# 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 = 'Fig5'

# 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()
varName = np.array(["Time", "pss", "kss", "ptc", "k1tc","k2tc", "ik2", "v"])
filename = '%s.csv' % (prefilename)
print(filename)
data = pd.read_csv(filename)
print('filename', filename)
data = pd.read_csv(filename)
time = data [ varName[0] ]
pss_data = data [varName[1]]
kss_data = data [varName[2]]
ptc_data = data [varName[3]]
k1tc_data = data [varName[4]]
k2tc_data = data [varName[5]]
ik2_data = data [varName[6]]
v_data = data [varName[7]]
axs[0].plot( v_data, pow(pss_data,2), 'b', v_data, kss_data, '--b')
axs[1].plot(v_data, ptc_data, 'b')
axs[2].semilogy(v_data, k1tc_data/1000, 'b',  v_data, k2tc_data/1000, '--b')

#axs[4].plot( v_data, ik2_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 = 'Fig5_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 = 'Fig5'

# 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 = 'Fig5_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 = (max(data ['ik2']))
        else:
            MAX_I_V = (max(data ['ik2']))
        I_max.append(max_ik2_data1 )

        print(max_ik2_data)
axs [ 3 ].plot(V_max, np.array(I_max)/9.308367141531372, '*k')

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

##axs[1].semilogy( v_data, htc_data, 'b', v_data, mtc_data, 'r')