def test_llr_const_single(t0, t_data, time_window):
with pytest.raises(ValueError):
llr = sn.LLR(sn.DetConfig(1, 2))
l = sn.LLR(sn.DetConfig(1, 1, time_window=time_window))
if (time_window[0] <= t_data - t0 <= time_window[1]):
assert l(t_data, t0) == np.log(2)
else:
assert l(t_data, t0) == 0
def ShapeAna(B, S, time_window="auto", *, dt=0.1, tChunk_min=1, delay=0):
""" Processing :term:`step`: shape analysis to calculate significance
Args:
B (rate)
Background rate
S (rate)
Signal rate
time_window (tuple (float, float) or "auto")
A relative time window to count the interactions, for example :code:`[-5,5]`.
Only interactions within the time window affect current significance.
If "auto", try to get the range from signal shape.
Keyword Args:
dt (float):
time step, seconds
tChunk_min (float):
minimal time duration of the produced chunk of data
delay (float):
processing delay in seconds.
This means that maximum assumed supernova time will be::
now()-delay-ana.time_window[1]
:Input:
data (list of float): list of events' timestamps
:Output:
:snap.datablock.DataBlock: with the SN observation significance
"""
ana = sn.ShapeAnalysis(sn.DetConfig(B=B, S=S, time_window=time_window))
return SignificanceCalculator(ana, dt, tChunk_min, delay)
def test_shapeana():
B = sn.rate(1)
S = sn.rate(2, range=[-1, 1])
det = sn.DetConfig(S=S, B=B)
ana = sn.ShapeAnalysis([det])
assert ana is not None
#define the rates S = sn.rate(([0, 1, 10], [0, 2, 0])) * 100 B = sn.rate(800) Tsn = 0. #total event rate vs time R = B + S.shift(Tsn) T0, T1 = -60, 60 #create the dataset via sampler s = sn.Sampler(R, time_window=[T0, T1]) ts = s.sample() #detector configuration det = sn.DetConfig(B=B, S=S) print(det) #assumed supernova start times t0s = np.linspace(T0 + det.time_window[0], T1 - det.time_window[1], 501) sa = sn.ShapeAnalysis(det) ca = sn.CountingAnalysis(det) #calculate TS vs time ls_s = sa.l_val(ts, t0s) ls_c = ca.l_val(ts, t0s) #calculate z vs time zs_s = sa(ts, t0s) zs_c = ca(ts, t0s)
#!/bin/env python
import sn_stat as sn
import numpy as np
import pylab as plt
#define the rates
sg1 = sn.signals.ccSN(S0=2)
sg2 = sn.signals.ccSN(S0=5, t_rise=2, t_decay=3)
#sg2 = sn.rate(([-5,0,1,5],[0,1,4,0]))
det1 = sn.DetConfig(B=0.1, S=sg1.at(1), time_window=[-5, 10])
det2 = sn.DetConfig(B=1, S=sg2.at(1), time_window=[-5, 10])
detectors = [det1, det2]
Tsn = 0.
T0, T1 = -60, 60
Rs = []
for det in [det1, det2]:
print(det)
#total event rate vs time
R = det.B + det.S.shift(Tsn)
det.rate = R
#create the dataset via sampler
s = sn.Sampler(R, time_window=[T0, T1])
det.data = s.sample()
#maximum time window:
tw = [
max(d.time_window[0] for d in detectors),
min(d.time_window[1] for d in detectors)