[英]How can I create custom break points in a matplotlib colorbar?
我從matplotlib自定義cmap示例頁面借用了一個示例:
https://matplotlib.org/examples/pylab_examples/custom_cmap.html
如bins數中指定的那樣,這將產生具有不同數量的陰影輪廓的同一圖像: n_bins
:
https://matplotlib.org/_images/custom_cmap_00.png
但是,我不僅對垃圾箱的數量感興趣,而且對顏色值之間的特定斷點感興趣。 例如,當右上角子圖中的nbins=6
時,如何指定垃圾箱的范圍,以便在這些自定義區域中填充陰影:
n_bins_ranges = ([-10,-5],[-5,-2],[-2,-0.5],[-0.5,2.5],[2.5,7.5],[7.5,10])
是否還可以指定斷點的包容性? 例如,我想在-2到0.5之間指定-2 < x <= -0.5
或-2 <= x < -0.5
。
使用下面的答案進行編輯:
使用下面接受的答案,這是繪制每個步驟的代碼,包括最終在中點添加自定義顏色條刻度。 請注意,由於我是新用戶,因此無法發布圖片。
設置數據和6個顏色檔:
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)
繪制不同的選項:
# 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()
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,如果您想要比例間距,即根據顏色值之間的距離,
fig.colorbar(im, ax=ax, spacing="proportional")
正如邊界規范文檔所述
如果
b[i] <= v < b[i+1]
則v映射到顏色j; 當我從0變到len(boundaries)-2時,j從0變到ncolors-1。
因此,顏色總是選擇為-2 <= x < -0.5
,為了獲得另一側的等號,您需要提供類似n_bins_ranges = np.array([-10,-5,-2,-0.5,2.5,7.5,10])-1e-9
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