import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import sys
import os

data = pd.read_csv('Fig05.csv')
data = data.loc[:, ~data.columns.str.contains('^Unnamed')]
data = data.drop(columns=["time"])
# print(data.T.iloc[3])
# print((data.T.iloc[3][700:1000].min())-(data.T.iloc[3][200:700].min()))
# print(data.index[data.T.iloc[3]==data.T.iloc[3][700:1500].min()].values[-1] - data.index[data.T.iloc[3]==data.T.iloc[3][200:700].min()].values[-1])

cl = []
for i in range(0, 7):
    cl.append(data.index[data.T.iloc[i] == data.T.iloc[i][700:1500].min()].values[-1] -
              data.index[data.T.iloc[i] == data.T.iloc[i][200:700].min()].values[-1])
print(cl)

y_shift = [-15, -10, -5, 0, 5, 10, 15]

plt.figure(figsize=(14, 14))
plt.subplot(2, 2, 1)
plt.plot(y_shift, cl, 'navy', linestyle='', marker='D', markersize='14', label='', linewidth=3)

plt.grid()
# plt.xlim(0, 1900)
plt.ylim(400, 1200)
plt.yticks(np.arange(400,1300,200))
plt.xlabel('y$_{\infty}$ shift', fontsize=16)
plt.tick_params(axis='both', labelsize=14)
plt.ylabel('CL (ms)', fontsize=16)
plt.title('A', loc= 'left', fontsize='20')
# plt.legend(loc='best',fontsize=16,
#        ncol=5, mode="expand", borderaxespad=0.)


DDR = []
for i in range(0, 7):
    A = data.T.iloc[i][200:700].min()
    end = (data.index[data.T.iloc[i] == data.T.iloc[i][200:700].min()][-1] + 100)
    B = data.T.iloc[i][end]
    DDR.append((B - A) * 10)
print(DDR)

plt.subplot(2, 2, 2)
plt.plot(y_shift, DDR, 'navy', linestyle='', marker='D', markersize='14', label='', linewidth=3)

plt.grid()
plt.ylim(20, 80)
plt.yticks(np.arange(20,90,20))
plt.xlabel('y$_{\infty}$ shift', fontsize='16')
plt.ylabel('DDR$_{100}$ (mV/s)', fontsize='16')
plt.tick_params(axis='both', labelsize=14)
plt.title('B', loc= 'left', fontsize='20')

MDP = []
for i in range(0, 7):
    A = data.T.iloc[i].min()
    MDP.append(A)
print(MDP)

plt.subplot(2, 2, 3)
plt.plot(y_shift, MDP, 'navy', linestyle='', marker='D', markersize='14', label='')

plt.grid()
plt.ylim(-65, -50)
plt.yticks(np.arange(-65, -45, 5))
plt.xlabel('y$_{\infty}$ shift', fontsize='16')
plt.ylabel('MDP (mV)', fontsize='16')
plt.tick_params(axis='both', labelsize=14)
plt.title('C', loc= 'left', fontsize='20')

APD = []
dys = []
for i in range(0, 7):
    # C = data.index[data.T.iloc[i] == data.T.iloc[i][700:1500].max()].values[-1]
    # D = data.index[data.T.iloc[i] == data.T.iloc[i][700:1500].min()].values[-1]

    y = data.T.iloc[i][:601]
    dy = np.gradient(y)
    dys.append(dy)
    a = dy[dy > 0.5]
    neg = dy[dy < 0]
    start = np.where(dy == a[0])
    # start = np.where(dy == neg[0])
    end = np.where(dy == neg[:int(neg.shape[0] * 0.9)][-1])
    duration = end[0][-1] - start[0][-1]
    APD.append(duration)
print(APD)
# print(dys[0])

plt.subplot(2, 2, 4)
plt.plot(y_shift, APD, 'navy', linestyle='', marker='D', markersize='14', label='')

plt.grid()
plt.ylim(150, 190)
plt.xlabel('y$_{\infty}$ shift', fontsize='16')
plt.ylabel('APD$_{90}$ (ms)', fontsize='16')
plt.tick_params(axis='both', labelsize=14)
plt.title('D', loc= 'left', fontsize='20')

plt.subplots_adjust(left=0.1,
                    bottom=0.1,
                    right=0.9,
                    top=0.9,
                    wspace=0.4,
                    hspace=0.4)
plt.savefig('C:/Nima/ABI/Physiome Journal/sinus/Python_codes/figure5.png')
plt.show()