matplotlib/lib/matplotlib/cm.py at v3.1.x · matplotlib/matplotlib · GitHub

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"""

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 and

: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

cmap_d = {}

# reverse all the colormaps.

# reversed colormaps have '_r' appended to the name.

def _reverser(f, x=None):

"""Helper such that ``_reverser(f)(x) == f(1 - x)``."""

if x is None:

# Returning a partial object keeps it picklable.

return functools.partial(_reverser, f)

return f(1 - x)

def revcmap(data):

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

data_r = {}

for key, val in data.items():

if callable(val):

valnew = _reverser(val)

# This doesn't work: lambda x: val(1-x)

# The same "val" (the first one) is used

# each time, so the colors are identical

# and the result is shades of gray.

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

def _reverse_cmap_spec(spec):

"""Reverses cmap specification *spec*, can handle both dict and tuple

type specs."""

if 'listed' in spec:

return {'listed': spec['listed'][::-1]}

if 'red' in spec:

return revcmap(spec)

else:

revspec = list(reversed(spec))

if len(revspec[0]) == 2: # e.g., (1, (1.0, 0.0, 1.0))

revspec = [(1.0 - a, b) for a, b in revspec]

return revspec

def _generate_cmap(name, lutsize):

"""Generates the requested cmap from its *name*. The lut size is

*lutsize*."""

spec = datad[name]

# Generate the colormap object.

if 'red' in spec:

return colors.LinearSegmentedColormap(name, spec, lutsize)

elif 'listed' in spec:

return colors.ListedColormap(spec['listed'], name)

else:

return colors.LinearSegmentedColormap.from_list(name, spec, lutsize)

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

# Generate the reversed specifications (all at once, to avoid

# modify-when-iterating).

datad.update({cmapname + '_r': _reverse_cmap_spec(spec)

for cmapname, spec in datad.items()})

# Precache the cmaps with ``lutsize = LUTSIZE``.

# Also add the reversed ones added in the section above:

for cmapname in datad:

cmap_d[cmapname] = _generate_cmap(cmapname, LUTSIZE)

cmap_d.update(cmaps_listed)

locals().update(cmap_d)

# Continue with definitions ...

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.

"""

if name is None:

try:

name = cmap.name

except AttributeError:

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

if not isinstance(name, str):

raise ValueError("Colormap name must be a string")

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

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.

If *name* is a :class:`matplotlib.colors.Colormap` instance, it will be

returned.

If *lut* is not None it must be an integer giving the number of

entries desired in the lookup table, and *name* must be a standard

mpl colormap name.

"""

if name is None:

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

if isinstance(name, colors.Colormap):

return name

if name in cmap_d:

if lut is None:

return cmap_d[name]

else:

return cmap_d[name]._resample(lut)

else:

raise ValueError(

"Colormap %s is not recognized. Possible values are: %s"

% (name, ', '.join(sorted(cmap_d))))

class ScalarMappable(object):

"""

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):

r"""

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}

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

def set_array(self, A):

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

Parameters

----------

A : ndarray

"""

self._A = A

self.update_dict['array'] = True

def get_array(self):

'Return the array'

return self._A

def get_cmap(self):

'return the colormap'

return self.cmap

def get_clim(self):

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

return self.norm.vmin, self.norm.vmax

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

"""

set the norm limits for image scaling; if *vmin* is a length2

sequence, interpret it as ``(vmin, vmax)`` which is used to

support setp

ACCEPTS: a length 2 sequence of floats; may be overridden in methods

that have ``vmin`` and ``vmax`` kwargs.

"""

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()

def get_alpha(self):

"""

Returns

-------

alpha : float

Always returns 1.

"""

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

return 1.

def set_cmap(self, cmap):

"""

set the colormap for luminance data

Parameters

----------

cmap : colormap or registered colormap name

"""

cmap = get_cmap(cmap)

self.cmap = cmap

self.changed()

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.

"""

if norm is None:

norm = colors.Normalize()

self.norm = norm

self.changed()

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()

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()

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

def check_update(self, checker):

"""

If mappable has changed since the last check,

return True; else return False

"""

if self.update_dict[checker]:

self.update_dict[checker] = False

return True

return False

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

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