Builtin colormaps, colormap handling utilities, and the
Colormap reference for a list of builtin colormaps.
Creating Colormaps in Matplotlib for examples of how to make colormaps and
Choosing Colormaps in Matplotlib an in-depth discussion of choosing colormaps.
Colormap Normalization for more details about data normalization
class matplotlib.cm.ScalarMappable(norm=None, cmap=None)
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.
Add an entry to a dictionary of boolean flags that are set to True when the mappable is changed.
Autoscale the scalar limits on the norm instance using the current array, changing only limits that are None
Call this whenever the mappable is changed to notify all the callbackSM listeners to the 'changed' signal
If mappable has changed since the last check, return True; else return False
cmap = None
The Colormap instance of this ScalarMappable.
colorbar = None
The last colorbar associated with this ScalarMappable. May be None.
norm = None
The Normalization instance of this ScalarMappable.
set_clim(self, vmin=None, vmax=None)
set the norm limits for image scaling; if vmin is a length2 sequence, interpret it as
(vmin, vmax) which is used to support setp
ACCEPTS: a length 2 sequence of floats; may be overridden in methods that have
set the colormap for luminance data
Set the normalization instance.
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.
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).
Get a colormap instance, defaulting to rc values if name is None.
Colormaps added with
register_cmap() take precedence over built-in colormaps.
If name is a
matplotlib.colors.Colormap instance, it will be returned.
If lut is not None it must be an integer giving the number of entries desired in the lookup table, and name must be a standard mpl colormap name.
matplotlib.cm.register_cmap(name=None, cmap=None, data=None, lut=None)
Add a colormap to the set recognized by
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
matplotlib.colors.Colormap instance. The name is optional; if absent, the name will be the
name attribute of the cmap.
In the second case, the three arguments are passed to the
LinearSegmentedColormap initializer, and the resulting colormap is registered.
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