"""
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 MutableMapping
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
def _reverser(f, x): # Deprecated, remove this at the same time as revcmap.
return f(1 - x) # Toplevel helper for revcmap ensuring cmap picklability.
[docs]@cbook.deprecated("3.2", alternative="Colormap.reversed()")
def revcmap(data):
"""Can only handle specification *data* in dictionary format."""
data_r = {}
for key, val in data.items():
if callable(val):
# Return a partial object so that the result is picklable.
valnew = functools.partial(_reverser, val)
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
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):
cbook.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."
)
_cmap_registry = _gen_cmap_registry()
locals().update(_cmap_registry)
# This is no longer considered public API
cmap_d = _DeprecatedCmapDictWrapper(_cmap_registry)
# Continue with definitions ...
[docs]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*.
The second case is deprecated. Here, the three arguments are passed to
the :class:`~matplotlib.colors.LinearSegmentedColormap` initializer,
and the resulting colormap is registered. Instead of this implicit
colormap creation, create a `.LinearSegmentedColormap` and use the first
case: ``register_cmap(cmap=LinearSegmentedColormap(name, data, lut))``.
Notes
-----
Registering a colormap stores a reference to the colormap object
which can currently be modified and inadvertantly change the global
colormap state. This behavior is deprecated and in Matplotlib 3.5
the registered colormap will be immutable.
"""
cbook._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 isinstance(cmap, colors.Colormap):
cmap._global = True
_cmap_registry[name] = cmap
return
if lut is not None or data is not None:
cbook.warn_deprecated(
"3.3",
message="Passing raw data via parameters data and lut to "
"register_cmap() is deprecated since %(since)s and will "
"become an error %(removal)s. Instead use: register_cmap("
"cmap=LinearSegmentedColormap(name, data, lut))")
# For the remainder, let exceptions propagate.
if lut is None:
lut = mpl.rcParams['image.lut']
cmap = colors.LinearSegmentedColormap(name, data, lut)
cmap._global = True
_cmap_registry[name] = cmap
[docs]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
cbook._check_in_list(sorted(_cmap_registry), name=name)
if lut is None:
return _cmap_registry[name]
else:
return _cmap_registry[name]._resample(lut)
[docs]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.callbacksSM = cbook.CallbackRegistry()
self._update_dict = {'array': False}
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:
cbook.warn_deprecated(
"3.3",
message="Passing parameters norm and vmin/vmax "
"simultaneously is deprecated since %(since)s and "
"will become an error %(removal)s. 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()
[docs] 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
[docs] def set_array(self, A):
"""
Set the image array from numpy array *A*.
Parameters
----------
A : ndarray
"""
self._A = A
self._update_dict['array'] = True
[docs] def get_array(self):
"""Return the data array."""
return self._A
[docs] def get_cmap(self):
"""Return the `.Colormap` instance."""
return self.cmap
[docs] def get_clim(self):
"""
Return the values (min, max) that are mapped to the colormap limits.
"""
return self.norm.vmin, self.norm.vmax
[docs] 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 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()
[docs] def get_alpha(self):
"""
Returns
-------
float
Always returns 1.
"""
# This method is intended to be overridden by Artist sub-classes
return 1.
[docs] 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.
[docs] 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.
"""
cbook._check_isinstance((colors.Normalize, None), norm=norm)
in_init = self.norm is None
if norm is None:
norm = colors.Normalize()
self.norm = norm
if not in_init:
self.changed() # Things are not set up properly yet.
[docs] 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()
[docs] 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):
"""Return whether mappable has changed since the last check."""
if self._update_dict[checker]:
self._update_dict[checker] = False
return True
return False
[docs] 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
update_dict = cbook._deprecate_privatize_attribute("3.3")
[docs] @cbook.deprecated("3.3")
def add_checker(self, checker):
return self._add_checker(checker)
[docs] @cbook.deprecated("3.3")
def check_update(self, checker):
return self._check_update(checker)
