# 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 = 'Fig2'

# Set figure dimension (width, height) in inches.
fw, fh = 15, 10
# 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", "hss", "mss", "htc", "mtc", "ina", "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] ]
hss_data = data [var_name[1]]
mss_data = data [var_name[2]]
htc_data = data [var_name[3]]
mtc_data = data [var_name[4]]
ina_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')

# Set ylable
ylab = ['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 = 'Fig2'

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([ -100, 50 ])
    axs [i].set_xlabel('V (mV)', fontsize=labelfontsize)
    axs [i].set_ylabel('%s' % (ylab[i]),fontsize=labelfontsize)
    if i == 0 or i == 1:
        y_d_2 = data [ 'Curve2' ]
        axs [ i ].plot(x_d, y_d_2, 'k*')
    elif i == 2:
        y_d_2 = data [ 'Curve2' ]
        y_d_3 = data [ 'Curve3' ]
        y_d_4 = data [ 'Curve4' ]
        y_d_5 = data [ 'Curve5' ]
        axs [ i ].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 ])
    # plt.tick_params(direction='in', axis='both')


I_V = []


for i in range(11):
    prefilename = 'Fig2_3'
    filename4 = '%s_%s.csv' % (prefilename, 5)
    data4 = pd.read_csv(filename4)
    print('filename', filename4)
    ina_data4 = data4['ina']
    max_ina_data4 = max(abs(data4['ina']))
    filename = '%s_%s.csv' % (prefilename, i)
    data = pd.read_csv(filename)
    print('filename', filename)
    time = data['Time']
    ina_data = data['ina']
    max_ina_data = max(data['ina'])
    print('max_ina_data', max_ina_data4)
    if 5 <= i < 11:
         axs[2].plot(time, ina_data/max_ina_data4, color=cycle[i % 4])
    if max_ina_data < 0:
        MAX_I_V = (min(ina_data / max_ina_data4))
    else:
        MAX_I_V = (max(ina_data / max_ina_data4))
    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')

figfiles = '%s.png' % (prefilename)
plt.savefig(figfiles)
plt.show()