def determine_offsets_for_pair(stations):
ref_station, station = stations
path = DT_DATAPATH % (ref_station, station)
with tables.open_file(path, 'r') as data:
table = data.get_node('/s%d' % station)
offsets = []
start = datetime(2010, 1, 1)
end = datetime(2015, 4, 1)
for dt0 in (start + timedelta(days=x)
for x in xrange(0, (end - start).days, 10)):
ts0 = datetime_to_gps(dt0)
CLUSTER.set_timestamp(ts0)
# dz is z - z_ref
r, _, dz = CLUSTER.calc_rphiz_for_stations(
CLUSTER.get_station(ref_station).station_id,
CLUSTER.get_station(station).station_id)
ts1 = datetime_to_gps(dt0 +
timedelta(days=max(int(r**1.12 / DAYS), 7)))
dt = table.read_where('(timestamp >= ts0) & (timestamp < ts1)',
field='delta')
if len(dt) < 100:
s_off = nan
else:
s_off = determine_station_timing_offset(dt, dz)
offsets.append((ts0, s_off))
write_offets(station, ref_station, offsets)
def plot_station_offset_matrix():
n = len(STATIONS)
stations = {
station: Station(station, force_stale=True)
for station in STATIONS
}
for type in ['offset', 'error']:
plot = MultiPlot(n, n, width=r'.08\textwidth', height=r'.08\textwidth')
for i, station in enumerate(STATIONS):
for j, ref_station in enumerate(STATIONS):
splot = plot.get_subplot_at(i, j)
if i == n:
splot.show_xticklabels()
if station == ref_station:
splot.set_empty()
splot.set_label(r'%d' % station, location='center')
continue
offsets = stations[station].station_timing_offsets(ref_station)
bins = list(offsets['timestamp'])
bins += [bins[-1] + 86400]
splot.histogram(offsets[type], bins, linestyle='very thin')
splot.set_axis_options('line join=round')
plot_cuts_vertically(splot, stations, station, ref_station)
# Even/row rows/columns
for row in range(0, n, 2):
plot.set_yticklabels_position(row, n - 1, 'right')
plot.set_xticklabels_position(n - 1, row, 'bottom')
plot.show_yticklabels(row, n - 1)
#plot.show_xticklabels(n - 1, row)
for row in range(1, n, 2):
plot.set_yticklabels_position(row, 0, 'left')
plot.set_xticklabels_position(0, row, 'top')
plot.show_yticklabels(row, 0)
#plot.show_xticklabels(0, row)
if type == 'offset':
plot.set_ylimits_for_all(None, min=-70, max=70)
else:
plot.set_ylimits_for_all(None, min=0, max=10)
plot.set_xlimits_for_all(None,
min=datetime_to_gps(date(2011, 1, 1)),
max=datetime_to_gps(date.today()))
plot.set_xticks_for_all(None, YEARS_TICKS)
# plot.set_xtick_labels_for_all(YEARS_LABELS)
plot.set_xlabel(r'Date')
plot.set_ylabel(r'Station offset [\si{\ns}]')
plot.save_as_pdf('station_offsets_%s' % type)
def test_get_cuts(self):
gps_station = (datetime_to_gps(datetime(2014, 1, 1, 10, 3)),
datetime_to_gps(datetime(2014, 3, 1, 11, 32)))
gps_ref_station = (datetime_to_gps(datetime(2014, 1, 5, 0, 1, 1)),
datetime_to_gps(datetime(2014, 3, 5, 3, 34, 4)))
elec_station = (datetime_to_gps(datetime(2014, 1, 3, 3, 34, 3)),
datetime_to_gps(datetime(2014, 3, 5, 23, 59, 59)))
elec_ref_station = (datetime_to_gps(datetime(2014, 1, 9, 0, 0, 0)),
datetime_to_gps(datetime(2014, 3, 15, 1, 2, 3)))
gps_mock = Mock()
elec_mock = Mock()
gps_mock.side_effect = [array(gps_station), array(gps_ref_station)]
elec_mock.side_effect = [array(elec_station), array(elec_ref_station)]
self.off._get_electronics_timestamps = elec_mock
self.off._get_gps_timestamps = gps_mock
cuts = self.off._get_cuts(sentinel.station, sentinel.ref_station)
elec_mock.assert_has_calls([call(sentinel.ref_station), call(sentinel.station)], any_order=True)
gps_mock.assert_has_calls([call(sentinel.ref_station), call(sentinel.station)], any_order=True)
self.assertEqual(len(cuts), 8)
six.assertCountEqual(self, sorted(cuts), cuts)
self.assertEqual(cuts[0], datetime(2014, 1, 1))
today = datetime.now()
self.assertEqual(cuts[-1], datetime(today.year, today.month, today.day))
def plot_offset_timeline(ref_station, station):
ref_s = Station(ref_station)
s = Station(station)
# ref_gps = ref_s.gps_locations
# ref_voltages = ref_s.voltages
# ref_n = get_n_events(ref_station)
# gps = s.gps_locations
# voltages = s.voltages
# n = get_n_events(station)
# Determine offsets for first day of each month
# d_off = s.detector_timing_offsets
s_off = get_station_offsets(ref_station, station)
graph = Plot(width=r'.6\textwidth')
# graph.scatter(ref_gps['timestamp'], [95] * len(ref_gps), mark='square', markstyle='purple,mark size=.5pt')
# graph.scatter(ref_voltages['timestamp'], [90] * len(ref_voltages), mark='triangle', markstyle='purple,mark size=.5pt')
# graph.scatter(gps['timestamp'], [85] * len(gps), mark='square', markstyle='gray,mark size=.5pt')
# graph.scatter(voltages['timestamp'], [80] * len(voltages), mark='triangle', markstyle='gray,mark size=.5pt')
# graph.shade_region(n['timestamp'], -ref_n['n'] / 1000, n['n'] / 1000, color='lightgray,const plot')
# graph.plot(d_off['timestamp'], d_off['d0'], markstyle='mark size=.5pt')
# graph.plot(d_off['timestamp'], d_off['d2'], markstyle='mark size=.5pt', linestyle='green')
# graph.plot(d_off['timestamp'], d_off['d3'], markstyle='mark size=.5pt', linestyle='blue')
graph.plot(s_off['timestamp'],
s_off['offset'],
mark='*',
markstyle='mark size=1.25pt',
linestyle=None)
graph.set_ylabel('$\Delta t$ [ns]')
graph.set_xlabel('Date')
graph.set_xticks(
[datetime_to_gps(date(y, 1, 1)) for y in range(2010, 2016)])
graph.set_xtick_labels(['%d' % y for y in range(2010, 2016)])
graph.set_xlimits(1.25e9, 1.45e9)
graph.set_ylimits(-150, 150)
graph.save_as_pdf('plots/offsets/offsets_ref%d_%d' %
(ref_station, station))
def get_detector_timing_offsets_source(request, station_number):
data = get_detector_timing_offsets(station_number)
if not len(data):
raise Http404
data = [
next(rows) for _, rows in groupby(data, key=itemgetter(1, 2, 3, 4))
]
data = [(clock.datetime_to_gps(r[0]), none_to_nan(r[1]), none_to_nan(r[2]),
none_to_nan(r[3]), none_to_nan(r[4])) for r in data]
buffer = StringIO()
writer = csv.writer(buffer, delimiter='\t', lineterminator='\n')
writer.writerows(data)
tsvdata = buffer.getvalue().strip('\n')
response = render(request,
'source/detector_timing_offsets.tsv', {
'tsvdata': tsvdata,
'station_number': station_number
},
content_type=MIME_TSV)
response['Content-Disposition'] = (
'attachment; filename=detector_timing_offsets-s%s.tsv' %
station_number)
return response
def coin_rate_since(start_ts=None):
"""Get coincidence rate since a given timestamp"""
if start_ts is None:
start_ts = datetime_to_gps(date(2015, 1, 1))
for s1, s2 in combinations([7001, 7002, 7003], 2):
ts12 = get_timestamp_ranges([s1, s2])
mts12 = modify_range(ts12, start_ts)
tot12 = get_total_exposure(mts12)
n_coin = get_coin_count(s1, s2, start_ts)
print s1, s2, tot12, n_coin, n_coin / tot12, tot12 / n_coin
def monthrange(start, stop):
"""Generator for datetime month ranges
This is a very specific generator for datetime ranges. Based on
start and stop values, it generates one month intervals.
:param start: a year, month tuple
:param stop: a year, month tuple
:return: generator for start and end timestamps of one month intervals
The stop is the last end of the range.
"""
startdt = datetime(start[0], start[1], 1)
stopdt = datetime(stop[0], stop[1], 1)
if stopdt < startdt:
return
if start == stop:
yield (datetime_to_gps(datetime(start[0], start[1], 1)),
datetime_to_gps(datetime(start[0], start[1] + 1, 1)))
return
else:
current_year, current_month = start
while (current_year, current_month) != stop:
if current_month < 12:
next_year = current_year
next_month = current_month + 1
else:
next_year = current_year + 1
next_month = 1
yield (datetime_to_gps(datetime(current_year, current_month, 1)),
datetime_to_gps(datetime(next_year, next_month, 1)))
current_year = next_year
current_month = next_month
return
def plot_timing_offsets(station_number):
"""Create a plot object from station configs"""
data = get_detector_timing_offsets(station_number)
data = [[clock.datetime_to_gps(row[0]), row[1:]] for row in data]
data = zip(*data)
if not data:
return None
timestamps = data[0]
values = zip(*data[1])
x_label = 'Date (month/year)'
y_label = 'Timing offset (ns)'
plot_object = create_plot_object(timestamps, values, x_label, y_label)
return plot_object
def plot_timing_offsets(station_number):
"""Create a plot object from station configs"""
data = get_detector_timing_offsets(station_number)
data = [[clock.datetime_to_gps(row[0]), row[1:]] for row in data]
data = zip(*data)
if not data:
return None
timestamps = data[0]
values = zip(*data[1])
x_label = 'Date (month/year)'
y_label = 'Timing offset (ns)'
plot_object = create_plot_object(timestamps, values, x_label, y_label)
return plot_object
def plot_luminosity(timestamp, aligned_data, aligned_data_all, i):
n_active_aligned = (aligned_data != 0).sum(axis=0)
cumsummed_data_all = aligned_data_all.sum(axis=0).cumsum()
summed_data = aligned_data.sum(axis=0)
cumsummed_data = summed_data.cumsum()
plot = Plot(width=r'.5\textwidth')
# plot.plot([t / 1e9 for t in timestamp[::100]], cumsummed_data_all[::100],
# linestyle='black!50!green, thick', mark=None)
plot.plot([t / 1e9 for t in timestamp[::100]],
cumsummed_data[::100],
linestyle='thick',
mark=None)
plot.set_xticks([datetime_to_gps(date(y, 1, 1)) / 1e9 for y in YEARS[::3]])
plot.set_xtick_labels(['%d' % y for y in YEARS[::3]])
plot.set_ylabel('Cummulative number of events')
plot.set_xlabel('Date')
plot.save_as_pdf('luminosity_%s' % ['network', 'spa'][i])
def get_station_timing_offsets_source(request, ref_station_number,
station_number):
ref_station_number = int(ref_station_number)
station_number = int(station_number)
if ref_station_number >= station_number:
raise Http404
data = get_station_timing_offsets(ref_station_number, station_number)
if not len(data):
try:
Station.objects.get(number=ref_station_number)
Station.objects.get(number=station_number)
except Station.DoesNotExist:
raise Http404
else:
# For existing pair without offsets return (nan, nan),
# to be handled by analysis software.
data = [(FIRSTDATE, nan, nan)]
data = [next(rows) for _, rows in groupby(data, key=itemgetter(1))]
data = [(clock.datetime_to_gps(r[0]), none_to_nan(r[1]), none_to_nan(r[2]))
for r in data]
buffer = StringIO()
writer = csv.writer(buffer, delimiter='\t', lineterminator='\n')
writer.writerows(data)
tsvdata = buffer.getvalue().strip('\n')
response = render(request,
'source/station_timing_offsets.tsv', {
'tsvdata': tsvdata,
'ref_station_number': ref_station_number,
'station_number': station_number
},
content_type=MIME_TSV)
response['Content-Disposition'] = (
'attachment; filename=station_timing_offsets-s%d-s%d.tsv' %
(ref_station_number, station_number))
return response
def get_station_timing_offsets_source(request, ref_station_number, station_number):
ref_station_number = int(ref_station_number)
station_number = int(station_number)
if ref_station_number >= station_number:
raise Http404
data = get_station_timing_offsets(ref_station_number, station_number)
if not len(data):
try:
Station.objects.get(number=ref_station_number)
Station.objects.get(number=station_number)
except Station.DoesNotExist:
raise Http404
else:
# For existing pair without offsets return (nan, nan),
# to be handled by analysis software.
data = [(FIRSTDATE, nan, nan)]
data = [next(rows) for _, rows in groupby(data, key=itemgetter(1))]
data = [(clock.datetime_to_gps(r[0]), none_to_nan(r[1]), none_to_nan(r[2]))
for r in data]
buffer = StringIO()
writer = csv.writer(buffer, delimiter='\t', lineterminator='\n')
writer.writerows(data)
tsvdata = buffer.getvalue().strip('\n')
response = render(request, 'source/station_timing_offsets.tsv',
{'tsvdata': tsvdata,
'ref_station_number': ref_station_number,
'station_number': station_number},
content_type=MIME_TSV)
response['Content-Disposition'] = (
'attachment; filename=station_timing_offsets-s%d-s%d.tsv' %
(ref_station_number, station_number))
return response
def get_eventtime_histogram_sources(station_number, start, end):
histograms = get_list_or_404(
DailyHistogram.objects.select_related('source'),
source__station__number=station_number,
source__date__gte=start, source__date__lt=end,
type__slug='eventtime')
bins = []
values = []
hours = arange(24) * 3600
no_data = [0] * 24
i = 0
for date in daterange(start, end):
ts = clock.datetime_to_gps(date)
bins.extend(ts + hours)
if histograms[i].source.date == date:
values.extend(histograms[i].values)
i += 1
if i == len(histograms):
break
else:
values.extend(no_data)
return izip(bins, values)
def get_eventtime_histogram_sources(station_number, start, end):
histograms = get_list_or_404(
DailyHistogram.objects.select_related('source'),
source__station__number=station_number,
source__date__gte=start, source__date__lt=end,
type__slug='eventtime')
bins = []
values = []
hours = arange(24) * 3600
no_data = [0] * 24
i = 0
for date in daterange(start, end):
ts = clock.datetime_to_gps(date)
bins.extend(ts + hours)
if histograms[i].source.date == date:
values.extend(histograms[i].values)
i += 1
if i == len(histograms):
break
else:
values.extend(no_data)
return izip(bins, values)
def get_detector_timing_offsets_source(request, station_number):
data = get_detector_timing_offsets(station_number)
if not len(data):
raise Http404
data = [next(rows) for _, rows in groupby(data, key=itemgetter(1, 2, 3, 4))]
data = [(clock.datetime_to_gps(r[0]), none_to_nan(r[1]), none_to_nan(r[2]), none_to_nan(r[3]), none_to_nan(r[4]))
for r in data]
buffer = StringIO()
writer = csv.writer(buffer, delimiter='\t', lineterminator='\n')
writer.writerows(data)
tsvdata = buffer.getvalue().strip('\n')
response = render(request, 'source/detector_timing_offsets.tsv',
{'tsvdata': tsvdata,
'station_number': station_number},
content_type=MIME_TSV)
response['Content-Disposition'] = (
'attachment; filename=detector_timing_offsets-s%s.tsv' %
station_number)
return response
from artist import MultiPlot, Plot
from sapphire.transformations.clock import datetime_to_gps
from sapphire.utils import pbar
from download_dataset import END, START, STATIONS
DATASTORE = "/Users/arne/Datastore/dataset"
DATA_PATH = os.path.join(DATASTORE,
'dataset_sciencepark_stations_110601_160201.h5')
COIN_PATH = os.path.join(DATASTORE, 'dataset_sciencepark_n2_110601_160201.h5')
TSV_PATH = os.path.join(DATASTORE, 'stats/s%d_%s.tsv')
# STATIONS = STATIONS[-2:]
START_TS = datetime_to_gps(date(*START, day=1))
END_TS = datetime_to_gps(date(*END, day=1))
BINS = arange(START_TS, END_TS + 1, 86400)
BIN_WIDTH = BINS[1] - BINS[0]
COLORS = ['black', 'red', 'green', 'blue']
YEARS = range(2011, date.today().year + 1)
YEARS_TICKS = array([datetime_to_gps(date(y, 1, 1)) for y in YEARS])
YEARS_LABELS = [str(y) for y in YEARS]
FIELDS = [
'event_rate', 'mpv', 'integrals', 't_trigger', ('t1', 't2', 't3', 't4'),
('n1', 'n2', 'n3', 'n4')
]
FIELD_NAMES = [''.join(field) for field in FIELDS]
graph.save_as_pdf('detector_offset_drift_%s_%d' % (type, station))
if __name__ == '__main__':
for station in pbar(STATIONS):
# Determine offsets for first day of each month
output = open('offsets_%d.tsv' % station, 'wb')
csvwriter = csv.writer(output, delimiter='\t')
offsets = []
timestamps = []
for y in range(2010, 2016):
for m in range(1, 13):
if y == 2015 and m >= 4:
continue
timestamps.append(datetime_to_gps(date(y, m, 1)))
path = os.path.join(DATA_PATH, str(y), str(m),
'%d_%d_1.h5' % (y, m))
with tables.open_file(path, 'r') as data:
offsets.append(determine_offset(data, station))
csvwriter.writerow([timestamps[-1]] + offsets[-1])
output.close()
plot_detector_offsets(offsets, 'month')
# Determine offsets for each day in one month
offsets = []
y = 2013
m = 1
for d in range(1, 32):
path = os.path.join(DATA_PATH, str(y), str(m),
'%d_%d_%d.h5' % (y, m, d))
def offsets_on_date(offsets, d, id):
timestamp = datetime_to_gps(d)
return [
o(timestamp)[id] for o in offsets.values()
if not isnan(o(timestamp)[1])
]
def test_datetime_to_gps(self):
for date, timestamp, _ in self.combinations:
dt = datetime.datetime.strptime(date, '%B %d, %Y')
self.assertEqual(clock.datetime_to_gps(dt), timestamp)
from datetime import date
from itertools import chain
from numpy import array
from artist import MultiPlot
from sapphire import Station
from sapphire.transformations.clock import datetime_to_gps
YEARS = range(2011, date.today().year + 1)
YEARS_TICKS = array([datetime_to_gps(date(y, 1, 1)) for y in YEARS])
YEARS_LABELS = [str(y) for y in YEARS]
STATIONS = [501, 502, 503, 504, 505, 506, 508, 509, 510, 511]
def plot_station_offset_matrix():
n = len(STATIONS)
stations = {
station: Station(station, force_stale=True)
for station in STATIONS
}
for type in ['offset', 'error']:
plot = MultiPlot(n, n, width=r'.08\textwidth', height=r'.08\textwidth')
for i, station in enumerate(STATIONS):
for j, ref_station in enumerate(STATIONS):
splot = plot.get_subplot_at(i, j)
if i == n:
import datetime
import numpy as np
from artist import Plot
from sapphire import Station
from sapphire.transformations.clock import datetime_to_gps, gps_to_datetime
from get_aligned_eventtimes import get_aligned, get_station_numbers
YEARS = range(2004, datetime.date.today().year + 1)
YEARS_TICKS = np.array(
[datetime_to_gps(datetime.date(y, 1, 1)) for y in YEARS])
YEARS_LABELS = [str(y) for y in YEARS]
def normalize_event_rates(data, station_numbers):
"""Normalize event rates using the number of detectors
Number per hour is divided by the expected number of events per hour for a
station with a certain number of detectors.
So after this a '1.3' would be on average 30% more events per hour than the
expected number of events per hour for such a station.
"""
scaled_data = data.copy()
for i, s in enumerate(station_numbers):
n = Station(s).n_detectors()
if n == 2:
MONTH = 30 * DAY
YEAR = 365 * DAY
SPA_STAT = [501, 502, 503, 504, 505, 506, 508, 509, 510]
CLUSTER = HiSPARCStations(SPA_STAT)
DATA_PATH = '/Users/arne/Datastore/station_offsets/'
def get_station_dt(data, station):
table = data.get_node('/s%d' % station)
return table
if __name__ == '__main__':
t_start = datetime_to_gps(datetime(2010, 1, 1))
t_end = datetime_to_gps(datetime(2015, 4, 1))
for i, station in enumerate(STATIONS, 1):
with tables.open_file(DATA_PATH + 'dt_ref501_%d.h5' % station, 'r') as data:
distance, _, _ = CLUSTER.calc_rphiz_for_stations(i, 0)
max_dt = max(distance / .3, 100) * 1.5
table = get_station_dt(data, station)
graph = Plot()
counts, x, y = histogram2d(table.col('timestamp'),
table.col('delta'),
bins=(arange(t_start, t_end, XWEEK),
linspace(-max_dt, max_dt, 150)))
graph.histogram2d(counts, x, y, bitmap=True, type='color')
graph.set_ylabel('$\Delta t$ [ns]')
graph.set_xlabel('Timestamp [s]')
def plot_active_stations(timestamps, stations, aligned_data, data, i):
first_ts = []
last_ts = []
stations_with_data = []
assert aligned_data.shape[0] == len(stations)
for n in range(aligned_data.shape[0]):
prev_ts = 0
for ts, has_data in zip(timestamps, aligned_data[n]):
if has_data:
if prev_ts > 30:
# Running for at least 30 hours.
first_ts.append(ts)
stations_with_data.append(stations[n])
break
else:
prev_ts += 1
else:
prev_ts = 0
for station in stations_with_data:
end_ts = get_station_end_timestamp(station, data)
if end_ts is not None:
last_ts.append(end_ts)
first_ts = sorted(first_ts)
last_ts = sorted(last_ts)
diff_stations = array([1] * len(first_ts) + [-1] * len(last_ts))
idx = argsort(first_ts + last_ts)
n_stations = diff_stations[idx].cumsum()
# Get maximinum number of simultaneaously active stations per 7 days
n_active_aligned = (aligned_data != 0).sum(axis=0)
n_binned, t_binned, _ = binned_statistic(timestamps,
n_active_aligned,
npmax,
bins=len(timestamps) / (7 * 24))
# Get average number of detected events per 7 days
# todo; scale 2/4 detector stations
summed_data = aligned_data.sum(axis=0)
e_binned, t_binned, _ = binned_statistic(timestamps,
summed_data,
average,
bins=len(timestamps) / (7 * 24))
plot = Plot(width=r'.5\textwidth')
plot.plot([t / 1e9 for t in sorted(first_ts + last_ts)],
n_stations,
linestyle='gray, thick',
mark=None,
use_steps=True)
plot.histogram(n_binned, t_binned / 1e9, linestyle='thick')
plot.histogram(e_binned * max(n_binned) / max(e_binned),
t_binned / 1e9,
linestyle='blue')
plot.set_axis_options('line join=round')
plot.set_ylabel('Number of stations')
plot.set_xlabel('Date')
plot.set_ylimits(min=0)
plot.set_xticks([datetime_to_gps(date(y, 1, 1)) / 1e9 for y in YEARS[::3]])
plot.set_xtick_labels(['%d' % y for y in YEARS[::3]])
plot.save_as_pdf('active_stations_%s' % ['network', 'spa'][i])
def test_datetime_to_gps(self):
for date, timestamp, _ in self.combinations:
dt = datetime.datetime.strptime(date, '%B %d, %Y')
self.assertEqual(clock.datetime_to_gps(dt), timestamp)
(data[sn]['counts'] < 5000))
return timestamps, extended_data
if __name__ == "__main__":
timestamps, eventtime = get_aligned()
summed_data = eventtime.sum(axis=0)
plot = Plot()
bins = arange(-.5, len(STATIONS) + 1.5)
counts, bins = histogram(summed_data, bins=bins)
counts_in_years = counts / 24. / 365.
plot.histogram(counts_in_years, bins, linestyle='semitransparent')
# Exluding data from before 26-09-2008
start = argmax(timestamps > datetime_to_gps(date(2008, 9, 26)))
counts, bins = histogram(summed_data[start:], bins=bins)
counts_in_years = counts / 24. / 365.
print[(i, sum(counts_in_years[i:])) for i in range(11)]
plot.histogram(counts_in_years, bins)
# Exluding data from before 08-03-2010
start = argmax(timestamps > datetime_to_gps(date(2010, 3, 8)))
counts, bins = histogram(summed_data[start:], bins=bins)
counts_in_years = counts / 24. / 365.
print[(i, sum(counts_in_years[i:])) for i in range(11)]
plot.histogram(counts_in_years, bins, linestyle='blue')
# Exluding data from before 01-07-2011
start = argmax(timestamps > datetime_to_gps(date(2011, 7, 1)))
counts, bins = histogram(summed_data[start:], bins=bins)
