Matplotlib imshow：更改颜色条的中心值时，NaN 的颜色会发生变化

Question

我用 np.NaN 绘制了一个数据。由于原始数据的分布，我还想更改颜色条的中心值。但是当我改变颜色条的Vmin、Vmax和vcenter值时，np.NaN值的颜色变成了白色以外的其他颜色。那么我该如何解决呢？以下是代码：

import numpy as np
import matplotlib.pyplot as plt
import matplotlib.colors as colors_tmp
class MidpointNormalize(colors_tmp.Normalize):
    def __init__(self, vmin=None, vmax=None, vcenter=None, clip=False):
        self.vcenter = vcenter
        colors_tmp.Normalize.__init__(self, vmin, vmax, clip)

    def __call__(self, value, clip=None):
        # I'm ignoring masked values and all kinds of edge cases to make a
        # simple example...
        x, y = [self.vmin, self.vcenter, self.vmax], [0, 0.5, 1]
        return np.ma.masked_array(np.interp(value, x, y))

img = np.linspace(1,1000,1000).reshape((20,50))
img[(img>700)*(img<800)] = np.nan

fig, ax = plt.subplots(1,1)
sc = ax.imshow(img)

axpos = ax.get_position()
cbar_ax = fig.add_axes(
    [axpos.x1, axpos.y0, 0.01, axpos.height])  # l, b, w, h
cbar = fig.colorbar(sc, cax=cbar_ax)

然后我像这样更改颜色条的 Vmin、Vmax 和 vcenter：

fig, ax = plt.subplots(1,1)
sc = ax.imshow(img)

axpos = ax.get_position()
cbar_ax = fig.add_axes(
    [axpos.x1, axpos.y0, 0.01, axpos.height])  # l, b, w, h
cbar = fig.colorbar(sc, cax=cbar_ax)
midnorm = MidpointNormalize(vmin=0, vcenter=200, vmax=500)
cbar.mappable.set_norm(midnorm)
cbar.mappable.set_cmap('BrBG')

结果如下，可以看到np.NaN的颜色还是白色

但是当我将其更改为 vmin=0, vcenter=800, vmax=1000 时，事情变得很奇怪：

fig, ax = plt.subplots(1,1)
sc = ax.imshow(img)

axpos = ax.get_position()
cbar_ax = fig.add_axes(
    [axpos.x1, axpos.y0, 0.01, axpos.height])  # l, b, w, h
cbar = fig.colorbar(sc, cax=cbar_ax)
midnorm = MidpointNormalize(vmin=0, vcenter=800, vmax=1000)
cbar.mappable.set_norm(midnorm)
cbar.mappable.set_cmap('BrBG')

那是为什么呢？我想将 np.NaN 值保持为白色，我尝试了 ax.set_patch 和 set_bad(color="white")，它们都不起作用...所以有没有人可以帮助我？非常感谢！

Answer 1

使用最新的 matplotlib 版本 (3.4.2)，代码似乎按预期工作。

import numpy as np
import matplotlib.pyplot as plt
import matplotlib.colors as colors_tmp

class MidpointNormalize(Normalize):
    def __init__(self, vmin=None, vmax=None, vcenter=None, clip=False):
        self.vcenter = vcenter
        colors_tmp.Normalize.__init__(self, vmin, vmax, clip)

    def __call__(self, value, clip=None):
        # I'm ignoring masked values and all kinds of edge cases to make a
        # simple example...
        x, y = [self.vmin, self.vcenter, self.vmax], [0, 0.5, 1]
        return np.ma.masked_array(np.interp(value, x, y))

img = np.linspace(1, 1000, 1000).reshape((20, 50))
img[(img > 700) * (img < 800)] = np.nan

fig, ax = plt.subplots(1, 1)
sc = ax.imshow(img)

axpos = ax.get_position()
cbar_ax = fig.add_axes(
    [axpos.x1 + 0.01, axpos.y0, 0.01, axpos.height])  # l, b, w, h
cbar = fig.colorbar(sc, cax=cbar_ax)
midnorm = MidpointNormalize(vmin=0, vcenter=800, vmax=1000)
cbar.mappable.set_norm(midnorm)
cbar.mappable.set_cmap('BrBG')
plt.show()

另外，你可以试试：

调用时直接设置范数和cmap imshow
使用 TwoSlopeNorm 而不是自定义规范
明确设置“不良”颜色（'none' 透明，显示背景，或 'white' 修复与背景无关的颜色）

import numpy as np
import matplotlib.pyplot as plt
import matplotlib.colors as mcolors

img = np.linspace(1, 1000, 1000).reshape((20, 50))
img[(img > 700) & (img < 800)] = np.nan

fig, ax = plt.subplots(1, 1)
cmap = plt.get_cmap('BrBG')
cmap.set_bad('white')
midnorm = mcolors.TwoSlopeNorm(vmin=0, vcenter=800, vmax=1000)
sc = ax.imshow(img, norm=midnorm, cmap=cmap)

axpos = ax.get_position()
cbar_ax = fig.add_axes(
    [axpos.x1 + 0.01, axpos.y0, 0.01, axpos.height])  # l, b, w, h
cbar = fig.colorbar(sc, cax=cbar_ax)
plt.show()

Matplotlib imshow：更改颜色条的中心值时，NaN 的颜色会发生变化

Matplotlib imshow: Color of NaN changes when change the center value of colorbar

python

matplotlib

nan

colorbar

imshow