- Author:
- Leyla <noroozbabaee@gmail.com>
- Date:
- 2022-05-10 14:01:08+12:00
- Desc:
- Adding Tong_2011 to PMR
- Permanent Source URI:
- https://models.cellml.org/workspace/85c/rawfile/a03f680a69226c515cd789723c8036028406852b/Experiments/Figure_8/Fig8_plt.py
# Author: Leyla Noroozbabaee
# Date: 12/12/2021
# To reproduce Figure 6 from original paper, the python file 'Fig8_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 = 'Fig8'
# 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()
ca_i = [ 0.000005,0.00001, 0.00005, 0.00008, 0.0001, 0.0005, 0.0008, 0.001, 0.005, 0.008, 0.01, 0.05, 0.1, 0.5, 0.8, 1.0, 5.0, 8, 10, 50, 80, 100]
print(ca_i)
xassz = []
xassvh = []
xabssz = []
xabssvh = []
iBk_tot = []
for j in range(len(ca_i)):
varName = np.array(["Time", "xass_z", "xass_vh", "xatc", "xass", "iBKa","xa", "v", "xabss_z", "xabss_vh",
"xabtc", "xabss", "iBKab","xab"])
filename = '%s_%s.csv' % (prefilename,j)
data = pd.read_csv(filename)
v_data = data [varName[7]]
xassz_data = max(data [ varName [ 1 ] ])
xassz.append(xassz_data)
xassvh_data = max(data [ varName [ 2 ] ])
xassvh.append(xassvh_data)
xabssz_data = max(data [ varName [ 8 ] ])
xabssz.append(xabssz_data)
xabssvh_data = max(data [ varName [ 9 ] ])
xabssvh.append(xabssvh_data)
xass_data = data [varName[4]]
xatc_data = data [varName[3]]
axs [ 2 ].plot(v_data, xass_data, 'b')
axs [ 4 ].semilogy(v_data, xatc_data, 'b')
xabss_data = data[varName [11]]
xabtc_data = data[varName [10]]
xab_data = data [ varName [13 ] ]
axs [ 3 ].plot(v_data, xabss_data, 'b')
axs [ 4 ].semilogy(v_data, xabtc_data, 'b')
if j == 1 or j == 6:
iBka_data = (data [ varName [ 5 ] ])
iBkab_data = (data [ varName [ 12 ] ])
iBk = 0.7 * iBka_data + 0.3 * iBkab_data
axs [ 5 ].plot(v_data, iBk, 'k')
ca_i = np.array(ca_i)*1000
axs[0].semilogx(ca_i, xassvh, '--b', ca_i, xabssvh, '--b')
axs[1].semilogx(ca_i, xassz, 'b', ca_i, xabssz, '--b')
# Set ylable
# tit = ['Steady state','Steady state','Time constant (ms)','Time constant (ms)','I (normalised)','I (normalised)']
# cycle = plt.rcParams [ 'axes.prop_cycle' ].by_key() [ 'color' ]
# # To add the extracted data from original paper to your plot, modify the path to have access to the
# # "Extracted_data"
# I_V = []
# prefilename = 'Fig8_5'
# # V =[-60]
# # V =[50, 40, 30, 25, 20, 10, 0,-10,-20,-30,-40,-50,-60, -70,-80]
# V = [-40,-30,-20,-10,0, 10 ]
# #for i in range(5,10):
# for i in range(len(V)):
# filename5 = '%s_%s.csv' % (prefilename, 5)
# data5 = pd.read_csv(filename5)
# print('filename', filename5)
# ik2_data5 = data5 ['ik2']
# max_ik2_data5 = max(abs(data5 ['ik2']))
# filename = '%s_%s.csv' % (prefilename, i)
# data = pd.read_csv(filename)
# print('filename', filename)
# time = data ['Time']
# ik2_data = data ['ik2']
# max_ik2_data = max(data['ik2'])
# print('max_ik2_data', max_ik2_data5)
# # if 5 <= i < 11:
# 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 [4].plot( time, ik2_data, color=cycle [i % 4])
# MAX_I_V = (min(ik2_data / max_ik2_data5))
# I_V.append(MAX_I_V )
#
#
# # v_clamp =[50, 40, 30, 25, 20, 10, 0,-10,-20,-30,-40,-50,-60, -70,-80]
# # print(I_V)
# # axs[5].plot(V, I_V, '-b')# y_data = data [ var [ i ]
# prefilename = 'Fig8'
#
# # for i in range(6):
# # filename = '%s_%s.csv' % (prefilename, i+1)
# # data = pd.read_csv(filename)
# # 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([ -90, 60 ])
# # axs [ i ].set_xlabel('V (mV)', fontsize=labelfontsize)
# # axs [ i ].set_ylabel('%s' % (tit [ i ]), fontsize=labelfontsize)
# # if i == 0 or i == 3:
# # y_d_2 = data [ 'Curve2' ]
# # axs [ 0 ].plot(x_d, y_d_2, 'k*')
# # #axs [ 3 ].plot(x_d, y_d_2, 'k*')
# # elif i == 1:
# # # y_d_2 = data [ 'Curve1' ]
# # axs [ 1 ].semilogy(x_d, y_d, 'k*')
# # elif i == 2:
# # y_d_2 = data [ 'Curve2' ]
# # axs [ i ].semilogy(x_d, y_d, 'k*', x_d, y_d_2, 'k*')
# #
# # elif i == 4 or i == 5 :
# # y_d_2 = data [ 'Curve2' ]
# # y_d_3 = data [ 'Curve3' ]
# # y_d_4 = data [ 'Curve4' ]
# # y_d_5 = data [ 'Curve5' ]
# # axs [ 4 ].plot(x_d, y_d_2, '*', x_d, y_d_3, '*', x_d, y_d_4, '*', x_d, y_d_5, '*', color=cycle [ i % 4 ])
# # axs [ 5 ].plot(x_d, y_d_2, '*', x_d, y_d_3, '*', x_d, y_d_4, '*', x_d, y_d_5, '*', color=cycle [ i % 4 ])
# # # axs [ i ].set_xlim([ 0, 50 ])
# # axs [ i ].set_xlabel('Time (ms)', fontsize=labelfontsize)
# # # axs [ i ].set_ylim([ -1, 0 ])
# #
# # axs [i].set_xlabel('V (mV)', fontsize=labelfontsize)
# # axs [i].set_ylabel('%s' % (tit[i]),fontsize=labelfontsize)
# #
#
#
# prefilename = 'Fig8_4'
# prename = 'max_ik2'
# # V =[-60]
# # V =[50, 40, 30, 25, 20, 10, 0,-10,-20,-30,-40,-50,-60, -70,-80]
# V_max = [ -30, -20, -10, 0, 10,20,30,40,-30, -20, -10, 0, 10,20,30,40]
# V = [ -30, -20, -10, 0, 10,20,30,40]
# I_max = []
# for j in range(2):
#
# name = '%s_%s' % (prename, j)
# name = []
# for i in range(len(V)):
# filename = '%s_%s_%s.csv' % (prefilename, j,i)# % (prefilename, 0)
# print('filename', filename)
# data = pd.read_csv(filename)
# # print('filename', filename5)
# ik2_data = data ['v']
# max_ik2_data1 = max((data ['ik2']))
#
# print(I_max)
# # filename = '%s_%s_%s.csv' % (prefilename, j,i)
# # data = pd.read_csv(filename)
# # print('filename', filename)
# # time = data ['Time']
# # ik2_data = data ['ik2']
# # v_data = data [ 'v' ]
# v_max = np.reshape(V_max, (1,-1))
# if max_ik2_data1 < 0:
# MAX_I_V = abs((max(data ['ik2'])))
# else:
# MAX_I_V =abs((max(data ['ik2'])))
# I_max.append(MAX_I_V )
#
# print(max_ik2_data)
# axs [ 3 ].plot(V_max, np.array(I_max), '*k')
#
# figfiles = '%s.png' % (prefilename)
# plt.savefig(figfiles)
plt.show()
##axs[1].semilogy( v_data, htc_data, 'b', v_data, mtc_data, 'r')
