matplotlib.colors.FuncNorm#
- class matplotlib.colors.FuncNorm(functions, vmin=None, vmax=None, clip=False)[source]#
Bases:
FuncNorm
Arbitrary normalization using functions for the forward and inverse.
- Parameters:
- functions(callable, callable)
two-tuple of the forward and inverse functions for the normalization. The forward function must be monotonic.
Both functions must have the signature
def forward(values: array-like) -> array-like
- 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)
callsautoscale_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. IfFalse
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
.
- 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)
callsautoscale_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. IfFalse
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 toself.clip
(which defaults toFalse
).
Notes
If not already initialized,
self.vmin
andself.vmax
are initialized usingself.autoscale_None(value)
.