def peak_detect(accum, sigma, neighbors):
""" Returns a dictionary with the fields:
accum
accum_cleared
accum_smooth
accum_smooth_max = (x, y)
centroid
"""
# smooth a little bit
accum_smooth = gaussian_filter(accum, sigma=sigma)
val, accum_smooth_max = md_argmax(accum_smooth)
if val == 0:
return None
# now take a weighted mean around val
accum_cleared, nevents, mi, mj = \
centroid_estimate2(accum, center=accum_smooth_max, neighbors=neighbors)
res = {}
res['accum'] = accum
res['accum_cleared'] = accum_cleared
res['accum_smooth'] = accum_smooth
res['accum_smooth_max'] = accum_smooth_max
res['centroid'] = (mi, mj)
res['centroid_nevents'] = nevents
res['quality'] = nevents # overall quality estimate
return res
def peak_detect(accum, sigma, neighbors):
""" Returns a dictionary with the fields:
accum
accum_cleared
accum_smooth
accum_smooth_max = (x, y)
centroid
"""
# smooth a little bit
accum_smooth = gaussian_filter(accum, sigma=sigma)
val, accum_smooth_max = md_argmax(accum_smooth)
if val == 0:
return None
# now take a weighted mean around val
accum_cleared, nevents, mi, mj = \
centroid_estimate2(accum, center=accum_smooth_max, neighbors=neighbors)
res = {}
res['accum'] = accum
res['accum_cleared'] = accum_cleared
res['accum_smooth'] = accum_smooth
res['accum_smooth_max'] = accum_smooth_max
res['centroid'] = (mi, mj)
res['centroid_nevents'] = nevents
res['quality'] = nevents # overall quality estimate
return res
def peak_detect_multiple(accum, min_distance, centroid_area,
npeaks, frequency, timestamp,
min_nevents=0.000001):
"""
Returns a set of peaks, at least min_distance from each other.
Returns a dictionary with the fields:
accum
accum_cleared
accum_smooth
accum_smooth_max = (x, y)
hypotheses = list of hypotheses
centroid
"""
# For each peak
hp = []
for i in range(npeaks):
# find the current peak
val, accum_smooth_max = md_argmax(accum)
if val <= min_nevents:
continue
# now take a weighted mean around val
_, nevents, mi, mj = \
centroid_estimate2(accum, center=accum_smooth_max,
neighbors=centroid_area)
h = create_track_observation(frequency=frequency,
timestamp=timestamp,
coords=(mi, mj),
quality=nevents,
peak=i, npeaks=npeaks)
hp.append(h)
m_before = np.max(accum)
# print(accum[mi, mj])
accum = remove_occupied(accum, occupied=[(mi, mj)],
distance=min_distance)
# print(accum[mi, mj])
assert accum[mi, mj] == 0
m_after = np.max(accum)
# print('%s >= %s' % (m_before, m_after))
# XXX: something is still not right (should be ">")
assert m_before >= m_after
# Note: because of centroid stuff, it is not guaranteed
# check_minimum_distance(hp, min_distance)
# check_minimum_distance(hp, min_distance)
for h in hp:
h['npeaks'] = len(hp)
return hp
def aer_stats_events_meat(log):
# events = collect_all(aer_load_log_generic(log))
events = aer_raw_events_from_file_all(log)
hist = aer_histogram(events)
_, coords = md_argmax(hist)
r = Report('index')
with r.subsection('sub') as sub:
report_for_one(sub, events, coords)
return r
def aer_stats_events_meat(log):
# events = collect_all(aer_load_log_generic(log))
events = aer_raw_events_from_file_all(log)
hist = aer_histogram(events)
_, coords = md_argmax(hist)
r = Report('index')
with r.subsection('sub') as sub:
report_for_one(sub, events, coords)
return r
