def test_analyze_lc_chunks(self):
lc = Lightcurve(self.times, self.counts, gti=self.gti)
def func(lc):
return lc.time[0]
start, stop, res = lc.analyze_lc_chunks(2, func)
assert start[0] == 0.5
assert np.all(start + lc.dt / 2 == res)
def test_analyze_lc_chunks(self):
lc = Lightcurve(self.times, self.counts, gti=self.gti)
def func(lc):
return lc.time[0]
start, stop, res = lc.analyze_lc_chunks(2, func)
assert start[0] == 0.5
assert np.all(start + lc.dt / 2 == res)
def test_analyze_lc_chunks_fvar_fracstep(self):
dt = 0.1
tstart = 0
tstop = 100
times = np.arange(tstart, tstop, dt)
gti = np.array([[tstart - dt / 2, tstop - dt / 2]])
# Simulate something *clearly* non-constant
counts = np.random.poisson(10000 + 2000 * np.sin(2 * np.pi * times))
lc = Lightcurve(times, counts, gti=gti)
def excvar(lc):
from stingray.utils import excess_variance
return excess_variance(lc, normalization='fvar')
start, stop, res = lc.analyze_lc_chunks(20, excvar, fraction_step=0.5)
# excess_variance returns fvar and fvar_err
res, res_err = res
assert np.allclose(start[0], gti[0, 0])
assert np.all(res > 0)
# This must be a clear measurement of fvar
assert np.all(res > res_err)
def test_analyze_lc_chunks_fvar_fracstep(self):
dt = 0.1
tstart = 0
tstop = 100
times = np.arange(tstart, tstop, dt)
gti = np.array([[tstart - dt/2, tstop - dt/2]])
# Simulate something *clearly* non-constant
counts = np.random.poisson(
10000 + 2000 * np.sin(2 * np.pi * times))
lc = Lightcurve(times, counts, gti=gti)
def excvar(lc):
from stingray.utils import excess_variance
return excess_variance(lc, normalization='fvar')
start, stop, res = lc.analyze_lc_chunks(20, excvar, fraction_step=0.5)
# excess_variance returns fvar and fvar_err
res, res_err = res
assert np.allclose(start[0], gti[0, 0])
assert np.all(res > 0)
# This must be a clear measurement of fvar
assert np.all(res > res_err)
