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

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.

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.

scaled()[source]

Return whether vmin and vmax are set.