""" 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