matplotlib.colors.Normalize#

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

Bases: object

A class which, when called, linearly normalizes data into the [0.0, 1.0] interval.

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.

Notes

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.

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).

autoscale(A)[source]#

Set vmin, vmax to min, max of A.

autoscale_None(A)[source]#

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

property clip#
inverse(value)[source]#
static process_value(value)[source]#

Homogenize the input value for easy and efficient normalization.

value can be a scalar or sequence.

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()[source]#

Return whether vmin and vmax are set.

property vmax#
property vmin#

Examples using matplotlib.colors.Normalize#

Multicolored lines

Multicolored lines

Multicolored lines
Mapping marker properties to multivariate data

Mapping marker properties to multivariate data

Mapping marker properties to multivariate data
Colormap Normalizations

Colormap Normalizations

Colormap Normalizations
Colormap Normalizations SymLogNorm

Colormap Normalizations SymLogNorm

Colormap Normalizations SymLogNorm
Contour Image

Contour Image

Contour Image
Creating annotated heatmaps

Creating annotated heatmaps

Creating annotated heatmaps
Image Masked

Image Masked

Image Masked
Blend transparency with color in 2D images

Blend transparency with color in 2D images

Blend transparency with color in 2D images
Multi Image

Multi Image

Multi Image
Pcolor Demo

Pcolor Demo

Pcolor Demo
pcolormesh

pcolormesh

pcolormesh
Histograms

Histograms

Histograms
Time Series Histogram

Time Series Histogram

Time Series Histogram
Axes Grid2

Axes Grid2

Axes Grid2
Shaded & power normalized rendering

Shaded & power normalized rendering

Shaded & power normalized rendering
Exploring normalizations

Exploring normalizations

Exploring normalizations
Hillshading

Hillshading

Hillshading
Left ventricle bullseye

Left ventricle bullseye

Left ventricle bullseye
Quick start guide

Quick start guide

Quick start guide
Constrained Layout Guide

Constrained Layout Guide

Constrained Layout Guide
Customized Colorbars Tutorial

Customized Colorbars Tutorial

Customized Colorbars Tutorial
Colormap Normalization

Colormap Normalization

Colormap Normalization