如何在matplotlib颜色栏中创建自定义断点？

Question

我从matplotlib自定义cmap示例页面借用了一个示例：

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

这会生成具有不同数量的着色轮廓的相同图像，如在箱数中指定的那样：n_bins：

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

但是，我不仅对箱子的数量感兴趣，而且对颜色值之间的特定断点感兴趣。例如，当右上方子图中的nbins=6时，如何指定二进制位的范围，以便在这些自定义区域中填充阴影：

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

是否也可以指定断点的包含性？例如，我想在-2到0.5之间指定它是-2 < x <= -0.5还是-2 <= x < -0.5。

以下答案编辑：

使用下面接受的答案，这里是绘制每个步骤的代码，包括最后在中点添加自定义颜色条标记。请注意，由于我是新用户，因此无法发布图片。

设置数据和6个颜色箱：

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)

绘制不同的选项：

# 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

您可以按如下方式使用BoundaryNorm：

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，如果你想要比例间距，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州

  

如果b[i] <= v < b[i+1]   然后v映射到颜色j;当i从0变化到len（边界）-2时，j从0变为ncolors-1。

所以颜色总是选为-2 <= x < -0.5，为了获得另一边的等号，你需要提供 像n_bins_ranges = np.array([-10,-5,-2,-0.5,2.5,7.5,10])-1e-9

这样的东西