"""

Builtin colormaps, colormap handling utilities, and the `ScalarMappable` mixin.

.. seealso::

:doc:`/gallery/color/colormap_reference` for a list of builtin colormaps.

:doc:`/tutorials/colors/colormap-manipulation` for examples of how to

make colormaps.

:doc:`/tutorials/colors/colormaps` an in-depth discussion of

choosing colormaps.

:doc:`/tutorials/colors/colormapnorms` for more details about data

normalization.

"""

from collections.abc import MutableMapping

import numpy as np

from numpy import ma

import matplotlib as mpl

from matplotlib import _api, colors, cbook

from matplotlib._cm import datad

from matplotlib._cm_listed import cmaps as cmaps_listed

LUTSIZE = mpl.rcParams['image.lut']

def _gen_cmap_registry():

"""

Generate a dict mapping standard colormap names to standard colormaps, as

well as the reversed colormaps.

"""

cmap_d = {**cmaps_listed}

for name, spec in datad.items():

cmap_d[name] = ( # Precache the cmaps at a fixed lutsize..

colors.LinearSegmentedColormap(name, spec, LUTSIZE)

if 'red' in spec else

colors.ListedColormap(spec['listed'], name)

if 'listed' in spec else

colors.LinearSegmentedColormap.from_list(name, spec, LUTSIZE))

# Generate reversed cmaps.

for cmap in list(cmap_d.values()):

rmap = cmap.reversed()

cmap._global = True

rmap._global = True

cmap_d[rmap.name] = rmap

return cmap_d

class _DeprecatedCmapDictWrapper(MutableMapping):

"""Dictionary mapping for deprecated _cmap_d access."""

def __init__(self, cmap_registry):

self._cmap_registry = cmap_registry

def __delitem__(self, key):

self._warn_deprecated()

self._cmap_registry.__delitem__(key)

def __getitem__(self, key):

self._warn_deprecated()

return self._cmap_registry.__getitem__(key)

def __iter__(self):

self._warn_deprecated()

return self._cmap_registry.__iter__()

def __len__(self):

self._warn_deprecated()

return self._cmap_registry.__len__()

def __setitem__(self, key, val):

self._warn_deprecated()

self._cmap_registry.__setitem__(key, val)

def get(self, key, default=None):

self._warn_deprecated()

return self._cmap_registry.get(key, default)

def _warn_deprecated(self):

_api.warn_deprecated(

"3.3",

message="The global colormaps dictionary is no longer "

"considered public API.",

alternative="Please use register_cmap() and get_cmap() to "

"access the contents of the dictionary."

)

_cmap_registry = _gen_cmap_registry()

globals().update(_cmap_registry)

# This is no longer considered public API

cmap_d = _DeprecatedCmapDictWrapper(_cmap_registry)

__builtin_cmaps = tuple(_cmap_registry)

# Continue with definitions ...

def register_cmap(name=None, cmap=None, *, override_builtin=False):

"""

Add a colormap to the set recognized by :func:`get_cmap`.

Register a new colormap to be accessed by name ::

LinearSegmentedColormap('swirly', data, lut)

register_cmap(cmap=swirly_cmap)

Parameters

----------

name : str, optional

The name that can be used in :func:`get_cmap` or :rc:`image.cmap`

If absent, the name will be the :attr:`~matplotlib.colors.Colormap.name`

attribute of the *cmap*.

cmap : matplotlib.colors.Colormap

Despite being the second argument and having a default value, this

is a required argument.

override_builtin : bool

Allow built-in colormaps to be overridden by a user-supplied

colormap.

Please do not use this unless you are sure you need it.

Notes

-----

Registering a colormap stores a reference to the colormap object

which can currently be modified and inadvertently change the global

colormap state. This behavior is deprecated and in Matplotlib 3.5

the registered colormap will be immutable.

"""

_api.check_isinstance((str, None), name=name)

if name is None:

try:

name = cmap.name

except AttributeError as err:

raise ValueError("Arguments must include a name or a "

"Colormap") from err

if name in _cmap_registry:

if not override_builtin and name in __builtin_cmaps:

msg = f"Trying to re-register the builtin cmap {name!r}."

raise ValueError(msg)

else:

msg = f"Trying to register the cmap {name!r} which already exists."

_api.warn_external(msg)

if not isinstance(cmap, colors.Colormap):

raise ValueError("You must pass a Colormap instance. "

f"You passed {cmap} a {type(cmap)} object.")

cmap._global = True

_cmap_registry[name] = cmap

return

def get_cmap(name=None, lut=None):

"""

Get a colormap instance, defaulting to rc values if *name* is None.

Colormaps added with :func:`register_cmap` take precedence over

built-in colormaps.

Notes

-----

Currently, this returns the global colormap object, which is deprecated.

In Matplotlib 3.5, you will no longer be able to modify the global

colormaps in-place.

Parameters

----------

name : `matplotlib.colors.Colormap` or str or None, default: None

If a `.Colormap` instance, it will be returned. Otherwise, the name of

a colormap known to Matplotlib, which will be resampled by *lut*. The

default, None, means :rc:`image.cmap`.

lut : int or None, default: None

If *name* is not already a Colormap instance and *lut* is not None, the

colormap will be resampled to have *lut* entries in the lookup table.

"""

if name is None:

name = mpl.rcParams['image.cmap']

if isinstance(name, colors.Colormap):

return name

_api.check_in_list(sorted(_cmap_registry), name=name)

if lut is None:

return _cmap_registry[name]

else:

return _cmap_registry[name]._resample(lut)

def unregister_cmap(name):

"""

Remove a colormap recognized by :func:`get_cmap`.

You may not remove built-in colormaps.

If the named colormap is not registered, returns with no error, raises

if you try to de-register a default colormap.

.. warning ::

Colormap names are currently a shared namespace that may be used

by multiple packages. Use `unregister_cmap` only if you know you

have registered that name before. In particular, do not

unregister just in case to clean the name before registering a

new colormap.

Parameters

----------

name : str

The name of the colormap to be un-registered

Returns

-------

ColorMap or None

If the colormap was registered, return it if not return `None`

Raises

------

ValueError

If you try to de-register a default built-in colormap.

"""

if name not in _cmap_registry:

return

if name in __builtin_cmaps:

raise ValueError(f"cannot unregister {name!r} which is a builtin "

"colormap.")

return _cmap_registry.pop(name)

class ScalarMappable:

"""

A mixin class to map scalar data to RGBA.

The ScalarMappable applies data normalization before returning RGBA colors

from the given colormap.

"""

def __init__(self, norm=None, cmap=None):

"""

Parameters

----------

norm : `matplotlib.colors.Normalize` (or subclass thereof)

The normalizing object which scales data, typically into the

interval ``[0, 1]``.

If *None*, *norm* defaults to a *colors.Normalize* object which

initializes its scaling based on the first data processed.

cmap : str or `~matplotlib.colors.Colormap`

The colormap used to map normalized data values to RGBA colors.

"""

self._A = None

self.norm = None # So that the setter knows we're initializing.

self.set_norm(norm) # The Normalize instance of this ScalarMappable.

self.cmap = None # So that the setter knows we're initializing.

self.set_cmap(cmap) # The Colormap instance of this ScalarMappable.

#: The last colorbar associated with this ScalarMappable. May be None.

self.colorbar = None

self.callbacksSM = cbook.CallbackRegistry()

self._update_dict = {'array': False}

def _scale_norm(self, norm, vmin, vmax):

"""

Helper for initial scaling.

Used by public functions that create a ScalarMappable and support

parameters *vmin*, *vmax* and *norm*. This makes sure that a *norm*

will take precedence over *vmin*, *vmax*.

Note that this method does not set the norm.

"""

if vmin is not None or vmax is not None:

self.set_clim(vmin, vmax)

if norm is not None:

_api.warn_deprecated(

"3.3",

message="Passing parameters norm and vmin/vmax "

"simultaneously is deprecated since %(since)s and "

"will become an error %(removal)s. Please pass "

"vmin/vmax directly to the norm when creating it.")

# always resolve the autoscaling so we have concrete limits

# rather than deferring to draw time.

self.autoscale_None()

def to_rgba(self, x, alpha=None, bytes=False, norm=True):

"""

Return a normalized rgba array corresponding to *x*.

In the normal case, *x* is a 1D or 2D sequence of scalars, and

the corresponding ndarray of rgba values will be returned,

based on the norm and colormap set for this ScalarMappable.

There is one special case, for handling images that are already

rgb or rgba, such as might have been read from an image file.

If *x* is an ndarray with 3 dimensions,

and the last dimension is either 3 or 4, then it will be

treated as an rgb or rgba array, and no mapping will be done.

The array can be uint8, or it can be floating point with

values in the 0-1 range; otherwise a ValueError will be raised.

If it is a masked array, the mask will be ignored.

If the last dimension is 3, the *alpha* kwarg (defaulting to 1)

will be used to fill in the transparency. If the last dimension

is 4, the *alpha* kwarg is ignored; it does not

replace the pre-existing alpha. A ValueError will be raised

if the third dimension is other than 3 or 4.

In either case, if *bytes* is *False* (default), the rgba

array will be floats in the 0-1 range; if it is *True*,

the returned rgba array will be uint8 in the 0 to 255 range.

If norm is False, no normalization of the input data is

performed, and it is assumed to be in the range (0-1).

"""

# First check for special case, image input:

try:

if x.ndim == 3:

if x.shape[2] == 3:

if alpha is None:

alpha = 1

if x.dtype == np.uint8:

alpha = np.uint8(alpha * 255)

m, n = x.shape[:2]

xx = np.empty(shape=(m, n, 4), dtype=x.dtype)

xx[:, :, :3] = x

xx[:, :, 3] = alpha

elif x.shape[2] == 4:

xx = x

else:

raise ValueError("Third dimension must be 3 or 4")

if xx.dtype.kind == 'f':

if norm and (xx.max() > 1 or xx.min() < 0):

raise ValueError("Floating point image RGB values "

"must be in the 0..1 range.")

if bytes:

xx = (xx * 255).astype(np.uint8)

elif xx.dtype == np.uint8:

if not bytes:

xx = xx.astype(np.float32) / 255

else:

raise ValueError("Image RGB array must be uint8 or "

"floating point; found %s" % xx.dtype)

return xx

except AttributeError:

# e.g., x is not an ndarray; so try mapping it

pass

# This is the normal case, mapping a scalar array:

x = ma.asarray(x)

if norm:

x = self.norm(x)

rgba = self.cmap(x, alpha=alpha, bytes=bytes)

return rgba

def set_array(self, A):

"""

Set the image array from numpy array *A*.

Parameters

----------

A : ndarray or None

"""

self._A = A

self._update_dict['array'] = True

def get_array(self):

"""Return the data array."""

return self._A

def get_cmap(self):

"""Return the `.Colormap` instance."""

return self.cmap

def get_clim(self):

"""

Return the values (min, max) that are mapped to the colormap limits.

"""

return self.norm.vmin, self.norm.vmax

def set_clim(self, vmin=None, vmax=None):

"""

Set the norm limits for image scaling.

Parameters

----------

vmin, vmax : float

The limits.

The limits may also be passed as a tuple (*vmin*, *vmax*) as a

single positional argument.

.. ACCEPTS: (vmin: float, vmax: float)

"""

if vmax is None:

try:

vmin, vmax = vmin

except (TypeError, ValueError):

pass

if vmin is not None:

self.norm.vmin = colors._sanitize_extrema(vmin)

if vmax is not None:

self.norm.vmax = colors._sanitize_extrema(vmax)

self.changed()

def get_alpha(self):

"""

Returns

-------

float

Always returns 1.

"""

# This method is intended to be overridden by Artist sub-classes

return 1.

def set_cmap(self, cmap):

"""

Set the colormap for luminance data.

Parameters

----------

cmap : `.Colormap` or str or None

"""

in_init = self.cmap is None

cmap = get_cmap(cmap)

self.cmap = cmap

if not in_init:

self.changed() # Things are not set up properly yet.

def set_norm(self, norm):

"""

Set the normalization instance.

Parameters

----------

norm : `.Normalize` or None

Notes

-----

If there are any colorbars using the mappable for this norm, setting

the norm of the mappable will reset the norm, locator, and formatters

on the colorbar to default.

"""

_api.check_isinstance((colors.Normalize, None), norm=norm)

in_init = self.norm is None

if norm is None:

norm = colors.Normalize()

self.norm = norm

if not in_init:

self.changed() # Things are not set up properly yet.

def autoscale(self):

"""

Autoscale the scalar limits on the norm instance using the

current array

"""

if self._A is None:

raise TypeError('You must first set_array for mappable')

self.norm.autoscale(self._A)

self.changed()

def autoscale_None(self):

"""

Autoscale the scalar limits on the norm instance using the

current array, changing only limits that are None

"""

if self._A is None:

raise TypeError('You must first set_array for mappable')

self.norm.autoscale_None(self._A)

self.changed()

def _add_checker(self, checker):

"""

Add an entry to a dictionary of boolean flags

that are set to True when the mappable is changed.

"""

self._update_dict[checker] = False

def _check_update(self, checker):

"""Return whether mappable has changed since the last check."""

if self._update_dict[checker]:

self._update_dict[checker] = False

return True

return False

def changed(self):

"""

Call this whenever the mappable is changed to notify all the

callbackSM listeners to the 'changed' signal.

"""

self.callbacksSM.process('changed', self)

for key in self._update_dict:

self._update_dict[key] = True

self.stale = True

update_dict = _api.deprecate_privatize_attribute("3.3")

@_api.deprecated("3.3")

def add_checker(self, checker):

return self._add_checker(checker)

@_api.deprecated("3.3")

def check_update(self, checker):

return self._check_update(checker)