"""
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.
"""
from collections.abc import Mapping
import functools
import numpy as np
from numpy import ma
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
_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))
# 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)
"""
def __init__(self, cmaps):
self._cmaps = cmaps
self._builtin_cmaps = tuple(cmaps)
# A shim to allow register_cmap() to force an override
self._allow_override_builtin = False
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
and not self._allow_override_builtin):
# We don't allow overriding a builtin unless privately
# coming from register_cmap()
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()
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 Colormap, simply return it
if isinstance(cmap, colors.Colormap):
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)
@_api.deprecated("3.7", alternative="``matplotlib.colormaps.register(name)``")
def register_cmap(name=None, cmap=None, *, override_builtin=False):
"""
Add a colormap to the set recognized by :func:`get_cmap`.
Register a new colormap to be accessed by name ::
LinearSegmentedColormap('swirly', data, lut)
register_cmap(cmap=swirly_cmap)
Parameters
----------
name : str, optional
The name that can be used in :func:`get_cmap` or :rc:`image.cmap`
If absent, the name will be the :attr:`~matplotlib.colors.Colormap.name`
attribute of the *cmap*.
cmap : matplotlib.colors.Colormap
Despite being the second argument and having a default value, this
is a required argument.
override_builtin : bool
Allow built-in colormaps to be overridden by a user-supplied
colormap.
Please do not use this unless you are sure you need it.
"""
_api.check_isinstance((str, None), name=name)
if name is None:
try:
name = cmap.name
except AttributeError as err:
raise ValueError("Arguments must include a name or a "
"Colormap") from err
# override_builtin is allowed here for backward compatibility
# this is just a shim to enable that to work privately in
# the global ColormapRegistry
_colormaps._allow_override_builtin = override_builtin
_colormaps.register(cmap, name=name, force=override_builtin)
_colormaps._allow_override_builtin = False
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)
# do it in two steps like this so we can have an un-deprecated version in
# pyplot.
get_cmap = _api.deprecated(
'3.7',
name='get_cmap',
alternative=(
"``matplotlib.colormaps[name]`` " +
"or ``matplotlib.colormaps.get_cmap(obj)``"
)
)(_get_cmap)
@_api.deprecated("3.7",
alternative="``matplotlib.colormaps.unregister(name)``")
def unregister_cmap(name):
"""
Remove a colormap recognized by :func:`get_cmap`.
You may not 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_cmap` 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 un-registered
Returns
-------
ColorMap or None
If the colormap was registered, return it if not return `None`
Raises
------
ValueError
If you try to de-register a default built-in colormap.
"""
cmap = _colormaps.get(name, None)
_colormaps.unregister(name)
return cmap
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 ScalarMappable:
"""
A mixin class to map scalar data to RGBA.
The ScalarMappable applies data normalization before returning RGBA colors
from the given colormap.
"""
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
self._norm = None # So that the setter knows we're initializing.
self.set_norm(norm) # The Normalize instance of this ScalarMappable.
self.cmap = None # So that the setter knows we're initializing.
self.set_cmap(cmap) # The Colormap instance of this ScalarMappable.
#: The last colorbar associated with this ScalarMappable. May be None.
self.colorbar = None
self.callbacks = cbook.CallbackRegistry(signals=["changed"])
def _scale_norm(self, norm, vmin, vmax):
"""
Helper for initial scaling.
Used by public functions that create a ScalarMappable 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.
"""
if vmin is not None or vmax is not None:
self.set_clim(vmin, vmax)
if isinstance(norm, 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.")
# always resolve the autoscaling so we have concrete limits
# rather than deferring to draw time.
self.autoscale_None()
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 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 `~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 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 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 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 value array from array-like *A*.
Parameters
----------
A : array-like or None
The values that are mapped to colors.
The base class `.ScalarMappable` does not make any assumptions on
the dimensionality and shape of the value array *A*.
"""
if A is None:
self._A = None
return
A = cbook.safe_masked_invalid(A, copy=True)
if not np.can_cast(A.dtype, float, "same_kind"):
raise TypeError(f"Image data of dtype {A.dtype} cannot be "
"converted to float")
self._A = A
def get_array(self):
"""
Return the array of values, that are mapped to colors.
The base class `.ScalarMappable` does not make any assumptions on
the dimensionality and shape of the array.
"""
return self._A
def get_cmap(self):
"""Return the `.Colormap` instance."""
return self.cmap
def get_clim(self):
"""
Return the values (min, max) that are mapped to the colormap limits.
"""
return self.norm.vmin, self.norm.vmax
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 the norm's limits are updated self.changed() will be called
# through the callbacks attached to the norm
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)
def get_alpha(self):
"""
Returns
-------
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 str or None
"""
in_init = self.cmap is None
self.cmap = _ensure_cmap(cmap)
if not in_init:
self.changed() # Things are not set up properly yet.
@property
def norm(self):
return self._norm
@norm.setter
def norm(self, norm):
_api.check_isinstance((colors.Normalize, str, None), norm=norm)
if norm is None:
norm = colors.Normalize()
elif isinstance(norm, str):
try:
scale_cls = scale._scale_mapping[norm]
except KeyError:
raise ValueError(
"Invalid norm str name; the following values are "
"supported: {}".format(", ".join(scale._scale_mapping))
) from None
norm = _auto_norm_from_scale(scale_cls)()
if norm is self.norm:
# We aren't updating anything
return
in_init = self.norm is None
# Remove the current callback and connect to the new one
if not in_init:
self.norm.callbacks.disconnect(self._id_norm)
self._norm = norm
self._id_norm = self.norm.callbacks.connect('changed',
self.changed)
if not in_init:
self.changed()
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
"""
if self._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
self.norm.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
"""
if self._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
self.norm.autoscale_None(self._A)
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)
self.stale = True
# The docstrings here must be generic enough to apply to all relevant methods.
mpl._docstring.interpd.update(
cmap_doc="""\
cmap : str or `~matplotlib.colors.Colormap`, default: :rc:`image.cmap`
The Colormap instance or registered colormap name used to map scalar data
to colors.""",
norm_doc="""\
norm : str or `~matplotlib.colors.Normalize`, optional
The normalization method used to scale scalar data to the [0, 1] range
before mapping to colors using *cmap*. By default, a linear scaling is
used, mapping the lowest value to 0 and the highest to 1.
If given, this can be one of the following:
- An instance of `.Normalize` or one of its subclasses
(see :doc:`/tutorials/colors/colormapnorms`).
- A scale name, i.e. one of "linear", "log", "symlog", "logit", etc. For a
list of available scales, call `matplotlib.scale.get_scale_names()`.
In that case, a suitable `.Normalize` subclass is dynamically generated
and instantiated.""",
vmin_vmax_doc="""\
vmin, vmax : float, optional
When using scalar data and no explicit *norm*, *vmin* and *vmax* define
the data range that the colormap covers. By default, the colormap covers
the complete value range of the supplied data. It is an error to use
*vmin*/*vmax* when a *norm* instance is given (but using a `str` *norm*
name together with *vmin*/*vmax* is acceptable).""",
)
def _ensure_cmap(cmap):
"""
Ensure that we have a `.Colormap` object.
For internal use to preserve type stability of errors.
Parameters
----------
cmap : None, str, Colormap
- if a `Colormap`, return it
- if a string, look it up in mpl.colormaps
- if None, look up the default color map in mpl.colormaps
Returns
-------
Colormap
"""
if isinstance(cmap, colors.Colormap):
return cmap
cmap_name = cmap if cmap is not None else mpl.rcParams["image.cmap"]
# use check_in_list to ensure type stability of the exception raised by
# the internal usage of this (ValueError vs KeyError)
_api.check_in_list(sorted(_colormaps), cmap=cmap_name)
return mpl.colormaps[cmap_name]