matplotlib imshow-默认颜色归一化 [英] matplotlib imshow - default colour normalisation

问题描述

使用imshow时，我的颜色映射始终存在问题，某些颜色似乎只是变成黑色.我终于意识到，imshow在默认情况下似乎可以归一化我给出的浮点值矩阵.

I have consistently had problems with my colour maps when using imshow, some colours seem to just become black. I have finally realised that imshow seems to, by default, normalise the matrix of floating point values I give it.

我本来希望像[[0,0.25],[0.5,0.75]]这样的数组显示对应于那些绝对值的映射中的适当颜色，但是0.75将被解释为1.在极端情况下，N x N数组为0.2 (例如)，只会产生一个大的黑色方块，而不是在颜色映射中期望0.2对应的任何地方(可能是20％的灰色).

I would have expected an array such as [[0,0.25],[0.5,0.75]] to display the appropriate colours from the map, corresponding to those absolute values but the 0.75 will be interpreted as a 1. In the extreme case, an N x N array of 0.2 (for example), would just produce one big black square, rather than whatever one would expect 0.2 to correspond to in the colour map (perhaps a 20% grey).

是否有防止这种行为的方法?当自定义颜色图具有许多不连续性时，这尤其令人烦恼，比例的微小变化可能会导致所有颜色完全改变.

Is there a way to prevent this behaviour? It is particularly annoying when custom colour maps have many discontinuities, a small change in scale could cause all the colours to completely change.

推荐答案

只需指定vmin=0, vmax=1.

默认情况下，imshow将数据标准化为最小值和最大值.您可以使用vmin和vmax参数或使用norm参数(如果要进行非线性缩放)来控制它.

By default, imshow normalizes the data to its min and max. You can control this with either the vmin and vmax arguments or with the norm argument (if you want a non-linear scaling).

作为一个简单的例子:

import matplotlib.pyplot as plt

data = [[0, 0.25], [0.5, 0.75]]

fig, ax = plt.subplots()
im = ax.imshow(data, cmap=plt.get_cmap('hot'), interpolation='nearest',
               vmin=0, vmax=1)
fig.colorbar(im)
plt.show()

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