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 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_hitlet_properties(hits_n_data):
"""
Function which tests refresh_hit_to_hitlets, hitlet_with_data_dtype,
and hitlet_properties.
:param hits_n_data:
:return:
"""
hits, data = hits_n_data
hits['time'] += 100
# Step 1.: Produce fake hits and convert them into hitlets:
nsamples = 0
if len(hits) >= 1:
nsamples = hits['length'].max()
nsamples = np.max((nsamples, 2))
hitlets = np.zeros(len(hits), dtype=strax.hitlet_with_data_dtype(nsamples))
if len(hitlets):
assert hitlets['data'].shape[
1] >= 2, 'Data buffer is not at least 2 samples long.'
strax.copy_to_buffer(hits, hitlets,
'_refresh_hit_to_hitlet_properties_test')
# Testing refresh_hit_to_hitlets for free:
assert len(hits) == len(
hitlets), 'Somehow hitlets and hits have different sizes'
# Testing interval fields:
dummy = np.zeros(0, dtype=strax.interval_dtype)
for name in dummy.dtype.names:
assert np.all(hitlets[name] == hits[name]), f'The entry of the field {name} did not match between hit and ' \
f'hitlets '
# Step 2.: Add to each hit(let) some data
for ind, d in enumerate(data):
h = hitlets[ind]
h['data'][:h['length']] = d[:h['length']]
# Step 3.: Add np.nan in data but outside of length:
for h in hitlets:
if h['length'] < len(h['data']):
h['data'][-1] = np.nan
# It is enough to test this for a single hitlet:
break
# Step 4.: Compute properties and apply tests:
strax.hitlet_properties(hitlets)
for ind, d in enumerate(data):
h = hitlets[ind]
d = d[:h['length']]
pos_max = np.argmax(d)
# Checking amplitude things:
assert pos_max == h[
'time_amplitude'], 'Wrong amplitude position found!'
assert d[pos_max] == h['amplitude'], 'Wrong amplitude value found!'
# Checking FHWM and FWTM:
fractions = [0.1, 0.5]
for f in fractions:
# Get field names for the correct test:
if f == 0.5:
left = 'left'
fwxm = 'fwhm'
else:
left = 'low_left'
fwxm = 'fwtm'
amplitude = np.max(d)
if np.all(d[0] == d) or np.all(d > amplitude * f):
# If all samples are either the same or greater than required height FWXM is not defined:
mes = 'All samples are the same or larger than require height.'
assert np.isnan(
h[left]
), mes + f' Left edge for {f} should have been np.nan.'
assert np.isnan(
h[left]), mes + f' FWXM for X={f} should have been np.nan.'
else:
le = np.argwhere(d[:pos_max] <= amplitude * f)
if len(le):
le = le[-1, 0]
m = d[le + 1] - d[le]
le = le + 0.5 + (amplitude * f - d[le]) / m
else:
le = 0
re = np.argwhere(d[pos_max:] <= amplitude * f)
if len(re) and re[0, 0] != 0:
re = re[0, 0] + pos_max
m = d[re] - d[re - 1]
re = re + 0.5 + (amplitude * f - d[re]) / m
else:
re = len(d)
assert math.isclose(
le, h[left], rel_tol=10**-4, abs_tol=10**
-4), f'Left edge does not match for fraction {f}'
assert math.isclose(
re - le, h[fwxm], rel_tol=10**-4,
abs_tol=10**-4), f'FWHM does not match for {f}'
def test_hitlet_properties(hits_n_data):
"""
Function which tests refresh_hit_to_hitlets, hitlet_with_data_dtype,
and hitlet_properties.
:param hits_n_data:
:return:
"""
hits, data = hits_n_data
hits['time'] += 100
# Step 1.: Produce fake hits and convert them into hitlets:
if len(hits) >= 1:
nsamples = hits['length'].max()
else:
nsamples = 2
hitlets = np.zeros(len(hits), dtype=strax.hitlet_with_data_dtype(nsamples))
if len(hitlets):
assert hitlets['data'].shape[
1] >= 2, 'Data buffer is not at least 2 samples long.'
strax.refresh_hit_to_hitlets(hits, hitlets)
# Testing refresh_hit_to_hitlets for free:
assert len(hits) == len(
hitlets), 'Somehow hitlets and hits have different sizes'
# Tetsing interval fields:
dummy = np.zeros(0, dtype=strax.interval_dtype)
for name in dummy.dtype.names:
assert np.all(hitlets[name] == hits[name]), f'The entry of the field {name} did not match between hit and ' \
f'hitlets '
# Step 2.: Add to each hit(let) some data
for ind, d in enumerate(data):
h = hitlets[ind]
h['data'][:h['length']] = d[:h['length']]
strax.hitlet_properties(hitlets)
# Step 4.: Apply tests.
for ind, d in enumerate(data):
h = hitlets[ind]
d = d[:h['length']]
pos_max = np.argmax(d)
# Checking amplitude things:
assert pos_max == h[
'time_amplitude'], 'Wrong amplitude position found!'
assert d[pos_max] == h['amplitude'], 'Wrong amplitude value found!'
# Checking FHWM and FWTM:
fractions = [0.1, 0.5]
for f in fractions:
amplitude = np.max(d)
le = np.argwhere(d[:pos_max] <= amplitude * f)
if len(le):
le = le[-1, 0]
m = d[le + 1] - d[le]
le = le + 0.5 + (amplitude * f - d[le]) / m
else:
le = 0
re = np.argwhere(d[pos_max:] <= amplitude * f)
if len(re) and re[0, 0] != 0:
re = re[0, 0] + pos_max
m = d[re] - d[re - 1]
re = re + 0.5 + (amplitude * f - d[re]) / m
else:
re = len(d)
if f == 0.5:
left = 'left'
fwxm = 'fwhm'
else:
left = 'low_left'
fwxm = 'fwtm'
assert math.isclose(
le, h[left], rel_tol=10**-4,
abs_tol=10**-4), f'Left edge does not match for fraction {f}'
assert math.isclose(re - le,
h[fwxm],
rel_tol=10**-4,
abs_tol=10**-4), f'FWHM does not match for {f}'
def test_hitlet_properties(hits_n_data):
"""
Function which tests refresh_hit_to_hitlets, hitlet_with_data_dtype,
and hitlet_properties.
:param hits_n_data:
:return:
"""
hits, data = hits_n_data
hits['time'] += 100
# Step 1.: Produce fake hits and convert them into hitlets:
if len(hits) >= 1:
nsamples = hits['length'].max()
else:
nsamples = 2
hitlets = np.zeros(len(hits), dtype=strax.hitlet_with_data_dtype(nsamples))
if len(hitlets):
assert hitlets['data'].shape[1] >= 2, 'Data buffer is not at least 2 samples long.'
strax.refresh_hit_to_hitlets(hits, hitlets)
# Testing refresh_hit_to_hitlets for free:
assert len(hits) == len(hitlets), 'Somehow hitlets and hits have different sizes'
# Tetsing interval fields:
dummy = np.zeros(0, dtype=strax.interval_dtype)
for name in dummy.dtype.names:
assert np.all(hitlets[name] == hits[name]), f'The entry of the field {name} did not match between hit and ' \
f'hitlets '
# Step 2.: Add to each hit(let) some data
for ind, d in enumerate(data):
h = hitlets[ind]
h['data'][:h['length']] = d[:h['length']]
strax.hitlet_properties(hitlets)
# Step 4.: Apply tests.
for ind, d in enumerate(data):
h = hitlets[ind]
d = d[:h['length']]
pos_max = np.argmax(d)
# Checking amplitude things:
assert pos_max == h['time_amplitude'], 'Wrong amplitude position found!'
assert d[pos_max] == h['amplitude'], 'Wrong amplitude value found!'
# Checking FHWM and FWTM:
fractions = [0.1, 0.5]
for f in fractions:
# Get field names for the correct test:
if f == 0.5:
left = 'left'
fwxm = 'fwhm'
else:
left = 'low_left'
fwxm = 'fwtm'
amplitude = np.max(d)
if np.all(d[0] == d) or np.all(d > amplitude*f):
# If all samples are either the same or greater than required height FWXM is not defined:
mes = 'All samples are the same or larger than require height.'
assert np.isnan(h[left]), mes + f' Left edge for {f} should have been np.nan.'
assert np.isnan(h[left]), mes + f' FWXM for X={f} should have been np.nan.'
else:
le = np.argwhere(d[:pos_max] <= amplitude * f)
if len(le):
le = le[-1, 0]
m = d[le + 1] - d[le]
le = le + 0.5 + (amplitude * f - d[le]) / m
else:
le = 0
re = np.argwhere(d[pos_max:] <= amplitude * f)
if len(re) and re[0, 0] != 0:
re = re[0, 0] + pos_max
m = d[re] - d[re - 1]
re = re + 0.5 + (amplitude * f - d[re]) / m
else:
re = len(d)
assert math.isclose(le, h[left],
rel_tol=10**-4, abs_tol=10**-4), f'Left edge does not match for fraction {f}'
assert math.isclose(re - le, h[fwxm], rel_tol=10**-4,
abs_tol=10**-4), f'FWHM does not match for {f}'
# Step 5.: Unity test for not defined get_fhwm-cases:
# This is a specific unity test for some edge-cases in which the full
# width half maximum is not defined.
odd_hitlets = np.zeros(3, dtype=strax.hitlet_with_data_dtype(10))
odd_hitlets[0]['data'][:5] = [2, 2, 3, 2, 2]
odd_hitlets[0]['length'] = 5
odd_hitlets[1]['data'][:2] = [5, 5]
odd_hitlets[1]['length'] = 2
odd_hitlets[2]['length'] = 3
for oh in odd_hitlets:
res = strax.get_fwxm(oh)
mes = (f'get_fxhm returned {res} for {oh["data"][:oh["length"]]}!'
'However, the FWHM is not defined and the return should be nan!'
)
assert np.all(np.isnan(res)), mes