def test_rebin_small_dt(self):
segment_size = 3
with warnings.catch_warnings():
warnings.simplefilter("ignore", category=UserWarning)
dps = DynamicalPowerspectrum(self.lc_test, segment_size=segment_size)
with pytest.raises(ValueError):
dps.rebin_time(dt_new=2.0)
def test_size_of_trace_maximum(self):
with warnings.catch_warnings():
warnings.simplefilter("ignore", category=UserWarning)
dps = DynamicalPowerspectrum(self.lc, segment_size=3)
max_pos = dps.trace_maximum()
nsegs = int(self.lc.tseg / dps.segment_size)
assert len(max_pos) == nsegs
def test_rebin_small_df(self):
segment_size = 3
with warnings.catch_warnings():
warnings.simplefilter("ignore", category=UserWarning)
dps = DynamicalPowerspectrum(self.lc, segment_size=segment_size)
with pytest.raises(ValueError):
dps.rebin_frequency(df_new=dps.df/2.0)
def test_trace_maximum_without_boundaries(self):
with warnings.catch_warnings():
warnings.simplefilter("ignore", category=UserWarning)
dps = DynamicalPowerspectrum(self.lc, segment_size=3)
max_pos = dps.trace_maximum()
assert np.max(dps.freq[max_pos]) <= 1 / self.lc.dt
assert np.min(dps.freq[max_pos]) >= 1 / dps.segment_size
def test_trace_maximum_with_boundaries(self):
dps = DynamicalPowerspectrum(self.lc, segment_size=3)
minfreq = 21
maxfreq = 24
max_pos = dps.trace_maximum(min_freq=minfreq, max_freq=maxfreq)
assert np.max(dps.freq[max_pos]) <= maxfreq
assert np.min(dps.freq[max_pos]) >= minfreq
def test_trace_maximum_with_boundaries(self):
dps = DynamicalPowerspectrum(self.lc, segment_size=3)
minfreq = 21
maxfreq = 24
max_pos = dps.trace_maximum(min_freq=minfreq, max_freq=maxfreq)
assert np.max(dps.freq[max_pos]) <= maxfreq
assert np.min(dps.freq[max_pos]) >= minfreq
def test_rebin_time_average_method(self):
segment_size = 3
dt_new = 4.0
rebin_time = np.array([ 2., 6., 10.])
rebin_dps = np.array([[0.59722222, 0.87301587, 0.21428571]])
dps = DynamicalPowerspectrum(self.lc_test, segment_size=segment_size)
dps.rebin_time(dt_new=dt_new, method='average')
assert np.allclose(dps.time, rebin_time)
assert np.allclose(dps.dyn_ps, rebin_dps)
assert np.isclose(dps.dt, dt_new)
def test_rebin_time_default_method(self):
segment_size = 3
dt_new = 4.0
rebin_time = np.array([ 2., 6., 10.])
rebin_dps = np.array([[0.7962963 , 1.16402116, 0.28571429]])
dps = DynamicalPowerspectrum(self.lc_test, segment_size=segment_size)
dps.rebin_time(dt_new=dt_new)
assert np.allclose(dps.time, rebin_time)
assert np.allclose(dps.dyn_ps, rebin_dps)
assert np.isclose(dps.dt, dt_new)
def test_rebin_time_default_method(self):
segment_size = 3
dt_new = 4.0
rebin_time = np.array([2., 6., 10.])
rebin_dps = np.array([[0.7962963, 1.16402116, 0.28571429]])
dps = DynamicalPowerspectrum(self.lc_test, segment_size=segment_size)
dps.rebin_time(dt_new=dt_new)
assert np.allclose(dps.time, rebin_time)
assert np.allclose(dps.dyn_ps, rebin_dps)
assert np.isclose(dps.dt, dt_new)
def test_rebin_time_average_method(self):
segment_size = 3
dt_new = 4.0
rebin_time = np.array([2., 6., 10.])
rebin_dps = np.array([[0.59722222, 0.87301587, 0.21428571]])
dps = DynamicalPowerspectrum(self.lc_test, segment_size=segment_size)
dps.rebin_time(dt_new=dt_new, method='average')
assert np.allclose(dps.time, rebin_time)
assert np.allclose(dps.dyn_ps, rebin_dps)
assert np.isclose(dps.dt, dt_new)
def test_rebin_time_mean_method(self):
segment_size = 3
dt_new = 4.0
rebin_time = np.array([1.5, 5.5, 9.5, 13.5])
rebin_dps = np.array([[0.59722222, 0.87301587, 0.21428571, 0.4921875]])
dps = DynamicalPowerspectrum(self.lc_test, segment_size=segment_size)
dps.rebin_time(dt_new=dt_new, method='mean')
assert np.allclose(dps.time, rebin_time)
assert np.allclose(dps.dyn_ps, rebin_dps)
assert np.isclose(dps.dt, dt_new)
def test_rebin_time_mean_method(self):
segment_size = 3
dt_new = 4.0
rebin_time = np.array([1.5, 5.5, 9.5, 13.5])
rebin_dps = np.array([[0.59722222, 0.87301587, 0.21428571, 0.4921875]])
dps = DynamicalPowerspectrum(self.lc_test, segment_size=segment_size)
dps.rebin_time(dt_new=dt_new, method='mean')
assert np.allclose(dps.time, rebin_time)
assert np.allclose(dps.dyn_ps, rebin_dps)
assert np.isclose(dps.dt, dt_new)
def test_trace_maximum_with_boundaries(self):
with warnings.catch_warnings():
warnings.simplefilter("ignore", category=UserWarning)
dps = DynamicalPowerspectrum(self.lc, segment_size=3)
minfreq = 21
maxfreq = 24
max_pos = dps.trace_maximum(min_freq=minfreq, max_freq=maxfreq)
assert np.max(dps.freq[max_pos]) <= maxfreq
assert np.min(dps.freq[max_pos]) >= minfreq
def test_works_with_events(self):
lc = copy.deepcopy(self.lc)
lc.counts = np.floor(lc.counts)
ev = EventList.from_lc(lc)
dps = DynamicalPowerspectrum(lc, segment_size=10)
with pytest.raises(ValueError):
# Without dt, it fails
_ = DynamicalPowerspectrum(ev, segment_size=10)
dps_ev = DynamicalPowerspectrum(ev, segment_size=10, dt=self.lc.dt)
assert np.allclose(dps.dyn_ps, dps_ev.dyn_ps)
def test_rebin_time_mean_method(self):
segment_size = 3
dt_new = 4.0
rebin_time = np.array([ 2., 6., 10.])
rebin_dps = np.array([[0.59722222, 0.87301587, 0.21428571]])
with warnings.catch_warnings():
warnings.simplefilter("ignore", category=UserWarning)
dps = DynamicalPowerspectrum(self.lc_test, segment_size=segment_size)
new_dps = dps.rebin_time(dt_new=dt_new, method='mean')
assert np.allclose(new_dps.time, rebin_time)
assert np.allclose(new_dps.dyn_ps, rebin_dps)
assert np.isclose(new_dps.dt, dt_new)
def test_rebin_frequency_average_method(self):
segment_size = 50
df_new = 10.0
rebin_freq = np.array(
[5.01000198, 15.01000198, 25.01000198, 35.01000198, 45.01000198])
rebin_dps = np.array([[1.15296690e-08], [1.41532979e-07],
[1.24993989e-02], [1.15516968e-07],
[3.53906336e-08]])
dps = DynamicalPowerspectrum(self.lc, segment_size=segment_size)
dps.rebin_frequency(df_new=df_new, method="average")
assert np.allclose(dps.freq, rebin_freq)
assert np.allclose(dps.dyn_ps, rebin_dps)
assert np.isclose(dps.df, df_new)
def test_rebin_frequency_default_method(self):
segment_size = 50
df_new = 10.0
rebin_freq = np.array(
[5.01000198, 15.01000198, 25.01000198, 35.01000198, 45.01000198])
rebin_dps = np.array([[5.76369293e-06], [7.07524761e-05],
[6.24846189e+00], [5.77470465e-05],
[1.76918128e-05]])
dps = DynamicalPowerspectrum(self.lc, segment_size=segment_size)
dps.rebin_frequency(df_new=df_new)
assert np.allclose(dps.freq, rebin_freq)
assert np.allclose(dps.dyn_ps, rebin_dps)
assert np.isclose(dps.df, df_new)
def test_rebin_frequency_default_method(self):
segment_size = 50
df_new = 10.0
rebin_freq = np.array(
[5.01000198, 15.01000198, 25.01000198, 35.01000198, 45.01000198])
rebin_dps = np.array([[5.76369293e-06], [7.07524761e-05],
[6.24846189e+00], [5.77470465e-05],
[1.76918128e-05]])
with warnings.catch_warnings():
warnings.simplefilter("ignore", category=UserWarning)
dps = DynamicalPowerspectrum(self.lc, segment_size=segment_size)
new_dps = dps.rebin_frequency(df_new=df_new)
assert np.allclose(new_dps.freq, rebin_freq)
assert np.allclose(new_dps.dyn_ps, rebin_dps)
assert np.isclose(new_dps.df, df_new)
def test_rebin_frequency_default_method(self):
segment_size = 50
df_new = 10.0
rebin_freq = np.array([5.01000198, 15.01000198, 25.01000198,
35.01000198, 45.01000198])
rebin_dps = np.array([[5.76369293e-06],
[7.07524761e-05],
[6.24846189e+00],
[5.77470465e-05],
[1.76918128e-05]])
dps = DynamicalPowerspectrum(self.lc, segment_size=segment_size)
dps.rebin_frequency(df_new=df_new)
assert np.allclose(dps.freq, rebin_freq)
assert np.allclose(dps.dyn_ps, rebin_dps)
assert np.isclose(dps.df, df_new)
def test_rebin_frequency_average_method(self):
segment_size = 50
df_new = 10.0
rebin_freq = np.array([5.01000198, 15.01000198, 25.01000198,
35.01000198, 45.01000198])
rebin_dps = np.array([[1.15296690e-08],
[1.41532979e-07],
[1.24993989e-02],
[1.15516968e-07],
[3.53906336e-08]])
dps = DynamicalPowerspectrum(self.lc, segment_size=segment_size)
dps.rebin_frequency(df_new=df_new, method="average")
assert np.allclose(dps.freq, rebin_freq)
assert np.allclose(dps.dyn_ps, rebin_dps)
assert np.isclose(dps.df, df_new)
def test_rebin_frequency_average_method(self):
segment_size = 50
df_new = 10.0
rebin_freq = np.array(
[5.01000198, 15.01000198, 25.01000198, 35.01000198, 45.01000198])
rebin_dps = np.array([[1.15296690e-08], [1.41532979e-07],
[1.24993989e-02], [1.15516968e-07],
[3.53906336e-08]])
with warnings.catch_warnings():
warnings.simplefilter("ignore", category=UserWarning)
dps = DynamicalPowerspectrum(self.lc, segment_size=segment_size)
new_dps = dps.rebin_frequency(df_new=df_new, method="average")
assert np.allclose(new_dps.freq, rebin_freq)
assert np.allclose(new_dps.dyn_ps, rebin_dps)
assert np.isclose(new_dps.df, df_new)
def test_matrix(self):
with warnings.catch_warnings():
warnings.simplefilter("ignore", category=UserWarning)
dps = DynamicalPowerspectrum(self.lc, segment_size=3)
nsegs = int(self.lc.tseg / dps.segment_size)
nfreq = int((1 / self.lc.dt) / (2 * (dps.freq[1] - dps.freq[0])) -
(1 / self.lc.tseg))
assert dps.dyn_ps.shape == (nfreq, nsegs)
def test_trace_maximum_without_boundaries(self):
dps = DynamicalPowerspectrum(self.lc, segment_size=3)
max_pos = dps.trace_maximum()
assert np.max(dps.freq[max_pos]) <= 1 / self.lc.dt
assert np.min(dps.freq[max_pos]) >= 1 / dps.segment_size
def test_size_of_trace_maximum(self):
dps = DynamicalPowerspectrum(self.lc, segment_size=3)
max_pos = dps.trace_maximum()
nsegs = int(self.lc.tseg / dps.segment_size)
assert len(max_pos) == nsegs
def test_rebin_small_df(self):
segment_size = 3
dps = DynamicalPowerspectrum(self.lc, segment_size=segment_size)
with pytest.raises(ValueError):
dps.rebin_frequency(df_new=dps.df / 2.0)
def test_rebin_small_dt(self):
segment_size = 3
dps = DynamicalPowerspectrum(self.lc_test, segment_size=segment_size)
with pytest.raises(ValueError):
dps.rebin_time(dt_new=2.0)
def test_size_of_trace_maximum(self):
dps = DynamicalPowerspectrum(self.lc, segment_size=3)
max_pos = dps.trace_maximum()
nsegs = int(self.lc.tseg / dps.segment_size)
assert len(max_pos) == nsegs
def test_rebin_small_dt(self):
segment_size = 3
dps = DynamicalPowerspectrum(self.lc_test, segment_size=segment_size)
with pytest.raises(ValueError):
dps.rebin_time(dt_new=2.0)
def test_trace_maximum_without_boundaries(self):
dps = DynamicalPowerspectrum(self.lc, segment_size=3)
max_pos = dps.trace_maximum()
assert np.max(dps.freq[max_pos]) <= 1 / self.lc.dt
assert np.min(dps.freq[max_pos]) >= 1 / dps.segment_size
def test_matrix(self):
dps = DynamicalPowerspectrum(self.lc, segment_size=3)
nsegs = int(self.lc.tseg / dps.segment_size)
nfreq = int((1 / self.lc.dt) / (2 * (dps.freq[1] - dps.freq[0])) -
(1 / self.lc.tseg))
assert dps.dyn_ps.shape == (nfreq, nsegs)
def test_with_long_seg_size(self):
with pytest.raises(ValueError):
dps = DynamicalPowerspectrum(self.lc, segment_size=1000)
def test_rebin_small_df(self):
segment_size = 3
dps = DynamicalPowerspectrum(self.lc, segment_size=segment_size)
with pytest.raises(ValueError):
dps.rebin_frequency(df_new=dps.df/2.0)
