# 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 = 'Fig11'
figfile = 'Figure_12_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 = 3, 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 = ['Time','Force', 'I_tot', 'v', 'cai']
# y_name =['fss'];
x_base =[0.4, 0.6]
sub = ['A','B','C','D']
I_st =[ -0.18, -0.13, -0.1, -0.5]
for i in range(len(I_st)):
    filename = '%s_%s.csv' % (prefilename, sub[i])
    data = pd.read_csv(filename)

    time = data [ var_name[0] ]
    Force_data = data [var_name[1]]
    I_tot_data = data [var_name[2]]
    v_data = data [var_name[3]]
    cai_data = data [var_name[4]]

    # axs[0].plot( time, Force_data, 'b')
    # axs[1].plot( time, I_tot_data, 'b')
    axs[i].plot( time, v_data, color=cycle [ i %5 ])# y_data = data [ var [ i ] ]
    # axs[3].plot( time, cai_data, 'b')
plt.show()



# import numpy as np
# import matplotlib.pyplot as plt
#
# def SquareWave(n,xmin=0,xmax=35,ymin=0,Nx=1000,ymax=2,offset=2):
#
#
#     x = np.sort(np.concatenate([np.arange(xmin, xmax)-1E-6,np.arange(xmin, xmax)+1E-6]))
#     #You can use np.linspace(xmin,xmax,Nx) if you want the intermediate points
#     y=np.array(x+n+offset,dtype=int)%2
#
#     plt.plot(x, y)
#     plt.axis([xmin, xmax, ymin, ymax])
#     # plt.grid()
#     plt.show()
# SquareWave(0, xmin=0, xmax=10, ymin=0, Nx=1000, ymax=2, offset=1)