- class matplotlib.colors.Normalize(vmin=None, vmax=None, clip=False)[source]#
A class which, when called, linearly normalizes data into the
- 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.,
- clipbool, default: False
Truevalues falling outside the range
[vmin, vmax], are mapped to 0 or 1, whichever is closer, and masked values are set to 1. If
Falsemasked 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
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.
Data to normalize.
- clipbool, optional
None, defaults to
self.clip(which defaults to
If not already initialized,
self.vmaxare initialized using
- property clip#
- static process_value(value)[source]#
Homogenize the input value for easy and efficient normalization.
value can be a scalar or sequence.
- resultmasked array
Masked array with the same shape as value.
Whether value is a scalar.
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.
- property vmax#
- property vmin#
Mapping marker properties to multivariate data
Colormap normalizations SymLogNorm
Blend transparency with color in 2D images
Shaded & power normalized rendering