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

If True values falling outside the range [vmin, vmax], are mapped to 0 or 1, whichever is closer, and masked values are set to 1. If False masked values remain masked.

Clipping silently defeats the purpose of setting the over, under, and masked colors in a colormap, so it is likely to lead to surprises; therefore the default is clip=False.

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: 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.