100 lines
3.5 KiB
Python
100 lines
3.5 KiB
Python
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#!/usr/bin/env python
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"""
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Created on Thu Jul 16 15:08:27 2020
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@author: Aloma Blanch
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"""
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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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from tkinter.filedialog import askdirectory
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# import pandas as pd
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# import tkinter as tk
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import os.path
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# from scipy.signal import find_peaks_cwt
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# from scipy import signal
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# import statistics
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# from fpdf import FPDF
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from functions import error_plot, periodicity, pressure, flow, inlet_flow_waveform, rcr, cycle, barPlot
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from generatePDF import generatePDF
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# Selct dir
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Tk().withdraw()
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folder = askdirectory()
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project_folder = os.path.dirname(folder)
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project = os.path.basename(project_folder)
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save_path = folder+'/'+project+'-report'
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os.mkdir(save_path)
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save_pdf = save_path + '/' + project + '-report.pdf'
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# Read important parameters from - solver.inp file
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mylines = [] # Declare an empty list named mylines.
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with open (project_folder + '/solver.inp', 'rt') as myfile: # Open lorem.txt for reading text data.
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for myline in myfile: # For each line, stored as myline,
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mylines.append(myline) # add its contents to mylines.
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# Number of Timesteps - idx 3
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# Idea: remove the text to extract the number, the text part will be the same no matter the simulation
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N_ts = int(mylines[3][20:-1])
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# Time Step Size - idx 4
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dt = float(mylines[4][16:-1])
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# Residual criteria - idx 4
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rc = float(mylines[26][18:-1])
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# Imesteps between Restarts - idx 6
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t_btw_rst = int(mylines[6][37:-1])
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# Extracting Outlet Boundary Conditions from - rcrt.dat fle
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Rc_C_Rd = rcr(project_folder)
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# Extracting number of cycles and period of cardiac cycle
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(T_cyc,n_cyc) = cycle(folder,dt,N_ts,save_path)
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T_cyc=0.4769
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n_cyc=4
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# Cehcking convergency and periodicity
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error_plot(folder,dt,rc,save_path)
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txt1 = periodicity(project,folder,dt,T_cyc,n_cyc,save_path)
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# Pressure - Outlets
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(DBP,MBP,SBP,PP) = pressure(folder,N_ts,T_cyc,dt,n_cyc,save_path)
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# Flow Rate - Outlets
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(Q_avg) = flow(folder,N_ts,T_cyc,dt,n_cyc,save_path)
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# Inlet Flow Rate + and t saved
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txt2 = inlet_flow_waveform(project_folder,t_btw_rst,N_ts,dt,T_cyc,n_cyc,save_path)
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# Extract bar plots CFD vs. aimed
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barPlot(project_folder,DBP,MBP,SBP,PP,Q_avg,save_path)
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# # Flow comparison specific for patient 120.
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# # Only ROI-5,6,8 because that is the PC-MRA data that we have.
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# def flow_comparison(folder,N_ts,T_cyc,dt,n_cyc,save_path):
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# flow_sim = np.loadtxt(folder+'/QHistRCR.dat',skiprows=2,)
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# flow_doc = np.loadtxt(os.path.dirname(os.path.dirname(os.path.dirname(os.path.dirname(os.path.dirname(folder)))))+'/Data/flow_waveforms.txt',skiprows=2,)
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# Nc = round(T_cyc/dt)
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# time_sim = np.linspace(0,T_cyc,Nc)
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# time_doc = np.linspace(0,T_cyc,flow_doc.shape[0])
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# Q = np.empty(flow_sim.shape[1])
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# for i in range(0,flow_sim.shape[1]):
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# fig, ax = plt.subplots()
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# ax.plot(time_sim,flow_sim[N_ts-Nc:N_ts,i],label='sim ROI-'+str(i+2))
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# ax.plot(time_doc,flow_doc[:,1+i],label='doc ROI-'+str(i+2))
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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_comparison.pdf')
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# fig.savefig(save_path + '/flow_comparison.jpg',dpi=150)
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# Create PDF report
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generatePDF(save_path,project,DBP,MBP,SBP,PP,Q_avg,txt1,txt2,Rc_C_Rd,T_cyc,n_cyc,N_ts,dt)
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