如何使用matplotlib获取密度/概率图

Question

I was trying to get the top-left and top-right panels of the figure 7 in this paper . I got the top-left panel of the figure 7 but couldn't get its top-right panel. The density part of my code generates the green and blue lines in output, which are not correct. How can I get the probability part (top-right panel of the figure 7) and combine it with my figure?

A small part of the input file ( 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 

Code:

#!/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)

Output:

Answer 1

The first thing that you have to notice is that the two plots are on separate axes though they share the same y-axis. It would be much more easier to solve the problem without the extra formatting first, then you can apply your special formatting.

#!/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)

The result looks like

The rest is mere formatting.