def plot_temporal_distribution(self, fname=None):
fname = self._fname(fname)
figname = 'temps1.Simulation.plot_temporal_distribution.' + fname.replace(
" ", "_")
fig, axs = plt.subplots(2,
1,
num=figname,
clear=True,
figsize=[6.4, 7.19])
axs[0].set_title(
f'{fname.capitalize()} filter\nTemporal distribution of S1 candidates'
)
ax = axs[0]
ax.set_xlabel('Time relative to true S1 location [ns]')
ax.set_ylabel('Inverse of neighbor temporal gap [ns$^{-1}$]')
for k in ['all', 'dcr']:
time = self.times[fname][k]
time = np.sort(time)
ddecdf = 1 / np.diff(time)
x = time - self.s1loc
y = np.concatenate([ddecdf, ddecdf[-1:]])
ax.plot(x, y, drawstyle='steps-post', **self.plotkw[k])
ax.axvspan(-self.deadradius,
self.deadradius,
color='#eee',
zorder=-9,
label='$\\pm$ dead radius')
ax.axvspan(-self.matchdist,
self.matchdist,
color='#ccc',
zorder=-8,
label='$\\pm$ match dist.')
ax.legend(loc='upper right')
ax.set_xlim(3.5 * max(2 * self.matchdist, self.deadradius) *
np.array([-1, 1]))
ax.set_yscale('log')
ax.minorticks_on()
ax.grid(True, which='major', linestyle='--')
ax.grid(True, which='minor', linestyle=':')
ax = axs[1]
ax.set_xlabel('Time relative to true S1 location [ns]')
ax.set_ylabel('Histogram bin density [ns$^{-1}$]')
times1 = self.hits1.reshape(-1) - self.s1loc
time = self.times[fname]['all'] - self.s1loc
time_match = time[np.abs(time) < self.matchdist]
idx = np.argsort(np.abs(time))
time_close = time[idx][:self.nmc]
# t = np.linspace(..., ..., 1000)
# ax.plot(t, pS1.p_S1_gauss(t, self.VL, self.tauV, self.tauL, self.tres), label='S1 pdf')
histkw = dict(bins='auto', density=True, histtype='step', zorder=10)
ax.hist(times1,
label=f'S1 photons ({len(times1)})',
linestyle=':',
**histkw)
ax.hist(
time_close,
label=
f'{self.nmc} closest candidates ($\\sigma_q$={qsigma.qsigma(time_close):.3g})',
linestyle='--',
**histkw)
ax.hist(
time_match,
label=
f'matching candidates ($\\sigma_q$={qsigma.qsigma(time_match):.3g})',
**histkw)
ax.axvspan(0,
self.deadradius,
color='#eee',
zorder=-9,
label='dead radius')
ax.axvspan(0,
self.matchdist,
color='#ccc',
zorder=-8,
label='match dist.')
textbox.textbox(ax, self.infotext(), loc='upper left', zorder=11)
ax.legend(loc='upper right', fontsize='small')
ax.set_yscale('log')
linthreshx = 10**np.ceil(np.log10(15 * qsigma.qsigma(time_match)))
ax.set_xscale('symlog', linthreshx=linthreshx)
ax.minorticks_on()
ax.xaxis.set_minor_locator(
symloglocator.MinorSymLogLocator(linthreshx))
ax.grid(True, which='major', linestyle='--')
ax.grid(True, which='minor', linestyle=':')
fig.tight_layout()
return fig
def simulation(
DCR=250e-9, # (ns^-1) Dark count rate per PDM, 25 or 250 Hz
VL=3, # fast/slow ratio, ER=0.3, NR=3
tauV=7, # (ns) fast component tau
tauL=1600, # (ns) slow component tau
T_target=4e6, # (ns) time window
T_sim=100e3, # (ns) actually simulated time window
npdm=8280, # number of PDMs
nphotons=10, # (2-100) number of photons in the S1 signal
tres=3, # (ns) temporal resolution (3-10)
nmc=10, # number of simulated events
deadradius=4000, # (ns) for selecting S1 candidates in filter output
matchdist=2000, # (ns) for matching a S1 candidate to the true S1
generator=None # random generator
):
info = f"""\
total DCR = {DCR * npdm * 1e3:.2g} $\\mu$s$^{{-1}}$
T (target) = {T_target * 1e-6:.1f} ms
T (sim.) = {T_sim * 1e-6:.3f} ms
fast/slow = {VL:.1f}
nphotons = {nphotons}
$\\tau$ = ({tauV:.1f}, {tauL:.0f}) ns
temporal res. = {tres:.1f} ns
dead radius = {deadradius:.0f} ns
match dist. = {matchdist:.0f} ns
nevents = {nmc}"""
hits1 = pS1.gen_S1((nmc, nphotons), VL, tauV, tauL, tres, generator)
hitdcr = dcr.gen_DCR(nmc, T_sim, DCR * npdm, generator)
s1loc = T_sim / 2
hitall = np.concatenate([hits1 + s1loc, hitdcr], axis=-1)
hitd = dict(all=hitall, dcr=hitdcr)
plotkw = {
'all': dict(label='Single S1 events'),
'dcr': dict(label='No S1 events', linestyle='--')
}
filt = {
k: filters.filters(hits,
VL,
tauV,
tauL,
tres,
midpoints=1,
pbar_batch=None)
for k, hits in hitd.items()
}
figs = []
for fname in filt['all'].dtype.names:
times = {}
values = {}
for k, fhits in filt.items():
time = fhits[fname]['time']
value = fhits[fname]['value']
times[k], values[k] = clustersort(time, value, deadradius)
figname = 'temps1.simulation_' + fname.replace(" ", "_")
fig, axs = plt.subplots(2,
1,
num=figname,
figsize=[6.4, 7.19],
clear=True,
sharex=True)
figs.append(fig)
axs[0].set_title(
f'{fname.capitalize()} filter detection performance\n(with explicit threshold)'
)
ax = axs[0]
ax.set_ylabel('Mean number of S1 candidates per event')
xy = {
k: [v, (1 + np.arange(len(v)))[::-1] / nmc]
for k, v in values.items()
}
interpkw = dict(kind='next',
assume_sorted=True,
copy=False,
bounds_error=False)
interp = {
k: interpolate.interp1d(x, y, fill_value=(y[0], 0), **interpkw)
for k, (x, y) in xy.items()
}
x = np.sort(np.concatenate([xy['all'][0], xy['dcr'][0]]))
xy['all'][0] = x
xy['all'][1] = interp['all'](
x) + interp['dcr'](x) * (T_target / T_sim - 1)
xy['dcr'][1] *= T_target / T_sim
for k, (x, y) in xy.items():
x = np.concatenate([x, x[-1:]])
y = np.concatenate([y, [0]])
ax.plot(x, y, drawstyle='steps-pre', **plotkw[k])
if fname == 'sample mode':
ax.set_xscale('log')
ax.set_yscale('log')
ax.minorticks_on()
ax.grid(True, which='major', linestyle='--')
ax.grid(True, which='minor', linestyle=':')
ax.legend(loc='upper right')
ax = axs[1]
ax.set_ylabel('True S1 detection probability')
ax.set_xlabel('Threshold on filter output')
time, value = times['all'], values['all']
close = np.abs(time - s1loc) < matchdist
s1value = value[close]
x = np.concatenate([s1value, s1value[-1:]])
y = np.arange(len(x))[::-1] / nmc
ax.plot(x, y, drawstyle='steps-pre', **plotkw['all'])
textbox.textbox(ax, info)
ax.minorticks_on()
ax.grid(True, which='major', linestyle='--')
ax.grid(True, which='minor', linestyle=':')
ax.set_ylim(0, max(1, np.max(y)))
figname = 'temps1.simulation_' + fname.replace(" ", "_") + '_combined'
fig, ax = plt.subplots(num=figname, clear=True)
figs.append(fig)
ax.set_title(f'{fname.capitalize()} filter detection performance')
ax.set_xlabel('S1 candidates per event')
ax.set_ylabel('S1 loss probability')
time, value = times['all'], values['all']
close = np.abs(time - s1loc) < matchdist
xs1 = value[close]
ys1 = 1 - (1 + np.arange(len(xs1)))[::-1] / nmc
fs1 = interpolate.interp1d(xs1,
ys1,
fill_value=(1 - ys1[0], 1),
**interpkw)
x, y = xy['all']
sel = (xs1[0] <= x) & (x <= xs1[-1])
x = x[sel]
y = y[sel]
s1cand = y
s1prob = fs1(x)
ax.plot(s1cand, s1prob, **plotkw['all'])
textbox.textbox(ax, info)
ax.minorticks_on()
ax.set_xscale('log')
ax.set_yscale('log')
ax.grid(True, which='major', linestyle='--')
ax.grid(True, which='minor', linestyle=':')
l, r = ax.get_xlim()
ax.set_xlim(max(0.1, l), r)
b, _ = ax.get_ylim()
ax.set_ylim(b, max(1, np.max(s1prob)))
autolinscale(ax)
figname = 'temps1.simulation_' + fname.replace(" ", "_") + '_time'
fig, axs = plt.subplots(2,
1,
num=figname,
clear=True,
figsize=[6.4, 7.19])
figs.append(fig)
axs[0].set_title(
f'{fname.capitalize()} filter\nTemporal distribution of S1 candidates'
)
ax = axs[0]
ax.set_xlabel('Time relative to true S1 location [ns]')
ax.set_ylabel('Inverse of neighbor temporal gap [ns$^{-1}$]')
for k, time in times.items():
time = np.sort(time)
ddecdf = 1 / np.diff(time)
x = time - s1loc
y = np.concatenate([ddecdf, ddecdf[-1:]])
ax.plot(x, y, drawstyle='steps-post', **plotkw[k])
ax.axvspan(-deadradius,
deadradius,
color='#eee',
zorder=-9,
label='$\\pm$ dead radius')
ax.axvspan(-matchdist,
matchdist,
color='#ccc',
zorder=-8,
label='$\\pm$ match dist.')
ax.legend(loc='upper right')
# ax.set_xscale('symlog', linthreshx=deadradius, linscalex=2)
ax.set_xlim(3.5 * max(2 * matchdist, deadradius) * np.array([-1, 1]))
ax.set_yscale('log')
ax.minorticks_on()
ax.grid(True, which='major', linestyle='--')
ax.grid(True, which='minor', linestyle=':')
ax = axs[1]
ax.set_xlabel('Time relative to true S1 location [ns]')
ax.set_ylabel('Histogram bin density [ns$^{-1}$]')
times1 = hits1.reshape(-1)
time = times['all'] - s1loc
time_match = time[np.abs(time) < matchdist]
idx = np.argsort(np.abs(time))
time_close = time[idx][:nmc]
# t = np.linspace(left, right, 1000)
# ax.plot(t, pS1.p_S1_gauss(t, VL, tauV, tauL, tres), label='S1 pdf')
histkw = dict(bins='auto', density=True, histtype='step', zorder=10)
ax.hist(times1,
label=f'S1 photons ({len(times1)})',
linestyle=':',
**histkw)
ax.hist(
time_close,
label=
f'{nmc} closest candidates ($\\sigma_q$={qsigma.qsigma(time_close):.3g})',
linestyle='--',
**histkw)
ax.hist(
time_match,
label=
f'matching candidates ($\\sigma_q$={qsigma.qsigma(time_match):.3g})',
**histkw)
ax.axvspan(0, deadradius, color='#eee', zorder=-9, label='dead radius')
ax.axvspan(0, matchdist, color='#ccc', zorder=-8, label='match dist.')
textbox.textbox(ax, info, loc='upper left', zorder=11)
ax.legend(loc='upper right', fontsize='small')
ax.set_yscale('log')
linthreshx = 10**np.ceil(np.log10(15 * qsigma.qsigma(time_match)))
ax.set_xscale('symlog', linthreshx=linthreshx)
ax.minorticks_on()
ax.xaxis.set_minor_locator(
symloglocator.MinorSymLogLocator(linthreshx))
ax.grid(True, which='major', linestyle='--')
ax.grid(True, which='minor', linestyle=':')
for fig in figs:
fig.tight_layout()
return figs
