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_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_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_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_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_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_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)
