Location: Computational analysis of the human sinus node action potential @ ee3b51bbb18e / Fig04-plot.py

Author:
nima <nafs080@aucklanduni.ac.nz>
Date:
2021-03-22 11:53:08+13:00
Desc:
Figure 06 and 07 Python codes
Permanent Source URI:
https://staging.physiomeproject.org/workspace/648/rawfile/ee3b51bbb18e879df1098bf0a5bd3dc16e3960fe/Fig04-plot.py

import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import sys
# import plot_func
# reload (plot_func)
import os

data = pd.read_csv('Fig04.csv')


X1 = data[data.keys()[5]]*1000
Y1 = data[data.keys()[0]]
Y2 = data[data.keys()[1]]
Y3 = data[data.keys()[2]]
Y4 = data[data.keys()[3]]
Y5 = data[data.keys()[4]]





plt.figure(figsize=(14,14))

plt.plot(X1, Y1, 'navy',linestyle='-',  label = 'Control', linewidth= 3)
plt.plot(X1, Y2, 'red',linestyle='-',  label = 'Block 30%', linewidth= 3)
plt.plot(X1, Y3, 'green',linestyle='-',  label = 'Block 70%', linewidth= 3)
plt.plot(X1, Y4, 'purple',linestyle='-',  label = 'Block 90%', linewidth= 3)
plt.plot(X1, Y5, 'black',linestyle='-',  label = 'Block 100%', linewidth= 3)


plt.grid()
plt.xlim(0, 1900)
plt.ylim(-70,30)
plt.xlabel ('time(ms)',fontsize=16)
plt.tick_params(axis='both', labelsize=14)
plt.ylabel ('V$_m$(mV)', fontsize=16)
# plt.title('A')
plt.legend(loc='best',fontsize=16,
       ncol=5, mode="expand", borderaxespad=0.)


plt.tight_layout(pad=0.4, w_pad=0.5, h_pad=1.0)
plt.savefig('C:/Nima/ABI/Physiome Journal/sinus/Python_codes/figure04.png')
plt.show()
#~ plt.legend(loc=0)