc = npd.FunctionDoc(fcn) _check_numpydoc_style(fcn, doc) - cls = list(class_factory_function.keys())[ - list(class_factory_function.values()).index(fcn)] + cls = list(iosys_class_factory_function.keys())[ + list(iosys_class_factory_function.values()).index(fcn)] # Make sure we reference parameters in class and factory function docstring - for argname in class_args[cls] + std_factory_args + factory_args[fcn]: + for argname in iosys_class_args[cls] + std_factory_args + factory_args[fcn]: _check_parameter_docs(fcn.__name__, argname, docstring) # Make sure we don't reference any class attributes - for argname in std_class_attributes + class_attributes[cls]: + for argname in std_iosys_class_attributes + iosys_class_attributes[cls]: if argname in std_factory_args: continue if re.search(f"[\\s]+{argname}(, .*)*[\\s]*:", docstring) is not None: @@ -900,12 +905,12 @@ def test_check_parameter_docs(docstring, exception, match): test_deprecated_functions for cls, fcn, args in [ - (cls, class_factory_function[cls], class_args[cls]) - for cls in class_args.keys()]: + (cls, iosys_class_factory_function[cls], iosys_class_args[cls]) + for cls in iosys_class_args.keys()]: _info(f"--- test_iosys_primary_classes(): {cls.__name__} ----", 0) test_iosys_primary_classes(cls, fcn, args) - for cls in class_args.keys(): + for cls in iosys_class_args.keys(): _info(f"--- test_iosys_attribute_lists(): {cls.__name__} ----", 0) with warnings.catch_warnings(): warnings.simplefilter('ignore', FutureWarning) diff --git a/control/tests/frd_test.py b/control/tests/frd_test.py index 9cceff389..54cc94b51 100644 --- a/control/tests/frd_test.py +++ b/control/tests/frd_test.py @@ -206,10 +206,10 @@ def testAppendSiso(self): d_app_2[2, 2, :] = d3 # Test appending two FRDs frd_app_1 = frd1.append(frd2) - np.testing.assert_allclose(d_app_1, frd_app_1.fresp) + np.testing.assert_allclose(d_app_1, frd_app_1.frdata) # Test appending three FRDs frd_app_2 = frd1.append(frd2).append(frd3) - np.testing.assert_allclose(d_app_2, frd_app_2.fresp) + np.testing.assert_allclose(d_app_2, frd_app_2.frdata) def testAppendMimo(self): # Create frequency responses @@ -232,10 +232,10 @@ def testAppendMimo(self): d_app_2[5:, 3:, :] = d3 # Test appending two FRDs frd_app_1 = frd1.append(frd2) - np.testing.assert_allclose(d_app_1, frd_app_1.fresp) + np.testing.assert_allclose(d_app_1, frd_app_1.frdata) # Test appending three FRDs frd_app_2 = frd1.append(frd2).append(frd3) - np.testing.assert_allclose(d_app_2, frd_app_2.fresp) + np.testing.assert_allclose(d_app_2, frd_app_2.frdata) def testAuto(self): omega = np.logspace(-1, 2, 10) @@ -417,47 +417,47 @@ def test_operator_conversion(self): sys_add = frd_tf + 2 chk_add = frd_tf + frd_2 np.testing.assert_array_almost_equal(sys_add.omega, chk_add.omega) - np.testing.assert_array_almost_equal(sys_add.fresp, chk_add.fresp) + np.testing.assert_array_almost_equal(sys_add.frdata, chk_add.frdata) sys_radd = 2 + frd_tf chk_radd = frd_2 + frd_tf np.testing.assert_array_almost_equal(sys_radd.omega, chk_radd.omega) - np.testing.assert_array_almost_equal(sys_radd.fresp, chk_radd.fresp) + np.testing.assert_array_almost_equal(sys_radd.frdata, chk_radd.frdata) sys_sub = frd_tf - 2 chk_sub = frd_tf - frd_2 np.testing.assert_array_almost_equal(sys_sub.omega, chk_sub.omega) - np.testing.assert_array_almost_equal(sys_sub.fresp, chk_sub.fresp) + np.testing.assert_array_almost_equal(sys_sub.frdata, chk_sub.frdata) sys_rsub = 2 - frd_tf chk_rsub = frd_2 - frd_tf np.testing.assert_array_almost_equal(sys_rsub.omega, chk_rsub.omega) - np.testing.assert_array_almost_equal(sys_rsub.fresp, chk_rsub.fresp) + np.testing.assert_array_almost_equal(sys_rsub.frdata, chk_rsub.frdata) sys_mul = frd_tf * 2 chk_mul = frd_tf * frd_2 np.testing.assert_array_almost_equal(sys_mul.omega, chk_mul.omega) - np.testing.assert_array_almost_equal(sys_mul.fresp, chk_mul.fresp) + np.testing.assert_array_almost_equal(sys_mul.frdata, chk_mul.frdata) sys_rmul = 2 * frd_tf chk_rmul = frd_2 * frd_tf np.testing.assert_array_almost_equal(sys_rmul.omega, chk_rmul.omega) - np.testing.assert_array_almost_equal(sys_rmul.fresp, chk_rmul.fresp) + np.testing.assert_array_almost_equal(sys_rmul.frdata, chk_rmul.frdata) sys_rdiv = 2 / frd_tf chk_rdiv = frd_2 / frd_tf np.testing.assert_array_almost_equal(sys_rdiv.omega, chk_rdiv.omega) - np.testing.assert_array_almost_equal(sys_rdiv.fresp, chk_rdiv.fresp) + np.testing.assert_array_almost_equal(sys_rdiv.frdata, chk_rdiv.frdata) sys_pow = frd_tf**2 chk_pow = frd(sys_tf**2, np.logspace(-1, 1, 10)) np.testing.assert_array_almost_equal(sys_pow.omega, chk_pow.omega) - np.testing.assert_array_almost_equal(sys_pow.fresp, chk_pow.fresp) + np.testing.assert_array_almost_equal(sys_pow.frdata, chk_pow.frdata) sys_pow = frd_tf**-2 chk_pow = frd(sys_tf**-2, np.logspace(-1, 1, 10)) np.testing.assert_array_almost_equal(sys_pow.omega, chk_pow.omega) - np.testing.assert_array_almost_equal(sys_pow.fresp, chk_pow.fresp) + np.testing.assert_array_almost_equal(sys_pow.frdata, chk_pow.frdata) # Assertion error if we try to raise to a non-integer power with pytest.raises(ValueError): @@ -467,7 +467,7 @@ def test_operator_conversion(self): sys_add = frd_2 + sys_tf chk_add = frd_2 + frd_tf np.testing.assert_array_almost_equal(sys_add.omega, chk_add.omega) - np.testing.assert_array_almost_equal(sys_add.fresp, chk_add.fresp) + np.testing.assert_array_almost_equal(sys_add.frdata, chk_add.frdata) # Test broadcasting with SISO system sys_tf_mimo = TransferFunction([1], [1, 0]) * np.eye(2) @@ -475,7 +475,7 @@ def test_operator_conversion(self): result = FrequencyResponseData.__rmul__(frd_tf, frd_tf_mimo) expected = frd(sys_tf_mimo * sys_tf, np.logspace(-1, 1, 10)) np.testing.assert_array_almost_equal(expected.omega, result.omega) - np.testing.assert_array_almost_equal(expected.fresp, result.fresp) + np.testing.assert_array_almost_equal(expected.frdata, result.frdata) # Input/output mismatch size mismatch in rmul sys1 = frd(ct.rss(2, 2, 2), np.logspace(-1, 1, 10)) @@ -493,14 +493,14 @@ def test_add_sub_mimo_siso(self): sys_siso = frd(ct.rss(2, 1, 1), omega) for op, expected_fresp in [ - (FrequencyResponseData.__add__, sys_mimo.fresp + sys_siso.fresp), - (FrequencyResponseData.__radd__, sys_mimo.fresp + sys_siso.fresp), - (FrequencyResponseData.__sub__, sys_mimo.fresp - sys_siso.fresp), - (FrequencyResponseData.__rsub__, -sys_mimo.fresp + sys_siso.fresp), + (FrequencyResponseData.__add__, sys_mimo.frdata + sys_siso.frdata), + (FrequencyResponseData.__radd__, sys_mimo.frdata + sys_siso.frdata), + (FrequencyResponseData.__sub__, sys_mimo.frdata - sys_siso.frdata), + (FrequencyResponseData.__rsub__, -sys_mimo.frdata + sys_siso.frdata), ]: result = op(sys_mimo, sys_siso) np.testing.assert_array_almost_equal(omega, result.omega) - np.testing.assert_array_almost_equal(expected_fresp, result.fresp) + np.testing.assert_array_almost_equal(expected_fresp, result.frdata) @pytest.mark.parametrize( "left, right, expected", @@ -573,7 +573,7 @@ def test_mul_mimo_siso(self, left, right, expected): result = frd(left, np.logspace(-1, 1, 10)).__mul__(right) expected_frd = frd(expected, np.logspace(-1, 1, 10)) np.testing.assert_array_almost_equal(expected_frd.omega, result.omega) - np.testing.assert_array_almost_equal(expected_frd.fresp, result.fresp) + np.testing.assert_array_almost_equal(expected_frd.frdata, result.frdata) @slycotonly def test_truediv_mimo_siso(self): @@ -589,17 +589,17 @@ def test_truediv_mimo_siso(self): # Test division of MIMO FRD by SISO FRD result = frd_mimo.__truediv__(frd_siso) np.testing.assert_array_almost_equal(expected.omega, result.omega) - np.testing.assert_array_almost_equal(expected.fresp, result.fresp) + np.testing.assert_array_almost_equal(expected.frdata, result.frdata) # Test division of MIMO FRD by SISO TF result = frd_mimo.__truediv__(tf_siso) np.testing.assert_array_almost_equal(expected.omega, result.omega) - np.testing.assert_array_almost_equal(expected.fresp, result.fresp) + np.testing.assert_array_almost_equal(expected.frdata, result.frdata) # Test division of MIMO FRD by SISO TF result = frd_mimo.__truediv__(ss_siso) np.testing.assert_array_almost_equal(expected.omega, result.omega) - np.testing.assert_array_almost_equal(expected.fresp, result.fresp) + np.testing.assert_array_almost_equal(expected.frdata, result.frdata) @slycotonly def test_rtruediv_mimo_siso(self): @@ -615,17 +615,17 @@ def test_rtruediv_mimo_siso(self): # Test division of MIMO FRD by SISO FRD result = frd_siso.__rtruediv__(frd_mimo) np.testing.assert_array_almost_equal(expected.omega, result.omega) - np.testing.assert_array_almost_equal(expected.fresp, result.fresp) + np.testing.assert_array_almost_equal(expected.frdata, result.frdata) # Test division of MIMO TF by SISO FRD result = frd_siso.__rtruediv__(tf_mimo) np.testing.assert_array_almost_equal(expected.omega, result.omega) - np.testing.assert_array_almost_equal(expected.fresp, result.fresp) + np.testing.assert_array_almost_equal(expected.frdata, result.frdata) # Test division of MIMO SS by SISO FRD result = frd_siso.__rtruediv__(ss_mimo) np.testing.assert_array_almost_equal(expected.omega, result.omega) - np.testing.assert_array_almost_equal(expected.fresp, result.fresp) + np.testing.assert_array_almost_equal(expected.frdata, result.frdata) @pytest.mark.parametrize( @@ -699,7 +699,7 @@ def test_rmul_mimo_siso(self, left, right, expected): result = frd(right, np.logspace(-1, 1, 10)).__rmul__(left) expected_frd = frd(expected, np.logspace(-1, 1, 10)) np.testing.assert_array_almost_equal(expected_frd.omega, result.omega) - np.testing.assert_array_almost_equal(expected_frd.fresp, result.fresp) + np.testing.assert_array_almost_equal(expected_frd.frdata, result.frdata) def test_eval(self): sys_tf = ct.tf([1], [1, 2, 1]) @@ -725,6 +725,12 @@ def test_freqresp_deprecated(self): with pytest.warns(FutureWarning): frd_tf.freqresp(1.) + with pytest.warns(FutureWarning, match="use complex"): + np.testing.assert_equal(frd_tf.response, frd_tf.complex) + + with pytest.warns(FutureWarning, match="use frdata"): + np.testing.assert_equal(frd_tf.fresp, frd_tf.frdata) + def test_repr_str(self): # repr printing array = np.array @@ -732,7 +738,7 @@ def test_repr_str(self): [1.0, 0.9+0.1j, 0.1+2j, 0.05+3j], [0.1, 1.0, 10.0, 100.0], name='sys0') sys1 = ct.frd( - sys0.fresp, sys0.omega, smooth=True, name='sys1') + sys0.frdata, sys0.omega, smooth=True, name='sys1') ref_common = "FrequencyResponseData(\n" \ "array([[[1. +0.j , 0.9 +0.1j, 0.1 +2.j , 0.05+3.j ]]]),\n" \ "array([ 0.1, 1. , 10. , 100. ])," @@ -740,17 +746,17 @@ def test_repr_str(self): ref1 = ref_common + " smooth=True," + \ "\nname='sys1', outputs=1, inputs=1)" sysm = ct.frd( - np.matmul(array([[1], [2]]), sys0.fresp), sys0.omega, name='sysm') + np.matmul(array([[1], [2]]), sys0.frdata), sys0.omega, name='sysm') assert ct.iosys_repr(sys0, format='eval') == ref0 assert ct.iosys_repr(sys1, format='eval') == ref1 sys0r = eval(ct.iosys_repr(sys0, format='eval')) - np.testing.assert_array_almost_equal(sys0r.fresp, sys0.fresp) + np.testing.assert_array_almost_equal(sys0r.frdata, sys0.frdata) np.testing.assert_array_almost_equal(sys0r.omega, sys0.omega) sys1r = eval(ct.iosys_repr(sys1, format='eval')) - np.testing.assert_array_almost_equal(sys1r.fresp, sys1.fresp) + np.testing.assert_array_almost_equal(sys1r.frdata, sys1.frdata) np.testing.assert_array_almost_equal(sys1r.omega, sys1.omega) assert(sys1._ifunc is not None) @@ -837,7 +843,7 @@ def test_to_pandas(): # Check to make sure the data make senses np.testing.assert_equal(df['omega'], resp.omega) - np.testing.assert_equal(df['H_{y[0], u[0]}'], resp.fresp[0, 0]) + np.testing.assert_equal(df['H_{y[0], u[0]}'], resp.frdata[0, 0]) def test_frequency_response(): @@ -891,9 +897,9 @@ def test_signal_labels(): # Make sure access via strings works np.testing.assert_equal( - fresp.magnitude['y[0]'], fresp.magnitude[0]) + fresp.magnitude['y[0]'], fresp.magnitude) np.testing.assert_equal( - fresp.phase['y[0]'], fresp.phase[0]) + fresp.phase['y[0]'], fresp.phase) # Make sure errors are generated if key is unknown with pytest.raises(ValueError, match="unknown signal name 'bad'"): @@ -911,20 +917,20 @@ def test_signal_labels(): fresp.phase['y[0]', 'u[1]'], fresp.phase[0, 1]) np.testing.assert_equal( - fresp.response['y[0]', 'u[1]'], - fresp.response[0, 1]) + fresp.complex['y[0]', 'u[1]'], + fresp.complex[0, 1]) # Make sure access via lists of strings works np.testing.assert_equal( - fresp.response[['y[1]', 'y[0]'], 'u[0]'], - fresp.response[[1, 0], 0]) + fresp.complex[['y[1]', 'y[0]'], 'u[0]'], + fresp.complex[[1, 0], 0]) # Make sure errors are generated if key is unknown with pytest.raises(ValueError, match="unknown signal name 'bad'"): fresp.magnitude['bad'] with pytest.raises(ValueError, match="unknown signal name 'bad'"): - fresp.response[['y[1]', 'bad']] + fresp.complex[['y[1]', 'bad']] with pytest.raises(ValueError, match=r"unknown signal name 'y\[0\]'"): - fresp.response['y[1]', 'y[0]'] # second index = input name + fresp.complex['y[1]', 'y[0]'] # second index = input name diff --git a/control/tests/freqresp_test.py b/control/tests/freqresp_test.py index 6c91a730b..a268d38eb 100644 --- a/control/tests/freqresp_test.py +++ b/control/tests/freqresp_test.py @@ -673,7 +673,7 @@ def test_singular_values_plot(tsystem): sys = tsystem.sys for omega_ref, sigma_ref in zip(tsystem.omegas, tsystem.sigmas): response = singular_values_response(sys, omega_ref) - sigma = np.real(response.fresp[:, 0, :]) + sigma = np.real(response.frdata[:, 0, :]) np.testing.assert_almost_equal(sigma, sigma_ref) diff --git a/control/tests/iosys_test.py b/control/tests/iosys_test.py index 42b09ff25..1eb1a1fdf 100644 --- a/control/tests/iosys_test.py +++ b/control/tests/iosys_test.py @@ -1582,7 +1582,7 @@ def test_linear_interconnection(): # Make sure call works properly response = io_connect.frequency_response(1) np.testing.assert_allclose( - response.fresp[:, :, 0], io_connect.C @ np.linalg.inv( + response.frdata[:, :, 0], io_connect.C @ np.linalg.inv( 1j * np.eye(io_connect.nstates) - io_connect.A) @ io_connect.B + \ io_connect.D) @@ -2276,7 +2276,7 @@ def test_signal_indexing(): # Implicitly squeezed response resp = ct.step_response(ct.rss(4, 1, 1, strictly_proper=True)) - for key in ['y[0]', ('y[0]', 'u[0]')]: + for key in [ ['y[0]', 'y[0]'], ('y[0]', 'u[0]') ]: with pytest.raises(IndexError, match=r"signal name\(s\) not valid"): resp.outputs.__getitem__(key) diff --git a/control/tests/lti_test.py b/control/tests/lti_test.py index bd50b46c8..9edf09013 100644 --- a/control/tests/lti_test.py +++ b/control/tests/lti_test.py @@ -342,7 +342,7 @@ def test_subsys_indexing(fcn, outdx, inpdx, key): match fcn: case ct.frd: np.testing.assert_almost_equal( - subsys_fcn.response, subsys_chk.response) + subsys_fcn.complex, subsys_chk.complex) case ct.ss: np.testing.assert_almost_equal(subsys_fcn.A, subsys_chk.A) np.testing.assert_almost_equal(subsys_fcn.B, subsys_chk.B) @@ -351,8 +351,8 @@ def test_subsys_indexing(fcn, outdx, inpdx, key): case ct.tf: omega = np.logspace(-1, 1) np.testing.assert_almost_equal( - subsys_fcn.frequency_response(omega).response, - subsys_chk.frequency_response(omega).response) + subsys_fcn.frequency_response(omega).complex, + subsys_chk.frequency_response(omega).complex) @pytest.mark.parametrize("op", [ diff --git a/control/tests/matlab_test.py b/control/tests/matlab_test.py index 83527cf76..b7e0d25d2 100644 --- a/control/tests/matlab_test.py +++ b/control/tests/matlab_test.py @@ -692,7 +692,7 @@ def testFRD(self): omega = np.logspace(-1, 2, 10) frd1 = frd(h, omega) assert isinstance(frd1, FRD) - frd2 = frd(frd1.fresp[0, 0, :], omega) + frd2 = frd(frd1.frdata[0, 0, :], omega) assert isinstance(frd2, FRD) @slycotonly diff --git a/doc/config.rst b/doc/config.rst index c49ee0802..d5c44036d 100644 --- a/doc/config.rst +++ b/doc/config.rst @@ -314,6 +314,13 @@ Plotting parameters If True, use Hertz for frequency response plots (otherwise rad/sec). +.. py:data:: freqplot.magnitude_label + :type: str + :value: 'Magnitude' + + Label to use on the magnitude portion of a frequeny plot. Set to + 'Gain' by `use_fbs_defaults()`. + .. py:data:: freqplot.number_of_samples :type: int :value: 1000 diff --git a/doc/develop.rst b/doc/develop.rst index f461ff0eb..880952abd 100644 --- a/doc/develop.rst +++ b/doc/develop.rst @@ -204,6 +204,102 @@ Signal arguments: either the number of each signal or a list of labels for the signals. +Order of arguments for functions taking inputs, outputs, state, time, +frequency, etc: + +* The default order for providing arguments in state space models is + ``(t, x, u, params)``. This is the generic order that should be + used in functions that take signals as parameters, but permuted so + that required arguments go first, common arguments go next (as + keywords, in the order listed above if they also work as positional + arguments), and infrequent arguments go last (in order listed + above). For example:: + + def model_update(t, x, u, params) + resp = initial_response(sys, timepts, x0) # x0 required + resp = input_output_response(sys, timepts, u, x0=x0) # u required + resp = TimeResponseData( + timepts, outputs, states=states, inputs=inputs) + + In the last command, note that states precedes inputs because not + all TimeResponseData elements have inputs (eg, `initial_response`). + +* The default order for providing arguments in the frequency domain is + system/response first, then frequency:: + + resp = frequency_response(sys, omega) + sys_frd = frd(sys_tf, omega) + sys = frd(response, omega) + +Time and frequency responses: + +* Use `timepts` for lists of times and `omega` for lists of + frequencies at which systems are evaluated. For example:: + + ioresp = ct.input_output_response(sys, timepts, U) + cplt = ct.bode_plot(sys, omega) + +* Use `inputs`, `outputs`, `states`, `time` for time response data + attributes. These should be used as parameter names when creating + `TimeResponseData` objects and also as attributes when retrieving + response data (with dimensions dependent on `squeeze` processing). + These are stored internally in non-squeezed form using `u`, `y`, + `x`, and `t`, but the internal data should generally not be accessed + directly. For example:: + + plt.plot(ioresp.time, ioresp.outputs[0]) + tresp = ct.TimeResponseData(time, outputs, states, ...) # (internal call) + + - Note that the use of `inputs`, `outputs`, and `states` for both + factory function specifications as well as response function + attributes is a bit confusing. + +* Use `frdata`, `omega` for frequency response data attributes. These + should be used as parameter names when creating + `FrequencyResponseData` objects and also as attributes when + retreiving response data. The `frdata` attribute is stored as a 3D + array indexed by outputs, inputs, frequency. + +* Use `complex`, `magnitude`, `phase` for frequency response + data attributes with squeeze processing. For example:: + + ax = plt.subplots(2, 1) + ax[0].loglog(fresp.omega, fresp.magnitude) + ax[1].semilogx(fresp.omega, fresp.phase) + + - The frequency response is stored internally in non-squeezed form + as `fresp`, but this is generally not accessed directly by users. + + - Note that when creating time response data the independent + variable (time) is the first argument wherease for frequency + response data the independent variable (omega) is the second + argument. This is because we also create frequency response data + from a linear system using a call ``frd(sys, omega)``, and + rename frequency response data using a call ``frd(sys, + name='newname')``, so the system/data need to be the first + argument. For time response data we use the convention that we + start with time and then list the arguments in the most frequently + used order. + +* Use `response` for generic response objects (time, frequency, + describing function, Nyquist, etc). + + - Note that when responses are evaluated as tuples, the ordering of + the dependent and independent variables switches between time and + frequency domain:: + + t, y = ct.step_response(sys) + mag, phase, omega = ct.frequency_response(sys) + + To avoid confusion, it is better to use response objects:: + + tresp = ct.step_response(sys) + t, y = tresp.time, tresp.outputs + + fresp = ct.frequency_response(sys) + omega, response = fresp.omega, fresp.response + mag, phase, omega = fresp.magnitude, fresp.phase, fresp.omega + Documentation Guidelines ======================== diff --git a/doc/linear.rst b/doc/linear.rst index 409fe671d..b877b7163 100644 --- a/doc/linear.rst +++ b/doc/linear.rst @@ -156,8 +156,8 @@ Frequency response data (FRD) systems The :class:`FrequencyResponseData` (FRD) class is used to represent systems in frequency response data form. The main data attributes are -`omega` and `fresp`, where `omega` is a 1D array of frequencies and -`fresp` is the (complex-value) value of the transfer function at each +`omega` and `frdata`, where `omega` is a 1D array of frequencies and +`frdata` is the (complex-value) value of the transfer function at each frequency point. FRD systems can be created with the :func:`frd` factory function: @@ -166,19 +166,19 @@ FRD systems can be created with the :func:`frd` factory function: sys_lti = ct.rss(2, 2, 2) lti_resp = ct.frequency_response(sys_lti) - fresp = lti_resp.response - freqpts = lti_resp.frequency + frdata = lti_resp.complex + omega = lti_resp.frequency .. testcode:: frdata - sys = ct.frd(fresp, freqpts) + sys = ct.frd(frdata, omega) FRD systems can also be created by evaluating an LTI system at a given set of frequencies: .. testcode:: frdata - frd_sys = ct.frd(sys_lti, freqpts) + frd_sys = ct.frd(sys_lti, omega) Frequency response data systems have a somewhat more limited set of functions that are available, although all of the standard algebraic @@ -283,9 +283,9 @@ response. mag, phase, omega = response(squeeze=False) -.. note:: The `fresp` data member is stored as a NumPy array and +.. note:: The `frdata` data member is stored as a NumPy array and cannot be accessed with signal names. Use - `response.response` to access the complex frequency response + `response.complex` to access the complex frequency response using signal names. From e2a93dd5586a0a2b305fbfacddb11d790a793d8d Mon Sep 17 00:00:00 2001 From: Richard Murray Date: Fri, 24 Jan 2025 15:59:05 -0800 Subject: [PATCH 84/93] Get rid of duplicative reshaping within bode_plot --- control/freqplot.py | 17 ++++++----------- 1 file changed, 6 insertions(+), 11 deletions(-) diff --git a/control/freqplot.py b/control/freqplot.py index 58b11416c..9428fbc0f 100644 --- a/control/freqplot.py +++ b/control/freqplot.py @@ -379,10 +379,8 @@ def bode_plot( else: raise ValueError("initial_phase must be a number.") - # Reshape the phase to allow standard indexing - phase = response.phase.copy().reshape((noutputs, ninputs, -1)) - # Shift and wrap the phase + phase = np.angle(response.frdata) # 3D array for i, j in itertools.product(range(noutputs), range(ninputs)): # Shift the phase if needed if abs(phase[i, j, 0] - initial_phase_value) > math.pi: @@ -405,12 +403,9 @@ def bode_plot( else: raise ValueError("wrap_phase must be bool or float.") - # Put the phase back into the original shape - phase = phase.reshape(response.magnitude.shape) - - # Save the data for later use (legacy return values) - mag_data.append(response.magnitude.reshape(noutputs, ninputs, -1)) - phase_data.append(phase.reshape(noutputs, ninputs, -1)) + # Save the data for later use + mag_data.append(np.abs(response.frdata)) + phase_data.append(phase) omega_data.append(response.omega) # @@ -685,8 +680,8 @@ def _make_line_label(response, output_index, input_index): for index, response in enumerate(data): # Get the (pre-processed) data in fully indexed form - mag = mag_data[index].reshape((noutputs, ninputs, -1)) - phase = phase_data[index].reshape((noutputs, ninputs, -1)) + mag = mag_data[index] + phase = phase_data[index] omega_sys, sysname = omega_data[index], response.sysname for i, j in itertools.product(range(noutputs), range(ninputs)): From 8081ac35bdaffd84785a61ef3338a5cf675f8fba Mon Sep 17 00:00:00 2001 From: Richard Murray Date: Fri, 24 Jan 2025 18:00:55 -0800 Subject: [PATCH 85/93] add __repr__ for NamedSignal --- control/iosys.py | 10 ++++++++++ control/tests/namedio_test.py | 16 ++++++++++++++++ 2 files changed, 26 insertions(+) diff --git a/control/iosys.py b/control/iosys.py index 7e129c70b..03cf2a873 100644 --- a/control/iosys.py +++ b/control/iosys.py @@ -108,6 +108,16 @@ def _parse_key(self, key, labels=None, level=0): def __getitem__(self, key): return super().__getitem__(self._parse_key(key)) + def __repr__(self): + out = "NamedSignal(\n" + out += repr(np.array(self)) # NamedSignal -> array + if self.signal_labels is not None: + out += f",\nsignal_labels={self.signal_labels}" + if self.trace_labels is not None: + out += f",\ntrace_labels={self.trace_labels}" + out += "\n)" + return out + class InputOutputSystem(): """Base class for input/output systems. diff --git a/control/tests/namedio_test.py b/control/tests/namedio_test.py index 2fc55f7d4..34feb5b35 100644 --- a/control/tests/namedio_test.py +++ b/control/tests/namedio_test.py @@ -363,3 +363,19 @@ def test_negative_system_spec(): np.testing.assert_allclose(negfbk_negsig.B, negfbk_negsys.B) np.testing.assert_allclose(negfbk_negsig.C, negfbk_negsys.C) np.testing.assert_allclose(negfbk_negsig.D, negfbk_negsys.D) + + +# Named signal representations +def test_named_signal_repr(): + from numpy import array + from ..iosys import NamedSignal + sys = ct.rss( + states=2, inputs=['u1', 'u2'], outputs=['y1', 'y2'], + state_prefix='xi') + resp = sys.step_response(np.linspace(0, 1, 3)) + + for signal in ['inputs', 'outputs', 'states']: + sig_orig = getattr(resp, signal) + sig_eval = eval(repr(sig_orig)) + assert sig_eval.signal_labels == sig_orig.signal_labels + assert sig_eval.trace_labels == sig_orig.trace_labels From f96276dab3ff4147e8d962ba629dd7f261047353 Mon Sep 17 00:00:00 2001 From: Richard Murray Date: Fri, 24 Jan 2025 23:46:20 -0800 Subject: [PATCH 86/93] address remaining @sawyerfuller comments + RMM copyedits --- control/bdalg.py | 10 ++- control/freqplot.py | 36 +++++------ control/iosys.py | 2 +- control/nlsys.py | 6 +- control/robust.py | 4 +- control/statesp.py | 4 +- control/tests/robust_test.py | 2 +- control/timeresp.py | 18 +++--- control/xferfcn.py | 2 +- doc/Makefile | 32 ++++++---- doc/classes.rst | 4 +- doc/config.rst | 27 ++++---- doc/descfcn.rst | 32 ++++++++++ doc/develop.rst | 72 ++++++++++----------- doc/figures/descfcn-pade-backlash.png | Bin 0 -> 44903 bytes doc/figures/timeplot-mimo_step-pi_cs.png | Bin 42935 -> 31881 bytes doc/figures/xferfcn-delay-compare.png | Bin 0 -> 36506 bytes doc/flatsys.rst | 14 ++--- doc/functions.rst | 2 +- doc/intro.rst | 73 ++++++++++++++++++---- doc/iosys.rst | 76 ++++++++++++----------- doc/linear.rst | 66 +++++++++----------- doc/nlsys.rst | 25 ++++---- doc/nonlinear.rst | 2 +- doc/optimal.rst | 2 +- doc/phaseplot.rst | 6 +- doc/response.rst | 54 ++++++++-------- doc/statesp.rst | 7 ++- doc/stochastic.rst | 11 +--- doc/xferfcn.rst | 28 +++++++-- examples/kincar-fusion.ipynb | 39 ++++++------ examples/python-control_tutorial.ipynb | 36 +++++------ 32 files changed, 408 insertions(+), 284 deletions(-) create mode 100644 doc/figures/descfcn-pade-backlash.png create mode 100644 doc/figures/xferfcn-delay-compare.png diff --git a/control/bdalg.py b/control/bdalg.py index 48e0f6365..0ed490084 100644 --- a/control/bdalg.py +++ b/control/bdalg.py @@ -27,9 +27,11 @@ def series(*sys, **kwargs): - r"""series(sys1, sys2[, ..., sysn]) + """series(sys1, sys2[, ..., sysn]) - Return series connection (`sysn` \* ...\ \*) `sys2` \* `sys1`. + Series connection of I/O systems. + + Generates a new system ``[sysn * ... *] sys2 * sys1``. Parameters ---------- @@ -100,7 +102,9 @@ def series(*sys, **kwargs): def parallel(*sys, **kwargs): r"""parallel(sys1, sys2[, ..., sysn]) - Return parallel connection `sys1` + `sys2` (+ ...\ + `sysn`). + Parallel connection of I/O systems. + + Generates a parallel connection ``sys1 + sys2 [+ ... + sysn]``. Parameters ---------- diff --git a/control/freqplot.py b/control/freqplot.py index 9428fbc0f..f1df3bf83 100644 --- a/control/freqplot.py +++ b/control/freqplot.py @@ -2621,24 +2621,24 @@ def _determine_omega_vector(syslist, omega_in, omega_limits, omega_num, """Determine the frequency range for a frequency-domain plot according to a standard logic. - If omega_in and omega_limits are both None, then omega_out is computed - on omega_num points according to a default logic defined by - _default_frequency_range and tailored for the list of systems syslist, and - omega_range_given is set to False. - - If omega_in is None but omega_limits is an array_like of 2 elements, then - omega_out is computed with the function np.logspace on omega_num points - within the interval [min, max] = [omega_limits[0], omega_limits[1]], and - omega_range_given is set to True. - - If omega_in is a list or tuple of length 2, it is interpreted as a - range and handled like omega_limits. If omega_in is a list or tuple of - length 3, it is interpreted a range plus number of points and handled - like omega_limits and omega_num. - - If omega_in is an array or a list/tuple of length greater than - two, then omega_out is set to omega_in (as an array), and - omega_range_given is set to True + If `omega_in` and `omega_limits` are both None, then `omega_out` is + computed on `omega_num` points according to a default logic defined by + `_default_frequency_range` and tailored for the list of systems + syslist, and `omega_range_given` is set to False. + + If `omega_in` is None but `omega_limits` is a tuple of 2 elements, then + `omega_out` is computed with the function `numpy.logspace` on + `omega_num` points within the interval ``[min, max] = [omega_limits[0], + omega_limits[1]]``, and `omega_range_given` is set to True. + + If `omega_in` is a tuple of length 2, it is interpreted as a range and + handled like `omega_limits`. If `omega_in` is a tuple of length 3, it + is interpreted a range plus number of points and handled like + `omega_limits` and `omega_num`. + + If `omega_in` is an array or a list/tuple of length greater than two, + then `omega_out` is set to `omega_in` (as an array), and + `omega_range_given` is set to True Parameters ---------- diff --git a/control/iosys.py b/control/iosys.py index 03cf2a873..1133fe9fb 100644 --- a/control/iosys.py +++ b/control/iosys.py @@ -115,7 +115,7 @@ def __repr__(self): out += f",\nsignal_labels={self.signal_labels}" if self.trace_labels is not None: out += f",\ntrace_labels={self.trace_labels}" - out += "\n)" + out += ")" return out diff --git a/control/nlsys.py b/control/nlsys.py index bae79d9a4..7073cae30 100644 --- a/control/nlsys.py +++ b/control/nlsys.py @@ -75,7 +75,7 @@ class NonlinearIOSystem(InputOutputSystem): following values: * `dt` = 0: continuous-time system (default) - * `dt` > 0: discrete-time system with sampling period 'dt' + * `dt` > 0: discrete-time system with sampling period `dt` * `dt` = True: discrete time with unspecified sampling period * `dt` = None: no timebase specified @@ -1388,7 +1388,7 @@ def nlsys(updfcn, outfcn=None, **kwargs): following values: * `dt` = 0: continuous-time system (default) - * `dt` > 0: discrete-time system with sampling period 'dt' + * `dt` > 0: discrete-time system with sampling period `dt` * `dt` = True: discrete time with unspecified sampling period * `dt` = None: no timebase specified @@ -2414,7 +2414,7 @@ def interconnect( values: * `dt` = 0: continuous-time system (default) - * `dt` > 0`: discrete-time system with sampling period 'dt' + * `dt` > 0`: discrete-time system with sampling period `dt` * `dt` = True: discrete time with unspecified sampling period * `dt` = None: no timebase specified diff --git a/control/robust.py b/control/robust.py index 55d453154..264611f16 100644 --- a/control/robust.py +++ b/control/robust.py @@ -15,7 +15,7 @@ def h2syn(P, nmeas, ncon): - """H_2 control synthesis for plant P. + """H2 control synthesis for plant P. Parameters ---------- @@ -87,7 +87,7 @@ def h2syn(P, nmeas, ncon): def hinfsyn(P, nmeas, ncon): # TODO: document significance of rcond - """H_{inf} control synthesis for plant P. + """H-infinity control synthesis for plant P. Parameters ---------- diff --git a/control/statesp.py b/control/statesp.py index 2908d1d6d..64134e8a8 100644 --- a/control/statesp.py +++ b/control/statesp.py @@ -60,7 +60,7 @@ class StateSpace(NonlinearIOSystem, LTI): r"""StateSpace(A, B, C, D[, dt]) - State space representation for LIT input/output systems. + State space representation for LTI input/output systems. The StateSpace class is used to represent state-space realizations of linear time-invariant (LTI) systems: @@ -109,7 +109,7 @@ class StateSpace(NonlinearIOSystem, LTI): when the system is constructed: * `dt` = 0: continuous-time system (default) - * `dt` > 0: discrete-time system with sampling period 'dt' + * `dt` > 0: discrete-time system with sampling period `dt` * `dt` = True: discrete time with unspecified sampling period * `dt` = None: no timebase specified diff --git a/control/tests/robust_test.py b/control/tests/robust_test.py index dde2423be..fc9c9570d 100644 --- a/control/tests/robust_test.py +++ b/control/tests/robust_test.py @@ -37,7 +37,7 @@ def testH2syn(self): """Test h2syn""" p = ss(-1, [[1, 1]], [[1], [1]], [[0, 1], [1, 0]]) k = h2syn(p, 1, 1) - # from Octave, which also uses SB10HD for H-2 synthesis: + # from Octave, which also uses SB10HD for H2 synthesis: # a= -1; b1= 1; b2= 1; c1= 1; c2= 1; d11= 0; d12= 1; d21= 1; d22= 0; # g = ss(a,[b1,b2],[c1;c2],[d11,d12;d21,d22]); # k = h2syn(g,1,1); diff --git a/control/timeresp.py b/control/timeresp.py index 5104a9895..87ba539e3 100644 --- a/control/timeresp.py +++ b/control/timeresp.py @@ -1682,15 +1682,15 @@ def step_info(sysdata, T=None, T_num=None, yfinal=None, params=None, steady_state_value = InfValue retij = { - 'RiseTime': rise_time, - 'SettlingTime': settling_time, - 'SettlingMin': settling_min, - 'SettlingMax': settling_max, - 'Overshoot': overshoot, - 'Undershoot': undershoot, - 'Peak': peak_value, - 'PeakTime': peak_time, - 'SteadyStateValue': steady_state_value + 'RiseTime': float(rise_time), + 'SettlingTime': float(settling_time), + 'SettlingMin': float(settling_min), + 'SettlingMax': float(settling_max), + 'Overshoot': float(overshoot), + 'Undershoot': float(undershoot), + 'Peak': float(peak_value), + 'PeakTime': float(peak_time), + 'SteadyStateValue': float(steady_state_value) } retrow.append(retij) diff --git a/control/xferfcn.py b/control/xferfcn.py index 2a9572954..ca8e5d114 100644 --- a/control/xferfcn.py +++ b/control/xferfcn.py @@ -122,7 +122,7 @@ class TransferFunction(LTI): 'timebase' dt when the system is constructed: * `dt` = 0: continuous-time system (default) - * `dt` > 0: discrete-time system with sampling period 'dt' + * `dt` > 0: discrete-time system with sampling period `dt` * `dt` = True: discrete time with unspecified sampling period * `dt` = None: no timebase specified diff --git a/doc/Makefile b/doc/Makefile index 16c9ec9b5..493fd7da5 100644 --- a/doc/Makefile +++ b/doc/Makefile @@ -2,16 +2,17 @@ # RMM, 15 Jan 2025 FIGS = figures/classes.pdf -RST_FIGS = figures/flatsys-steering-compare.png \ - figures/iosys-predprey-open.png \ - figures/timeplot-servomech-combined.png \ - figures/steering-optimal.png figures/ctrlplot-servomech.png \ - figures/phaseplot-dampedosc-default.png \ - figures/timeplot-mimo_step-default.png \ - figures/freqplot-siso_bode-default.png \ - figures/pzmap-siso_ctime-default.png \ - figures/rlocus-siso_ctime-default.png \ - figures/stochastic-whitenoise-response.png +RST_FIGS = figures/flatsys-steering-compare.png \ + figures/iosys-predprey-open.png \ + figures/timeplot-servomech-combined.png \ + figures/steering-optimal.png figures/ctrlplot-servomech.png \ + figures/phaseplot-dampedosc-default.png \ + figures/timeplot-mimo_step-default.png \ + figures/freqplot-siso_bode-default.png \ + figures/pzmap-siso_ctime-default.png \ + figures/rlocus-siso_ctime-default.png \ + figures/stochastic-whitenoise-response.png \ + figures/xferfcn-delay-compare.png figures/descfcn-pade-backlash.png # You can set these variables from the command line. SPHINXOPTS = @@ -47,13 +48,22 @@ figures/timeplot-mimo_step-default.png \ figures/stochastic-whitenoise-response.png: stochastic.rst @$(SPHINXBUILD) -M doctest "$(SOURCEDIR)" "$(BUILDDIR)" +figures/xferfcn-delay-compare.png: xferfcn.rst + @$(SPHINXBUILD) -M doctest "$(SOURCEDIR)" "$(BUILDDIR)" + +figures/descfcn-pade-backlash.png: descfcn.rst + @$(SPHINXBUILD) -M doctest "$(SOURCEDIR)" "$(BUILDDIR)" + # Other figure rules figure/classes.pdf: figure/classes.fig make -C figures classes.pdf # Catch-all target: route all unknown targets to Sphinx using the new # "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS). -html latexpdf doctest clean: Makefile $(FIGS) $(RST_FIGS) +html latexpdf: Makefile $(FIGS) $(RST_FIGS) + @$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) + +doctest clean: Makefile @$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) distclean: clean diff --git a/doc/classes.rst b/doc/classes.rst index 0654a773a..82ca42c21 100644 --- a/doc/classes.rst +++ b/doc/classes.rst @@ -55,8 +55,8 @@ These classes are used as the outputs of `_response`, `_map`, and TimeResponseData TimeResponseList -More informaton on the functions used to create these classes can be -found in the :ref:`iosys-module` chapter. +More information on the functions used to create these classes can be +found in the :ref:`response-chapter` chapter. Nonlinear System Classes diff --git a/doc/config.rst b/doc/config.rst index d5c44036d..5716da29a 100644 --- a/doc/config.rst +++ b/doc/config.rst @@ -126,14 +126,16 @@ System creation parameters :value: '' Prefix to add to system name when sampling a system at a set of - frequency points in :func:`frd`. + frequency points in :func:`frd` or converting a continuous-time + system to discrete time in :func:`sample_system`. .. py:data:: iosys.sampled_system_name_suffix :type: str :value: '$sampled' Suffix to add to system name when sampling a system at a set of - frequency points in :func:`frd`. + frequency points in :func:`frd` or converting a continuous-time + system to discrete time in :func:`sample_system`. .. py:data:: iosys.state_name_delim :type: str @@ -318,7 +320,7 @@ Plotting parameters :type: str :value: 'Magnitude' - Label to use on the magnitude portion of a frequeny plot. Set to + Label to use on the magnitude portion of a frequency plot. Set to 'Gain' by `use_fbs_defaults()`. .. py:data:: freqplot.number_of_samples @@ -367,10 +369,10 @@ Plotting parameters :type: bool :value: False - If wrap_phase is False, then the phase will be unwrapped so that it - is continuously increasing or decreasing. If wrap_phase is True the + If `wrap_phase` is False, then the phase will be unwrapped so that it + is continuously increasing or decreasing. If `wrap_phase` is True the phase will be restricted to the range [-180, 180) (or [:math:`-\pi`, - :math:`\pi`) radians). If `wrap_phase` is specified as a float, the + :math:`\pi`) radians). If ``wrap_phase`` is specified as a float, the phase will be offset by 360 degrees if it falls below the specified value. @@ -452,9 +454,10 @@ Plotting parameters :value: ['--', ':'] Linestyles for mirror image of the Nyquist curve in - :func:`nyquist_plot`. The first element is used for unscaled portions - of the Nyquist curve, the second element is used for portions that are - scaled (using `max_curve_magnitude`). If False then omit completely. + :func:`nyquist_plot`. The first element is used for unscaled + portions of the Nyquist curve, the second element is used for + portions that are scaled (using `max_curve_magnitude`). If False + then omit the mirror image curve completely. .. py:data:: nyquist.primary_style :type: list of str @@ -509,7 +512,7 @@ Plotting parameters :type: float :value: 0.1 - In :func:`phaseplot.separatrices`, set the offset from the equlibrium + In :func:`phaseplot.separatrices`, set the offset from the equilibrium point to the starting point of the separatix traces, in the direction of the eigenvectors evaluated at that equilibrium point. @@ -537,7 +540,7 @@ Plotting parameters If True plot omega-damping grid in :func:`pole_zero_plot`. If False or None show imaginary axis for continuous-time systems, unit circle for - discrete-time systems. If `empty`, do not draw any additonal lines. + discrete-time systems. If 'empty', do not draw any additional lines. Note: this setting only applies to pole/zero plots. For root locus plots, the 'rlocus.grid' parameter value is used as the default. @@ -562,7 +565,7 @@ Plotting parameters If True, plot omega-damping grid in :func:`root_locus_plot`. If False or None show imaginary axis for continuous-time systems, unit circle for - discrete-time systems. If `empty`, do not draw any additonal lines. + discrete-time systems. If 'empty', do not draw any additional lines. .. py:data:: sisotool.initial_gain :type: float diff --git a/doc/descfcn.rst b/doc/descfcn.rst index 7aa7d80bb..55d218f85 100644 --- a/doc/descfcn.rst +++ b/doc/descfcn.rst @@ -86,6 +86,38 @@ nonlinearity:: These functions use the :class:`DescribingFunctionNonlinearity` class, which allows an analytical description of the describing function. + +Example +------- + +The following example demonstrates a more complicated interaction +between a (non-static) nonlinearity and a higher order transfer +function, resulting in multiple intersection points: + +.. testcode:: descfcn + + # Linear dynamics + H_simple = ct.tf([1], [1, 2, 2, 1]) + H_multiple = ct.tf(H_simple * ct.tf(*ct.pade(5, 4)) * 4, name='sys') + omega = np.logspace(-3, 3, 500) + + # Nonlinearity + F_backlash = ct.friction_backlash_nonlinearity(1) + amp = np.linspace(0.6, 5, 50) + + # Describing function plot + cplt = ct.describing_function_plot( + H_multiple, F_backlash, amp, omega, mirror_style=False) + +.. testcode:: descfcn + :hide: + + import matplotlib.pyplot as plt + plt.savefig('figures/descfcn-pade-backlash.png') + +.. image:: figures/descfcn-pade-backlash.png + + Module classes and functions ---------------------------- .. autosummary:: diff --git a/doc/develop.rst b/doc/develop.rst index 880952abd..ace58c3a8 100644 --- a/doc/develop.rst +++ b/doc/develop.rst @@ -37,13 +37,13 @@ GitHub repository file and directory layout: + bdalg.py, delay.py, canonical.py, margins.py, sysnorm.py, modelsimp.py, passivity.py, robust.py, - statefbk.py, stochsys.py - analysis and synthesis routintes + statefbk.py, stochsys.py - analysis and synthesis routines + ctrlplot.py, descfcn.py, freqplot.py, grid.py, nichols.py, pzmap.py, rlocus.py, sisotool.py, - timeplot.py, timeresp.py - response and plotting rouintes + timeplot.py, timeresp.py - response and plotting routines - + ctrlutil.py, dtime.py, exception.py, mateqn.py - utility funcitons + + ctrlutil.py, dtime.py, exception.py, mateqn.py - utility functions + phaseplot.py - phase plot module @@ -135,11 +135,11 @@ Function names * Function names are lower case with words separated by underscores. * Function names usually describe what they do - (`create_statefbk_iosys`, `find_operating_points`) or what they + (`create_statefbk_iosystem`, `find_operating_points`) or what they generate (`input_output_response`, `find_operating_point`). * Some abbreviations and shortened versions are used when names get - very long (e.g., `create_statefbk_iosys` instead of + very long (e.g., `create_statefbk_iosystem` instead of `create_state_feedback_input_output_system`. * Factory functions for I/O systems use short names (partly from MATLAB @@ -257,7 +257,7 @@ Time and frequency responses: * Use `frdata`, `omega` for frequency response data attributes. These should be used as parameter names when creating `FrequencyResponseData` objects and also as attributes when - retreiving response data. The `frdata` attribute is stored as a 3D + retrieving response data. The `frdata` attribute is stored as a 3D array indexed by outputs, inputs, frequency. * Use `complex`, `magnitude`, `phase` for frequency response @@ -271,7 +271,7 @@ Time and frequency responses: as `fresp`, but this is generally not accessed directly by users. - Note that when creating time response data the independent - variable (time) is the first argument wherease for frequency + variable (time) is the first argument whereas for frequency response data the independent variable (omega) is the second argument. This is because we also create frequency response data from a linear system using a call ``frd(sys, omega)``, and @@ -281,8 +281,8 @@ Time and frequency responses: start with time and then list the arguments in the most frequently used order. -* Use `response` for generic response objects (time, frequency, - describing function, Nyquist, etc). +* Use `response` or `resp` for generic response objects (time, + frequency, describing function, Nyquist, etc). - Note that when responses are evaluated as tuples, the ordering of the dependent and independent variables switches between time and @@ -323,8 +323,8 @@ General docstring info ---------------------- The guiding principle used to guide how docstrings are written is -similar to NumPy (as articuated in the `numpydoc style guide -`_: +similar to NumPy (as articulated in the `numpydoc style guide +`_): A guiding principle is that human readers of the text are given precedence over contorting docstrings so our tools produce nice @@ -352,7 +352,7 @@ guidelines: generate a link to the :mod:`sys` Python module. To avoid this, `conf.py` includes code that converts \`sys\` in docstrings to \:code\:\`sys`, which renders as :code:`sys` (code style, with no - link). In ``.rst`` files, this construction should be done + link). In ``.rst`` files this construction should be done manually, since ``.rst`` files are not pre-processed as a docstring. @@ -398,11 +398,11 @@ guidelines: - The rationale here is similar to built-ins: adding 4 backticks just to get them in a code font seems unnecessary. - - Note that if a string is is included in Python assignment - statement (e.g., ``method='slycot'``) it looks quite ugly in text - form to have it enclosed in double backticks - (\`\`method='slycot'\`\`), so OK to use method='slycot' (no - backticks). + - Note that if a string is included in Python assignment statement + (e.g., ``method='slycot'``) it looks quite ugly in text form to + have it enclosed in double backticks (\`\`method='slycot'\`\`), so + OK to use method='slycot' (no backticks) or `method` = 'slycot' + (backticks with extra spaces). * References to the `defaults` dictionary should be of the form \`config.defaults['module.param']\` (like a parameter), which @@ -412,8 +412,7 @@ guidelines: documentation for that parameter (in the :ref:`package-configuration-parameters` section of the Reference Manual), but the special processing to do that hasn't been - implemented. If we go that route at some point, we could perhaps - due a global change to single backticks. + implemented. - Depending on placement, you can end up with lots of white space around defaults parameters (also true in the docstrings). @@ -497,9 +496,9 @@ Follow numpydoc format with the follow additional details: (in the "Parameters" or "Additional Parameters" section of the class docstring). -* Attributes that are created within a class and might be of interest - to the user should be documented in the "Attributes" section of the - class docstring. +* Attributes that are created within a class and that might be of + interest to the user should be documented in the "Attributes" + section of the class docstring. * Classes should not include a "Returns" section (since they always return an instance of the class). @@ -526,9 +525,9 @@ design to get up and running quickly. The User Guide consists of chapters that are each their own separate `.rst` file and each of them generates a separate page. Chapters are -divided into sections whose names appear in the indexo on the left of +divided into sections whose names appear in the index on the left of the web page when that chapter is being viewed. In some cases a -section may be in its own file, including in the chapter page by using +section may be in its own file, included in the chapter page by using the `include` directive (see `nlsys.py` for an example). Sphinx files guidelines: @@ -544,7 +543,7 @@ Sphinx files guidelines: prompt and the expected response) and code blocks (using the `testcode` directive). -* When refering to the python-control package, several different forms +* When referring to the python-control package, several different forms can be used: - Full name: "the Python Control Systems Library (python-control)" @@ -553,12 +552,13 @@ Sphinx files guidelines: - Adjective form: "the python-control package" or "a python-control module" (this is the most common form). - - Noun form: "`python-control`" (only used occassionally). + - Noun form: "`python-control`" (only used occasionally). -* Unlike docstrings, use backticks and \:math\: more liberally when it - is appropriate to highlight/format code properly. However, Python - built-ins should still just be written as True, False, and None (no - backticks). +* Unlike docstrings, the documentation in the User Guide should use + backticks and \:math\: more liberally when it is appropriate to + highlight/format code properly. However, Python built-ins should + still just be written as True, False, and None (no backticks), for + formatting consistency. - The Sphinx documentation is not read in "raw" form, so OK to add the additional annotations. @@ -573,16 +573,18 @@ Sphinx files guidelines: Reference Manual ---------------- -The reference manual should provide a fairly comprehensive description -of every class, function and configuration variable in the package. +The Reference Manual should provide a fairly comprehensive description +of every class, function, and configuration variable in the package. +All primary functions and classes bust be included here, since the +Reference Manual generates the stub files used by Sphinx. Modules and subpackages ----------------------- -When documenting (independent) modules and subpackages (refered to +When documenting (independent) modules and subpackages (refereed to here collectively as modules), use the following guidelines for -documentatation: +documentation: * In module docstrings, refer to module functions and classes without including the module prefix. 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zNk~c2gdJKMR~2whdUH*))sBai-~9~dJ>%ozXr&{VL`6kG$;PByGa)aJAHy?{$^F*Q zWn^Sr>+P{$jW@X}XemW~Q<#LDT+rjk!u}8a_X=Lb)}%JNZ*Tss=Xzqd;+tt!R<>H$ z*|BA3XUoXTKR>wwAClE89&K&y`OMPp5o1$Rhz`55wKJoz#BFSCF9in&JJp6O7x&lxWF-n%zSeSiWxd;74nk#xjXX=$x6{*aIme4ethvd?}_?)_!sP-Ero z@9+7CY~+A2LT}o?u+!v}R8&1^*mW MQQiAO$>`Gm1LUG=3IG5A literal 0 HcmV?d00001 diff --git a/doc/flatsys.rst b/doc/flatsys.rst index cb17c2901..dda35d9a3 100644 --- a/doc/flatsys.rst +++ b/doc/flatsys.rst @@ -7,7 +7,7 @@ Differentially Flat Systems The `flatsys` subpackage contains a set of classes and functions to compute trajectories for differentially flat systems. The objects in -this subpackage must be explictly imported:: +this subpackage must be explicitly imported:: import control as ct import control.flatsys as fs @@ -67,7 +67,7 @@ system, using equation :eq:`flat2state` to determine the full state space and input trajectories. In particular, given initial and final conditions on :math:`z` and its -derivatives that satisfy the initial and final conditions any curve +derivatives that satisfy the initial and final conditions, any curve :math:`z(\cdot)` satisfying those conditions will correspond to a feasible trajectory of the system. We can parameterize the flat output trajectory using a set of smooth basis functions :math:`\psi_i(t)`: @@ -119,8 +119,8 @@ Assuming that :math:`M` has a sufficient number of columns and that it is full column rank, we can solve for a (possibly non-unique) :math:`\alpha` that solves the trajectory generation problem. -Sub-package usage ------------------ +Subpackage usage +---------------- To access the flat system modules, import `control.flatsys`:: @@ -361,11 +361,11 @@ The results of the two approaches can be shown using the :align: center -Sub-package classes and functions ---------------------------------- +Subpackage classes and functions +-------------------------------- The flat systems subpackage `flatsys` utilizes a number of classes to -define the flatsystem, the basis functions, and the system trajetory: +define the flat system, the basis functions, and the system trajectory: .. autosummary:: :template: custom-class-template.rst diff --git a/doc/functions.rst b/doc/functions.rst index 2201d8c82..db9a5a08c 100644 --- a/doc/functions.rst +++ b/doc/functions.rst @@ -38,6 +38,7 @@ Functions that transform systems from one form to another: modal_form observable_form reachable_form + sample_system similarity_transform ss2tf tf2ss @@ -319,7 +320,6 @@ Utility Functions mag2db reset_defaults reset_rcParams - sample_system set_defaults ssdata timebase diff --git a/doc/intro.rst b/doc/intro.rst index 871578174..7f0d510b3 100644 --- a/doc/intro.rst +++ b/doc/intro.rst @@ -74,12 +74,8 @@ The python-control package can also be used with `Google Colab `_ by including the following lines to import the control package:: - try: - import control as ct - print("python-control", ct.__version__) - except ImportError: - !pip install control - import control as ct + !pip install control + import control as ct Note that Google Colab does not currently support Slycot, so some functionality may not be available. @@ -94,15 +90,15 @@ The python-control package makes use of a few naming and calling conventions: words (`frequency_response`). * Class names use camel case (`StateSpace`, `ControlPlot`, etc) and - instances of the class are created with "factory functions" (`ss`) - or as the output of an operation (`bode_plot`). + instances of the class are created with "factory functions" (`ss`, `tf`) + or as the output of an operation (`bode_plot`, `step_response`). * Functions that return multiple values use either objects (with elements for each return value) or tuples. For those functions that return tuples, the underscore variable can be used if only some of the return values are needed:: - K, _, _ = lqr(sys) + K, _, _ = ct.lqr(sys) * Python-control supports both single-input, single-output (SISO) systems and multi-input, multi-output (MIMO) systems, including @@ -134,10 +130,65 @@ some things to keep in mind: [1, 2, 3] and matrices are written using 2D nested lists, e.g., [[1, 2], [3, 4]]. * Functions that in MATLAB would return variable numbers of values - will have a parameter of the form `return_` that is used to + will have a parameter of the form `return_\` that is used to return additional data. (These functions usually return an object of a class that has attributes that can be used to access the information and this is the preferred usage pattern.) * You cannot use braces for collections; use tuples instead. * Time series data have time as the final index (see - :ref:`time-series-convention`). + :ref:`time series data conventions `). + + +Documentation Conventions +========================= + +This documentation has a number of notional conventions and functionality: + +* The left panel displays the table of contents and is divided into + two main sections: the User Guide, which contains a narrative + description of the package along with examples, and the Reference + Manual, which contains documentation for all functions, classes, + configurable default parameters, and other detailed information. + +* Class, functions, and methods with additional documentation appear + in a bold, code font that link to the Reference Manual. Example: `ss`. + +* Links to other sections appear in blue. Example: :ref:`nonlinear-systems`. + +* Parameters appear in a (non-bode) code font, as do code fragments. + Example: `omega`. + +* Example code is contained in code blocks that can be copied using + the copy icon in the top right corner of the code block. Code + blocks are of three primary types: summary descriptions, code + listings, and executed commands. + + Summary descriptions show the calling structure of commands but are + not directly executable. Example:: + + resp = ct.frequency_response(sys[, omega]) + + Code listings consist of executable code that can be copied and + pasted into a Python execution environment. In most cases the + objects required by the code block will be present earlier in the + file or, occasionally, in a different section or chapter (with a + reference near the code block). All code listings assume that the + NumPy package is available using the prefix `np` and the python-control + package is available using prefix `ct`. Example: + + .. code:: + + sys = ct.rss(4, 2, 1) + resp = ct.frequency_response(sys) + cplt = resp.plot() + + Executed commands show commands preceded by a prompt string of the + form ">>> " and also show the output that is obtained when executing + that code. The copy functionality for these blocks is configured to + only copy the commands and not the prompt string or outputs. Example: + + .. doctest:: + + >>> sys = ct.tf([1], [1, 0.5, 1]) + >>> ct.bandwidth(sys) + np.float64(1.4839084518312828) diff --git a/doc/iosys.rst b/doc/iosys.rst index 7a9ae084d..e8ef247eb 100644 --- a/doc/iosys.rst +++ b/doc/iosys.rst @@ -43,7 +43,8 @@ The following operators are defined to operate between I/O systems: If either of the systems is a scalar or an array of appropriate dimension, then the appropriate scalar or matrix operation is -performed. +performed. In addition, if a SISO system is combined with a MIMO +system, the SISO system will be broadcast to the appropriate shape. Systems of different types can be combined using these operations, with the following rules: @@ -100,10 +101,10 @@ the following command will also work:: Gyu = G1.feedback(G2) -All block diagram algebra functions allow the names of the system and -signals to be specified using the usual `name`, `inputs`, and -`outputs` keywords, as described in the :class:`InputOutputSystem` -class. For state space systems, the names of the states can also be +All block diagram algebra functions allow the name of the system and +labels for signals to be specified using the usual `name`, `inputs`, +and `outputs` keywords, as described in the :class:`InputOutputSystem` +class. For state space systems, the labels for the states can also be given, but caution should be used since the order of states in the combined system is not guaranteed. @@ -234,10 +235,12 @@ constructed using :func:`nlsys` with no update function: .. testcode:: predprey + def output(t, x, u, params): + return -K @ (u[1:] - xeq) + kf * (u[0] - ueq) + controller = ct.nlsys( - None, - lambda t, x, u, params: -K @ (u[1:] - xeq) + kf * (u[0] - ueq), - inputs=['Ld', 'H', 'L'], outputs=1, name='control') + None, output, + inputs=['Ld', 'H', 'L'], outputs=1, name='control') The input to the controller is `u`, consisting of the desired lynx population followed by the vector of hare and lynx populations. @@ -336,7 +339,7 @@ interconnecting systems, especially when combined with the :func:`summing_junction` function. For example, the following code will create a unity gain, negative feedback system: -.. testcode: autoconnect +.. testcode:: autoconnect P = ct.tf([1], [1, 0], inputs='u', outputs='y') C = ct.tf([10], [1, 1], inputs='e', outputs='u') @@ -441,7 +444,9 @@ connection of signals with the same name, the :func:`interconnect` has a variety of other mechanisms available for specifying signal names. The following forms are recognized for the `connections`, `inplist`, and `outlist` -parameters:: +parameters: + +.. code-block:: text (subsys, index, gain) tuple form with integer indices ('sysname', 'signal', gain) tuple form with name lookup @@ -726,7 +731,7 @@ an LQR controller: K, _, _ = ct.lqr(sys, np.eye(2), np.eye(1)) We construct an estimator for the system assuming disturbance and -noise intensity of 0.1: +noise intensity of 0.01: .. testcode:: statefbk @@ -829,12 +834,11 @@ Adding integral action ---------------------- Integral action can be included using the `integral_action` keyword. -The value of this keyword can either be a matrix (ndarray) or a -function. If a matrix :math:`C` is specified, the difference between -the desired state and system state will be multiplied by this matrix -and integrated. The controller gain should then consist of a set of -proportional gains :math:`K_\text{p}` and integral gains -:math:`K_\text{i}` with +The value of this keyword should be a matrix (ndarray). The +difference between the desired state and system state will be +multiplied by this matrix and integrated. The controller gain should +then consist of a set of proportional gains :math:`K_\text{p}` and +integral gains :math:`K_\text{i}` with .. math:: @@ -844,21 +848,23 @@ and the control action will be given by .. math:: - u = u_\text{d} - K\text{p} (x - x_\text{d}) - + u = u_\text{d} - K_\text{p} (x - x_\text{d}) - K_\text{i} \int C (x - x_\text{d}) dt. -If `integral_action` is a function ``h``, that function will be called -with the signature ``h(t, x, u, params)`` to obtain the outputs that -should be integrated. The number of outputs that are to be integrated +.. TODO: If `integral_action` is a function ``h``, that function will + be called with the signature ``h(t, x, u, params)`` to obtain the + outputs that should be integrated. + +The number of outputs that are to be integrated must match the number of additional columns in the `K` matrix. If an estimator is specified, :math:`\hat x` will be used in place of :math:`x`. As an example, consider the servo-mechanism model `servomech` -described in the :ref:`sec-nonlinear-models`. We construct a state -space controller by linearizing the system around an equilibrium -point, augmenting the model with an integrator, and computing a state -feedback that optimizes a quadratic cost function: +described in :ref:`creating nonlinear models `. +We construct a state space controller by linearizing the system around +an equilibrium point, augmenting the model with an integrator, and +computing a state feedback that optimizes a quadratic cost function: .. testsetup:: integral_action @@ -872,7 +878,6 @@ feedback that optimizes a quadratic cost function: 'k': 1, # Spring constant 'r': 1, # Location of spring contact on arm 'l': 2, # Distance to the read head - 'eps': 0.01, # Magnitude of velocity-dependent perturbation } # State derivative @@ -943,11 +948,12 @@ the integral action: Adding gain scheduling ---------------------- -Finally, for the trajectory generation design pattern, gain scheduling on -the desired state, desired input, or system state can be implemented by -setting the gain to a 2-tuple consisting of a list of gains and a list of -points at which the gains were computed, as well as a description of the -scheduling variables:: +Finally, for the trajectory generation design pattern, gain scheduling +on the desired state :math:`x_\text{d}`, desired input +:math:`u_\text{d}`, or current state :math:`x` can be implemented by +setting the gain to a 2-tuple consisting of a list of gains and a list +of points at which the gains were computed, as well as a description +of the scheduling variables:: ctrl, clsys = ct.create_statefbk_iosystem( sys, ([g1, ..., gN], [p1, ..., pN]), gainsched_indices=[s1, ..., sq]) @@ -961,10 +967,10 @@ controller implemented in this case has the form u = u_\text{d} - K(\mu) (x - x_\text{d}) -where :math:`\mu` represents the scheduling variables. See -:ref:`steering-gainsched.py` for an example implementation of a gain -scheduled controller (in the alternative formulation section at the -bottom of the file). +where :math:`\mu` represents the scheduling variables. See :ref:`gain +scheduled control for vehicle steering ` for an +example implementation of a gain scheduled controller (in the +alternative formulation section at the bottom of the file). As an example, consider the following simple model of a mobile robot ("unicycle" model), which has dynamics given by diff --git a/doc/linear.rst b/doc/linear.rst index b877b7163..b7b8f7137 100644 --- a/doc/linear.rst +++ b/doc/linear.rst @@ -69,11 +69,6 @@ customized access to system information: A, B, C, D, name='sys', states=['x1', 'x2'], inputs=['u1', 'u2'], outputs=['y']) -State space models can be manipulated using standard arithmetic -operations as well as the :func:`feedback`, :func:`parallel`, and -:func:`series` function. A full list of functions can be found in -:ref:`function-ref`. - The :func:`rss` function can be used to create a random state space system with a desired number or inputs, outputs, and states: @@ -101,20 +96,21 @@ transfer functions where :math:`n` is greater than or equal to :math:`m` for a proper transfer function. Improper transfer functions are also allowed. -To create a transfer function, use the :func:`tf` function: - -.. testsetup:: xferfcn - - num = np.array([1, 2]) - den = np.array([3, 4]) +To create a transfer function, use the :func:`tf` function:: -.. testcode:: xferfcn + num = [a0, a1, ..., am] + den = [b0, b1, ..., bn] sys = ct.tf(num, den) The system name as well as input and output labels can be specified in the same way as state space systems: +.. testsetup:: xferfcn + + num = [1, 2] + den = [3, 4] + .. testcode:: xferfcn sys = ct.tf(num, den, name='sys', inputs=['u'], outputs=['y']) @@ -156,9 +152,9 @@ Frequency response data (FRD) systems The :class:`FrequencyResponseData` (FRD) class is used to represent systems in frequency response data form. The main data attributes are -`omega` and `frdata`, where `omega` is a 1D array of frequencies and -`frdata` is the (complex-value) value of the transfer function at each -frequency point. +`omega` and `frdata`, where `omega` is a 1D array of frequencies (in +rad/sec) and `frdata` is the (complex-value) value of the transfer +function at each frequency point. FRD systems can be created with the :func:`frd` factory function: @@ -232,7 +228,7 @@ Similarly, MIMO frequency response data (FRD) systems are created by providing the :func:`frd` function with a 3D array of response values,with the first dimension corresponding to the output index of the system, the second dimension corresponding to the input index, and -the 3rd dimension corresponding to the frequency points in omega. +the 3rd dimension corresponding to the frequency points in `omega`. Signal names for MIMO systems are specified using lists of labels: @@ -275,11 +271,11 @@ response. .. note:: If a system is single-input, single-output (SISO), `magnitude` and `phase` default to 1D arrays, indexed by frequency. If the system is not SISO or `squeeze` is set to - False, the array is 3D, indexed by the output, input, and - frequency. If `squeeze` is True for a MIMO system then - single-dimensional axes are removed. The processing of the - `squeeze` keyword can be changed by calling the response - function with a new argument:: + False generating the response, the array is 3D, indexed by + the output, input, and frequency. If `squeeze` is True for + a MIMO system then single-dimensional axes are removed. The + processing of the `squeeze` keyword can be changed by + calling the response function with a new argument:: mag, phase, omega = response(squeeze=False) @@ -294,7 +290,7 @@ response. Discrete Time Systems ===================== -A discrete-time system is created by specifying a nonzero "timebase", +A discrete-time system is created by specifying a nonzero "timebase" `dt` when the system is constructed: .. testsetup:: dtime @@ -311,22 +307,22 @@ A discrete-time system is created by specifying a nonzero "timebase", The timebase argument is interpreted as follows: * `dt` = 0: continuous-time system (default) -* `dt` > 0: discrete-time system with sampling period 'dt' +* `dt` > 0: discrete-time system with sampling period `dt` * `dt` = True: discrete time with unspecified sampling period * `dt` = None: no timebase specified (see below) Systems must have compatible timebases in order to be combined. A -discrete-time system with unspecified sampling time (`dt` = True) -can be combined with a system having a specified sampling time; the -result will be a discrete-time system with the sample time of the -other system. Similarly, a system with timebase None can be combined -with a system having a specified timebase; the result will have the -timebase of the other system. For continuous-time systems, the +discrete-time system with unspecified sampling time (`dt` = True) can +be combined with a system having a specified sampling time; the result +will be a discrete-time system with the sample time of the other +system. Similarly, a system with timebase None can be combined with a +system having a specified timebase; the result will have the timebase +of the other system. For continuous-time systems, the :func:`sample_system` function or the :meth:`StateSpace.sample` and :meth:`TransferFunction.sample` methods can be used to create a -discrete-time system from a continuous-time system. See -:ref:`utility-and-conversions`. The default value of `dt` can be -changed by changing the value of `config.defaults['control.default_dt']`. +discrete-time system from a continuous-time system. The default value +of `dt` can be changed by changing the value of +`config.defaults['control.default_dt']`. Functions operating on LTI systems will take into account whether a system is continuous time or discrete time when carrying out operations @@ -365,8 +361,8 @@ Conversion between representations ---------------------------------- LTI systems can be converted between representations either by calling -the constructor for the desired data type using the original system as -the sole argument or using the explicit conversion functions +the factory function for the desired data type using the original +system as the sole argument or using the explicit conversion functions :func:`ss2tf` and :func:`tf2ss`. In most cases these types of explicit conversions are not necessary, since functions designed to operate on LTI systems will work on any subclass. @@ -566,7 +562,7 @@ Information about an LTI system can be obtained using the Python [-0. 0.]] A loadable description of a system can be obtained just by displaying -the system object:: +the system object: .. doctest:: diff --git a/doc/nlsys.rst b/doc/nlsys.rst index dbe693941..f063cd13d 100644 --- a/doc/nlsys.rst +++ b/doc/nlsys.rst @@ -11,9 +11,10 @@ of the form \frac{dx}{dt} &= f(t, x, u, \theta), \\ y &= h(t, x, u, \theta), -where :math:`t` represents the current time, :math:`x` is the system -state, :math:`u` is the system input, :math:`y` is the system output, -and :math:`\theta` represents a set of parameters. +where :math:`t` represents the current time, :math:`x \in +\mathbb{R}^n` is the system state, :math:`u \in \mathbb{R}^m` is the +system input, :math:`y \in \mathbb{R}^p` is the system output, and +:math:`\theta` represents a set of parameters. Discrete time systems are also supported and have dynamics of the form @@ -37,11 +38,11 @@ The `updfcn` argument is a function returning the state update function:: updfcn(t, x, u, params) -> array -where `x` is a 1-D array with shape (n,), `u` is a 1-D array -with shape (m,), `t` is a float representing the current time, -and `params` is a dict containing the values of parameters used by the -function. The dynamics of the system can be in continuous or discrete -time (use the `dt` keyword to create a discrete-time system). +where `t` is a float representing the current time, `x` is a 1-D array +with shape (n,), `u` is a 1-D array with shape (m,), and `params` is a +dict containing the values of parameters used by the function. The +dynamics of the system can be in continuous or discrete time (use the +`dt` keyword to create a discrete-time system). The output function `outfcn` is used to specify the outputs of the system and has the same calling signature as `updfcn`. If it is not @@ -62,7 +63,7 @@ simple model of a spring loaded arm driven by a motor: :width: 240 :align: center -The dynamics of this system can be modeling using the following code: +The dynamics of this system can be modeled using the following code: .. testcode:: @@ -73,7 +74,6 @@ The dynamics of this system can be modeling using the following code: 'k': 1, # Spring constant 'r': 1, # Location of spring contact on arm 'l': 2, # Distance to the read head - 'eps': 0.01, # Magnitude of velocity-dependent perturbation } # State derivative @@ -114,7 +114,7 @@ of the model (via the Python `~python.print` function): Inputs (1): ['tau'] Outputs (2): ['y', 'thdot'] States (2): ['theta', 'thdot'] - Parameters: ['J', 'b', 'k', 'r', 'l', 'eps'] + Parameters: ['J', 'b', 'k', 'r', 'l'] Update: Output: @@ -165,7 +165,8 @@ Time responses can be plotted using the :func:`time_response_plot` function or (equivalently) the :func:`TimeResponseData.plot` method:: - cplt = ct.time_response_plot(resp) + cplt = ct.time_response_plot(resp) # function call + cplt = resp.plot() # method call The resulting :class:`ControlPlot` object can be used to access different plot elements: diff --git a/doc/nonlinear.rst b/doc/nonlinear.rst index b0ea3058f..66de61c38 100644 --- a/doc/nonlinear.rst +++ b/doc/nonlinear.rst @@ -8,7 +8,7 @@ The Python Control Systems Library contains a variety of tools for modeling, analyzing, and designing nonlinear feedback systems, including support for simulation and optimization. This chapter describes the primary functionality available, both in the core -python-control package and in specalized modules and subpackages. +python-control package and in specialized modules and subpackages. .. include:: nlsys.rst diff --git a/doc/optimal.rst b/doc/optimal.rst index aa2420f47..07131b536 100644 --- a/doc/optimal.rst +++ b/doc/optimal.rst @@ -165,7 +165,7 @@ A nonlinear constraint is satisfied if lb <= f(x, u) <= ub The `constraints` are taken as trajectory constraints holding at all -points on the trajectory. The `terminal_constraint` parameter can be +points on the trajectory. The `terminal_constraints` parameter can be used to specify a constraint that only holds at the final point of the trajectory. diff --git a/doc/phaseplot.rst b/doc/phaseplot.rst index 612568843..324b3baf4 100644 --- a/doc/phaseplot.rst +++ b/doc/phaseplot.rst @@ -21,9 +21,11 @@ The default method for generating a phase plane plot is to provide a .. testcode:: phaseplot + def sys_update(t, x, u, params): + return np.array([[0, 1], [-1, -1]]) @ x sys = ct.nlsys( - lambda t, x, u, params: np.array([[0, 1], [-1, -1]]) @ x, - states=['position', 'velocity'], inputs=0, name='damped oscillator') + sys_update, states=['position', 'velocity'], + inputs=0, name='damped oscillator') axis_limits = [-1, 1, -1, 1] T = 8 ct.phase_plane_plot(sys, axis_limits, T) diff --git a/doc/response.rst b/doc/response.rst index 59a024020..0058a500d 100644 --- a/doc/response.rst +++ b/doc/response.rst @@ -54,12 +54,12 @@ system in response to a standard input (such as a step function or impulse function), the initial state with no input, a custom function of time, or any combination of the above. Time responses are useful for evaluating system performance of either linear or nonlinear -systems, in continous or discrete time. The time response for a +systems, in continuous or discrete time. The time response for a linear system to a standard input can be often computed exactly while -the responses of nonlinear systems or linear systems with arbitary +the responses of nonlinear systems or linear systems with arbitrary input signals must be computed numerically. -Continous time signals in `python-control` are represented by the +Continuous time signals in `python-control` are represented by the value of the signal at a set of specified time points, with linear interpolation between the time points. The time points need not be uniformly spaced. Discrete time signals are represented by the value @@ -106,8 +106,8 @@ function is described in more detail in the :ref:`iosys-module` section. .. _time-series-convention: -Time series data ----------------- +Time series data conventions +---------------------------- A variety of functions in the library return time series data: sequences of values that change over time. A common set of conventions is used for @@ -122,7 +122,7 @@ and :func:`initial_response`. .. note:: The convention used by `python-control` is different from the convention used in the `scipy.signal `_ - library. In Scipy's convention the meaning of rows and columns is + library. In SciPy's convention the meaning of rows and columns is interchanged. Thus, all 2D values must be transposed when they are used with functions from `scipy.signal`_. @@ -232,12 +232,13 @@ a pandas dataframe:: df = response.to_pandas() -The column labels for the data frame are `time` and the labels for the input, -output, and state signals ('u[i]', 'y[i]', and 'x[i]' by default, but these -can be changed using the `inputs`, `outputs`, and `states` keywords when -constructing the system, as described in :func:`ss`, :func:`tf`, and other -system creation functions. Note that when exporting to pandas, "rows" in the -data frame correspond to time and "cols" (DataSeries) correspond to signals. +The column labels for the data frame are :code:`time` and the labels +for the input, output, and state signals ('u[i]', 'y[i]', and 'x[i]' +by default, but these can be changed using the `inputs`, `outputs`, +and `states` keywords when constructing the system, as described in +:func:`ss`, :func:`tf`, and other system creation functions. Note +that when exporting to pandas, "rows" in the data frame correspond to +time and "cols" (DataSeries) correspond to signals. Time response plots ------------------- @@ -264,6 +265,7 @@ for a two-input, two-output can be plotted using the commands: :hide: plt.savefig('figures/timeplot-mimo_step-default.png') + plt.close('all') which produces the following plot: @@ -296,6 +298,7 @@ yields the following plot: :hide: plt.savefig('figures/timeplot-mimo_step-pi_cs.png') + plt.close('all') .. image:: figures/timeplot-mimo_step-pi_cs.png :align: center @@ -738,11 +741,11 @@ various ways. The following general rules apply: sequentially, the :func:`matplotlib.pyplot.figure` command should be used to explicitly create a new figure. -* The `ax` keyword argument can be used to direct the plotting function - to use a specific axes or array of axes. The value of the `ax` keyword - must have the proper number of axes for the plot (so a plot generating a - 2x2 array of subplots should be given a 2x2 array of axes for the `ax` - keyword). +* The `ax` keyword argument can be used to direct the plotting + function to use a specific axes or array of axes. The value of the + `ax` keyword must have the proper number of axes for the plot (so a + plot generating a 2x2 array of subplots should be given a 2x2 array + of axes for the `ax` keyword). * The `color`, `linestyle`, `linewidth`, and other matplotlib line property arguments can be used to override the default line properties. @@ -829,7 +832,7 @@ various ways. The following general rules apply: specified in the `rcParams` keyword argument. To override the defaults for all control plots, update the `config.defaults['ctrlplt.rcParams']` dictionary entries. For convenience, - this dictionary can alse be accessed as `ct.rcParams`. + this dictionary can also be accessed as `ct.rcParams`. The default values for style parameters for control plots can be restored using :func:`reset_rcParams`. @@ -847,13 +850,14 @@ various ways. The following general rules apply: and phase portions of the plot, and `share_frequency` can be used instead of `sharex`. -* The `title` keyword can be used to override the automatic creation of - the plot title. The default title is a string of the form " plot - for " where is a list of the sys names contained in - the plot (which is updated if the plotting is called multiple times). - Use `title` = False to suppress the title completely. The title can also - be updated using the :func:`ControlPlot.set_plot_title` method - for the returned control plot object. +* The `title` keyword can be used to override the automatic creation + of the plot title. The default title is a string of the form + " plot for " where is a list of the sys + names contained in the plot (which is updated if the plotting + function is called multiple times). Use `title` = False to suppress + the title completely. The title can also be updated using the + :func:`~ControlPlot.set_plot_title` method for the returned control + plot object. The plot title is only generated if `ax` is None. diff --git a/doc/statesp.rst b/doc/statesp.rst index 46fb537a0..752c488bb 100644 --- a/doc/statesp.rst +++ b/doc/statesp.rst @@ -7,7 +7,8 @@ This section describes the functions the are available to analyze state space systems and design state feedback controllers. The functionality described here is mainly specific to state space system representations; additional functions for analysis of linear -input/output systems are defined in the next section and can also be +input/output systems, including transfer functions and frequency +response data systems, are defined in the next section and can also be applied to LTI systems in state space form. @@ -41,7 +42,7 @@ Similarity transformations and canonical forms ---------------------------------------------- State space systems can be transformed into different internal -representations representing a variety of standard cananonical forms +representations representing a variety of standard canonical forms that have the same input/output properties. The :func:`similarity_transform` function allows a change of internal state variable via similarity transformation and the @@ -121,7 +122,7 @@ that minimizes the quadratic cost J = \int_0^\infty (x' Q x + u' R u + 2 x' N u) dt -by solving the approriate Riccati equation. It returns the gain +by solving the appropriate Riccati equation. It returns the gain matrix `K`, the solution to the Riccati equation `S`, and the location of the closed loop eigenvalues `E`. It can be called in one of several forms: diff --git a/doc/stochastic.rst b/doc/stochastic.rst index 9169b967a..b09213f10 100644 --- a/doc/stochastic.rst +++ b/doc/stochastic.rst @@ -311,7 +311,7 @@ horizon estimator* (MHE). The formulation for the moving horizon estimation problem is very general and various situations can be captured using the conditional probability -function :math:`p(x[0], \dots, x[N] \mid y[0], \dots, y[N-1]`. We start by +function :math:`p(x[0], \dots, x[N] \mid y[0], \dots, y[N-1])`. We start by noting that if the disturbances are independent of the underlying states of the system, we can write the conditional probability as @@ -373,21 +373,16 @@ optimal estimation problem over a finite horizon:: Given noisy measurements :math:`y` and control inputs :math:`u`, an estimate of the states over the time points can be computed using the -:func:`optimal.OptimalEstimationProblem.compute_estimate` method:: +:func:`~optimal.OptimalEstimationProblem.compute_estimate` method:: estim = oep.compute_optimal(Y, U[, X0=x0, initial_guess=(xhat, v)]) xhat, v, w = estim.states, estim.inputs, estim.outputs For discrete-time systems, the -:func:`optimal.OptimalEstimationProblem.create_mhe_iosystem` method +:func:`~optimal.OptimalEstimationProblem.create_mhe_iosystem` method can be used to generate an input/output system that implements a moving horizon estimator. -An example showing the use of the optimal estimation problem and -moving horizon estimation (MHE) applied to a planar vertical takeoff -and landing (PVTOL) model is given in the :doc:`Moving Horizon -Estimation Jupyter notebook `. - Several functions are available to help set up standard optimal estimation problems: diff --git a/doc/xferfcn.rst b/doc/xferfcn.rst index dbba9c628..627c47c33 100644 --- a/doc/xferfcn.rst +++ b/doc/xferfcn.rst @@ -52,8 +52,8 @@ Input/output norms Continuous and discrete-time signals can be represented as a normed linear space with the appropriate choice of signal norm. For -continous time signals, the three most common norms are the 2-norm and -the :math:`\infty`-norm: +continuous time signals, the three most common norms are the 1-norm, +2-norm, and the :math:`\infty`-norm: .. list-table:: :header-rows: 1 @@ -89,7 +89,7 @@ following table summarizes the induced norms for a transfer function - :math:`\| G \|_2` - :math:`\| g \|_1` -The 2-norm and :math:`\infty`-norm can be computed using +The system 2-norm and :math:`\infty`-norm can be computed using :func:`system_norm`:: sysnorm = ct.system_norm(sys, p=) @@ -144,12 +144,12 @@ where S = \frac{1}{1 + P C}, \qquad T = \frac{P C}{1 + P C} -are the sensitivty function and complementary sensitivity function, +are the sensitivity function and complementary sensitivity function, and :math:`P(s)` represents the process dynamics. The :func:`h2syn` and :func:`hinfsyn` functions compute a feedback controller :math:`C(s)` that minimizes the 2-norm and the -:math:`\infty`-norm of the sensitity function for the closed loop +:math:`\infty`-norm of the sensitivity function for the closed loop system, respectively. @@ -172,3 +172,21 @@ a time delay to a given order: -s^3 + 120 s^2 - 6000 s + 1.2e+05 --------------------------------- s^3 + 120 s^2 + 6000 s + 1.2e+05 + +The plot below shows how the Pade approximation compares to a pure +time delay. + +.. testcode:: + :hide: + + import matplotlib.pyplot as plt + omega = np.logspace(0, 2) + delay_exact = ct.FrequencyResponseData(np.exp(-0.1j * omega ), omega) + cplt = ct.bode_plot( + [delay_exact/0.98, delay*0.98], omega, legend_loc='upper right', + label=['Exact delay', '3rd order Pade approx'], + title="Pade approximation versus pure time delay") + cplt.axes[0, 0].set_ylim([0.1, 10]) + plt.savefig('figures/xferfcn-delay-compare.png') + +.. image:: figures/xferfcn-delay-compare.png diff --git a/examples/kincar-fusion.ipynb b/examples/kincar-fusion.ipynb index 7258d507a..da320a502 100644 --- a/examples/kincar-fusion.ipynb +++ b/examples/kincar-fusion.ipynb @@ -23,10 +23,10 @@ "import numpy as np\n", "import scipy as sp\n", "import matplotlib.pyplot as plt\n", + "\n", "import control as ct\n", "import control.optimal as opt\n", "import control.flatsys as fs\n", - "from IPython.display import Image\n", "\n", "# Define line styles\n", "ebarstyle = {'elinewidth': 0.5, 'capsize': 2}\n", @@ -76,11 +76,11 @@ "Outputs (3): ['x', 'y', 'theta']\n", "States (3): ['x', 'y', 'theta']\n", "\n", - "Update: \n", - "Output: \n", + "Update: \n", + "Output: \n", "\n", - "Forward: \n", - "Reverse: \n" + "Forward: \n", + "Reverse: \n" ] } ], @@ -112,7 +112,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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", 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" ] @@ -160,10 +160,11 @@ "name": "stdout", "output_type": "stream", "text": [ - ": sys[3]\n", + ": sys[1]\n", "Inputs (2): ['u[0]', 'u[1]']\n", "Outputs (3): ['y[0]', 'y[1]', 'y[2]']\n", "States (3): ['x[0]', 'x[1]', 'x[2]']\n", + "dt = 0.1\n", "\n", "A = [[ 1.0000000e+00 0.0000000e+00 -5.0004445e-07]\n", " [ 0.0000000e+00 1.0000000e+00 1.0000000e+00]\n", @@ -179,10 +180,7 @@ "\n", "D = [[0. 0.]\n", " [0. 0.]\n", - " [0. 0.]]\n", - "\n", - "dt = 0.1\n", - "\n" + " [0. 0.]]\n" ] } ], @@ -219,7 +217,7 @@ "outputs": [ { "data": { - "image/png": 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RSFh8CvWsTXiha81Y/7U4ShzYRUREMGPGDBwcKmd9tSNHjjBlyhROnTrFvn37yMjIYMCAASQmJlZKe4QQQoja4GxwLADtXa1RqVTo62nXW61vY0pwdDJTfvMjvZLKoMQlpfPTIe18v7eeaIaxQc3Pgn1UiQO7UaNGcfjw4TJsSvHs3r0bLy8vWrduTfv27Vm1ahV37tzB19e30tokhBBC1HTnsgK7Dq7Wum11zAxZPq4zZoZ6nLgVxRc7r+R7fGhoKJ988gmhoaFFvmZRj1ly5BbxKRk0d7Cgm6O6WNcpz3ZVpBIHdj/88ANbtmzBy8uLb7/9lu+++y7Hq6LFxWkzdGxsbCr82kIIIURtcfaRwC47uLFUHrDg+Q6Adjj0d+/gPI8PDQ1l7ty5xQ6gCjsmLC6FVccDAHhnYHMiwsOKdZ3yaldFK3HyxG+//caePXswMTHh8OHDqFT/1YdRqVS88cYbZdLAolAUhRkzZtCzZ0/atGmT736pqamkpqbq3sfHx1dE84QQQlSyk7eiWLjvOl0b2TDK3QU3W7PKblKVFBoaytKlS5k8eTJOTk65Pk9ISefm/QcAtHOx1h0zd+5chg0bxsBOnXirfzMW7r/O7G0XaFzXDHc3G91+oaGh+Pn5Aej+dHR0xN7eHn19fSIjI0lNTcXU1BS1Ws2ZM2cICQkhISEBgDVr1nDq1ClGjBjB8ePHycjIoFGjRjg6OvLqVyuJvHWf9h3duXrwD44cOaK7zq+//oqhoSE9evSgdevWrFu3DkVRGD16NKdPn+bo0aOEhYUB8Nlnn2FtbY27uztDhw5lyZIlAIwePZqQkBCOHDnCgwcPGD9+PIsWLcpxL05OTnl+3SpSiQO7Dz/8kE8//ZT3338ftbpyy+FNnTqV8+fPc+zYsQL3mzdvHnPnzq2gVgkhhCjMgn3X8Q6IZsmLnbA2NSyXayiKwqc7LnMlNJ4zgdF8f/AmXRra8Ky7C0+2dcLMqFSVv6q0wgK1vPbPDtIe3V9RFPwC7qPJzMQmI5rTR/YBcPbsWQDmz5/P5MmTSfP3wcLfj+AHKl7Wy6Suz1KM9NXo6+uzdetW3fkmTZoEQLNmzejXrx/fffcd7733HkZGRgwcOJBGjRrxxRdfcPToUd0x2SOCERERNG7cGD09PdRqNffi0/C7r6BvaY9LjD/Tvv4h13UmTpxIq1atsLOz46mnnkKtVuPo6Mi5c+dYuXKlbv9t27YBYG1tjb29PePGjQOgXr16ODk50bBhQ3744Qe6du2a6xpz5szhk08+KfTrXJ5UiqKUqFy0jY0N3t7eNG7cuKzbVCzTpk1j27ZtHD16lIYNGxa4b149dq6ursTFxcnSZ0IIUcEiElLwnHeQTI3ClMcb887AFuVyHd+gaEYuOYmRvpqujWz598Z9sn/ymRrq8WRbJ551d6FLQ5sco09VTXGDNND2JLm7u+Pr60unTp0AbYAGcO/ePUJCQtBoNNjZ2fHDDz9w7do1du/ezdSpUzl37hzGxsasXbuWRYsWcenSJVLMHLnmNADbGzuwSb7LwYMHc11z+vTpvDz5dd748yq3EtS0dbZk8+s9iImM0PXYTZo0ieXLl9OpU6cCe7ke7uUr6JjX1/vyz4Uw+resy2cDXIp1naJeo7THlEZ8fDxWVlZFildK/GvK+PHj2bRpEx988EFJT1EqiqIwbdo0tm7dyuHDhwsN6gCMjIwwMqo91aeFEKIq2+Z/j8yspahWHw/k5Z6NqGNW9r12604GAfB0B2fmj2pPaFwyW/zu8YdPMIFRSWz2vctm37u42ZoyqpMLI91dcLYu+rroFaWw3jSAgIAAgoKCiIiIwNHRkc8//xyAFStW0KRJE3bt2oWRkRErV67kzz//JC4ujubNm7Njxw4WL16sO98PP2h7vObMmYOjoyNfffUVAJPX+XDjUjivvfsxQ5uaFhjcrHndhWE/HONiaALv/3mehc93yNHuTp066YLN/DwaKOV1zLngWP65EIZKBe8MbIGTo0WxrlOUa5TFMRWlxIFdZmYm8+fPZ8+ePbRr1w4Dg5wFABcsWFDqxhVkypQp/Pbbb2zfvh0LCwvd2LiVlRUmJlXvH6QQQoj/KIrCHz53ATDUU5OYlskvxwJ4e2DzMr1O5INU/rmg/fkwtlsDAJysTJjyeBNe79MYn6AY/vAJZuf5UIKikvh233UW7L9OzyZ2jHJ3YWBrx3IpmVGc3re7d+9y/vx5zpw5A8BXX32lG660srJi2bJlgDZ427NnDykpKfj4+PDbb7/pzpE9T+zhocLp06frPn/sscd45pln8gzSHpa9lFgH1zo4OdkUGNy42pjy0wvuvPjLabadDaGlkyWTezfGycmJOXPmFKtnK79jFEXh691XARjRsR7NHS0KPaa41yjrY8pbiYdiH3/88fxPqlLl2T1blvLrLl+1ahVeXl5FOkdxujaFEEKUnXPBsTz943GM9NV8PrwN72w+j7mRPsfee7xM59r9eOgm/9tzjQ6u1myb0iPf/ZLSMth1IYw/fIM5dTtat72BrSnbp/TEyrTg1QuKO0z68BBphw4ddD1tjo6OxMXFsXTpUqKiovj5558ZM2YM//77b65zfPDBB3zxxRf5tqckQ4V5Dd1mC4tLodu8A6hVcHHuQEwN9Yt072tPBvLx9kuoVLDKy4M+zesW+vUpqqPX7zNu5RkM9dQcfLs3LnVMy+zcVUmFDMUeOnSopIeWiRLGo0IIIaqAP3y1pTAGtXFkZCcXVh4P5EpoPL8cC2DmgLLptcvUKPx2+g4AY7u5FbivqaE+I921w7B3opLY7HeXdScDCYxKYtfFUEZ3KXgFg4KGSQESExO5cuUK3t7eNGvWjJ9++gmAGTNm8OWXX7Jy5Upat26Nra0tLVu25JtvvsHW1haVSsWmTZvyDdLyU9KhwoJ6oM7djQWgmYOFLqjLPqaghIGx3dy4EhrPhjPBTNvgz7YpPWhsb15oWwqj0fzXW/diN7caG9QVV7HSWc+fP49GU/Rq0pcuXSIjI6PYjRJCCFFzpaRn8tfZEACedXdFrVYxvV8TAFYdDyQ2qWyWpDp4NYJ7scnUMTXgqXZFHyozSIsj/vhvjGqpLYmy93J4vvtmB1zZ5S62bt3KokWLuHfvHl5eXgwZMoQVK1Zw+fJl/v77b/bt20f//v3ZsmULoF1FqUePHri4uPDWW2/Rvn17rK2tsbOz041MOTk55QjMsv9elJ7BkgxHfvLJJ3kHdnkUJi4KlUrF3GFt6OxWh4SUDCat9SE+Jb1Y58jLjguhXAqJx9xIn6l9m5T6fDVFsXrsOnbsSFhYGPb29kXa39PTk7Nnz9KoUaMSNU4IIQqy/ew9wuJSeOWxRlU6m1HktO9yOPEpGThbGdO9sS0AA1o50sLRgqthCaw8FsCMMui1W3dKmzTxnIdrsebJZfe+bdnbD4BjNyN5kJqBuZE+iqIQHh5OYmIiiqIwZswYfHx8dMdmJyvExsaycOFC6tSpo/vMw8OD0NBQPvzww2L1vmUr6Rywsiq/8Whh4uIw1Fez5EV3hv1wjNv3E5m+wZ/l4zqjr1eycmlpGRq+3atdH/6VxxphUw5JN9VVsQI7RVH46KOPMDUtWndnWlrlLgQshKi5tp+9x/SNZwHwbGyrK5Yqqr4/fLVJEyPdXVCrtQG5tteuKa+t92PV8UAm9mxU6Ly2ggREJnL0+n1UKnihS8HDsNkeLaAbfusSpsEhhATd4tmrv7Fj02omTJiAoaEhQ4YMoW/fvvz000+oVCrOnj2bK1B7OKjLVppsyrIM0opLo1E4f1ebONG+BIEdgL2FEcvGdmbUzyc4dO0+0zeeZdHoDhiUILjb5H2HoKgk7MyNmNiz8KoYtUmxArvHHnuMa9euFXl/T09PyVAVQpQ536AY3tl8XvfeOzBGArtqIjQumX9v3AdglLtLjs8Gtv6v1+6X4wHMeKJZia+zPqu3rktdFSu/n19gUkNCQgLnz59nyZIlrF+/Xrf9tddeA8C09eM4/5+2GP/atWtzHOvh4QGgK9RfFnPZqqLbkQ94kJqBiYEeTeuWfH5cWxcrfvy/Try23pedF0LJ0Gj4fkwnDPWLHtwlpmaw+MBNAN7o16RGF5guiWJ9NQ4fPlxOzRBCiKIJjk5i8jof0jI0WBjrk5CSgU9gtPzWXk1s8buHokCXhja5lvVSq1W80a8pr6/3Y9WxACb2aFiiXrvktExdr2Df+oa8Okqb1ODo6EhycjJBQUH8/vvvXLx4kR9++IGffvoJQ0NDRowYwVtvvYWfnx+vvPIKy5cvx8SpMe/tusuZCIX0TAVD/byH/Es6l626OJtV5qRtPasSD59m69/KgWVjOzP5V1/2XArn9fW+/PhCJ4z0izZcvup4AJEPUqlvY8poj4KTWmqjyl0LTAghiiEhJZ2X1/gQ+SCNVk6WfDemIwA+QTGSKV8NKIrC5qyA69HeumyDWjvS3MGChNQMVmYt6P6w7AXnC1p0/e9zIUSFh2AcfJJzB7RJCl5eXjz22GNs2LABY2NjBg0axOrVq3FwcGDu3LnMnj2bkSNH4u7ujru7O6DtfRszuA+Ojk4kpGRw6nZUvtcsKOmgJjgbHANAh/rWZXK+x1vUZcW4zhjpq9l/JYJX1/mSkp5Z6HExiWksPXIbgJkDmhWrp6+2kK+IEKJayMjUMG2DP9fCE6hrYcQvXp3xbGSLgZ6K+wmp3IlOquwmikL4BsUQEJmIqaEeT7XNOwDK7rUDWHk8gLjknNmT2YkNjwZ2UVFRrF69milTpvD9H3uI2vU91377QleY98KFCxw7dozg4GAaNmxI165dMTPL2WOY7eHeN7VaxROtHADYezmsVPdfnWUXJm5fhlMeHmtmz0ovD4wN1By6dp9Ja30KDe5+PHSThNQMWjlZMrSdc5m1pSaRwE4IUS18vvMKh6/dx9hAzS/jPXCyMsHYQI829awA8AmMqeQWisJk99YNbuNU4LyowW0caeZgTkJKBquyeu2ykxqyM1B9fHyYNGkS/fr148svvyQ1NRVTU1MGjXmZOypHnIbN4OCxUyxfvhyA5cuX4+vry+TJkwtt56O9bwNbZwV2l8LRaGpfz3BKeiZXQuMBaO9qVabn7tHEjtUTumBqqMe/NyJ5abU3SWl5l0m7F5vM2qzl4d4d1FyXeCNyksBOCFHlrT0ZyOoTgQAser4DbV3+++Hi0cAGAJ+g6LwOFVVEUloGO85re9me7awdhs1vWPXhXrulu/3Y8McW5syZg7u7uy4wmzx5MitWrKBnz5588MEHODs789xzz3EoRIVKpWJ4j9Y83qNriWq/PcqzsS3mRvpEJKTqivTWJpdD48nQKNiZG1KvHNbQ7dbIljUvdcHMUI8Tt6LwWuVNYmru4G7hvuukZWro1siG3s2KVnatNpLATghRpR25fp+5f18GtL+lD2qT8wezu5u2pIT02FVtuy+G8SA1g/o2pnTJCsbzGla9cOECixYt4uDqb2hkrU/wnl/YevwSL7/8Mr6+vrl64F599VXdsdGJabrgcZxnA6Bssk+N9PXo01wbSOy5lH+x4prq7J1YQDsMW171Ij0a2LDu5a5YGOlzJiCa8SvPkPBQEePr4Qls8dP2+L43qIXUrSxAqXKEDxw4wIEDB4iIiMi1IsXKlStL1TAhhLgensDU9X5kahRGubvwWu/GufbpnBXY3Yh4QGxSWpmuMyrKTvYw7MhOLoSHhxEaGsrJkycBePfdd0lOTubjjz9Go9HQsmVLxo8fz7E7yUyLfZOrxvq0aNcRS+P/MmTzKivyu08waRka2tazon1Wr25ZZZ8ObO3IjvOh7L0cxvuDW5T6fNVJdi9lSQoTF0en+nX49eWujP3lND5BMYxbeYY1L3XB0tiA+buvoVG0yTUd6+euDyj+U+Ieu7lz5zJgwAAOHDhAZGQkMTExOV5CCFEakQ9SeWm1NwmpGXRpaMOXI9rm+Vu6rbkRjey0k+B9g+T/nsqU39BqcHQSx29EkB5xm6fbOzBixAjc3d2ZOnUqoO0kOHHiBCdPnmTw4MEMHDiQOnXq8GRbJ5rWNSc+JYPVxwOB/HvgMjUK609r51+N7eZW5j06fZrbY6Cn4vb9RG5GPCj1+SISUoqUBVoVZC8lVtLCxMXR3tWa3yZ1w9rUAP87sby44jQHr4az/0o4ahW8PbBs1hGuyUrcY/fzzz+zevVqxo4dW5btEUIIUtIzmbzOl7sxybjZmrL0RfcCyxp0blCH25GJeAfG0K+lQwW2VDwse2h12LBhWFpacvr0aSwsLFi2x4/7W36hYbMW2Bp78dtvvxEbG1voslp6ahXT+jXljQ3+rPj3Nl49GuTbA3f0+n2Co5OxMjFgaPuyz5a0MDage2M7jly/z55LYTSpW/K1SY9ev8/La3ywMzdk4yue1LetuovXxyalERilzThv51K2iRP5aVPPit9e7saLv5zm/N04XlqtTZh5rrMrTUpRHLm2KHGPXVpaGt27dy/LtgghBIqi8N6f5/ENisHSWJ+VXh7UKWQdyM5u2jlbvpJAUSlCQ0M5cOAAK1asAMDb25sRI0Zw6NAhzMzMuWrYlLqj5vD5F19iYWFBo0aNiryo/VNtnWiS1Wu3JqvXLi9rT2o/e9bdBRPDoq8LWxwDWzsCsPdyyefZXQtL4PX1fqRlagiJS2HM8lMEV+FSPeeylhFraGdWodMcWjlbsmFSN+zMtdc00lczvX/TCrt+dVbiwO7ll1/mt99+K8u2CCEE3x+8yfazIeirVSx50Z3G9oX/ht65gXbOzbm7caRmVI/hraqusELAiYmJbNq0iddff51Zs2bRv39/Xc24V199lX379qGnp0ecUV2Co5MxN9JnUOucgVtREhv01Cqm9dX2jq04FpBjQn22O1FJHL6uXabshW5FWxe2JPq3qotKpR2aDItLKfbxEfEpTFh1hgepGXg0qEMjezPuxSYzelnVDe6yEyfKe35dXpo7WrDxlW70aW7PlyPa4mQlS5QWRYmHYlNSUli2bBn79++nXbt2GBjkXPZlwYIFpW6cEKJ2+ftcCAv2XQfgs+Ft6NHErkjHNbQzw9bMkKjENC7ei8M9qwdPaMUmpfEgNQOXOkUf8nt4WNXJyQlFUdixYwd//fUXlpaWzJ49m+TkZN555x2MjIx444038hxa/eZfbdLEkHZOuXrSiprYMKSdM98duMGt+4msORHI1L45e27Wnw5CUbQFbxva5V10uCzUtTCmU/06+AbFsO9yGGOzMm+LIiktg4lrfAiJS6GRvRnLx3UmNUPD6GWnCIhMZMzyU2ya7Fku5URKIztxon0FDcM+qkldC1ZP6FIp166uStxjd/78eTp06IBarebixYv4+/vrXmfPni3DJgohaoNzwbHM/OMcAJN6NWRMl6KvAalSqaTsST5SMzJ55qcT9F9wpEi9QtmFgE+dOgXA1KlT6d69O6dOncLW1paPP/6Yb7/9FhsbG7y8vGjYsCHOzs55Dq1a2Njzz4WctetKQu+hunbL/83Za5eSnskmn2BAmzRR3gboVqEo+nBspkbhjQ1nuXAvDhszQ1Z5eWBtaoiDpTEbJnWjga0pd2OSGb3sJCGxyeXV9GJTFKVCEydE2Shxj92hQ4fKsh1CiCpIURRW/BuAnYUhIzqW/AdzUSw+cIO0DA39W9bl/cEti3185wZ12Hs5HO/AGCb3LocGVlPrTgZxPTCYB2d38VtHa957xjPP/UJCQnBycmLEiBGcPn1atz27JMmePXsK7V17dGj1n/OhJKdn0sjOjE6lLFExpJ0ziw/c4Pb9RNaeDGLK49rh2Z3nQ4lNSqeetQl9W9Qt1TWKYkBrR+btusrJW1HEJaVjZWpQ6DFf7LzC/ivhGOqrWT7OHTfb/3oVHa2M2fBKN0YvO0VQVBJjlp9i4yvdqsSw492YZKIS0zDQU9HSybKymyOKSAoUCyHydSkkni/+ucLbf5wnPo+5TWUlLimdf29o50i9P7gFeiVYKqhzg/8SKBSl9i37lJf4lHR+OHSTzAfRxB3fwK4zV3Sf3b9/nzt37rBjxw4GDRrEhx9+SFJSEitXrsyzEHBJluLS1a5zdyl1+RE9tYo3+mb32t3mQdbKBGtPaUuc/F/X+iX6vimuhnZmNHMwJ0OjcOhaRKH7rzkRyMqsZdEWPNc+z2kCTlYmbJjUjfo2pgRFJfF/y0+XaA5fWTub1VvXyskSY4PySUgRZa9UgV1sbCzffvstL7/8MpMmTWLBggXExcWVVduEEJXs5K0oQDuUdOZ2+WWc7r0cRnqmQgtHC5rUtSjROdo4W2GkryYmKZ1b9xPLuIXV0/+2nCT89hXMH9wBwO/A3/xz8F/69+/PjBkzCAoKYuDAgezevZuVK1diZmZGq1atipyxWpDAyETOBEajVmmLEpeFoe2daWRnRmxSOmtOBHL+bizngmMx1FPzvIdrmVyjKAa0ys6ODStwvwNXwpn79yVAu2rKkAIWrXe2NmHDK91wqWNCQGQi/7f8FBHxlRvcyTBs9VTiwM7Hx4fGjRuzcOFCoqOjiYyMZOHChTRu3Bg/P7+ybKMQopKcvB2l+/uxm5Hldp2dWfOwnmpb8mWfDPXVuh9AUvYEAsJiWPzVXMLWvMnNP7XJbAk+23iq32P06NGDdevW0atXr1yJb9lKuxRXdm9dr6b2OFoZl+wmHqGta6cdgl3+722WHrkNwJNtHbEzNyqTaxRFdtmTw9fu51tk+OK9OKZt8EejwGgP1zxXTXlUPWttz109axNuRyYyevkpIhIqL7j7L3HCutLaIIqvxIHdW2+9xbBhwwgMDGTLli1s3bqVgIAAhgwZwptvvlmGTRRCVIb0TA2nHwrsTtwqn8AuNimNYze0536yXckDO/hveTHvapZAcfp2FC+t9mbjmTtF2j+vUiSZmZl4e3uzceNGwsPDGThsJCpze7q//j+WLVsGgM2gaTwxe1WO9VXz8+iwanFkahT+zFrXc5R72c7NHNruv1677F8IxnqWf9LEw9rUs8TJypiktEyO5/ELT0hsMi+t9iYpLZNeTe34bHibIg9Fu9qYsvGVbjhbGXP7fiJjlp3ifkJqiduaqVFKtMJFRqaGC/e0I3DSY1e9lDh5wsfHh+XLl6Ov/98p9PX1effdd+ncuXOZNE4IUXku3IsjMS0TcyN9EtMyuB7+gIiEFOpalE3vS7a9l8LJ0Ci0dLIsUs26gng0sAFuVZulxe4npDJv1xW2+N0D4NiNSHo1sy+05EV2KZJ27doRFhbGwIED2bhxI8nJyQwaNIgElRkZ/WZSR4HFr3qijg4EwNChMTcz7TG0KN9yMCduRRIal4KlsT5PtCrblUD09dRM7duEGb9rM6hbOlmWOjGjuFQqFQNaObDmZBB7L4XnWO0kISWdl1Z7E5GQSnMHC358oRMGesXrQ9EGd548v+wkt+5rh2U3vNItV69kSnomYXEphMWnEB6fQmhcCmFx2r+HxacQHpdCeFZQOLabGx882bLAFVwedj38ASnpGiyM9XVL9onqocSBnaWlJXfu3KFFi5yLIQcHB2NhUbI5MqJ07sYk8SA1gxaOkr0kSi97fl2PJrbcjUnmUkg8J25GMbxjvTK9zo6sXpchpeytA3Q/4AMiE7mfkIq9RcUNzxVHpkbht9NBzN9zjYSUDFQqsDM34n5CKov2Xed/z7bP87jz58/z119/cf26ttbfjz/+yJAhQwCYPXu2br/J63zQKPBEKwc6N7Ah1CiVOXPmcEy/PjcfwP4r4TzvUfRyMsX1h4+2t25YB+dymXQ/rL0zPxy8ye3IRMZ7lv26sEUxsLUja04Gsf9KOJkaBT21ioxMDVN/8+dqWAL2FkasnOCBpXHhWbN5qW9ryoZJ2mzZGxEPGL3sFO716+QI4uKSi57QtPpEIGeDY/nxhU5FqpWXnTjR3sUadQUkpYiyU+LA7vnnn2fixIl88803dO/eHZVKxbFjx3jnnXcYM2ZMWbZRFEFSWgYjfjpBXHI6h9/ug3MVK3Ipqp/swK57YztCYrWB3fGbkWUa2MUkpumGsp4sxfy6bFamBjR3sOBaeAK+QTEMauNY6nOWtXPBsXy47aJumKuJeRpNok4z4pnxTP7zNn/63WVy70Y0qWtBcnIyR48eZd++fbz99ttMnz6dw4cP68518OBBDh48SFxcnK4UiW9QDHsuaRdMfzdrwfTsYdUfDt7gm73X2XUxrNwCu7jkdPZc0iYVPOtePgkN+npqVnp5cCYwmlFllJhRXB4NbbAyMSAqMQ3foBg8GtTh478uceT6fUwM9PhlfOdSFxtuYGeWVQrlJDcjHnAz4kGufUwM9HC0MsbR0hhHK2McLI1xtDTSbrMywdHSmIv34pj5xznOBsfy1Hf/suj5DvRpXnBpmP8SJyqnMLEouRIHdt988w0qlYpx48aRkaFNOzcwMOC1117jq6++KrMGiqLZ5B2sm4dx7EYkz1VghpioeVIzMvEO1CYgeDa2JTQuhaVHb3P8ZiSKopRZD8meS2FkahRaO1uW2YoB7g3qcC08AZ/A6CoV2MUlpfO/vVdZf/oOigIWRvq8M6g5LQ2i6OLxDK+MfY4BrRz4++BxXpi6m0kD2tOmTRsuXrzIuHHjcHBw4LffftMVEH50hQfQ1h38etdVQBtUNXXIOXoyqI0j3+y9zvGbkcSnpJe4N6kgO86HkJqhoZmDebkuGt/AzowGlThEaKCnpl+Lumzxv8eeS2GcDY7ht9N3UKlg8egOtCujhIOGdmZsfrU7m7yDMdRX42hpjIOVMU5ZQZylsX6h/x4drYzZMa0nU37z4/zdOCas9mbq4014s3+zfEvESOJE9VXi5AlDQ0MWL15MTEwMZ8+exd/fn+joaBYuXIiRUcUNf/z00080bNgQY2Nj3N3d+ffffyvs2lVFeqaGFf8G6N4/nMkoREn434klNUODnbkhTeua49GgDgZ6KkLiUgiMKrs1LXXZsGUwDJvNI2vdWJ8qMs9OURT+9L1L328P8+spbVA3omM9fnuxGW2MojlyWFvs/fXXX6e/bRzJN04SpOdM+z5D6datGzNnzqRdu3aoVCqcnJwKLEVy4EoEZwKjMdJX89YTzXK1pUldCxrbm5GeqXDoauE12Eoiexh2VBnUrqvqBmRlx/7uE8y8rID6w6da6baXFVcbU94e2Jw3+jXlOQ9Xejezp5mDBVYmBsVKyvjjVU/GdnNDUbRrMo9beZrIB7kTMxJTM7gengBUzhqxonRKXaDY1NSUtm3b0q5dO0xNi74OYVnYtGkTb775JrNnz8bf359evXoxePBg7twpWmZZTfH3uRDuxSbrfvM6eStKCrSKUskehvVsbIdKpcLUUF83fy2vLMCSiHqQyoms65SmzMmjOmcVgL14L47ktOJnA5ala2EJPL/0FDP/OEdUYhpN6pqzbkJn+poGM/GF53F3d+edd94B4PTp0zw/pD8tna0xdmvHosMB+Z43r1IkmRqFr3drg4uXejbMt8RIdi/m7osF12AriZsRCZwNjkVPrSrzuZhV0WPN7DDSV5OQkoGiwHhPN17q0aCym5UvI309PhvehsWjO2BqqMfxm1E8ufhfzgTkLA904V4cGgWcrYypa1m2yVKi/BVrKHbGjBl89tlnmJmZMWPGjAL3XbBgQakaVhQLFixg4sSJvPzyywAsWrSIPXv2sGTJEubNm1fu168KNBqFn4/cAmDK401YcvgmYfHaXpXyXAxb1Gz/za+z1W3r0cSO0wHRnLgVyYtlsCbnnkvaSedt61nlWGKptFzqmOBgaUR4fCrn7sbSrZFt4QeVkdDQUJYuXcpYr4lsuvyAlccCyNAoqGPv0ib9GmZBYfR481eWHbrHggULMDExwd/fP8ewqsbYijHrr3H8ZhTHbkTSs6ldrutkz5l72J9+d7kR8QArEwNeLaBm2qDWTvx46BaHr90nOS0TE8OyS274I6t2XZ9m9mWePV0VmRrq83jzuuy+FEbfFnX5aEiratFL+XSHerR2tuS1X/24EfGAMctP8e7A5rzyWCNUKpUUJq7mihXY+fv7k56ervt7firiGzstLQ1fX1/ef//9HNsHDBjAiRMnyv36VcXBqxFcD3+AhZE+L/dqyKlbUZwJjObkrSgJ7ESJJKdl4h+sHcb0bPRwYGfLgn1w4lYUGo1S6ky5nRdCgLIdhgXt/z+d3WzYeSEUn8DoCg3sAoPvMnfuXP4ItSYs6BYpQefoM+x5JgxtioNlK7p164Zarc5RRy77/8uHh1hf6JrC6hOBzN9zlR5NehT6f2pKeiYL92kzZac+3gQrk/znzrWpZ0k9axPuxSZz9MZ9XbHd0srI1OjKtjzbuXISGirD3Kdb83gLe4a1r4d+McuaVKYmdS3YPrUHH2y5wLazIczbdRWfoBi+ebb9f/PrJLCrlooV2B06dEj39zVr1uDi4oJanfMbWVEUgoODy6Z1BYiMjCQzMxMHh5w1khwcHAgLy3uIITU1ldTU/+YTxMfHl2sbK0J2b93/dauPpbEB3RrbciYwmlO3o/i/ruVXzkDUXD5B0aRnKjhbGeNm+9/0inYu1pgZ6hGblM7l0Hja1Cv5xPjIB6m6XsGyHIbN1rlBHW1gV0Hz7K4H3OGTH9by99bNAITevoaVgYZvvvmCCU/1KvDYvIZVp/Ztwu8+wZy/G8fui2EMLuRrtOZEIKFxKdSzNim0WK9KpWJQG0d+ORbA7othZRbY/XsjkvsJqdQxNaBvi7KtXVeVOVgal2vpmPJkaqjPwuc74NHQhrl/XWbf5XCGfP8vSanaKQySOFE9lfjXi4YNGxIZmXuuTXR0NA0bNixVo4rj0d9kC8rYmzdvHlZWVrqXq2v1zhz1DozGJygGQz01E3tov+bZPSwnb8s8O1EyJx6ZX5fNQE+t6/0q7Ty73RfD0CjQ3sUKV5uyn5ubPc/ONygGjabk/w7yWuEhW0REBO9/+DEtu/Sm26hX2bDgIx4EaIvmxuz/mcBdywjyPlDoNfJa4cHO3IiXe2r/TX+z9xoZmZp8j49NSuPHQzcBmPFEsyLVjcueZ7f/SjhpGfmfuzj+8NX+Qv90h3pFLoIrKp9KpeKFrm78+Vp3XG1MCI5OJioxDZUK2pZjVrMoPyX+15df0PDgwQOMjct/boWdnR16enq5euciIiJy9eJlmzVrFnFxcbpXRfQslqclh7W9dSPdXXQTXDvWt8ZQX839hFRZCF2UyH+BXe4hzO5NtPO9jt8qXeb1zvNlnw37sJZOFpga6pGQksH1iIQSnyd7hYfQ0FAURWHDhg08//zzvPH2e3yz9wa/BVuS1HsGpp5j8HhrGa9+OB+A5cuX4+vry+TJk0t87Zcfa0QdUwNu3U/UDXHmZcnhW8SnZNDC0aLICQud6tfBztyIhJSMMsmij0lMY/9lbZZtbRqGrUnaulixY2ov+metotG2nhXmRiWuiCYqUbGfWnbShEql4uOPP86RCZuZmcnp06fp0KFDmTUwP4aGhri7u7Nv3z5GjBih275v3z6efvrpPI8xMjKq0FIs5elqWDwHr0agUsErjzXSbTc20KNTfWtO3Y7m5O0omtQt3RJNonaJT0nnQtb8mrwCux5NtNvOBESRmpGJkX7xJ95HJKRwOkAbTJRFUeK86Oup6VjfmuM3o/AOjCn2aiyhoaHcu3ePPXv2APDiiy/yv//9jyTFENtBU/nnRgIZF2NRO7WkvYsVr/XpyoBWDpw968/Pn+ecL1dSlsYGTHm8CZ/vvMLC/dfzXMUhJDaZVScCAXhvUIt8a5I9Sk+tYkBrB347fYfdF8Po3cy+xO1UFIW5f18iLVNDKydLWjtLL091ZWVqwPJx7hy6FkHTurKCVHVV7B47f39//P39URSFCxcu6N77+/tz9epV2rdvz+rVq8uhqbnNmDGDFStWsHLlSq5cucJbb73FnTt3irTAdXW39MhtAJ5s45QrScKzkbZX5VQpe1VE7eMdEI1GgQa2pnlWzW/uYIGduSEp6Rr878SW6Bp7soZhO7ha41Kn/Eok6YZjswotFzSsCugSw+bNm8djjz2Gh4cHH374IQBXrlxhyJAhzPzpT/65Fk+GRqFHE1vWv9yVbVN6MKiNI2q1Ks/5cqXxYjc3nKyMCY1L4ddTQbk+X7jvOmkZGro2tKFP8+IFZ4Oy5tbtu6wtEl1SG84Es+1sCHpqFZ8Ma13i84iqQaVS0beFQ7lMkRAVo9g9dtkJFBMmTGDx4sVYWlbeuqTPP/88UVFRfPrpp4SGhtKmTRv++ecf3NxKX4qhKguOTuKvc9qMwrzKGng2tmXhfjiVNc+uOqTfi6rh4fl1eVGpVHRvbMdf50I4cTOyRBmnO86X3dqwBen8SKHi7GHVYcOG4eTkhKIopKSk4Ofnx4IFC0hNTWXjxo0MHz6cF198kfv373Ps1BmmT3kNm0HTMHRojJ65DQNaOfD6403yLNyaVxmS0jA20OPN/k15788L/HjoJs97uGKRtVrEtbAE/vTTlhd5f3CLYv8792xsi6WxPpEP0vAJjKZrCZ7lxXtxfPL3JQDeGdicLg1tin0OIUTZKvEcu1WrVlVqUJft9ddfJzAwkNTUVHx9fXnssccqu0nl7pdjAWRqFHo2sctzcmt7VyuMDdREJaZxI4+1BYXIz8kC5tdlyx6OPVaCBIqI+BTOZPWgFZbpWVod69dBrYLA4HvsPXISPz8/AHx8fHj66afp27cvO3fupHnz5qxevZodO3Zgbm5Oy5YtcXV1pVOnTlxK1d6riVMTxjzZm0MfDWfZuM4VWo1/ZCcXGtmbEZOUnmOFmf/tuYpGgSfbOtIxq3h0cRjoqenfSjufavel4hcrjktO5/X1fqRlaOjfsi6v9GpU+EFCiHJXrQsU10ZRD1LZ6K1dWeO1PnkXITXS16Ozmw3HbkZy8lYUzRxkroQoXExiGpdDtSWAPAvoveme1Zt37m4cCSnpuh6koth1MQxFgU71rUu9QHphzI30aWqjz8lD2xn445+67dkJDXPmzGHUqFH5Hh8Sm8y+wDSseozh+5f6MqJnm3Jtb3709dS8PaA5r6/3Y8W/txnn6cat+4nsvxKBnlrF2wOal/jcg1o7ssXvHnsuhvFxMYrrKorCO3+c4050Ei51TPj22Q6lrmsohCgb1bZAcW215mQQKeka2tazyrEqwKM8G9vqArvx3RtUXANFtXUqKzuymYM59hb5Jxm52pjiZmtKUFQSZwKi6dey6DXL/suGdS5dY/lvlYfJkyfr5rSlp6dz8+ZNHB0dGTt2LMHRqRjX686oeU/jaZvCK6+8olvhobB5cD8cuonGxJqBY6cxvEflzh0b3MaRtvWsuHAvjh8O3eRs1soAoz1caWRf8gSpx5rZY2KgR0hcChfuxRV54fpfjgWw93I4hnpqfnqhE1amRQ/uhRDlq8QFih/+u6gYiakZrMnKgHutT+MCA+jsuU+nAspmlQBR82WXvSioty5b98Z2BEXd4djNyCIHdmFxKXgHaYdhn2xb+qK42XPmWrVqRd++ffnll184duwYjz/+OG+99RZbt25l9+X7TNvgT4SZJe7u2iSjomSsBkcn8bu3thzSjCeaVfovqyqVincHNWfsL2dYdTwQABMDPab3a1qq8xob6PF4C3v+uRDG7othRQrsfIOi+Sp7wfshLYscDAohKkaJ59glJyeTlJSkex8UFMSiRYvYu3dvmTRM5LbhzB3iktNpaGdWaLX4di5WmGatEnA1rOR1vETtUVjixMOy59mduFn0zOtdF0NRFOjsVgcnq5INwyqKwqlTp1ixYgX//PMPACtWrOD06dOMHTuWv//+mxkzZqBSqTAwMNAlUFwOicfSxr7IGavfH7xBRtY81pIkFZSHnk3scvTSv9yrYZks0D6ojfbrsftiWKFFzaMT05j6mz8ZGoWh7Z0ZWwZrBgshylaJA7unn36atWvXAhAbG0uXLl349ttvefrpp1myZEmZNVBopWVo+OWYduL0K481KrRelYGems4NtBlqZVGAVNRsEfEp3Ix4gEoF3RoVntmY3at3LTyB+wmpheytVVBR4oJKkQQHB7N27VpiYmKYOXMm06ZNY9KkSXz00UeAtnblkCFDWLZsWa5jnaxMqGdtgkaBsHSTXCs85CUwMpE/swoCzxjQrEj3VhG0vXYtUKvAztwwR/3K0ni8uT2GempuRyYWmGyl0Si8ueksoXEpNLI3Y94zbSu9J1MIkVuJAzs/Pz969dKugbh582YcHR0JCgpi7dq1fPfdd2XWQKG1/ew9QuNSsLcwYkQRq8tn//A9JYGdKER28N/KyRJrU8NC97c1N6KVkzYr/sStwrNjQ+OS8QmKQaWCwW3yDuyyV3gIDw9n48aNbN++nSNHjjB37lz09fUxNDRkwYIF/PXXX/j6+rJ8+XKg8FUePLJ67byzsnELs/jADTI1Co83t6dTCbJNy1MHV2u2TenB1td7FCtppSAWxgb0bKrtpd19Mf/s2B8O3eTo9fsYG6hZ8oK7rEogRBVV4n+ZSUlJWFhosy337t3LM888g1qtplu3bgQF5S6kKUpOo1H4+Yh2+bCJPRsWaS1I+K9kxenbUWRqlCJXpRe1T3aZk4ISch7Vo4ktl0PjOX4zkqc7FPzLxj8XtAGDh5sNjlb/DR+GhoZy5coVNm/eDMCZM2c4evQoXbp0YdSoUbi4uNC7d+8c53JycsrR61bYnDn3BjZsOxuCb1Y9u4LcjEhg+9ms3ronSp5tWp7KY07boNaOHLwawe6LYbyRx7y94zcjWbj/OgCfD29Lc0fJtBeiqipxj12TJk3Ytm0bwcHB7NmzhwEDBgDatVqrQn27mmT/lXBu3U/EwlifF7rWL/JxbZwtMTfSJz4lgytZZSyEyMsJXWBX+Py6bLp1Y29G5Zibldew6s7z2oLaT7VzIi0tjU2bNvH666/z5ptv0q9fP930jddee40NGzYQGxuLi0vBa44WdZWH7B47vzsxZGQWvOD9ov030CjwRCuHWrUAev9WDqhVcDk0njtRSTk+C49PYfpGfxQFnu/syih3WQtWiKqsxIHdxx9/zNtvv02DBg3o2rUrnp6egLb3rmPHjmXWwNpOURSWZPXWvdjNrVjDL/p6al0l+JOyvJjIx92YJO5EJ6GnVuFRjJUDujSwwUBPxb3YZO5E/xcMPDysmn3+40cOEbX7e3b9+BEqlYrk5GTefvttFi5cWKxh1Ydlr/JQWGDXrK4FFsb6JKVlFphIdDUsXrcqxownqs7cuopgY2ZI14ba3to9DxUrzsjUMG2DP5EP0mjhaMHcp2XJMCGquhIPxY4aNYqePXsSGhpK+/btddv79evHiBEjyqRxAs4ERON/JxZDfTUTejQo9vHdGtlw8GoEJ29HMamMJluLmiU76G/nYlWseVNmRvp0dK3DmcBojt2MxDAtnpCQEI4dOwbAtGnTUBQFj+ET0TOzovfzk9k4S/t/g5eXl+48zs7/1bQrSimS4lKrVbi71eHwtft4B0bTpl7ePXGL9t0A4Km2TrR0qn2jDoPbOnLydhS7L4Xp/q/4Zu91zgREY26kz5IX3Ys8DUQIUXlK3GMH4OjoSMeOHVGr/ztNly5daNGiRakbJrSye+tGubtQ16L4pQ08G2mHy7wDogsdhhK1U2Hz6wrKWO3awJLU0OscvXyPkSNH0rlzZ958800ATpw4wcmTJ9lz+DiGdRvxfJ/8e/KLOqxaUp3dcq4b+6iL9+LYfSkMlQqm9y9dbbjqakArbQkl36AYwuNTOHAlXDe3d/6odjS0M6vM5gkhiqhUaU2xsbH88ssvXLlyBZVKRcuWLZk4cSJWVrVnbkp5uhwSz+Fr91GrKPE6jK2cLbE01s6zuxQST/sKXONSVH2KohQ6vy57aHXYsGGYmZlx8uRJTExMSE9PZ9v//kdySh1OONfn7982EB0dhZ+fH5MmTWL58uU4N27JK3/exlAFA9vkX3sxe1i1vGSX/vEJjEZRlFxlOhbu0yYGDGvvXGuX4HO0MqZjfWv878Sy8lgAG7MKNHt1b8CT5byurxCi7JS4x87Hx4fGjRuzcOFCoqOjiYyMZOHChTRu3Fi32LYonaVHtb8tP9nWiQYl/G1ZT62iS9bcmepQzy4lPZNzwbGFFkoVZSMwKomw+BQM9dS4u+Us7REaGsru3bv5+eefAe2/+RdeeIFTp05Rt25d+vXrx8G9/1Cv33gSFGMeGFjnGErt1KkTd/Uc0Te3oWtD2xL1OJeV9i7W6KtVhMencjcmOcdn/ndiOHA1ArWKUq/kUN0Nyip8vvTobeKS0+ngas0HT7as5FYJIYqjxIHdW2+9xbBhwwgMDGTLli1s3bqVgIAAhgwZohuKESV3JyqJv89pMwlf7d24VOfKLntSHRIo5u++xtM/Hmf2tosS3JWDR4dVs2vQdaxvjR4aYmNjmTdvHiNHjuS9995j8ODBusSGyZMns2PHDhRF0U23MHgoQSf7XA8PqxZUlLgimRjq6ebW+QTlrGe3cL92bt0znVxKte5qTTDooV5Va1MDfnyhE4b6pZqxI4SoYKXqsXvvvffQ1/9vNFdfX593330XHx+fMmlcbbb839toFOjV1C7fyd5FlV2o2DswmvQqPM8uU6Poaoj9dvoOPx2+Vcktqnmyh1Xv3bvHpUuXOHLpLnEnf+f8L+8xe/ZsDA0N6dWrF+vWrePrr78uUsZqj4fKnsB/w6oZRlacuxuHWpUzYKgsunl2gf/Ns/MJjObo9fvoq1W80bd299YBuNma4dGgDnpqFQuea08965It/SaEqDwlnmNnaWnJnTt3ciVKBAcH6woXi5KJfJDK7z7a+S2vlbK3DqCFowXWpgbEJqVz/m5criG3quJMQDRRiWnoq1VkaBT+t+caztbGjOhY8+tmpWZksmj/DRramTGqkwvqIhaTDg0NZenSpUyePDnfxANFUfDx8eHIkSPcu6cNnF966SXatWuHr1UvLDoNYe30fnhkzUPr2bMnAKampkUqBJwd2J0JiCYtQ6Pr4dl5Qdtb59nYFjtzoyLdT3nq3MCGFccCcgR23+7Vzq17trML9W1NK6tpVcpKLw9ik9JxtZGvhxDVUYl77J5//nkmTpzIpk2bCA4O5u7du2zcuJGXX36ZMWPGlGUba53VxwNJzdDQ3sVKN4xaGmq1iq5Zw2VVeXmx3Re1gcDwjvV062C+u/k8J24WvmRVdfe7z12WHL7Fu5vPM2LJCS7cjSvScY/WjFMUhYyMDLy9vfnwww959tlnURSF6dOn884777Bo0SIALly4wPr16wk9dwQzcwvaF7CaQWEZq80dLLA1MyQ5PRP/O/8FTbph2LbOeR5X0bJ/obkekUBcUjonbkVy8nYUhnpqpkpvnY6FsYEEdUJUYyUO7L755hueeeYZxo0bR4MGDXBzc8PLy4tRo0bx9ddfl2Uba5XIB6msORkIwGt9GpfZIttVfd1YjUZhd1Zh1MFtHHl/UAuGtHMiPVNh8jpfrobV7JUzsldmADgXHMuwH4/x0baLxCWl57l/aGgovr6+HDhwAICDBw/y+OOP079/f44dO4aRkREjR45k/fr1qNVq/vzzz1zDqnN++QvzDoPp3KBOgfOoCisErFardL+AHM+axxkUlciFe3HoqVUMbO1Q/C9IObC3MKKhnRmKol2FIjsTdnQXVxlyFELUGCUeijU0NGTx4sXMmzePW7duoSgKTZo0wdRUftMrjS92XiEhJYOWTpY80ars5iV5ZpWy8AmMyTFcVlX4B8cSHp+KuZE+PZvaoVar+ObZ9kQkpHImIJoJq7zZ8np3nKwq9wdwRqYGfb2y/dpFJKRwJiCajAfRPGVwkcxm/dgflM66U0H8cyGUWU+2ZGSneoSEhLBv3z7OnTuHnp4e3377re4c77zzDgBz5syhT58+ua6R1/qqZy5q0DdXF2sZsfz0aGLHjvOhnLgZyYwnmumGYbs3tsW2CgzDZnN3q0NAZCLfH7yBX1bh79f7NKnsZgkhRJkp9U8oU1NT2rRpQ9u2bSWoK6XjNyPZ6n8PlQrmPdMWvSLOsyqKZg7muuGyc3djy+y8ZSV7GLZvi7oY6Wur2xsb6LFsrDuN7c0IjUthwipv4lPy7sEqb4qi8Onfl2n18R5Ol3Gv556LYWgUaGyezrJF83muMbzfCRrZGnNt45eMe24EXbw+wvfGXUxNTXn11Vd58803S7QUV/awal0HR13vbVkM9/fMmmd3NjiWB6kZDw3DVq36Z/+tGxsLwItd3XC0qrwyLEIIUdZKFdj98ssvtGnTBmNjY4yNjWnTpg0rVqwoq7bVKinpmXy47SIAY7u50aGMCwmrVCq6NaqaZU8UReGfC9ph2Cfb5uyltDY1ZPWELthbGHE1LIHXfvUlLaPiM3t/OnyLlccDSMvU8NuZO4XuX9BqDdlSUlIIDw/nf/O/5v5fX2MR6g/Ap59+SpD/MVaNbsFnXy+gwQufcd/Jk7f2RnHVpA3Obo1wcXHJVTOuU6dOha7ckD2sGqOYEZ+SgYWRPm2cS798lquNKa42JmRoFH73DuZSSHzWMGzlZ8M+zN3tv7VwTQz0eK1P6ZOThBCiKilxYPfRRx8xffp0hg4dyh9//MEff/zB0KFDeeutt/jwww/Lso21wk+HbxEQmUhdCyPeHti8XK7RrYrWs7t4L557scmYGOjRu1ndXJ+72piyyssDU0M9jt+M4v0t5yu0xt02/3vM+/MkscfWk/EgmoNXIwoNLh9OaoiMjGT//v388MMPaDQaxo8fz1NPPcWyZcu4n5DMrWuXSbryLzvX/gDA8ePH+eqrr1j1y3LeGtKR/TN682RbRzI1CiuPB9Dv2yNsP3sPRVFKvBTXydvahJSujWzKbGi5R9aQbvbctR5N7KhjZlgm5y4rje3NqGNqAMC47m7YW1SdYWIhhCgLJZ5jt2TJEpYvX54jA3bYsGG0a9eOadOm8fnnn5dJA2uDmxEP+DmrZtucoa2xNDYol+t4NtL2VvjdiSElPbPKLOi9K2sYtk9ze0wM825Tm3pW/PRCJyau8WGL3z3qWZswc0D5BMAPO3Ezknc2nyPzQTRxxzfg3L4XCSk2nLwdRe9m9rr94uPjuXnzJsHBwZiYmOiWx1q3bh0NGjQgNjaWrl27oigKq1ev1iXFrD0ZSJ2+L+MxbDwvNtHoluJ6uPfN2dqEn15w58j1+3zy1yUCIhOZvvEsG88E89nw1iVaiit7GbHsXtyy0KOJHRu9g0lIzQBgSBUbhgVtz/U7A1tw+FoEr/eWuXVCiJqnxL+qZ2Zm0rlz51zb3d3dycjIKFWjahNFUZi99QJpmRr6NLfPNRRZlhrbm2NvYURqhkYXTFU2RVHYfVE7DFtYEds+zevy5Yg2AHx/8CYbijAkmpeiDJMCXAtL4KWf9vLg3g1a6Gt7uGwizpJ44zTfrVyPl5cXTz31FNHR0fzvf/9j27ZtbNq0iYEDB3Ly5EkAFi1axJtvvomiKAwaNAg9Pb0cmc47zoeib27Di0/1KXRYtXcze3a/2YuZTzTDSF/NydtRDFr0L/N2XSExtej/5tIzNZwJ0K6+UBaJE9m6PzRXT1+tYkAVyYZ91P91rc+ycZ2xMi2fX6CEEKIylbjH7sUXX2TJkiUsWLAgx/Zly5bxwgsvlLphtcWffvc4HRCNsYGaz55uU2blTfKiUqkY7+nGN3uv8/Wuawxs7YipYYm/BcrE9fAH3I5MxFBPTd8WuYdhH/W8R33uxSTz3cGbfLjtIqrkWK4e2lJggd5HPbyo/cPHJCUlcevWLYKCgmjRoSuDXv+Uuye2kxEVTFjWPsc3L9P+2fNZzm3+gbp17VGpVHz22We6c7/99tv4+fnl2fv2sIj4FLwDtQHW4LZOqJNNCh1WNdLXY1q/pgzvWI+5f19m/5Vwlh65zd5L4az08qBhEdYUPn83lqS0TOqYGtDCseyKiduaG9HC0YKrYQn0bGqHtWnVGoYVQojaoFQ/1X/55Rf27t1Lt27dADh16hTBwcGMGzeOGTNm6PZ7NPgTWtGJaXyx8zIA0/s1q5CioC/3asRG72DuxiTz85HbzHiiWblfsyDZPYe9mtphYWxQpJUU3nqiGfdiU/jT7y4zVx8iZPVchg4dWmBAlJKSwv379zlx4gSHDh0CtN+vb7/9Nnp6enh5eVG/fn127dqFg7MLiy6oSXf1wP3lbnw9tAk3rlxg0qRJLPl5Kd/4ppFiaMXdFH0cHgnE8yorktdqDdp7D0NRtOu01rM2AWuTIg+rutqYsmJ8Zw5cCeejbRcJiEzkmZ+Os3xcZzo3sCnw2Ow5lp6NbYu8wkVRPdfZlc92Xma8Z4MyPa8QQoiiKXFgd/HiRd0PrFu3tPPD7O3tsbe35+LFi7r9yqMHKjAwkM8++4yDBw8SFhaGs7MzL774om6ty+pi3j9XiElKp7mDBS/3algh1zQ20OODJ1vy+no/lh65xXOdXXCpUz4BZVGCtOxh2MFZ87Hy6017mEqlYnp3OyIDr7Dj7E0AJs39kXFPdOKZp4exdOlS7t27R9++fXFxcWHhwoUYGxtTr149Fi9erDvPlClTAG3tt+xe5m7de/DSam9u3o3E0cmZP17vjquNKRYm2mG7Lh6dedJSxV/nQthzKTxHluXDipLUkF3rrTQlQfq1dKCtixUvr/Hh/N04/m/FaRY8154h7fJf7SF7fp1nGc6vyzahRwNe6FZfV7JGCCFExSpxYJfd61EZrl69ikajYenSpTRp0oSLFy8yadIkEhMT+eabbyqtXXnJL7g5dTuKP3zvAvDlM20weCgzsSgBUVGukZ/BbRxpZ6Ph6F/r+chJzaopA0t1LwXtX1CQdvv+A66GJaCvVtHWWoO3tzcnTpwA4O+//+bIkSNYWFgwfvx4Zs2axf379+nbty9ubm688sor3LhxQ3cu/79X4f/3KgKDQ3jpxdHUq1cPGxsbVCoVffv21bVn3Lhx+Q6TKorCrC0X+PdGJCYGeqz06qzrRX04UBuoT1ZgF8aswS3y/OUlu6xIfsIfGYYtjboWxmx8pRvTN55l3+Vwpv7mT3B0Mq/2bpSrbSnpmfgEaZf98izD+XXZVCqVBHVCCFGZlBpi/vz5SsOGDYt1TFxcnAIocXFx5dQqRfH19VUAxdfXV7ctJT1D6fvNIcXtvR3K+3+eL9Ixxb1GYX7ffVgBFMfxi5TTt6NKdZ3U1FQlPj5eycjIUK5evars3r1b2blzp/LVV18pgDJ27FjlwIEDyoIFC5QpU6Yo77zzjpKcnKy06fa4YtLYQ+n+0sfKuHHjFCDX64UXXlA0Go1y9epVJSoqSsnMzFQURVFCQkIUX19fZfny5QqguD39luI4fpHScuZvyuFrESW6jwV7rylu7+1QGr6/Q9l/OSzf4x+kpCtNZ/+juL23Q7kSWrLvnVXHbitu7+1QRvx4rETH5yUjU6N88tdFxe29HbrvrfSMzBz7nLgZqbi9t0Px+HyfotFoyuzaQgghyk9x4pXKnTlfhuLi4rCxKXhuUWpqKqmpqbr38fHlt/5oaGgooaGh+Pn5AbBnzx6io6Np1KgRq48HcPV6AHUd6vKyu61uKNvU1BQfHx/Onz8PaOeABQQEYGdnR/v27UlOTiYiIgKNRkP79u05cuQId+/eJSgoCIBNmzbh4+ODh4cHFhYWXLt2jYyMDAYPHszBgweJiopCpVLh4uLCX79qC0kn3zjFyx+G8rhdMkZGRnzwwQcsXbqUq1ev4urqyptvvsno0aNJSUmhdevWAEyYMAErKysWLVrEn3/+ydWrV2nWrBkfffQRy5cvx8fHhyNHjui+FuvWrWPdunVMnz6dd999F2tra4yNjWnwf5+ScC+OKSPa8nh9A6ZPn55nb5pKpaJ585ylTR6dy7bsrWf56aKGs8GxTFh1hncGtsizxyr72EeHSX/3DmbxAW0P4GfD29CvZf4ZnWZG+vRqYseBqxHsvRROC8fiF/j9ryBz2ZUE0VOrmDO0NfVtTPl0x2U2nLlDSGwyP77QCXMj7T/1k7e02b2ejW3LNVFHCCFEJamAQLPc3bx5U7G0tFSWL19e4H5z5szJs1eoPHrs8rvWuImTFesuwxWLzk8ri37fp2zevFl56623lBkzZuR7TI8ePRR/f39l165dyocffqh89NFHSlpamjJo0KA893/ttdeUM2fOKEuXLlVWrFihJCcnKzt37lQ2bdqkjB8/Ps9j+o94Qbl8+bKSlpam3L9/X4mMjFQSExMVjUajfPzxx3keM2fOnDzv/dHetOXLlyu+vr5KSEiIbp87UYm63rH7CSm67cXtfQwJCVHmzJmjhISEKCnpGcp7m8/peqxe+9VHeZCSXug5Dl+LUBrN2qm4vbdD+XrXlSJdd9OZO4rbezuUJxcfLdL+DwuNTVYavK9t472YpGIfXxR7LoYqzT/U9ioOWnRUCYnVXmfkT8cVt/d2KBvPBJXLdYUQQpS94vTYqRSlZCX8g4ODcXV1Lcmh+frkk0+YO3dugft4e3vnqJ8XEhJC79696d27d6HLmeXVY+fq6kpcXByWlqVfVulhD/fYZfdAdezYkS8PheEbqc0CXftSlxy9Jnkdk91rldccteLun9cx4979kr3hptR1cODY3JFY5FEcuSTXAfDz88Pd3R1fX99cmaEr/r3N5zuv0LWhDZsme+a4VnHm8eXlt9N3mPPXRdIzFZo5mLNsbGca5FMG5FJIHM/9fJLEtEyGd3Bm4fMditSTFfUgFY8v9qNR4N93Hy9WRvOq4wHM/fsy7m51+PO17kU+rrjOBccycY0PkQ9ScbQ05scXOvL80lNkaJRit1kIIUTliY+Px8rKqkjxSomHYlu0aMGMGTN4//33MTMrvHZWUUydOpXRo0cXuE+DBg10fw8JCeHxxx/H09OTZcuWFXp+IyMjjIwqZgmhvMpe3FE54BsZhqF+3jXrilMqoyT753XM6yOfIPiotpbcD4duMmtwyzK5TvZx+WWG7rqY91BkYUkHRfF/XevT3NGcV3/143r4A4b9cIzFYzryePOcdfLuxSYzYZU3iWmZeDayZf6o9kUenrQ1N8KjgQ2nA6LZezmciT2LntX8T1Y2bFkOw+alvas1W1/vzoTV3tyMeMCzP59Eo4BLHRMJ6oQQooYq8coT+/btY+/evTRt2pRVq1aVSWPs7Oxo0aJFgS9jY2MA7t27R58+2mr9q1atQq0um/Uuy1p2cGNmZcfnWTXr3ujbJN8epIePKWqPVUnWC80+pr5rPT4cog3mVh4LIDAysUzb9cknn+TaPywuBd+szMzyWiTe3c2GHdN60qm+NfEpGby02psfDt7QrTEbl5zOhFVniEhIpZmDOT+PdcdQv3jfQ9lt33MprJA9/xMWl4J3oPbey3OVkWyuNqb8+Vp3PBvZosnqmy+PMidCCCGqhhIPxWZbu3Yts2fPxs7OjoULF9KnT58yalr+sodf69evz9q1a9HT+6+8gqNj0X9YFqdrs7RmbbnAhjN3aFLXnH/e6FXsIKI8KYqC1ypvjly/zxOtHFg+LvdScWVpzYlA5vx1iU71rdnyeo9yvVZahoa5f19i/Wnt8mODWjsy75m2vLbel1O3o3GwNGLr6z1wtjYp9rnvxiTR8+tDqFXgPbs/tuaF9wavPBbApzsu09mtDpvLcRj2UWkZGj7cdoFt/iGsmuBBjyZlX+pECCFE+ShOvFLq6GLcuHFcv36doUOH8tRTTzFixAhu3rxZ2tMWaO/evdy8eZODBw/i4uKiGyos6Zys8uYTGK1b1/SL4W2qVFAH2tpjHw1piZ5axb7L4Ry7EVmu18tebWJwm/J/Xob6ar4Y0ZavnmmLoZ6a3ZfC6P7VQU7djsbcSJ9VXl1KFNQBuNQxpU09SzQK7L8SXqRjKmoY9lGG+mrmj2rPpU8HSlAnhBA1WJlEGIqiMGDAAF555RX++usv2rRpw8yZM0lISCiL0+fi5eWFoih5vqqa9EwNs7dqV+J4rrMLXavoMFiTuhaM83QD4NMdl8jI1JTLdaIepOoWoB/UpvyHIrON7lKfTZO74WBpRHJ6JvpqFUte7EQr59L11A5slT0cW3hgFxqXrCsOXNGBXbaHC2ELIYSoeUr8v/zPP//MxIkTadeuHVZWVvTv35/jx48zZcoUfvrpJ86ePUurVq3w8fEpy/ZWO9fCEgiJTcbGzDDPxISq5M1+zahjasD18Ae6HsaytvdyOBoF2tSzrPAJ/B3r1+HvaT3x6t6A5eM606upfanPOTArOD12I5IHqRkF7rsrq3ZdZ7c6OFoZl/raQgghxKNKHNh98cUXxMfHM378eA4fPkxcXBxnzpzhu+++46WXXuLAgQO89tpreHl5lWFzq5829azYP7M3P/5fJ+qYVe11bK1MDZgxQFsI+Nt914lNSivza2Rnw1bEMGxe6loY88mw1jzeom7hOxdB07rmNLQzIy1Tw+FrEQXuq1sbtl3VnDIghBCi+itxYBccHMwff/zBzJkz6dGjR55lRCZOnMiVK1dK1cCawMHSGM/GVXMI9lFjPFxp7mBBbFI6i/bfKPyAYohLSufETe38vcEVOAxbnlQqFQNaa1epKGg4NiQ2Gd+gGFSqygtqhRBC1HzlOuGmbt26HDx4sDwvIcqYvp6aj4e2AmDdqSBuhJfdPMl9V8LJ0Cg0d7Cgkb15mZ23smWXPTl0NYLUjMw898nuqZRhWCGEEOWpXAM7lUpF7969y/MSohz0aGLHgFYOZGoUPt1xucySUnZnZcNWZNJERejgYk1dCyMepGZw8lZUnvvsPB8CwFOVlDQhhBCidpAUOZGn2U+1xFBPzb83Ijl4teC5Y0XxIDWDo1llVAZXQGHeiqRWq3iiVf7DsSGxyfjdidUOw0pgJ4QQohxJYCfy5GZrxktZy2R9vvMKaRmlK39y8GoEaRkaGtqZ0dzBoiyaWKVkD8fuuxxOpiZnD2d27ToPNxscLGUYVgghRPmRwE7ka2rfJthbGBEQmciaE4GlOtfDw7BFXY+1OunWyBYLY30iH6Tifycmx2eSDSuEEKKiSGAn8mVupM87A7XlT747cIOQ2OQSnSc5LZNDV+8D8GQNzQg11FfTL6uEysNrx96LTcY/exi2hs0tFEIIUfVIYCcKNKqTC+1drEhIzWDsL6eJepBa7HMcuR5BcnomLnVMaFOvfNfkrUzZw7F7LoXrEk52ZQ/DNrChrgzDCiGEKGcS2IkCqdUqfnrRHWcrY27dT2T8qjPEp6QX6xzZpT4Gta6Zw7DZeje3x0hfzZ3oJK6GacvE7DivDeyGyDCsEEKICiCBnShUPWsT1r3cFVszQy7ei+fl1T4kp+Vdr+1RqRmZHLyizaqtadmwjzI11NctU7bnUhh3Y5I4G6wdhq1pJV6EEEJUTRLYiSJpbG/Ompe6YGGsz5nAaF5b71ukTNnjNyNJSM3AwdKIjq51KqCllWvgQ6tQZK8N26WBDXUtZBhWCCFE+ZPAThRZm3pWrPLywNhAzeFr95nx+9lcpT0elR3cDGztiFpdc4dhs/Vv6YCeWsWV0HjWnAwEZBhWCCFExZHAThRL5wY2LB3bGQM9FTvOh/Lhtgv5rkyRnqlh3xVtwd7asj5qHTNDujSwAeBuTDIqFQyUYVghhBAVRAI7UWy9m9mz6PmOqFWw4Uww83ZdzTO4O3U7itikdGzNDOnS0KYSWlo5sodjAbo2lGFYIYQQFUcCO1EiT7Vz4qtn2gGw7Ohtfjp8K9c+2dmwA1prhydriwGt/+uhk7VhhRBCVCQJ7ESJPefhyodPtQTgf3uusTZrThlApkZhb1ah3kG1ZBg2m7O1CUPbO+Nma8qQds6V3RwhhBC1iH5lN0BUby/3akR8cjrfHbzJx9svYWGsz4iOLvgERhP5IA1LY308G9lWdjMr3PdjOlZ2E4QQQtRCEtiJUnvriWbEp2Sw+kQgb/9xHnMjA47fjASgfysHDPWlY1gIIYSoCBLYiVJTqVR8PKQV8SnpbPG7x5Tf/DAx0ANq7tqwQgghRFUkgZ0oE2q1ivkj2/EgJYO9l8NJy9BgZqhHz6Z2ld00IYQQotaQMTJRZvT11Hw3piM9mmjn1A1o7YhxVs+dEEIIIcqf9NiJMmVsoMeKcR7suRTGY83sK7s5QgghRK0igZ0ocyaGegzvWK+ymyGEEELUOjIUK4QQQghRQ0hgJ4QQQghRQ0hgJ4QQQghRQ9TqOXbZC9fHx8dXckuEEEIIIfKWHadkxy0FqdWBXUJCAgCurq6V3BIhhBBCiIIlJCRgZWVV4D4qpSjhXw2l0WgICQnBwsIClUpVLteIj4/H1dWV4OBgLC0ty+UaVZXcu9y73HvtUpvvX+5d7r08711RFBISEnB2dkatLngWXa3usVOr1bi4uFTItSwtLWvdN3w2uXe599qmNt871O77l3uXey8vhfXUZZPkCSGEEEKIGkICOyGEEEKIGkICu3JmZGTEnDlzMDIyquymVDi5d7n32qY23zvU7vuXe5d7rypqdfKEEEIIIURNIj12QgghhBA1hAR2QgghhBA1hAR2QgghhBA1hAR2QgghhBA1hAR25einn36iYcOGGBsb4+7uzr///lvZTaoQn3zyCSqVKsfL0dGxsptVLo4ePcrQoUNxdnZGpVKxbdu2HJ8risInn3yCs7MzJiYm9OnTh0uXLlVOY8tYYffu5eWV6/ugW7duldPYMjZv3jw8PDywsLCgbt26DB8+nGvXruXYp6Y++6Lce0199kuWLKFdu3a6YrSenp7s2rVL93lNfeZQ+L3X1Geel3nz5qFSqXjzzTd126rSs5fArpxs2rSJN998k9mzZ+Pv70+vXr0YPHgwd+7cqeymVYjWrVsTGhqqe124cKGym1QuEhMTad++PT/88EOen8+fP58FCxbwww8/4O3tjaOjI0888YRuneLqrLB7Bxg0aFCO74N//vmnAltYfo4cOcKUKVM4deoU+/btIyMjgwEDBpCYmKjbp6Y++6LcO9TMZ+/i4sJXX32Fj48PPj4+9O3bl6efflr3A7ymPnMo/N6hZj7zR3l7e7Ns2TLatWuXY3uVevaKKBddunRRXn311RzbWrRoobz//vuV1KKKM2fOHKV9+/aV3YwKByhbt27VvddoNIqjo6Py1Vdf6balpKQoVlZWys8//1wJLSw/j967oijK+PHjlaeffrpS2lPRIiIiFEA5cuSIoii169k/eu+KUruefZ06dZQVK1bUqmeeLfveFaV2PPOEhASladOmyr59+5TevXsr06dPVxSl6v17lx67cpCWloavry8DBgzIsX3AgAGcOHGiklpVsW7cuIGzszMNGzZk9OjR3L59u7KbVOECAgIICwvL8X1gZGRE7969a833weHDh6lbty7NmjVj0qRJREREVHaTykVcXBwANjY2QO169o/ee7aa/uwzMzPZuHEjiYmJeHp61qpn/ui9Z6vpz3zKlCk89dRT9O/fP8f2qvbs9Sv8irVAZGQkmZmZODg45Nju4OBAWFhYJbWq4nTt2pW1a9fSrFkzwsPD+fzzz+nevTuXLl3C1ta2sptXYbKfdV7fB0FBQZXRpAo1ePBgnn32Wdzc3AgICOCjjz6ib9+++Pr6Vqkq7aWlKAozZsygZ8+etGnTBqg9zz6ve4ea/ewvXLiAp6cnKSkpmJubs3XrVlq1aqX7AV6Tn3l+9w41+5kDbNy4ET8/P7y9vXN9VtX+vUtgV45UKlWO94qi5NpWEw0ePFj397Zt2+Lp6Unjxo1Zs2YNM2bMqMSWVY7a+n3w/PPP6/7epk0bOnfujJubGzt37uSZZ56pxJaVralTp3L+/HmOHTuW67Oa/uzzu/ea/OybN2/O2bNniY2N5c8//2T8+PEcOXJE93lNfub53XurVq1q9DMPDg5m+vTp7N27F2Nj43z3qyrPXoZiy4GdnR16enq5euciIiJyRfS1gZmZGW3btuXGjRuV3ZQKlZ0JLN8HWk5OTri5udWo74Np06bx119/cejQIVxcXHTba8Ozz+/e81KTnr2hoSFNmjShc+fOzJs3j/bt27N48eJa8czzu/e81KRn7uvrS0REBO7u7ujr66Ovr8+RI0f47rvv0NfX1z3fqvLsJbArB4aGhri7u7Nv374c2/ft20f37t0rqVWVJzU1lStXruDk5FTZTalQDRs2xNHRMcf3QVpaGkeOHKmV3wdRUVEEBwfXiO8DRVGYOnUqW7Zs4eDBgzRs2DDH5zX52Rd273mpSc/+UYqikJqaWqOfeX6y7z0vNemZ9+vXjwsXLnD27Fndq3PnzrzwwgucPXuWRo0aVa1nX+HpGrXExo0bFQMDA+WXX35RLl++rLz55puKmZmZEhgYWNlNK3czZ85UDh8+rNy+fVs5deqUMmTIEMXCwqJG3ntCQoLi7++v+Pv7K4CyYMECxd/fXwkKClIURVG++uorxcrKStmyZYty4cIFZcyYMYqTk5MSHx9fyS0vvYLuPSEhQZk5c6Zy4sQJJSAgQDl06JDi6emp1KtXr0bc+2uvvaZYWVkphw8fVkJDQ3WvpKQk3T419dkXdu81+dnPmjVLOXr0qBIQEKCcP39e+eCDDxS1Wq3s3btXUZSa+8wVpeB7r8nPPD8PZ8UqStV69hLYlaMff/xRcXNzUwwNDZVOnTrlKAdQkz3//POKk5OTYmBgoDg7OyvPPPOMcunSpcpuVrk4dOiQAuR6jR8/XlEUbRr8nDlzFEdHR8XIyEh57LHHlAsXLlRuo8tIQfeelJSkDBgwQLG3t1cMDAyU+vXrK+PHj1fu3LlT2c0uE3ndN6CsWrVKt09NffaF3XtNfvYvvfSS7v90e3t7pV+/frqgTlFq7jNXlILvvSY/8/w8GthVpWevUhRFqbj+QSGEEEIIUV5kjp0QQgghRA0hgZ0QQgghRA0hgZ0QQgghRA0hgZ0QQgghRA0hgZ0QQgghRA0hgZ0QQgghRA0hgZ0QQgghRA0hgZ0QQgghRA0hgZ0QQgghRA0hgZ0QQgghRA0hgZ0QQhRRnz59ePPNN0u9T1m1RaVSoVKpOHv2bKnO5eXlpTvXtm3byqR9QojKIWvFCiFEEUVHR2NgYICFhQWgDa46dOjAokWL8t2nvPTp04dmzZrx6aefYmdnh76+fonPFRcXR3JyMk5OTmzdupXhw4eXXUOFEBWq5P8TCCFELWNjY1Mm+5QVU1NTHB0dS30eKysrrKysyqBFQojKJkOxQogqacOGDRgbG3Pv3j3dtpdffpl27doRFxeXa/8+ffowdepUpk6dirW1Nba2tnz44Yc8PCiRmprKG2+8Qd26dTE2NqZnz554e3vnOM/mzZtp27YtJiYm2Nra0r9/fxITE3XXyB5m9fLy4siRIyxevFg3jBkYGJhrKLYo1+zTpw9vvPEG7777LjY2Njg6OvLJJ58U+2vWp08fpk2bxptvvkmdOnVwcHBg2bJlJCYmMmHCBCwsLGjcuDG7du0q9rmFENWDBHZCiCpp9OjRNG/enHnz5gEwd+5c9uzZw65du/LtXVqzZg36+vqcPn2a7777joULF7JixQrd5++++y5//vkna9aswc/PjyZNmjBw4ECio6MBCA0NZcyYMbz00ktcuXKFw4cP88wzz5DXjJXFixfj6enJpEmTCA0NJTQ0FFdX11z7FXbNh9tuZmbG6dOnmT9/Pp9++in79u0r9tdtzZo12NnZcebMGaZNm8Zrr73Gs88+S/fu3fHz82PgwIGMHTuWpKSkYp9bCFENKEIIUUX9/fffipGRkfLFF18oderUUS5evJjvvr1791ZatmypaDQa3bb33ntPadmypaIoivLgwQPFwMBAWb9+ve7ztLQ0xdnZWZk/f76iKIri6+urAEpgYGC+15g+fXq+7x/dVpRrZh/Ts2fPHOfx8PBQ3nvvvQLvN69rP3yejIwMxczMTBk7dqxuW2hoqAIoJ0+ezHVOQNm6dWu+1xRCVH3SYyeEqLKGDBlCq1atmDt3Llu3bqV169YF7t+tWzdUKpXuvaenJzdu3CAzM5Nbt26Rnp5Ojx49dJ8bGBjQpUsXrly5AkD79u3p168fbdu25dlnn2X58uXExMSUuP1FuWa2du3a5Xjv5OREREREsa/58Hn09PSwtbWlbdu2um0ODg4AJTq3EKLqk8BOCFFl7dmzh6tXr5KZmakLSEpKyRpOfTjwy96evU1PT499+/axa9cuWrVqxffff0/z5s0JCAgot2tmMzAwyPFepVKh0WiKfc28zvPwtuzrluTcQoiqTwI7IUSV5Ofnx7PPPsvSpUsZOHAgH330UaHHnDp1Ktf7pk2boqenR5MmTTA0NOTYsWO6z9PT0/Hx8aFly5a6bSqVih49ejB37lz8/f0xNDRk69ateV7P0NCQzMzMfNtT1GsKIURZkXInQogqJzAwkKeeeor333+fsWPH0qpVKzw8PPD19cXd3T3f44KDg5kxYwaTJ0/Gz8+P77//nm+//RYAMzMzXnvtNd555x1sbGyoX78+8+fPJykpiYkTJwJw+vRpDhw4wIABA6hbty6nT5/m/v37+QZhDRo04PTp0wQGBmJubp6r1ElRrimEEGVJAjshRJUSHR3N4MGDGTZsGB988AEA7u7uDB06lNmzZ7N79+58jx03bhzJycl06dIFPT09pk2bxiuvvKL7/KuvvkKj0TB27FgSEhLo3Lkze/bsoU6dOgBYWlpy9OhRFi1aRHx8PG5ubnz77bcMHjw4z+u9/fbbjB8/nlatWpGcnJznkG1h1xRCiLIkK08IIWqEvFaBqMnK435VKpWsPCFENSdz7IQQopr66aefMDc358KFC6U6z6uvvoq5uXkZtUoIUZmkx04IUSPUth67e/fukZycDED9+vUxNDQs8bkiIiKIj48HtGVWzMzMyqSNQoiKJ4GdEEIIIUQNIUOxQgghhBA1hAR2QgghhBA1hAR2QgghhBA1hAR2QgghhBA1hAR2QgghhBA1hAR2QgghhBA1hAR2QgghhBA1hAR2QgghhBA1hAR2QgghhBA1hAR2QgghhBA1hAR2QgghhBA1hAR2QgghhBA1hAR2QgghhBA1hAR2QgghhBA1hAR2QgghhBA1hAR2Qohq4/Tp04wYMYL69etjZGSEg4MDnp6ezJw5s7KbVqE6duzIrFmzAIiJiUGtVnP48OEc+1y/fp23334bd3d3rK2tsbGxoUePHmzevLkSWiyEqCgS2AkhqoWdO3fSvXt34uPjmT9/Pnv37mXx4sX06NGDTZs2VXbzKkxKSgoXL16ka9euAJw6dQqVSoW7u3uO/fbu3cvOnTsZOXIkf/zxB+vXr6dp06Y8++yzfPrpp5XRdCFEBVApiqJUdiOEEKIwvXv35t69e1y9ehV9ff0cn2k0GtTq6vt7anp6OiqVKtd95eXkyZN0796de/fu4ezszJw5c/jzzz+5ePFijv0iIyOxtbVFpVLl2D5kyBAOHTpEdHQ0RkZGZXofQojKV33/JxRC1CpRUVHY2dnlGfzkFdRt2rQJT09PzMzMMDc3Z+DAgfj7++fYx8vLC3Nzc27evMmTTz6Jubk5rq6uzJw5k9TU1Bz7LlmyhPbt22Nubo6FhQUtWrTggw8+yLHPxYsXefrpp6lTpw7GxsZ06NCBNWvW5Njn8OHDqFQq1q1bx8yZM6lXrx5GRkbcvHmzSF8Hb29vXFxccHZ2BrTD0126dMm1n52dXa6gDqBLly4kJSURHR1dpOsJIaoXCeyEENWCp6cnp0+f5o033uD06dOkp6fnu++XX37JmDFjaNWqFb///jvr1q0jISGBXr16cfny5Rz7pqenM2zYMPr168f27dt56aWXWLhwIV9//bVun40bN/L666/Tu3dvtm7dyrZt23jrrbdITEzU7XPt2jW6d+/OpUuX+O6779iyZQutWrXCy8uL+fPn52rjrFmzuHPnDj///DN///03devWzfd+PvnkE1QqFSqViunTp3P37l3d+z179rBq1Srd+8IcOnQIe3v7Aq8nhKjGFCGEqAYiIyOVnj17KoACKAYGBkr37t2VefPmKQkJCbr97ty5o+jr6yvTpk3LcXxCQoLi6OioPPfcc7pt48ePVwDl999/z7Hvk08+qTRv3lz3furUqYq1tXWB7Rs9erRiZGSk3LlzJ8f2wYMHK6ampkpsbKyiKIpy6NAhBVAee+yxIt97aGio4u/vr/j5+SmmpqbKZ599pvj7+ysrV65UAOXAgQOKv7+/4u/vX+B5li9frgDK4sWLi3xtIUT1Ij12QohqwdbWln///Rdvb2+++uornn76aa5fv86sWbNo27YtkZGRAOzZs4eMjAzGjRtHRkaG7mVsbEzv3r1zZY+qVCqGDh2aY1u7du0ICgrSve/SpQuxsbGMGTOG7du36671sIMHD9KvXz9cXV1zbPfy8iIpKYmTJ0/m2D5y5Mgi37ujoyMdOnRArVaTlJTE6NGj6dChA5GRkTRo0IC+ffvSoUMHOnTokO85du3axZQpUxg1ahTTpk0r8rWFENVL4TN1hRCiCuncuTOdO3cGtMOo7733HgsXLmT+/PnMnz+f8PBwADw8PPI8/tH5eKamphgbG+fYZmRkREpKiu792LFjycjIYPny5YwcORKNRoOHhweff/45TzzxBKCdA+jk5JTretlz4aKionJsz2vfvCiKQmZmJqCdn+fo6EiDBg3IyMjg6NGj9OzZk4yMDIB8ky/27NnDM888wxNPPMH69euLNGQrhKieJLATQlRbBgYGzJkzh4ULF+qyQu3s7ADYvHkzbm5uZXatCRMmMGHCBBITEzl69Chz5sxhyJAhXL9+HTc3N2xtbQkNDc11XEhISI52ZStqcLVmzRomTJiQY5uBgUGO97/++isAAQEBNGjQIMdne/bsYfjw4fTu3Zs///wTQ0PDIl1XCFE9SWAnhKgWQkND8+zlunLlCvBfz9jAgQPR19fn1q1bxRruLCozMzMGDx5MWloaw4cP59KlS7i5udGvXz+2bt1KSEiIri0Aa9euxdTUlG7dupXoekOHDsXb25vMzEz69u3L7NmzGTBgAH5+frz66qvs27cPKysrgBzXBW0tu+HDh9OzZ0+2bdsm5U2EqAUksBNCVAsDBw7ExcWFoUOH0qJFCzQaDWfPnuXbb7/F3Nyc6dOnA9CgQQM+/fRTZs+eze3btxk0aBB16tQhPDycM2fOYGZmxty5c4t17UmTJmFiYkKPHj1wcnIiLCyMefPmYWVlpRvynTNnDjt27ODxxx/n448/xsbGhvXr17Nz507mz5+vC76Ky9bWFltbW44ePUpKSgqTJk3C3t6ebdu24e7uTr9+/fI87tixYwwfPhxHR0c++OADzp49m+PzVq1aYWlpWaI2CSGqLgnshBDVwocffsj27dtZuHAhoaGhpKam4uTkRP/+/Zk1axYtW7bU7Ttr1ixatWrF4sWL2bBhA6mpqTg6OuLh4cGrr75a7Gv36tWL1atX8/vvvxMTE4OdnR09e/Zk7dq12NvbA9C8eXNOnDjBBx98wJQpU0hOTqZly5asWrUKLy+vUt//9u3b6dq1q+56f//9d4E9kvv37yc5OZnAwED69u2b6/NDhw7Rp0+fUrdLCFG1yMoTQgghhBA1hJQ7EUIIIYSoISSwE0IIIYSoISSwE0IIIYSoISSwE0IIIYSoISSwE0IIIYSoISSwE0IIIYSoISSwE0IIIYSoIWp1gWKNRkNISAgWFhayKLYQQgghqiRFUUhISMDZ2Rm1uuA+uVod2IWEhODq6lrZzRBCCCGEKFRwcDAuLi4F7lOrAzsLCwtA+4WSNROFEEIIURXFx8fj6uqqi1sKUqsDu+zhV0tLSwnshBBCCFGlFWXamCRPCCGEEELUEBLYCSGEEELUELV6KFYIIYQQRXf+bixBUUk4WxvjbG1CXQtj9NRSVaIqkcBOCCGEEIXKyNQw6ueTpGVodNv01CocLY1xstIGek7WxtSzNsHJygQnK+3frU0NpKRYBaq2gd28efPYsmULV69excTEhO7du/P111/TvHnzym6aEEIIUePo66mZ/Fgjlh29TWpWcJepUbgXm8y92GQIisnzOGMDNc7WJjhbmeBsbYyTlYk2+Mvq9XO2MsHEUK8ib6VGUymKolR2I0pi0KBBjB49Gg8PDzIyMpg9ezYXLlzg8uXLmJmZFekc8fHxWFlZERcXJ1mxQgghRBFEPUhlzckg1p4MJDYpXbe9jqkBfZrXJSktg9C4FEJik4l8kFakc9YxNcDJykQb6GUFf9nDvc7WJjhYGKGvV7y0gNDQUJYuXcrkyZNxcnKqUscUV3HilWob2D3q/v371K1blyNHjvDYY48V6RgJ7IQQQoiSSUrL4HfvYFYcC+BuTDIAJgZ6PO/hysSeDXG1MSUlPZOwuBRC4pIJiU0hNDaZkLhkbgbexXfP7+i3GkCakVWh11KroA6JJJ3fTbcnn6dpw/o4WxnjZG1C98a2WBgb5DrGz88Pd3d3fH196dSpU5HuqaKOKa7ixCvVdij2UXFxcQDY2Njku09qaiqpqam69/Hx8eXeLiGEEKImMjXUx6tHQ17s5savh87xxbffkdC4L6tPZLLuVBBPtnVi8mONaFPPigZ2OUfS/PwycJ+5Bp8vp9GkVTtC45IJidUGfyGxyboev5C4ZMLiUkjPVAgJCyXsn1XE2rfnUHCG7lxdGtrw+2RP3fvQ0FBCQ0Px8/PLupb2Tycnp3x71Ep6TEhISLGOqQg1osdOURSefvppYmJi+Pfff/Pd75NPPmHu3Lm5tkuPnRBCCFFy2b1WK7bu53CUOf/eiNR91rOJHa881oheTe0ICwvTBVCTJk1i2bJltG7dGgsLC5o1a6YLltLS0ujTpw/r1q0jKCgIfWNz7oTdZ8m3X9K8YzfaD3iOG7cCuHbpHKaG+tw/e4D/+7//48GDB6SkpLBv375cbWzWrBnNmzdn+/btTJw4kcjISDw9PRk5ciRDhw7l+vXruY6ZPXs2UVFR3Lt3j44dOzJx4kSmTZsGaFevWr9+fa5j5syZwyeffFJ2X1xq4VDslClT2LlzJ8eOHStwDbW8euxcXV0lsBNCCCGyFGfOWHZPl6+vL6+88grfffcdrVq1wu9aIAeuRHBFXZ+kwPOk3r1MHVN9PFs1ZNOyhbnO06FDBzZt2sSFCxe4ceMGtra2TJo0iW3btrF58+Y8A6iJU2ew16A79WwsOPnhgFxtyg4ely9fTqdOnYrcYzdp0iR++OlnmrRqi9rMhkxjazq5WVPXwrjAY4pynZKqVUOx06ZN46+//uLo0aOFLoxrZGSEkZFRBbVMCCGEqHjh8SmsOh5IXHI6D2Lu47/3DzoOGIV5HXvdPo926Tz8Puz2FdbOnUuAaQscGrVEURRSkx4QeecmCdERuLbqzG3/fwk8d5Kou7eJvHNDd+wbb7wBgFPrbrh0GYiNozGZzi0wcm6OYmzBsfQUPGcs54UmmUx9/dVcwVCzZs1ytGv48OF07dqVGTNm5Aqg4jBj/+83SVNUnLgZSXJ6pvaVlklKug0BivZ+90eYstc7lfD4q4TFndVl9OYlNSwRgM9Pp2AUFAP8l+nr82F/7Mz/iyEeDeA6depUbnPsiqPaBnaKojBt2jS2bt3K4cOHadiwYWU3SQghhKhUJ25F8sYGf102amrYTcJ+/5lbJi0wcmyS5zGa9FRUKjXJAf6k3DlHevRdADZ8Oxu1oTEmTbpg0qgzybe80TO35WxmGOg1QdW1NUYdk3F8EE1a+C2id3+PzaBpGDo0Rm1uQ4S5DWhA3+q/ni61kSkPjPVp074OULRgKL8A6nJIPHCTqMQ0/m/F6VzHZTyIx6rHGI6FZKIfH12kr5+euQ1WPcagZ55zvn4dUwPMjfIOmZycnJgzZ06lzqt7WLUN7KZMmcJvv/3G9u3bsbCwICwsDAArKytMTEwquXVCCCFExdFoFJYcucW3e6+hUaChSRoeDiqC0x+wEehpEUXDBm7cu3WZhNhorO0ccHJrys7VizEwMuIpr+kcuHSJUz7bdedMj7gNQN++/RgydiAwUPeZipwFh+9cv8hXu7/nrdEDad66HYb6aoz09TAyUGOkp8bIQI2hnva9k5UxD2Iiix0MPRpANXUwp1dTOwKjEgmOTs61v765DdY9X8j3fA6WRjhYGme9jHDM+ruj1SAcLI2pa2GEhbFBoStrODk5lfmcutKotnPs8qtivWrVKry8vIp0Dil3IoQQojrJa/5bbFIaM34/x8GrESgZ6fSoE0/sid/Z9c/OXMePGjWK6dOn07x5c+zt7XOdu6RzxiqilltBQmKTOR0QhaWxAVYm2pdl1p9G+upqv/JFrUueKCkJ7IQQQlQn2dmnp06dwtTUlHQLJ56Z8iERN89jUrcBX7z/FpH+e6hXrx5ubm7cvn272EFaRdRlE8VTq5InhBBCiOqoqL1cGRkZ7Ny5k6NHj5KZmQnAxEmvYO3ShOD6g8ms3wX3niP4eXw3WjtbwVMeumOtra2B4k3sr2pzxkTxSI+d9NgJIUS1lZyWyeRffbkXk4SBnho9tQp9PTX6apX2padCX63+7++6zx7a9vAxeir01GoM1Cr09FQYqLXnNMjanmv/XNd4eH8VBg9f76Fj9dQqLp73p2e3rnh7+9C5szuKohCTmEpobBI7d+1l3+4dPEhKoc9LH7Dm48mEXvXJdf9WPcbw/OQZ/O/Z9ljmsfpCZQ+RirIhQ7FFJIGdEEJUb3djkuj59aHKbkaxZDyIJvNBNGlhN4ne8wNWPUZjYN+ARP9doG+Ide/xAKiNzNCzsEOlUv13TFb2qf3gN3Bq3JKxfTswc3jXaj+HTBRMArsiksBOCCGqv5XHAvh0x2UAhrZ35un2zmRoFDI0GjI1CumZCpkaTdafCumZ2u0ZGoWMTO1+2r9rHtqmfZ+pUUjX5HP8Q8c+iL5P0PHtOHYbip6ZTY79sv9My9SQEhlM5IFfSLnlnes+LLuPpk6vF7EyMdBmaFoZ42BhhKNVVrampTFRQVcZ/WQfXS+fqB1kjp0QQoha46WeDYl8kMpPh2+x83wIQ9o5MaiNY4W2wc/PD/c5a/hj3hu6uWyKonDp0iWOHDlC/fr1UalU7N9/hTbvT6Z5y0VcuHCRKa9NZtEPS2jdrj12dR1o3tANE0O9fK8TWkfDnDlzqFfPuaJuTVQzEtgJIYSo9t4Z2JyoB2ls8glm2gZ/1r3Uha6NbMv9uo8uHr9r1y42bNjA3bt3+fXXX/n111/p0aMHvXr1wtramiFDhuiONTPRFu7t5dmlWIkNValmmqh6ZChWhmKFEKJGyMjU8Np6P/ZdDsfCWJ/fJ3vS0ql8/2//4IMPmDdvXq7t7733Hl999VWBx0pigygqmWNXRBLYCSFEzZKUlsFT3x0jIDKRuhZG/Plad1xtTIt9noKCrtOnT7NlyxYSEhKYMWMGmzdvxtjYmLfeeqtcF4IXtZcEdkUkgZ0QQtQMGo3CPxdDWbz/BjciHui217cxZcvr3XMs3l4UDxfpbdSoERs3buTAgQO8//77xMTEYGdnR/v27XXZqFLUV5QnSZ4QQghRK2g0CnsuhbFo/w2uhSfk+vxOdBKdP9/PhU8GYJFHnbdHZc+Z27BhAwAffvghM2bMwMDAgF9++SXfH6pS1FdUFcXqsfvrr7+KfYEnnngCExOTYh9XEaTHTgghqidFUdhzKZxF+69zNUwb0FkY6fNSz4aMcndhm/89vt13Xbf/9H5NeeuJZvmeLzExkX/++YfPPvuMCxcu5Pp8zpw5krQgKk25DcWq1epiNUSlUnHjxg0aNWpUrOMqigR2QghRvSiKwv4rESzaf51LIfEAmBvp81KPBkzs2Qgr0/965ZLTMvl0x2Uu3gjE/t6/fDHrrRw9avfv3+fvv/8mJiaGYcOGcejQIbp27UpmZiZ+fn7FXmNViPJSrkOxYWFh1K1bt0j7WlhYFPf0QgghRC6KonDoWgSL9t/g/N04AMwM9fDq0YBJvRphbWqY6xgTQz3mPdMWP7903N3/x1Sv0aSmprJt2zZGjRrFunXraNCgASNHjsTKyoqmTZvmOkdx1lgVoiooVmA3fvz4Yg2rvvjii9ITJoQQosQUReHI9fss3H+Dc8GxAJga6jHOswGvPNYIG7PcAV227PlyO3fuBGDr1q1cvHiRZ555Bnt7e2bNmpXvsTJnTlRXkhUrQ7FCCFHlKIrCvzciWbj/Ov53YgEwNlAzPiugsy0kyzUwMJBly5blWWNO5suJ6kayYoUQQlRLiqJw4lYUC/ddxycoBgAjfTVju7kxuXdjMh5E8/038/KsL5eens7t27d56y3tXLqpU6cyatSoPOfLCVFTlSqwS0lJ4fz580RERKDRaHJ8NmzYsFI1TAghRO1y8lYUC/df50xANACG+mpe6Fqf13o3pq6ldvktvxuhzJ07l2HDhukCtM2bN7Nu3To6dOjABx98wJYtWzA2Ns51fpkvJ2qDEgd2u3fvZty4cURGRub6TKVSkZmZWaqGCSGEqB3OBESzcN91Tt6OAsBQT83/da3Pa30a45AV0D26JuvPP//M7du3+eCDD2jYsCHr16/H3Nw8z/PLfDlRm5R4jl2TJk0YOHAgH3/8MQ4ODmXdrgohc+yEEKJyZGoUvAOj+eHgTY7d1HYQGOipGO1Rn9cfb4yTVc5EvTlz5vDpp5/mOo/MlxO1QYXMsYuIiGDGjBnVNqgTQghRsUJikzl2I5KjN+5z/GYkMUnpAOirVTzn4cqUx5tQzzpnQBcYGIiRkRFpaWm88sorNGzYkFmzZsl8OSHyUeLAbtSoURw+fJjGjRuXZXuEEELUEElpGZy+Hc3RG/f590YkNx9awxW0hYX7uOpjcOMg07pNxSkrqMvMzCQ+Pp7x48djZ2fHrFmzdNmtfn5+zJo1S+bLCZGPEg/FJiUl8eyzz2Jvb0/btm0xMMi5Bt8bb7xRJg0syNGjR/nf//6Hr68voaGhbN26leHDhxf5eBmKFUKIsqPRKFwOjdcGctcj8QmKJj3zvx8xahW0d7WmV1N7HmtqR3tXay6cO4u7uzu+vr4A/PTTT6SmprJ27VoSExNzzZsLDQ1l6dKleWbFClFTVchQ7G+//caePXswMTHh8OHDqFQq3WcqlapCArvExETat2/PhAkTGDlyZLlfTwghRG5xyel8vuMyB65GEJ2YluOzetYmPNZMG8h1b2ynW/IrNDSUC+fOsm/fPgBWrFhB9+7dmThxIp6engB5JkM4OTnJnDohClDiHjtHR0feeOMN3n///WKvIVseVCqV9NgJIUQl2H0xlFd/9cuxrX/Lurw/uCWN7c1y/OIPkJGRgZeXF+vXr891LkmGECK34sQrJY7I0tLSeP7556tEUFdUqampxMfH53gJIYQonSdaOTL7SW0Ql23/lQheWHGKr3Zd5Xp4AqCdO3fw4EEGDRpE48aNOXXqFMuXLwdg+fLl+Pr6Mnny5Eq5ByFqihJHZePHj2fTpk1l2ZZyN2/ePKysrHQvV1fXym6SEEJUe3pqFZMea8S6Mc0YlPovI5qbYm1qQHh8KkuP3qbX1AXU6/g4X327iO7du7Nv3z7mzp1L165ddQkQ2ckQMm9OiNIp8Ry7zMxM5s+fz549e2jXrl2u5IkFCxaUunFlbdasWcyYMUP3Pj4+XoI7IYQoA4qicPZaAEsXfc24rztgkKIi9vjfmLfpi5KZhrrXJHoNfyLXihBSPFiIslXiwO7Chf9v777DorzSh49/hzJDR3qxEBQLqFggKpaIMdGYoqa4McWYXXXV2FBjmnFtMfoaa6LGEpPobkyyKbrJzzVKYouxAhJRMSpFUAZRkA4DDM/7B2FWBBSQOtyf65rLmaec5xzPiDenRtGjRw8Azp49W+bcneMpGguNRoNGc/eNo4UQQtyboijE3czhRFwaByL+4PjZGK5dLvm/4D8//h+6a9HY+j9K904+9H6iDwM7uPBQB5dy6chkCCFqV40DuwMHDtRmPoQQQjRixcUKl1KyORmXyvG4NE7GpXEjSwdA+pEvyPjtS8O1GUdKJkWEvPg4S2c81CD5FaK5qlZgd+bMGbp06VLlCRPnzp2jY8eOmJnVOH68q+zsbC5fvmz4HBcXR2RkJI6OjrRp06ZOnimEEM2BvlghWpvJibg0TsSmcio+zbBTRCklNR6zP0J5csAAur06HMuCW0yb8prsCiFEA6rWciempqYkJyfj4lK+Ob0idnZ2REZG0rZt2xpn8G4OHjzIoEGDyh0fO3Ysn3/++T3vl+VOhBDifzLyCvkmLJGjMSWBXFZ+UZnzluam9Ghpi03qOV5+5gm+3fQBr02aRKdOnYCSXSFKFxuWXSGEqD11tkCxoijMmzcPKyurKl1fUFBw74vuQ3BwMDVchk8IIcSfsvIL+ey3eLb8GmsI5oqy0yiI2kvwyBcZ1KMjDz7gwI0/wlix/B8EjRxJ3/ZuPLRmTZl0ZCKEEA2vWi12wcHB1Z4YsWPHjkb7j1xa7IQQzVm2rohtR+PZfDiWjLySbtYObjb8JbA1ttmJjH48mG3btrF//366devGxIkTsbCwaFLrlwphDOqsxe7gwYP3ky8hhBCNQG5BEduPXWHToRjDuLl2LtaEPNKBns5w7dpVtmz/FwD79+/n2WefJTAwsMq9NUKIhlPjLcWMgbTYCSGak7wCPV+cuMLHB2NI/XNP17bO1sx4pD1P+nuiFOuZM2cOa+7oYgXZ6kuIhlRnLXZCCCGanvxCPTtOJPDxoRjDEiVtHK2YMbg9I7p7Eh8Xy/RpU8nPz2fx4sW89NJLREZGMmHCBJnhKkQTI4GdEEIYKV2Rnq9PJbL+wGWuJWnJjtxDh4FP8/rIPozs4cmRw4c4dPAPVCoVEydOxN/fHwBPT0/DOLrSrb6EEE2DBHZCCGFkdEV6vg2/yrr9l9Fm5APgSA7XfvuSlf9vKv0DWzFq1Cg6d+7Ma6+9hpubW7k0ZIarEE2TjLGTMXZCiCauUF/MmavpHI9N41hMKmFX0sgvLAZKArqnfa2xTL3InNmz8PX1Zd26dXTs2JGWLVs2cM6FEFVRb2PsfvnlF3755RdSUlIoLi4uc+7TTz+9n6SFEEJUokhfzJlrGRyPTeVYTCrhV26RW6Avc427nQWTBrbl5I6V/OPV9Ybj0dHRDB48WCZDCGGkahzYLVy4kEWLFhEYGIiHh0e117cTQghRNUX6Ys4lZXIsNpXjsamciksj545AzsHKnD5tnejT1omgdk54O1qwZs0aUm/e4KuvviIrK0smQwjRDNQ4sNu4cSOff/45Y8aMqc38CCFEs6cvVjiflMmx2Jscj03jVFwaWbqy23vZW5rT29uRoHYlgVwHV1tA4T//+Q8zFm9mxowZzJ492zAJIiIiApDJEEIYuxoHdgUFBfTt27c28yKEEM1ScbHCeW0mx/9skTsRV36fVlsLM3p7lwRx7awLOLBrB5MenYSHhwd5eXl89tmnvPjii1y/fp2vvvoKe3v7MvfLZAghmocaT5548803sbGxYd68ebWdp3ojkyeEEA1FV6TnP5FJhJ6/zsm4NMOWXqVsNWb08nY0dK36ethhalIy5CUiIoKAgADCwsIoLCxk3rx5vPLKK4wePRpzc/OGKI4Qog7Vy+SJ/Px8Nm/ezM8//4y/v3+5HyarVq2qadJCCGG0cguK+PJkIlsOx5KcmW84bq025UFvR4L+DOT8POwwMy27J6tWq0Wr1bJ3714A5s2bx4IFC9i2bRuenp71Wg4hRONU4xa7QYMGVZ6oSsX+/ftrnKn6Ii12Qoj6kplfyD+PXWHrkTjS/tzOy81Ow8u9vejf3pmuLe3LBXJ3mjhxIps3by53XGa4CmHc6qXF7sCBAzW9VQghmo3UbB2f/hbH9qNXDBMg2jhaMWlgO54NaInGzPSeaRQUFDB69GhatGjB7t27SUpKkhmuQogKyc4TQghRB7QZeWw5HMeXJxPIKyxZmqS9qw1TBvnwpL/HPVvn9Ho933//Pdu3b+ebb77h888/N/ymLjNchRCVua/ALj09na1btxIdHY1KpcLX15dx48aVm40lhBDNxZXUHDYeiuHb8KsU6ktGunRtac+UQT4M8XPDxKT8mp9arZZNmzYxceJEHBwciI6OJjs7m6tXr/Lll19iYWGBhYWF4XqZ4SqEqEyNx9iFhYUxdOhQLC0t6dWrF4qiEBYWRl5eHvv27WsSv0XKGDshRG25eD2LDQcu88PvSRT/+VO1l7cjUwf5MKC9810XcS+d5Tp37lyOHz/O5MmTefbZZ+sp50KIxq5extjNnDmT4cOHs2XLFszMSpIpKipi/PjxhISEcPjw4ZomLYQQTcaZq+ms23+ZfeevG44Fd3RhyiAfHnzA8a73arVazpw5Y1hFwNbWlmXLlskerkKIGqtxi52lpSWnT5+mU6dOZY6fP3+ewMBAcnNzayWDdUla7IQQNaUr0jNtx+kyAZ2V2pQpg3wI7uiCq60FTtbqCrteARITE9m0aRNLliwpd05muQohblcvLXZ2dnYkJCSUC+wSExOxtbWtabLVtmHDBj744AO0Wi2dO3dmzZo1DBgwoN6eL4Ronq7eyisT1AHkFuj5YO8ffLD3DwBMTVQ426hxtbXA1VaDq50GMpM5tGM91pYaZr75Lj89+jhxF84yedJEmeUqhLhvNQ7snn/+ecaNG8eKFSvo27cvKpWKI0eOMGfOHF544YXazGOlvv76a0JCQtiwYQP9+vVj06ZNDBs2jPPnz9OmTZt6yYMQonlq52LDd5P7EnU1nZQsneF1I0vHjax8UnMK0BcrXM/UkZyRjy4hioIb8Vh1CIJ2IzCzd+O1HxIB0CXnAfBpNBxSFeJ6OQVXu4ySYNDWAlc7jeG9pfrey6MIIZqvGnfFFhQUMGfOHDZu3EhRUcnaTObm5kyePJlly5ah0WhqNaMV6d27Nz179uTjjz82HPP19WXkyJEsXbr0nvdLV6wQoraVznD92/gJmFk7cC01k7Url5OdX0jA4y+SZ2ZDSmZJ8FcaCOZnppIduQeb7sMws7n7uDxbjRkutwV6pS2BrrYWeNhbEPiAo2HrMSGEcahOvFLjwK5Ubm4uMTExKIqCj48PVlZW95NclRUUFGBlZcU333zD008/bTg+Y8YMIiMjOXTo0D3TkMBOCFHbSme4zp8/nyNHjrBw4UL69etX6fX6YoVrt/K4fCOLS9ezuZRS8rp8PYucAn21nz9lUDvmDO107wuFEE1GvYyxK2VlZUXXrl3vN5lqu3nzJnq9Hjc3tzLH3dzcSE5OrvAenU6HTqczfM7MzKzTPAohmofiYoVLcYmcvXCJTz/ZBMDR3/9gwOipHLluwpGfLpCRV0hGXiGZf/55++fi+/r1umQsn72lOU7W6nvOxBVCGLdqBXazZs1i8eLFWFtbM2vWrLteWzp9v67duTaUoiiVrhe1dOlSFi5cWB/ZEkI0MUX6YjLzi8oEXbcHYncGZLefz8wv5OZ/15IT9bMhvdBdXxG66yvs+71Ai/4v3fP5alMT7CzNsbc0w97SvMzL7o4/7zxnrTa96zp5Qojmo1qB3enTpyksLDS8r0x9/IBxdnbG1NS0XOtcSkpKuVa8Um+//XaZgDQzM5PWrVvXaT6FEI3f7jNaZv47koKi4mrdV5R5g8xTu1CZW9Ci34s4Pzgck7R44natIuClN2nVvjOu7u609PQsCcIsbgvKrMoGaBozEwnOhBD3rVqB3YEDBwzvt23bRqtWrTAxKbvfoaIoJCYm1k7u7kKtVhMQEEBoaGiZMXahoaGMGDGiwns0Gk29TOoQQjQtGXmFZYI6F1sNzjYa7CzKt57ZW5mTlXwFe2tLLkRcovtTMxjYvy92lmZozExLxtjtWsXmWX9pEjvwCCGMS43H2Hl7e6PVanF1dS1zPC0tDW9vb/T66g/6ra5Zs2YxZswYAgMDCQoKYvPmzSQkJDBp0qQ6f7YQwni80Ks1+YV6lvw3Gn2xgpudhk1jAjHJSzfs4erh4UFBQQGvvPIK1tbWvPvuu7w8JKRcWrKPqxCiIdV4VqyJiQnJycnlArsrV67g5+dHTk5OrWTwXjZs2MDy5cvRarV06dKF1atX89BDD1XpXpkVK4S43dGYm0z5IoJbuYU4WauZ3s2UV0cMZv369Rw4cIDt27eTnZ2Ni4tLQ2dVCNGM1OlyJ6Vj1NauXcuECRPKLG+i1+s5ceIEpqam/PbbbzXIev2SwE4IcafEtFzGrttLdEw8uvjT3Dr8T5566ilCQkLw9fWVljghRL2r0+VOSidNKIpCVFQUarXacE6tVtOtWzdef/316iYrhBCNQmtHK+xO/5PkXTsNx3788Ud+/PFH2cNVCNHo1bgr9q9//Str165t0i1d0mInhCiVnp7Ohg0b6NOnD2ZmZmg0GtZ/t59/fvAOjo9No0u37qz9azDdO7Zt6KwKIZqZ6sQrJnc9exefffaZBENCiCbv5s2b6HQ6xo8fT7du3Rg0aBAPPfQQvXv3JmT0UACcvDpyBTfGfxPLqfi0Bs6xEEJUrlotdo1xgeL7IS12QjQvpfu4Tpw4EZ1Ox3vvvUdWVhbbtm3DwsKi0uufGDWGeaHXuJCchZmJigXDO/NyH68GKIEQojmqszF2jWmBYiGEqC6tVsvChQspKChgwoQJzJ49G19f30qv9/DwMIyp+769F3O+PcPuM1re3XWWc0kZLBjeGY2ZaT3lXggh7q3GCxTf/l4IIRqza9eukZiYyHvvvQeAg4MDt27dqtYMVyu1Gete6EEXT3uW773AlycT+SM5i49fDsDNrnxrnxBCNIQaj7HLy8sjNzfX8PnKlSusWbOGffv21UrGhBDifhUWFrJ161YGDBhAUFAQu3fvBuCNN94gICCATZs2VSs9lUrF2L5eTBvkA0BEQjpPfXSEpPS8Ws+7EELURI13nhgxYgTPPPMMkyZNIj09nV69eqFWq7l58yarVq1i8uTJtZlPIYSosrS0NLZv386kSZOwsLDgwIEDpKamEhERwYQJE9iyZQs9e/a8Z4tdXoGe89pMoq6mE3Utk6hr6VxOyab4tpHJKVk6oq5l4NnCso5LJYQQ91bjwC4iIoLVq1cD8O233+Lu7s7p06f57rvv+Mc//iGBnRCiTt0+EaI0QCsqKmL//v2sW7eOSZMmodFoeOmllwDw8vrfZIeePXuW28c1v7AkiDt7LYMzVzM4ey2DSynZ6IvLzy9zs9PQtaU9XVu24MEHHAhq51SHJRVCiKqrcWCXm5uLra0tAPv27eOZZ57BxMSEPn36cOXKlVrLoBBCVKR0IsTw4cNJSkpi9erVdO3alTlz5jBkyJAK7yndx9XB2ZXIxPQ/W+JKArnKgjgXWw3+Le3p0tIe/1b2dG1pj6uMqRNCNFI1Dux8fHzYtWsXTz/9NHv37mXmzJkApKSkyNIhQog6o9Vq0Wq1hIeHA7Bnzx4Apk+fTq9evcpdryvSc0GbRdS1DKKuZhDlMJh/bTxDUQVBnLONuqQlrlULuv4ZyMnECCFEU1LjwO4f//gHL774IjNnzmTw4MEEBQUBJa13PXr0qLUMCiHE7TZu3MiiRYsMn999912gZKJEj4BA/kjO4szVDKKupXPmagYXr2dRqC8fxDlZq+n6ZwtcSTBnj7udhSzXJIRo0mq8pRhAcnIyWq2Wbt26YWJSMsH25MmT2NnZ0alTp1rLZF2RBYqFaDqSk5NZv349YWFhLF68mFOnwnjttclMenc5RQ5eJOZriM1VU1BUXO5eR2t1SVfqbV2qHvYSxAkhmoY6W6D4Tu7u7ri7u5c5VlFXiBBC1FRkZCTJyclkFqqw9OpGv96jWR6eSVhEEQC7EtVoCq3/vLoYe0tzw1g4/1YlgVzLFpYSxAkhmoX7CuzS09PZunUr0dHRqFQqfH19GTduHPb29rWVPyFEM3D7DFd3d3e06Xn8fjWdBW+GkKFTUHV5nDxLV8ASLscDUKSxx/mhlwj0bUvvzt74t26Bf0t7vJysJIgTQjRbNe6KDQsLY+jQoVhaWtKrVy8URSEsLIy8vDz27dtXbimBxki6YoVoeGk5BXyz9zCTnn2UJ+du4czvv5MSdRjHx6ZjZueCyqRkyy61qQm+nnb4/9kS1611C9q52GBqIkGcEMK4VSdeqXFgN2DAAHx8fNiyZQtmZiUNf0VFRYwfP57Y2FgOHz5ck2TrlQR2QtSvrPxCw/IiZ66mE3Y+lsRrSeTHR5J+6HMcBo1DKSrEtusg/Nq3pVurFvi3tse/ZQs6utuiNqvxZjlCCNFk1csYu7CwsDJBHYCZmRlvvPEGgYGBNU1WCGFE0nMLOBmXxom4NE7EpXI+KdOwa4OiKKQf/obM498Yrr91YCsArw30YknIqw2QYyGEaNpqHNjZ2dmRkJBQbvZrYmKiYeFiIUTzciunwBDEHY9N40JyJnf2CXjaaTC79AspZw4z9oWXGLxyNpfOR1Vrqy8hhBAVq3Fg9/zzzzNu3DhWrFhB3759UalUHDlyhDlz5vDCCy/UZh6FEI3UzWxdSYtcbCon4tK4kJwFQFF2GtmRe7DpPoyO3q3p3daJVibpnPn5W1bOe5+DB3U8umkharUaAFsLc6Dirb6EEEJUXY0DuxUrVqBSqXjllVcoKipZdsDc3JzJkyezbNmyWsugEKLxuJGl+7M1LpUTsWlcSskud017VxvauGTz6fov+feSKXTybk1sbCz/+te/eO2117C2tuaJJ54oc0/pVl/SUieEEPfnvhYohpI9Y2NiYlAUBR8fH6ysrGorb3e1ZMkSdu/eTWRkJGq1mvT09GqnIZMnhKicoihcS88j/MotTsSlcTw2ldgbOeWu6+hmS5+2jvRu60QbSx0FmWlEREQwYcIEOnXqxPPPP8/EiRMlaBNCiBqqtwWKAaysrOjSpQtAva4dVVBQwKhRowgKCmLr1q319lwhjFGRvpiYGzmcS8rgfFIm55IyOa/NJCOvsMx1KhV0crejt7cjfdo60cvbEUfrku5URVGYMGFCmX+PFy5cYOHChQAsWLCg3sojhBDN1X0Fdlu3bmX16tVcunQJgPbt2xMSEsL48eNrJXN3U/qfxeeff17nzxLCmOQWFBGtzeJ8UgbntSVB3IXkrAq34jIzUdHR3ZY+bZ3o7e1IL29HWlipy1yTkZHBoUOH6NWrFxqNhl27dnHjxg2ZDCGEEA2gxoHdvHnzWL16NdOmTSMoKAiAY8eOMXPmTOLj43nvvfdqLZO1RafTodPpDJ8zMzMbMDdC1L8FP5xj27H4cjNVAazVpvh52uHnYUdnT3v8PO1o72ZD2o0UNm3aRNeAiYagTlEUCgsL2b59Oz/++CNjxozB3d2d9evXAxAREQHIZAghhKhvNQ7sPv74Y7Zs2VJmBuzw4cPx9/dn2rRpjTKwW7p0qaGlT4jm6OL1rDJBna2FGbMf7UBwR1faOFphUsEuDlqtloULFzJ8+HBcXV3ZsmULO3fuZOrUqYwdO7bCFnqZDCGEEA2jxsu46/X6ChciDggIMMySra4FCxagUqnu+goLC6tplnn77bfJyMgwvBITE2uclhBN0ZZXAlnwlB9tHEsmOWXlF/H+fy+w/sDlcjNctVotERERhta32bNnc/LkSSwsLPjhhx946qmnMDc3r/A5Hh4eLFiwQAI7IYSoZzWeFTtt2jTMzc1ZtWpVmeOvv/46eXl5hi6Z6rh58yY3b9686zUPPPAAFhYWhs+ff/45ISEhMitWiGrQFyuEnk9my69xhF+5ZTg+oL0zEwa0ZUB7Z958800++OCDcvfOnz9fJkIIIUQ9qrdZsVu3bmXfvn306dMHgOPHj5OYmMgrr7zCrFmzDNfdGfxVxtnZGWdn5/vJkhCiCkxNVDzWxYPHungQkXCLT36N5aezyRy+kMzhiykov32Ki0kW6z7+GI2ZmUyEEEKIJqLGgd3Zs2cNg6JjYmIAcHFxwcXFhbNnzxquq6slUBISEkhLSyMhIQG9Xk9kZCQAPj4+2NjY1MkzhWjqtFotmzZtKrOuXM82DswNdqfo6DZ+ORqGSfAUCvq8QqqZms9TNTzsVNJFKxMhhBCi8bvvBYobyquvvsq2bdvKHT9w4ADBwcFVSkO6YkVzExERQUBAAOHh4Xh5efHll1+SmprKxIkT0Wq1dO/encz8Ir46mcBnv8WTnJlPUXYa+Wd+4qVXxxEyvDfeztYNXQwhhGhWqhOvNNnArjZIYCeaC61Wi1ar5dSpU0yaNIlly5aRlJSEv78/Y8aMMezZertCfTG7z2jZ8mss55JKlgZSqWDdCz15wl+6Y4UQor7U684TQojGrbi4mKVLl/LRRx8Zjr311ltAyUSIioI6AHNTE0b2aMkjfm78ZeMxzmszURTIKajZrHchhBB1r8bLnQghGi9FUSgqKmLDhg089thjODs7Ex4ezpYtWwDYsmUL4eHhTJw48a7pxNzIZuT63zivzURtasLSZ7ryl8DW9VEEIYQQNVDjFrvExERat5Yf8EI0pDsnQ2RkZLBixQqOHz/OkiVLePHFF5k8eXK5SUxVmQjx8/nrzPw6kixdEW52Gj5+OYCebRzqsjhCCCHuU40Du06dOjFr1izeeustrK1lMLUQDaF0V4jU1FQKCgpYs2YNw4YNY9GiRRXOSK/KjhDFxQprf7nE2l9K9oB+8AEH1r/UE1dbi0rvEUII0TjUuCs2NDSUffv20b59ez777LPazJMQ4h6OHDnC9OnT+frrrwEwNzdn3LhxpKen07dv30qXGbrXjhCZ+YVM2B5mCOrGBnnxxfg+EtQJIUQTcd+zYrdv387cuXNxdnZm9erVVV5qpDGQWbGiMalojblSiqIQERFBWFgYzz33HI899liF2+vdz64Ql65nMfGf4cTezEFtZsL7T3fluYBWNUpLCCFE7an35U7y8vJYunQpK1euZMiQIXzwwQf4+Pjcb7J1TgI70ZjcvsZcz549KSoq4vDhw3h5efHDDz+QkpLC008/Ta9evQzLl0RERJTZFcLc1oGzt0zZfyGFvAI93s7WtHWxpp2LDW1drPG0t8TEpHxr3k9ntcz+9+/kFOhp2cKSjS8H0LWVfQP8LQghhLhTvS93oigKQ4YMISsriw8//JA9e/YwZcoUFixYgK2tbW08QgijdXuQBvDdd9+RmprK+++/z6OPPkrHjh2ZOXNmmXs8PDzw8PCg9Peyy3oX/u9YHpGJ2jLXHblcdu9lC3MTHnCypp2rDe2crWnrYkN0ciabDsUCENTWiXUv9sDJRlNXxRVCCFGHatxit3HjRk6dOsWpU6eIjo7G1NQUf39/+vTpQ/fu3fniiy+4ePEiO3fuJDAwsLbzXSukxU40BjNmzODDDz8sd7yybtUifTHhV24Rev46/3fiPBcOfI9N92GY2TgC0K2VPY/4uuFub0HszRxib2QTcyOHK6k5FOor/+c+YYA3bz7WCTNTWQVJCCEak3rpim3dujV9+vQxvAIDA9Foyv6W//7777Njx44ye8c2JhLYibpyt/FyeXl5xMbGYmVlxaRJk3B1deW5554jJSWFv//974Zu1dJWOYAcXRG/XrrBvvPXOXAhhVu5hYb01KYm9PVx4lE/NwZ3KgnoKlKkL+bqrTxibmQTeyOH2JvZxKTkkFNQxN8fasuI7i3r7i9ECCFEjTWaLcWuX7+Op6cner2+rh5xXySwMy45uiKydUW42VUc2GTkFnLo0g2crdV4tLDEw94CC3PTOsnL7ePlevTowcWLF/H09GTZsmWcO3eOkSNHMnbsWADDDNY7x9ilZObzc3QKoeeT+S0mlYKiYkP6LazMebijK4/6uTGggws2GtlERgghjFWj2VLM1dWV/fv31+UjhDCYsiOCYzGp7Jv5EF5OZddWzMwv5PnNx7iQnFXmuJO1Go8WFnjaW+LZwhLPFhZ43Pbe1dYC0womG1SmdLzcr7/+CsCuXbuYMmUKfn5+LF68mPfee6/SpUjc3d2ZMvstforNZ+Gx3/g9Mb3M+TaOVjzq58ajfm4EejlIl6kQQohy6jSwU6lUDBw4sC4fIQRQsmH90cupFOiL+fXSzTKBna5Iz8Tt4VxIzsLe0hxnGzVJ6fnkFepJzSkgNaeAs9cyK0zX1ESFu50FDqpsbpzczcPPvIRv2zZ/Bn8lAWELK3NSU1M5fPgwERERLFmyxHD/4sWLARg6dCienp7l0i/SF3Mq/hY/R18n9Px1Esz6Q9gtw/lurVsw5M9grr2rTaVBoRBCCAF1HNgJUV+upOZQoC/pqvw9MZ2X+3gBJbsozP737xyLTcVGY8YX43vTpaU9iqKQkVfItfQ8tOn5JGXkkZSejzYjj6T0kvfJmfnoixWupecRmxxH8vebSbD1Q+OehT4nnfzEs5hY2KBW6Sm8+CtefgF0692fWeu/J/vaJTa//yZLVn1EcN/eeLf533pw2boiDl+8wc/nr7P/jxTSbx8vZ2ZCv3ZOPOrnziO+rrhW0q0shBBCVEQCO2EU/kjONryPvK0Lc8l/o/m/M1rMTVVsfDmALi1L1mZTqVS0sFLTwkpNZ8+K12vTFyucvRTP2ctX2H8gjU8Bq9+/xt97Iid//hfWrbtS5NgRUzsXTL0CuA7suwqgRpdcMpHoo9/1bL6egoPVLTzsL2NjYUZkQrohCAVwsDLn4U5uPOrnyoD2LljLeDkhhBA1JP+DCKPwx/X/jZ27fCObrPxCvjqZyNYjcQCsGNWN/u2dgbvPWAUoKCggJyeHjRs38s9//pPo6GjDudjIY8RGHjMsRZJfqCc547YWv/Q8kjLyuByvInzIWMwdXdABt3ILy8xkfcCpZLzcI75uBMh4OSGEELWkTmfFNnYyK9Z4TP5XOHvOJhs+v9i7DTtOJADwzuOd+PtD7Qznbp992rlzZ86cOUPnzp1Zvnw5p06donv37rz99tucPn0aT09PMjIyyu3wcPtSJPeSmV9I0p9dvqk5BXRrZY+PjJcTQghRRY1mVqwQ9UGr1fLT9o8o6jAYNzd3UnMKDEHd3/p5M2FAWwCSkpIIDQ01zNTesWMHYWFh9OzZk7lz5/LGG29gZWVlSHfAgAHlntWzZ0969uxZrfzZWZhj525OJ3f55UEIIUTdksBONHnxiVeJD92Gu2cPnn4sgC2HLlF4K4lhD3agffYZnntuHmZmZnTq1IlFixYZ7lu5ciUAwcHBODk53fUZHh4ezJ8/v8qtdEIIIURDkMBONCr3Gv92uytXrnDz5k0+/uRzAPIi/kOLh5y48f1qvNv7Mmv2IFycuvL00yOxsLBAq9UyYsSICrtV78XDw6PC7b2EEEKIxkTG2MkYuzpTnSCt1J27LxQVFaHT6QgNDeXy5cv079+f2NhYduzYQUxMDBcuXCiXxjvvzuO9RQsrHcN25zOEEEKIxszox9jFx8ezePFi9u/fT3JyMp6enrz88svMnTsXtVrd0Nmrc7oiPTeydIZXa0crfD3qPjCtbqCm1WpZuHAhw4cPL3d9cbFCXmERySmp6IoVoqLOcuDnUKLPlewrPH7qTPLz83mgQ2eGjpnOr4fOonbw4GxkLrkqbwoGz0HdOZlWQdfJ1V4m7aePePy1+SweV/Ksu01MkG5VIYQQxqpJBnYXLlyguLiYTZs24ePjw9mzZ5kwYQI5OTmsWLGiobNXRk1arUrvee7lV/kpRsfVW7mk/BnEpWTpyMgrLHO9aV46z1pHM3PalGo/o7r5Kg3UrB2cSUjNJTEtl4S0XG5m68gr1JNXUMzNlGRuXddy9VIkACOmLcK5+2BuXIwg5/oVsLTHps9fSN29GhMLG6y7DCb3j6NkR/7X8KzTxw4DkGTVjvO/XgNND8gF4vOAvJKLVDaYutqgLi5pdP7L0Ieq1AIn3apCCCGMldF0xX7wwQd8/PHHxMbGVvme+uiKrUm3X+k9b23axZexFcfealMTXGw1pOcWkJbwB8nbQmr0jMruKSzSE5+SQVJmAQePHOPC5RiuJF7l2I61OHR/lEJTC+x6PE7uxWPotBcxs3fDvt8LpO5eRWHqVYpuJZVL0zZwBC36v4hKbVWmRU1jZoKZLh3TvAx012O4+O0Kerz4Jm5tfWnh5IqbuzsO1mocrNQ4WJkb3rewMsfRWk1+RirbPv2kWkGqEEII0VQYfVdsRTIyMnB0dLzrNTqdDp1OZ/icmVnx/qC1oXQz+IiICAB2797NjRs38PHxMeShdevWZGdnk5aWBoCjoyNHjx4lKioKAOv0GLiUQq7KAq9O/rz+cBuKMm5gZ2HK40MG88MPP7B5zymuXowBYPPmzbRq1Yrg4GDUajVHjx5Fp9Mxffp0PvnkExISErC3t6dPnz7Mnz8fgJXrNmHRwpWjvx5ArzKn+0tvc+z7T0jTJmDm/AB2vZ4m5d/zKdD+YSjbrchQAGysbej71Ct4uzvi6WiLldoUi6d3oMtIJS/jJlcvn2f9ojm88d4qunbrTktPT1q3aoml2hRL85KXxswEE5P/BXkREREEfLuCT2b/perj3+xbSQucEEIIgZG02MXExNCzZ09WrlzJ+PHjK71uwYIFLFy4sNzxumixq+xZ06ZNM6yVNmbMGC5fvszx48dRqVSo1eoK77H08qfF4Ik83t6aTmY3MDU1Zdq0aYwaNYpdu3aVu3769OlMmzaN2Lh4sgrAybszEecuci09l192fsHve74od499vxdo0f+lMsfUZia0drDE2SQXeyUb3fUYti9/h0UrPuThfr1p69X6ri1kNWmtrEkXsRBCCGHMqtNi16gCu8qCodudOnWKwMBAw+ekpCQGDhzIwIED+eSTT+56b0Utdq1bt66TwO72Fruq7lhQ2T2/p8LCX7QALHumK6N7tTFcfyE2gefe+4K0nz7i+ZnvYenpQwbW3Ci2Iim9ZBP72xVlp6HPTqPgegxpP32E18iZtO3UFR+vVnRq50UbRyvDy9VWU741rRqBmgRpQgghxP1rsl2xU6dOZfTo0Xe95oEHHjC8T0pKYtCgQQQFBbF58+Z7pq/RaNBoNPebzSq5M4Cryo4Fld3TE0jnImt/ucS7u87S2tGKfj7Ohus7+kVx7Cc4lGaDRm3z590lEww0Zia0vi1Ya+3oRxtHK7KuXuTZnz7i+3kvV7k1rbqzSWWSghBCCFG/GlVg5+zsjLOzc5WuvXbtGoMGDSIgIIDPPvsME5PGuYl6TZbWqOiekEfaE3czhx9+T2Lyv8L5/rV++LiWBHEzhvci+9I4+vTxw7e01c2pJJBzsSnb6lZK61Bco3xJoCaEEEI0Xo2qK7aqSrtf27Rpw/bt2zE1NTWcc3d3r3I6TW2B4vxCPS9uOU5EQjpeTlbsfK0fjtbGv26fEEII0Zw12a7Yqtq3bx+XL1/m8uXLtGrVqsy5JhinVpmFuSmbXwlk5PrfcLOzoPIleIUQQgjRHDXJFrva0tRa7EolpuXiaqdBY2Z674uFEEII0aQZfYtdc9fa0aqhsyCEEEKIRqhxzjgQQgghhBDVJoGdEEIIIYSRkMBOCCGEEMJINOsxdqXzRupyz1ghhBBCiPtRGqdUZb5rsw7ssrKyAGjdunUD50QIIYQQ4u6ysrKwt7e/6zXNermT4uJikpKSsLW1RaWqm1XhSvejTUxMbFJLqtQGKXvzLDs07/JL2aXsza3s0LzLXx9lVxSFrKwsPD0977nTVrNusTMxMSm3wHFdsbOza3Zf9lJS9uZZdmje5ZeyS9mbo+Zc/rou+71a6krJ5AkhhBBCCCMhgZ0QQgghhJGQwK6OaTQa5s+fj0ajaeis1Dspe/MsOzTv8kvZpezNUXMuf2Mre7OePCGEEEIIYUykxU4IIYQQwkhIYCeEEEIIYSQksBNCCCGEMBIS2NWhDRs24O3tjYWFBQEBAfz6668NnaV6sWDBAlQqVZmXu7t7Q2erThw+fJinnnoKT09PVCoVu3btKnNeURQWLFiAp6cnlpaWBAcHc+7cuYbJbC27V9lfffXVct+DPn36NExma9nSpUt58MEHsbW1xdXVlZEjR/LHH3+UucZY674qZTfWuv/444/x9/c3rFcWFBTEnj17DOeNtc5L3av8xlrvFVm6dCkqlYqQkBDDscZS/xLY1ZGvv/6akJAQ5s6dy+nTpxkwYADDhg0jISGhobNWLzp37oxWqzW8oqKiGjpLdSInJ4du3bqxbt26Cs8vX76cVatWsW7dOk6dOoW7uzuPPvqoYTu7puxeZQd47LHHynwP/vvf/9ZjDuvOoUOHmDJlCsePHyc0NJSioiKGDBlCTk6O4RpjrfuqlB2Ms+5btWrFsmXLCAsLIywsjIcffpgRI0YY/vM21jovda/yg3HW+51OnTrF5s2b8ff3L3O80dS/IupEr169lEmTJpU51qlTJ+Wtt95qoBzVn/nz5yvdunVr6GzUO0DZuXOn4XNxcbHi7u6uLFu2zHAsPz9fsbe3VzZu3NgAOaw7d5ZdURRl7NixyogRIxokP/UtJSVFAZRDhw4pitK86v7OsitK86p7BwcH5ZNPPmlWdX670vIrSvOo96ysLKV9+/ZKaGioMnDgQGXGjBmKojSuf/PSYlcHCgoKCA8PZ8iQIWWODxkyhKNHjzZQrurXpUuX8PT0xNvbm9GjRxMbG9vQWap3cXFxJCcnl/keaDQaBg4c2Gy+BwcPHsTV1ZUOHTowYcIEUlJSGjpLdSIjIwMAR0dHoHnV/Z1lL2Xsda/X6/nqq6/IyckhKCioWdU5lC9/KWOv9ylTpvDEE0/wyCOPlDnemOq/We8VW1du3ryJXq/Hzc2tzHE3NzeSk5MbKFf1p3fv3mzfvp0OHTpw/fp13nvvPfr27cu5c+dwcnJq6OzVm9K6ruh7cOXKlYbIUr0aNmwYo0aNwsvLi7i4OObNm8fDDz9MeHh4o1nIszYoisKsWbPo378/Xbp0AZpP3VdUdjDuuo+KiiIoKIj8/HxsbGzYuXMnfn5+hv+8jb3OKys/GHe9A3z11VdERERw6tSpcuca0795CezqkEqlKvNZUZRyx4zRsGHDDO+7du1KUFAQ7dq1Y9u2bcyaNasBc9Ywmuv34Pnnnze879KlC4GBgXh5ebF7926eeeaZBsxZ7Zo6dSpnzpzhyJEj5c4Ze91XVnZjrvuOHTsSGRlJeno63333HWPHjuXQoUOG88Ze55WV38/Pz6jrPTExkRkzZrBv3z4sLCwqva4x1L90xdYBZ2dnTE1Ny7XOpaSklIvmmwNra2u6du3KpUuXGjor9ap0JrB8D0p4eHjg5eVlVN+DadOm8cMPP3DgwAFatWplON4c6r6yslfEmOperVbj4+NDYGAgS5cupVu3bqxdu7ZZ1DlUXv6KGFO9h4eHk5KSQkBAAGZmZpiZmXHo0CE+/PBDzMzMDHXcGOpfArs6oFarCQgIIDQ0tMzx0NBQ+vbt20C5ajg6nY7o6Gg8PDwaOiv1ytvbG3d39zLfg4KCAg4dOtQsvwepqakkJiYaxfdAURSmTp3K999/z/79+/H29i5z3pjr/l5lr4gx1f2dFEVBp9MZdZ3fTWn5K2JM9T548GCioqKIjIw0vAIDA3nppZeIjIykbdu2jaf+63WqRjPy1VdfKebm5srWrVuV8+fPKyEhIYq1tbUSHx/f0Fmrc7Nnz1YOHjyoxMbGKsePH1eefPJJxdbW1ijLnpWVpZw+fVo5ffq0AiirVq1STp8+rVy5ckVRFEVZtmyZYm9vr3z//fdKVFSU8sILLygeHh5KZmZmA+f8/t2t7FlZWcrs2bOVo0ePKnFxccqBAweUoKAgpWXLlkZR9smTJyv29vbKwYMHFa1Wa3jl5uYarjHWur9X2Y257t9++23l8OHDSlxcnHLmzBnlnXfeUUxMTJR9+/YpimK8dV7qbuU35nqvzO2zYhWl8dS/BHZ1aP369YqXl5eiVquVnj17llkOwJg9//zzioeHh2Jubq54enoqzzzzjHLu3LmGzladOHDggAKUe40dO1ZRlJIp8PPnz1fc3d0VjUajPPTQQ0pUVFTDZrqW3K3subm5ypAhQxQXFxfF3NxcadOmjTJ27FglISGhobNdKyoqN6B89tlnhmuMte7vVXZjrvu//e1vhp/pLi4uyuDBgw1BnaIYb52Xulv5jbneK3NnYNdY6l+lKIpSf+2DQgghhBCirsgYOyGEEEIIIyGBnRBCCCGEkZDATgghhBDCSEhgJ4QQQghhJCSwE0IIIYQwEhLYCSGEEEIYCQnshBBCCCGMhAR2QgghhBBGQgI7IYQQQggjIYGdEEIIIYSRkMBOCCGqKDg4mJCQkPu+prbyolKpUKlUREZG3ldar776qiGtXbt21Ur+hBANQ/aKFUKIKkpLS8Pc3BxbW1ugJLjq3r07a9asqfSauhIcHEyHDh1YtGgRzs7OmJmZ1TitjIwM8vLy8PDwYOfOnYwcObL2MiqEqFc1/0kghBDNjKOjY61cU1usrKxwd3e/73Ts7e2xt7evhRwJIRqadMUKIRqlL7/8EgsLC65du2Y4Nn78ePz9/cnIyCh3fXBwMFOnTmXq1Km0aNECJycn3n33XW7vlNDpdEyfPh1XV1csLCzo378/p06dKpPOt99+S9euXbG0tMTJyYlHHnmEnJwcwzNKu1lfffVVDh06xNq1aw3dmPHx8eW6YqvyzODgYKZPn84bb7yBo6Mj7u7uLFiwoNp/Z8HBwUybNo2QkBAcHBxwc3Nj8+bN5OTk8Ne//hVbW1vatWvHnj17qp22EKJpkMBOCNEojR49mo4dO7J06VIAFi5cyN69e9mzZ0+lrUvbtm3DzMyMEydO8OGHH7J69Wo++eQTw/k33niD7777jm3bthEREYGPjw9Dhw4lLS0NAK1WywsvvMDf/vY3oqOjOXjwIM888wwVjVhZu3YtQUFBTJgwAa1Wi1arpXXr1uWuu9czb8+7tbU1J06cYPny5SxatIjQ0NBq/71t27YNZ2dnTp48ybRp05g8eTKjRo2ib9++REREMHToUMaMGUNubm610xZCNAGKEEI0Uj/++KOi0WiUJUuWKA4ODsrZs2crvXbgwIGKr6+vUlxcbDj25ptvKr6+voqiKEp2drZibm6ufPHFF4bzBQUFiqenp7J8+XJFURQlPDxcAZT4+PhKnzFjxoxKP995rCrPLL2nf//+ZdJ58MEHlTfffPOu5a3o2benU1RUpFhbWytjxowxHNNqtQqgHDt2rFyagLJz585KnymEaPykxU4I0Wg9+eST+Pn5sXDhQnbu3Ennzp3ven2fPn1QqVSGz0FBQVy6dAm9Xk9MTAyFhYX069fPcN7c3JxevXoRHR0NQLdu3Rg8eDBdu3Zl1KhRbNmyhVu3btU4/1V5Zil/f/8ynz08PEhJSan2M29Px9TUFCcnJ7p27Wo45ubmBlCjtIUQjZ8EdkKIRmvv3r1cuHABvV5vCEhqSvmzO/X2wK/0eOkxU1NTQkND2bNnD35+fnz00Ud07NiRuLi4OntmKXNz8zKfVSoVxcXF1X5mRencfqz0uTVJWwjR+ElgJ4RolCIiIhg1ahSbNm1i6NChzJs37573HD9+vNzn9u3bY2pqio+PD2q1miNHjhjOFxYWEhYWhq+vr+GYSqWiX79+LFy4kNOnT6NWq9m5c2eFz1Or1ej1+krzU9VnCiFEbZHlToQQjU58fDxPPPEEb731FmPGjMHPz48HH3yQ8PBwAgICKr0vMTGRWbNmMXHiRCIiIvjoo49YuXIlANbW1kyePJk5c+bg6OhImzZtWL58Obm5uYwbNw6AEydO8MsvvzBkyBBcXV05ceIEN27cqDQIe+CBBzhx4gTx8fHY2NiUW+qkKs8UQojaJIGdEKJRSUtLY9iwYQwfPpx33nkHgICAAJ566inmzp3LTz/9VOm9r7zyCnl5efTq1QtTU1OmTZvG3//+d8P5ZcuWUVxczJgxY8jKyiIwMJC9e/fi4OAAgJ2dHYcPH2bNmjVkZmbi5eXFypUrGTZsWIXPe/311xk7dix+fn7k5eVV2GV7r2cKIURtkp0nhBBGoaJdIIxZXZRXpVLJzhNCNHEyxk4IIZqoDRs2YGNjQ1RU1H2lM2nSJGxsbGopV0KIhiQtdkIIo9DcWuyuXbtGXl4eAG3atEGtVtc4rZSUFDIzM4GSZVasra1rJY9CiPongZ0QQgghhJGQrlghhBBCCCMhgZ0QQgghhJGQwE4IIYQQwkhIYCeEEEIIYSQksBNCCCGEMBIS2AkhhBBCGAkJ7IQQQgghjIQEdkIIIYQQRkICOyGEEEIIIyGBnRBCCCGEkZDATgghhBDCSPx/TuN+5x/ZGSsAAAAASUVORK5CYII=\n", + "image/png": 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", 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" ] @@ -278,13 +276,14 @@ "name": "stdout", "output_type": "stream", "text": [ - ": sys[4]\n", + ": sys[2]\n", "Inputs (6): ['y[0]', 'y[1]', 'y[2]', 'y[3]', 'u[0]', 'u[1]']\n", "Outputs (3): ['xhat[0]', 'xhat[1]', 'xhat[2]']\n", "States (12): ['xhat[0]', 'xhat[1]', 'xhat[2]', 'P[0,0]', 'P[0,1]', 'P[0,2]', 'P[1,0]', 'P[1,1]', 'P[1,2]', 'P[2,0]', 'P[2,1]', 'P[2,2]']\n", + "dt = 0.1\n", "\n", - "Update: ._estim_update at 0x166ac1120>\n", - "Output: ._estim_output at 0x166ac0dc0>\n" + "Update: ._estim_update at 0x115c63e20>\n", + "Output: ._estim_output at 0x115c63880>\n" ] } ], @@ -324,7 +323,7 @@ "outputs": [ { "data": { - "image/png": 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\n"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", 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" ] @@ -390,7 +389,7 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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", 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" ] @@ -451,7 +450,7 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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", 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\n", + "image/png": 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vcjBCxKPC58zhXNy0NC5XqhT/moYPt3/y3dhYtdUhd3B2//1As2amwYhOx+WPH+efc99bndG94w0sdTHVq8dda8o1zt3YYk1QgAHVSiejWtA1VDZeRuW0f1H9znEEZKXg4vhZINKhdGkgeMkcUEwMCDoQdDD6+SMzuDyySpdDzdA7qPL1WwgMzP5bWj2L/zh8fPiPI/fXsDAeHVWunFN/L95AghcJXoTwSpmZnA/x2298I966FQgvcYL7Iq5f5xFFK1YU+nXsDTKIuNXEXP7JuHGc/uAqREBUFM9f9+ef6va2bTkBOPccd82bA6++CgwcKNOSFJa9fxvJycDo0eZb/JTcqsRE59fv4YeBBbqhqPXnAvMFfHy4r07pb/z3X26qVLKbNUyCFwlehPA6BgPPR7ZsGeef/L4+C51ipvP093fu8LDfLVvcnifgjNaUwoqO5kFC5rozBgzgyXG12l2jZbb+NtLTuScoLg64eBGYO5eTuvOqXZtb8HS67AeMOBmTimvn76A47uIayiMFplFp/dDreKzOafSofQqdqp5FoE8m/xMB3Gyn6NABd/cfxelWz+Jk/cdx0L8NDsWVwYULQFISDxnv0YOTlHOGjf/7rzok/uRJ/vrKK9ys50ESvEjwIoRXIeKujnnzOG9k3Yf/oMcPAzmjFeB32LVrOQHmHmWpO8PVLUDCeRwJhHOXJeJ8pr/+4kB2/341TgE4wbtVK54nsGpVDpQuXAASLhtxfm8CLmZVsVm3MiUz8KrfAgzUz8b9dCR/gdyJWxcvcjLywIHcFORb+CHx9pDgRYIXIbwGEa93+Pnn3OK9dNIJ9JvUkJ8MDeVPkUOG3PPJi97QAiS8w+3bwB9/ABs38sPc30VeZYqlor7fWdRLjkZDHEd9nMQ69MZ3eDFf2WY+hzCw0jYMaHUG1VuU44Sf8HB1+YdPP+Xh4gB3SfXvz02DrVq5tAlQghcJXoTwGh99xKOFAGDBAuClF4kXzatfH/jwQ5kzQwgriIBBg3iUWl5du/LqD/Xr8+cAnQ5IPH4LqWs3I3jXemSVDsXlcdOQmgocOUzY8OMNbPy7HDIz1QCkUyduYHnmmVzD9g8e5H/Wn3/mGRcVdety4dGjXfJ/K8GLBC9CeIUZM3iyMgCY9lEqxr5TjH/IyrIwQ5gQIi9ntsrdvMk58UuW8Ig/ha8vT01TvTo/mjQBHmieibqXtyFsw2Lo1q7hoYLFivEY/FKlCnVO5kjwIsGLEB73/fSbGDyGP8q9jymY8tZd00RDIYRHxcVxb9DOndbLlSoF1K1tQA2fOFT3jUfZXu1QvTpPIeCU5S6yOXL/lo8+QginSUgArpzSI/rTPzBsYx8AwChMx5tDrgPjP/Bs5YQQJqpV4yUTzAUvNWpwN9SFCzxs/GCsLw6iJoCawH7Tsp5IKpfgRQjhNDt6for1MRXxIyJB8MFjWI9t6IxJZUfjS0ltEcLrvPEGt77klXs4+NmzPJp62jSeVNIbSPAixL1qzx5g/XqeZvWRRwqVgJeWxqMsP/xnNFIRBACojdPYgB4AfPCIk6oshHAuW10+gYG8DEfDhrxEhKXcG3eT4EWIe9XevTwUCOBhym3a8ExWjz7KQyKtze1gNAIbNoC++BIrus/DW/Pq4vx5AAhC0xq3Mea9YDRrUTenuAz1FUL7vGnYviTsCnEvSUvj6W0BngUrMtJ0MRtF2bK8imD79qbbU1OBH34AvvoK0SdKYCy+wi50BMALF37yCY+kvMenbBFCFIAk7AohTKWn8zT8a9fyFJ7Fi3PLypIl/HxcHPD77/yIiuLFderXV/dfvBjYtQv49VdcvuaPd/AxvsdgAEDxYka89bYP3nijSCyvIoTQAGl5EaKoO3AAGDxYXVr4hx94kSFLsrKAI0dMlxDu3Bl3d+zHl/gPPtFNwF0qDoAbbj7+mKcsF0KIwpCWFyEEkJEB/Pe/HF0YDECFCrxyXJ8+1vfz8zMJXI4cAX4oMQM/Fq+G+LulAeIc348/Bh57zJUnIIQQ5knwItzj8mXuiqhYkfMpJCnCqRISgN27genTgfh44MFGVzH4n7HocHEJdACvTTJjBs8hbgci7iWaPZtnCDcYmpg8/88/wJ9/SvAihPAMCV6Ewy5eBJYt45tXQICagX7//UDLljzxUWBgduF//+UZjH79le+IAH+yr1CBA5lBg3idDICTSdeu5e116nAGqLApNRV48klg3z5127//VsBC/ITqmIq+vVLRd3R9tCkL2AoZk5K4V2nOHG5xEUIIbyTBi7DbpUvA/Pk8k2JKivWyFSvyDI1hpaugblRn1KRqaF8iBi1SdnNORXw8P3r0MH2BAQP4e52OA5vJk4GaNV11Su6VlgZs2sSR319/AYsW8cpqhfD778Brr/EkUrkF4S4IPriI6pj2GzDtN44Fn34a6NsXaNfOdCR0TAy3sixZol7b4sV55FDfvkD58vlf21uGTAoh7kHkBjNnzqSaNWtSYGAgtWzZknbs2GG1/LZt26hly5YUGBhItWrVotmzZ9v9Wnq9ngCQXq8vbLUFERkMRFFRRE8+SeTrS8TNJ+YfoaUzqJhfutUyOmRRRVymac9FE/32G9HRo+qLnTxJ1LkzUb166g7+/kSjRhFdueKx30GhZGQQbdhANGgQUXCwel7FixMlJanldu0iOnHC7sNevkzU/xlDzuGqII6+v/8Tio4mOnCAH7t2ES1cSDRwIFGpUqbXoVw5omeeIXr7baLGjU2fa9iQ6JtviG7dcv6vQwghLHHk/u3y4GXZsmXk7+9P8+fPp2PHjtHo0aOpRIkSdOHCBbPlz549S8WLF6fRo0fTsWPHaP78+eTv708rVqyw6/UkeHGOmzeJpk0jqlvX9MbWqRPRrFlEe/dm3ySjjXRi4S66+cgzZPT1JSNAVxFK+5ecopUrORYxF8RERBClpFipwL59RF26qDuUKMEvqCVr1xKVLWt64lWrEv3nP0RbtpiWbd6cn2/XjmjBAtPAJpesLKJv302k4IBUAoh8kEVj8SUloST/srOyzO6Xmkq0bh3HUIGB5q9J/fpE27YRGY1O/j0IIYQdvCp4ad26NQ0fPtxkW4MGDWj8+PFmy7/11lvUoEEDk23Dhg2jtm3b2vV6Erw4Lj5e/bT+ww9Ejz9ueoMrVYpoxAiiw4dz7WQ0Ev34I1F4uOkd8KGHiNasybmJ5j72d98RNW1qeh//8Udu3bEoKoqoVSu+s2ZmuvT3UBDK+f22zkA/vxNDR36MoQMHeDsdPconGhZGNHIk0c6d5k/2zh2i3r1Nm7aKF+dII1c0ER1NFB56LqdIa+ylg2UeJnrjDaJjx+yu8+jR5oOXceOc8zsRQoiC8JrgJT09nXx9fWnVqlUm20eNGkWdOnUyu0/Hjh1p1KhRJttWrVpFfn5+lJGRka98Wloa6fX6nEdcXJzrgpfoaG6OuH3b+cf2oEmTzN/MwsKI5syx0AgwfbpaMCiI6OWXiQ4dsvlaRiPR0qVE1auru7duTbR7t42d4uPVn9PSiDp04D4RDwU0qalEv/9OFN7CQMG4lXMuFRFPANHrr2cX3LXLYmtIPvHxRJ99RtSggcmFuD1kNL3+OpGPD28KwS2a1WQWZa1Yzd1SDsodUOZ+5P4VCyGEu3lN8HL58mUCQH/99ZfJ9o8++ojq1atndp+6devSRx99ZLLtr7/+IgAUb+bdddKkSQQg38MlwUvt2nz3WL/e+cf2oJgYojFj1Ptlu3ZE779PdPCglZ2SkogiI/lT/7VrDr/m3btEH31EVLKk+roDBhCdP2/HzrNnqzs1aED0888cTbiQ0Uh06hTngvToQVSsmNEk0PNBFpVEUs7PgYGcTxIXV7AXy9ixh9Y9PI1e8VtAZUqoeUQ9HsmgQ5sTnH5+QgjhaY4EL26ZbEOn0+VNEs63zVZ5c9sBYMKECdDr9TmPuLg4J9TYgs6d+au3rAnuJN99B3z9tfrz7t3ABx/wqGWLSpUCvv8e+Pxzu+cOya1YMeCdd3iZ9Rdf5MFFy5YBDRoAEycCyclWdh4yhIc8lSsHnDgB9OvH9WnZEhg2DEhMdLg+uSUkAAcP8oCgr7/mw1erBtSrB4waBWzcCKSm6lAFl/ASFmBpiRdx6OVv8PsGI95+m8umpwOffgrUqsUjdvbvt/266enAb78BL7yoQ8U+bfH4lrGYl/USbqUE5JTZ+Ic/vttUsVDnJ4QQmufKKMod3UZ5uTTn5bvv1GzTIiIjQ01buf/+XIm45roRjEai1atdktF58CC3+CgtDMHBnK/79ttEW7daeEm9nui997h/K2c4k463K2bO5Galn37iphM76j54sPluNB8foocfJvqs9w46jPvJWK060bff5ss8Nhg4V/fBB03379CBaOVK016klBTeNnCg6WAkgKhYMclNEULcO7ym24iIE3ZfffVVk20NGza0mrDbsGFDk23Dhw/3joTds2f57uHnZ2OojHa8/XZ2HkUIn55V337Lhfv2dUkAM3as5SHWFSoQ9evHI52OH8/z8kYj0YULRCtWcM5Ibh06mB6odGlOAO7dm7OQs2VkEP0yI5EejEgzKe6LTGqPHRSBv2jkyOzCd+4Q/e9/duWbHDzIvWv+/uoxq1QhGjaMg7OgINPqVa7Mub1bt3KXk+SmCCHuFV4VvChDpRcuXEjHjh2jMWPGUIkSJeh8dnLD+PHjKTIyMqe8MlR67NixdOzYMVq4cKH3DJU2GnmIDED055/OP76bbdqk3jR/+cVG4ZgYooAALvztty6pz7hx5gMXPz/zwUz37kQTJ3LLxd9/W7ixL1/O2bNt2uQfI1ylCsXHE02ZwkFD7vyVx/030K9Vh1GWL0cdd+s0ofjLBQ/YLl8meued/MGK8ggP56RlqyOvhBCiCPOq4IWIJ6mrUaMGBQQEUMuWLWn79u05zw0ePJg6d+5sUn7btm3UokULCggIoJo1a3rXJHUDB/LdZtIk1xzfTS5fJipfnk/ltddsFE5OVieOe+IJl00EYmkUzLlzPMr4gw94JLaleUoAojp1iCZM4B6+3buJbtzI9QLp6UQxMWRcs5Z2/GcN9W912iQwquB/gybiv3QRVU0P+uCDRJs3O+W8X39duoKEEMIcR+7fOqLsbNgiwpEltQtk3jxOCu3SBfjjD+cf3w0MBp6VfutWXh14714gKMjKDoMHc3Ju1apAbCwnynpQWhqvHPDLL/aVDw3lFQYqV+YllbZtA86cUZ9v1w4YMYKnzg/0MwBXr3LWbkIC79SihdPqrhw2L2V9KCGEuFc5cv+W4MVRiYk8wqVNGx4yo0EffABMmgSUKMGjaurVs1L4++85ePHx4bt+x47uqqZVuYOArCz+/uJFXrg6MRE4eZJHMl26ZP04zz/PCxEKIYTwLEfu37Iwo6MqVuSHRm3bBkyZwt/PmWMjcElOBsaM4e8nT/aawAWwv6Xizh3g9GngvfeA9evzP1+hgvPrJoQQwrWk5eUecu0adxMlJAAvvMCLGtv099/ArFnAwoWmyxBrjHTXCCGEd5OWF1c7cwb49lvAaOSvGmA0cu9PQgLQsKED1W7dmh8aJ0GKEEIUHW6ZYbfISUkBvvmGp6bNyvJ0bezy5Zc8M2xQELB8Oee7WPTHH8ChQ26rmxBCCOEICV4KonFjoHRpTqiIjfV0bWzau5en4gc45mrSxErhS5eA/v25teWvv9xSPyGEEMIRErwUhK8v0KEDf++l6xwp6/Ns2wY89RQ3EHXvDvTsaWWnrCxeiOfmTQ7QWrVyV3WFEEIIu0nwUlCdOvHX7ds9Ww8L5s4FwsOBhx5SE1V//52nqbHoiy+AnTuBkiV5lcTAQLfUVQghhHCEJOwWlBK87NzJ2bA+3hUH9ukDFC8OvP02//zBB8B99wH3329hh7Q0YNo0/n76dKBOHXdUUwghhHCYd91xtaRlS44Obt0Cjh71dG3yWbNGDVwA4P33eUK2NWss7PDzzzyWumpVnr5WCCGE8FISvBSUvz/PK1+1qvkJRDxswAB1yv85c4ADB/gxbJiZwkTq2OnXXgP8pEFOCCGE95K7VGGsXs1jjnU6T9ckn40buSeocWPglVdsVNFg4IV9kpKAoUPdVkchhBCiIKTlpTBKlvTKwMVgAGbM4O9ff92OKvr5AePH85pNoaEur58QQghRGBK8OIPRCGRkeLoWOTZuBM6eBcqUAZ57zoEdvTAQE0IIIfKS4KWw3nsPKF+eV1/2Et98w19fesnGTLoA13v5ciAz0+X1EkIIIZxBghdnuHnTayarO34ciIrikdsjRtgonJHBQ5IGDLAyDEkIIYTwLhK8FJYy34uXBC9Krkvv3kDNmjYKr1gBJCbyioV9+ri4ZkIIIYRzSPBSWBERvFzAhQv88CC9Hvjf//j7UaPs2EEZHj18OA/9FkIIITRAgpfCKlmS5+EHeLZdD1q8mBe8vv9+XhbAqv37ecVGf38eSy2EEEJohAQvztC5M3/1YNeR0ejg8Gil1aVfP6BiRZfWTQghhHAmCV6cwQsWady4Efj3X6B0aV4GwKqrV3mEEWBn/5IQQgjhPWSGXWdo3x7o0IFbYAwGzoFxM6Uh5cUX7Rgefe0a8MADPDy6dWuX100IIYRwJh0Rkacr4UxJSUkICQmBXq9HcHCwp6vjFidPAg0acFfRmTO8erRdkpOBUqVcWjchhBDCHo7cv6XbqAhQcl169XIgcAEkcBFCCKFJErw40+3bwNatbn3JpCTgu+/4e7vSV374Abhxw5VVEkIIIVxKcl6c5eZNXiaACLh+HShb1i0v+913wJ07QMOGQJcuNgrHxgKDBnGLy+XL0vIihBBCk6TlxVnKlgXq1ePg5a+/3PKSBR4e/dhjErgIIYTQLGl5caZOnYATJ3jIdO/edu2SkMCPvCpV4oc1v/8OnD4NhIQAkZE2XujGDWDJEv7+9dftqpsQQgjhjaTlxZkKsM7R3Lk8QW/ex9y5Fnb4919eh+i11/DtNzxQ7MUXeaJfqxYsANLSgJYtgXbt7K6fEEII4W0keHEmJXg5eJCHIduQkQFcuWK6rWRJ4IUXgKeeMrPDTz8BLVoAa9fi1Fk/bNykg05nx+rRWVnArFn8vV39S0IIIYT3kuDFmapVA2rV4onq9uyxWvTMGZ7bbs4c/vnRR4FixTj5dvFioE0bDkrOnQMHQoMH89S5yclA7dqYGTweANCzJ1Bbdxbo3h344w/Oucnr11+BixeB0FBgwAAnn7QQQgjhXi4NXm7duoXIyEiEhIQgJCQEkZGRuH37ttV9Vq1ahe7duyM0NBQ6nQ6xsbGurKLz2dF19OOP3IASHc0BCwBs2gSkpqpl0tK4saRuXcLzVbbgyPcHAB8fYPJkJEefxOJNlQFkp6988QWweTPQtSvPnLtiBQdQilOneAHGoUOBoCAnn7AQQgjhXi6dYbdHjx64dOkS5s2bBwB45ZVXULNmTfz6668W9/nhhx9w7tw5VK5cGUOHDkVMTAyaN29u92t6fIbd7ds5WHjkEW6FySU5mVtTfviBf+7cGfjyy/y9OETc4jJ/Rho2b1eDjW6tbmDAa+Vw4gTw2WdAjRocp1SnC6jww5ec16JEQHXrAm++yUOjAwOBxETAz49bX4QQQggv49D9m1zk2LFjBID27t2bs23Pnj0EgE6cOGFz/3PnzhEAiomJceh19Xo9ASC9Xu9olV1q/36iOnWIACIfH6IPPiDKyrK9X/SQb+iZKrtIpzMShzX5H5MmZRe+epXo/feJypRRn2zUiMhgcOWpCSGEEIXmyP3bZUOl9+zZg5CQELRp0yZnW9u2bRESEoLdu3ejfv36Tnmd9PR0pKen5/yclJTklOPm5shw5rxljUbOs505k9dBrF6dRyy3b2/hxf78E6hZE6hdGwAQvuA1/Ozjg1OndZgyBfj5Z86/9fXlyXxLlMhVh/LlgSlTuMVl/nxg2jQemeQjqU1CCCGKDpcFL4mJiahQoUK+7RUqVEBiYqLTXmfq1KmYMmWK045nzty5HBPkNWkSMHly/rL/m3IOj2EDzqMGNqBXznNPP80xRZkyZl4kMxN4/33g00+BVq2AXbuAgICcFarr1ePUlshIYP167p2yuHp0yZLA2LHcR5WRUaBzFkIIIbyVwx/JJ0+eDJ1OZ/URHR0NANCZGZJLRGa3F9SECROg1+tzHnFxcU47tmLYMODAAX507Kh+P2yYaTm9HmjbFhjX+zTK4xr26rh5JTAQ+Pxz4JdfcgUu6enAsWPA2rX8ZIcOwCefcGdPy5amCbfZ5s4FevTgWXUPH7ZjTpiAADsmgBFCCCG0xeGWl5EjR2KAjeG2NWvWxKFDh3Al7yQmAK5du4awsDBHX9aiwMBABAYGOu145uTuHipZknthTp/mEUKnTvH3p08D164pe3TjBwFN6qVh2Zh9aJQeA5ztndMdhEWLgNdeM32h0qW5aaZvX7P1GDYMePxx8/UTQggh7hUOBy+hoaEItWPESkREBPR6Pf7++2+0bt0aALBv3z7o9Xq00+AMr1eu8PDmhARg40bL5SpW5OHNdf/+Cc3T9+HlUwtQ7LU0frJsWTV4qVsXCA7mr3XrAvXr81S51atbPLY9SwYIIYQQRZ3Lcl4aNmyIRx99FEOHDsXc7H6NV155Bb169TJJ1m3QoAGmTp2KJ598EgBw8+ZNXLx4EfHx8QCAkydPAgAqVqyIihUruqq6NoWG8sLRAFCuHMcb9eqpsUfdukCdOkBKCpCQoEPVT/eiws8zYQwshrvVm0JXry6K5Y48Hn4YuH1bZrsVQgghHOXKYU83btyg5557jkqVKkWlSpWi5557jm7dumVSBgAtXrw45+fFixcTgHyPSTnjga1z5VDpf/4h6t7deplJk5RRykYqi+ukg8F0OLMQQggh8nHk/u3SSeo8wdWT1D3+OLBuneXnC7NKtBBCCHGvcuT+7bJuo6Ikd0By+zavuwiYD0gkSBFCCCFcS2Yvs8Pcueqw5J077RiiLIQQQgiXkZYXO8gQZSGEEMJ7SPBiB+kKEkIIIbyHdBsJIYQQQlMkeBFCCCGEpkjwIoQQQghNkeBFCCGEEJoiwYsQQgghNEWCFyGEEEJoigQvQgghhNAUCV6EEEIIoSkSvAghhBBCUyR4EUIIIYSmSPAihBBCCE2R4EUIIYQQmiLBixBCCCE0RYIXIYQQQmiKBC9CCCGE0BQJXoQQQgihKRK8CCGEEEJTJHgRQgghhKZI8CKEEEIITZHgRQghhBCaIsGLEEIIITRFghchhBBCaIoEL0IIIYTQFAlehBBCCKEpErwIIYQQQlMkeBFCCCGEpkjwIoQQQghNcWnwcuvWLURGRiIkJAQhISGIjIzE7du3LZbPzMzE22+/jSZNmqBEiRKoXLkyBg0ahPj4eFdWUwghhBAa4tLgZeDAgYiNjcWmTZuwadMmxMbGIjIy0mL5u3fv4uDBg3jvvfdw8OBBrFq1CqdOncLjjz/uymoKIYQQQkN0RESuOPDx48fRqFEj7N27F23atAEA7N27FxEREThx4gTq169v13H279+P1q1b48KFC6hevbrN8klJSQgJCYFer0dwcHChzkEIIYQQ7uHI/dvPVZXYs2cPQkJCcgIXAGjbti1CQkKwe/duu4MXvV4PnU6H0qVLm30+PT0d6enpJuUB/iUIIYQQQhuU+7Y9bSouC14SExNRoUKFfNsrVKiAxMREu46RlpaG8ePHY+DAgRajsKlTp2LKlCn5tlerVs2xCgshhBDC45KTkxESEmK1jMPBy+TJk80GC7nt378fAKDT6fI9R0Rmt+eVmZmJAQMGwGg0YtasWRbLTZgwAePGjcv52Wg04ubNmyhXrpxdr+OIpKQkVKtWDXFxcUWyS6qonx9Q9M9Rzk/7ivo5FvXzA4r+Obrq/IgIycnJqFy5ss2yDgcvI0eOxIABA6yWqVmzJg4dOoQrV67ke+7atWsICwuzun9mZib69euHc+fOYcuWLVZ/OYGBgQgMDDTZZqmLyVmCg4OL5B+koqifH1D0z1HOT/uK+jkW9fMDiv45uuL8bLW4KBwOXkJDQxEaGmqzXEREBPR6Pf7++2+0bt0aALBv3z7o9Xq0a9fO4n5K4HL69Gls3boV5cqVc7SKQgghhCjCXDZUumHDhnj00UcxdOhQ7N27F3v37sXQoUPRq1cvk2TdBg0aYPXq1QCArKws9O3bF9HR0fjpp59gMBiQmJiIxMREZGRkuKqqQgghhNAQl87z8tNPP6FJkybo1q0bunXrhqZNm+KHH34wKXPy5MmcEUKXLl3CunXrcOnSJTRv3hyVKlXKeezevduVVbVLYGAgJk2alK+bqqgo6ucHFP1zlPPTvqJ+jkX9/ICif47ecH4um+dFCCGEEMIVZG0jIYQQQmiKBC9CCCGE0BQJXoQQQgihKRK8CCGEEEJTJHjJY9asWahVqxaCgoIQHh6OnTt3Wi2/fft2hIeHIygoCPfddx/mzJnjppoWjCPnt23bNuh0unyPEydOuLHG9tuxYwd69+6NypUrQ6fTYc2aNTb30dr1c/QctXQNp06digceeAClSpVChQoV0KdPH5w8edLmflq6hgU5Ry1dw9mzZ6Np06Y5k5dFRERg48aNVvfR0vUDHD9HLV0/c6ZOnQqdTocxY8ZYLefu6yjBSy7Lly/HmDFjMHHiRMTExKBjx47o0aMHLl68aLb8uXPn8Nhjj6Fjx46IiYnBO++8g1GjRmHlypVurrl9HD0/xcmTJ5GQkJDzqFu3rptq7JiUlBQ0a9YMM2bMsKu81q4f4Pg5KrRwDbdv344RI0Zg7969iIqKQlZWFrp164aUlBSL+2jtGhbkHBVauIZVq1bFJ598gujoaERHR+Phhx/GE088gaNHj5otr7XrBzh+jgotXL+89u/fj3nz5qFp06ZWy3nkOpLI0bp1axo+fLjJtgYNGtD48ePNln/rrbeoQYMGJtuGDRtGbdu2dVkdC8PR89u6dSsBoFu3brmhds4FgFavXm21jNauX172nKOWr+HVq1cJAG3fvt1iGa1fQ3vOUcvXkIioTJkytGDBArPPaf36Kaydo1avX3JyMtWtW5eioqKoc+fONHr0aItlPXEdpeUlW0ZGBg4cOIBu3bqZbO/WrZvFCfL27NmTr3z37t0RHR2NzMxMl9W1IApyfooWLVqgUqVK6NKlC7Zu3erKarqVlq5fYWnxGiqTV5YtW9ZiGa1fQ3vOUaG1a2gwGLBs2TKkpKQgIiLCbBmtXz97zlGhtes3YsQI9OzZE4888ojNsp64jhK8ZLt+/ToMBkO+RSPDwsKQmJhodp/ExESz5bOysnD9+nWX1bUgCnJ+lSpVwrx587By5UqsWrUK9evXR5cuXbBjxw53VNnltHT9Ckqr15CIMG7cOHTo0AGNGze2WE7L19Dec9TaNTx8+DBKliyJwMBADB8+HKtXr0ajRo3MltXq9XPkHLV2/QBg2bJlOHjwIKZOnWpXeU9cR4cXZizqdDqdyc9ElG+brfLmtnsLR86vfv36JutQRUREIC4uDl988QU6derk0nq6i9aun6O0eg1HjhyJQ4cOYdeuXTbLavUa2nuOWruG9evXR2xsLG7fvo2VK1di8ODB2L59u8WbuxavnyPnqLXrFxcXh9GjR2Pz5s0ICgqyez93X0dpeckWGhoKX1/ffK0QV69ezRdRKipWrGi2vJ+fn9ethl2Q8zOnbdu2OH36tLOr5xFaun7O5O3X8PXXX8e6deuwdetWVK1a1WpZrV5DR87RHG++hgEBAahTpw5atWqFqVOnolmzZpg+fbrZslq9fo6coznefP0OHDiAq1evIjw8HH5+fvDz88P27dvxzTffwM/PDwaDId8+nriOErxkCwgIQHh4OKKioky2R0VFoV27dmb3iYiIyFd+8+bNaNWqFfz9/V1W14IoyPmZExMTg0qVKjm7eh6hpevnTN56DYkII0eOxKpVq7BlyxbUqlXL5j5au4YFOUdzvPUamkNESE9PN/uc1q6fJdbO0Rxvvn5dunTB4cOHERsbm/No1aoVnnvuOcTGxsLX1zffPh65ji5LBdagZcuWkb+/Py1cuJCOHTtGY8aMoRIlStD58+eJiGj8+PEUGRmZU/7s2bNUvHhxGjt2LB07dowWLlxI/v7+tGLFCk+dglWOnt9XX31Fq1evplOnTtGRI0do/PjxBIBWrlzpqVOwKjk5mWJiYigmJoYA0LRp0ygmJoYuXLhARNq/fkSOn6OWruGrr75KISEhtG3bNkpISMh53L17N6eM1q9hQc5RS9dwwoQJtGPHDjp37hwdOnSI3nnnHfLx8aHNmzcTkfavH5Hj56il62dJ3tFG3nAdJXjJY+bMmVSjRg0KCAigli1bmgxhHDx4MHXu3Nmk/LZt26hFixYUEBBANWvWpNmzZ7u5xo5x5Pw+/fRTql27NgUFBVGZMmWoQ4cOtH79eg/U2j7KkMS8j8GDBxNR0bh+jp6jlq6hufMCQIsXL84po/VrWJBz1NI1fPHFF3PeX8qXL09dunTJuakTaf/6ETl+jlq6fpbkDV684TrqiLKzaoQQQgghNEByXoQQQgihKRK8CCGEEEJTJHgRQgghhKZI8CKEEEIITZHgRQghhBCaIsGLEEIIITRFghchhBBCaIoEL0IIIYTQFLcEL7NmzUKtWrUQFBSE8PBw7Ny502LZVatWoWvXrihfvjyCg4MRERGB33//3R3VFEIIIYQGuDx4Wb58OcaMGYOJEyciJiYGHTt2RI8ePXDx4kWz5Xfs2IGuXbtiw4YNOHDgAB566CH07t0bMTExrq6qEEIIITTA5csDtGnTBi1btsTs2bNztjVs2BB9+vTB1KlT7TrG/fffj/79++P999/P91x6errJap5GoxE3b95EuXLloNPpCn8CQgghhHA5IkJycjIqV64MHx/rbSt+rqxIRkYGDhw4gPHjx5ts79atG3bv3m3XMYxGI5KTk1G2bFmzz0+dOhVTpkwpdF2FEEII4XlxcXGoWrWq1TIuDV6uX78Og8GAsLAwk+1hYWFITEy06xhffvklUlJS0K9fP7PPT5gwAePGjcv5Wa/Xo3r16oiLi0NwcHDBKy+EEEIIt0lKSkK1atVQqlQpm2VdGrwo8nbfEJFdXTpLly7F5MmTsXbtWlSoUMFsmcDAQAQGBubbHhwcLMGLEEIIoTH2xAcuDV5CQ0Ph6+ubr5Xl6tWr+Vpj8lq+fDleeukl/PLLL3jkkUdcWU0hhBBCaIhLRxsFBAQgPDwcUVFRJtujoqLQrl07i/stXboUQ4YMwZIlS9CzZ09XVlEIIYQQGuPybqNx48YhMjISrVq1QkREBObNm4eLFy9i+PDhADhn5fLly/j+++8BcOAyaNAgTJ8+HW3bts1ptSlWrBhCQkJcXV0hhBBCeDmXBy/9+/fHjRs38MEHHyAhIQGNGzfGhg0bUKNGDQBAQkKCyZwvc+fORVZWFkaMGIERI0bkbB88eDC+++47V1dXCCGEEF7O5fO8uFtSUhJCQkKg1+slYVcIIYTQCEfu37K2kRBCCCE0RYIXIYQQQmiKBC9CCCGE0BQJXoQQQgihKRK8CCGEEEJTJHgRQgghhKZI8CKEEEIITZHgRQghhBCaIsGLEEIIITRFghchhBBCaIoEL0IIIYTQFAlehBBCCKEpErwIIYQQQlMkeBFCCCGEpkjwIoQQQghNkeBFCCGEEJoiwYsQQgghNEWCFyGEEEJoigQvQgghhNAUCV6EEEIIoSkSvAghhKOiooCxY4EbNzxdEyHuSX6eroAQQmjKzp1Ar15ARgZw+DCwaRPgJ2+lQriTtLwIIYS9Tp8G+vThwAUAkpMBvd6jVRLiXiTBixBC2OPGDaBnT+DmTaB1a2DrVmD7dqBcOU/XTIh7jrR1CiGELenpwFNPcctLjRrAunVAWJhpmTt3gJIlPVM/Ie4x0vIihBC2pKcD/v5AcDCwfr1p4GIwAO+8AzRrJgm8QriJBC9CCGFLcDCwcSOwaxdw//2mzyUnA8uXA2fPAs8+y8GMEMKlJHgRQghLTp1Sv/f3B5o0yV+mdGlg9WqgeHEeQv3OO26rnhD3KglehBDCnN27gaZNgZEjgaws62WbNgUWL+bvP/sM+Pln19dPiHuYBC9CCJHXv/8CTzzBuS6XLgE6ne19+vUD3nqLv3/hBZ4DRgjhEhK8CCFEbrdu8ZDo69eB8HDgp58AX1/79v34Y6BrV+DuXZ4PJjXVpVUV4l7lluBl1qxZqFWrFoKCghAeHo6dO3daLJuQkICBAweifv368PHxwZgxY9xRRSGE4Mnnnn4aOHkSqFqVh0SXKGH//r6+wNKlQKNGwAcfAMWKua6uQtzDXB68LF++HGPGjMHEiRMRExODjh07okePHrh48aLZ8unp6ShfvjwmTpyIZs2aubp6Qoh7XEICcPAgcPAA4Xq/V4GtW2EoXhLXvlsPVK7s+AHLlQP++Qd47jnnV1YIAcANwcu0adPw0ksv4eWXX0bDhg3x9ddfo1q1apg9e7bZ8jVr1sT06dMxaNAghISE2Dx+eno6kpKSTB5CCGGvuXO5d+jRVtfw1tr2uIUQ9L67HDN3Ni34QXOvdXTlCvDnn4WvqBAih0uDl4yMDBw4cADdunUz2d6tWzfs3r3bKa8xdepUhISE5DyqVavmlOMKIe4NQ4cC3bsD11ABi/Ei2pX/F69veAzDhjnh4OfOcWT0xBPAokVAXJwTDiqEcGnwcv36dRgMBoTlmUY7LCwMiYmJTnmNCRMmQK/X5zzi5M1BCGEnIuC994Dff+d0FX9/4MS1chg8GDhyxAkvUK0a0KABkJICvPQSUL06UKcOR0xLlwLXrjnhRYS497glYVeXZ5ghEeXbVlCBgYEIDg42eQghhD0++kidnsVgADIz+ftr14BevYBNmwr5An5+wMqVwLvv8mKOPj48DHvBAmDgQGDVKrWsXs8jnIQQNrk0eAkNDYWvr2++VparV6/ma40RQgh3mjOHW10A4Gvf/+Bq39dw4ACwbRvwwAM88KhXL2DevEK+UEgI8OGHwL59vCL1r78C48YBLVoADz2klvvpJ6B8eZ7w7rXXgIULOfFXiaiEEDlcGrwEBAQgPDwcUVFRJtujoqLQrl07V760EEJYtGIFxwcA8G7zXzHaMA3lS9xFy5ZA5868hNGgQdwaM2wYMGECYDQ64YVDQjgi+vJLHuJUr5763OnT/PXwYWD2bODll4HmzXldpbZtOX9GCAHADd1G48aNw4IFC7Bo0SIcP34cY8eOxcWLFzF8+HAAnLMyaNAgk31iY2MRGxuLO3fu4Nq1a4iNjcWxY8dcXVUhxD3gzz95FDMRMOzFTHxweiA/8eKLOWUCAoDvvgMmT+afP/mE90lLc2HFvvqKRyb98gvw5pvcKhMczC+6fz9QoYJadvx4oH17YNQoTtgR4l5DbjBz5kyqUaMGBQQEUMuWLWn79u05zw0ePJg6d+5sUh5AvkeNGjXsei29Xk8ASK/XO/EMhBBFwf79RCVLEgFEffsSZS36H/9QuzaR0Wh2n+++I/Lz42IdOhBdv+7GChsMRKdOEa1bZ7o9IoIrpDy+/daNlRLCNRy5f+uIiDwYOzldUlISQkJCoNfrJXlXCJHj5EmgQwfOiX34YWDDBiCw+4PA9u3Af/8LTJxocd8//+SJd/V6oEYNYPp0HkikqFSJH25z6hS3xmzaBPz4I2/78UeZGE9omiP3bwlehBBF3uXLQLt2wMWLPO3K1q1AqWtngdq1edHFCxdMoxEzjh7lnhq9Pv9zkyapXUxuRQSMGQN88w2P9V67ltdlEkKDHLl/+1l9VgghNO7mTaBbNw5c6tUDNm4ESpUC8Pl3XKBbN5uBCwDcfz+PRHr2WeDECd4WFgZUqQIcO8YBTK1aQM2a/LVKFR5ynZCQ/1hOa6nR6ThX5uZNbnl55RXgzBlZU0kUeRK8CCGKnIQEfqSm8qiiY8d4FPKPP/JXAHyjDwoCWra0+7jNm3NvTWQksGYN59deucIDh/Ly8wNKlgRu387/3Cuv8Ojp0FCe+iV3nfOyGej4+PDsvUFBnMArgYu4B0i3kRCiyJk8GZgyJf/2wnbv5A4wXnsNePVV7pK6c4dzac6f58eFCzxPjC3+/rz2Y+XK3B1lblBlgetsNKqRkRAaIN1GQoh72rBhfO/+8EO+fy9YADRrVviumrlzTYOiffv466RJppPZGY0c5Ozfz8sMxMcDq1dzt9XVq8CNGxzsZGZyoHPhgunr+PhwDnH37gWs844dwOuvA+vXA1WrFuAAQng3CV6EEEWO0agOwqlRgwOXHLducb9PZCTwzDMOtU4MGwY8/nj+7XkDDB8fznmZP9802FEmG580iQc3JSRwy83ly8Dx47xywD//ALGxwDvvAGfPFqDVxWjk7qNDhzifZ8cO7p8SogiRbiMhRJEzcCCve5jXpEnA5LDZ3OfTpAlHCk5aZ80cR/JYLHV1NWzILTglSjjwwhcu8LjwS5d4rYM//8zOUhbCe0m3kRDinnb+PH8dNAgYPVrdXqkSgCeyV2J84QWXBi7K69nb7ZO3VWfdOl448vhxoFMn/rlKFTtfuEYNYPNmoGNHjnz69OEupKAgR09BCK8kLS9CiCJl/35ewNnPj5cDMkn5OHKEW1z8/DgRJWfokXfauRN46inOj6lUiadxeeABBw4QHc3LDNy5Azz5JPDzz3zuQnghR+7fkoouhChSvvySvz77rJlc1cXZrS69e3t94AJww8nff/McMwkJ3AKzfLkDB2jViiOegADOGJ4xw2V1FcKdJHgRQhQZ58/zuoYA8MYbeZ7MzAR++IG/f+EFd1arUGrVAnbvBh57jNdoHDCA82PsXuX64Yc54hk4UF1KWwiNk+BFCFFkfP0139S7dQOaNs3z5Pr1POVtxYpAjx6eqF6BBQdzzsu4cfzzlCnAo48Cf/3FE+QpD3PJwQA45+Wnn7gFBuCZ8158kYczCaFBErwIIYqEW7d4PhfATKsLwMN12rXjLF4N5n34+nKX2IIFPBQ7KooHFIWHq4+5c+082Mcfcxdaw4bAm2+anwZYCC8mwYsQokiYMwdISeEWl0ceMVOga1duqvj4Y7fXzZleeglYsQIICeGf/fy4N2jLFh6xZJfISP59ZGQAX3wB1KkDzJzJXWtCaIAEL0IIzUtP54WVAW51sToC2tfXLXVypSef5G6ixo2BrCxg1iygVy9g6lQ7e4KaNAF+/x3YsIFbX27cAEaO5O2//eby+gtRWBK8CCE0b8kSnr22ShWgf/88TxJxF8mNGx6pmyskJHBPz4IFQP36/Lh7F/j2W6BuXaBfPx6lZJVOx7k/hw5x9FO+PHDyJLBqlTtOQYhCkeBFCKFpRNzzAfCEdEpOao6//+bk1Dp1uImmCJg7l3Nc2rbleOPkSd5epw4nLP/yC9CmjTq5ndWRSX5+vMLk6dPAhAm8qJIQXk4mqRNCaNrGjTyMuFQpIC5OzQXJMXw43+2ff14dKq1x1pYduH6dE3uXLFFTWGrU4NN/7DF1kl2bs/8mJ/MCUUOGAMWKOfsUhMjHkfu3BC9CCE17+GFg61bgP/9RW2By3L3Ld+ikJM5ofeghj9TREy5f5m6kr7823+A0aZKNRR+bNeMupYULueVKCBeTGXaFEPeEAwc4cPHzM13DKMfq1Ry41KwJdO7s7up5VJUqwCefAIcPc2BXsSJvf+AB/r3ZHJn0/PP89dtvuW9OCC8iwYsQQrOUpQD69weqVTNTYNEi/jpkCE+Ocg8qWZIn112wgHN09++3I5kX4DHZxYoBsbHArl2urqYQDrk3/5uFEI47fJjnp/cSFy7wOoMAtyzk8+ef3FWk03Hwco9Sknt79VIbUF59lefFsapsWbX1RRmHLoSXkOBFCGHbihU8+5sXrQk0fTpgMABdugAtWpgpUKkS4O/PizDWqOH2+nmLYcO4m+jAAWDzZjVht2ZNO3Z+/XX+uno1Z0ML4SUkeBFC2Pbuu/x12TLg+HHP1gU8x8n8+fz9m29mbyQCoqPVQo0a8TjhH390d/W8SqVKQMuW/OjaVV0fado0Dv6satKEk5wNBmD2bJfXVQh7SfAihLDuzBl1IhEA2LPHc3XJNncucOcOzzDbrRt4Wtnu3Xlyk9wBzKOP8hhqkeONN4DSpYEjRzgWten11zlf6OpVV1dNCLtJ8CKEsO677/hrnTrAxYseHzabkcFdRgDwxlgDdF98zlFMVBTPUHfsmEfr5+3KlFFbqyZNsmM5o8cfB/79V131UggvIMGLEMIyg0ENXj7+2MKQHvdaupQnaKtcPgPPfhMBvPUWkJrKE74cOsSrRgurRo0CKlTgmGTxYhuFfX3tTJARwn0keBFCWJaaCjz7LC/e9/jj6vbDh4GbN91WjYQEXojwwAHgww952+vX30fAP/u5KWHRIuCPP3hhH2FTyZLAxIn8/Qcf8GW2y7lzvIyAEB4mwYsQ9xglEMj7MDfdPEqWBD7/HDh6FAgM5G1vvskjj2bMcFudleG+rVpxa4E/MjCc5uBw42c5gfiFF2wsJS3yGjaMG9IuX7YzF3fmTKB2bTXqEcKDJHgR4h6jBAJ5H3PnWtkpd2DwwAP89ZtvgJQUl9ZV8fDDQPv26s/9BvrjyoJfEbp5CRAW5pY6FDWBgeryAB9/zBMRW9WpE4/oWrVKhk0Lj5PgRYh7jDLvx2OP8c9jx1qYLn7TJk6Czbsk8dNP8yfwGzd43RsXunmTp/3v0gX46y/A15dQuTIwYqQOKS06uvS17wWDBgH16vGl/PprG4WbNAEefJDzoGzOcCeEa7kleJk1axZq1aqFoKAghIeHY+fOnVbLb9++HeHh4QgKCsJ9992HOfKPIoRTXbzIsQnA96HExDwFiIC33+ZxyHkDFF9fTpIFeCVEm8NVHJeZyUvq1KnDDTxZWUAVXMJuQ2sMjZ+Mdu3saC0SNvn5qTlEX3zBQYxVo0bx17lzgdRUx7oghXAmcrFly5aRv78/zZ8/n44dO0ajR4+mEiVK0IULF8yWP3v2LBUvXpxGjx5Nx44do/nz55O/vz+tWLHCrtfT6/UEgPR6vTNPQ4giY9IkIo5OTB+TJuUqFB3NGwMDiW7cyH+Q1FSiihW5zP/+59T6bdhA1KCBWq/GjYmWLSO61fkJIoDiX36PDhwgOnCAKD7eqS99TzIYiJo359/1m2/aKJyZSVSjBhdetMi+vyUh7OTI/dvlwUvr1q1p+PDhJtsaNGhA48ePN1v+rbfeogYNGphsGzZsGLVt29Zs+bS0NNLr9TmPuLg41wYvS5cSXbzommML4QYHDxL5+/NN5u23iXx9+fslS3IVeu013jhggOUDffopl2nUiO+ADoqPp5wg5MABol9+IWrXTr0BhoYSzZ7N90vS64kCAviJw4cdfi1h3W+/8a82KIjo8mUbhZXr3rw5xcYYqXt3/tHXl2jVKgkqRcF5TfCSnp5Ovr6+tGrVKpPto0aNok6dOpndp2PHjjRq1CiTbatWrSI/Pz/KyMjIV37SpEkEIN/DJcHL6dP8rl+sGH+0SElx/msI4WJvvsk3m/bt+eexY/nnOnW4QYXu3iUqXZo3RkVZPpBeTxQSQlShAtGZMw7Xw9Kndh8fov/8h+jWrVyFf/iBn2zQgMhodPi1hHVGoxo4vvqqjcI3bpAhqDjNCRpNwSUNJteubl2iv/6yELwsW8bXkYiD3fnziZKSnH0qQsMcCV5cmvNy/fp1GAwGhOUZDRAWFobEfJ3sLDEx0Wz5rKwsXL9+PV/5CRMmQK/X5zziXJAFr/TrHj1kQHKTCJ4UYcoUGOrU5xmzlKVahfByt26pw2LHj+evkyfz+jdnzvCoaKxZw4sHVa/Ow3wsCQ4Gfv8dOH+eE3gdNGwYz+TftKm6rXNnYMcOzr8oXTpXYWX56H79ZEi0C+h0POII4DWjzp61XPbQpbLoUDsew9O+RtId01vI6dM8KswkF4mIDz5gAM/OHBvLq1UPHaqumSWEg9ySsKvL82ZDRPm22SpvbjsABAYGIjg42OThbMrQ0sZP10fwwW3oi19wHjXgm3AJGDgQ6NAB2L/f6a97Tzp/nodkTprk6ZoUSbNmqWsC9ezJ24KDeZE+gO8xZ2ds4B9eeIHXtLGmTRugWLEC1+eff3hSXB8fDqqmTQPuuy9Podu3OUgCOHgRLtG5M+dnZ2UBU6bkfz4lhfO0W7YE9hwNQcmSPMHd33/zaLXZs9W4MiQke6fMTODll9W5YUaO5FFLQ4bwz99+ywcQwlGubAJyR7dRXq5I2M3dN9+xI389+NddSnr7v0QlSnB7acmSRLdvO+01i4q8eQ02Ey2V7gGdjigykujvv91a3yLhzBmi/fvzbU5JISpfnn+9P/5o+pzRSPTww/xcz5JbyQgd0blz9r+mwUC0Y4dD1bQ72fN//1Nza4RL7d+v/vsdOaJu//VXNU8XIHrqKaK4OOI/nPPnc8p9+KGaO/PPTj1Rly5qX+CMGaYv9vzz/FyTJkR2vLeLos9rcl6IOGH31TydqA0bNrSasNuwYUOTbcOHD7eYsJuXq0cb9e6dZ8OlS3yT/egj0+2u/mf84Qeibdtc+xpO4PBohOHDTQsOHuy+yhYFf//N+SqVKxMlJpo8NWMG/0pr1sxOgs3j+HE1kXfNN+ZHA5qVlkZ0//28Y0yM3bvFxKjJwj/+aCWwPX2a6N13OXtXuNxjj/E1efhhoo0b1fgDIKpenQMZIiK6cIEDj9DQ7GQpjmEffTQ7/yXgHOlRij/g/fZb/he6epWoXDkuPHWq+05QeC2vCl6UodILFy6kY8eO0ZgxY6hEiRJ0PjtaHz9+PEVGRuaUV4ZKjx07lo4dO0YLFy70iqHSt24RffEFf/owK3cS4ZYtfIfI0+LkNIsX8z98pUpEV6645jWcJD6eaO9efgOsWJFH4FpteWnShM/trbfUobrXrrm1zpq0ezf/DSYlETVsyL+7zp1zopSMDPWTc94PwLlNmMBlatRwMB/92Wdtj07KY8oU3qVdOwdeR7icMtAs76NdO6I7d3IVzMzkaAbg96Rs164RVQvRE0DUN+hXMh44aPnFlFa1oCAOUsU9zauCFyKimTNnUo0aNSggIIBatmxJ27dvz3lu8ODB1LlzZ5Py27ZtoxYtWlBAQADVrFmTZjvwicuVwYtOx/9neT7Q5tejh/ofb6Pbw+Fulawsfgdp1IiP37VrgYaputMff6i/jpxPbebk/SW3bMnff/aZu6qqTRs28O+pb1++oRw/TlSqFG8bN46IuGUD4G6ju3ctHCc+nu5cTcm5H73zjgN1iI1VuwfsGHmUkcGxd74h2sLj4uOJevZU/2ebNOGBQmbfk5Rh0y1amHyA27PbSP4+mQQQTZ9u5cWMRqJHHuFjdOvm9HMR2uJ1wYs7ubLbqHFj/h9bvdpGwTt3iDp04MLffmu1qMPdKiNH8vDUoUOJihfnwv/9r8Pn4i7x8UTPPaeeV+3aHM+ZfSPcuFEtRES0cCH/XKsWB233GLsC2/PnicqW5d/Ta6+p21euzPmlG5cuy/nbzdu7aeLZZ4mCg2n12O0EcBfS8eMOVFgJ2vPM62TO8uVcNCyMKD3dSsHPPiNasyanW0K4Xnw80ebNRL168fD5/futfKC6fp1bTQCi8eNNmuu+/po3+/kR7dlj5QXPnOH3y4NWWmjEPUGCFxcFL6+8YucslETq5Bk2CptLBrba8vLEE3zcWbOIvvtO/bTrpfkvDgVn777LTw4axD+npBCVKcPbzPWZF3E2f3dpaUQPPMAbH3iAf87t7beJAPo18GkCuDHGZO6U3G7e5C46gIz7o3PyHrp0cWBalR071K6+hASrRTt25KLvv2+l0M2bfOcDHIyiRGE4/IHq5ZfVQk8/ndMSbDRyYyBAVK0axzlC25T71d69dvYUOEiCFxcFL0qsoEzuZdVXX3Hh/v1tFs3MJNq5kz+42qR0pSj9L0OGeHX+y+7dXD1fX6KXXuLvK1YkOnvWTOF33+UEvrlz1W3jxvFOjz3mtjp7i/h4oo8/5p60gACiFSvyvFEoyQllypiM+MiRmUnUpQu1x07bcfTMmXyspk2JjEY6cyYnlqFly+yscO6Zziwk5BOpPUx+fjZmc120SO23EG7jcFf2P/+oQ5SmTTOJdvV6nrgO4EReu3q44+Kcch7C+Vy9HIQEL04OXpR/5tWr+UIFBHAzqNVoU2m2j4iweXzlvlGihB0jg0NDuXBsLP+cO/8lV9Kct/jmG67agw9yrkWVKvzztGkWdjAaTUdqnT7Nyc+ffHLPzay6ZIna8ABw79Dy5dl/dz/9pD6xfr3FY+xcdzP7b9ZoPVAID+djff11ziYlobZyZQcmQl23jnd65BGL12voUC7Sr5+NYynDVj780M4XFx6zYQN/AjPjn3/UniWbl3LyZO6v3LDB+XUUhRYfTzRxovrh488/peXFaVwRvBQo2lQmTKhSxebxlSZ6pYXiww/ND2Wlu3fVgrkXyzt61Gv/2ZX7j5Jzu2AB/1yunAPT4nh5QrIr7N2rDlvO+xg5JInzngBurbJCSbx85RUrhZSmEH9/k5FdqamcfgTk5P3aZjDwTcxC4HLzJq+uAdiYFub6dTVyO3HCzhcX3kppRPPx4QR+i5Tu9ho18gxtEt7g5Ek1xe6++6TbyKlcPUld58584caOtXHBbt7kEHX+fKvHzswkCg5Wb+jKDSoiwsygjVOn+MnixTXRCnHnjtr1cPQob8vMVFcMnjgxV2GLQ2DuPceOqX8LbdtyILN1qzqVSunSRPvnx/AkX1YSmZXWfB+f7FGoWVk8FnrzZtOCo0dzwb598x1DyaH29SU6dKjw5/bll2pPkNU/YSXKbdas8C8qvEL//moL4qZNFm58ycnqmH67I2Yvc+YM0ZNPEu3b5+maOF379tJt5DKunqTuk0/UvDRnUG4OJUtywv2HH5pO2rtoUa43+T//5CfyrLpt4tw5Tur1gvyXX39VP0TlvlGtWqXGYDlvXP36EVWtyt1t5qSnc/LFxo2urrZHXbzIvwaAqHVrfi9X3L7NQS3AAe/u3daPpYzyyumeUfKwypVTc2QyM9WuSAutd0rLYPPm6jw9dn3aunaN6PPPcy6+waC25OROazKrWzcuaHV4lNCSd96x88anDP338TE7U7Q7OZz/Q0Q0cCDXPzxcEx8y7fXvv+rC7l99JQm7Tufq4EUZUFGxonP+Lrt2Nf8PnXcq7mvXiO9WPXpY7gMwGtXQuFs3j3e3vPoqVyXvKrVGI7co5DxnNKrJMFu3mj/YtGn8fKtWrq62x1y/rrZK1a9vZm6+JUsoad8x6tRJzZHKNWWSibNn1dlrc0agpqby7095Y1WGH//7LwcJFlpxxowpwKet1FT1j3jhQiJS70khITZ6BDIz1cT0U6esFBRaEh/PeYPKh7NPPrFy41MmPWze3EIfuns4nDKQa9QeAUS7drmxtq6ljByzks5WaBK8uDB4uXtX7Yq3ufRLfDzfXf7912IRJXgZM8Y0ko2L439uJe+hUiVO4LT5KeDIETWpwIOfWo1GdfJNc6Oct29Xk75Obb2k/mBpWterV9Wwvwiud5ScTNSmDZ9e1ao887oJJQmmeHG6E308Zx2iYsXM5xAoA5G6d8/zxPnzap/USy/ZVbf4eKJRo9QPw//7n52ftpQJzEqWJPr335wWnLFj7XpZq/83QnuUVowXX1S7RC3+HV25ok6T4MFJKuPjeRYKJeDq1cvG374y+kJ5PPOMW+vrKsr7tY+Pc7qPLZHgxYXBCxE35wM84MOqF17gghYmkcvKUvMuLbWOHjyozvZu6ZHvU0Du7DhLH81d7MgRrkJQkOV4RLmZ9Wtznr954AHrB42M5HJFbL2j9HS1l6RsWTU/KMf16zxRhtIMZzTS3btqMnRQkGlvWmKiOrrDbENWVBT/bQBE8+bZrF98PMeLyryL5cpxQGozeMnKIqWZ6HSLZ0inMxIgs8DfqxxuxVi0iPuWra1n4WLx8abLJeh0REuXWvnbf/pp9YNBs2Y8v4bGGQzqYESrif9OIMGLi4MXpRl9xAgbBZX/VgtX/OBBfrpUKesto3fvEr3+uvoPVKsWt6pb7G80GnmiN4DHuV696sjpOcVnn/HLW5u75p9/1NUAotGSf7HW7N3LhYvQekcGAy8HpOQA5ZuJNPdKd3XqmAzRSkvjhUIBbpRat463K7kFbdpYad6dOlX9g/rzT6t1tHTTefttO07w/HmiUqVoHL6w+fdARBzp5k70EUVG7vwRpZXx5ZetBAJGIy9860HKWl92B1xGI4+2u3GjyOS7KPOblSrl+lRKCV5cHLz8/DNfzBYtbBRUprd/9FGzTytpHPbOv7ax+itUUZeQ0xJvVXKymkDRvbvb/5GUUVk2VkfIaUx5BJuJfvnFemGjsUitd2Q08moPSo+Z2VxkZXKhoCCO9vJIT1c/7Pn68ptqyZL88xdf2LgxPPMMF7SUJJ0t901n/Xo1v/fBB+1LR7gz7ycqDZ5vZv3XNnJY5s7lvrAJE2wfWGjWsmX8N1SlikdTWmxSAveqVflfUUkZuFfW40pOVtcg+/RT17+eBC8uDl7i4tReGasfEqOiuGCjRmafVmb6t/uPolQpSkBYTvO7jRnYiQ4f5jtamTIWprR1jdu31WRRW2kL5w4nUwDSCCDavNSO+cOL0HpHH36ofpKz+GaozKNvpZkjM1Ndd8vuT4hE3KpjbmZeG6Kj1WW1Xn3Vdlw8by7/vd6HM2ToZj6Qz9GlCx946lSH6yW0Iy1NTb2yulgrEf+Bb9nCk4y4UWqqeuNWZrxQpqFp3DjP209GhvnpHpKT+ROcrQ9mXuq99/h877sv/+ojriDBi4uDFyI1GdVqi/vJk2rCYp53eINBzUezazqA27dz7kiNGxkIsGOBSCLOfndzq8svv3BVrY3oznHlCo1pvpUAblSxOUBKWe+ofXs7ojfvo7Ri5G6OfuMNCy0kykSHfn42m8/j4kxXAp482blDGPNavVrt8vvqK8vljEZecQAg+rLDKuszE169qubi2LEytdA2JRB44gkbBYcP54KjR7uhVqpZs9RWF2Xx0Bs3eJ4lIGcQHVu+nJ/44APTg0yfrn6A1Vg30oULau7cihXueU0JXtwQvCh5Clanu849I26eFfGUSU1LlrSz2fTQISKAMkPK0pNPqnmrrrxBFZSy3JK9c0xdu8b9qYCd6+h4wRw2BeVQ0qLyqc2OLpT4eI51XnqJV2pWFk5z5d/GF1+oSYxr15ovo0wtUKwYx9FWzZnDhcPDnV5X4X2OHlW7O63+na5dq0YRbpr+ISNDHen/zTemzyl/95Ur5xryr2Tc553x+vZttR837+SQXk6ZrqZTJ/fFXRK8uCF4UdbssZmAWL48F8yTr6AE5PmGslqyfj0RQDFo5ljXgJsZDHzzBGxMA57HBx/wPrVrq59yiqILF9Q3xbAwddK3wgYZrl4wzRyjUV1pvXhxPo+8+vXj54cOzbPjokX5lxlWxn+7o3NdeAVlHc+PP7ZSKDVV/XSTL6PdNRYv5perUCF/b1BaGi+3lvPh9cIFtRnSXD+5MtqiZ093VN0plLEROh2/R7mLBC9uCF6io/nili5t48PAt9/yJ8o8rQVK64ndXfvZn0pvd+xFK1bwvoGB/Edm9cZ38iRPTmAhadjZlJ6OkiXtCEIyM3ksb0oKJSerQc/MmXa+2PXrlie181JKnkvZsjxbrrNmqCzQTKBOkJGhzlVUubJp79bly2qCo7KOKBGpyxE884z6kS4xUe0ycmN+lvAsJUi47z4b76NKM4AblgzIyiKqV8/6uIAlS9T3ucQ3s5tiHnrIfOFTp9TgRgOTLuaeRHTIEPe+tgQvbgheMjLUpMV883LYYDCoyWq2pnjPoSzl+dprZDCo/a42o+ILF9S8idyrNbuIshLxk0/aUVgZKx4aSmQ00scf84/lyvFoQ6s34JgYjt7KltXMuki5FyW02kKSnMyJuosXe/dQjGy3bqkLm7dooSaxK61BHTvm2SE6Wo1qvv+etykJBrbm+hFFyp076tpuVvMHlTVFqld3eR+GMhKqTBnLq6kbDOpk1a+W+p6/+fFHywdVEtJef901lXYiJTArUYKsr0TvAhK8uCF4IeKhorkz0e11+LDa1G53PLF4MfdRZWeJKVN/2BqKTEaj2ud67JhjFS0AZf4Gu34n335r0nf27rsOdH1kZan9L4sWOfEMXGfcOLVrbN8+Ky0kSp9kvXoeX+LBXmfPqj2kvXtzS3/Fivzz8uVmdvjvf/nJ4GAe8XTxIn/MtTnzoyhqlHzcAQOsFLp7V53m1oUzbBsMvGgowB/ErNm2LTtnB5l0vGQr6x+iNm9Wm2qsJa172N276nyYVvM5XUSCFzcFL8pkYC+8YKXQzZv8V75jR84m5Z7dtWvBX3vyZD7Gc8/ZUfiBB9ySMn71qto6alfErmQ9Z2fox8dTTuuLry9/ErPa9aFMtKaB9Y7OnFGXerC6tmRWFrehA9waoSF79qjLuigrYIeGWujazMxUV5ns1Enzw95FwR04wH8GAQH506BMKAlUeUf0OJGSG1yqlB0J5kT0eJVoAogerxlrvaDRyFFRnz5m1v7wHspnimrVPNOgLcGLm4KX337jC12/vpVCyox27dvnbFIWuLKwaoBdfv9d7Su2SRn+48J/eiLuAQB4LTW7KCF+rvZig0EdWps3yz+f3Osdefny88pEcjYTtJXm8bJlbaxe6J2U87Sr9ezMGbVVUJJ072ktWvCfgbVh9xQby0l1Luo2MhrVz3njx9u3z/GN58hXl0WAHSuxuGOilEK4fFlt3PLUJHyO3L99IAqsbVv+evIkcP26hULVq/PXixcB8Fv59u286cEH7XwhIiA52WRTmzaATgecPQtcvWpj/0aN+OuxY3a+YMFs2MBfe/a0o3BcHD98fYHWrQEACQlAbCzQrRsXmTsXOHiQt5tVvjzQrx9///33ham6S+3aBaxcCfj4AF98YaPwtGn8dfhwoEQJl9fN2b79FnjtNf5epwM2bQIOHACGDTNTuHZt4Ouv+fu1a4E7d9xVTeFlhg7lrwsW8NudWc2aAa1a8R+WC/zxB7B/P1CsGDB2rH37NHi0JoYO8wUAvPEGYDRaKRwYWPhKukBCAr/PvvoqkJICNGkC1Ktn5X3XS0jwUgjlygENGvD3e/daKKQEL5cvA1lZOH4cuHaN/0EeeMDOF7p6FQgOBsqWBbKyAAAhIWpMYvG1FW4IXrKy+EYFAI89ZscOf/3FX5s1A0qWBMDBSni4eoM/epR/njvXynGU4GX9eivveuo/aN6Hq/9BjUZg3Dj+/uWXgcaNrRT++2+OdPz9gREjXFsxF3rxRWDiRKBhQ44vbRZ++WXg5k3g7l231E94n4ED+T3x6FE73s8Aq//rBfXf//LXV14BKlSwf7/Jk/ktbP9+4Oef7djh7Flg5syCVNEllPfddev458OHOUa0+r7rDdzQEuRW7uw2IlKXd7c4j5jBoCY7XLiQM6ji4YcdeBFl/HGlSiabX3rJzibOs2e5eb5jR5c1ue7cqfZ22JW+oCzqkyv7Pvdw3/bt1flBrA73TUnhaSB1OqtrERRoHpS0tEJ3/P74Y64hlYk2Cis5QIMGFeo1PckT882IomHwYP5befFFK4UuX+Ykw7Ztnfpepkym6O/Ps1XbFBvLK7z//jsRqfNU1axpo3fo+nX1fnDokFPqXljx8USPPKLmqNmcZuHkSV5E12T+A+eQnBc3Bi8LFvBF79zZSiElAXPnzoLlnCl5EK1bO/7aRPxP7uLhhcp09wMH2rnDkSNEX39N9NdfZp/+6Sc1p8dm1aOibK6cHR+vJkr7+/P6f1b/QTMyeBXnsDDzs6/ZIXfmvtVJuBSbNvG7SExMgV7PG3hqvhmhfbt2qaMwLb596/VqVrgTb/7du/MhX3nFzh2UieeeeYaIOD1NWQfpyy9t7KskPb78cqHq7Cy7d6tTLLVsacf/rPJm74JJ9yR4cWPwcuwYX8dixawMe84eU2388aecidhsJnflpkzH27evyWZleu3ixT0/HYiSZGttqgNH3LmjJo/ZPReOFfHx6sRLymimceOsjIratEktHBJiMciy5qOPePfq1TUzFY0QHmM0EjVsyP8zc+ZYKfj441zo/fed8rpKw7avr+2FZImI5wFQFqbbtClns7JmbJkyvAaSRUozdVAQr43iYcp8NXa1lmZl8TINgEsWm5SEXTeqXx8oUwZITQX++cdCoey8l1MH7+DKFSAoKCdH1T7Zyb6oVs1kc4MGnPty9y73U9rFBX3Fly4Bhw5xHl337s45ZokSwFNP8fc//lj44338sWlfusHAubHdu3OSWj4rV/LXgABAr+csYiVPxw6JicDUqfz91Kncny+EsEyn4/QngBN3Lerbl7+uWOGU1/3oI/46cCBw33127LB2LXDrFr8fP/JIzubBgznP69YtYNQoK7l17dsDLVsCaWnA/PlOOYeCunYNOHKEv58zh5PrlYfZJPvLl/nNrEwZoHdvt9Y1LwleCsnHB4iI4O9377ZQKDISmDsX20rwMJy2bTmAsVtcHH/NE7z4+PCoIwDYs8fGMVat4sTdl15y4IXto4wyatsWCA21Y4fffgMWL+aox4rnn+evy5cDGRk2jvnDD0DnzhYz5tLS+GunTkB0NPDmmzzQ6cgRvn7//pursMEArFnD3//yCwcu1atzCr6dJk3iwTOtWwMDBtgovGgR8O673p/eL4SLRUZyvnp0NI88NOvxx7nQsWOFHoRw5Aj/q+t0wIQJdu60aBF/HTKE30Sy+fpyTAIAP/3ESbDKwyT5VacDxozh72fOBDIzC3UOhTFzJr83tmrFicotW6qPSpXM7FC9Og+vPXjQ86OnnN7u42Hu7jYiUif26d/ferlnny1g8qIymZeZZjolQfL5520cY80atVPTyZ54wsEZGZUVWGfMsFosM1OdpXXdOhvHHD/eYtJNcrI6BXmuVl7asUNdT6l0aaING7KfSE3luvXuzX2BaWl2ZNuqDh1S+5B37bJROCtLXeVt9my7X0OIokrJCxwxwkohZbr9Qs5dpbwn5+mRt+z8eauLMF6+rM4yXqYMd0mZzR9JS1PffJYtK9Q5FNSdO+oyNT//7JEq5CPdRm7Wrh1/tdjyAu6t2baNv7d7fhfFQw8BPXpwH1Ueylwzdg+XPn7cxmQEjklP5/kRADuHSBsMajNR+/ZWi/r5Ac8+y9/b7Drq1Yu/btyYM5xc8dNPQFISULcu0LWrur1jR24ebdsWuH2b56f5738BY0AQD1Vet44/4QUGAmFh6o4LFqifvsxQ5nvo29fmKfLHvvPnedz9oEE2CgtR9CldRz/+aGX0vNJ19MsvDh9fmTZhzRpu1QW4i9quhs///Y/fzB96yGwfk07H//+Bgdx9tHSpheMEBvLEKiEhPE2AByxeDNy4waehdNFbde6c2oTtDdwQTLmVJ1pekpM52QvgJVryycigUz/tI4AnhHVm8ubNm2qCldUBN5mZ6my0Tly1V1myo1IlOwc0xcaq82/bMaZamTo8KMjGkiBZWTxOGzBZisFoJGrc2PrsnWlp6voqALckWfzz2btX/eRlZgrgjRsp5zqfOWPz9IjateMd3n3XjsJCFH0GA1GtWvxvoazbmc+NGzzd/rvvOjxaoVDD+b//nl/XwsgEh46t16urmLpZZqba4Dtzpp07dejATdS5m6+dTEYbuTl4IeLeGMDCInR37tB8vEQAUad2zh8WpGTo2+xaUVYc++03p732mDF2zM2Q28yZvIOdCzvlHoFgc/3F557jgm+/nbNJWTyteHFe/diahQuJAvwNPF9D1UxascLMcF+jUT3pPGOgMzPV1ZX/8x87Tm7vXjXSSUiwYwch7g1KV3y+Fcmd4ORJ7hFW/oUXL3ZwOL/RaHHBVGWqgOhodeHeGjXs/CDjRsrK2aGhPFWWTadP8w4+PkSXLrmsXl7TbXTr1i1ERkYiJCQEISEhiIyMxO3bt63us2rVKnTv3h2hoaHQ6XSItZi15V2sdh2VKIFtATwMp3MTB5sIU1O5z8MKh7uOnDjTrkNLAgDqiB3lF2aDTsdJfIADXUe//ZazSZnIMjISKF3a+u4vvgjseuYbVEUczl/yQ9++pkl3c+ZkV2jaNOC993ind97h6WSJsHAh/2rLleP8W5u++oq/DhwIVKxoxw5C3BuGDOEBCTt3cn6os+zaxQOEfv2V/5WrVweaNnXwIDodV86MSpU42TU8nAdDVakCXLigjjw0i4hHMB086GBFCoYI+Owz/v7114Hixe3YSVl+pWtXPilv4LIQiogeffRRaty4Me3evZt2795NjRs3pl69elnd5/vvv6cpU6bQ/PnzCQDFODhhl6daXpYs4cD0gQfyP2c0ElX1TyCA6I+P9jp24KVL+cCPPmqxyLx5XOShh2wca8oULmh1GWz7KcG4n5+Vbpa8atTgnTZvtvt1zp/nXXQ6G7Nf3ryp9t+dPUuXLqk/2jWfldFIVKMGXUF5alP7ar6m35IledHJp58meustorlPbaI/8DCdQw06+dTbVKaMkQB+zuanuPPn1cr984/dvwsh7hVK68gbb1gpdPcuD0awMS1uejrRO++oifQOdxvdvMlNsw5282zZovYym22VJyL6/HMu0Lq1xRYdZ/rjD3VuMrummTEY1PdtF6/Y6BXdRseOHSMAtHeverPes2cPAaATJ07Y3P/cuXN2BS9paWmk1+tzHnFxcR4JXpQbrJ9f/ma4M2f4OX+kU8r0+Y4d+NNPeefnnrNY5PBhLlKihI3u3zVriJo1c9pc7crceTaDJkViotr0mJTk0Gt16sS7fvaZjYKPPkrUqxfR4cP0/vu8T6dOdr5I9mxVhmLF6e9tKfTaazw/nTIowNpDB4PJz5++ec36G9GlS5xo8/jjdlZOiHtHfDzRtGnqqJ29ey107fTqlf0PZ3lV8uPHicLD1f/Nfv14klCHZoGeMYN3btfO4XOZOJF3DQkhOnfOwsmWKsWFFixw+PiOUgZ7jhxp5w5btvAOwcEun23TK4KXhQsXUkhISL7tISEhtMhm8oL9wcukSZMIQL6Hu4MXo5GocmW+xnlnz1VmXmyPnVYWQbJAWQPIygJGBoM6FNgdM8sr/brKCO4xYxzoM752jejPPx1+TaV1qWlTGwWzs4bT09Wgw+Innryyh1sfafSM2QDltdeIfv2VVzV4/XWixx4jql2bKMA3K7uMkaZPJzoQbSRjQACvQ1C9Ov+innmGf1Gff27a6uTiZRuE0CJLia/53gbnzLHY5G00csxRrJgaBBVoUtiDB9UZdb/+2uHdMzLU2b3btbPwAfPLL9UklJs3C1BJ+yjjJXx8HBi3oSw6NXSoy+ql8Irg5aOPPqK6devm2163bl362I6FXrTW8kKkLlkxdarp9kGDePtEfGi1BcUsZRKVWbOsFlMW1nLHVCGeWHzv5k11sJQ9vSxKN16lSlaWbcjNaCSqW5cIoJuzl9m9Pk98PNHff3MOdKtWXC72z+tkVNqKzT2eftrh8xfiXqJ8QBo71vRfx9+fF7WdNo0TbykxUe0LOn/eZP9HH1X369q1gHmmuQOXtm3tzG7N7+xZ9QOm2VUNMjLUkQl2N4k4ThnTMGCAnTvcvcv95YAdk1YVniPBi5+jOTKTJ0/GlClTrJbZv38/AECn05nLsTG7vaACAwMR6OmZ/rK1a8dJWnmTdpX5XTpjO3Ax3bGDWlgaIK+ICJ5vZc8eYPhwG8c0GHhWx+xpfhMSzM9xUKmSOstiRgbPeLl3Ly+DUKkS7xMYyDm4Op2FGRmdpEwZzsddtYrnbbGVZDdzWhqAIAwbxlO12HTkCHD6NBAYiDLPPYYypeyr19y5QO5/h/BwACiHKe+m4/2hCTyddt6HMiWzEMKq55/n97WmTTnZ9sIFYMsWfowbB9StG4aelZeh16U5qDVtNW4PHoMtW3i+Jr2e358++wwYOdJijq1lMTFAly48YUvbtsDvv9uZ3ZpfrVqc8D9wINetSxee7TuHvz8wYwY/MWsWz4TevHmBXsuSCxeAZcv4+zfftHOnYsV4Mqw1a+weZOE2jkZG165do+PHj1t9pKamuq3bKC9PJewSqSNfy5VTewPOnVNyYYx0Z/oCoq1bHTtoaCgfwMby4xs2cDEzjV2mxo3jVVlzTXpiqSWlb18uHhGhLuRqkueh46HBdrlzh5uH3nvPzqaQ/JTFtatUsZ5OcvDVefw712XZP/xx8WJOoHUwB0VWURbC+Sy9J40cyW9dXbpwK0zu54rjTr7yr75awArExJi2uDjpfqL0wFStamHxRmV64fbtnd6lPHo0H7pLF6ce1qm8ottISdjdt29fzra9e/cS4NyE3bw8Gbykp6s3+ZMnedvixfxzREQBDnj3rvpfaHWZUn5aKXr9upWC772Xr/9SuQGvWsWxUvny5t84ypXjWbnfeot7sbZt43kY7LphK0lfVaoU+J8yLY3nSAL4cJa81D2Om0aLrXbsta5f5yFUQgiPsudDgV5PtGIF0Qv9U6gCEk3eqwYP5g+TBf4Qce4cj7BxYuBCxOMUsnun6amnzLw9XbzIQxpz1ipxjhs3eEAHQPT77049tFN5RfBCxEOlmzZtSnv27KE9e/ZQkyZN8g2Vrl+/Pq1atSrn5xs3blBMTAytX7+eANCyZcsoJiaGEuycxMuTwQsRT0IIcNBCRDRkiM18W8tu3SIaNoyoTx+7bsL16/NrWZ2DThl63b69yWaDgej++9V/fl9fnnjvtdd4UsnTp9UqFCjn5cMP1VT/QnjlFT6MpUnxbtwgKlaMhyzvRHvusxZCFGl3W3eiv9GK3uu+j5o1c1IL6PnzTg1cFNHRaqvR3LlmCrggiV+Z9K9ZMwcO/+mnHGH99ZfT62OJ1wQvN27coOeee45KlSpFpUqVoueee45u5ZnmFAAtVu70RLR48WIC8o8emmRnNqing5e33jJt2FCmud60iYj27eNIwOwaAoWnBEoTJ1op9M8/aup9rr9iZcZFPz/u2bKWl1agrhIle276dEdPy8SOHXwYS6P2vvgi+580+CwZAfsWbktLK1SdhBCeNfflv6keThRuEMGBA0Rr17qqiiaUqV2KFSM6etRKQScMTb57l6hCBX49C6sa5Gc08lBKwMoaDc7nNcGLJ3g6eFEWb77/fqILF9RWjKQkUicrWbrUJa+tjBp8+GErhVJT1ez87GgjLk5d56JGDRfkbRw+zJMcAHzgQsg9X1LelVANBqL77uPn5kVmRzmtW9s+aHg4t0QdPlyougkhPKPQuWcHDvAHOj+/Ak3l4CiDgahzZzVPcfduM8uQfP0199UfOVKo11LuC9WrO5BuuHMn71SyJOcruonXLA9wL1IGkhw9yjM+AzwCpVQp8FzUgDqCyJbr120uDWDutf/+mwcUmRUUBNSuzd9nLxMwahQvbAxwRroyHf7cuXa/tGUvvQQ0acKp/6VLF2AublM+PsBzz/H3eZcL2LQJOHuWX2bg+3V44/79wJUrlg949ixn0+/ZY7pytBBCM5Rp+Vu2ILQsex4tmxvRsqWdIyAPHuQ1A27dAh54AGjVyuX19fFRX+b0aR7IY/K+q9PxMNUbN/gNmqhAr2MwAF9+yd+PG2fnyEuAV88GgGeeAUqUKNBru5oEL05mMKijmpX1LBo2zB6K7Gjw8s47vGT6xx/bVfz++4GSJYE7dzh4sijXGkdZWTwEGuDhhAcOqI9hw+yrponTp4GUFPXnFi0AX19ec/333wE/h0fn56MELxs2cHynmDGDv77wAlCiTva7GRGwcaPlg61axV8ffBAoX77QdRNCeMiJE3yzrVULCA7mT3PDhvHQ47/+Mv9BMHfgEhHBn4CCg91S3f/8B5g+Xf25Th0eypzzvjttGn/Y3LIF+OWXAr3G2rX8llymDH+OtMvdu8Dy5fz94MEFel23cENLkFt5utvIajKr0n7Xu7d9B+vencsvXGj36z/8MO8yZ46VQtOn80Rp69bRokVcvnz5QqzOnpHBU1d26cIHyz3FdVIS0eXLBTywZcoq3srcfadPq2uI5AwYWr2ahyNY+1tQpr60e114IYRXun6dl3IOCjL/Jpwnz4/mz1dnjouIcElyrjVKV9dXX6kJvP7+RO++S5SVlV1o8mR1bLWdb9C5V7ZWBmG89JIDXWg//sg71arllrWWcpOcFw8GL/HxvACY8v/i48PLBcTHkzoZS7Nm9h2sUSMu78Aihso6GkOG2C6bkaHmunzxhd0voTp/nl+wYkXTyV+srqTmHMq6J8pSI+PG8c89ejhwkLg4tc4yMYsQRUNmJi9otGwZvxn37Mk3/7yLnClvfh4IXIgsf9AFuKrnzxNn2yqjPuxcWqbQM6B37VqAbGfnkODFg8ELEa9grPzBtGqV64kjR9RPAPZQFus6ftzu1/7tN96lfn3bZefO5bJhYQWY9XrfPtOZ68LCOJDJNUW3K8XHq3nHhw+r879YHSael7KyZJ5h40KIIij3qMKMDE7m79PHI4ELUf4k4+hobnUpXlwdUfm//xEZ16xVm2WUCcQsSE/nVvc2bdS35qefdiB52Wgk+uYbohYtiP791zkn6gAJXjwcvGRlqa2R//mPSeXUvyhbqyrfvq2WdSDb+9o1dTdr89qlpRqpWuVMAgq01hhRZCS/SIsW3GVUwFlzC0NZHVVZEuS++3I1tyrOnOGmV3MnqYz+yjXbsBBCeNKZM+qitxx8GOnaIwN42Or8+Wb3OXmSG7yVCdmVBuUyZYj++EM7s35L8OLh4IWIaOBA/gPauTPPEwsWEG3caHtuEaX5pmxZh19bmcFx/XrLZWZU+i8BRJVD0yg11eGX4BaWzz7z2CRw8fE8hUvuZtHRo838g65cyU/WqZP/ID/9xAtfXrjghhoLIYR9MjN5Yjk/P377qlg+kzbOMl0GOjWV01OUIdfKQ1lHsVBz3niII/dvHVEBx2B5qaSkJISEhECv1yPYTVnj5qSkAL17c6J4gWzYAPTsCTRrpg4HstPgwcD33wPvvQd88EH+51NTgTplriM+PRQzB+zEa0s7FrCSnjN5sumCiIpJk/i5HMnJQLlyvBDlyZNAvXpuqqEQQhTOgQO8OOWJE/zzM88ATzwBrF+XhfWb/HIGUPn4AI89BgwdygM8r13Lf6zcC+2adfgwTy3xzDPZc3u4nyP3bxkq7SIlSvCw5QKrVInHzD31lMO7KvO97Nlj/vl584D49FBUxwW8VHZ1ISrpOcOG8T92jx788xNPWBjeXaoU0Lkzf//bb26toxBCFEZ4OI/mbt2af/7lFw5mlv7MgUtwMH+Iu3AB+PVX4PHHeaqOli3zP2zOeaOsZj1qlMvPyxkkeHGyhAT+Yzt4ELh9W/0+ISG7wOnTPLva9u3WD9SiBa+h/v77DtehbVv+um9f/snq7t5V5595F/9F4MlDjh387l0OqL7/HsjKcrhuzjZuHFCzJjB2rJVCvXrxVyV4uXED+PxznqBOCCG8WLFiwJo1wMyZPBWVLwzog9VYr+uJ8yO/wPvvEapWLeSLpKXxJDMAR0da4PJOLDfzdM6LzWFqn3zCGyIjXVaHzEx1BdG8M94ra2rUqpxKGfAjqlTJsYP//DMfoGZNlywgZi+HhgOeOcNP+vlxIrQyuY29Q9aFEMKDlJFJu3cTdWp9l673HqK+6fXta3sAiDX//KMOj65Wze1zu+TmyP278NOdChPDhnHTXV45TXb2zrJ74QJPi1iAvB0/P25m3LqVu44aN+btycnAp5/y9++/R/B/NYubhG7d4teyhzLzYr9+PIW1h9j8PedWuzbQoAF3HG/eDKxYwdufftqldRRCCGeYOzd3jl8xhGIRhqM1ZviMhu+KFbzUy6pVQP369h80Pp4TIxcv5jDI35+b5X200SEjwYuT2UyKsjd46dIF+PdfYOdOoEMHh+vRtq0avAwdyttmzODp9OvWBZ5/uRjwcTUgLg44fpwX17AlORlYv56/79/f4To5k83fc149e3Kg9u+/QFQUb5PgRQihAfk/rOkAvIpbl5ohdHhfDl4eeIBzBRo2tO+gP/0ELFrE3z/zDAcuyrp3GiDBi7spwculS5yQ4uubv4zRyM8DKGhnZt6kXb2e0zwAHpHj5wdeBCgtzf41fX79lcvXqcM5OVry3nv8z/nzzzzyqGFDdY0nIYTwYhY/rLVsB7Q+yC3hISHWW14MBm5tURbfe/11IDoaGDNGvWFoiAQv7lapEgcsmZm82nHlyvnLXLsGpKdzt0yVKgV6GSVp98QJ7hX69lv+2rAhMGBAdiFzY42tUbqM+vf3aJdRgYSE8NeVK/mrtLoIIYqCihWBP//kD5ZKl09qKj/KluUuoU2bgLfe4g/G//zDn16DgtT3dA3SRudWUeLnpwYklrqO4uL4a8WKDqxhbqp8ebUF8PffeYFSgOdAMdfYY9Pt2/wPAHi8y6jA7txRV5iW4EUIUVT4+6tzsxABw4cDrVpxfl+3bjwJzJEjQGKiOmmMxknw4gm28l6U4EUpVwAJCWoL4muvcbdRnTpA+/a5ChFx68+uXbYPeOUKZwHff7+aAaw1+/fzpxOAJ/8TQoii5sYNfk8/d45zWf74AwgIAN54AzhzRrvv33lI8OIJkyfzDLoPPWT+eSV4UfomC2DuXH4JgLuLAP67nT8/VyG9nlt3OnZEzlSNltSvz8nD+/drr8tI0akTD7fat0+75yCEENaEhnIuS8+e/POAAdza8vnn9o8q1QDJefGELl2sP6+0yBQieBk2LHtUUfZ8Q/Xrc3K5SYpN6dKcg5OQwH/cyjSO1hQrVuA6eZyvL/f7CiFEUVamDA+wSEpS8/2KGGl58Ubt23P00alTgQ9RqRKnpijTxHzxBU81nS9jXRlxc+yY5YOdOMFjrIUQQmiDTldkAxdAghfPuHGDlwhYsMD8808+yUsD9OlTqJfx8+PBNc2aqS2I+ShzAlgLXkaN4u6lJUsKVR8hhBDCGaTbyBPi44HISF7t+OWXXfISCQn8KFuWW19iYnh7vvkCbLW8XLvGS2MbDPZ1KwkhhBAuJi0vnqCMIrpxA0hJMX3OYACOHrWdQGvD3LncTRQeznm2yvdz5+YpaCt4WbmS69SyJQ9XEkIIITxMWl48ISSEm0OSknhkUYMG6nOXL/NQNn9/00mHHGT32j9K8HL+PK8YXby46fM//8xftTq3ixBCiCJHghdPqV6dJw26eNE0eFGGSVetWqgFsuxe+6d8eWD8eOC++3jel9wSE4Ht2/n7fv0KXBchhBDCmSR48RQleFGCFYUThkk7bOpU89tXrODppNu0AWrWdF99hBBCCCsk58VTLM2y64QJ6pxmxQr+Kl1GQgghvIi0vHiKNwUvKSm8WFdKCtC1q7r9l1+AVauA3r3dVxchhBDCBglePKVvX6BFi/xLmDthXSOH7d0LPPIIUK8ecPKkur18ec78FUIIIbyIBC+eUrcuP/LyRM6LMuLozBkgPR0IDHTfawshhBAOkpwXbzNkCLd2KAGFO1SsyOscGY3AqVPAhQu8NMHs2e6rgxBCCGEnlwYvt27dQmRkJEJCQhASEoLIyEjcvn3bYvnMzEy8/fbbaNKkCUqUKIHKlStj0KBBiI+Pd2U1PWfpUuCjj9RlnwGein/OHB667C46nelkdT//zDPbKXO8CCGEEF7EpcHLwIEDERsbi02bNmHTpk2IjY1FZGSkxfJ3797FwYMH8d577+HgwYNYtWoVTp06hcfNzbZWFLz1FvDuu8Dp056uiWnwsnw5fy+jjIQQQnghl+W8HD9+HJs2bcLevXvRpk0bAMD8+fMRERGBkydPon7eRFUAISEhiIqKMtn27bffonXr1rh48SKqm0liTU9PR3p6es7Per0eAJBUyOn13aJyZeDSJV61uUED4OZNnhiualV1OWh3UVp6Vq8GDh/m1phu3Qq9TIEQQghhD+W+TXknTDWHXGThwoUUEhKSb3tISAgtWrTI7uNERUWRTqcjvV5v9vlJkyYRAHnIQx7ykIc85FEEHnFxcTZjA5e1vCQmJqJChQr5tleoUAGJiYl2HSMtLQ3jx4/HwIEDEWyhJWLChAkYN25czs9GoxE3b95EuXLloNPpClZ5C5KSklCtWjXExcVZrI+WFfXzA4r+Ocr5aV9RP8eifn5A0T9HV50fESE5ORmVK1e2Wdbh4GXy5MmYMmWK1TL79+8HALPBAxHZFVRkZmZiwIABMBqNmDVrlsVygYGBCMwztLd06dI2j18YwcHBRfIPUlHUzw8o+uco56d9Rf0ci/r5AUX/HF1xfiEhIXaVczh4GTlyJAYMGGC1TM2aNXHo0CFcuXIl33PXrl1DWFiY1f0zMzPRr18/nDt3Dlu2bCnSF18IIYQQjnE4eAkNDUVoaKjNchEREdDr9fj777/RunVrAMC+ffug1+vRrl07i/spgcvp06exdetWlCtXztEqCiGEEKIIc9lQ6YYNG+LRRx/F0KFDsXfvXuzduxdDhw5Fr169TEYaNWjQAKtXrwYAZGVloW/fvoiOjsZPP/0Eg8GAxMREJCYmIiMjw1VVtVtgYCAmTZqUr5uqqCjq5wcU/XOU89O+on6ORf38gKJ/jt5wfjoie8YkFczNmzcxatQorFu3DgDw+OOPY8aMGSY5KTqdDosXL8aQIUNw/vx51KpVy+yxtm7digcffNBVVRVCCCGERrg0eBFCCCGEcDZZ20gIIYQQmiLBixBCCCE0RYIXIYQQQmiKBC9CCCGE0BQJXvKYNWsWatWqhaCgIISHh2Pnzp1Wy2/fvh3h4eEICgrCfffdhzlz5rippgXjyPlt27YNOp0u3+PEiRNurLH9duzYgd69e6Ny5crQ6XRYs2aNzX20dv0cPUctXcOpU6figQceQKlSpVChQgX06dMHJ0+etLmflq5hQc5RS9dw9uzZaNq0ac7MqxEREdi4caPVfbR0/QDHz1FL18+cqVOnQqfTYcyYMVbLufs6SvCSy/LlyzFmzBhMnDgRMTEx6NixI3r06IGLFy+aLX/u3Dk89thj6NixI2JiYvDOO+9g1KhRWLlypZtrbh9Hz09x8uRJJCQk5Dzq1q3rpho7JiUlBc2aNcOMGTPsKq+16wc4fo4KLVzD7du3Y8SIEdi7dy+ioqKQlZWFbt26ISUlxeI+WruGBTlHhRauYdWqVfHJJ58gOjoa0dHRePjhh/HEE0/g6NGjZstr7foBjp+jQgvXL6/9+/dj3rx5aNq0qdVyHrmOdi/vfA9o3bo1DR8+3GRbgwYNaPz48WbLv/XWW9SgQQOTbcOGDaO2bdu6rI6F4ej5bd26lQDQrVu33FA75wJAq1evtlpGa9cvL3vOUcvX8OrVqwSAtm/fbrGM1q+hPeeo5WtIRFSmTBlasGCB2ee0fv0U1s5Rq9cvOTmZ6tatS1FRUdS5c2caPXq0xbKeuI7S8pItIyMDBw4cQLdu3Uy2d+vWDbt37za7z549e/KV7969O6Kjo5GZmemyuhZEQc5P0aJFC1SqVAldunTB1q1bXVlNt9LS9SssLV5DvV4PAChbtqzFMlq/hvaco0Jr19BgMGDZsmVISUlBRESE2TJav372nKNCa9dvxIgR6NmzJx555BGbZT1xHSV4yXb9+nUYDIZ8i0aGhYUhMTHR7D6JiYlmy2dlZeH69esuq2tBFOT8KlWqhHnz5mHlypVYtWoV6tevjy5dumDHjh3uqLLLaen6FZRWryERYdy4cejQoQMaN25ssZyWr6G956i1a3j48GGULFkSgYGBGD58OFavXo1GjRqZLavV6+fIOWrt+gHAsmXLcPDgQUydOtWu8p64jg4vzFjU6XQ6k5+JKN82W+XNbfcWjpxf/fr1TdahioiIQFxcHL744gt06tTJpfV0F61dP0dp9RqOHDkShw4dwq5du2yW1eo1tPcctXYN69evj9jYWNy+fRsrV67E4MGDsX37dos3dy1eP0fOUWvXLy4uDqNHj8bmzZsRFBRk937uvo7S8pItNDQUvr6++Vohrl69mi+iVFSsWNFseT8/P69bDbsg52dO27Ztcfr0aWdXzyO0dP2cyduv4euvv45169Zh69atqFq1qtWyWr2GjpyjOd58DQMCAlCnTh20atUKU6dORbNmzTB9+nSzZbV6/Rw5R3O8+fodOHAAV69eRXh4OPz8/ODn54ft27fjm2++gZ+fHwwGQ759PHEdJXjJFhAQgPDwcERFRZlsj4qKQrt27czuExERka/85s2b0apVK/j7+7usrgVRkPMzJyYmBpUqVXJ29TxCS9fPmbz1GhIRRo4ciVWrVmHLli0WF2nNTWvXsCDnaI63XkNziAjp6elmn9Pa9bPE2jma483Xr0uXLjh8+DBiY2NzHq1atcJzzz2H2NhY+Pr65tvHI9fRZanAGrRs2TLy9/enhQsX0rFjx2jMmDFUokQJOn/+PBERjR8/niIjI3PKnz17looXL05jx46lY8eO0cKFC8nf359WrFjhqVOwytHz++qrr2j16tV06tQpOnLkCI0fP54A0MqVKz11ClYlJydTTEwMxcTEEACaNm0axcTE0IULF4hI+9ePyPFz1NI1fPXVVykkJIS2bdtGCQkJOY+7d+/mlNH6NSzIOWrpGk6YMIF27NhB586do0OHDtE777xDPj4+tHnzZiLS/vUjcvwctXT9LMk72sgbrqMEL3nMnDmTatSoQQEBAdSyZUuTIYyDBw+mzp07m5Tftm0btWjRggICAqhmzZo0e/ZsN9fYMY6c36effkq1a9emoKAgKlOmDHXo0IHWr1/vgVrbRxmSmPcxePBgIioa18/Rc9TSNTR3XgBo8eLFOWW0fg0Lco5auoYvvvhizvtL+fLlqUuXLjk3dSLtXz8ix89RS9fPkrzBizdcRx1RdlaNEEIIIYQGSM6LEEIIITRFghchhBBCaIoEL0IIIYTQFAlehBBCCKEpErwIIYQQQlMkeBFCCCGEpkjwIoQQQghNkeBFCCGEEJoiwYsQQgghNEWCFyGEEEJoigQvQgghhNCU/wPOZs8FpSNrQQAAAABJRU5ErkJggg==", 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" ] @@ -570,7 +569,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.6" + "version": "3.13.1" } }, "nbformat": 4, diff --git a/examples/python-control_tutorial.ipynb b/examples/python-control_tutorial.ipynb index d1fc446bf..b5094a97d 100644 --- a/examples/python-control_tutorial.ipynb +++ b/examples/python-control_tutorial.ipynb @@ -348,24 +348,24 @@ { "data": { "text/plain": [ - "[[{'RiseTime': np.float64(0.6153902252990775),\n", - " 'SettlingTime': np.float64(89.02645259326653),\n", - " 'SettlingMin': NamedSignal(-0.13272846),\n", - " 'SettlingMax': NamedSignal(0.90059949),\n", - " 'Overshoot': NamedSignal(170.17984629),\n", - " 'Undershoot': np.float64(39.81853696610825),\n", - " 'Peak': np.float64(0.9005994876222034),\n", - " 'PeakTime': np.float64(2.3589958636464634),\n", - " 'SteadyStateValue': np.float64(0.33333333333333337)}],\n", - " [{'RiseTime': np.float64(0.6153902252990775),\n", - " 'SettlingTime': np.float64(73.6416969607896),\n", - " 'SettlingMin': NamedSignal(0.22760198),\n", - " 'SettlingMax': NamedSignal(1.13389338),\n", - " 'Overshoot': NamedSignal(70.08400657),\n", - " 'Undershoot': 0,\n", - " 'Peak': np.float64(1.13389337710215),\n", - " 'PeakTime': np.float64(6.564162403190159),\n", - " 'SteadyStateValue': np.float64(0.6666666666666665)}]]" + "[[{'RiseTime': 0.6153902252990775,\n", + " 'SettlingTime': 89.02645259326653,\n", + " 'SettlingMin': -0.13272845655369417,\n", + " 'SettlingMax': 0.9005994876222034,\n", + " 'Overshoot': 170.17984628666102,\n", + " 'Undershoot': 39.81853696610825,\n", + " 'Peak': 0.9005994876222034,\n", + " 'PeakTime': 2.3589958636464634,\n", + " 'SteadyStateValue': 0.33333333333333337}],\n", + " [{'RiseTime': 0.6153902252990775,\n", + " 'SettlingTime': 73.6416969607896,\n", + " 'SettlingMin': 0.2276019820782241,\n", + " 'SettlingMax': 1.13389337710215,\n", + " 'Overshoot': 70.08400656532254,\n", + " 'Undershoot': 0.0,\n", + " 'Peak': 1.13389337710215,\n", + " 'PeakTime': 6.564162403190159,\n", + " 'SteadyStateValue': 0.6666666666666665}]]" ] }, "execution_count": 7, From 8297a28f68a0274c9cd712d7f238f70ea8ac9484 Mon Sep 17 00:00:00 2001 From: Richard Murray Date: Sat, 25 Jan 2025 11:35:28 -0800 Subject: [PATCH 87/93] update userguide examples (copyedits + additional narrative) --- control/dtime.py | 3 +- control/stochsys.py | 4 +- doc/examples/kalman-pvtol.ipynb | 47 ++++--- examples/kincar-fusion.ipynb | 141 ++++++++++---------- examples/mhe-pvtol.ipynb | 219 ++++++++++++++++++++++---------- examples/pvtol-outputfbk.ipynb | 203 ++++++++++++++++++++--------- 6 files changed, 406 insertions(+), 211 deletions(-) diff --git a/control/dtime.py b/control/dtime.py index 481615671..11d2d90d3 100644 --- a/control/dtime.py +++ b/control/dtime.py @@ -62,7 +62,8 @@ def sample_system(sysc, Ts, method='zoh', alpha=None, prewarp_frequency=None, Notes ----- See `StateSpace.sample` or `TransferFunction.sample` for further - details. + details on implementation for state space and transfer function + systems, including available methods. Examples -------- diff --git a/control/stochsys.py b/control/stochsys.py index cd01fe291..95749f5b9 100644 --- a/control/stochsys.py +++ b/control/stochsys.py @@ -471,8 +471,8 @@ def create_estimator_iosystem( # Set the output matrices if C is not None: - # Make sure that we have the full system output - if not np.array_equal(sys.C, np.eye(sys.nstates)): + # Make sure we have full system output (allowing for numerical errors) + if not np.allclose(sys.C, np.eye(sys.nstates)): raise ValueError("System output must be full state") # Make sure that the output matches the size of RN diff --git a/doc/examples/kalman-pvtol.ipynb b/doc/examples/kalman-pvtol.ipynb index 150000c20..cef836d09 100644 --- a/doc/examples/kalman-pvtol.ipynb +++ b/doc/examples/kalman-pvtol.ipynb @@ -29,8 +29,12 @@ "metadata": {}, "source": [ "## System definition\n", - "The dynamics of the system\n", - "with disturbances on the $x$ and $y$ variables is given by\n", + "\n", + "We consider the dynamics of a planar vertical takeoff and landing (PVTOL) aircraft model:\n", + "\n", + "![PVTOL diagram](https://murray.cds.caltech.edu/images/murray.cds/7/7d/Pvtol-diagram.png)\n", + "\n", + "The dynamics of the system with disturbances on the $x$ and $y$ variables is given by\n", "$$\n", " \\begin{aligned}\n", " m \\ddot x &= F_1 \\cos\\theta - F_2 \\sin\\theta - c \\dot x + d_x, \\\\\n", @@ -62,19 +66,19 @@ "States (6): ['x0', 'x1', 'x2', 'x3', 'x4', 'x5']\n", "Parameters: ['m', 'J', 'r', 'g', 'c']\n", "\n", - "Update: \n", - "Output: \n", + "Update: \n", + "Output: \n", "\n", - "Forward: \n", - "Reverse: \n", + "Forward: \n", + "Reverse: \n", "\n", ": pvtol_noisy\n", "Inputs (7): ['F1', 'F2', 'Dx', 'Dy', 'Nx', 'Ny', 'Nth']\n", "Outputs (6): ['x0', 'x1', 'x2', 'x3', 'x4', 'x5']\n", "States (6): ['x0', 'x1', 'x2', 'x3', 'x4', 'x5']\n", "\n", - "Update: \n", - "Output: \n" + "Update: \n", + "Output: \n" ] } ], @@ -84,12 +88,12 @@ "from pvtol import pvtol, pvtol_noisy, plot_results\n", "\n", "# Find the equilibrium point corresponding to the origin\n", - "xe, ue = ct.find_eqpt(\n", + "xe, ue = ct.find_operating_point(\n", " pvtol, np.zeros(pvtol.nstates),\n", " np.zeros(pvtol.ninputs), [0, 0, 0, 0, 0, 0],\n", " iu=range(2, pvtol.ninputs), iy=[0, 1])\n", "\n", - "x0, u0 = ct.find_eqpt(\n", + "x0, u0 = ct.find_operating_point(\n", " pvtol, np.zeros(pvtol.nstates),\n", " np.zeros(pvtol.ninputs), np.array([2, 1, 0, 0, 0, 0]),\n", " iu=range(2, pvtol.ninputs), iy=[0, 1])\n", @@ -133,7 +137,7 @@ "source": [ "## Control system design\n", "\n", - "To design the control system, we first construct an estimator for the state (given the commanded inputs and measured outputs. Since this is a nonlinear system, we use the update law for the nominal system to compute the state update. We also make use of the linearization around the current state for the covariance update (using the function `pvtol.A(x, u)`, which is defined in `pvtol.py`, making this an extended Kalman filter)." + "To design the control system, we first construct an estimator for the state (given the commanded inputs and measured outputs). Since this is a nonlinear system, we use the update law for the nominal system to compute the state update. We also make use of the linearization around the current state for the covariance update (using the function `pvtol.A(x, u)`, which is defined in `pvtol.py`, making this an extended Kalman filter)." ] }, { @@ -151,8 +155,8 @@ "Outputs (6): ['xh0', 'xh1', 'xh2', 'xh3', 'xh4', 'xh5']\n", "States (42): ['x[0]', 'x[1]', 'x[2]', 'x[3]', 'x[4]', 'x[5]', 'x[6]', 'x[7]', 'x[8]', 'x[9]', 'x[10]', 'x[11]', 'x[12]', 'x[13]', 'x[14]', 'x[15]', 'x[16]', 'x[17]', 'x[18]', 'x[19]', 'x[20]', 'x[21]', 'x[22]', 'x[23]', 'x[24]', 'x[25]', 'x[26]', 'x[27]', 'x[28]', 'x[29]', 'x[30]', 'x[31]', 'x[32]', 'x[33]', 'x[34]', 'x[35]', 'x[36]', 'x[37]', 'x[38]', 'x[39]', 'x[40]', 'x[41]']\n", "\n", - "Update: \n", - "Output: \n" + "Update: \n", + "Output: \n" ] } ], @@ -298,8 +302,7 @@ " * xh2 <- sys[1].xh2\n", " * xh3 <- sys[1].xh3\n", " * xh4 <- sys[1].xh4\n", - " * xh5 <- sys[1].xh5\n", - "\n" + " * xh5 <- sys[1].xh5\n" ] } ], @@ -367,7 +370,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -387,6 +390,8 @@ "W = ct.white_noise(timepts, Qw)\n", "plt.plot(timepts, V[0], label=\"V[0]\")\n", "plt.plot(timepts, W[0], label=\"W[0]\")\n", + "plt.xlabel(\"Time $t$ [s]\")\n", + "plt.ylabel(\"Disturbance, sensor noise\")\n", "plt.legend();" ] }, @@ -519,7 +524,7 @@ "id": "2039578e", "metadata": {}, "source": [ - "Note the lag in tracking changes in the $\\dot x$ and $\\dot y$ states (varies from simulation to simulation, depending on the specific noise signal)." + "Note the (slight) lag in tracking changes in the $\\dot x$ and $\\dot y$ states (varies from simulation to simulation, depending on the specific noise signal)." ] }, { @@ -586,6 +591,14 @@ "plt.axis('equal')\n", "plt.legend(frameon=False);" ] + }, + { + "cell_type": "markdown", + "id": "ffd7d082-2add-4440-99d9-2bab551b51a0", + "metadata": {}, + "source": [ + "The warning here can be ignored. It comes from the way that the `pvtol` dynamics are defined." + ] } ], "metadata": { diff --git a/examples/kincar-fusion.ipynb b/examples/kincar-fusion.ipynb index da320a502..062345ad3 100644 --- a/examples/kincar-fusion.ipynb +++ b/examples/kincar-fusion.ipynb @@ -76,11 +76,11 @@ "Outputs (3): ['x', 'y', 'theta']\n", "States (3): ['x', 'y', 'theta']\n", "\n", - "Update: \n", - "Output: \n", + "Update: \n", + "Output: \n", "\n", - "Forward: \n", - "Reverse: \n" + "Forward: \n", + "Reverse: \n" ] } ], @@ -133,11 +133,10 @@ "\n", "# Create the desired trajectory between the initial and final condition\n", "Ts = 0.1\n", - "# Ts = 0.5\n", - "T = np.arange(0, Tf + Ts, Ts)\n", - "xd, ud = traj.eval(T)\n", + "timepts = np.arange(0, Tf + Ts, Ts)\n", + "xd, ud = traj.eval(timepts)\n", "\n", - "plot_lanechange(T, xd, ud)" + "plot_lanechange(timepts, xd, ud)" ] }, { @@ -160,7 +159,7 @@ "name": "stdout", "output_type": "stream", "text": [ - ": sys[1]\n", + ": sys[0]$sampled\n", "Inputs (2): ['u[0]', 'u[1]']\n", "Outputs (3): ['y[0]', 'y[1]', 'y[2]']\n", "States (3): ['x[0]', 'x[1]', 'x[2]']\n", @@ -170,9 +169,9 @@ " [ 0.0000000e+00 1.0000000e+00 1.0000000e+00]\n", " [ 0.0000000e+00 0.0000000e+00 1.0000000e+00]]\n", "\n", - "B = [[0.1 0. ]\n", - " [0. 0. ]\n", - " [0. 0.33333333]]\n", + "B = [[ 9.99999999e-02 -8.33407417e-08]\n", + " [ 0.00000000e+00 1.66666667e-01]\n", + " [ 0.00000000e+00 3.33333333e-01]]\n", "\n", "C = [[1. 0. 0.]\n", " [0. 1. 0.]\n", @@ -190,13 +189,16 @@ "#\n", "\n", "# Linearize about the starting point\n", - "linsys = ct.linearize(vehicle, x0, u0)\n", + "veh_lin = ct.linearize(vehicle, x0, u0)\n", "\n", "# Create a discrete-time model by hand\n", - "Ad = np.eye(linsys.nstates) + linsys.A * Ts\n", - "Bd = linsys.B * Ts\n", - "discsys = ct.ss(Ad, Bd, np.eye(linsys.nstates), 0, dt=Ts)\n", - "print(discsys)" + "veh_lin_dt = ct.sample_system(veh_lin, Ts)\n", + "\n", + "# Update the model to have full-state output\n", + "# veh_lin_dt = ct.ss(\n", + "# veh_lin_dt.A, veh_lin_dt.B, np.eye(veh_lin_dt.nstates), 0, dt=veh_lin_dt.dt,\n", + "# name=\"vehicle-lin-dt\")\n", + "print(veh_lin_dt)" ] }, { @@ -217,7 +219,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -228,23 +230,24 @@ ], "source": [ "# Sensor #1: longitudinal\n", - "C_lon = np.eye(2, discsys.nstates)\n", + "C_lon = np.eye(2, veh_lin_dt.nstates)\n", "Rw_lon = np.diag([0.1 ** 2, 1 ** 2])\n", - "W_lon = ct.white_noise(T, Rw_lon, dt=Ts)\n", + "W_lon = ct.white_noise(timepts, Rw_lon, dt=Ts)\n", "\n", "# Sensor #2: lateral\n", - "C_lat = np.eye(2, discsys.nstates)\n", + "C_lat = np.eye(2, veh_lin_dt.nstates)\n", "Rw_lat = np.diag([1 ** 2, 0.1 ** 2])\n", - "W_lat = ct.white_noise(T, Rw_lat, dt=Ts)\n", + "W_lat = ct.white_noise(timepts, Rw_lat, dt=Ts)\n", "\n", "# Plot the noisy signals\n", "plt.subplot(2, 1, 1)\n", "Y = xd[0:2] + W_lon\n", - "plt.plot(Y[0], Y[1])\n", - "plt.plot(xd[0], xd[1], **xdstyle)\n", + "plt.plot(Y[0], Y[1], label=\"measured\")\n", + "plt.plot(xd[0], xd[1], **xdstyle, label=\"actual\")\n", "plt.xlabel(\"$x$ position [m]\")\n", "plt.ylabel(\"$y$ position [m]\")\n", "plt.title(\"Sensor #1\")\n", + "plt.legend()\n", " \n", "plt.subplot(2, 1, 2)\n", "Y = xd[0:2] + W_lat\n", @@ -282,8 +285,8 @@ "States (12): ['xhat[0]', 'xhat[1]', 'xhat[2]', 'P[0,0]', 'P[0,1]', 'P[0,2]', 'P[1,0]', 'P[1,1]', 'P[1,2]', 'P[2,0]', 'P[2,1]', 'P[2,2]']\n", "dt = 0.1\n", "\n", - "Update: ._estim_update at 0x115c63e20>\n", - "Output: ._estim_output at 0x115c63880>\n" + "Update: ._estim_update at 0x141142d40>\n", + "Output: ._estim_output at 0x141142700>\n" ] } ], @@ -301,7 +304,7 @@ "C = np.vstack([C_lon, C_lat])\n", "Rw = sp.linalg.block_diag(Rw_lon, Rw_lat)\n", "\n", - "estim = ct.create_estimator_iosystem(discsys, Rv, Rw, C=C, P0=P0)\n", + "estim = ct.create_estimator_iosystem(veh_lin_dt, Rv, Rw, C=C, P0=P0)\n", "print(estim)" ] }, @@ -310,7 +313,7 @@ "id": "d9e2e618", "metadata": {}, "source": [ - "Finally, we estimate the position of the vehicle based on sensor measurements. We assume that the input to the vehicle (velocity and steering angle) is available, though we can also explore what happens if that information is not available (see commented out code).\n", + "Finally, we estimate the position of the vehicle based on sensor measurements. We assume that the input to the vehicle (velocity and steering angle) is available, though we can also explore what happens if that information is not available (see commented out code below).\n", "\n", "We also carry out a prediction of the position of the vehicle by turning off the correction term in the Kalman filter." ] @@ -323,7 +326,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -340,25 +343,28 @@ "X0 = np.hstack([xd[:, 0], P0.reshape(-1)])\n", "\n", "# Run the estimator on the trajectory\n", - "estim_resp = ct.input_output_response(estim, T, U, X0)\n", + "estim_resp = ct.input_output_response(estim, timepts, U, X0)\n", "\n", "# Run a prediction to see what happens next\n", - "T_predict = np.arange(T[-1], T[-1] + 4 + Ts, Ts)\n", - "U_predict = np.outer(U[:, -1], np.ones_like(T_predict))\n", + "timepts_predict = np.arange(timepts[-1], timepts[-1] + 4 + Ts, Ts)\n", + "U_predict = np.outer(U[:, -1], np.ones_like(timepts_predict))\n", "predict_resp = ct.input_output_response(\n", - " estim, T_predict, U_predict, estim_resp.states[:, -1],\n", + " estim, timepts_predict, U_predict, estim_resp.states[:, -1],\n", " params={'correct': False})\n", "\n", "# Plot the estimated trajectory versus the actual trajectory\n", "plt.subplot(2, 1, 1)\n", "plt.errorbar(\n", " estim_resp.time, estim_resp.outputs[0], \n", - " estim_resp.states[estim.find_state('P[0,0]')], fmt='b-', **ebarstyle)\n", + " estim_resp.states[estim.find_state('P[0,0]')], \n", + " fmt='b-', **ebarstyle, label=\"estimated\")\n", "plt.errorbar(\n", " predict_resp.time, predict_resp.outputs[0], \n", - " predict_resp.states[estim.find_state('P[0,0]')], fmt='r-', **ebarstyle)\n", - "plt.plot(T, xd[0], 'k--')\n", + " predict_resp.states[estim.find_state('P[0,0]')],\n", + " fmt='r-', **ebarstyle, label=\"predicted\")\n", + "plt.plot(timepts, xd[0], 'k--', label=\"actual\")\n", "plt.ylabel(\"$x$ position [m]\")\n", + "plt.legend()\n", "\n", "plt.subplot(2, 1, 2)\n", "plt.errorbar(\n", @@ -368,9 +374,9 @@ " predict_resp.time, predict_resp.outputs[1], \n", " predict_resp.states[estim.find_state('P[1,1]')], fmt='r-', **ebarstyle)\n", "# lims = plt.axis(); plt.axis([lims[0], lims[1], -5, 5])\n", - "plt.plot(T, xd[1], 'k--');\n", + "plt.plot(timepts, xd[1], 'k--');\n", "plt.ylabel(\"$y$ position [m]\")\n", - "plt.xlabel(\"Time $t$ [s]\");" + "plt.xlabel(\"Time $t$ [sec]\");" ] }, { @@ -389,7 +395,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -403,12 +409,16 @@ "plt.subplot(2, 1, 1)\n", "plt.errorbar(\n", " estim_resp.time, estim_resp.outputs[0] - xd[0], \n", - " estim_resp.states[estim.find_state('P[0,0]')], fmt='b-', **ebarstyle)\n", + " estim_resp.states[estim.find_state('P[0,0]')],\n", + " fmt='b-', **ebarstyle, label=\"estimated\")\n", "plt.errorbar(\n", " predict_resp.time, predict_resp.outputs[0] - (xd[0] + xd[0, -1]), \n", - " predict_resp.states[estim.find_state('P[0,0]')], fmt='r-', **ebarstyle)\n", + " predict_resp.states[estim.find_state('P[0,0]')],\n", + " fmt='r-', **ebarstyle, label=\"predicted\")\n", "lims = plt.axis(); plt.axis([lims[0], lims[1], -0.2, 0.2])\n", "# lims = plt.axis(); plt.axis([lims[0], lims[1], -2, 0.2])\n", + "plt.ylabel(\"$x$ error [m]\")\n", + "plt.legend(loc='lower right')\n", "\n", "plt.subplot(2, 1, 2)\n", "plt.errorbar(\n", @@ -417,7 +427,9 @@ "plt.errorbar(\n", " predict_resp.time, predict_resp.outputs[1] - xd[1, -1], \n", " predict_resp.states[estim.find_state('P[1,1]')], fmt='r-', **ebarstyle)\n", - "lims = plt.axis(); plt.axis([lims[0], lims[1], -0.2, 0.2]);" + "lims = plt.axis(); plt.axis([lims[0], lims[1], -0.2, 0.2])\n", + "plt.ylabel(\"$y$ error [m]\")\n", + "plt.xlabel(\"Time $t$ [sec]\");" ] }, { @@ -428,6 +440,7 @@ "### Things to try\n", "\n", "To gain a bit more insight into sensor fusion, you can try the following:\n", + "\n", "* Remove the input (and update P0)\n", "* Change the sampling rate" ] @@ -450,7 +463,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -461,14 +474,14 @@ ], "source": [ "# System matrices\n", - "A, B, F = discsys.A, discsys.B, discsys.B\n", + "A, B, F = veh_lin_dt.A, veh_lin_dt.B, veh_lin_dt.B\n", "\n", "# Create an array to store the results\n", - "xhat = np.zeros((discsys.nstates, T.size))\n", - "P = np.zeros((discsys.nstates, discsys.nstates, T.size))\n", + "xhat = np.zeros((veh_lin_dt.nstates, timepts.size))\n", + "P = np.zeros((veh_lin_dt.nstates, veh_lin_dt.nstates, timepts.size))\n", "\n", "# Update the estimates at each time\n", - "for i, t in enumerate(T):\n", + "for i, t in enumerate(timepts):\n", " # Prediction step\n", " if i == 0:\n", " # Use the initial condition\n", @@ -488,14 +501,16 @@ " # xhat[:, i], P[:, :, i] = xkkm1, Pkkm1 # For comparison to Kalman form\n", " \n", "plt.subplot(2, 1, 1)\n", - "plt.errorbar(T, xhat[0], P[0, 0], fmt='b-', **ebarstyle)\n", - "plt.plot(T, xd[0], 'k--')\n", + "plt.errorbar(timepts, xhat[0], P[0, 0], fmt='b-', **ebarstyle, label=\"estimated\")\n", + "plt.plot(timepts, xd[0], 'k--', label=\"actual\")\n", "plt.ylabel(\"$x$ position [m]\")\n", + "plt.legend()\n", "\n", "plt.subplot(2, 1, 2)\n", - "plt.errorbar(T, xhat[1], P[1, 1], fmt='b-', **ebarstyle)\n", - "plt.plot(T, xd[1], 'k--')\n", - "plt.ylabel(\"$x$ position [m]\");" + "plt.errorbar(timepts, xhat[1], P[1, 1], fmt='b-', **ebarstyle)\n", + "plt.plot(timepts, xd[1], 'k--')\n", + "plt.ylabel(\"$x$ position [m]\")\n", + "plt.xlabel(\"Time $t$ [sec]\");" ] }, { @@ -514,7 +529,7 @@ "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", "text/plain": [ "
" ] @@ -526,14 +541,18 @@ "source": [ "# Plot the estimated errors (and compare to Kalman form)\n", "plt.subplot(2, 1, 1)\n", - "plt.errorbar(T, xhat[0] - xd[0], P[0, 0], fmt='b-', **ebarstyle)\n", - "plt.plot(estim_resp.time, estim_resp.outputs[0] - xd[0], 'r--')\n", + "plt.errorbar(timepts, xhat[0] - xd[0], P[0, 0], fmt='b-', **ebarstyle, label=\"predictor-corrector\")\n", + "plt.plot(estim_resp.time, estim_resp.outputs[0] - xd[0], 'r--', label=\"Kalman filter\")\n", "lims = plt.axis(); plt.axis([lims[0], lims[1], -0.2, 0.2])\n", + "plt.ylabel(\"$x$ error [m]\")\n", + "plt.legend()\n", "\n", "plt.subplot(2, 1, 2)\n", - "plt.errorbar(T, xhat[1] - xd[1], P[1, 1], fmt='b-', **ebarstyle)\n", + "plt.errorbar(timepts, xhat[1] - xd[1], P[1, 1], fmt='b-', **ebarstyle)\n", "plt.plot(estim_resp.time, estim_resp.outputs[1] - xd[1], 'r--')\n", - "lims = plt.axis(); plt.axis([lims[0], lims[1], -0.2, 0.2]);" + "lims = plt.axis(); plt.axis([lims[0], lims[1], -0.2, 0.2])\n", + "plt.ylabel(\"$y$ error [m]\")\n", + "plt.xlabel(\"Time $t$ [sec]\");" ] }, { @@ -543,14 +562,6 @@ "source": [ "Note that the estimates are not the same! It turns out that to get the correspondence of the two formulations, we need to define $\\hat{x}_\\text{KF}(k) = \\hat{x}_\\text{PC}(k|k-1)$ (see commented out code above)." ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "0796fc56", - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { diff --git a/examples/mhe-pvtol.ipynb b/examples/mhe-pvtol.ipynb index a832df2aa..734cd062b 100644 --- a/examples/mhe-pvtol.ipynb +++ b/examples/mhe-pvtol.ipynb @@ -9,7 +9,7 @@ "\n", "Richard M. Murray, 24 Feb 2023\n", "\n", - "In this notebook we illustrate the implementation of moving horizon estimation (MHE)" + "In this notebook we illustrate the implementation of moving horizon estimation (MHE)." ] }, { @@ -35,8 +35,11 @@ "source": [ "## System Description\n", "\n", - "The dynamics of the system\n", - "with disturbances on the $x$ and $y$ variables is given by\n", + "We consider a planar vertical takeoff and landing (PVTOL) aircraft model:\n", + "\n", + "![PVTOL diagram](https://murray.cds.caltech.edu/images/murray.cds/7/7d/Pvtol-diagram.png)\n", + "\n", + "The dynamics of the system with disturbances on the $x$ and $y$ variables is given by\n", "\n", "$$\n", " \\begin{aligned}\n", @@ -69,20 +72,21 @@ "Inputs (2): ['F1', 'F2']\n", "Outputs (6): ['x0', 'x1', 'x2', 'x3', 'x4', 'x5']\n", "States (6): ['x0', 'x1', 'x2', 'x3', 'x4', 'x5']\n", + "Parameters: ['m', 'J', 'r', 'g', 'c']\n", "\n", - "Update: \n", - "Output: \n", + "Update: \n", + "Output: \n", "\n", - "Forward: \n", - "Reverse: \n", + "Forward: \n", + "Reverse: \n", "\n", ": pvtol_noisy\n", "Inputs (7): ['F1', 'F2', 'Dx', 'Dy', 'Nx', 'Ny', 'Nth']\n", "Outputs (6): ['x0', 'x1', 'x2', 'x3', 'x4', 'x5']\n", "States (6): ['x0', 'x1', 'x2', 'x3', 'x4', 'x5']\n", "\n", - "Update: \n", - "Output: \n" + "Update: \n", + "Output: \n" ] } ], @@ -90,7 +94,6 @@ "# pvtol = nominal system (no disturbances or noise)\n", "# noisy_pvtol = pvtol w/ process disturbances and sensor noise\n", "from pvtol import pvtol, pvtol_noisy, plot_results\n", - "import pvtol as pvt\n", "\n", "# Find the equiblirum point corresponding to the origin\n", "xe, ue = ct.find_eqpt(\n", @@ -117,7 +120,9 @@ "id": "5771ab93", "metadata": {}, "source": [ - "### Control Design" + "### Control Design\n", + "\n", + "We first synthesize an LQR controller that we will use for trajectory tracking, using a physically motivated weighting:" ] }, { @@ -130,20 +135,56 @@ "name": "stdout", "output_type": "stream", "text": [ - ": sys[4]\n", + ": sys[2]\n", "Inputs (13): ['xd[0]', 'xd[1]', 'xd[2]', 'xd[3]', 'xd[4]', 'xd[5]', 'ud[0]', 'ud[1]', 'Dx', 'Dy', 'Nx', 'Ny', 'Nth']\n", "Outputs (8): ['x0', 'x1', 'x2', 'x3', 'x4', 'x5', 'F1', 'F2']\n", "States (6): ['pvtol_noisy_x0', 'pvtol_noisy_x1', 'pvtol_noisy_x2', 'pvtol_noisy_x3', 'pvtol_noisy_x4', 'pvtol_noisy_x5']\n", "\n", - "Update: .updfcn at 0x167b58dc0>\n", - "Output: .outfcn at 0x167b58e50>\n" + "Subsystems (2):\n", + " * ['x0', 'x1', 'x2', 'x3', 'x4', 'x5']>\n", + " * ['F1', 'F2']>\n", + "\n", + "Connections:\n", + " * pvtol_noisy.F1 <- sys[1].F1\n", + " * pvtol_noisy.F2 <- sys[1].F2\n", + " * pvtol_noisy.Dx <- Dx\n", + " * pvtol_noisy.Dy <- Dy\n", + " * pvtol_noisy.Nx <- Nx\n", + " * pvtol_noisy.Ny <- Ny\n", + " * pvtol_noisy.Nth <- Nth\n", + " * sys[1].xd[0] <- xd[0]\n", + " * sys[1].xd[1] <- xd[1]\n", + " * sys[1].xd[2] <- xd[2]\n", + " * sys[1].xd[3] <- xd[3]\n", + " * sys[1].xd[4] <- xd[4]\n", + " * sys[1].xd[5] <- xd[5]\n", + " * sys[1].ud[0] <- ud[0]\n", + " * sys[1].ud[1] <- ud[1]\n", + " * sys[1].x0 <- pvtol_noisy.x0\n", + " * sys[1].x1 <- pvtol_noisy.x1\n", + " * sys[1].x2 <- pvtol_noisy.x2\n", + " * sys[1].x3 <- pvtol_noisy.x3\n", + " * sys[1].x4 <- pvtol_noisy.x4\n", + " * sys[1].x5 <- pvtol_noisy.x5\n", + "\n", + "Outputs:\n", + " * x0 <- pvtol_noisy.x0\n", + " * x1 <- pvtol_noisy.x1\n", + " * x2 <- pvtol_noisy.x2\n", + " * x3 <- pvtol_noisy.x3\n", + " * x4 <- pvtol_noisy.x4\n", + " * x5 <- pvtol_noisy.x5\n", + " * F1 <- sys[1].F1\n", + " * F2 <- sys[1].F2\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "/Users/murray/src/python-control/murrayrm/control/statefbk.py:783: UserWarning: cannot verify system output is system state\n", + "/Users/murray/Library/CloudStorage/Dropbox/macosx/src/python-control/murrayrm/control/statefbk.py:788: UserWarning: cannot verify system output is system state\n", " warnings.warn(\"cannot verify system output is system state\")\n" ] } @@ -200,7 +241,7 @@ "outputs": [ { "data": { - 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\n", + "image/png": 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", "text/plain": [ "
" ] @@ -222,6 +263,8 @@ "# plt.plot(timepts, V0[0], 'b--', label=\"V[0]\")\n", "plt.plot(timepts, V[0], label=\"V[0]\")\n", "plt.plot(timepts, W[0], label=\"W[0]\")\n", + "plt.xlabel(\"Time $t$ [s]\")\n", + "plt.ylabel(\"Disturbance, sensor noise\")\n", "plt.legend();" ] }, @@ -233,7 +276,7 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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", 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\n", 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", "text/plain": [ "
" ] @@ -291,7 +334,7 @@ "plt.xlabel('$x$ [m]')\n", "plt.ylabel('$y$ [m]')\n", "plt.axis('equal')\n", - "plt.legend(frameon=False)\n", + "plt.legend()\n", "\n", "plt.figure()\n", "plot_results(timepts, lqr_resp.states, lqr_resp.outputs[6:8]);" @@ -315,7 +358,12 @@ " label=actual_label.format(i=i))\n", " plt.plot(timepts[start:], est_states[i, start:], 'b', \n", " label=estimated_label.format(i=i))\n", + " if i % 3 == 0:\n", + " plt.ylabel(\"State, estimate\")\n", + " if i > 2:\n", + " plt.xlabel(\"Time $t$ [s]\")\n", " plt.legend()\n", + " plt.gcf().align_labels()\n", " plt.tight_layout()\n", " \n", "# Define a function to plot out all of the relevant signals\n", @@ -356,6 +404,7 @@ " plt.ylabel(f'W[{i}]')\n", " plt.xlabel('Time [s]')\n", "\n", + " plt.gcf().align_labels()\n", " plt.tight_layout()" ] }, @@ -364,7 +413,9 @@ "id": "73dd9be3", "metadata": {}, "source": [ - "## State Estimation" + "## State Estimation\n", + "\n", + "We first construct a standard linear estimator (Kalman filter). To do so, we create a new nonlinear system that has limited outputs (the original system had full state output):" ] }, { @@ -375,9 +426,8 @@ "outputs": [], "source": [ "# Create a new system with only x, y, theta as outputs\n", - "# TODO: add this to pvtol.py?\n", - "sys = ct.NonlinearIOSystem(\n", - " pvt._noisy_update, lambda t, x, u, params: x[0:3], name=\"pvtol_noisy\",\n", + "sys = ct.nlsys(\n", + " pvtol_noisy.updfcn, lambda t, x, u, params: x[0:3], name=\"pvtol_noisy\",\n", " states = [f'x{i}' for i in range(6)],\n", " inputs = ['F1', 'F2'] + ['Dx', 'Dy'],\n", " outputs = ['x', 'y', 'theta']\n", @@ -394,15 +444,14 @@ "name": "stdout", "output_type": "stream", "text": [ - ": sys[7]\n", + ": sys[5]\n", "Inputs (5): ['y[0]', 'y[1]', 'y[2]', 'F1', 'F2']\n", "Outputs (6): ['xhat[0]', 'xhat[1]', 'xhat[2]', 'xhat[3]', 'xhat[4]', 'xhat[5]']\n", "States (42): ['xhat[0]', 'xhat[1]', 'xhat[2]', 'xhat[3]', 'xhat[4]', 'xhat[5]', 'P[0,0]', 'P[0,1]', 'P[0,2]', 'P[0,3]', 'P[0,4]', 'P[0,5]', 'P[1,0]', 'P[1,1]', 'P[1,2]', 'P[1,3]', 'P[1,4]', 'P[1,5]', 'P[2,0]', 'P[2,1]', 'P[2,2]', 'P[2,3]', 'P[2,4]', 'P[2,5]', 'P[3,0]', 'P[3,1]', 'P[3,2]', 'P[3,3]', 'P[3,4]', 'P[3,5]', 'P[4,0]', 'P[4,1]', 'P[4,2]', 'P[4,3]', 'P[4,4]', 'P[4,5]', 'P[5,0]', 'P[5,1]', 'P[5,2]', 'P[5,3]', 'P[5,4]', 'P[5,5]']\n", "\n", - "Update: ._estim_update at 0x1685997e0>\n", - "Output: ._estim_output at 0x16859a4d0>\n", - "xe=array([ 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00,\n", - " -1.766654e-27, 0.000000e+00]), P0=array([[1., 0., 0., 0., 0., 0.],\n", + "Update: ._estim_update at 0x1533a9bc0>\n", + "Output: ._estim_output at 0x1533a9620>\n", + "xe=array([0., 0., 0., 0., 0., 0.]), P0=array([[1., 0., 0., 0., 0., 0.],\n", " [0., 1., 0., 0., 0., 0.],\n", " [0., 0., 1., 0., 0., 0.],\n", " [0., 0., 0., 1., 0., 0.],\n", @@ -412,7 +461,7 @@ }, { "data": { - "image/png": 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\n", 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", "text/plain": [ "
" ] @@ -441,12 +490,22 @@ "plot_state_comparison(timepts, kf_resp.outputs, lqr_resp.states)" ] }, + { + "cell_type": "markdown", + "id": "6417b46d-b527-47c3-a13c-0a8c0d9006d9", + "metadata": {}, + "source": [ + "We see that the Kalman filter does a good job of estimating most states, but the estimates for $x_2$ ($y$) and $x_4$ ($\\dot y$) are not very close." + ] + }, { "cell_type": "markdown", "id": "654dde1b", "metadata": {}, "source": [ - "### Extended Kalman filter" + "### Extended Kalman filter\n", + "\n", + "To try to improve the performance of the estimator, we construct an extended Kalman filter, which uses the linearization of the dynamics at the current state rather than a fixed linearization." ] }, { @@ -459,13 +518,13 @@ "name": "stdout", "output_type": "stream", "text": [ - ": sys[8]\n", + ": sys[6]\n", "Inputs (8): ['x0', 'x1', 'x2', 'x3', 'x4', 'x5', 'F1', 'F2']\n", "Outputs (6): ['xh0', 'xh1', 'xh2', 'xh3', 'xh4', 'xh5']\n", "States (42): ['x[0]', 'x[1]', 'x[2]', 'x[3]', 'x[4]', 'x[5]', 'x[6]', 'x[7]', 'x[8]', 'x[9]', 'x[10]', 'x[11]', 'x[12]', 'x[13]', 'x[14]', 'x[15]', 'x[16]', 'x[17]', 'x[18]', 'x[19]', 'x[20]', 'x[21]', 'x[22]', 'x[23]', 'x[24]', 'x[25]', 'x[26]', 'x[27]', 'x[28]', 'x[29]', 'x[30]', 'x[31]', 'x[32]', 'x[33]', 'x[34]', 'x[35]', 'x[36]', 'x[37]', 'x[38]', 'x[39]', 'x[40]', 'x[41]']\n", "\n", - "Update: \n", - "Output: \n" + "Update: \n", + "Output: \n" ] } ], @@ -524,7 +583,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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", 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" ] @@ -540,6 +599,24 @@ "plot_state_comparison(timepts, ekf_resp.outputs, lqr_resp.states)" ] }, + { + "cell_type": "markdown", + "id": "b4ee15d5-fc01-40d1-aaf6-194d4c734c80", + "metadata": {}, + "source": [ + "This estimator does a considerably better job, though still with fairly significant errors (~15%) in the $\\dot y$ estimate." + ] + }, + { + "cell_type": "markdown", + "id": "09c3c9db-f781-4009-897a-da5fe93d286b", + "metadata": {}, + "source": [ + "## Fixed horizon, maximum likelihood estimator (MLE)\n", + "\n", + "We now create an estimator that tries to find the disturbances and noise that maximumize the likelihood of the signals given a Gaussian noise model. This estimator will compute the estimated state over a finite horizon." + ] + }, { "cell_type": "code", "execution_count": 12, @@ -551,13 +628,13 @@ "output_type": "stream", "text": [ "Summary statistics:\n", - "* Cost function calls: 5051\n", - "* Final cost: 354.3319137685172\n" + "* Cost function calls: 5050\n", + "* Final cost: 485.715540533845\n" ] }, { "data": { - "image/png": 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\n", 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", 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" ] @@ -586,7 +663,7 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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", 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" ] @@ -611,13 +688,13 @@ "output_type": "stream", "text": [ "Summary statistics:\n", - "* Cost function calls: 9464\n", - "* Final cost: 212754409.97292745\n" + "* Cost function calls: 10947\n", + "* Final cost: 212754409.96759257\n" ] }, { "data": { - "image/png": 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\n", 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", 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" ] @@ -645,7 +722,7 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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", 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" ] @@ -665,7 +742,7 @@ "source": [ "### Bounded disturbances\n", "\n", - "Another thing that the MHE can handled is input distributions that are bounded. We implement that here by carrying out the optimal estimation problem with constraints." + "Another thing that the maximum likelihood estimator can handle is input distributions that are bounded. We implement that here by carrying out the optimal estimation problem with constraints." ] }, { @@ -676,7 +753,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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", "text/plain": [ "
" ] @@ -691,6 +768,8 @@ "plt.plot(timepts, V[0], label=\"V[0]\")\n", "plt.plot(timepts, V_clipped[0], label=\"V[0] clipped\")\n", "plt.plot(timepts, W[0], label=\"W[0]\")\n", + "plt.xlabel(\"Time $t$ [s]\")\n", + "plt.ylabel(\"Disturbance, sensor noise\")\n", "plt.legend();" ] }, @@ -705,14 +784,14 @@ "output_type": "stream", "text": [ "Summary statistics:\n", - "* Cost function calls: 3572\n", - "* Constraint calls: 3756\n", - "* Final cost: 531.7451775567271\n" + "* Cost function calls: 3896\n", + "* Constraint calls: 4082\n", + "* Final cost: 715.5190193022809\n" ] }, { "data": { - "image/png": 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\n", 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1apXjPrNnz6ZixYpMnz4dgOrVq3PgwAGmTZtWoKQO4KOPZK33+fMwdizMnFmgw1m0jIwM3QdT2bJlzR2O2ZR4cPvj2rVreHl5qVuxj3FygrfegjFjZLOFsDAozn8iVW4kT09PYmJiSE9Px97e3tzhWJZq1eDff+XvS5fC66+bN55CkJ6eTnJyMr6+vjg7O5s7HLMw1mdNkUqHExISAHB3d891m927d9OxY8cs6zp16sSBAwdybL+SkpJCYmJiliU3zs7wXeffAJg1S35RKq4y/5bFtYA9KvNvUFzbRz3J0KHg5gZnzsCKFeaOxrxUuZEyb7tmZGSYORIL9OyzD3+fO9d8cRSizPdJcb9db4zPmiKT1AkhCA8Pp0WLFtSqVSvX7eLi4vD29s6yztvbm/T0dOLj47NtHxERgaurq27Jc8qW+/dpu+g1BiML0qCuMSQv+FlWgRdT6taJ+hs8iYsLjBwpf//kExDCvPEUBcX9PVPcr79ABg2C+HjZQDUhQf5eTBT3940xrr/IJHUjRozg2LFjLF269InbPn7h4sGnSE5/kHHjxpGQkKBboqKicj/w/fswciSfV56NH1f4764vEwdGgacndO8OK1cadlGKUkyMHAklS8KRI/BYqwpFUQxVtiwcOiRvw3p4mDsaxYIUiaTu//7v/1i1ahVbtmx54gTP5cqVIy4uLsu6a9euYWdnl2MbFkdHR90ULU+cqsXNDT7+GNf/DjL7G1n9+SXh7E+tA6tXw4QJ8KAho6IoD5Ut+7DpzyefmDcWRbFYaWlw+jTcugU1a0Ix6zCgFJxZ3zFCCEaMGMGKFSvYvHkzAQEBT9wnJCSEjRs3Zlm3YcMGGjZsaLwGuRoNz/xfAH36gBZbBjy1g9T1W2SXWC8v45xDUaxMeLi8Y/TPP7Bjh7mjURQLdOkSVK8uxwjKdP8+nDtnvpgUi2LWpO6NN95g8eLF/PTTT7i4uBAXF0dcXBz37t3TbTNu3Dj69u2rezx06FAuXbpEeHg4p06dYsGCBcyfP5+3337b6PFNny5rvk/850TE7jZGP75iGkuXLsXJyYno6GjdukGDBlGnTh1dZxzF+Pz8Hg5pomrrLI8qN0VA5p2gzMqDbdugfHl46SXzxaTkqaiVG7MmdbNmzSIhIYE2bdrg4+OjW5YtW6bbJjY2lsuXL+seBwQEsGbNGrZu3Uq9evX48MMP+eabbwo8nElOPD3hf/+Tv3/8sayoIz3d6OdRjKt3794EBgYSEREBwOTJk1m/fj1r167F1dXVzNFZtzFj5B2jdetkkyDFcqhyUwRcvSp/ZnYGrFkTkpJkYTp40HxxKbkqauXGrOPUCT26yS1cuDDbutatW3OokD4xevWSQwWtWgUDmp1id3pjbC9fKL6NV+/ezf05W1s5cJk+29rYZJ0CJ7dtcxmEOi8ajYaPP/6YF154AV9fX77++mt27NiBn58fAH/++SdvvfUWWq2Wd999l0GDBhl8DiVnVapA795yIOKICPj1V3NHVERYQbnp0aMHW7dupX379vz2228GH1/RQ2ZNXWZS5+EBzz8vP4TmzoU5c8wXmzlYeLmJiooiLCxM1+5/woQJvPjiiwafwyCimElISBCASEhI0Huf6GghXFyEACH+oZkQq1ebMMKi4d69eyIyMlLcu3cv6xNyxIqcly5dsm7r7Jz7tq1bZ93WwyPn7Qqgfv36wsHBQWzdulW3Li0tTVStWlVcuXJFJCYmiqeeekrcuHEjz+Pk+rcQ+Xs/FdS2bdvEM888I3x8fAQgVq5c+cR9tm7dKho0aCAcHR1FQECAmDVrlkHnNOQ6jx+XL51GI8SpUwadxuJZa7kRQojNmzeLVatWieeff/6JxyhqZcZcDL7WyZPl6zd48MN1W7bIdaVKCZGYaJI4zc1ay01MTIw4fPiwEEKIq1evCj8/P3Hnzp1cj2GMcqO61ujB1xc6dJC//0ML2L3bvAEpT7R+/XpOnz5NRkZGlnEN9+3bR82aNfHz88PFxYUuXbqwfv16M0ZquLt371K3bl2+/fZbvbbPnFqvZcuWHD58mPHjxzNy5EiWL19ukvhq1ZLjpwoBU6ea5BSKieRWbgDatm2Li4uLmSIrJh6vqQM5B2y1anDnDvz8s3niUvKUW7nx8fGhXr16AHh5eeHu7s7NmzdNGotK6vTUvLn8uZPmsGuXeYMxpzt3cl8eTxKuXct927Vrs2578WLO2+XDoUOHePHFF5kzZw6dOnViwoQJuudiYmJ0t5MAypcvn6WBqyUIDQ3lo48+omfPnnpt/+jUetWrV2fQoEEMGDCAadOmmSzGcePkz8WLZYe+Ys/Cy421mDlzJgEBATg5OREcHMwOPbtp79y5Ezs7O90HtMlktql7dJQFjQaGDJG/F5MZJnSsqNwcOHAArVab9wQIRmDWNnWWpEUL+XMXzRB7X0aTng52xfDPZ0ibA1Ntm4eLFy/StWtXxo4dS1hYGDVq1KBRo0YcPHiQ4ODgHNtxWvso5rlNrTd//nzS0tJyHAooJSWFlJQU3eO8ptfj3j0YPFhOmPxgNpgmTaB9e9i0CT7/HPSsVLReFl5urMGyZct48803mTlzJs2bN2fOnDmEhoYSGRlJxYoVc90vISGBvn370r59e65mJl2m0rWrTOge/5v36wfjx8OBA3I+vsBA08ZRVFhJublx4wZ9+/blu+++M8p586Jq6vRUvz44OQlu4MGZexXg+HFzh6Q85ubNm4SGhtK9e3fGjx8PQHBwMN26deO9994DwM/PL0vN3JUrV/Dx8TFLvIXF0Kn1wMDp9T7/HJYsgXr14M035dRGyBwPYNEieCQ/VIoYfcqNNfjyyy8ZOHAggwYNonr16kyfPp0KFSowa9asPPd7/fXX6dOnDyEhIaYPsn9/mDEDmjXLut7DA777Dk6dKj4JXRGnb7lJSUmhR48ejBs3jmaPv64mUAyrmvLHwQEaN9awfbtsVxe0a5fM9JQiw93dnVOnTmVb/8cff+h+b9y4MSdOnCA6OprSpUuzZs0aPvjgg8IM0ywMmVoP5PiQ4eHhuseJiYm5J3b9+sHRo7BiBXz9tWz389lntOvzKj4+NsTGwubNEBpqnGtRjEufcmPpUlNTOXjwIGMzv2k80LFjR3bl0Zzm+++/59y5cyxevJiPPvrI1GHmLSzMvOdXstCn3Agh6N+/P+3atSOskF4/VVNngMxbsDvL98464rdiMezs7Pjiiy9o27Yt9evX55133slxejlrYujUemDg9HqVKsn2LevXy1qEq1ehXz9sWrfk2eayJlBNm2z5OnXqxIsvvsiaNWsoX748+/fvN3dIeouPj8+x84e3t3e2spHp7NmzjB07liVLlmCnZ1OblJQUEhMTsyx6y8iQNXE3b8peRorF27lzJ8uWLeP333+nXr161KtXj+MmvsunauoMoOssUaIDdDdvLEr+de/ene7di88LGBISwurVq7OsM/rUegAdO8KxY3IqlilTYNcuejT7ltlM4o8/YNYsObSUYpksrZd4TnKqsc6ptjojI4M+ffowefJkqlWrpvfxIyIimDx5cv6Ci42FGjVkW+2UFNlB4nFr18qx6po0edgbSSmyWrRogVarLdRzqpo6A2Q2qTh79mEnJUUpbHfu3OHIkSMcOXIEkEOWHDlyRDfzijmn1sPBQU4rcfo0vPoqbRb0w9VVdkxTIwEp5uLh4YGtrW2ONdaP194BJCUlceDAAUaMGIGdnR12dnZMmTKFo0ePYmdnx+bNm3M8z7hx40hISNAtUVFR+gf56BRhNrl8NMfEwB9/FO8RGJQ8qaTOAGXKyFlbAHatigcLGwpDsQ4HDhygfv361H/QpjM8PJz69evr2gaac2o9nfLl4ccfcQgM4Jln5Cp1C1YxFwcHB4KDg9m4cWOW9Rs3bsyx8Xrp0qU5fvy47svTkSNHGDp0KIGBgRw5coQmTZrkeB6Dmi08LqfhTB6X2Vwilw5OiqJuvxqoRQs4eRJ2DllIjzHX1eiqSqFr06ZNnlPsmXtqvcf16JLCkiWOrFwJ06blfFdJUUwtPDycsLAwGjZsSEhICHPnzuXy5csMHToUkLVs0dHRLFq0CBsbG2o9GJ4nk5eXF05OTtnWG01OAw8/LjOpu3HDNDEoFk/V1BkoyyDE6n6SouTt/n06/19VnLjHhQuyyZ2imEOvXr2YPn06U6ZMoV69emzfvp01a9ZQqVIlIHsNd6HTp6Yuc85xldQpuVBJnYEyk7qDBHNv/wlISzNvQIpSlDk5UbJNIzqyAVC3YBXzGj58OBcvXiQlJYWDBw/SqlUr3XMLFy5k69atue47adIkXTtWkzCkpu7WLdlbVlEeU6Ck7v79+8aKw2IEBICPjyANB/bfryXH51IUJXevvUYPZDa3coUaqkFRcqRPTZ27u/wphEzsFOUxBid1Wq2WDz/8ED8/P0qVKsX58+cBmDBhAvPnzzd6gEWNRgPNm8tGQeoWrKLooXNnunnuxZZ0jh3X8OBfhqIoj+rcGd54Axo3zn0aUjs7cHOTvcxVUqfkwOCk7qOPPmLhwoV89tlnODg46NbXrl27UOY1Kwoyb8H+QwuV1CnKk9jZUbbfM7RiO6BuwSpKjl55Bb79lh8utsbFRU7Ukpycw3bR0XD/PlStWughKkWfwUndokWLmDt3Lq+88gq2j4wkWqdOHU6fPm3U4IqqzJkldtEM7U6V1CnKE/Xv//AW7C+pZg5GUYqur7+WPxctkmOjnjv32AbOzqoLuZIrg5O66OhonnrqqWzrtVotacWk00DduuDsLLhNGU6NmKGmdFGUJ6lZk+fqXABg1357NXi3ojxKq4XISE7suMXhw2BvL5vWHTsGwcHw55/mDlCxFAYndTVr1mTHjh3Z1v/666+6wVCtnb09NGnyoF2daxf1rakIu3XrFpMnTyY2NtbcoRR7Fb6bSMPa9xFCgxXNFW+VVLkpZDdvQs2a/NhqLgBdu8KhQ7KmLiEBunWDDz540OF1/nx47jlYutSsISvZFYVyY3BSN3HiREaMGMHUqVPRarWsWLGCwYMH88knn+hGtC8OdO3q/jFvHEreRo4cyf79+xk2bJi5Q1EaNaJHbydAtasr6lS5KWTXrpGBDUs0YQCEhYGfH2zdCiNGyE0+/FAmezf2n5dThZlyeBUlX4pCuTE4qevWrRvLli1jzZo1aDQaPvjgA06dOsXq1at5+umnTRFjkZTZrm7npnuy8YNS5KxatYo7d+7w559/4ubmxpIlS8wdUrHXo4f8uWmTICHBvLEoOVPlxgyuXmULbYkWvpQpI5M3kJ1c//c/+PFHKFEC1q+H4GXv8B9V1FRhRUxRKTcakdd8Q1YoMTERV1dXEhISDJuX7zEJCVCmjEAIDTEO/vgkngFHRyNGal7379/nwoULBAQE4OTkZO5wzCqvv4Wx3k9FndGuMz6eIP/7nLlbnp9+SOPlvvbGC7IIUOVGUmVG0vtaly2jX+/7LKIfQ4fCrFnZNzl2DHr2lB0nhjCHOc+uhd9/N1nshUmVG8kY5cbgmrrKlStzI4cpSm7fvk3lypUNPZzFcnWF2rXl7ztTG8Lhw+YNSFEsQZky9LD5HYCVs+PMG4uiFBF3L99gOc8D0LdvztvUqQPvvy9/v4i/mipMyZHBSd3FixfJyGF6kpSUFKKjow061vbt2+nWrRu+vr5oNBp+f8K3jq1bt6LRaLIt5hpKpUULNQixohjE1pYez8uhkNbuK0sxnJRGUbJZ+Y8ndynFU67XaNo09+3Kl5c/o/FTt1+VHOmd1K1atYpVq1YBsH79et3jVatWsXLlSj788EP8/f0NOvndu3epW7cu3377rUH7nTlzhtjYWN1S1UyDMGZ2llBJXdGydOlSnJycsnzJGDRoEHXq1CFBNeQyu4bvtsePK9zJcObvZaq2oahQ5cZ8Fh2sAUBYg8g8B1Pw85M/r1Be1dQVEUWt3Njpu+Fzzz0HgEajoV+/flmes7e3x9/fny+++MKgk4eGhhIaGmrQPgBeXl64ubkZvJ+xZSZ1h6nP3Z1HKGnecExKiFxGNy8Eho612bt3bz799FMiIiL49ttvmTx5MuvXr2fPnj24urqaLlBFLzZB1Xiu3HJmxJVn5bfRPNOvrLlDMhlVbpQniY6GTTHVAXj1tbzbmGYmdQm4cfculBTCKofUUuUm//RO6rRaLQABAQHs378fDw8PkwX1JPXr1+f+/fvUqFGD999/n7Zt2+a6bUpKCikpKbrHiYmJRoujYkXw89USHWPPvhg/2kZFQYUKRjt+UZKcDKVKmefcd+5ASQMyZo1Gw8cff8wLL7yAr68vX3/9NTt27MAv8z8i8Oeff/LWW2+h1Wp59913GTRokAkiV3LT45USzPgCVh/2Q6sFG4MbglgGays3PXr0YOvWrbRv357ffvvNBFEXPz/9BFphQ/PmUDmseZ7bli4NpUoJ7tzREH3oKtWsMKED6yo3UVFRhIWFce3aNezs7JgwYQIvvviiiaLPR5u6CxcumC2h8/HxYe7cuSxfvpwVK1YQGBhI+/bt2b59e677RERE4OrqqlsqGDHp0migRUv5J9xJc4iMNNqxlYJ55plnqFGjBpMnT2blypXUrFlT91x6ejrh4eFs3ryZQ4cOMXXqVG7evGnGaIufVmOb40Ii1zPKcvCPK+YOR3kgr3IDchyuRWoIJ6P68Uf5M7cOEo8rX14mctEx1pnQWaK8yo2dnR3Tp08nMjKSv//+m9GjR3P37l2TxaJ3Td2j7t69y7Zt27h8+TKpqVnncRw5cqRRAstJYGAggYGBuschISFERUUxbdo0WrVqleM+48aNIzw8XPc4MTHRqIld8+awbBnsbPo2NLN98g4WytlZfoMx17kNtX79ek6fPk1GRgbe3t5Zntu3bx81a9bUfZPq0qUL69ev5+WXXzZGuIoe7D1c6VDtBCv/rcXave406mHuiEzDmsoNQNu2bdm6dWvBg1MAOHoUjh8HB3stL7a7BcL9iff+/Pzg9Gm4YsXfhayp3Pj4+ODj4wPIpmPu7u7cvHmTkoZUBxrA4KTu8OHDdOnSheTkZO7evYu7uzvx8fE4Ozvj5eVl0qQuJ02bNmXx4sW5Pu/o6IijCcePy2xXtyvSjQxnsNa0TqMxrEranA4dOsSLL77InDlz+Pnnn5kwYQK//vqr7vmYmJgst5TKly9vcM9tpeBC367FyiGwbrsz1joXjTWVG8X4Mis9u6ctp0zVlyAp6Yn3HTP/dUVHLIKnqsq5xKyMtZabAwcOoNVqjVqx9DiDk7rRo0fTrVs3Zs2ahZubG3v27MHe3p5XX32VUaNGmSLGPB0+fFiXBZtDnTqyDCYmyruvmWPXKeZx8eJFunbtytixYwkLC6NGjRo0atSIgwcPEhwcDEBO421rrLRtSlGW2Udq71459aW7u3njKc70KTeKcaWny/Z0AGH8KKuI9GhIpkvqTiXA2bNWmdRZCkPKzY0bN+jbty/fffedSWMyuE3dkSNHeOutt7C1tcXW1paUlBQqVKjAZ599xvjx4w061p07dzhy5AhHHsxhd+HCBY4cOcLly5cBeeu07yMNDaZPn87vv//O2bNnOXnyJOPGjWP58uWMyJwczwzs7KBudXkL+uQXa80WhwI3b94kNDSU7t27696LwcHBdOvWjffee0+3nZ+fX5aauStXrpj1i0FxVb481ApMQ6uFDTPPmjucYkvfcqMY199/Q1wceLil0Zl14OWl135ZxqpTw5qYjSHlJiUlhR49ejBu3DiaNWtm0rgMrqmzt7fX1Wp4e3tz+fJlqlevjqurqy4Z09eBAwey9FzNbPvWr18/Fi5cSGxsbJZjpqam8vbbbxMdHU2JEiWoWbMmf/31F126dDH0MowqyDeRnXhw+reTsNDwIVoU43B3d+fUqVPZ1v/xxx9ZHjdu3JgTJ04QHR1N6dKlWbNmDR98YK03AIu2UOetnOBp1v4YT+/3zTPeZHGnb7lRjCuzg0TvZlE4rEmDHNow5iTLWHXxB00UnfIk+pYbIQT9+/enXbt2hIWFmTwug5O6+vXrc+DAAapVq0bbtm354IMPiI+P58cff6S2gfce27Rpk+OtsEwLFy7M8njMmDGMGTPG0JBNLqiBM/wBp++Wh3v35MzLSpFlZ2fHF198Qdu2bdFqtYwZM4ayZa13rLSiLPSl0nx+GNb99xTaDIGNrboNXpR16tSJQ4cOcffuXcqXL8/KlStp1KiRucOyOElJsHKl/D2szlFYg941dbrbr6qmziLs3LmTZcuWUadOHd2sWfnJl/Rl8O3XTz75RHer6sMPP6Rs2bIMGzaMa9euMXfuXKMHaAmC6ssk7jRBYGBtpWIe3bt3599//+W///5jyJAh5g6n2Go+rA6lSOKa1pPDv/1n7nCUJ1i/fj3Xr18nOTmZK1euWFxCN3PmTN1k6cHBwezYsSPXbVesWMHTTz+Np6cnpUuXJiQkhPXr1xsljuXL5ff/wEBo5HxSrjSwpi6OcqRfv2WUeBTTadGiBVqtVtfU7MiRIyZL6CAfSV3Dhg11t0w9PT1Zs2YNiYmJHDp0iLp16xo9QEsQGCRrF/6lGtrzF80bjKJYEAfXErT3PgHAugUxZo5GsWbLli3jzTff5L333uPw4cO0bNmS0NDQXJsNbd++naeffpo1a9Zw8OBB2rZtS7du3Th8+HCBY8kctzksDDTXrsoHetbUeXmBna0WLbZcjdUWOBbFuljpOO6FKyAA7DVp3MOZqMNqkmVFMURoO9nRaO2eMmaORLFmX375JQMHDmTQoEFUr16d6dOnU6FCBWbNmpXj9tOnT2fMmDE0atSIqlWr8sknn1C1alVWr15d4Fh+/RV+/hn69QPatoURI6BFC732tbUFH3dZZq7czMegaopVMzipu3HjBm+88QY1atTAw8MDd3f3LEtxZGcHVcvIZO700ZQnbK0oyqNCR1QBYHdiTW5duG3eYBSrlJqaysGDB+nYsWOW9R07dmTXrl16HUOr1ZKUlJTn51xKSgqJiYlZlpyUKAG9ej3oydqzJ/zvfw/H+NGDX2UHAKI/Xqj3PkrxYHBHiVdffZVz584xcOBAvL291fheDwT5JRF504fTZ23pZO5gFMWCVGxWnhqO/xGZ8hQb51/mpY/czB2SYmXi4+NzHO3f29ubuLg4vY7xxRdfcPfuXV566aVct4mIiGDy5MkFilUffuVtYK+aKkzJzuCk7p9//uGff/4ptu3nchPUuhwch9N1TDdRrznk1Tu5uFB/A9ML7VWayEWwNroOuX9kWo7i/p4pqtf/eCWEEEKviomlS5cyadIk/vjjD7zyaPuWr2kpT50CDw+56FlJohurzsomwimq75vCYozrN/j2a1BQEPfu3Svwia1NUOPSAJy+ZB1tHOzt7QFITk42cyTml/k3yPybKMYX2ld+UK5bB5b8f12VGylzTnBb26IxcaKHhwe2trbZauWuXbuW4xy3j1q2bBkDBw7kl19+oUOHDnlu6+joSOnSpbMseUpLgxo1ZO8HA4Yn0Y1Vt3grxFh+B6PM98njc8kXN8b4rDG4pm7mzJmMHTuWDz74gFq1amU7+RPfxFYqKEj+PH3avHEYi62tLW5ubly7dg0AZ2fnYnerXQhBcnIy165dw83Nrch8QFmjFi3kXI9xcXD0iKBefct8r6lyI9ueXb9+HWdnZ+zsDP6IMQkHBweCg4PZuHEjPXr00K3fuHEjzz77bK77LV26lAEDBrB06VK6du1q/MCuX5c/bW0NmidPN1ZdtJBJna+v8WMrRHZ2djg7O3P9+nXs7e2xsSlefTiN+VljcIlzc3MjISGBdu3aZQtKo9GQkZGR72AsWWCg/BkXB7dPRuNW0y/vHSxAuXLlAHQfUMWVm5ub7m+hmIajI7SrEcvq/T6sfXsT9TblXSNSlKlyAzY2NlSsWLFIJbTh4eGEhYXRsGFDQkJCmDt3LpcvX2bo0KGAvHUaHR3NokWLAJnQ9e3bl6+//pqmTZvqavlKlCiBq6urcYK6+mA4E09PMCCRyToA8QXjxGJGGo0GHx8fLly4wKVLl8wdjtkY47PG4KTulVdewcHBgZ9++kl1lHhE6dLga3eVmHRvzmy6QhMrSOoyC5qXlxdpaWnmDscs7O3tVQ1dIQmtfkkmdfvcGWfuYApAlRtZM1bUalt69erFjRs3mDJlCrGxsdSqVYs1a9ZQqVIlgGzTUs6ZM4f09HTeeOMN3njjDd36zGksjSIz8ddz4OFMj87/KuIPYA2fwg4ODlStWrXY3oI11meNwUndiRMnOHz4MIGZVVOKTpBrHDE3vDl9OJkm5g7GiGxtbVVio5hc6IgqsAh23anD7dNxuAVZdu2oKjdFz/Dhwxk+fHiOzz2eqG3dutX0AV01bODhTJl3W5Mpye2oJKxlhEcbGxucnJzMHYZFy9eMElFRUaaIxeIF+SYAcPqMNXxvUpTC5d/IkyCnC2Rgx6ZvTpo7HEUxvXzW1JUoAe6OdwCIvpRu7KgUC2ZwTd3//d//MWrUKN555x1q166draNEnTp1jBacpQmqki6HNblc0tyhKIpFCq0Xx+k9AaxdI3je3MEoiqnls6YOwM/1DjevlSL6iqCWkcNSLJfBSV2vXr0AGDBggG6dRqMp9h0lAIJq28PvcOamh7lDURSL1LmPO1/tgXWXqyNSUtE4Opg7JEUxnZYtISUFWrc2eNfyZe9x/BpEX1dlRHnI4NuvFy5cyLacP39e97M4C2wie0T9d8+PYto+WikkM2fOJCAgACcnJ4KDg9mxY0eu227duhWNRpNtOV0Ex99pNbAqziQTLfw4vqjgE6crSpHWvTt88438aSC/JnJQ4yuhg40dlWLBDK6py+wppGRXvrEfztwlmZJcOHWfanVUg0/F+JYtW8abb77JzJkzad68OXPmzCE0NJTIyEgqVqyY635nzpzJMo6kp6dnYYRrECdnG9r6n+evi7VYe8SH4tuYQ1Hy5ldJfnyrqcKUR+mV1K1atYrQ0FDs7e1ZtWpVntt2z8c3Dmth4+FOYLW7HP4XTl9wpJr6RFJM4Msvv2TgwIEMGjQIgOnTp7N+/XpmzZpFRERErvt5eXnh5uZWSFHmX+jbtfhrBKw9WZF3zR2MopjS6dNy0GEPD4PGqYNHByA2QVyKxdIrqXvuueeIi4vDy8uL5557LtftinubOjQagoJLyaTujIbim94qppKamsrBgwcZO3ZslvUdO3Zk165dee5bv3597t+/T40aNXj//fdp27ZtrtumpKSQkpKie5yYmFiwwA0QGip/7twJt2+DBeShimI4rRZq14b0dIiKejj4nJ7KuyYBLlz55wIQYJIQFcuj11cDrVarm8RYq9XmuhTrhO4Ba5suTCla4uPjycjIyDZfpbe3d7Z5LTP5+Pgwd+5cli9fzooVKwgMDKR9+/Zs37491/NERETg6uqqW544KbkRVa4M1avLz7r1M/4rtPMqSqG6fVu+ySF/vV/Ly9uu0QmlQM3HrjxgcEeJRYsWZfkGnyk1NVU3vUpxFiROAXD6n+tmjkSxZo/P5JLZ+zwngYGBDB48mAYNGhASEsLMmTPp2rUr06ZNy/X448aNIyEhQbcU9tiU3SqfAODPr1VSp1ipzOFM3NzAwfAerH7V5NBZ8XiSEnPDiIHpSQj47TdVg1HEGJzUvfbaayQkJGRbn5SUxGuvvWaUoCxZUOI+AE5fKoEQZg5GsToeHh7Y2tpmq5W7du1attq7vDRt2pSzZ8/m+ryjoyOlS5fOshSmZ/rLThxrrjciPfpqoZ5bUQpFPgcezuReVoMj9wGIOZNkrKj09+ef8OKL0LAhFMbsG4peDE7qcqsRuHLlivEmObZgVYNLo0HLrdRSXFeVdYqROTg4EBwczMaNG7Os37hxI82aNdP7OIcPH8bHx8fY4RlNyHPeuNve5iZl2f3NfnOHoyjGV4CBhwE0GijvIBPDK/8mGysq/c2YIX/evSsbwq5ZU/gxKNnoPaRJ/fr1deNbtW/fHju7h7tmZGRw4cIFOnfubJIgLUmJwIr4c5ELVOb06XyXV0XJVXh4OGFhYTRs2JCQkBDmzp3L5cuXGTp0KCBvnUZHR+uaQ0yfPh1/f39q1qxJamoqixcvZvny5Sxfvtycl5EnOzvoUusyi4+6sfq3FFpONXdEimJkBaypA/ArcZNzqRWJvpBqpKD0dO4crF8vM8s2bWDLFnjuOfjjj4c9nRSz0Dupy+z1euTIETp16kSpUqV0zzk4OODv78/zzxs2sc/27dv5/PPPOXjwILGxsaxcuTLP3rUA27ZtIzw8nJMnT+Lr68uYMWN0H2ZFgr8/QeyVSd3xNFq1sn/yPopigF69enHjxg2mTJlCbGwstWrVYs2aNboxJGNjY7l8+bJu+9TUVN5++22io6MpUaIENWvW5K+//qJLly7mugS9dHvFlcVH4c8LNfjs7l0oqabfU6xIAWvqAPxckiABoq8Ucluf27ehWTMoXRpWrYK+feHIEQgOLtw4lGz0TuomTpwIgL+/P71798bR0bHAJ7979y5169bltdde0yshvHDhAl26dGHw4MEsXryYnTt3Mnz4cDw9PQ1OKE3G3Z0g+/OsTYPTB+4AZcwdkWKFhg8fzvDhw3N8buHChVkejxkzhjFjxhRCVMbVaXBF7MakcUpU59yiDVQZ1tHcISmK8TRrBiNHQqtW+T6En3syXIHoOFsjBqaH4GCurthJ2zZaYjxtcHD4CQc7LQ5NbXF0lP0+HB3h5Zdh9OjCDa24M3hGiXbt2nH9+nXKPxhTZ9++ffz000/UqFGDIUOGGHSs0NBQQg2oqp09ezYVK1Zk+vTpAFSvXp0DBw4wbdq0opPUaTQEed6AGDgTmW7uaBTFYrm6aWhV4QKbo6qxeuEN3hxm7ogUxYhCQwt8q7J8n9ZwDK74NTFSUPpbtAhOnc5slq8BsieW+/fLofg6dCjU0Io1gztK9OnThy1btgAQFxdHhw4d2LdvH+PHj2fKlClGD/BRu3fvpmPHrN/WO3XqxIEDB0jLZbLVlJQUEhMTsyymFlhJ9kg6fb7gtZmKUpx16yN73a4u8ZKZI1GUosevipyKslBnlfjxR4iP55df5MOPP4bjx+HgQdi9G7YuOM8GOvIqPwLw2mvybq1SOAxO6k6cOEHjxo0B+OWXX6hduza7du3ip59+ynbbx9ji4uJyHHQ1PT2d+Pj4HPcxxyCqQdNlG78LN1y4f9/kp1MUq/XMoHIAbN9pSw4jKSmK5Tp9Wrar02rzfYhCnyrs+HHo25fzldpy4ICc2WzQIKhVCxo0gKZNofVrlXl6agdmM5SnOMuVKzBiRCHFV0QJIV/uL76A9u1l08OffsIkw54ZnNSlpaXp2tP9/fffurleg4KCiI2NNW50Ochp0NWc1mcyxyCqXo0q4eYGQmjIYygwRVGe4Kmn5Cwt6emys52iWI2GDaFcOTh/Pt+H8Lv7LwAxUekFyQ31N3s2AL9Vlm1027bNpZ/HmDGUHNCbHwnDhgyWLEFXs1dc3L8P69bB//2f/D9WvTq8/TZs3gyHDsErr8gELzLSuOc1OKmrWbMms2fPZseOHWzcuFE3jElMTAxly5Y1bnSPKVeuXI6DrtrZ2eV6bnMMoqrRqOnCFMVYuj0tp0BaPXqzab7aKkphu3tXLlCgIU18HG+iQUua1s7046LeuSNvvQK/pD4LwEt5tYr44gua+l1hHBEADB0KMTEmjrEIEAImTgR3d9lk8ttvZd7u4AAdO8LXX8OUKVCihBwJpm5dGDNG/nmNweCkburUqcyZM4c2bdrw8ssvU7duXQBWrVqluy1rKiEhIdkGXd2wYQMNGzbE3r4IDR0SH0/QHTlgqkrqFKVguj0jf66JqUvG0RPmDUZRjCFzjDonJ3hkeDBD2Xu7440cGsXkt2CXLIGkJM77t+Pgv6WxtYUePfLY3s0N5s3jA6ZQn0PcugUDBlj/97KpU2XSdu+evD0+ZAj8/jvcuCHvNowcCRMmyBq6Z5+VdyE+/1xWBP36a8H/PgYndW3atCE+Pp74+HgWLFigWz9kyBBmP6ia1dedO3c4cuQIR44cAeSQJUeOHNGNsTVu3Dj69u2r237o0KFcunSJ8PBwTp06xYIFC5g/fz5vv/22oZdhWhoNQSd+A+D0yQwzB6Moli2kXQnK2CfJ2SVmHDJ3OIoFmzlzJgEBATg5OREcHMyOHTvy3H7btm0EBwfj5ORE5cqVDf6My9WjAw/n0nRIL2XL4ofM5qIv5txZ0CiEgFmzAPi15iRA3nr19HzCfqGhOAwIYzGv4miXzvr1uju4JpeWBhs3wrBh4OsLlSrJNm3GqhHLyQ8/wLhx8vcvvoCoKJgzRyZvj+fu/v4y2fvzT6hcWSblL70EnTrBmTMFCELkQ1pamti4caOYPXu2SExMFEIIER0dLZKSkgw6zpYtWwSQbenXr58QQoh+/fqJ1q1bZ9ln69aton79+sLBwUH4+/uLWbNmGXTOhIQEAYiEhASD9jOIVit+d+olQIgGNe+Z7jyK2RXK+6kIMPd1vtLkrAAhxpRbaJbzK8ZjrvfSzz//LOzt7cW8efNEZGSkGDVqlChZsqS4dOlSjtufP39eODs7i1GjRonIyEgxb948YW9vL3777Te9z5nrtf7xhxAgRKNGBbkkIdLTRXf+ECDErE9vF+xYedm9W8br5CQa1E0TIMTcuXrue+uWENu2ia++kodwdhbi339NE2ZyshC//y5E375ClCkjz/f4UqaMEJMmCXHjhnHPvWaNELa28hzvvGPYvvfuyZgcHeX+06Zl30bfcmNwUnfx4kURFBQknJ2dha2trTh37pwQQohRo0aJ119/3dDDFbrC+odyuuoz8g3smC4yMkx6KsWMzJ3sFBZzX+fPs28JEKI6J4WIijJLDIpxmOu91LhxYzF06NAs64KCgsTYsWNz3H7MmDEiKCgoy7rXX39dNG3aVO9z5nqt8+bJT+9nntH7WLkZ7vidACHeG3y1wMfK1axZQtjbi7M93hEgk5fr1w07REaGEO3ayctu0kSItDTjhZecLMTrrwtRsmTWBM7LS4ghQ4RYOz9azH9pnaha/q7uuZIlhXj7bSFiYgp+/n37hHB21goQ4tUGJ0VGx85CBARk3ahvXyFcXGRW6+YmRMOGQrzyihAffijEL78IkZwszp0TYsQIIVJTs59D33Jj8O3XUaNG0bBhQ27dukWJEiV063v06MGmTZsKUGdoXSo/ZYMdaSSn2BbuGEKKYoU69XLDTpPOKWpwbv5Wc4ejWJjU1FQOHjyYbZzTjh07smvXrhz3yc+4qHozwhRhmfxc5Nir0VEm7P46dChcucKvVccD0K4deHgYdggbG/h+0iVK2yezd68c384Ybkbd5el615kzR/Y9qchl3uQrtr++hJgYefuzc/B1BvzSmVNXXFim6U1dp9PcvQvTpoF/JS0jhmdw61Y+Th4Tw9mpK+jaMoHkZA0dWc/8Q/Ww2bAOLlzIOlzN/fuQlATJyXLgvgMHZDvFCRPkfVetlsqV4X//g4J0ETA4qfvnn394//33cXBwyLK+UqVKRKvsRce+cgWe4j9AdZZQlIJyc4OWleX/lz+XJpk3GMXixMfHk5GRkeM4p4+PqJApP+Oi6j3YfePGMGqU7A5ZQH5ushdtdGwB2ubpw8uLXze6AU/o9ZobrZaKgzrybdrrAEyaBA8mh8qfmBguPTuSFpUus/NfT1y5zVo6c5FKfEU4LV2PYZs5yUWlStCjB7blfXlJLOPw/er8RReasZPUNBtmzLKlenU57Iq4dh3WrJEN4jJ7LSQmwp49sGABjw6YGffuV3QaW4/rKa404CC/leiLQ2gH+PJL2LUra3vJr76Cs2dlsnfiBKxYAZ98Av36QffuRpvb2uBpwrRaLRkZ2Rv/X7lyBRcXF6MEZRX8/QniNKepzpkz8PTT5g5IUSxbt5dKsCUCVie1ZpQQBWtgrhRLOY1zmtsYp7ltn9P6TBEREUyePPnJgTz9tNE+FPymjoTnITot/0Oj5EqrlYlIYCBnz8Lhwzy512tubGxg+nRe7dKFE9TmM8YwerTsJZrZuSBPQkBcHPj4AHD0chlCV48jVvhQ3jaWtQN+pdar46HCLLmNk9PDfd3cZBIFEBODZv9+uuzbR+jeSWzZU4I3yvzE6Sul6NULFjVIZ8ahYVTiMri6yh4Oj1ZYPfUUtGpFUhJ03TWeC5ShstsN1sy/j0vXy3LS25z4+mZ9XLOm3n86QxhcU/f000/r5l4F+ea+c+cOEydOpEuXLsaMzbI9SOpA1dQpijF0GyBvVW27VoOERJXQKfrz8PDA1tY2x3FOH6+Ny5SfcVHNMdh9+Rpy7NUrV0xw8F275Fgb3bvz6y8yoW3fHvI9JG1oKJrXXuNT3mWizYcAjB8PH7yXkftQHjExEBEBVavKrqFCsGkTtOxYgljhQ60qyey+UI5ac0dCq1YQEJA1oXucr6/sjvrxx2j+3ki7xN85cqYEkybJseT+OuRDDc0pvrJ5i/SEOw8TOl9folq8zKIN5XjtNahRAw6dL4OnJ6zbVxbvns1zT+gKk6ENAqOjo0W1atVE9erVhZ2dnWjatKkoW7asCAwMFFevmrChppEUWiPdpCSx8KubAoRo3960p1LMx9wdCApLUbnOwEDZyPmXX8wahlIA5uwoMWzYsCzrqlevnmdHierVq2dZN3ToUON0lDCixMSHHQMMHIDiyaZPlwfu0UPUqyd//e67Ah7z1i0hmjUTAsSnjNHF/vbbQmi1D7ZJTRVi5UrZkcTG5uEFuriIJV9fF/b28mHr1vJwxnLqlBAtWz48XXCNu2Le2P/E4L73RZUq2XvSurvLThKFwWS9X4UQIjk5WSxYsEC88cYbYtiwYWLevHkiOTk5X4EWtsL8h5LZC9zPz+SnUsykqCQ7plZUrvPtt2WZCnvmRs5dxJQiz9xDmsyfP19ERkaKN998U5QsWVJcvHhRCCHE2LFjRVhYmG77zCFNRo8eLSIjI8X8+fONN6SJMW3eLFzskwUIcfq0kY89eLAQIM4M+0qAEHZ2QsTHG+G4Wq0Qhw8LMXq0+MZlvC5JGj5ciIyr14Xw9MySPaU3byUOT/5DjH87Rbf6pZeEuH/fCLE8JiNDdk52c8uexNnYyFFoxowRYu1aEyTReTBpUmfJCvMfys2bD98MD4bzU6xMUUl2TK2oXOe2bbI8leW6SPt7q1ljUfLHnO+lGTNmiEqVKgkHBwfRoEEDsW3bNt1zFjsu6rx5IohIAUJs2mTkYz+oUfvopaMChOjUycjHF0KItDQx99v7QqORZXtAm/9EMk5iZ5muIqLVGtGldZJwdc2aXI0eLUw+VFhsrBADBggREiJEeLgQq1cLcduEQwE+ib7vJYM7Sij6K/PDdLyd+nP1vhunT0OjRuaOSFEsW7NmUNYxiRspHvwz/wxt2rc2d0iKBRk+fDjDhw/P8bmFCxdmW9e6dWsOHSris5h4eFCeK5ymunHb1Qmhm23+16NVgXz2en0SOzsGv2FHCVfZEXTB1ir8YHOXjFs2sP3hZi4usvz36QOPTDRlMuXKwfz5pj+PsRncUUIxwN9/U+O+/Idw8qSZY1EUK2BnB882lWN8Ld+getsrSpapwow5qlhsLNy+zRlNEEfPlMDODp57zojHf8yrr8KyZbKMZ2ht8PSEnj3lSCAHD8LNm7BuXeEkdJZM1dSZkr8/dTjGFtpx7Ji5g1EU69BziCcLtsHKGy35+koMNuV9n7yTolgrDw/8HlRpGTWpy6ylc38dbsgRWNzdjXj8HLzwAjRoIOdtrVZNjVqUH6qmzpQeJHWASuoUxUja93TFxeYO0ZRn/+yD5g5HUczr0Zq6K0acVcLDAwYP5heb3gC8+KLxDp2XypUhMFAldPmlkjpTUkmdohidkxN0rX4BgOW/mXBqJEWxBO7ulEc2prtyKfvEAPlWrx5n3prL8evlsLc37a1XxXiMmtQFBAQwcOBANV1YJn9/ahCJDRlcv/5wuj9FUQrm+T5ykM8VZ2sj0tLNHI2imJGdHX6lHsz/auSP3s2b5c/WraFMGeMeWzENoyZ1/fr1Q6vV0qpVK2Me1nL5++PMPd0csKq2TlGMo/MbVXCyS+OctjLHT6j7NErx5vfPMgCu3rAn3RjfcYSAo0fZu0vW/IWEGOGYSqEwalI3adIkvv/+e86dO2fMw1qusmWhZEnqaE4AKqlTFGMp5WpLp672AKz4w/YJWyuKdfOq7Y2dnZyq9bGZzfInLg7q1WPv4rMANGlihGMqhSLfSV1qaipnzpwh3ShfC6yURgP//UediXL2Y5XUKYrx9Owpfy5fbt44FMXcbGwezhdvlLHqTp7kNq6cJgiAxo2NcEylUBic1CUnJzNw4ECcnZ2pWbMmly9fBmDkyJF8+umnRg/Q4pUrR5168s+skjpFMZ5u3cDOVsuJE/Dvrnhzh6Mo5rN0KX73ZK2aUdrVnTzJARoCsjeqp6cRjqkUCoOTunHjxnH06FG2bt2Kk5OTbn2HDh1YtmyZUYOzFnXqyJ+RkXL8HUVRCq5MGWjnvBeAlV9fMnM0imJG+/bhd/0IYKSkLjKSvch7rqqWzrIYnNT9/vvvfPvtt7Ro0QLNIwPJ1KhRQ7Wly8nx41Qa+zIu9vdITYWzZ80dkKJYj54trgGw4m9XM0eiKGbk4WHcWSVOntQldao9nWUxOKm7fv06Xl5e2dbfvXs3S5KnPJCWhs0vP1NLHAfULVhFMaZnh/qgQcu+m08RddGIY3QpiiUpW1Y3Vt2lglZaC4E4GamSOgtlcFLXqFEj/vrrL93jzERu3rx5hKh+z9kFBgJQJ13OAauSOkUxnnJdGtDc9sEt2G+izByNophJ2bLUQE7rdfx4AY8VF8el26W5hjf29oL69QsenlJ4DJ77NSIigs6dOxMZGUl6ejpff/01J0+eZPfu3Wzbts0UMVq2kiWhUiXqXFIzSyiK0dnZ8XytM/xzNIQVKwQjvzR3QIpiBh4e1OUoAGfOwP37cuaVfLG3Z1+fr+EnqFtXk//jKGZhcE1ds2bN2LlzJ8nJyVSpUoUNGzbg7e3N7t27CQ4ONkWMlq96dTVdmKKYSI9XnAHYcaki166ZORhFMYeyZfElhrKaG2RkwMmTBTiWhwd7yz0LqE4Slihf49TVrl2bH374gRMnThAZGcnixYupXbt2vgKYOXMmAQEBODk5ERwczI4dO3LdduvWrWg0mmzL6dOn83XuQlO9OrWRdeJRUXDrlpnjURQrUumVFgRzAC22/PFrqrnDUZTC5+GBBqgrZG3dkSMFO9xe2aJBtaezQAYndba2tlzL4evwjRs3sLU1bGT3ZcuW8eabb/Lee+9x+PBhWrZsSWhoqG7su9ycOXOG2NhY3VK1alWDzlvoqlfHlUQqOcnJX0+cMHM8imJNfH3pOdofgBV/Opg3FkUxB29vuHKFeiPlFJ1Hj+b/UGl/b+PgAS2gkjpLZHBSJ4TIcX1KSgoODob9Q/3yyy8ZOHAggwYNonr16kyfPp0KFSowa9asPPfz8vKiXLlyusXQZLLQVa8OdnbUKXUeULdgFcXYeg7xAGDTJrh927yxKEqhs7UFPz/qBstm8vlO6oTgxPMTuZ9ig5tLOkW9vkTJTu+OEt988w0ge7t+9913lCpVSvdcRkYG27dvJygoSO8Tp6amcvDgQcaOHZtlfceOHdm1a1ee+9avX5/79+9To0YN3n//fdq2bav3ec2iaVNITqb2JHtWf6KSOkUxtqAgqFFDDvD952rBq2FqeCWl+KlbV/48ehSEkDNVGuTqVfYmys/xRo012Bh1dnilMOid1H311VeArKmbPXt2ltoxBwcH/P39mT17tt4njo+PJyMjA29v7yzrvb29ictlRmIfHx/mzp1LcHAwKSkp/Pjjj7Rv356tW7fSqlWrHPdJSUkhJSVF9zgxMVHvGI3GTv6ZM2eWUEmdUlAzZ87k888/JzY2lpo1azJ9+nRatmyZ6/bbtm0jPDyckydP4uvry5gxYxg6dGghRmx6PZ3WEEkXViy4zathZcwdjqIUrunTqX7gGPZ235GQYMPly1CpkoHHeGQmiSYhRfwOmJIjvZO6CxcuANC2bVtWrFhBmTLG+af5+IDFQohcBzEODAwk8MG4bwAhISFERUUxbdq0XJO6iIgIJk+ebJRYCyozqTt+HLRa1LcgJV8y26LOnDmT5s2bM2fOHEJDQ4mMjKRixYrZtr9w4QJdunRh8ODBLF68mJ07dzJ8+HA8PT15/vnnzXAFptHTbTMf0YV1O0uRlAQuLuaOSFEK0dq1OGzYQPUK0zgW5c7Ro/lI6k6eZC/tAdWezlIZnFZs2bLFKAmdh4cHtra22Wrlrl27lq32Li9NmzblbB5zb40bN46EhATdEhVlpgFKFy6kalhTHO3SuXsXLl40TxiK5TO0Lers2bOpWLEi06dPp3r16gwaNIgBAwYwbdq0Qo7ctOr1CqQaZ7iXZs8vv5g7GqUouXXrFmFhYbi6uuLq6kpYWBi382h8mZaWxrvvvkvt2rUpWbIkvr6+9O3bl5iYmMIL2lBlywJQ11t+puanXV3C4fOcRt5+VUmdZcpXXdGVK1eYOXMmY8eOJTw8PMuiLwcHB4KDg9m4cWOW9Rs3bqRZs2Z6H+fw4cP4+Pjk+ryjoyOlS5fOsphFQgJ2B/dSs5Scw0XdglXyI7MtaseOHbOsz6st6u7du7Nt36lTJw4cOEBaWlqO+6SkpJCYmJhlKeo0XUIZwAIA5s9WQ5soD/Xp04cjR46wbt061q1bx5EjRwgLC8t1++TkZA4dOsSECRM4dOgQK1as4N9//6V79+6FGLWBMpM614tA/pK6A/sFAhsCPJPw9DRibEqhMXhGiU2bNtG9e3cCAgI4c+YMtWrV4uLFiwghaNCggUHHCg8PJywsjIYNGxISEsLcuXO5fPmyrq3PuHHjiI6OZtGiRQBMnz4df39/atasSWpqKosXL2b58uUsX77c0MsofNWrA1BHHOUQVTh2DJ57zrwhKZYnP21R4+Lictw+PT2d+Pj4HL8UFaVmC3orX56+9Y7z3pF0dh9w4NQpXbFTCllMDCxeDMHB0L69eWM5deoU69atY8+ePTR5UP2UOa3lmTNnsjTpyeTq6pqtwuF///sfjRs35vLlyzk2czA7D9kDvK7Tv0AXw5M6Idh7Th6jcf1048amFBqDa+rGjRvHW2+9xYkTJ3BycmL58uVERUXRunVrXnzxRYOO1atXL6ZPn86UKVOoV68e27dvZ82aNVR60BAgNjY2y5h1qampvP3229SpU4eWLVvyzz//8Ndff9GzZ09DL6PwPfh0qZ20G1A1dUrBGNIWNbftc1qfqcg0WzCQT/9OdGENAAsWmDmYYiY5GX76CTp1ggoV4N134X//M3dUsqba1dVVl9CBbLbj6ur6xJEWHpWQkIBGo8HNzS3Xbcxaw12uHAB17+0B4L//ICnJgP2FYG/NAQA0aV/S2NEphcTgpO7UqVP069cPADs7O+7du0epUqWYMmUKU6dONTiA4cOHc/HiRVJSUjh48GCWDg8LFy5k69atusdjxozhv//+4969e9y8eZMdO3bQpUsXg89pFuXLQ6lS1NEeBlRSp+RPftqilitXLsft7ezsKPvgls3jikyzBUO99BIDNd8DsOj7dHK5u6wYiRCwYwcMGgQ+PvDKK7Bhg+wI1qJF0bgbERcXh5eXV7b1Xl5eudZuP+7+/fuMHTuWPn365FkWIiIidO32XF1dqVChQr7jNljNmgB4/rcbX1+56vhx/XcXGhv2Rskdm7RQg3hbKoOTupIlS+qGCPH19eXcuXO65+Lj440XmbXRaCAoSDcH7H//wd27Zo5JsTj5aYsaEhKSbfsNGzbQsGFD7O3tTRarWfj40OU1b7xLJnHthh1//WXugKzTnTswfTpUrQqtWsH8+ZCYCP7+8MEH8v/bjh3Qv7/pYpg0aVKO00Y+uhw4cADIuUb6SbXbmdLS0ujduzdarZaZM2fmua1Za7gfJHVcu0bdWvL2qSG3YC9fhqtX5Qhc9eubID6lUBjcpq5p06bs3LmTGjVq0LVrV9566y2OHz/OihUraNq0qSlitB7Vq+N14ADepe5w9U4pIiOhUSNzB6VYGkPbog4dOpRvv/2W8PBwBg8ezO7du5k/fz5Lly4152WYjP382fQtC59/Lm/BFoXaImsRFydvqc6a9XAO61Kl4MUXoV8/aNmy8IZqGjFiBL17985zG39/f44dO8bVq1ezPXf9+vUnjrSQlpbGSy+9xIULF9i8efMTa6wdHR1xdHR8cvCmUKaMzKb9/an7vi1rNxiW1O2bdxSoS92aaZQoYWVf9ooTYaBz586Jo0ePCiGEuHv3rhg2bJioXbu26NGjh7h48aKhhyt0CQkJAhAJCQmFf/KpU4WoUkU8Xe2CACG++67wQ1CMy1zvpxkzZohKlSoJBwcH0aBBA7Ft2zbdc/369ROtW7fOsv3WrVtF/fr1hYODg/D39xezZs0y6HxmLTf5cOqUECCEra0QMTHmjsbynT4txODBQjg6yr8rCFG1qhCzZwtx584jG965I8TKlUIMGCDEDz/keKzCfi9FRkYKQOzdu1e3bs+ePQIQp0+fznW/1NRU8dxzz4maNWuKa9eu5evc5io3S5fK16hpU/33eav8UgFCDGuf+99EMR9930sGJ3WWrih8OL31lixwI0eaLQTFSIrC+6kwWNx13rsnmgXGCxDi00/NHYzlOnxYiOeeE0KjeZjMhYQIsWKFEOnpj2yo1coNH836nn46x2Oa473UuXNnUadOHbF7926xe/duUbt2bfHMM89k2SYwMFCsWLFCCCFEWlqa6N69uyhfvrw4cuSIiI2N1S0pKSl6n9dc5SYyUr4EJUsKkZGhxw5arWhht0uAEAsnF/3KmeJI3/eSwRXllStX5saNG9nW3759m8qVK+ezvrB4qV1b/lSdJRTFRP76i4Fn3gFgwQLBg86+ip4uX5a3Uxs0gN9/l1la9+7wzz+waxf0CInD9rdlD3fQaGT315QUCAiAUaNg/Hizxf+4JUuWULt2bTp27EjHjh2pU6cOP/74Y5Ztzpw5Q0JCAiDHYl21ahVXrlyhXr16+Pj46BZDeswWuuPH4ZVXqPrFUJycZLvtR5q95yot+hoH0+XEsU26Ze9UolgOg9vUXbx4kYyMjGzrU1JSiI6ONkpQ1q5ObQFoOHYsn5MuK4qSty5deNFlJCOT7vDvv6XYuVP2xlTydusWRETAN9/I/Aygd2/Z+aF65RRYvRqeWQjr1skurs2ayfFLAD75BL78EmrUKHL/1Nzd3Vm8eHGe24hHMn9/f/8sjy1GSgr89BN2np7UqjWbAwdku7qqVfPe7cSay9yjEa42iVSrayE93ZUc6Z3UrVq1Svf7+vXrcXV11T3OyMhg06ZN+Pv7GzU4qxQWRvXVG7G1ieHmTRtiYsDPz9xBKYqVKVECl55P0+uHZSxgIPPnq6QuLykpMGMGfPTRww4QbdrA558JGmoOwoyFchC6zCcBQkLg+vWHSV1wcGGHrTyuenWZUF+/Tt2n73HgQAmOHoUXXsh7t32b7wDQyP08Njb1TB+nYjJ6J3XPPehCptFodOPUZbK3t8ff358vvvjCqMFZpXv3cEq4SqD3LSKvluX4cZXUKYpJ9OnDgB8ms4CB/PKL4JtvNLi4mDuookUIWLkS3nrr4XzUNWvCZ59BaCholiyBR6fTKl8e+vaVSw4zMShmVrIkVK4M585R1z0KqMaRI0/ebe8R2WO3SbWbJg1PMT2929RptVq0Wi0VK1bk2rVrusdarZaUlBTOnDnDM888Y8pYrUOQnCy5TinZ0EG1q1MUE2nXjmae/xHIaZKTNSxb9uRdipNLl2Q7ueeflwmdry/Mn6fl6J9RdOny4A7qc8/JJ/r0kaMKX7wIH3+sErqirFYtAOrayJGH9RnWZG+UnCqwSZOiddtcMZzBHSUuXLiAx4M55pR8yJwDNkPNLKEoJmVnh6Z3LwYg5wtT04ZJaWlyDL8aNeDPP8HeHt4bm8HZ9xYy4LMgbJ99Bl3PklKl4Px5WLIEnn4abG3NG7zyZJlJXeJ2AKKi4GYeFXCJiXDqnj8ATfqqZN3S6Z3U7d27l7Vr12ZZt2jRIgICAvDy8mLIkCG6mSaUPGQmdTe3ASqpUxST6tOHvizClgx274ZTp8wdkHnt2QMNG8KYMbKzasuWgiOTfuejxf44v/EanD0LV67IRC6TuQbTVfLnQVLn+u8BMpu55/U5s3s3CKHB3x+86vmaPDzFtPRO6iZNmsSxR94Zx48fZ+DAgXTo0IGxY8eyevVqIiIiTBKkVXlw26J24j+A/JBJTTVnQIpixZo0odyZ7XTtLmuYimttXUICDB8uO6seOwbu7jB/SjRbRRtqvNdDJnK+vrL36qVLUKWKuUNW8qtWLVmjmpFBXTlKSa63YIWQvZ0BOnYsnPAU09I7qTty5Ajt27fXPf75559p0qQJ8+bNIzw8nG+++YZffvnFJEFalZIloVIlKhCFa6l00tPh9GlzB6UoVkqjgWrVGDhQPly0CO7fN29IhW3TJjk25qxZ8kO8Xz84/fMRBkzxx+af7eDsDJ9+KmvnRo+Wt1wVy1Wjhhygbs+eJyZ1f/4J27aBk10a7796sdBCVExH76Tu1q1bWebJ27ZtG507d9Y9btSoUeFOXmzJ2rVD06kTDardBeCxudYVRTGy0FCoUF7LtWtyIvriIDkZ/u//oEMH2a6qShXYsgUWLgTP9nWgSRN49lmIjIR331W3Wa2FjY3utcwrqUtPl7fhAd5Mn0aFw6uyb6RYHL2TOm9vby5cuABAamoqhw4dIiQkRPd8UlIS9vZqEmC9LFgA69bRa4gc62/hQtSI94piQvYfjCMibgAgO2/GxZk5IBPbswfq14dvv5WPh4Xd4Uiz4bRpKMcjw8ZGDiD8++9QqZLZ4lRMKzOpO3lSJnGPWrBA3iUqSzxj+RQeuROnWC69k7rOnTszduxYduzYwbhx43B2dqZly5a6548dO0YV1Q7DIL16yS9UJ07A4cPmjkZRrFiZMrycvogmLpHcuQPvv2/ugEwjNVVeW/Pm8O+/4OcnWP/2Rmb+4UepH2fBpEkPN1a3Wa3Xn39C06YETHuDUqXk4NJnzjx8+s4dOUsIwAdMwdW7hLxtq1g8vZO6jz76CFtbW1q3bs28efOYN28eDg4OuucXLFhAR9XS0iBu3KZHD/n7woVmDUVRrFvv3tgg+CppECBrKazti9SJE/KO6scfyxm8Xn3hPsfr9aXjtI5y3IqmTWHAAHOHqRSG9HTYuxebvbupU0euenQQ4i++gKtXoUqZGwxlNrRrV+SmdlPyR++kztPTkx07dnDr1i1u3bpFj8xs5IFff/2ViRMnGj1Aq5SUBOXKQZky9O91D5DDQKkRYRTFRCpWhJ49CWE3L/tsRQjZJ8Aamj0IIedqbdhQfnCXLQu/jdnHj9srUeavxXIguk8+gR07VG1McfFgWBMiI6lbRws8bFcXFyfHKQSI8PwKB9LUrVcrYvDgw66urtjmMAClu7t7lpo7JQ8uLpCRAUAH30h8feXgkH/+aea4FMWaffop2NnxaWxfnBwy2LZNTpFlyeLioEsXGDVKfins0gVOjJ7P8581gWvX5If7vn0wbhzY6T0rpGLpAgKgRAlISaGe73XgYVI3caLsHNukYQYvnJsqV6qkzmoYnNQpRvJgEGLbf0/Rt69cpW7BKooJVa0Kb7xBRaJ4x20eAO+8Y7k15KtXy6FK1q0DJyeYMUN+MSw3oAt4eMiL278f6tUzd6hKYbO11dXK1rWPBGRSd+oUfPed3GTaoNNo7GzlXLGZoxQrFk8ldeaSeRvk1Cn69ZO/rl1r/b3yFMWsPvgA3NwYc28Kvl5pnD8vb11akuRkGDZMztsaHw916wgOfr6Z4cMfNIvy8ZEzQ3z2mcz2lOLpwS3YWnf2oNHINnSDBsn2ls89By1erwm3bsFff5k3TsWoVFJnLg9q6jh1iqAg2YY5I0O2rVNMJyUFYmLkqPpbtsBvv8Hs2bLJ0Q8/qKTa6rm7w/LllDp3lIjP5RBMH34oP/AswcGD0KCBfM8CvDXwNnsdW1Hj/9pnvZfs5maW+JQi5EFSV/Lfw1StKlft2iUr8T799ME2JUpAUJB54lNMQjWyMJdHkjqA/v3l2FILF0J4uOqIZCwpKXJE/ZUr5a0pfZK2evWgc2c5YG1IiGxnrliRdu0AePVV+N//4MABWYE3Z46Z48pDWprs1frxx7Jjo6+v4IdX/6bDzJ5yfApXV3OHqBQ1tWvLtnVeXtStK4e4ARgyRDdbpWKFNEJYQ/8v/SUmJuLq6kpCQgKlS5c2XyBRUbJHnp0dJCdz+649Pj5yCqMDByA42AjnuHdP3oY5fVouZ87A9euyDv6ll4xwgqIpMRHWrJGJ3Jo18jPvUba2sodg5uLhISs2TpyQf/tHubjIEflDQ6FPHznLW9ZzFZH3k4lZ63Xu/GIPLd5uio0NHDr0cLDWouTECTm116FD8vGLz6Uyy/b/KLt8rlzRsiUsXiz/n1gAa30v5aSoXOvHH8vxC0uVgv/+A+8dv8GUKfKN9dZbZotL0Z/e7yVhZjNmzBD+/v7C0dFRNGjQQGzfvj3P7bdu3SoaNGggHB0dRUBAgJg1a5ZB50tISBCASEhIKEjYBafVChEaKsTw4ULcvi2EEOLll4UAIUaMKOCxt2wRwt9fCI1GHvDx5bvvHm6bmipjsWB37gixebMQH34oRMeOQjg4ZL1cX1/5Z964UYhbt/K+3KtXhfjxRyFeeUUID4+Hx3BwECIpKfv2Reb9ZGJWeZ1jxggB4iX/vQKEaNtWiIwMcwf1UHq6EFOnPnw/u7sL8fOH/8qyDULY2so3fXq6uUM1iFW+l3JRVK71wgUhgoOFWLTowYrXX5fvoVGjzBiVYgh930tmTep+/vlnYW9vL+bNmyciIyPFqFGjRMmSJcWlS5dy3P78+fPC2dlZjBo1SkRGRop58+YJe3t78dtvv+l9zqJSyHKyfv3Df9737xfgQIcOPcxGypQRtxt1EL+2/Va8FnxUBPvFiv6vpIoFC4Q4d04I7Tf/E6JaNSE+/1yIa9eMdi3GlpYmRGKiEHFxQpw9K8SyZUKMHClEw4bys+3xvDUwUIixY4XYs0fPD+qUFPlHfyTjy8gQYv9+IaZMyf1/X1F+PxmTVV7nnj1CgLiAv3B0yBAgxIABRSNH+vdfIUJCHr6fu3YVIiZGCPH773JFQIAQu3aZO8x8scr3Ui6KxLVqtfLL+6Oeekq+j1atMk9MisEsIqlr3LixGDp0aJZ1QUFBYuzYsTluP2bMGBEUFJRl3euvvy6aNm2q9zmLRCHLRXq6EH5+sqz9+quBOycn637VJt8TJ344IKZOSBKtW2uFnV3OFXYghJ/DVdGHxWI2Q8Qp25pC27qNEB9/LLMZI3+6aTO0IvpUgli7LEFMnSpE375C9OghRGjz26JtkzuiWcMU0aBehqhRQyuqVBHCx0cIV9fsNW85LeXLC9GrlxDffCNEZKSQ/8guXxZi+3YhfvhBiMmThXjtNSG6dROidWsh3nnnkcC0WWs1HR3lib28hKhYUYg+fXK9pqL8fjImq73O3r2FALGk5sfCxkYrQNbSpqWZJ5yUFPn9qkQJ+VZ0cRFiwXcZWWuXf/hBV7tviczxXrp586Z49dVXRenSpUXp0qXFq6++Km7duqX3/kOGDBGA+Oqrrww6r9nLzbRpQpQtK8T48Q/XXbr0sKbX2sqzFdP3vWS2jhKpqakcPHiQsWPHZlnfsWNHdu3aleM+u3fvzjYVWadOnZg/fz5paWnY59CiPSUlhZRHBqJKTEw0QvRGlJ4uu7wKgW3//vTtCxERssPECy/oeYxLl6B1a8T7E5iTPpCICCcuX87aKC8wELp2hcaN5XhF27bJIayiU734iVf4iVcgAwK2nefZbX/w7Htv08I/Grvz/z7stZGQICcCf3wRQjYGzLzPf+8e2ikfcSpSsPe/shy7Vo5jif4cSw3kBh45XIBhjbxL2KcR6HWL5n6XaO51lualj1ORy1CzJvzfeLnR/ZS82xg5Oj78XaORjecy3xspKVkHL7t2zaD4FAsSEQErVtDn5HvYj+1En2nButldliyBwhxPfe1aOctF5hyd7drBgj5/U+nzEdB5E/j5yScyB7ZU9NanTx+uXLnCunXrABgyZAhhYWGsXr36ifv+/vvv7N27F19fX1OHaXyOjnDjhmyYmWnzZvmzUaOH/7MVq2G2pC4+Pp6MjAy8vb2zrPf29iYuly6KcXFxOW6fnp5OfHw8Pj4+2faJiIhg8uTJxgvc2H77TXZ99fCA55+nXz8XIiLkgKKxsXLIqTxduwZPP03MpVQGvBnI+rtytZMTtG0rR5gPDYUqVR7u0quX/JmcLHvcbt8ul107tVxIrcx0RjOd0bhHJ9G1n4Znn4WOTwtc8hgm4UaPQewZOI89e2DPbif2bRpDYg7Jmi3pBJaKps4zlahVCzzKZOA44R2c7t3G6d4tHLmPE/dxJAVnknFuF0KJhbNwdgbnEgKnMiXQpKZANHJ5VLt2MH78wz9AxYqy66q/v+wF5u8PXl6yp2CFCln3vXxZ/rx/X36iP/rT2fkJL4Jisfz95XQMn3/Oi7+/iuOSw7wY5sRvv8mX/9dfs+b/pnD2rEzmMocL8/KCTyan8drJt7EZ9GAQvU8+kaMLKwY7deoU69atY8+ePTRp0gSAefPmERISwpkzZwjMoytodHQ0I0aMYP369XTt2rWwQjaezOnCHk3qNm2SPx/0Alesi9mHNNE8NnaHECLbuidtn9P6TOPGjSM8PFz3ODExkQqPf6Cb0wsvyPEUzp6Fb74h8L33CAmB3btlTcHbb+exb0ICdO7MsrP1GWYzh1t33XB0lJUPr7/+5FzE2VmW68yyffeuDRs2wB9/wJ9/Cm7ccOHHH+HHH2WP0ZLcxo70bEs6dlxeWQl0w2RpAFdKOqTSsPJNgmvco04dqNPIiepNXXFyr/RIFLYw/Ev5q1Yr569JTJTXdvOm7G6a+XKlZ0C/vnK9vb2sXStdWv50ccmauQJcvKj/2DCZQ0KooSGKn/HjZdX46dN0/+VV/vjjN3r0kDM2PPus7EVdooTxT5uUBB99BF99JYcssbODkSPhg15ncB3S6+G8TqNHy0Kt5Mvu3btxdXXVJXQATZs2xdXVlV27duWa1Gm1WsLCwnjnnXeoWbNmYYVrXJlxnz8v/7c6Oz9M6tTUYFbJbEmdh4cHtra22Wrlrl27lq02LlO5cuVy3N7Ozo6yZcvmuI+joyOOpv6qXRB2djB5shwv4/PPYfhw+vcvw+7d8nPmrbdyyUvu3eNm6CuMOPw2S+kDWjkMyqJF+Z+zu2RJ6NFDLunpGnbtkgneH3/AuXOaHGveHhUYKAdRDgmRP2vWdMDOrpz+AdjYPEzQMm81PcrODubO1f94arA/RR9ubvJN/vLLMH48nRvIWrNu3WD9etlsYfXq7MPZ5NetW/KLUkTEw3ETO3eGr6ZlELT2K2j1vqwm9PSU/wS6dDHOiYupuLg4vLy8sq338vLK9a4QwNSpU7Gzs2PkyJF6n6vINffx9ARvbzm6dmSk/HDo2BF27oRmzcwbm2ISZptRwsHBgeDgYDZu3Jhl/caNG2mWy5stJCQk2/YbNmygYcOGObansxi9eslq8oQE+PJLevWSdw9PnoSGDeVnzQcfyA+C3bshPjaNDW0jqL17Dkvpg62tYMIE+Vx+E7rH2dlBq1bwxReyEvHKFTl4ZWSknI3h0CE5T/iuXfDPP7LZxunT8jPo9dfleF9q/nDFYoSEyDd4gwaArL1ev15+v9iyBdq0kS0l7t/P3+GFkOWkb1/w9ZV3fOPi4KmnZMK4Zg0Ebfzfw8loO3eWNXUqocvVpEmT0Gg0eS4HHgw8mdOdnLzuCh08eJCvv/6ahQsX5nnn6HERERG4urrqliJxV+jRW7AlS8p/0mfPqinkrFVh9NrITeaQJvPnzxeRkZHizTffFCVLlhQXL14UQggxduxYERYWpts+c0iT0aNHi8jISDF//nzrGdJkxQrZI6lUKSGuXxfDhj25xycIUa3CXbFnj7mDL76K7PvJyIrLders2iVEr15iz/YU4er6sLy5uQkxeLDsVK3PUDnx8UJ8+aUQ1atnLbd16ggxY8ZjQxfdvStEo0ZyHEkLHzsyL8Z6L12/fl2cOnUqz+XevXti/vz5wtXVNdv+rq6uYsGCBTke+6uvvhIajUbY2trqFkDY2NiISpUq5RrT/fv3RUJCgm6Jiooyf7kZNUq+6cLDzReDUmBFvvcrQK9evbhx4wZTpkwhNjaWWrVqsWbNGipVkm2uYmNjuZzZgB0ICAhgzZo1jB49mhkzZuDr68s333zD888/b65LMJ7nnpO1BIcOwWefMWPGZ4wYISsP/vtPfrE6e1b+HhUld3mjywU++zVAteNXFGO6d0+2Qbh6lSZ373J4z2/MWejIkiWyxnrePLn4+8Mrr8havZs35R2uuLiHy9WrsrItNVUe1tlZ1roPGSI7HmrOn4N3/yerw21t5QZ79shmCMoTeXh44OGRU2/6rEJCQkhISGDfvn00btwYgL1795KQkJDrXaGwsDA6dOiQZV2nTp0ICwvjtddey/VcRbK5T0iIvMXy1FNw+LC8jaLeY9arkJLMIqNI1zj89ZcQLVvKaoDHXb6sG5sqOVnOjKCYX5F+PxlRcblOnU2bhHBykjUczz4rREqKyMiQM5e89pocP06fmnQQol49IWbNemRIsFu35FiQzs5ygy+/NOOFFj5zvJc6d+4s6tSpI3bv3i12794tateuLZ555pks2wQGBooVK1bkeoxKlSpZ3jh1jzpw4OHA1VZcE2ytLKKmTnlMaKhcHm/Dkdm2JjAQ1q6lRAlHk/TGUxTlgXbtZGO3bt1kJ4qePbGZOpW2bWvStq0cXWTVKjnl6pkzchiScuVkm/RHf1apItu5ajRATAx8NB1mz5ZdX0GOO/Tcc2a80OJhyZIljBw5UjfOaffu3fn222+zbHPmzBkSEhLMEV7hyOz1WquW6kRmxVRSV5TkVNA2bZK3gpKSZC+9W7fkp4WiKKbVoQP8/rsc1+Svv+Tywgvw66+UKCH7N2WO+Zin9HQYPhx++OHhvdhateDdd2Wvd3UrzOTc3d1ZvHhxntuIB8Nj5ebixYtGjMgMli+XP9VQJlZN/Tcpim7elGNntWsna+6SkqB1a9l9TiV0ilJ4OnWSI3P37CnbvVWv/vC59HTZFTzTvXtydpf9+2UCmDmchZ2dXJ+aCi1awJ9/yv1efVUldErh6NVLDlcAKqmzcqqmrig6dSrrYKMvvSQHoCtqDXAVpTho3FjWcsTGykGvM61dC927yzEVExLgzp2s++3bJ3tEAHz6qRyXqHnzwotbUTLFxj783VIHUlb0or4mFkXNm8uaAYDwcFi6VCV0imJuPj5yOr9Mp07JWrjo6IcJnYODnIIuOFj2k8hUv75K6BTzmTRJ/hw+XLWns3Kqpq6oWrZMjp/g72/uSBRFycmYMfDaa3KcIU9P2VvCxUV9aCpFT7t2cO4clC9v7kgUE1M1dUWVnZ1K6JRsbt26RVhYmG7E+rCwMG7fvp3nPv3798820n7Tpk0LJ2Br5+kpxwF76ik5D7FK6JSiqnJlWZOsWDVVU6coFqRPnz5cuXKFdevWATBkyBDCwsJYvXp1nvt17tyZ77//XvfYQf1zVxRFsToqqVMUC3Hq1CnWrVvHnj17aNKkCQDz5s0jJCSEM2fOEBgYmOu+jo6OlFM9pxVFUayauv2qKBZi9+7duLq66hI6gKZNm+Lq6squXbvy3Hfr1q14eXlRrVo1Bg8ezLVr1/LcPiUlhcTExCyLoiiKUrSppE5RLERcXBxeXl7Z1nt5eREXF5frfqGhoSxZsoTNmzfzxRdfsH//ftq1a0dKSkqu+0REROja7bm6ulKhQgWjXIOiKIpiOsXu9mvmqOGq5kExhsz30ZNGo8/LpEmTmDx5cp7b7N+/HwBNDg3xhRA5rs/U65FpD2rVqkXDhg2pVKkSf/31Fz0zh855zLhx4wgPD9c9TkhIoGLFiqrcKAVmjDJjKdTnjWIs+pabYpfUJT2Yc1HVPCjGlJSUhKura772HTFiBL17985zG39/f44dO8bVq1ezPXf9+nW8vb31Pp+Pjw+VKlXi7NmzuW7j6OiI4yNjI2b+Q1HlRjGWgpQZS6E+bxRje1K5KXZJna+vL1FRUbi4uGSr3UhMTKRChQpERUVRunRpM0VoOur6jE8IQVJSEr6+vvk+hoeHBx6PDmqbi5CQEBISEti3bx+NGzcGYO/evSQkJNCsWTO9z3fjxg2ioqLw8fHRex9Vbqzz+iy1zFiK3MqNNb+nQF2fKehbbjSiONSB6ykxMRFXV1cSEhKs9o2ors+yhYaGEhMTw5w5cwA5pEmlSpWyDGkSFBREREQEPXr04M6dO0yaNInnn38eHx8fLl68yPjx47l8+TKnTp3CxcWlwDFZ+9/dmq/Pmq+tKLP2v7u6PvNRHSUUxYIsWbKE2rVr07FjRzp27EidOnX48ccfs2xz5swZEhISALC1teX48eM8++yzVKtWjX79+lGtWjV2795tlIROURRFKTqK3e1XRbFk7u7uLF68OM9tHq18L1GiBOvXrzd1WIqiKEoRoGrqHuHo6MjEiROzNBC3Jur6FFOw9r+7NV+fNV9bUWbtf3d1feaj2tQpiqIoiqJYAVVTpyiKoiiKYgVUUqcoiqIoimIFVFKnKIqiKIpiBVRSpyiKoiiKYgWKXVI3c+ZMAgICcHJyIjg4mB07duS5/bZt2wgODsbJyYnKlSsze/bsQorUMBERETRq1AgXFxe8vLx47rnnOHPmTJ77bN26FY1Gk205ffp0IUWtv0mTJmWLs1y5cnnuYymvnSWwxnKjykx2lvC6WQprLDOgyk1OitRrJ4qRn3/+Wdjb24t58+aJyMhIMWrUKFGyZElx6dKlHLc/f/68cHZ2FqNGjRKRkZFi3rx5wt7eXvz222+FHPmTderUSXz//ffixIkT4siRI6Jr166iYsWK4s6dO7nus2XLFgGIM2fOiNjYWN2Snp5eiJHrZ+LEiaJmzZpZ4rx27Vqu21vSa1fUWWu5UWUmK0t53SyBtZYZIVS5eVxRe+2KVVLXuHFjMXTo0CzrgoKCxNixY3PcfsyYMSIoKCjLutdff100bdrUZDEay7Vr1wQgtm3blus2mQXt1q1bhRdYPk2cOFHUrVtX7+0t+bUraopLuVFlxjJft6KouJQZIVS5KWqvXbG5/ZqamsrBgwfp2LFjlvUdO3Zk165dOe6ze/fubNt36tSJAwcOkJaWZrJYjSFzmih3d/cnblu/fn18fHxo3749W7ZsMXVo+Xb27Fl8fX0JCAigd+/enD9/PtdtLfm1K0qKU7lRZcYyX7eipjiVGVDlpqi9dsUmqYuPjycjIwNvb+8s6729vYmLi8txn7i4uBy3T09PJz4+3mSxFpQQgvDwcFq0aEGtWrVy3c7Hx4e5c+eyfPlyVqxYQWBgIO3bt2f79u2FGK1+mjRpwqJFi1i/fj3z5s0jLi6OZs2acePGjRy3t9TXrqgpLuVGlRnLfN2KouJSZkCVGyh6r12xm/tVo9FkeSyEyLbuSdvntL4oGTFiBMeOHeOff/7Jc7vAwEACAwN1j0NCQoiKimLatGm0atXK1GEaJDQ0VPd77dq1CQkJoUqVKvzwww+Eh4fnuI8lvnZFlbWXG1VmJEt73Yoyay8zoMpNpqL02hWbmjoPDw9sbW2zfVO6du1atiw7U7ly5XLc3s7OjrJly5os1oL4v//7P1atWsWWLVsoX768wfs3bdqUs2fPmiAy4ypZsiS1a9fONVZLfO2KouJQblSZkSztdSuqikOZAVVuMhW1167YJHUODg4EBwezcePGLOs3btxIs2bNctwnJCQk2/YbNmygYcOG2NvbmyzW/BBCMGLECFasWMHmzZsJCAjI13EOHz6Mj4+PkaMzvpSUFE6dOpVrrJb02hVl1lxuVJnJylJet6LOmssMqHLzuCL32pmhc4bZZHYznz9/voiMjBRvvvmmKFmypLh48aIQQoixY8eKsLAw3faZXZVHjx4tIiMjxfz584tsN/Nhw4YJV1dXsXXr1ixdsZOTk3XbPH59X331lVi5cqX4999/xYkTJ8TYsWMFIJYvX26OS8jTW2+9JbZu3SrOnz8v9uzZI5555hnh4uJiFa9dUWet5UaVGct83SyBtZYZIVS5KeqvXbFK6oQQYsaMGaJSpUrCwcFBNGjQIEs37H79+onWrVtn2X7r1q2ifv36wsHBQfj7+4tZs2YVcsT6AXJcvv/+e902j1/f1KlTRZUqVYSTk5MoU6aMaNGihfjrr78KP3g99OrVS/j4+Ah7e3vh6+srevbsKU6ePKl73pJfO0tgjeVGlRnLfN0shTWWGSFUuSnqr51GiAct+hRFURRFURSLVWza1CmKoiiKolgzldQpiqIoiqJYAZXUKYqiKIqiWAGV1CmKoiiKolgBldQpiqIoiqJYAZXUKYqiKIqiWAGV1CmKoiiKolgBldQpiqIoiqJYAZXUKYqiKIqiWAGV1CmKoiiKolgBldQpiqIoiqJYAZXUKYqiKIqiWAGV1CmKoiiKolgBldQpiqIoiqJYAZXUKYqiKIqiWAGV1CmKoiiKolgBldQpiqIoiqJYAZXUKYqiKIqiWAE7cwdQ2LRaLTExMbi4uKDRaMwdjmLhhBAkJSXh6+uLjU3hfUeaOXMmn3/+ObGxsdSsWZPp06fTsmXLHLddsWIFs2bN4siRI6SkpFCzZk0mTZpEp06d9D6fKjeKsZirzJiDKjeKsehdbkQxExUVJQC1qMWoS1RUVKG9h3/++Wdhb28v5s2bJyIjI8WoUaNEyZIlxaVLl3LcftSoUWLq1Kli37594t9//xXjxo0T9vb24tChQ3qfU5UbtRh7KcwyYy6q3KjF2MuTyo1GCCEoRhISEnBzcyMqKorSpUubOxzFwiUmJlKhQgVu376Nq6troZyzSZMmNGjQgFmzZunWVa9eneeee46IiAi9jlGzZk169erFBx98oNf2qtwoxmKOMmMuqtwoxqJvuSl2t18zq8BLly6tCpliNIV1ayU1NZWDBw8yduzYLOs7duzIrl279DqGVqslKSkJd3f3XLdJSUkhJSVF9zgpKQlQ5UYxnuJwO1J93ijG9qRyY90NGhTFysTHx5ORkYG3t3eW9d7e3sTFxel1jC+++IK7d+/y0ksv5bpNREQErq6uuqVChQoFiltRFEUxPZXUKYoFevzbmhBCr5qPpUuXMmnSJJYtW4aXl1eu240bN46EhATdEhUVVeCYFUVRFNMqdrdfFcWSeXh4YGtrm61W7tq1a9lq7x63bNkyBg4cyK+//kqHDh3y3NbR0RFHR8cCx6soiqIUHpXUKYoFcXBwIDg4mI0bN9KjRw/d+o0bN/Lss8/mut/SpUsZMGAAS5cupWvXroURqpJfJ07AqlWQmAhJSXKJiAA/PzIy4OLcDaT8vJJUD19S3cuRVrYcqa6epLp5YVfOg8YdSuPiYu6LUBTDZWRkkJaWZu4wzMLW1hY7O7sCtzVVSZ0lu3YNPD2hGDQ4Vh4KDw8nLCyMhg0bEhISwty5c7l8+TJDhw4F5K3T6OhoFi1aBMiErm/fvnz99dc0bdpUV8tXokQJq+99aFFu3oQPPoBZs0Cr1a2Opyzrq01mzSlYtw5u3uwIdMz1MHa2Wpo1t6FTJ+jU4i71G9tj4+RQCBegGMXKlbB0KbRrBw/KdHFw584drly5QjEbkCMLZ2dnfHx8cHDIf3lVSZ2l0WrBxgbmzoW334Zvv4W+fc0dlVKIevXqxY0bN5gyZQqxsbHUqlWLNWvWUKlSJQBiY2O5fPmybvs5c+aQnp7OG2+8wRtvvKFb369fPxYuXFjY4Ss5+ecfeO45uHEDARxqPpI12s6suVyLvTHlERMefnFzdNBSyj4Ve00aDqTioE3BPuM+DhnJJKSX5GJGANu3w/bt8B4l8eA6T3scpnOjm3T/tBludSqa7TIVPfz7L/z6K5QqVWySuoyMDK5cuYKzszOenp7Fomf0o4QQpKamcv36dS5cuEDVqlXzPTC3Suoszf/+JxO69HR5W+att6BrVyhb1tyRKYVo+PDhDB8+PMfnHk/Utm7davqAlIKpWROA/57qzGtOS/lnp1uWp+vWhS5d5NK0qQ12dk6AU/bjJCVx7hps2ADr18Pmv5KJT/dkaXxHlq4Fh7UpdA06xSsTKtO1pyNOORxCMbOSJeXPO3fMG0chSktLQwiBp6cnJUqUMHc4ZlGiRAns7e25dOkSqampOOWzcKrer5bmjz8gMhIGDpQfBPHx8NiYZYqiFHFxcbKd3INbTVrXMnw75Bh1Y9bwzwk3SpSAHj1g3jy4cgWOHIFPPoEWLcAur6/iLi5UqQLDhsHvv8ONuyXYviSK97sfo3bJc6TiyMrT1XnhFUfKlU1l4EDYvBkyMgrjohW9jB8vf547Z944zKC41dA9zhjT5qmkzpLcvCnvqQC88ALMni1//+472LnTfHEpiqK/lBR4+mn54b1iBRcvyof/F+FLcrKGtm3l97YVK2DQIPDzy/+p7B00tOxTgQ//qMOxpMoc+3w977rMpAKXSUh2YMECaN8eKlWS4Zw/b7SrVPIjI0PegQG4dcu8sSgWSSV1lmTtWlnoa9WCypXl1/aBA+VzQ4dCMe01pCgW5cMP4cQJRFkPvjvSkNq1ZW2Zs7NsIvv33+Dvb4LzajTUfrsTn8b15+IH37NteTxDhoCbG0RHy4rDKlWgQwdYtkzmnkohu3bt4e+lSpkvDsViqaTOkvzxh/z56NAVU6fK9nQnTsBXX5knLkVR9HPwIHz6KTH40LXicQZ/VIk7d6B5czh6FN54Q/aDMilnZ2wmT6RVTw/mzJF3gn9t8jmdHDaj0Qg2bYLevaF8edlk9/RpE8ejPBQd/fD3+/fNF4disVRSZylSUmRNHUD37g/Xly0L06aBre3DantFUYqe1FTo35/4DDdalzrI2sPlcHSUxXfbNnjqKfOE5ZiSyAsJC1iX2p7zIoAJTy3Fr1w68fHw5ZdQvbrsoKHn1MJKQVy58vD3YtRRQjEeldRZii1bZCH38YGGDbM+168fnDwpb+soilI0ffghKSf+pYfdn/x3x4dKleDwYVkbZmtrxrhKl5Y9MSZNwt8hlin/9eFigjurB66kezctNjby+2Tz5nLotM2bdf07FGPLrKlr3x7U1HwWYenSpTg5ORH9SC3roEGDqFOnDgkJCYUej0rqLIW3N/TvD/37s26DDe3bw2uvwRdfwLr1Gq6UDFT/aBWlqIqORnw6lUF8xz/pTSldGtaskbVgRYKjI0ycKO8Bt2qF3b0knpnfkz+iG/HvpigGDQJ7e/ndsn172Zx37VrrT+62b99Ot27d8PX1RaPR8Pvvv5v2hFeuIIArFUIQNubM9IuIu3dzXx6/PZ3Xtvfu6bdtPvTu3ZvAwEAiIiIAmDx5MuvXr2ft2rXmGdxdFDMJCQkCEAkJCeYOJd+aNBFC/jvNupQuLURI3TtidNXV4tblRHOHWSxYw/tJH8XlOk1pUv8LAoSwtRViwwZzR5OHjAwhvvtOiDJlhKhYUYikJCGEEJcuCTFihBCOjg//5zRoIMQPPwhx967+h7ek99KaNWvEe++9J5YvXy4AsXLlSoP2N/haw8LEfF4TIMS33xoer6W6d++eiIyMFPfu3cv6RE4fdJlLly5Zt3V2zn3b1q2zbuvhkfN2+bR69Wrh6OgoPv74Y1GmTBlx4sQJ3XO2traibt26om7dumLgwIH5+zsI/d9LKqmzMElJ8kMBhBgzRogXXxSiRo2H6zKXPrWPmjvUYsHS30/6Ki7XaSqLFz8sm3PnmjsaPcXFCbF//8PHKSlC9OwpYr77S7wdniFKlsz6hfL11+XmWm3eh7XU91KhJHWpqWJIn0QBQgyuvFGIW7cMjtMSWXpSJ4QQ9evXFw4ODmLr1q1Z1pctW1bvYxgjqVMzSliCzZtlu5fgYHbu1JCRIYc8mDr14SapqXJ2mT3fHmDInAb8dLwOr6+9S6vQkmYLW1GKvchIdhxzZcAAOdjcO+/A4MFmjklf3t5yyfTrr7BiBT4rVvB55cq8O/5d5tzvx4Iljpw/D3PmyKVOHTm+3iuvgLu7+cK3SPb2xCTaA5B4Pl6OVefmZt6YzCmvziKPN0R9dDiYxz3epfzixXyHlJP169dz+vRpMjIy8H60zJiBalNnCUaPhkaN4Oef2bZNrmrTJusmDg5y+LpBM+oz1G0ZACMG3CU9vXBDVRTlgdRU/nv+XXq87EhqKvTsCZ9+au6gCqBtWzlCsbs7nD+Px3uv897XXpyt9yKbhv1GnxdScHSEY8dg5Ejw9S02U5fqpKSkkJiYmGUxVEyM/JmAq+oBW7Jk7svj02jlte3jU4/ltl0+HDp0iBdffJE5c+bQqVMnJkyYkOX5xMREgoODadGiBdsyP8BNSCV1Rd2FC/K/pK0tdOxI5jSerVvnsr2tLR99YkNZ4jke58Wsr1MLK1JFUR5x84PpdD09jRt40KheGj/+WAhj0JmSry98/LHslTlrFlSrBomJ2Kz4jXazXmTJt7eJiZHTU9etnERKChS3WZ8iIiJwdXXVLRUqVNB/58REeOUVYs7IRFAldUXfxYsX6dq1K2PHjiUsLIwpU6awfPlyDh48mGWbgwcPMnv2bPr27ZuvRN8QlvwvpnhYtUr+bNGCu05l2b9fPny8pu5R7oOf5+OyciDiCe+LPGulrcm5c7KG4I03YOZMOaPajRvmjkoplm7dou+02vxLIBU97rJqrT3OzuYOykicnWUV3KlTsGOHnIpi0CDw9sbdHUaMgMNVXuBAm7d4+21zB1u4xo0bR0JCgm6JMmRYkqgo0n9axtW7ciaJBFzz3SNTMb2bN28SGhpK9+7dGf9gvt7g4GC6devGe++9p9vO19cXgFq1alGjRg3+/fdfk8al2tQVdZlJ3bPPsmsXpKdDxYpPmEbIzo5BHwcwd+hBDt0PZtyYDOYvLPzu8TExcqKL+vXB09N050lIkBUIX38t2xY+zttb3pquWRP69IEmTUwXi6IAbH57DX9lvIK9Jo3VfztTrpy5IzIBGxs5tkmLFtme0tSqSXDdOlDFDHGZkaOjI46OjvnbOTqaq3gjHtS1yJq6s0aMTjEmd3d3Tp06lW39H5kzPwG3bt3C2dkZR0dHrly5QmRkJJUrVzZpXCqpK8pu3ULXiK57d7Z9L3/Nq5Yuk23/ML793wianZzHgh9sGTLMtMlMRoZM4HbulCPP79z5sC2qgwO89BIMHw5Nmxrvlkx6Onz3HXzwAVy/Ltd17CiTyJMnZTwXL8LVq3LZtEneNVqyBF580TgxKMrjxL37jF1UA4DXO5yjTt0gM0dkBl9+ae4IjOLOnTv8999/uscXLlzgyJEjuLu7U7FiReOe7MoVYvDVPUyktLr9auFOnTrF66+/jo2NDRqNhq+//hp3U/ce0ruvbREzY8YM4e/vLxwdHUWDBg3E9u3b9drPorrTZ46DULOmEEKI5s3lw/nz9T9E//5yn+BgIdLTjR/i8eOyZ3np0tl7h9vYCFGhQtZ19erJIR3u3CnYeTdsEKJWrYfHDQwU4q+/sg+nkJQkxL59QixYIETXrg/jWrCgYOfPZFHvpwIoLtdpDL+9vkGAECU1d0RcVKq5wylyLOm9tGXLFgFkW/r166fX/gZd65Qp4ne6Z/l/mT5jdsEuQB9XrwrRqpXx/inmQ15DeRQnxXZIk2XLlvHmm28yc+ZMmjdvzpw5cwgNDSUyMtL4357MacMG+fPZZ0lOhn375EN9auoyffoprFgh5xFfsMC4wyls2AAvvPBwytlSpaBprSSaa3bTvMQhmqTvpPTty+yv1oWZd8L4+UgQR47YMGSIHNqhf385cXijRvpNk5SUJEfhX7gQ1q2T68qUgcmTZRMfe3vkyOGtW4OdHdjbU8renkZ2djSyt6evvSNDG03gu/11GTBAHm/kSOP9PRQlPR3eW9EAgPAOx/Eu39TMESkF0aZNG0RhTZvxWE0dQNJLA3Ez9XmXL5cNkPfulR8uAQGmPqNiSiZKOE2qcePGYujQoVnWBQUFibFjxz5xX0v6lijS0oTYskWIc+fE33/Lb27lyz95cM/HjzH91f0ChChbVitu3DBOaHNnZwhbmww5rmODRHHo0IOawNWrcx0AMh53Me3Z7aJKlaxPubsL0auX/KIYHZ31PNevy5rJrl2FcHB4uI+dnVaM6h0rboR/JMT06Q93uHUrzwErtX1eEeHhD1d9VO9Xof1quqzOS0kx+O9gUe+nAigu11lQ8+bJ95VH2QyREFPA6mgrVZzeSwZda9euYgKTs/zLunjR9DGKN998eMKePQvhhNmpmjqpWNbUpaamcvDgQcaOHZtlfceOHdm1a1e27VNSUkhJSdE9fmJ34vv3ZbWRvb1R4i0QOztdtdy2hXJVmzYGtkm7d483/gzlOzZz4kZtJkyAGTPyH5L27j3G9TzDZxvqARDGIr6r+jcO9RfJDWrUkOPq+fjIIRDc3ORk4Tt2UHbXLt6a4Mzo+rBxI8yfcIEN+8tw86Yby5bBMjm8HrVry+s8flx+gdRqH56/qtdtevrs5rXYCAJ/3vFgZVVZ5abRyJ55q1fLKpO0NLlk/p6SgqZmTaa1kmM5T5oE7x95gYQjnzGVxmjs7WVvinr15NKunQxGUfRw7558TwG8974NpX3UwN+KAaKjs9XUmXj0C+nR3pgrVsgezS1bFsKJFZMwVcZpKtHR0QIQO3fuzLL+448/FtWqVcu2/cSJE3NsE5Fjttu7t5zYcNMmU4Wfby1byi9S8+blY+fJk8UWWj9oT6YV27bl4xjXr4vk9z8WLzj8oftSN8npE6EdO06ImBj9jpGWJueVzDRihEjDVvxDMzGByaIRe4WGjGwVbPXrC/FhuW/FCWoI7aNPlColxAsvyLaHjx5XT198cFt3qNcdF4h0bLKeeNKkJx7DXLUOhrYp3bp1q2jQoIFwdHQUAQEBYtasWQadrzjVruTXZ6NjBMjpUot5hUOeitN7yaBrTU0VoW2Ts/wL2vHuatMH+dRT8mQdOggxc6ZpGl8/gaqpk4rl3K+ZSd2uXbuyrP/oo49EYGBgtu3v378vEhISdEtUVFTuf5hXXpFv7okTTRS9AUaNEuK114Q4eVIkJz+89Xj2bD6OdfOmEC4u4mWWCJDHmjPHgNu4ycniqlct0ZRdAoSwJ0X8+Opa3UTf+abVyp4WX38txLPPCuHqKq5TViyll3jDZqb48vN0ceHCg23DwoTw8xOic2ch3nlH9oowwj+AuXOF0Gjk3/aFLnfE7SV/ytf/2WeF2Ljxifub4wPq559/Fvb29mLevHkiMjJSjBo1SpQsWVJcunQpx+3Pnz8vnJ2dxahRo0RkZKSYN2+esLe3F7/99pve5yxOH8T5ceuWEGXs5ZydC18ohA9iC1ac3kuGXmvdulm/V/7Z5nPTBiiE/D9++HDBe68VgErqJLMndeZ4AVJSUoStra1YsWJFlvUjR44UrVq1euL+ef5hZs+WJaltW2OFm3+Z3Ub37BGbN8tffX0NbE/3qHHjRCKlRM8ym3T/MAYOfHJepNUKsXmzEAEe8gOrTMn7YuumtHwG8QTp6bJt26efCtG9uxCxsQ+fSzVdL8KlS4Wws5N/E39/If75R/99zfEBZWib0jFjxoigoKAs615//XXRtGlTvc9ZnD6I82Pc4OuyozrHRXrkGXOHU6QVp/eSodfq6fmgTWYpWWO3JPgLE0eYi+Rk+U2lkKikTjJGUmfwjBJarZYPP/wQPz8/SpUqxfnz5wGYMGEC8+fPz/dtYH05ODgQHBzMxo0bs6zfuHEjzZo1K9jBW7WSP/fsyXkU28Ki1UJsrPzdzy/LfK/5HuNt9GhcSmTw2632fBryOzY2gvnzZdOJy5cf2zY9HfFJBBs/2U+rVrJp2YV4FypXFuw+6EjrdiZqimlrK7vCvvsu/PEHWUZsNWEbx969Zdu9gAA5rl2rVjBxIkVy3tzMNqUdO3bMsj63NqUAu3fvzrZ9p06dOHDgAGlpaTnuo/cclmlpclDAzPnriqHYWJi+wAWAT5quxrZ6NTNHpFico0dJ7d1XN95mkJ8cUiDhTuEPGs+WLbJtdHGbDsRKGJzUffTRRyxcuJDPPvsMBwcH3fratWvz3XffGTW43ISHh/Pdd9+xYMECTp06xejRo7l8+TJDCzp7dFAQeHjIFs+PzN1W6K5dkxmFjQ2UK/fk+V714ekJ4eFogHfjwlm3RuDuDgcOQHCwYNMmuZk4cZK1Nd6i2Xtt6PheI/75Rw4ePHw47N2rITCwgNdWRIWEyP4cYWEyp54yRSa8D76zFBnx8fFkZGTg7e2dZb23tzdxcXE57hMXF5fj9unp6cTHx+e4j95zWJ45I6freO45WQFcDE159y73Mhxpxk66fdnW3OEoligykrhlWwH5/bWy730AEpNN3Jfx11/leFBr1jxc5+Agv90uWIBuXkrFYhic1C1atIi5c+fyyiuvYPvI4GJ16tTh9OnTRg0uN7169WL69OlMmTKFevXqsX37dtasWUOlSpXyfczUVFj5u4Ypnv9DgKy6MZcrV+TPcuW4n27Hnj3yoSHj0+Xoww/h99/h0095upMNBw9C/Xpa4uM1dOyQwVuNttO4zj26nP2aPYTgZJ/OqJGC8+dlj1kPjwKev4grXRoWLYKffgJXV1lhW7euXFfU8hXNY1W2Qohs6560fU7rM+k9h2XmlDcJCXDzpp7RW4+zZ2HeYicAPq2zFE2IGpdOyYdHxqjz8QE3N1kuE+455LVXwf39N8yZg+5DBqB5c3j1VflPb+TIrMMPKEWewUlddHQ0Tz31VLb1Wq0211s5pjB8+HAuXrxISkoKBw8epFXmrdMC6NULJp7qzUX8zZvURUfLn35+7N0LKSnyTmTVqgU8rkYDzz4r5+xCzh+786Ot9GMhWmz58kArDoiGONve563X73Dhsh3Tv9bg51fA81qYl1+Go0dlTd2dO9Cvn1x365a5IwMPDw9sbW2z1cpdu3YtW21cpnLlyuW4vZ2dHWXLls1xH0dHR0qXLp1lyZGzM7o3yLlzhl2MFZg4PpUMYUtX/qTlR53MHY5iqR4ZzsTXF1zLPEjqUpxMe97M4UyqPdZkYOpUKFlSJntLlpg2BsWoDE7qatasyY4dO7Kt//XXX6lfv75RgjIHBwd5FwngcMMh8Mwz5gsmM6krX9447enyUKJrO74/0ZiZHVcSVOIiY7se52KMI9Nml7LOScj1VKmSbFry8ceyqd/mzTK5Nrf8tCkNCQnJtv2GDRto2LAh9sZoq1jlwaztj8yRWRxcvQq/rJR/v48CFkDXrmaOSLFY0dHE4gM8SOrc5W3XhNTCSepSKwdlbUPs6wsTJsjfx4x5OG2Qkqdbt24xefJkYjPbxJuBwTfsJ06cSFhYGNHR0Wi1WlasWMGZM2dYtGgRf/75pyliLDT16sHhw3AkdBw9h5kxkBs35E8/P+O0p3sCTc0aDFtfA3NeclFkawvjx0OHDnIQ0KKS5IaHhxMWFkbDhg0JCQlh7ty5WdqUjhs3jujoaBYtkgNCDx06lG+//Zbw8HAGDx7M7t27mT9/PkuXLjVOQE89JWu2i1lN3U8/QUaGhiaNBfWWfSnbwCpKfly5Qgz1AJlPla4s27oktHnWdOe8cwdiYjhJDRo/Hcy9e+Dl9XDceB+vt/EtUxK/uON0H/8/fP433nSxWImRI0dy69YtDh8+zO+//26WGAxO6rp168ayZcv45JNP0Gg0fPDBBzRo0IDVq1fz9NNPmyLGQlO/Pnz/vUzszGrCBHj7bVKSUtn9oJlggdvTKfnWuLG5I8iqV69e3LhxgylTphAbG0utWrWytCmNjY3l8iNdmgMCAlizZg2jR49mxowZ+Pr68s033/D/7Z15XJTV+sC/w77JoCIooQKm4p67mKWWuWdpevNalGW2XVPb7tWs1NJsscV285bWT2+2WpblkrmmqKloiqIpqGyCioOAbMP5/XGYgVHAGZhhhpnz/XzezwzvnPc9z8vMmXneZ73rrrusI5AhHMOFLHVCyO8KgIkPaGQsg0JRU650vzaWP805toypO34cgF8DxpGfK91AZ8/KLT4ewB2YAsCXy+PZ/J7tRHEGVq9eTW5uLj///DMTJ05kxYoV3HPPPXUuR41Sa4YMGcKQIc4XP2LwHu/fD5w6JTOAbGkiqw5fX3b/6UtBAYSG4rRZp4qa8fjjj/P4449X+tqyZcuu2te/f3/27dtnG2Fc0P0aHy/b2Hl7C+6+2wZxEQrXobQUMjNNlTqtfEmns+G8Za7XBL+ekAtPPSXzI9LTIS1NPqac0vPJp+5s1d3AuXPOnyxXG0aNGsWoUaOAyr+D6wqLlbqoqCj27NlzVYD1xYsX6datm7FuXX2kSxf5mJoKWRE9aNJEI29bbBHMZgaGeLr+/e0mgkJxbXr2lMGHLtQnd+knxYAnd4ofaHjxBmgYaW+RFPUVNzfIzSWto4BEqdQ1kGUP0Z08B+c1UEVCU60oy2hPENEA9OkjDRumofHuxO2Bgwdh/XqYMMH6Yiisi8VBIMnJyej1+qv2FxYWkmoI8K+nNGhQ7kmK9+wFWVmmzY7rijvugAceYPMGWQDZXsZChcIsIiNl8OHtt9tbkjqhsBBWLJdlHiY2Wq1cr4ra4+FBWpZMujGx1F1ygypqSdaaZ55BXNSRcFnekLRvX/mw4cPlY8VSdgrHxWxL3erVq43P161bh9bwqQP0ej0bN24kwgm+3Lp2lV6k/eG3c1vSLzIAvC59nzk5sHo1RXiyw/czQMXTKRSOxJo1cCHXmzBSue3hSGVGV9SagoLyMo9hYZCfL5/nEIi4lIStPmGplwK5lCuTwqoqmTU86iivEs3ab3LQfx6Iux2aXDgyX375JQ888AAnTpzgurLyTg899BC7d+9m27ZtJrpSXWC2UnfnnXcCsljp/fffb/Kap6cnERERvPnmm1YVzh507SqLbO/3KysPsXUrTJ5cdwKUWTv3+A/kcp6GJk2gXbu6m16hqBFJSbJdWNu2Viio6Ngs+/gy4Ess/4f7/ffaWxxFfeeHH0j/bBOwCG9vCAoq74qox4P885fxt9HUR47Ix9atZVmvyojpXoSWi5wvCmJPnJ4+N9peqxOiXLGta/z8LLtPGz9+PK+++ioLFizg/fffZ+7cuaxbt464uLg6V+jAAqWutKyqdGRkJHv27CHYSSMmDfEE8Tllwd91XYS4TKnb4jcM8lQ8naKe8OKLsHw5vPqq7N3rpJw9C79s9AZgYre/IGqGnSVS1Ht27SLtpz8BaaXTaGTdX3dK0OOBLrPQ+kpdVhbccw8JBY8Ad1XpegXw6NKBIe4/8LX+LtZ8cZ4+N4ZYW5qryM+HgACbT1Mpubny/28uGo2G+fPnM3bsWMLCwli0aBHbtm0zWu0APDw86FhWCLdHjx42balqcaJEUlKSLeRwGG64QT4mpviR59YA/9OnZSZsLVqQWUSZUveHiAFkc3mFwuFxkbImK5YL9KVu9GEn0Y8OsLc4CmfginImIBW7QI98sksC0WUVlb1qRRITYcMGEgImAlXH0wHg7s7w1sf5+ij8staNl60tixMwcuRI2rdvz9y5c1m/fj0dOnQweT0oKIh4WSfG5tSopEleXh5btmzh9OnTFBUVmbw2depUqwhmL5o2lVtGhoaD0eOIOfIZbNtWd0pdWd/X40VyPhdKKFTUZ1ygrIkQsHRxIeDDRI8VMG6evUVSOAMpKaQjrThhFbQ3rWeZUne+pIoDa4GhnIlHZ+AaSh0w7LYSOAr7TgeTni4LFNsSPz9pMbMHfn6WH7Nu3TqOHj2KXq+vsl1jXWGxUrd//36GDx9Ofn4+eXl5NGrUiHPnzuHn50dISEi9V+pAumB//RX23zSVmJeGwcCBdTd5aip63EjOle7tSFUpQVEfcAFL3f79cOi4D95epdy9qJ8MflIoaktqKmkMBkyVukCvQrgMOdlXV5uoNceOIYDDZZmv14rbDrmlIz3f280eerF2LTzwgPVFqojBBV0f2LdvH+PGjWPx4sWsXLmSF154gW+++cZkTE5ODt27d8fX15f58+fT34YlLSwuafLkk09y++23c+HCBXx9fYmLi+PUqVN0796dhQsX2kLGOsfggo0XXWDsWNvUCKqKnBzSCKO41AMPDwgPr7upFYoaY1DqUlLg8mX7ymIjDPVER49xI+jR8XaVReEkCFGp+xVAGy37Eupihlp/3mPHyCSE7EJ/NBozCjz07s1wZE2TX34str489ZTk5GRGjBjBjBkziI2N5aWXXuK7775j7969V43bu3cvH3/8Mffddx85OTk2k8lipS4+Pp6nn34ad3d33N3dKSwspHnz5rz++us895xz9IYz6SxR1yxfzsn10trRsiUqfVxRP2jcuLy4lhPG3RYWwooVAoCJE+0ri8KJ0OkgL69ypa6JTMjRFfpYf95jx0hA+lyjosDX9xrjmzVjeGv5u7R+ozvFSq/jwoULDBs2jFGjRhl1n+7du3P77bcza9Ysk7FhZW9sx44dad++PcdsWP/WYqXO09MTTVk6ZmhoqLHHpFarNek3WZ8xKHV//QXFu/bCvHkyrq6OSEqVizkqqs6mVChqh0bj1HF1a9bAhQsawnwvMKjZYXuLo3AWzp4FNzfS3KRLxkSps1WrML0e/v7bqNRdK57OQI+jy2nSBHJy3dixw8oy1UMaNWrEkSNHWLx4scn+H3/8kbVr1xr/zs7OprCwEICUlBQSEhKIsuGPu8VKXdeuXfnzT5l+PXDgQF588UVWrFjB9OnT6eQkUf1RUbK7RGEhJL6zFl54Ab79ts7mN3RaU/F0inrFc8/Bl1/KtmFOxrIl0jRx3+XFuJcU2lkahdPQti0UFpIW0Aa4QqnLTwNAt+2AdefMyoIGDUhwk7/X5ip1bm4wtMwTrLpLmM+RI0fo0aMHXbp0YeTIkSxatIhGjRrZbD6LlbpXXnmFZmWpLy+//DKNGzfmscceIzMzk08++cTqAtoDN7fyPrD7G90qn9RFvbq0NLjtNpK+jAOUpU5Rz7jrLhg/3vapcXXM2bPwy3oZB3H/9TuubI6pUNSKvEIPdDnyp7ji0gm8JJW6nIQU607YtClkZZFw40OA+UodwIgR8nHNGmFdmZyYvn378tdff3HgwAHi4+ONjRxshcXZrz169DA+b9KkCb84qcretSts3w77izsSC3DggLSD27JC9KlT8NtvnPReAChLnULhCCxfTnltusk3qWrgCquSni4f/fwgMLB8v9H9ml9Fq4dacuSY/Pk3W6krLWXwCzG4sYPDh93rtHyrwnwsttS5CsZkieMBMrNPCPjjD9tOWlZ4OKk0AlCWOkU9Iz9fBp8tXWpvSazKF/+V7taJfA733GNnaRROxZtvkvboS0B5NwkD2obyD12Bt9WnPX9eWqABoqPNPMjNjYZBgr7IgLpff7W6WAorYLFSd/78ef71r3/Rvn17goODadSokcnmLBjbhcWDuKmsrYOtXbApKVzGh/RiVaNOUQ/JzoaRI2WvZCdJj0tKgoNHvXGnhHEDsqBC6x+FotZs2kT6Rpl4E3ZF2whtQ+ny1xVZOfv1oYc4MuIZQFraLGrH1adPeWkT53TS1Xssdr/ee++9nDhxgkmTJhEaGmrMhHU22reXTZUvXoRT7YcRwWe2z4BNTSWZCECa4Z1IR1a4As2aydoIly/LUAJD7bp6zJqfBaDhRv6g0aTR9hZH4WykpJCGLG5/lVIXLH+ec4quVW/EQn7/nYSkQcC1iw5fRe/eDH/vdZ5jARs3QkEB+Nig4oqi5lis1G3fvp3t27fTxZBJ4KR4eUGHDtJSt9+/n1S1jh6VFghPT9tMmprKSaTPNTJShe4o6hlubrKsyaFDsqyJMyh1a6RSN7LlIbjjPnuLo3A2qig8DBDYWMbS6Uqs2FqhsBCSky0uZ2KkTx86c5AwUknLv46tW2HwYOuJJ4RrJ2BY4/otdr9GR0dz2Ukrxl+JMa4uLRT27pVBCLZS6ABSU0lC+lxVPJ2iXmKoVXfihH3lsAJ5ebBps/yKHPHLv2SdI4XCWhQUwLlzVSp12pCy4sOllvhHr8GJEyCE2T1fryIqCk1wsNVdsO5lVfav7CXvauTn5wOyHnBNsVip+/DDD5k1axZbtmzh/Pnz5OTkmGzOhLFd2AENdOsGHhYbNi3j8mUTS51CcSXZ2dnExsai1WrRarXExsZy8eLFKscXFxfzn//8h06dOuHv709YWBj33XcfaWlpthHQiXrAbtwoDRuRkTVwUykU16JsDaZpri48DKCNlvVNdJ5NrDdnWSeDBE0HoAZKnUYDvXszgjWA9ZQ6Dw8P/Pz8yMrKIj8/n4KCApfaLl++zPnz58nMzCQoKMio5Nbof2npAUFBQeh0Om655RaT/UIINBoNer0Nmg/biTpvF7Z7N0mj9PCTUuoUlTNhwgRSUlKMFcsffvhhYmNj+emnnyodn5+fz759+3jhhRfo0qUL2dnZTJ8+nVGjRhmLiFsVJ1Lqfv6+CPBi5G2FaDTWz0BUuDhl1Q7SPJpDcSVKXRPpfi0s0lBYCN7W+AgeO4aOQFKLQ4Ea3qyMHs2toX/h+UUpx4+7cfw4tG5dO7E0Gg3NmjUjKSmJU6dO1e5k9ZigoCCaNm1aq3NYrNTdc889eHl58b///c+pEyWgvABxSgqcW/wdwTtWw513wmjbBUyfPCU1dOV+VVzJkSNHWLt2LXFxcfTu3RuAJUuWEBMTQ2JiIm0r6cqt1WrZsGGDyb733nuPXr16cfr0aVq0aGFdIQ1KXT13vwoBa1bLG9QR66YCi6s/QKGwlHPnZIuwUvkjfmXN7ore/pwcaGINg92xYxxBanJhYRAUVINzTJpEg0lw8ylpzf7lF5g2rfaieXl50bp1a5d1wXp6etbKQmfAYqXu0KFD7N+/v9IfEGcjMFD+Rv39N+xfe5bbfvhCrgQbKXVClPdCV5Y6xZXs3LkTrVZrVOgA+vTpg1arZceOHWavSZ1Oh0ajIaiab/TCwkJjv0LA/NCKrl1ltd42bcwb76DEx0Nati/+5NL/zob2FkfhjIwezaVzheQ2kj/DVyp17u4Q4FVIbpE3uqQLNGlihXII7u4k+PaAyzVwvV7B8OFSqVuzxjpKHYCbmxs+Kp22VlgcU9ejRw/OnDljC1kcEmNc3eWyH8zMTNtMtG0b5/uP4dIl+WdEhG2mUdRfMjIyCAkJuWp/SEgIGRkZZp2joKCAGTNmMGHCBAIrlq+/ggULFhjj9rRaLc2bNzdPyMaNZYHeet7/9efVpQAM4jd87hhiZ2kUzkpaplToGjSoPA9HW3IeAF3SBetMuHgxRx5/D6ilUldYyO3N4wH4/XdpdHRVLl+Gw4fhhx/gjTfgkUfglluk0nvBSm+bJVis1D3xxBNMmzaNZcuWsXfvXg4ePGiyORvGuLoLZf1QsrJsM9HRoyRtk8qyodyXwjWYM2cOGo2m2s0Q/1ZZuIMhnvVaFBcXM378eEpLS/nwww+rHTtz5kx0Op1xc6UbOYA13+QBMNJnI/TrZ2dpFM6KIV/pyng6A1r3XAB0WdZzSSYckd8VtVLq/vUvWv+jK92apqLXw7ffWke2+sSiRdCiBfj7Q8eO0oH373/DJ5/Apk2y48a8eXUvl8Xu17vvvhuABx980LhPo9E4ZaIEXFHWBGyn1KWkGDNfVTydazFlyhTGjx9f7ZiIiAgOHjzIWUNvnwpkZWURGhpa7fHFxcX84x//ICkpid9//71aKx2At7c33jWNzN67F3buhO7dISamZuewI2fPwu7DsjbY8EFFti1jpHBdHn+c9P3tgCeqVuo886EYdOes16ElIUE+1iqju1cv+PRT/hnwM/t4hJUr4dFHrSJelRQXw2OPSauYn5/p5u8vH2+5BYYOta0cAHFxMH16+d+BgTJZpHVrGbLl7g5z58IHH8hx1g5drg6LlbokQ9CXi2BQ6hLTAsjDD39bKXUVatSpeDrXIjg4mODg4GuOi4mJQafTsXv3bnr16gXArl270Ol09O3bt8rjDArd8ePH2bRpE40bN7aa7JXy+efw3nvytrUeKnW//goCN7qxl7BxN9pbHIWz8vPPpJ3xA6q21AV6FgCQc6Gk9vN99RV5zy8gOTkeqKWlriyu9x/pi3iWR9i6VSbz2rKL3rx58Omn1Y954w0Z0mvLFs16PfzrX/L5P/8pLXbBwabNAoSALVtg82aYMwc++8x28lyJxUpdy5YtbSGHw9K0KYSGwtmzGv6iE30yE2wzUWoqScg4JGWpU1RGu3btGDp0KJMnT2bxYpmN+fDDDzNy5EiTJIno6GgWLFjA6NGjKSkpYezYsezbt4+ff/4ZvV5vjL9r1KgRXl5e1he0npc1WfNdAeDDCH6BYTY2PyhcE70e0tOrLDxsQOtTADrQZZfWfs6EBI7+LbMrmzSRikiN6dAB/P1pkXeEft3y2L7Pn6+/hiefrL2YlbFjR7krc/58afjIz5cFwvPz5XboEKxaBQ8+CM2bw80320aWJUtg3z7QauHttyvPStZo4NVXoU8feY/7zDO1T0wxF7OUutWrVzNs2DA8PT1ZvXp1tWNHjRplFcEcia5dYe1a2E9X+lzahfWKBlXgihZhCkVlrFixgqlTpzK4rDfPqFGjeP/9903GJCYmotPpAEhJSTGu2RsMWT9lbNq0iQEDBlhfyHrcVaKoCNZtkdl3I+fHWKmOhEJxBZmZUFJCGtK0VaVS5ytj6XQ6K7TPOnas5u3BrsTDA3r0gC1bGB99gO37+vLll7ZR6nJypOWttBTuvSWN57r/BX37XpVZUloK//gHfPedjG/budP6SfjnzsFzz8nnL70kDT5V0bu3lGPVKpg1Sz7WCcIMNBqNOHv2rPF5VZubm5s5p7MrOrk6hE6nM/uYmTOFACEmT8gVoqDANoI1aiRacVyAEFu22GYKhfWpyeepPmLRdR49KheMv78QpaW2F86KbNwoRQ8JEUKvt7c0zkl9WzMffPCBiIiIEN7e3qJbt25i69atZh9b5bXu2SMEiJu8dgoQ4quvKj/+mfZrBAjx9M27a3EFZXTrJmbwigAhHnus9qcT//mPECDO3vuUcHOT6+bvv61wXgOlpUIcPizu63FIgBARJImLBMqJfvihfFxKihDHjwtRWiry84Xo3VsOadVKiMxMK8ojhJg8WZ67c2chiovLdlbzRZGQIIz/m507aze3uevGrOzX0tJSYymF0tLSKjdnS5IwYCxrctzf+hY6gKIi9O5enEK6tpWlTlGviYgANzfpG6kkscOR+fln+Th8uLwEhWvz1VdfMX36dGbNmsX+/fu56aabGDZsGKdPn67diVNSAEjTSEvdlTXqDGiH9AEgp2Wn2s0nhHUtdSB9i0DIzh+59VZpSVy50grnBVn8LiKCrzrM5Ys/O+CGnuXcg7ZlQxneUTGG+JNPZIZC06b43v8PVsd+Q0QLPSdOyF4BBQXWEWnPHvjvf+V1vv9+ha6h06ZJi36PHjB2LDz9tIwpXr+edhGXmThRDpsxQ74NNsdSbfHzzz8XBZVYqwoLC8Xnn39u6enqnJrcJR47JjVtH58K2rmVSU6Wc3h5lYqSEtvMobA+9c3qUFMsvs6WLeUHets2m8plbdqE5woQ4ttp5ltjFJZRn9ZMr169xKOPPmqyLzo6WsyYMcOs46u81vffF6UgfN0LqrVwLVokl9E//lET6SuQliYEiOs5JkBapGtNbq4Q06cLceKE+OwzKWfHjlY4rxBC7N4tThMugrggQIgXBu8U4siRyi3/06YJ4eUlBSjbEtw6iCCPHAFC3D22uHZW96NHhf6VV0VPf2kxvJcvpFJgYORIk7lNNl9fcWpnqvD2ln/++mvNxbCqpa4iDzzwgDFepyKXLl3igQceqKWK6Zi0agUBAVLjPzZmhqy2aGVOnpSPLVtqsEKnEIXCvtTDdmHHjsGxFH88KeI2nQsW3lKYUFRUxN69e43xqwYGDx7Mjh07Kj2msLCQnJwck61ScnLQaRpyWS89P1Va6rTysZKfXMs4dowCvI1x21ax1Pn7y0yBqChGjwYvL5mscOhQDc9XWp4Mor+hO7GdDnCRhvTqBS/83Aeio01TTA288478B23bJoPXOnWiXelhvi8ZhQfFfPWtBy+8UDZ23z7zKgIfPiwDBKOjITqaT5/7mz15HWhADq93+wqys8vHLl8uW9D8+KNMhX3qKRgzBsLDITSUFr2bGbNlZz6QTunsufDHH7J9VE6O1c13Fit1oopCpykpKWgNn0Anw82tvGHxiZ8Ow4EDVp/DUClGZb4qnIJ58+QX15132lsSs1nzs/xyvZmtBI4ZZGdpFPbm3Llz6PX6q2pAhoaGVtnBxexOLDNnknZAlscKCpI11ipDe07eFOlO1rJlQ0EBx6LvoBR3GjasPsC/JgQFwbAhUimrkQs2N1e6c1esAGDhW25s+asR/v5y1zVLRfr4yCLh8+bBwYNw4gQD37mT/45dB8Arr8isVe68U3a96dhRFtZbvhxOnZKboRI0yJvRd96BxEQueIQw03MhAHPnQLO9P8s6fQa0WtkoftQomDoV3nxTZmucPi2zNTQannsOAgMF8RnN+PqlI1LWqCh5rLe31OrLagDXFrOVuq5du9KtWzc0Gg233nor3bp1M25dunThpptuYtAg5/0iNChbSURav1XYZ59x8mX5YVbxdAqnoE8fGfdSj270DF0kRrivk1VMFQqu7uJSlWEDLOvEkp4pXTJVZb4CaFNlCS1dxmULpb6CIUNImP0VIIsOm9GAxnySk2HsWMYnzgHgyy8tND4JAZMmyaC1Z59l37Y8o2Xt3XfLjf4WERUF06Zx/zcjjed6+GEYemEFe+kmLXGLF0NsrIwBjoiAil12brlFyvTtt8y67zTni7V06ABTnqu+aLsJGo2siYbUI599UuYcPO//NkXXRUpFFGRV5YwMU+tfLTC7Tt2dZXfc8fHxDBkyhICAAONrXl5eREREcNddd1lFqOqYP38+a9asIT4+Hi8vLy5evGjzOaFc2TpJFGTV1L5cBQcOkJQsNX9lqVMo6p6cHNiyW/bmG3ljtnQtKVya4OBg3N3dr7LKZWZmVtnBxZJOLNdqEQagbSQVv5yi2je5N3SSsHq9NF9fWLOG2wt+xc9nNidPuvPnnxa0f37rLfj6a/D0RLdsFRMm+1NcLD2Y1ojomjtXhk69/Tasy7uJdexlTJ80Xmq7gg5Hv5MdcEpLTZO6AgLgv/9l715YvFTu+uCD2jWXmf6MB+99BCcym/HpGyd57DFkgb3z52WtFA+LywZXitlnmT17NiDbFY0fP77mLYRqSVFREePGjSMmJoZPr1Ve2oqYWOqyNln35KpGncLZyMuTVTdTUqTvw8HZsAFKSt1pzTFa/6OrvcVROABeXl50796dDRs2MHr0aOP+DRs2cMcdd9T6/OYodYHBsji4rrgK/6wF2EypCw2FSZPw/+ADRmm3srJgIF9+aaZS9/vvsvMMUPjGu4x+tTeJifJ/8skn1rEoajTw+uvwyCNSwVu+HL6PC2PVrme5555nmfPfy7SK0FPiE0DiYdi/X4be7d8Pf/4pDYkTJkD//rWTIyAAXngBnngCZs+WXmI3N7+yrTlubjLUy91ddqroWtOvIUszME6fPi3OnDlj/HvXrl1i2rRpYvHixZaeqlYsXbpUaLVai4+raebVr7+WZfdwUIiYGIvnrZbevUUo6QKE2LvXuqdW2Jb6lMlXGyy+ztzc8gyw8+dtK5wVmPhPmYX4JG8KkZRkb3Gcmvq0ZlauXCk8PT3Fp59+KhISEsT06dOFv7+/SE5ONuv46q516lS5PKpLpM38dotxGdWqKsJdd4l2XrIO6tq1tThPVSQnC+HhIX5glAAhwsLMqPN4+rQQwcFCgNDfN1GMH18qQIiAACH27bOBjGUcPizEXXeVfz15eAjRrZsQvr6VJ7C2aiVEaqp15i4sFCIqqupkWcP2v/9dfay568Zie9+ECRN4+OGHiY2NJSMjg0GDBtGxY0eWL19ORkYGL774Yg3VS9tQWFhIYWGh8e8qs5GuQUVLncjMwpohCXlnLnAW6XtXljqFU+DvL4N/09Nlu7CKgcUOhhDw6wbp5hrZ6qiMr1EogLvvvpvz58/z0ksvkZ6eTseOHfnll1+s0i7TLPdraLnbNScHGjas2VxFx5I5XiRltkm7qpYtYcIEhn6xEq1nHmlp/mzbVo11q6AA7rpLuh27duXfDT9h5SINHh7w/fe1sFKZQfv28O230uv6/POyW9S+ffI1f385d9eu0K2bfGzfvnZu14p4eckSfN9/DyUl0utr2PT68ufR0bWYxFJNMygoSBw9elQIIcSiRYtE3759hRBCrFu3TkRGRlp6uhpjrqVu9uzZArhqs/QusaBACI1G3kmcDYiqodSVUFIiDrl1EiBEkFaVsK9v1CerQ22o0XXedFPVt50OxPHjhhqRQhTk2qgQpcKIq6wZIaq/1r595efum2+qOcGhQ8KHfAG1MyAfbtJfWsH8SmzX5CUhQQiNRjzApwKEeOSRasaWlMhWTY0bi7eeP2+0UH3xhY1kq4bdu4X4+mshEhMdu4uMzerUFRcXG+PpfvvtN2Ov1+joaNLT02ukWM6ZMweNRlPt9ueff9bo3JZkI1WHtzdcJ4t/k/Tr0Rqdo1IyMzlZaugkYU37n0JhZwxpa3//bV85roGh5Fj37uDtb51gZYXiWphjqcPfHy2ySF0NnUwgBAkXpCeofZsS62a+VqRdOxg9mn/yJSCtYcXFV4wx1KJzd4dXXuGrV5N4al4jAF59VSaj1jU9e8K4cbJPrDN0kbH4G6xDhw58/PHHjBgxgg0bNvDyyy8DkJaWRuPGjWskxJQpUxg/fny1YyJq6BKxJBvpWkRGakhJgZNnPOltlTMCFy6Q5N8J8iCqlVLqFE5EPVHqdm4tBjxNOg8pFLZECDOVumbNCAwv5WxKLQoQ63Qk6WW9vOvb2vim5fnnGdh3KyGvCTKzNPz2GwwbhrzgL76AhQth+3bQatm8Ge77VwMApkwx5ksoaonF7/Brr73G6NGjeeONN7j//vvp0qULAKtXr6ZXDeNmgoODCQ4OrtGxdUlUlCxabSgUbBU6dODkQ6/AIhVPp6ifFBfLIu1XVXlo1Uo+OnhXiR3rLwGNiNn1DjDdvsIoXIILF6CoSD6vqpsEAN7eaJsCtVHqMjNJRbqZwlvauF1R1654dO3KuCRZAuS55+CPDXlc9/tywg78wnV4EzZ/KZkTpnPnnfJ/MGaMrPNrMwuii2GxUjdgwADOnTtHTk4ODStEbT788MP4VVUW24qcPn2aCxcucPr0afR6PfHx8QBcf/31JrXzbIGxVt1nm2FooIyktAKqm4SivrJ6tYx3jomBrVuveNGYXWTNuyDrkpMDh1KCAIjpUFP/lkJhGQYrXePGMrSnOmrdKiwrixTCAdm5qi64916p1MXHQ3y8P/BI2Qa8UbYhGyssX45qjWlFamSLFUKwd+9eTpw4wYQJE2jQoAFeXl51otS9+OKLfP7558a/u5alyWzatIkBAwbYdG7jb9QJPRw9ajWlztD3VVnqFPWN8HCZxXW0sjDTDh0gLu4apgj7sns3lAo3WpJM2CBbpAUqFFdjCD+v1vVahvZCEhBJTuoloEGN5kvxawv5dafU9cn9jV94k310I40wUhu0I61Fb1Kz/cnIkKF1HTvKdqm+vnUjk6tgsVJ36tQphg4dyunTpyksLOS2226jQYMGvP766xQUFPDxxx/bQk4jy5YtY9myZTadoypMukpk/mWVc4oZM0k68iLgqyx1inpHmzbyMStLFkY3Cav184PeVos+tQk7txYBXvRlB/S+yd7iKFwEs+LpytAm7gIi0Z3JoUZK3Y03ktKQOlXqKCxkGGsZplkHTz8NL08ytsXS6+X3RaNGssSHwrpYnOsxbdo0evToQXZ2Nr4VVOzRo0ezceNGqwrnaBiUrjM0pzjjvFXOmfXHMfL0vmg0AiuUPlIo6pSAADD0LE9MtK8sNWHH+lwAYgIT6vAXT+HqWKLUBXrLOqu67NIazVVSUm4ZrLOP+IgR8PPPsp/rG2+U9zlFulqbNlUKna2w2FK3fft2/vjjD7yueEdatmxJamqq1QRzRJo2BW+PEgpLPDiTrMcahrWk02VNnRsX4u1d+/5+CkVdEx0NZ85IF+xVGaT/+x8cOAD33AOdO9tFvqooLYW4gzJkpG/3QhWpragzLLLU+ZQpdRdrptQZ3J0eHhASUqNT1IwRI+pwMoUBiy11paWl6PX6q/anpKTQoEHN/P31BTc3iAy+BEDSGeukhp88KxuHR0XUbMEqFPbGUP280ri6L76QjRcNJdsdiKNH4eJlH3zJp/PgpvYWR+FCDB8OM2fCwIHXHqv1lcXeapookfKvBQCEBeWphAQXwGKl7rbbbuOdd94x/q3RaMjNzWX27NkMHz7cmrI5JJHN5F3TybNWSArJySGpUAaRR7axUh8ShVOTnZ1NbGwsWq0WrVZLbGwsFy9eNPv4Rx55BI1GY7KGa4tBqTtypJIXm5YpSzUsTG5Ldu6Uj72ansZzUC27dSsUFjB8OLzyCtx667XHav1LAMi5VDNLcspJWTslvGFejY5X1C8sVurefvtttmzZQvv27SkoKGDChAlERESQmprKa6+9ZgsZHYqoCGmlTLoQVPuTpaTIpAsgSil1CjOYMGEC8fHxrF27lrVr1xIfH0+smWXYf/jhB3bt2kWYOT4fC2jXTj5WaqkzZL5mZFh1TmtgUOpiJkZDjx72FUahqAJtgPzN0eXWzMyWcl7Gvoc3U94gV8BiH2JYWBjx8fGsXLmSvXv3UlpayqRJk7jnnntMEieclcheIbAKTg56uPYnS00lCZlSq8qZKK7FkSNHWLt2LXFxcfQuyypdsmQJMTExJCYm0rZt2yqPTU1NZcqUKaxbt44RVo51MVjqTp6EwsIr6m45sKXO0B5MdZJQODKBgfJRl1ezkJ9UnazfGt7CCXpgKa5JjT4lvr6+PPDAAzzwwAPWlsfhiWorLWpJp6ywQC5f5qTbDVCqCg8rrs3OnTvRarVGhQ6gT58+aLVaduzYUaVSV1paSmxsLM8++ywdOnQwa67CwkIKCwuNf+dU03iyaVP5w5OTIzuCmUxhsNQ5mFKXnV3uLu7To4QafhUqFDZHO3E0rAOdRw3acJaWknJZ9la9LlKlm7oCSnW3EGOtupO1P1fxsFGc0bQwOa9CURUZGRmEVJK+FhISQkY17s3XXnsNDw8Ppk6davZcCxYsMMbtabVamhvqllSCRlNNsoSDul/j4uRja47RZMP/7CuMQlEN2hvkj4MutwY3HhcvkiLKWoS19bemWAoHRSl1FmJQvs6dg0sHaqfZnTkjCzF6ezt00X2FjZkzZw4ajaba7c8//wRkYtKVCCEq3Q+wd+9eFi1axLJly6ocUxkzZ85Ep9MZtzNnzlQ7vkqlzkHdrzu3yzilGHZCnz52lkahqBpDm7CcHBDCwoMrtgiLVHHbroDyOViIVguN3C9yQR9E0q5MOnepud/U0BIzIkKWS1G4JlOmTGH8+PHVjomIiODgwYOcPXv2qteysrIIDQ2t9Lht27aRmZlJixYtjPv0ej1PP/0077zzDsnJyZUe5+3tjfe1mlJWoEqlrmVL2LVL3rUI4TC14HZsyAMCifE7AK3vs7c4CkWVaNOPAtGUlkJeniz4bS6lhcWkaqJAqNraroJS6mpAlF8GFy4FkZRYRG3KqZ589iPgMaKCc4BAK0mnqG8EBwcTHBx8zXExMTHodDp2795Nr169ANi1axc6nY6+VUT7x8bGMmjQIJN9Q4YMITY21qoxsVUqdV5eUCaro6DXw66DstB3364FDqNoKhSV4Ru/E3euR48HOp1lSl1WaEeKy+6llDfINVD2oRoQqc0Gah9Xl3RUBqJHXldUW5EULkC7du0YOnQokydPJi4ujri4OCZPnszIkSNNkiSio6NZtWoVAI0bN6Zjx44mm6enJ02bNq02W9ZSKip1FruI6phDhyC30IsG5NBhkPqlUzg2mgYBaJGVhy0tQJySIh+bNgVP5X11Cayq1EVGRjJp0iSnbxcWFVLWVSKlFqukoICTl2W8UVRHKxQyVrgEK1asoFOnTgwePJjBgwfTuXNn/u///s9kTGJiIrqalp+vIddfL9sQ5ebCVcv/m2/g3/+WblgHwFCfrje7cO/bu/rBCoW98fc3KnXVJKFXikGpU65X18Gq7tf777+fU6dOcfPNN3PixAlrntqhiAwrgn1wMrMW2UQpKZygFQBRHZy/vp/COjRq1Ijly5dXO0Zcw1RWVRxdbfD0hFatIDFRWutMfkS++UZu4eHQ2/5K1I5NBYCPTJLoZX5GsEJhFwJqbqlL/fBH4A7Ci0+CVbqVKxwdqyp1c+bMsebpHJaoSPmjmZQdVONzlJ46QwIy1qhdexXTo6j/REeXK3UmYXwOlgG7c6+s19X38a4QFGRfYRSKa1ELpS7ljPytCm9Qt5Z7hf2osfu1qKiIxMRESkpKrClPvSCytdSFk/JCahw/lLw/m3z88dIUcf31VhROobAT16xV5wBKXWYm/H1Cfu31nne7naVRKMwgIIBApN/VYqUuW4b2XHedtYVSOCoWK3X5+flMmjQJPz8/OnTowOnTpwGYOnUqr776qtUFdERa3Hszbm6Cy6U+VFJhwiwOH5DKcHRQBh4qB1nhBNSHAsSGosPt20PDhvaVRaEwi4qWuouWWRFSLskid+Et1Y+Mq2CxUjdz5kwOHDjA5s2b8fHxMe4fNGgQX331lVWFc1S8GvoTHi5dpjXNgD2U2QSAjtddtJJUCoV9qQ8FiHf8IX8UY5omQZHKOlfUA4KD0Y64CahBokSBLJUU3lrFbbsKFit1P/zwA++//z79+vUzqVDfvn17p06OuBJDr1ZDAWFLOdxkIAAdxneykkQKhX0xVEhJTYVLlyq84ECWup2/5wPQd/vrquK3on7g5YW2SwQAuhzz46+FvpQUvVx74e0a2EIyhQNisU02Kyur0v6TeXl5FrUhqu9EZu1mM704eSgPsDwL9tAh+dixk+P/z/R6PcXFxfYWwy54enri7u5ubzHqBQ0bQmgonD0rrXU9e5a9YFDqsrKgpAR7xRsUF8Oeg7JLRkznPJvKUVpaSpELWwK9vLxwU0qz1TC0CrMkpi476SKXaQTAdR1VrIGrYPG3Ws+ePVmzZg1PPPEEUN6LcsmSJcTExFhXOgcmKmkj0IukI4VYqtSVlJS7qDp0sLpoVkMIQUZGBhcvXrS3KHYlKCiIpk2butRNS01p164SpS44GHbvlm5YOyrIBw7A5SIPGnKBtrfYLnK8qKiIpKQkSktLbTaHo+Pm5kZkZCReXl72FsUpCDy+F+iOLrMQMK99X8pJeVMR7H4Bn8BGthNO4VBYrNQtWLCAoUOHkpCQQElJCYsWLeLw4cPs3LmTLVu22EJGhySyYTbkQ1Ky5T/0J/7Kp7DQD1+3AiLDNJi7SOsag0IXEhKCn5+fyyk1Qgjy8/PJzMwEoJnqs3NNoqNh8+Yr4urc3CpoePbDUHS4D3G4xdimXp4QgvT0dNzd3WnevLlLWqtKS0tJS0sjPT2dFi1auNz3hi3Q/vgFUqkrwGylrkTGsoZ3UgqdK2GxUte3b1/++OMPFi5cSKtWrVi/fj3dunVj586ddOrkOvFhUU1yIRVOplp+J3p4ezbgR3sScPPtZn3hrIBerzcqdI0bN7a3OHbD11cGGGdmZhISEqJcsdegymQJB2DHlmLAk77sgN7/sskcJSUl5OfnExYWhp+f63aKadKkCWlpaZSUlOCp+lPVGq2fDH/JsSCmztDZRXWTcC1qFFTSqVMnPv/8c2vLUq+IDC+GeEg570tRkexbbi6H/iwAoGOD04BjKnWGGDpX/mEyYPgfFBcXK6XuGlSp1P3wA/zxBwwfDgMH1rVYAOzcXgJ4EhNy0mbdzfV6PYDLux0N16/X65VSZwW0AfJzpcs13/KrWoS5Jhb7Btzd3Y3uqIqcP3/epX7wQpt74Us+pcKNslJ9ZnP4iPy3dwzNsoFk1kW5TtT/wBIMSt3x4zJ21MiaNbBwIWzbZhe5Tp2CU2d9caeEXv1sr3C5+mfG1a/f2gQ2kPGZujzz7TApvx4E4LoTW20ik8IxsVipq6qvZGFhoUvdnWpCmhCJrGdiaVmTQ8kysaJDRJ61xVIo7Erz5uDrKzNNTdaFncuabN4sH3vcUEKDV2baRQaFoqZoA+WjLt98q2dKhhwb7u34xgOF9TBb7X/33XcBeQf23//+l4CAAONrer2erVu3Em24TXcFmjQhipMk0MGiAsRFRXDsnAxc7dihhj3GFAoHxc1N1quLj4cjR6B167IX7FyA2KDUDRzqU15QT6GoJ2iDpOWzqMSdwkLwNiNXIkUnf6PDW7heso4rY7ZS9/bbbwPSUvfxxx+buFq9vLyIiIjg448/tr6EjkpsLJH73eFTyyx1x45BifAgEB3hHbS2k0+hsBPR0VKpO3oURo0q22lnS92mTfJxwAC7TK9Q1IoGDT3QUIrADZ0OKikVexUpebI2XXikiml0JcxW6pLKNJeBAwfy/fff09DVGycGBhLVUT61xFJ3+LB87OB5DE3LFtaXS6GwM+3ayUeTZAk7WuqSk2VMnYemhBuP/R8MeaDOZVAoaoPbI5Np8H96cvLNU+pyciBHLy1117UNqH6wwqmw2C67adMmpdCVERkpHy2x1Bk6SXS4vycMGmR9oRR8+eWX+Pj4kGrI6QceeughOnfujM6SkuyKGlFpBqzBUpeeDlXE5doKg5Wup9hNQNxvdTp3fUKtGwema1cCG0mLmzlvheEt1HKRBi1VnTpXokYlTVJSUli9ejWnT5++qhXOW2+9ZRXBHJ7Ll4n65l3gP5w8KQDzsr2M7cE62kwy25NXTYKHuzv4+Jg31s1NRtVXN9bf8hZs48eP59VXX2XBggW8//77zJ07l3Xr1hEXF4dWq1zetqaiUicEaDSUW+qKiuDiRdlTrI4wxtOxCbp0qbN5TajLNQNq3TghWq0sU2KOUpdySg+4E06Keb5ahdNgsVK3ceNGRo0aRWRkJImJiXTs2JHk5GSEEHTr5pg112yClxeRK+YB/+HCBQ06XXl/vuowul8duD3YNQmoxpw/fLgsX2EgJATy8ysf279/+S8uQEQEnDtnOqYGVh2NRsP8+fMZO3YsYWFhLFq0iG3btnHddddx6dIlbrnlFoqLi9Hr9UydOpXJkydbPIeialq3lopcdrZs9xoSglRa/vxTNoetQwVBiArxdGyGG56ts7lNqMs1A1ZfNwby8/Np164d48aNY+HChRbPoaghSUloi/yBEHJyrj08NakI8CXc5zw0VolBroTF7teZM2fy9NNPc+jQIXx8fPjuu+84c+YM/fv3Z9y4cbaQ0TFxdycg2IcmyJp95rhgL1+Gv/+WX7Ydlz5tS+lcnpEjR9K+fXvmzp3LqlWr6FCmRfv5+bFlyxbi4+PZtWsXCxYs4Pz583aW1rnw9ZW6Blzhgu3eXVZCrcPWWUlJcOYMeFIkO0nYy1JXT6hq3RiYP38+vXvbpsWaohr++APt8T8BMy1156U1N/ye/qCKP7sUFn+7HjlyhPvvvx8ADw8PLl++TEBAAC+99BKvvfaa1QV0aJpYVqtOuqM0NOI8odkO2EfJXHJzq96++850bGZm1WN//dV0bHLy1WNqyLp16zh69Ch6vZ7Q0FDjfnd3d2OHiIKCAvR6fZW1Fx2R7OxsYmNj0Wq1aLVaYmNjuXjx4jWPO3LkCKNGjUKr1dKgQQP69OnDaUurZluAo7QLM1jperEb/6aB0lJoD+pyzdhg3QAcP36co0ePMnz48BqfX1FDAgLQIrU5s5Q61U3CZbFYqfP396ewsBCAsLAwTpw4YXztXGVuAGemrFYdmJcBa4yn4xCaFs1tKJiN8feveqsYG3StsRVjg6oaWwP27dvHuHHjWLx4MUOGDOGFF14wef3ixYt06dKF8PBw/v3vfxMcHFyjeezBhAkTiI+PZ+3ataxdu5b4+HhiY2OrPebEiRP069eP6OhoNm/ezIEDB3jhhRfwufK9siIGpe7IkQo7f/kFnnnG1NVoYxwing7qds3YaN0888wzLFiwoEbnVtSSgAACkX5XS5S6Cp5zhYtgcUxdnz59+OOPP2jfvj0jRozg6aef5q+//uL777+nT58+tpDRcbHQUmeMp+OwuoWyEcnJyYwYMYIZM2YQGxtL+/bt6dmzJ3v37qV79+4ABAUFceDAAc6ePcuYMWMYO3bsVVYJR+TIkSOsXbuWuLg4owtsyZIlxMTEkJiYSNsqiurOmjWL4cOH8/rrrxv3RUVF2VTWSi11v/8Ob74p471GjLDp/HBFPJ2ncr1Wx7XWzY8//kibNm1o06YNO3bssLe4dcL8+fNZs2YN8fHxeHl5mWURtxmWWuoOngcaE77+M5j8oG1lUzgUFlvq3nrrLeMPypw5c7jtttv46quvaNmyJZ9++qnVBXRoQkJqbKmjeT221DkoFy5cYNiwYYwaNYrnnnsOgO7du3P77bcza9asq8aHhobSuXNntm6tH70Rd+7ciVarNYlp6tOnD1qttsof2tLSUtasWUObNm0YMmQIISEh9O7dmx9++KHauQoLC8nJyTHZLKHSWnV1XID4xAlZ2sHLC2LO/QTPP18n89Y3zFk3cXFxrFy5koiICJ555hmWLFnCSy+9ZE+xbU5RURHjxo3jscces7coJkqdOUsx5VxZTB0ptpRK4YBYbKmreIfv5+fHhx9+aFWB6hVNmhDJdqAGlrrmY20omGvSqFEjjpj4+yQ//vij8fnZs2fx9fUlMDCQnJwctm7d6hhf2maQkZFBSCXlCUJCQsioQlHKzMwkNzeXV199lXnz5vHaa6+xdu1axowZw6ZNm+jfv3+lxy1YsIC5c+fWWFaDpe7UKZnI6edHnRcgNljpevcGv0APoEGdzFvfMGfdLFiwwOh6XbZsGYcOHeLFF1+sMxntgeHzv2zZMvsKAhZZ6i5fhvOXZdxweESNqpYp6jEWW+qioqIqzRa8ePGizV06Dse0aUTtWglIpa60tOqhubkyphkMSp2y1NmDlJQUbr75Zrp06UK/fv2YMmUKnTt3tqtMc+bMQaPRVLv9+afMfNNorq6HKISodD9ISx3AHXfcwZNPPskNN9zAjBkzGDlyZLVt/WbOnIlOpzNuZ86cseiagoOhUSPpAj1+vGxnxQLEdYBBqRs4sE6mUyhqbeGukopK3cXqE7sMhYf9yCOoubqRcTUsVuOTk5PR6/VX7S8sLDSpRG4LkpOTefnll/n999/JyMggLCyMe++9l1mzZuHl5WXTuSulUSOad5O1QwsLpVcpLKzyoQkJ8jHU4zzBjT1UTJ2d6N69O/Hx8fYWw4QpU6Ywfvz4asdERERw8OBBzp49e9VrWVlZVcYEBgcH4+HhQfv27U32t2vXju3bt1c5n7e3N97mdA2vAo1GWut27JAu2C5dqFP3qxDlSRIDlj8E7i3AyS1LdcXEiRPtLYLDUlsLd5VotQQ++RC8fW1LnTHzlRQ0oarwsKthtlK3evVq4/N169aZVBjX6/Vs3LiRCENxKhtx9OhRSktLWbx4Mddffz2HDh1i8uTJ5OXl2a0QpoeHbBf2998yDvzeeysfZ4yn698YfrNPU3OFYxIcHGxWBm5MTAw6nY7du3fTq1cvAHbt2oVOp6Nv376VHuPl5UXPnj1JTEw02X/s2DFatmxZe+GroaJSB5S7Xy9elD6iKzM5rcixY9Ig6O1eTMzJ5XCm+gxhhWswZ86caypde/bsoUePHjU6/8yZM3nqqaeMf+fk5NDcGl4ZT0+0o/rD25BzqfruRQbbSjgp0KRJ7edW1CvMVuruvPNOQLp/DHXqDHh6ehIREcGbb75pVeGuZOjQoQwdOtT4d1RUFImJiXz00Uf2UeqysmDePCYGDeR57mTBApgwofLaqk7RSUJhV9q1a8fQoUOZPHkyixcvBuDhhx9m5MiRJpmv0dHRLFiwgNGjRwPw7LPPcvfdd3PzzTczcOBA1q5dy08//cTmip0JbMBVGbBBQeDtXW7WNjRPtgGGS4sJOoLP+UKV+aoAzLeK15TaWrirw2BHscRSR0h3m8iicFzMVuoMsTmRkZHs2bPHYWp76XQ6GjWqumFxYWGhsa4eYL0YB5BBdO++yxQ+Z2FQNgkJGr7/HsZWkgPhFD1fFXZnxYoVTJ06lcGDBwMwatQo3n//fZMxiYmJJg3YR48ezccff8yCBQuYOnUqbdu25bvvvqNfv342ldWg1BlCD9BoYPduaNy43GpnI4ylTIp/k09uuMGm8ynqB+ZaxR0R7cFtwE1lMXVVW+tSzsjXwwMvqb6vLojFMXVJ5qR51hEnTpzgvffeq9ZCaLMYB5A/ToAWHVMn5fPSm/7Mmwd33VXWxLwCRkvd3H9A/o0wbZptZFI4NY0aNWL58uXVjqmsQ8aDDz7Igw/Wbb0qgx51+LBMFAoIAOogKaViPN3AnB/kEzsnwyjqH6dPn+bChQucPn0avV5vjMW9/vrrCaiul6+N0L78DLCLvHwNJSUy9KcyUlLlj891C6aA0ulcDrOzX3ft2sWvV7So+eKLL4iMjCQkJISHH37YxCJmCZZk/xlIS0tj6NChjBs3joceeqjKc9c2i69aPDxkih8wbcwZAgLgwAH4+WfTYRcvlsc5dEhdJ+OJFAonp3lzaNEC9HrYtavu5j16FM6eBR8vPb3ZJd28gYF1J4DCKXjxxRfp2rUrs2fPJjc3l65du9K1a9erfovqioof4eocTqpFmGtjtlI3Z84cDh48aPz7r7/+YtKkSQwaNIgZM2bw008/1biFzJQpUzhy5Ei1W8cKfsu0tDQGDhxITEwMn3zySbXn9vb2JjAw0GSzKmWBqI0K0/nXv+Sul1+W1gIDBitduHcmWnJUOROFy2Dw8BoTbTdulK3Cvv3WZnMarHR9W6biTZFyvSpqxLJlyxBCXLUNGDDALvJ4NvDBl3xAKXWKqjFbqYuPj+fWW281/r1y5Up69+7NkiVLeOqpp3j33Xf5+uuvayREcHAw0dHR1W6GPpWpqakMGDCAbt26sXTpUtwqy0qoSwzZRVlZPPWUTOjbswfWry8fYoyncysr8KmUOoWLYFDqtm0r27Fjh2wVtnatzeY0xtO1zYC2baFbN5vNpVDUGWYUIC4qgrNnpUUh/N1/15VkCgfCbI0oOzvbpBbWli1bTDJRe/bsaV3XZiWkpaUxYMAAmjdvzsKFC8nKyiIjI6PKavp1giEQNSuLkBB49FH5Z0VrnTGermiffKKUOoWLcNNN8jEuDoqLsXkBYpN4uv/0kr7YSlrEKRT1DjOUuvR0EEKDF4UEX3Kc+HdF3WG2UhcaGmpMkigqKmLfvn3ExMQYX7906RKenp7Wl7AC69ev5++//+b3338nPDycZs2aGTe7UcFSB9Kz5O0Nf/xR/uNitNTpD8gMiuuuq3s5FQo70L69rGSSlyfjTW1dgDghQS5FX1/o2bNsZxXdNhSKeoUZSp3B9XodqbiFqhp1rojZSt3QoUOZMWMG27ZtY+bMmfj5+XGT4TYcOHjwIK1atbKJkAYmTpxYaYxDZdl+dcbLL8uo7BdeAGRHiUmT5Evz5slHk56voaGyw7iiTsjOzmbu3Lmk11FrKoUpbm5w443y+bZt2Lz/q+FG6sa+pXh7VtO3T1Etat04IAEBBCKD6apS6lThYYXZSt28efNwd3enf//+LFmyhCVLlpi05vrss8+MtbNciiZNpAvW3d246z//AU9P2WHixx8hM1Pub9/VB7p2tZOgrsnUqVPZs2cPjz32mL1FcVlMkiUMlrrMTJkWa2WM8XRBB2S1VkP2ksIi1LpxQCZMQNuzDVB1ooRp4WFVz8QVMVupa9KkCdu2bSM7O5vs7GxjtXoD33zzDbNnz7a6gPWRFi3A0HTD8J0YFQX++7bBL7/YTzAXY/Xq1eTm5vLzzz8TFBTEihUr7C2SS2Iw6G/fDqJJiHSH6vVw7pxV57l0qUI8ned2WRyvws2WwjzUunFQevdG20m29ruW+1VZ6lwXi4sPV+z5WpHqujo4NcnJ8NZb0qVaoVXZzJmwdGm5l0m1B6t7Ro0axahRowBZnkBhH3r0kHGmmZnwd7IHrZs0kX+kp8twBCtQUgJ33w3nz8uQ1R4ZZcUiVXswi1HrxnG5VquwijF1hPSqG6EUDoWd64E4ATk58N578MUXJrujomQfWAOqPZjCVfH2Lk9a2LYN6SNNTYVOnaxyfiFg+nT49VeZIPH9dwKvv/bKF1WNOoWzkJ5Ow7OykfIvv1Qelmq01DUpstoNk6J+oZS62mKIWzh//qoYoeeeK0+86/Dzq9C6tQy0UyhcjIouWNq3lxlFVnKNvvsufPCBXGv/93/QKzxNrkd3d2UiVzgP27bxz/+NpJGHjr/+gl69YP9+0yFGpe6nj6Bdu7qXUWF3lFJXW8r6v1JaChcumLwUHS3dsF26wNCLX8Hff0uzhcKmfPnll/j4+JBqSAUDHnroITp37mzS6F5Rd1zVWcJKrF4NTz4pn7/2muy7TFmPTqKjoaxoueLaqHXj4AQEcD0n2B19H9HRUoHr1w9++EG+rNdDWpp8rrpJuC5Kqastnp7QsKF8bkhzrcD8+RC/r5TGGWV1TVThYZszfvx42rZta2xbN3fuXNatW8evv/5aZUyowrb07SstacePw9kfdsJTT8Hnn9fqnPv2wT//Kd2vkyfLGpFAWUE8VDydhah14+AEBADQqjiRnTth8GDIz4cxY+QNTUaGVOzc3csrBylcD4sTJRSVEBUFe/fCb79V7u45e1aW03dzk26neooQ8kukrvHzs6x+rEajYf78+YwdO5awsDAWLVrEtm3buK6s6POlS5e45ZZbKC4uRq/XM3XqVCZPnmwj6RUgCxB36gQHD8L21Re4a+nbMG5ceZp4RfR6GbvQtGm5Ge4KzpyBkSPl5/G228rdrwC0agUjRoCdenRWxF5rBqy/bgzk5+fTrl07xo0bx8IKyWEKG1Om1JGbS1AQrFkjY0k/+ABmzICfy3KDmpGG+71Pw5df2ktShR1RSp01mDxZKnXvvCPrYnlc8W81BDo0a3b1a/WI/Pzy75W6JDcX/P0tO2bkyJG0b9+euXPnsn79ejpUULb9/PzYsmULfn5+5Ofn07FjR8aMGUNjgytdYRP69StT6s625i6ougDx0qXw+uvyeViYTGutwKVLUqFLT5f3UN98Iw3mRu6++6pj7IW91gxYf90YmD9/Pr1797aSlAqzqaDUgfwpef99GTo3bVp5aEO4/pTNinsrHB/lfrUG990HwcEy26iy9keGnrjK9VpnrFu3jqNHj6LX6016FgO4u7vj5+cHQEFBAXq93r5dSVwEY1zdyWpahel00kpn4IoM2eJiGD9eKoehodI6oTyD1qO6dQNw/Phxjh49yvDhw+0gnYtj0NDz8sobiyPtCL/8Ur4OWnBaFR52Yeqv2ciR8PWVwdlhYZX7O5xEqfPzM94k1vm8lrBv3z7GjRvH4sWLWblyJS+88ALffPONyZiLFy/Sv39/jh8/zhtvvEFwcLAVJVZUhiEDdv/xAHLxJ6Aya8LLL8vmra1bS+t3gwbGl0pLYeJE+QPm6yuTJCIirjj+4kW4fFm6bh2g56u91oxhbkswZ90888wzvPHGG+zYscOKkirMwmCpKymBoiKTpLvBg2HnTnhrwp88Gv86NOljJyEV9kYpddbiirgTE4KCZAXWel6sTqOx3J1T1yQnJzNixAhmzJhBbGws7du3p2fPnuzdu5fu3bsbxwUFBXHgwAHOnj3LmDFjGDt2bKWWCYX1CA+Hli3h1CkNcfRhUN5G6Us1KG6JibBokXz+3nsmCp04dZrHF7Tgf/+Tbqevv5YlHa7iq6/g0UdlvN7XX9v+oq5BfVgzYN66+fHHH2nTpg1t2rRRSp09CAiQAXQBAZXesLRrB0t6LYH4fRAyyg4CKhwB5X61NtnZMsinIvffD3v2wIsv2kcmF+HChQsMGzaMUaNG8VyZC6979+7cfvvtzJo1q9JjQkND6dy5M1u3bq1LUV0WowvW8xb5pKIL9umnpRVixAgYMkTuEwIxbz7/ifyaxYvlb9ny5TKmrlIMma9RUTaR3xkxd93ExcWxcuVKIiIieOaZZ1iyZAkvvfSSvcR2Pdzd4fHHZbhPhb7rJmRlyUfVIsxlUUqdNblwQZoi7r5bWh0UdUqjRo04cuQIixcvNtn/448/snbtWuPfZ8+eJaesI3ZOTg5bt26lbdu2dSprTcnOziY2NhatVotWqyU2NpaLFy9We0xubi5TpkwhPDwcX19f2rVrx0cffVQ3Al+BsQixR3/5xOCCFQKGDpWBcm+9VX6ARsMrazrzhpD1Spa8cbH6HAhDjTpVzsRszF03CxYs4MyZMyQnJ7Nw4UImT57Mi+pG1bEwlNVSMXUui1LqrEmjRrKMghDw9tvl+1UQvkORkpLCzTffTJcuXejXrx9Tpkyhc+fO9hbLLCZMmEB8fDxr165l7dq1xMfHExsbW+0xTz75JGvXrmX58uUcOXKEJ598kieeeIIff/yxjqQux2Cp20kMxafS4MYb5Q6NBqZMgVOnoE0b4/h334Xn424H4C2eZNIvd13VuQWQZYMWLJDF60ApdQrnZNcuWcvk/PnKXw8Lg8hIWWlB4ZoIF0On0wlA6HQ620ywZYsQIISPjxCZmUKUlAgRECDE9dcLce6cbea0AZcvXxYJCQni8uXL9hbF7lT3v7D556kCCQkJAhBxcXHGfTt37hSAOHr0aJXHdejQQbz00ksm+7p16yaef/55s+e21nXq9UI0bCiXyO7dZTtLSysdu3SpHAdCzJmSJYS/v/xj3jzTge++K4SnZ/ngNm2EKC6ulZw1Ra0biaOsGXtj9Wvt2FF+xjdutM75FPUGcz9LylJnbW66SSZFFBTAhx9K91JuLiQny4QJhaKG7Ny5E61Wa1IjrE+fPmi12moD1/v168fq1atJTU1FCMGmTZs4duwYQwxxa5VQWFhITk6OyWYN3NzKjXPbtyNjgHr2hB9/NLFof/stTJoknz/1FLz4brAMEgd4/nn4/vvyk3bpImud9OkDy5ZJF2w9rgepUFTJFbXqFIorUUqdtdFoyvsVffCB7IsEVm1grnBNMjIyCKkkViYkJISMymq+lfHuu+/Svn17wsPD8fLyYujQoXz44Yf0M/hCK2HBggXGuD2tVktzK5bjMcTVbfvwoIz92bsX5s6F0lL0elnVZPx4WcLkoYdg4cKyZL/77oN775UH/9//mZ7wr79kTYf775f1ThQKZ0QpdYproJQ6W3DXXTJhIisLXnlF7qvnNeoUtmPOnDloNJpqtz///BOQrZyuRAhR6X4D7777LnFxcaxevZq9e/fy5ptv8vjjj/Pbb79VeczMmTPR6XTG7Yyh1qIVMGbA/t0Uo21u0SJOp7ozcKBMEtfr4cEH4eOPK1Rv0Gik9fvmm2UpFINlT6Op9+WCFAqzMNTHqUypO3RIFm4cNqxORVI4FspHYQs8PGRTvmefhT/+kPuUUqeogilTpjB+/Phqx0RERHDw4EHOnj171WtZWVlV1ti7fPkyzz33HKtWrWLEiBEAdO7cmfj4eBYuXMigQYMqPc7b2xvvCsVNrUn37uDtqSerOITjtKbNP7ryXeZNPDRK1g4OCICPPio3ypnQoAFs2WITuRQKh6c6S11Ghkw0sldfOoVDoJQ6W/HQQzB2LLz5puwJGx5ub4kUDkpwcLBZHS1iYmLQ6XTs3r2bXmWVd3ft2oVOp6Nv376VHlNcXExxcTFubqZGeXd3d0pLS2svfA3w9obeNxSydY8f6xjCQvdXWDJWvtarF/zvf9CqlV1EUygcm6qUusxMMMTVqnImLo1yv9qKgACpyNXzFmH2+uF3JBzlf9CuXTuGDh3K5MmTiYuLIy4ujsmTJzNy5EiTOnvR0dGsWrUKgMDAQPr378+zzz7L5s2bSUpKYtmyZXzxxReMHj3aXpdCv9tkD6tpmndZ8mUDNBqYOVMmTziDQidcvIyRq1+/zbhSqfvPf2RLvNBQmD1b7lPlTFwaZamzNa1by2zYCrW36gNeXl64ubmRlpZGkyZN8PLyqjZuyxkRQlBUVERWVhZubm54VVXFvQ5ZsWIFU6dOZfDgwQCMGjWK999/32RMYmIiOp3O+PfKlSuZOXMm99xzDxcuXKBly5bMnz+fRx99tE5lr4ghrk4IDWFhMu/hllvsJo7V8PT0RKPRkJWVRZMmTVxuzYBcN1lZWWg0Gjw9Pe0tjnNxxx0ybs7Q8rCwUNZo1Gjk3VCXLvLuSOGyaISL3VLl5OSg1WrR6XQEBgbaWxyHpqioiPT0dPLz8+0til3x8/OjWbNmlSp1rvJ5svZ1FhTIbmDNmsl2r40bW0FIByE3N5eUlBSXtlZpNBrCw8MJqCS+y1XWDNTBtSYmgk4HHTrUjybDihpj7mdJWeoUVeLl5UWLFi0oKSlBX1kVfxfA3d0dDw8Pl7S42BIfH9i40d5S2IaAgABat25NcXGxvUWxG56enrirEk62p560N1TUHUqpU1SLwYWi3CgKhfm4u7srpUahUNQ5KlFCoVAoFAqFwglQSp1CoVAoFAqFE6CUOoVCoVAoFAonwOVi6gwZadZqUK5wbQyfI2fPdFTrRmEtXGXNgFo3Cuth7rpxOaXu0qVLAFZtUK5QXLp0Ca1Wa28xbIZaNwpr4+xrBtS6UVifa60bl6tTV1paSlpaGg0aNLiqTEVOTg7NmzfnzJkzTlk/SV2f9RFCcOnSJcLCwq5qxeVMqHXjnNen1oxtqWrdOPNnCtT12QJz143LWerc3NwIv0Yf1sDAQKf8IBpQ12ddnN3aAGrdgHNfn1oztuFa68aZP1Ogrs/amLNunPs2SaFQKBQKhcJFUEqdQqFQKBQKhROglLoKeHt7M3v2bLy9ve0tik1Q16ewBc7+f3fm63Pma3NknP3/rq7PfrhcooRCoVAoFAqFM6IsdQqFQqFQKBROgFLqFAqFQqFQKJwApdQpFAqFQqFQOAFKqVMoFAqFQqFwApRSp1AoFAqFQuEEuJxS9+GHHxIZGYmPjw/du3dn27Zt1Y7fsmUL3bt3x8fHh6ioKD7++OM6ktQyFixYQM+ePWnQoAEhISHceeedJCYmVnvM5s2b0Wg0V21Hjx6tI6nNZ86cOVfJ2bRp02qPqS/vXX3AGdeNWjNXUx/et/qCM64ZUOumMhzqvRMuxMqVK4Wnp6dYsmSJSEhIENOmTRP+/v7i1KlTlY4/efKk8PPzE9OmTRMJCQliyZIlwtPTU3z77bd1LPm1GTJkiFi6dKk4dOiQiI+PFyNGjBAtWrQQubm5VR6zadMmAYjExESRnp5u3EpKSupQcvOYPXu26NChg4mcmZmZVY6vT++do+Os60atGVPqy/tWH3DWNSOEWjdX4mjvnUspdb169RKPPvqoyb7o6GgxY8aMSsf/+9//FtHR0Sb7HnnkEdGnTx+byWgtMjMzBSC2bNlS5RjDQsvOzq47wWrI7NmzRZcuXcweX5/fO0fDVdaNWjP1831zRFxlzQih1o2jvXcu434tKipi7969DB482GT/4MGD2bFjR6XH7Ny586rxQ4YM4c8//6S4uNhmsloDnU4HQKNGja45tmvXrjRr1oxbb72VTZs22Vq0GnP8+HHCwsKIjIxk/PjxnDx5ssqx9fm9cyRcad2oNVM/3zdHw5XWDKh142jvncsodefOnUOv1xMaGmqyPzQ0lIyMjEqPycjIqHR8SUkJ586ds5mstUUIwVNPPUW/fv3o2LFjleOaNWvGJ598wnfffcf3339P27ZtufXWW9m6dWsdSmsevXv35osvvmDdunUsWbKEjIwM+vbty/nz5ysdX1/fO0fDVdaNWjP1831zRFxlzYBaN+B4751Hnc9oZzQajcnfQoir9l1rfGX7HYkpU6Zw8OBBtm/fXu24tm3b0rZtW+PfMTExnDlzhoULF3LzzTfbWkyLGDZsmPF5p06diImJoVWrVnz++ec89dRTlR5TH987R8XZ141aM5L69r45Ms6+ZkCtGwOO9N65jKUuODgYd3f3q+6UMjMzr9KyDTRt2rTS8R4eHjRu3NhmstaGJ554gtWrV7Np0ybCw8MtPr5Pnz4cP37cBpJZF39/fzp16lSlrPXxvXNEXGHdqDUjqW/vm6PiCmsG1Lox4GjvncsodV5eXnTv3p0NGzaY7N+wYQN9+/at9JiYmJirxq9fv54ePXrg6elpM1lrghCCKVOm8P333/P7778TGRlZo/Ps37+fZs2aWVk661NYWMiRI0eqlLU+vXeOjDOvG7VmTKkv75uj48xrBtS6uRKHe+/skJxhNwxp5p9++qlISEgQ06dPF/7+/iI5OVkIIcSMGTNEbGyscbwhVfnJJ58UCQkJ4tNPP3XYNPPHHntMaLVasXnzZpNU7Pz8fOOYK6/v7bffFqtWrRLHjh0Thw4dEjNmzBCA+O677+xxCdXy9NNPi82bN4uTJ0+KuLg4MXLkSNGgQQOneO8cHWddN2rN1M/3rT7grGtGCLVuHP29cymlTgghPvjgA9GyZUvh5eUlunXrZpKGff/994v+/fubjN+8ebPo2rWr8PLyEhEREeKjjz6qY4nNA6h0W7p0qXHMldf32muviVatWgkfHx/RsGFD0a9fP7FmzZq6F94M7r77btGsWTPh6ekpwsLCxJgxY8Thw4eNr9fn964+4IzrRq2Z+vm+1Reccc0IodaNo793GiHKIvoUCoVCoVAoFPUWl4mpUygUCoVCoXBmlFKnUCgUCoVC4QQopU6hUCgUCoXCCVBKnUKhUCgUCoUToJQ6hUKhUCgUCidAKXUKhUKhUCgUToBS6hQKhUKhUCicAKXUKRQKhUKhUDgBSqmrxwwYMIDp06fbW4wqGTBgABqNBo1GQ3x8vFnHTJw40XjMDz/8YFP5FK6JWjcKheWodVM/UEqdg2L4oFW1TZw4ke+//56XX37ZLvJNnz6dO++885rjJk+eTHp6Oh07djTrvIsWLSI9Pb2W0ilcFbVuFArLUevGefCwtwCKyqn4Qfvqq6948cUXSUxMNO7z9fVFq9XaQzQA9uzZw4gRI645zs/Pj6ZNm5p9Xq1Wa9frUtRv1LpRKCxHrRvnQVnqHJSmTZsaN61Wi0ajuWrflebwAQMG8MQTTzB9+nQaNmxIaGgon3zyCXl5eTzwwAM0aNCAVq1a8euvvxqPEULw+uuvExUVha+vL126dOHbb7+tUq7i4mK8vLzYsWMHs2bNQqPR0Lt3b4uu7dtvv6VTp074+vrSuHFjBg0aRF5ensX/I4XiStS6USgsR60b50EpdU7G559/TnBwMLt37+aJJ57gscceY9y4cfTt25d9+/YxZMgQYmNjyc/PB+D5559n6dKlfPTRRxw+fJgnn3ySe++9ly1btlR6fnd3d7Zv3w5AfHw86enprFu3zmz50tPT+ec//8mDDz7IkSNH2Lx5M2PGjEEIUfuLVyhqiFo3CoXlqHXjgAiFw7N06VKh1Wqv2t+/f38xbdo0k7/79etn/LukpET4+/uL2NhY47709HQBiJ07d4rc3Fzh4+MjduzYYXLeSZMmiX/+859VyrNq1SrRuHHja8p9pXxCCLF3714BiOTk5GqPBcSqVauuOYdCURVq3SgUlqPWTf1GxdQ5GZ07dzY+d3d3p3HjxnTq1Mm4LzQ0FIDMzEwSEhIoKCjgtttuMzlHUVERXbt2rXKO/fv306VLlxrJ16VLF2699VY6derEkCFDGDx4MGPHjqVhw4Y1Op9CYQ3UulEoLEetG8dDKXVOhqenp8nfGo3GZJ9GowGgtLSU0tJSANasWcN1111ncpy3t3eVc8THx9d4kbm7u7NhwwZ27NjB+vXree+995g1axa7du0iMjKyRudUKGqLWjcKheWodeN4qJg6F6Z9+/Z4e3tz+vRprr/+epOtefPmVR73119/mdyhWYpGo+HGG29k7ty57N+/Hy8vL1atWlXj8ykUdYlaNwqF5ah1UzcoS50L06BBA5555hmefPJJSktL6devHzk5OezYsYOAgADuv//+So8rLS3l4MGDpKWl4e/vb1FK+K5du9i4cSODBw8mJCSEXbt2kZWVRbt27ax1WQqFTVHrRqGwHLVu6gZlqXNxXn75ZV588UUWLFhAu3btGDJkCD/99FO1pul58+bx1Vdfcd111/HSSy9ZNF9gYCBbt25l+PDhtGnThueff54333yTYcOG1fZSFIo6Q60bhcJy1LqxPRohnDm3V2FPBgwYwA033MA777xj8bEajYZVq1aZVUVcoXAm1LpRKCxHrRuJstQpbMqHH35IQEAAf/31l1njH330UQICAmwslULh2Kh1o1BYjlo3ylKnsCGpqalcvnwZgBYtWuDl5XXNYzIzM8nJyQGgWbNm+Pv721RGhcLRUOtGobActW4kSqlTKBQKhUKhcAKU+1WhUCgUCoXCCVBKnUKhUCgUCoUToJQ6hUKhUCgUCidAKXUKhUKhUCgUToBS6hQKhUKhUCicAKXUKRQKhUKhUDgBSqlTKBQKhUKhcAKUUqdQKBQKhULhBCilTqFQKBQKhcIJUEqdQqFQKBQKhRPw/1iBcYhaJuMJAAAAAElFTkSuQmCC", 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\n", 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XkZFBcHCw5n1lrWS9kQylutQZkPVGMhxd6021S+oKm8A9PDxkJZMMxtpvrch6IxmatdcZkPVGMrzy6o1ZOzTExsbSunVr3N3d8fPz46GHHuLEiRPl7rdlyxaioqJwcnKifv36zJ492wTRSpJl2Lp1K3379iUoKAiVSsWKFSvK3UfWGUmSJOtn1qRuy5YtjBgxgt27d7Nhwwby8/Pp1q0bWVlZpe5z5swZevXqRceOHTl48CBvvPEGI0eOZOnSpSaMXJLMJysri+bNm/PVV1/pVF7WGUmSpOrBrLdf165dq/X7d999h5+fH/v376dTp04l7jN79mzq1KnD9OnTAWjcuDH79u3jk08+oX///sYOWZLMrmfPnvTs2VPn8rLOSJIkVQ8W1acuPT0dAG9v71LL7Nq1i27dumlt6969O/PmzSMvLw97e3ut53JycsjJydH8npGRUXYQa9dCSAiEhekZvfUqKCggLy/P3GGYhb29Pba2tuYOo1L0rTNQgXpz5Ag0aWKQeK1Fda43Dg4OVj9dibFcuwbz58Nzz0GNGuaOxrTUajW5ubnmDsMsDPVZYzFJnRCCsWPH0qFDB5qU8eGQkpKCv7+/1jZ/f3/y8/NJS0sjMDBQ67nY2FimTJmiWxAFBfDMM3DpEjRrBo8+qjyqaYInhCAlJYXr16+bOxSz8vLyIiAgoMp27Na3zoCe9WbbNujUCR5/HGbMAF9fQ4RdZcl6AzY2NoSEhODg4GDuUKqcL7+EyZPh1CmYOdPc0ZhObm4uZ86cQa1WmzsUszHEZ43FJHUvv/wyhw4dYvv27eWWvfuChRAlbgeYOHEiY8eO1fxeOCy4RFeuQGQkbNwIhw4pj0mT7iR4TzwB9evrcVVVW+EHk5+fHy4uLlU2qakoIQQ3b94kNTUVoMTkp6rQp86AnvVm/36wsYFFi2D9evjiCyXBq2bvl0LVvd4UTribnJxMnTp1qt31V1ZiovLvunVmDcOkhBAkJydja2tLcHBwtWvlNeRnjUUkda+88gqrVq1i69at5U5GGRAQQEpKita21NRU7OzsqFmzZrHyjo6OODo66haInx/8+SdcvQorVsCvv2oSPHHoEAXTPsLu7Klq0RJRUFCg+WAq6e9aXTg7OwPKe8zPz69K3orVt86AnvVm9Gjo0EG5X3T4MPzvf/DzzzBrFlSziVdlvVH4+vpy8eJF8vPzS7y9L5Xu9uc6p08rCV69euaMxjTy8/O5efMmQUFBuLi4mDscszDUZ41Z02EhBC+//DLLli3jr7/+IiQkpNx92rVrx4YNG7S2rV+/nlatWhnuPw9vb+UD6s8/lVux8+bxSMQJartf56qt9Sd0gKYvUHWtYEUV/g2qav8ok9SZVq1g3z54911wcIDVqyEiAn74wTDHryJkvVEU3nYtKCgwcyRVT2FSB7Bpk/niMKXC90l1v11viM8asyZ1I0aMYOHChfz888+4u7uTkpJCSkoKt27d0pSZOHEiTz31lOb3YcOGcfbsWcaOHcuxY8eYP38+8+bNY9y4ccYJ8naCt/l8Qy6l2vDXX8Y5jaWSt04s72+QmZlJfHw88fHxgDJlSXx8POfOnQPMWGccHOCtt+DgQYiOhhs3WLA9lD17DHuaqsDS3jOmZo7rnzlzJiEhITg5OREVFcW2bdtKLRsXF4dKpSr2OH78uFa5pUuXEh4ejqOjI+Hh4SxfvtzYl1Etk7pCst4Y4PqFGQElPr777jtNmaefflrcd999WvvFxcWJyMhI4eDgIOrVqydmzZql8znT09MFINLT03XeR60WwsZGCBBi7Fidd6vSbt26JRISEsStW7fMHYrZlfW3qMj7qbI2b95cYr15+umnhRCGrzNCVOA68/PFP5+sFyBEzZpCZGfrdboqS9YbhanrzOLFi4W9vb2YO3euSEhIEKNGjRKurq7i7NmzJZYvrEMnTpwQycnJmkd+fr6mzM6dO4Wtra2YNm2aOHbsmJg2bZqws7MTu3fv1jkufa9VrRbC2Vn5rAEh/P2VbdZO1huFIeqNSojbPaariYyMDDw9PUlPT9d52ZasLHBzU36Odj/CrpT6YOW3V7Kzszlz5ozmm291VtbfoiLvp6qoIte5ejX06aP8/OuvMGCAEQO0ELLeKExdZ9q2bUvLli2ZNWuWZlvjxo156KGHiI2NLVY+Li6Ozp07c+3aNby8vEo85sCBA8nIyODPP//UbOvRowc1atRg0aJFOsWl77VmZkLh0p4ODpCbq8wWFBGh0+mqLFlvFIaoN9VriEkF3bhx5+cDNxqQs+uA+YKRpCri6tU7P88fd9R8gUhWLTc3l/379xebi7Fbt27s3LmzzH0jIyMJDAwkJiaGzZs3az1X2vyOZR0zJyeHjIwMrYc+Cm+9urjAffcpP1e3W7BS5cikTgdF62UujhxYlmi2WKTyLVq0CCcnJy5cuKDZNmTIEJo1a6aZ4FoyvqJJ3bqzjbiw86z5gpHKVVXrTVpaGgUFBSXOxXj3qO9CgYGBzJkzh6VLl7Js2TLCwsKIiYlh69atmjKlze9Y2jFBmd/R09NT8yh1GqBSFCZ1fm5ZxKiVwU0yqbNsllZvZFKng6ItdQC7tlbNUZDVxaBBgwgLC9PcdpkyZQrr1q3jzz//xNPT08zRVR9Fkzo1tvww5bT5gpHKVdXrTUlzMZbW8TwsLIyhQ4fSsmVL2rVrx8yZM+nduzeffPJJhY8JyiCl9PR0zSMpKUmva9AkdalHiNk0EYC4OMjP1+swkglZWr2xiHnqLN3dSd3Ok76MFaJ6Tq6alVX6c7a2ULQfQFllbWzg9rw8ZZZ1ddUvPpT/iN9//30GDBhAUFAQM2bMYNu2bdSqVQuAP/74g1dffRW1Ws3rr7/OkCFD9D6HVL4rV5R/63hncu6qG9/FhTChmlYba6g3Dz/8MHFxccTExPDbb7/pfXxj8fHxwdbWtsS5GO9uaStLdHQ0Cxcu1Pxe2vyOZR1Tr/kdS6BJ6kglkoN4cY3rGTXYvx/atq3wYauuKl5vkpKSGDx4sGZe0EmTJvHoo4/qfQ59yJY6HRTefrWzU8aU7MqJRCRW01tJbm6lP+5eHN7Pr/Sydy9IX69eyeUqqE+fPoSHhzNlyhSWL19OxO2exvn5+YwdO5a//vqLAwcO8OGHH3K1aJOSZDCFf9bnX7DFlUxO5tZj58Jq2lpXxesNwMiRI/nBAucddHBwICoqqthcjBs2bKB9+/Y6H+fgwYNaM/mXNr+jPsfUV9GkztbPh84o/fyq7S3YKl5v7OzsmD59OgkJCWzcuJExY8aQVVbyaQAyqdNBYUtd69Yq7FT5XKQWSX/8Y96gpDKtW7eO48ePF+trs2fPHiIiIqhVqxbu7u706tWLddVpPR4TKkzq6jZy5tHgvwGY/+k1M0Yklae0egPQuXNn3AuHZlqYsWPH8u233zJ//nyOHTvGmDFjOHfuHMOGDQOKz904ffp0VqxYwcmTJzl69CgTJ05k6dKlvPzyy5oyo0aNYv369Xz44YccP36cDz/8kI0bNzJ69GijXUfqReU+qx+p8N57xKBkc5s2VN/1UKuC0upNYGAgLVq0AMDPzw9vb2+jNyLI2686KGypCwiA5r4X2Z9ah12H3ahj3rDMIzOz9OfuXtak6Cyad7t7bb/CBQ8N4MCBAzz66KN88803LF68mEmTJvHrr78CcPHiRc3tJIDatWtrdXCVDKfw/67CBVoWTIFfDoUx44bAzb2a3YOt4vXG0g0cOJArV64wdepUkpOTadKkCWvWrKFu3boAJCcnaybnBmXE7Lhx47hw4QLOzs5ERESwevVqevXqpSnTvn17Fi9ezFtvvcWkSZMIDQ1lyZIltDXifdDUpGzADT/bq/DMOGLe6gKpsGOH4NYt7TuI1YIV1Zt9+/ahVqv1HjyjL5nU6aCwpc7dHdr3D2L/LNjpHMNA84ZlHvr0OTBW2TIkJibSu3dvJkyYwODBgwkPD6d169bs37+fqKgoSpqWsbrPYm4shX3qataEduOiuWfqKf4Tofz2fSbPvFzxWx1VUhWvN1XB8OHDGT58eInPLViwQOv38ePHM378+HKPOWDAAAaYcILF1IvKcll+Xrlgb0/Yy10JevsCF/NqsXMnxMSYLBTLYCX15sqVKzz11FN8++23BjlvWeTtVx0UttR5eEC7jkoevGuXGQOSSnT16lV69uxJv379eOONNwCIioqib9++vPnmmwDUqlVLq2Xu/PnzWv1oJMMp2lKncnPlmbdqA/Ddr9UsobNwutQbyTRSLyv/+vkqXz5VLwwlxnYLAJvWylkXLImu9SYnJ4eHH36YiRMnGrU/ZiHZUqeDoi117dopPx88KLiVkY+zh4EWRJcqzdvbm2PHjhXbvnLlSs3Pbdq04ciRI1y4cAEPDw/WrFnD22+/bcowq4WCArh+XfnZ21v59+kXHJn0HmzdCv/9B/fcY7bwpCJ0qTeSaaRmKV94/J7trWzw9yfmywf5cThs2io/ayyJLvVGCMEzzzzDAw88wODBg00Sl2yp00HRlrq6dSHAPZP8fBX7J1rOsH5JN3Z2dnz66ad07tyZyMhIXnvtNWrWrGnusKxOYUIHUKOG8m/t2lA4Qf+CGZY7ma1Usu7du/Poo4+yZs0aateuzd69e80dklVRq+HyVaWfmN/g7prtMX2VW4X79mnXK8ny7dixgyVLlrBixQpatGhBixYtOHz4sFHPKVvqdFC0pU6lgvZ1L7DsSBg7t+bRwbyhSRXQr18/+vXrZ+4wrFphfzoPD7Av0sDwnO/vrKMv339XwJTpxfs6S5ZLjhI3rqtXlcQOwMfnzvbataFhQ/j3X9jyxT88+HZz8wQo6a1Dhw6o1aYduSxb6nRQNKkDaNdR+ZTaddIXSuh4L0nVXdH+dEX1e8KNGlzlfJY3G/+UfYQkqVDh4E1v9zzsM65oPRcTpqxMsenj/XJ5CalMMqnTQdHbrwDtBihTYuzKiUSc028ZGEmqDkpL6py6deJ/zssB+O6jyyaOSpIsl2bi4Rv/wfbtWs898HgAAH9ltgHZ11Eqg0zqdHB3S11Ue0fsVXlcIoDEVYfMF5gkWajSkjpsbXm2n9IKsXyHL3IxD0lSFF1NgoAArec6d7NHhZqjNCHl05/MEJ1UVcikTgd3t9Q5OUFLv/MA7FwtZ8iXpLsVnaPubpEjO9KMf8hV27Po+1zTBiZJFir1ktKVp6SkrmZNaNFEue361y4nMHJne6nqqlRSl52dbag4LNrdLXUA7SJzANgV71TCHpJUvZXaUgeo2kXznPcKAL77qowZ4yWpGklNUj5T/EiFu5ZoA4jp4QDAJmLg++9NGptUdeid1KnVat59911q1aqFm5sbp08rC3RPmjSJefPmGTxAcxOieEsdQLveyqfVLow/maAkVTVlJXWoVDzxpC02FLD/tLchV+yRpCor9dwtAPycMpTbQXcpXE1iEzGIxLOmDE2qQvRO6t577z0WLFjARx99hIODg2Z706ZNTbIEhqndunVnmHnRlrr2D/kB8E9aLbKyzBCYJFmwMpM6wHfEY9zbQBkosWqViYKSJAuWelG5vernWXKXhI4dwd62gLPU43SSnIhYKpneSd0PP/zAnDlz+N///odtkUmmmjVrxvHjxw0anCUovPUK2svF1a6tPAoKQM7BKUnayupTB0DDhjz4otJvSA7mk6QiAyV8Sp7XzNUVotsoz20a+I2pwpKqGL2TugsXLnBPCev7qNVq8vKsb96pwluv7u5gc9dfq3DJsF3LU0wblCRZuPJa6gAefFD5d8sWuCbHG0nVXGqOJwB+j8eUWub+LkoL3d9H3UstI1Vveid1ERERbNu2rdj2X3/9lcjISIMEZUlKGiRRqF2tcwDsmiOnNZGkonRJ6u7xSiPC/zIFBbBmjWnikiRLlXpN6c7kN6BTqWVCQpR/L140RURSVaR3Ujd58mRefvllPvzwQ9RqNcuWLWPo0KFMmzbNKhdGL2mQRKH2D/oCsCtbTkJsqa5du8aUKVNITk42dyjVii5JHXl5PHhpDgArfskxflCSzmS9Ma2cHEi/vRyyn1/p5QIDlGlPkvedv/PhJFkMS6g3eid1ffv2ZcmSJaxZswaVSsXbb7/NsWPH+P333+natasxYjSrslrqIts746jKIQ1f/lsu5w2yRCNHjmTv3r289NJL5g6l2igouLPweKl96gACA3kwRKk3a9fbkCPzOosh641pFfans7NV46UufUbuwCAVAMlp9iATbotjCfWmQvPUde/enS1btpCZmcnNmzfZvn073bp10/s4W7dupW/fvgQFBaFSqVixYkWZ5ePi4lCpVMUexhygUVZLnYMDRPkrkxDvWiM7BVmaVatWkZmZyR9//IGXlxc//SRnYjeF69fvLIlco0bZZVv1CyKQi2Rm2/PXX0YPTdKBrDempxkkUZCMau+eUssFBir/XsaX/JQ0E0Qm6cpS6o3eSV39+vW5cuVKse3Xr1+nfv36eh0rKyuL5s2b89VXX+m134kTJ0hOTtY8GjRooNf++iirpQ6KTkLsbLQYpIrp168fy5cr64wuWLCA//3vf2aOqHoovPXq7g725cy8YNM1hgdRhr/KUbCWQdYb0ytribCifHzATpWPwIZL/90otZxkepZSb/RO6hITEykoKCi2PScnhwsXLuh1rJ49e/Lee+/xyCOP6LWfn58fAQEBmkfRqVUMrayWOoD2fW5PQnw5FHn/SJJ07E9X6L77eNDmDwBWLc/XzAkpSdVJaoryxi8vqbOxAX/H6wAkn6keKzpJ+rHTteCqIjOErlu3Dk9PT83vBQUFbNq0iXr16hk0uNJERkaSnZ1NeHg4b731Fp07dy61bE5ODjlFkq0MPTuXlttS95A/jIDDogk3dsXjfn+UXseXJGtT7hx1Rbm50bldNu47MkhO9WDfPmjTxqjhSZLFST2TBbjjx2Xw9S2zbKDrDS5k+5B8zvqmEJMqT+eWuoceeoiHHnoIlUrF008/rfn9oYceYtCgQWzYsIFPP/3UmLESGBjInDlzWLp0KcuWLSMsLIyYmBi2bt1a6j6xsbF4enpqHsHBwXqds7ykLjBIRZ0aGaixJf6KfseWjGPRokU4OTlptRwPGTKEZs2akV44xEwyGr1a6gDH7vfTw3YDAOV0q5WMSNYb80k9e3uJMJdMKOfOU6CXUlaOk7AMllZvdG6pU9++LxISEsLevXvx8fExWlClCQsLIywsTPN7u3btSEpK4pNPPqFTp5Ln9pk4cSJjx47V/J6RkaFXYlfe7VeAsFYenNsAp2740VHnI1ctQsDNm+Y5t4sLqFS6lx80aBAffPABsbGxfPXVV0yZMoV169axe/durRZmyTj0TeoYNYoHg5359VmlX920aUYLzeRkvZF0kXpRaXXz8yz/lmqgTx6cguRU43U7MjdZbypO56Su0JkzZ4wRR4VFR0ezcOHCUp93dHTE0dGxwscvr6UOoHB8yKlTFT6Nxbt5E9zczHPuzEztJdrKo1KpeP/99xkwYABBQUHMmDGDbdu2UatWLU2ZP/74g1dffRW1Ws3rr7/OkCFDjBB59aR3UufhQa8HlQaKhAT47z8oYdGaKsna6s3DDz9MXFwcMTEx/Pbbb0aIunpKvaQMF/erWby/+t0CO4TC35DcspexwzIba6o3SUlJDB48mNTUVOzs7Jg0aRKPPvqokaKvQFIHyqjVLVu2cO7cOXJztRcfHjlypEEC09XBgwcJLBznbQS6tNSFBucCDpzanQqUMXOkZDJ9+vQhPDycKVOmsH79eiIiIjTP5efnM3bsWDZv3oyHhwctW7bkkUcewVvnLEQqi1596m6rUQPuvx82bVJa61591SihSeUoq96A8v/7c889x/fff2+mCK1Tqlq58+X3cIdyywY2ULKd5KtORo1J0l1Z9cbOzo7p06fTokULUlNTadmyJb169cJVn8xRD3ondQcPHqRXr17cvHmTrKwsvL29SUtLw8XFBT8/P72SuszMTP777z/N72fOnCE+Ph5vb2/q1KnDxIkTuXDhAj/88AMA06dPp169ekRERJCbm8vChQtZunQpS5cu1fcydKZLS11o7jGgOae2p2CtSZ2Li/INxlzn1te6des4fvw4BQUF+Pv7az23Z88eIiIiNN+kevXqxbp163j88ccNEW61p3dLHcC2bTx4ZCubeNOqkjprqjcAnTt3Ji4urvLBSVpS05UEza9v23LLFrZhWPNSYdZUbwIDAzUNT35+fnh7e3P16lXLSerGjBlD3759mTVrFl5eXuzevRt7e3uefPJJRo0apdex9u3bpzVytbDv29NPP82CBQtITk7m3Llzmudzc3MZN24cFy5cwNnZmYiICFavXk2vXsZrhtappS7KC4BT2UFKZwB9bshXESqVfk3S5nTgwAEeffRRvvnmGxYvXsykSZP49ddfNc9fvHhR65ZS7dq19Z6ORypdhZI6Pz/6XZrDSN5kxw5BWpoKM3TbNThrqjeScQhRZJ46HdoEgjyzAFeSj18HvIwXmBlZa73Zt28farVa7wGb+tB7nrr4+HheffVVbG1tsbW1JScnh+DgYD766CPeeOMNvY51//33I4Qo9liwYAGgTOBX9Fvh+PHj+e+//7h16xZXr15l27ZtRk3oQMc+de2VeYWu4EP66eITM0umk5iYSO/evZkwYQKDBw9m6tSpLF26lP3792vKiMLlDopQWWEibi4VSuoaNqRusKAFB1GrVfzxh1FCk0qhS72xdDNnziQkJAQnJyeioqLYtm1bqWWXLVtG165d8fX1xcPDg3bt2rFu3TqtMgsWLChxBaPsbMPOD5eRAYW9mPzsy1+ZKNBfGbR4KdMVdaaZRhNIgH715sqVKzz11FPMmTPHqDHpndTZ29trPgD9/f01LWmenp5arWrWQpekzt3HEV8bZcmW07sumSAqqSRXr16lZ8+e9OvXT/MFIyoqir59+/Lmm29qytWqVUurZe78+fNG7ZdZ3VSkTx0qFXTtqlldQk5tYjq61htLtmTJEkaPHs2bb77JwYMH6dixIz179iz1M2nr1q107dqVNWvWsH//fjp37kzfvn05ePCgVjkPDw+t1YuSk5NxcjJsX7bCVjp3MnD+959yy/uHuqFCTT72pP1b+jqxknHpU29ycnJ4+OGHmThxIu3btzdqXHrffo2MjGTfvn00bNiQzp078/bbb5OWlsaPP/5I06ZNjRGj2Qih2+1XgFCXFC5n+nDqYAaRTxo/Nqk4b29vjh07Vmz7yrvWn2rTpg1HjhzhwoULeHh4sGbNGt5++21ThWn1KtRSB9ClCw/O/4gpvMP69coIuIr0b5H0o2u9sWSfffYZzz//vGYU+/Tp01m3bh2zZs0iNja2WPnp06dr/T5t2jRWrlzJ77//TmRkpGa7SqUioIwVHgxB1yXCCtnZq/C1SSNV7UvyiQz8Who1PKkUutYbIQTPPPMMDzzwAIMHDzZ6XHq31E2bNk3TqvHuu+9Ss2ZNXnrpJVJTU43erGhq2dlQuCJaWS11AKE1rwNw6ric5dvS2dnZ8emnn9K5c2ciIyN57bXXqKlXs5JUmoICuH5d+VnvpC4mhhbEU4ez3LoFGzcaOjqpsrp3786jjz7KmjVrqF27Nnv37jV3SOTm5rJ//366deumtb1bt27s3LlTp2Oo1Wpu3LhRbAR8ZmYmdevWpXbt2vTp06dYS97dcnJyyMjI0HqUJzVJWfFI16QOINBB+eaUfPqWTuUl89mxYwdLlixhxYoVtGjRghYtWnD48GGjnU/vlrpWrVppfvb19WXNmjUGDciSFK2P5c2ZE1o7B87CqUTrnRDSmvTr149+/fqZOwyrk56utHCDMk2JXvz8ULVowYPxK/mSkaxcCfIlsix39zuzBGlpaSWOOvT39yclJUWnY3z66adkZWXx2GOPabY1atSIBQsW0LRpUzIyMpgxYwb33nsv//zzDw0aNCjxOLGxsUyZMkWv+FNP3wAc8bNJAx0nqw10TeefbEg+K9cbt3QdOnTQLN5gCnq31FUnhf3p3NyUhZTLEtq5DgCnXSLKLihJVqywP527Ozg4VOAATzzBgz2VXuO//36npVySynP3YCchhE4DoBYtWsQ777zDkiVL8Csy/DQ6Oponn3yS5s2b07FjR3755RcaNmzIl19+WeqxJk6cSHp6uuaRlJRU7vlTE4ssEabjgK1AD2WARPKF4oO+pOpN76TuypUrjBgxgvDwcHx8fPD29tZ6WBNd+9MB1O+ufHM7leZlvIAkycJVuD9doddeo9PKcdSoAZcvw44dBgtNslI+Pj7Y2toWa5VLTU0tca69opYsWcLzzz/PL7/8QpcuXcosa2NjQ+vWrTl58mSpZRwdHfHw8NB6lCf1wu0lwjx0b3ULrKmUTb4k22UkbXrffn3yySc5deoUzz//PP7+/lY9FYQuI18LhYYq/547pwxPr1ArhSTpaObMmXz88cckJycTERHB9OnT6dix5JWH4+LitOaDLHTs2DEaNWpk0LgqndQB9vbQpw/8+CMsXw6lLOssSQA4ODgQFRXFhg0bePjhhzXbN2zYwIMPPljqfosWLeK5555j0aJF9O7du9zzCCGIj483+IDA1EvKrTk/73yd9wns2xr2QXKtVuUXlqoVvZO67du3s337dpo3b26MeCxKYVKnS0tdQAC4OBVwM9uWswlZNGhRRWZOlKqcwukbZs6cyb333ss333xDz549SUhIoE6dOqXud+LECa2WA19fX4PHZoikjoICHml6ih9pyPLl8NlnVjmft2RAY8eOZfDgwbRq1Yp27doxZ84czp07x7BhwwCKrU60aNEinnrqKWbMmEF0dLSmlc/Z2VmzCPuUKVOIjo6mQYMGZGRk8MUXXxAfH8/XX39t0NhTbYMA8OvdWud9AiOUCpacZm/QWKSqT++220aNGnHrVvUYcVN4+1WXljqVCurn/wvAqe3JRozKtEqaqLe6sbS/QdHpGxo3bsz06dMJDg5m1qxZZe7n5+dHQECA5mFra/hBPRWao+5un31Gt/EtcLbN4exZiI83RGSmZWnvGVMz9fUPHDiQ6dOnM3XqVFq0aMHWrVtZs2YNdevWBSi2OtE333xDfn4+I0aM0CzjFBgYqLUq0vXr13nhhRdo3Lgx3bp148KFC2zdupU2bdoYNPbUTGXeHr9uLXTex1qXCpP1pvLXr3dSN3PmTN588022bNnClStX9B6+XZXoc/sVINRdmXDo9CEzLVpnQPb2yjfAmzfljOWFf4PCv4k5VWb6hsjISAIDA4mJiWHz5s1llq3I1AxgoJa6zp1x4RY9VMpIy2XLKnEsE5P1RpF7e4kEY3xxKM3w4cNJTEwkJyeH/fv306nIffu7VyeKi4srczUjgM8//5yzZ8+Sk5NDamoq69ato127dgaPW58lwgoF2iiT3Cefz8ca8qDC90nh+6a6MsRnjd63X728vEhPT+eBBx7Q2l440qjAioar6TNQAqC+7w24Bqf+rfp/A1tbW7y8vEi9/T+Oi4uLVfefLIkQgps3b5KamoqXl5dJP6BKU5HpGwIDA5kzZw5RUVHk5OTw448/EhMTQ1xcnNYHX1EVmZoBDJTURUaCjw8Pp/3KcvqxfDm8+24ljmdCst4oc75dvnwZFxcX7Oz0/oipVvLz4coVAajwc0wHdJzSxD4N8Ccn347r1yswfZCFsbOzw8XFhcuXL2Nvb49NedNNWBlDftboXeP+97//4eDgwM8//ywHStwltE4+/AunkszfomMIhTOpF35AVVdeXl5Gn1VeX/pM3xAWFkZYWJjm93bt2pGUlMQnn3xSalI3ceJExo4dq/k9IyNDp0WoDZLU2dpC9+70+ekP7GwKOHrUlpMnoZSpwSyOrDfKSNE6depY9eeDIVy5AkKoUKGm5qUEaKBbS6BTbR9qcJVreJN8voAaNcz/hbMyVCoVgYGBnDlzhrNnz5o7HLMxxGeN3kndkSNHOHjwoNaHhLXSt6UuNMwONsKpyzruYOEKK5qfnx95edVzpQx7e3uLaKErVJnpG4qKjo5m4cKFpT7v6OiIo6Oj3vEZpE8dQM+e1PjpJzq77GFDZjuWL4fx4yt5TBOR9UYZkVrdWlsqIvWS0krnQxq2tfT4MK9Zk0BOKEndvzcIb+plrBBNxsHBgQYNGlTbW7CG+qyp0IoSSUlJ1SKp07ulroVS8HSWP0JYz4g9W1tbi0psqrOKTt9wt4MHD2qW+zMkg7TUAXTvDioVD2f+wAaqVlJXSNYbqTypiVmAm7JEmH993Xe0syPQ7jIJ+ZD8XxbgZaQITcvGxgYnJydzh1Gl6Z3UvfLKK4waNYrXXnuNpk2bFuvQ16xZM4MFZ276ttTVbeWLDQXcVDuTknJnhJIkGZK+0zdMnz6devXqERERQW5uLgsXLmTp0qUsXbrU4LEZLKnz8YHWrXlwz0qGM4vdu+HCBahVq9IhSpLFSD2ZAbjhZ3sVXJrotW+gczrcgOTEbOMEJ1VJeid1AwcOBOC5557TbFOpVFY5UELfljqHsBCCa2Rx9poHp08JAgOtpKlOsigDBw7kypUrTJ06leTkZJo0aVLm9A25ubmMGzeOCxcu4OzsTEREBKtXr6ZXr14Gj81gSR3AtGkEOTrS7jXBrt0qVq6E4cMNcFxJshCpicpoRz+XG3rvG+iRqSR15023rqhk+fRO6s6cOWOMOCySvkkdzs6ERjpz9i84dVrFvR2MFppUzQ0fPpzhpWQ4RadlABg/fjzjTXDvsqAArl1Tfq50nzqAmBgAHn4Edu1WVpeQSZ1kTVLPK/3H/Nz1b20LrJEDFyC55EHvUjWld1JX2BpQHeh7+xWU5cL++gtOnTJOTJJkqdLT0cyZZcgpFh5+WOlPFxentARa2RLTUjWWmqL/EmGFAp/rCWMh2UmPvniS1dMpqVu1ahU9e/bE3t6eVatWlVm2X79+BgnMEujdUgeEeqYBPpzaexWQnz5S9VF469XNzYBrH+/fzz3z5tEkcCpHkn344w946ikDHVuSzCzVRWkk8YvRfz3ZwJZKp+3kVDkYR7pDp6TuoYceIiUlBT8/Px566KFSy1lbn7qKtNTVP/EnMJhT8RnIpE6qTgzan67Q0aMwaxaPBLTgCC+wfLlM6iTrkXpTaTHwuz9c730LB+IlW8+qlJIB6DSRkFqtxu/2GiZqtbrUhzUldEJUsKUuXGmiOH3Vy/BBSZIFM9gcdUV17w7AwykzAVi3Dqr5ClySFanIEmGFAm8oa43fuAGZVX9lSslA9J4d8ocffiAnJ6fY9tzcXM0UCtYgO1tZwgX07FMXqSzzkprjpUkKJak6MEpLnb8/REXRnH+o53ODW7eUxE6SrEHqReVDxs9Z/w8L99RTuKJkc7K1Tiqkd1L37LPPkp6eXmz7jRs3ePbZZw0SlCUompC5uem+n2dEbWqSBsDp0wYOSpIsmFGSOoCePVEBD3tvBWDZMgMfX5LM4OZNyMxWekD5ZVbgw8LXlyAuAjKpk+7QO6krbY3J8+fP4+mp22LEhbZu3Urfvn0JCgpCpVKxYsWKcvfZsmULUVFRODk5Ub9+fWbPnq3XOXVVmNS5uoJeq93UrUsoytDXU4flfSKp+jBmUgfwSPJXAPzxB1TT1bckK3I5Remu5Eg27qEVuP/q40MgSjaXfFEYMjSpCtN5SpPIyEhUKhUqlYqYmBjs7O7sWlBQwJkzZ+jRo4deJ8/KyqJ58+Y8++yz9O/fv9zyZ86coVevXgwdOpSFCxeyY8cOhg8fjq+vr07766MigyQAcHenvsN59uS25dSBdHjSxaBxSZKlMkqfOoC2baFGDdpdW49fjVxSrzkQFwdduxr4PJJkQqn/Xgdq4kcqKr8g/Q/g60sguwFITswB5PJakh5JXeGo1/j4eLp3745bkXuSDg4O1KtXT+/EqmfPnvS8/S1cF7Nnz6ZOnTpMnz4dgMaNG7Nv3z4++eQTgyd1FRkkUSjU+xqkwOnjxfseSpK1MlpLna0tdOuG7d69PNggmbnr6rJ8uUzqpKot9WQ6UBM/u6tgV0f/A7i6Emh7GQog+Uw2Jk/qMjNh2DDlS9fLL1vPYudVnM5J3eTJkwGoV68egwYNwtHR0WhBlWbXrl1069ZNa1v37t2ZN28eeXl5xdahBcjJydEa2JFR2ARXjgq31AGhj7WCL+DUTbn4q1R9GC2pA5g/H5ydeXitirnrYMUK+OorPbtGSJIFSU3MAio2SKJQoNsNSIfkJP0nL66spA9+ot1PH6Ba6kjnvSoeeAA6d4ZqtD6BRdL7v8QHHniAy5cva37fs2cPo0ePZs6cOQYNrCQpKSn4+/trbfP39yc/P5+0tLQS94mNjcXT01PzCA4O1ulclWqp698CgFNJpk98JclcjJrUubiASvngcHNTOoYfOGCE80iSiaQmFS4RdqvCxwj0UvZNTjZxn7r8fBZ8lckFanM+25cff4Rnn4V69aB+fXj+eVi4EDkDhBnondQ98cQTbN68GVCSrC5durBnzx7eeOMNpk6davAA73b3IA1xe12ikgZvAEycOJH09HTNIykpSafzVKqlLlT59+xZ2aFbqj6M1qeuCEebPLrdp7S8r15tvPNIkrGlJisDJfxqVPxDIvDN5wBIzjFipSvJypX8kq7cNRs3Oo+JEyG6rRpbVQFnzigN64MHQ3S0nFfS1PRO6o4cOUKbNm0A+OWXX2jatCk7d+7k559/LraQuKEFBASQkqK9enFqaip2dnbULOWTxNHREQ8PD62HLirTUhfodA1H+wIKCuDcOf33l6SqyKgtdQA//ww+PvS+vACQSZ1UtaXWCAPAr2OjCh8jsH0IAMmXTNsPIWHaCo7QFHubfN54255p02BX0xe5JrxYE/wir72YgY8PJCTAG2+YNLRqT+93Ql5enqY/3caNGzVrvTZq1IhkI0+W065dOzZs2KC1bf369bRq1arE/nSVUZmWOpvz56ifdwKQc9VJ1YNaDdeuKT8bLamrUwcyMuj173QA9u6Fu77jSVKVkZqjTAHm1y60wscoXCrs6lUoYU0A4zh4kF8OKDF365xPjRq3t48ciXstT3omzeGj3xvzw5QzAMyYAbdv7kkmoHdSFxERwezZs9m2bRsbNmzQTGNy8eLFUlvLSpOZmUl8fDzx8fGAMmVJfHw85243b02cOJGniiz0OGzYMM6ePcvYsWM5duwY8+fPZ968eYwbN07fyyhXZVrqtOaqS5AjYCXrl56uLK0HRkzqoqPBy4uA68dp1UiZSf/PP410LkkyssosEVaoxun9ONoqgyRM9QVHTJ/BEgYCMPDpIiNumzaFXbsgPBwuXqTnxBa80FeZHPnZZ+80lFQ3QsDly7BvHyxdCp99BrNmKatWGYPeSd2HH37IN998w/3338/jjz9O8+bNAVi1apXmtqyu9u3bR2RkJJGRkQCMHTuWyMhI3n77bQCSk5M1CR5ASEgIa9asIS4ujhYtWvDuu+/yxRdfGHw6E6hkUuflRajDeQBO/SN7ikrWr7A/nZsbODgY6SR2dpp5THr77QXkLVip6ko9pwxy8HPNqvAxVAf2E1CgfNZcvGiQsMp1xLczx2mMg72a2zfq7ggOhu3boWNHyMjgk61tCQnO5+xZGDvWNPFZgtWroUcPaNRIWcDAzw9at4YBA+DVV2H4cLj/fuMk4jpPaVLo/vvvJy0tjYyMDGpo2l3hhRdewMVFv4l277//fs1Ah5KU1Efvvvvu44AJhr1V5vYrQKhPOlyEU8dNP9RckkzN6P3pCvXsCb/+Su/LC5hCZ9avh9xcIyaSkmQEQkDq1dtLhOVfBBpU7EC+vgSSzFnqmWypsCVOTwPQs5cNJS4iVaMGrF8P992H+549LKg/jvvPf868eSoefhh69zZNnEUJYbpp9Pbtg4cfLj5IMjBQme6lTh3YuBH+/ltJ9FatgtvtWgZRod6VQgj279/PN998w43bTVoODg56J3WWrFItdUD9YOUVPXVO77xZkqockyV13bsDEHVsIf5+am7cgG3bjHxOSTKw65dyyEfpB+7bqBIjV318TLr+qxDwyy/Kz489VkZBJyf46SeIiKDTjP6MGaNkVEOG3GnVN3acR47AF1/Agw8qeWZQEHz0kXGnWcnIgEGDlISuVy8leTt5UrnVevGicnd6yRIloWvUCM6fhw4dDLuetd5J3dmzZ2natCkPPvggI0aM0MxZ99FHHxmlb5u5VLqlrqEtAKdS3SmjMVKSrILJkrqgIAgPxwY1vSLOAvIWrFT1pB5TMhtPruMYUKOc0mW43VIHJkjq1q8n/pONnDyp5Gx9+5ZT/p574NAh6NiR996Dxo2V240jRhgnvJQUmDtXSaoCApQufqNGKS1h6enK3+f115W59N57D65fN+z5hYAXX4RTp5QWuYULISZG+TPcvVbDPfcoCV737sqUL/37KzEZIlfQO6kbNWoUrVq14tq1azg7O2u2P/zww2zatKnyEVmIyrbUhTR1Q4WarDxHiszVLElWyRRz1GkMHw5Tp9L7EaWT9h9/mOCcksWbOXMmISEhODk5ERUVxbZymnC3bNlCVFQUTk5O1K9fn9mzZxcrs3TpUsLDw3F0dCQ8PJzly5cbJFZl3Vfwt7tSufuCPj53kroLagNEVgohYOJEfhmv9GXt1UvHz8bbS744O8P3U89iaytYskRprTKkPXsgLAxeeEE5dmqqcs5u3eDDD5XnFyyAhg2VL6CTJimJ16RJhms5nDcPFi9WVjVctAhqlJOre3kp/3eNGqX8PmkS/O9/cKvic1ErhJ5q1qwpjh8/LoQQws3NTZw6dUoIIcSZM2eEs7OzvoczufT0dAGI9PT0MsvVrSsECLF7dwVP9O+/ItgnS4AQO3dW8BiSxdP1/VTVlXed77yj1JcXXzRlTELY2yvn/fdf051Xqhxj1JnFixcLe3t7MXfuXJGQkCBGjRolXF1dxdmzZ0ssf/r0aeHi4iJGjRolEhISxNy5c4W9vb347bffNGV27twpbG1txbRp08SxY8fEtGnThJ2dnditx4dCadf62+t7BAjRwe1gha5XIz9fzFM9L0CIng/cqtyxyrJtm1CDCFGdFiDEkiV67v/TT0I4O4tJUX8IEMLbW4iLFw0T2oEDQnh5Kf8PREQo/xdt3SpETk7xsvn5QixapJRTMlUhXF2FmDhRiKysisdw5IgQzs7K8T74oMgTarUQeXlC3LolRGam8p/W1atC3Lihtf833whhZ6fs36ZNyX8bXeuN3kldjRo1xNGjR4UQ2kndtm3bhJ+fn76HMzld/zDe3sof+PalVsj99yvH+PHHih9DsmwyqVOMHKm81ydONG1cMTHKeT//3LTnlSrOGHWmTZs2YtiwYVrbGjVqJCZMmFBi+fHjx4tGjRppbXvxxRdFdHS05vfHHntM9OjRQ6tM9+7dxaBBg3SOq7RrnfnEVgFCPBKwQ+djlWaNx0ABQrRodLPSxypV//5iL1EChHBxUfITvWzcKASIHOxFZP1rApS6e7OSIR86JETNmsr/AffeK8SNq7naBRYsEOLNN4V47jkhhg0TYs4cIfbvFwW3csTSpUJERt5J7ho0EGL7dv1jyMoSIjxcOUb35smi4OixO0/u2HHnBHc//P2F+PRTTdHN63KEt2eeACEmTy5+Hl3rjd63X7t27cr06dM1v6tUKjIzM5k8eTK9evWqZLuhZRCi8rdfQVkDD5R77JJkzUzWp65QWpoyCjYiEZC3YKuz3Nxc9u/fT7du3bS2d+vWjZ07d5a4z65du4qV7969O/v27SPv9rDF0sqUdkyAnJwcMjIytB4luXRRuVXqVyO37IvTQeDMSQAkX3Mqp2QFnT0Ly5dr5qbr00eZpkMvMTHw6qs4kMcPV/vi5CTYtEkZzF7R+euOH4cuXQRXrkCbgHOsudAct+gm2oVmzID331fWLZs9W7k/GxWFjYcbj3zekf37BMuXQ61ayoCGjh2VqVfKXdpMCGWUw7JljGq9k4QECCCZH/5phs3in++Us7Ut/RiXLmluTwPc73OEv9Mb8XJMAm+9pf/fo5DeSd3nn3/Oli1bCA8PJzs7myeeeIJ69epx4cIFPvzww4pHYkFycu4MR67oQAmA0LzjAJw6Ihe/k6ybSfvUAXz6KTz2GH3OzQRg61a5eHh1lZaWRkFBAf7+/lrb/f39iy0rWSglJaXE8vn5+aSlpZVZprRjAsTGxuLp6al5BAcHl1gu1b8pAH5tQ8q+OB0EdolQjpmqIt8YM2h9/TVCreYXJ2UhgDJHvZbl/feheXOaXN/Oumbj8fAQbNkCnTujX79ztZr/luzngVbppKaqiOQAa1Oa45F4qPhokf79lZEZ770H48cryWWNGsoHfF4eKhsVDz2kjJR9znMpQsDnn0OLWpfZPuEP2L0bDh+Go0fvHDMlRWntCQ5mcf9f+DahPSrULORJ/JoGaM8mHRWl/Od4/TpkZiod5nJzld/37YNHH71TNi2Ne7yv8eU7V7GrzKQZ+jc2CnHz5k0xf/58MWLECPHSSy+JuXPnipuVbUc1EV2aMFNT77SQ5udX/FyLg8cpzcLhVyt+EMmiyduvirZtlfqyYoWJAtqwQTlhrVqiQQO1ACGWLjXRuaVKMXSduXDhggDEzrs6L7/33nsiLCysxH0aNGggpk2bprVt+/btAhDJyclCCCHs7e3Fzz//rFVm4cKFwtHRsdRYsrOzRXp6uuaRlJRU4rXu3CnE118L8fffOl9mqQoKhLC1VarDhQuVP56WzEwhvLzELtoKEMLNrZK3TI8cEcLJSQgQ+5//Wvj6FAgQIixMiFK6PxZz5uExIpizAoRoyj/ismtdIR5/XIjffhPiypXyD6BWC3H6tNIZr1BurhB2dmINPUQtkgQIoaJAjOYzkYWzEAMGCLVaiOxsIa5fLRApzvXEHpu2wt3mhgAh3uqfoCQOhqBWl7hZ13pToXzQ2dmZZ599lmeffbYS6aTlKmwOdnUtu/W0PKF18yEJTp2XM6NK1s3kt1/vvVeZJ+DCBfo8fY3PT3rzxx/wyCMmOr9kMXx8fLC1tS3Wgpaamlqspa1QQEBAieXt7Ow0y12WVqa0YwI4Ojpq1kYvS7t2ysMQbLbG4e/Skos3PEhOVmb9MZjjx6FePX45NwyuQr9+yqjSCouIgI8/hldeoeW8EWzb9CBdn6nFiRNw772CDRtUNGpUpPzZs8rcIM88A7VqkZQED2yfQhLuNPK4yMYvL+Lz2HFljhVdqVQQclcLqb09XLpEz2PHOLJvE6/OCWN+QjTTGcNshiGW2ZKjua9pA5zR7NqxI0xe3LgCSzmUEV8lVGjyYWtniP50AKGNlMklUzJcyar4SjCSZPFMntQ5O0P79gD09twOwJo1oDbirA6SZXJwcCAqKooNGzZobd+wYQPtb79H7tauXbti5devX0+rVq2wt7cvs0xpxzSbTZsIvPEvYIS56qKiUO8/yK8uyioSFb71WtSIEco9zqeeIuyBWuzYUTgRr4qOLTLY/+lm+PZbuO8+cuo1ZNdbf/Dpi//Svz+0aAFnLrtzT6iaTceC8Huqh34JXVm8veHee/Ea9TTzjkazZo3S1y4bZ3LUxRtmnJygTRv4+Wcqd7vU0AzTXlh16NKEuWWL0pTdsGElT/bJJ8KLqwKUUTqS9ZG3X5XbPzY2Sp25fefKNN59VxlR9/BA4e6unH/vXhOeX6oQY05pMm/ePJGQkCBGjx4tXF1dRWJiohBCiAkTJojBgwdryhdOaTJmzBiRkJAg5s2bV2xKkx07dghbW1vxwQcfiGPHjokPPvjAYFOaGNSMGaIPqwQoU2MY2vbtSt3y8FBm5jCG1BNXRRT7BAjhTrp4hRmiPduFA9nFBo3ec48Q584ZJ4675eQIceKEcmv40iUhrl9XbsGWcofUqIw2+rU6MFRLHXXrcg//AfDvv5U8liRZqPT0Oy1k5U24aVAxMQA4bNlAt67KVOxyFGz1NHDgQKZPn87UqVNp0aIFW7duZc2aNdStWxeA5ORkzp07pykfEhLCmjVriIuLo0WLFrz77rt88cUX9O/fX1Omffv2LF68mO+++45mzZqxYMEClixZQtu2bU1+fWUqOgGxoVvqCgo0y4I9+KDhGsXu5tvAi782Ce6v/R838OBLRrKTe8nFEV9f5dwffqgsCXjkCJQy/sTgHByUCYvr1FHGP3h6Kr0+TLWObEVYUqOhxajsEmEa9eoRzlH20ZpjxyodliRZpMJbr66uxZfDMarWrZVvXlev0rv5eZYuC2b1anjnHRPGIFmM4cOHM3z48BKfW7BgQbFt9913HwcOHCjzmAMGDGDAgAGGCM94fH0Jwgi3X7OyKPDx59f8M4AvAwca8Nh3U6nweKAVf56EN99UBoq2b690nQ0NtewkytLIpK4EBmupq1ePcH4F4OihAqASoy4kyUKZvD9dITs7WLoUwsLo6RAMk5VZAlJSlLUfJalaMFZL3dGj7MpuQTK+eHlB164GPHYpnJyU2YqkijPo7deQkBCef/55Lly4YMjDmpzBkrqaNYl44yEAEo7LO92SdTL5HHVFde0KdeoQEKA03IEyYEKSqg1f3yJJnQFWhC90+DDb6Ago1cxBTuJQJRg003j66adRq9V06tTJkIc1OYPdflWpCH9eGbd+4l8jTQwpSWZmtpa6u/Turfy7erV545AkkyraUnfBgEndoUPsJhow3PQrkvEZNKl75513+O677zhVxdfFMlhLHVC3rjL7Qk4OnDlTfnlJqmrMntTNmwd9+tC7zmEA1q9X6pskVQtOTgQunwVASqrKYNP6iEOHNUlddLRhjikZX4WTutzcXE6cOEG+FTY/GaylDrD97wSNvC8B2iuNSJK1MHtSt349rF5Ny8Rl1KqldLJev95MsUiSGfj3boVKBfn5Kk13iEoRgsR/0knFH3s7NZGRBjimZBJ6J3U3b97k+eefx8XFhYiICM0w8ZEjR/LBBx8YPEBzMGRLHXv2EHFB+YRJSDDA8STJwpi1Tx1opjax2bxJs5Ri4TQMklQd2NuDj4/ys0EGS1y6xO5rDQGIbCGMNpWJZHh6J3UTJ07kn3/+IS4uDqcir3SXLl1YsmSJQYMzF0O21NG4MeEo2ZxM6iRrZPaWuttJHbt381jfWwCsXAnZ2WaKR5JM7Y8/CFQpS5oZJKm7dYtdoYMBiG4vZ22oSvRO6lasWMFXX31Fhw4dUBWZPCY8PLzK96UrZNCWurCwO0ndYeu7VS1JZk/q6tdXOq/m5dE2ZyvBwUodXrvWTPFIkqn99huBqfGAgZK6kBB2e/cC5CCJqkbvpO7y5cv4+fkV256VlaWV5FVlBk3q3N0J91c+9Y4dV1FQYIBjSpIFMXtSp1LBAw8A8hasVE0VGQF78WLlD3frFhw8qPwsB0lULXonda1bt2Z1kTkDChO5uXPn0s5KUnqD3n4F6jdxwZFssnNtSUw0zDElyVKYvU8d3LkFu2mTZtHxVauUDydJsnpac9VV/nAHN6SRnw/+/kojuFR16L2iRGxsLD169CAhIYH8/HxmzJjB0aNH2bVrF1u2bDFGjCZn0JY6wDY8jEabjvMPLUhIUJY9kSRrYfaWOlBa6hwdwcuLNi3zqVvXjrNn4c8/4ZFHzBiXJJmCry/B7AUMMHVWQQG7+38MfEh081uoVM6VDk8yHb1b6tq3b8+OHTu4efMmoaGhrF+/Hn9/f3bt2kVUVJTeAcycOZOQkBCcnJyIiopi27ZtpZaNi4tDpVIVexw/flzv85bF0C11crCEZGj61BuALVu2EBUVhZOTE/Xr12f27NkGiUOthmvXlJ/NmtQFBsL167BpEyp7O01rnbwFK1ULPj404xAA//xTyWP99x+785XP8uj7TbmYs2QIFVr7tWnTpnz//feVPvmSJUsYPXo0M2fO5N577+Wbb76hZ8+eJCQkUKdOnVL3O3HiBB5FMi5fX99Kx1IoJwfy8pSfDdVSx8MPE36oAGbLpE6qPH3rzZkzZ+jVqxdDhw5l4cKF7Nixg+HDh+Pr60v//v0rFUtGBprJTs29okTReRceeww+/hh+/x1u3gQXFzPGJUnG5utLMw6hQs358zakpd2Z4kRvh4tMOtxOLm9Z1ej9itna2pKamlps+5UrV7C11W/o82effcbzzz/PkCFDaNy4MdOnTyc4OJhZs2aVuZ+fnx8BAQGah77nLUthKx0YMKkLCCC8ay1AJnVS5elbb2bPnk2dOnWYPn06jRs3ZsiQITz33HN88sknlY6lsD+dq6ty99MiXL1KVBSEhCgJnVwLVrJ6Pj64k8k9qtMAxMdX/FAXtp8hiTrYqNS0amWY8CTT0TupE6LkteVycnJw0GPF39zcXPbv30+3bt20tnfr1o2dO3eWuW9kZCSBgYHExMSwefPmMsvm5OSQkZGh9ShLYX86FxcwYK5IeLjyb0ICBlvGRap+KlJvdu3aVax89+7d2bdvH3mFzdJ30bXeWER/ukL5+dCqFfj4oDpzWnML1kqmz5Sk0tWpA9u306K7P1C5pO7vXcoHVNPAK7i5GSA2yaR0vv36xRdfAMpo12+//Ra3Iq92QUEBW7dupVGjRjqfOC0tjYKCAvz9/bW2+/v7k5KSUuI+gYGBzJkzh6ioKHJycvjxxx+JiYkhLi6OTp06lbhPbGwsU6ZM0TkuQw+SKHTPP0uxt3mQmzftOHcO6tUz7PGl6qEi9SYlJaXE8vn5+aSlpREYGFhsH13rjUUldXZ24OkJQsCyZTz22Dg+/BBWr1aWDpMfUJLVcnSEe++lRUf4dW3lkrpdJ5TK3C5KLqBcFemc1H3++eeA0lI3e/ZsrVueDg4O1KtXr0Kdr++e204IUep8d2FhYYSFhWl+b9euHUlJSXzyySelJnUTJ05k7Nixmt8zMjIIDg4uNR6DD5K4zW7tH4SpG3KEpiQkyKROqhx96k1p5UvaXkjXetOypdJvzd5e59CNq39/+OsvWLaMyFfHERoKp04pid3AgeYOTpKMq0UL5d8KJ3WZmexOVxpnorsa+ENQMgmdk7ozt8dJd+7cmWXLllGjRo1KndjHxwdbW9tirQupqanFWhXKEh0dzcKFC0t93tHREUc9OvsYq6WucARsYVLXq5eBjy9VCxWpNwEBASWWt7Ozo2Ypk8vpWm98faFPHx2DN4WHHoIRI2DXLlQXL/DYY7WIjVVGwcqkTrJqixfT4u8k4DWOH1fmaHTWczaSvKxc9tlFQz5Ed5NJXVWkd5+6zZs3VzqhA6V1Lyoqig0bNmht37BhA+3bt9f5OAcPHizx9lFFGauljkaN5LQmUqVVpN60a9euWPn169fTqlUr7C2mic1AgoKg8O+wfLmmX92aNXe+sEmmU0oXbMkY5s4lcPp4fD2yKSiAI0f0P8Sh895k59tTowY0aGD4ECXjq9CUJufPn2fVqlWcO3eO3Nxcrec+++wznY8zduxYBg8eTKtWrWjXrh1z5szh3LlzDBs2DFBuAV24cIEffvgBgOnTp1OvXj0iIiLIzc1l4cKFLF26lKVLl1bkMkpkzJa6CJTrOHpUANaxpJpkevrWm2HDhvHVV18xduxYhg4dyq5du5g3bx6LFi0y52UYzyOPwM6dsGwZzUe8TIMGcPIk/PEHPP64uYOzbkIot/5+/11Z0aN/f5g40dxRVRM+PqiAFoGX2JBRl/h4aN1av0Ps3q3827Yt2MjZTKokvZO6TZs20a9fP0JCQjhx4gRNmjQhMTERIQQtW7bU61gDBw7kypUrTJ06leTkZJo0acKaNWuoe3tdkuTkZM6dO6cpn5uby7hx47hw4QLOzs5ERESwevVqehnwXqbRWupCQgi3Pwl5kHBUIIQKK1kqVzIxfetNSEgIa9asYcyYMXz99dcEBQXxxRdfVHqOOov1yCMwbhxs2YIq7TKPPebL++8rt2BlUmd4OTmwebOSyP3+OyQl3XnO3l4mdSZze77WSO9zbKBuhfrV7V5zBagp13utyoSeWrduLSZNmiSEEMLNzU2cOnVK3LhxQ/Tr10/MnDlT38OZXHp6ugBEenp6ic9PniwECDFsmOHPndO4ubAjV4AQ584Z/viS6ZX3frIWVe46x40T4uefhcjKEv/8o9RpR0chqkr4li4/X4g//hCif38h3NyUv2/hw0WVJR5imZjXcb5ISSm+b5V7L1WCSa/19ofXzzFzBQjRvr2e+6vV4h6bUwKEWPvVSWNEKFWCru8lvRtYjx07xtNPPw2AnZ0dt27dws3NjalTp/Lhhx8aMN00D6O11AEOEQ1owElA9quTJKP6+GOlWc7FhaZNISxMaVFatcrcgVVtqakQGwv33KMMkFm6VJkuJtAmhRf4hj/oTZqoyXK3p3iu6T70GPMmVVYtZYL7Ftl/A8pyYfrMiZp29BL/qesD0ObhWgYPTzINvZM6V1dXcnKU+WuCgoI4deqU5rm0tDTDRWYmRutTB/D554T3vQeQSZ0kmYpKdWfkq1wLVn9CwNatSo5cuza88QYkJkKNGjBmDOyduZfz6iC+8RhP7ye9cV6xWMn+vv7a3KFXL02bAtDw1J84O0NWljKdj67+Xn4RgMYO/1EjSM9hs5LF0LtPXXR0NDt27CA8PJzevXvz6quvcvjwYZYtW0a0FdyIN2pSV7s2EZGw9Hc4etQIx5ck6Y7//lOyuJYteeyxHkydCuvWKfmGn5+5g7N8BQWwaBF88IH2/1dtVX/zUtdTPLbiCWXKjPxIqPM7dOliQWvFVUNNmoBKhW3KBZq2zGPPAXsOHtR9FOuuLcqgx+igc8A9xotTMiq9W+o+++wz2rZtC8A777xD165dWbJkCXXr1mXevHkGD9DUjHn7FbSXC5MkyYi+/x7efBPmzCEiAtq0gdxcMMCSt1ZNCOW2arNmMHiwktC5qG4ylDkcIJLdIpqn1d/h7HR7vhI7O+jdWyZ05ubmpoxYSU6mRStlqiJ9BkvsPqq0ZEQ3vWmE4CRT0Tupq1+/Ps2aNQPAxcWFmTNncujQIZYtW6YZfVeVGbWlTgjC//gIgISjajmHkyQZ0yOPKP+uXQtZWUyerPz69ddKa52kTQj4809l+dwBA5Qvnl6q60xjIhdFIHOcRxP5fBTs3QsbNmApw/evXbvG4MGD8fT0xNPTk8GDB3P9+vVSy+fl5fH666/TtGlTXF1dCQoK4qmnnuLixYta5e6//35UKpXWY9CgQUa+mkq67z4ICCAyUvlV16SuoAD2pCqf39Gd5a3XqqxCSd2VK1eKbb9+/Tr169c3SFDmZNSWOpWKhlu/xYYC0jNsSE42wjkkSVK0aAEhIcrU+mvX0rOnMm/XzZuyte5ucXHQsaOy0s2BA0qjz6S26zgj6jExZAmeX74PFy/Ct98qWZ8FeeKJJ4iPj2ft2rWsXbuW+Ph4Bg8eXGr5mzdvcuDAASZNmsSBAwdYtmwZ//77L/369StWdujQoSQnJ2se33zzjTEvxWD0XS7s2OF8bqjdcCWTiF5Vv3GmOtO7T11iYiIFBQXFtufk5HDhwgWDBGVORm2pAxwb1+eec//xL2EcPapMgC9JkhGoVMrst598AkuXourfn3feUe4Ufv21MpVdde9bd/YsjBoFK1cqvzs5qnn5FRvGjwdfEQkrPoJnn7WgxX21HTt2jLVr17J7925Nt6C5c+fSrl07Tpw4obVWeCFPT89iK6x8+eWXtGnThnPnzlGnTh3NdhcXFwICAox7EYZ08SJ8/TVNr2SjUn1KcjJcukS5o5B371JuG7UJTsa2QagJApWMReeWulWrVrHq9nwA69at0/y+atUqli9fzrvvvks9K1il3th96pSVJZRex7JfnSQZWeEEy3/8ATk5srXutpwcmDYNGjdWEjo78hjO15xq8wQff3x7Hls/P3jhBYtN6AB27dqFp6enJqEDZTCfp6cnO3fu1Pk46enpqFQqvLy8tLb/9NNP+Pj4EBERwbhx47hRzlpzOTk5ZGRkaD1MKjcXpk3Ddf6XNGygJGr//FP+brv3K69x9JMN5FISVZzOLXUPPfQQACqVSjNPXSF7e3vq1avHp59+atDgzMHYLXU0bkw4CSznEZnUSZKxtWmjNIdfvAgbN6Lq3bvat9Zt2gQjRsCJE8rv9xHH14wgwusi9HhNmdysinywp6Sk4FfCC+jn50dKSopOx8jOzmbChAk88cQTeBT5Nv+///2PkJAQAgICOHLkCBMnTuSff/4p1spXVGxsLFOmTNH/Qgylbl3w9IT0dFqEpHPiXy8OHoRu3crerXB5MCuYwKLa07nmqtVq1Go1derUITU1VfO7Wq0mJyeHEydO0KdPH2PGanQ5OcoXHTBiUteoEeEo2ZxM6iTJyGxslAETbm6a9auqa2vdxYvKXHNduigJnb/NZRbyPzbTmYhhneDMGWUSOgtI6N55551igxTufuzbtw9QGhruJoQocfvd8vLyGDRoEGq1mpkzZ2o9N3ToULp06UKTJk0YNGgQv/32Gxs3buTAgQOlHm/ixImkp6drHklF10wzBZVKGbYMtPA8A5Tfry49HRISlFa96GZy5GtVp3efujNnzhgjDotQtGXd2C11AEflGrCSZHyTJysrTDg5Acrn3uTJyooIX38Nr72mWTbTKqnVMHs2TJig/B9nYyMYrv6Kd9WT8KrrBfM2QkyMucPU8vLLL5c70rRevXocOnSIS5cuFXvu8uXL+JfTkSwvL4/HHnuMM2fO8Ndff2m10pWkZcuW2Nvbc/LkyVLXOXd0dMTR3FO7NGsG27bRomA/EFluUrdzJwihoj6n8DtzDup1NkWUkpHo/JXs77//5s8//9Ta9sMPPxASEoKfnx8vvPCCZqWJqqowqXN2VqZeMgofH8JqXMaGAq5dU1HC/0eSJBmSj48moSvUq5cyiNPaW+sSE6FrV+V2640b0LYt7N2Vz5dR3+P10hNw+LDFJXQAPj4+NGrUqMyHk5MT7dq1Iz09nT179mj2/fvvv0lPT6d9+/alHr8woTt58iQbN26kZs2a5cZ09OhR8vLyCAwMNMg1Gk3z5gBEXl4PKK2yWVmlF/9qej4A3VmnWZVCqrp0TureeecdDh06pPn98OHDPP/883Tp0oUJEybw+++/Exsba5QgTcXogyQAVCqcTx+l/j3Kn17egpUkExFCM0GdSgXvvKNs/uoruHzZfGEZgxDwzTfKZ/Rff4GzfR5ffF7Azp3Qso09bN8OM2ca8ZaEaTRu3JgePXowdOhQdu/eze7duxk6dCh9+vTRGvnaqFEjli9fDkB+fj4DBgxg3759/PTTTxQUFJCSkkJKSgq5t/vfnDp1iqlTp7Jv3z4SExNZs2YNjz76KJGRkdx7771muVad3U7q/E9sJSBAeS8cOVJy0YMHYc16O2wo4FWfH5QvQFKVpnNSFx8fT0yRb3SLFy+mbdu2zJ07l7Fjx/LFF1/wSxVfWNHogyQKeXkRHq7cc5VJnSSZwH//KUM9mzdXZlrFelvrzp2D7t1h2DDIzIQObvEcymvMKxcn3ukud1fLZVX2008/0bRpU7p160a3bt1o1qwZP/74o1aZEydOkJ6eDsD58+dZtWoV58+fp0WLFgQGBmoehSNmHRwc2LRpE927dycsLIyRI0fSrVs3Nm7ciK2trcmvUS9Nmij9IrOyaBGRB5Ter66wHWYQiwmN8jJJeJJx6XyT8dq1a1p9FLZs2UKPHj00v7du3dr0nUINzCQtdbeFh8OqVTKpkySTqFNHaY67elVZ0PTJJzWtdX36KK1148ZV7b51QsD8+TBmjPIF1cmhgFibt3gl8yNsa3gqswtbIW9vbxYuXFhmGVFk+Z569epp/V6S4OBgtmzZYpD4TM7FBU6fhuBgWrxpw9pNSovc3U6cgN9+U36ewAcQ85Rp45SMQueWOn9/f80gidzcXA4cOEC7du00z9+4cQN7C57PSBcma6k7cYLwP5XpX2RSJ0km4OCgZG0AU6ZAntKCYS2tdefPK9O0DBmi/D/WLuAM8bnhjM7+ANvoNkpTTd++5g5TMpW6dcHGpsyVJT74QPki0M/mD5pyRGnelao8nZO6Hj16MGHCBLZt28bEiRNxcXGhY5FvfocOHSI0tGrPRG2yljoHByL++QlQRsBKkmQCr7yiNMX99x/88ANQvG/dv/+aL7yKEAIWLFDuuP35Jzg6qPm49gy2pdxDGP8qQ3u3blVaKqVqpzCpO3RI0+sAUFYSKWzcfEP9LgQGykESVkLnpO69997D1taW++67j7lz5zJ37lwcHBw0z8+fP59u5c1waOFM1lJXpw6NHBNRoSYtTWV1nbQlySK5uSnzegC8+65mUspevZR10G/ehH79oIy14C3KxYtK49uzzypzjbVpAweXn2XctTex9fZSVtH46COLXhFCMpL//oOBA7nntYdxdVWWPz558s7Tn3wC+fkQ84Cg7a4ZyqAZObeWVdA5qfP19WXbtm1cu3aNa9eu8fDDD2s9/+uvvzJ58mSDB2hKJkvqbG1xaVSHeiQCyrcoSZJM4KWXlFaJs2dh3jxA+SxbsgSCg5V+RoMGKR94lkoI+PFHiIiA1avBwUHwwQewYwc07hUCS5cq99t69zZ3qJK5ODjAL79g++cfNGuqBu7cgr10Cb79Vvn5jTdVyjISt1eMkqo+vacO9/T0LHH0j7e3t1bLXVVkyoESNGpER7YB8P33JjifJEnKJJRvvqn8vH69ZrO/v7IGqosLrFsH48ebKb5yJCcrn79PPaW0KLZqkceB1sN4vd3WO3Nrdu+uZKhS9RUcDF5ekJ9Pi+CrwJ2k7vPPITtbmbOws5xn2OqYfz0YC2KyljqAxo0ZwdcALF4MOi5TKElSZQ0ZojRxLVumtTky8s4XrM8/V0aSWoqCAvjyS2jUSBk1b28P7w9LYtflBkTsmAPPPWfZzYuSaalUmvnqWrgqHUUPHoRr15Q7rQBvxuxG9eILsHmzuaKUjEAmdUWYtKWucWPasJdotyPk5SnL+BjT5ctK8jh1asnD2yWp2nB0VDrSldCHaMCAOwMnhg1T5ug1tz17lLVqR45U/o9q3Vqw//VfeGNeKHYXzkJYmJLpGW0ZHKlKKkzqcpTVNg4eVAYD3bihjInoffpLmDsXNm0yZ5SSgcmkrgiTttQ1agRubowKWQUoSZ0hV1m7dUu5uzR+vNIC4eenLOY9eTK0bAkPPKD0o1arDXdOSapyMjKUxS+LmDRJSe7y8uCRR5Sltszh2jWlC2B0tPKB7OUFs2bksqvRczR9b6ASYP/+StYXHm6eICXLdTupa5K8ARsb5Yv9xx8rT018XY3NxtvdD+RUJlbF7EndzJkzCQkJwcnJiaioKLZt21Zm+S1bthAVFYWTkxP169dntgGbuEzaUte0KWRk0H//G9SqpXReNcSCHLu359O15RVqeOTTvbtSiQv7UjS3OUS/vgJbW6XFvW9fCA8XfPONMvJPkqqVo0chJEQZ8lr4jQ5lMv4FC5QvQ5cvw4MPKiszmErhQIhGjZQve0LA4MFw/O90hn3XFtsfFyhBfvQR/Pqrif7Dkqqc20mdy5E9hIUpU2fduAGhofBo6AFIS1NaMKKjzRmlZGBmTeqWLFnC6NGjefPNNzl48CAdO3akZ8+enDt3rsTyZ86coVevXnTs2JGDBw/yxhtvMHLkSJYuXWqQeEzaUqdSgUqFvT0MH65smjFD+Q+8ovbuEXS9P5eNB2uSk29HLc7zDN/xE0+Qgj/xgb1YOS+NM2eU6as8bW9w4oSKYcOgTrCat9/WLI1p9W7dUtZDXL5c+Wx88UV49VXlw/TwYc3ctJI1CwtT1rq8ckWpfEW4uioDJ/z9ldHpTzxh/MROCOUuatu2ykCI1FRlZbO4OGVaPf8GHkqm5+sLGzcqlVhOQyGVJjxcGRhUqxaRTe70t5wwAew2rVN+iYmRU95YG2FGbdq0EcOGDdPa1qhRIzFhwoQSy48fP140atRIa9uLL74ooqOjdT5nenq6AER6enqx5+rXFwKE2LFD58NVnlotLv+5Vzg5Kefevr1ihzlyRAhvb+UYnVV/iWMxI4T6jTeF+PFHIfbuFSIjQ3uHpCSR4VBTzOAVEcIpoXykCOHkkC9efjFHJCZW/tJKkpsrxNmzynX+9psQO3cKceOGcc5VVPyum+LVZ9JETNRVUad2gVCphOaaS3o42OaJlrWSxXMDrosvvhBi61Yl9ruV9X6yJlZ7nT//rLzgnp5CXL1a7OmdO4VwcFCK1KolxKJFQqjVhg0hP1+IX34RolmzO+8/FxchYmOFyMlWC3Hr1p3CmZlCJCUZNgATs9r3UgnMfq15eUIIIT766M57ODtbCNGxo7Jh1izzxCXpTdf3ktmSupycHGFrayuWLVumtX3kyJGiU6dOJe7TsWNHMXLkSK1ty5YtE3Z2diK3pE9cIUR2drZIT0/XPJKSkkr9w/j4KO/zw4creFH6yssT4t57hQDxfN8UAUI8+qj+hzl1SojAQCX2Nm2EyEi8otuOV64IMX26yG8UIX7jEdGG3ZoPFVubAjF4sJIs6k2tFlfPXBfLZ5wVrw44I/q3Piva1EkWQW7pwsZGXWISFepyUTwSvEe80/oPsbz/D+LUazOF+sS/d46ZkSHExYu3/0e6LSdHiLQ0IU6fFiIr6872v/8WYsIEkf7IM2J2vVjRyu5Aief09BSiVd1L4nF+EpOYIkYyXXQiTnhwvVhZGxvtUxQy+3/aJmK111lQIESTJsqL/NZbJRbZsEGIkJA774X77hPi0KHKnzovT/nO1ajRnWO7uQkxYYIQly4J5T3/yCPKw9CZpBlZ7XupBJZyrZcvCzFggPJeFunpQtjZKW+406fNGpekO13fS2YbLpWWlkZBQQH+/v5a2/39/UkpZX6PlJSUEsvn5+eTlpZGYGBgsX1iY2OZMmWKTjGZ9PYrKKPVwsJgxw5Gpk5iHnNYtgySknSfZuriRejSNoPkNA/NUkHu3t667eztDaNGYTtyJP137OCRH79j88oZxF56lo3qrvz4o3I78sH7rvO67cc0q3cDF8cCVLY2Sp8eGxuwtYWhQ8mqHcb27fDXJ/vZ9JeKA+oWCDxLPK29PdSuDf6qS5w9XUAyQZy6Gcipm4EsSwL2KuV85+TQMQY6dYJO6XE0m/wQtqiVycTy8zUrAgCwdi10744QsPPXi3z7SUN+4S1u4qqck1wesl9NL++/afjmozQYFIWPD6j+Pg0/71b6JdlcgezViFtLSUxzIz4lgPiGjxGfEkBWlnJaycrY2ChrwfbvD9Onw//+p9ziLKJLF2WN5o8/hthY2LJF6W83YoSyq5eX7qfLy4N9++Cvv+C77+DUKWW7lxeMGqWMcPX2RpkF+f6H4dgxpcLExysnlaQK8Kkp+PXX27fqD59VOtap1UqfUsmqqISoTC+uirt48SK1atVi586dtGvXTrP9/fff58cff+T48ePF9mnYsCHPPvssEydO1GzbsWMHHTp0IDk5mYCAgGL75OTkkFNkWGlGRgbBwcGkp6fjcVcH4+PHlcSueXNlQm6TSEyEBg0gP5/OLa4RF+/FhAnKh0d5rlyBTk2ukJBSk1D7s2w7WpPABm6Vi0cIOHWKvReC+OALF5Yv1+7np0KNG5m4kYk7N3AjE5uG9/DPGc9i/dAa2Zygs+dBGvtcJtgvm9pBguDhffHt1BgbG5Re6Pv3czkpm0PH7PnnpAuHznlxKNmHo1cCyFVr9/XwIJ172UEHtuPCTa7irTxs/bja7H6u2vlx8SJcuHBnn8a1MxjyWAaDX3LH956Sk8zKyMjIwNPTs8T3kzWx6usUQvnmsH27skbqvn1Kv7USnD2r9L0s7Mbr6wsTJypV2MdH+d3XV/liqFIp88v984+SxG3erCzDWrRvno8PjB2r9Kv1LHx7Ll8OTz+t/GcUFKSczIo6s1v1e+kuZr/Ww4fhmWeUN+O+fdrPpacXedNJlk7n95JJ2g1LYKrbr3ezlOZwLS++KASI5RFvClD6xpV0q6+o9HQhWtVPU/pJkCTOjPrcKKEdOybEM/3ShINtXpl90ECIOnWEePaJbPHjJ8ni/MmblTpvdrbS7y42VoiePYXw8Cj73EUfLi5CPPec0h/K2HetLPL9ZARWf52pqUI0bCjEqFHKLdlyrF8vRFhYGX0yHYQIChLCy6v4c97eQvTvL8Ts2UoXOY1bt4QYPvxOwY4dhUhJMdolm4vVv5eKMPu1nj9/uz+NrXbfTKnK0fW9ZLaWOoC2bdsSFRXFzMIproHw8HAefPBBYktoqnr99df5/fffSUhI0Gx76aWXiI+PZ9euXTqd0+zfnEqSlAT33ENBbj4NAjI5k+LMnDkwdGjJxW/dgp5tr7LlsDc1SWPb47No/NNbRh0Jp1Yr057cuKG0NGRm3vk5O1tp3axf33ghFBQooxC3boXdu5XzeHsXf9SoAc2ame4WukW+n4ygWlzn9etKy4WOb+LcXGV2/nXrlEbntDTl37unB3J3VxoCH3hAeTRrptz1LebBB5XhrwDjxsG0aVY5MrFavJduM/u1CqE0B1+9Cvv3K10L7OxMeCtKMhSLb6kTQojFixcLe3t7MW/ePJGQkCBGjx4tXF1dReLtoZcTJkwQgwcP1pQ/ffq0cHFxEWPGjBEJCQli3rx5wt7eXvz22286n9Ps35xK88orQoD4tN4MAUJERBRvZcrOFmLuXCFCa90SIIQH18W+rhN0almQjMNi308GVl2uUyM7W4jPPtOMHtRHVpYywnvfPiH279fjEDt3Ks17a9fqfc6qpDq9lyziWjt3Vlrr5s8X4ssvhXB1FeLNN80Xj1QhFj9QAmDgwIFcuXKFqVOnkpycTJMmTVizZg1169YFIDk5WWvOupCQENasWcOYMWP4+uuvCQoK4osvvqB///7mugTDmTgR5s7lOefFvO3yCkePqvjrL2UaoawsZTWXTz4p7C/mhA+XWdb6A6L+iC3la78kSRUiBDz6KPz+uzJB8dy5ejVBu7goXfPq1CmnYHo67N2rjMQAaNcOTp9WljGTJENp3lzp0PnPP8rInKwscKtk32vJYpn19qs5mL05vCzx8dCsGS+PtOHrr6FbN+W2zfTpyq0dgKCAAl7Li2Vo8z24rvxZVk4zs+j3kwFVl+vUWLlSWSNMrYY334T33jPs8ePi4LnnIDlZSeyaNDHs8S1YdXovWcS1LlgAzz4L7dsriV1WFhw4IEdTVzG6vpdkE48ladECbGx45RXl1/Xr4a23lISufn2YMwdOJ9oy+p9ncV27VCZ0kmQsDz4I33yj/Pz++8o3K0N8/921C7p2hc6d4cwZCAgw7KLPknS3Zs2Uf3fuVBI6Pz/NEmKS9ZFJnQUKC77JIy3PABARIfipy3ecmLiAoUNv35mpVcsqO1BLkkUZMuROC92YMcqXrvnzK5bc7dkDPXsqrSUbNyr1d9gwOHgQoqIMGrYkaQkPV+ZDLdStm+yyY8XkK2tpCgogMpIfD0QQ//5qDjUYwBMbn8Nu+AvKKFlJkkznjTeUGYZdXJTh14sW6T/EOytL+SBdu1aZrHvIEPj3X5g1S7+ZiyWpIpyclElYC2+3du9u3ngkozLrQAmpBLa2MHAgLu++S/M3+yjbHBxg4ULdl5mQJMkwVCp4+2145RX49lvtVrXkZGUm4ocfVqZDSUnRfvz0E9SrB66uyhQlp04p/SlCQ811NVJ1demS0ioMyu1/yWrJpM4SjRkDX3xxZ8bvlSvhvvvMHZUkVV81asBrr2lvmz1bablbtKjkfc6fV5I6UJI5STIXW1tlmaLTp+GupTYl6yKTOktUo4YyYmnBAnj3XWja1NwRSZJ0t/79lTmGjhxROp8HBCgfmAEByuOuNWQlyWx8fGDCBHNHIZmATOos1UMPKQ9JkixTs2bKLVlJkiQLIQdKSFIVcu3aNQYPHoynpyeenp4MHjyY69evl7nPM888g0ql0npEW9EC8VL1Zqw6kZOTwyuvvIKPjw+urq7069eP8+fPG/FKJKnyZFInSVXIE088QXx8PGvXrmXt2rXEx8czePDgcvfr0aMHycnJmseaNWtMEK0kGZ+x6sTo0aNZvnw5ixcvZvv27WRmZtKnTx8KCgqMdSmSVGny9qskVRHHjh1j7dq17N69m7Zt2wIwd+5c2rVrx4kTJwgrOhfVXRwdHQkICDBVqJJkEsaqE+np6cybN48ff/yRLreXcVu4cCHBwcFs3LiR7nJaEMlCyZY6Saoidu3ahaenp+bDCyA6OhpPT0927txZ5r5xcXH4+fnRsGFDhg4dSmpqapnlc3JyyMjI0HpIkqUxVp3Yv38/eXl5dOvWTbMtKCiIJk2alHlcWW8kc6t2LXWFS93KyiYZQuH7yBRLKKekpODn51dsu5+fHykpKaXu17NnTx599FHq1q3LmTNnmDRpEg888AD79+/HsZTF42NjY5kyZUqx7bLeSJVlyDpjrDqRkpKCg4MDNWrU0NrP39+/zOPKeiMZi871RlQzSUlJApAP+TDoIykpqcLvycmTJ5d7/L1794r3339fNGzYsNj+99xzj4iNjdX5fBcvXhT29vZi6dKlpZbJzs4W6enpmkdCQoLZ/8byYV2PsuqMuevETz/9JBwcHIqV69Kli3jxxRdlvZEPsz3K+6ypdi11QUFBJCUl4e7ujuqu5X4yMjIIDg4mKSkJDw8PM0VoPPL6DE8IwY0bNwgKCqrwMV5++WUGDRpUZpl69epx6NAhLl26VOy5y5cv46/HhKKBgYHUrVuXkydPllrG0dFRqxXPzc1N1hsrvD5LrTPmrhMBAQHk5uZy7do1rda61NRU2rdvX+pxdK031vyeAnl9xqDrZ021S+psbGyoXbt2mWU8PDys8o1YSF6fYXl6elZqfx8fH3x8fMot165dO9LT09mzZw9t2rQB4O+//yY9Pb3MD5q7XblyhaSkJAIDA3XeR9Yb674+S6sz5q4TUVFR2Nvbs2HDBh577DEAkpOTOXLkCB999JHOxy2v3ljzewrk9RmaLp81cqCEJFURjRs3pkePHgwdOpTdu3eze/duhg4dSp8+fbRG+TVq1Ijly5cDkJmZybhx49i1axeJiYnExcXRt29ffHx8ePjhh811KZJkEMaqE56enjz//PO8+uqrbNq0iYMHD/Lkk0/StGlTzWhYSbJE1a6lTpKqsp9++omRI0dqRuX169ePr776SqvMiRMnSE9PB8DW1pbDhw/zww8/cP36dQIDA+ncuTNLlizB3d3d5PFLkqEZq058/vnn2NnZ8dhjj3Hr1i1iYmJYsGABtra2prs4SdKTTOqKcHR0ZPLkyaWOCKzq5PVVfd7e3ixcuLDMMqLI6ChnZ2fWrVtn1Jis/e9uzddnDddmrDrh5OTEl19+yZdfflnpGO9mDX/3ssjrMx+VECaYi0GSJEmSJEkyKtmnTpIkSZIkyQrIpE6SJEmSJMkKyKROkiRJkiTJCsikTpIkSZIkyQpUu6Ru5syZhISE4OTkRFRUFNu2bSuz/JYtW4iKisLJyYn69esze/ZsE0Wqn9jYWFq3bo27uzt+fn489NBDnDhxosx94uLiUKlUxR7Hjx83UdS6e+edd4rFGRAQUOY+VeW1qwqssd7IOlNcVXjdqgprrDMg601JLOq103lxPCuwePFiYW9vL+bOnSsSEhLEqFGjhKurqzh79myJ5U+fPi1cXFzEqFGjREJCgpg7d66wt7cXv/32m4kjL1/37t3Fd999J44cOSLi4+NF7969RZ06dURmZmap+2zevFkA4sSJEyI5OVnzyM/PN2Hkupk8ebKIiIjQijM1NbXU8lXptbN01lpvZJ3RVlVet6rAWuuMELLe3M3SXrtqldS1adNGDBs2TGtbo0aNxIQJE0osP378eNGoUSOtbS+++KKIjo42WoyGkpqaKgCxZcuWUssUVrRr166ZLrAKmjx5smjevLnO5avya2dpqku9kXWmar5ulqi61BkhZL2xtNeu2tx+zc3NZf/+/ZpZxwt169aNnTt3lrjPrl27ipXv3r07+/btIy8vz2ixGkLh7One3t7llo2MjCQwMJCYmBg2b95s7NAq7OTJkwQFBRESEsKgQYM4ffp0qWWr8mtnSapTvZF1pmq+bpamOtUZkPXG0l67apPUpaWlUVBQgL+/v9Z2f39/UlJSStwnJSWlxPL5+fmkpaUZLdbKEkIwduxYOnToQJMmTUotFxgYyJw5c1i6dCnLli0jLCyMmJgYtm7dasJoddO2bVt++OEH1q1bx9y5c0lJSaF9+/ZcuXKlxPJV9bWzNNWl3sg6UzVfN0tUXeoMyHoDlvfaVbtlwlQqldbvQohi28orX9J2S/Lyyy9z6NAhtm/fXma5sLAwrUWv27VrR1JSEp988gmdOnUydph66dmzp+bnpk2b0q5dO0JDQ/n+++8ZO3ZsiftUxdfOUll7vZF1RlHVXjdLZu11BmS9KWRJr121aanz8fHB1ta22Del1NTUYll2oYCAgBLL29nZUbNmTaPFWhmvvPIKq1atYvPmzdSuXVvv/aOjozl58qQRIjMsV1dXmjZtWmqsVfG1s0TVod7IOqOoaq+bpaoOdQZkvSlkaa9dtUnqHBwciIqKYsOGDVrbN2zYQPv27Uvcp127dsXKr1+/nlatWmFvb2+0WCtCCMHLL7/MsmXL+OuvvwgJCanQcQ4ePEhgYKCBozO8nJwcjh07VmqsVem1s2TWXG9kndFWVV43S2fNdQZkvbmbxb12ZhicYTaFw8znzZsnEhISxOjRo4Wrq6tITEwUQggxYcIEMXjwYE35wqHKY8aMEQkJCWLevHkWO8z8pZdeEp6eniIuLk5rKPbNmzc1Ze6+vs8//1wsX75c/Pvvv+LIkSNiwoQJAhBLly41xyWU6dVXXxVxcXHi9OnTYvfu3aJPnz7C3d3dKl47S2et9UbWmar5ulUF1lpnhJD1xtJfu2qV1AkhxNdffy3q1q0rHBwcRMuWLbWGYT/99NPivvvu0yofFxcnIiMjhYODg6hXr56YNWuWiSPWDVDi47vvvtOUufv6PvzwQxEaGiqcnJxEjRo1RIcOHcTq1atNH7wOBg4cKAIDA4W9vb0ICgoSjzzyiDh69Kjm+ar82lUF1lhvZJ2pmq9bVWGNdUYIWW8s/bVTCXG7R58kSZIkSZJUZVWbPnWSJEmSJEnWTCZ1kiRJkiRJVkAmdZIkSZIkSVZAJnWSJEmSJElWQCZ1kiRJkiRJVkAmdZIkSZIkSVZAJnWSJEmSJElWQCZ1kiRJkiRJVkAmdZIkSZIkSVZAJnWSJEmSJElWQCZ1kiRJkiRJVkAmdZIkSZIkSVZAJnWSJEmSJElWQCZ1kiRJkiRJVkAmdZIkSZIkSVZAJnWSJEmSJElWQCZ1kiRJkiRJVkAmdZIkSZIkSVbAztwBmJparebixYu4u7ujUqnMHY5UxQkhuHHjBkFBQdjYWO93JFlvJEOpLnUGZL2RDEfXelPtkrqLFy8SHBxs7jAkK5OUlETt2rXNHYbRyHojGZq11xmQ9UYyvPLqTbVL6tzd3QHlD+Ph4WHmaKSqLiMjg+DgYM37ylrJeiMZSnWpMyDrjWQ4utabapfUFTaBe3h4yEomGYy131qR9UYyNEuvM7GxsSxbtozjx4/j7OxM+/bt+fDDDwkLC9P5GLLeSIZWXr2x7g4NkiRJklQBW7ZsYcSIEezevZsNGzaQn59Pt27dyMrKMndoklSqatdSJ0mSJEnlWbt2rdbv3333HX5+fuzfv59OnTqZKSpJKptsqZMkSZKkcqSnpwPg7e1t5kgkqXSypa4qSk+HAwfg/vvBwvulSJJUAdnZcOOG8sjMhLAwcHRUnjt9Gs6dg6AgqFULXF3NG2s1IIRg7NixdOjQgSZNmpRaLicnh5ycHM3vGRkZep8rJwe2bYMOHcDJqULhVllqtZrc3Fxzh2EW9vb22NraVvo4MqmrioYPh59/hthYmDDB3NFIkmQoJ07Aq6/C6tXa2xMSoHFj5efFi+HNN+885+GhJHdBQVCnDowbB+Hhpou5Gnj55Zc5dOgQ27dvL7NcbGwsU6ZMqdS5Zs6EsWNh2jSYOLFSh6pScnNzOXPmDGq12tyhmI2XlxcBAQGVGkQkk7qq5uZNJaEDpcY/+iiEhpo3JkmSKicjA957D6ZPh7y8O9tdXMDNDYq2Xri6QsOGcOECZGUp+2ZkwLFjyvNjxtwpu3Il7NwJ0dHQvj34+5vkcqzJK6+8wqpVq9i6dWu58+pNnDiRsWPHan4vnIZCH4Uv4+nTeodaZQkhSE5OxtbWluDgYKuflPpuQghu3rxJamoqAIGBgRU+lkzqqpqNG+/8/NZbyjd0SZKqtr//ho8/Vn7u3Vv5uWFDKOF2jBg5iltDRyl3Zy/e4MaZNDISr3LjfDr5yZcJtwvnHjXY2ABLl8KPP97Z+amn4IMPoBIfGtWFEIJXXnmF5cuXExcXR0hISLn7ODo64lh4m7yCbn+uU4E7t1VWfn4+N2/eJCgoCBcXF3OHYxbOzs4ApKam4ufnV+FbsTKpq2pWrCCNmmzvFUuvSUNxcDB3QJIkVcjVq1DY6b5rVxg1Svm3d2+tYjk5sGsXbNigPA4ehPz8wmfdbz+KJByLlca9Fi0g0mMyLe/rTOSF1YT/txL7H36AZctg0iTlfJVMQKzZiBEj+Pnnn1m5ciXu7u6kpKQA4OnpqfkANobqmNQVFBQA4FDNP9AKE9q8vDyZ1FUL+fmwahWv8CWL1zxO9H3wyy8QHJivdKx2czN3hJIk6SI2Fj76SLnXFhCgbJs+HQAh4MiRO0nc1q1Kr4uSuLtrP4SAo0eVsRXbt8N2QoFQ4FkcHdR0dt5N3/SF9H79a+pu2VK8756kMWvWLADuv/9+re3fffcdzzzzjNHOWx2TukKWPiG1sRni+mVSV5UkJqK2sWOdqgcI2L0bIpvl85PPaLp3ugXz5pk7QkmSyrNzpzLQQQilz9uLL2qeOnECXnhBSeSK8veHLl2UR6dO4OurdK0rqetRfr5ynAMHlFa9wkd6ug1rc9uzlvaMAJomZND3TejTB9q0KfFOb7UmhDDLeatzUidVnkzqqpJ77uHw2gtci7LF1VWZ5eDAATt6Xv+Ct/+byqT/bcH2gfvMHaUkSaW5dQuefVZJ6J56SpPQ5eXBJ5/AlCnK7VYnJ2XGoq5dlUeTJrrPXmRnBxERymPwYGWbEMoA2tWr4fffYedOweFEDw5PU0ZZ+rrd4tGwf3jyk0ii73OUMyWZya1byiw2IJM6qWKq1xATKxC3Tfk63bEj7NgBw4aBwIYpvEOvvrZcPp9TzhEkSTKbt9+Gf/9VBircvt26f7/SUvbGG0pC16MHHD8Of/6pTG3RtGnlp6NUqZQkb/x4ZQ601FQVP/4IAweCp4fgcqYzM/dH076zI/fUusXkyUqYkmldvnznZ5nUSRUhk7qq4tYtUKuJi1N+vf9+5dv8rFnww6wsXFQ3WX+zAy0jstm1y5yBSpJUol274LPPlJ/nzOGWUw1efx3atoX4eGXMxA8/wJo1ULeucUOpWROefFKZ8u7yZVj71nYGO/+GK5mcTnZm6lTlTkCbNvDFFzLBMJXCW6+g/M3NdAdYqsJkUldVfPwx6qDabFmfDShJXaHBw1z5+6OthHGc8xmedOokNMmfJEkWoPC2q1oNTz3Fbp8+NGumjJUoKFBazI4dU26XmvrWp72Diu7vduCH5K5cGjqJn/gfPVmDLfns3asMkq1fHz79VLkMyXguXbrzs1pd+gAZyXIsWrQIJycnLly4oNk2ZMgQmjVrpllazpRkUldVrFjB4Uu+XLvphJsbtGyp/XSTV7uzt9tb9OF38vNVTJ4sv+JJksXIyoKQEAgM5O8nv6RrV/jvP2WayVWrlBYzPz8zx+jpieucz3li58usafI6F6jFDNUowkJyuHJFWaiiQQP49tuiU6pIhlS0pQ5kCylZWaU/srN1L3v3t5HSylXAoEGDCAsLIzY2FoApU6awbt06/vzzTzw9PSt0zMqQSV1VcPYsHDxInKozoKwJaG9/VxmVCvdvPmG20xjsVPls3apizx7ThypJUgl8fGDNGo78cICeAz3IzISYGGX6kb59zR3cXdq1gwMH8J82mpET3TjyryPz5kHt2soiFkOHKv3zfv1VaU2SDEcmdXdxcyv90b+/dlk/v9LL9uypXbZevZLLVYBKpeL999/n22+/Zdq0acyYMYO1a9dS6/bCAHZ2drRo0YIWLVowZMiQCp1DHzKpqwpWrgQgzlt5E981bdId9epRa/dSnnhSGUzx6acmiE2SpNIV6RT13ykVXQcHcO2asmrXihVghi/yurG3V5YhfP997Ozguefg5LLDfMYYfGyu8u+/8Nhj0Lo1LFmiDPCQKk8mdVVTnz59CA8PZ8qUKSxfvpyIiAjNc15eXsTHxxMfH8+3335r9FhkUlcVrFiBGhVbbrYGykjqAJo359VxSqec336DxESjRydJUmkmToTnnuP80XS6dIGUFGjWTBkMUdXmCnc6e4IxXgs4pa7HZN7BjRscOACDBkHt2oLXXpMjZivr7qTODF2yLEtmZumPpUu1y6amll72zz+1yyYmllyugtatW8fx48cpKCjA38zrK8ukztJduQJbt3KYply7VXJ/urs1awbdHshHrYbpn8u+dZJkFnv2wMcfc/m73+naw4azZ5U+aevXQ40a5g6uAgYMgKQkPL6K5Z17fuI09ZnEVIK4QFqaik8+UUbMPvAALFpYIFvvKkC21N3F1bX0h5OT7mXvXtattHIVcODAAR599FG++eYbunfvzqRJk7Sez8jIICoqig4dOrBly5YKnUMfMqmzdKtXQ0EBcUFPAKX0p7tbQQHjEp4D4NtvCrh2zcgxSpKkTQh46SXS1W70qLGH4+fdCQ6GjRuV1SGqLDc3GDECjh/Hd+U8pj54gLPeLVnp8jh9egtsbGDzZnhisC21PDJ46y1zB1y1FCZ1dreXBaj2SZ2FS0xMpHfv3kyYMIHBgwczdepUli5dyv79+7XK7N+/n9mzZ/PUU0+RYeQXVSZ1li4qCsaNI873UaCcW6+FbG3p8kJ9mvEPWTl2fDNb9maWJJPauJGbB47Rx2YNB66F4OurrONap465AzMQW1vo1w9WrMAuLYV+/37C73+oSEyEyZOhtm0yV3I9SEoyd6BVS2FSV6+e8q9M6izX1atX6dmzJ/369eONN94AICoqir59+/Lmm29qygUFBQHQpEkTwsPD+dfIfRRkUmfpIiJQf/gxW87VB3RM6gDV6FGMc/oagC8+zpG3QiTJlD7+mGdYwHb1vXh6Krdcw8LMHZSRqFTK3CxAcDC88w6cSXbi9x+u8dpr5g2tKhHiTlJ3zz3KvzKps1ze3t4cO3aMb775Rmv7ypUrWbt2LQDXrl0j5/aH7/nz50lISKB+/fpGjavKJnUzZ84kJCQEJycnoqKi2LZtm7lDMprDh+HaNXTqT6dRowYDRwdSi/MkX3Nm0c+m71uXlwcXL8pZ0aVq5uBB1m+AX3kMOzvB6tXQooW5gzItO98a9BlcgyZNzB1J1XHt2p35/2RSZx2OHTtGq1ataN68OX369GHGjBl4e3sb9Zx2Rj26kSxZsoTRo0czc+ZM7r33Xr755ht69uxJQkICdazm/gbw44/g50fckRjATrf+dEU4vPoKIz+dzut57/HJOzd4+hkPo8xWn5uVx8ndV0jYk8nRQwUknLQnITuEf/9VkZcHbZtkMv/lg4QPbApeXoYPAJSJIzduZN8PCVy47ID9Q72xb9oIe3uwTzyJ/bo/cHCAiNYu2N/bRllQ065Kvv0lC5f36ReMZjoAr7yi4t57zRuPVDUUttJ5eSnTGoJM6qq69u3bc/jwYZOes0p+qn322Wc8//zzmon8pk+fzrp165g1a5ZmVueKEsL0y/SUqKBAWc07LY24e1MBXzp31vMYPj688JIt735xg6PnPFi3VtCjpwEvLj6ehS/vZtiOJ8kioNRifx9xI3JYG94e9i7jm63FvmO0MuKjQwdlRtOKSkmBP/6AlSvJ3bCFMTmxzGSi8pxWw20DYAwAYT8cZyNdqO1yTZlkq107ZW2m8PCKxyFJRcwK/5JjuOFbI4+339bjW5hUrRUmdX5+4OGh/CyTOklfVe72a25uLvv376dbt25a27t168bOnTuLlc/JySEjI0PrUZLr16F3bwgMFORkW8D9wp07IS0NtZc3WxKUr2269qcryuvNEQy1/x6ATz4y4ICJN97g+8jPeWrHC2ThhjsZRDvs5zmfVXwSPp81P13j7Fk4dw56hx4nF0fe4j3aHJpL/Nfb4fHHlQ4499yj24KSQmiX++svCAyEoUNJ/mMfD+SsYSYjAGgdnELLiGyaNoVGjSA0OIe6Htdws8/mBI24z2YbZ2/6wJYt8MEH2pP57dgBkyYpk/z995+cMl/Sy+XLMPljZQK69z+0N1rDtGR9ZFInGUKVS+rS0tJKnODP39+flJSUYuVjY2Px9PTUPIKDg0s8rqcn7Iu7waVLKvbMM21zaYlWrADgcIeXuHZNpV9/uqL8/Bi1bzC2trApzpb4+ArGc+uW1kyYC/Ke4Fm+Q2DDS/1TuZ7nxq6cKOZd7serR5+j5xM1qFNHydt+P9mIhQvBu4aaeCJpbbOftwPnkmPjXHwOoWefhWHD4OefYdYsGD5cadGrUQNef/1OuTZtwMmJXY2fI8r9X3bQAQ8Pwe+/w55zAew/4sShQ8oi6f+dcyQxvQZH/3Oifn04rQ7hvqCTnI5dAkOGQNu2d467Zg289x48+qgyqZinJ7Rvb9KVtfXtL7plyxaioqJwcnKifv36zJ49u1iZpUuXEh4ejqOjI+Hh4SxfvtxY4VdfBQW8/bbyBTEyUlmFQZJ0JZM6ySBEJdy6dasyu1fIhQsXBCB27typtf29994TYWFhxcpnZ2eL9PR0zSMpKUkAIj09vVjZx2rvECDElAfijBa/zu69VwgQ0wfvEyBEz56VO9zjjwsBQjz5pJ47qtVCzJ0rhK+vEBMmCCGEmD9fCJVKLUCI4cOVIrpISRFiwAAlDhAivFG+2Dgv8c7+N28K4eh4p8Ddj86dtcKa/VmmsLe/faxwIU6cKD+G8+eFaNBA2ad2bSH+/feuAr//LsTzzwvRqtWdWAICSj1eenp6qe+nili8eLGwt7cXc+fOFQkJCWLUqFHC1dVVnD17tsTyp0+fFi4uLmLUqFEiISFBzJ07V9jb24vffvtNU2bnzp3C1tZWTJs2TRw7dkxMmzZN2NnZid27d+scl6Gv0xrFD/9G2JAvQIitW80djeWqTu8lfa518mTlv5thw4RYu1b5uXlzo4doEW7duiUSEhLMklNYkrL+Drq+l/RO6goKCsTUqVNFUFCQsLW1FadOnRJCCPHWW2+Jb7/9Vt/D6S0nJ0fY2tqKZcuWaW0fOXKk6NSpU7n7l/WHmfnYZiV3qHHQUOFWXN26QoB4qNMVAUJ8+GHlDrdvn/KfhJ1Nvjh3TsedTp8WIibmTlLVooWY961aqFTKryNG6J7QFfXrr0L4+d05bHS0EKtWCVFwK0eIlSuFGD1aiLZthejVS4jXXxfip5+EOHRIiJwcIYQQt24peVfh/v37C5GRofv5L14UolEjZd/AQCGOHSulYF6eEEePCrFpU6nHMvQHVJs2bcSwYcO0tjVq1EhMuJ1Q3238+PGiUaNGWttefPFFER0drfn9scceEz169NAq0717dzFo0CCd46pOH8QVoc66Ke6z3y5AiIHRieYOx6JVp/eSPtf60kvK/0lvvy3Erl3KzyEhJgjSAsikTmGWpG7KlCmifv36YuHChcLZ2VmT1C1ZsuT/7Z15XFRV/8ffwy4Io4hsLqCguKGhlkuWW4HmklmWlT5qatvPTNOnR1vcymh/Mp8sNVtsMSs1LU0zc80tUVJTEBdEFAQVAUVZz++Pw4wgizMwwwwz5/163dfcuXPuvd87M2fuZ875LqVuJObkjjvuEM8880ypba1bt67wxleSyt6Yoz/FCRDClWviWlaeyew1msJCIZydRSEaUV8r//nv2VPNY16+LHo7bBEgREe/ZJG6+1Tl558/XwgPD/nL4uYmxHvviSWLCvSCbsKEqgk6HRcuSFFYcmAuPFyIb7+VWupmsrKE+OMPId54Q4jbbpPtNRohoqOrZkdqqhDt2snj+PkJcfhw1a7DlDeoqvxhueuuu8TEiRNLbVu5cqVwcnISeXnyO9ykSRPx/vvvl2rz/vvvi6ZNm1Zoi6Ej3Nu2yUFlI/ShTfL9uA0ChKijyRGnT5TzBVboUaKufB58UP4e/e9/8r8kCNGgQQ0YaQUoUScxhagz2qdu6dKlLFq0iMcffxxHR0f99vbt2xMXF1f1eWAjeOGFF/j000/57LPPOHr0KJMnTyYpKYmnn366WscNG9gCf00qubix5+sEE1lbBdLTIT+fQ7QnI9Ox6v50JdFq+XDEXhqSxv7zjejRNZ9TnYfBwoWUqiN28iT07AnPPSfThNx9Nxw8yBLtC4x90hEh5Esffli9KOEGDeB//5MxCv/5D3h6ynx8jz0mgxsWLoTPP4ennoIOHWSYf58+8NJLEBsrXex+/RWmTauaHX5+MtaiQwc4f14GoezfX/XrMQXG+osCpKamltu+oKCACxcuVNqmomOC4b6ompSz/Pkn7Pqp4mPZOjnZhUz9oi0A/4n6m6bNa2VSAYWFqcinTuX5VBiD0aLu7NmzhOoyI5agqKiI/Px8kxh1Kx555BE++OAD5syZw2233ca2bdtYt24dQUFB1TquxtGBXoGyhMfmlRYsmOrtDQcPsuXZ7wG46y7TpFRr99kL/PnuboLdUjlOC7rHfMjBpz8Cf39Ys0Y2EkKqGw8P+OgjMlZuZvKCFhRnj2HiRJg3z3RpX/z9ZQDq6dPw2mtS7J04IWMlnngCFi2CgwdlEGrTpvDww/Dee1IARkVV79wNG0ph16kTXLggH/v2hWXL4Pp101xfVdDc9OYKIcpsu1X7m7cbe8zp06eTmZmpX85UUO+pVVv5xTx93Z+cy3kVHs+WeXd8HEkFjWjqcIZ/Lw23tDmKWsr58/LRz++GqMvPR1UDUhiF0aKubdu25Ubj/fDDD0RERJjEKEN49tlnSUxMJDc3l5iYGO6++26THLdXN3lj2rLf0yTHqxLOzhAezpZzLaVNvUx0XEdHWkwZzJ8n/AlvnU8qAdztsINt+d1kNClASAh88w35Bw7zYcGzhLZ04IMP5EuTJsEHH5gnj1/9+vDKK1Lc/fe/0L69HCR88UVYuRLOnpWvLV8u0/cVVyWqNt7essj6/ffL6/rjDzlaGBgIzz8vxWNN4ePjg6OjY5kRtLS0tDIjbTr8/f3Lbe/k5ESDBg0qbVPRMQFcXV3x8vIqtZRrcxtfGnARgGNbUyq/QBsk6bTgzR9CAHhnyE7cG3pY2CJFbaXkSF3duje2qwhYhTEYLepmzpzJhAkTeOuttygqKmLlypWMHz+eN954gxkzZpjDxhql9+Oy+O6uzDYGpU8zF0VFMo0amFDUFRMYCNt2OtOjB2QWeRHp/Aer98jkwULAGochtBsYzPPPw6VL0LYtbNggxZa5EzN7eEjx+Pff8vrfegseeEDabC7q1ZMZZE6dksXImzSRM9IffijFZZcusHix+bOauLi40KlTJzZu3Fhq+8aNG+nevXu5+3Tr1q1M+99++43OnTvjXFx+pKI2FR3TKDQaWrknARC381L1j1fLmD4unWtFbtyt2c6wBcZmB1coJHl5MhUOSFHn4CBdUkCJutpERkYGs2fPJiXFgn9wq+LMt379enH33XcLDw8PUadOHXHnnXeKDRs2VOVQNc6tnA2LimREJEjHfIuwerWInbBYgBB165YfOGAKcnKEGDRIXquDgxCvvy6zhugCF3x9hVi40Hznt1YKCoT49VfpuOzkJN8LFxch0tPLtjVXSpMlS5aII0eOiEmTJgkPDw+RmCgjKqdNmyZGjhypb69LaTJ58mRx5MgRsWTJkjIpTf7880/h6Ogo3nzzTXH06FHx5ptvmjSlyRNBvwsQYlZ/w49nCyQlCeHgIFP7xEz8wtLm1BpUoERZkpOLsxM4CVEYe1CIv/8WjQIL5XcrpoaMtSC2EigxYsQIMWDAAHH//fdXaX+LRL/Wdgx5Yx57THawV1+tQcNKMnKk+ICJJslPdyvy84UYM6Z0OjhXVyGmTxfCDn5zb8n580K8847MrFIe5rhBffTRRyIoKEi4uLiIjh07iq1bt+pfGzVqlOjZs2ep9lu2bBERERHCxcVFBAcHi48//rjMMX/44QcRFhYmnJ2dRatWrcSKFSuMsqmy63y75y8ChBjecp9Rx6ztvPJKmfSJCgNQoq4s+/ffSLGk+6fd2v+iZQcXahBbEHWrV68WQ4YMEULI3+mvv/7a6GNYRNQ1a9ZMXLhwocz2jIwM0awWJNUx5I1ZtEh2sLs6XqlBy0rQp48Yyo8ChHjzTfOfrqhIiJdeEsLZWaamSFRptgzGXm5QlV3nmmd/lWkMvY5bwDLLkJsrU+GAzLuoMBx76TNCGH6tpZINt2kjBIgurTIECPHTTzViqkWxBVFnCiyS0iQxMZHCwsIy23Nzczl79mwVJoCtj96NZTqT3fudybligdqfyckcR0YY33ab+U+n0cDcudJnbNkyqGYQscLOaNVFC0D81cZ2Uyp35QrB+fMQ6JXN/T0vW9ocRS1HHyTRUMCRIwB4nT0KKJ86hXEYnChjjS7lBbBhwwa0Wq3+eWFhIZs2bSI4ONikxlmKkL7BNNKc5axoxK5lJ+k7vnnNnVwISE4miaaATONRU5gibYrC/mj2aFecx8G1fFfOnLGPPwUL3rkK1OXJqx/g7DTB0uYoajl6Ued1IzrPS5MNKFGnMA6Db+NDhgwBZL6rUaNGlXrN2dmZ4OBg3nvvPZMaZyk0Ls709j/K1ymN2LziUs2KusxMsnIcuUx9QEZiKhTWjJOzhhYt5ABDXJzti7pDh2D7gbo4UsD4/skyH49CUQ10Oep8nW5EkHsVZQJK1Fk7y5YtY8yYMZw4cYJGxbm2xo0bx969e9m+fXupAbCawODp16KiIoqKimjatClpaWn650VFReTm5hIfH8/AgQPNaWuN0quLzD67ZV/dW7Q0MSVG6by9S+crUiislVat5GN8vGXtqAk+/kjOMT/AKgKfGmRhaxS2gG6kzq/oRj5Jr3yZ/9EeRZ0QsqCRJRZjK3gMHz6csLAwoqOjAZg9ezYbNmzg119/rXFBB0aM1Ok4deqUOeywOnoP94OfYO/F5ly9IvCoa+YEbTosNPWqUFSHsPQdQA/iVh2Fia0tbY7ZyMqCr5YWAQ48q/0Wor63tEkKM7Jt2zbeeecdYmJiSElJYdWqVfpZK1Oin369nqTf5pUny/zZo6jLybHcgMaVKzJfqqFoNBrmzp3LQw89RGBgIPPmzWP79u36UTsAJycn2rVrB0Dnzp359NNPTW32jXNVZaerV6+ydetWkpKSyMsrXRpo4sSJJjHM0jS7vz1NOMMZmrDz+zPc+0QNzYP27EnSq+HwmhJ1itpDqzqngR7EnXC2tClm5auv4Mo1J1pzhF7/aiqrvyhslqtXr9KhQwfGjBnDgw8+aLbz6EVd77Zw2yvw+ut4icuAfYq62sbAgQNp06YNs2fP5rfffqNt27alXq9Xrx6xsbE1YovRou7AgQPcd9995OTkcPXqVby9vblw4QLu7u74+vrajKjTuLnS2+8flp5vwuYfLtScqKtTh6QCqfBt3TdJYTu0CneG3yAu3dvSppgNIWDB/woBR55lAZpRYyxtksLM9O/fn/79+5v9PHpRd1cYdJ4D0dFoC+3Xp87dXY6YWercxrJhwwbi4uIoLCystPxiTWB0SpPJkyczaNAgLl26RJ06ddi9ezenT5+mU6dOvPvuu+aw0WL0fkA6QG8536pGz5tUPAKvRuoUtYWwblLMpVz3JjPTwsaYiW3b4EicIx5O1xl522Ho2NHSJimsjNzcXLKyskott0KI0nVf0WigVSu8Gsl6y/Yo6jQaOQVqicXYUpj79+9n2LBhLFy4kKioKF599dUybbKysujUqRM9evRgq67+p5kwWtTFxsYyZcoUHB0dcXR0JDc3lyZNmvD222/z0ksvmcNGi9HrP10A+OtQnZr71/DRR5zecQZQok5Re9B2CCaAcwDExxnpaVxL+Ogj+ThirBvamD/MXwhZUeuIjo5Gq9XqlyYGpC/IzobcXLnecM8vcPgwHD6M12KZTcIeRV1tITExkQEDBjBt2jRGjhzJnDlzWLFiBTExMWXaxcTE8Mknn/Cvf/3LILFfVYwWdc7OzmiKf8z8/PxIKh5W0mq1+nVbIThYLgUF8OefNXTShQtJOi2j65SoU9QagoJohQx9jd9z2bK2mIFz52DVKrn+zDPIiusKxU1Mnz6dzMxM/XLmzJlb7qMbpavrXoj7I4Ogd28AvORAnRJ1VsqlS5fo378/gwcP1g9oderUiUGDBvHyyy+XahsYGAhAu3btaNOmDceOHTObXUb71EVERLBv3z5atmxJ7969mTFjBhcuXOCrr74iPDzcHDZalF63X+WLRA82f3WGqCjz+9UVnEnhLNKnTok6Ra3B2Zkwz3Nszoa4v7IB28rd9umn8s/dnRFX6dDBiNA4hV3h6uqKq6urUfvop149r0EOEBICKFFn7Xh7e3P06NEy21evXl3qeUZGBu7u7ri6upKcnMyRI0do3tx8uW+N/rv5xhtvEBAQAMBrr71GgwYNeOaZZ0hLS2PRokUmN9DS9L62DoAtG/Ju0dIE5OSQctmNQpxwdhb4+5v/lAqFqWjVXPaRuNNuFrbEtOTnw8KFckr5/w6Mg59+sqxBCptCn3jYtdgZNSQE/vMfvIbeAyhRV9s5evQonTt3pkOHDgwcOJB58+bh7W2+gDKjR+o6d+6sX2/YsCHr1q0zqUHWRq9hDeEX2HchiOxs8PQ048nOntXnqGvSRM3wKGoXrd4aA/0g7qKvpU0xKWvWwLlzGnw5z1D3DXDPYkubpKghrly5wvHjx/XPT506RWxsLN7e3jQ10VSKPvGwJl2uhITAqVN4HZd+WdevQ14euLiY5HRl2bxZlknp3RtscLbN0nTv3p1Dhw7V2PmUbLgFTR/oRHNOUIgTO1alm/dkpRIPKydsRe1CV1UiIUFOVdoKCxbIx3F8iutDg1SZFzti3759REREEBERAcALL7xAREQEM2bMMNk59NOvBWflSvPmoNXiSba+TXZ2OTuaiu++g+efl4+KWo/Rou7ixYv83//9H23atMHHxwdvb+9Si83h6Ukvn38A2Py9+UXdaWRyOuVPp6htNGkCderI6UpbKTyTnAx//CHXn2QR/OtfljVIUaP06tULIUSZ5YsvvjDZOfSi7kpxpwkJAa0WJwpxd5JhsWadgtUlxb3tNvjxR3j2WeNrZSmsBqOnX0eMGMGJEycYO3Ysfn5++khYW6b37Vf47FfYsreOeU9UYvpViTpFbcPh7BnCiq4QS2vijgpatKj9vw3Ll8vHHmwnqHER9OplUXsUtode1GUVT/MWizoAL+dr5BS4mi/3Y0EBHDwo1728YMAAKCyEzp3hiSfMdFKFOTFa1O3YsYMdO3bQoUMHc9hjlfQa1hB+hZj0pmRm6vub6ZkwgaRfBWxTok5RC/HxoVXuDmJpTfyBHAYNrv1RoroZqUdZBiNGgKOjZQ1S2Bx6UffcIxDgDwEBN0Sdw1VSqWe+kbpjx6TTnocH3HsvvP46TJ8OEyZAly5wU7krhfVj9PRrq1atuHbtmjlssVoaD+lMI5IpwpGjOy6a70R165KUISMxlKhT1Drq1CHMMwWAuJirFjam+iQkwL594EgBD/GjFHUKhYnRi7r7u8G0aTKptV7USWc6s4k63dRrhw4yMu/FFyEyEq5dg4cfhpwcM524fISdT/ua4vqNFnULFizg5ZdfZuvWrVy8eNHokii1kvr1adRW5t06n1vPrKdSJcIUtZlWjWXplbh4CxtiAnRTr33vccB32wpo08ayBilsklIlwnT4+EBgIF5uMk2Q2UXdbbfJRwcH+Oor8PeHI0eghmq5OxaPgOfl1UDqMCsmp1hEOzs7V/kYRk+/1qtXj8zMTPr06VNquxACjUZDYWFhlY2xZvxDPOAfSE033/RL5qSZZGbOBpSoU9ROWoUJOApxZ2p3hKgQsGyZXH/0cQe46y7LGqSwSQoK4GLx5I/vgQ3gHQ6BgXDPPXD2LF4PAD+ZX9SdDb4TkSxnfh19feHbb6FvX1iyRKY6efxxMxkgcXJywt3dnfT0dJydnXGws3xeQghycnJIS0ujXr16epFbFYwWdY8//jguLi58++23dhMoAfLLDpCSYqYT5OeTNG8VMJsG3kV4eNjXl1phG7SM8ICf4OI1dy5ckAMOtZHDh+VAhYuLYMgQ+/iNU9Q8Fy7IPxAOFNLgX/fB/HnSn60Ys1eVWLGC3ctO0e2p9vAiODlB48YQFNSbpuEHCDq4hqBv3Rl0D/j5mckGQKPREBAQwKlTpzh9+rT5TmTl1KtXD/9qVh0wWtQdPnyYAwcOEBYWVq0T1zb8r54AQkjdcxqK046YlNRUkpBlyJoGqZuIonbi3jqIIBI5TTBxcdCjh6Utqhq6Ubr7nDdS791t0oFcoTAxuqlXH6fLOBYU6UuE6TC7qPP0ZO2Z9vqnBQWQmCgX6CCXddDjIdi+3Uw2FOPi4kKLFi3sdgrW2dm5WiN0OqpUUeLMmTP2J+rSDgIhpB4zU2y5SjyssAXCwmjleZbT2cHEx9dOUSfEjajX4VeXwJF8yxqksFn0/nSFqXJFVxP0+nXo2xftsX8BT5k1T92ePfLxf/+D+++Xft2nT99YFi+GHTtkoGzLluazA8DBwQE3N9sqM1jTGC3qnnvuOZ5//nn+/e9/Ex4eXsahr3379hXsWbvxD5TToamZZspVV0rUmecUCoXZad+esDGw4UOIi7O0MVVj716ZPNnDIYeBRb/AwPmWNklho+hFnUiVUa/BwXKDqyvs2YNX4Z2AmUbqvv6aoj1/sXfne4AT3bvLqdfGjaF79xvNkpJg3TpY+uFlXv9fPTMYojAlRou6Rx55BIAnSiQm1Gg0Nh8oERAkC++lXjVT8VeVeFhhI+jKhdVWUacbpRtc9BMe5MB991nWIIXNohd1pMmSLK6ucoNGA15eeGVINWcWUbd6Ncd+PEQm83Bzg3btym/2L+dlrONRvlpaxJwPVU1ya8doUXfKVur/GIl/c3cAUnK9EUL2OZOSnEwSnQAl6hS1G72oO1pEbSsvXVh4I5XJoyyDTp1kegeFwgyUEnU3+dOh1ZpX1B04wF7kkFynTlBRFo3Bw93Rrr5MUrY3W7fKYFiF9WK0qAsKMkOQQC3Ar6VMBpknXLh8GerXN/EJSky/2ulbrLARWq18A3iJkychN/fG4ENtYPt2GeFez/kKkfm/wcDpljZJYcOUEnU6fzodWi1emEnUZWXBiRPsYTIgi0dURJ3+vXhY8z2LxXiWLrhC7961O12RrWPQ3+g1a9aQn5+vX69ssVXcGvtQjwwAUlNMn/W6YMEizjqq6Vd7JiMjg5EjR6LVatFqtYwcOZLLly9X2D4/P5///Oc/hIeH4+HhQWBgIP/61784d+5cqXa9evVCo9GUWoYPH2626/D3KcCLTIqEA8ePm+00ZkEX9fogK3AlT9bCVCjMhF7Ujbu/bK1Vc4q64nqve5xl/sXKRB1aLaPa7Qfgx59duFr7i8XYNAaN1A0ZMoTU1FR8fX0ZMmRIhe1s2aeOhg3x5ySXqU/q8Su0bmNa37pzV7woLJRD4ObMB6SwXh577DGSk5NZv349AE8++SQjR47k559/Lrd9Tk4O+/fv59VXX6VDhw5kZGQwadIkBg8ezL59+0q1HT9+PHPmzNE/r1PHTAE/gCY0hFbEsZcuxMfXnvKR+fnw449yfXjUZUjtLOelFAozoRd1A++A7je9qNXihSwxZHJRFxvLdVz5u0BWSbnjjsqbd38wgOaHTnAyN4RVq1TFPGvGIFFXVFRU7rpd4epKQOdGxO2DlCzTFyrXlQdr0kQ5otojR48eZf369ezevZsuxX+bFy9eTLdu3YiPjy83hZBWq2Xjxo2lts2fP5877riDpKQkmpYY8nV3d692UkuDCQ3Vi7raFCyxcSNcuiT/VPX+6XlwfN7SJilsnPPn5WOpEmE6/P3x8k2HNPOIugNEUCCc8PW9tcuPpl8U/5q1lFnMZumXRYwYoW5S1orRn8zSpUvJzc0tsz0vL4+lS5eaxChrxb+FzASZmmbiL/SlSyTN+gxQU6/2yq5du9BqtXpBB9C1a1e0Wi07d+40+DiZmZloNBrq1atXavs333yDj48Pbdu2ZerUqWRnZ1d6nNzc3KrXdQ4JIQxZ/DXunwLD97MwuqjXYcPABDlAFYpbkpYmXXn8jvwhEySWZNEivP7ZBcDVqzKIx2ScOcMe5G9Nly4GBP517sxIL+le9fsmDcnJJrRFYVKMVidjxowhM7NsAt7s7GzGjBljEqOsFd1AR2qqiQ+cmEjSpmOAEnX2is694WZ8fX1JNfALd/36daZNm8Zjjz2Gly4VPbK037Jly9iyZQuvvvoqK1asYOjQoZUeKzo6Wu/bp9VqadKkieEX06ABrdzPABB3sHZkh792DVatkuuPtj1o4juoQlGWq1chJ0eqKd+po8pVVp4lvHxu8T/MODZsYO8D0cCtp14BcHSk+Xv/x11tLiCEhm++MaEtCpNitKjT5aO7meTkZLRarUmMslb8Lx0BIDXWxKpOJR62WWbNmlUmSOHmRef/Vl6/qqi/3Ux+fj7Dhw+nqKiIBQsWlHpt/Pjx3HPPPbRr147hw4fz448/8vvvv7N///4Kjzd9+nQyMzP1y5kzZwy/aI2GVsHXAYg74VxmAMIaWbcOrlyBoPqZdHumAzz0kKVNUtg4On+6OuTgEVK+a4Sr643ocVNPwe75W/rVVhokUZJx4/jXZFnM+csvyw4sKqwDg1OaRERE6G9Cffv2xcnpxq6FhYWcOnWKfv36mcVIayEgcRfQhtSTJg7/SU7mdHE9WZXOxLaYMGHCLSNNg4ODOXjwIOd1DjYlSE9Px+8WkTP5+fk8/PDDnDp1ij/++KPUKF15dOzYEWdnZxISEujYsWO5bVxdXXGtRi6SkH4tcDxaSPY1Z1JSIDCwyoeqEXRRr494/YomA+jZ06L2KGyfkulMNCHNyzb49Vd4/XW8HNaTjiflTJBVmfR0OHlSrt9+u+H7DRsGzz0HR49CTAx07mw6mxSmwWBRp4t6jY2NJSoqirp1b+SqcXFxITg4mAcffNDkBt7M3LlzWbt2LbGxsbi4uFSa8sHU+AfKEZOUDBNHDp49SxIytFyN1NkWPj4++Pj43LJdt27dyMzMZO/evdxRPB+yZ88eMjMz6d795rC4G+gEXUJCAps3b6ZBgwa3PNc///xDfn4+AQEBhl+Ikbi+9wbNf4aEBFlZwppFXV6eHKkDGJ78nlwZONByBinsgkoTDwNkZsLOnXi5ZZGOp+lG6l5/nb0/5QOzadUKbnK/rRTt8RiGNHfmuyPt+fJLJeqsEYNF3cyZMwE5qjB8+PBq/YuvDnl5eQwbNoxu3bqxZMmSGj23fxNdqTATJ19U0692T+vWrenXrx/jx49n4cKFgExpMnDgwFKRr61atSI6OpoHHniAgoICHnroIfbv388vv/xCYWGh3v/O29sbFxcXTpw4wTfffMN9992Hj48PR44cYcqUKURERHDnnXea9ZpatZKiLj4e+vQx66mqxV9/SZ86X+11bsvcJ6uWh4Za2iyFjXNLUVfszuSlyQIamU7U/fEHe2PkSLRB/nQlWbqUUUfi+Y71LFsG770HLi4mskthEoz2qevTpw/p6en653v37mXSpEksWrTIpIZVxOzZs5k8eTLh4eE1cr6S+DeTI3QXcr0ozsVsEjITM8hCdmBj/NEVtsU333xDeHg4kZGRREZG0r59e7766qtSbeLj4/WBSsnJyaxZs4bk5GRuu+02AgIC9IsuYtbFxYVNmzYRFRVFWFgYEydOJDIykt9//x1HM4d4tgqTTjfWntZk61b5eHf9Q2hAJRxW1AgGizoh+7tJRJ0QEBtbKvLVKKKiuIff8XdM4+LFGyPcCuvB6DJhjz32mD4pampqqt4B++uvvyY1NZUZM2aYw84qk5ubWyoFi1GpGW6iQXMtTuRTgDNpadCokSksvJGjroFXPh4eFRTgU9g83t7efP3115W2ESW8k4ODg0s9L48mTZqwVadaapL0dMI+nAF8TNwR664Bq3t7el5YKVfU1KuiBkhLlf1Clgi7p2yD4nlRbaGsZGQSUZeUhMjIYC9yiM5oUdezJ04ujjyet5T3mMrSpVBJPQKFBTD6l/bw4cN6n5/vv/+e8PBwdu7cybfffssXX3xhavuqTbVSM9yEg19D/JDO7CkpprIQkt6SXtpNm1nvjU+hMAofH8KQaXrij1pvepD8fPjzT7ne88ovModEjx6WNUphF5zXjdQ9Hln+CIFupK7gImAiURcby3FCycAbV1cwesLLwwN69GAUXwLwyy9w8aIJ7FKYDKNVRH5+vt6f7vfff2fw4MGA9PVJqaLSMSbtg7FUKzXDzfj64o/0WUo9Z7rKGqcvyAoVQc1UxlOFjaDR6OuTJ6c4UmClOYj375f5wry9BW3/XAwLFyonIUWNkJYub79+/TuWn+3aHNOvJaZeO3as4lc9MpJwDnOb1wny828k7VZYB0aLurZt2/LJJ5+wfft2Nm7cqE9jcu7cOYMi78pjwoQJHD16tNKlXbt2VTq2q6srXl5epZYq4+uL/53SgTr1/K1zhxmKbvpVBUkobAn/MC0u5FJY5MDZs5a2pnz0/nR3a3Do3hUefdSyBinsBr1PXXklwkCOinl54VVXDiCYWtQZPfWqIyoKgFHXZUDXZ5+pnHXWhNE+dW+99RYPPPAA77zzDqNGjaJDhw4ArFmzRj8tayyGpn2wOI6O+LeqB3+aUNTt30/SikLgdiXqFDaFQ4sQmpLEcVpw+rR15mDU+9OptHSKGibtzHXADd+s40A50dYaDWRm4hUNvGQiUefmxl6HblBUDVHXvj34+vJ43k9M403273dg504wczC9VVJUJHP+paTAuXOlHz08YM4cqGPiDGi3wmhR16tXLy5cuEBWVhb169fXb3/yySdxd3c3qXHlkZSUxKVLl0hKSqKwsJDY2FgAQkNDS+XOMxe61F4mKxV24ABJx2XKCiXqFDZFaChBnOY4LUhMhLvvtrRBpSkshB075HrPP9+AZm3h/vsta5TCLigqgvTLcu7T9/Af8GDFKXR0k0umEHW5XywjdqWAvCqkM9Hh4AC7d9OwaVMef9KBzz6DefNqRtQdOQIHDshpYxcXcHYuvd6mjX7W2qwIAf/3f7B4MZW6lhQWwvvvm9+ekhgt6kBG4MXExHDixAkee+wxPD09cXFxqRFRN2PGDL788kv984iICAA2b95Mr169zH5+/3P7gY6kHLkEeFf/gMnJJHEvoESdwsYICSGYRABOn7asKeURGytvlFq367T/8VVwfkSJOkWNcOkSFBV7P/m0rzwztylFXWws5OVp8PGBZs2qcaDinZ9/Xk6/rlwp3YjMeQ/76Sd48EEpiCtCq4U1a8z/B/Ldd+Hjj+W6RiOn0AMD5aBPYKAs7fbRR/DBB/InpSZnAowWdadPn6Zfv34kJSWRm5vLvffei6enJ2+//TbXr1/nk08+MYeder744guLRtn6/7MJ6Ejq6dxbtjWEgjMpnEVGPilRp7ApWrQgqM0FOAKJiZY2piy6qde7vA7ieL0Iuna1rEEKuyHtvAA0eHMR57BySoTpmD4dr58E8Gb1RV1REXv2SCHZpYsUI9Wlfbigdy/YvEXDRx/BW29V/5jlsWULDB8uBV2HDlK85eXJ6PW8PLlkZEg/xago+P57GDTIPLZs2wbTp8v1+fPh6afBqRwllZsLn34KY8bA33/LwPqawOhAieeff57OnTuTkZFBnRKTxQ888ACbNm0yqXHWiH9x3eXUDNNU1Dh74jpFOOLiVMgtSnwqFLWLoCCC/iPr3lrjSJ3en+7KWrmiRJ2iHBYsWECzZs1wc3OjU6dObN++vdrHTDsuFZovaZUPmR05glfcHsAEI3XjxrH3ldVANaZeSzJ3LgQF8XzENkBORV41cVl0kBHqgwcLcnNhSJ9M9u2TfXfXLti3Dw4elAnOExOlkLt+HR54AEpM6JmMlBR45BE5rTpihJyCLU/Qgay2ERQEp07B1Kmmt6UijBZ1O3bs4JVXXsHlpljooKAgzlpriJsJ8W8sP8HUbA+TRPzoIl+bNMzFQaWpU9gYwcHy0dpEXVER6O7NPXPWSaec4qAvhULH8uXLmTRpEi+//DIHDhzgrrvuon///iTpfrirSNo/siqTr8vlyj3ptVq8kGqu2qJu/372ZLcBqhEkUZKsLDhzhoHpn9O8uRwpu0XudMPJzoZNmzg2aQH9ul0mO1tDLzazzO2J0iIqMhIeewzefJM6m9ex8uPzjBolRdfo0ab1ZysokKOFqanQti188gloft8o1ewvv0BMjIySKHay8/KCzz+X+y5aBOvXm86WyjBaRhQVFVFYWDaZaHJyMp41Nb5oQXSlwnIKXMnOrv7xks7LEb+mTUyX906hsBaCir/XSUmiUl+YmubQIXkTquuWTwQHZNIuC9WzVlgv77//PmPHjmXcuHG0bt2aDz74gCZNmvCxzqGqipw/dhkAX6/rlTc0lajLzeXi4RSO0wIw0Uhd//4AOK74nucevwTIgIlqDXZcuAD33gv16pF8zyjunTeA9Lx6dCSG1a6P4OZS4kfk8mXYuBGWLZPzoQMG4NTYn892tmJK+AYApkyRL1XLJiEgIYGXB/7Ntm3g6SlYsUJGt/L55/Dkk3KIsHNnmUTaxUVO6XXtSu+QJCZOlIcZO1b+5pgbo0XdvffeywcffKB/rtFouHLlCjNnzuS+++4zpW1WiUfj+ngWd7JqR8Beu0bSVRls0TREJTxV2B6NRt2DIwXk5WlMFzFuAnRTr3f6JuBEoZp6VZQhLy+PmJgYIiMjS22PjIzU11a+mdzcXLKyskot5ZGWeA0Avwa3qLZSQtRlZ1ceJFApR47wV6EMKmzZUlAicUXV6dkTeveGa9d4Iub/8PSEo0elzqoy7u5QVMTFonpEOf9BEkG09M3g1/UOeGUlw6pVN9q6ucHatRAdLfNLtmkDGg0OCfG8c6gfb96+AoA334QnxxVR+NEnsHMnXLlSuQ35+XJe98MP5dBc48asbjmVtzfIkfzPZicTFlbctlMnWSu6Y0cZJeHgIEXg+fNyXtjfn+hoaNlSDuJNvH2nFIKHD8u53GvXTJ/kTxjJ2bNnRcuWLUXr1q2Fk5OT6Nq1q2jQoIEICwsT58+fN/ZwNU5mZqYARGZmZtUOsGmTaEG8ACG2bq2+PU8/kStAiFdfKar+wRQ1TrW/T7WEKl/nQw+JIE4JEOLPP81jW1UYOlQIEOKN1l/Kle++s7RJdkNt6TNnz54VgPjzpi/u3LlzRcuWLcvdZ+bMmQIos9x8rU+Oui5AiNnPplZuxNtvixzchLzzC5GVVcWL+eILMYsZAoQYMaKKxyiPf/4RwslJCBATB50UIET//tU7ZPb5q6JL+FUBQjRqJERiohE7X7okxM8/C/Hvfwuxfr1YtEgIBwf53vVjndjNHaIIjRChoUI8+KAQr70mxOrVQly9euMYr7wi9G84iOM0F1oyBAgx6Y4/hUhKqvj8BQVCpKYKceCAEBs36jfv2iWEAwUChFjBA6WOL5ydhWjb9paXZmi/MXqkLjAwkNjYWP7973/z1FNPERERwZtvvsmBAwfwrTA1tg1RslSYCUYeklLlCF3TINNVqFAorIaAAKtLayKEjGAD6Pnpv2RHHjDAskYprBbNTWGiQogy23QYWpZy2kxXNm6ERyfdIjpOq8WN6zhppJ9WZqbx9gOQlFT9ShLl0aYNTJ4MwHMHnkCjEfz6K8THG3GM9HSZ/0MIcnJg6Ah39hxyx9sbfvvNyKTl9evDwIHw9tsQFcX48fDDD+DiXMR6+tOVPXTmL5Ycv5ucFevg1VdlzpHNm28co2tXqFcP7ruPazPf5MGQWDKpR/fu8PaO7lBZ/XhHR/Dzg9tug3vuKXXI/zx8CoCnnJaQ5hWK3ok+P7/yZHdGUqU8dXXq1GHMmDGMGTPGZIbUGkJDCbgnB343kahTJcIUtoy/P0FINWctaU2OHJGuO3XqSDcYXFTYuaIsPj4+ODo6knrTD31aWhp+FaQqcHV11ddGr4xmzQzME6fVonF3x6vgGpfyPKvsVyfOJLOXZwATizqAGTPg228JTdvJwK4X+XmXD/Pnw//+Z8C+6enQty8cOkT6+SIG/fYce/ZIf7V166RmrC5Dh8Levxz4739lndr9uZ0YxxKmuH3EmOAtPF20gLBjx7jedwAnT8Lx/H4cf+UiJ046sPtn+PsENGwo06Q4O1fdjplLQ1kbBwcP1mdMjwT+71mBU/41nK5fwUnk47RTRtI6OckAM++qpsG95ZifjWGKof+JE+Wo6bRp1bOl6PsfhKdzjgAhjh6t3rEUlqG2TCVVlypf52efiRnMEiDEU0+ZxzZj+egj2X/79rW0JfZJbeozd9xxh3jmmWdKbWvdurWYZuCPf7WvtUi65QQHy+/srl1VO8zxKQsECOHiVCCuX6/aMSpl61YhEhLEpk3STg8PITIybrFPWpoQ4eFCgEjw6SpCg6Qrkre3EDt2mMFGIcSFC0K8844QzZuXngENCBBCoym9Tbc4Ogrx+++mOX9srJxtLe88JZevvy67r6HfpSqN1Nk7+lx11Rypy/xtD9n5DwFqpE5ho/j7E4TMHWItI3X6/HSXf4J+n8Arr0CPHha1SWGdvPDCC4wcOZLOnTvTrVs3Fi1aRFJSEk8//XTNGFA8zasrfVXVkbq/OstRuohOjuYJ8i4u4dA7BNq1k3EAS5bI6NNyKTFCt9v7PgYVrubCaSeCg2XqD30ggolp0EDmjHvhBTm1u2CBzEaSkiJf9/SEFi0gNPTGcuedMtDBFHToINObLFokEyYXFJS/VKfiqRJ1VcD/xJ/AnaQevwJU/d1POpEPgE/da7i713DVX4WiJggI0E+/WoNPnRAlRF3S1xCzAaZNs6xRCqvlkUce4eLFi8yZM4eUlBTatWvHunXrCDLK0av6VLdUWFycfGzf3jT2VIRGA88POsn4w82ZP1+WESuTnHf7dnjqKTh6lNX1RvFozmdcu+5Ap05SYOkGTcyJgwP06yeXM2fg7FkICQEfH9NU2qiM0aPlYi5Uutsq4L9HZuVOTc6v1nGSzshvT1M/05QcUyisjiZNCO7XGoDTp4XJo/eN5dgxmW3A1VVwR/ov8te9c2fLGqWwap599lkSExPJzc0lJiaGu81dWLQkly/DgAF4/SNTqFRJ1AnB8eOy44WGms60clm1isej29HA4RKnT8Pq1Te9npkpg5KOHuUjz2kMzfqca9cduO8+WQqsJgTdzTRpIgMZGjY0v6CrCZSoqwIBAbKDpFysXm45feSrmnpV2CoNGtBk9f/QaODaNQ3p6ZY1RzdK1zX0Am7kQnh49eY6FApz4uwM69bhdSkRqKKoO32a49/uBSA0xMz/qiIjqdPEh6eKZHLmhx4CrVcRISFSOA18XMuYljsYHrKXCdnRFBVpGD9eij/VDU2DSUVds2bNGDt2rM2XC/NvJMeT06/UoZziGoaRm0vSFZkBsmmoymSvsF1cXCAwUK5begpWP/XqFStXVNJhhTXj7g6OjtWrKnH2LMdFCAChLcw8FOXhAR98wHPMpxHJAGRlO3DyJOzZI3MFfxHTnuUnbgfgtddg4cKK66cqjMekb+WoUaM4ffo0d999NydOnDDloa2Khk3r4EAhRcKR9PQqDhknJ3Ma6ZfRtKWbaQ1UKKyJoiKCAgs4e9aFxES4/XbLmCGEnOIB6HllrVwxeX4HhcKEaDSyqsSlqou6jLjzXOROQPqNmZ0HHsC/32KS1jflMvVIpyEXBo8lfcyLXLgg0wldvAh9+ugrjSlMiElF3axZs0x5OKvF0c+HhqRzHn9SU6so6s6evSHqVOJhhS1z//0E/zWcnTxu0ZG6EydkqR5nZ0HXhK/kRjVSp7B2qinqjh+SJckC3C7h4VHV5GdGoNHA/Pk49OiBd4MGeL/xBmGDB4O6zdUIVRZ1eXl5nDp1ipCQEJzsbey0YUMCSNGLuipx990c9ymCCzX070mhsBS+vlaRgFg39XpHRD7unhEyasJcuRMUClNRov5rlURdQnGQRIPLQA2IOpARGefO3aiaoKgxjH7Hc3JyGDt2LO7u7rRt25ak4pIIEydO5M033zS5gVZJiVJhuvw2xpKZCekX5NvfooWpDFMorBArKRWm96e7xwV+/10ao246CmunuqIuSQbktWiUY0qrbo3qWxbB6Hd9+vTp/P3332zZsgU3txu+YPfccw/Lly83qXFWS8eO+A+UjkFVHak7flw++vnJhIcKhc1SolSYpURdqfx0PYs32kL+AoXto9Xi5SLTXlVJ1KXJG0xo8yJTWqWwUoyeN/3pp59Yvnw5Xbt2LVXUuE2bNjYdHFEKT0/823nCL1UXdQnRPwIPERpQvQTGNUFhYSH5+dXLyVdbcXZ2xtHR0dJm1G5KJCBOTJQCq6b11PHjss6yszN0b3MZqGfW8xUVFZGXl2fWc1gzLi4uOKiRGtOwahVefzjAvVUUdQ6yHELobR4mNkxhjRgt6tLT0/H19S2z/erVq6VEnq0TECAfqzxSt13O27ZomIm1ijohBKmpqVy+fNnSpliUevXq4e/vb/bvd0ZGBhMnTmTNmjUADB48mPnz51OvXr0K9xk9ejRffvllqW1dunRh9+7d+ue5ublMnTqVZcuWce3aNfr27cuCBQto3LixWa6jDCVEXXa2zKdav37NnFrHhg3ysUeXfOo29ZY+P3//DXVMX8lF529cVGS/IyMODg40a9YMF5fq5fJUAA4O1aoocbyoOJ1JpHLetgeMFnW33347a9eu5bnnngPQ3+gWL15Mt27dTGudFeN/aCNwL6mJ1wHjU5IkZPgA0KKNs2kNMyE6Qefr64u7u7tdiXaQojYnJ4e0tDQAAnRK3kw89thjJCcns379egCefPJJRo4cyc8//1zpfv369ePzzz/XP7/5Rjpp0iR+/vlnvvvuOxo0aMCUKVMYOHAgMTExNTMK6e9PHa7jy3nS8CMxseZFXfFbSr+WJ2FH8VChGQSdEIKUlBQcHR1p0qSJXY5WFRUVce7cOVJSUmjatKnd/W6Yg6qKuqwsKP75UgF5doLRoi46Opp+/fpx5MgRCgoKmDdvHv/88w+7du1iq85pxQ7w3/QNcC8pZ6uQfTgri4R8mc4kNMI6HeoKCwv1gq5BgwaWNsdi1Cm+8aelpeHr62s2EXT06FHWr1/P7t276VKcO033Ryk+Pp6wSqI0XV1d8a8gr05mZiZLlizhq6++4p577gHg66+/pkmTJvz+++9ERUWZ/mJuJiAAHn6Y4K3XSDsv/eoiIsx/Wh25ubB5s1yPcv5DrpgpP11BQQE5OTkEBgbi7u5ulnPUBho2bMi5c+coKCjA2dl6/7jWCtauxeu/y4GlZGUZ575w4lgh4Iiv7w1hqLBtjP4b2b17d/78809ycnIICQnht99+w8/Pj127dtGpUydz2GiV+PvJMPHUqpQKO3OG48gifC3am360wBTofOjs+cakQ/cemNOvcNeuXWi1Wr2gA+jatStarZadO3dWuu+WLVvw9fWlZcuWjB8/Xj+yCBATE0N+fj6RkZH6bYGBgbRr167S4+bm5pKVlVVqqTJ16sDy5QTdHQzUfFqTHTsgJ0fmk2x/qrgYpZny0xUWl5ix92lH3fUXVrnkjkLP2bN4bVoJQGEhXLtm+K7HV8QCEJp/xAyGKayRKiWYCw8PL+PHY2/4B0o9nH3dmatXZXUUQ8mMSyGdtkANFFiuJmrqpGbeg9TU1HJ9VX19fUmtxHGzf//+DBs2jKCgIE6dOsWrr75Knz59iImJwdXVldTUVFxcXKh/03ynn59fpceNjo5m9uzZVb+gcggOlo81HQGrm3qNihRo1uyRT8ycdNje+429X79J0Wrx4KqsYoQjmZmyepghJByVojrUIwVoYz4bFVaD0SN1jo6OpUYCdFy8eNGuogQ9Az1x5yoA588bt2/C/mwA/FwyVDoTG2fWrFloNJpKl3379gHl3wiFEJXeIB955BEGDBhAu3btGDRoEL/++ivHjh1j7dq1ldp1q+NOnz6dzMxM/XLmzBkDr7gCiooI8pbfe0uJun7tz8kojTp1IDy8Zo1QKKqKVosG8HKU9xtjBs2PJ8pxm9CAq2YwTGGNGD1SJ4Qod3tubq5dTTlo/GQC4pOEkJICzZsbvq8uw3cL74tADXuMK2qUCRMmMHz48ErbBAcHc/DgQc6X8+8gPT0dPz8/g88XEBBAUFAQCQkJAPj7+5OXl0dGRkap0bq0tDS6d+9e4XFcXV1xdXU1+Ly3ZORIgr+9DKyt0enXs2fh8GHpg3Sv6za5sXNnmdtEoagNFEe/e5HNZbyME3WpMrNCi+ACMximsEYMFnUffvghIEcTPv30U+rWvZGGo7CwkG3bttGqVSvTW2itNGyoF3XGpjVJCB8KP0Bo7xpKKaGwGD4+Pvj4+NyyXbdu3cjMzGTv3r3ccccdAOzZs4fMzMxKxdfNXLx4kTNnzugjdTt16oSzszMbN27k4YcfBiAlJYXDhw/z9ttvV+GKqoifH0H8DdTsSJ0ulcntt0ODiKYwfjy0bVtzBigU1UWrBcCLTKCRcaLusgxyC21lZ6U87RiDP+n//ve/gByp++STT0pNtbq4uBAcHMwnn3xiegutlRKlwowVdbpqEi3aGZ8KRWGbtG7dmn79+jF+/HgWLlwIyJQmAwcOLBX52qpVK6Kjo3nggQe4cuUKs2bN4sEHHyQgIIDExEReeuklfHx8eOCBBwDQarWMHTuWKVOm0KBBA7y9vZk6dSrh4eH6aNgaoUSuukuXZL66mnA90E+99gPuvFMuCkVtQifqCi8Dhk+/Xr0KKblS1IXcpvx87AWDfepOnTrFqVOn6NmzJ3///bf++alTp4iPj2fDhg2lIvdsnl69CHj4LsB4UVc8M6ZqvpqJZcuW4ebmxtmzZ/Xbxo0bR/v27cnMzLSgZZXzzTffEB4eTmRkJJGRkbRv356vvvqqVJv4+Hj9NTg6OnLo0CHuv/9+WrZsyahRo2jZsiW7du3Cs4Ri+u9//8uQIUN4+OGHufPOO3F3d+fnn3+uWR/YgAA8uYK3k7wj1cRoXUGBLPEKxaJOUSm1td/YPDpR53AFMFzU6Qo8NeAC9VsZ7sKhqN0YPSa7WZfwyd6pXx//cOB7I0WdECTEZAFaQv2ygVr4D+pqJU63jo5QoiZwpW0dHEongC2vrTFhxcUMHz6cN998k+joaP73v/8xe/ZsNmzYwO7du9EW/0BaI97e3nz99deVtinp01qnTh026OYXK8HNzY358+czf/78attYZYrz6AU5JXOpoA2JidCunXlP+ddfkJEhXZJuD0qDmDNy6tXNAiPkNdlnwK76jc3j7g65uXiNdIHvDRd1x4tz1IXWTYVGTcxqosJ6qNJEe3JyMmvWrCEpKalMfcP333/fJIbVBnT5XlNSDN/n8omLXMiTPlahbWppYEndSsqa3XcflIy89PWVScLKo2dP2LLlxvPgYLhwoXSbCgJzKkOj0TB37lweeughAgMDmTdvHtu3b6dRo0ZkZ2fTp08f8vPzKSwsZOLEiYwfP97ocyiMpNjHL7jwJAdoUyMjdTq9e++94LRujfSni4y88UJNUpN9Bkzeb3Tk5OTQunVrhg0bxrvvvmv0ORRVQKMBFxejq0ocPylH4kPvbwdKk9sNRou6TZs2MXjwYJo1a0Z8fDzt2rUjMTERIQQdO3Y0h41Wi//un4AhpCbnA4ZF0x3ffQHwwc8hDU+fsnnJFKZh4MCBtGnThtmzZ/Pbb7/Rttg53t3dna1bt+Lu7k5OTg7t2rVj6NChdl01o0YoFnVB+dL3oCYiYPX56aKAA7HySfv25j9xLaaifqNj7ty59uVmY0UYLeqKfbetPReqwrQYLeqmT5/OlClTmDNnDp6enqxYsQJfX18ef/xx+tmZ44r/T58AQ0hNMfxfccIB6RfRwv0cUEtF3ZUrFb92s59WOTkN9dxcF9OEd/oNGzYQFxdHYWFhqZQgjo6O+goR169fp7CwsMI0PQoTUr8+PPoowWeDYJv5feouXoS9e+V6VBTwhYy8pUMH8564ImpBn4GK+w1AQkICcXFxDBo0iMOHD5v0vIpb8MoreP3cGnjccFGXUAQ4KFFnZxidfPjo0aOMGjUKACcnJ65du0bdunWZM2cOb731lskNtGYC/IoAOH/RiaIiw/Y5Hi/zBbVocNFcZpkfD4+Kl5v9lSpre3NB9fLaVIH9+/czbNgwFi5cSFRUFK+++mqp1y9fvkyHDh1o3LgxL774okEpRxTVRKOBb78laPJQwPyi7vff5Qxku3bQOLAI/i4WdbfdZt4TV0RN9hkz9ZupU6cSHR1dpWMrqsmOHXglyCTlhoo63QBC6C8fmMkohTVitKjz8PAgNzcXkDUkT+hCbIAL5fl22DC+/vLtKyh04KKBGi3hlJymDW103Vxm2TWJiYkMGDCAadOmMXLkSObMmcOKFSuIiYnRt6lXr54+gvvbb78tN+mvwjzoSoWZe/q11NRrYqLMoeLiAiXSwyhucKt+s3r1alq2bEnLli0tbKmdotXihVRzhoi6a9cgOVPO18qAPIW9YLSo69q1K3/++ScAAwYMYMqUKcydO5cnnniCrmaup2htOPs3wId0wPAI2ITzMtq1RaiBQ3sKg7l06RL9+/dn8ODBvPTSS4BMvjto0CBefvnlMu39/Pxo374927Ztq2lT7ZPCQoLcpIBOSzOuMLkxCHEjFqJfP26M0rVtqypJlIMh/Wb37t189913BAcHM3XqVBYvXsycOXMsabZ9YaSoO3lSPtYjgwahqmqRPWG0T93777/PlWL/kFmzZnHlyhWWL19OaGioPkGx3VCcgPgCDUlNNayc5PEs6UfXop0JSzApAJkS5OjRo2W2r169Wr9+/vx56tSpg5eXF1lZWWzbto1nnnmmJs20X557jnoff4ynyzWy89w4fRrMUYTm0CEZkV6nDvToAbxp4alXK8eQfhMdHa2fev3iiy84fPgwM2bMqDEb7R6tFi/kHyJDRJ0+SILjaBo3qryxwqYwWtQ1L1Hk1N3dnQULFpjUoFpFw4YEkMJhwg0aqbt8GS7k1wMgZNRdZjVNUT7JycmMHTsWIQRCCCZMmEB7FRFZM/j7owGC617g0KXGZhN1uqnX3r2L3dXuv1/6mRnyr0uhsEaMHKkrKepoFGJGwxTWRpVE3V9//VUmBcTly5fp2LEjJ3XjvmYgMTGR1157jT/++IPU1FQCAwMZMWIEL7/8Mi4uFsj5ZmSpMF1H8/cHT986lTdWmIVOnToRGxtraTPsE30C4nMcorHZ/OpKlQYDiIiQi8IkjB492tIm2B/Gijpd5CvHodHd5rVNYVUYLeoSExMpLCwssz03N7dUeRlzEBcXR1FREQsXLiQ0NJTDhw8zfvx4rl69aplEmIMH479dwFLDEhDryoOpEHOFXaJLQMwp4I4KI2ALCmDSJDh1Cho2LH8JC5OVIm7myhXYsUOuR0WZ4yIUCgug1aJFlmozSNQdyQdcCdWcBD9VIsyeMFjUrVmzRr++YcOGUmVjCgsL2bRpE8G60DYz0a9fv1K58Jo3b058fDwff/yxZUSdry/+xWmvDBmpS9ieCvjTIjsG6GROyxQK60OXgPj6MaDitCZvvw0ffVT5oZycpGgbPlzOrupK3W7eDPn50KxZcW3lEydg3z7o3BlC1DSUopYyejReQ0dDQ8jLg+vXK692dzyxuJpEd1/ZWRR2g8Gf9pAhQwBZSkaXp06Hs7MzwcHBvPfeeyY1zhAyMzPx9vau8PXc3Fx9ChaALEOT/BiIrlSYQdOvh2UakxaZ+1CiTmF3FHeW4CsycW1506+HD8OsWXJ96lRo0ADS00sv589DcrKsrLV2rby5DRggBd6vv8p9o6JkajzWroXnn4dBg6DEH1OF4lbMnTuXtWvXEhsbi4uLC5cvX7acMS4u1C1xm8vKqljU5eZC0ll5aw9dYV+5YxVGiLqi4uy6zZo146+//rKKhK0nTpxg/vz5lYrJ6OhoZs+ebR4DCgsJ2PoDMJzUFOnDUBkJp6XfX2hjlaNOYYcUTwMFFUm/25tH6goKYMwYOdI2aJAcsdNoyj/U0aOwfDksWwbHjsGKFXLRoR/Qt3TSYUWtJS8vj2HDhtGtWzeWLFliaXNwcJAj0tnZUtT5VlCQ6NQpKCqS5YYraqOwXYzOU3fq1CmTC7pZs2ah0WgqXfbt21dqn3PnztGvXz+GDRvGuHHjKjz29OnTyczM1C9nzpwxneEODvh/LsP8U87dutRUQppMBtlC+dQp7BFnZxg7luDxkQCcOyenknS8846cKa1XDz75pGJBB9C6tRzRi4uD/fvhxRehaVP5Wr16MvIVAF1QjKXKgylqLbNnz2by5MmEW0PU9PnzMGIEXgUyy31lE043ar6KSvuQwjYxeKRuz549+iSVOpYuXcrMmTO5evUqQ4YMYf78+bi6Gp9/bcKECQwfPrzSNiX99c6dO0fv3r3p1q0bixYtqnQ/V1fXKtlkEBoN/g0L4RxcznKs1M8hIwMuXq8LQGi4inxV2CmffoqPgDpfy+TDZ85IV7eS064ffgiBgYYdTqO5EdwaHS0FXv36xcXPCwrgn39kQzVSp6jNFBbCN9/gxUucpYFBoq7F3yvgo/Pwf/9XMzYqrAKDRd2sWbPo1auXXtQdOnSIsWPHMnr0aFq3bs0777xDYGAgs3S/zEbg4+Nj8Ojf2bNn6d27N506deLzzz/H4eYC1zVMPT9XXM9dJxc3zp+HoKDy2+nTmZBC3RYBNWegQmFlaDSyn8TFSb+6oCA57ZqXBwMHwogRVTuug4OMh9ATHy8djOrWlZETCoWZMZsPd3FgoiFpTfQjdeIYaJua5vyKWoPBiig2Npa+ffvqn3/33Xd06dKFxYsX88ILL/Dhhx/y/fffm8VIHefOnaNXr140adKEd999l/T0dFJTU0k1tEaXGdD4ygTEAEeOVNxO/++JBGjSpAYsUyiskMJCSE0lOFDe+E6fLj3tunBh5dOuRlFy6tXCf/4U1kFVXH2MITo6Gq1Wq1+amOq33t0dHB2NE3Uch0aqmoS9YfBIXUZGBn4l8t1s3bq1VHqR22+/3bT+auXw22+/cfz4cY4fP07jxo1LvSbErX3azIKvLwNYy0dMYMkSKDE7XYqEuALAqVjUDalJCxUK6+GVV+DNNwlqtwO4k3Xr4Oef5Uvz5hk+7WoQuiAJ5U+nKMZYVx9jmT59Oi+88IL+eVZWlmmEnUYjExBfMkTUCUCjRJ2dYvDfVz8/P06dOgXIqKD9+/fTrVs3/evZ2dk4m7lY9ujRo/XlnW5eLEbDhjzNJwD89JN0/i6PhJPFIeYzR0AlKVgUpiUjI4PZs2eTYkh2aIX50aU1cZChrytW3Jh2HTnSxOeaOBFWrpRzuwqjsNV+4+PjQ6tWrSpd3CpLAHcLXF1d8fLyKrWYDAOqSuTn30gVpESdfWKwqOvXrx/Tpk1j+/btTJ8+HXd3d+6660b90oMHDxJij8k9fX1pxz/08I2nsBAqinzXT7+2czXh/JLiVkycOJG//vqLZ555xtKmKOBGqbC84/pNJp921dG4MTzwwE2OdgpDUP0GkpKSiI2NJSkpicLCQmJjY4mNjeXKlSuWMcgAUXf6NBQWaqhDDgHaa7LmscKuMFjUvf766zg6OtKzZ08WL17M4sWLS9Vb/eyzz4iMjDSLkVbNiBGwfz/PzJbBD4sWyaC7m1ElwmqeNWvWcOXKFX755Rfq1avHN998Y2mTFLpSYcUJiAE++MDE066KaqH6jWTGjBlEREQwc+ZMrly5QkREBBEREdXyuasWJURdfDyUN0FV0p9O01iN0tkjGmHk3GVmZiZ169bF0dGx1PZLly5Rt27dUkLPGsnKykKr1ZKZmWnSofHcXDkwcOGCnIa9//4br2Vk3JhxzV74LXWffMxk5zUX169f59SpUzRr1qxa0xG2QGXvhbm+T9aGya7z2DEICyOvrjeD77xIixYyhYnJR+liY2U1iR49oGdPEx+8YlS/kag+IzHptWZlsXmXG336yXvs22/Dv/9dusn8+dLr4IFGe1j58HJ4//3qnVNhNRj6XTI6JEyr1ZYRdADe3t5WL+jMiasrPPGEXP/449Kv6f49BXCOuueO1axhCoU1UTxS53LlEutXXGX+fDN5I2zYIIMybu6MCkVtxcuL3lEu6Mqcv/giLF1auol+pO6xLkrQ2Skqzr+6XL0Kb70FL77IU0/KQc8NG+DkyRtN9FOvHFfpTBT2Td26Mj0DgDmd8FV5MIWNMmWKXEAOJOjqHUMJ3+0WNW+XwjpQos4UTJsG77xDc7+rREXJTQsX3nhZ5airWZYtW4abmxtnz57Vbxs3bhzt27cnMzPTgpYp0Ghg/HiYPBnqmLGyiioPZjSq31g5P/8sfbgXLuTtt+Hxx2Xax4cegj17ZBP9SF3zIsvZqbAoStRVFw+PGzentDR0wWKffSb97ODGSJ0SdTXD8OHDCQsLIzpa1uWdPXs2GzZs4Ndff0VbnJldYUE++EBODZkr3cK1a9KTHJSoMwLVb6ycuDj45hvYsQMHB3mPiYyEnBwYMEAmvy/OOkZoVAhs3GhZexUWweDkw4pK8PeXvenIEQYMaE7jxpCcLHNwPfbYjcTDtX36VQj5A1LTuLsb53el0WiYO3cuDz30EIGBgcybN4/t27fTqFhEZGdn06dPH/Lz8yksLGTixImMHz/eTNYrapzDh6GoCBo21PvwWQpL9Rkwfb/RkZOTQ+vWrRk2bBjv6hy8FOZHJ6yLR01dXOQ9pk8f+OsvGQ+Unw+uXKdR4WkwsPSmwrZQI3WmYPBg+ThvHk5OcnYJbvho66dfPc9Ln6JaSk6ONL+ml6rcFAcOHEibNm2YPXs2q1atom3btvrX3N3d2bp1K7GxsezZs4fo6GguXrxowneqamRkZDBy5Eh9iaGRI0dy+fLlSvepqNTRO++8o2/Tq1evMq/fKqu+WSkslP505vKpK1lJwsI5IS3VZ8zRb3TMnTuXLl26mODdURjFTaIO5Oe8dq30obtwQW4L4QQOCJV42E5Ros4UTJ4Mjo7w+++wfz/jxsmnO3bA9u1w8bIcEA1pmm9hQ+2HDRs2EBcXR2FhYanydgCOjo64FzvrX79+ncLCQstWJSnmscceIzY2lvXr17N+/XpiY2MZeYsyCykpKaWWzz77DI1Gw4MPPliq3fjx40u1W1jS6bOmeestmZjupZcqbpOUdOMuZSyqPFiVqazfACQkJBAXF8d9991nAevsnHr15ONN/o0NG8rgvOK83nJGyNlZjdTZKWr61RQEBcEjj8C338K77xL47bfcf7+sUDR1qmwSEAB1Y7Za1s5q4u4OlkimrguWNJT9+/czbNgwFi5cyHfffcerr77KDz/8UKrN5cuX6dmzJwkJCbzzzjv4WPgH8OjRo6xfv57du3frR0EWL15Mt27diI+PJywsrNz9/HW/5MWsXr2a3r1707x581Lb3d3dy7S1GLop0YpG6o4dg4gIOTKxf/+Nu5WhvP8+PPmkVYyKW6rP6M5tDIb0m6lTp/LOO++wc+dOE1qqMIhyRup0NGsmhd2r/3eJyTv+K/80OagxG3tEiTpT8e9/SxXn7Q1C8MwzGlauhL175cstWiCT2dViNBrrrzqTmJjIgAEDmDZtGiNHjqRNmzbcfvvtxMTE0KlTJ327evXq8ffff3P+/HmGDh3KQw89VO7IRE2xa9cutFptqWmtrl27otVq2blzZ4WiriTnz59n7dq1fPnll2Ve++abb/j666/x8/Ojf//+zJw5E09PzwqPlZubS64u0geZ+NJk6ERdamr5r7/wgpw7zMmB1avhqaeMO76zM4SHV89GE1Eb+gwY1m9Wr15Ny5YtadmypRJ1lqASUQfQvj2snrgJdmyFRt1r0DCFNaGkvKm47TY58vC//4FGQ58+pXMFqfJg5ufSpUv079+fwYMH81Lx1F6nTp0YNGgQL7/8crn7+Pn50b59e7Zt21aTppYhNTUVX1/fMtt9fX1JrUj83MSXX36Jp6cnQ4cOLbX98ccfZ9myZWzZsoVXX32VFStWlGlzM9HR0XrfPq1WSxNTBvjoRt7KG6n79VfpJAQyZb6xgk5hNIb2m927d/Pdd98RHBzM1KlTWbx4MXPmzLGU2fZHSVFXkbuILh1N48Y1Y5PC+hB2RmZmpgBEZmam2c/17rtCyN4nRHTLz4TYvdvs5zQV165dE0eOHBHXrl2ztCkmJzU1Vf/5Z2ZmijZt2oi///67wvaVvRe3+j7NnDlTAJUuf/31l5g7d65o2bJlmf1DQ0NFdHS0QdcVFhYmJkyYcMt2+/btE4CIiYmpsM3169dFZmamfjlz5ozp+k1qquwUGo0Q+fk3tuflCREWJl+bMqVqx/7jDyFGjRJi2bLq21kFbLnflOTzzz8XUyr5jKrTZ2wJk15rYaEQ588Lcf16xW1WrxZi2DAhPvyw+udTWBWGfpfU9Ks5iImBM2cYPXoIL78s89WFHlsLuSGWtkwBJCcnM3bsWIQQCCGYMGEC7du3N8u5JkyYcMtI0+DgYA4ePMj58+fLvJaenm7QtPD27duJj49n+fLlt2zbsWNHnJ2dSUhIoGPHjuW2cXV1xdVc7gI+PjKSqLAQ0tKk/w/ARx/J/HING8Krr95on5wM//mPHAWvX7/yY2/ZAl9+Kf2JLBnhq1CYGgcHKGc0vxSDB9/IxqCwS5SoMzW//QZRUeDrS4PEKN5+y411kzfST6yHJu/cen+F2enUqROxuooDZsbHx8egIIxu3bqRmZnJ3r17ueOOOwDYs2cPmZmZdO9+a/+YJUuW0KlTJzoYEPH5zz//kJ+fT4Clcrg5Osqbky6tSWCgHND+6iv5+ty5N6aahLiRMj83F374ofI0JSrytUYYPXq0pU1QKBTloHzqTE3v3tC0qRyBWLqUiY+ms15EUVeTo/IGKSqkdevW9OvXj/Hjx7N79252797N+PHjGThwYKkgiVatWrFq1apS+2ZlZfHDDz8wbty4Msc9ceIEc+bMYd++fSQmJrJu3TqGDRtGREQEd955p9mvq0JGj5YBEbqRN40G/vwTPvlEFrTUodHIETpnZ5lptbJULFlZcpQcVM1XhW0yezaMHAlHj5b/enq6TLytsFuUqDM1zs7yZgXw3ntw+rRc9/OTKcAVigr45ptvCA8PJzIyksjISNq3b89XutGrYuLj48vU4fzuu+8QQvDoo4+WOaaLiwubNm0iKiqKsLAwJk6cSGRkJL///juOjo5mvZ5KeeMN2T9Kpl5xc5OBETfb1bkzFJeuYtIkOHSo9OuHDskUJoGBcqrWwUGGAioUtsZPP8HXX0NiYtnXhJAVi1xdZZ5HhV2ipl/Nwdix8h9VQoIcZQA5eqdQVIK3tzdff/11pW1EOVFvTz75JE8++WS57Zs0acLWrVacH1EImQro/vvBqZKfo8mTYdMmGR37yCOyLpIuV8jvv8PixXK9dWuZ1PhWvncKRW2ksrQmly7dKDhuwfRMCsuiRurMQd268Oyzcn3pUvlYi2u+KhQmp6AAzp2DefOkz9ydd8rAiYpwcIAvvpA57o4eLZ3qZNQoePxx2LoV/vkHRowwu/kKhUWoTNTp0pk0bFjrc6Iqqo4SdebiuedKdywl6hSKGyxaJH1MJ0+Wz/v1KzvtejO+vnLqSaOBEydubPf2ltvvvtvitV4VCrNSQakwQLoegPLdtnPU9Ku58POTIwjr1skp2IEDLW1RlShSTrfqPTAHJUt/NW4ML75o2H59+siiyteuyalbKxVx5U2T2xP2fv1mQzdSFx9/Y1tKCgwdemObEnV2jRJ15uTNN2H+/FoZIOHi4oKDgwPnzp2jYcOGuLi4oLHSG6i5EEKQl5dHeno6Dg4OuNTCz9FqKZlO5e23jaulZUCKF0vh7OyMRqMhPT2dhg0b2l2fAdlv0tPT0Wg0ODs7W9oc20In6r74ApYskev168uUP0JINwWVp86uUaLOnNRiZ20HBweaNWtGSkoK586ds7Q5FsXd3Z2mTZvioApkm44OHWR91rAwm0oS7OjoSOPGjUlOTiaxvAhFO0Gj0dC4cWPLRljbIlFRcuanT58b29zcZI3kJk0gJAQqqemssH2UqFNUiIuLC02bNqWgoIDCypzYbRhHR0ecnJzscsTFrLi7w8GDlrbCLNStW5cWLVqQn59vaVMshrOzsxJ05qBHDxnlevPv0aBBlrFHYXUoUaeoFN0UippGUSgMx9HRUYkahXlQfzAVlaDmkxQKhUKhUChsACXqFAqFQqFQKGwAJeoUCoVCoVAobAC786nT5U/KysqysCUKW0D3PbL1vFyq3yhMhb30GVD9RmE6DO03difqsrOzAVkTU6EwFdnZ2Wh1OaRsENVvFKbG1vsMqH6jMD236jcaYQ9/l0pQVFTEuXPn8PT0LJOmIisriyZNmnDmzBm8vLwsZKH5UNdneoQQZGdnExgYaNN57FS/sc3rU33GvFTUb2z5OwXq+syBof3G7kbqHBwcaNy4caVtvLy8bPKLqENdn2mx9dEGUP0GbPv6VJ8xD7fqN7b8nQJ1fabGkH5j23+TFAqFQqFQKOwEJeoUCoVCoVAobAAl6krg6urKzJkzcXV1tbQpZkFdn8Ic2Pr7bsvXZ8vXZs3Y+vuurs9y2F2ghEKhUCgUCoUtokbqFAqFQqFQKGwAJeoUCoVCoVAobAAl6hQKhUKhUChsACXqFAqFQqFQKGwAJeoUCoVCoVAobAC7E3ULFiygWbNmuLm50alTJ7Zv315p+61bt9KpUyfc3Nxo3rw5n3zySQ1ZahzR0dHcfvvteHp64uvry5AhQ4iPj690ny1btqDRaMoscXFxNWS14cyaNauMnf7+/pXuU1s+u9qALfYb1WfKUhs+t9qCLfYZUP2mPKzqsxN2xHfffSecnZ3F4sWLxZEjR8Tzzz8vPDw8xOnTp8ttf/LkSeHu7i6ef/55ceTIEbF48WLh7Owsfvzxxxq2/NZERUWJzz//XBw+fFjExsaKAQMGiKZNm4orV65UuM/mzZsFIOLj40VKSop+KSgoqEHLDWPmzJmibdu2pexMS0ursH1t+uysHVvtN6rPlKa2fG61AVvtM0KofnMz1vbZ2ZWou+OOO8TTTz9dalurVq3EtGnTym3/4osvilatWpXa9tRTT4muXbuazUZTkZaWJgCxdevWCtvoOlpGRkbNGVZFZs6cKTp06GBw+9r82Vkb9tJvVJ+pnZ+bNWIvfUYI1W+s7bOzm+nXvLw8YmJiiIyMLLU9MjKSnTt3lrvPrl27yrSPiopi37595Ofnm81WU5CZmQmAt7f3LdtGREQQEBBA37592bx5s7lNqzIJCQkEBgbSrFkzhg8fzsmTJytsW5s/O2vCnvqN6jO183OzNuypz4DqN9b22dmNqLtw4QKFhYX4+fmV2u7n50dqamq5+6SmppbbvqCggAsXLpjN1uoihOCFF16gR48etGvXrsJ2AQEBLFq0iBUrVrBy5UrCwsLo27cv27Ztq0FrDaNLly4sXbqUDRs2sHjxYlJTU+nevTsXL14st31t/eysDXvpN6rP1M7PzRqxlz4Dqt+A9X12TjV+Rguj0WhKPRdClNl2q/blbbcmJkyYwMGDB9mxY0el7cLCwggLC9M/79atG2fOnOHdd9/l7rvvNreZRtG/f3/9enh4ON26dSMkJIQvv/ySF154odx9auNnZ63Yer9RfUZS2z43a8bW+wyofqPDmj47uxmp8/HxwdHRscw/pbS0tDIqW4e/v3+57Z2cnGjQoIHZbK0Ozz33HGvWrGHz5s00btzY6P27du1KQkKCGSwzLR4eHoSHh1doa2387KwRe+g3qs9IatvnZq3YQ58B1W90WNtnZzeizsXFhU6dOrFx48ZS2zdu3Ej37t3L3adbt25l2v/222907twZZ2dns9laFYQQTJgwgZUrV/LHH3/QrFmzKh3nwIEDBAQEmNg605Obm8vRo0crtLU2fXbWjC33G9VnSlNbPjdrx5b7DKh+czNW99lZIDjDYujCzJcsWSKOHDkiJk2aJDw8PERiYqIQQohp06aJkSNH6tvrQpUnT54sjhw5IpYsWWK1YebPPPOM0Gq1YsuWLaVCsXNycvRtbr6+//73v2LVqlXi2LFj4vDhw2LatGkCECtWrLDEJVTKlClTxJYtW8TJkyfF7t27xcCBA4Wnp6dNfHbWjq32G9VnaufnVhuw1T4jhOo31v7Z2ZWoE0KIjz76SAQFBQkXFxfRsWPHUmHYo0aNEj179izVfsuWLSIiIkK4uLiI4OBg8fHHH9ewxYYBlLt8/vnn+jY3X99bb70lQkJChJubm6hfv77o0aOHWLt2bc0bbwCPPPKICAgIEM7OziIwMFAMHTpU/PPPP/rXa/NnVxuwxX6j+kzt/NxqC7bYZ4RQ/cbaPzuNEMUefQqFQqFQKBSKWovd+NQpFAqFQqFQ2DJK1CkUCoVCoVDYAErUKRQKhUKhUNgAStQpFAqFQqFQ2ABK1CkUCoVCoVDYAErUKRQKhUKhUNgAStQpFAqFQqFQ2ABK1CkUCoVCoVDYAErU1WJ69erFpEmTLG1GhfTq1QuNRoNGoyE2NtagfUaPHq3f56effjKrfQr7RPUbhcJ4VL+pHShRZ6XovmgVLaNHj2blypW89tprFrFv0qRJDBky5Jbtxo8fT0pKCu3atTPouPPmzSMlJaWa1insFdVvFArjUf3GdnCytAGK8in5RVu+fDkzZswgPj5ev61OnTpotVpLmAbAX3/9xYABA27Zzt3dHX9/f4OPq9VqLXpditqN6jcKhfGofmM7qJE6K8Xf31+/aLVaNBpNmW03D4f36tWL5557jkmTJlG/fn38/PxYtGgRV69eZcyYMXh6ehISEsKvv/6q30cIwdtvv03z5s2pU6cOHTp04Mcff6zQrvz8fFxcXNi5cycvv/wyGo2GLl26GHVtP/74I+Hh4dSpU4cGDRpwzz33cPXqVaPfI4XiZlS/USiMR/Ub20GJOhvjyy+/xMfHh7179/Lcc8/xzDPPMGzYMLp3787+/fuJiopi5MiR5OTkAPDKK6/w+eef8/HHH/PPP/8wefJkRowYwdatW8s9vqOjIzt27AAgNjaWlJQUNmzYYLB9KSkpPProozzxxBMcPXqULVu2MHToUIQQ1b94haKKqH6jUBiP6jdWiFBYPZ9//rnQarVltvfs2VM8//zzpZ736NFD/7ygoEB4eHiIkSNH6relpKQIQOzatUtcuXJFuLm5iZ07d5Y67tixY8Wjjz5aoT2rVq0SDRo0uKXdN9snhBAxMTECEImJiZXuC4hVq1bd8hwKRUWofqNQGI/qN7Ub5VNnY7Rv316/7ujoSIMGDQgPD9dv8/PzAyAtLY0jR45w/fp17r333lLHyMvLIyIiosJzHDhwgA4dOlTJvg4dOtC3b1/Cw8OJiooiMjKShx56iPr161fpeAqFKVD9RqEwHtVvrA8l6mwMZ2fnUs81Gk2pbRqNBoCioiKKiooAWLt2LY0aNSq1n6ura4XniI2NrXInc3R0ZOPGjezcuZPffvuN+fPn8/LLL7Nnzx6aNWtWpWMqFNVF9RuFwnhUv7E+lE+dHdOmTRtcXV1JSkoiNDS01NKkSZMK9zt06FCpf2jGotFouPPOO5k9ezYHDhzAxcWFVatWVfl4CkVNovqNQmE8qt/UDGqkzo7x9PRk6tSpTJ48maKiInr06EFWVhY7d+6kbt26jBo1qtz9ioqKOHjwIOfOncPDw8OokPA9e/awadMmIiMj8fX1Zc+ePaSnp9O6dWtTXZZCYVZUv1EojEf1m5pBjdTZOa+99hozZswgOjqa1q1bExUVxc8//1zp0PTrr7/O8uXLadSoEXPmzDHqfF5eXmzbto377ruPli1b8sorr/Dee+/Rv3//6l6KQlFjqH6jUBiP6jfmRyOELcf2KixJr169uO222/jggw+M3lej0bBq1SqDsogrFLaE6jcKhfGofiNRI3UKs7JgwQLq1q3LoUOHDGr/9NNPU7duXTNbpVBYN6rfKBTGo/qNGqlTmJGzZ89y7do1AJo2bYqLi8st90lLSyMrKwuAgIAAPDw8zGqjQmFtqH6jUBiP6jcSJeoUCoVCoVAobAA1/apQKBQKhUJhAyhRp1AoFAqFQmEDKFGnUCgUCoVCYQMoUadQKBQKhUJhAyhRp1AoFAqFQmEDKFGnUCgUCoVCYQMoUadQKBQKhUJhAyhRp1AoFAqFQmEDKFGnUCgUCoVCYQMoUadQKBQKhUJhA/w/OxqUKJ3XrdcAAAAASUVORK5CYII=", "text/plain": [ "
" ] @@ -746,7 +825,7 @@ "est_clipped = oep_clipped.compute_estimate(\n", " Y_clipped, U_clipped, X0=lqr0_resp.states[:, 0])\n", "plot_state_comparison(timepts, est_clipped.states, lqr_resp.states)\n", - "plt.suptitle(\"MHE with constraints\")\n", + "plt.suptitle(\"MLE with constraints\")\n", "plt.tight_layout()\n", "\n", "plt.figure()\n", @@ -767,7 +846,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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", "text/plain": [ "
" ] @@ -833,13 +912,14 @@ "output_type": "stream", "text": [ "Sample time: Ts=0.1\n", - ": sys[9]\n", + ": sys[7]\n", "Inputs (4): ['F1', 'F2', 'Dx', 'Dy']\n", "Outputs (3): ['x', 'y', 'theta']\n", "States (6): ['x0', 'x1', 'x2', 'x3', 'x4', 'x5']\n", + "dt = 0.1\n", "\n", - "Update: at 0x168af1360>\n", - "Output: at 0x168598940>\n" + "Update: at 0x153eb9da0>\n", + "Output: at 0x1533aa5c0>\n" ] } ], @@ -863,7 +943,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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", 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" ] @@ -884,6 +964,8 @@ "# plt.plot(timepts, V0[0], 'b--', label=\"V[0]\")\n", "plt.plot(timepts, V[0], label=\"V[0]\")\n", "plt.plot(timepts, W[0], label=\"W[0]\")\n", + "plt.xlabel(\"Time $t$ [s]\")\n", + "plt.ylabel(\"Disturbance, sensor noise\")\n", "plt.legend();" ] }, @@ -909,7 +991,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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", 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" ] @@ -932,6 +1014,14 @@ "plot_state_comparison(timepts, mhe_resp.states, lqr_resp.states)" ] }, + { + "cell_type": "markdown", + "id": "d94b5d23-e482-440e-b449-4cd905d6269e", + "metadata": {}, + "source": [ + "We see that while the estimates eventually converge to the correct values, the initial estimates for the state trajectory are not close to the actual values. This is in large part due to the fact that we started far from an equilibrium point for the closed loop system. We can see an improved response if we change the control problem to start at the origin and then move to the final point, allowing the MHE estimator to have an initial estimate that is closer to the actual state." + ] + }, { "cell_type": "code", "execution_count": 24, @@ -939,7 +1029,7 @@ "metadata": {}, "outputs": [], "source": [ - "# Resimulate starting at the origin and moving to the \"initial\" condition\n", + "# Resimulate starting at the origin and moving to the original initial condition\n", "uvec = [x0, ue, V, W*0]\n", "lqr_resp = ct.input_output_response(lqr_clsys, timepts, uvec, xe)\n", "U = lqr_resp.outputs[6:8] # controller input signals\n", @@ -954,7 +1044,7 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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", "text/plain": [ "
" ] @@ -964,6 +1054,7 @@ } ], "source": [ + "# Create a new optimal estimation problem with a slightly shorter horizon\n", "mhe_timepts = timepts[0:8]\n", "oep = opt.OptimalEstimationProblem(\n", " dsys, mhe_timepts, traj_cost, terminal_cost=init_cost,\n", @@ -978,12 +1069,12 @@ ] }, { - "cell_type": "code", - "execution_count": null, - "id": "4158e922", + "cell_type": "markdown", + "id": "29d5d904-f6bc-463b-8e53-c0b5fbbeeded", "metadata": {}, - "outputs": [], - "source": [] + "source": [ + "We see now that the MHE estimtor is able to quickly converge to values that are close to the actual state and maintain a very good estimate throughout the trajectory." + ] } ], "metadata": { @@ -1002,7 +1093,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.6" + "version": "3.13.1" } }, "nbformat": 4, diff --git a/examples/pvtol-outputfbk.ipynb b/examples/pvtol-outputfbk.ipynb index 7d8bc8529..bc999c140 100644 --- a/examples/pvtol-outputfbk.ipynb +++ b/examples/pvtol-outputfbk.ipynb @@ -13,7 +13,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 1, "id": "544525ab", "metadata": {}, "outputs": [], @@ -30,7 +30,7 @@ "metadata": {}, "source": [ "## System definition\n", - "We consider a (planar) vertical takeoff and landing aircraf model:\n", + "We consider a (planar) vertical takeoff and landing aircraft model:\n", "\n", "![PVTOL diagram](https://murray.cds.caltech.edu/images/murray.cds/7/7d/Pvtol-diagram.png)\n", "\n", @@ -63,7 +63,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 2, "id": "ffafed74", "metadata": {}, "outputs": [ @@ -71,15 +71,25 @@ "name": "stdout", "output_type": "stream", "text": [ - "Object: pvtol\n", - "Inputs (2): F1, F2, \n", - "Outputs (6): x0, x1, x2, x3, x4, x5, \n", - "States (6): x0, x1, x2, x3, x4, x5, \n", + ": pvtol\n", + "Inputs (2): ['F1', 'F2']\n", + "Outputs (6): ['x0', 'x1', 'x2', 'x3', 'x4', 'x5']\n", + "States (6): ['x0', 'x1', 'x2', 'x3', 'x4', 'x5']\n", + "Parameters: ['m', 'J', 'r', 'g', 'c']\n", "\n", - "Object: pvtol_noisy\n", - "Inputs (7): F1, F2, Dx, Dy, Nx, Ny, Nth, \n", - "Outputs (6): x0, x1, x2, x3, x4, x5, \n", - "States (6): x0, x1, x2, x3, x4, x5, \n" + "Update: \n", + "Output: \n", + "\n", + "Forward: \n", + "Reverse: \n", + "\n", + ": pvtol_noisy\n", + "Inputs (7): ['F1', 'F2', 'Dx', 'Dy', 'Nx', 'Ny', 'Nth']\n", + "Outputs (6): ['x0', 'x1', 'x2', 'x3', 'x4', 'x5']\n", + "States (6): ['x0', 'x1', 'x2', 'x3', 'x4', 'x5']\n", + "\n", + "Update: \n", + "Output: \n" ] } ], @@ -117,7 +127,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 3, "id": "1e1ee7c9", "metadata": {}, "outputs": [], @@ -143,7 +153,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 4, "id": "3647bf15", "metadata": {}, "outputs": [ @@ -151,10 +161,13 @@ "name": "stdout", "output_type": "stream", "text": [ - "Object: sys[3]\n", - "Inputs (5): x0, x1, x2, F1, F2, \n", - "Outputs (6): xh0, xh1, xh2, xh3, xh4, xh5, \n", - "States (42): x[0], x[1], x[2], x[3], x[4], x[5], x[6], x[7], x[8], x[9], x[10], x[11], x[12], x[13], x[14], x[15], x[16], x[17], x[18], x[19], x[20], x[21], x[22], x[23], x[24], x[25], x[26], x[27], x[28], x[29], x[30], x[31], x[32], x[33], x[34], x[35], x[36], x[37], x[38], x[39], x[40], x[41], \n" + ": sys[1]\n", + "Inputs (5): ['x0', 'x1', 'x2', 'F1', 'F2']\n", + "Outputs (6): ['xh0', 'xh1', 'xh2', 'xh3', 'xh4', 'xh5']\n", + "States (42): ['x[0]', 'x[1]', 'x[2]', 'x[3]', 'x[4]', 'x[5]', 'x[6]', 'x[7]', 'x[8]', 'x[9]', 'x[10]', 'x[11]', 'x[12]', 'x[13]', 'x[14]', 'x[15]', 'x[16]', 'x[17]', 'x[18]', 'x[19]', 'x[20]', 'x[21]', 'x[22]', 'x[23]', 'x[24]', 'x[25]', 'x[26]', 'x[27]', 'x[28]', 'x[29]', 'x[30]', 'x[31]', 'x[32]', 'x[33]', 'x[34]', 'x[35]', 'x[36]', 'x[37]', 'x[38]', 'x[39]', 'x[40]', 'x[41]']\n", + "\n", + "Update: \n", + "Output: at 0x13771f7e0>\n" ] } ], @@ -209,7 +222,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 5, "id": "9787db61", "metadata": {}, "outputs": [ @@ -217,10 +230,10 @@ "name": "stdout", "output_type": "stream", "text": [ - "Object: control\n", - "Inputs (14): xd[0], xd[1], xd[2], xd[3], xd[4], xd[5], ud[0], ud[1], xh0, xh1, xh2, xh3, xh4, xh5, \n", - "Outputs (2): F1, F2, \n", - "States (0): \n", + ": sys[2]\n", + "Inputs (14): ['xd[0]', 'xd[1]', 'xd[2]', 'xd[3]', 'xd[4]', 'xd[5]', 'ud[0]', 'ud[1]', 'xh0', 'xh1', 'xh2', 'xh3', 'xh4', 'xh5']\n", + "Outputs (2): ['F1', 'F2']\n", + "States (0): []\n", "\n", "A = []\n", "\n", @@ -228,20 +241,72 @@ "\n", "C = []\n", "\n", - "D = [[-3.16227766e+00 -1.31948924e-07 8.67680175e+00 -2.35855555e+00\n", - " -6.98881806e-08 1.91220852e+00 1.00000000e+00 0.00000000e+00\n", - " 3.16227766e+00 1.31948924e-07 -8.67680175e+00 2.35855555e+00\n", - " 6.98881806e-08 -1.91220852e+00]\n", - " [-1.31948923e-06 3.16227766e+00 -2.32324805e-07 -2.36396241e-06\n", - " 4.97998224e+00 7.90913288e-08 0.00000000e+00 1.00000000e+00\n", - " 1.31948923e-06 -3.16227766e+00 2.32324805e-07 2.36396241e-06\n", - " -4.97998224e+00 -7.90913288e-08]]\n", - " \n", + "D = [[-3.16227766e+00 -1.31948922e-07 8.67680175e+00 -2.35855555e+00\n", + " -6.98881821e-08 1.91220852e+00 1.00000000e+00 0.00000000e+00\n", + " 3.16227766e+00 1.31948922e-07 -8.67680175e+00 2.35855555e+00\n", + " 6.98881821e-08 -1.91220852e+00]\n", + " [-1.31948921e-06 3.16227766e+00 -2.32324826e-07 -2.36396240e-06\n", + " 4.97998224e+00 7.90913276e-08 0.00000000e+00 1.00000000e+00\n", + " 1.31948921e-06 -3.16227766e+00 2.32324826e-07 2.36396240e-06\n", + " -4.97998224e+00 -7.90913276e-08]] \n", + "\n", + ": sys[3]\n", + "Inputs (13): ['xd[0]', 'xd[1]', 'xd[2]', 'xd[3]', 'xd[4]', 'xd[5]', 'ud[0]', 'ud[1]', 'Dx', 'Dy', 'Nx', 'Ny', 'Nth']\n", + "Outputs (14): ['x0', 'x1', 'x2', 'x3', 'x4', 'x5', 'F1', 'F2', 'xh0', 'xh1', 'xh2', 'xh3', 'xh4', 'xh5']\n", + "States (48): ['pvtol_noisy_x0', 'pvtol_noisy_x1', 'pvtol_noisy_x2', 'pvtol_noisy_x3', 'pvtol_noisy_x4', 'pvtol_noisy_x5', 'sys[1]_x[0]', 'sys[1]_x[1]', 'sys[1]_x[2]', 'sys[1]_x[3]', 'sys[1]_x[4]', 'sys[1]_x[5]', 'sys[1]_x[6]', 'sys[1]_x[7]', 'sys[1]_x[8]', 'sys[1]_x[9]', 'sys[1]_x[10]', 'sys[1]_x[11]', 'sys[1]_x[12]', 'sys[1]_x[13]', 'sys[1]_x[14]', 'sys[1]_x[15]', 'sys[1]_x[16]', 'sys[1]_x[17]', 'sys[1]_x[18]', 'sys[1]_x[19]', 'sys[1]_x[20]', 'sys[1]_x[21]', 'sys[1]_x[22]', 'sys[1]_x[23]', 'sys[1]_x[24]', 'sys[1]_x[25]', 'sys[1]_x[26]', 'sys[1]_x[27]', 'sys[1]_x[28]', 'sys[1]_x[29]', 'sys[1]_x[30]', 'sys[1]_x[31]', 'sys[1]_x[32]', 'sys[1]_x[33]', 'sys[1]_x[34]', 'sys[1]_x[35]', 'sys[1]_x[36]', 'sys[1]_x[37]', 'sys[1]_x[38]', 'sys[1]_x[39]', 'sys[1]_x[40]', 'sys[1]_x[41]']\n", + "\n", + "Subsystems (3):\n", + " * ['x0', 'x1', 'x2', 'x3', 'x4', 'x5']>\n", + " * ['F1',\n", + " 'F2']>\n", + " * ['xh0', 'xh1',\n", + " 'xh2', 'xh3', 'xh4', 'xh5']>\n", + "\n", + "Connections:\n", + " * pvtol_noisy.F1 <- sys[2].F1\n", + " * pvtol_noisy.F2 <- sys[2].F2\n", + " * pvtol_noisy.Dx <- Dx\n", + " * pvtol_noisy.Dy <- Dy\n", + " * pvtol_noisy.Nx <- Nx\n", + " * pvtol_noisy.Ny <- Ny\n", + " * pvtol_noisy.Nth <- Nth\n", + " * sys[2].xd[0] <- xd[0]\n", + " * sys[2].xd[1] <- xd[1]\n", + " * sys[2].xd[2] <- xd[2]\n", + " * sys[2].xd[3] <- xd[3]\n", + " * sys[2].xd[4] <- xd[4]\n", + " * sys[2].xd[5] <- xd[5]\n", + " * sys[2].ud[0] <- ud[0]\n", + " * sys[2].ud[1] <- ud[1]\n", + " * sys[2].xh0 <- sys[1].xh0\n", + " * sys[2].xh1 <- sys[1].xh1\n", + " * sys[2].xh2 <- sys[1].xh2\n", + " * sys[2].xh3 <- sys[1].xh3\n", + " * sys[2].xh4 <- sys[1].xh4\n", + " * sys[2].xh5 <- sys[1].xh5\n", + " * sys[1].x0 <- pvtol_noisy.x0\n", + " * sys[1].x1 <- pvtol_noisy.x1\n", + " * sys[1].x2 <- pvtol_noisy.x2\n", + " * sys[1].F1 <- sys[2].F1\n", + " * sys[1].F2 <- sys[2].F2\n", "\n", - "Object: xh5\n", - "Inputs (13): xd[0], xd[1], xd[2], xd[3], xd[4], xd[5], ud[0], ud[1], Dx, Dy, Nx, Ny, Nth, \n", - "Outputs (14): x0, x1, x2, x3, x4, x5, F1, F2, xh0, xh1, xh2, xh3, xh4, xh5, \n", - "States (48): pvtol_noisy_x0, pvtol_noisy_x1, pvtol_noisy_x2, pvtol_noisy_x3, pvtol_noisy_x4, pvtol_noisy_x5, sys[3]_x[0], sys[3]_x[1], sys[3]_x[2], sys[3]_x[3], sys[3]_x[4], sys[3]_x[5], sys[3]_x[6], sys[3]_x[7], sys[3]_x[8], sys[3]_x[9], sys[3]_x[10], sys[3]_x[11], sys[3]_x[12], sys[3]_x[13], sys[3]_x[14], sys[3]_x[15], sys[3]_x[16], sys[3]_x[17], sys[3]_x[18], sys[3]_x[19], sys[3]_x[20], sys[3]_x[21], sys[3]_x[22], sys[3]_x[23], sys[3]_x[24], sys[3]_x[25], sys[3]_x[26], sys[3]_x[27], sys[3]_x[28], sys[3]_x[29], sys[3]_x[30], sys[3]_x[31], sys[3]_x[32], sys[3]_x[33], sys[3]_x[34], sys[3]_x[35], sys[3]_x[36], sys[3]_x[37], sys[3]_x[38], sys[3]_x[39], sys[3]_x[40], sys[3]_x[41], \n" + "Outputs:\n", + " * x0 <- pvtol_noisy.x0\n", + " * x1 <- pvtol_noisy.x1\n", + " * x2 <- pvtol_noisy.x2\n", + " * x3 <- pvtol_noisy.x3\n", + " * x4 <- pvtol_noisy.x4\n", + " * x5 <- pvtol_noisy.x5\n", + " * F1 <- sys[2].F1\n", + " * F2 <- sys[2].F2\n", + " * xh0 <- sys[1].xh0\n", + " * xh1 <- sys[1].xh1\n", + " * xh2 <- sys[1].xh2\n", + " * xh3 <- sys[1].xh3\n", + " * xh4 <- sys[1].xh4\n", + " * xh5 <- sys[1].xh5\n" ] } ], @@ -292,20 +357,18 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 6, "id": "c2583a0e", "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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", 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\n", 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", 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" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -368,30 +429,42 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 8, "id": "c5f24119", "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "<>:16: SyntaxWarning: invalid escape sequence '\\h'\n", + "<>:16: SyntaxWarning: invalid escape sequence '\\h'\n", + "<>:16: SyntaxWarning: invalid escape sequence '\\h'\n", + "<>:16: SyntaxWarning: invalid escape sequence '\\h'\n", + "/var/folders/3h/8vlrqzts6wnd_p5xvy01zclc0000gn/T/ipykernel_62492/1696903767.py:16: SyntaxWarning: invalid escape sequence '\\h'\n", + " [h1, h2, h3, h4], ['$x$', '$y$', '$\\hat{x}$', '$\\hat{y}$'],\n", + "/var/folders/3h/8vlrqzts6wnd_p5xvy01zclc0000gn/T/ipykernel_62492/1696903767.py:16: SyntaxWarning: invalid escape sequence '\\h'\n", + " [h1, h2, h3, h4], ['$x$', '$y$', '$\\hat{x}$', '$\\hat{y}$'],\n" + ] + }, { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 18, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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cx24p5SQp5Xop5Ubzr9xKFgUhRH1h+I6EEF1R5d97JMsQjZIIR3o6vPkmwGDOPfdZIJH334effy7fsmk0Gk1ZUxqL42EhxFvAr5jzoAJSyq/KqjBCiAlAHyBNCLEFeBhjIgsp5VhgCHC9EMIP5AIXyZKGPh0CN954Iy6Xi1dffbXI7UoiHGDOzwFC5AKLgB4sWACnnXY4pdRoNJojS2mE40qgDaoiN11VEigz4ZBSDi1m/WuocN0jwpIlS/B4ir9FpRWOWbPGAHcCWxGi4SGXT6PRaCqC0gjHcVLKDuVWkkpISYyZefPs4bZFYwpHu3YnGUv+xOU6/9AKp9FoNBVEafo4/hJCtCu3klRCSjKOo3Pn0gtH8+bHAwnAzBJbKxqNRlNZKI3FcSIwTAixHtXHURHhuEeckozjKCmmcCQkeFFjJv/QwqHRaKocpRGOgeVWikpK+/btiYmJKbPjmcKh5u04EXiMgoKDQEqZnUOj0WjKmxILx5EOva0MjBs3rkyP5xSOYUB/7r03gXvuKdPTaDQaTblSrKNECLGgLLap8mRlHfYhnMLRHOiJnktLo9FUNUpSa7UVQvxTxHoBpJZReSoVl112GQkJCcrySE6GwxwyYgpHbKy5ZAZqIP6NgEpN8lWZBTdrNBpN+VAS4WhTgm2q5Xx2a9euJSkpqcyO57Q4QKXaGkN+/jXExsayenWZnUqj0WjKjWKF47/Yt2FS1oPSzZyGlnCcCLzI3LnzqVevJ/v3K6OmDAO5NBqNpszRDvYiKOl8HCUlunDA5Ml/sHRpT3JywOeDMgzk0mg0mjJHjyIohrIUDrN/3es1l9QFWjFr1kxycqCgALKzy+x0Go1GUy6UWDiEEL8IIY4rz8JUNk444QQ6dixy1txS4fer/870Vz3YsGE12dlKOHJyyux0Go1GUy6IkvrxhRCdgOeAjcADUsrt5VmwsqRLly5y3rx5h3cQIQ47qkpKOHgQUlNhwwZo3hwgh5Ej4/n2W8GSJVC/PmyvMndWo9FUV4QQ86WUXaKtK7HFIaVcIKU8BfgemCKEeFgIEV9WhawW/PADBAJQp07U1UIo0QA1P8f48QAJ/PKL4MABtXzHjiNRUI1Gozl0StXHYUyitBIYA9wMrBZCXFYeBasMnH322Vx11VUl3+HMM2HPHvVXAlRneQ4LFgxj164vD6mMGo1Gc6QpTR/HH8BW4EWgEXAFatKlrkKI8eVRuIpm69at7Ny5s3QuqlJsqzaNp6DgW/LyppW6fBqNRlMRlCYc9zpgaZQZ924WQiwvwzJVKoQQyv0EsGoVtGxpxdVGIz+/8HVhqDspEOIYpCxqcL5Go9FUHkrTx/FvEdO0nllG5alUhC7X51P/W7eG2bOL3qkUwhEMmuc5HliMNbGiRqPRVF7KZByHlHJdWRynMiIyM2H+fGtBcaPzSm1xAHQCsoA1AHzwgWXkaDQaTWVDDwAsgr59+9Ld64U//rAW5ufD6tWQkRF9p5071f8S9HUEQwZGZ6AtsA+AYcNK3L+u0Wg0R5xDFg4hRAMhRGzxW1ZdnnvuOUbUqQO5udbCrCxo0waef965cUGB+n/aaep/CUwGS1uOA5YB3UPr9u491FJrNBpN+XI4FseHwAohxHNlVZjKSN7SpeRmZloLsrOVqeAJiyswB2KYmP0iRdCunYrgNenb17JSdu06lNJqNBpN+XPISQ6llP2McR3tyrA8lYq+3bqxYMkS+mRl8S1Ao0YE9u/HBQiPR43WCwTUcPDxYRHJPh/Ex8OaNWo4eJT07H36qD+VDmssf/89GlgLeLSrSqPRVFpKM47jJRGW8U8qlpZ9sSoHO5Yv5yDQrVYt8oEVMTF0uece1oPKVDh0KFx0EfzyC7z0knNn0+I4+mh4/XX1OS8Pliwp5Gw1yM7eBKiwXDOvlUaj0VQ2SuOqygImCSESAYQQ/YUQf5ZlYYQQ7wghdgkh/i1kvRBCvCKEWCOE+MfIn1VuZBjuqAGxscQBbdevZ1FGBqsB1q6F6dPVzIDxUTKv2F1V992n/k+aBMceG7GpywWXXXay8W0qYO8412g0mspFacZxjAQmANONUeR3AveVcXneAwYWsf504Gjjbzgq9Um5kRsMkhgXx0mzZjmWrwFC0/UlJ1sTbAwfbm0UrY+jkIGDS5fCY481oEuXLqhUYDocV6PRVF5K46o6FbgWyAbqALdIKWeWZWGklDMwY1KjczbwgeEi+wuoIYRoUJZlCEcIgRlTlXHGGQC8DFY4rt3i6GQzgKIJR3iHukGbNtCsGQwaNAiYDezhm29gXbUdHaPRaKoypXFVjQAelFL2AYYAnwkhTimXUhVOI2Cz7fsWY1kEQojhQoh5Qoh5u3fvPqSTde/enW7t27MCeBtIrVWL2sBqYLeZxjYmxrIkatWydr7pJti/3/o+bZpzPEgUzj33XOBewMdXX8Gnnx5SsTUajaZcKXFUlZFS3fy8RAhxOjAR6FkeBSuEaNPxRR1pJ6UcD4wHNR/HoZxsypQpsHAhdOpEa4CkJKYAYuhQdk2cyCTglMxMmps92WbOdIAff3SaDA8+CHaXV0aG6mBPTAwt6tChAzA69L0QA0Wj0WgqlEMex2FM5HRqGZalJGwBmti+Nwa2lesZrXleITmZLkCn9HRuLijgGqDDp5+SYbitcmNimG/f195REa4C/frB7bdHnO6KK3KBhRGn1mg0msrCYaUckVLmFr9VmTIJuNyIruoOHCjPmQhr1qxJrV69rAXJyQCIpCSmovLKZ/v9zH7iCQAuHDmSLsByYDewav16a99w4cjIiDpPbGrqo0A3IFdbHBqNplJSqXJVCSEmoHqHWwshtgghrhZCXCeEuM7Y5AdgHSqw6U3ghvIsTyAQIGiPizVNgORkvMBrgBuYsWEDe+68k+/+/JM4IA54A7ji8cetfc3IKxMpzZF/Dnr27AX4gLm89ZaV+kqj0WgqC5WqTSulHFrMegnceISKo9Kq2yt3l6GzRid4ItAXZV3MNNxSPwPNUR0vfy1dyj6gljpY5An++EMpQ716oUX9+pldRn/wzz+92bPHsVqj0WgqnBILh5HQ8Dwg3b6flPLRsi9W5cExWN78XLt2aNFU1LSIvT78kLS0NE4wcoWcBjwiJdNQIWiOEX3Ll1sd5x99BHfeGVpVq1YtGjduy5YtamylHs+h0WgqG6VxVX2LGkfhR43lMP/+O5jCYYuecgENgRFDhjBx4kTMdMFdgeSEBH42N7Rn2B0xwvqckBBxmnbtegGzgKAeQa7RaCodpXFVNZZSFjWqu9rRt29fvFLC92o0d8hVFTaZkxv4v0GDoHfv0DIv0LdDB6bOmYMEhL0j3G7FRElXcs45t/PTT8MAbXFoNJrKR2ksjllCiA7lVpJKyKRJk5j43XdW/4RZ4bvdkbGyZgjUBx+EFl3dty8vowafyKwsa9ultryQsbFg70QHei/5jXPZCbi0xaHRaCodpbE4TgSuEEKsB/Ix6kMpZWTWvmpCTk4Obreb2FjDAWVaHB6PmrjJbjmYQtKuHbRvD0uXctZxx8HgwcxKSeHsCRO4EZXgK3nlSmu/Sy5RwrRzJ7z6KgDNvn2ZC1hPI07HtWMsyhmm0Wg0lYPSWBxmgsH+wGBgkPG/2lKnTh0aNrRV2qZQmNaFPVLKXNa5sxo1DkpcgkHSmzRhTzDII0AKKpNjCPMY//xjnUYGeQQ/6/iOhKVzy+6CNBqNpgwoTXbcjUANlFgMBmoYy/4bzJ8Pl1+uPkfLcmvv9zCtj/x8CARoWK8eW2JjMXtAXgAKwve3TcAhggG6AnOBgDTEypx5UKPRaCqY0mTHvRX4GKhr/H0khLi5vApWWQiF43bqBDVqqM/hQ7oXLICuXa3v5vply2DKFIiPp1F+Pr8DGVdcwTzA2b2OoxdcyCDdgF3A5v3GVIB16sBnn5XFJWk0Gs1hURpX1dVANynlQ1LKh4DuqDTr1RYppXMch0m4cDRu7LRCzPVzDTeTLXIqtVs3YlC548fZj2ETDk/2QTobn1ds36A+5OaqKWqL4tZbYfToorfRaDSaw6Q0wiEAe3BogOjZaqs/4a6qwiKs9hlTi9hDbg2BeBm4DhhlX75kCeTk4MnKCE3kvmaXPYt8MUyfDnPmlHx7jUajOQRKE1X1LjBHCPE1SjD+B7xTHoWqLAwaNIhkI7Ghg3CLozjhSEmx1hnCcTMwGXgE2Ak8k59P8rHHhvZJAjYBfzQpMguLEyGipzbRaDSaMqQ0neMvAFcCe42/YVLKF8urYJWBzz//nLfffjtyRUktjr171f/+/eHvv9VnQzjSgBnARcBYoMWyZawF1aFu0AQY/7a35IMAo7nVNBqNpowp1uIQQvwhpTxRCJGJyt0nbOuklDKl8L2rNps2bcLj8ThDcsHZqu/QIVI4TGExI6WEgBNOUJ9tKpCAmsT9JtSsgi0BTjsttP4vYAXP4vF0t2aruvNOaNIEbrsteqG1xaHRaMqZYi0OKeWJxv9kKWWK8d/8q7aiAdC6dWs62ecRN7ELxT//RLb0ze+vvx65byCgJnGy0Qs1twe9e1Pw77+h5VuAHfwI2EaaT54Mf/0VvcDaVaXRaI4ApQnHfboky6oTMlolHAw6p4gtihuiTBfi98PPP8NbbzmXL13Khzt30gQwZjPn+NDKRdZ2gUD0cSSgXVUajeaIUJqoqtOiLDu9rApSWYkIxy1p5bxgQfTlpquqbVvncq+XLnFxZKLmwx2JmtfDQxI1+Mm5f2HCAdri0Gg05U5J+jiuR82010II8Y9tVTLwZ3kVrMrTsaP6/8wzzuWmcIT3eLvdtM3LYwJqwMwTwDIghVb4+du5vykcq1YpoWjdWn3XriqNRnMEKInF8QkqxcgkrHQjg4HOUspLy7FslYKoAwBLw913W59dLksw2rWDBg3sJ4K9ezkb2O5ycRcqHPdCViAI4APlJtu0yRKO6dNhxgznMezznGs0Gk05UKzFIaU8ABwASjGgoHpw/vnnU7du3bI7oMdjCUft2jBrFjRvbq3PVvNiuV1ungkGCQBucqjBCLyMgLw8tZ0pHNnZzrnMhVCzC2o0Gk05cijhuGCF5FbrcNwPP/ywbA/odjuSGZKebn2uWRN8PvVZCATqx9kN7K7zHXI3CHNfu3CED0a0s20bhIcSazQazWFyKOG4Kf+VcNz58+ezvCxb8BddpNKuhzNpkkqg6PdDYiKuvn1Cqz4C3tr9F33BEpYxY+CrryAnx1oGVse9adU0aqQz6mo0mjKnNOG45wshko3PI4UQXwkhOpZf0Sqenj17ctpp0YLJDpF33lHiURT336/Gf3g8TDl5NLcAaS43M4Ctu/Za22Vnw4YNTgvGFI6ffrISIprrH30UXnmljC5Eo9H8lylNOO6DUspMIcSJwADgfVS2jGpLodlxy5L+/aFlS+t7TAwcdZSyJIIBNZ95MIAEBrz8Ar7atdV2zz4LEyY4LQ6Tt9+G1avVZ1M4vv1WjR/RaDSaw6Q0wmHGj54JjJFSfkuUaSUOByHEQCHESiHEGiHEfVHW9xFCHBBCLDL+HirL81cIU6eqCCsT26h0YbiZzjGSJC4F3jTzX+XkqP92i8Nk/34w5zj3+SAzU4Xp2qesjcbp1X5YjkajKQNKIxxbhRDjgAuAH4QQsaXcv0iEEG7gddSgwnbAUCFEuyibzpRSHm/8PVpW5y+iXOV9Cif2dCaGcBzboQNxQG/gUnMyKbPvwhSOZ55RggEqw64pHH6/ys67cKFlhXz/ffRzT5lSVleh0WiqMaWp+C8ApgIDpZQZQC3g7iL3KB1dgTVSynVSygLgU+DsMjx+5WfAADjmGOu7IQ7eY47hGuBCICkQ5ADwmDlew3RV3XsvrFmjPodbHOEMrtZTxWs0mnKmNGnVc4C1wAAhxE1AXSnlT8XsVhoaAfZZi7YYy8LpIYRYLIT4UQjRvrCDCSGGCyHmCSHm7d69+5AKdOWVV3LttUdwksMpU+Dkk0NfhQwSwAUXXsirqOH7Qdx8AjwEzASnqyo2Vv23Wxy7djnP8dJL5VV6jUZziPz9d/HbVCYq05zj0XxC4fkzFgDNpJTHAa8C3xR2MCnleCllFylllzp16hxSgcaNG8eDDz54SPuWCcEgQVzQty8ABUBGwxb8yVjqAbcCOWZfB1gDBDMzLeE47jjnMW+/vbxLrdFoSkm3bhVdgtJRmeYc34Kau8ikMbDNvoGU8qCUMsv4/APgFUKklWEZHEydOpW55rzhFUCMO6CEA1iDIAH46uBegiTzOrAQuP/nn+GWW9QO9jxVpnBoNBpNGVOZ5hyfCxwthGguhIhBTY43yVEAIeoLo7daCNEVVf69EUcqIwYNGsQFF1xQXocvlhN7BomJUz/Rr2MLcAnByvxc/Hg4DzVn+etr1rDq1VchLUw/ixOOdesiM/RqNBpNCTjUOcdBzTkeZV7VQ0NK6Tf6TqYCbuAdKeVSIcR1xvqxwBDgeiGEH8gFLpJRJ82oHnhcQZUYEYiL89AiNollubmYs6A/glLOFFDpS/bssXYuTjhycmDFCti4EZo1s5YHrXNqNBpNNEosHFLKF4QQ04ETUZbGlVLKhWVZGMP99EPYsrG2z68Br5XlOYspz5EPx7Vjq8S9Xqidl8JSdtPLMPxSk2rxetY+AAo8HuegmszMoo9tCkt6utPF5ferUedjx8LIkWVzHRqNplpRbNNSCBEnhLhNCPEacALwhpTy5bIWjcpKhQuHkdDQ44HGpLCFAiSqQ/zmrNEAbD/9dI7dsIFn7ftmZBR97CFDoi/3+WDpUnjwQfj888Mrv0ajqZaUxCfxPtAFWIIanPdcuZaoklGhwuF2Q3w8APn5cCwNuIO6CFQIrtlxntSyJW1btuQebLOTf/tt0cfeutX53T6g0LxmPbdH9eS+++CJJ8rl0FUtOqgiWb1aTalTFSmJcLSTUl4qpRyH6mPoXc5lqjTcfPPN3HbbbRVXgFGjQlPQ5uRAAsfSj47EosZr+A1Po7vAw9innyYRGAjMO5RzmeNBfD6rj0PPYV49+fVX+LN8Ju+sauMRiiR89s4y5pVX4Nxzy/UU5UZJhCM09FhKGSUxUvXlxRdf5Kabbqq4AiQnh2YJzM2Fu3mWwbzNLlYAcJAUMl0pjB8vqdexIzNRUQUnAP/aEyeWBDMV+8GD1nwfWjiqJ1Lq37Yk3HtvuR6+Ks/0XBLhOE4IcdD4ywSONT8LIQ6WdwErko8//php06ZVdDEANc9TAA9+nuInXsYHTGEg/qALF0GIj6cjytp48P/+j4bNmvEBxujykmBaHMceq6Oqyovp0+Gssyq6FKq2+g//xuY42QgmTQrNwnkkcLmqsXBIKd1hEzh5bJ+r9UROl19+Oddff31FFwOAyy83P51MgDximE0uCQRxIbBakGkzZ/Lo2LHUiolhO8qveA/OAThRMS2O7OwKc1Xl5MDOnUf0lEeWf/+F776r6FKo/iwhiu8HqySU5VjWvLxQt2Ekn30GO3aoz0XU6B9+WDbp3lwu5zxrVUlE/rvNjiqGVYefZPyfAagOche2p69Fi9DHi4GewLNAG+Bxc0WHDlZeK4Arr3TmvHrWEZ91xHjqKWeG+WpHZakZTFfV//5Xqt0KCkq+bVk2AJKTi9+mRHzxBd4Bp1jfFy1SYmGSn2+9B0XMnLlkCfzyi21Bfn7kRsFgkcEl5i7BoHWqqjRZpxaOYqjQqKqo1EPJwO8A/EI/lmLL9WhzQTQB/gDeu/9+tgPPAAeBXV4vOfaH/ZdfnMJhpl2/uyyTHxdPQYHOlHJEKKKPo6jHPTY2MhivMOrXLz4i/Iji98PKlbhn/GYte/99GD7c+m4XDtMC79s3oka3j5H97jsgLg7++MN5vpkzoUULZs1yLp4zBxo3hlat4M031U8RfsqqgBaOKsnJuN0zSUoq4GImMIYbrFVmx7bRuhXAsEsu4SDwobHJG8uX0wp4omZNssxt7fOU5+YWfuoDB+CNN8r0akyqss+3RAUvy4t75hm44opS7bJ+PXz0rq9Q4ShJi7fQ/oEoHEqb66LzA9jzdpaK33+3phYwMS/qhhvU2KRw7L+JXTjM/9Onq2ce1KDa7793dBFtNvJ5r/4rLPOR8Q716uVc/OGHSnw3bVKNJPurF/X+X3ZZ1EutaLRwFEPlszgA7iYQWEhBgRorbmoFEL3T0+PBhZrcJAU4JTeXTGDk/v3KfRUIqBele3fVOV4U//wDN96o5kU/55yyuJgQlT7K5NlnCzeJSuL0LsuLW7w4spVbDN9/D5deFaPKEZ4tYMmSEglHSV4H89CBgHpMTjklykZff03ewuURiz/90kPmepU6p9TRsH/8ofxIdhISYNgwilWjV15Rz3a05r856+a778LgwQ6Lw5x3bepkP199ZTteIX695s2tzx5PMRbHP//ARx+pfsdK9mJo4SiCESNGMGLEiIouhoMbbwRoCbQMPZv27gqHcMTGqkSG9lkFgd7JyawCWiYlMRGQPp+K7d+2LTKq5NdfoxfkzTdhxoxSlf2uu+CnImZwqVTCMXYsrFrlXHbPPSo5ZDTMTtXwFm95cQgNmtBzEgw6/fK//w7HHlsiV0lRp7ULhvl/zRpVn5v1dmhqnIULObvTJkDNaHz88dZx0gZ0Bg4hGjYQiBSI/Hz44IPofiD7xdx6K+zcycLrxvHX/55ypNvZvVKl9TEFxC4cHiNpU36Wz5EqLuoEakBSkvU5NtZpcUQU0ZwSoVatorM4lLKvqizQwlEEjz76KJdeemlFF8PBoEGqEXX11WtQeSYXkZBge+jMJ1pK9TYuWxYhHCQkUA8YdeyxbASWHzgAV19t2c92+vWLXpDFi0vdm/fqq/Dbb85l+/ZZ5n6lEo41ayInwYLoBQwErIri6KPLt1zFlaUI4uJs+9l9Tsbv6L6u+FkSQnXtK69YnRjp6fDXXxGzGQcC6tA+H5hJpuvWNfb3+XAbsX579qjHycS9dVPEeV98MfrPEWL7dvUgFWZZlLADoeO8N+n+7f3q+gwuH7SXbdtgz9Y8iI11CIfp1RUBf+iW7lu2g/07o1scBQXW62gKR9T++Ouuc+5kusuiUQHRcVo4iuCll17i+8Lm564gvF71DiQkpALfARNJTbXVA9FcVeHCERMD11zDJUcdxRygnfHkzgJmlma2xFIKh9utPAnffYfyOUvJbbdBnz5qfWmFY9u24rc5ZIJBVQmFO/WjFbCgoGQVU1mq4uFYHFI6+7GM31EstqWfW7hQ+ZlsSARy125Vgd96q+UW2rgRdu2KcLnEP/cY9ff8C1jenhA24YgWHmsXod/pzR13qFHp4V62EJdcAm+9Vfg4DHvwRymJI49Ro2DShGxISHAIh/IAgCvgCz0qtdo34MWnowtHfr5ldcTEFGFxjBvn3DH8HTa2r6iGlhaOIrjjjju47777KroYDszY76SkOsDJwIckJ/utcWXRKnPTnrYf5M03EV4vHY1FY4BeQO/NmynxfMClfGrdbpXJfeFCYMwYyMoiELDqwNIKx/DhhUfulOZnGz8+Sr0fCMAdd0D//s7lUka2asOFoxA3Ran47DM46aSitynFzZo5E9atCVr7RREOs9K95Rbghx8gStaEgF9yXL3t6suWLVYZPJ4I4Uh9/iFabCnEnWkTjmiPrHkL9++H3sYwVp8PTjxRWa4RJCYCMP+PHOrWjezqKFQ4SjCK3ouPxERIKDgAqakEg8pK2rfPWeBFi5TRDuAOOEN0jzpK/S8osIQjNlZdewkigCOFY84cBvfP59FHiyx6uaGFowoSCJgd4rcBG9m160umTQOuv97ZfDNfiPCHznwrbb3qXmBI//40FoKhwMSSFKSUFofHowJTQu/w/v2O1ltpG9G5uZF19J9/qsanI87eYNu26AFj//d/0KRJ2MJgUIW/bNniWPz0UzJUSYXw+ZzCUVhUWgkr+txc2Pz72qI7v0upspdeChc83MYqh9lB5vNZQmiU+9VXYd326KPk1q+TbKeh+nLxxaFcani9od/CfisunKGa5Nu3q9RrIQoKQsJhFsXe7WIey77M74f588NCgs1xS8ZvMmViNrt3w5QpYQU/DIujWyc/iYkQ7z9Ibkwq2w3d3LDB2iaYX8CECTCgv/pN6vi2O46xdq11283HJybGKRxFGq3h73D37hz1z1e4/v7LufzgQWUBAvPmlVsuSy0cxeGqZKkZhIATTjCjMwYBR7Njxz3ATo766Q31NNrw+Yh86Mwn1WaJXAN88d57/OZ20xIYm5Bgbb95Mzz5ZGRhDsFVlZlpq+z37YsqHDt3quc/grw8RwhqXl6kcMyfD598EmYUGAdu3LjwSOLt28MWmM75sMp56b8lcFUdcjypYu1amDuvmOeuFCr7yiuqJdwysFotsNVWuzqcas1Db7uGRSudwnHaaer/sMvCajezc2Lt2pAbPloFuHEjPPKIbYHN4jCFwz79i98Pt/MC/v1qXhk3/pA+O6II16+Hl14KDeRLMKYciOgSsBVKIpR1VdJ76PfTsGADCeSweFVcqEvB/mjcse5m0tgdeiB7Z36PTErCgw9uu41EsggG1bWar5+p/YEANGMD/zw+iQXnPg7vvBNRhIArcuqkJsGNPPhDD8eyMc1GQ3o6W9bmk5VVukGbpaFy1YqaYhEC/voLrroK1M/3KcHgA0Aia9faNhw+nFUDblY6UpjF4fHA6aerqA2A1FSO8vv5E3jaiNSYAXz6yScqNDAcm3AMGKBM94cfLrzsHk+YlbB3L8GADFUExy3+gImcy4knqgCmCA4cUIO2DPLyIhuSZv0Qre6WMiwCrSjsHd7hBwknXDiiWRxt2pTsLd65k8C2nQRlKeJei2Hq1Cj7GTfOu3KJ1ay3XUN2IC70+dhjLQvOkaUArM6L668nPV19bFinQFXOBh6i3McowmFvLPh88AJ34lqnotRiycfvh6A/iNslyc2FT5oqf+SWX1aE9jOFI6JNE/6gRBvtXQjS5+eGZ5tzGr/gx3qXws8RQwGb1qlrrePbhsjK4nrGwN9/k0wmfr86rTnHmr0fZzDf0f/1s0ma9DGzHw43l+COu9RL0tuWmzzW5byvwSDsz1D3/X9HLSE/3xYQUcZo4SiGyjaOI7I4nejR4zogybn4vPNY0eli9Tm8j8MuHB6P9fIbbi4v0OnEEwEYB9z81FNkmCEt9gKY8ZZC4PtpGgcPwvLI0PwQZjGef95Y0K8fXTd/Geq3qbtrCQOYGhoYFUHYQtPiMDsoATyZ++nBrEIb/eHpK5RbX1KPHc4VZqu8sMp5zhz42phFuSQWx8qVJausJk0i8bfvCciyszgikDL0DOSQgIxiKuTlGtd93HEsWaJa/BBFOKIEU3hwVtKn8iuu8GxpNuFoOeZOQDoeU3++WmfW97Hk4/PBH/Si17yXyciAkzerIa0/TLbKlIjqp/nnn7CMH+HCUYTo+kXY+2LbV7oscyf8EHHkcZMxQalHqvv7CrfC7NnEkYffrx4VlwuOY1Fof78ftqOyYAcDkk1bIn/b/XvU/Zg5U7WfpBB4wwR5zx5rjp4YCuh8R++SN5RKiRaOInjmmWd4/PHHi9/wCBKtvvjttwLatHkDcDr2Q8+7fad+/eDll9Vnj8fyEz31lHM7wwy4G9h74ACPLlsWeWK/XyXuA1qzMvRiFEa4fgEk5Gewfr06nYsgQVyR7ohCMK0Xu/up9YIJzKJXiYVj927oyEJ2GC9uCDOWNKx2GLzHcCOMHQvmXC2mcJjbFhcSKoRzfng7OTkE8v34AiV4NUtocUQ8MzbhyCYRYU+cZODPMX5Iw9KMR1lR7nABiBIjG16hPcddNMUKsd2xA76baPVxtPzmBdwEnGMSM1RYuHnLzIr3OBaTmrWV22+3rItYLEE2l02eDI4hWOE3wThwIBB5D6Vw3nu39OH3qBo4ECUpYb4xaXMSWTyNsoJ8wllj24VDCFhER9aulUgEPh9kGQ0/F0GSiZz2uWayJV6bN4NPerh+z2OObS67zCkcactmauGoCO666y4Gl0UazDLE/vxv3qzedY/HzcaNzwCjGTgQ3n5brY/aH9ili+nncgpH+Ggro5Y/Hrh6wABe2rmTDz/8kAiM/V0EQ6Z4OHl5KpNJNDHIF3GhCEqXVMLh96vTm8UMYbypZuRKNFeV+eLk5DjrVfMc4eK1c6d6ySIwKtE9e6Tjms7fPcZRFsASDrOWK6xz3Ob6Wre2kEo/N5dAvp+C4oRDCAL+Q4zFlBJp3Lh8Yikw3C8H9lmiEMx1/pBmhRxucQSzrWt9k2uASOFoyDbqYFkmDRpAsMDnECE3Afx+mMmJ7DvtQgIHlHD4c9VvE0cezz9vCJfHzRefBahJBhBdOCDMQxsussZMl9FSqASF80H1SD/SrR4cgTpOPDk0u0TlE8n21gBgDsfzHfA68A3SIbFx5OHzOd+PFJRvrqDAEuRWrKYVYQNPgRbN/KG5t3bsAB9O9/Po0Wpwrfn8m/dEC0cFMHLkSL744ouKLkahNG6shMTtdtO06XBgGnPmbGT+fLU+qnBcaxvkZRcOO0lJjlr+lUsuoY/Hw7XXXsuqTZuc2xpKJpCFWhwxdVLYti26xZEn4kNi6JIBh3AsfHch/PhjaNv8bHVBzZoZ+0bpHDddPFIaL6lRYZgz04V33BY6qMwQjtwcyaZNYR27wMIFQUvFTeEwL952EzZvBv9fxpyMtsI++EAhITQ5OcSv+5erltzhXP7mm46Us/6gYGth41iCQXjssUJWAlKSnaHK4ibANiNKqhb7+ZGBAGxcHV04wt1QgRxru2t4GxeBCOGoxX7q4rzRXpRwmKGpHvz4/RDAjS+pBvKganXv3KiOH0ceq1cr4erx+9OOVrldOExXFUCCO0qYlsFrL6rviTJy3IcpDgCrgI25a0JWyE7q0YRNPMhj1F+rMhhmxtRmLdANOAu4CbjevxVzCGEQy+LIy1OvXD5Qj7WhotlF9CjW8hkXOMqUFOvnuuugG3/x7TMrycepCCMeCFKPHVzs/sxxH3QfRwXwxBNPMHr06IouRohHHlEZRKLRsOHZAOTm/hIa2+B4V37+Wf23pV1X/qEoj0Dz5pZwxMURn5nJJ34/T40eTdPQ0F8Dm8WR9vRdnLXecIPt2RPyf7uy1EtenHAEfaoV+b/8z/B4oA0rKJhvBeS3PtpZ2RYlHGB4jAylMAcL+v2q/jUzhBSWeipjr9pPIDm/1SJajnJmEFi2JFAi4WjaVJJxsZrTZfUyq7BJcWGq/u67PHzlJiUc22xpTfLyVFNy+HBHeK4MGhWc+bsCr72mXDQUFMBDD4WWfz85zE3j8yGDqnKMcQfYQf3Qql6oZm0s+SEXzFA+CQlHHFYTvUDEEMh1thQasi10DDumxeEiQABXSDhMS9BNIPRb+uOSCB5Qz8wTD+eHyqP2V4Juus7s68BpcTTOWcVyjBDksBbNqmWFh+cKqc6xAOgEPL/tNWYFg6wFbmEWB+lHfb5jOgCPkuWtyR3ASuAl4B9gZExrbgBmA0cBG3mA6dOnkZ2tAh8XAavpzEBg5cql7OAPw36CPGAD6Y4ySZ+f2nlb+YsevPpzG3JxRr158XFzw4m0CqhAAdOa0RaHhocegtq1o6+rUaMdUJ/8/B9DLnaHxRFtx8Isjjp1LOGoWRP27aM+cFv79sSFR2gZLfMaZJD29Vu0PTBbLR86VMX5G30gEN1VZTe5589Tx7rF9xxut/IZZ+7zha7F3to1s2ZECIftkc7NxWFiuN3q69Ch0LOnigCLZiFJaXW4CiTtWcqlfOzYxl6W4G13EPQHog5IiCUfT56qHX/5wVqenBBQneudO8Pq1XDVVfz73lzIycHvskKqg3Pnq5A1AJcrNP4w1B3Rvz98+ik7j+7F0zdv5t13Ye+1yu24Jv1U+vSKEtHk94da1R7hFA6zQo4lnzxUczWdDaHl9oo5350Q4dK6jZf4kvMjTmkKRwwFuJAh4YgxKv0aSQE2b1ahsnOWJoeEwxSqB3mMM5iM2xCOFA6yzeiXOgPLKrULW/ttP/Mwj/ANZ5OdUYAPq+US47J+vzyXsxLODBbQBOiMmjf7nBr96OaO4UlgNzvIYhNXsZS+QALfIN0e0hjBLGAIKpFot4KjWI+aD2c9sJ3pDB16Kvl/X05afDadgGQaMBW46qpjmMuzIRuqM/A07/ERhJ4y6fMTG7DuvfnbmKSxhxHbrAGbp6BmLo2NKZ+h5ZVKOIQQA4UQK4UQa4QQEWN/heIVY/0/QohO5V2myjaOozA8HgEMBbLw+ZwRKYX2oUYTjjFjoFs3p3CYUVcDBjD+2295BNgMvAPkG032u3kWT/YB+u/7DH79lR1rMgn4g2rSKINowlE7dzPpQWWym63JenIHLpcyt33ZqjLOznaa81OnRqZcgsItDlBBY+bX3bvVCPb8fNU5buePP5y+/AKcY2MALuTzkMXhWjif3KzoFkcKB3EFLD+9yQNTe6t8RAsWqMkZgFQOKOGwVXA7t9rUXwimT1cDyPLtgjd0KPXWzKIz89m7F2p/pJwkR22cxuJZ6vepwf7Q5tLlCpXFi4+d1Aut8xDASwExFIQqp800CQnGDKx40ALhxZ+XH3LsZAI5rMUHDEOlsNmLqjhrs5oWvMnDKJ9fTfbjJkC+cY7UpAC/qylm+PWfNORuFTyQQA7z6My5fM14rLkzGrGVTMIiHYBkMmmDCu1LydvFZpqQSzzL1mZzOyrYYwVQN89yuea6k5CA4eFlK7AFSEdZEWel9gKXmzZAPdoRIB830BbYzEI2y1wm8wmdgcZAa2AbXlKAB4E73B15C/C63fy860NOO/52coFzuZ+NQAqdSKA2KYAErgCCxHCZcbxBKOFw+y2RDuB8mWrjzOlS34gSdIvymR2q0tSKQgg3ql/pdKAdMFQIET4f3OnA0cbfcFSmDA1mpfwMMIX8fPVQmcLRpk0hOxnCYe/X5brr1Cw85kDCXbtUhjmALl1Ys3Uro4CmwNXAiffey36scEIA/viDHRvycP1uZTRs0SJibCIAdy2/hhdRA9DMyroJWyjI8ZNEFu+MVzmAsrKcrfxbblH/TVfTwYPKLJ/0vVM4CnLVheXnK+Ew70mf7Mkkr/+H//s/HPOZ5Gzaw0m9BRehfMUCGdERGcIWqeAmwMMPGK17m3Akk4kroJbbhaNWZmRCyTrshpwchM/aPzfLmd47a53qK/jiy8jwurrscqbBQFltACtpbS10u/EYIrwzthk7qUc+8AKqwo/nWZrVz2crXq4F5vAW93AnEtiKnxOB2kB9325qLPqTo1CV7WbgIb6lNvA5MMSbyDF0ZyDwCG+xjuFs42nmAS1Z5vg9ayQHyCOWzsxnJ/Wo89GLrAP8rOUp9vMWyhrJA3KAxmwhB9sgVYN6bGYp7fiUCzll7tPkE0smCTzPdl7Hz3PGNd7zS39WZ2RwHvBY0McgoAswFeiAmmp5JdAImJE5l5nADUCDFjlcA/xFIr8AccTzRs5GdrGPnvSiC3AekEciDYFHgbMTGnI1MPGi6+kPHN+yAT8B33ELtwCXkclgXmQJvRDArUAqKlnmTmAysP7gOly+bK4DLgIuYTtNcYVSBsVzkFtECtej5t15XiziH2DPjkMfMV8UlUY4gK7AGinlOillAfApagoJO2cDH0jFX0ANIUSD8AOVJZVtHEdhqHQOHs48E7KytrHLlnRu1SqgfXtHRzMQEo5LL1U5B0MMHw5nnqk+m6md77mHzYmtefyyy7gQOKVLFx4HOm7eTCpOFwYYfmebiZHu3sykxc2iln0n9ejJn1zLWwRQra78jFwSySSGAqZMiRQOM+2E6SOvUUPV10HbI/3NN1A3TVWQublKOMaNUxl6O8jFJGwPS4EuJQnN6kSUL5rFEcJndTJ/+mEBOcQ7XFXJZOLyR1ocgDPHNsrdsGN9Dnu25hvn9ZKXbROOa66h6/MX0INZ1GJfRCe0m0BEn43ZiVzXFtVkH4uwLvk4PsZHInAn8DNQg07s9e1kMDt5C1jNDAYzj3GcxHbgT2Afyo0igROBeFSfQC2U5ZEHbPdls5OraEBsqEfiVeAEIBk/q5jF8ajEOWt2XMJTFJBCJjuoy9kr/6Il8BUjmMg6ZqOsjJFAK6Aef7I/rIP4a5SbKBXYhOq4zieWN5nBZ+RyCQmsQuVkA1j59tt8BbwYyOBn4HQ6c79HWZ8uIAa429uAd/f9wOSAukeznnueN4EuZNMQ1bC4JqkJkim05mrmAh8DEss1/HbK7Xx9wpN0d8cyFRhy8CD9gT6ovo7XWY2fyxnPUACeADbxG4OBK4HRQCN3HZZmPME44DNgHllsJsgKYDM1OMAk3pW5jAUuB36Su7kf6Nm1fIQjSndlhdEI1Wgx2YIKVChum0ZAeMIIhBDDUVYJTZs2PaQCjR8/npycHEaNGsV5553H9OnT2bt3L8OHD2f8+PF06NCBpKQkZs+ezdChQ/n+++/Jz8/n4osv5r333qNzZzWvwPz587niiiv45JNPiI2NZdCgQUyYMIEePXqQlZXFkiVLQsesXbs2ffr0YeLEifTp04dt27axatWq0PoGDRrQpUsXvvvuO/r378+qVavYsGEDw4cPB8bTrFkdJk++hQsv7M3xxz8HzAO2s23PcMb/9Ret9u2jYcOGTJ8+nfN8PqavXMnMraNo2XI4o0aFXRPwfaNG5MfEcDFw/+9zubjPUVwCzO/alUvmzeMT4Fm3h4zAHhYCtwC5y5axhBUM98N4VOv0pdVNGQP04SHm0ZBRxo8zHljNvxzkFQYBP6FM801/PUomn7KGrQTPWci4cQ+xi08Md8I4cnK2A8N5773xQCukbAhMZx9xqBx4o5g/fzj5vMRE4L77ptJJvsbs7c8B3/MXv5K8NwEYxXfG7z1/xAiuAD4BYo1yjOcgO1jMRGCJrcy1gT4FBUwcOZI+wBb87OclVpPA669/TbusACed1IX9vMbf+dlsAH5hCd2M/dOBVhkZ/AQMNn6lP/mZ41Yl8unBzfQDUvEwadL73ARMR7l9zsnLIZk72Mxm3OxjAcpB+T2wiB8oKDidUSg/OcABXmBD2DV9fOAAvVCt2bt2f8Ju9lHDWHcPMIAvGbHvWzIMp5kfcCN4jVW8DwwAEgC3J5HpfuWvv8dVm7eDyl0yBJXC5jm68QtbmcNShnAU+UA2sAsVVbSJlWwDXgbI/JVRQHtgGR8SZ5Q3nVSa0oZNzOF9YA7KunmO8TwHNAMuQ02kvN3YfwNwP1vYCWzjDZLIoAbQiFjmkkMKsM245yOBBa402gf34HMfzUL/jzyEcmncSR12+3bQKqYhiDzmA/Om/MB223OQjt9o0ExhvzeOV33qd8pFMgplvfyQ72dzwXYabN3GB0D+Sy9xsbFuGPAlykpow9c8iYrCqssIOvEEXXCTT4D39v5CE9cguvAdzYzn7zXacjfLeZL2HOArsoyGRBrQiBS8HGT3rj85mrBEnWWBlLJS/AHnA2/Zvl8GvBq2zWTgRNv3X4HOxR27c+fO8r8ASDltmpQpKT1k586d5aOPmkmoC9lh3Dgpr7xS9ugh5WmnWYuzsmwHnDhRysaN1bYg5QknqP8rV8pMbw0pQe654zHZCyEBeaGV+Trqnx9kewbLl43vS0B6cElU41UKkA+BHNh2g7yWk2RbGktAnnLKUHkCc6QECVKezG/yKt6SIGUSB0OnuJAJoW1OOknKOuwMfZcgvV613WOMCO0/A+RUkMt//z2ivDtEPdmPn6Jfz1FHSbl+feh7Z+bKNbSQt/GCvPusFfLnMavlNPrIgrgkKUH+xsmF3pfMgefJ7zhTLkvtJv+mi3wm/iH5Jz0ittvespd8jRvkJhpHrJtHJ3l+g5mOZWfzdehzNsgbQV5OqtwMMgAy1VNLQpocDPJKkKtpLiXIW0D2IFVeA/LNQf8nf046S47jWrmSo+UBkqUEud7TUh4AuRbkHncd+SnId2znfpwHJEjpJb/Q694DMg5kCshpxveGbJE+kA2NZ8L866mCyeRdIBNAdrA9M/eDrB+2fRrIusyUjagpaxrPZxeQm8LK8Jv7VClBTur3sjyKVXKPsX88CbJdnYvk5y2HywUcr7Zftsz5u5Eo1zQ+WYKU/1frcylB3sQr8n6ekG+2flZKkGlpUt583lY5iUGF3oefQPbhjtB3CMqFHCczSJES5BsNH5MD+UFKkNuadZMS5BW8IyXIUTwkJ4FMd9eU3xr7z47prY61Z89h1CfMk4XUqZXJVbUFsOcobYxqGJR2m/80MTFQu/Zg5s+fz0MPbS16Y48HhIhItJqUZEUrSQnUqEHQa7gF5s4FYNZcL4N77QfAX6cR05DcgjKji8qs+wawlO+4FfgAOAZ4lGNpxkAeQbk5HgHIzaUf9XmAk2nLEKZNm8AO3kYCUxjAxXzC46iseJmkhI6/gT+5AbiSjqxe/ZWjk/tFwOc7HViGF58RKTSF3qhWdNuTT+ajsPKuxscufmYuhI+ZVtgSLMWRRxZJbGIxayd9wL6XPqAv0yNcVb8B7wPvAcuA7DrNOCuvEZcwlV4H5nEu83k57x1yyCED5VoZAbwG7PKpjmv7eAWTzizg1O3OQZrNWc8y1PiCRFQn4gcc4APgitRvadrmKWAP3wHvAl8Y9+tloBljOInLuLDz0bRsVEAOCXjwk2K4v/L8HlKAFkDtwG4uRLlWTMzOdX8Rjo3awN+omnowqrW8h7dwoZ6l44EUavAVKtRVoNwQOSgL8BqUBfIwqjO+J8qfPbFVK04H8tjNC/RjEi04XdRgHjDBOPcrQD9vXS4L/KWsrS1f8OOkfGqj+jquYyS9jhpB/USPNW6ibVt+twUIJJFNvX2qM/6q65RLcy+1ySaRffGNAOVmXbcjgcEUPrfPacAxtTrblgi+ZEjoHuZl+UNhxw1aJDju6ygeYQBenq59CWcBz3C3FUhyGFmBi6IyCcdc4GghRHMhRAyqD2hS2DaTgMuN6KruwAEpZYSb6r+MEg5zco7w2xeGMY5DiMiEbebz9vnnsHZfDdZs8PA094R6uC+81MNsI/I2q11XYoAnUR1VF6AqIZMFRhfejyhXSCNacCaE4kB6kEprbuMhVOUG4C7IpUWdLDp5l7CAL2nYsAuSLCTwHj/xLfN5m2zgAgYBsB/wscY1mTHAuyxix47zyOJ+44ivo4bUTQHa8zxvkpiyE6jDsShXR2MSeRQlEA8CNYCT5D7+4Vl6oUIz56Aqs4XArp1BmDGDA6jKfQ0PkUEiC5jBVzzJcxuVO+UYfwFjgE3spw1wCipy5kqUybxjt5tZs/NpSgLZBPAikRIyyCbVOO+TwM3ACZvm8SPzIoQjBy9/Ab+xnE9QLpjBQDrrSEe9XAAXovzqnwOTcx4hvfnlwDLmoPouGmP18UzmTCYwlBjhw1WQRx5xtGB9aH14ZA/AxlbWjJFmpVdc+7QDarxDZ6A+0KTFabhQfScLgLd7P8g5qL4RULPQPAq4+II5LKQByq3V3LiGb4Bz69blAyBIP1I4iJfaPOFtzQyU0AEsEi5+9e0ilwA/Alu2LCUuRjW2+gMFJCM9XuI8fgqI4UCrLlGvOylHBSy0PkYFUfzI6UxgaCh2Ii8Pdmc7O/LnXPZa6HO20cn/6htuR5bQAO7QPUzM2sk3nKNWGDnl7IIcg498tzrOT/S3EktWd+GQUvpRgy6nAsuBz6WUS4UQ1wkhzHkUfwDWAWuAN8EWDqMBVL2ekNAOFZj2cdEbG53jdovDzJZhPm+ffi5Ytq0G02Z4eJSHQhFDfjzk56sBXQfcKrtuIjAN1eJLR2XOWg98T4AslK88HXi+2zl8D0YslepgnGqMWDZ5Z+cZJIssEjwFxAH9+/9Nc67ABZwD/MACHiQTmMSvAMzBi2Ts0Pv5FhhGPHAKiZxrHHEPKuXjbOA+OtKSy0/3Mofr+RvoDbxENj2ADFSFlQyk46ILd/EDEIea7X0xamDYGZnr+PDmmxmIau3GE08eyVzCuXTkJJbk7+ZuY7+rgSVksBa4DmVtzQCuQlUQ+fmv8wqdWEl91gGXcwPt8CKMY68ElgKnJtTiN1bgpYAMlDjMANYRxxDgM2ZyCaqD9V8ER7OUBGA18fhQre2LOJ7FQK3EnsTFxQJt6YpqrddKtAaI5hOLHw8xwocMBknDyq+1LP30qMIR9MY59g8fcV8Y7bH6KU4/o2douQCkx1np1kEJe5AhLOH40PKW2IIdjNGm+cRyNW8zhC/xuWI5CdUHA/CmDPJk3ct5O/5MVgCvv76Mxn36gDHrpw8v194YQ0wgD4lgwVglv/bMv3bc8apRlUFNdlPXYe3uz3JG5hUY1zT743XWffR44LzzQtsEcIcsnQ7BRdbOhnCE339TOPx4aNHUeIGru3AASCl/kFK2klK2lFI+YSwbK6Uca3yWUsobjfUdpJTzKrbElQ81HaVAOUK+LHrjKMJhBgSFxoAgyKAGBUEPOSF7wBq41627i50ZVnRLonHW0Xg5DVXRPsw/HI9yS80AGrnd+F2FhLgCe2MaUDewgxh/Nt6gEqqcHEGM8cJeANzJuTzjTaBJkwOo3K4D+avWmfxv+hOcBTxFCvArCRwDgJsRhox2A0bzP7oQRz5dmRuKzTkP5UKqjYrQ2QTMd9eiKd0x29FpqE7VQai4/8uN7d4EunI5+6hFXQrozxn8IpuyHCVCMcDVxDOTYxiD6sA7ybhfqgJQlYAXayT+HgK8bZTnSVRT4OWcRC6gDfejRON7VOv4BDLZBpzHKYwB1gLfJnahJwtYATyBn53A/cBKFnMD0KLRfRGj+VfHWBHwBcTQqJkX8fBDpG+cAbbkf7mJafjx0IwNXCveCi0PxlrCkUecI3NxSbFN9w3A5Gkly5sRwM2jPMgGmoVyl/vwso1GbKEJGXnOKCw30DSmAbhjORqoU6e+iuk2IgpvvcND14G1iM9TMc7mkKcux0evjBNrOqPv7MKRle0UG2m8cP6YhJDrD7fbYfoHcYXes/YstXY+Rj3T4S5An9tyDaYm/IeEQ3P4xMSYz8oJKMN/I2BN1PHDD2r4wc6dhITD5bKEw3zOQn0chnDkB50VvfnQHnUU7NivXsh1NAOUz/oXfDRD4ALqkcx6lIvgRgBZgF942YZyaY1PXBNqK+4Ebgvm0Af4LG8v7/r34QPWfD4/5Lf14yadBtzkjSMlOSYULNsqfjPunVtD5QZr0KD533yZvfiIK7BNABF+H1GtXSGsMOAgypdeD+UEvBT1An2P8rXHkcca0ZAGbMWLj47spI1xHIz1biIrwaBNLMz7upbl9Gc116DExUz0OpVNjGcRzwB/oNxpzYE4XHQDmlGffsDTwNCCtfTiAMcAGQi+pSNPA7Vpwj0ASUkRwrHLZY9uFwRsKcZj3Oo+vMLNrDt6IAHcbKIZv7W42rqWGGsU9sNPxkVNM1MkSUnKxTPPahPWa1KyvBkNGnt4mEdJv+pUW9+TuvvDhkWGVQ9nHBu2uAm61bMdPkC13bEeSEkhLv+gY31qotXb9bkxSj4/H0Ss8/j2wXdjx9pW3HQTGClffIk1bDu4QwOqbrzRyNtlCMdRrGHB7R+ycl6mGmtFZKJD0zcWwG29wFo4NCXB67Wn4ZAow/wy8vPzOXgQ3ntPrdm1C4fFEQyqtEdm0j/787afmhQEnTWAXTi27ollMqrVeyfKV/0bKSz21CIXeISh/IMatTsFiAvk83rQTxOUq+aT7G30R4Vo/g58EshhER7uz9nIg4EMxgMX8HmoAs8hgSAuPL5c+nYzx49IdS1GwU3fsCkY5r7mdy8+YrMs10s461Gd0UHc+NjHycBgavMo8AS1ESjRW4oSxCuBL7mFh+QLvMwvJLGMBHLpxbtcCNwHfMsBPmJPaDTHRrAlG1fjFDbRlPHAZ3xMLTz8hvLNqoxH8C9wKQlMNj7vR/l1/6YFs4E2JODBCHP2H0AAp5LMhbSgHSlsAs5hhJL4xMSIyjJXOoVtyFCrckqIDbCBZtzKK+xo0TPkKlGzUSryalrC0+johKgZbezsT2rCq1ipMkLzqrazLJ/rbyuZcCQkGxcTHw/JyWSlNQutq1EjsqJ9k+EEKFw4SEoCIfD6c8gl3roW28vxtdHvEG3CNLOR8s03cNZZthWvvkr7dko4Ah7btbndjmMHcIfes93UZe/pl9K6c1LIVbWZJmzEGmqQEDQyCuOxjqOFQ1MSPB67cAhUdT2bnj3v5pprrJTkbrfaON/nYvt2ZXH07w/fGYMa7K6qz7iQVcmdHecxX8IWLWDuwv2cC/gI0A01EdQxeAgIN17UXAPtUePaNwDeggN8LoPURvni/+rYkVeBhxlNU2DwWUuYEH8CIxJb8rSICyWaMCv9hNrxSAReXy6vblceaw9+x5u/j1pcwkehhHtjYm51HMOLj9jM6MLhBy5BdUZPlj7WM40ZwA/spV27PjxstGI9QBvUoLMCoCUHqeWpxZ8c5H0+JQC4aM4m0nkauI48XmEDA1BRQZ+hxiH0YxUQx+msZTZ1uQw4j3P5kDbUogNxwAjU5NFjgQ/J4QxUv4BJ/WaqAkokmw2crI7f5lr+BTYxB4FKTtkEGP+Wm5ymrYlN9ERU7AVh80icfZ7VYBh8eiAkFu74mNBnM+/luLN/YMkFtqy8deo4jv9vnb4U4OWGltaUhGtanBYaAb6JJmqmInCkGXAlOMXsdl5wfF/dTtXKIeGIiwO/n0XP/hLaplEjp3CYeatM4WjANqdw7N4N5yhRqLFvHetoYa23VcYR85OgDIK6dS3hOPtstXz/W1a8Ye1aSjjs0Yy43Wqg7iefqMYcLuKSVJk//xy6dze2M4RjMceTzkYADpBCUlBZRn481rughUNTEuyNloEDQfUI3MKCBa+ybt2MkKhs2gR4PPwxy8XKldYDHP68SQTLaM8et5XPCCyLY/Xqt5g0qS4FwFn8nyMZ9P91VJ2JJ5xs9Y3UApLyDzDBncZa4AGgW0oKZ6JetO5A/fqtScyP4coajbhZuPAC+8lkH/PJAN5wQxZGbo1Vau4CLz6HcGSRxGju5x2UG+V0v+rwtAuH94BTOKbUG4YPNaBuNvA2cJorieM4jT+BntRm5MiPqB3KYwpMncq6toP4GJUV9Y12d9CJ1xiKGujmI5ZLBo/lV1QY7rW0oSkqhvwTxtIGSMMDNGQNkEk88cBxtCOJeLaiQjpnYXUYRyO5tqrwzRH88UBSDVWjywTnCHXcbpZ8voIEZ58zAPkybJS82Yr+919iPZZweBNjQs+AOTmWP7lmqIMYgNq1HZWxPyZeRSd5almHdwdDx9xEUzj66FAZTVzxlpj1b7wsVCE3b64yAQQ+VAG2Sak24cjMpFY9VfaaNaFpU6dwmKHRIx/2IN1edtDAKRxpaaFOjficfewhzRLBQEAJ2xdf4MdDXnwNtbxzZ/D7GTNGzcYcnieqZvMa1hfjhQsGUbn3e/dW15yYCENVRFYAN36hynz++bZJyMITjQIZ1CAxoHr7kmrHWdto4dCUBLuryvxfu/aTQAsWLz6PvDyVdvn00wG3OzTPgCkc5sth7+Mwv9szqgdRLq6Cgr1Ac54BkrB8FgLJrjhlRvc+zdmKjc/dj4vEiBR1LoJ8We9G4uOhTStJg8au0BSc3/ATf/IoLYFbd+3iQ3O2Q+PFmMoAcFuP88nMoAlbrPIa1+kmQDw5pHIAT4ZTOP4JZJGC6tx/AeVGa+DbxMOMpifwBMfQqlUjVht5hNTFxPPlsO/oa2QjrRd/kD0M4kGgAcqvfvKpXk5BpZg4k1Z8CLRAsJkhLAc+pQWwDgnkGKHLbgL48IbSihQ1FgIIuXjsqV9EirrDmTLJGQnkduP1qoar+bvf1FqlZ4+YstbspGjfHgJKOFasgGHXxtDrJFXTmhWaOz7G2adRq5bD4gjExBsuQ1WWLTTC45Yh4QhPFW5itzhWutqGMvuuW6ey47Rpr/aPSzME0hCOtAZWBRsb67yHQdxceSV449ykH+11XGo0HNloExKU72nIED7nAsY8aBsRYKhPMBhlmt1mluuMY47hS85T979xY+UWsxXgzDONPg5XmJCD6stY5ZzsaSWtCRjbzlzXyOpkX7268Is6DLRwVCN27FAtHbORYebaS0pKBKYQHz+QmBjr4d2+2xOqbM1tzVbXOmNKCLNDsaAAkpL+pr9wGa6jr/B4JLfffi+whrshFDr4CjcDtsS7tkkBAmedQ2xeBtnSVkkYk2MIJC8d9Rrx8VC3RgEJqTG4pbqYMzk1FNM16uijuQKVGPmcvXu5GtjOH+wKRroNAsAXwC+B7UxCubTeZxj9+RnPdktYfK4Yasc14hKUcNxuO0ZzNoQ+16kTFuESF0cgYE39mSoz2GvLU+TDizvWqhDWG+KaQ0KowrRPHLSdBuQSh4sgBcSEZieMJhx+ezim0dFgn5vi5NOVSZEl1Z2rWcNqHSQmQmqqJRz/1ldxY8OuCAs1DZtj3o+HZs0gJjkWT4o6fkg44ryq7vv5Z9iwARo2dLTig944vPhCIubHw/4OJ9O6rdooPFW42TFgtzj8/igVsnGS5DRjuzCL46yz1CNoWhxfH6vGSrzzjtq3ay8v99wDTZpQKD68ViLQ778PTb8scRHwRgY8SBmlnOnp1ueePTmfLy1XVWKiw8oaMADatHWFLI4Ijj7a8fVsvmVsOyMcLSVFWUVNmqgKoRzQwlGNqGd4k8zGxv/+p/6rqTiOplGjDxEiHtUtO4bf/jqIMGp3M4jFfJBXr4b6bOcXIxD14MFdbN16DjNkkE8AuAC3+x/D3aGOYZ+VTGDTC+McA/mRwK13EJe7n5ygTThWrlSbEaRBA2PWsoICiInBZVSqadTibXqxCsHD3brR0vCLZxUU8A5qYFuHtf8yEriMNylAhal6UM66S4EXaqbhJqDSl6NCB2YBZwB/BgvwxtbmLeDs+DDXjg31HgqSjYlyiItjxAh4+S21z9+njyKbxNAkSH1Oi3EIxyM8zPtczgFSI+Lwb+cFptOH2HgXLoI0ah5Dj86FC0d3/iK//2D1pW9fALoxhz6o/OTeBFXpzJ6vynJMB0MEPB5at4bnn7eeFcNtTp++YcJhnz/d7yeAW9VvsbHwpQr3Tk6GuXThuAH1VaRov36h1rXd4gjGxuMmGNKiTb+s5tgXr+SCwWrwUITFcc01qrhJVsUctUI2KtzUVON7XBzk5+OJV9f/3ntO4Zh8wijnvl4vTz+tGv6FEcRlCUfNmtCwYeEbAxdcALXP7A533OE8V9hI25BwJCRE9M4HcBMoTDiwMlq3bg2uhHiV3WHWLGtlz56q47Ic0MJRDQkGVfih+cympalGiOVquhu4gavGDuXtvJ1ga/GqcRw7WLduNTupH2odrls3Ab9/L5OpZXj4lxET0zBU4QBh01lKa9pKI6Z+KgMR7doSk5PBTE4k0MjZxOtzUpCBA41KzBAOO3Xx8uboICQn0ywth5xOvfg+Lo51qNDUM2SQV4C1zGYqauDd+bi4GhUy+9Qxx5HOBpaTzSDUmIxewC8ijgOAmTR22QhDGoc4X3IXwVDQj2mJvTvBiN5KVcLhS2sACPaQBsCr45zCUUAM8eRykJSQcJj3+CVuZzsNweXGRZDW7b14pdNVtYjjQsd68o2axNZOUq3PPn0AiLNZHKbro107ZdWELBujgrLXU/HxsMbTOrLZ3bGjZX4arqrQfsYPnJQEXZlL1zPr0LKlc3e7wSKN7bd7mzK94cX0PtWrKvv9KnVNhMUxeDAcc4zD4njlFaeFZp5kGn1JMTPPmA+e18uBA6GP+PDSjb9Cv2HoJkTpM4hG+LTDoeuSkcsefxz+90hHpc5hZY26b5jFAbAhsT3rEjtQGKYor1gBt94KJ58M9OhhFTbaBDhlhBaOaoiUzpZehw6q3856Zj8CfiG/4BQmLH0fNRpApZLYtWsb0J6XX26FshseBeDAgUbcfPP3HEUSe+sdC7QmJqYOHo/13pktwa85h6djH7YsjowMMg1XjisuBq8vl/U0x3/HvY5yCxkM+d4pKHC80GblGhcHxMfTtW0mCW+9Smx+Ps1RAvAFsBtoQQ8GAykJX/E+MdyV1oszgW7rVzKTbrzCUiajUoe8BVzR6APOBjxGZ2ZMsiq4y+18yT34EQImTrRG7T40WlVSwViloKbWhdxVMU7h8OMhhYMcIDWqFXHVVSA8Lu66zeiANXyIAdy4CHAPz1j3y2NUDFJGd9DbKo5kMm0+JWu5WXHFx0O/Rivg+OOdx3C5rHhbQzjCI7FCFXYxuI5pz+LEHmR40ni+ky2rgTGvb7/BUfo4vF7ciXF8bIz7T0iIYnEApzLNsjjM1ozXGypbQYFyDxYQw0knhVKu4XiAiyE8LY9JqceqRDtmly6Wy8BgZWpXZtc4vUTHefJJuOce24JA4PAKVgxaOKohwaCzYfPUUzBlivqsKooY4FTUMLY3UEmkf+WkkwJ8+eUjqNkWTgD6ohKd7wSGcMYZ/XiDG/itm5qc0XRvmdE5Zsds7IC+jIm9DY8HpiUMgtxcshLUS+GKUzVrDgl43GFNtWCQY46BTp1QLhJH01CRnk7Ih43XG6pY8wxrJxbwGpW6y2vEwRunER4PscDTnEIGKk/U1YBwq9rF7VIbhoQj7O0wO6rPPdcSDrOVnBmbBnv3RgqH19nHEcDNa9zE44wMHcPeSu/WDeU+DAbhrrvUj4cSHIkrZOl4KcDXKN3aMVrrMjY2pAxt6uxFpBllsl2YWXG5XGHTg5gzZdkJBCLca2CL9imGE56/iOOyZoU3umHLFkhNpWHzKCPEPR48ibFcxTuA8v13PyGyBt+7V0VOAdYDabNY8/PhIR5lEceTmqrqaeCwLY6tW+H660u0e1RCFseVVzr7QAz+STkxcn7kaPuH89NPoWenPNDCUQ2xWxwrVqiGh/kObXPkEhbA9agkGteQni7ZtOk0VJapv1GZp/4EY2rRevXgGe5ldrqacMYMEjFbeitow8F/N9GmjRIulwuuqfcdPPUUDdb+oZLxxhgRLMkJEeGKu2u2olMn5ZolOztCOOqkwQknoC4oO9vxwv/MaaHPZovUFetBIENiZUYNDOh6kFQsAsLDEz0n82fLywFLONLSbPfU4wlzkQhW0irUKZ6dDdSqFaoUGzx/t/oQE4MnzikckxnEZAaFRozH2Tx8LhdqVGWdOsrtYKS/MCts89r8eJVWmPliotUgtjC4hEAWogiLIyYmTDjCXSwAgQA9TnS2YocNs1XYhfDPP+q/+dvXr++8t7zxhpqy2LHQwOvFkxgbss7cbkhOirzWWrVsFXtqasT6zp3h2ZdimDBBWOMhzAOWQDg6dBC0aBG5vGFDyzN2KBRmxZgIwaFZDq1aQYPym+Ou/GwZTYVhtzha22YMNed0LoykJA9WCjiLM8+EyZNVn+CwYZG5hFq2BO56hd+GtCGhQaojtDcYRA3bxWgIGor20vgE2GG8Nf/3fzBuHPvPGmYdNDvbUZNJhOp4bYR6kVavtlwozz9Pp85nQR81VsOs4D0xbuLJ5aiOySr3tlGzJB8woqkmT4Ybb2SntzH767cO6VRMSiy0a8dzz4ExKxTC6yUonRVMG1aGPhuXGKq84s49Q02pF+aqMkNRJ02Cs86KTJYnBGoQXFhlIXExdSo8P8BqfXo8WD+03eJ46y3VsWx3feTmWjVcmHCkpqrZgW1ZPqJbMCkpEQJlZiLYsCFyc5PQtPPGNU2cGObq79nTaC1EwevFkxSnRHboUKvQUQhVwlGEo1491Q8QQQmF44orwBZtXib8+WekZ7CqoIWjGhIMRrpZSkK0AWEAI0eqOjYuLvq84T/+CMTeHJoBWmXotQmHgd2AiE9LhG3GyquvhnHjGD7cWk9BgWOHeHKtF9ysVM3CxMbSqJ1VWZitck+sGzdBSDVHqBlxyps3q/9nnAHr17Ohg5qO1O+HIILEpmmwdKkzs1GTJnStU3hnoxHUFBKOkNchzOKAItwLGL9bbGzUdaeeCg9Qm2DNWjRNtgX2SKlqxgMHVKUZDCqHtz0XiJmQ7I8/DF+gtWvdukoTTjnFdjIhQtcU4qOPCi23fYhCoRi/W6la6I88YliMAj5RQQtCRm+mF2VxFEpCQvEt+gsusKZQLkMK00qTop6Tika7qqoh4Z3jJvYX9thjYfRo5/r4KH2TYL1XhQlHeD338MOwYEFk9KHDFZKQYL0Zxx2nYv/tvPSSlasBqMl+6wDhL3ogYDX5sbmqYoztTBdNIKBiLnNyYPFiR1Hi4mDCBHATJKF1lID+unUd5/30U2vVSsvwiPSDu90RwhGNkSONshfxRrpcMJ8uZG3Yy8aN0LatscK8j2ZPcCAATz/t/EHNXDS9ejmWSxkR6IM5cfm0aWHLExOj9juViGOPPbTWzMknI1zOAubHJLOVyHDYUEVbGuG48EK49NKit/nsM6XaFUDEb1NJ0MJRDQkEoj9w4cIQLgKFWRxmQ78w4QgnKUn5sYVwVqSOOichQYV6XnqpOmi/fs6D3HprKH00zzzDsJPWFy4ceXkOd0PIVRXrtgoEyqQwK1fbhfz4o3KxG8MSIu/d+vVKAW3nvfBCa7V9jJUjw0NcHAhhCVhh3HILjxkpnqLWrTfc4CiX4/ITEqxJVEyiOc49nqjpJ6IKx6GKQ1EsXlxmteDsrrfSluURy888E5YvRwnHRReV7GBmZ5ymVOg7Vg3JzS1cBCA0Y6yZnTmEuU+3bs79zTrW61Wf7ZVmUZx3ngoWMXFYHPHxyjfy4YfFH6h9e1xLlxQuHGEV54MPWK6qxxkBpxkd536/ZXbZhKNWLaUnJ51UyPm9XrVv2HnNbgC7JWdaL/ZyJacar1n4DQeasEn18RhErVtff93x1VGMGjUgI8O5fbTwn9IIRyUn6PY6pgs2iYmBNm2MDxMmRO5YxbjqqtAYyEqHFo5qSG5u9EajacobgTohcXjhBef3Cy+06tVvvnHmqFJT05asHAMHqvhyE8NFrShsNFU04uPVRZnCYe+4jYmxhMMYSNawvjEeI8HDZ8c8bvVAFiIcJoVel5k5Mkw4zHrY7qobNiyUQcXCbNGOGRNx6C00ibppUTiKUbOmsrhMevZ09m2Y1KkTte+kKgpHZfb9lyWXXKKep8qI7hyvpjha9wZSKg/Q+vXO5Wa9mpCgKvvbb7dCwM8+2/milkY4wgkNSl60yGgalpCEBCNJkVGr2mu6sWMtl5bZz2EU+MFRbp7qDew33FiBQJHC4XarbNoRuFxRhcPEvtjtjuJit5W3uEqvOOE466ywbcJ/jD//jL7jjBlVWjhs/flVpszVGW1xVFOiWRz2MF37i2fPV2T2I9vrVSGcyw87b9pxx5XOrxwfrzp2zUrSXvteeaUxuMOGcUGJKYZlYp6rGOGAKEMJ7rpLCZKUkX0JqHkeiq3EkpIiy0hkWLO9qIXx7bdhC4YNgzVrom7roGbNqP7LqlIJz59vfe7RA2cEnuaIoy2Oakq0NBCFVRKmcMTGWvuFN07NmThNi+OIugtiY+GLL1REUEkwL8g0BWJjlbkzYIAadPLDDyXr5Qd49ln1/7PPQtFGdrZsiVgUSXw8/P13xOKbb47s9ih1JR4bS0SCqFJQVYTDzhlnqD9NxaGFoxqyfr1qCYdjhunaK/1TTlEx+FddZYzKNSwLW3SrgwsuKHSYQfkREwNDbAMTi1Ot8Fmp4uKsqQ+XLoX77y+5cJiEYl/LlvCxZ0c6wKcqCoem4tGuqmpIenr0wbDRXFW//qqyE7z9tqpfTcG4997I/UENOjvUPo5DJvxiihOOcIvDjrmsHDOHHioffRRKcnvE0MKhORS0xfEforhK4qSTVCguqKkqKwWzZhU790EE9sx94ZgiVAlry0suOfLnbNHi8HItaf6bVArhEELUAj4D0oENwAVSyv1RttsAZKImdvNLKbuEb6MpnMJGlJu43YWPHq8wzPkF7JTU4ohGOaaaropEy2Wo0RRHZXFV3Qf8KqVU+b3V98LoK6U8XotG6XnjDWVJlLRjO2poamVn1iyV+6owSphCu7IxZ075HFeISml8aSo5laX5dTbQx/j8PjAdKMTLrjlUrrpK/S+pXztalutKQVHKZ7dQol1kFbU4unat6BJoNBaVxeKoJ6XcDmD8r1vIdhL4SQgxXwhRZCS3EGK4EGKeEGLe7irZdC5f/rOtzCpqcWg0lYkj1vwSQvwC1I+yakQpDtNLSrlNCFEX+FkIsUJKOSPahlLK8cB4gC5duvxHkhSUjCqfsqGkFxBtFGQVtTg0msrEEXuLpJT9ClsnhNgphGggpdwuhGgA7CrkGNuM/7uEEF8DXYGowqEpmiptcaSmFj/RdWHioi0OjeawqSyuqkmAmc5rGBCeWAEhRKIQItn8DPQH/j1iJaxGdOsGJ55Y0aU4DC6/XE32fChoi0OjOWwqy1v0FPC5EOJqYBNwPoAQoiHwlpTyDNTE118L1VT2AJ9IKadUUHmrNA89VNElOExcruhZHEtCJRz4p9FUNSqFcEgp9wIRU2wZrqkzjM/rgLKfv1Hz3+Ozzyq6BBpNlaayuKo0miPHBRdUdAk0miqNFg6NRqPRlAotHBqNRqMpFVo4NBqNRlMqtHBoNBqNplRo4dBoNBpNqdDCodFoNJpSoYVDo9FoNKVCC4dGo9FoSoWQVT5VavEIIXYDGw9x9zRgTxkWpyqgr7n681+7XtDXXFqaSSnrRFvxnxCOw0EIMe+/Ntugvubqz3/tekFfc1miXVUajUajKRVaODQajUZTKrRwFM/4ii5ABaCvufrzX7te0NdcZug+Do1Go9GUCm1xaDQajaZUaOHQaDQaTanQwlEIQoiBQoiVQog1Qoj7Kro85Y0QookQ4jchxHIhxFIhxK0VXaYjhRDCLYRYKIT4vqLLciQQQtQQQnwphFhh/N49KrpM5Y0Q4nbjuf5XCDFBCBFX0WUqa4QQ7wghdgkh/rUtqyWE+FkIsdr4X7MszqWFIwpCCDfwOnA60A4YKoRoV7GlKnf8wJ1SyrZAd+DG/8A1m9wKLK/oQhxBXgamSCnboKZjrtbXLoRoBNwCdJFSHgO4gYsqtlTlwnvAwLBl9wG/SimPBn41vh82Wjii0xVYI6VcJ6UsAD4Fzq7gMpUrUsrtUsoFxudMVGXSqGJLVf4IIRoDZwJvVXRZjgRCiBSgN/A2gJSyQEqZUaGFOjJ4gHghhAdIALZVcHnKHCnlDGBf2OKzgfeNz+8D/yuLc2nhiE4jYLPt+xb+A5WoiRAiHegIzKngohwJXgLuAYIVXI4jRQtgN/Cu4Z57SwiRWNGFKk+klFuB54BNwHbggJTyp4ot1RGjnpRyO6jGIVC3LA6qhSM6Isqy/0TcshAiCZgI3CalPFjR5SlPhBCDgF1SyvkVXZYjiAfoBIyRUnYEsikj90VlxfDrnw00BxoCiUKISyu2VFUbLRzR2QI0sX1vTDU0bcMRQnhRovGxlPKrii7PEaAXcJYQYgPKHXmKEOKjii1SubMF2CKlNK3JL1FCUp3pB6yXUu6WUvqAr4CeFVymI8VOIUQDAOP/rrI4qBaO6MwFjhZCNBdCxKA60iZVcJnKFSGEQPm9l0spX6jo8hwJpJT3SykbSynTUb/xNClltW6JSil3AJuFEK2NRacCyyqwSEeCTUB3IUSC8ZyfSjUPCLAxCRhmfB4GfFsWB/WUxUGqG1JKvxDiJmAqKgLjHSnl0gouVnnTC7gMWCKEWGQse0BK+UPFFUlTTtwMfGw0itYBV1ZwecoVKeUcIcSXwAJU9OBCqmH6ESHEBKAPkCaE2AI8DDwFfC6EuBoloOeXybl0yhGNRqPRlAbtqtJoNBpNqdDCodFoNJpSoYVDo9FoNKVCC4dGo9FoSoUWDo1Go9GUCi0cGo1GoykVWjg0mkIQQtQWQiwy/nYIIbbavscIIWaV03kbCyEujLI8XQiRaxtnE23feKN8BUKItPIon0ajBwBqNIUgpdwLHA8ghBgFZEkpn7NtUl5pK05FpfP/LMq6tVLK4wvbUUqZCxxvpFHRaMoFbXFoNIeIECLLsAJWGFlm/xVCfCyE6CeE+NOYPKerbftLhRB/GxbBOGPel/Bjngi8AAwxtmtexPkThRCThRCLjXNHWCkaTXmghUOjOXyOQk2OdCzQBrgYOBG4C3gAQAjRFrgQ6GVYDAHgkvADSSn/QOVKO1tKebyUcn0R5x0IbJNSHmdMUDSlzK5IoykC7arSaA6f9VLKJQBCiKWoGdekEGIJkG5scyrQGZir8uwRT+GZSlsDK0tw3iXAc0KIp4HvpZQzD/0SNJqSo4VDozl88m2fg7bvQax3TADvSynvL+pAQojaqImGfMWdVEq5SgjRGTgDGC2E+ElK+WipS6/RlBLtqtJojgy/ovot6gIIIWoJIZpF2a45JZz7RQjREMiRUn6EmuGuus+roakkaItDozkCSCmXCSFGAj8JIVyAD7gR2Bi26QpUWux/geFSyqJCfjsAzwohgsbxri+Homs0Eei06hpNFcGYC/57oyO8uG03AF2klHvKu1ya/x7aVaXRVB0CQGpJBgACXlQfi0ZT5miLQ6PRaDSlQlscGo1GoykVWjg0Go1GUyq0cGg0Go2mVGjh0Gg0Gk2p0MKh0Wg0mlKhhUOj0Wg0pUILh0aj0WhKhRYOjUaj0ZSK/wenXwZvSP5iXQAAAABJRU5ErkJggg==\n", 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", 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" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -427,20 +500,26 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 9, "id": "3b6a1f1c", "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/murray/Library/CloudStorage/Dropbox/macosx/src/python-control/murrayrm/control/statefbk.py:788: UserWarning: cannot verify system output is system state\n", + " warnings.warn(\"cannot verify system output is system state\")\n" + ] + }, { "data": { - "image/png": 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\n", 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", "text/plain": [ - "
" + "
" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -503,7 +582,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.1" + "version": "3.13.1" } }, "nbformat": 4, From f566656099f4e2d64618da5c9b55d04948107432 Mon Sep 17 00:00:00 2001 From: Richard Murray Date: Sat, 25 Jan 2025 13:10:54 -0800 Subject: [PATCH 88/93] fix unit test issue (change in error message) --- control/mateqn.py | 20 ++++++++++---------- control/modelsimp.py | 6 +++--- control/robust.py | 2 +- control/statefbk.py | 6 +++--- control/statesp.py | 4 ++-- control/stochsys.py | 3 ++- control/sysnorm.py | 4 ++-- 7 files changed, 23 insertions(+), 22 deletions(-) diff --git a/control/mateqn.py b/control/mateqn.py index 8efa2531c..7f6580ac0 100644 --- a/control/mateqn.py +++ b/control/mateqn.py @@ -108,9 +108,9 @@ def lyap(A, Q, C=None, E=None, method=None): method = _slycot_or_scipy(method) if method == 'slycot': if sb03md is None: - raise ControlSlycot("Can't find Slycot module 'sb03md'") + raise ControlSlycot("Can't find slycot module 'sb03md'") if sb04md is None: - raise ControlSlycot("Can't find Slycot module 'sb04md'") + raise ControlSlycot("Can't find slycot module 'sb04md'") # Reshape input arrays A = np.array(A, ndmin=2) @@ -170,7 +170,7 @@ def lyap(A, Q, C=None, E=None, method=None): from slycot import sg03ad except ImportError: - raise ControlSlycot("Can't find Slycot module 'sg03ad'") + raise ControlSlycot("Can't find slycot module 'sg03ad'") # Solve the generalized Lyapunov equation by calling Slycot # function sg03ad @@ -236,11 +236,11 @@ def dlyap(A, Q, C=None, E=None, method=None): if method == 'slycot': # Make sure we have access to the right slycot routines if sb03md is None: - raise ControlSlycot("Can't find Slycot module 'sb03md'") + raise ControlSlycot("Can't find slycot module 'sb03md'") if sb04qd is None: - raise ControlSlycot("Can't find Slycot module 'sb04qd'") + raise ControlSlycot("Can't find slycot module 'sb04qd'") if sg03ad is None: - raise ControlSlycot("Can't find Slycot module 'sg03ad'") + raise ControlSlycot("Can't find slycot module 'sg03ad'") # Reshape input arrays A = np.array(A, ndmin=2) @@ -403,12 +403,12 @@ def care(A, B, Q, R=None, S=None, E=None, stabilizing=True, method=None, try: from slycot import sb02md except ImportError: - raise ControlSlycot("Can't find Slycot module 'sb02md'") + raise ControlSlycot("Can't find slycot module 'sb02md'") try: from slycot import sb02mt except ImportError: - raise ControlSlycot("Can't find Slycot module 'sb02mt'") + raise ControlSlycot("Can't find slycot module 'sb02mt'") # Solve the standard algebraic Riccati equation by calling Slycot # functions sb02mt and sb02md @@ -449,7 +449,7 @@ def care(A, B, Q, R=None, S=None, E=None, stabilizing=True, method=None, try: from slycot import sg02ad except ImportError: - raise ControlSlycot("Can't find Slycot module 'sg02ad'") + raise ControlSlycot("Can't find slycot module sg02ad") # Solve the generalized algebraic Riccati equation by calling the # Slycot function sg02ad @@ -568,7 +568,7 @@ def dare(A, B, Q, R, S=None, E=None, stabilizing=True, method=None, try: from slycot import sg02ad except ImportError: - raise ControlSlycot("Can't find Slycot module 'sg02ad'") + raise ControlSlycot("Can't find slycot module sg02ad") # Initialize optional matrices S = np.zeros((n, m)) if S is None else np.array(S, ndmin=2) diff --git a/control/modelsimp.py b/control/modelsimp.py index 307ab59bc..8ad3ee144 100644 --- a/control/modelsimp.py +++ b/control/modelsimp.py @@ -295,7 +295,7 @@ def balanced_reduction(sys, orders, method='truncate', alpha=None): ValueError If `method` is not 'truncate' or 'matchdc'. ImportError - If Slycot routine ab09ad, ab09md, or ab09nd is not found. + If slycot routine ab09ad, ab09md, or ab09nd is not found. ValueError If there are more unstable modes than any value in orders. @@ -314,12 +314,12 @@ def balanced_reduction(sys, orders, method='truncate', alpha=None): from slycot import ab09ad, ab09md except ImportError: raise ControlSlycot( - "can't find Slycot subroutine ab09md or ab09ad") + "can't find slycot subroutine ab09md or ab09ad") elif method == 'matchdc': try: from slycot import ab09nd except ImportError: - raise ControlSlycot("can't find Slycot subroutine ab09nd") + raise ControlSlycot("can't find slycot subroutine ab09nd") # Check for ss system object, need a utility for this? diff --git a/control/robust.py b/control/robust.py index 264611f16..197222390 100644 --- a/control/robust.py +++ b/control/robust.py @@ -34,7 +34,7 @@ def h2syn(P, nmeas, ncon): Raises ------ ImportError - If Slycot routine sb10hd is not loaded. + If slycot routine sb10hd is not loaded. See Also -------- diff --git a/control/statefbk.py b/control/statefbk.py index e4f0bac27..c88356a53 100644 --- a/control/statefbk.py +++ b/control/statefbk.py @@ -163,7 +163,7 @@ def place_varga(A, B, p, dtime=False, alpha=None): try: from slycot import sb01bd except ImportError: - raise ControlSlycot("can't find slycot module 'sb01bd'") + raise ControlSlycot("can't find slycot module sb01bd") # Convert the system inputs to NumPy arrays A_mat = _ssmatrix(A, square=True, name="A") @@ -1181,7 +1181,7 @@ def gram(sys, type): # Compute Gramian by the Slycot routine sb03md # make sure Slycot is installed if sb03md is None: - raise ControlSlycot("can't find slycot module 'sb03md'") + raise ControlSlycot("can't find slycot module sb03md") if type == 'c': tra = 'T' C = -sys.B @ sys.B.T @@ -1199,7 +1199,7 @@ def gram(sys, type): elif type == 'cf' or type == 'of': # Compute Cholesky factored Gramian from Slycot routine sb03od if sb03od is None: - raise ControlSlycot("can't find slycot module 'sb03od'") + raise ControlSlycot("can't find slycot module sb03od") tra = 'N' n = sys.nstates Q = np.zeros((n, n)) diff --git a/control/statesp.py b/control/statesp.py index 64134e8a8..f66bc3b04 100644 --- a/control/statesp.py +++ b/control/statesp.py @@ -1182,7 +1182,7 @@ def minreal(self, tol=0.0): return StateSpace(A[:nr, :nr], B[:nr, :self.ninputs], C[:self.noutputs, :nr], self.D, self.dt) except ImportError: - raise TypeError("minreal requires Slycot tb01pd") + raise TypeError("minreal requires slycot tb01pd") else: return StateSpace(self) @@ -2004,7 +2004,7 @@ def linfnorm(sys, tol=1e-10): """ if ab13dd is None: - raise ControlSlycot("Can't find slycot module 'ab13dd'") + raise ControlSlycot("Can't find slycot module ab13dd") a, b, c, d = ssdata(_convert_to_statespace(sys)) e = np.eye(a.shape[0]) diff --git a/control/stochsys.py b/control/stochsys.py index 95749f5b9..756d83e13 100644 --- a/control/stochsys.py +++ b/control/stochsys.py @@ -472,7 +472,8 @@ def create_estimator_iosystem( # Set the output matrices if C is not None: # Make sure we have full system output (allowing for numerical errors) - if not np.allclose(sys.C, np.eye(sys.nstates)): + if sys.C.shape[0] != sys.nstates or \ + not np.allclose(sys.C, np.eye(sys.nstates)): raise ValueError("System output must be full state") # Make sure that the output matches the size of RN diff --git a/control/sysnorm.py b/control/sysnorm.py index 603c16b81..fecdd7095 100644 --- a/control/sysnorm.py +++ b/control/sysnorm.py @@ -28,13 +28,13 @@ def _h2norm_slycot(sys, print_warning=True): try: from slycot import ab13bd except ImportError: - ct.ControlSlycot("Can't find slycot module `ab13bd`!") + ct.ControlSlycot("Can't find slycot module ab13bd") try: from slycot.exceptions import SlycotArithmeticError except ImportError: raise ct.ControlSlycot( - "Can't find slycot class `SlycotArithmeticError`!") + "Can't find slycot class SlycotArithmeticError") A, B, C, D = ct.ssdata(ct.ss(sys)) From 49e37401066148809a74afdbe7ee581d12ce224e Mon Sep 17 00:00:00 2001 From: Richard Murray Date: Sat, 25 Jan 2025 17:53:04 -0800 Subject: [PATCH 89/93] fix bug in predprey controller (was generating wrong steady state) --- doc/figures/iosys-predprey-closed.png | Bin 79692 -> 80113 bytes doc/iosys.rst | 12 ++++++++---- 2 files changed, 8 insertions(+), 4 deletions(-) diff --git a/doc/figures/iosys-predprey-closed.png b/doc/figures/iosys-predprey-closed.png index 5ec611ba8fe2ec3b2dc9c74c33e2af118302272f..09b159ba7eb4c23181e187f504754d424ccf81f0 100644 GIT binary patch literal 80113 zcmdSBWmJ{_`tJM8NjDOTbc3LjNGc^DpaLQSBGMu#-OZ#05fD^Rx-&q)*1SSN%1DYJ2!*5oqs@--$5GphDznC}jxi$y}LDZEMbv=`p zQapTb&K$ML^U2!8t*Bq^9JPCT=JE;UdLI>KJt4-D-q@n-*QKLtRfc?Mz7^nFE{vSg~IPfKh9r47(#Og)J1q1{pYqK8> z6q(^Mgeg9J_^>8{lbDBx$8&EXSEV+rAA& zM{R64*Zk?b$}b=gq?*;hlVCJix?=O~^+dGn%9_mI8n4YF&vo;oy{s!SlV4<4r;lGK zppR(cdjcMlLq|tvt-tPw9$DL+OS9-o6xDvKJ3m$@ua|Eyv9*2aV6AC>s9gA&1`jDI 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function: .. testcode:: predprey def output(t, x, u, params): - return -K @ (u[1:] - xeq) + kf * (u[0] - ueq) + Ld, x, ye = u[0], u[1:], xeq[1] + return ueq - K @ (x - xeq) + kf * (Ld - ye) controller = ct.nlsys( None, output, @@ -259,7 +260,7 @@ To connect the controller to the predatory-prey model, we use the ], inplist=['control.Ld'], inputs='Ld', outlist=['predprey.Hares', 'predprey.Lynxes', 'control.y[0]'], - outputs=['Hares', 'Lynxes', 'u0'], name='closed' + outputs=['Hares', 'Lynxes', 'u0'], name='closed loop' ) Finally, we simulate the closed loop system: @@ -267,8 +268,11 @@ Finally, we simulate the closed loop system: .. testcode:: predprey # Simulate the system - resp = ct.input_output_response(closed, timepts, 30, [15, 20]) - resp.plot(plot_inputs=False, overlay_signals=True, legend_loc='upper left') + Ld = 30 + resp = ct.input_output_response(closed, timepts, U=Ld, X0=[15, 20]) + cplt = resp.plot( + plot_inputs=False, overlay_signals=True, legend_loc='upper left') + cplt.axes[0, 0].axhline(Ld, linestyle='--', color='black') .. testcode:: predprey :hide: From 81742f0d91ea153c9e7b24308c74922a6113310b Mon Sep 17 00:00:00 2001 From: Richard Murray Date: Sat, 25 Jan 2025 22:46:23 -0800 Subject: [PATCH 90/93] improve class documentation in Reference Manual --- control/ctrlplot.py | 4 ++-- control/descfcn.py | 2 +- control/freqplot.py | 15 ++++++++++---- control/iosys.py | 13 +++++++----- control/lti.py | 10 +++++----- control/nichols.py | 4 ++-- control/phaseplot.py | 10 +++++----- control/pzmap.py | 2 +- control/rlocus.py | 2 +- control/tests/ctrlplot_test.py | 4 ++-- control/timeplot.py | 2 +- doc/_templates/custom-class-template.rst | 25 +++++++++++++++++++++--- doc/_templates/list-class-template.rst | 8 ++++++++ doc/classes.rst | 16 ++++++++++++--- doc/config.rst | 2 +- 15 files changed, 83 insertions(+), 36 deletions(-) create mode 100644 doc/_templates/list-class-template.rst diff --git a/control/ctrlplot.py b/control/ctrlplot.py index 9abfbe5c1..dbdb4e1ec 100644 --- a/control/ctrlplot.py +++ b/control/ctrlplot.py @@ -685,7 +685,7 @@ def _get_color_offset(ax, color_cycle=None): Parameters ---------- - ax : matplotlib.axes.Axes + ax : `matplotlib.axes.Axes` Axes containing already plotted lines. color_cycle : list of matplotlib color specs, optional Colors to use in plotting lines. Defaults to matplotlib rcParams @@ -727,7 +727,7 @@ def _get_color( Offset into the color cycle (for multi-trace plots). fmt : str, optional Format string passed to plotting command. - ax : matplotlib.axes.Axes, optional + ax : `matplotlib.axes.Axes`, optional Axes containing already plotted lines. lines : list of matplotlib.lines.Line2D, optional List of plotted lines. If not given, use ax.get_lines(). diff --git a/control/descfcn.py b/control/descfcn.py index 3c044b7e3..6c8cc9241 100644 --- a/control/descfcn.py +++ b/control/descfcn.py @@ -444,7 +444,7 @@ def describing_function_plot( point_label : str, optional Formatting string used to label intersection points on the Nyquist plot. Defaults to "%5.2g @ %-5.2g". Set to None to omit labels. - ax : matplotlib.axes.Axes, optional + ax : `matplotlib.axes.Axes`, optional The matplotlib axes to draw the figure on. If not specified and the current figure has a single axes, that axes is used. Otherwise, a new figure is created. diff --git a/control/freqplot.py b/control/freqplot.py index f1df3bf83..8d8f53cf7 100644 --- a/control/freqplot.py +++ b/control/freqplot.py @@ -66,8 +66,15 @@ # class FrequencyResponseList(list): + """List of FrequencyResponseData objects with plotting capability. + + This class consists of a list of `FrequencyResponseData` objects. + It is a subclass of the Python `list` class, with a `plot` method that + plots the individual `FrequencyResponseData` objects. + + """ def plot(self, *args, plot_type=None, **kwargs): - """List of FrequencyResponseData objects with plotting capability. + """Plot a list of frequency responses. See `FrequencyResponseData.plot` for details. @@ -149,7 +156,7 @@ def bode_plot( Other Parameters ---------------- - ax : array of matplotlib.axes.Axes, optional + ax : array of `matplotlib.axes.Axes`, optional The matplotlib axes to draw the figure on. If not specified, the axes for the current figure are used or, if there is no current figure with the correct number and shape of axes, a new figure is @@ -1617,7 +1624,7 @@ def nyquist_plot( 8 and can be set using `config.defaults['nyquist.arrow_size']`. arrow_style : matplotlib.patches.ArrowStyle, optional Define style used for Nyquist curve arrows (overrides `arrow_size`). - ax : matplotlib.axes.Axes, optional + ax : `matplotlib.axes.Axes`, optional The matplotlib axes to draw the figure on. If not specified and the current figure has a single axes, that axes is used. Otherwise, a new figure is created. @@ -2398,7 +2405,7 @@ def singular_values_plot( Other Parameters ---------------- - ax : matplotlib.axes.Axes, optional + ax : `matplotlib.axes.Axes`, optional The matplotlib axes to draw the figure on. If not specified and the current figure has a single axes, that axes is used. Otherwise, a new figure is created. diff --git a/control/iosys.py b/control/iosys.py index 1133fe9fb..40367595d 100644 --- a/control/iosys.py +++ b/control/iosys.py @@ -321,7 +321,7 @@ def repr_format(self): Format used in creating the representation for the system: - * 'info' : [outputs]> + * 'info' : [outputs]> * 'eval' : system specific, loadable representation * 'latex' : HTML/LaTeX representation of the object @@ -536,7 +536,8 @@ def find_inputs(self, name_list): # Property for getting and setting list of input signals input_labels = property( lambda self: list(self.input_index.keys()), # getter - set_inputs) # setter + set_inputs, # setter + doc="List of labels for the input signals.") def set_outputs(self, outputs, prefix='y'): """Set the number/names of the system outputs. @@ -597,7 +598,8 @@ def find_outputs(self, name_list): # Property for getting and setting list of output signals output_labels = property( lambda self: list(self.output_index.keys()), # getter - set_outputs) # setter + set_outputs, # setter + doc="List of labels for the output signals.") def set_states(self, states, prefix='x'): """Set the number/names of the system states. @@ -658,7 +660,8 @@ def find_states(self, name_list): # Property for getting and setting list of state signals state_labels = property( lambda self: list(self.state_index.keys()), # getter - set_states) # setter + set_states, # setter + doc="List of labels for the state signals.") @property def shape(self): @@ -941,7 +944,7 @@ def iosys_repr(sys, format=None): format : str Format to use in creating the representation: - * 'info' : [outputs]> + * 'info' : [outputs]> * 'eval' : system specific, loadable representation * 'latex' : HTML/LaTeX representation of the object diff --git a/control/lti.py b/control/lti.py index a4fde0d82..e4c9b2f4e 100644 --- a/control/lti.py +++ b/control/lti.py @@ -23,10 +23,10 @@ class LTI(InputOutputSystem): """Parent class for linear time-invariant system objects. - LTI is the parent to the `StateSpace` and `TransferFunction` child - classes. It contains the number of inputs and outputs, and the timebase - (dt) for the system. This class is not generally accessed directly by - the user. + LTI is the parent to the `FrequencyResponseData`, `StateSpace`, and + `TransferFunction` child classes. It contains the number of inputs and + outputs, and the timebase (dt) for the system. This class is not + generally accessed directly by the user. See Also -------- @@ -76,7 +76,7 @@ def __call__(self, x, squeeze=None, warn_infinite=True): Notes ----- - See `FrequencyResponseData`.__call__`, `StateSpace.__call__`, + See `FrequencyResponseData.__call__`, `StateSpace.__call__`, `TransferFunction.__call__` for class-specific details. """ diff --git a/control/nichols.py b/control/nichols.py index a88dbdfb8..3c4edcdbd 100644 --- a/control/nichols.py +++ b/control/nichols.py @@ -65,7 +65,7 @@ def nichols_plot( Other Parameters ---------------- - ax : matplotlib.axes.Axes, optional + ax : `matplotlib.axes.Axes`, optional The matplotlib axes to draw the figure on. If not specified and the current figure has a single axes, that axes is used. Otherwise, a new figure is created. @@ -190,7 +190,7 @@ def nichols_grid(cl_mags=None, cl_phases=None, line_style='dotted', ax=None, line_style : string, optional :doc:`Matplotlib linestyle \ `. - ax : matplotlib.axes.Axes, optional + ax : `matplotlib.axes.Axes`, optional Axes to add grid to. If None, use `matplotlib.pyplot.gca`. label_cl_phases : bool, optional If True, closed-loop phase lines will be labeled. diff --git a/control/phaseplot.py b/control/phaseplot.py index e9225b904..5f5016f57 100644 --- a/control/phaseplot.py +++ b/control/phaseplot.py @@ -89,7 +89,7 @@ def phase_plane_plot( Plot all elements in the given color (use ``plot_`` = {'color': c} to set the color in one element of the phase plot (equilpoints, separatrices, streamlines, etc). - ax : matplotlib.axes.Axes, optional + ax : `matplotlib.axes.Axes`, optional The matplotlib axes to draw the figure on. If not specified and the current figure has a single axes, that axes is used. Otherwise, a new figure is created. @@ -275,7 +275,7 @@ def vectorfield( dict with key 'args' and value given by a tuple (passed to callable). color : matplotlib color spec, optional Plot the vector field in the given color. - ax : matplotlib.axes.Axes + ax : `matplotlib.axes.Axes`, optional Use the given axes for the plot, otherwise use the current axes. Returns @@ -376,7 +376,7 @@ def streamlines( dict with key 'args' and value given by a tuple (passed to callable). color : str Plot the streamlines in the given color. - ax : matplotlib.axes.Axes + ax : `matplotlib.axes.Axes`, optional Use the given axes for the plot, otherwise use the current axes. Returns @@ -497,7 +497,7 @@ def equilpoints( dict with key 'args' and value given by a tuple (passed to callable). color : str Plot the equilibrium points in the given color. - ax : matplotlib.axes.Axes + ax : `matplotlib.axes.Axes`, optional Use the given axes for the plot, otherwise use the current axes. Returns @@ -589,7 +589,7 @@ def separatrices( separatrices. If a tuple is given, the first element is used as the color specification for stable separatrices and the second element for unstable separatrices. - ax : matplotlib.axes.Axes + ax : `matplotlib.axes.Axes`, optional Use the given axes for the plot, otherwise use the current axes. Returns diff --git a/control/pzmap.py b/control/pzmap.py index 8fd224535..42ba8e087 100644 --- a/control/pzmap.py +++ b/control/pzmap.py @@ -215,7 +215,7 @@ def pole_zero_plot( Other Parameters ---------------- - ax : matplotlib.axes.Axes, optional + ax : `matplotlib.axes.Axes`, optional The matplotlib axes to draw the figure on. If not specified and the current figure has a single axes, that axes is used. Otherwise, a new figure is created. diff --git a/control/rlocus.py b/control/rlocus.py index 355731ce8..c4ef8b40e 100644 --- a/control/rlocus.py +++ b/control/rlocus.py @@ -151,7 +151,7 @@ def root_locus_plot( Other Parameters ---------------- - ax : matplotlib.axes.Axes, optional + ax : `matplotlib.axes.Axes`, optional The matplotlib axes to draw the figure on. If not specified and the current figure has a single axes, that axes is used. Otherwise, a new figure is created. diff --git a/control/tests/ctrlplot_test.py b/control/tests/ctrlplot_test.py index 78ec7d895..b7192c844 100644 --- a/control/tests/ctrlplot_test.py +++ b/control/tests/ctrlplot_test.py @@ -247,10 +247,10 @@ def test_plot_ax_processing(resp_fcn, plot_fcn): # Make sure that docstring documents ax keyword if plot_fcn not in legacy_plot_fcns: if plot_fcn in multiaxes_plot_fcns: - assert "ax : array of matplotlib.axes.Axes, optional" \ + assert "ax : array of `matplotlib.axes.Axes`, optional" \ in plot_fcn.__doc__ else: - assert "ax : matplotlib.axes.Axes, optional" in plot_fcn.__doc__ + assert "ax : `matplotlib.axes.Axes`, optional" in plot_fcn.__doc__ @pytest.mark.parametrize("resp_fcn, plot_fcn", resp_plot_fcns) diff --git a/control/timeplot.py b/control/timeplot.py index eda6fd1ea..139bf9b48 100644 --- a/control/timeplot.py +++ b/control/timeplot.py @@ -109,7 +109,7 @@ def time_response_plot( add_initial_zero : bool Add an initial point of zero at the first time point for all inputs with type 'step'. Default is True. - ax : array of matplotlib.axes.Axes, optional + ax : array of `matplotlib.axes.Axes`, optional The matplotlib axes to draw the figure on. If not specified, the axes for the current figure are used or, if there is no current figure with the correct number and shape of axes, a new figure is diff --git a/doc/_templates/custom-class-template.rst b/doc/_templates/custom-class-template.rst index 2432f11d2..1f01e7e8f 100644 --- a/doc/_templates/custom-class-template.rst +++ b/doc/_templates/custom-class-template.rst @@ -5,17 +5,36 @@ .. autoclass:: {{ objname }} :members: :show-inheritance: - :inherited-members: list dict + :inherited-members: :special-members: + {% block attributes %} + {% if attributes %} + .. rubric:: {{ _('Attributes') }} + + .. autosummary:: + :nosignatures: + + {% for item in attributes %} + {%- if not item.startswith('_') %} + ~{{ name }}.{{ item }} + {%- endif -%} + {%- endfor %} + {% endif %} + {% endblock %} + {% block methods %} {% if methods %} .. rubric:: {{ _('Methods') }} .. autosummary:: :nosignatures: - {% for item in methods %} - {%- if not item.startswith('_') %} + + {% for item in members %} + {%- if not item.startswith('_') and item not in attributes %} + ~{{ name }}.{{ item }} + {%- endif -%} + {%- if item == '__call__' %} ~{{ name }}.{{ item }} {%- endif -%} {%- endfor %} diff --git a/doc/_templates/list-class-template.rst b/doc/_templates/list-class-template.rst new file mode 100644 index 000000000..3c85596b3 --- /dev/null +++ b/doc/_templates/list-class-template.rst @@ -0,0 +1,8 @@ +{{ fullname | escape | underline}} + +.. currentmodule:: {{ module }} + +.. autoclass:: {{ objname }} + :members: plot + :no-inherited-members: + :show-inheritance: diff --git a/doc/classes.rst b/doc/classes.rst index 82ca42c21..0ab508a3a 100644 --- a/doc/classes.rst +++ b/doc/classes.rst @@ -47,12 +47,22 @@ These classes are used as the outputs of `_response`, `_map`, and :nosignatures: ControlPlot - FrequencyResponseList + FrequencyResponseData NyquistResponseData - NyquistResponseList PoleZeroData - PoleZeroList TimeResponseData + +In addition, the following classes are used to store lists of +responses, which can then be plotted using the ``.plot()`` method: + +.. autosummary:: + :toctree: generated/ + :template: list-class-template.rst + :nosignatures: + + FrequencyResponseList + NyquistResponseList + PoleZeroList TimeResponseList More information on the functions used to create these classes can be diff --git a/doc/config.rst b/doc/config.rst index 5716da29a..9313fdded 100644 --- a/doc/config.rst +++ b/doc/config.rst @@ -166,7 +166,7 @@ System display parameters Set the default format used by :func:`iosys_repr` to create the representation of an :class:`InputOutputSystem`: - * 'info' : [outputs]> + * 'info' : [outputs]> * 'eval' : system specific, loadable representation * 'latex' : latex representation of the object From 74edc2418d3ad0e011dce0dae03246ee82af2bdf Mon Sep 17 00:00:00 2001 From: Richard Murray Date: Sun, 26 Jan 2025 14:48:03 -0800 Subject: [PATCH 91/93] add (user-oriented) release notes to Reference Manual --- doc/conf.py | 7 +- doc/develop.rst | 14 +-- doc/index.rst | 1 + doc/iosys.rst | 4 + doc/releases.rst | 64 +++++++++++ doc/releases/0.10.0-notes.rst | 184 ++++++++++++++++++++++++++++++ doc/releases/0.10.1-notes.rst | 200 +++++++++++++++++++++++++++++++++ doc/releases/0.3-7.x-notes.rst | 25 +++++ doc/releases/0.8.x-notes.rst | 32 ++++++ doc/releases/0.9.0-notes.rst | 87 ++++++++++++++ doc/releases/0.9.1-notes.rst | 51 +++++++++ doc/releases/0.9.2-notes.rst | 126 +++++++++++++++++++++ doc/releases/0.9.3-notes.rst | 129 +++++++++++++++++++++ doc/releases/0.9.4-notes.rst | 137 ++++++++++++++++++++++ doc/releases/template.rst | 82 ++++++++++++++ 15 files changed, 1133 insertions(+), 10 deletions(-) create mode 100644 doc/releases.rst create mode 100644 doc/releases/0.10.0-notes.rst create mode 100644 doc/releases/0.10.1-notes.rst create mode 100644 doc/releases/0.3-7.x-notes.rst create mode 100644 doc/releases/0.8.x-notes.rst create mode 100644 doc/releases/0.9.0-notes.rst create mode 100644 doc/releases/0.9.1-notes.rst create mode 100644 doc/releases/0.9.2-notes.rst create mode 100644 doc/releases/0.9.3-notes.rst create mode 100644 doc/releases/0.9.4-notes.rst create mode 100644 doc/releases/template.rst diff --git a/doc/conf.py b/doc/conf.py index 9a0d76c9a..f07ee3fa2 100644 --- a/doc/conf.py +++ b/doc/conf.py @@ -29,7 +29,7 @@ # -- Project information ----------------------------------------------------- project = u'Python Control Systems Library' -copyright = u'2024, python-control.org' +copyright = u'2025, python-control.org' author = u'Python Control Developers' # Version information - read from the source code @@ -92,8 +92,9 @@ # List of patterns, relative to source directory, that match files and # directories to ignore when looking for source files. # This pattern also affects html_static_path and html_extra_path . -exclude_patterns = [u'_build', 'Thumbs.db', '.DS_Store', - '*.ipynb_checkpoints'] +exclude_patterns = [ + u'_build', 'Thumbs.db', '.DS_Store', '*.ipynb_checkpoints', + 'releases/template.rst'] # The name of the Pygments (syntax highlighting) style to use. pygments_style = 'sphinx' diff --git a/doc/develop.rst b/doc/develop.rst index ace58c3a8..78cb1c995 100644 --- a/doc/develop.rst +++ b/doc/develop.rst @@ -239,13 +239,13 @@ Time and frequency responses: ioresp = ct.input_output_response(sys, timepts, U) cplt = ct.bode_plot(sys, omega) -* Use `inputs`, `outputs`, `states`, `time` for time response data - attributes. These should be used as parameter names when creating - `TimeResponseData` objects and also as attributes when retrieving - response data (with dimensions dependent on `squeeze` processing). - These are stored internally in non-squeezed form using `u`, `y`, - `x`, and `t`, but the internal data should generally not be accessed - directly. For example:: +* Use `inputs`, `outputs`, `states`, :code:`time` for time response + data attributes. These should be used as parameter names when + creating `TimeResponseData` objects and also as attributes when + retrieving response data (with dimensions dependent on `squeeze` + processing). These are stored internally in non-squeezed form using + `u`, `y`, `x`, and `t`, but the internal data should generally not + be accessed directly. For example:: plt.plot(ioresp.time, ioresp.outputs[0]) tresp = ct.TimeResponseData(time, outputs, states, ...) # (internal call) diff --git a/doc/index.rst b/doc/index.rst index ae6f7c33c..c21eeccd8 100644 --- a/doc/index.rst +++ b/doc/index.rst @@ -68,6 +68,7 @@ or the GitHub site: https://github.com/python-control/python-control. config matlab develop + releases *********** Development diff --git a/doc/iosys.rst b/doc/iosys.rst index a985e306f..cd2747fd5 100644 --- a/doc/iosys.rst +++ b/doc/iosys.rst @@ -668,6 +668,10 @@ closed loop systems `clsys`, both as I/O systems. The input to the controller is the vector of desired states :math:`x_\text{d}`, desired inputs :math:`u_\text{d}`, and system states :math:`x`. +If an `InputOutputSystem` is passed instead of the gain `K`, the error +e = x - xd is passed to the system and the output is used as the +feedback compensation term. + The above design pattern is referred to as the "trajectory generation" ('trajgen') pattern, since it assumes that the input to the controller is a feasible trajectory :math:`(x_\text{d}, u_\text{d})`. Alternatively, a diff --git a/doc/releases.rst b/doc/releases.rst new file mode 100644 index 000000000..88a76775a --- /dev/null +++ b/doc/releases.rst @@ -0,0 +1,64 @@ +************* +Release Notes +************* + +This chapter contains a listing of the major releases of the Python +Control Systems Library (python-control) along with a brief summary of +the significant changes in each release. + +The information listed here is primarily intended for users. More +detailed notes on each release, including links to individual pull +requests and issues, are available on the `python-control GitHub +release page +`_. + + +Version 0.10 +============ + +Version 0.10 of the python-control package introduced the +``_response/_plot`` pattern, described in more detail in +:ref:`response-chapter`, in which input/output system responses +generate an object representing the response that can then be used for +plotting (via the ``.plot()`` method) or other uses. Significant +changes were also made to input/output system functionality, including +the ability to index systems and signal using signal labels. + +.. toctree:: + :maxdepth: 1 + + releases/0.10.1-notes + releases/0.10.0-notes + + +Version 0.9 +=========== + +Version 0.9 of the python-control package included significant +upgrades the the `interconnect` functionality to allow automatic +signal interconnetion and the introduction of an :ref:`optimal control +module ` for optimal trajectory generation. In +addition, the default timebase for I/O systems was set to 0 in Version +0.9 (versus None in previous versions). + +.. toctree:: + :maxdepth: 1 + + releases/0.9.4-notes + releases/0.9.3-notes + releases/0.9.2-notes + releases/0.9.1-notes + releases/0.9.0-notes + + +Earlier Versions +================ + +Summary release notes are included for these collections of early +releases of the python-control package. + +.. toctree:: + :maxdepth: 1 + + releases/0.8.x-notes + releases/0.3-7.x-notes diff --git a/doc/releases/0.10.0-notes.rst b/doc/releases/0.10.0-notes.rst new file mode 100644 index 000000000..dc44a7ec2 --- /dev/null +++ b/doc/releases/0.10.0-notes.rst @@ -0,0 +1,184 @@ +.. currentmodule:: control + +.. _version-0.10.0: + +Version 0.10.0 Release Notes +---------------------------- + +* Released: 31 March 2024 +* `GitHub release page + `_ + +This release changes the interface for plotting to use a +``_response/_plot`` calling pattern, adds multivariable interconnect +functionality, restructures I/O system classes, and adds the `norm` +(now `system_norm`) function to compute input/output system norms. +Support for the NumPy `~numpy.matrix` class has been removed. + +This version of `python-control` requires Python 3.10 and higher. + + +New classes, functions, and methods +................................... + +The following new classes, functions, and methods have been added in +this release: + +* `time_response_plot`, `TimeResponseData.plot`: Plot simulation + results for time response functions. + +* `InterconnectedSystem.connection_table`: Print out a table of each + signal name, where it comes from (source), and where it goes + (destination), primarily intended for systems that have been + connected implicitly. + +* `nyquist_response`, `NyquistResponseData`: Compute the Nyquist curve + and store in an object that can be used to retrieve information + (e.g., `~NyquistResponseData.count`) or for plotting (via + the `~NyquistResponseData.plot` method). + +* `describing_function_response`, `DescribingFunctionResponse`: Compute + describing functions and store in a form that can be used for + analysis (e.g., `~DescribingFunctionResponse.intersections`) or plotting + (via `describing_function_plot` or the + `~DescribingFunctionResponse.plot` method). + +* `gangof4_response`, `gangof4_plot`: Compute the Gang of Four + response and store in a `FrequencyResponseData` object for plotting. + +* `singular_values_response`: Compute the Gang of Four response and store in a + `FrequencyResponseData` object for plotting. + +* `FrequencyResponseData.plot`: Plot a frequency response using a Bode, + Nichols, or singular values plot. + +* `pole_zero_map`, `PoleZeroData`: New "response" (map) functions for + pole/zero diagrams. The output of `pole_zero_map` can be plotted + using `pole_zero_plot` or the `~PoleZeroData.plot` method. + +* `root_locus_map`: New "response" (map) functions for root locus + diagrams. The output of `root_locus_map` can be plotted using + `root_locus_plot` or the `~PoleZeroData.plot` method. + +* `norm` (now `system_norm`): Compute H2 and H-infinity system norms. + +* `phase_plane_plot`: New implementation of phase + plane plots. See :ref:`phase-plane-plots` for more information. + + +Bug fixes +......... + +The following bugs have been fixed in this release: + +* `sample_system`: Fixed a bug in which the zero frequency (DC) gain + for the 'matched' transformation was being computed incorrectly. + +* `TimeResponseData.to_pandas`: Fixed a bug when the response did not + have state data. + + +Improvements +............ + +The following additional improvements and changes in functionality +were implemented in this release: + +* `interconnect`: Allows a variety of "multivariable" specifications + for connections, inputs, and outputs when systems have variables + with names of the form 'sig[i]'. + +* `nlsys`: Factory function for `NonlinearIOSystem`. + +* Block diagram functions (`series`, `parallel`, `feedback`, `append`, + `negate`) now work on all I/O system classes, including nonlinear + systems. + +* Simulation functions (`initial_response`, `step_response`, + `forced_response`) will now work for nonlinear functions (via an + internal call to `input_output_response`). + +* Bode and Nyquist plots have been significantly enhanced in terms of + functionality for display multiple tracing and other visual + properties. See `bode_plot` and `nyquist_plot` for details, along + with the :ref:`response-chapter` chapter. + +* Properties of frequecy plots can now be set using the + `config.defaults['freqplot.rcParams']` (see + :ref:`package-configuration-parameters` for details). + +* `create_statefbk_iosystem`: Allows passing an I/O system instead of + the a gain (or gain schedule) for the controller. + +* `root_locus_plot`: Interactive mode is now enabled, so clicking on a + location on the root locus curve will generate markers at the + locations on the loci corresponding to that gain and add a message + above the plot giving the frequency and damping ratio for the point + that was clicked. + +* `gram`: Computation of Gramians now supports discrete-time systems. + +* All time response functions now allow the `params` keyword to be + specified (for nonlinear I/O systems) and the parameter values used + for generating a time response are stored in the `TimeResponseData` + object.. + + +Deprecations +............ + +The following functions have been newly deprecated in this release and +generate a warning message when used: + +* `connect`: Use `interconnect`. + +* `ss2io`, `tf2io`: These functions are no longer required since the + `StateSpace` and `TransferFunction` classes are now subclasses of + `NonlinearIOSystem`. + +* `root_locus_plot`, `sisotool`: the `print_gain` keyword has been + replaced `interactive`. + +* In various plotting routines, the (already deprecated) `Plot` + keyword is now the (still deprecated) `plot` keyword. This can be + used to obtain legacy return values from ``_plot`` functions. + +* `phase_plot`: Use `phase_plane_plot` instead. + +The listed items are slated to be removed in future releases (usually +the next major or minor version update. + + +Removals +........ + +The following functions and capabilities have been removed in this release: + +* `use_numpy_matrix`: The `numpy.matrix` class is no longer supported. + +* `NamedIOSystem`: renamed to `InputOutputSystem` + +* `LinearIOSystem`: merged into the `StateSpace` class + +* `pole`: use `poles`. The `matlab.pole` function is still available. + +* `zero`: use `zeros`. The `matlab.zero` function is still available. + +* `timebaseEqual`: use `common_timebase`. + +* The `impulse_response` function no longer accepts the `X0` keyword. + +* The `initial_response` function no longer accepts the :code:`input` + keyword. + +* The deprecated default parameters 'bode.dB', 'bode.deg', + 'bode.grid', and 'bode.wrap_phase' have been removed. They should + be accessed as 'freqplot.dB', 'freqplot.deg', 'freqplot.grid', and + 'freqplot.wrap_phase'. + +* Recalculation of the root locus plot when zooming no longer works + (you can still zoom in and out, you just don't get a recalculated + curve). + +Code that makes use of the functionality listed above will have to be +rewritten to work with this release of the python-control package. diff --git a/doc/releases/0.10.1-notes.rst b/doc/releases/0.10.1-notes.rst new file mode 100644 index 000000000..d0545f7b7 --- /dev/null +++ b/doc/releases/0.10.1-notes.rst @@ -0,0 +1,200 @@ +.. currentmodule:: control + +.. _version-0.10.1: + +Version 0.10.1 Release Notes (current) +-------------------------------------- + +* Released: 17 Aug 2024 +* `GitHub release page + `_ + +This release provides a number of updates to the plotting functions to +make the interface more uniform between the various types of control +plots (including the use of the `ControlPlot` object as the return +type for all :code:`_plot` functions, adds slice access for state space +models, includes new tools for model identification from data, as well +as compatibility with NumPy 2.0. + +New functions +............. + +The following new functions have been added in this release: + +* `hankel_singular_values`: renamed `hsvd`, with a convenience alias + available for backwards compatibility. + +* `balanced_reduction`: renamed `balred`, with a convenience alias + available for backwards compatibility. + +* `model_reduction`: renamed `modred`, with a convenience alias + available for backwards compatibility. + +* `minimal_realization`: renamed `minreal`, with a convenience alias + available for backwards compatibility. + +* `eigensys_realization`: new system ID method, with a convenience + alias `era` available. + +* All plotting functions now return a `ControlPlot` object with lines, + axes, legend, etc available. Accessing this object as a list is + backward compatible with 10.0 format (with deparecation warning). + + +Bug fixes +......... + +The following bugs have been fixed in this release: + +* Fixed bug in `matlab.rlocus` where `kvect` was being used instead of + `gains`. Also allow `root_locus_plot` to process `kvects` as a + legacy keyword. + +* Fixed a bug in `nyquist_plot` where it generated an error if called + with a `FrequencyResponseData` object. + +* Fixed a bug in processing `indent_radius` keyword when + `nyquist_plot` is passed a system. + +* Fixed a bug in `root_locus_plot` that generated an error when you + clicked on a point outside the border window. + +* Fixed a bug in `interconnect` where specification of a list of + signals as the input was not handled properly (each signal in the + list was treated as a separate input rather than connecting a single + input to the list). + +* Fixed a bug in `impulse_response` where the `input` keyword was not + being handled properly. + +* Fixed bug in `step_info` in computing settling time for a constant + system. + + +Improvements +............ + +The following additional improvements and changes in functionality +were implemented in this release: + +* Added support for NumPy 2. + +* `frequency_response` now properly transfer labels from the system to + the response. + +* I/O systems with no inputs and no outputs are now allowed, mainly + for use by the `phase_plane_plot` function. + +* Improved error messages in `input_output_response` when the number + of states, inputs, or outputs are incompatible with the system size + by telling you which one didn't match. + +* `phase_plane_plot` now generate warnings when simulations fail for + individual initial conditions and drops individual traces (rather + than terminating). + +* Changed the way plot titles are created, using + `matplotlib.axes.set_title` (centers title over axes) instead of + `matplotlib.fig.suptitle` (centers over figure, which is good for + multi-axes plots but otherwise looks funny). + +* Updated arrow placement in `phase_plane_plot` so that very short + lines have zero or one arrows. + +* Subsystem indexing now allows slices as indexing arguments. + +* The `label` keyword is now allowed in frequency response commands to + override default label generation. + +* Restored functionality that allowed omega to be specified as a list + of 2 elements (indicating a range) in all frequency + response/plotting routines. This used to work for + `nyquist_response` but got removed at some point. It now works for + all frequency response commands. + +* Fixed up the `ax` keyword processing to allow arrays or lists + + uniform processing in all frequency plotting routines. + +* Fixed processing of `rcParam` to provide more uniformity. + +* Added new `ControlPlot.set_plot_title` method to set/add titles that are + better centered (on axes instead of figure). + +* Set up `frd` as factory function with keywords, including setting + the signal/system names. + +* Bode and Nyquist plots now allow FRD systems with different omega + vectors as well as mixtures of FRD and other LTI systems. + +* Added unit circle, sensitivity circles, and complementary + sensitivity cicles to `nyquist_plot`. + +* `time_response_plot` improvements: + + - Fixed up the `ax` keyword processing to allow arrays or lists + + uniform processing for all (time and frequency) plot routines. + + - Allow time responses for multiple systems with common time vector + and inputs to find a single time interval. + + - Updated sequential plotting so that different colors are used and + plot title is updated (like Bode and Nyquist). + + - Allow label keyword in various time response commands to override + default label generation. + + - Allow legends to be turned on and off using `show_legend` keyword. + +* `NonlinearIOSystem` improvements: + + - Allow system name to be overridden in `linearize`, even if + `copy_names` is `False`. + + - Allows renaming of system/signal names in bdalg functions + + - New `update_names` method for that allows signal and system names + to be updated. + + - `x0`, `u0` keywords in `linearize` and `input_output_response` + provide common functionality in allowing concatenation of lists + and zero padding ("vector element processing"). + + - Improved error messages when `x0` and `u0` don't match the expected size. + + - If no output function is given in `nlsys`, which provides full + state output, the output signal names are set to match the state + names. + +* `markov` now supports MIMO systems and accepts a `TimeResponseData` + object as input. + +* Processing of the `ax` and `title` keywords is now consistent across + all plotting functions. + +* Set up uniform processing of the `rcParams` keyword argument for + plotting functions (with unit tests). + +* Updated legend processing to be consistent across all plotting + functions, as described in the user documention. + +* Default configuration parameters for plotting are now in + `control.rcParams` and can be reset using `reset_rcParams`. + +* Unified `color` and `*fmt` argument processing code, in addition to + color management for sequential plotting. + + +Deprecations +............ + +The following functions have been newly deprecated in this release and +generate a warning message when used: + +* Assessing the output of a plotting function to a list is now + deprecated. Assign to a `ControlPlot` object and access lines and + other elements via attributes. + +* Deprecated the `relabel` keyword in `time_response_plot`. + +The listed items are slated to be removed in future releases (usually +the next major or minor version update. diff --git a/doc/releases/0.3-7.x-notes.rst b/doc/releases/0.3-7.x-notes.rst new file mode 100644 index 000000000..23b7d03b5 --- /dev/null +++ b/doc/releases/0.3-7.x-notes.rst @@ -0,0 +1,25 @@ +.. currentmodule:: control + +.. _version-0.3-7.x: + +Versions 0.3-0.7 Release Notes +------------------------------ + +* Released: 10 June 2010 - 23 Oct 2015 +* `Detailed release notes `_ + on python-control GitHub wiki. + +[ChatGPT summary] Between versions 0.3d and 0.7.0, the python-control +package underwent significant enhancements and refinements. Key +additions included support for discrete-time systems with a timebase +variable and the introduction of the c2d function for MIMO state-space +systems. New functionality such as rlocus, pade, and nichols was +added, along with minimal realization tools and model reduction +methods like hsvd, modred, and balred. Plotting capabilities were +expanded with more flexible Bode and Nyquist plots, frequency +labeling, and a phase_plot command for 2D nonlinear +systems. Performance improvements included faster versions of freqresp +and forced_response, bug fixes in tools like dare and tf2ss, and +enhanced stability margin and root-locus calculations. Installation +became easier via pip and conda, Python 3 compatibility improved, and +extensive documentation updates ensured a smoother user experience. diff --git a/doc/releases/0.8.x-notes.rst b/doc/releases/0.8.x-notes.rst new file mode 100644 index 000000000..9b6b89742 --- /dev/null +++ b/doc/releases/0.8.x-notes.rst @@ -0,0 +1,32 @@ +.. currentmodule:: control + +.. _version-0.8.x: + +Version 0.8.x Release Notes +---------------------------- + +* Released: 7 Jul 2018 - 28 Dec 2020 +* `Detailed release notes `_ + on python-control GitHub wiki. + +[ChatGPT summary] Between versions 0.8.0 and 0.8.4, the +python-control package introduced significant updates and +enhancements. Notable additions include improved support for nonlinear +systems with a new input/output systems module and functions for +linearization and differential flatness analysis, the ability to +create non-proper transfer functions, and support for dynamic +prewarping during continuous-to-discrete system +conversion. Visualization improvements were made across several +functions, such as enhanced options for Nyquist plots, better +pole-zero mapping compatibility with recent matplotlib updates, and +LaTeX formatting for Jupyter notebook outputs. Bugs were fixed in +critical areas like discrete-time simulations, forced response +computations, and naming conventions for interconnected systems. The +release also focused on expanded configurability with a new +`use_legacy_defaults` function and dict-based configuration handling, +updated unit testing (switching to pytest), and enhanced documentation +and examples, including for `sisotool` and trajectory +planning. Improvements to foundational algorithms, such as pole +placement, transfer function manipulation, and discrete root locus, +rounded out this series of releases, ensuring greater flexibility and +precision for control systems analysis. diff --git a/doc/releases/0.9.0-notes.rst b/doc/releases/0.9.0-notes.rst new file mode 100644 index 000000000..00f20f6df --- /dev/null +++ b/doc/releases/0.9.0-notes.rst @@ -0,0 +1,87 @@ +.. currentmodule:: control + +.. _version-0.9.0: + +Version 0.9.0 Release Notes +---------------------------- + +* Released: 21 Mar 2021 +* `GitHub release page + `_ + +Version 0.9.0 of the Python Control Toolbox (python-control) contains +a number of enhanced features and changes to functions. Some of these +changes may require modifications to existing user code and, in +addition, some default settings have changed that may affect the +appearance of plots or operation of certain functions. + +Significant new additions including improvements in the I/O systems +modules that allow automatic interconnection of signals having the +same name (via the `interconnect` function), generation and plotting +of describing functions for closed loop systems with static +nonlinearities, and a new :ref:`optimal control module +` that allows basic computation of optimal controls +(including model predictive controllers). Some of the changes that may +break use code include the deprecation of the NumPy `~numpy.matrix` +type (2D NumPy arrays are used instead), changes in the return value +for Nyquist plots (now returns number of encirclements rather than the +frequency response), switching the default timebase of systems to be 0 +rather than None (no timebase), and changes in the processing of +return values for time and frequency responses (to make them more +consistent). In many cases, the earlier behavior can be restored by +calling ``use_legacy_defaults('0.8.4')``. + +New features +............ + +* Optimal control module, including rudimentary MPC control +* Describing functions plots +* MIMO impulse and step response +* I/O system improvements: + + - `linearize` retains signal names plus new `interconnect` function + - Add summing junction + implicit signal interconnection + +* Implementation of initial_phase, wrap_phase keywords for bode_plot +* Added IPython LaTeX representation method for StateSpace objects +* New `~StateSpace.dynamics` and `~StateSpace.output` methods in `StateSpace` +* `FRD` systems can now be created from a discrete time LTI system +* Cost and constraints are now allowed for `flatsys.point_to_point` + + +Interface changes +................. + +* Switch default state space matrix type to 'array' (instead of 'matrix') +* Use `~LTI.__call__` instead of `~LTI.evalfr` in LTI system classes +* Default dt is now 0 instead of None +* Change default value of `StateSpace.remove_useless_states` to False +* Standardize time response return values, `return_x`/`squeeze` + keyword processing +* Standardize `squeeze` processing in frequency response functions +* Nyquist plot now returns number of encirclements +* Switch `LTI` class and subclasses to use ninputs, noutputs, nstates +* Use standard time series convention for `markov` input data +* TransferFunction array priority plus system type conversion checking +* Generate error for `tf2ss` of non-proper transfer function +* Updated return values for frequency response evaluated at poles + + +Improvements, bug fixes +....................... + +* Nyquist plot improvements: better arrows, handle poles on imaginary axis +* Sisotool small visual cleanup, new feature to show step response of + different input-output than loop +* Add `bdschur` and fox modal form with repeated eigenvalues +* Fix rlocus timeout due to inefficient _default_wn calculation +* Fix `stability_margins`: finding z for ``|H(z)| = 1`` computed the wrong + polynomials +* Freqplot improvements +* Fix rlocus plotting problem in Jupyter notebooks +* Handle empty pole vector for timevector calculation +* Fix `lqe` docstring and input array type +* Updated `markov` to add tranpose keyword + default warning +* Fix impulse size for discrete-time impulse response +* Extend `returnScipySignalLTI` to handle discrete-time systems +* Bug fixes and extensions for `step_info` diff --git a/doc/releases/0.9.1-notes.rst b/doc/releases/0.9.1-notes.rst new file mode 100644 index 000000000..d0ef8b733 --- /dev/null +++ b/doc/releases/0.9.1-notes.rst @@ -0,0 +1,51 @@ +.. currentmodule:: control + +.. _version-0.9.1: + +Version 0.9.1 Release Notes +---------------------------- + +* Released: 31 Dec 2021 +* `GitHub release page + `_ + +This is a minor release that includes new functionality for discrete +time systems (`dlqr`, `dlqe`, `drss`), flat systems (optimization and +constraints), a new time response data class, and many individual +improvements and bug fixes. + +New features +............ + +* Add optimization to flat systems trajectory generation +* Return a discrete time system with `drss` +* A first implementation of the singular value plot +* Include InfValue into settling min/max calculation for `step_info` +* New time response data class +* Check for unused subsystem signals in `InterconnectedSystem` +* New PID design function built on `sisotool` +* Modify discrete-time contour for Nyquist plots to indent around poles +* Additional I/O system type conversions +* Remove Python 2.7 support and leverage @ operator +* Discrete time LQR and LQE + +Improvements, bug fixes +....................... + +* Change `step_info` undershoot percentage calculation +* IPython LaTeX output only generated for small systems +* Fix warnings generated by `sisotool` +* Discrete time LaTeX repr of `StateSpace` systems +* Updated rlocus.py to remove warning by `sisotool` with `rlocus_grid` = True +* Refine automatic contour determination in Nyquist plot +* Fix `damp` method for discrete time systems with a negative real-valued pole +* Plot Nyquist frequency correctly in Bode plot in Hz +* Return frequency response for 0 and 1-state systems directly +* Fixed prewarp not working in `c2d` and `sample_system`, margin docstring + improvements +* Improved lqe calling functionality +* Vectorize `FRD` feedback function +* BUG: extrapolation in ufun throwing errors +* Allow use of SciPy for LQR, LQE +* Improve `forced_response` and its documentation +* Add documentation about use of axis('equal') in `pzmap`, `rlocus` diff --git a/doc/releases/0.9.2-notes.rst b/doc/releases/0.9.2-notes.rst new file mode 100644 index 000000000..2adec3fb1 --- /dev/null +++ b/doc/releases/0.9.2-notes.rst @@ -0,0 +1,126 @@ +.. currentmodule:: control + +.. _version-0.9.2: + +Version 0.9.2 Release Notes +---------------------------- + +* Released: 28 May 2022 +* `GitHub release page + `_ + +This is a minor release that includes I/O system enhancements, optimal +control enhancements, new functionality for stochastic systems, +updated system class functionality, bug fixes and improvements to +Nyquist plots and Nichols charts, and L-infinity norm for linear +systems. + +New features +............ + +* I/O system enhancements: + + - Modify the `ss`, `rss`, and `drss` functions to return + `LinearIOSystem` objects (instead of `StateSpace` objects). + This makes it easier to create LTI state space systems that can + be combined with other I/O systems without having to add a + conversation step. Since `LinearIOSystem` objects are also + `StateSpace` objects, no functionality is lost. (This change is + implemented through the introduction of a internal + `NamedIOSystem` class, to avoid import cycles.) + + - Added a new function `create_statefbk_iosystem` that creates an + I/O system for implementing a linear state feedback controller + of the form u = ud - Kp(x - xd). The function returns an I/O + system that takes xd, ud, and x as inputs and generates u as an + output. The `integral_action` keyword can be used to define a + set of outputs y = C x for which integral feedback is also + included: u = ud - Kp(x - xd) - Ki(C x - C xd). + + - The `lqr` and `dlqr` commands now accept an `integral_action` + keyword that allows outputs to be specified for implementing + integral action. The resulting gain matrix has the form K = + [Kp, Ki]. (This is useful for combining with the + `integral_action` functionality in `create_statefbk_iosystem`). + +* Optimal control enhancements: + + - Allow `t_eval` keyword in `input_output_response` to allow a + different set of time points to be used for the input vector and + the computed output. + + - The final cost is now saved in optimal control result. + +* Stochastic systems additions: + + - Added two new functions supporting random signals: + `white_noise`, which creates a white noise vector in continuous + or discrete time, and `correlation`, which calculates the + correlation function (or [cross-] correlation matrix), R(tau). + + - Added a new function `create_estimator_iosystem` that matches + the style of `create_statefbk_iosystem` (#710) and creates an + I/O system implementing an estimator (including covariance + update). + + - Added the ability to specify initial conditions for + `input_output_response` as a list of values, so that for + estimators that keep track of covariance you can set the initial + conditions as `[X0, P0]`. In addition, if you specify a fewer + number of initial conditions than the number of states, the + remaining states will be initialized to zero (with a warning if + the last initial condition is not zero). This allows the + initial conditions to be given as `[X0, 0]`. + + - Added the ability to specify inputs for `input_output_response` + as a list of variables. Each element in the list will be + treated as a portion of the input and broadcast (if necessary) + to match the time vector. This allows input for a system with + noise as `[U, V]` and inputs for a system with zero noise as + `[U, np.zero(n)]` (where U is an input signal and `np.zero(n)` + gets broadcast to match the time vector). + + - Added new Jupyter notebooks demonstrate the use of these + functions: `stochresp.ipynb`, `pvtol-outputfbk.ipynb`, + `kincar-fusion.ipynb`. + +* Updated system class functionality: + + - Changed the `LTI` class to use `poles` and `zeros` for + retrieving poles and zeros, with `pole` and `zero` generating a + `PendingDeprecationWarning` (which is ignored by default in + Python). (The MATLAB compatibility module still uses `pole` and + `zero`.) + + - The `TimeResponseData` and `FrequencyResponseData` objects now + implement a `to_pandas` method that creates a simple pandas + dataframe. + + - The `FrequencyResponseData` class is now used as the output for + frequency response produced by `freqresp` and a new function + `frequency_response` has been defined, to be consistent with the + `input_output_response` function. A `FrequencyResponseData` + object can be assigned to a tuple to provide magnitude, phase, + and frequency arrays, mirroring `TimeResponseData` functionality. + + - The `drss`, `rss`, `ss2tf`, `tf2ss`, `tf2io`, and `ss2io` + functions now all accept system and signal name arguments (via + `_process_namedio_keywords`. + + - The `ss` function can now accept function names as arguments, in + which case it creates a `NonlinearIOSystem` (I'm not sure how + useful this is, but `ss` is a sort of wrapper function that + calls the appropriate class constructor, so it was easy enough + to implement.) + +* Added `linform` to compute linear system L-infinity norm. + + +Improvements, bug fixes +....................... + +* Round to nearest integer decade for default omega vector. +* Interpret str-type args to `interconnect` as non-sequence. +* Fixes to various optimization-based control functions. +* Bug fix and improvements to Nyquist plots. +* Improvements to Nichols chart plotting. diff --git a/doc/releases/0.9.3-notes.rst b/doc/releases/0.9.3-notes.rst new file mode 100644 index 000000000..d8f956a81 --- /dev/null +++ b/doc/releases/0.9.3-notes.rst @@ -0,0 +1,129 @@ +.. currentmodule:: control + +.. _version-0.9.3: + +Version 0.9.3 Release Notes +---------------------------- + +* Released: date of release +* `GitHub release page + `_ + +This release adds support for collocation in finding optimal +trajectories, adds the ability to compute optimal trajectories for +flat systems, adds support for passivity indices and passivity tests +for discrete time systems, and includes support for gain scheduling +(in `create_statefbk_iosystem`. Setup is now done using setuptools +(`pip install .` instead of `python setup.py install`). + +This release requires Python 3.8 or higher. + + +New classes, functions, and methods +................................... + +The following new classes, functions, and methods have been added in +this release: + +* `ispassive`: check to see if an LTI system is passive (requires + `cvxopt`). + +* `get_output_fb_index`, `get_input_ff_index`: compute passivity indices. + +* `flatsys.BSplineFamily`: new family of basis functions for flat + systems. + +* `flatsys.solve_flat_ocp`: allows solution of optimal control + problems for differentially flat systems with trajectory and + terminal costs and constraints, mirroring the functionality of + `optimal.solve_ocp`. + +* `zpk`: create a transfer funtion from a zero, pole, gain + representation. + +* `find_eqpts` (now `find_operating_system`) now works for + discrete-time systems. + +Bug fixes +......... + +The following bugs have been fixed in this release: + +* Fixed `timebase` bug in `InterconnectedSystem` that gave errors for + discrete-time systems. + +* Fixed incorect dimension check in `matlab.lsim` for discrete-time + systems. + +* Fixed a bug in the computation of derivatives for the Bezier family + of basis functions with rescaled final time, and implemented a final + time rescaling for the polynomial family of basis functions. + +* Fixed bug in the processing of the `params` keyword for systems + without states. + +* Fixed a problem that was identified in PR #785, where + interconnecting a LinearIOSystem with a StateSpace system via the + interconnect function did not work correctly. + +* Fixed an issued regarding the way that `StateSpace._isstatic` was + defining a static system. New version requires nstates == 0. + +* Fixed a bug in which system and system name were not being handled + correctly when a `TransferFunction` system was combined with other + linear systems using interconnect. + +* Fixed a bug in `find_eqpt` where when y0 is None, dy in the root + function could not be calculated (since it tries to subtract + None). + + +Improvements +............ + +The following additional improvements and changes in functionality +were implemented in this release: + +* Handle `t_eval` for static systems in `input_output_response`. + +* Added support for discrete-time passive systems. + +* Added a more descriptive `__repr__` for basis functions (show the + family + information on attributes). + +* `StateSpace.sample` and `TransferFunction.sample` return a system + with the same input and output labels, which is convenient when + constructing interconnected systems using `interconnect`. + +* `optimal.solve_ocp`: add collocation method for solving optimal + control problems. Use `trajectory_method` parameter that be set to + either 'shooting' (default for discrete time systems) or + 'collocation' (default for continuous time systems). When + collocation is used, the `initial_guess` parameter can either be an + input trajectory (as before) or a tuple consisting of a state + trajectory and an input trajectory. + +* `StateSpace` objects can now be divided by a scalar. + +* `rlocus`, `sisotool`: Allow `initial_gain` to be a scalar (instead + of requiring and array). + +* `create_statefbk_iosystem` now supports gain scheduling. + +* `create_estimator_iosystem` now supports continous time systems. + + +Deprecations +............ + +The following functions have been newly deprecated in this release and +generate a warning message when used: + +* In the :ref:`optimal module `, constraints are + specified in the form ``LinearConstraint(A, lb, ub)`` or + ``NonlinearConstraint(fun, lb, ub)`` instead of the previous forms + ``(LinearConstraint, A, lb, ub)`` and ``(NonlinearConstraint, fun, + lb, ub)``. + +The listed items are slated to be removed in future releases (usually +the next major or minor version update. diff --git a/doc/releases/0.9.4-notes.rst b/doc/releases/0.9.4-notes.rst new file mode 100644 index 000000000..5d2c19bb6 --- /dev/null +++ b/doc/releases/0.9.4-notes.rst @@ -0,0 +1,137 @@ +.. currentmodule:: control + +.. _version-0.9.4: + +Version 0.9.4 Release Notes +---------------------------- + +* Released: date of release +* `GitHub release page + `_ + +This release adds functions for optimization-based estimation and +moving horizon estimation, better handling of system and signal names, +as well a number of bug fixes, small enhancements, and updated +documentation. + + +New classes, functions, and methods +................................... + +The following new classes, functions, and methods have been added in +this release: + +* Added the `optimal.OptimalEstimationProblem` class, the + `optimal.compute_oep` function, and the + `optimal.create_mhe_iosystem` function, which compute the optimal + estimate for a (nonlinear) I/O system using an explicit cost + function of a fixed window of applied inputs and measured outputs. + +* Added `gaussian_likelyhood_cost` to create cost function + corresponding to Gaussian likelihoods for use in optimal estimation. + +* Added `disturbance_range_constraint` to create a range constraint on + disturbances. + +* Added `LTI.bandwidth` to compute the bandwidth of a linear system. + + +Bug fixes +......... + +The following bugs have been fixed in this release: + +* Fixed a bug in `interconnect` in which the system name was being + clobbered internally. + +* Fixed a bug in `bode_plot` where phase wrapping was not working when + there were multiple systems. + +* Fixed a bug in `root_locus_plot` in which the `ax` parameter was not + being handled correctly. + +* Fixed a bug in `create_statefbk_iosystem` that didn't proper handle + 1D gain schedules. + +* Fixed a bug in `rootlocus_pid_designer` where the Bode plot was + sometimes blank. + +* Fixed a bug in which signal labels for a `StateSpace` system were + lost when computing `forced_response`. + +* Fixed a bug in which the `damp` command was assuming a + continuous-time system when printing out pole locations (but the + return value was correct). + +* Fixed a bug in which signal names could be lost for state transfer + functions when using the `interconnect` function. + +* Fixed a bug in the block-diagonal schur matrix computation used in + `bdschur`. + + +Improvements +............ + +The following additional improvements and changes in functionality +were implemented in this release: + +* Added an `add_unused` keyword parameter to `interconnect` that + allows unused inputs or outputs to be added as inputs or outputs of + the interconnected system (useful for doing a "partial" + interconnection). + +* Added `control_indices` and `state_indices` to + `create_statefbk_iosystem` to allow partial interconnection (eg, for + inner/outer loop construction). + +* `create_mpc_iosystem` now allows system and signal names to be + specified via appropriate keywords. + +* `TransferFunction` objects can now be displayed either in polynomial + form or in zpk form using the `display_format` parameter when + creating the system. + +* Allow discrete-time Nyquist plots for discrete-time systems with + poles at 0 and 1. + +* Generate a warning if `prewarp_frequency` is used in `sample_system` + for a discretization type that doesn't support it. + +* Converting a system from state space form to transfer function form + (and vice versa) now updates the system name to append "$converted", + removing an issue where two systems might have the same name. + + +Deprecations +............ + +The following functions have been newly deprecated in this release and +generate a warning message when used: + +* Changed `type` keyword for `create_statefbk_iosystem` to + `controller_type` ('linear' or 'nonlinear'). + +* `issys`: use ``isinstance(sys, ct.LTI)``. + +The listed items are slated to be removed in future releases (usually +the next major or minor version update. + + +Removals +........ + +The following functions and capabilities have been removed in this release: + +* `function`: function that was removed. + +* Other functionality that has been removed. + +Code that makes use of the functionality listed above will have to be +rewritten to work with this release of the python-control package. + + +Additional notes +................ + +Anything else that doesn't fit above. diff --git a/doc/releases/template.rst b/doc/releases/template.rst new file mode 100644 index 000000000..2c09c8df8 --- /dev/null +++ b/doc/releases/template.rst @@ -0,0 +1,82 @@ +.. currentmodule:: control + +.. _version-M.nn.p: + +Version M.nn.p Release Notes +---------------------------- + +* Released: date of release +* `GitHub release page + `_ + +Summary of the primary changes for this release. This should be a +paragraph describing the key updates in this release. The individual +subsections below can provide more information, if needed. Any +sections that are empty can be removed. + +This version of `python-control` requires Python 3.x or higher, NumPy +2.y or higher, etc. + + +New classes, functions, and methods +................................... + +The following new classes, functions, and methods have been added in +this release: + +* `function`: what it does + + +Bug fixes +......... + +The following bugs have been fixed in this release: + +* `function`: short description of the bug and what was fixed. + +* Other bug fixes that are not necessarily associated with a specific + function. + + +Improvements +............ + +The following additional improvements and changes in functionality +were implemented in this release: + +* `function`: improvements made that relate to a specific function. + +* Other changes that are not necesarily attached to a specific function. + + +Deprecations +............ + +The following functions have been newly deprecated in this release and +generate a warning message when used: + +* `function`: functions that are newly deprecated. + +* Other calling patterns that will not be supported in the future. + +The listed items are slated to be removed in future releases (usually +the next major or minor version update. + + +Removals +........ + +The following functions and capabilities have been removed in this release: + +* `function`: function that was removed. + +* Other functionality that has been removed. + +Code that makes use of the functionality listed above will have to be +rewritten to work with this release of the python-control package. + + +Additional notes +................ + +Anything else that doesn't fit above. From 719fcb2a1855691398895ea50ea6ea2cb886aed2 Mon Sep 17 00:00:00 2001 From: Richard Murray Date: Sat, 1 Feb 2025 22:03:53 -0800 Subject: [PATCH 92/93] update !pip to %pip per #1113 --- doc/intro.rst | 2 +- examples/python-control_tutorial.ipynb | 4 ++-- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/doc/intro.rst b/doc/intro.rst index 7f0d510b3..9eb214b7c 100644 --- a/doc/intro.rst +++ b/doc/intro.rst @@ -74,7 +74,7 @@ The python-control package can also be used with `Google Colab `_ by including the following lines to import the control package:: - !pip install control + %pip install control import control as ct Note that Google Colab does not currently support Slycot, so some diff --git a/examples/python-control_tutorial.ipynb b/examples/python-control_tutorial.ipynb index b5094a97d..4d718b050 100644 --- a/examples/python-control_tutorial.ipynb +++ b/examples/python-control_tutorial.ipynb @@ -47,7 +47,7 @@ " import control as ct\n", " print(\"python-control\", ct.__version__)\n", "except ImportError:\n", - " !pip install control\n", + " %pip install control\n", " import control as ct" ] }, @@ -64,7 +64,7 @@ " \n", "If you are using [Google Colab](https://colab.research.google.com), use the following command at the top of the notebook to install the `control` package:\n", "\n", - " !pip install control\n", + " %pip install control\n", "\n", "(The import code above automatically runs this command if needed.)\n", " \n", From 7da8c00b33bdc35132991931a193426df7b759e8 Mon Sep 17 00:00:00 2001 From: Richard Murray Date: Sun, 2 Feb 2025 17:57:01 -0800 Subject: [PATCH 93/93] addressed @slivingston review comments --- .gitignore | 2 +- README.rst | 10 +++++----- control/__init__.py | 2 +- control/config.py | 2 +- control/freqplot.py | 2 +- control/modelsimp.py | 4 ++-- control/statefbk.py | 2 +- control/statesp.py | 2 +- control/timeplot.py | 2 +- control/timeresp.py | 2 +- doc/develop.rst | 12 ++++++------ doc/examples/template.py | 6 +++--- doc/index.rst | 2 +- doc/intro.rst | 2 +- doc/iosys.rst | 6 +++--- doc/releases/0.10.0-notes.rst | 2 +- doc/releases/0.10.1-notes.rst | 2 +- doc/releases/0.9.3-notes.rst | 2 +- doc/releases/0.9.4-notes.rst | 4 ++-- doc/releases/template.rst | 2 +- examples/.gitignore | 1 - 21 files changed, 35 insertions(+), 36 deletions(-) diff --git a/.gitignore b/.gitignore index 6a262f045..9359defa9 100644 --- a/.gitignore +++ b/.gitignore @@ -14,7 +14,7 @@ record.txt .coverage doc/_build doc/generated -examples/.ipynb_checkpoints/ +.ipynb_checkpoints/ .settings/org.eclipse.core.resources.prefs .pydevproject .project diff --git a/README.rst b/README.rst index ebcf77c43..984862469 100644 --- a/README.rst +++ b/README.rst @@ -31,7 +31,7 @@ Try out the examples in the examples folder using the binder service. The package can also be installed on Google Colab using the commands:: - !pip install control + %pip install control import control as ct Features @@ -49,11 +49,11 @@ Features Links ----- -- Project home page: http://python-control.org +- Project home page: https://python-control.org - Source code repository: https://github.com/python-control/python-control -- Documentation: http://python-control.readthedocs.org/ +- Documentation: https://python-control.readthedocs.io/ - Issue tracker: https://github.com/python-control/python-control/issues -- Mailing list: http://sourceforge.net/p/python-control/mailman/ +- Mailing list: https://sourceforge.net/p/python-control/mailman/ Dependencies ------------ @@ -158,7 +158,7 @@ License ------- This is free software released under the terms of `the BSD 3-Clause -License `_. There is no +License `_. There is no warranty; not even for merchantability or fitness for a particular purpose. Consult LICENSE for copying conditions. diff --git a/control/__init__.py b/control/__init__.py index 5d3d288e0..d2929c799 100644 --- a/control/__init__.py +++ b/control/__init__.py @@ -9,7 +9,7 @@ The initial goal for the package is to implement all of the functionality required to work through the examples in the textbook -`Feedback Systems `_ by Astrom and Murray. In +`Feedback Systems `_ by Astrom and Murray. In addition to standard techniques available for linear control systems, support for nonlinear systems (including trajectory generation, gain scheduling, phase plane diagrams, and describing functions) is diff --git a/control/config.py b/control/config.py index c49913145..852849126 100644 --- a/control/config.py +++ b/control/config.py @@ -272,7 +272,7 @@ def use_matlab_defaults(): def use_fbs_defaults(): """Use Feedback Systems (FBS) compatible settings. - The following conventions from `Feedback Systems `_ + The following conventions from `Feedback Systems `_ are used: * Bode plots plot gain in powers of ten, phase in degrees, diff --git a/control/freqplot.py b/control/freqplot.py index 8d8f53cf7..78305cfff 100644 --- a/control/freqplot.py +++ b/control/freqplot.py @@ -1030,7 +1030,7 @@ def gen_zero_centered_series(val_min, val_max, period): # # Because plots can be built up by multiple calls to plot(), the legend # strings are created from the line labels manually. Thus an initial - # call to plot() may not generate any legends (eg, if no signals are + # call to plot() may not generate any legends (e.g., if no signals are # overlaid), but subsequent calls to plot() will need a legend for each # different response (system). # diff --git a/control/modelsimp.py b/control/modelsimp.py index 8ad3ee144..3352cc156 100644 --- a/control/modelsimp.py +++ b/control/modelsimp.py @@ -598,7 +598,7 @@ def markov(*args, m=None, transpose=False, dt=None, truncate=False): .. [1] J.-N. Juang, M. Phan, L. G. Horta, and R. W. Longman, Identification of observer/Kalman filter Markov parameters - Theory and experiments. Journal of Guidance Control and Dynamics, 16(2), - 320-329, 2012. http://doi.org/10.2514/3.21006 + 320-329, 2012. https://doi.org/10.2514/3.21006 Examples -------- @@ -696,7 +696,7 @@ def markov(*args, m=None, transpose=False, dt=None, truncate=False): # J.-N. Juang, M. Phan, L. G. Horta, and R. W. Longman, Identification # of observer/Kalman filter Markov parameters - Theory and # experiments. Journal of Guidance Control and Dynamics, 16(2), - # 320-329, 2012. http://doi.org/10.2514/3.21006 + # 320-329, 2012. https://doi.org/10.2514/3.21006 # # Set up the full problem diff --git a/control/statefbk.py b/control/statefbk.py index c88356a53..b6e9c9655 100644 --- a/control/statefbk.py +++ b/control/statefbk.py @@ -558,7 +558,7 @@ def create_statefbk_iosystem( ctrl, clsys = ct.create_statefbk_iosystem(sys, K) where `sys` is the process dynamics and `K` is the state (+ integral) - feedback gain (eg, from LQR). The function returns the controller + feedback gain (e.g., from LQR). The function returns the controller `ctrl` and the closed loop systems `clsys`, both as I/O systems. A gain scheduled controller can also be created, by passing a list of diff --git a/control/statesp.py b/control/statesp.py index f66bc3b04..bafc196e6 100644 --- a/control/statesp.py +++ b/control/statesp.py @@ -1749,7 +1749,7 @@ def ss(*args, **kwargs): warn("state labels specified for " "non-unique state space realization") - # Allow method to be specified (eg, tf2ss) + # Allow method to be specified (e.g., tf2ss) method = kwargs.pop('method', None) # Create a state space system from an LTI system diff --git a/control/timeplot.py b/control/timeplot.py index 139bf9b48..545618f75 100644 --- a/control/timeplot.py +++ b/control/timeplot.py @@ -573,7 +573,7 @@ def _make_line_label(signal_index, signal_labels, trace_index): # # Because plots can be built up by multiple calls to plot(), the legend # strings are created from the line labels manually. Thus an initial - # call to plot() may not generate any legends (eg, if no signals are + # call to plot() may not generate any legends (e.g., if no signals are # combined nor overlaid), but subsequent calls to plot() will need a # legend for each different line (system). # diff --git a/control/timeresp.py b/control/timeresp.py index 87ba539e3..d708dd2e5 100644 --- a/control/timeresp.py +++ b/control/timeresp.py @@ -1961,7 +1961,7 @@ def impulse_response( # # We can't put the impulse into U because there is no numerical # representation for it (infinitesimally short, infinitely high). - # See also: http://www.mathworks.com/support/tech-notes/1900/1901.html + # See also: https://www.mathworks.com/support/tech-notes/1900/1901.html # if isctime(sys): X0 = sys.B[:, i] diff --git a/doc/develop.rst b/doc/develop.rst index 78cb1c995..2a14a6cc7 100644 --- a/doc/develop.rst +++ b/doc/develop.rst @@ -16,11 +16,11 @@ Package Structure The python-control package is maintained on GitHub, with documentation hosted by ReadTheDocs and a mailing list on SourceForge: - * Project home page: http://python-control.org + * Project home page: https://python-control.org * Source code repository: https://github.com/python-control/python-control - * Documentation: http://python-control.readthedocs.org/ + * Documentation: https://python-control.readthedocs.io/ * Issue tracker: https://github.com/python-control/python-control/issues - * Mailing list: http://sourceforge.net/p/python-control/mailman/ + * Mailing list: https://sourceforge.net/p/python-control/mailman/ GitHub repository file and directory layout: - **python-control/** - main repository @@ -56,7 +56,7 @@ GitHub repository file and directory layout: + **matlab/** - MATLAB compatibility subpackage - - __init.py, timeresp.py, wrappers.py - subpackage files + - __init__.py, timeresp.py, wrappers.py - subpackage files + **tests/** - unit tests @@ -222,7 +222,7 @@ frequency, etc: timepts, outputs, states=states, inputs=inputs) In the last command, note that states precedes inputs because not - all TimeResponseData elements have inputs (eg, `initial_response`). + all TimeResponseData elements have inputs (e.g., `initial_response`). * The default order for providing arguments in the frequency domain is system/response first, then frequency:: @@ -566,7 +566,7 @@ Sphinx files guidelines: - The Python built-ins occur frequently and are capitalized, and so the additional formatting doesn't add much and would be inconsistent if you jump from the User Guide to the Reference - Manual (eg, to look at a function more closely via a link in the + Manual (e.g., to look at a function more closely via a link in the User Guide). diff --git a/doc/examples/template.py b/doc/examples/template.py index 29bd70e2b..77da7e1a1 100644 --- a/doc/examples/template.py +++ b/doc/examples/template.py @@ -78,7 +78,7 @@ def __init__(self, sys): self.data = sys.name # Attribute created within class def sample_method(self, data): - """Sample method within in a class. + """Sample method within a class. This is an example of a method within a class. Document using numpydoc format. @@ -92,10 +92,10 @@ def sample_function(data, option=False, **kwargs): This is an example of a public function within the template module. This function will usually be placed in the `control` namespace by - updating `__init.py` to import the function (often by importing the + updating `__init__.py` to import the function (often by importing the entire module). - Docstring should be in standard numpy doc format. The extended summary + Docstring should be in standard numpydoc format. The extended summary (this text) should describe the basic operation of the function, with technical details in the "Notes" section. diff --git a/doc/index.rst b/doc/index.rst index c21eeccd8..44da952c7 100644 --- a/doc/index.rst +++ b/doc/index.rst @@ -24,7 +24,7 @@ feedback control systems. - GitHub repository: https://github.com/python-control/python-control - Issue tracker: https://github.com/python-control/python-control/issues -- Mailing list: http://sourceforge.net/p/python-control/mailman/ +- Mailing list: https://sourceforge.net/p/python-control/mailman/ .. rubric:: How to cite diff --git a/doc/intro.rst b/doc/intro.rst index 9eb214b7c..e1e5fb8e6 100644 --- a/doc/intro.rst +++ b/doc/intro.rst @@ -115,7 +115,7 @@ Some Differences from MATLAB Users familiar with the MATLAB control systems toolbox will find much of the functionality implemented in `python-control`, though using Python constructs and coding conventions. The python-control package -makes heavy use of `NumPy `_ and `SciPy +makes heavy use of `NumPy `_ and `SciPy `_ and many differences are reflected in the use of those . A list of general differences between NumPy and MATLAB can be found `here diff --git a/doc/iosys.rst b/doc/iosys.rst index cd2747fd5..c1d58fb6c 100644 --- a/doc/iosys.rst +++ b/doc/iosys.rst @@ -139,7 +139,7 @@ Illustrative example To illustrate the use of the :func:`interconnect` function, we create a model for a predator/prey system, following the notation and parameter -values in `Feedback Systems `_. +values in `Feedback Systems `_. We begin by defining the dynamics of the system: @@ -219,7 +219,7 @@ system and computing the linearization about that point. We next compute a controller that stabilizes the equilibrium point using eigenvalue placement and computing the feedforward gain using the number of lynxes as the desired output (following `Feedback Systems -`_, Example 7.5): +`_, Example 7.5): .. testcode:: predprey @@ -692,7 +692,7 @@ A reference gain controller can be created with the command:: sys, K, kf, feedfwd_pattern='refgain') This reference gain design pattern is described in more detail in -`Feedback Systems `_, Section 7.2 (Stabilization +`Feedback Systems `_, Section 7.2 (Stabilization by State Feedback) and the trajectory generation design pattern is described in Section 8.5 (State Space Controller Design). diff --git a/doc/releases/0.10.0-notes.rst b/doc/releases/0.10.0-notes.rst index dc44a7ec2..360bd9a79 100644 --- a/doc/releases/0.10.0-notes.rst +++ b/doc/releases/0.10.0-notes.rst @@ -146,7 +146,7 @@ generate a warning message when used: * `phase_plot`: Use `phase_plane_plot` instead. The listed items are slated to be removed in future releases (usually -the next major or minor version update. +the next major or minor version update). Removals diff --git a/doc/releases/0.10.1-notes.rst b/doc/releases/0.10.1-notes.rst index d0545f7b7..dd0939021 100644 --- a/doc/releases/0.10.1-notes.rst +++ b/doc/releases/0.10.1-notes.rst @@ -197,4 +197,4 @@ generate a warning message when used: * Deprecated the `relabel` keyword in `time_response_plot`. The listed items are slated to be removed in future releases (usually -the next major or minor version update. +the next major or minor version update). diff --git a/doc/releases/0.9.3-notes.rst b/doc/releases/0.9.3-notes.rst index d8f956a81..72ff4c8e8 100644 --- a/doc/releases/0.9.3-notes.rst +++ b/doc/releases/0.9.3-notes.rst @@ -126,4 +126,4 @@ generate a warning message when used: lb, ub)``. The listed items are slated to be removed in future releases (usually -the next major or minor version update. +the next major or minor version update). diff --git a/doc/releases/0.9.4-notes.rst b/doc/releases/0.9.4-notes.rst index 5d2c19bb6..6cdff2f42 100644 --- a/doc/releases/0.9.4-notes.rst +++ b/doc/releases/0.9.4-notes.rst @@ -82,7 +82,7 @@ were implemented in this release: interconnection). * Added `control_indices` and `state_indices` to - `create_statefbk_iosystem` to allow partial interconnection (eg, for + `create_statefbk_iosystem` to allow partial interconnection (e.g., for inner/outer loop construction). * `create_mpc_iosystem` now allows system and signal names to be @@ -115,7 +115,7 @@ generate a warning message when used: * `issys`: use ``isinstance(sys, ct.LTI)``. The listed items are slated to be removed in future releases (usually -the next major or minor version update. +the next major or minor version update). Removals diff --git a/doc/releases/template.rst b/doc/releases/template.rst index 2c09c8df8..6212f410e 100644 --- a/doc/releases/template.rst +++ b/doc/releases/template.rst @@ -60,7 +60,7 @@ generate a warning message when used: * Other calling patterns that will not be supported in the future. The listed items are slated to be removed in future releases (usually -the next major or minor version update. +the next major or minor version update). Removals diff --git a/examples/.gitignore b/examples/.gitignore index 3d8dcfb71..ad3049346 100644 --- a/examples/.gitignore +++ b/examples/.gitignore @@ -1,2 +1 @@ .ipynb-clean -.ipynb_checkpoints/