matplotlib.colors.LogNorm

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

Bases: matplotlib.colors.LogNorm

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

__call__(value, clip=None)

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).

__init__(vmin=None, vmax=None, clip=False)

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.