matplotlib.colors.LogNorm

classmatplotlib.colors.LogNorm(vmin=None, vmax=None, clip=False)[source]

Bases: Normalize

Normalize a given value to the 0-1 range on a log scale.

Parameters:

vmin, vmaxfloat or None

Values within the range [vmin, vmax] from the input data will be linearly mapped to [0, 1]. If either vmin or vmax is not provided, they default to the minimum and maximum values of the input, respectively.

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, resulting in values outside [0, 1]. This behavior is usually desirable, as colormaps can mark these under and over values with specific colors.

If clipping is on, values below vmin are mapped to 0 and values above vmax are mapped to 1. Such values become indistinguishable from regular boundary values, which may cause misinterpretation of the data.

Notes

If vmin == vmax, input data will be mapped to 0.

__call__(value, clip=None)[source]

Normalize the data and return the normalized data.

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]

Maps the normalized value (i.e., index in the colormap) back to image data value.

Parameters:

value

Normalized value.

Examples using matplotlib.colors.LogNorm

Colormap normalizations

Colormap normalizations

pcolor images

pcolor images

Histograms

Histograms

Quick start guide

Quick start guide

Colormap normalization

Colormap normalization