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plot_data.py
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from postprocessdatalib2 import *
from matplotlib import pylab as plt
from matplotlib import rc
import numpy as np
# Plot effects of contractility cases
rc('text', usetex=False)#True)
rc('font',**{'family':'sans-serif','sans-serif':['Helvetica'],'size':16})
BCLs = [
1000,\
1000,\
]
cycle = 5
casenames = [
#'HCM2_7',\
'HCM2_7',\
#'HCM2_7_testa',\
]
labels = [
#'HCM2_7',\
'HCM2_7_test',\
'HCM2_7_testa',\
]
colors=["k",
"r",
"b"]
PVoutfilename = "comparePV.png"
LVPoutfilename = "compareLVP.png"
LVVoutfilename = "compareLVV.png"
Qmvoutfilename = "compareQmv.png"
Vmvoutfilename = "compareVmv.png"
Qladoutfilename = "compareQlad.png"
IMPoutfilename = "compareIMP.png"
Qdisfilename = "compareQdist.png"
IMPdisfilename = "compareIMPdist.png"
WDoutfilename = "compareWD.png"
WDdistfilename = "compareWDdist.png"
RelQdisfilename = "comparerelQdist.png"
caseno = 0
MV_area = 2.5 # Assuming MV_area = 4cm^2 and Vmax = Q/(A/2) based on poiseulle
#Qtotal_arr = []
for (casename, label, color) in zip(casenames, labels, colors):
#radialpos = np.load(casename+".npz")['radialpos']
#Sff = np.load(casename+".npz")['Sff']
#WD = np.load(casename+".npz")['WD']
#Eff = np.load(casename+".npz")['Eff']
#Ecc = np.load(casename+".npz")['Ecc']
#Ell = np.load(casename+".npz")['Ell']
#imp = np.load(casename+".npz")['imp']
##Qtotal = np.load(casename+".npz")['Qtotal']
homo_tptt = np.load(casename+".npz")['homo_tptt']
homo_LVP = np.load(casename+".npz")['homo_LVP']
homo_LVV = np.load(casename+".npz")['homo_LVV']
homo_Qmv = np.load(casename+".npz")['homo_Qmv']
homo_Qao = np.load(casename+".npz")['homo_Qao']
homo_Qper = np.load(casename+".npz")['homo_Qper']
homo_Qla = np.load(casename+".npz")['homo_Qla']
homo_Qlad = np.load(casename+".npz")['homo_Qlad']
homo_Pven = np.load(casename+".npz")['homo_Pven']
homo_LVPP = np.load(casename+".npz")['homo_LVPP']
homo_Part = np.load(casename+".npz")['homo_Part']
homo_PLA = np.load(casename+".npz")['homo_PLA']
BCL = np.load(casename+".npz")['BCL']
tpt = np.load(casename+".npz")['tpt']
ncycle = np.load(casename+".npz")['ncycle']
# Plot PV Loop
plt.figure(1)
plt.plot(homo_LVV, homo_LVP, label=label, color=color)
# Plot Pressure waveform
plt.figure(2)
plt.plot(homo_tptt - homo_tptt[0], homo_LVPP, label=label + r"$P_{LV}$", color=color, linestyle='-', linewidth=2)
plt.plot(homo_tptt - homo_tptt[0], homo_Pven, label=label + r"$P_{ven}$", color=color, linestyle='--', linewidth=2)
plt.plot(homo_tptt - homo_tptt[0], homo_PLA, label=label + r"$P_{LA}$", color=color, linestyle='-.', linewidth=2)
plt.plot(homo_tptt - homo_tptt[0], homo_Part, label=label + r"$P_{a,p}$", color=color, linestyle=':', linewidth=2)
# Plot Volume waveform
plt.figure(3)
plt.plot(homo_tptt - homo_tptt[0], homo_LVV , label=label, color=color)
# Plot Mitral valve velocity
plt.figure(4)
plt.plot(homo_tptt - homo_tptt[0], homo_Qmv*1000 , label=label, color=color)
# Plot Mitral valve velocity normalized with MV area
plt.figure(5)
plt.plot(homo_tptt - homo_tptt[0], homo_Qmv/MV_area*1000 , label=label, color=color)
'''
# Get transmural variation of IMP
plt.figure(9)
nbins = 8
n, __ = np.histogram(radialpos, bins=nbins)
sy, __ = np.histogram(radialpos, bins=nbins, weights=imp)
sy2, __ = np.histogram(radialpos, bins=nbins, weights=imp*imp)
mean_imp = sy / n
std_imp = np.sqrt(sy2/n - mean_imp*mean_imp)
plt.plot(radialpos, imp, 'x', alpha=0.1, color=color)
plt.errorbar((__[1:] + __[:-1])/2, mean_imp, yerr=std_imp, color=color, label=label)
# Get transmural variation of IMP/LVP
plt.figure(10)
sy, _ = np.histogram(radialpos, bins=nbins, weights=imp/max(homo_LVPP))
sy2, _ = np.histogram(radialpos, bins=nbins, weights=imp*imp/(max(homo_LVPP)*max(homo_LVPP)))
mean = sy / n
std = np.sqrt(sy2/n - mean*mean)
plt.plot(radialpos, imp/max(homo_LVPP), 'x', alpha=0.1, color=color)
plt.errorbar((_[1:] + _[:-1])/2, mean, yerr=std, color=color, label=label)
plt.ylim(-0.25,1.25)
plt.ylabel(r'IMP/LVP', fontsize=16)
plt.xlabel(r"Radial position $R$")
#if(isexpt):
# plt.plot(IMP_Expt[:,0], IMP_Expt[:,1], 'o', color=color, label="Expt")
if(len(casenames)<=1):
plt.twinx().errorbar((__[1:] + __[:-1])/2, mean_imp, yerr=std_imp, color=color, label=label)
plt.ylim(-0.25*max(homo_LVPP),1.25*max(homo_LVPP))
plt.ylabel(r'IMP (mmHg)', fontsize=16)
# Get transmural variation of WD
plt.figure(11)
n, _ = np.histogram(radialpos, bins=nbins)
sy, _ = np.histogram(radialpos, bins=nbins, weights=WD)
sy2, _ = np.histogram(radialpos, bins=nbins, weights=WD*WD)
mean = sy / n
std = np.sqrt(sy2/n - mean*mean)
plt.plot(radialpos, WD, 'x', alpha=0.1, color=color)
plt.errorbar((_[1:] + _[:-1])/2, mean, yerr=std, color=color, label=label)
# Get endo midwall epi value for IMP, WD
plt.figure(12)
n, _ = np.histogram(radialpos, bins=3)
sy, _ = np.histogram(radialpos, bins=3, weights=WD)
sy2, _ = np.histogram(radialpos, bins=3, weights=WD*WD)
mean = sy / n
std = np.sqrt(sy2/n - mean*mean)
print "Work done at endo = ", mean[0] , " +/- ", std[0]
print "Work done at midwall = ", mean[1] , " +/- ", std[1]
print "Work done at epi = ", mean[2] , " +/- ", std[2]
n, _ = np.histogram(radialpos, bins=3)
sy, _ = np.histogram(radialpos, bins=3, weights=imp)
sy2, _ = np.histogram(radialpos, bins=3, weights=imp*imp)
mean = sy / n
std = np.sqrt(sy2/n - mean*mean)
print "IMP at endo = ", mean[0] , " +/- ", std[0]
print "IMP at midwall = ", mean[1] , " +/- ", std[1]
print "IMP at epi = ", mean[2] , " +/- ", std[2] '''
# Plot Wf vs Q ratio
# n, _ = np.histogram(radialpos, bins=4)
# sy, _ = np.histogram(radialpos, bins=4, weights=WD)
# sy2, _ = np.histogram(radialpos, bins=4, weights=WD*WD)
# mean = sy / n
# std = np.sqrt(sy2/n - mean*mean)
#
# cnt = 0
# loc_array = []
# for m_ in mean:
# if(cnt == 0):
# loc = " Endo"
# loc_array.append(loc)
# elif(cnt == 3):
# loc = " Epi"
# loc_array.append(loc)
# else:
# loc = " Midwall " + str(cnt+1)
# loc_array.append(loc)
#
# cnt += 1
#
# width = 0.8/len(casenames)
# xloc = np.arange(len(mean))+caseno*width
# plt.bar(xloc, mean/Qtotal_arr[caseno], yerr=std, color=color, label=label,alpha=0.5, width=width)
# if(caseno == 0):
# plt.xticks(np.arange(len(mean))+0.5*len(casenames)*width - 0.5*width, loc_array)
#
'''
# Plot Sff vs Eff loop
plt.figure(13)
nbins = 3
n, _ = np.histogram(radialpos, bins=nbins)
Sff_bin_array = []
Eff_bin_array = []
Sff_err_bin_array = []
Eff_err_bin_array = []
for p in range(0, len(Sff[:,1])):
Sff_av, _ = np.histogram(radialpos, bins=nbins, weights=Sff[p,:])
Eff_av, _ = np.histogram(radialpos, bins=nbins, weights=Eff[p,:])
Sff_err, _ = np.histogram(radialpos, bins=nbins, weights=Sff[p,:]*Sff[p,:])
Sff_err = np.sqrt(Sff_err/n - Sff_av*Sff_av/(n*n))
Eff_err, _ = np.histogram(radialpos, bins=nbins, weights=Eff[p,:]*Eff[p,:])
Eff_err = np.sqrt(Eff_err/n - Eff_av*Eff_av/(n*n))
Sff_bin_array.append(Sff_av/n)
Eff_bin_array.append(Eff_av/n)
Sff_err_bin_array.append(Sff_err)
Eff_err_bin_array.append(Eff_err)
Sff_bin_array = np.array(Sff_bin_array)
Eff_bin_array = np.array(Eff_bin_array)
Sff_err_bin_array = np.array(Sff_err_bin_array)
Eff_err_bin_array = np.array(Eff_err_bin_array)
for p in range(0, nbins):
if(p == 0):
location = "Endo"
linestyle= "-"
elif(p == nbins-1):
location = "Epi"
linestyle = "--"
else:
location = "Midwall"
linestyle = "-."
plt.plot(Eff_bin_array[:,p], Sff_bin_array[:,p]*0.0075, label=label + location, linestyle=linestyle, color=color)
# Get Ecc
plt.figure(14)
peakEcc = np.max(np.abs(np.mean(Ecc, axis=1)*100))
plt.plot(tpt - (ncycle-1)*BCL, np.mean(Ecc, axis=1)*100, linestyle='-', color=color, \
label=label+"("+str(int(peakEcc))+"%)",\
)
print "Peak Ecc = ", peakEcc
# Get Ell
plt.figure(15)
peakEll = np.max(np.abs(np.mean(Ell, axis=1)*100))
plt.plot(tpt - (ncycle-1)*BCL, np.mean(Ell, axis=1)*100, linestyle='-', color=color, \
label=label+"("+str(int(peakEll))+"%)",\
)
print "Peak Ell = ", peakEll
'''
caseno += 1
plt.figure(1)
plt.legend()
plt.rc('font', **{'family': 'serif', 'serif': ['Computer Modern']})
plt.title(r'LV PV loop')
plt.xlabel(r"LV volume (ml)", fontsize=16)
plt.ylabel(r'LV pressure (mmHg)', fontsize=16)
plt.xticks(fontsize=14, rotation=0)
plt.yticks(fontsize=14, rotation=0)
plt.savefig(PVoutfilename)
plt.figure(2)
#plt.legend()
plt.title(r"LV pressure waveform")
plt.xlabel(r"Time (ms)")
plt.ylabel(r"LV pressure (mmHg)")
plt.savefig(LVPoutfilename)
plt.figure(3)
plt.legend()
plt.title("LV volume waveform")
plt.xlabel("Time (ms)")
plt.ylabel("LV volume (ml)")
plt.savefig(LVVoutfilename)
plt.figure(4)
plt.legend()
plt.title("Mitral flow rate")
plt.xlabel("Time (ms)")
plt.ylabel("Flow rate (ml/s)")
plt.savefig(Qmvoutfilename)
plt.figure(5)
plt.legend()
plt.title("Mitral flow velocity")
plt.xlabel("Time (ms)")
plt.ylabel("Velocity (cm/s)")
plt.savefig(Vmvoutfilename)
plt.figure(6)
plt.legend()
plt.title("Coronary flow")
plt.xlabel("Time (ms)")
plt.ylabel("Flow rate (ml/min)")
plt.savefig(Qladoutfilename)
plt.figure(7)
plt.legend()
plt.title("Q distribution")
plt.xlabel("Location")
plt.ylabel("Q")
plt.legend()
plt.savefig(Qdisfilename)
plt.figure(8)
plt.legend(loc='best')
plt.title("Relative Q distribution")
plt.xlabel("Location")
plt.tight_layout()
plt.savefig(RelQdisfilename)
plt.figure(9)
plt.rc('font', **{'family': 'serif', 'serif': ['Computer Modern']})
plt.legend()
plt.xlabel(r"Radial position $R$")
plt.ylabel(r"IMP (mmHg)")
plt.xlim(0,1)
plt.ylim(-10, 1.6*max(homo_LVPP))
plt.tight_layout()
plt.savefig("transmuralvariation_of_imp.png")
plt.figure(10)
plt.legend()
plt.xlim(0,1)
plt.tight_layout()
plt.savefig("transmuralvariation_of_imp_LVP_ratio.png")
plt.figure(11)
plt.legend()
plt.xlabel(r"Radial position")
plt.ylabel(r"$W_{f}$ (mmHg)")
plt.ylim(0,150)
plt.tight_layout()
plt.savefig("WD_distribution.png")
plt.figure(12)
plt.xlabel(r"Radial position")
plt.ylabel(r"$W_{f}/Q$ (mmHg/ml)")
plt.legend()
plt.savefig("QWf_ratio.png")
plt.figure(13)
plt.legend()
plt.xlabel(r"$E_{ff}$")
plt.ylabel(r"$S_{ff}$ (mmHg)")
plt.tight_layout()
plt.savefig("Stress_vs_strain.png")
plt.figure(14)
plt.legend()
#if(isexpt):
# plt.plot(Hoit2_Ecc_Expt[:,0]*BCL, Hoit2_Ecc_Expt[:,1], 'o', label="Hoit 2", color='k')
# plt.plot(Gorcsan_Ecc_Expt[:,0]*BCL, Gorcsan_Ecc_Expt[:,1], '*', label="Gorcsan", color='k')
plt.xlabel(r"Time (ms)")
plt.ylabel(r"$E_{cc}$")
plt.legend()
plt.tight_layout()
plt.savefig("Ecc.png")
plt.figure(15)
plt.legend()
#if(isexpt):
# plt.plot(Gorcsan_Ell_Expt[:,0]*BCL, Gorcsan_Ell_Expt[:,1], '*', label="Gorcsan", color='k')
# plt.plot(Smiseth_Ell_Expt[:,0]*BCL, Smiseth_Ell_Expt[:,1], 'o', label="Smiseth", color='k')
plt.xlabel(r"Time (ms)")
plt.ylabel(r"$E_{ll}$")
plt.legend()
plt.tight_layout()
plt.savefig("Ell.png")