# 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)` 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 by the function, 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.

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_None(A)[source]#

If vmin or vmax are not set, use the min/max of A to set them.

inverse(value)[source]#

## Examples using `matplotlib.colors.FuncNorm`#

Colormap normalization

Colormap normalization