Current File : //proc/self/root/lib/python3/dist-packages/matplotlib/cm.py
"""
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, MutableMapping
import numpy as np
from numpy import ma
import matplotlib as mpl
from matplotlib import _api, colors, cbook
from matplotlib._cm import datad
from matplotlib._cm_listed import cmaps as cmaps_listed
@_api.caching_module_getattr # module-level deprecations
class __getattr__:
LUTSIZE = _api.deprecated(
"3.5", obj_type="", alternative="rcParams['image.lut']")(
property(lambda self: _LUTSIZE))
_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._global = True
rmap._global = True
cmap_d[rmap.name] = rmap
return cmap_d
class _DeprecatedCmapDictWrapper(MutableMapping):
"""Dictionary mapping for deprecated _cmap_d access."""
def __init__(self, cmap_registry):
self._cmap_registry = cmap_registry
def __delitem__(self, key):
self._warn_deprecated()
self._cmap_registry.__delitem__(key)
def __getitem__(self, key):
self._warn_deprecated()
return self._cmap_registry.__getitem__(key)
def __iter__(self):
self._warn_deprecated()
return self._cmap_registry.__iter__()
def __len__(self):
self._warn_deprecated()
return self._cmap_registry.__len__()
def __setitem__(self, key, val):
self._warn_deprecated()
self._cmap_registry.__setitem__(key, val)
def get(self, key, default=None):
self._warn_deprecated()
return self._cmap_registry.get(key, default)
def _warn_deprecated(self):
_api.warn_deprecated(
"3.3",
message="The global colormaps dictionary is no longer "
"considered public API.",
alternative="Please use register_cmap() and get_cmap() to "
"access the contents of the dictionary."
)
class ColormapRegistry(Mapping):
r"""
Container for colormaps that are known to Matplotlib by name.
.. admonition:: Experimental
While we expect the API to be final, we formally mark it as
experimental for 3.5 because we want to keep the option to still adapt
the API for 3.6 should the need arise.
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
def __getitem__(self, item):
try:
return self._cmaps[item].copy()
except KeyError:
raise KeyError(f"{item!r} is not a known colormap name")
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-compatibilty 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.
"""
name = name or cmap.name
if name in self and not force:
raise ValueError(
f'A colormap named "{name}" is already registered.')
register_cmap(name, cmap.copy())
_cmap_registry = _gen_cmap_registry()
globals().update(_cmap_registry)
# This is no longer considered public API
cmap_d = _DeprecatedCmapDictWrapper(_cmap_registry)
__builtin_cmaps = tuple(_cmap_registry)
# 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(_cmap_registry)
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.
Notes
-----
Registering a colormap stores a reference to the colormap object
which can currently be modified and inadvertently change the global
colormap state. This behavior is deprecated and in Matplotlib 3.5
the registered colormap will be immutable.
"""
_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
if name in _cmap_registry:
if not override_builtin and name in __builtin_cmaps:
msg = f"Trying to re-register the builtin cmap {name!r}."
raise ValueError(msg)
else:
msg = f"Trying to register the cmap {name!r} which already exists."
_api.warn_external(msg)
if not isinstance(cmap, colors.Colormap):
raise ValueError("You must pass a Colormap instance. "
f"You passed {cmap} a {type(cmap)} object.")
cmap._global = True
_cmap_registry[name] = cmap
return
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.
Notes
-----
Currently, this returns the global colormap object, which is deprecated.
In Matplotlib 3.5, you will no longer be able to modify the global
colormaps in-place.
Parameters
----------
name : `matplotlib.colors.Colormap` or str or None, default: None
If a `.Colormap` instance, it will be returned. Otherwise, the name of
a colormap known to Matplotlib, which will be resampled by *lut*. The
default, None, means :rc:`image.cmap`.
lut : int or None, default: None
If *name* is not already a Colormap instance and *lut* is not None, the
colormap will be resampled to have *lut* entries in the lookup table.
"""
if name is None:
name = mpl.rcParams['image.cmap']
if isinstance(name, colors.Colormap):
return name
_api.check_in_list(sorted(_cmap_registry), name=name)
if lut is None:
return _cmap_registry[name]
else:
return _cmap_registry[name]._resample(lut)
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.
"""
if name not in _cmap_registry:
return
if name in __builtin_cmaps:
raise ValueError(f"cannot unregister {name!r} which is a builtin "
"colormap.")
return _cmap_registry.pop(name)
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 : `matplotlib.colors.Normalize` (or subclass thereof)
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 `~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()
callbacksSM = _api.deprecated("3.5", alternative="callbacks")(
property(lambda self: self.callbacks))
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 norm is not None:
raise ValueError(
"Passing parameters norm and vmin/vmax simultaneously 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 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 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
cmap = get_cmap(cmap)
self.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, None), norm=norm)
if norm is None:
norm = colors.Normalize()
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 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
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