def plot_all_ranges(ax1,ct):
for i in range(len(ct['ct']['data'])):
x,y,fo=ct['ct']['x'],ct['ct']['data'][i],ct['ct']['falloff'][i]
ax1.step(x, y, label=ct['name']+'ct', color='steelblue', lw=1, where='mid',clip_on=False, alpha=0.5)
ax1.axvline(fo, color='steelblue', ls='-',lw=1, alpha=0.5)
ax1.set_xlabel('Depth [mm]', fontsize=FONTSIZE)
ax1.set_ylabel('PG detected [counts]', fontsize=FONTSIZE)
ax1.set_title(plot.sn(ct['nprim'],0)+' primaries', fontsize=Fontsize)
plot.texax(ax1)
for i in range(len(ct['rpct']['data'])):
x,y,fo=ct['rpct']['x'],ct['rpct']['data'][i],ct['rpct']['falloff'][i]
ax1.step(x, y, label=ct['name']+'ct', color='indianred', lw=1, where='mid',clip_on=False, alpha=0.5)
ax1.axvline(fo, color='indianred', ls='-',lw=1, alpha=0.5)
if str(ct['nprim'])[1] == '0':
ax1.set_title('PG detection for '+plot.sn_mag(ct['nprim'])+' primaries', fontsize=Fontsize)
else:
ax1.set_title('PG detection for '+plot.sn(ct['nprim'])+' primaries', fontsize=Fontsize)
ax1.set_ylim(bottom=0)
ct_labelextra = ''
rpct_labelextra = ''
if True:
ct_labelextra += 'CT DE: '+plot.sn(ct['ct']['detyieldmu']) #yield per prot
rpct_labelextra += 'RPCT DE: '+plot.sn(ct['rpct']['detyieldmu'])
ax1.text(0.05, 0.95, ct_labelextra , ha='left', va='center', fontsize=Fontsize, transform=ax1.transAxes)
ax1.text(0.05, 0.8, rpct_labelextra , ha='left', va='center', fontsize=Fontsize, transform=ax1.transAxes)
plot.texax(ax1)
def get_effhist(ax1,image,name=None):
colors = plot.colors
get_effhist.color_index+=1
data = image.imdata[~np.isnan(image.imdata)].flatten()
if np.nanmin(data) == 0:
binn = np.logspace(1,np.log10(np.nanmax(data)))
label = plot.sn_mag(image.nprim)+" primaries\nMin: "+str(0)+"\nMax: "+plot.sn(np.nanmax(data))
else:
binn = np.logspace(np.log10(np.nanmin(data)),np.log10(np.nanmax(data)))
label = plot.sn_mag(image.nprim)+" primaries\nMin: "+plot.sn(np.nanmin(data))+"\nMax: "+plot.sn(np.nanmax(data))
hist,bins = np.histogram(data, bins=binn)
center = (bins[:-1] + bins[1:]) / 2
bins=center
# hist[0] = 0
# hist[-1] = 0
if name is None:
ax1.plot(bins,hist/float(hist.max()),label=label,color=colors[get_effhist.color_index],drawstyle='steps')
else:
ax1.plot(bins,hist/float(hist.max()),label=name+'\nMedian gain: '+plot.sn(np.median(data)),color=colors[get_effhist.color_index],drawstyle='steps')
ax1.semilogx()
plot.texax(ax1)
# print 'Median of efficiency:',np.median(data)
# print 'Mean of efficiency:',np.mean(data)
return [np.nanmin(data),np.median(data),np.nanmax(data),np.mean(data)]
def get_effhist(ax1, image, name=None):
colors = plot.colors
get_effhist.color_index += 1
data = image.imdata[~np.isnan(image.imdata)].flatten()
if np.nanmin(data) == 0:
binn = np.logspace(1, np.log10(np.nanmax(data)))
label = plot.sn_mag(
image.nprim) + " primaries\nMin: " + str(0) + "\nMax: " + plot.sn(
np.nanmax(data))
else:
binn = np.logspace(np.log10(np.nanmin(data)),
np.log10(np.nanmax(data)))
label = plot.sn_mag(image.nprim) + " primaries\nMin: " + plot.sn(
np.nanmin(data)) + "\nMax: " + plot.sn(np.nanmax(data))
hist, bins = np.histogram(data, bins=binn)
center = (bins[:-1] + bins[1:]) / 2
bins = center
# hist[0] = 0
# hist[-1] = 0
if name is None:
ax1.plot(bins,
hist / float(hist.max()),
label=label,
color=colors[get_effhist.color_index],
drawstyle='steps')
else:
ax1.plot(bins,
hist / float(hist.max()),
label=name + '\nMedian gain: ' + plot.sn(np.median(data)),
color=colors[get_effhist.color_index],
drawstyle='steps')
ax1.semilogx()
plot.texax(ax1)
# print 'Median of efficiency:',np.median(data)
# print 'Mean of efficiency:',np.mean(data)
return [np.nanmin(data), np.median(data), np.nanmax(data), np.mean(data)]
def get_effhist_oud(ax1, image, name=None):
colors = plot.colors
get_effhist.color_index += 1
data = image.imdata[~np.isnan(image.imdata)].flatten()
# data=data/np.nanmax(data)
# with np.errstate(divide='ignore', invalid='ignore'):
# data = np.true_divide(data,np.nanmax(data))
#data = np.nonzero(data)]
#print image.nprim,np.nanmin(data),np.nanmax(data)
if np.nanmin(data) == 0:
label = plot.sn_mag(
image.nprim) + " primaries\nMin: " + str(0) + "\nMax: " + plot.sn(
np.nanmax(data))
y, x, _ = ax1.hist(data,
bins=np.logspace(1, np.log10(np.nanmax(data))),
label=label,
color=colors[get_effhist.color_index],
histtype='step',
lw=1)
else:
label = plot.sn_mag(image.nprim) + " primaries\nMin: " + plot.sn(
np.nanmin(data)) + "\nMax: " + plot.sn(np.nanmax(data))
y, x, _ = ax1.hist(data,
bins=np.logspace(np.log10(np.nanmin(data)),
np.log10(np.nanmax(data))),
label=label,
color=colors[get_effhist.color_index],
histtype='step',
lw=1)
print y.max()
# label=str(image.nprim)+" primaries\nMin: "+str(np.nanmin(data))+"\nMax: "+str(np.nanmax(data))
# ax1.hist(data, bins = np.linspace(np.nanmin(data),np.nanmax(data)) ,label=label,color=colors[get_effhist.color_index],histtype='step', lw=1)
ax1.semilogx()
plot.texax(ax1)
# print 'Median of efficiency:',np.median(data)
# print 'Mean of efficiency:',np.mean(data)
return [np.nanmin(data), np.median(data), np.nanmax(data), np.mean(data)]
def get_effhist_oud(ax1,image,name=None):
colors = plot.colors
get_effhist.color_index+=1
data = image.imdata[~np.isnan(image.imdata)].flatten()
# data=data/np.nanmax(data)
# with np.errstate(divide='ignore', invalid='ignore'):
# data = np.true_divide(data,np.nanmax(data))
#data = np.nonzero(data)]
#print image.nprim,np.nanmin(data),np.nanmax(data)
if np.nanmin(data) == 0:
label=plot.sn_mag(image.nprim)+" primaries\nMin: "+str(0)+"\nMax: "+plot.sn(np.nanmax(data))
y, x, _=ax1.hist(data, bins = np.logspace(1,np.log10(np.nanmax(data))) ,label=label,color=colors[get_effhist.color_index],histtype='step', lw=1)
else:
label=plot.sn_mag(image.nprim)+" primaries\nMin: "+plot.sn(np.nanmin(data))+"\nMax: "+plot.sn(np.nanmax(data))
y, x, _=ax1.hist(data, bins = np.logspace(np.log10(np.nanmin(data)),np.log10(np.nanmax(data))) ,label=label,color=colors[get_effhist.color_index],histtype='step', lw=1)
print y.max()
# label=str(image.nprim)+" primaries\nMin: "+str(np.nanmin(data))+"\nMax: "+str(np.nanmax(data))
# ax1.hist(data, bins = np.linspace(np.nanmin(data),np.nanmax(data)) ,label=label,color=colors[get_effhist.color_index],histtype='step', lw=1)
ax1.semilogx()
plot.texax(ax1)
# print 'Median of efficiency:',np.median(data)
# print 'Mean of efficiency:',np.mean(data)
return [np.nanmin(data),np.median(data),np.nanmax(data),np.mean(data)]
def plot_single_range(ax1,ct):
for i in range(len(ct['ct']['data'])):
x,y,fo=ct['ct']['x'],ct['ct']['data'][i],ct['ct']['falloff'][i]
ax1.step(x, y, label=ct['name']+'ct', color='steelblue', lw=1, where='mid',clip_on=False, alpha=0.5)
ax1.axvline(fo, color='steelblue', ls='-',lw=1, alpha=0.5)
break
ax1.set_xlabel('Depth [mm]', fontsize=FONTSIZE)
ax1.set_ylabel('PG detected [counts]', fontsize=FONTSIZE)
ax1.set_title(plot.sn(ct['nprim'],0)+' primaries', fontsize=FONTSIZE)
plot.texax(ax1)
for i in range(len(ct['rpct']['data'])):
x,y,fo=ct['rpct']['x'],ct['rpct']['data'][i],ct['rpct']['falloff'][i]
ax1.step(x, y, label=ct['name']+'ct', color='indianred', lw=1, where='mid',clip_on=False, alpha=0.5)
ax1.axvline(fo, color='indianred', ls='-',lw=1, alpha=0.5)
break
ax1.set_title('PG detection for '+plot.sn_mag(ct['nprim'])+'primaries', fontsize=FONTSIZE)
ax1.set_ylim(bottom=0)
plot.texax(ax1)
# y_tle = [np.nansum(value['var'].imdata/np.sum(mask90pc.imdata)) for key, value in tlerelunc.iteritems()]
y_tle = [
np.median(value['var'].imdata[~np.isnan(value['var'].imdata)])
for key, value in tlerelunc.iteritems()
]
tle_2pc = tle.get_reluncconv(tl, x_tle / ppstle, y_tle)
x_an = np.array(anrelunc.keys())
# y_an = [np.nansum(value['var'].imdata/np.sum(mask90pc.imdata)) for key, value in anrelunc.iteritems()]
y_an = [
np.median(value['var'].imdata[~np.isnan(value['var'].imdata)])
for key, value in anrelunc.iteritems()
]
an_2pc = tle.get_reluncconv(tl, x_an / ppsan, y_an)
tl.set_title('Median relative uncertainty\nGain: ' + plot.sn(an_2pc / tle_2pc))
tl.set_ylabel('Relative Uncertainty [\%]')
tl.set_xlabel('Runtime t [s]')
tl.semilogx()
tl.legend(bbox_to_anchor=(1.1, 1), frameon=False, prop={'size': 6})
#efficiencies + speedups
a = tle.get_effhist(tr, tleeff[1e3]['var'])
b = tle.get_effhist(tr, tleeff[1e4]['var'])
c = tle.get_effhist(tr, tleeff[1e5]['var'])
d = tle.get_effhist(tr, tleeff[1e6]['var'])
lmin = min([a[0], b[0], c[0], d[0]])
lmed = np.average([a[1], b[1], c[1], d[1]])
lmax = max([a[2], b[2], c[2], d[2]])
lmean = np.average([a[3], b[3], c[3], d[3]])
print 'Glob mean/median:', lmean, lmed
###########################################################################################################
#volume_offset=-141.59+7.96#spot sources
volume_offset=150-141.59+7.96#spot sources
###########################################################################################################
# BOTTOM ROW IBA
f, ((ax4,ax5,ax6),(ax1,ax2,ax3)) = plot.subplots(nrows=2, ncols=3, sharex=True, sharey=False)
typ = 'iba-auger-notof-3.root'
yield_av = auger.plot_all_ranges_2(ax1,auger.getctset(840964615,'fromclus-sorted/layers-beide/run.e8ai','fromclus-sorted/layers-beide/run.96ra',typ,manualshift=volume_offset),firstcolor='steelblue',secondcolor=None)
yield_av = auger.plot_all_ranges_2(ax1,auger.getctset(848710335,'fromclus-sorted/layers-beide/run.m4RU','fromclus-sorted/layers-beide/run.tLiY',typ,manualshift=volume_offset),firstcolor='seagreen',secondcolor=None)
ax1.set_title('KES, Spot A,\nyield: '+plot.sn(yield_av), fontsize=8)
ax1.set_ylabel('PG detected [counts]')
yield_av = auger.plot_all_ranges_2(ax2,auger.getctset(1068263318,'fromclus-sorted/layers-beide/run.E2tF','fromclus-sorted/layers-beide/run.qE8o',typ,manualshift=volume_offset),firstcolor='steelblue',secondcolor=None)
yield_av = auger.plot_all_ranges_2(ax2,auger.getctset(1071596684,'fromclus-sorted/layers-beide/run.r48i','fromclus-sorted/layers-beide/run.c0rg',typ,manualshift=volume_offset),firstcolor='seagreen',secondcolor=None)
ax2.set_title('KES, Spot B,\nyield: '+plot.sn(yield_av), fontsize=8)
ax2.set_xlabel('Position [mm]')
yield_av = auger.plot_all_ranges_2(ax3,auger.getctset(1068263318,'fromclus-sorted/layers-beide/run.E2tF','fromclus-sorted/layers-beide/run.qE8o',typ,manualshift=volume_offset),firstcolor='steelblue',secondcolor=None)
yield_av = auger.plot_all_ranges_2(ax3,auger.getctset(979533764,'fromclus-sorted/layers-beide/run.c4UF','fromclus-sorted/layers-beide/run.bj2R',typ,manualshift=volume_offset),firstcolor='seagreen',secondcolor=None)
ax3.set_title('KES, Spot C,\nyield: '+plot.sn(yield_av), fontsize=8)
######## TopRow IPNL
typ = 'ipnl-auger-tof-1.root'
f, (tr,tl) = plt.subplots(nrows=1, ncols=2, sharex=False, sharey=False)
#swapped
#Relative uncertainties 90pc region
x_tle = np.array(tlerelunc.keys())
# y_tle = [np.nansum(value['var'].imdata/np.sum(mask90pc.imdata)) for key, value in tlerelunc.iteritems()]
y_tle = [np.median(value['var'].imdata[~np.isnan(value['var'].imdata)]) for key, value in tlerelunc.iteritems()]
tle_2pc = tle.get_reluncconv(tl,x_tle/ppstle,y_tle)
x_an = np.array(anrelunc.keys())
# y_an = [np.nansum(value['var'].imdata/np.sum(mask90pc.imdata)) for key, value in anrelunc.iteritems()]
y_an = [np.median(value['var'].imdata[~np.isnan(value['var'].imdata)]) for key, value in anrelunc.iteritems()]
an_2pc = tle.get_reluncconv(tl,x_an/ppsan,y_an)
tl.set_title('Median relative uncertainty\nGain: '+plot.sn(an_2pc/tle_2pc))
tl.set_ylabel('Relative Uncertainty [\%]')
tl.set_xlabel('Runtime t [s]')
tl.semilogx()
tl.legend(bbox_to_anchor=(1.1, 1),frameon=False,prop={'size':6})
#efficiencies + speedups
a=tle.get_effhist(tr,tleeff[1e3]['var'])
b=tle.get_effhist(tr,tleeff[1e4]['var'])
c=tle.get_effhist(tr,tleeff[1e5]['var'])
d=tle.get_effhist(tr,tleeff[1e6]['var'])
lmin = min([a[0],b[0],c[0],d[0]])
lmed = np.average([a[1],b[1],c[1],d[1]])
lmax = max([a[2],b[2],c[2],d[2]])
lmean = np.average([a[3],b[3],c[3],d[3]])
print 'Glob mean/median:',lmean,lmed
sharex=False,
sharey=False) #,figsize=(28,10))
for axrow, yim, uim, label in zip(axes, imyields, imuncs, labels):
print yim, uim, label
yim_ = image.image(yim, type='yield')
uim_ = image.image(
uim, type='relunc'
) #the doseactor outputs relative uncertainties as per Chetty, see GateImageWithStatistic
#uim_.applymask(mask90pc)
uim_.imdata = uim_.imdata.squeeze() * 100. #fractions to percentages
fracties_isocentrum.append(yim_.getcenter().mean())
fracties_totaal.append(yim_.imdata.sum())
axrow[0].imshow(yim_.imdata.squeeze(), extent=[0, 41, 0, 41], cmap='gray')
axrow[0].set_title(label + '\n$\sum$ Dose: ' +
plot.sn(fracties_totaal[-1]))
axrow[1].set_title(label + ' Profile')
axrow[1].plot(*yim_.getprofile('y'), color='steelblue', label='x')
axrow[1].plot(*yim_.getprofile('x'), color='indianred', label='y')
axrow[1].legend(loc='upper right', bbox_to_anchor=(1., 1.), frameon=False)
axrow[1].axvline(41. / 2. - 8., color='#999999',
ls='--') #10x10 at isoc is 16x16 at epid level
axrow[1].axvline(41. / 2. + 8., color='#999999', ls='--')
axrow[1].set_xlim(0, 41)
plot.set_metric_prefix_y(axrow[1])
axrow[2].set_title(label + ' relunc')
plot.plot1dhist(axrow[2],
uim_.imdata.flatten(),
bins=np.linspace(0, 100, 50),
for spot in rtplan.spots:
MSW.append(spot[-1])
#spot_weight_mean = np.median(MSW)
spot_weight_mean = np.mean(MSW)
nrfields=len(rtplan.fields)
fontsize=6
mpl.rcParams['xtick.labelsize'] = fontsize
mpl.rcParams['ytick.labelsize'] = fontsize
mpl.rcParams['axes.titlesize'] = fontsize
mpl.rcParams['axes.labelsize'] = fontsize
plotke = plot.subplots(nrows=2, ncols=nrfields, sharex='col', sharey='row')
plotke[0].get_axes()[0].annotate('Total nr. of primaries: '+plot.sn(rtplan.TotalMetersetWeight)+'. Mean spot weight: '+plot.sn(spot_weight_mean), (0.5, 0.975), xycoords='figure fraction', ha='center', fontsize=fontsize)
if nrfields == 1:
spotaxes = [plotke[-1][0]] #0 is eerste kolom
layaxes = [plotke[-1][1]] #0 is eerste kolom
spotloop= [spotdata[0]]
layloop = [layerdata[0]]
else:
spotaxes = plotke[-1][0] #0 is eerste kolom
layaxes = plotke[-1][1] #0 is eerste kolom
spotloop = spotdata
layloop = layerdata
first = True
layer_min_y = 1e10
layer_max_y = 1e1
#volume_offset=-141.59+7.96#spot sources
volume_offset=0
addnoise = False
#addnoise = 'onlynoise'
###########################################################################################################
# BOTTOM ROW IBA
f, ((ax4,ax5,ax6),(ax1,ax2,ax3)) = plot.subplots(nrows=2, ncols=3, sharex=True, sharey=False)
typ = 'iba-auger-tof-1.root'
typ = 'iba-auger-notof-3.root'
yield_av = auger.plot_all_ranges_2(ax1, auger.getctset(1e9,'run.o5zC','run.o5zC',typ,manualshift=volume_offset,addnoise=addnoise) )
ax1.set_title('KES, 1e9,\nyield: '+plot.sn(yield_av), fontsize=8)
ax1.set_ylabel('PG detected [counts]')
yield_av = auger.plot_all_ranges_2(ax2, auger.getctset(1e8,'run.fFpk','run.fFpk',typ,manualshift=volume_offset,addnoise=addnoise) )
ax2.set_title('KES, 1e8,\nyield: '+plot.sn(yield_av), fontsize=8)
ax2.set_xlabel('Position [mm]')
yield_av = auger.plot_all_ranges_2(ax3, auger.getctset(1e7,'run.Gfu4','run.Gfu4',typ,manualshift=volume_offset,addnoise=addnoise) )
ax3.set_title('KES, 1e7\nyield: '+plot.sn(yield_av), fontsize=8)
######## TopRow IPNL
typ = 'ipnl-auger-tof-1.root'
typ = 'ipnl-auger-notof-3.root'
#shift = 13.76-19.44
f, (tr,tl) = plt.subplots(nrows=1, ncols=2, sharex=False, sharey=False)
#swapped left and right!!!!!!
#Relative uncertainties 90pc region
x_tle = np.array(tlerelunc.keys())
# y_tle = [np.nansum(value['var'].imdata/np.sum(mask90pc.imdata)) for key, value in tlerelunc.iteritems()]
y_tle = [np.median(value['var'].imdata[~np.isnan(value['var'].imdata)]) for key, value in tlerelunc.iteritems()]
tle_2pc = tle.get_reluncconv(tl,x_tle/ppstle,y_tle)
x_an = np.array(anrelunc.keys())
# y_an = [np.nansum(value['var'].imdata/np.sum(mask90pc.imdata)) for key, value in anrelunc.iteritems()]
y_an = [np.median(value['var'].imdata[~np.isnan(value['var'].imdata)]) for key, value in anrelunc.iteritems()]
an_2pc = tle.get_reluncconv(tl,x_an/ppsan,y_an)
tl.set_title('Median relative uncertainty\nGain: '+plot.sn(an_2pc/tle_2pc))
tl.set_ylabel('Relative Uncertainty [\%]')
tl.set_xlabel('Runtime t [s]')
tl.semilogx()
tl.legend(bbox_to_anchor=(1.1, 1),frameon=False,prop={'size':6})
#efficiencies + speedups
a=tle.get_effhist(tr,tleeff[1e3]['var'])
b=tle.get_effhist(tr,tleeff[1e4]['var'])
c=tle.get_effhist(tr,tleeff[1e5]['var'])
d=tle.get_effhist(tr,tleeff[1e6]['var'])
lmin = min([a[0],b[0],c[0],d[0]])
lmed = np.average([a[1],b[1],c[1],d[1]])
lmax = max([a[2],b[2],c[2],d[2]])
lmean = np.average([a[3],b[3],c[3],d[3]])
print 'Glob mean/median:',lmean,lmed
else:
shifts.append(shift)
pgshifts.append(pgshift)
diffshifts.append(diffshift)
if args.plotspot is not None and spindex == int(args.plotspot):
if args.ctonly:
f, ax1 = plot.subplots(nrows=1, ncols=1, sharex=False, sharey=False)
ax1.step(x,ct/ct.max(), color='steelblue',lw=1., label='Dose,\nFOP: '+str(auger.get_fop(x,ct))[:4]+' mm')
ax1.step(x,pg/pg.max(), color='indianred',lw=1., label='PG production,\nFOP: '+str(auger.get_fop(x,pg))[:4]+' mm')
ax1.set_xlabel('Position w.r.t. isocenter [mm]')
ax1.set_ylabel('Dose/PG production, rescaled [a.u.]')
ax1.legend(frameon = False)
ax1.set_xlim(-75,75)
f.suptitle('Spotid '+str(spindex)+', '+plot.sn(MSW[spindex][-1])+' protons', fontsize=8)
plot.texax(ax1)
f.savefig('CT-dose-pg-'+str(args.plotspot)+'.pdf', bbox_inches='tight')
plot.close('all')
quit()
else:
f, (ax1,ax2) = plot.subplots(nrows=1, ncols=2, sharex=False, sharey=False)
ax1.step(x,ct, color='steelblue',lw=1., label='FOP: '+str(auger.get_fop(x,ct))[:5])
ax1.step(x,rpct, color='indianred',lw=1., label='FOP: '+str(auger.get_fop(x,rpct))[:5])
ax1.set_xlabel('FOP [mm]')
ax1.set_ylabel('Integrated Dose [a.u.]')
ax1.set_title('Integrated Dose Profile (along x)'+', shift '+str(shift)[:4], fontsize=8)
ax1.legend(prop={'size':6},loc='best',frameon = False)
#from scipy.ndimage.filters import gaussian_filter
ncols=3,
sharex=True,
sharey=False)
typ = 'iba-auger-tof-1.root'
typ = 'iba-auger-notof-3.root'
yield_av = auger.plot_all_ranges_2(
ax1,
auger.getctset(1e9,
'run.o5zC',
'run.o5zC',
typ,
manualshift=volume_offset,
addnoise=addnoise))
ax1.set_title('KES, 1e9,\nyield: ' + plot.sn(yield_av), fontsize=8)
ax1.set_ylabel('PG detected [counts]')
yield_av = auger.plot_all_ranges_2(
ax2,
auger.getctset(1e8,
'run.fFpk',
'run.fFpk',
typ,
manualshift=volume_offset,
addnoise=addnoise))
ax2.set_title('KES, 1e8,\nyield: ' + plot.sn(yield_av), fontsize=8)
ax2.set_xlabel('Position [mm]')
yield_av = auger.plot_all_ranges_2(
ax3,
color='steelblue',
lw=1.,
label='Dose,\nFOP: ' + str(auger.get_fop(x, ct))[:4] +
' mm')
ax1.step(x,
pg / pg.max(),
color='indianred',
lw=1.,
label='PG production,\nFOP: ' +
str(auger.get_fop(x, pg))[:4] + ' mm')
ax1.set_xlabel('Position w.r.t. isocenter [mm]')
ax1.set_ylabel('Dose/PG production, rescaled [a.u.]')
ax1.legend(frameon=False)
ax1.set_xlim(-75, 75)
f.suptitle('Spotid ' + str(spindex) + ', ' +
plot.sn(MSW[spindex][-1]) + ' protons',
fontsize=8)
plot.texax(ax1)
f.savefig('CT-dose-pg-' + str(args.plotspot) + '.pdf',
bbox_inches='tight')
plot.close('all')
quit()
else:
f, (ax1, ax2) = plot.subplots(nrows=1,
ncols=2,
sharex=False,
sharey=False)
ax1.step(x,
ct,
color='steelblue',
lw=1.,
x_tle = np.array(tle_d.keys())
y_tle = [
sqrt(value['var'][0]) / value['yield'][0]
for key, value in tle_d.iteritems()
]
tle_2pc = tle.get_reluncconv(tl, x_tle / ppstle, y_tle)
x_an = np.array(an_d.keys())
y_an = [
sqrt(value['var'][0]) / value['yield'][0]
for key, value in an_d.iteritems()
]
an_2pc = tle.get_reluncconv(tl, x_an / ppsan, y_an)
tl.set_title('Median relative uncertainty\nGain: ' + plot.sn(an_2pc / tle_2pc))
tl.set_ylabel('Relative Uncertainty [\%]')
tl.set_xlabel('Runtime [s]')
tl.semilogx()
tl.legend(bbox_to_anchor=(1.1, 1), frameon=False, prop={'size': 6})
#efficiencies + speedups
a = tle.get_effhist(tr, tleeff[1e3]['var'])
b = tle.get_effhist(tr, tleeff[1e4]['var'])
c = tle.get_effhist(tr, tleeff[1e5]['var'])
d = tle.get_effhist(tr, tleeff[1e6]['var'])
lmin = min([a[0], b[0], c[0], d[0]])
lmed = np.average([a[1], b[1], c[1], d[1]])
lmax = max([a[2], b[2], c[2], d[2]])
lmean = np.average([a[3], b[3], c[3], d[3]])
print 'Glob mean/median:', lmean, lmed
tle.seteff(tlebox8)
tle.seteff(anbox8)
tle.seteffratio(tlebox2,anbox2[max(anbox2.keys())]['var'])
tle.seteffratio(tlebox8,anbox8[max(anbox8.keys())]['var'])
f, ((tl,tr)) = plt.subplots(nrows=1, ncols=2, sharex=True, sharey=False)
a=tle.get_effhist(tl,tlebox2[1e3]['var'])
b=tle.get_effhist(tl,tlebox2[1e4]['var'])
c=tle.get_effhist(tl,tlebox2[1e5]['var'])
d=tle.get_effhist(tl,tlebox2[1e6]['var'])
# print [a[1],b[1],c[1],d[1]]
lmed2 = np.average([a[1],b[1],c[1],d[1]])
tl.set_title("Medium 2 (Bone)")
tl.text(0.05, 0.9,"Median gain: "+plot.sn(lmed2), ha='left', va='center', transform=tl.transAxes, fontsize=6)
a=tle.get_effhist(tr,tlebox8[1e3]['var'])
b=tle.get_effhist(tr,tlebox8[1e4]['var'])
c=tle.get_effhist(tr,tlebox8[1e5]['var'])
d=tle.get_effhist(tr,tlebox8[1e6]['var'])
# print [a[1],b[1],c[1],d[1]]
lmed8 = np.average([a[1],b[1],c[1],d[1]])
tr.set_title("Medium 8 (Muscle)")
tr.text(0.05, 0.9,"Median gain: "+plot.sn(lmed8), ha='left', va='center', transform=tr.transAxes, fontsize=6)
f.savefig('boxes-eff.pdf', bbox_inches='tight')
plt.close('all')
### Reldiff of two boxes.
tle_2 = tle4dbox2 #no deepcopy of 4D images
#e_spot_list.append(spindex)
#ct61_geo = map(add, ct61_geo, dosespot)
ct61_elay = [0] * len(ct[0])
e_spot_list = []
for spindex, (dosespot, spotid) in enumerate(zip(ct, MSW)):
if spotid[1] == E61: #in the right elayer
e_spot_list.append(spindex)
ct61_elay = map(add, ct61_elay, dosespot)
f, ax1 = plot.subplots(nrows=1, ncols=1, sharex=False, sharey=False)
ax1.step(ct_x,
ct61_geo / max(ct61_geo),
color='indianred',
lw=1.,
label='Geometric layer ' + plot.sn(new_spot_prot_sum) + ' protons',
where='mid')
ax1.step(ct_x,
ct61_elay / max(ct61_elay),
color='steelblue',
lw=1.,
label='Energy Layer' + plot.sn(lay61_prots) + ' protons',
where='mid')
ax1.set_xlabel('Position [mm]')
ax1.set_ylabel('Number of spots')
#ax1.set_xlim(-60,16)
#ax1.set_ylim(0,110)
plot.texax(ax1)
ax1.legend(
frameon=False, loc='upper right'
ax4.step(x,
dose[29] / dose[29].max(),
color='steelblue',
lw=1.,
alpha=1,
label='CT',
where='mid')
ax4.step(x,
rpdose[29] / rpdose[29].max(),
color='indianred',
lw=1.,
alpha=1,
label='RPCT',
where='mid')
ax4.set_title('Spot A, Shift: ' + str(rpct29_fo - ct29_fo)[:3] + ' mm\n' +
plot.sn(MSW[29][-1], 1) + ' protons',
fontsize=10)
ax4.legend(frameon=False, loc='upper left')
ax4.set_xlim(-80, 60)
ax4.set_ylabel('Scaled Dose [a.u.]')
plot.texax(ax4)
ax5.step(x,
dose[40] / dose[40].max(),
color='steelblue',
lw=1.,
alpha=1,
label='CT',
where='mid')
ax5.step(x,
rpdose[40] / rpdose[40].max(),
fracties_totaal = []
fracties_isocentrum = []
f, axes = plot.subplots(nrows=len(imyields)+1, ncols=4, sharex=False, sharey=False)#,figsize=(28,10))
for axrow,yim,uim,label in zip(axes,imyields,imuncs,labels):
print yim,uim,label
yim_ = image.image(yim,type='yield')
uim_ = image.image(uim,type='relunc') #the doseactor outputs relative uncertainties as per Chetty, see GateImageWithStatistic
#uim_.applymask(mask90pc)
uim_.imdata = uim_.imdata.squeeze()*100. #fractions to percentages
fracties_isocentrum.append( yim_.getcenter().mean() )
fracties_totaal.append( yim_.imdata.sum() )
axrow[0].imshow( yim_.imdata.squeeze() , extent = [0,41,0,41], cmap='gray')
axrow[0].set_title(label + '\n$\sum$ Dose: '+ plot.sn(fracties_totaal[-1]))
axrow[1].set_title(label + ' Profile')
axrow[1].plot(*yim_.getprofile('y'), color = 'steelblue' , label='x')
axrow[1].plot(*yim_.getprofile('x'), color = 'indianred' , label='y')
axrow[1].legend(loc='upper right', bbox_to_anchor=(1., 1.),frameon=False)
axrow[1].axvline(41./2.-8., color='#999999', ls='--') #10x10 at isoc is 16x16 at epid level
axrow[1].axvline(41./2.+8., color='#999999', ls='--')
axrow[1].set_xlim(0,41)
plot.set_metric_prefix_y(axrow[1])
axrow[2].set_title(label+' relunc')
plot.plot1dhist( axrow[2], uim_.imdata.flatten(), bins=np.linspace(0,100,50), log=True)
frac_trans=1.
frac_nontrans=1.
'fromclus-sorted/layers-beide/run.e8ai',
'fromclus-sorted/layers-beide/run.96ra',
typ,
manualshift=volume_offset),
firstcolor='steelblue',
secondcolor=None)
yield_av = auger.plot_all_ranges_2(ax1,
auger.getctset(
848710335,
'fromclus-sorted/layers-beide/run.m4RU',
'fromclus-sorted/layers-beide/run.tLiY',
typ,
manualshift=volume_offset),
firstcolor='seagreen',
secondcolor=None)
ax1.set_title('KES, Spot A,\nyield: ' + plot.sn(yield_av), fontsize=8)
ax1.set_ylabel('PG detected [counts]')
yield_av = auger.plot_all_ranges_2(ax2,
auger.getctset(
1068263318,
'fromclus-sorted/layers-beide/run.E2tF',
'fromclus-sorted/layers-beide/run.qE8o',
typ,
manualshift=volume_offset),
firstcolor='steelblue',
secondcolor=None)
yield_av = auger.plot_all_ranges_2(ax2,
auger.getctset(
1071596684,
'fromclus-sorted/layers-beide/run.r48i',
ct61_geo = map(add, ct61_geo, ct[spindex])
#for spindex,(dosespot,spotid) in enumerate(zip(ct,MSW)):
#if spotid[1]==E61: #in the right elayer
#e_spot_list.append(spindex)
#ct61_geo = map(add, ct61_geo, dosespot)
ct61_elay = [0]*len(ct[0])
e_spot_list = []
for spindex,(dosespot,spotid) in enumerate(zip(ct,MSW)):
if spotid[1]==E61: #in the right elayer
e_spot_list.append(spindex)
ct61_elay = map(add, ct61_elay, dosespot)
f, ax1 = plot.subplots(nrows=1, ncols=1, sharex=False, sharey=False)
ax1.step(ct_x,ct61_geo/max(ct61_geo), color='indianred',lw=1., label='Geometric layer '+plot.sn(new_spot_prot_sum)+' protons', where='mid')
ax1.step(ct_x,ct61_elay/max(ct61_elay), color='steelblue',lw=1., label='Energy Layer'+plot.sn(lay61_prots)+' protons', where='mid')
ax1.set_xlabel('Position [mm]')
ax1.set_ylabel('Number of spots')
#ax1.set_xlim(-60,16)
#ax1.set_ylim(0,110)
plot.texax(ax1)
ax1.legend(frameon = False,loc = 'upper right')#,fancybox = True,ncol = 1,fontsize = 'x-small',loc = 'upper right')
f.savefig('rtplan.geogroup.pdf', bbox_inches='tight')
plot.close('all')
print 'geoplan protcount',new_spot_prot_sum
print 'eplan protcount',lay61_prots
rtplan.rtplan([rtpfile],noproc=True,spotlist=e_spot_list,fieldind=field,relayername='elayspot'+str(SPOTID_GROUPAROUND))