def test_to_short_data_field(self):
hitlets_to_short = np.zeros(len(self.hitlets),
dtype=strax.hitlet_with_data_dtype(2))
strax.copy_to_buffer(self.hitlets, hitlets_to_short,
'_refresh_hit_to_hitlet')
with self.assertRaises(ValueError):
strax.get_hitlets_data(hitlets_to_short, self.records,
np.ones(3000))
def compute(self, records_nv, start, end):
# Search again for hits in records:
hits = strax.find_hits(
records_nv, min_amplitude=self.config['hit_min_amplitude_nv'])
# Merge concatenate overlapping within a channel. This is important
# in case hits were split by record boundaries. In case we
# accidentally concatenate two PMT signals we split them later again.
hits = strax.concat_overlapping_hits(
hits, self.config['save_outside_hits_nv'],
self.config['channel_map']['nveto'], start, end)
hits = strax.sort_by_time(hits)
# Now convert hits into temp_hitlets including the data field:
if len(hits):
nsamples = hits['length'].max()
else:
nsamples = 0
temp_hitlets = np.zeros(
len(hits), strax.hitlet_with_data_dtype(n_samples=nsamples))
# Generating hitlets and copying relevant information from hits to hitlets.
# These hitlets are not stored in the end since this array also contains a data
# field which we will drop later.
strax.refresh_hit_to_hitlets(hits, temp_hitlets)
del hits
# Get hitlet data and split hitlets:
strax.get_hitlets_data(temp_hitlets, records_nv, to_pe=self.to_pe)
temp_hitlets = strax.split_peaks(
temp_hitlets,
records_nv,
self.to_pe,
data_type='hitlets',
algorithm='local_minimum',
min_height=self.config['min_split_nv'],
min_ratio=self.config['min_split_ratio_nv'])
# Compute other hitlet properties:
# We have to loop here 3 times over all hitlets...
strax.hitlet_properties(temp_hitlets)
entropy = strax.conditional_entropy(temp_hitlets,
template='flat',
square_data=False)
temp_hitlets['entropy'][:] = entropy
strax.compute_widths(temp_hitlets)
# Remove data field:
hitlets = np.zeros(len(temp_hitlets), dtype=strax.hitlet_dtype())
drop_data_field(temp_hitlets, hitlets)
return hitlets
def test_inputs_are_empty(self):
hitlets_empty = np.zeros(0, dtype=strax.hitlet_with_data_dtype(2))
records_empty = np.zeros(0, dtype=strax.record_dtype(10))
hitlets_result = strax.get_hitlets_data(hitlets_empty, self.records,
np.ones(3000))
assert len(hitlets_result
) == 0, 'get_hitlet_data returned result for empty hitlets'
hitlets_result = strax.get_hitlets_data(hitlets_empty, records_empty,
np.ones(3000))
assert len(hitlets_result
) == 0, 'get_hitlet_data returned result for empty hitlets'
with self.assertRaises(ValueError):
strax.get_hitlets_data(self.hitlets, records_empty, np.ones(3000))
def compute(self, records_nv, start, end):
hits = strax.find_hits(records_nv, min_amplitude=self.config['hit_min_amplitude_nv'])
hits = remove_switched_off_channels(hits, self.to_pe)
temp_hitlets = strax.create_hitlets_from_hits(hits,
self.config['save_outside_hits_nv'],
self.channel_range,
chunk_start=start,
chunk_end=end)
del hits
# Get hitlet data and split hitlets:
temp_hitlets = strax.get_hitlets_data(temp_hitlets, records_nv, to_pe=self.to_pe)
temp_hitlets = strax.split_peaks(temp_hitlets,
records_nv,
self.to_pe,
data_type='hitlets',
algorithm='local_minimum',
min_height=self.config['min_split_nv'],
min_ratio=self.config['min_split_ratio_nv']
)
# Compute other hitlet properties:
# We have to loop here 3 times over all hitlets...
strax.hitlet_properties(temp_hitlets)
entropy = strax.conditional_entropy(temp_hitlets, template='flat', square_data=False)
temp_hitlets['entropy'][:] = entropy
# Remove data field:
hitlets = np.zeros(len(temp_hitlets), dtype=strax.hitlet_dtype())
strax.copy_to_buffer(temp_hitlets, hitlets, '_copy_hitlets')
return hitlets
def test_get_hitlets_data_without_data_field(self):
hitlets_empty = np.zeros(len(self.hitlets), strax.hitlet_dtype())
strax.copy_to_buffer(self.hitlets, hitlets_empty,
'_copy_hitlets_to_hitlets_without_data')
hitlets = strax.get_hitlets_data(hitlets_empty, self.records,
np.ones(3000))
self._test_data_is_identical(hitlets, [self.test_data_truth])
def test_get_hitlets_data():
dummy_records = [ # Contains Hitlet #:
[
[1, 3, 2, 1, 0, 0],
], # 0
[
[0, 0, 0, 0, 1, 3], # 1
[2, 1, 0, 0, 0, 0]
], #
[
[0, 0, 0, 0, 1, 3], # 2
[2, 1, 0, 1, 3, 2],
], # 3
[
[0, 0, 0, 0, 1, 2], # 4
[2, 2, 2, 2, 2, 2],
[2, 1, 0, 0, 0, 0]
],
[[2, 1, 0, 1, 3, 2]], # 5, 6
[[2, 2, 2, 2, 2, 2]] # 7
]
# Defining the true parameters of the hitlets:
true_area = [7, 7, 7, 6, 18, 3, 6, 12]
true_time = [10, 28, 46, 51, 68, 88, 91, 104]
true_waveform = [[1, 3, 2, 1], [1, 3, 2, 1], [1, 3, 2, 1], [1, 3, 2],
[1, 2, 2, 2, 2, 2, 2, 2, 2, 1], [2, 1], [1, 3, 2],
[2, 2, 2, 2, 2, 2]]
records = _make_fake_records(dummy_records)
hits = strax.find_hits(records, min_amplitude=2)
hits = strax.concat_overlapping_hits(hits, (1, 1), (0, 1), 0, float('inf'))
hitlets = np.zeros(
len(hits),
strax.hitlet_with_data_dtype(n_samples=np.max(hits['length'])))
strax.refresh_hit_to_hitlets(hits, hitlets)
strax.get_hitlets_data(hitlets, records, np.array([1, 1]))
for i, (a, wf, t) in enumerate(zip(true_area, true_waveform, true_time)):
h = hitlets[i]
assert h['area'] == a, f'Hitlet {i} has the wrong area'
assert np.all(h['data'][:h['length']] ==
wf), f'Hitlet {i} has the wrong waveform'
assert h['time'] == t, f'Hitlet {i} has the wrong starttime'
def test_get_hitlets_data(self):
dummy_records = [ # Contains Hitlet #:
[
[1, 3, 2, 1, 0, 0],
], # 0
[
[0, 0, 0, 0, 1, 3], # 1
[2, 1, 0, 0, 0, 0]
], #
[
[0, 0, 0, 0, 1, 3], # 2
[2, 1, 0, 1, 3, 2],
], # 3
[
[0, 0, 0, 0, 1, 2], # 4
[2, 2, 2, 2, 2, 2],
[2, 1, 0, 0, 0, 0]
],
[[2, 1, 0, 1, 3, 2]], # 5, 6
[[2, 2, 2, 2, 2, 2]] # 7
]
# Defining the true parameters of the hitlets:
true_area = [7, 7, 7, 6, 18, 3, 6, 12]
true_time = [10, 28, 46, 51, 68, 88, 91, 104]
true_waveform = [[1, 3, 2, 1], [1, 3, 2, 1], [1, 3, 2, 1], [1, 3, 2],
[1, 2, 2, 2, 2, 2, 2, 2, 2, 1], [2, 1], [1, 3, 2],
[2, 2, 2, 2, 2, 2]]
records, hitlets = self.make_records_and_hitlets(dummy_records)
hitlets = strax.get_hitlets_data(hitlets, records, np.ones(2))
for i, (a, wf, t) in enumerate(zip(true_area, true_waveform,
true_time)):
h = hitlets[i]
assert h['area'] == a, f'Hitlet {i} has the wrong area'
assert np.all(h['data'][:h['length']] ==
wf), f'Hitlet {i} has the wrong waveform'
assert h['time'] == t, f'Hitlet {i} has the wrong starttime'
def test_empty_overlap(self):
records = np.zeros(3, strax.record_dtype(10))
# Create fake records for which hitlet overlaps with channel 0
# although hit is in channel 1. See also github.com/AxFoundation/strax/pull/549
records['channel'] = (0, 1, 1)
records['length'] = (10, 3, 10)
records['time'] = (0, 0, 5)
records['dt'] = 1
records['data'][-1] = np.ones(10)
# Assume we extend our hits by 1 sample hence hitlet starts at 4
hitlet = np.zeros(1, strax.hitlet_with_data_dtype(11))
hitlet['time'] = 4
hitlet['dt'] = 1
hitlet['length'] = 11
hitlet['channel'] = 1
hitlet = strax.get_hitlets_data(hitlet, records, np.ones(10))
assert hitlet['time'] == 5
assert hitlet['length'] == 10
assert np.sum(hitlet['data']) == 10
assert hitlet['data'][0, 0] == 1
def test_data_field_is_empty(self):
hitlets = strax.get_hitlets_data(self.hitlets, self.records,
np.ones(3000))
with self.assertRaises(ValueError):
strax.get_hitlets_data(hitlets, self.records, np.ones(3000))
self._test_data_is_identical(hitlets, [self.test_data_truth])
def test_get_hitlets_data_for_single_hitlet(self):
hitlets = strax.get_hitlets_data(self.hitlets[0], self.records,
np.ones(3000))
self._test_data_is_identical(hitlets, [self.test_data_truth])
def test_to_pe_wrong_shape(self):
self.hitlets['channel'] = 2000
with self.assertRaises(ValueError):
strax.get_hitlets_data(self.hitlets, self.records, np.ones(10))
def __call__(self,
peaks,
hits,
records,
rlinks,
to_pe,
data_type,
do_iterations=1,
min_area=0,
**kwargs):
if not len(records) or not len(peaks) or not do_iterations:
return peaks
# Build the *args tuple for self.find_split_points from kwargs
# since numba doesn't support **kwargs
args_options = []
for i, (k, value) in enumerate(self.find_split_args_defaults):
if k in kwargs:
value = kwargs[k]
if k == 'threshold':
# The 'threshold' option is a user-specified function
value = value(peaks)
args_options.append(value)
args_options = tuple(args_options)
# Check for spurious options
argnames = [k for k, _ in self.find_split_args_defaults]
for k in kwargs:
if k not in argnames:
raise TypeError(f"Unknown argument {k} for {self.__class__}")
is_split = np.zeros(len(peaks), dtype=np.bool_)
new_peaks = self._split_peaks(
# Numba doesn't like self as argument, but it's ok with functions...
split_finder=self.find_split_points,
peaks=peaks,
is_split=is_split,
orig_dt=records[0]['dt'],
min_area=min_area,
args_options=tuple(args_options),
result_dtype=peaks.dtype)
if is_split.sum() != 0:
# Found new peaks: compute basic properties
if data_type == 'peaks':
strax.sum_waveform(new_peaks, hits, records, rlinks, to_pe)
strax.compute_widths(new_peaks)
elif data_type == 'hitlets':
# Add record fields here
new_peaks = strax.sort_by_time(
new_peaks
) # Hitlets are not necessarily sorted after splitting
new_peaks = strax.get_hitlets_data(new_peaks, records, to_pe)
# ... and recurse (if needed)
new_peaks = self(new_peaks,
hits,
records,
rlinks,
to_pe,
data_type,
do_iterations=do_iterations - 1,
min_area=min_area,
**kwargs)
if np.any(new_peaks['length'] == 0):
raise ValueError(
'Want to add a new zero-length peak after splitting!')
peaks = strax.sort_by_time(
np.concatenate([peaks[~is_split], new_peaks]))
return peaks
