179 lines
7.1 KiB
Python
179 lines
7.1 KiB
Python
#!/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
|
|
|
|
|
|
|