#!/usr/bin/env python
"""
Created on Thu Jul 16 15:08:27 2020

@author: Aloma Blanch
"""


import numpy as np
import matplotlib.pyplot as plt
import matplotlib.ticker as mtick
from tkinter import Tk
from tkinter.filedialog import askopenfilename, asksaveasfile, askdirectory
import pandas as pd
import tkinter as tk
from statistics import mean 
from scipy.signal import find_peaks
import matplotlib.transforms as mtransforms


def error_plot(folder,t_step,r_criteria,save_path):
    # Load iterations and residual error
    histor = folder + '/histor.dat'
    input = open(histor, 'r')
    output = open(folder + "/histor_better.dat", 'w')
    output.writelines(line.strip() +'\n' for line in input)
    input.close()
    output.close()
    error_info = pd.read_csv(folder + "/histor_better.dat", sep=' ', header=None, usecols=(0,1,2))

    # Select only last iteration of residual error
    error=[] 
    for n in range(1,error_info.shape[0]):
        if (error_info[0][n]>error_info[0][n-1]):
            error.append(error_info[2][n-1])

    time = np.linspace(start = t_step, stop = len(error)*t_step, num = len(error))

    # Liniar Scale
    fig, ax = plt.subplots()
    ax.plot(time,error)
    ax.plot(time,r_criteria*np.ones(len(error)),'r')
    ax.set(xlabel='Time steps', ylabel='Residual error',
        title='Last nonlinear residual error for each time step')
    ax.spines['right'].set_visible(False)
    ax.spines['top'].set_visible(False)
    plt.show()
    plt.savefig(save_path + '/Last_nonlin_res_error.pdf')
    # Semilog scale
    fig, ax = plt.subplots()
    ax.semilogy(time,error)
    ax.semilogy(time,r_criteria*np.ones(len(error)),'r')
    ax.set(xlabel='Time steps', ylabel='Residual error',
        title='Log - Last nonlinear residual error for each time step')
    ax.spines['right'].set_visible(False)
    ax.spines['top'].set_visible(False)
    plt.show()
    plt.savefig(save_path + '/Log_Last_nonlin_res_error.pdf')
    fig.savefig(save_path + '/Log_Last_nonlin_res_error.jpg',dpi=150)


def periodicity(project,folder,dt,T_cyc,n_cyc,save_path):
    pressure = np.loadtxt(folder+'/PHistRCR.dat',skiprows=2,)
    time = np.linspace(0,T_cyc*n_cyc,round(T_cyc/dt*n_cyc))
    peak_P = []
    peak_P_pos = []
    Nc = round(T_cyc/dt)
    for i in range(0,n_cyc):
        peak_P.append(np.amax(pressure[i*Nc:Nc*(i+1),-1])/1333.22)
        peak_P_pos.append(np.where(pressure[i*Nc:Nc*(i+1),-1] == np.amax(pressure[i*Nc:Nc*(i+1),-1]))[0][0]+Nc*i)
    
    peak_Pdiff = [peak_P[n]-peak_P[n-1] for n in range(1,len(peak_P))]
    peak_Pdiff = list(map(abs, peak_Pdiff)) 
    
    fig, ax = plt.subplots()
    ax.plot(time,pressure[:,-1]/1333.22,'b')
    ax.plot(time[peak_P_pos], peak_P,'ro',label='Cylce pike')
    ax.set(xlabel='time [s]', ylabel='Pressure [mmHg]',
        title='Pressure at last outlet')
    ax.spines['right'].set_visible(False)
    ax.spines['top'].set_visible(False)
    ax.legend(loc=0)
    plt.show()
    plt.savefig(save_path + '/periodicity.pdf')
    fig.savefig(save_path + '/periodicity.jpg',dpi=150)
    
    if (peak_Pdiff[-1]<=1): 
        print('The numerical simulation \'{0}\' has achieve periodicity!\nSystolic Blood Pressure (SBP):\nsecond-last cycle = {1:.2f} mmHg,\nlast cycle = {2:.2f} mmHg,\n\u0394mmHg = {3:.2f} mmHg'.format(project,peak_P[-2],peak_P[-1],peak_Pdiff[-1]))  
        # txt = ['The numerical simulation \'{0}\' has achieve periodicity!'.format(project), 'Systolic Blood Pressure (SBP):', 'Second-last cycle = {0:.2f} mmHg'.format(peak_P[-2]), 'Last cycle = {0:.2f} mmHg'.format(peak_P[-1]), 'Delta_mmHg = {0:.2f} mmHg'.format(peak_Pdiff[-1])]
        txt = ['The numerical simulation \'{0}\' has achieve periodicity!'.format(project), 'Systolic Blood Pressure (SBP):','Second-last cycle = {0:.2f} mmHg - Last cycle = {1:.2f} mmHg - Delta_mmHg = {2:.2f} mmHg'.format(peak_P[-2],peak_P[-1],peak_Pdiff[-1])]
    return txt
    
def pressure(folder,N_ts,T_cyc,dt,n_cyc,save_path):
    pressure = np.loadtxt(folder+'/PHistRCR.dat',skiprows=2,)
    Nc = round(T_cyc/dt)
    time = np.linspace(0,T_cyc,Nc)
    fig, ax = plt.subplots()
    SBP = np.empty(pressure.shape[1])
    DBP = np.empty(pressure.shape[1])
    MBP = np.empty(pressure.shape[1])
    for i in range(0,pressure.shape[1]):
        ax.plot(time,pressure[N_ts-Nc:N_ts,i]/1333.22,label='ROI-'+str(i+2)) 
        SBP[i] = (np.amax(pressure[N_ts-Nc:N_ts,i]/1333.22))
        DBP[i] = (np.amin(pressure[N_ts-Nc:N_ts,i]/1333.22))
        MBP[i] = (mean(pressure[N_ts-Nc:N_ts,i]/1333.22)) 
    PP = SBP-DBP    
    ax.set(xlabel='time [s]', ylabel='Pressure [mmHg]',
        title='Pressure at each outlet')
    ax.spines['right'].set_visible(False)
    ax.spines['top'].set_visible(False)
    ax.legend(loc=0)
    plt.show()
    plt.savefig(save_path + '/pressure.pdf')
    fig.savefig(save_path + '/pressure.jpg',dpi=150)
    return (DBP,MBP,SBP,PP)


def flow(folder,N_ts,T_cyc,dt,n_cyc,save_path):
    flow = np.loadtxt(folder+'/QHistRCR.dat',skiprows=2,)
    Nc = round(T_cyc/dt)
    time = np.linspace(0,T_cyc,Nc)
    fig, ax = plt.subplots()
    Q = np.empty(flow.shape[1])
    for i in range(0,flow.shape[1]):
        ax.plot(time,flow[N_ts-Nc:N_ts,i],label='ROI-'+str(i+2))
        Q[i] = (mean(flow[N_ts-Nc:N_ts,i]))
   
    ax.set(xlabel='time [s]', ylabel='Flow [mL/s]',
        title='Flow at each outlet')
    ax.spines['right'].set_visible(False)
    ax.spines['top'].set_visible(False)
    ax.legend(loc=0)
    plt.show()
    plt.savefig(save_path + '/flow.pdf')
    fig.savefig(save_path + '/flow.jpg',dpi=150)
    return Q
    
def inlet_flow_waveform(project_folder,t_btw_rst,N_ts,dt,T_cyc,n_cyc,save_path):
    x = np.loadtxt(project_folder+'/ROI-1.flow')
    t = x[:,0]
    Q = -x[:,1]
    Nt_pts = np.linspace(t_btw_rst,N_ts,int(N_ts/t_btw_rst))
    t_pts = Nt_pts*dt
    # Put all the time values on a single cardiac cylce            
    for n in range(len(t_pts)):
        tmp=divmod(t_pts[n],T_cyc)
        t_pts[n]=tmp[1]
        if round(tmp[1],3) ==  0:
            t_pts[n]=T_cyc
    # Interpolate the flow rate to obtain the location of the point
    Q_pts = np.interp(t_pts, t, Q)

    fig, ax = plt.subplots()
    ax.plot(t, Q, 'r')
    ax.plot(t_pts, Q_pts, 'ob',label='Time steps saved')
    trans_offset = mtransforms.offset_copy(ax.transData, fig=fig,
                                       x=-0, y=0.15, units='inches')
    ax.set(xlabel='Time [s]', ylabel='Flow Rate - Q [mL/s]',
       title='Inlet Flow Rate Waveform')
    ax.set_ylim([-10, 90])
    ax.spines['right'].set_visible(False)
    ax.spines['top'].set_visible(False)
    # Adding label to the points
    
    time = []
    for i in range(0,np.unique(np.round(t_pts,3)).shape[0]):
        time.append('$t_'+str(i+1)+'$')
    for x, y, t in zip(t_pts[(-n_cyc-1):], Q_pts[(-n_cyc-1):], time):
        plt.text(x, y, t, transform=trans_offset, fontsize=12)    
    ax.legend(loc=0)
    plt.show()
    plt.savefig(save_path + '/inlet_waveform.pdf')
    fig.savefig(save_path + '/inlet_waveform.jpg',dpi=150)
    print('{0} time steps saved, available to visualize in ParaView.'.format(np.unique(np.round(t_pts,3)).shape[0]))
    txt = '{0} time steps saved, available to visualize in ParaView.'.format(np.unique(np.round(t_pts,3)).shape[0])
    return txt