matplotlib.colors.Colormap

class matplotlib.colors.Colormap(name, N=256) [source]

Bases: object

Baseclass for all scalar to RGBA mappings.

Typically, Colormap instances are used to convert data values (floats) from the interval [0, 1] to the RGBA color that the respective Colormap represents. For scaling of data into the [0, 1] interval see matplotlib.colors.Normalize. Subclasses of matplotlib.cm.ScalarMappable make heavy use of this data -> normalize -> map-to-color processing chain.

Parameters:	`namestr` The name of the colormap. `Nint` The number of rgb quantization levels.

__call__(self, X, alpha=None, bytes=False) [source]

Parameters:

Parameters:	`Xfloat or int, ndarray or scalar` The data value(s) to convert to RGBA. For floats, X should be in the interval `[0.0, 1.0]` to return the RGBA values `X100` percent along the Colormap line. For integers, X should be in the interval `[0, Colormap.N)` to return RGBA values indexed* from the Colormap with index `X`. `alphafloat, None` Alpha must be a scalar between 0 and 1, or None. `bytesbool` If False (default), the returned RGBA values will be floats in the interval `[0, 1]` otherwise they will be uint8s in the interval `[0, 255]`.
Returns:	Tuple of RGBA values if X is scalar, otherwise an array of `RGBA values with a shape of X.shape + (4, ).`

Xfloat or int, ndarray or scalar: The data value(s) to convert to RGBA. For floats, X should be in the interval [0.0, 1.0] to return the RGBA values X*100 percent along the Colormap line. For integers, X should be in the interval [0, Colormap.N) to return RGBA values indexed from the Colormap with index X.
alphafloat, None: Alpha must be a scalar between 0 and 1, or None.
bytesbool: If False (default), the returned RGBA values will be floats in the interval [0, 1] otherwise they will be uint8s in the interval [0, 255].

Returns:

Tuple of RGBA values if X is scalar, otherwise an array of
RGBA values with a shape of X.shape + (4, ).

__copy__(self) [source]

__dict__ = mappingproxy({'__module__': 'matplotlib.colors', '__doc__': '\n Baseclass for all scalar to RGBA mappings.\n\n Typically, Colormap instances are used to convert data values (floats)\n from the interval ``[0, 1]`` to the RGBA color that the respective\n Colormap represents. For scaling of data into the ``[0, 1]`` interval see\n `matplotlib.colors.Normalize`. Subclasses of `matplotlib.cm.ScalarMappable`\n make heavy use of this ``data -> normalize -> map-to-color`` processing\n chain.\n ', '__init__': <function Colormap.__init__>, '__call__': <function Colormap.__call__>, '__copy__': <function Colormap.__copy__>, 'set_bad': <function Colormap.set_bad>, 'set_under': <function Colormap.set_under>, 'set_over': <function Colormap.set_over>, '_set_extremes': <function Colormap._set_extremes>, '_init': <function Colormap._init>, 'is_gray': <function Colormap.is_gray>, '_resample': <function Colormap._resample>, 'reversed': <function Colormap.reversed>, '__dict__': <attribute '__dict__' of 'Colormap' objects>, '__weakref__': <attribute '__weakref__' of 'Colormap' objects>})

__init__(self, name, N=256) [source]

Parameters:	`namestr` The name of the colormap. `Nint` The number of rgb quantization levels.

__module__ = 'matplotlib.colors'

__weakref__: list of weak references to the object (if defined)

colorbar_extend: When this colormap exists on a scalar mappable and colorbar_extend is not False, colorbar creation will pick up colorbar_extend as the default value for the extend keyword in the matplotlib.colorbar.Colorbar constructor.