matplotlib.colors.Normalize#

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

Bases: object

A class which, when called, linearly normalizes data into the [0.0, 1.0] interval.

Parameters:

vmin, vmaxfloat or None

If vmin and/or vmax is not given, they are initialized from the minimum and maximum value, respectively, of the first input processed; i.e., __call__(A) calls autoscale_None(A).

clipbool, default: False

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

If clipping is off, values outside the range [vmin, vmax] are also transformed linearly, resulting in values outside [0, 1]. For a standard use with colormaps, this behavior is desired because colormaps mark these outside values with specific colors for over or under.

If True values falling outside the range [vmin, vmax], are mapped to 0 or 1, whichever is closer. This makes these values indistinguishable from regular boundary values and can lead to misinterpretation of the data.

Notes

Returns 0 if vmin == vmax.

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

Normalize value data in the [vmin, vmax] interval into the [0.0, 1.0] interval and return it.

Parameters:

value

Data to normalize.

clipbool, optional

See the description of the parameter clip in Normalize.

If None, defaults to self.clip (which defaults to False).

Notes

If not already initialized, self.vmin and self.vmax are initialized using self.autoscale_None(value).

autoscale(A)[source]#: Set vmin, vmax to min, max of A.

autoscale_None(A)[source]#: If vmin or vmax are not set, use the min/max of A to set them.

property clip#

inverse(value)[source]#

static process_value(value)[source]#

Homogenize the input value for easy and efficient normalization.

value can be a scalar or sequence.

Returns:

resultmasked array: Masked array with the same shape as value.
is_scalarbool: Whether value is a scalar.

Notes

Float dtypes are preserved; integer types with two bytes or smaller are converted 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.