@@ -3281,27 +3281,19 @@ def errorbar(self, x, y, yerr=None, xerr=None,
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kwargs = {k : v for k , v in kwargs .items () if v is not None }
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kwargs .setdefault ('zorder' , 2 )
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- self . _process_unit_info ([( "x" , x ), ( "y" , y )], kwargs , convert = False )
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-
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- # Make sure all the args are iterable; use lists not arrays to preserve
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- # units.
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- if not np .iterable ( x ):
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- x = [ x ]
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-
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- if not np .iterable ( y ):
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- y = [ y ]
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-
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+ # Casting to object arrays preserves units.
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+ if not isinstance ( x , np . ndarray ):
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+ x = np . asarray ( x , dtype = object )
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+ if not isinstance ( y , np . ndarray ):
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+ y = np .asarray ( y , dtype = object )
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+ if xerr is not None and not isinstance ( xerr , np . ndarray ):
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+ xerr = np . asarray ( xerr , dtype = object )
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+ if yerr is not None and not isinstance ( yerr , np .ndarray ):
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+ yerr = np . asarray ( yerr , dtype = object )
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+ x , y = np . atleast_1d ( x , y ) # Make sure all the args are iterable.
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if len (x ) != len (y ):
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raise ValueError ("'x' and 'y' must have the same size" )
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- if xerr is not None :
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- if not np .iterable (xerr ):
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- xerr = [xerr ] * len (x )
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-
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- if yerr is not None :
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- if not np .iterable (yerr ):
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- yerr = [yerr ] * len (y )
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-
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if isinstance (errorevery , Integral ):
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errorevery = (0 , errorevery )
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if isinstance (errorevery , tuple ):
@@ -3313,10 +3305,8 @@ def errorbar(self, x, y, yerr=None, xerr=None,
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raise ValueError (
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f'errorevery={ errorevery !r} is a not a tuple of two '
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f'integers' )
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-
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elif isinstance (errorevery , slice ):
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pass
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-
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elif not isinstance (errorevery , str ) and np .iterable (errorevery ):
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# fancy indexing
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try :
@@ -3328,6 +3318,8 @@ def errorbar(self, x, y, yerr=None, xerr=None,
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else :
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raise ValueError (
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f"errorevery={ errorevery !r} is not a recognized value" )
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+ everymask = np .zeros (len (x ), bool )
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+ everymask [errorevery ] = True
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label = kwargs .pop ("label" , None )
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kwargs ['label' ] = '_nolegend_'
@@ -3410,13 +3402,8 @@ def errorbar(self, x, y, yerr=None, xerr=None,
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xlolims = np .broadcast_to (xlolims , len (x )).astype (bool )
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xuplims = np .broadcast_to (xuplims , len (x )).astype (bool )
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- everymask = np .zeros (len (x ), bool )
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- everymask [errorevery ] = True
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-
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- def apply_mask (arrays , mask ):
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- # Return, for each array in *arrays*, the elements for which *mask*
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- # is True, without using fancy indexing.
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- return [[* itertools .compress (array , mask )] for array in arrays ]
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+ # Vectorized fancy-indexer.
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+ def apply_mask (arrays , mask ): return [array [mask ] for array in arrays ]
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def extract_err (name , err , data , lolims , uplims ):
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"""
@@ -3437,24 +3424,14 @@ def extract_err(name, err, data, lolims, uplims):
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Error is only applied on **lower** side when this is True. See
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the note in the main docstring about this parameter's name.
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"""
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- try : # Asymmetric error: pair of 1D iterables.
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- a , b = err
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- iter (a )
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- iter (b )
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- except (TypeError , ValueError ):
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- a = b = err # Symmetric error: 1D iterable.
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- if np .ndim (a ) > 1 or np .ndim (b ) > 1 :
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+ try :
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+ low , high = np .broadcast_to (err , (2 , len (data )))
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+ except ValueError :
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raise ValueError (
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- f"{ name } err must be a scalar or a 1D or (2, n) array-like" )
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- # Using list comprehensions rather than arrays to preserve units.
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- for e in [a , b ]:
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- if len (data ) != len (e ):
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- raise ValueError (
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- f"The lengths of the data ({ len (data )} ) and the "
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- f"error { len (e )} do not match" )
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- low = [v if lo else v - e for v , e , lo in zip (data , a , lolims )]
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- high = [v if up else v + e for v , e , up in zip (data , b , uplims )]
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- return low , high
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+ f"'{ name } err' (shape: { np .shape (err )} ) must be a scalar "
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+ f"or a 1D or (2, n) array-like whose shape matches "
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+ f"'{ name } ' (shape: { np .shape (data )} )" ) from None
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+ return data - low * ~ lolims , data + high * ~ uplims # low, high
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if xerr is not None :
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left , right = extract_err ('x' , xerr , x , xlolims , xuplims )
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