def set_data(self, d):
LogLikelihoodBase.set_data(self, d)
# Bin the data in the analysis space
dimnames, bins = zip(*self.base_model.config['analysis_space'])
self.data_events_per_bin = Histdd(bins=bins, axis_names=dimnames)
self.data_events_per_bin.add(
*self.base_model.to_analysis_dimensions(d))
def xes(request):
# warnings.filterwarnings("error")
data = pd.DataFrame([
dict(s1=56.,
s2=2905.,
drift_time=143465.,
x=2.,
y=0.4,
z=-20,
r=2.1,
theta=0.1,
event_time=1579784955000000000),
dict(s1=23,
s2=1080.,
drift_time=445622.,
x=1.12,
y=0.35,
z=-59.,
r=1.,
theta=0.3,
event_time=1579784956000000000)
])
if request.param == 'ER':
x = fd.ERSource(data.copy(), batch_size=2, max_sigma=8)
elif request.param == 'NR':
x = fd.NRSource(data.copy(), batch_size=2, max_sigma=8)
elif request.param == 'WIMP':
x = fd.WIMPSource(data.copy(), batch_size=2, max_sigma=8)
elif request.param == 'ER_spatial':
nbins = 100
r = np.linspace(0, 47.9, nbins + 1)
z = np.linspace(-97.6, 0, nbins + 1)
theta = np.linspace(0, 2 * np.pi, nbins + 1)
# Construct PDF histogram
h = Histdd(bins=[r, theta, z], axis_names=['r', 'theta', 'z'])
h.histogram = np.ones((nbins, nbins, nbins))
# Calculate bin volumes for cylindrical coords (r dr dtheta)
r_c, _, _ = h.bin_centers()
bin_volumes = h.bin_volumes() * r_c[:, np.newaxis, np.newaxis]
# Convert to events per bin histogram
h.histogram *= bin_volumes
class ERSpatial(fd.ERSource):
spatial_rate_hist = h
spatial_rate_bin_volumes = bin_volumes
x = ERSpatial(data.copy(), batch_size=2, max_sigma=8)
return x
def xes(request):
# warnings.filterwarnings("error")
data = pd.DataFrame([
dict(s1=56.,
s2=2905.,
drift_time=143465.,
x=2.,
y=0.4,
z=-20,
r=2.1,
theta=0.1,
event_time=1483488000000000000),
dict(s1=23,
s2=1080.,
drift_time=445622.,
x=1.12,
y=0.35,
z=-59.,
r=1.,
theta=0.3,
event_time=1483488000000000000)
])
if request.param == 'ER':
x = fd.ERSource(data.copy(), batch_size=2, max_sigma=8)
elif request.param == 'NR':
x = fd.NRSource(data.copy(), batch_size=2, max_sigma=8)
elif request.param == 'WIMP':
x = fd.WIMPSource(data.copy(), batch_size=2, max_sigma=8)
elif request.param == 'ER_spatial':
nbins = 100
r = np.linspace(0, 47.9, nbins + 1)
z = np.linspace(-97.6, 0, nbins + 1)
theta = np.linspace(0, 2 * np.pi, nbins + 1)
h = Histdd(bins=[r, theta, z], axis_names=['r', 'theta', 'z'])
h.histogram = np.ones((nbins, nbins, nbins))
class ERSpatial(fd.ERSource):
spatial_hist = h
x = ERSpatial(data.copy(), batch_size=2, max_sigma=8)
return x
def build_histogram(self):
# Get the events to estimate the PDF
dimnames, bins = zip(*self.config['analysis_space'])
mh = Histdd(bins=bins, axis_names=dimnames)
# Get a generator function which will give us the events
get = self.get_events_for_density_estimate
if not inspect.isgeneratorfunction(get):
def get():
return [self.get_events_for_density_estimate()]
n_events = 0
for events, n_simulated in get():
n_events += n_simulated
mh.add(*utils._events_to_analysis_dimensions(
events, self.config['analysis_space']))
self.fraction_in_range = mh.n / n_events
# Convert the histogram to a density estimate
# This means we have to divide by
# - the number of events IN RANGE received
# (fraction_in_range keeps track of how many events were not in range)
# - the bin sizes
self._pdf_histogram = mh.similar_blank_hist()
self._pdf_histogram.histogram = mh.histogram.astype(np.float) / mh.n
# For the bin widths we need to take an outer product of several vectors, for which numpy has no builtin
# This reduce trick does the job instead, see http://stackoverflow.com/questions/17138393
self._bin_volumes = reduce(np.multiply,
np.ix_(*[np.diff(bs) for bs in bins]))
self._pdf_histogram.histogram /= self._bin_volumes
self._n_events_histogram = mh
return mh
def plot_peaks_aft_histogram(
context, run_id, peaks,
pe_bins=np.logspace(0, 7, 120),
rt_bins=np.geomspace(2, 1e5, 120),
extra_labels=tuple(),
rate_range=(1e-4, 1),
aft_range=(0, .85),
figsize=(14, 5)):
"""Plot side-by-side (area, width) histograms of the peak rate
and mean area fraction top.
:param pe_bins: Array of bin edges for the peak area dimension [PE]
:param rt_bins: array of bin edges for the rise time dimension [ns]
:param extra_labels: List of (area, risetime, text, color) extra labels
to put on the plot
:param rate_range: Range of rates to show [peaks/(bin*s)]
:param aft_range: Range of mean S1 area fraction top / bin to show
:param figsize: Figure size to use
"""
livetime_sec = straxen.get_livetime_sec(context, run_id, peaks)
mh = Histdd(peaks,
dimensions=(
('area', pe_bins),
('range_50p_area', rt_bins),
('area_fraction_top', np.linspace(0, 1, 100))
))
f, axes = plt.subplots(1, 2, figsize=figsize)
def std_axes():
plt.gca().set_facecolor('k')
plt.yscale('log')
plt.xscale('log')
plt.xlabel("Area [PE]")
plt.ylabel("Range 50% area [ns]")
labels = [
(12, 8, "AP?", 'white'),
(3, 150, "1PE\npileup", 'gray'),
(30, 200, "1e", 'gray'),
(100, 1000, "n-e", 'w'),
(2000, 2e4, "Train", 'gray'),
(1200, 50, "S1", 'w'),
(45e3, 60, "αS1", 'w'),
(2e5, 800, "S2", 'w'),
] + list(extra_labels)
for x, w, text, color in labels:
plt.text(x, w, text, color=color,
verticalalignment='center',
horizontalalignment='center')
plt.sca(axes[0])
(mh / livetime_sec).sum(axis=2).plot(
log_scale=True,
vmin=rate_range[0], vmax=rate_range[1],
colorbar_kwargs=dict(extend='both'),
cblabel='Peaks / (bin * s)')
std_axes()
plt.sca(axes[1])
mh.average(axis=2).plot(
vmin=aft_range[0], vmax=aft_range[1],
colorbar_kwargs=dict(extend='max'),
cmap=plt.cm.jet, cblabel='Mean area fraction top')
std_axes()
plt.tight_layout()
def plot_peaks_aft_histogram(context,
run_id,
peaks,
pe_bins=np.logspace(0, 7, 120),
width_bins=np.geomspace(2, 1e5, 120),
extra_labels=tuple(),
rate_range=(1e-4, 1),
aft_range=(0, .85),
figsize=(14, 5)):
"""Plot side-by-side (area, width) histograms of the peak rate
and mean area fraction top."""
try:
md = context.run_metadata(run_id, projection=('start', 'end'))
livetime_sec = (md['end'] - md['start']).total_seconds()
except strax.RunMetadataNotAvailable:
livetime_sec = (strax.endtime(peaks)[-1] - peaks[0]['time']) / 1e9
mh = Histdd(peaks,
dimensions=(('area', pe_bins), ('range_50p_area', width_bins),
('area_fraction_top', np.linspace(0, 1, 100))))
f, axes = plt.subplots(1, 2, figsize=figsize)
def std_axes():
plt.gca().set_facecolor('k')
plt.yscale('log')
plt.xscale('log')
plt.xlabel("Area [PE]")
plt.ylabel("Range 50% area [ns]")
labels = [
(12, 8, "AP?", 'white'),
(3, 150, "1PE\npileup", 'gray'),
(30, 200, "1e", 'gray'),
(100, 1000, "n-e", 'w'),
(2000, 2e4, "Train", 'gray'),
(1200, 50, "S1", 'w'),
(45e3, 60, "αS1", 'w'),
(2e5, 800, "S2", 'w'),
] + list(extra_labels)
for x, w, text, color in labels:
plt.text(x,
w,
text,
color=color,
verticalalignment='center',
horizontalalignment='center')
plt.sca(axes[0])
(mh / livetime_sec).sum(axis=2).plot(log_scale=True,
vmin=rate_range[0],
vmax=rate_range[1],
colorbar_kwargs=dict(extend='both'),
cblabel='Peaks / (bin * hour)')
std_axes()
plt.sca(axes[1])
mh.average(axis=2).plot(vmin=aft_range[0],
vmax=aft_range[1],
colorbar_kwargs=dict(extend='max'),
cmap=plt.cm.jet,
cblabel='Mean area fraction top')
std_axes()
plt.tight_layout()
df = aft_peak_cut(df)
#Binning
window_length = 10**8
t_bins = np.linspace(0, window_length, 201)
t_bin_width = t_bins[1:] - t_bins[:-1]
r_bins = np.linspace(0, (R_tpc)**2, 101)
dist_bins = np.linspace(-R_tpc, R_tpc, 101)
s2_bins = np.linspace(2, 6, 51)
s2_p_bins = np.linspace(0, 4, 51)
#Define Blank Hists
livet_histogram = Histdd(bins=[
t_bins,
s2_bins,
],
axis_names=[
'delta_T',
's2_area',
])
livet_weights_histogram = Histdd(bins=[
t_bins,
s2_bins,
],
axis_names=[
'delta_T',
's2_area',
])
events_histogram = Histdd(bins=[
t_bins,
def setUp(self):
self.m = Histdd(range=test_range_2d,
bins=test_bins_2d,
axis_names=['foo', 'bar'])
for idx, x_edge in tqdm(enumerate(xv[:-1])):
for idy, y_edge in enumerate(yv[:-1]):
df_xy = df.loc[(df.x_s2_tpf > xv[idx][idy])
& (df.x_s2_tpf < xv[idx][idy + 1])
& (df.y_s2_tpf > yv[idx][idy]) &
(df.y_s2_tpf < yv[idx + 1][idy])]
unique_s2s = pd.unique(df_xy[['s2_time', 'x_s2_tpf',
'y_s2_tpf']].values)
num_events = len(unique_s2s)
#Define Blank Hists
dt_r2_histogram = Histdd(bins=[
t_reduced_bins,
r_bins,
],
axis_names=[
'delta_T',
'r_dist',
])
xy_histogram = Histdd(bins=[
t_reduced_bins,
x_bins,
y_bins,
],
axis_names=[
'delta_T',
'x_p_pos',
'y_p_pos',
])
