- 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_4/Fig4_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 = 'Fig4'
# Set figure dimension (width, height) in inches.
fw, fh = 15, 15
# Set subplots
subpRow, subpCol = 2, 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", "yss", "ya", "yb", "ytc", "ih", "v" ])
filename = '%s.csv' % (prefilename)
print(filename)
data = pd.read_csv(filename)
print('filename', filename)
data = pd.read_csv(filename)
time = data [ var_name[0] ]
yss_data = data [var_name[1]]
ya_data = data [var_name[2]]
yb_data = data [var_name[3]]
ytc_data = data [var_name[4]]
ih_data = data [var_name[5]]
v_data = data [var_name[6]]
axs[0].plot(v_data, yss_data, 'b')
axs[1].plot(v_data, ytc_data/350, 'b')# y_data = data [ var [ i ] ]
axs [ 1 ].set_xlim([ -150, 0 ])
axs [ 1 ].set_ylim([ 0,5 ])
# Set ylable
tit = ['Steady state','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"
prefilename = 'Fig4'
for i in range(4):
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([ -150, 0 ])
axs [i].set_xlabel('V (mV)', fontsize=labelfontsize)
axs [i].set_ylabel('%s' % (tit[i]),fontsize=labelfontsize)
if i == 2:
y_d_2 = data [ 'Curve2' ]
y_d_3 = data [ 'Curve3' ]
axs [ i ].plot(x_d, y_d_2, '*', x_d, y_d_3,'*', color=cycle [ i%4 ])
axs [ i ].set_xlim([ 0,3 ])
axs [ i ].set_xlabel('Time (ms)', fontsize=labelfontsize)
# axs [ i ].set_ylim([ -1, 0 ])
# # plt.tick_params(direction='in', axis='both')
# #
I_V = []
for i in range(9):
prefilename = 'Fig4_3'
filename5 = '%s_%s.csv' % (prefilename, 1)
data5 = pd.read_csv(filename5)
print('filename', filename5)
ih_data5 = data5 ['ih']
max_ih_data5 = max(abs(data5 ['ih']))
filename = '%s_%s.csv' % (prefilename, i)
data = pd.read_csv(filename)
print('filename', filename)
time = data ['Time']
ih_data = data ['ih']
max_ih_data = max(data ['ih'])
print(' max_ih_data ', max_ih_data5)
if 0<=i<10:
axs [2].plot( time/100, ih_data/1.8, color=cycle [i % 4])
axs [2].set_xlim([ 0,3 ])
# axs [2].set_ylim([ -3, 1 ])
if max_ih_data < 0:
MAX_I_V = (min(ih_data/1.8))
else:
MAX_I_V = (max(ih_data/1.8))
I_V.append(MAX_I_V )
V = [-130, -110, -90, -80, -70, -60, -50, -40, -30]
print(I_V)
axs[3].plot(V, I_V, '-b')
axs[3].set_ylim([ -3, 1 ])
axs[3].set_xlim([ -150, 0 ])
figfiles = '%s.png' % (prefilename)
plt.savefig(figfiles)
plt.show()
# # import matplotlib.pyplot as plt
# # import pandas as pd
# # from sklearn import preprocessing
# # import numpy as np
# # # Figure name
# # prefilename = 'Fig2'
# # #figfile = 'Figure_8_origin'
# # # Read data from the files
# # # x_name = 'Time'
# # # y_name = 'fss'
# # # z_name = ['I_inj']
# # # current = r'$I_{Aon1}(pA)$'
# # # print(current)
# # # y_labels = [ '%s' % current ]
# #
# # suffix = [ 'h', 'g', 'f', 'e', 'd', 'c', 'b', 'a' ]
# # c = ['a','b','c','d']
# # # Set figure dimension (width, height) in inches.
# # fw, fh = 15, 10
# # # Set subplots
# # subpRow, subpCol = 2, 2
# # ax, lns = {}, {}
# # # Set Title
# # tit = ['1s','2 s','1s','2 s','1s','2 s']
# # # 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 = .1, wspace=.1)
# # axs = axs.ravel()
# # var_name = np.array(["Time", "hss", "mss", "htc", "mtc", "ina", "v"])
# # # y_name =['fss'];
# # x_base =[0.4, 0.6]
# #
# # filename = '%s.csv' % (prefilename)
# # print(filename)
# # data = pd.read_csv(filename)
# # print('filename', filename)
# # data = pd.read_csv(filename)
# # time = data [ var_name[0] ]
# # hss_data = data [var_name[1]]
# # mss_data = data [var_name[2]]
# # htc_data = data [var_name[3]]
# # mtc_data = data [var_name[4]]
# # ih_data = data [var_name[5]]
# # v_data = data [var_name[6]]
# #
# # axs[0].plot( v_data, hss_data, 'b', v_data, pow(mss_data,3), 'r')
# # axs[1].semilogy( v_data, htc_data, 'b', v_data, mtc_data, 'r')# y_data = data [ var [ i ] ]
# # #axs[3].plot( , v_data, f2tc_data, 'k')
# # #axs[3].plot( v_data, ih_data, 'k')
# #
# # prefilename = 'Fig2'
# # # for i in range(1,2):
# # # filename = '%s_%s.csv' % (prefilename, i)
# # # print('filename', filename, i)
# # # data = pd.read_csv(filename)
# # # y_d = data [ 'Curve1' ]
# # # #y_d1 = data [ 'Curve2' ]
# # # x_d = data [ 'x' ]
# # # axs [ i ].plot(x_d, y_d, 'k*')
# # # plt.tick_params(direction='in', axis='both')
# #
# # filename = '%s_%s.csv' % (prefilename, 2)
# # print('filename', filename, 2)
# # data = pd.read_csv(filename)
# # y_d = data [ 'Curve1' ]
# # y_d1 = data [ 'Curve2' ]
# # x_d = data [ 'x' ]
# # axs [ 2 ].plot(x_d, y_d, 'k*',x_d, y_d1, 'k*')
# # plt.tick_params(direction='in', axis='both')
# # I_V = []
# #
# # prefilename = 'Fig2_3'
# # for i in range(11):
# #
# # filename5 = '%s_%s.csv' % (prefilename,5)
# # data5 = pd.read_csv(filename5)
# # print('filename', filename5)
# # ih_data5 = data5 ['ina']
# # max_ih_data5 = max(abs(data5 ['ina']))
# # filename = '%s_%s.csv' % (prefilename, i)
# # data = pd.read_csv(filename)
# # print('filename', filename)
# # time = data ['Time']
# # ih_data = data ['ina']
# # max_ih_data = max(data ['ina'])
# # print(' max_ih_data ',max_ih_data5)
# # if 5<=i<11:
# #
# # axs [2].plot( time, ih_data/max_ih_data5, color=cycle [i % 4])#, label='%d mV ' % (V))
# # # axs [6 ].plot(time, itot_data [ 0 ], color=cycle [ i % 4 ]) # , label='%d mV ' % (V))
# # # axs [7 ].plot(time, ih_tot_data [ 0 ], color=cycle [ i % 4 ]) # , label='%d mV ' % (V))
# # MAX_I_V = (min(ih_data / max_ih_data5))
# # I_V.append(MAX_I_V )
# #
# # V =[50, 40, 30, 20, 10, 0,-10,-20,-30,-40,-50]
# # print(I_V)
# # axs[3].plot(V, I_V, '-b')# y_data = data [ var [ i ]
# # prefilename = 'Fig2'
# # filename = '%s_%s.csv' % (prefilename, 4)
# # print('filename', filename, 4)
# # data = pd.read_csv(filename)
# # y_d = data [ 'Curve1' ]
# #
# # x_d = data [ 'x' ]
# # axs [ 3 ].plot(x_d, y_d, 'k*')
# # figfiles = '%s.png' % (prefilename)
# # plt.savefig(figfiles)
# # plt.show()
# #
# # figfiles = '%s.png' % (prefilename)
# # plt.savefig(figfiles)
# # plt.show()
# #
