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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.colors — Matplotlib 3.4.2 documentation</title>

title="Search within Matplotlib 3.4.2 documentation"

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

</head><body>

<div id="unreleased-message"> You are reading an old version of the documentation (v3.4.2). For the latest version see <a href="/stable/">https://matplotlib.org/stable/</a> </div>

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<div style="float: left; position: absolute; width: 496px; bottom: 0; padding-bottom: 24px">Version 3.4.2</div>

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

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<ul>

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<li class="nav-item nav-item-1"><a href="../index.html" >Module code</a> »</li>

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<li class="nav-item nav-item-this"><a href="">matplotlib.colors</a></li>

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</div>

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

"""

A module for converting numbers or color arguments to *RGB* or *RGBA*.

*RGB* and *RGBA* are sequences of, respectively, 3 or 4 floats in the

range 0-1.

This module includes functions and classes for color specification conversions,

and for mapping numbers to colors in a 1-D array of colors called a colormap.

Mapping data onto colors using a colormap typically involves two steps: a data

array is first mapped onto the range 0-1 using a subclass of `Normalize`,

then this number is mapped to a color using a subclass of `Colormap`. Two

subclasses of `Colormap` provided here: `LinearSegmentedColormap`, which uses

piecewise-linear interpolation to define colormaps, and `ListedColormap`, which

makes a colormap from a list of colors.

.. seealso::

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

make colormaps and

:doc:`/tutorials/colors/colormaps` for a list of built-in colormaps.

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

normalization

More colormaps are available at palettable_.

The module also provides functions for checking whether an object can be

interpreted as a color (`is_color_like`), for converting such an object

to an RGBA tuple (`to_rgba`) or to an HTML-like hex string in the

"#rrggbb" format (`to_hex`), and a sequence of colors to an (n, 4)

RGBA array (`to_rgba_array`). Caching is used for efficiency.

Matplotlib recognizes the following formats to specify a color:

* an RGB or RGBA (red, green, blue, alpha) tuple of float values in closed

interval ``[0, 1]`` (e.g., ``(0.1, 0.2, 0.5)`` or ``(0.1, 0.2, 0.5, 0.3)``);

* a hex RGB or RGBA string (e.g., ``'#0f0f0f'`` or ``'#0f0f0f80'``;

case-insensitive);

* a shorthand hex RGB or RGBA string, equivalent to the hex RGB or RGBA

string obtained by duplicating each character, (e.g., ``'#abc'``, equivalent

to ``'#aabbcc'``, or ``'#abcd'``, equivalent to ``'#aabbccdd'``;

case-insensitive);

* a string representation of a float value in ``[0, 1]`` inclusive for gray

level (e.g., ``'0.5'``);

* one of the characters ``{'b', 'g', 'r', 'c', 'm', 'y', 'k', 'w'}``, which

are short-hand notations for shades of blue, green, red, cyan, magenta,

yellow, black, and white. Note that the colors ``'g', 'c', 'm', 'y'`` do not

coincide with the X11/CSS4 colors. Their particular shades were chosen for

better visibility of colored lines against typical backgrounds.

* a X11/CSS4 color name (case-insensitive);

* a name from the `xkcd color survey`_, prefixed with ``'xkcd:'`` (e.g.,

``'xkcd:sky blue'``; case insensitive);

* one of the Tableau Colors from the 'T10' categorical palette (the default

color cycle): ``{'tab:blue', 'tab:orange', 'tab:green', 'tab:red',

'tab:purple', 'tab:brown', 'tab:pink', 'tab:gray', 'tab:olive', 'tab:cyan'}``

(case-insensitive);

* a "CN" color spec, i.e. 'C' followed by a number, which is an index into the

default property cycle (:rc:`axes.prop_cycle`); the indexing is intended to

occur at rendering time, and defaults to black if the cycle does not include

color.

.. _palettable: https://jiffyclub.github.io/palettable/

.. _xkcd color survey: https://xkcd.com/color/rgb/

"""

import base64

from collections.abc import Sized, Sequence

import copy

import functools

import inspect

import io

import itertools

from numbers import Number

import re

from PIL import Image

from PIL.PngImagePlugin import PngInfo

import matplotlib as mpl

import numpy as np

from matplotlib import _api, cbook, scale

from ._color_data import BASE_COLORS, TABLEAU_COLORS, CSS4_COLORS, XKCD_COLORS

class _ColorMapping(dict):

def __init__(self, mapping):

super().__init__(mapping)

self.cache = {}

def __setitem__(self, key, value):

super().__setitem__(key, value)

self.cache.clear()

def __delitem__(self, key):

super().__delitem__(key)

self.cache.clear()

_colors_full_map = {}

# Set by reverse priority order.

_colors_full_map.update(XKCD_COLORS)

_colors_full_map.update({k.replace('grey', 'gray'): v

for k, v in XKCD_COLORS.items()

if 'grey' in k})

_colors_full_map.update(CSS4_COLORS)

_colors_full_map.update(TABLEAU_COLORS)

_colors_full_map.update({k.replace('gray', 'grey'): v

for k, v in TABLEAU_COLORS.items()

if 'gray' in k})

_colors_full_map.update(BASE_COLORS)

_colors_full_map = _ColorMapping(_colors_full_map)

_REPR_PNG_SIZE = (512, 64)

<div class="viewcode-block" id="get_named_colors_mapping"><a class="viewcode-back" href="../../api/_as_gen/matplotlib.colors.get_named_colors_mapping.html#matplotlib.colors.get_named_colors_mapping">[docs]</a>def get_named_colors_mapping():

"""Return the global mapping of names to named colors."""

return _colors_full_map</div>

def _sanitize_extrema(ex):

if ex is None:

return ex

try:

ret = ex.item()

except AttributeError:

ret = float(ex)

return ret

def _is_nth_color(c):

"""Return whether *c* can be interpreted as an item in the color cycle."""

return isinstance(c, str) and re.match(r"\AC[0-9]+\Z", c)

<div class="viewcode-block" id="is_color_like"><a class="viewcode-back" href="../../api/_as_gen/matplotlib.colors.is_color_like.html#matplotlib.colors.is_color_like">[docs]</a>def is_color_like(c):

"""Return whether *c* can be interpreted as an RGB(A) color."""

# Special-case nth color syntax because it cannot be parsed during setup.

if _is_nth_color(c):

return True

try:

to_rgba(c)

except ValueError:

return False

else:

return True</div>

def _check_color_like(**kwargs):

"""

For each *key, value* pair in *kwargs*, check that *value* is color-like.

"""

for k, v in kwargs.items():

if not is_color_like(v):

raise ValueError(f"{v!r} is not a valid value for {k}")

<div class="viewcode-block" id="same_color"><a class="viewcode-back" href="../../api/_as_gen/matplotlib.colors.same_color.html#matplotlib.colors.same_color">[docs]</a>def same_color(c1, c2):

"""

Return whether the colors *c1* and *c2* are the same.

*c1*, *c2* can be single colors or lists/arrays of colors.

"""

c1 = to_rgba_array(c1)

c2 = to_rgba_array(c2)

n1 = max(c1.shape[0], 1) # 'none' results in shape (0, 4), but is 1-elem

n2 = max(c2.shape[0], 1) # 'none' results in shape (0, 4), but is 1-elem

if n1 != n2:

raise ValueError('Different number of elements passed.')

# The following shape test is needed to correctly handle comparisons with

# 'none', which results in a shape (0, 4) array and thus cannot be tested

# via value comparison.

return c1.shape == c2.shape and (c1 == c2).all()</div>

<div class="viewcode-block" id="to_rgba"><a class="viewcode-back" href="../../api/_as_gen/matplotlib.colors.to_rgba.html#matplotlib.colors.to_rgba">[docs]</a>def to_rgba(c, alpha=None):

"""

Convert *c* to an RGBA color.

Parameters

----------

c : Matplotlib color or ``np.ma.masked``

alpha : float, optional

If *alpha* is not ``None``, it forces the alpha value, except if *c* is

``"none"`` (case-insensitive), which always maps to ``(0, 0, 0, 0)``.

Returns

-------

tuple

Tuple of ``(r, g, b, a)`` scalars.

"""

# Special-case nth color syntax because it should not be cached.

if _is_nth_color(c):

from matplotlib import rcParams

prop_cycler = rcParams['axes.prop_cycle']

colors = prop_cycler.by_key().get('color', ['k'])

c = colors[int(c[1:]) % len(colors)]

try:

rgba = _colors_full_map.cache[c, alpha]

except (KeyError, TypeError): # Not in cache, or unhashable.

rgba = None

if rgba is None: # Suppress exception chaining of cache lookup failure.

rgba = _to_rgba_no_colorcycle(c, alpha)

try:

_colors_full_map.cache[c, alpha] = rgba

except TypeError:

pass

return rgba</div>

def _to_rgba_no_colorcycle(c, alpha=None):

"""

Convert *c* to an RGBA color, with no support for color-cycle syntax.

If *alpha* is not ``None``, it forces the alpha value, except if *c* is

``"none"`` (case-insensitive), which always maps to ``(0, 0, 0, 0)``.

"""

orig_c = c

if c is np.ma.masked:

return (0., 0., 0., 0.)

if isinstance(c, str):

if c.lower() == "none":

# Named color.

try:

# This may turn c into a non-string, so we check again below.

c = _colors_full_map[c]

except KeyError:

if len(orig_c) != 1:

try:

c = _colors_full_map[c.lower()]

except KeyError:

pass

if isinstance(c, str):

# hex color in #rrggbb format.

match = re.match(r"\A#[a-fA-F0-9]{6}\Z", c)

if match:

return (tuple(int(n, 16) / 255

+ (alpha if alpha is not None else 1.,))

# hex color in #rgb format, shorthand for #rrggbb.

match = re.match(r"\A#[a-fA-F0-9]{3}\Z", c)

if match:

for n in [c[1]*2, c[2]*2, c[3]*2])

# hex color with alpha in #rrggbbaa format.

match = re.match(r"\A#[a-fA-F0-9]{8}\Z", c)

if match:

color = [int(n, 16) / 255

for n in [c[1:3], c[3:5], c[5:7], c[7:9]]]

if alpha is not None:

color[-1] = alpha

return tuple(color)

# hex color with alpha in #rgba format, shorthand for #rrggbbaa.

match = re.match(r"\A#[a-fA-F0-9]{4}\Z", c)

if match:

if alpha is not None:

color[-1] = alpha

return tuple(color)

# string gray.

try:

c = float(c)

except ValueError:

pass

else:

if not (0 <= c <= 1):

raise ValueError(

f"Invalid string grayscale value {orig_c!r}. "

f"Value must be within 0-1 range")

return c, c, c, alpha if alpha is not None else 1.

raise ValueError(f"Invalid RGBA argument: {orig_c!r}")

# turn 2-D array into 1-D array

if isinstance(c, np.ndarray):

if c.ndim == 2 and c.shape[0] == 1:

c = c.reshape(-1)

# tuple color.

if not np.iterable(c):

if len(c) not in [3, 4]:

raise ValueError("RGBA sequence should have length 3 or 4")

if not all(isinstance(x, Number) for x in c):

# Checks that don't work: `map(float, ...)`, `np.array(..., float)` and

# `np.array(...).astype(float)` would all convert "0.5" to 0.5.

# Return a tuple to prevent the cached value from being modified.

c = tuple(map(float, c))

if len(c) == 3 and alpha is None:

alpha = 1

if alpha is not None:

c = c[:3] + (alpha,)

if any(elem < 0 or elem > 1 for elem in c):

raise ValueError("RGBA values should be within 0-1 range")

return c

<div class="viewcode-block" id="to_rgba_array"><a class="viewcode-back" href="../../api/_as_gen/matplotlib.colors.to_rgba_array.html#matplotlib.colors.to_rgba_array">[docs]</a>def to_rgba_array(c, alpha=None):

"""

Convert *c* to a (n, 4) array of RGBA colors.

Parameters

----------

c : Matplotlib color or array of colors

If *c* is a masked array, an ndarray is returned with a (0, 0, 0, 0)

row for each masked value or row in *c*.

alpha : float or sequence of floats, optional

If *alpha* is not ``None``, it forces the alpha value, except if *c* is

``"none"`` (case-insensitive), which always maps to ``(0, 0, 0, 0)``.

If *alpha* is a sequence and *c* is a single color, *c* will be

repeated to match the length of *alpha*.

Returns

-------

array

(n, 4) array of RGBA colors.

"""

# Special-case inputs that are already arrays, for performance. (If the

# array has the wrong kind or shape, raise the error during one-at-a-time

# conversion.)

if np.iterable(alpha):

alpha = np.asarray(alpha).ravel()

if (isinstance(c, np.ndarray) and c.dtype.kind in "if"

and c.ndim == 2 and c.shape[1] in [3, 4]):

mask = c.mask.any(axis=1) if np.ma.is_masked(c) else None

c = np.ma.getdata(c)

if np.iterable(alpha):

if c.shape[0] == 1 and alpha.shape[0] > 1:

c = np.tile(c, (alpha.shape[0], 1))

elif c.shape[0] != alpha.shape[0]:

raise ValueError("The number of colors must match the number"

" of alpha values if there are more than one"

" of each.")

if c.shape[1] == 3:

result = np.column_stack([c, np.zeros(len(c))])

result[:, -1] = alpha if alpha is not None else 1.

elif c.shape[1] == 4:

result = c.copy()

if alpha is not None:

result[:, -1] = alpha

if mask is not None:

result[mask] = 0

if np.any((result < 0) | (result > 1)):

raise ValueError("RGBA values should be within 0-1 range")

return result

# Handle single values.

# Note that this occurs *after* handling inputs that are already arrays, as

# `to_rgba(c, alpha)` (below) is expensive for such inputs, due to the need

# to format the array in the ValueError message(!).

if cbook._str_lower_equal(c, "none"):

return np.zeros((0, 4), float)

try:

if np.iterable(alpha):

return np.array([to_rgba(c, a) for a in alpha], float)

else:

return np.array([to_rgba(c, alpha)], float)

except (ValueError, TypeError):

pass

if isinstance(c, str):

raise ValueError("Using a string of single character colors as "

"a color sequence is not supported. The colors can "

"be passed as an explicit list instead.")

# Quick path if the whole sequence can be directly converted to a numpy

# array in one shot.

if isinstance(c, Sequence):

lens = {len(cc) if isinstance(cc, (list, tuple)) else -1 for cc in c}

if lens == {3}:

rgba = np.column_stack([c, np.ones(len(c))])

elif lens == {4}:

rgba = np.array(c)

else:

rgba = np.array([to_rgba(cc) for cc in c])

else:

if alpha is not None:

rgba[:, 3] = alpha

return rgba</div>

<div class="viewcode-block" id="to_rgb"><a class="viewcode-back" href="../../api/_as_gen/matplotlib.colors.to_rgb.html#matplotlib.colors.to_rgb">[docs]</a>def to_rgb(c):

"""Convert *c* to an RGB color, silently dropping the alpha channel."""

return to_rgba(c)[:3]</div>

<div class="viewcode-block" id="to_hex"><a class="viewcode-back" href="../../api/_as_gen/matplotlib.colors.to_hex.html#matplotlib.colors.to_hex">[docs]</a>def to_hex(c, keep_alpha=False):

"""

Convert *c* to a hex color.

Uses the ``#rrggbb`` format if *keep_alpha* is False (the default),

``#rrggbbaa`` otherwise.

"""

c = to_rgba(c)

if not keep_alpha:

c = c[:3]

return "#" + "".join(format(int(round(val * 255)), "02x") for val in c)</div>

### Backwards-compatible color-conversion API

cnames = CSS4_COLORS

hexColorPattern = re.compile(r"\A#[a-fA-F0-9]{6}\Z")

rgb2hex = to_hex

hex2color = to_rgb

class ColorConverter:

"""

A class only kept for backwards compatibility.

Its functionality is entirely provided by module-level functions.

"""

colors = _colors_full_map

cache = _colors_full_map.cache

to_rgb = staticmethod(to_rgb)

to_rgba = staticmethod(to_rgba)

to_rgba_array = staticmethod(to_rgba_array)

colorConverter = ColorConverter()

### End of backwards-compatible color-conversion API

def _create_lookup_table(N, data, gamma=1.0):

r"""

Create an *N* -element 1D lookup table.

This assumes a mapping :math:`f : [0, 1] \rightarrow [0, 1]`. The returned

data is an array of N values :math:`y = f(x)` where x is sampled from

[0, 1].

By default (*gamma* = 1) x is equidistantly sampled from [0, 1]. The

*gamma* correction factor :math:`\gamma` distorts this equidistant

sampling by :math:`x \rightarrow x^\gamma`.

Parameters

----------

N : int

The number of elements of the created lookup table; at least 1.

data : (M, 3) array-like or callable

Defines the mapping :math:`f`.

If a (M, 3) array-like, the rows define values (x, y0, y1). The x

values must start with x=0, end with x=1, and all x values be in

increasing order.

A value between :math:`x_i` and :math:`x_{i+1}` is mapped to the range

:math:`y^1_{i-1} \ldots y^0_i` by linear interpolation.

For the simple case of a y-continuous mapping, y0 and y1 are identical.

The two values of y are to allow for discontinuous mapping functions.

E.g. a sawtooth with a period of 0.2 and an amplitude of 1 would be::

[(0, 1, 0), (0.2, 1, 0), (0.4, 1, 0), ..., [(1, 1, 0)]

In the special case of ``N == 1``, by convention the returned value

is y0 for x == 1.

If *data* is a callable, it must accept and return numpy arrays::

data(x : ndarray) -> ndarray

and map values between 0 - 1 to 0 - 1.

gamma : float

Gamma correction factor for input distribution x of the mapping.

See also https://en.wikipedia.org/wiki/Gamma_correction.

Returns

-------

array

The lookup table where ``lut[x * (N-1)]`` gives the closest value

for values of x between 0 and 1.

Notes

-----

This function is internally used for `.LinearSegmentedColormap`.

"""

if callable(data):

xind = np.linspace(0, 1, N) ** gamma

lut = np.clip(np.array(data(xind), dtype=float), 0, 1)

return lut

try:

adata = np.array(data)

except Exception as err:

raise TypeError("data must be convertible to an array") from err

shape = adata.shape

if len(shape) != 2 or shape[1] != 3:

raise ValueError("data must be nx3 format")

x = adata[:, 0]

y0 = adata[:, 1]

y1 = adata[:, 2]

if x[0] != 0. or x[-1] != 1.0:

raise ValueError(

"data mapping points must start with x=0 and end with x=1")

if (np.diff(x) < 0).any():

raise ValueError("data mapping points must have x in increasing order")

# begin generation of lookup table

if N == 1:

# convention: use the y = f(x=1) value for a 1-element lookup table

lut = np.array(y0[-1])

else:

x = x * (N - 1)

xind = (N - 1) * np.linspace(0, 1, N) ** gamma

ind = np.searchsorted(x, xind)[1:-1]

distance = (xind[1:-1] - x[ind - 1]) / (x[ind] - x[ind - 1])

lut = np.concatenate([

[y1[0]],

distance * (y0[ind] - y1[ind - 1]) + y1[ind - 1],

[y0[-1]],

])

# ensure that the lut is confined to values between 0 and 1 by clipping it

return np.clip(lut, 0.0, 1.0)

def _warn_if_global_cmap_modified(cmap):

if getattr(cmap, '_global', False):

_api.warn_deprecated(

"3.3",

removal="3.6",

message="You are modifying the state of a globally registered "

"colormap. This has been deprecated since %(since)s and "

"%(removal)s, you will not be able to modify a "

"registered colormap in-place. To remove this warning, "

"you can make a copy of the colormap first. "

f'cmap = mpl.cm.get_cmap("{cmap.name}").copy()'

)

<div class="viewcode-block" id="Colormap"><a class="viewcode-back" href="../../api/_as_gen/matplotlib.colors.Colormap.html#matplotlib.colors.Colormap">[docs]</a>class Colormap:

"""

Baseclass for all scalar to RGBA mappings.

Typically, Colormap instances are used to convert data values (floats)

from the interval ``[0, 1]`` to the RGBA color that the respective

Colormap represents. For scaling of data into the ``[0, 1]`` interval see

`matplotlib.colors.Normalize`. Subclasses of `matplotlib.cm.ScalarMappable`

make heavy use of this ``data -> normalize -> map-to-color`` processing

chain.

"""

<div class="viewcode-block" id="Colormap.__init__"><a class="viewcode-back" href="../../api/_as_gen/matplotlib.colors.Colormap.html#matplotlib.colors.Colormap.__init__">[docs]</a> def __init__(self, name, N=256):

"""

Parameters

----------

name : str

The name of the colormap.

N : int

The number of rgb quantization levels.

"""

self.name = name

self.N = int(N) # ensure that N is always int

self._rgba_bad = (0.0, 0.0, 0.0, 0.0) # If bad, don't paint anything.

self._rgba_under = None

self._rgba_over = None

self._i_under = self.N

self._i_over = self.N + 1

self._i_bad = self.N + 2

self._isinit = False

#: When this colormap exists on a scalar mappable and colorbar_extend

#: is not False, colorbar creation will pick up ``colorbar_extend`` as

#: the default value for the ``extend`` keyword in the

#: `matplotlib.colorbar.Colorbar` constructor.

self.colorbar_extend = False</div>

<div class="viewcode-block" id="Colormap.__call__"><a class="viewcode-back" href="../../api/_as_gen/matplotlib.colors.Colormap.html#matplotlib.colors.Colormap.__call__">[docs]</a> def __call__(self, X, alpha=None, bytes=False):

"""

Parameters

----------

X : float or int, ndarray or scalar

The data value(s) to convert to RGBA.

For floats, X should be in the interval ``[0.0, 1.0]`` to

return the RGBA values ``X*100`` percent along the Colormap line.

For integers, X should be in the interval ``[0, Colormap.N)`` to

return RGBA values *indexed* from the Colormap with index ``X``.

alpha : float or array-like or None

Alpha must be a scalar between 0 and 1, a sequence of such

floats with shape matching X, or None.

bytes : bool

If False (default), the returned RGBA values will be floats in the

interval ``[0, 1]`` otherwise they will be uint8s in the interval

``[0, 255]``.

Returns

-------

Tuple of RGBA values if X is scalar, otherwise an array of

RGBA values with a shape of ``X.shape + (4, )``.

"""

if not self._isinit:

self._init()

mask_bad = X.mask if np.ma.is_masked(X) else np.isnan(X) # Mask nan's.

xa = np.array(X, copy=True)

if not xa.dtype.isnative:

xa = xa.byteswap().newbyteorder() # Native byteorder is faster.

if xa.dtype.kind == "f":

with np.errstate(invalid="ignore"):

xa *= self.N

# Negative values are out of range, but astype(int) would

# truncate them towards zero.

xa[xa < 0] = -1

# xa == 1 (== N after multiplication) is not out of range.

xa[xa == self.N] = self.N - 1

# Avoid converting large positive values to negative integers.

np.clip(xa, -1, self.N, out=xa)

xa = xa.astype(int)

# Set the over-range indices before the under-range;

# otherwise the under-range values get converted to over-range.

xa[xa > self.N - 1] = self._i_over

xa[mask_bad] = self._i_bad

if bytes:

lut = (self._lut * 255).astype(np.uint8)

else:

lut = self._lut.copy() # Don't let alpha modify original _lut.

rgba = np.empty(shape=xa.shape + (4,), dtype=lut.dtype)

lut.take(xa, axis=0, mode='clip', out=rgba)

if alpha is not None:

if np.iterable(alpha):

if alpha.shape != xa.shape:

raise ValueError("alpha is array-like but its shape"

" %s doesn't match that of X %s" %

(alpha.shape, xa.shape))

alpha = np.clip(alpha, 0, 1)

if bytes:

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

rgba[..., -1] = alpha

# If the "bad" color is all zeros, then ignore alpha input.

if (lut[-1] == 0).all() and np.any(mask_bad):

if np.iterable(mask_bad) and mask_bad.shape == xa.shape:

rgba[mask_bad] = (0, 0, 0, 0)

else:

rgba[..., :] = (0, 0, 0, 0)

rgba = tuple(rgba)

return rgba</div>

<div class="viewcode-block" id="Colormap.__copy__"><a class="viewcode-back" href="../../api/_as_gen/matplotlib.colors.Colormap.html#matplotlib.colors.Colormap.__copy__">[docs]</a> def __copy__(self):

cls = self.__class__

cmapobject = cls.__new__(cls)

cmapobject.__dict__.update(self.__dict__)

if self._isinit:

cmapobject._lut = np.copy(self._lut)

cmapobject._global = False

return cmapobject</div>

<div class="viewcode-block" id="Colormap.get_bad"><a class="viewcode-back" href="../../api/_as_gen/matplotlib.colors.Colormap.html#matplotlib.colors.Colormap.get_bad">[docs]</a> def get_bad(self):

"""Get the color for masked values."""

if not self._isinit:

self._init()

return np.array(self._lut[self._i_bad])</div>

<div class="viewcode-block" id="Colormap.set_bad"><a class="viewcode-back" href="../../api/_as_gen/matplotlib.colors.Colormap.html#matplotlib.colors.Colormap.set_bad">[docs]</a> def set_bad(self, color='k', alpha=None):

"""Set the color for masked values."""

_warn_if_global_cmap_modified(self)

self._rgba_bad = to_rgba(color, alpha)

if self._isinit:

self._set_extremes()</div>

<div class="viewcode-block" id="Colormap.get_under"><a class="viewcode-back" href="../../api/_as_gen/matplotlib.colors.Colormap.html#matplotlib.colors.Colormap.get_under">[docs]</a> def get_under(self):

"""Get the color for low out-of-range values."""

if not self._isinit:

self._init()

<div class="viewcode-block" id="Colormap.set_under"><a class="viewcode-back" href="../../api/_as_gen/matplotlib.colors.Colormap.html#matplotlib.colors.Colormap.set_under">[docs]</a> def set_under(self, color='k', alpha=None):

"""Set the color for low out-of-range values."""

_warn_if_global_cmap_modified(self)

self._rgba_under = to_rgba(color, alpha)

if self._isinit:

self._set_extremes()</div>

<div class="viewcode-block" id="Colormap.get_over"><a class="viewcode-back" href="../../api/_as_gen/matplotlib.colors.Colormap.html#matplotlib.colors.Colormap.get_over">[docs]</a> def get_over(self):

"""Get the color for high out-of-range values."""

if not self._isinit:

self._init()

<div class="viewcode-block" id="Colormap.set_over"><a class="viewcode-back" href="../../api/_as_gen/matplotlib.colors.Colormap.html#matplotlib.colors.Colormap.set_over">[docs]</a> def set_over(self, color='k', alpha=None):

"""Set the color for high out-of-range values."""

_warn_if_global_cmap_modified(self)

self._rgba_over = to_rgba(color, alpha)

if self._isinit:

self._set_extremes()</div>

<div class="viewcode-block" id="Colormap.set_extremes"><a class="viewcode-back" href="../../api/_as_gen/matplotlib.colors.Colormap.html#matplotlib.colors.Colormap.set_extremes">[docs]</a> def set_extremes(self, *, bad=None, under=None, over=None):

"""

Set the colors for masked (*bad*) values and, when ``norm.clip =

False``, low (*under*) and high (*over*) out-of-range values.

"""

if bad is not None:

self.set_bad(bad)

if under is not None:

self.set_under(under)

if over is not None:

self.set_over(over)</div>

<div class="viewcode-block" id="Colormap.with_extremes"><a class="viewcode-back" href="../../api/_as_gen/matplotlib.colors.Colormap.html#matplotlib.colors.Colormap.with_extremes">[docs]</a> def with_extremes(self, *, bad=None, under=None, over=None):

"""

Return a copy of the colormap, for which the colors for masked (*bad*)

values and, when ``norm.clip = False``, low (*under*) and high (*over*)

out-of-range values, have been set accordingly.

"""

new_cm = copy.copy(self)

new_cm.set_extremes(bad=bad, under=under, over=over)

return new_cm</div>

def _set_extremes(self):

if self._rgba_under:

else:

self._lut[self._i_under] = self._lut[0]

if self._rgba_over:

else:

self._lut[self._i_over] = self._lut[self.N - 1]

def _init(self):

"""Generate the lookup table, ``self._lut``."""

raise NotImplementedError("Abstract class only")

<div class="viewcode-block" id="Colormap.is_gray"><a class="viewcode-back" href="../../api/_as_gen/matplotlib.colors.Colormap.html#matplotlib.colors.Colormap.is_gray">[docs]</a> def is_gray(self):

"""Return whether the colormap is grayscale."""

if not self._isinit:

self._init()

return (np.all(self._lut[:, 0] == self._lut[:, 1]) and

np.all(self._lut[:, 0] == self._lut[:, 2]))</div>

def _resample(self, lutsize):

"""Return a new colormap with *lutsize* entries."""

raise NotImplementedError()

<div class="viewcode-block" id="Colormap.reversed"><a class="viewcode-back" href="../../api/_as_gen/matplotlib.colors.Colormap.html#matplotlib.colors.Colormap.reversed">[docs]</a> def reversed(self, name=None):

"""

Return a reversed instance of the Colormap.

.. note:: This function is not implemented for base class.

Parameters

----------

name : str, optional

The name for the reversed colormap. If it's None the

name will be the name of the parent colormap + "_r".

See Also

--------

LinearSegmentedColormap.reversed

ListedColormap.reversed

"""

raise NotImplementedError()</div>

def _repr_png_(self):

"""Generate a PNG representation of the Colormap."""

X = np.tile(np.linspace(0, 1, _REPR_PNG_SIZE[0]),

(_REPR_PNG_SIZE[1], 1))

pixels = self(X, bytes=True)

png_bytes = io.BytesIO()

title = self.name + ' colormap'

author = f'Matplotlib v{mpl.__version__}, https://matplotlib.org'

pnginfo = PngInfo()

pnginfo.add_text('Title', title)

pnginfo.add_text('Description', title)

pnginfo.add_text('Author', author)

pnginfo.add_text('Software', author)

Image.fromarray(pixels).save(png_bytes, format='png', pnginfo=pnginfo)

return png_bytes.getvalue()

def _repr_html_(self):

"""Generate an HTML representation of the Colormap."""

png_bytes = self._repr_png_()

png_base64 = base64.b64encode(png_bytes).decode('ascii')

def color_block(color):

hex_color = to_hex(color, keep_alpha=True)

return (f'<div title="{hex_color}" '

'style="display: inline-block; '

'width: 1em; height: 1em; '

'margin: 0; '

'vertical-align: middle; '

'border: 1px solid #555; '

f'background-color: {hex_color};"></div>')

return ('<div style="vertical-align: middle;">'

f'{self.name} '

'</div>'

'<div class="cmap"><img '

f'alt="{self.name} colormap" '

f'title="{self.name}" '

'style="border: 1px solid #555;" '

f'src="data:image/png;base64,{png_base64}"></div>'

'<div style="vertical-align: middle; '

f'max-width: {_REPR_PNG_SIZE[0]+2}px; '

'display: flex; justify-content: space-between;">'

'<div style="float: left;">'

f'{color_block(self.get_under())} under'

'</div>'

'<div style="margin: 0 auto; display: inline-block;">'

f'bad {color_block(self.get_bad())}'

'</div>'

'<div style="float: right;">'

f'over {color_block(self.get_over())}'

'</div>')

<div class="viewcode-block" id="Colormap.copy"><a class="viewcode-back" href="../../api/_as_gen/matplotlib.colors.Colormap.html#matplotlib.colors.Colormap.copy">[docs]</a> def copy(self):

"""Return a copy of the colormap."""

return self.__copy__()</div></div>

<div class="viewcode-block" id="LinearSegmentedColormap"><a class="viewcode-back" href="../../api/_as_gen/matplotlib.colors.LinearSegmentedColormap.html#matplotlib.colors.LinearSegmentedColormap">[docs]</a>class LinearSegmentedColormap(Colormap):

"""

Colormap objects based on lookup tables using linear segments.

The lookup table is generated using linear interpolation for each

primary color, with the 0-1 domain divided into any number of

segments.

"""

<div class="viewcode-block" id="LinearSegmentedColormap.__init__"><a class="viewcode-back" href="../../api/_as_gen/matplotlib.colors.LinearSegmentedColormap.html#matplotlib.colors.LinearSegmentedColormap.__init__">[docs]</a> def __init__(self, name, segmentdata, N=256, gamma=1.0):

"""

Create colormap from linear mapping segments

segmentdata argument is a dictionary with a red, green and blue

entries. Each entry should be a list of *x*, *y0*, *y1* tuples,

forming rows in a table. Entries for alpha are optional.

Example: suppose you want red to increase from 0 to 1 over

the bottom half, green to do the same over the middle half,

and blue over the top half. Then you would use::

cdict = {'red': [(0.0, 0.0, 0.0),

(0.5, 1.0, 1.0),

(1.0, 1.0, 1.0)],

'green': [(0.0, 0.0, 0.0),

(0.25, 0.0, 0.0),

(0.75, 1.0, 1.0),

(1.0, 1.0, 1.0)],

'blue': [(0.0, 0.0, 0.0),

(0.5, 0.0, 0.0),

(1.0, 1.0, 1.0)]}

Each row in the table for a given color is a sequence of

*x*, *y0*, *y1* tuples. In each sequence, *x* must increase

monotonically from 0 to 1. For any input value *z* falling

between *x[i]* and *x[i+1]*, the output value of a given color

will be linearly interpolated between *y1[i]* and *y0[i+1]*::

row i: x y0 y1

/

row i+1: x y0 y1

Hence y0 in the first row and y1 in the last row are never used.

See Also

--------

LinearSegmentedColormap.from_list

Static method; factory function for generating a smoothly-varying

LinearSegmentedColormap.

"""

# True only if all colors in map are identical; needed for contouring.

self.monochrome = False

super().__init__(name, N)

self._segmentdata = segmentdata

self._gamma = gamma</div>

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