# 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 = '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"])
filename = '%s.csv' % (prefilename)
print(filename)
data = pd.read_csv(filename)
print('filename', 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[2].plot( v_data, btc_data, 'b')
axs[3].plot( v_data, gtc_data, 'b')
#axs[4].plot( v_data, icat_data, '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 = 'Fig3_4'
# V =[-60]
# V =[50, 40, 30, 25, 20, 10, 0,-10,-20,-30,-40,-50,-60, -70,-80]
V  = [-60, -50, -40, -30, -20, -10, 0, 10, 20]
for i in range(len(V)):
    filename5 = '%s_%s.csv' % (prefilename, 8)
    data5 = pd.read_csv(filename5)
    print('filename', filename5)
    icat_data5 = data5 ['icat']
    max_icat_data5 = max(abs(data5 ['icat']))
    filename = '%s_%s.csv' % (prefilename, i)
    data = pd.read_csv(filename)
    print('filename', filename)
    time = data ['Time']
    icat_data = data ['icat']
    max_icat_data = max(data['icat'])
    print('max_icat_data', max_icat_data5)
    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 )


# 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 = 'Fig3'

for i in range(2):
    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' % (tit[i]),fontsize=labelfontsize)

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