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
