- 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_3/Fig3_plt.py
# Author: Leyla Noroozbabaee
# Date: 12/12/2021
# To reproduce Figure 3 from original paper, the python file 'Fig3_sim.py' should be run at the prompt in the OpenCOR Python console.
import matplotlib.pyplot as plt
import pandas as pd
from sklearn import preprocessing
import numpy as np
# Figure name
prefilename = 'Fig3'
# 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 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()
var_name = np.array(["Time", "bss", "gss", "btc", "gtc", "icat", "v"])
# Load Data
filename = '%s.csv' % (prefilename)
data = pd.read_csv(filename)
data = pd.read_csv(filename)
time = data [ var_name[0] ]
bss_data = data [var_name[1]]
gss_data = data [var_name[2]]
btc_data = data [var_name[3]]
gtc_data = data [var_name[4]]
icat_data = data [var_name[5]]
v_data = data [var_name[6]]
axs[1].plot( v_data, pow(bss_data, 2), 'b')
axs[0].plot( v_data, gss_data, 'b')
axs[3].plot( v_data, gtc_data, 'b')
# Set ylable
ylab = ['Steady state','Steady state','Time-topeak (ms)','Time constant (ms)','I (normalised)','I (normalised)']
cycle = plt.rcParams [ 'axes.prop_cycle' ].by_key() [ 'color' ]
I_V = []
Time_Max = []
V = [20, 10, 0, -10, -20,-30,-35, -40, -45,-50]
prefilename = 'Fig3_2'
for i in range(len(V)):
filename = '%s_%s.csv' % (prefilename, i)
data = pd.read_csv(filename)
time = data ['Time']
btc_data = np.array(data ['btc'])
max_btc = max(btc_data)
Time_Max.append(pow(max_btc,2))
axs [ 2 ].plot(V, Time_Max, '*b')
prefilename = 'Fig3_4'
V = [-60, -50, -40, -30, -20, -10, 0, 10, 20]
filename5 = '%s_%s.csv' % (prefilename, 8)
data5 = pd.read_csv(filename5)
icat_data5 = data5 [ 'icat' ]
max_icat_data5 = max(abs(data5 [ 'icat' ]))
for i in range(len(V)):
filename = '%s_%s.csv' % (prefilename, i)
data = pd.read_csv(filename)
time = data ['Time']
icat_data = np.array(data ['icat'])
axs [4].plot( time, icat_data/0.20810 , color=cycle [i % 4])
MAX_I_V = (min(icat_data / max_icat_data5))
I_V.append(MAX_I_V)
axs[5].plot(V, I_V, '-b')
for i in range(5):
axs [i].set_xlabel('V (mV)', fontsize=labelfontsize)
axs [ i ].set_ylabel('%s' % (ylab [ i ]), fontsize=labelfontsize)
if i == 4: axs [i].set_xlabel('Time (ms)', fontsize=labelfontsize)
# To add the extracted data from original paper to your plot, modify the path to have access to the
# "Extracted_data"
# prefilename = 'Extracted_Data/Fig3'
#
# for i in range(5):
#
# if i== 4:
# for j in range(1,5):
# filename = 'Extracted_Data/Fig3_55.csv'
# data = pd.read_csv(filename)
# x_name = 'x'
# y_name = 'Curve%s' %(j)
# x_data = data [ x_name ]
# y_data = data [ y_name ]
# axs [ i ].plot(x_data, y_data, '^')
# else:
# filename = '%s_%s.csv' % (prefilename, i + 1)
# data = pd.read_csv(filename)
# y_d = data [ 'Curve1' ]
# x_d = data [ 'x' ]
# axs [ i ].plot(x_d, y_d, 'k*')
# axs [i].set_xlim([ -100, 50 ])
# axs [i].set_xlabel('V (mV)', fontsize=labelfontsize)
# axs [ i ].set_ylabel('%s' % (ylab [ i ]), fontsize=labelfontsize)
figfiles = 'Figure_3.png'
plt.savefig(figfiles)
plt.show()
