def test_get_new_df():
np.random.seed(150)
amplitude_0 = 200.0
amplitude_1 = 100.0
amplitude_2 = 50.0
x_0_0 = 0.5
x_0_1 = 2.0
x_0_2 = 7.5
fwhm_0 = 0.1
fwhm_1 = 1.0
fwhm_2 = 0.5
whitenoise = 100.0
model = models.Lorentz1D(amplitude_0, x_0_0, fwhm_0) + \
models.Lorentz1D(amplitude_1, x_0_1, fwhm_1) + \
models.Lorentz1D(amplitude_2, x_0_2, fwhm_2) + \
models.Const1D(whitenoise)
freq = np.linspace(0.01, 10.0, 10.0 / 0.01)
p = model(freq)
noise = np.random.exponential(size=len(freq))
power = p * noise
cs = Crossspectrum()
cs.freq = freq
cs.power = power
cs.df = cs.freq[1] - cs.freq[0]
cs.n = len(freq)
cs.m = 1
assert np.isclose(cs.df, spec.get_new_df(cs, cs.n), rtol=0.001)
def test_get_new_df():
np.random.seed(150)
amplitude_0 = 200.0
amplitude_1 = 100.0
amplitude_2 = 50.0
x_0_0 = 0.5
x_0_1 = 2.0
x_0_2 = 7.5
fwhm_0 = 0.1
fwhm_1 = 1.0
fwhm_2 = 0.5
whitenoise = 100.0
model = models.Lorentz1D(amplitude_0, x_0_0, fwhm_0) + \
models.Lorentz1D(amplitude_1, x_0_1, fwhm_1) + \
models.Lorentz1D(amplitude_2, x_0_2, fwhm_2) + \
models.Const1D(whitenoise)
freq = np.linspace(0.01, 10.0, 1000)
p = model(freq)
noise = np.random.exponential(size=len(freq))
power = p * noise
cs = Crossspectrum()
cs.freq = freq
cs.power = power
cs.df = cs.freq[1] - cs.freq[0]
cs.n = len(freq)
cs.m = 1
assert np.isclose(cs.df, spec.get_new_df(cs, cs.n), rtol=0.001)
def test_compute_rms():
np.random.seed(150)
amplitude_0 = 200.0
amplitude_1 = 100.0
amplitude_2 = 50.0
x_0_0 = 0.5
x_0_1 = 2.0
x_0_2 = 7.5
fwhm_0 = 0.1
fwhm_1 = 1.0
fwhm_2 = 0.5
whitenoise = 100.0
model = models.Lorentz1D(amplitude_0, x_0_0, fwhm_0) + \
models.Lorentz1D(amplitude_1, x_0_1, fwhm_1) + \
models.Lorentz1D(amplitude_2, x_0_2, fwhm_2) + \
models.Const1D(whitenoise)
freq = np.linspace(-10.0, 10.0, 10.0 / 0.01)
p = model(freq)
noise = np.random.exponential(size=len(freq))
power = p * noise
cs = Crossspectrum()
cs.freq = freq
cs.power = power
cs.df = cs.freq[1] - cs.freq[0]
cs.n = len(freq)
cs.m = 1
rms = np.sqrt(np.sum(model(cs.freq) * cs.df)).mean()
assert rms == spec.compute_rms(cs, model, criteria="all")
rms_pos = np.sqrt(np.sum(model(cs.freq[cs.freq > 0]) * cs.df)).mean()
assert rms_pos == spec.compute_rms(cs, model, criteria="posfreq")
optimal_filter = Window1D(model)
optimal_filter_freq = optimal_filter(cs.freq)
filtered_cs_power = optimal_filter_freq * np.abs(model(cs.freq))
rms = np.sqrt(np.sum(filtered_cs_power * cs.df)).mean()
assert rms == spec.compute_rms(cs, model, criteria="window")
with pytest.raises(ValueError):
spec.compute_rms(cs, model, criteria="filter")
def test_compute_rms():
np.random.seed(150)
amplitude_0 = 200.0
amplitude_1 = 100.0
amplitude_2 = 50.0
x_0_0 = 0.5
x_0_1 = 2.0
x_0_2 = 7.5
fwhm_0 = 0.1
fwhm_1 = 1.0
fwhm_2 = 0.5
whitenoise = 100.0
model = models.Lorentz1D(amplitude_0, x_0_0, fwhm_0) + \
models.Lorentz1D(amplitude_1, x_0_1, fwhm_1) + \
models.Lorentz1D(amplitude_2, x_0_2, fwhm_2) + \
models.Const1D(whitenoise)
freq = np.linspace(-10.0, 10.0, 1000)
p = model(freq)
noise = np.random.exponential(size=len(freq))
power = p * noise
cs = Crossspectrum()
cs.freq = freq
cs.power = power
cs.df = cs.freq[1] - cs.freq[0]
cs.n = len(freq)
cs.m = 1
rms = np.sqrt(np.sum(model(cs.freq) * cs.df)).mean()
assert rms == spec.compute_rms(cs, model, criteria="all")
rms_pos = np.sqrt(np.sum(model(cs.freq[cs.freq > 0]) * cs.df)).mean()
assert rms_pos == spec.compute_rms(cs, model, criteria="posfreq")
optimal_filter = Window1D(model)
optimal_filter_freq = optimal_filter(cs.freq)
filtered_cs_power = optimal_filter_freq * np.abs(model(cs.freq))
rms = np.sqrt(np.sum(filtered_cs_power * cs.df)).mean()
assert rms == spec.compute_rms(cs, model, criteria="window")
with pytest.raises(ValueError):
spec.compute_rms(cs, model, criteria="filter")
