具有轮廓级别的连续色条 [英] Continuous colorbar with contour levels

问题描述

我试图在轮廓图上添加一个颜色条，但是颜色条不是连续的.

该图是用以下代码制作的.

将 numpy 导入为 np导入matplotlib.pyplot作为plt导入 matplotlib.cmcmap = matplotlib.cm.viridisSilhouette_start = 500轮廓数 = 20outline_factor = 1.20#计算轮廓线水平cl=contour_start*contour_factor**np.arange(contour_num)negcl = cl[::-1] * -1supercl = np.concatenate([negcl，cl])#创建图fig = plt.figure(figsize=(6,5), dpi=150)ax = fig.add_subplot(111)#绘制轮廓cp = ax.contour(datab，supercl，cmap = cmap，)cbar = plt.colorbar(cp)# cbar.set_alpha(1)# cbar.draw_all()plt.show()

我尝试添加

cbar.set_alpha(1)cbar.draw_all()

但它没有用，所以我实际上认为这与谨慎的轮廓水平有关.我不确定.

我意识到不包含数据，并且代码无法按原样工作.我没有包括，因为数据加载部分依赖于另一个不常见的库.不过，我想指出的是matplotlib文档，该文档具有类似的情节，具有相似的颜色条(最后一个示例，右边的颜色条):

I am trying to make add a colorbar to my contour plot, but the bar is not continuous.

The plot was made with the following code.

import numpy as np
import matplotlib.pyplot as plt
import matplotlib.cm

cmap = matplotlib.cm.viridis
contour_start = 500
contour_num = 20
contour_factor = 1.20

# calculate contour levels
cl = contour_start * contour_factor ** np.arange(contour_num) 
negcl = cl[::-1] * -1
supercl = np.concatenate([negcl, cl])

# create the figure
fig = plt.figure(figsize=(6,5), dpi=150)
ax = fig.add_subplot(111)
# plot the contours
cp = ax.contour(datab, supercl, cmap=cmap,)

cbar = plt.colorbar(cp)

# cbar.set_alpha(1)
# cbar.draw_all()
plt.show()

I tried to add

cbar.set_alpha(1)
cbar.draw_all()

But it didn't work, so I actually think this has to do with the discreet contour levels. I'm unsure.

I realize that the data is not included and the code will not work as it is. I didn't include as the data loading part depends on a another library which is not usual. Nevertheless I would like to point to the matplotlib docs which have a similar plot with a similar colorbar (last example, right colorbar): https://matplotlib.org/examples/pylab_examples/contour_demo.html

解决方案

A solution can be to create a colorbar from a different ScalarMappable than the contour plot itself. The newly created ScalarMappable would then take the range of colors from the contour plot via a Normalize instance.

The following code is the adapted version of the contour-demo example.

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

x = np.arange(-3.0, 3.0, 0.025)
X, Y = np.meshgrid(x, x)
Z1 = mlab.bivariate_normal(X, Y, 1.0, 1.0, 0.0, 0.0)
Z2 = mlab.bivariate_normal(X, Y, 1.5, 0.5, 1, 1)
Z = 10.0 * (Z2 - Z1)

fig, ax = plt.subplots()

cs = plt.contour(X, Y, Z, cmap="viridis")

norm= matplotlib.colors.Normalize(vmin=cs.cvalues.min(), vmax=cs.cvalues.max())
# a previous version of this used
#norm= matplotlib.colors.Normalize(vmin=cs.vmin, vmax=cs.vmax)
# which does not work any more
sm = plt.cm.ScalarMappable(norm=norm, cmap = cs.cmap)
sm.set_array([])
fig.colorbar(sm, ticks=cs.levels)

plt.show()

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