MATPLOTLIB：如何在 python 中的同一绘图上堆叠 2 个颜色图？

如何解决MATPLOTLIB：如何在 python 中的同一绘图上堆叠 2 个颜色图？

我想生成如下图：

目前我正在尝试使用 alpha 参数：

import numpy as np
from matplotlib import pyplot as plt

xlocations_edensity = np.loadtxt("edensity_xaxis.txt") 
ylocations_edensity = np.loadtxt("edensity_yaxis.txt") 
xlocsedensity,ylocsedensity = np.meshgrid(xlocations_edensity,ylocations_edensity)

xlocations_Efield = np.loadtxt("Efield_x_axis.txt")
ylocations_Efield = np.loadtxt("Efield_y_axis.txt")
xlocsEfield,ylocsEfield = np.meshgrid(xlocations_Efield,ylocations_Efield)

edensitytensor = np.load("edensitytensor.npy") # shape (76,257,65)
Efieldtensor = np.load("Efieldtensor.npy")

fig,ax = plt.subplots()
ax.set(xlabel="x position [um]",ylabel="y position [um] \n")
pos2 = ax.pcolor(xlocations_Efield,ylocations_Efield,Efieldtensor[40,:,:].T,cmap="Reds",alpha=0.9)
fig.colorbar(pos2,ax=ax,label="\n Efield value [MV/m]")
pos1 = ax.pcolor(xlocations_edensity,ylocations_edensity,edensitytensor[100,cmap="Blues",alpha=0.5)
fig.colorbar(pos1,label="\n electron density value [cm^(-3)]")
plt.savefig("Efield_edensity_map.pdf")

但是改变绘图顺序，我得到了不同的结果。一张颜色图“隐藏”了另一个。 假设我先绘制了 Reds 一个，它出现了，而 Blues 一个隐藏了。 反过来，蓝军先红军，蓝军隐藏红军。

以上代码的结果是：

你有什么想法我该怎么办？

谢谢！

解决方法

设置 pcolor 调用的 alpha 值不太好，因为它对颜色图上的所有颜色应用相同的透明度。

您可以使用具有不断变化的透明度的自定义颜色图，我尝试使用 alpha 的线性和 sigmoidal 演变，您可以尝试其他方法。我用高斯脉冲创建了虚拟噪声数据来模拟您的示例中的数据。

import numpy as np
from matplotlib import pyplot as plt
from matplotlib.colors import ListedColormap


# generating dummy data
nx,ny = 257,65

x_field,y_field = np.linspace(0,10,nx),np.linspace(0,6,ny)
field = np.random.rand(nx,ny)
# normalizing
field -= np.min(field); field /= np.max(field)

x_density,y_density = np.linspace(1,np.linspace(1,ny)
Y,X = np.meshgrid(y_density,x_density,)
density = np.random.rand(nx,ny) # shape (76,257,65)
gaussian_center = (4.0,4.0)
distance_square = (X - gaussian_center[0])**2 + (Y - gaussian_center[1])**2
density += 5.0*np.exp(-distance_square/4.0)
# normalizing
density -= np.min(density); density /= np.max(density)

# getting the original colormap
orig_cmap = plt.get_cmap('Blues')
cmap_n = orig_cmap.N
derived_cmap = orig_cmap(np.arange(cmap_n))

fig,axs = plt.subplots(
    4,3,gridspec_kw={"width_ratios":[1,0.025,0.025]},figsize=(10,8),constrained_layout=True)

# original
row_subplot = 0
ax = axs[row_subplot,0]
ax.set_ylabel("original")

image_field = ax.pcolor(
    x_field,y_field,field.T,cmap="Reds",shading='auto')
fig.colorbar(image_field,cax=axs[row_subplot,-2],)
image_density = ax.pcolor(
    x_density,y_density,density.T,cmap=orig_cmap,shading="auto")
fig.colorbar(image_density,-1],)

# option 1 - transparent pseudocolor for the above image
row_subplot = 1
ax = axs[row_subplot,0]
ax.set_ylabel("transparent pcolor")

image_field = ax.pcolor(
    x_field,alpha=1.0,alpha=0.5,)


# option 2 - linear gradient colormap
linear_cmap = derived_cmap.copy()
linear_cmap[:,-1] = np.arange(cmap_n)/cmap_n
linear_cmap = ListedColormap(linear_cmap)

row_subplot = 2
ax = axs[row_subplot,0]
ax.set_ylabel("linear gradient")
image_field = ax.pcolor(
    x_field,cmap=linear_cmap,)


# option 3 - sigmoid gradient
sigmoid_cmap = derived_cmap.copy()
x = np.linspace(-10,cmap_n)
sigmoid_cmap[:,-1] = np.exp(x)/(np.exp(x) + 1)
sigmoid_cmap = ListedColormap(sigmoid_cmap)

row_subplot = 3
ax = axs[row_subplot,0]
ax.set_ylabel("sigmoid gradient")

image_field = ax.pcolor(
    x_field,cmap=sigmoid_cmap,)