matplotlib.cm

Builtin colormaps, colormap handling utilities, and the ScalarMappable mixin.

See also

Colormap reference for a list of builtin colormaps.

Creating Colormaps in Matplotlib for examples of how to make colormaps.

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)

Bases: object

A mixin class to map scalar data to RGBA.

The ScalarMappable applies data normalization before returning RGBA colors from the given colormap.

Parameters:

normmatplotlib.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.

cmapstr or Colormap

The colormap used to map normalized data values to RGBA colors.

add_checker(checker)

[Deprecated]

Notes

Deprecated since version 3.3:

autoscale()

Autoscale the scalar limits on the norm instance using the current array

autoscale_None()

Autoscale the scalar limits on the norm instance using the current array, changing only limits that are None

changed()

Call this whenever the mappable is changed to notify all the callbackSM listeners to the 'changed' signal.

check_update(checker)

[Deprecated]

Notes

Deprecated since version 3.3:

colorbar

The last colorbar associated with this ScalarMappable. May be None.

get_alpha()

Returns:

float

Always returns 1.

get_array()

Return the data array.

get_clim()

Return the values (min, max) that are mapped to the colormap limits.

get_cmap()

Return the Colormap instance.

set_array(A)

Set the image array from numpy array A.

Parameters:

Andarray

set_clim(vmin=None, vmax=None)

Set the norm limits for image scaling.

Parameters:

vmin, vmaxfloat

The limits.

The limits may also be passed as a tuple (vmin, vmax) as a single positional argument.

set_cmap(cmap)

Set the colormap for luminance data.

Parameters:

cmapColormap or str or None

set_norm(norm)

Set the normalization instance.

Parameters:

normNormalize 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.

to_rgba(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).

property update_dict

matplotlib.cm.get_cmap(name=None, lut=None)

Get a colormap instance, defaulting to rc values if name is None.

Colormaps added with register_cmap() take precedence over built-in colormaps.

Parameters:

namematplotlib.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 rcParams["image.cmap"] (default: 'viridis').

lutint 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.

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.

matplotlib.cm.register_cmap(name=None, cmap=None, data=None, lut=None)

Add a colormap to the set recognized by 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 matplotlib.colors.Colormap instance. The name is optional; if absent, the name will be the name attribute of the cmap.

The second case is deprecated. Here, the three arguments are passed to the 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.

matplotlib.cm.revcmap(data)

[Deprecated] Can only handle specification data in dictionary format.

Notes

Deprecated since version 3.2.