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_reluncconv(ax1,xlist,ylist,func=None):
colors = 'rk'
labels = ['vpgTLE','Analog']
get_reluncconv.color_index+=1
#set default fit 1/sqrt(x)
if func is None:
def func(x,b):#var, params
return b/np.sqrt(x)
# return a+b/np.sqrt(x)
parameter, covariance_matrix = curve_fit(func, xlist, ylist)
threshold = fsolve(lambda x: func(x,*parameter)-.02, 1e1) #initial guess at 1e3
x = np.linspace(min(xlist), max([threshold,max(xlist)]), 1e5)
y = func(x, *parameter)
#'Fit: '+plot.sn(parameter[0])+'+\n'+plot.sn(parameter[1])+'/$\sqrt{runtime}$'
fitlabel = labels[get_reluncconv.color_index]+', Fit:\n'+r'$\frac{%s}{\sqrt{t}}$'%plot.snS(parameter[0])
ax1.scatter(xlist, [100*i for i in ylist],marker='x',s=100,color=colors[get_reluncconv.color_index])
ax1.plot(x, 100*y, 'b-',label=fitlabel,color=colors[get_reluncconv.color_index])
ax1.autoscale(axis='x',tight=True)
ax1.autoscale(axis='y',tight=True)
ax1.axhline(100*0.02, color='#666666', ls='--')
ax1.axvline(threshold, color='#999999', ls='--')
plot.texax(ax1)
print "Crossing 2pc threshold at %.2e"%threshold
return threshold
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 plotrange(ax1,ct):
x = ct['ct']['x']
y = ct['ct']['av']
um = ct['ct']['uncm']
up = ct['ct']['uncp']
ymax = max(y)
if len(um)>0:
ymax = max(ymax,max(up))
plot.fill_between_steps(ax1, x, um, up, alpha=0.2, color='steelblue', lw=0.5,clip_on=False) #doesnt support where=mid
else:
ax1.step(x, y, label=ct['name']+'ct', color='steelblue', lw=1, where='mid',clip_on=False)
#ax1.step(x, y, label=ct['name']+'ct', color='steelblue', lw=1, where='mid')
x = ct['rpct']['x']
y = ct['rpct']['av']
um = ct['rpct']['uncm']
up = ct['rpct']['uncp']
ymax = max(ymax,max(y))
if len(um)>0:
ymax = max(ymax,max(up))
plot.fill_between_steps(ax1, x, um, up, alpha=0.2, color='indianred', lw=0.5,clip_on=False)
else:
ax1.step(x, y, label=ct['name']+'rpct', color='indianred', lw=1, where='mid',clip_on=False)
#ax1.step(x, y, label=ct['name']+'rpct', color='indianred', lw=1, where='mid')
ax1.set_xlabel('Depth [mm]', fontsize=FONTSIZE)
ax1.set_ylabel('PG detected [counts]', fontsize=FONTSIZE)
ax1.set_ylim(bottom=0,top=ymax)
#ax1.set_title('PG detection for '+plot.sn_mag(ct['nprim'])+'primaries', fontsize=FONTSIZE)
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_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_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_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 get_reluncconv(ax1, xlist, ylist, func=None):
colors = 'rk'
labels = ['vpgTLE', 'Analog']
get_reluncconv.color_index += 1
#set default fit 1/sqrt(x)
if func is None:
def func(x, b): #var, params
return b / np.sqrt(x)
# return a+b/np.sqrt(x)
parameter, covariance_matrix = curve_fit(func, xlist, ylist)
threshold = fsolve(lambda x: func(x, *parameter) - .02,
1e1) #initial guess at 1e3
x = np.linspace(min(xlist), max([threshold, max(xlist)]), 1e5)
y = func(x, *parameter)
#'Fit: '+plot.sn(parameter[0])+'+\n'+plot.sn(parameter[1])+'/$\sqrt{runtime}$'
fitlabel = labels[
get_reluncconv.
color_index] + ', Fit:\n' + r'$\frac{%s}{\sqrt{t}}$' % plot.snS(
parameter[0])
ax1.scatter(xlist, [100 * i for i in ylist],
marker='x',
s=100,
color=colors[get_reluncconv.color_index])
ax1.plot(x,
100 * y,
'b-',
label=fitlabel,
color=colors[get_reluncconv.color_index])
ax1.autoscale(axis='x', tight=True)
ax1.autoscale(axis='y', tight=True)
ax1.axhline(100 * 0.02, color='#666666', ls='--')
ax1.axvline(threshold, color='#999999', ls='--')
plot.texax(ax1)
print "Crossing 2pc threshold at %.2e" % threshold
return threshold
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 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_2(ax1,ct,firstcolor='steelblue',secondcolor='indianred'):
yield_ct=0
for i in range(len(ct['ct']['data'])):
yield_ct+=sum(ct['ct']['data'][i])
x,y,fo=ct['ct']['x'],ct['ct']['data'][i],ct['ct']['falloff'][i]
ax1.step(x, y, label=ct['name']+'ct', color=firstcolor, lw=1, where='mid',clip_on=True, alpha=0.5)
ax1.axvline(fo, color=firstcolor, ls='-',lw=1, alpha=0.5)
yield_ct = yield_ct/len(ct['ct']['data']) #per realisatie
if secondcolor is not None:
yield_rpct=0
for i in range(len(ct['rpct']['data'])):
yield_rpct+=sum(ct['rpct']['data'][i])
x,y,fo=ct['rpct']['x'],ct['rpct']['data'][i],ct['rpct']['falloff'][i]
ax1.step(x, y, label=ct['name']+'ct', color=secondcolor, lw=1, where='mid',alpha=0.5, clip_on=True)
ax1.axvline(fo, color=secondcolor, ls='-',lw=1, alpha=0.5)
yield_rpct = yield_rpct/len(ct['rpct']['data']) #per realisatie
plot.texax(ax1)
return (yield_ct+yield_rpct)/(2.*ct['nprim'])
return yield_ct/ct['nprim']
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 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)]
#a = ax.pcolormesh(xbins, ybins, counts.T,cmap='Greys',norm=matplotlib.colors.LogNorm(), vmin=0.7)
ax.set_xlim(min(xbins),max(xbins))
ax.set_ylim(min(spot[1]),max(spot[1])*1.05)
ax.semilogy()
except Exception as e:
print str(e)
pass
if first:
ax.set_ylabel('Spots\nNr. protons')
first=False
#ax.set_xlabel('$E$ [MeV]')
plot.texax(ax)
ax.set_title('Field '+str(fieldnr+1)+"\n"+str(len(spot[0]))+" spots\n"+str(len(set(spot[0])))+" layers")
first = True
for layer,ax in zip(layloop,layaxes): #1 is tweede rij
try:
xbins = plot.bincenters_to_binedges(sorted(layer[0]))
ax.hist(layer[0],bins=xbins,weights=layer[1],bottom=min(layer[1])/100.,histtype='bar',color='grey',linewidth=0.2)
new_layer_min_y = min(layer[1])/1.5
new_layer_max_y = max(layer[1])*1.5
if new_layer_min_y < layer_min_y:
layer_min_y=new_layer_min_y
ax.set_ylim(ymin=layer_min_y)
if new_layer_max_y > layer_max_y:
layer_max_y=new_layer_max_y
#dose3dCT.imdata = dose3dCT.imdata.astype(np.float32,copy=False)
#dose3dCT.saveas('TEST')
rmse.append(np.sqrt(np.mean(np.square(spot*mask))))
#rmse.append(np.sqrt(np.mean(np.square(spot))))
nprim.append(newspots[i][4])
E.append(newspots[i][1])
Eh, Ehbin = np.histogram(E,bins=30)
nprimh, nprimhbin = plot.getloghist(nprim,30)
f, ((ax1,ax2),(ax3,ax4)) = plot.subplots(nrows=2, ncols=2, sharex=False, sharey=False)
ax1.set_title('Dose Diff (nprim)')
ax1.set_xlabel('Number of Primaries')
ax1.set_ylabel('Dose Diff [RMSE]')
plot.texax(ax1)
ax2.set_title('Dose Diff (E)')
ax2.set_xlabel('Energy [MeV]')
ax2.set_ylabel('Dose Diff [RMSE]')
plot.texax(ax2)
ax3.set_title('Dose Diff (nprim)')
ax3.set_xlabel('Number of Primaries')
ax3.set_ylabel('Dose Diff [RMSE]')
plot.texax(ax3)
ax4.set_title('Dose Diff (E)')
ax4.set_xlabel('Energy [MeV]')
ax4.set_ylabel('Dose Diff [RMSE]')
plot.texax(ax4)
def get_fop_fow_contrast(x,y,**kwargs):
plotten = False
plottenname = ''
label=''
threshold = 0.5
ax1 = ''
smooth=2
fitlines=False
globmax=False
filename = ''
if 'filename' in kwargs:
filename = kwargs['filename']
if 'plot' in kwargs:
plotten = True
fitlines = True
plottenname = str(kwargs['plot'])
import plot
if 'threshold' in kwargs:
threshold = float(kwargs['threshold'])
if 'globmax' in kwargs:
globmax = float(kwargs['globmax'])
if 'width' in kwargs:
FOW = True
if 'ax' in kwargs:
if plotten == True:
ax1 = kwargs['ax']
else:
return
if 'ax2' in kwargs:
if plotten == True:
ax2 = kwargs['ax2']
else:
return
if 'ax3' in kwargs:
if plotten == True:
ax3 = kwargs['ax3']
else:
return
if 'smooth' in kwargs:
smooth = kwargs['smooth']
if 'label' in kwargs:
label = kwargs['label']
if 'fitlines' in kwargs:
fitlines = kwargs['fitlines']
if 'nprim' in kwargs:
nprim = kwargs['nprim']
if 'contrast_divisor' in kwargs:
contrast_divisor = kwargs['contrast_divisor']
y=np.array(y)
# if plotten: import plot
# if plotten: f, ax1 = plot.subplots(nrows=1, ncols=1, sharex=False, sharey=False)
if plotten: ax1.scatter( x,y, color='black',marker="x",clip_on=False) #bindata
#smooth that shit out.
#https://en.wikipedia.org/wiki/Smoothing_spline
y2 = scipy.signal.cspline1d(y,lamb=smooth)
y_intpol_f = scipy.interpolate.interp1d(x,y2,kind='cubic') #interpolate function
x_intpol = np.linspace(x[0],x[-1],2048,endpoint=False)
y_intpol = y_intpol_f(x_intpol) #interpolate for x_intpol
y_intpol = y_intpol.clip(min=0) #set any possible negatives to zero
####################################################################### CURVE IS COMPLETE, NOW ANALYSIS
#if plotten: ax1.step(x,y2,color='steelblue', where='mid')
if plotten: ax1.plot(x_intpol,y_intpol,color='indianred')
#get 25 percentile, and take mean as baseline.
baseline = y_intpol[y_intpol < np.percentile(y_intpol, 25)].mean()
maxind=0
if globmax:
maxind = np.where(y_intpol == y_intpol.max())[0][-1] #so that we find the LAST index where argmax(y)
else:
#global max
globmaxind = np.where(y_intpol == y_intpol.max())[0][-1]
globfalloff = y_intpol[globmaxind]-baseline
#find distal local max within 30% of FO
maxind = np.sort( scipy.signal.argrelextrema(y_intpol, np.greater)[0] )[::-1]
for locmac in maxind[::-1]: #search from back
if y_intpol[locmac] > (baseline+globfalloff*0.3):
maxind = locmac
#if fitlines: ax1.axhline(baseline+globfalloff*0.3,color='green')
falloff = y_intpol[maxind]-baseline
falloff_index=len(x_intpol)-1
print falloff
try:
while y_intpol[falloff_index] < threshold*falloff+baseline: #default threshold is 0.5
falloff_index-=1
except ValueError:
print y_intpol[falloff_index] < threshold*falloff+baseline
print y_intpol[falloff_index]
print threshold
print falloff
print baseline
#contrast = (y_intpol[maxind]-baseline)/(y_intpol[maxind]+baseline)
contrast = y_intpol[maxind]-baseline/contrast_divisor
falloff_pos=x_intpol[falloff_index]
if fitlines: ax1.axvline(falloff_pos,color='green')
if fitlines: ax1.axhline(baseline,color='green')
if fitlines: ax1.axhline(y_intpol[maxind],color='green')
if fitlines: ax1.axvline(x_intpol[maxind],color='green')
if plotten: plot.texax(ax1)
if plotten: ax1.set_xlabel('Position [mm]', fontsize=8)
if plotten: ax1.set_ylabel('PG yield [counts]', fontsize=8)
if plotten: from matplotlib.ticker import MaxNLocator
if plotten: ax1.yaxis.set_major_locator(MaxNLocator(integer=True))
# if plotten: ax1.set_ylim(bottom=0)
if plotten: ax1.annotate('FOP: '+str(falloff_pos)[:4],xy=(falloff_pos,falloff/2.+baseline))
if plotten: ax1.annotate('Contrast: '+str(contrast)[:4],xy=(x_intpol[maxind],y_intpol[maxind]))
if plotten: print 'FOP is',falloff_pos
if plotten: print 'Contrast is',contrast
################################################# FOW
# first diff:
y_intpol_diff = np.diff(y_intpol)
# second diff:
y_intpol_diff2 = np.diff(y_intpol_diff)
# find inflex in second diff and plot
# print "len y_intpol_diff2:", len(y_intpol_diff2)
if plotten: ax3.plot(x_intpol[:-2],y_intpol_diff2,color='darkseagreen')
diff2_zero_index = falloff_index
while y_intpol_diff2[diff2_zero_index]*y_intpol_diff2[diff2_zero_index-1] >=0 or y_intpol_diff2[diff2_zero_index] > y_intpol_diff2[diff2_zero_index-1]:
#zolang product positief, dan geen kruising van x as
#zolang positieve kruising van xas ga dan verder
diff2_zero_index+=1
if fitlines: ax3.axvline(x_intpol[diff2_zero_index],color='green')
if fitlines: ax3.annotate('Inflex: '+str(x_intpol[diff2_zero_index])[:4],xy=(x_intpol[diff2_zero_index],0 ) )
# use inflex to set interval for gauss fit in first diff
if plotten: ax2.plot(x_intpol[:-1],y_intpol_diff,color='steelblue')
# fit_interval = x_intpol[maxind : falloff_index+(falloff_index-maxind)]
fit_interval = x_intpol[maxind : diff2_zero_index]
fit_points = y_intpol_diff[maxind : diff2_zero_index]
#offset = y_intpol_diff[diff2_zero_index]
offset = 0 #actually, lets remove this param
if plotten: ax2.plot(fit_interval,fit_points,color='steelblue')
try:
popt,y_fitted = fitgauss_tuned(fit_interval,fit_points,falloff_pos,offset)
except RuntimeError as e:
raise RuntimeError("in scipy.optimize.curve_fit for filename: "+filename+'. '+e)
#except ValueError as e:
#raise ValueError("in scipy.optimize.curve_fit for filename: "+filename+'.')
def guassianfunc(xVar, a, b, c):
return a * np.exp(-(xVar - b) ** 2 / (2 * c ** 2)) + offset
if plotten: ax2.plot(x_intpol[:-1], guassianfunc(x_intpol[:-1], *popt) ,color='grey',linestyle='--') #full function over full window
if plotten: ax2.plot(fit_interval,y_fitted,color='black')
g_fwhm = abs(popt[2]*2.35482) #can be neg?
g_center = popt[1]#np.argmin(y_fitted)
# print 'FWHM = ',g_fwhm
# stride = np.diff(fit_interval)[0]
# fwhm_ind_l = fit_interval[ g_center - int( (g_fwhm*0.5)/stride) ]
# fwhm_ind_r = fit_interval[ g_center + int( (g_fwhm*0.5)/stride) ]
fwhm_ind_l = g_center - (g_fwhm*0.5)
fwhm_ind_r = g_center + (g_fwhm*0.5)
# print g_center,g_fwhm,fwhm_ind_l, fwhm_ind_r
if fitlines: ax2.axvline(fwhm_ind_l,color='green')
if fitlines: ax2.axvline(fwhm_ind_r,color='green')
if fitlines: ax2.annotate('FOW: '+str(g_fwhm)[:4],xy=(fwhm_ind_r,min(y_fitted)/2.) )#,xycoords='axes points')
if plotten: print 'FOW is',g_fwhm
return falloff_pos,g_fwhm,contrast
def get_fop(x,y,**kwargs):
plotten = False
plottenname = ''
label=''
threshold = 0.5
ax1 = ''
smooth=True
fitlines=False
globmax=False
if 'plot' in kwargs:
plotten = True
fitlines = True
plottenname = str(kwargs['plot'])
import plot
if 'threshold' in kwargs:
threshold = float(kwargs['threshold'])
if 'globmax' in kwargs:
globmax = float(kwargs['globmax'])
if 'ax' in kwargs:
if plotten == True:
ax1 = kwargs['ax']
else:
return
if 'smooth' in kwargs:
smooth = kwargs['smooth']
if 'label' in kwargs:
label = kwargs['label']
if 'fitlines' in kwargs:
fitlines = kwargs['fitlines']
y=np.array(y)
#if plotten: import plot
#if plotten: f, ax1 = plot.subplots(nrows=1, ncols=1, sharex=False, sharey=False)
if plotten: ax1.scatter( x,y, color='black',marker="x",clip_on=False) #bindata
#smooth that shit out.
#https://en.wikipedia.org/wiki/Smoothing_spline
y2 = scipy.signal.cspline1d(y,lamb=2)
y_intpol_f = scipy.interpolate.interp1d(x,y2,kind='cubic') #interpolate function
x_intpol = np.linspace(x[0],x[-1],2048,endpoint=False)
y_intpol = y_intpol_f(x_intpol) #interpolate for x_intpol
y_intpol = y_intpol.clip(min=0) #set any possible negatives to zero
#if plotten: ax1.step(x,y2,color='steelblue', where='mid')
if plotten: ax1.plot(x_intpol,y_intpol,color='indianred')
#get 25 percentile, and take mean as baseline.
baseline = y_intpol[y_intpol < np.percentile(y_intpol, 25)].mean()
maxind=0
if globmax:
maxind = np.where(y_intpol == y_intpol.max())[0][-1] #so that we find the LAST index where argmax(y)
else:
#global max
globmaxind = np.where(y_intpol == y_intpol.max())[0][-1]
globfalloff = y_intpol[globmaxind]-baseline
#find distal local max within 30% of FO
maxind = np.sort( scipy.signal.argrelextrema(y_intpol, np.greater)[0] )[::-1]
for locmac in maxind[::-1]: #search from back
if y_intpol[locmac] > (baseline+globfalloff*0.3):
maxind = locmac
#if fitlines: ax1.axhline(baseline+globfalloff*0.3,color='green')
falloff = y_intpol[maxind]-baseline
falloff_index=len(x_intpol)-1
#print falloff
try:
while y_intpol[falloff_index] < threshold*falloff+baseline: #default threshold is 0.5
falloff_index-=1
except ValueError:
print y_intpol[falloff_index] < threshold*falloff+baseline
print y_intpol[falloff_index]
print threshold
print falloff
print baseline
falloff_pos=x_intpol[falloff_index]
if fitlines: ax1.axvline(falloff_pos,color='green')
if fitlines: ax1.axhline(baseline,color='green')
if fitlines: ax1.axhline(y_intpol[maxind],color='green')
if fitlines: ax1.axvline(x_intpol[maxind],color='green')
if plotten: plot.texax(ax1)
if plotten: ax1.set_xlabel('Position [mm]', fontsize=8)
if plotten: ax1.set_ylabel('PG yield [counts]', fontsize=8)
if plotten: from matplotlib.ticker import MaxNLocator
if plotten: ax1.yaxis.set_major_locator(MaxNLocator(integer=True))
if plotten: ax1.set_ylim(bottom=0)
#if plotten: ax1.set_title('FOP = '+str(falloff_pos), fontsize=8)
#if plotten: f.savefig(plottenname, bbox_inches='tight')
#if plotten: print 'Plotted PG profile to',plottenname
#if plotten: plot.close('all')
if plotten: print 'FOP is',falloff_pos
#if plotten: quit()
return falloff_pos
rects1 = barax1.bar(ind, full[:3] + [0], width, color='red')
rects2 = barax1.bar(ind + width, [0] + full[3:], width, color='blue')
rects3 = barax2.bar(ind, layer[:3] + [0], width, color='red')
rects4 = barax2.bar(ind + width, [0] + layer[3:], width, color='blue')
rects5 = barax3.bar(ind, spot[:3] + [0], width, color='red')
rects6 = barax3.bar(ind + width, [0] + spot[3:], width, color='blue')
# axes and labels
barax1.set_xlim(-width, len(ind) + width)
barax1.set_ylabel('Shift [mm]')
barax1.set_title('Full field')
xTickMarks = labelbar
barax1.set_xticks(ind + width)
xtickNames = barax1.set_xticklabels(xTickMarks)
plt.setp(xtickNames, rotation=45, fontsize=10)
plot.texax(barax1)
# axes and labels2
barax2.set_xlim(-width, len(ind) + width)
barax2.set_ylabel('Shift [mm]')
barax2.set_title('Layer')
xTickMarks = labelbar
barax2.set_xticks(ind + width)
xtickNames = barax2.set_xticklabels(xTickMarks)
plt.setp(xtickNames, rotation=45, fontsize=10)
plot.texax(barax2)
barax3.set_xlim(-width, len(ind) + width)
barax3.set_ylabel('Shift [mm]')
barax3.set_title('Spot')
xTickMarks = labelbar
y,
color='indianred',
lw=1.,
label='Spot count',
where='mid')
dose, = ax1.step(ct_x,
ct1d / ct1d.max() * float(y.max()),
color='steelblue',
lw=1.,
label='Integral dose (scaled)',
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)
ax2 = ax1.twinx()
ax2.semilogy()
geo_prot, = ax2.step(geo_x,
y_msw,
color='forestgreen',
lw=1.,
label='Protons',
where='mid')
ax2.set_xlim(-58, 18)
ax2.set_ylim(5e7, 5e9)
ax2.set_ylabel('Number of Protons')
plot.texax(ax2, 'twinx')
#ax1.axvline(falloffs[61],color='green') #plot FOP of spot 61
im = image.image(doseimage)
ct = im.imdata.reshape(im.imdata.shape[::-1]).squeeze()
ct1d=np.add.reduce(ct)
ct_xhist = np.linspace(-150,150,301) #2mm voxels, endpoints
ct_x = np.linspace(-149.5,149.5,300) #bincenters
f, ax1 = plot.subplots(nrows=1, ncols=1, sharex=False, sharey=False)
geo_spot, = ax1.step(geo_x,y, color='indianred',lw=1., label='Spot count', where='mid')
dose, = ax1.step(ct_x,ct1d/ct1d.max()*float(y.max()), color='steelblue',lw=1., label='Integral dose (scaled)', 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)
ax2=ax1.twinx()
ax2.semilogy()
geo_prot, = ax2.step(geo_x,y_msw, color='forestgreen',lw=1., label='Protons', where='mid')
ax2.set_xlim(-58,18)
ax2.set_ylim(5e7,5e9)
ax2.set_ylabel('Number of Protons')
plot.texax(ax2,'twinx')
#ax1.axvline(falloffs[61],color='green') #plot FOP of spot 61
f.legend([geo_spot,dose,geo_prot],['Spot count','Dose (scaled)','Proton count'],frameon = False)#,fancybox = True,ncol = 1,fontsize = 'x-small',loc = 'upper right')
f.savefig('rtplan.geolayers.pdf', bbox_inches='tight')
plot.close('all')
def get_fop_fow_contrast(x,y,**kwargs):
plotten = False
plottenname = ''
label=''
threshold = 0.5
ax1 = ''
smooth=2
fitlines=False
globmax=False
filename = ''
nokillax3=True
killfirst=False
if 'killfirst' in kwargs:
killfirst=True
if 'filename' in kwargs:
filename = kwargs['filename']
if 'plot' in kwargs:
plotten = True
fitlines = True
plottenname = str(kwargs['plot'])
import plot
if 'threshold' in kwargs:
threshold = float(kwargs['threshold'])
if 'globmax' in kwargs:
globmax = float(kwargs['globmax'])
if 'width' in kwargs:
FOW = True
if 'ax' in kwargs:
if plotten == True:
ax1 = kwargs['ax']
else:
return
if 'ax2' in kwargs:
if plotten == True:
ax2 = kwargs['ax2']
else:
return
if 'ax3' in kwargs:
if plotten == True:
ax3 = kwargs['ax3']
else:
nokillax3 = False
else:
nokillax3 = False
if 'smooth' in kwargs:
smooth = kwargs['smooth']
if 'label' in kwargs:
label = kwargs['label']
if 'fitlines' in kwargs:
fitlines = kwargs['fitlines']
if 'nprim' in kwargs:
nprim = kwargs['nprim']
if 'contrast_divisor' in kwargs:
contrast_divisor = kwargs['contrast_divisor']
y=np.array(y)
# if plotten: import plot
# if plotten: f, ax1 = plot.subplots(nrows=1, ncols=1, sharex=False, sharey=False)
if plotten:
if killfirst:
ax1.scatter( x[1:],y[1:], color='black',marker="x",clip_on=False) #bindata
else:
ax1.scatter( x,y, color='black',marker="x",clip_on=False) #bindata
#smooth that shit out.
#https://en.wikipedia.org/wiki/Smoothing_spline
y2 = scipy.signal.cspline1d(y,lamb=smooth)
y_intpol_f = scipy.interpolate.interp1d(x,y2,kind='cubic') #interpolate function
x_intpol = np.linspace(x[0],x[-1],2048,endpoint=False)
y_intpol = y_intpol_f(x_intpol) #interpolate for x_intpol
y_intpol = y_intpol.clip(min=0) #set any possible negatives to zero
####################################################################### CURVE IS COMPLETE, NOW ANALYSIS
#if plotten: ax1.step(x,y2,color='steelblue', where='mid')
if plotten: ax1.plot(x_intpol,y_intpol,color='indianred')
#get 25 percentile, and take mean as baseline.
baseline = y_intpol[y_intpol < np.percentile(y_intpol, 25)].mean()
maxind=0
if globmax:
maxind = np.where(y_intpol == y_intpol.max())[0][-1] #so that we find the LAST index where argmax(y)
else:
#global max
globmaxind = np.where(y_intpol == y_intpol.max())[0][-1]
globfalloff = y_intpol[globmaxind]-baseline
#find distal local max within 30% of FO
maxind = np.sort( scipy.signal.argrelextrema(y_intpol, np.greater)[0] )[::-1]
for locmac in maxind[::-1]: #search from back
if y_intpol[locmac] > (baseline+globfalloff*0.3):
maxind = locmac
#if fitlines: ax1.axhline(baseline+globfalloff*0.3,color='green')
falloff = y_intpol[maxind]-baseline
falloff_index=len(x_intpol)-1
print falloff
try:
while y_intpol[falloff_index] < threshold*falloff+baseline: #default threshold is 0.5
falloff_index-=1
except ValueError:
print y_intpol[falloff_index] < threshold*falloff+baseline
print y_intpol[falloff_index]
print threshold
print falloff
print baseline
#contrast = (y_intpol[maxind]-baseline)/(y_intpol[maxind]+baseline)
contrast = (y_intpol[maxind]-baseline)/contrast_divisor
falloff_pos=x_intpol[falloff_index]
if fitlines: ax1.axvline(falloff_pos,color='green')
if fitlines: ax1.axhline(baseline,color='green')
if fitlines: ax1.axhline(y_intpol[maxind],color='green')
if fitlines: ax1.axvline(x_intpol[maxind],color='green')
if plotten: plot.texax(ax1)
if plotten: ax1.set_ylim(bottom=0)
if plotten: ax1.set_xlabel('Position [mm]', fontsize=8)
if plotten: ax1.set_ylabel('PG yield [counts]', fontsize=8)
if plotten: from matplotlib.ticker import MaxNLocator
if plotten: ax1.yaxis.set_major_locator(MaxNLocator(integer=True))
# if plotten: ax1.set_ylim(bottom=0)
if fitlines: ax1.annotate('FOP: '+str(falloff_pos)[:4],xy=(falloff_pos,falloff/2.+baseline))
if fitlines: ax1.annotate('Contrast: '+str(contrast)[:4],xy=(x_intpol[maxind],y_intpol[maxind]))
if plotten: print 'FOP is',falloff_pos
if plotten: print 'Contrast is',contrast
################################################# FOW
# first diff:
y_intpol_diff = np.diff(y_intpol)
# second diff:
y_intpol_diff2 = np.diff(y_intpol_diff)
# find inflex in second diff and plot
# print "len y_intpol_diff2:", len(y_intpol_diff2)
if nokillax3: ax3.plot(x_intpol[:-2],y_intpol_diff2,color='darkseagreen')
diff2_zero_index = falloff_index
try:
while y_intpol_diff2[diff2_zero_index]*y_intpol_diff2[diff2_zero_index-1] >=0 or y_intpol_diff2[diff2_zero_index] > y_intpol_diff2[diff2_zero_index-1]:
#zolang product positief, dan geen kruising van x as
#zolang positieve kruising van xas ga dan verder
diff2_zero_index+=1
except IndexError:
diff2_zero_index-=1 #reached end
if nokillax3: ax3.axvline(x_intpol[diff2_zero_index],color='green')
if nokillax3: ax3.annotate('Inflex: '+str(x_intpol[diff2_zero_index])[:4],xy=(x_intpol[diff2_zero_index],0 ) )
# use inflex to set interval for gauss fit in first diff
if plotten: ax2.plot(x_intpol[:-1],y_intpol_diff,color='steelblue')
# fit_interval = x_intpol[maxind : falloff_index+(falloff_index-maxind)]
fit_interval = x_intpol[maxind : diff2_zero_index]
fit_points = y_intpol_diff[maxind : diff2_zero_index]
#offset = y_intpol_diff[diff2_zero_index]
offset = 0 #actually, lets remove this param
if plotten: ax2.plot(fit_interval,fit_points,color='steelblue')
try:
popt,y_fitted = fitgauss_tuned(fit_interval,fit_points,falloff_pos,offset)
except RuntimeError as e:
print(kleur.fail("[auger/__init__.py]")+" RuntimeError in scipy.optimize.curve_fit for filename: "+filename+'. '+str(e))
raise e
except ValueError as e:
print(kleur.fail("[auger/__init__.py]")+" ValueError in scipy.optimize.curve_fit for filename: "+filename+'. '+str(e))
raise e
def guassianfunc(xVar, a, b, c):
return a * np.exp(-(xVar - b) ** 2 / (2 * c ** 2)) + offset
if plotten: ax2.plot(x_intpol[:-1], guassianfunc(x_intpol[:-1], *popt) ,color='grey',linestyle='--') #full function over full window
if plotten: ax2.plot(fit_interval,y_fitted,color='black')
if plotten: ax2.set_xlabel('Position [mm]', fontsize=8)
if not nokillax3: ax2.set_ylabel('First Derivative', fontsize=8)
g_fwhm = abs(popt[2]*2.35482) #can be neg?
g_center = popt[1]#np.argmin(y_fitted)
# print 'FWHM = ',g_fwhm
# stride = np.diff(fit_interval)[0]
# fwhm_ind_l = fit_interval[ g_center - int( (g_fwhm*0.5)/stride) ]
# fwhm_ind_r = fit_interval[ g_center + int( (g_fwhm*0.5)/stride) ]
fwhm_ind_l = g_center - (g_fwhm*0.5)
fwhm_ind_r = g_center + (g_fwhm*0.5)
# print g_center,g_fwhm,fwhm_ind_l, fwhm_ind_r
if fitlines: ax2.axvline(fwhm_ind_l,color='green')
if fitlines: ax2.axvline(fwhm_ind_r,color='green')
if fitlines: ax2.annotate('FOW: '+str(g_fwhm)[:4],xy=(fwhm_ind_r,min(y_fitted)/2.) )#,xycoords='axes points')
if plotten: print 'FOW is',g_fwhm
return falloff_pos,g_fwhm,contrast
dosebar.append(dose_shift)
ipnlbar.append(ipnl_shift)
ibabar.append(iba_shift)
dosebar2.append(dose_corr_af)#-dose_corr_bef)
ipnlbar2.append(ipnl_corr_af)#-ipnl_corr_bef)
ibabar2.append(iba_corr_af)#-iba_corr_bef)
labelbar.append(key+", CMU:"+value['CMU'])
plotter(pl,value['orig'],'dose','orig')
plotter(pl,value['replan'],'dose',formatlabel(dose_shift,dose_corr_bef,dose_corr_af))
plotter(pm,value['orig'],'ipnl','orig')
plotter(pm,value['replan'],'ipnl',formatlabel(ipnl_shift,ipnl_corr_bef,ipnl_corr_af))
plotter(pr,value['orig'],'iba','orig')
plotter(pr,value['replan'],'iba',formatlabel(iba_shift,iba_corr_bef,iba_corr_af))
plot.texax(pl)
plot.texax(pm)
plot.texax(pr)
pl.set_title("Dose")
pm.set_title("PG IPNL")
pr.set_title("PG IBA")
# plo.set_ylim([1e-1,1.1e2])
pl.set_ylabel('Dose [gy], PG Yield [counts]')
pl.set_xlabel('Depth [mm]')
#plo.xaxis.set_label_coords(1.1,-0.1)
lgd = pl.legend(loc='upper right', bbox_to_anchor=(1.4, 1.),frameon=False)
[label.set_linewidth(1) for label in lgd.get_lines()]
lgd = pm.legend(loc='upper right', bbox_to_anchor=(1.4, 1.),frameon=False)
[label.set_linewidth(1) for label in lgd.get_lines()]
ax.step(xax,emit, color=color,lw=1., alpha=1, label=name+', yield: '+yld(emit), where='mid')
#ax.step(xax,dete, color=color,lw=1., alpha=0.5, label=name+' PSF', where='mid')
return ax
###########################################################################################################
f, (ax1,ax2) = plot.subplots(nrows=1, ncols=2, sharex=True, sharey=False)
plotprof(ax1,pgsrc_ct_x,pgelay.imdata,detprof_pgelay,'CT')
plotprof(ax1,pgsrc_ct_x,rppgelay.imdata,detprof_rppgelay,'RPCT')
ax1.set_title('Iso-energy layer, PG shift: '+str(rppgelay_fo-pgelay_fo)[:4]+' mm', fontsize=10)
ax1.legend(frameon = False,loc='upper left')
ax1.set_xlim(-80,60)
ax1.set_ylim(0,0.003)
ax1.set_ylabel('Cumulative PG emission per proton')
plot.texax(ax1)
plotprof(ax2,pgsrc_ct_x,pggeolay.imdata,detprof_pggeolay,'CT')
plotprof(ax2,pgsrc_ct_x,rppggeolay.imdata,detprof_rppggeolay,'RPCT')
ax2.set_title('Iso-depth layer, PG shift: '+str(rppggeolay_fo-pggeolay_fo)[:4]+' mm', fontsize=10)
ax2.legend(frameon = False,loc='upper left')
#ax2.set_xlim(-80,70)
ax2.set_ylim(0,0.003)
ax2.set_xlabel('Position [mm]')
ax2.xaxis.set_label_coords(-0.1, -0.1)
plot.texax(ax2)
######## TopRow
#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')
f, (tl,tr) = plt.subplots(nrows=1, ncols=2, sharex=True, sharey=True)
# tleeff[1e6]['var'].saveas('speedup.mhd')
tl.imshow(speedim,interpolation='nearest',origin='lower',norm = mpl.colors.LogNorm(),cmap="hot",clim=[1e2,1e4])
pica=tr.imshow(speedim,interpolation='nearest',origin='lower',norm = mpl.colors.LogNorm(),cmap="hot",clim=[1e2,1e4])
tr.imshow(ctim,interpolation='nearest', origin='lower',cmap="gray",alpha=0.5)
tl.set_ylim([5,75])
tr.set_ylim([5,75])
tl.set_xlim([5,30])
tr.set_xlim([5,30])
tr.yaxis.set_visible(False)
tl.set_ylabel('Depth [mm]')
tl.set_xlabel('Width [mm]')
tr.set_xlabel('Width [mm]')
tl.set_title(r'Gain vpgTLE $10^6$'+'\n w.r.t. Reference')
tr.set_title('CT Image')
plot.texax(tl)
plot.texax(tr)
f.colorbar(pica)#, ticks=[1e2, 1e3, 1e4])
# f.colorbar(tl,shrink=.92)
f.savefig('slice1.pdf', bbox_inches='tight')
f.savefig('slice1.png', bbox_inches='tight',dpi=300)
plt.close('all')
### Speedup slice + material slice
speedim = tleeff[1e6]['var'].getslice('x',14)
ctim = patient.getslice('x',14)
#ctim[ctim == 0] = np.nan
f, (tl,tr) = plt.subplots(nrows=2, ncols=1, sharex=True, sharey=True)
#rmse.append(np.sqrt(np.mean(np.square(spot))))
nprim.append(newspots[i][4])
E.append(newspots[i][1])
Eh, Ehbin = np.histogram(E, bins=30)
nprimh, nprimhbin = plot.getloghist(nprim, 30)
f, ((ax1, ax2), (ax3, ax4)) = plot.subplots(nrows=2,
ncols=2,
sharex=False,
sharey=False)
ax1.set_title('Dose Diff (nprim)')
ax1.set_xlabel('Number of Primaries')
ax1.set_ylabel('Dose Diff [RMSE]')
plot.texax(ax1)
ax2.set_title('Dose Diff (E)')
ax2.set_xlabel('Energy [MeV]')
ax2.set_ylabel('Dose Diff [RMSE]')
plot.texax(ax2)
ax3.set_title('Dose Diff (nprim)')
ax3.set_xlabel('Number of Primaries')
ax3.set_ylabel('Dose Diff [RMSE]')
plot.texax(ax3)
ax4.set_title('Dose Diff (E)')
ax4.set_xlabel('Energy [MeV]')
ax4.set_ylabel('Dose Diff [RMSE]')
plot.texax(ax4)
def get_fop(x,y,**kwargs):
plotten = False
plottenname = ''
label=''
threshold = 0.5
ax1 = ''
smooth=True
fitlines=False
globmax=False
if 'plot' in kwargs:
plotten = True
fitlines = True
plottenname = str(kwargs['plot'])
import plot
if 'threshold' in kwargs:
threshold = float(kwargs['threshold'])
if 'globmax' in kwargs:
globmax = float(kwargs['globmax'])
if 'ax' in kwargs:
if plotten == True:
ax1 = kwargs['ax']
else:
return
if 'smooth' in kwargs:
smooth = kwargs['smooth']
if 'label' in kwargs:
label = kwargs['label']
if 'fitlines' in kwargs:
fitlines = kwargs['fitlines']
y=np.array(y)
#if plotten: import plot
#if plotten: f, ax1 = plot.subplots(nrows=1, ncols=1, sharex=False, sharey=False)
if plotten: ax1.scatter( x,y, color='black',marker="x",clip_on=False) #bindata
#smooth that shit out.
#https://en.wikipedia.org/wiki/Smoothing_spline
y2 = scipy.signal.cspline1d(y,lamb=2)
y_intpol_f = scipy.interpolate.interp1d(x,y2,kind='cubic') #interpolate function
x_intpol = np.linspace(x[0],x[-1],2048,endpoint=False)
y_intpol = y_intpol_f(x_intpol) #interpolate for x_intpol
y_intpol = y_intpol.clip(min=0) #set any possible negatives to zero
#if plotten: ax1.step(x,y2,color='steelblue', where='mid')
if plotten: ax1.plot(x_intpol,y_intpol,color='indianred')
#get 25 percentile, and take mean as baseline.
baseline = y_intpol[y_intpol < np.percentile(y_intpol, 25)].mean()
maxind=0
if globmax:
maxind = np.where(y_intpol == y_intpol.max())[0][-1] #so that we find the LAST index where argmax(y)
else:
#global max
globmaxind = np.where(y_intpol == y_intpol.max())[0][-1]
globfalloff = y_intpol[globmaxind]-baseline
#find distal local max within 30% of FO
maxind = np.sort( scipy.signal.argrelextrema(y_intpol, np.greater)[0] )[::-1]
for locmac in maxind[::-1]: #search from back
if y_intpol[locmac] > (baseline+globfalloff*0.3):
maxind = locmac
#if fitlines: ax1.axhline(baseline+globfalloff*0.3,color='green')
falloff = y_intpol[maxind]-baseline
falloff_index=len(x_intpol)-1
print falloff
try:
while y_intpol[falloff_index] < threshold*falloff+baseline: #default threshold is 0.5
falloff_index-=1
except ValueError:
print y_intpol[falloff_index] < threshold*falloff+baseline
print y_intpol[falloff_index]
print threshold
print falloff
print baseline
falloff_pos=x_intpol[falloff_index]
if fitlines: ax1.axvline(falloff_pos,color='green')
if fitlines: ax1.axhline(baseline,color='green')
if fitlines: ax1.axhline(y_intpol[maxind],color='green')
if fitlines: ax1.axvline(x_intpol[maxind],color='green')
if plotten: plot.texax(ax1)
if plotten: ax1.set_xlabel('Position [mm]', fontsize=8)
if plotten: ax1.set_ylabel('PG yield [counts]', fontsize=8)
if plotten: from matplotlib.ticker import MaxNLocator
if plotten: ax1.yaxis.set_major_locator(MaxNLocator(integer=True))
if plotten: ax1.set_ylim(bottom=0)
#if plotten: ax1.set_title('FOP = '+str(falloff_pos), fontsize=8)
#if plotten: f.savefig(plottenname, bbox_inches='tight')
#if plotten: print 'Plotted PG profile to',plottenname
#if plotten: plot.close('all')
if plotten: print 'FOP is',falloff_pos
#if plotten: quit()
return falloff_pos
