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:

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`.

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__(self, 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).

__dict__ = mappingproxy({'__module__': 'matplotlib.colors', '__doc__': '\n A class which, when called, linearly normalizes data into the\n ``[0.0, 1.0]`` interval.\n ', '__init__': <function Normalize.__init__>, 'process_value': <staticmethod object>, '__call__': <function Normalize.__call__>, 'inverse': <function Normalize.inverse>, 'autoscale': <function Normalize.autoscale>, 'autoscale_None': <function Normalize.autoscale_None>, 'scaled': <function Normalize.scaled>, '__dict__': <attribute '__dict__' of 'Normalize' objects>, '__weakref__': <attribute '__weakref__' of 'Normalize' objects>, '__slotnames__': []})

__init__(self, vmin=None, vmax=None, clip=False) [source]

Parameters:

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`.

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.

__module__ = 'matplotlib.colors'

__slotnames__ = []

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

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

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

inverse(self, 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.