Python (numpy) - 关联两个分箱图

Question

我的问题是如何关联我的两个分箱图并输出 Pearson 相关系数？

我不确定如何正确提取 np.corrcoef 函数所需的合并数组。这是我的脚本：

import numpy as np 
import matplotlib.pyplot as plt

A = np.genfromtxt('data1.txt') 
x1 = A[:,1] 
y1 = A[:,2]

B=np.genfromtxt('data2.txt') 
x2 = B[:,1] 
y2 = B[:,2]

fig = plt.figure() 
plt.subplots_adjust(hspace=0.5) 
plt.subplot(121) 
AA = plt.hexbin(x1,y1,cmap='jet',gridsize=500,vmin=0,vmax=450,mincnt=1) 
plt.axis([-180,180,-180,180]) 
cb = plt.colorbar() 
plt.title('Data1')

plt.subplot(122) 
BB = plt.hexbin(x2,y2,cmap='jet',gridsize=500,vmin=0,vmax=450,mincnt=1) 
plt.axis([-180,180,-180,180]) 
cb = plt.colorbar() 
plt.title('Data 2')

array1 = np.ndarray.flatten(AA)  
array2 = np.ndarray.flatten(BB)

print np.corrcoef(array1,array2)

plt.show()

Answer 1

答案可以在documentation中找到：

Returns: object

a PolyCollection instance; use get_array() on this PolyCollection to get the counts in each hexagon.

这是您的代码的修订版：

A = np.genfromtxt('data1.txt') 
x1 = A[:,1] 
y1 = A[:,2]

B = np.genfromtxt('data2.txt') 
x2 = B[:,1] 
y2 = B[:,2]

# make figure and axes
fig, (ax1, ax2) = plt.subplots(1, 2)

# define common keyword arguments
hex_params = dict(cmap='jet', gridsize=500, vmin=0, vmax=450, mincnt=1)

# plot and set titles   
hex1 = ax1.hexbin(x1, y1, **hex_params) 
hex2 = ax2.hexbin(x2, y2, **hex_params) 
ax1.set_title('Data 1')
ax2.set_title('Data 2')

# set axes lims
[ax.set_xlim(-180, 180) for ax in (ax1, ax2)]
[ax.set_ylim(-180, 180) for ax in (ax1, ax2)]

# add single colorbar
fig.subplots_adjust(right=0.8, hspace=0.5)
cbar_ax = fig.add_axes([0.85, 0.15, 0.05, 0.7])
fig.colorbar(hex2, cax=cbar_ax)

# get binned data and corr coeff
binned1 = hex1.get_array()
binned2 = hex2.get_array()    
print np.corrcoef(binned1, binned2)

plt.show()

但有两条评论：您确定要皮尔逊相关系数吗？你到底想展示什么？如果您想显示分布是 same/different，您可能需要使用 Kolmogorov-Smirnov 检验。

也不要使用 jet 作为颜色图。 Jet is bad。

Python (numpy) - 关联两个分箱图

Python (numpy) - correlate two binned plots

python

numpy

pearson-correlation