173 lines
7.1 KiB
Python
173 lines
7.1 KiB
Python
#!/usr/bin/env python
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import numpy as np
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import matplotlib.pyplot as plt
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import matplotlib.ticker as mtick
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from tkinter import Tk
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from tkinter.filedialog import askopenfilename, asksaveasfile, askdirectory
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import pandas as pd
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import tkinter as tk
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from statistics import mean
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from scipy.signal import find_peaks
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import matplotlib.transforms as mtransforms
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def error_plot(folder,t_step,r_criteria,save_path):
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# Load iterations and residual error
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histor = folder + '/histor.dat'
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input = open(histor, 'r')
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output = open(folder + "/histor_better.dat", 'w')
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output.writelines(line.strip() +'\n' for line in input)
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input.close()
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output.close()
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error_info = pd.read_csv(folder + "/histor_better.dat", sep=' ', header=None, usecols=(0,1,2))
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# Select only last iteration of residual error
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error=[]
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for n in range(1,error_info.shape[0]):
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if (error_info[0][n]>error_info[0][n-1]):
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error.append(error_info[2][n-1])
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time = np.linspace(start = t_step, stop = len(error)*t_step, num = len(error))
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# Liniar Scale
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fig, ax = plt.subplots()
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ax.plot(time,error)
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ax.plot(time,r_criteria*np.ones(len(error)),'r')
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ax.set(xlabel='Time steps', ylabel='Residual error',
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title='Last nonlinear residual error for each time step')
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ax.spines['right'].set_visible(False)
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ax.spines['top'].set_visible(False)
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plt.show()
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plt.savefig(save_path + '/Last_nonlin_res_error.pdf')
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# Semilog scale
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fig, ax = plt.subplots()
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ax.semilogy(time,error)
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ax.semilogy(time,r_criteria*np.ones(len(error)),'r')
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ax.set(xlabel='Time steps', ylabel='Residual error',
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title='Log - Last nonlinear residual error for each time step')
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ax.spines['right'].set_visible(False)
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ax.spines['top'].set_visible(False)
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plt.show()
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plt.savefig(save_path + '/Log_Last_nonlin_res_error.pdf')
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fig.savefig(save_path + '/Log_Last_nonlin_res_error.jpg',dpi=150)
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def periodicity(project,folder,dt,T_cyc,n_cyc,save_path):
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pressure = np.loadtxt(folder+'/PHistRCR.dat',skiprows=2,)
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time = np.linspace(0,T_cyc*n_cyc,round(T_cyc/dt*n_cyc))
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peak_P = []
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peak_P_pos = []
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Nc = round(T_cyc/dt)
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for i in range(0,n_cyc):
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peak_P.append(np.amax(pressure[i*Nc:Nc*(i+1),-1])/1333.22)
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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)
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peak_Pdiff = [peak_P[n]-peak_P[n-1] for n in range(1,len(peak_P))]
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peak_Pdiff = list(map(abs, peak_Pdiff))
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fig, ax = plt.subplots()
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ax.plot(time,pressure[:,-1]/1333.22,'b')
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ax.plot(time[peak_P_pos], peak_P,'ro',label='Cylce pike')
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ax.set(xlabel='time [s]', ylabel='Pressure [mmHg]',
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title='Pressure at last outlet')
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ax.spines['right'].set_visible(False)
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ax.spines['top'].set_visible(False)
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ax.legend(loc=0)
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plt.show()
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plt.savefig(save_path + '/periodicity.pdf')
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fig.savefig(save_path + '/periodicity.jpg',dpi=150)
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if (peak_Pdiff[-1]<=1):
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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]))
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# 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])]
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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])]
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return txt
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def pressure(folder,N_ts,T_cyc,dt,n_cyc,save_path):
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pressure = np.loadtxt(folder+'/PHistRCR.dat',skiprows=2,)
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Nc = round(T_cyc/dt)
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time = np.linspace(0,T_cyc,Nc)
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fig, ax = plt.subplots()
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SBP = np.empty(pressure.shape[1])
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DBP = np.empty(pressure.shape[1])
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MBP = np.empty(pressure.shape[1])
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for i in range(0,pressure.shape[1]):
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ax.plot(time,pressure[N_ts-Nc:N_ts,i]/1333.22,label='ROI-'+str(i+2))
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SBP[i] = (np.amax(pressure[N_ts-Nc:N_ts,i]/1333.22))
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DBP[i] = (np.amin(pressure[N_ts-Nc:N_ts,i]/1333.22))
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MBP[i] = (mean(pressure[N_ts-Nc:N_ts,i]/1333.22))
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PP = SBP-DBP
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ax.set(xlabel='time [s]', ylabel='Pressure [mmHg]',
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title='Pressure at each outlet')
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ax.spines['right'].set_visible(False)
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ax.spines['top'].set_visible(False)
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ax.legend(loc=0)
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plt.show()
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plt.savefig(save_path + '/pressure.pdf')
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fig.savefig(save_path + '/pressure.jpg',dpi=150)
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return (DBP,MBP,SBP,PP)
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def flow(folder,N_ts,T_cyc,dt,n_cyc,save_path):
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flow = np.loadtxt(folder+'/QHistRCR.dat',skiprows=2,)
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Nc = round(T_cyc/dt)
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time = np.linspace(0,T_cyc,Nc)
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fig, ax = plt.subplots()
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Q = np.empty(flow.shape[1])
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for i in range(0,flow.shape[1]):
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ax.plot(time,flow[N_ts-Nc:N_ts,i],label='ROI-'+str(i+2))
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Q[i] = (mean(flow[N_ts-Nc:N_ts,i]))
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ax.set(xlabel='time [s]', ylabel='Flow [mL/s]',
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title='Flow at each outlet')
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ax.spines['right'].set_visible(False)
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ax.spines['top'].set_visible(False)
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ax.legend(loc=0)
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plt.show()
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plt.savefig(save_path + '/flow.pdf')
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fig.savefig(save_path + '/flow.jpg',dpi=150)
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return Q
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def inlet_flow_waveform(project_folder,t_btw_rst,N_ts,dt,T_cyc,n_cyc,save_path):
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x = np.loadtxt(project_folder+'/ROI-1.flow')
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t = x[:,0]
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Q = -x[:,1]
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Nt_pts = np.linspace(t_btw_rst,N_ts,int(N_ts/t_btw_rst))
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t_pts = Nt_pts*dt
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# Put all the time values on a single cardiac cylce
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for n in range(len(t_pts)):
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tmp=divmod(t_pts[n],T_cyc)
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t_pts[n]=tmp[1]
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if round(tmp[1],3) == 0:
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t_pts[n]=T_cyc
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# Interpolate the flow rate to obtain the location of the point
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Q_pts = np.interp(t_pts, t, Q)
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fig, ax = plt.subplots()
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ax.plot(t, Q, 'r')
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ax.plot(t_pts, Q_pts, 'ob',label='Time steps saved')
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trans_offset = mtransforms.offset_copy(ax.transData, fig=fig,
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x=-0, y=0.15, units='inches')
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ax.set(xlabel='Time [s]', ylabel='Flow Rate - Q [mL/s]',
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title='Inlet Flow Rate Waveform')
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ax.set_ylim([-10, 90])
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ax.spines['right'].set_visible(False)
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ax.spines['top'].set_visible(False)
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# Adding label to the points
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time = []
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for i in range(0,np.unique(np.round(t_pts,3)).shape[0]):
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time.append('$t_'+str(i+1)+'$')
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for x, y, t in zip(t_pts[(-n_cyc-1):], Q_pts[(-n_cyc-1):], time):
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plt.text(x, y, t, transform=trans_offset, fontsize=12)
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ax.legend(loc=0)
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plt.show()
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plt.savefig(save_path + '/inlet_waveform.pdf')
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fig.savefig(save_path + '/inlet_waveform.jpg',dpi=150)
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print('{0} time steps saved, available to visualize in ParaView.'.format(np.unique(np.round(t_pts,3)).shape[0]))
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txt = '{0} time steps saved, available to visualize in ParaView.'.format(np.unique(np.round(t_pts,3)).shape[0])
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return txt
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