def test_epoch_folding_search_expocorr_fails(self):
"""Test pulse phase calculation, frequency only."""
frequencies = np.arange(9.8, 9.99, 0.1/self.tseg)
with pytest.raises(ValueError) as excinfo:
freq, stat = epoch_folding_search(self.event_times, frequencies,
nbin=23, expocorr=True)
assert 'To calculate exposure correction' in str(excinfo)
def test_epoch_folding_search_expocorr_fails(self):
"""Test pulse phase calculation, frequency only."""
frequencies = np.arange(9.8, 9.99, 0.1 / self.tseg)
with pytest.raises(ValueError) as excinfo:
freq, stat = epoch_folding_search(self.event_times,
frequencies,
nbin=23,
expocorr=True)
assert 'To calculate exposure correction' in str(excinfo)
def test_epoch_folding_search_weights(self):
"""Test pulse phase calculation, frequency only."""
frequencies = np.arange(9.8, 9.99, 0.1/self.tseg)
freq, stat = epoch_folding_search(self.times, frequencies,
nbin=16, weights=self.counts)
minbin = np.argmin(np.abs(frequencies - self.pulse_frequency))
maxstatbin = freq[np.argmax(stat)]
assert np.allclose(maxstatbin, frequencies[minbin], atol=0.1/self.tseg)
def test_epoch_folding_search_expocorr(self):
"""Test pulse phase calculation, frequency only."""
frequencies = np.arange(9.8, 9.99, 0.1/self.tseg)
freq, stat = epoch_folding_search(self.event_times, frequencies,
nbin=42, expocorr=True, gti=self.gti)
minbin = np.argmin(np.abs(frequencies - self.pulse_frequency))
maxstatbin = freq[np.argmax(stat)]
assert np.allclose(maxstatbin, frequencies[minbin], atol=0.1/self.tseg)
def test_epoch_folding_search_expocorr(self):
"""Test pulse phase calculation, frequency only."""
frequencies = np.arange(9.89, 9.91, 0.1/self.tseg)
freq, stat = epoch_folding_search(self.event_times, frequencies,
nbin=16, expocorr=True)
minbin = np.argmin(np.abs(frequencies - self.pulse_frequency))
maxstatbin = freq[np.argmax(stat)]
assert maxstatbin == frequencies[minbin]
def test_epoch_folding_search(self):
"""Test pulse phase calculation, frequency only."""
frequencies = np.arange(9.85, 9.95, 0.1 / self.tseg)
freq, stat = epoch_folding_search(self.event_times,
frequencies,
nbin=16)
minbin = np.argmin(np.abs(frequencies - self.pulse_frequency))
maxstatbin = freq[np.argmax(stat)]
assert maxstatbin == frequencies[minbin]
def test_epoch_folding_search_fdot(self):
"""Test pulse phase calculation, frequency only."""
frequencies = np.arange(9.8, 9.99, 0.1/self.tseg)
fdots = [-0.1, 0, 0.1]
freq, fdot, stat = epoch_folding_search(self.event_times, frequencies,
nbin=43, fdots=fdots)
minbin = np.argmin(np.abs(frequencies - self.pulse_frequency))
maxstatbin = freq.flatten()[np.argmax(stat)]
assert np.allclose(maxstatbin, frequencies[minbin], atol=0.1/self.tseg)
maxfdot = fdot.flatten()[np.argmax(stat)]
assert np.allclose(maxfdot, 0.0, atol=0.1/self.tseg)
def test_epoch_folding_search_weights(self):
"""Test pulse phase calculation, frequency only."""
frequencies = np.arange(9.8, 9.99, 0.1 / self.tseg)
freq, stat = epoch_folding_search(self.times,
frequencies,
nbin=16,
weights=self.counts)
minbin = np.argmin(np.abs(frequencies - self.pulse_frequency))
maxstatbin = freq[np.argmax(stat)]
assert np.allclose(maxstatbin,
frequencies[minbin],
atol=0.1 / self.tseg)
def test_epoch_folding_search_expocorr(self):
"""Test pulse phase calculation, frequency only."""
frequencies = np.arange(9.8, 9.99, 0.1 / self.tseg)
freq, stat = epoch_folding_search(self.event_times,
frequencies,
nbin=42,
expocorr=True,
gti=self.gti)
minbin = np.argmin(np.abs(frequencies - self.pulse_frequency))
maxstatbin = freq[np.argmax(stat)]
assert np.allclose(maxstatbin,
frequencies[minbin],
atol=0.1 / self.tseg)
def test_epoch_folding_search_fdot(self):
"""Test pulse phase calculation, frequency only."""
frequencies = np.arange(9.8, 9.99, 0.1 / self.tseg)
fdots = [-0.1, 0, 0.1]
freq, fdot, stat = epoch_folding_search(self.event_times,
frequencies,
nbin=43,
fdots=fdots)
minbin = np.argmin(np.abs(frequencies - self.pulse_frequency))
maxstatbin = freq.flatten()[np.argmax(stat)]
assert np.allclose(maxstatbin,
frequencies[minbin],
atol=0.1 / self.tseg)
maxfdot = fdot.flatten()[np.argmax(stat)]
assert np.allclose(maxfdot, 0.0, atol=0.1 / self.tseg)
c1 = fits.Column(name='TIMES', array=TIME, format='D')
t = fits.BinTableHDU.from_columns([c1], name='VALUES')
t.writeto('fits_folder/' + add_space(source) + '_nustar_times.fits',
overwrite=True)
times = TIME
print('Search for the best frequency')
# We will search for pulsations over a range of frequencies around the known pulsation period.
df = (period_ranges[1] - period_ranges[0]) / period_bins
frequencies = 1 / np.arange(period_ranges[0], period_ranges[1], df)
freq, efstat = epoch_folding_search(times, frequencies, nbin=nbin)
pulse_frequency = freq[np.where(efstat == max(efstat))[0][0]]
print('pulse frequency', pulse_frequency)
_ = write_files('pulse_frequency_NuSTAR', pulse_frequency)
#fitting epoch folding distribution with Lorentzian curve
g_init = models.Lorentz1D(
amplitude=max(efstat) - min(efstat),
x_0=pulse_frequency,
fwhm=pulse_frequency / 500) + models.Const1D(amplitude=min(efstat))
fit_g = fitting.LevMarLSQFitter()
bin_max = [np.where(efstat == max(efstat))[0][0]][0]
bin_left_min = 0
bin_right_min = len(efstat)
