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

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

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<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. Colormapping typically involves two steps: a data array is first

mapped onto the range 0-1 using an instance of :class:`Normalize` or of a

subclass; then this number in the 0-1 range is mapped to a color using an

instance of a subclass of :class:`Colormap`. Two are provided here:

:class:`LinearSegmentedColormap`, which is used to generate all the built-in

colormap instances, but is also useful for making custom colormaps, and

:class:`ListedColormap`, which is used for generating a custom colormap from a

list of color specifications.

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

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

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

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

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

Matplotlib recognizes the following formats to specify a color:

* an RGB or RGBA tuple of float values in ``[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 ``'#0F0F0F0F'``);

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

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

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

* a X11/CSS4 color name;

* a name from the `xkcd color survey <https://xkcd.com/color/rgb/>`__;

prefixed with ``'xkcd:'`` (e.g., ``'xkcd:sky blue'``);

* one of ``{'tab:blue', 'tab:orange', 'tab:green',

'tab:red', 'tab:purple', 'tab:brown', 'tab:pink',

'tab:gray', 'tab:olive', 'tab:cyan'}`` which are the Tableau Colors from the

'T10' categorical palette (which is the default color cycle);

* a "CN" color spec, i.e. `'C'` followed by a single digit, which is an index

into the default property cycle (``matplotlib.rcParams['axes.prop_cycle']``);

the indexing occurs at artist creation time and defaults to black if the

cycle does not include color.

All string specifications of color, other than "CN", are case-insensitive.

"""

from __future__ import (absolute_import, division, print_function,

unicode_literals)

import six

from six.moves import zip

from collections import Sized

import itertools

import re

import warnings

import numpy as np

import matplotlib.cbook as cbook

from ._color_data import BASE_COLORS, TABLEAU_COLORS, CSS4_COLORS, XKCD_COLORS

class _ColorMapping(dict):

def __init__(self, mapping):

super(_ColorMapping, self).__init__(mapping)

self.cache = {}

def __setitem__(self, key, value):

super(_ColorMapping, self).__setitem__(key, value)

self.cache.clear()

def __delitem__(self, key):

super(_ColorMapping, self).__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)

<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 = np.asscalar(ex)

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, six.string_types) 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 same_color(c1, c2):

"""

Compare two colors to see if they are the same.

Parameters

----------

c1, c2 : Matplotlib colors

Returns

-------

bool

``True`` if *c1* and *c2* are the same color, otherwise ``False``.

"""

return (to_rgba_array(c1) == to_rgba_array(c2)).all()

<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

alpha : scalar, 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 = _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.lower() == "none":

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

# Named color.

try:

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

c = _colors_full_map[c.lower()]

except KeyError:

pass

# hex color with no alpha.

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

if match:

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

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

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

# hex color with alpha.

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

if match:

color = [int(n, 16) / 255

if alpha is not None:

color[-1] = alpha

return tuple(color)

# string gray.

try:

return (float(c),) * 3 + (alpha if alpha is not None else 1.,)

except ValueError:

pass

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

# tuple color.

c = np.array(c)

if not np.can_cast(c.dtype, float, "same_kind") or c.ndim != 1:

# Test the dtype explicitly as `map(float, ...)`, `np.array(...,

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

# Test dimensionality to reject single floats.

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

c = tuple(c.astype(float))

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

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

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.

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

``"none"`` (case-insensitive) or an empty list, an empty array is returned.

"""

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

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

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 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 isinstance(c, six.string_types) and c.lower() == "none":

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

try:

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

except (ValueError, TypeError):

pass

# Convert one at a time.

result = np.empty((len(c), 4), float)

for i, cc in enumerate(c):

result[i] = to_rgba(cc, alpha)

return result</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(np.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(object):

"""

Provides methods for converting color specifications to *RGB* or *RGBA*

Caching is used for more efficient conversion upon repeated calls

with the same argument.

Ordinarily only the single instance instantiated in this module,

*colorConverter*, is needed.

"""

colors = _colors_full_map

cache = _colors_full_map.cache

@staticmethod

def to_rgb(arg):

"""

Returns an *RGB* tuple of three floats from 0-1.

*arg* can be an *RGB* or *RGBA* sequence or a string in any of

several forms:

1) a letter from the set 'rgbcmykw'

2) a hex color string, like '#00FFFF'

3) a standard name, like 'aqua'

4) a string representation of a float, like '0.4',

indicating gray on a 0-1 scale

if *arg* is *RGBA*, the *A* will simply be discarded.

"""

return to_rgb(arg)

@staticmethod

def to_rgba(arg, alpha=None):

"""

Returns an *RGBA* tuple of four floats from 0-1.

For acceptable values of *arg*, see :meth:`to_rgb`.

In addition, if *arg* is "none" (case-insensitive),

then (0,0,0,0) will be returned.

If *arg* is an *RGBA* sequence and *alpha* is not *None*,

*alpha* will replace the original *A*.

"""

return to_rgba(arg, alpha)

@staticmethod

def to_rgba_array(arg, alpha=None):

"""

Returns a numpy array of *RGBA* tuples.

Accepts a single mpl color spec or a sequence of specs.

Special case to handle "no color": if *c* is "none" (case-insensitive),

then an empty array will be returned. Same for an empty list.

"""

return to_rgba_array(arg, alpha)

colorConverter = ColorConverter()

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

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

"""Create an *N* -element 1-d lookup table

*data* represented by a list of x,y0,y1 mapping correspondences.

Each element in this list represents how a value between 0 and 1

(inclusive) represented by x is mapped to a corresponding value

between 0 and 1 (inclusive). The two values of y are to allow

for discontinuous mapping functions (say as might be found in a

sawtooth) where y0 represents the value of y for values of x

<= to that given, and y1 is the value to be used for x > than

that given). The list must start with x=0, end with x=1, and

all values of x must be in increasing order. Values between

the given mapping points are determined by simple linear interpolation.

Alternatively, data can be a function mapping values between 0 - 1

to 0 - 1.

The function returns an array "result" where ``result[x*(N-1)]``

gives the closest value for values of x between 0 and 1.

"""

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:

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

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

x = x * (N - 1)

lut = np.zeros((N,), float)

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

lut[0] = y1[0]

lut[-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)</div>

<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(object):

"""

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

:class:`matplotlib.colors.Normalize`. It is worth noting that

:class:`matplotlib.cm.ScalarMappable` subclasses make heavy use of this

``data->normalize->map-to-color`` processing chain.

"""

"""

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

#: :class:`matplotlib.colorbar.Colorbar` constructor.

self.colorbar_extend = False

def __call__(self, X, alpha=None, bytes=False):

"""

Parameters

----------

X : scalar, ndarray

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, None

Alpha must be a scalar between 0 and 1, 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, )``.

"""

# See class docstring for arg/kwarg documentation.

if not self._isinit:

self._init()

mask_bad = None

if not cbook.iterable(X):

vtype = 'scalar'

xa = np.array([X])

else:

vtype = 'array'

xma = np.ma.array(X, copy=True) # Copy here to avoid side effects.

mask_bad = xma.mask # Mask will be used below.

xa = xma.filled() # Fill to avoid infs, etc.

del xma

# Calculations with native byteorder are faster, and avoid a

# bug that otherwise can occur with putmask when the last

# argument is a numpy scalar.

if not xa.dtype.isnative:

xa = xa.byteswap().newbyteorder()

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

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

if mask_bad is not None:

if mask_bad.shape == xa.shape:

np.copyto(xa, self._i_bad, where=mask_bad)

elif mask_bad:

xa.fill(self._i_bad)

if bytes:

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

else:

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

if alpha is not None:

alpha = min(alpha, 1.0) # alpha must be between 0 and 1

alpha = max(alpha, 0.0)

if bytes:

alpha = int(alpha * 255)

if (lut[-1] == 0).all():

lut[:-1, -1] = alpha

# All zeros is taken as a flag for the default bad

# color, which is no color--fully transparent. We

# don't want to override this.

else:

lut[:, -1] = alpha

# If the bad value is set to have a color, then we

# override its alpha just as for any other value.

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

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

if vtype == 'scalar':

rgba = tuple(rgba[0, :])

return rgba

def __copy__(self):

"""Create new object with the same class, update attributes

"""

cls = self.__class__

cmapobject = cls.__new__(cls)

cmapobject.__dict__.update(self.__dict__)

if self._isinit:

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

return cmapobject

<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 color to be used for masked values.

"""

self._rgba_bad = colorConverter.to_rgba(color, alpha)

if self._isinit:

self._set_extremes()</div>

<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 color to be used for low out-of-range values.

Requires norm.clip = False

"""

self._rgba_under = colorConverter.to_rgba(color, alpha)

if self._isinit:

self._set_extremes()</div>

<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 color to be used for high out-of-range values.

Requires norm.clip = False

"""

self._rgba_over = colorConverter.to_rgba(color, alpha)

if self._isinit:

self._set_extremes()</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):

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 color map 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):

"""

Make a reversed instance of the Colormap.

.. note :: Function 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".

Notes

-----

See :meth:`LinearSegmentedColormap.reversed` and

:meth:`ListedColormap.reversed`

"""

raise NotImplementedError()</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.

"""

def __init__(self, name, segmentdata, N=256, gamma=1.0):

"""Create color map 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.

.. seealso::

:meth:`LinearSegmentedColormap.from_list`

Static method; factory function for generating a

smoothly-varying LinearSegmentedColormap.

:func:`makeMappingArray`

For information about making a mapping array.

"""

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

self.monochrome = False

Colormap.__init__(self, name, N)

self._segmentdata = segmentdata

self._gamma = gamma

def _init(self):

self._lut = np.ones((self.N + 3, 4), float)

self._lut[:-3, 0] = makeMappingArray(

self.N, self._segmentdata['red'], self._gamma)

self._lut[:-3, 1] = makeMappingArray(

self.N, self._segmentdata['green'], self._gamma)

self._lut[:-3, 2] = makeMappingArray(

self.N, self._segmentdata['blue'], self._gamma)

if 'alpha' in self._segmentdata:

self._lut[:-3, 3] = makeMappingArray(

self.N, self._segmentdata['alpha'], 1)

self._isinit = True

self._set_extremes()

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

"""

Set a new gamma value and regenerate color map.

"""

self._gamma = gamma

self._init()</div>

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

def from_list(name, colors, N=256, gamma=1.0):

"""

Make a linear segmented colormap with *name* from a sequence

of *colors* which evenly transitions from colors[0] at val=0

to colors[-1] at val=1. *N* is the number of rgb quantization

levels.

Alternatively, a list of (value, color) tuples can be given

to divide the range unevenly.

"""

raise ValueError('colors must be iterable')

if (isinstance(colors[0], Sized) and len(colors[0]) == 2

and not isinstance(colors[0], six.string_types)):

# List of value, color pairs

vals, colors = zip(*colors)

else:

vals = np.linspace(0, 1, len(colors))

cdict = dict(red=[], green=[], blue=[], alpha=[])

for val, color in zip(vals, colors):

r, g, b, a = colorConverter.to_rgba(color)

cdict['red'].append((val, r, r))

cdict['green'].append((val, g, g))

cdict['blue'].append((val, b, b))

cdict['alpha'].append((val, a, a))

return LinearSegmentedColormap(name, cdict, N, gamma)</div>

def _resample(self, lutsize):

"""

Return a new color map with *lutsize* entries.

"""

return LinearSegmentedColormap(self.name, self._segmentdata, lutsize)

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

"""

Make a reversed instance of the Colormap.

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".

Returns

-------

LinearSegmentedColormap

The reversed colormap.

"""

if name is None:

name = self.name + "_r"

# Function factory needed to deal with 'late binding' issue.

def factory(dat):

def func_r(x):

return dat(1.0 - x)

return func_r

data_r = dict()

for key, data in six.iteritems(self._segmentdata):

if callable(data):

data_r[key] = factory(data)

else:

new_data = [(1.0 - x, y1, y0) for x, y0, y1 in reversed(data)]

data_r[key] = new_data

return LinearSegmentedColormap(name, data_r, self.N, self._gamma)</div></div>

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

"""Colormap object generated from a list of colors.

This may be most useful when indexing directly into a colormap,

but it can also be used to generate special colormaps for ordinary

mapping.

"""

def __init__(self, colors, name='from_list', N=None):

"""

Make a colormap from a list of colors.

*colors*

a list of matplotlib color specifications,

or an equivalent Nx3 or Nx4 floating point array

(*N* rgb or rgba values)

*name*

a string to identify the colormap

*N*

the number of entries in the map. The default is *None*,

in which case there is one colormap entry for each

element in the list of colors. If::

N < len(colors)

the list will be truncated at *N*. If::

N > len(colors)

the list will be extended by repetition.

"""

self.monochrome = False # True only if all colors in map are

# identical; needed for contouring.

if N is None:

self.colors = colors

N = len(colors)

else:

if isinstance(colors, six.string_types):

self.colors = [colors] * N

self.monochrome = True

elif cbook.iterable(colors):

if len(colors) == 1:

self.monochrome = True

self.colors = list(

itertools.islice(itertools.cycle(colors), N))

else:

try:

gray = float(colors)

except TypeError:

pass

else:

self.colors = [gray] * N

self.monochrome = True

def _init(self):

rgba = colorConverter.to_rgba_array(self.colors)

self._lut = np.zeros((self.N + 3, 4), float)

self._lut[:-3] = rgba

self._isinit = True

self._set_extremes()

def _resample(self, lutsize):

"""

Return a new color map with *lutsize* entries.

"""

colors = self(np.linspace(0, 1, lutsize))

return ListedColormap(colors, name=self.name)

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

"""

Make a reversed instance of the Colormap.

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".

Returns

-------

ListedColormap

A reversed instance of the colormap.

"""

if name is None:

name = self.name + "_r"

colors_r = list(reversed(self.colors))

return ListedColormap(colors_r, name=name, N=self.N)</div></div>

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

"""

A class which, when called, can normalize data into

the ``[0.0, 1.0]`` interval.

"""

def __init__(self, vmin=None, vmax=None, clip=False):

"""

If *vmin* or *vmax* is not given, they are initialized from the

minimum and maximum value respectively of the first input

processed. That is, *__call__(A)* calls *autoscale_None(A)*.

If *clip* is *True* and the given value falls outside the range,

the returned value will be 0 or 1, whichever is closer.

Returns 0 if::

vmin==vmax

Works with scalars or arrays, including masked arrays. If

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