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 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_unc(ax1,dataset,refdata,ax,typ='parodi'):
colors = plot.colors
color_index=0
#asume voxels are 2mm
if 'Z' in ax and 'parodi' in typ:
x_axis = np.linspace(0,222,111)
if 'Y' in ax and 'head' in typ:
x_axis = np.linspace(0,170,85)
if 'E' in ax:
x_axis = np.linspace(0,10,250)
for key, valuee in iter(sorted(dataset.iteritems())):
value = valuee[ax]
ax1.step(x_axis,[x*100. for x in value['unc']], label=plot.sn_mag(key)+' primaries', color=colors[color_index])
color_index+=1
refdata = refdata[ax]
ax1.step(x_axis,[x*100. for x in refdata['unc']], label='Reference', color='k')
ax1.semilogy()
ax1.autoscale(axis='x',tight=True)
#ax1.yaxis.set_major_formatter(mpl.ticker.FormatStrFormatter('%.0f%%'))
#PG falloff line
spectcutoff = [1,8]
if ax == 'E':
ax1.set_xlim(spectcutoff)
# if ax == 'E':
# ax1.axvline(spectcutoff[0], color='#999999', ls='--')
# ax1.axvline(spectcutoff[1], color='#999999', ls='--')
plot.texax(ax1)
def get_yield(ax1,dataset,refdata,ax,typ='parodi'):
colors = plot.colors
color_index=0
#asume voxels are 2mm
if 'Z' in ax and 'parodi' in typ:
x_axis = np.linspace(0,222,111)
if 'Y' in ax and 'head' in typ:
x_axis = np.linspace(0,170,85)
if 'E' in ax:
x_axis = np.linspace(0,10,250)
for key, valuee in iter(sorted(dataset.iteritems())):
# if TLE
value = valuee[ax]
# print ax, len(value['yield'])
ax1.step(x_axis,value['yield'], label=plot.sn_mag(key)+' primaries', color=colors[color_index], lw=0.5)
plot.fill_between_steps(ax1, x_axis, value['yield-unc'], value['yield+unc'], alpha=0.5, color=colors[color_index], lw=0.5)
color_index+=1
#analog reference
refdata = refdata[ax]
ax1.step(x_axis,refdata['yield'], label='Reference', color='k', lw=0.5)
plot.fill_between_steps(ax1, x_axis, refdata['yield-unc'], refdata['yield+unc'], alpha=0.5, color='k', lw=0.5)
ax1.ticklabel_format(style='sci', axis='y', scilimits=(0,0))
ax1.autoscale(axis='x',tight=True)
# #PG falloff line parodi
spectcutoff = [1,8]
if ax == 'E':
ax1.set_xlim(spectcutoff)
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_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_yield(ax1, dataset, refdata, ax, typ='parodi'):
colors = plot.colors
color_index = 0
#asume voxels are 2mm
if 'Z' in ax and 'parodi' in typ:
x_axis = np.linspace(0, 222, 111)
if 'Y' in ax and 'head' in typ:
x_axis = np.linspace(0, 170, 85)
if 'E' in ax:
x_axis = np.linspace(0, 10, 250)
for key, valuee in iter(sorted(dataset.iteritems())):
# if TLE
value = valuee[ax]
# print ax, len(value['yield'])
ax1.step(x_axis,
value['yield'],
label=plot.sn_mag(key) + ' primaries',
color=colors[color_index],
lw=0.5)
plot.fill_between_steps(ax1,
x_axis,
value['yield-unc'],
value['yield+unc'],
alpha=0.5,
color=colors[color_index],
lw=0.5)
color_index += 1
#analog reference
refdata = refdata[ax]
ax1.step(x_axis, refdata['yield'], label='Reference', color='k', lw=0.5)
plot.fill_between_steps(ax1,
x_axis,
refdata['yield-unc'],
refdata['yield+unc'],
alpha=0.5,
color='k',
lw=0.5)
ax1.ticklabel_format(style='sci', axis='y', scilimits=(0, 0))
ax1.autoscale(axis='x', tight=True)
# #PG falloff line parodi
spectcutoff = [1, 8]
if ax == 'E':
ax1.set_xlim(spectcutoff)
plot.texax(ax1)
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_relunc(ax1, dataset, refdata, ax, typ='parodi'):
colors = plot.colors
color_index = 0
#asume voxels are 2mm
if 'Z' in ax and 'parodi' in typ:
x_axis = np.linspace(0, 222, 111)
if 'Y' in ax and 'head' in typ:
x_axis = np.linspace(0, 170, 85)
if 'E' in ax:
x_axis = np.linspace(0, 10, 250)
for key, valuee in iter(sorted(dataset.iteritems())):
value = valuee[ax]
try:
ax1.step(x_axis, [x * 100. for x in value['relunc']],
label=plot.sn_mag(key) + ' primaries',
color=colors[color_index])
except TypeError:
ax1.step(x_axis, [x * 100. for x in value['relunc']],
label=key,
color=colors[color_index])
color_index += 1
if refdata is not None:
refdata = refdata[ax]
ax1.step(x_axis, [x * 100. for x in refdata['relunc']],
label='Reference',
color='k')
ax1.semilogy()
ax1.autoscale(axis='x', tight=True)
#ax1.yaxis.set_major_formatter(mpl.ticker.FormatStrFormatter('%.0f%%'))
#PG falloff line
spectcutoff = [1, 8]
if ax == 'E':
ax1.set_xlim(spectcutoff)
# if ax == 'E':
# ax1.axvline(spectcutoff[0], color='#999999', ls='--')
# ax1.axvline(spectcutoff[1], color='#999999', ls='--')
plot.texax(ax1)
def get_reldiff(ax1, dataset, ax, typ='parodi'):
colors = plot.colors
color_index = 0
#asume voxels are 2mm
if 'Z' in ax and 'parodi' in typ:
x_axis = np.linspace(0, 222, 111)
if 'Y' in ax and 'head' in typ:
x_axis = np.linspace(0, 170, 85)
if 'E' in ax:
x_axis = np.linspace(0, 10, 250)
for key, valuee in iter(sorted(dataset.iteritems())):
value = valuee[ax]
ax1.step(x_axis, [x * 100. for x in value['reldiff']],
label=plot.sn_mag(key) + ' primaries',
color=colors[color_index],
lw=0.5)
print key, __withinsigma(value)
plot.fill_between_steps(ax1,
x_axis,
[x * 100. for x in value['reldiff-2sigma']],
[x * 100. for x in value['reldiff+2sigma']],
alpha=0.25,
color=colors[color_index],
lw=0.01)
color_index += 1
ax1.autoscale(axis='x', tight=True)
#ax1.set_ylim([-4,4])
# #PG falloff line parodi
spectcutoff = [1, 8]
if ax == 'E':
ax1.set_xlim(spectcutoff)
# if ax == 'E':
# ax1.axvline(spectcutoff[0], color='#999999', ls='--')
# ax1.axvline(spectcutoff[1], color='#999999', ls='--')
plot.texax(ax1)
def plot1d(ax1,dataset):
colors = plot.colors
color_index=0
#asume voxels are 2mm
x_axis = np.linspace(0,10,250)
key = 1e4
ax1.step(x_axis,[x*100. for x in dataset], label=plot.sn_mag(key)+' primaries', color=colors[color_index])
color_index+=1
ax1.semilogy()
ax1.autoscale(axis='x',tight=True)
#ax1.yaxis.set_major_formatter(mpl.ticker.FormatStrFormatter('%.0f%%'))
#PG falloff line
spectcutoff = [1,8]
ax1.set_xlim(spectcutoff)
plot.texax(ax1)
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)
def get_reldiff(ax1,dataset,ax,typ='parodi'):
colors = plot.colors
color_index=0
#asume voxels are 2mm
if 'Z' in ax and 'parodi' in typ:
x_axis = np.linspace(0,222,111)
if 'Y' in ax and 'head' in typ:
x_axis = np.linspace(0,170,85)
if 'E' in ax:
x_axis = np.linspace(0,10,250)
for key, valuee in iter(sorted(dataset.iteritems())):
value = valuee[ax]
ax1.step(x_axis,
[x*100. for x in value['reldiff']],
label=plot.sn_mag(key) +' primaries', color=colors[color_index], lw=0.5)
print key, __withinsigma(value)
plot.fill_between_steps(ax1, x_axis,
[x*100. for x in value['reldiff-2sigma']],
[x*100. for x in value['reldiff+2sigma']],
alpha=0.25, color=colors[color_index], lw=0.01)
color_index+=1
ax1.autoscale(axis='x',tight=True)
#ax1.set_ylim([-4,4])
# #PG falloff line parodi
spectcutoff = [1,8]
if ax == 'E':
ax1.set_xlim(spectcutoff)
# if ax == 'E':
# ax1.axvline(spectcutoff[0], color='#999999', ls='--')
# ax1.axvline(spectcutoff[1], color='#999999', ls='--')
plot.texax(ax1)
def plot1d(ax1, dataset):
colors = plot.colors
color_index = 0
#asume voxels are 2mm
x_axis = np.linspace(0, 10, 250)
key = 1e4
ax1.step(x_axis, [x * 100. for x in dataset],
label=plot.sn_mag(key) + ' primaries',
color=colors[color_index])
color_index += 1
ax1.semilogy()
ax1.autoscale(axis='x', tight=True)
#ax1.yaxis.set_major_formatter(mpl.ticker.FormatStrFormatter('%.0f%%'))
#PG falloff line
spectcutoff = [1, 8]
ax1.set_xlim(spectcutoff)
plot.texax(ax1)
### Make efficiency histo
tleeff = copy.deepcopy(tle3d)
aneff = copy.deepcopy(an3d)
tle.seteff(tleeff)
tle.seteff(aneff)
tle.seteffratio(tleeff,aneff[max(aneff.keys())]['var'])
f, ((tl,tr),(bl,br)) = plt.subplots(nrows=2, ncols=2, sharex=True, sharey=True)
lab1=tle.get_effhist(tl,tleeff[1e3]['var'])
lab2=tle.get_effhist(tr,tleeff[1e4]['var'])
lab3=tle.get_effhist(bl,tleeff[1e5]['var'])
lab4=tle.get_effhist(br,tleeff[1e6]['var'])
tl.set_title("TLE "+plot.sn_mag(1e3)).set_fontsize(8)
tl.text(0.05, 0.9,"Min: "+plot.sn(lab1[0])+"\nMedian: "+plot.sn(lab1[1])+"\nMax: "+plot.sn(lab1[2]), ha='left', va='center', transform=tl.transAxes, fontsize=6)
tr.set_title("TLE "+plot.sn_mag(1e4)).set_fontsize(8)
tr.text(0.05, 0.9,"Min: "+plot.sn(lab2[0])+"\nMedian: "+plot.sn(lab2[1])+"\nMax: "+plot.sn(lab2[2]), ha='left', va='center', transform=tr.transAxes, fontsize=6)
bl.set_title("TLE "+plot.sn_mag(1e5)).set_fontsize(8)
bl.text(0.05, 0.9,"Min: "+plot.sn(lab3[0])+"\nMedian: "+plot.sn(lab3[1])+"\nMax: "+plot.sn(lab3[2]), ha='left', va='center', transform=bl.transAxes, fontsize=6)
br.set_title("TLE "+plot.sn_mag(1e6)).set_fontsize(8)
br.text(0.05, 0.9,"Min: "+plot.sn(lab4[0])+"\nMedian: "+plot.sn(lab4[1])+"\nMax: "+plot.sn(lab4[2]), ha='left', va='center', transform=br.transAxes, fontsize=6)
tl.set_ylabel('Number of voxels (scaled)')
tl.yaxis.set_label_coords(-0.2, 0.)
bl.set_xlabel('Gain factor w.r.t. Reference')
bl.xaxis.set_label_coords(1.1,-0.2)
#f.savefig('eff-histo.pdf', bbox_inches='tight')
plt.close('all')
aneff = copy.deepcopy(an3d)
tle.seteff(tleeff)
tle.seteff(aneff)
tle.seteffratio(tleeff, aneff[max(aneff.keys())]['var'])
f, ((tl, tr), (bl, br)) = plt.subplots(nrows=2,
ncols=2,
sharex=True,
sharey=True)
lab1 = tle.get_effhist(tl, tleeff[1e3]['var'])
lab2 = tle.get_effhist(tr, tleeff[1e4]['var'])
lab3 = tle.get_effhist(bl, tleeff[1e5]['var'])
lab4 = tle.get_effhist(br, tleeff[1e6]['var'])
tl.set_title("TLE " + plot.sn_mag(1e3)).set_fontsize(8)
tl.text(0.05,
0.9,
"Min: " + plot.sn(lab1[0]) + "\nMedian: " + plot.sn(lab1[1]) +
"\nMax: " + plot.sn(lab1[2]),
ha='left',
va='center',
transform=tl.transAxes,
fontsize=6)
tr.set_title("TLE " + plot.sn_mag(1e4)).set_fontsize(8)
tr.text(0.05,
0.9,
"Min: " + plot.sn(lab2[0]) + "\nMedian: " + plot.sn(lab2[1]) +
"\nMax: " + plot.sn(lab2[2]),
ha='left',
va='center',