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

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_d(): """ 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 return cmap_d cmap_d = _gen_cmap_d() locals().update(cmap_d) # 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*. In the second case, the three arguments are passed to the :class:`~matplotlib.colors.LinearSegmentedColormap` initializer, and the resulting colormap is registered. """ cbook._check_isinstance((str, None), name=name) if name is None: try: name = except AttributeError: raise ValueError("Arguments must include a name or a Colormap") if isinstance(cmap, colors.Colormap): cmap_d[name] = cmap return # For the remainder, let exceptions propagate. if lut is None: lut = mpl.rcParams['image.lut'] cmap = colors.LinearSegmentedColormap(name, data, lut) cmap_d[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. 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_d), name=name) if lut is None: return cmap_d[name] else: return cmap_d[name]._resample(lut)
[docs]class ScalarMappable: """ This is a mixin class to support scalar data to RGBA mapping. The ScalarMappable makes use of data normalization before returning RGBA colors from the given colormap. """ def __init__(self, norm=None, cmap=None): """ Parameters ---------- norm : :class:`matplotlib.colors.Normalize` instance 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 :class:`~matplotlib.colors.Colormap` instance The colormap used to map normalized data values to RGBA colors. """ self.callbacksSM = cbook.CallbackRegistry() if cmap is None: cmap = get_cmap() if norm is None: norm = colors.Normalize() self._A = None #: The Normalization instance of this ScalarMappable. self.norm = norm #: The Colormap instance of this ScalarMappable. self.cmap = get_cmap(cmap) #: The last colorbar associated with this ScalarMappable. May be None. self.colorbar = None self.update_dict = {'array': False}
[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 array' return self._A
[docs] def get_cmap(self): 'return the colormap' return self.cmap
[docs] def get_clim(self): 'return the min, max of the color limits for image scaling' 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 ------- alpha : 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 registered colormap name """ cmap = get_cmap(cmap) self.cmap = cmap self.changed()
[docs] def set_norm(self, norm): """Set the normalization instance. Parameters ---------- norm : `.Normalize` 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) if norm is None: norm = colors.Normalize() self.norm = norm self.changed()
[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()
[docs] 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
[docs] def check_update(self, checker): """ If mappable has changed since the last check, return True; else return False """ 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