色彩图的非线性缩放以增强对比度 [英] Non-linear scaling of a colormap to enhance contrast

问题描述

以下 python 代码创建包含正态分布值的矩阵的热图

The following python code creates a heatmap of a matrix that contains normally distributed values

import numpy as np
from matplotlib import pylab as plt


np.random.seed(123) #make sure we all have same data
m = np.random.randn(200).reshape(10, 20)
plt.imshow(m, cmap='RdYlGn', interpolation='nearest')
plt.colorbar()

这是这段代码的输出

我想通过淡入"接近零的值来增强此图像的对比度.我可以通过如下方式对原始数据进行双曲线缩放来轻松实现此目的:

I would like to enhance the contrast of this image by "fading out" the values close to zero. I can easily do this by using disigmoid scaling of the original data as follows:

def disigmoidScaling(values, steepnessFactor=1, ref=None):
    ''' Sigmoid scaling in which values around a reference point are flattened
    arround a reference point

    Scaled value y is calculated as 
        y = sign(v - d)(1 - exp(-((x - d)/s)**2)))
    where v is the original value,  d is the referenc point and s is the 
    steepness factor
    '''
    if ref is None:
        mn = np.min(values)
        mx = np.max(values)
        ref = mn + (mx - mn) / 2.0

    sgn = np.sign(values - ref)
    term1 = ((values - ref)/steepnessFactor) ** 2
    term2 = np.exp(- term1) 
    term3 = 1.0 - term2 
    return sgn * term3


plt.imshow(disigmoidScaling(m, 4), cmap='RdYlGn', interpolation='nearest')
plt.colorbar()

这是输出.

我对结果很满意，除了在这个版本中原来的值已交换为缩放值.

I'm pleased with the result, except the fact that in this version the original values have been exchanged for scaled ones.

有没有办法将值非线性映射到颜色图?

Is there a way to perform a non-linear mapping of values to colormap?

推荐答案

颜色表包含在间隔[0,1]上映射的红色，绿色和蓝色值的字典.Linear Segmented Colormap 类文档给出了示例

A colormap contains a dictionary of red, green and blue values mapped over the interval [0,1]. The Linear Segmented Colormap class docs give the example

cdict = {'red':   [(0.0,  0.0, 0.0),
               (0.5,  1.0, 1.0),
               (1.0,  1.0, 1.0)],

     'green': [(0.0,  0.0, 0.0),
               (0.25, 0.0, 0.0),
               (0.75, 1.0, 1.0),
               (1.0,  1.0, 1.0)],

     'blue':  [(0.0,  0.0, 0.0),
               (0.5,  0.0, 0.0),
               (1.0,  1.0, 1.0)]}

表中给定颜色的每一行都是一个x，y0，y1元组的序列.在每个序列中，x必须从0到1单调递增.对于任何介于x [i]和x之间的输入值z[i+1]，给定颜色的输出值将在 y1[i] 和 y0[i+1] 之间进行线性插值:"

"Each row in the table for a given color is a sequence of x, y0, y1 tuples. In each sequence, x must increase monotonically from 0 to 1. For any input value z falling between x[i] and x[i+1], the output value of a given color will be linearly interpolated between y1[i] and y0[i+1]:"

RdYlGn 颜色图具有11个值，每种颜色从0到1.0的步长为0.1.您可以通过调用

The RdYlGn colormap has 11 x values for each color going from 0 to 1.0 in steps of 0.1. You can get the cdict values by calling

plt.cm.RdYlGn._segmentdata

然后您可以将x值更改为所需的任何步长(只要它们单调递增且范围从0到1)，并通过调用 matplotlib.colors.LinearSegmentedColormap 获得新的颜色图.您的新 cdict .Matplotlib Cookbook 中有几个很好的例子.

You can then change the x values to whatever steps you want (as long as they are monotonically increasing and range from 0 to 1) and get a new colormap by calling matplotlib.colors.LinearSegmentedColormap on your new cdict. There are several great examples of this in the Matplotlib Cookbook.

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