Source code for

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

from 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 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:`` 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 = 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)