如何使用matplotlib獲取密度/概率圖

Question

我試圖讓這個圖7的左上和右上面板紙 。 我得到了圖7的左上方面板，但無法獲得其右上方面板。 我的代碼的密度部分在輸出中生成綠線和藍線，這是不正確的。 如何獲得概率部分（圖7的右上方面板）並將其與我的圖結合？

輸入文件的一小部分（ input.txt ）：

0.0000000 0.0000474 0.0000393 
400.0000000 0.1775423 0.1091695 
800.0000000 0.2363394 0.1158220 
1200.0000000 0.2146373 0.1323802 
1600.0000000 0.2629943 0.1379013 
2000.0000000 0.2353280 0.1205457 
2400.0000000 0.2548243 0.1285356 
2800.0000000 0.2507923 0.1243078 
3200.0000000 0.3038598 0.1328937 
3600.0000000 0.2438334 0.1171351 
4000.0000000 0.2399136 0.1386342
4400.0000000 0.2263989 0.1232137 
4800.0000000 0.2036292 0.1274123 
5200.0000000 0.2136007 0.1262307 
5600.0000000 0.2685070 0.1408818 
6000.0000000 0.2805652 0.1222442 
6400.0000000 0.2328329 0.1256370 
6800.0000000 0.2660308 0.1135865 
7200.0000000 0.2446094 0.1089109 
7600.0000000 0.2729914 0.1254719 
8000.0000000 0.3119634 0.1378875 
8400.0000000 0.3347659 0.1309574 
8800.0000000 0.3206002 0.1289072 
9200.0000000 0.2670084 0.1275363 
9600.0000000 0.2712551 0.1324258 
10000.0000000 0.2453061 0.1368878 

碼：

#!/usr/bin/python
import numpy as np
import pylab as plot
import matplotlib.pyplot as plt
import numpy, scipy, pylab, random
from matplotlib.ticker import MultipleLocator
import matplotlib as mpl
from matplotlib.ticker import MaxNLocator
from scipy import stats

with open("input.xvg", "r") as f:
    x=[]
    y1=[]
    y2=[]
    for line in f:
        if not line.strip() or line.startswith('@') or line.startswith('#'): continue
        row = line.split()
        x.append(float(row[0])*0.001)
        y1.append(float(row[1]))
        y2.append(float(row[2]))


fig = plt.figure(figsize=(3.2,2.2), dpi=300)
ax = plt.subplot(111)


plt.xlim(0, 1000)
plt.ylim(0, 0.7)
ax.xaxis.set_major_locator(MaxNLocator(10))
ax.yaxis.set_major_locator(MaxNLocator(7))
ax.xaxis.set_minor_locator(MultipleLocator(50))
ax.yaxis.set_minor_locator(MultipleLocator(0.05))

plt.plot(x, y1, 'orange', label='A',  linewidth=0.5)
plt.plot(x, y2, 'black', label='B',  linewidth=0.5)


plt.xlabel('Time (ns)', fontsize=8)
plt.ylabel('RMSD (nm)', fontsize=8)


for axis in ['top','bottom','left','right']:
  ax.spines[axis].set_linewidth(0.5)

plt.subplots_adjust(top=0.95)
plt.subplots_adjust(bottom=0.18)
plt.subplots_adjust(left=0.14)
plt.subplots_adjust(right=0.95)


plt.tick_params(axis='both', which='major', labelsize=7)
plt.tick_params(axis='both', which='minor', labelsize=0)

#for the density part
density1 = stats.kde.gaussian_kde(y1)
density2 = stats.kde.gaussian_kde(y2)
plt.plot(x, density1(y1))
plt.plot(x, density2(y2))

plt.savefig("output.png", dpi=300)

輸出：

Answer 1

首先要注意的是，盡管兩個圖共享相同的y軸，但它們在不同的軸上。 如果不首先進行額外的格式化，則解決該問題將更加容易，然后您可以應用特殊的格式化。

#!/usr/bin/python
import numpy as np
import pylab as plot
import matplotlib.pyplot as plt
import numpy, scipy, pylab, random
from matplotlib.ticker import MultipleLocator
import matplotlib as mpl
from matplotlib.ticker import MaxNLocator
from scipy import stats

with open("input.txt", "r") as f:
    x=[]
    y1=[]
    y2=[]
    for line in f:
        if not line.strip() or line.startswith('@') or line.startswith('#'): continue
        row = line.split()
        x.append(float(row[0])*0.001)
        y1.append(float(row[1]))
        y2.append(float(row[2]))



fig, (ax1, ax2) =plt.subplots(1, 2, sharey=True)

ax1.axis([0, 10, 0, 0.7])


ax1.plot(x, y1, 'orange', label='A',  linewidth=1)
ax1.plot(x, y2, 'black', label='B',  linewidth=1)

#for the density part

density1 = stats.kde.gaussian_kde(y1)
density2 = stats.kde.gaussian_kde(y2)

# plot the pdf for the full range of y-axis 
y_range = np.linspace(0, 0.7, 100)
ax2.plot(density1(y_range), y_range, 'orange')
ax2.plot(density2(y_range), y_range, 'black')

# display y-axis tick on the right 
ax2.yaxis.tick_right()
# remove the spacing between the two axes
plt.subplots_adjust(wspace=0, hspace=0)

# deal with the overlaping x-axis label at the center
# you can remove the label corresponding to the last element of the frist axis 
xticks = ax1.xaxis.get_major_ticks()
xticks[-1].label1.set_visible(False)

# modifying the number of y ticks 
ax2.yaxis.set_major_locator(MaxNLocator(4.0)) 
ax2.yaxis.set_minor_locator(MultipleLocator(0.1))

plt.savefig("output.png", dpi=300)

結果看起來像

剩下的僅僅是格式化。