RankNormalize

class RankNormalize(levels, vmin=None, vmax=None, clip=False)

Color distribution normalization class based on rank and not value.

This class is used to manage the “rank” norm for Contour drawables

Attributes:

clip

vmax

vmin

Methods

autoscale(A)	Set vmin, vmax to min, max of A.
autoscale_None(A)	If vmin or vmax are not set, use the min/max of A to set them.
inverse(value)	Maps the normalized value (i.e., index in the colormap) back to image data value.
process_value(value)	Homogenize the input value for easy and efficient normalization.
scaled()	Return whether vmin and vmax are both set.

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

Construct the normalization based on rank

Parameters:

levelslist of floats

List of level values.

vminfloat, optional

Minimum value for color distribution

vmaxfloat, optional

Maximum value for color distribution

clipbool, optional

Indicator for cutting color distribution out of vmin and vmax Required by the parent class, but unused.

autoscale(A)

Set vmin, vmax to min, max of A.

autoscale_None(A)

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

inverse(value)

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

Parameters:

value

Normalized value.

static process_value(value)

Homogenize the input value for easy and efficient normalization.

value can be a scalar or sequence.

Parameters:

value

Data to normalize.

Returns:

resultmasked array

Masked array with the same shape as value.

is_scalarbool

Whether value is a scalar.

Notes

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.

scaled()

Return whether vmin and vmax are both set.