def test_flat_top_window(self):
result = np.array([
8.67361738e-17, -2.62000000e-01, 4.63600000e+00, -2.62000000e-01,
8.67361738e-17
])
window = utils.create_window(self.N, self.flattop_window)
assert np.allclose(window, result)
def _get_window(self):
"""
Returns a window function of self.window_name type
"""
N = 2 * self.maxlag + 1
window_even = utils.create_window(N, self.window_name)
# 2d even window
window2d = np.array([window_even, ] * N)
## One-sided window with zero padding
window = np.zeros(N)
window[:self.maxlag + 1] = window_even[self.maxlag:]
window[self.maxlag:] = 0
# 2d window function to apply to bispectrum
row = np.concatenate(([window[0]], np.zeros(2 * self.maxlag)))
toep_matrix = toeplitz(window, row)
toep_matrix += np.tril(toep_matrix, -1).transpose()
window = toep_matrix[..., ::-1] * window2d * window2d.transpose()
return window
def test_bad_N(self):
N_bad = 'abc'
with pytest.raises(TypeError):
window = utils.create_window(N_bad, self.uniform_window)
def test_bad_window_type(self):
window_bad = 123
with pytest.raises(TypeError):
window = utils.create_window(self.N, window_bad)
def test_welch_window(self):
result = np.array([0, 0.75, 1, 0.75, 0])
window = utils.create_window(self.N, self.welch_window)
assert np.allclose(window, result)
def test_hanning_window(self):
result = np.array([0, 0.5, 1, 0.5, 0])
window = utils.create_window(self.N, self.hanning_window)
assert np.allclose(window, result)
def test_parzen_window(self):
result = np.array([0, 0.25, 1, 0.25, 0])
window = utils.create_window(self.N, self.parzen_window)
assert np.allclose(window, result)
def test_N_equals_zero(self):
N = 0
window = utils.create_window(N)
assert len(window) == 0
def test_triangular_window(self):
result = np.array([0.6, 0.8, 1, 0.8, 0.6])
window = utils.create_window(self.N, self.triangular_window)
assert np.allclose(window, result)
def test_hanning_window(self):
result = np.array([0, 0.5, 1, 0.5, 0])
window = utils.create_window(self.N, self.hanning_window)
assert np.allclose(window, result)
def test_hamming_window(self):
result = np.array([0.08, 0.54, 1, 0.54, 0.08])
window = utils.create_window(self.N, self.hamming_window)
assert np.allclose(window, result)
def test_parzen_window(self):
result = np.array([0, 0.25, 1, 0.25, 0])
window = utils.create_window(self.N, self.parzen_window)
assert np.allclose(window, result)
def test_uniform_window(self):
result = np.ones(self.N)
window = utils.create_window(self.N)
assert np.allclose(window, result)
def test_N_equals_zero(self):
N = 0
window = utils.create_window(N)
assert len(window) == 0
def test_not_available_window(self):
window_not = 'kaiser'
with pytest.raises(ValueError):
window = utils.create_window(self.N, window_not)
def test_bad_window_type(self):
window_bad = 123
with pytest.raises(TypeError):
window = utils.create_window(self.N, window_bad)
def test_not_available_window(self):
window_not = 'kaiser'
with pytest.raises(ValueError):
window = utils.create_window(self.N, window_not)
def test_welch_window(self):
result = np.array([0, 0.75, 1, 0.75, 0])
window = utils.create_window(self.N, self.welch_window)
assert np.allclose(window, result)
def test_uniform_window(self):
result = np.ones(self.N)
window = utils.create_window(self.N)
assert np.allclose(window, result)
def test_blackmann_window(self):
result = np.array([0.006879, 0.349741, 0.999999, 0.349741, 0.006879])
window = utils.create_window(self.N, self.blackmann_window)
assert np.allclose(window, result)
def test_hamming_window(self):
result = np.array([0.08, 0.54, 1, 0.54, 0.08])
window = utils.create_window(self.N, self.hamming_window)
assert np.allclose(window, result)
def test_flat_top_window(self):
result = np.array([8.67361738e-17, -2.62000000e-01, 4.63600000e+00, -2.62000000e-01,
8.67361738e-17])
window = utils.create_window(self.N, self.flattop_window)
assert np.allclose(window, result)
def test_triangular_window(self):
result = np.array([0.6, 0.8, 1, 0.8, 0.6])
window = utils.create_window(self.N, self.triangular_window)
assert np.allclose(window, result)
cadence = 12.
# Number of exposures
n_exposure = 234
# Times at which the exposures are made
times = cadence*np.arange(0, n_exposure) # seconds
# Counts per second
signal = 300 * np.sin(2.*np.pi*times/0.5) + 1000 # counts/s
# Noisy version of the true signal
noisy = np.random.poisson(signal*exposure_time) # counts
uniform = create_window(len(signal))
hanning = create_window(len(signal), window_type='hanning')
lc = []
for window_type in [uniform, hanning]:
data = noisy*window_type
lc.append(Lightcurve(times, data))
ps1 = Powerspectrum(lc[0])
ps2 = Powerspectrum(lc[1])
fig, ax1 = plt.subplots(1, 1, figsize=(9, 6), sharex=True)
ax1.plot(ps1.freq, ps1.power, lw=1, color='blue', label='no window')
ax1.plot(ps2.freq, ps2.power, lw=1, color='red', label='Hanning')
ax1.set_xlabel("Frequency (Hz)")
def test_blackmann_window(self):
result = np.array([0.006879, 0.349741, 0.999999, 0.349741, 0.006879])
window = utils.create_window(self.N, self.blackmann_window)
assert np.allclose(window, result)
def test_bad_N(self):
N_bad = 'abc'
with pytest.raises(TypeError):
window = utils.create_window(N_bad, self.uniform_window)
