matplotlib.colors.
Normalize
(vmin=None, vmax=None, clip=False)¶A class which, when called, can normalize data into
the [0.0, 1.0]
interval.
If vmin or vmax is not given, they are initialized from the minimum and maximum value respectively of the first input processed. That is, __call__(A) calls autoscale_None(A). If clip is True and the given value falls outside the range, the returned value will be 0 or 1, whichever is closer. Returns 0 if:
vmin==vmax
Works with scalars or arrays, including masked arrays. If clip is True, masked values are set to 1; otherwise they remain masked. Clipping silently defeats the purpose of setting the over, under, and masked colors in the colormap, so it is likely to lead to surprises; therefore the default is clip = False.
autoscale
(A)¶Set vmin, vmax to min, max of A.
autoscale_None
(A)¶autoscale only None-valued vmin or vmax.
inverse
(value)¶process_value
(value)¶Homogenize the input value for easy and efficient normalization.
value can be a scalar or sequence.
Returns result, is_scalar, where result is a masked array matching value. 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, can greatly improve speed for large arrays.
Experimental; we may want to add an option to force the use of float32.
scaled
()¶return true if vmin and vmax set