303 lines
12 KiB
Python
303 lines
12 KiB
Python
#!/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 matplotlib
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import numpy as np
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import pandas as pd
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import matplotlib.pyplot as plt
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import matplotlib.transforms as mtransforms
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from math import floor
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from statistics import mean
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from scipy.signal import find_peaks
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def cycle(folder,dt,N_ts,save_path):
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pressure = np.loadtxt(folder + '/PHistRCR.dat',skiprows=1)
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T = round(dt*N_ts,3)
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time = np.linspace(0,T,N_ts+1)
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# Find peaks, keep only the maximums
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peaks, _ = find_peaks(pressure[:,-1])
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idx = np.where(pressure[peaks,-1] >= np.mean(pressure))
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peaks_loc = peaks[idx]
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P_peaks = pressure[peaks,-1][pressure[peaks,-1] >= np.mean(pressure)]
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# Rmove the multiple picks found in the same location
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idx_del = []
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t_pk_loc = time[peaks_loc].tolist()
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peak_tdiff = [t_pk_loc[n]-t_pk_loc[n-1] for n in range(1,len(t_pk_loc))]
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for i in range(0,len(peak_tdiff)):
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if peak_tdiff[i]<= dt*10:
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idx_del.append(i)
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t_pk_loc[i] = []
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peak_tdiff[i] = []
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t_pk_loc[:] = [x for x in t_pk_loc if x]
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peak_tdiff[:] = [x for x in peak_tdiff if x]
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P_peaks = np.delete(P_peaks,idx_del)
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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(t_pk_loc, P_peaks/1333.22, "or",label='peak location')
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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 + '/Pressure_max_n_cycles.pdf')
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return (floor(mean(peak_tdiff[1:])*1000)/1000,len(t_pk_loc))
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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=1,)
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time = np.linspace(0,T_cyc*n_cyc,round(T_cyc/dt*n_cyc)+1)
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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='Cycle 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 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])]
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else:
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print('The numerical simulation \'{0}\' has not 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 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])]
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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=1,)
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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=1,)
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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+1:N_ts+1,i],label='ROI-'+str(i+2))
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Q[i] = (mean(flow[N_ts-Nc+1:N_ts+1,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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if (x[4,1] < 0):
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Q = -x[:,1]
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else:
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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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def rcr(project_folder):
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rcr_lines = []
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with open (project_folder + '/rcrt.dat', "rt") as myfile:
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for myline in myfile:
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rcr_lines.append(myline)
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n_out = int((len(rcr_lines)-1)/6)
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Rc_C_Rd = np.empty([n_out,3])
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for i in range(0,n_out):
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Rc_C_Rd[i][0] = float(rcr_lines[2+i*6][:-1])
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Rc_C_Rd[i][1] = float(rcr_lines[3+i*6][:-1])
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Rc_C_Rd[i][2] = float(rcr_lines[4+i*6][:-1])
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return Rc_C_Rd
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def barPlot(project_folder,DBP,MBP,SBP,PP,Q_avg,save_path):
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aimed_all = [1] #No li agrada fer append en una llista buida, no recordo perquè i segur que es pot fer millor
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f = open(project_folder+'/aimed.txt', 'r') #obrir el fitxer
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for line in f:
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aimed_all.append(line.split()) #fas una llista amb cada valor de la línia carregan-te els espais buits
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del aimed_all[0] #esborrar el primer valor que has posat perquè no estigues buida la llista
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def plot_bar(CFD,aimed,name,save_path,unit,decimals):
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labels = []
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for i in range(0,len(CFD)):
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labels.append('ROI-'+str(i+2))
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x = np.arange(len(labels)) # the label locations
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width = 0.38 # the width of the bars
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fig, ax = plt.subplots()
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rects1 = ax.bar(x - width/2, CFD, width, label='CFD',color='k')
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rects2 = ax.bar(x + width/2, aimed, width, label='Aimed',color='white', edgecolor='black', hatch='/')
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ax.set_ylabel(name + unit)
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ax.set_title(name)
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ax.set_xticks(x)
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ax.set_xticklabels(labels)
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ax.legend()
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high = max(max(aimed,CFD))
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ax.set_ylim(top = 1.7*high)
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def autolabel(rects,decimals):
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"""Attach a text label above each bar in *rects*, displaying its height."""
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for rect in rects:
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height = rect.get_height()
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ax.annotate(decimals.format(height),
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xy=(rect.get_x() + rect.get_width() / 2, height),
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xytext=(0,3), # 3 points vertical offset
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textcoords="offset points",
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ha='center', va='bottom',
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fontsize = 7.5)
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autolabel(rects1,decimals)
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autolabel(rects2,decimals)
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# Create labels
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err = []
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zip_object = zip(CFD, aimed)
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for x1_i, x2_i in zip_object:
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err.append(round((x1_i-x2_i)/x2_i*100,2))
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# Text on the top of each barplot
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for i in range(len(x)):
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plt.text(x = x[i]- width/2, y = max(CFD[i],aimed[i])+0.15*max(max(CFD,aimed)), s = str(err[i])+'%', fontsize = 8, color='r')
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fig.tight_layout()
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# plt.show()
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plt.savefig(save_path + '/'+name+'_bar.pdf')
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fig.savefig(save_path + '/'+name+'_bar.jpg',dpi=150)
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# DBP
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CFD = [float(i) for i in DBP]
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aimed = [float(i) for i in aimed_all[0]]
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plot_bar(CFD,aimed,'DBP',save_path,' [mmHg]','{0:.1f}')
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# MBP
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CFD = [float(i) for i in MBP]
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aimed = [float(i) for i in aimed_all[1]]
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plot_bar(CFD,aimed,'MBP',save_path,' [mmHg]','{0:.1f}')
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# SBP
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CFD = [float(i) for i in SBP]
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aimed = [float(i) for i in aimed_all[2]]
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plot_bar(CFD,aimed,'SBP',save_path,' [mmHg]','{0:.1f}')
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# PP
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CFD = [float(i) for i in PP]
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aimed = [float(i) for i in aimed_all[3]]
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plot_bar(CFD,aimed,'PP',save_path,' [mmHg]','{0:.1f}')
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# Q_avg
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CFD = [float(i) for i in Q_avg]
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aimed = [float(i) for i in aimed_all[4]]
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plot_bar(CFD,aimed,'Q_avg',save_path,' [mL/s]','{0:.2f}') |