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<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN"

"http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd">

<head>

<title>matplotlib.cm — Matplotlib 3.2.0 documentation</title>

title="Search within Matplotlib 3.2.0 documentation"

href="../../_static/opensearch.xml"/>

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<div id="unreleased-message"> You are reading an old version of the documentation (v3.2.0). For the latest version see <a href="/stable/">https://matplotlib.org/stable/</a> </div>

<div style="float: left; position: absolute; width: 496px; bottom: 0; padding-bottom: 24px">Version 3.2.0</div>

<div id="forkongithub"><a href="https://github.com/matplotlib/matplotlib">Fork me on GitHub</a></div>

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<li><a href="../../users/installing.html">Installation</a></li>

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<li><a href="../../tutorials/index.html">Tutorials</a></li>

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<h1>Source code for matplotlib.cm</h1><div class="highlight"><pre>

"""

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.

"""

import functools

import numpy as np

from numpy import ma

import matplotlib as mpl

import matplotlib.colors as colors

import matplotlib.cbook as cbook

from matplotlib._cm import datad

from matplotlib._cm_listed import cmaps as cmaps_listed

def _reverser(f, x): # Deprecated, remove this at the same time as revcmap.

return f(1 - x) # Toplevel helper for revcmap ensuring cmap picklability.

<div class="viewcode-block" id="revcmap"><a class="viewcode-back" href="../../api/cm_api.html#matplotlib.cm.revcmap">[docs]</a>@cbook.deprecated("3.2", alternative="Colormap.reversed()")

def revcmap(data):

"""Can only handle specification *data* in dictionary format."""

data_r = {}

for key, val in data.items():

if callable(val):

# Return a partial object so that the result is picklable.

valnew = functools.partial(_reverser, val)

else:

# Flip x and exchange the y values facing x = 0 and x = 1.

valnew = [(1.0 - x, y1, y0) for x, y0, y1 in reversed(val)]

data_r[key] = valnew

return data_r</div>

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

def _gen_cmap_d():

"""

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_d[rmap.name] = rmap

return cmap_d

cmap_d = _gen_cmap_d()

locals().update(cmap_d)

# Continue with definitions ...

<div class="viewcode-block" id="register_cmap"><a class="viewcode-back" href="../../api/cm_api.html#matplotlib.cm.register_cmap">[docs]</a>def register_cmap(name=None, cmap=None, data=None, lut=None):

"""

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

It can be used in two ways::

register_cmap(name='swirly', cmap=swirly_cmap)

register_cmap(name='choppy', data=choppydata, lut=128)

In the first case, *cmap* must be a :class:`matplotlib.colors.Colormap`

instance. The *name* is optional; if absent, the name will

be the :attr:`~matplotlib.colors.Colormap.name` attribute of the *cmap*.

In the second case, the three arguments are passed to

the :class:`~matplotlib.colors.LinearSegmentedColormap` initializer,

and the resulting colormap is registered.

"""

cbook._check_isinstance((str, None), name=name)

if name is None:

try:

name = cmap.name

except AttributeError:

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

if isinstance(cmap, colors.Colormap):

cmap_d[name] = cmap

return

# For the remainder, let exceptions propagate.

if lut is None:

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

cmap = colors.LinearSegmentedColormap(name, data, lut)

cmap_d[name] = cmap</div>

<div class="viewcode-block" id="get_cmap"><a class="viewcode-back" href="../../api/cm_api.html#matplotlib.cm.get_cmap">[docs]</a>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.

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

cbook._check_in_list(sorted(cmap_d), name=name)

if lut is None:

return cmap_d[name]

else:

return cmap_d[name]._resample(lut)</div>

<div class="viewcode-block" id="ScalarMappable"><a class="viewcode-back" href="../../api/cm_api.html#matplotlib.cm.ScalarMappable">[docs]</a>class ScalarMappable:

"""

This is a mixin class to support scalar data to RGBA mapping.

The ScalarMappable makes use of data normalization before returning

RGBA colors from the given colormap.

"""

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

"""

Parameters

----------

norm : :class:`matplotlib.colors.Normalize` instance

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 :class:`~matplotlib.colors.Colormap` instance

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

"""

self.callbacksSM = cbook.CallbackRegistry()

if cmap is None:

cmap = get_cmap()

if norm is None:

norm = colors.Normalize()

self._A = None

#: The Normalization instance of this ScalarMappable.

self.norm = norm

#: The Colormap instance of this ScalarMappable.

self.cmap = get_cmap(cmap)

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

self.colorbar = None

self.update_dict = {'array': False}

<div class="viewcode-block" id="ScalarMappable.to_rgba"><a class="viewcode-back" href="../../api/cm_api.html#matplotlib.cm.ScalarMappable.to_rgba">[docs]</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 1-D or 2-D 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</div>

<div class="viewcode-block" id="ScalarMappable.set_array"><a class="viewcode-back" href="../../api/cm_api.html#matplotlib.cm.ScalarMappable.set_array">[docs]</a> def set_array(self, A):

"""Set the image array from numpy array *A*.

Parameters

----------

A : ndarray

"""

self._A = A

self.update_dict['array'] = True</div>

<div class="viewcode-block" id="ScalarMappable.get_array"><a class="viewcode-back" href="../../api/cm_api.html#matplotlib.cm.ScalarMappable.get_array">[docs]</a> def get_array(self):

'Return the array'

return self._A</div>

<div class="viewcode-block" id="ScalarMappable.get_cmap"><a class="viewcode-back" href="../../api/cm_api.html#matplotlib.cm.ScalarMappable.get_cmap">[docs]</a> def get_cmap(self):

'return the colormap'

return self.cmap</div>

<div class="viewcode-block" id="ScalarMappable.get_clim"><a class="viewcode-back" href="../../api/cm_api.html#matplotlib.cm.ScalarMappable.get_clim">[docs]</a> def get_clim(self):

'return the min, max of the color limits for image scaling'

return self.norm.vmin, self.norm.vmax</div>

<div class="viewcode-block" id="ScalarMappable.set_clim"><a class="viewcode-back" href="../../api/cm_api.html#matplotlib.cm.ScalarMappable.set_clim">[docs]</a> 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()</div>

<div class="viewcode-block" id="ScalarMappable.get_alpha"><a class="viewcode-back" href="../../api/cm_api.html#matplotlib.cm.ScalarMappable.get_alpha">[docs]</a> def get_alpha(self):

"""

Returns

-------

alpha : float

Always returns 1.

"""

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

return 1.</div>

<div class="viewcode-block" id="ScalarMappable.set_cmap"><a class="viewcode-back" href="../../api/cm_api.html#matplotlib.cm.ScalarMappable.set_cmap">[docs]</a> def set_cmap(self, cmap):

"""

set the colormap for luminance data

Parameters

----------

cmap : colormap or registered colormap name

"""

cmap = get_cmap(cmap)

self.cmap = cmap

self.changed()</div>

<div class="viewcode-block" id="ScalarMappable.set_norm"><a class="viewcode-back" href="../../api/cm_api.html#matplotlib.cm.ScalarMappable.set_norm">[docs]</a> def set_norm(self, norm):

"""Set the normalization instance.

Parameters

----------

norm : `.Normalize`

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.

"""

cbook._check_isinstance((colors.Normalize, None), norm=norm)

if norm is None:

norm = colors.Normalize()

self.norm = norm

self.changed()</div>

<div class="viewcode-block" id="ScalarMappable.autoscale"><a class="viewcode-back" href="../../api/cm_api.html#matplotlib.cm.ScalarMappable.autoscale">[docs]</a> 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()</div>

<div class="viewcode-block" id="ScalarMappable.autoscale_None"><a class="viewcode-back" href="../../api/cm_api.html#matplotlib.cm.ScalarMappable.autoscale_None">[docs]</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')

self.norm.autoscale_None(self._A)

self.changed()</div>

<div class="viewcode-block" id="ScalarMappable.add_checker"><a class="viewcode-back" href="../../api/cm_api.html#matplotlib.cm.ScalarMappable.add_checker">[docs]</a> 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</div>

<div class="viewcode-block" id="ScalarMappable.check_update"><a class="viewcode-back" href="../../api/cm_api.html#matplotlib.cm.ScalarMappable.check_update">[docs]</a> def check_update(self, checker):

"""

If mappable has changed since the last check,

return True; else return False

"""

if self.update_dict[checker]:

return True

return False</div>

<div class="viewcode-block" id="ScalarMappable.changed"><a class="viewcode-back" href="../../api/cm_api.html#matplotlib.cm.ScalarMappable.changed">[docs]</a> 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</div></div>

</pre></div>

</div>

</div>

Last updated on Mar 04, 2020.

Created using

<a href="http://sphinx-doc.org/">Sphinx</a> 2.4.3.

Doc version v3.2.0-3-gea62ccde8.

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