def test_noisy(self):
candidate_table = accelsearch(self.times, self.noisy, zmax=10,
candidate_file='bubu.csv', delta_z=0.5,
nproc=1)
best = np.argmax(candidate_table['power'])
assert np.isclose(candidate_table['frequency'][best], self.freq,
atol=5 * self.df)
assert np.isclose(candidate_table['fdot'][best] * self.rescale_fdot,
self.fdot * self.rescale_fdot,
atol=2 * self.dfdot * self.rescale_fdot)
def test_signal(self):
candidate_table = accelsearch(self.times, self.signal, zmax=10,
candidate_file='bubu.csv', delta_z=0.5,
gti=[[self.tstart, self.tstop]],
debug=True,
interbin=True, nproc=1)
best = np.argmax(candidate_table['power'])
assert np.isclose(candidate_table['frequency'][best], self.freq,
atol=5 * self.df)
print(candidate_table['fdot'][best] * self.rescale_fdot,
self.fdot * self.rescale_fdot)
assert np.isclose(candidate_table['fdot'][best] * self.rescale_fdot,
self.fdot * self.rescale_fdot,
atol=2 * self.dfdot * self.rescale_fdot)