如何在matplotlib颜色条中创建自定义断点？

Question

我借用从matplotlib定制CMAP示例页面的示例：

https://matplotlib.org/examples/pylab_examples/custom_cmap.html

这产生相同的图像具有不同数量的阴影的轮廓，如在段的数目指定：n_bins：

https://matplotlib.org/_images/custom_cmap_00.png

但是，我不仅对桶的数量感兴趣，而且还对颜色值之间的具体断点感兴趣。例如，当nbins=6右上角的插曲，我怎么能指定的垃圾箱的范围，以使得阴影填充这些自定义方面：

n_bins_ranges = ([-10,-5],[-5,-2],[-2,-0.5],[-0.5,2.5],[2.5,7.5],[7.5,10]) 

是否也可以指定休息的包容性点？例如，我想在-2和0.5之间的范围内指定是-2 < x <= -0.5还是-2 <= x < -0.5。

与回答以下编辑：

使用下面的接受的答案，这里是绘制每一步，包括代码最后加入定制的彩条蜱连线的中点处。请注意，由于我是新用户，因此无法发布图片。

设置数据和6个色垃圾桶：

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

# Make some illustrative fake data: 
x = np.arange(0, np.pi, 0.1) 
y = np.arange(0, 2*np.pi, 0.1) 
X, Y = np.meshgrid(x, y) 
Z = np.cos(X) * np.sin(Y) * 10 

# Create colormap with 6 discrete bins 
colors = [(1, 0, 0), (0, 1, 0), (0, 0, 1)] # R -> G -> B 
n_bin = 6 
cmap_name = 'my_list' 
cm = matplotlib.colors.LinearSegmentedColormap.from_list(
     cmap_name, colors, N=n_bin) 

情节不同的选择：

# Set up 4 subplots 
fig, axs = plt.subplots(2, 2, figsize=(6, 9)) 
fig.subplots_adjust(left=0.02, bottom=0.06, right=0.95, top=0.94, wspace=0.05) 

# Plot 6 bin figure 
im = axs[0,0].imshow(Z, interpolation='nearest', origin='lower', cmap=cm) 
axs[0,0].set_title("Original 6 Bin") 
fig.colorbar(im, ax=axs[0,0]) 

# Change the break points 
n_bins_ranges = [-10,-5,-2,-0.5,2.5,7.5,10] 
norm = matplotlib.colors.BoundaryNorm(n_bins_ranges, len(n_bins_ranges)) 
im = axs[0,1].imshow(Z, interpolation='nearest', origin='lower', cmap=cm, norm=norm) 
axs[0,1].set_title("Custom Break Points") 
fig.colorbar(im, ax=axs[0,1]) 

# Arrange color labels by data interval (not colors) 
im = axs[1,0].imshow(Z, interpolation='nearest', origin='lower', cmap=cm, norm=norm) 
axs[1,0].set_title("Linear Color Distribution") 
fig.colorbar(im, ax=axs[1,0], spacing="proportional") 

# Provide custom labels at color midpoints 
# And change inclusive equality by adding arbitrary small value 
n_bins_ranges_arr = np.asarray(n_bins_ranges)+1e-9 
norm = matplotlib.colors.BoundaryNorm(n_bins_ranges, len(n_bins_ranges)) 
n_bins_ranges_midpoints = (n_bins_ranges_arr[1:] + n_bins_ranges_arr[:-1])/2.0 
im = axs[1,1].imshow(Z, interpolation='nearest', origin='lower', cmap=cm ,norm=norm) 
axs[1,1].set_title("Midpoint Labels\n Switched Equal Sign") 
cbar=fig.colorbar(im, ax=axs[1,1], spacing="proportional", 
     ticks=n_bins_ranges_midpoints.tolist()) 
cbar.ax.set_yticklabels(['Red', 'Brown', 'Green 1','Green 2','Gray Blue','Blue']) 

plt.show() 

Answer 1

可以使用BoundaryNorm如下：

import matplotlib.pyplot as plt 
import matplotlib.colors 
import numpy as np 
x = np.arange(0, np.pi, 0.1) 
y = np.arange(0, 2*np.pi, 0.1) 
X, Y = np.meshgrid(x, y) 
Z = np.cos(X) * np.sin(Y) * 10 

colors = [(1, 0, 0), (0, 1, 0), (0, 0, 1)] # R -> G -> B 
n_bin = 6 # Discretizes the interpolation into bins 
n_bins_ranges = [-10,-5,-2,-0.5,2.5,7.5,10] 
cmap_name = 'my_list' 
fig, ax = plt.subplots() 

# Create the colormap 
cm = matplotlib.colors.LinearSegmentedColormap.from_list(
      cmap_name, colors, N=n_bin) 
norm = matplotlib.colors.BoundaryNorm(n_bins_ranges, len(n_bins_ranges)) 
# Fewer bins will result in "coarser" colomap interpolation 
im = ax.imshow(Z, interpolation='nearest', origin='lower', cmap=cm, norm=norm) 
ax.set_title("N bins: %s" % n_bin) 
fig.colorbar(im, ax=ax) 

plt.show() 

或者，如果你想比例间隔，即颜色之间的距离根据它们的值，

fig.colorbar(im, ax=ax, spacing="proportional") 

作为boundary norm documentation状态

如果b[i] <= v < b[i+1] 则v被映射到色彩焦耳;当我从0变到len（边界）-2时，j从0变为ncolors-1。

所以颜色总是选择为-2 <= x < -0.5，以获得对方的等号，你将需要提供 像n_bins_ranges = np.array([-10,-5,-2,-0.5,2.5,7.5,10])-1e-9