"""

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

.. seealso::

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

:ref:`colormap-manipulation` for examples of how to make

colormaps.

:ref:`colormaps` an in-depth discussion of choosing

colormaps.

:ref:`colormapnorms` for more details about data normalization.

"""

from collections.abc import Mapping

import functools

import numpy as np

from numpy import ma

import matplotlib as mpl

from matplotlib import _api, colors, cbook, scale

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))

# Register colormap aliases for gray and grey.

cmap_d['grey'] = cmap_d['gray']

cmap_d['gist_grey'] = cmap_d['gist_gray']

cmap_d['gist_yerg'] = cmap_d['gist_yarg']

cmap_d['Grays'] = cmap_d['Greys']

# Generate reversed cmaps.

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

rmap = cmap.reversed()

cmap_d[rmap.name] = rmap

return cmap_d

class ColormapRegistry(Mapping):

r"""

Container for colormaps that are known to Matplotlib by name.

The universal registry instance is `matplotlib.colormaps`. There should be

no need for users to instantiate `.ColormapRegistry` themselves.

Read access uses a dict-like interface mapping names to `.Colormap`\s::

import matplotlib as mpl

cmap = mpl.colormaps['viridis']

Returned `.Colormap`\s are copies, so that their modification does not

change the global definition of the colormap.

Additional colormaps can be added via `.ColormapRegistry.register`::

mpl.colormaps.register(my_colormap)

To get a list of all registered colormaps, you can do::

from matplotlib import colormaps

list(colormaps)

"""

def __init__(self, cmaps):

self._cmaps = cmaps

self._builtin_cmaps = tuple(cmaps)

def __getitem__(self, item):

try:

return self._cmaps[item].copy()

except KeyError:

raise KeyError(f"{item!r} is not a known colormap name") from None

def __iter__(self):

return iter(self._cmaps)

def __len__(self):

return len(self._cmaps)

def __str__(self):

return ('ColormapRegistry; available colormaps:\n' +

', '.join(f"'{name}'" for name in self))

def __call__(self):

"""

Return a list of the registered colormap names.

This exists only for backward-compatibility in `.pyplot` which had a

``plt.colormaps()`` method. The recommended way to get this list is

now ``list(colormaps)``.

"""

return list(self)

def register(self, cmap, *, name=None, force=False):

"""

Register a new colormap.

The colormap name can then be used as a string argument to any ``cmap``

parameter in Matplotlib. It is also available in ``pyplot.get_cmap``.

The colormap registry stores a copy of the given colormap, so that

future changes to the original colormap instance do not affect the

registered colormap. Think of this as the registry taking a snapshot

of the colormap at registration.

Parameters

----------

cmap : matplotlib.colors.Colormap

The colormap to register.

name : str, optional

The name for the colormap. If not given, ``cmap.name`` is used.

force : bool, default: False

If False, a ValueError is raised if trying to overwrite an already

registered name. True supports overwriting registered colormaps

other than the builtin colormaps.

"""

_api.check_isinstance(colors.Colormap, cmap=cmap)

name = name or cmap.name

if name in self:

if not force:

# don't allow registering an already existing cmap

# unless explicitly asked to

raise ValueError(

f'A colormap named "{name}" is already registered.')

elif name in self._builtin_cmaps:

# We don't allow overriding a builtin.

raise ValueError("Re-registering the builtin cmap "

f"{name!r} is not allowed.")

# Warn that we are updating an already existing colormap

_api.warn_external(f"Overwriting the cmap {name!r} "

"that was already in the registry.")

self._cmaps[name] = cmap.copy()

# Someone may set the extremes of a builtin colormap and want to register it

# with a different name for future lookups. The object would still have the

# builtin name, so we should update it to the registered name

if self._cmaps[name].name != name:

self._cmaps[name].name = name

def unregister(self, name):

"""

Remove a colormap from the registry.

You cannot 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` 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 removed.

Raises

------

ValueError

If you try to remove a default built-in colormap.

"""

if name in self._builtin_cmaps:

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

"colormap.")

self._cmaps.pop(name, None)

def get_cmap(self, cmap):

"""

Return a color map specified through *cmap*.

Parameters

----------

cmap : str or `~matplotlib.colors.Colormap` or None

- if a `.Colormap`, return it

- if a string, look it up in ``mpl.colormaps``

- if None, return the Colormap defined in :rc:`image.cmap`

Returns

-------

Colormap

"""

# get the default color map

if cmap is None:

return self[mpl.rcParams["image.cmap"]]

# if the user passed in a Colormap, simply return it

if isinstance(cmap, colors.Colormap):

return cmap

if isinstance(cmap, str):

_api.check_in_list(sorted(_colormaps), cmap=cmap)

# otherwise, it must be a string so look it up

return self[cmap]

raise TypeError(

'get_cmap expects None or an instance of a str or Colormap . ' +

f'you passed {cmap!r} of type {type(cmap)}'

)

# public access to the colormaps should be via `matplotlib.colormaps`. For now,

# we still create the registry here, but that should stay an implementation

# detail.

_colormaps = ColormapRegistry(_gen_cmap_registry())

globals().update(_colormaps)

# This is an exact copy of pyplot.get_cmap(). It was removed in 3.9, but apparently

# caused more user trouble than expected. Re-added for 3.9.1 and extended the

# deprecation period for two additional minor releases.

@_api.deprecated(

'3.7',

removal='3.11',

alternative="``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap()``"

" or ``pyplot.get_cmap()``"

)

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

"""

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

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.

Returns

-------

Colormap

"""

if name is None:

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

if isinstance(name, colors.Colormap):

return name

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

if lut is None:

return _colormaps[name]

else:

return _colormaps[name].resampled(lut)

def _auto_norm_from_scale(scale_cls):

"""

Automatically generate a norm class from *scale_cls*.

This differs from `.colors.make_norm_from_scale` in the following points:

- This function is not a class decorator, but directly returns a norm class

(as if decorating `.Normalize`).

- The scale is automatically constructed with ``nonpositive="mask"``, if it

supports such a parameter, to work around the difference in defaults

between standard scales (which use "clip") and norms (which use "mask").

Note that ``make_norm_from_scale`` caches the generated norm classes

(not the instances) and reuses them for later calls. For example,

``type(_auto_norm_from_scale("log")) == LogNorm``.

"""

# Actually try to construct an instance, to verify whether

# ``nonpositive="mask"`` is supported.

try:

norm = colors.make_norm_from_scale(

functools.partial(scale_cls, nonpositive="mask"))(

colors.Normalize)()

except TypeError:

norm = colors.make_norm_from_scale(scale_cls)(

colors.Normalize)()

return type(norm)

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 : `.Normalize` (or subclass thereof) or str or None

The normalizing object which scales data, typically into the

interval ``[0, 1]``.

If a `str`, a `.Normalize` subclass is dynamically generated based

on the scale with the corresponding name.

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.callbacks = cbook.CallbackRegistry(signals=["changed"])

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 isinstance(norm, colors.Normalize):

raise ValueError(

"Passing a Normalize instance simultaneously with "

"vmin/vmax is not supported. 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 `~numpy.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 `~numpy.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 `~numpy.uint8`, or it can be floats with

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

Any NaNs or masked elements will be set to 0 alpha.

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 preexisting 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 `~numpy.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 any of R, G, B, or A is nan, set to 0

if np.any(nans := np.isnan(x)):

if x.shape[2] == 4:

xx = xx.copy()

xx[np.any(nans, axis=2), :] = 0

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)

# Account for any masked entries in the original array

# If any of R, G, B, or A are masked for an entry, we set alpha to 0

if np.ma.is_masked(x):

xx[np.any(np.ma.getmaskarray(x), axis=2), 3] = 0

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 value array from array-like *A*.

Parameters

----------

A : array-like or None

The values that are mapped to colors.

The base class `.ScalarMappable` does not make any assumptions on

the dimensionality and shape of the value array *A*.

"""

if A is None:

self._A = None

return

A = cbook.safe_masked_invalid(A, copy=True)

if not np.can_cast(A.dtype, float, "same_kind"):

raise TypeError(f"Image data of dtype {A.dtype} cannot be "

"converted to float")

self._A = A

if not self.norm.scaled():

self.norm.autoscale_None(A)

def get_array(self):

"""

Return the array of values, that are mapped to colors.

The base class `.ScalarMappable` does not make any assumptions on

the dimensionality and shape of the 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 the norm's limits are updated self.changed() will be called

# through the callbacks attached to the norm

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)

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

self.cmap = _ensure_cmap(cmap)

if not in_init:

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

@property

def norm(self):

return self._norm

@norm.setter

def norm(self, norm):

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

if norm is None:

norm = colors.Normalize()

elif isinstance(norm, str):

try:

scale_cls = scale._scale_mapping[norm]

except KeyError:

raise ValueError(

"Invalid norm str name; the following values are "

f"supported: {', '.join(scale._scale_mapping)}"

) from None

norm = _auto_norm_from_scale(scale_cls)()

if norm is self.norm:

# We aren't updating anything

return

in_init = self.norm is None

# Remove the current callback and connect to the new one

if not in_init:

self.norm.callbacks.disconnect(self._id_norm)

self._norm = norm

self._id_norm = self.norm.callbacks.connect('changed',

self.changed)

if not in_init:

self.changed()

def set_norm(self, norm):

"""

Set the normalization instance.

Parameters

----------

norm : `.Normalize` or str 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.

"""

self.norm = norm

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')

# If the norm's limits are updated self.changed() will be called

# through the callbacks attached to the norm

self.norm.autoscale(self._A)

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')

# If the norm's limits are updated self.changed() will be called

# through the callbacks attached to the norm

self.norm.autoscale_None(self._A)

def changed(self):

"""

Call this whenever the mappable is changed to notify all the

callbackSM listeners to the 'changed' signal.

"""

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

self.stale = True

# The docstrings here must be generic enough to apply to all relevant methods.

mpl._docstring.interpd.update(

cmap_doc="""\

cmap : str or `~matplotlib.colors.Colormap`, default: :rc:`image.cmap`

The Colormap instance or registered colormap name used to map scalar data

to colors.""",

norm_doc="""\

norm : str or `~matplotlib.colors.Normalize`, optional

The normalization method used to scale scalar data to the [0, 1] range

before mapping to colors using *cmap*. By default, a linear scaling is

used, mapping the lowest value to 0 and the highest to 1.

If given, this can be one of the following:

- An instance of `.Normalize` or one of its subclasses

(see :ref:`colormapnorms`).

- A scale name, i.e. one of "linear", "log", "symlog", "logit", etc. For a

list of available scales, call `matplotlib.scale.get_scale_names()`.

In that case, a suitable `.Normalize` subclass is dynamically generated

and instantiated.""",

vmin_vmax_doc="""\

vmin, vmax : float, optional

When using scalar data and no explicit *norm*, *vmin* and *vmax* define

the data range that the colormap covers. By default, the colormap covers

the complete value range of the supplied data. It is an error to use

*vmin*/*vmax* when a *norm* instance is given (but using a `str` *norm*

name together with *vmin*/*vmax* is acceptable).""",

)

def _ensure_cmap(cmap):

"""

Ensure that we have a `.Colormap` object.

For internal use to preserve type stability of errors.

Parameters

----------

cmap : None, str, Colormap

- if a `Colormap`, return it

- if a string, look it up in mpl.colormaps

- if None, look up the default color map in mpl.colormaps

Returns

-------

Colormap

"""

if isinstance(cmap, colors.Colormap):

return cmap

cmap_name = cmap if cmap is not None else mpl.rcParams["image.cmap"]

# use check_in_list to ensure type stability of the exception raised by

# the internal usage of this (ValueError vs KeyError)

if cmap_name not in _colormaps:

_api.check_in_list(sorted(_colormaps), cmap=cmap_name)

return mpl.colormaps[cmap_name]