SimVascular_report_PostProcess/functions.py

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

@author: Aloma Blanch
"""

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.transforms as mtransforms

from math import floor
from statistics import mean 
from scipy.signal import find_peaks

def cycle(folder,dt,save_path):
    pressure = np.loadtxt(folder + '/PHistRCR.dat',skiprows=2)
    N_ts = pressure.shape[0]
    T = round(dt*N_ts,3)
    time = np.linspace(0,T,N_ts)
    # Find peaks, keep only the maximums
    peaks, _ = find_peaks(pressure[:,-1])
    idx = np.where(pressure[peaks,-1] >= np.mean(pressure))
    peaks_loc = peaks[idx]
    P_peaks = pressure[peaks,-1][pressure[peaks,-1] >= np.mean(pressure)]
    # Rmove the multiple picks found in the same location
    idx_del  = []
    t_pk_loc = time[peaks_loc].tolist()
    peak_tdiff = [t_pk_loc[n]-t_pk_loc[n-1] for n in range(1,len(t_pk_loc))]
    for i in range(0,len(peak_tdiff)):
        if peak_tdiff[i]<= dt*10:
            idx_del.append(i)
            t_pk_loc[i] = []
            peak_tdiff[i] = []    
    t_pk_loc[:] = [x for x in t_pk_loc if x]
    peak_tdiff[:] = [x for x in peak_tdiff if x]
    P_peaks = np.delete(P_peaks,idx_del)

    fig, ax = plt.subplots()
    ax.plot(time,pressure[:,-1]/1333.22,'b')
    ax.plot(t_pk_loc, P_peaks/1333.22, "or",label='peak location')
    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 + '/Pressure_max_n_cycles.pdf')
    return (floor(mean(peak_tdiff)*1000)/1000,len(t_pk_loc))

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 achieved 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])]
    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
      

def rcr(project_folder):
    rcr_lines = []
    with open (project_folder + '/rcrt.dat', "rt") as myfile:
        for myline in myfile:
            rcr_lines.append(myline)
    n_out = int((len(rcr_lines)-1)/6)
    Rc_C_Rd = np.empty([n_out,3])
    for i in range(0,n_out):
        Rc_C_Rd[i][0] = float(rcr_lines[2+i*6][:-1])
        Rc_C_Rd[i][1] = float(rcr_lines[3+i*6][:-1])
        Rc_C_Rd[i][2] = float(rcr_lines[4+i*6][:-1])
    return Rc_C_Rd