- Author:
- leyla <noroozbabaee@gmail.com>
- Date:
- 2022-06-20 15:25:51+12:00
- Desc:
- A Computational Model of the Ionic Currents, Ca Dynamics and Action Potentials Underlying Contraction of Isolated Uterine Smooth Muscle
- Permanent Source URI:
- https://staging.physiomeproject.org/workspace/8aa/rawfile/539364647a9050858e906191c88d1552b6711c57/Experiments/Figure_6/Fig6_plt.py
# Author: Leyla Noroozbabaee
# Date: 12/12/2021
# To reproduce Figure 6 from original paper, the python file 'Fig6_sim.py' should be run.
import matplotlib.pyplot as plt
import pandas as pd
from sklearn import preprocessing
import numpy as np
# Figure name
prefilename = 'Fig6'
# Set figure dimension (width, height) in inches.
fw, fh = 15, 10
# Set subplots
subpRow, subpCol = 3, 2
ax, lns = {}, {}
# This gives list with the colors from the cycle, which you can use to iterate over.
cycle = plt.rcParams [ 'axes.prop_cycle' ].by_key() [ 'color' ]
# Set ylable
ylab = ['Steady state','Time constant (ms)','Time constant (ms)','I (normalised)','I (normalised)','I (normalised)']
# Set subplots
lfontsize, labelfontsize = 10, 15 # legend, label fontsize
fig, axs = plt.subplots(subpRow, subpCol, figsize=(fw, fh), facecolor='w', edgecolor='k')
fig.subplots_adjust(hspace = .3, wspace=.3)
axs = axs.ravel()
varName = np.array(["Time", "pss", "kss", "ptc", "k1tc","k2tc", "ik2", "v"])
filename = '%s.csv' % (prefilename)
data = pd.read_csv(filename)
time = data [ varName[0] ]
pss_data = data [varName[1]]
kss_data = data [varName[2]]
ptc_data = data [varName[3]]
k1tc_data = data [varName[4]]
k2tc_data = data [varName[5]]
ik2_data = data [varName[6]]
v_data = data [varName[7]]
axs[0].plot( v_data, pow(pss_data,2), 'b', v_data, kss_data, '--b')
axs[1].plot(v_data, ptc_data, 'b')
axs[2].semilogy(v_data, k1tc_data/1000, 'b', v_data, k2tc_data/1000, '--b')
I_V = []
prefilename = 'Fig6_5'
V = [-40,-30,-20,-10,0, 10 ]
for i in range(len(V)):
filename5 = '%s_%s.csv' % (prefilename, 5)
data5 = pd.read_csv(filename5)
ik2_data5 = data5 ['ik2']
max_ik2_data5 = max(abs(data5 ['ik2']))
filename = '%s_%s.csv' % (prefilename, i)
data = pd.read_csv(filename)
time = data ['Time']
ik2_data = data ['ik2']
max_ik2_data = max(data['ik2'])
axs [ 4 ].plot(time / 1000, ik2_data/max_ik2_data5 , color=cycle [ i % 4 ])
axs [ 5 ].plot(time / 1000, ik2_data / max_ik2_data5, color=cycle [ i % 4 ])
axs [ 4 ].set_xlim([ 0, 10 ])
axs [ 5 ].set_xlim([ 0, 0.5 ])
axs [3].plot( V[i], max_ik2_data/5, '^')
MAX_I_V = (min(ik2_data / max_ik2_data5))
I_V.append(MAX_I_V )
# To add the extracted data from original paper, modify the path to have access to the folder
# "Extracted_data"
prefilename = 'Extracted_Data/Fig6'
for i in range(6):
filename = '%s_%s.csv' % (prefilename, i+1)
data = pd.read_csv(filename)
axs [ i ].set_xlim([ -70, 60 ])
if i == 1 or i==3 :
y_d = data [ 'Curve1' ]
x_d = data [ 'x' ]
axs [ i ].plot(x_d, y_d, 'k*')
elif i == 4 or i == 5 :
for j in range(1, 6):
x_name = 'x'
y_name = 'Curve%s' % (j)
x_data = data [ x_name ]
y_data = data [ y_name ]
if i == 4:
axs [ i ].set_xlim([ 0, 10 ])
axs [ i ].plot(x_data - 0.233, y_data, '^')
if i == 5:
axs [ i ].set_xlim([ 0, 0.4 ])
axs [ i ].plot(x_data, y_data, '^')
axs [ i ].set_xlabel('Time (s)', fontsize=12)
axs [ i ].set_ylim([ 0, 1.2 ])
axs [i].set_xlabel('V (mV)', fontsize=labelfontsize)
axs [i].set_ylabel('%s' % (ylab[i]),fontsize=labelfontsize)
else:
y_d = data [ 'Curve1' ]
x_d = data [ 'x' ]
y_d_2 = data [ 'Curve2' ]
axs [ i ].plot(x_d, y_d_2, 'k*')
axs [ i ].plot(x_d, y_d, 'k*')
axs [ i ].set_xlabel('V (mV)', fontsize=labelfontsize)
axs [ i ].set_ylabel('%s' % (ylab [ i ]), fontsize=labelfontsize)
axs [i].set_xlabel('V (mV)', fontsize=labelfontsize)
axs [i].set_ylabel('%s' % (ylab[i]),fontsize=labelfontsize)
figfiles = 'Figure_6.png'
plt.savefig(figfiles)
plt.show()
