matplotlib.colors.Normalize #

classmatplotlib.colors.Normalize(vmin=None,vmax=None,clip=False)[source]#

Bases:Norm

A class which, when called, maps values within the interval[vmin,vmax] linearly to the interval[0.0,1.0]. The mapping ofvalues outside[vmin,vmax] depends onclip.

See also

Colormap normalization

Examples

x=[-2,-1,0,1,2]norm=mpl.colors.Normalize(vmin=-1,vmax=1,clip=False)norm(x)# [-0.5, 0., 0.5, 1., 1.5]norm=mpl.colors.Normalize(vmin=-1,vmax=1,clip=True)norm(x)# [0., 0., 0.5, 1., 1.]

Parameters:

vmin, vmaxfloat or None

Values within the range[vmin,vmax] from the input data will belinearly mapped to[0,1]. If eithervmin orvmax is notprovided, they default to the minimum and maximum values of the input,respectively.

clipbool, default: False

Determines the behavior for mapping values outside the range[vmin,vmax].

If clipping is off, values outside the range[vmin,vmax] arealso transformed, resulting in values outside[0,1]. Thisbehavior is usually desirable, as colormaps can mark theseunderandover values with specific colors.

If clipping is on, values belowvmin are mapped to 0 and valuesabovevmax are mapped to 1. Such values become indistinguishablefrom regular boundary values, which may cause misinterpretation ofthe data.

Notes

Ifvmin==vmax, input data will be mapped to 0.

__call__(value,clip=None)[source]#

Normalize the data and return the normalized data.

Parameters:

value

Data to normalize.

clipbool, optional

See the description of the parameterclip inNormalize.

IfNone, defaults toself.clip (which defaults toFalse).

Notes

If not already initialized,self.vmin andself.vmax areinitialized usingself.autoscale_None(value).

autoscale(A)[source]#: Setvmin,vmax to min, max ofA.

autoscale_None(A)[source]#: Ifvmin orvmax are not set, use the min/max ofA to set them.

propertyclip#

Determines the behavior for mapping values outside the range[vmin,vmax].

See theclip parameter inNormalize.

inverse(value)[source]#

Maps the normalized value (i.e., index in the colormap) back to imagedata value.

Parameters:

value: Normalized value.

propertyn_components#

The number of distinct components supported (1).

This is the number of elements of the parameter to__call__ and ofvmin,vmax.

This class support only a single component, as opposed toMultiNormwhich supports multiple components.

staticprocess_value(value)[source]#

Homogenize the inputvalue for easy and efficient normalization.

value can be a scalar or sequence.

Parameters:

value: Data to normalize.

Returns:

resultmasked array: Masked array with the same shape asvalue.
is_scalarbool: Whethervalue is a scalar.

Notes

Float dtypes are preserved; integer types with two bytes or smaller areconverted to np.float32, and larger types are converted to np.float64.Preserving float32 when possible, and using in-place operations,greatly improves speed for large arrays.