Python：在contourf图中设置零值颜色，其中需要日志比例颜色条

Question

我想从某些数据文件中制作等高线图。我遇到的麻烦是我想要颜色栏上的最小值以下的z值与最小值的颜色相同。 当使用例如线性比例尺时，这很容易。 contourf的extend="both"选项，或者使用cmap.set_under()作为颜色映射。不幸的是，在使用logscale时，这些选项都不起作用。任何人都可以提出一个解决方法？我只是想摆脱在下面的图中的白色区域：

#!/usr/bin/env python 
import numpy as np 
import matplotlib.pyplot as plt 
import scipy.interpolate 
from matplotlib import colors, ticker, cm 
from matplotlib.colors import LogNorm 
N = 100 #number of points for plotting/interpolation 

y, x, z = np.genfromtxt(r'40Ca_208Pb_39K_Ex_115deg.dat', unpack=True) 

xi = np.linspace(x.min(), x.max(), N) 
yi = np.linspace(y.min(), y.max(), N) 
zi = scipy.interpolate.griddata((x, y), z, (xi[None,:], yi[:,None]), method='linear') 

hfont = {'fontname':'Palatino'} 

fig = plt.figure(facecolor="white") 

zi = np.ma.masked_less(zi, 1e-7) 

plt.contourf(xi, yi, zi,levels=[1e-7,1e-6,1e-5,1e-4,1e-3,1e-2,1e-1],cmap=plt.cm.jet,norm = LogNorm()) 

plt.xlabel("$E_{x}$") 
plt.ylabel("$E/V_{B}$") 
plt.colorbar() 
plt.show() 

Answer 1

看来，extend关键字不是对数刻度工作是known issue与matplotlib。

粗的解决方法可能是将所有的值对象转换成绘制的范围（注意在min_drawn_value和max_drawn_value的意见，值必须在该范围内）：

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

N = 100 # number of points for plotting/interpolation 
min_exp = -8 
max_exp = -2 

min_drawn_value = 1.000001 * 10.**min_exp # above 10.**min_exp 
max_drawn_value = 0.999999 * 10.**max_exp # below 10.**max_exp 

xi = np.linspace(0, 1, N) 
yi = np.linspace(0, 1, N) 
zi = np.random.rand(N, N) *\ 
    10. ** np.random.randint(min_exp - 1, max_exp + 2, (N, N)) 
zi = np.sort(zi.flatten()).reshape(N,N) 

# Coerce values outside of colorbar range to lie within 
zi_masked = np.where(zi < 10.**min_exp, min_drawn_value, zi) 
zi_masked = np.where(zi_masked > 10.**max_exp, max_drawn_value, zi_masked) 


fig, (ax,ax2) = plt.subplots(ncols=2) 

c1 = ax.contourf(xi, yi, zi, levels=10.**np.arange(min_exp, max_exp+1), 
      cmap=plt.cm.jet, norm=LogNorm()) 

c2 = ax2.contourf(xi, yi, zi_masked, levels=10.**np.arange(min_exp, max_exp+1), 
      cmap=plt.cm.jet, norm=LogNorm()) 

ax.set_title("direct plot of array") 
ax2.set_title("coerce outlier values") 
fig.colorbar(c1, ax=ax) 
fig.colorbar(c2, ax=ax2) 
fig.subplots_adjust(wspace=0.3) 
plt.show()