"""

Builtin colormaps, colormap handling utilities, and the `VectorMappable` and

`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 numbers

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

from matplotlib._cm_multivar import cmap_families as multivar_cmaps

from matplotlib._cm_bivar import cmaps as bivar_cmaps

from typing import Union

ColorMapType = Union[colors.Colormap,

colors.BivarColormap,

colors.MultivarColormap]

_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.

aliases = {

# alias -> original name

'grey': 'gray',

'gist_grey': 'gist_gray',

'gist_yerg': 'gist_yarg',

'Grays': 'Greys',

}

for alias, original_name in aliases.items():

cmap = cmap_d[original_name].copy()

cmap.name = alias

cmap_d[alias] = cmap

# 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 valid colormap type, simply return it

if isinstance(cmap, (colors.Colormap,

colors.BivarColormap,

colors.MultivarColormap)):

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)

_multivar_colormaps = ColormapRegistry(multivar_cmaps)

globals().update(_multivar_colormaps)

_bivar_colormaps = ColormapRegistry(bivar_cmaps)

globals().update(_bivar_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 Colorizer():

"""

Class that holds (multiple) norm and (one) colormap object.

"""

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

self._cmap = None

self._set_cmap(cmap)

self._id_norm = [None] * self.cmap.n_variates

self._norm = [None] * self.cmap.n_variates

self.norm = norm # The Normalize instance of this Colorizer

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

self.colorbar = None

# @property

# def n_variates(self):

# return self.cmap.n_variates

@property

def norm(self):

return self._norm

@norm.setter

def norm(self, norm):

norm = _ensure_multivariate_norm(self.cmap.n_variates, norm)

changed = False

for i, n in enumerate(norm):

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

if n is None:

n = colors.Normalize()

elif isinstance(n, str):

try:

scale_cls = scale._scale_mapping[n]

except KeyError:

raise ValueError(

"Invalid norm str name; the following values are "

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

) from None

n = _auto_norm_from_scale(scale_cls)()

if n is self._norm[i]:

continue

if self._norm[i] is not None:

# Remove the current callback and connect to the new one

self._norm[i].callbacks.disconnect(self._id_norm[i])

# emit changed if we are changing norm

# do not emit during initialization (self.norm[i] is None)

changed = True

self._norm[i] = n

self._id_norm[i] = self._norm[i].callbacks.connect('changed',

self.changed)

if changed:

self.changed()

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

"""

Helper for initial scaling.

Used by public functions that create a VectorMappable 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.

"""

norm = _ensure_multivariate_norm(self.cmap.n_variates, norm)

vmin, vmax = _ensure_multivariate_clim(self.cmap.n_variates, vmin, vmax)

for i, _ in enumerate(self._norm):

if vmin[i] is not None or vmax[i] is not None:

if isinstance(norm[i], 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.")

self._set_clim_i(i, vmin[i], vmax[i])

# always resolve the autoscaling so we have concrete limits

# rather than deferring to draw time.

self.autoscale_None(A)

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 VectorMappable.

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 self.cmap.n_variates == 1 and x.ndim == 3:

# looks like imega data, try to process it without cmap

return self._pass_image_data(x, alpha, bytes, norm)

except AttributeError:

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

pass

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

if self.cmap.n_variates == 1:

x = ma.asarray(x)

if norm:

x = self.normalize(x)

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

else:

if norm:

x = self.normalize(x)

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

return rgba

@staticmethod

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

"""

Helper function to pass ndarray of shape (...,3) or (..., 4)

through `to_rgba()`, see `to_rgba()` for docstring.

"""

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

def normalize(self, x):

"""

Normalize the data in x.

Parameters

----------

x : np.array or sequence of arrays. Must be compatible with the number

of variates (`Colorizer.n_variates`).

- If there is a single norm, x may be of any shape.

- If there are two norms x may be a sequce of length 2, an array with

complex numbers, or an array with a dtype containing two fields

- If there more than two norms, x may be a sequce of length n, or an array

with a dtype containing n fields.

Returns

-------

np.array, or if more than one variate, a list of np.arrays.

"""

if self.cmap.n_variates == 1:

return self._norm[0](x)

elif hasattr(x, 'dtype') and len(x.dtype.descr) > 1:

x = _iterable_variates_in_data(x)

elif np.iscomplexobj(x):

# NOTE: when data is passed to plotting methods, i.e.

# imshow(data), and the data is complex, it is converted

# to a dtype with two fields.

# Therefore, complex data should only arrive here if

# the user invokes VectorMappable.to_rgba(data) or

# Colorizer.to_rgba(data) etc. with complex data directly.

x = [x.real, x.imag]

return [norm(xx) for norm, xx in zip(self._norm, x)]

def autoscale(self, A):

"""

Autoscale the scalar limits on the norm instance using the

current array

"""

if 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

for n, a in zip(self._norm, _iterable_variates_in_data(A)):

n.autoscale(a)

def autoscale_None(self, A):

"""

Autoscale the scalar limits on the norm instance using the

current array, changing only limits that are None

"""

if 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

for n, a in zip(self._norm, _iterable_variates_in_data(A)):

n.autoscale_None(a)

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 = _ensure_cmap(cmap)

if not in_init:

if not cmap.n_variates == self.cmap.n_variates:

raise ValueError('The selected colormap does not have'

' the correct number of variates')

self._cmap = cmap

if not in_init:

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

@property

def cmap(self):

return self._cmap

@cmap.setter

def cmap(self, cmap):

self._set_cmap(cmap)

def _set_clim_i(self, i, vmin, vmax):

"""

Set the norm limits for the norm at index i

"""

if vmin is not None and vmax is not None:

# this block exists to avoid calling _changed twice.

vmin = colors._sanitize_extrema(vmin)

vmax = colors._sanitize_extrema(vmax)

if vmin != self._norm[i]._vmin or vmax != self._norm[i]._vmax:

self._norm[i]._vmin = vmin

self._norm[i]._vmax = vmax

self._norm[i]._changed()

else:

if vmin is not None:

self._norm[i].vmin = vmin

if vmax is not None:

self._norm[i].vmax = vmax

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

"""

Set the norm limits for image scaling.

Parameters

----------

vmin, vmax : float

The limits.

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

"""

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

# through the callbacks attached to the norm

# we can add logic here to avoid multiple calls to self.changed() if the

# data is multivariate. This would be a minor efficiency improvement, but

# the use case of repeated calls to multivariate set_clim is limited,

# so the performance improvement is not prioritized at this moment.

vmin, vmax = _ensure_multivariate_clim(self.cmap.n_variates, vmin, vmax)

for i, _ in enumerate(self._norm):

self._set_clim_i(i, vmin[i], vmax[i])

def get_clim(self):

"""

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

"""

return [n.vmin for n in self._norm], [n.vmax for n in self._norm]

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.stale = True

@property

def vmin(self):

return self.get_clim[0]

@vmin.setter

def vmin(self, vmin):

if not np.iterable(vmin):

vmin = [vmin]

self.set_clim(vmin=vmin)

@property

def vmax(self):

return self.get_clim[1]

@vmax.setter

def vmax(self, vmax):

if not np.iterable(vmax):

vmax = [vmax]

self.set_clim(vmax=vmax)

@property

def clip(self):

return [n.clip for n in self._norm]

@clip.setter

def clip(self, clip):

if not np.iterable(clip):

clip = [clip]*len(self._norm)

for n, c in zip(self._norm, clip):

n.clip = c

def __getitem__(self, index):

"""

Returns a Colorizer object containing the norm and colormap for one axis

"""

if self.cmap.n_variates > 1:

if index >= 0 and index < self.cmap.n_variates:

part = Colorizer(cmap=self._cmap[index], norm=self._norm[index])

part._super_colorizer = self

part._super_colorizer_index = index

part._id_parent_cmap = id(self.cmap)

part._id_parent_norm = id(self._norm[index])

self.callbacks.connect('changed', part._check_update_super_colorizer)

return part

elif self.cmap.n_variates == 1 and index == 0:

return self

raise ValueError(f'Only 0..{self.cmap.n_variates-1} are valid indexes'

' for this Colorizer object.')

def _check_update_super_colorizer(self):

"""

If this `Colorizer` object was created by __getitem__ it is a

one-dimensional component of another `Colorizer`.

In this case, `self._super_colorizer` is the Colorizer this was generated from.

This function propagetes changes from the `self._super_colorizer` to `self`.

"""

if hasattr(self, '_super_colorizer'):

# _super_colorizer, the colorizer this is a component of

if id(self._super_colorizer.cmap) != self._id_parent_cmap:

self.cmap = self._super_colorizer.cmap[self._super_colorizer_index]

super_colorizer_norm =\

self._super_colorizer._norm[self._super_colorizer_index]

if id(super_colorizer_norm) != self._id_parent_norm:

self.norm = [super_colorizer_norm]

def _format_cursor_data(self, data):

"""

Return a string representation of *data*.

Uses the colorbar's formatter to format the data.

"""

if (np.ndim(data) == 0 or len(data) == self.cmap.n_variates):

if self.cmap.n_variates == 1:

# This if test is equivalent to `isinstance(self.cmap, Colormap)`

data = [data]

num_colors = [self.cmap.N]

else:

if isinstance(self.cmap, colors.BivarColormap):

num_colors = [self.cmap.N, self.cmap.M]

else: # i.e. a MultivarColormap object

num_colors = [component.N for component in self.cmap]

out_str = '['

for nn, dd, nc in zip(self._norm, data, num_colors):

if np.ma.getmask(dd):

out_str += ", "

else:

# Figure out a reasonable amount of significant digits

normed = nn(dd)

if np.isfinite(normed):

if isinstance(nn, colors.BoundaryNorm):

# not an invertible normalization mapping

cur_idx = np.argmin(np.abs(nn.boundaries - dd))

neigh_idx = max(0, cur_idx - 1)

# use max diff to prevent delta == 0

delta = np.diff(

nn.boundaries[neigh_idx:cur_idx + 2]

).max()

else:

# Midpoints of neighboring color intervals.

neighbors = nn.inverse(

(int(normed * nc) + np.array([0, 1])) / nc)

delta = abs(neighbors - dd).max()

g_sig_digits = cbook._g_sig_digits(dd, delta)

else:

g_sig_digits = 3 # Consistent with default below.

out_str += f"{dd:-#.{g_sig_digits}g}, "

return out_str[:-2] + ']'

else:

# This point is reacehd if the number of colormaps does not match the length

# of data. This happens in the as-of-yet hypothetical case that the norm

# converts from one data field to two.

raise ValueError

try:

data[0]

except (TypeError, IndexError):

data = [data]

data_str = ', '.join(f'{item:0.3g}' for item in data

if isinstance(item, numbers.Number))

return "[" + data_str + "]"

class ColorizerShim:

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

self.colorizer._scale_norm(norm, vmin, vmax, self._A)

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 VectorMappable.

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).

"""

return self.colorizer.to_rgba(x, alpha=alpha, bytes=bytes, norm=norm)

def get_cmap(self):

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

return self.colorizer.cmap

def get_clim(self):

"""

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

"""

if self.colorizer.cmap.n_variates == 1:

return self.colorizer._norm[0].vmin, self.colorizer._norm[0].vmax

return self.colorizer.get_clim()

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

"""

Set the norm limits for image scaling.

Parameters

----------

vmin, vmax : float

The limits.

For scalar data, 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 self.cmap.n_variates == 1:

try:

vmin, vmax = vmin

except (TypeError, ValueError):

pass

self.colorizer.set_clim(vmin, vmax)

def get_alpha(self):

"""

Returns

-------

float

Always returns 1.

"""

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

return 1.

@property

def cmap(self):

return self.colorizer.cmap

@cmap.setter

def cmap(self, cmap):

self.colorizer.cmap = cmap

def set_cmap(self, cmap):

"""

Set the colormap for luminance data.

Parameters

----------

cmap : `.Colormap` or str or None

"""

self.colorizer.cmap = cmap

@property

def norm(self):

if self.cmap.n_variates == 1:

return self.colorizer.norm[0]

return self.colorizer.norm

@norm.setter

def norm(self, norm):

self.colorizer.norm = norm

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

"""

self.colorizer.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

"""

self.colorizer.autoscale_None(self._A)

def _parse_multivariate_data(self, data):

"""

Parse data to a dtype with self.cmap.n_variates.

Input data of shape (n_variates, n, m) is converted to an array of shape

(n, m) with data type np.dtype(f'{data.dtype}, ' * n_variates)

Complex data is returned as a view with dtype np.dtype('float64, float64')

or np.dtype('float32, float32')

If n_variates is 1 and data is not of type np.ndarray (i.e. PIL.Image),

the data is returned unchanged.

If data is None, the function returns None

Parameters

----------

data : np.ndarray, PIL.Image or None

Returns

-------

np.ndarray, PIL.Image or None

"""

return _ensure_multivariate_data(self.cmap.n_variates, data)

@property

def colorbar(self):

return self.colorizer.colorbar

@colorbar.setter

def colorbar(self, colorbar):

self.colorizer.colorbar = colorbar

class ScalarMappable(ColorizerShim):

"""

A mixin class to map one or multiple sets of scalar data to RGBA.

The VectorMappable applies data normalization before returning RGBA colors

from the given `~matplotlib.colors.Colormap`, `~matplotlib.colors.BivarColormap`,

or `~matplotlib.colors.MultivarColormap`.

"""

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

if isinstance(norm, Colorizer):

self.colorizer = norm

else:

self.colorizer = Colorizer(cmap, norm)

self._id_colorizer = self.colorizer.callbacks.connect('changed', self.changed)

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

self.get_alpha = lambda: 1

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 `.VectorMappable` does not make any assumptions on

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

"""

if A is None:

self._A = None

return

A = _ensure_multivariate_data(self.cmap.n_variates, A)

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

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

if A.dtype.fields is None:

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

f"converted to float")

else:

for key in A.dtype.fields:

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

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

f"converted to a sequence of floats")

self._A = A

self.colorizer.autoscale_None(A)