Python matplotlib imshow - 默认颜色归一化
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matplotlib imshow - default colour normalisation
提问by oLas
I have consistently had problems with my colour maps when using imshow, some colours seem to just become black. I have finally realised that imshowseems to, by default, normalise the matrix of floating point values I give it.
使用时我的颜色图一直有问题imshow，有些颜色似乎变成黑色。我终于意识到，imshow默认情况下，似乎对我给它的浮点值矩阵进行了归一化。
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).
我本来期望一个数组，例如[[0,0.25],[0.5,0.75]]显示地图中适当的颜色，对应于那些绝对值，但 0.75 将被解释为 1。在极端情况下，0.2 的 N x N 数组（例如），将只需生成一个大的黑色方块，而不是在颜色图中对应于 0.2 的任何内容（可能是 20% 的灰色）。
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.
有没有办法防止这种行为？当自定义颜色映射有许多不连续性时，尤其令人讨厌，比例的微小变化可能会导致所有颜色完全改变。
采纳答案by Joe Kington
Just specify vmin=0, vmax=1.
只需指定vmin=0, vmax=1.
By default, imshownormalizes the data to its min and max. You can control this with either the vminand vmaxarguments or with the normargument (if you want a non-linear scaling).
默认情况下，imshow将数据标准化为其最小值和最大值。您可以使用vmin和vmax参数或使用norm参数（如果您想要非线性缩放）来控制它。
As a quick example:
举个简单的例子：
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()
