def build_peak(self, hits, detector, **kwargs):
"""Return a peak object made from hits. Compute a few basic properties which are needed during the clustering
stages.
Any kwargs will be passed to the peak constructor.
"""
hits.sort(order='index_of_maximum'
) # Hits must always be in sorted time order
peak = Peak(detector=detector, hits=hits, **kwargs)
peak.area_per_channel = dsputils.count_hits_per_channel(
peak, self.config, weights=hits['area'])
peak.n_contributing_channels = np.sum(peak.does_channel_contribute)
if peak.n_contributing_channels == 0:
raise RuntimeError(
"Every peak should have at least one contributing channel... what's going on?"
)
if peak.n_contributing_channels == 1:
peak.type = 'lone_hit'
peak.lone_hit_channel = hits[0]['channel']
peak.area = peak.area_per_channel.sum()
peak.left = peak.hits[0]['left']
peak.right = peak.hits['right'].max()
return peak
def test_s1_helper_method(self):
e = Event.empty_event()
e.peaks.append(Peak({'area': 2.0,
'index_of_maximum': 0,
'type': 's1',
'detector': 'tpc', }))
self.assertEqual(len(e.S1s()), 1)
self.assertIsInstance(e.S1s()[0], Peak)
self.assertEqual(e.S1s()[0].area, 2.0)
def test_peaks_append(self):
e = Event.empty_event()
e.peaks.append(Peak({'area': 2.0,
'index_of_maximum': 0,
'type': 'S1',
'detector': 'tpc'}))
self.assertEqual(len(e.peaks), 1)
self.assertIsInstance(e.peaks[0], Peak)
self.assertEqual(e.peaks[0].area, 2.0)
def test_peaks(self):
e = Event.empty_event()
e.peaks = [Peak(
area=0,
index_of_maximum=0,
detector='tpc')]
self.assertEqual(len(e.peaks), 1)
self.assertIsInstance(e.peaks[0], Peak)
self.assertEqual(e.peaks[0].area, 0)
def example_event(channels_with_something):
bla = np.zeros(242)
bla[np.array(channels_with_something)] = 1
e = Event.empty_event()
e.peaks.append(
Peak({
'left': 5,
'right': 9,
'type': 'unknown',
'detector': 'tpc',
'area_per_channel': bla,
}))
return e
def example_event(self, channels_with_something):
channels = np.array(channels_with_something, dtype='float64')
e = Event.empty_event()
e.peaks.append(
Peak({
'left': 5,
'right': 9,
'type': 'S2',
'detector': 'tpc',
'area': 123,
'area_per_channel': channels
}))
return e
def setUp(self): # noqa
self.pax = core.Processor(config_names='XENON100',
just_testing=True,
config_dict={
'pax': {
'plugin_group_names': ['test'],
'test':
'WeightedSum.PosRecWeightedSum'
}
})
self.posrec_plugin = self.pax.get_plugin_by_name('PosRecWeightedSum')
self.e = Event.empty_event()
self.e.peaks.append(Peak({'left': 5, 'right': 9, 'type': 's2'}))
def test_s1_helper_method_sort(self):
areas = [3.0, 1.0, 2.0, 1.2]
e = Event.empty_event()
for area in areas:
e.peaks.append(Peak({'area': area,
'type': 's2',
'detector': 'tpc'}))
s2s = e.S2s()
self.assertEqual(len(s2s), len(areas))
# Please note the areas should come out in reverse order (largest first)
areas = sorted(areas, reverse=True)
for i, area in enumerate(areas):
self.assertIsInstance(s2s[i], Peak)
self.assertEqual(s2s[i].area, area)
def example_event():
top_hits = [
7, 8, 8, 5, 8, 10, 6, 9, 3, 7, 6, 4, 5, 2, 1, 0, 7, 1, 3, 1, 4, 2,
5, 1, 4, 3, 1, 3, 2, 4, 3, 0, 4, 4, 1, 6, 2, 4, 9, 12, 8, 10, 9, 6,
9, 1, 2, 1, 2, 1, 4, 10, 0, 0, 1, 2, 1, 0, 2, 3, 6, 1, 3, 2, 3, 5,
2, 6, 30, 18, 24, 10, 8, 3, 4, 2, 4, 2, 1, 4, 3, 4, 5, 5, 2, 1, 2,
2, 2, 4, 12, 48, 139, 89, 19, 9, 3, 4, 2, 3, 1, 1, 6, 0, 3, 1, 2,
4, 12, 97, 87, 15, 6, 3, 4, 4, 0, 2, 3, 6, 13, 21, 3, 4, 3, 1, 7
]
hits = np.append(top_hits, np.zeros(254 - 127))
e = Event.empty_event()
e.peaks.append(
Peak({
'left': 5,
'right': 9,
'type': 'S2',
'detector': 'tpc',
'area': 123,
'area_per_channel': hits
}))
return e
def build_peak(self, hits, detector, **kwargs):
"""Return a peak object made from hits. Compute a few basic properties which are needed during the clustering
stages.
Any kwargs will be passed to the peak constructor.
"""
hits.sort(order='left_central') # Hits must always be in sorted time order
peak = Peak(detector=detector, hits=hits, **kwargs)
peak.area_per_channel = dsputils.count_hits_per_channel(peak, self.config, weights=hits['area'])
peak.n_contributing_channels = np.sum(peak.does_channel_contribute)
if peak.n_contributing_channels == 0:
raise RuntimeError("Every peak should have at least one contributing channel... what's going on?")
if peak.n_contributing_channels == 1:
peak.type = 'lone_hit'
peak.lone_hit_channel = hits[0]['channel']
peak.area = peak.area_per_channel.sum()
peak.left = peak.hits[0]['left']
peak.right = peak.hits['right'].max()
return peak
def example_event():
# Hitpattern taken from "S2_5e3Phts_1e5Evts.root"
# MC truth (x,y) = (100.833, 177.593) [mm]
# Recontructed by Yuehuans c++/ROOT code at (102.581, 177.855) [mm]
# Reconstucted by the PAX implementation at (10.258076101568305 17.785535721706857) [cm]
top_hits = [
7, 8, 8, 5, 8, 10, 6, 9, 3, 7, 6, 4, 5, 2, 1, 0, 7, 1, 3, 1, 4, 2,
5, 1, 4, 3, 1, 3, 2, 4, 3, 0, 4, 4, 1, 6, 2, 4, 9, 12, 8, 10, 9, 6,
9, 1, 2, 1, 2, 1, 4, 10, 0, 0, 1, 2, 1, 0, 2, 3, 6, 1, 3, 2, 3, 5,
2, 6, 30, 18, 24, 10, 8, 3, 4, 2, 4, 2, 1, 4, 3, 4, 5, 5, 2, 1, 2,
2, 2, 4, 12, 48, 139, 89, 19, 9, 3, 4, 2, 3, 1, 1, 6, 0, 3, 1, 2,
4, 12, 97, 87, 15, 6, 3, 4, 4, 0, 2, 3, 6, 13, 21, 3, 4, 3, 1, 7
]
hits = np.append(top_hits, np.zeros(254 - 127))
e = Event.empty_event()
e.peaks.append(
Peak({
'left': 5,
'right': 9,
'type': 'S2',
'detector': 'tpc',
'area_per_channel': hits
}))
return e
def test_peak_instantiation(self):
p = Peak({'area': 3.0,
'index_of_maximum': 0,
'detector': 'tpc', })
self.assertIsInstance(p, Peak)
self.assertEqual(p.area, 3.0)