def test_correct_parameters_without_whitenoise(self):
parest, res = fit_lorentzians(self.ps,
self.nlor,
self.t0[:-1],
fit_whitenoise=False)
assert len(parest.lpost.model.parameters) / 3 == self.nlor
def setup_class(cls):
np.random.seed(150)
cls.nlor = 3
cls.x_0_0 = 0.5
cls.x_0_1 = 2.0
cls.x_0_2 = 7.5
cls.amplitude_0 = 200.0
cls.amplitude_1 = 100.0
cls.amplitude_2 = 50.0
cls.fwhm_0 = 0.1
cls.fwhm_1 = 1.0
cls.fwhm_2 = 0.5
cls.whitenoise = 2.0
cls.priors = {
'x_0_0': cls.x_0_0,
'x_0_1': cls.x_0_1,
'x_0_2': cls.x_0_2,
'amplitude_0': cls.amplitude_0,
'amplitude_1': cls.amplitude_1,
'amplitude_2': cls.amplitude_2,
'fwhm_0': cls.fwhm_0,
'fwhm_1': cls.fwhm_1,
'fwhm_2': cls.fwhm_2,
'whitenoise': cls.whitenoise
}
cls.model = models.Lorentz1D(cls.amplitude_0, cls.x_0_0, cls.fwhm_0) +\
models.Lorentz1D(cls.amplitude_1, cls.x_0_1, cls.fwhm_1) + \
models.Lorentz1D(cls.amplitude_2, cls.x_0_2, cls.fwhm_2) + \
models.Const1D(cls.whitenoise)
freq = np.linspace(0.01, 10.0, 1000)
p = cls.model(freq)
noise = np.random.exponential(size=len(freq))
power = p * noise
cls.ps = Powerspectrum()
cls.ps.freq = freq
cls.ps.power = power
cls.ps.power_err = np.array([0.] * len(power))
cls.ps.df = cls.ps.freq[1] - cls.ps.freq[0]
cls.ps.m = 1
cls.cs = Crossspectrum()
cls.cs.freq = freq
cls.cs.power = power
cls.cs.power_err = np.array([0.] * len(power))
cls.cs.df = cls.cs.freq[1] - cls.cs.freq[0]
cls.cs.m = 1
cls.t0 = np.asarray(
[200.0, 0.5, 0.1, 100.0, 2.0, 1.0, 50.0, 7.5, 0.5, 2.0])
cls.parest, cls.res = fit_lorentzians(cls.ps, cls.nlor, cls.t0)
def setup_class(cls):
np.random.seed(150)
cls.nlor = 3
cls.x_0_0 = 0.5
cls.x_0_1 = 2.0
cls.x_0_2 = 7.5
cls.amplitude_0 = 200.0
cls.amplitude_1 = 100.0
cls.amplitude_2 = 50.0
cls.fwhm_0 = 0.1
cls.fwhm_1 = 1.0
cls.fwhm_2 = 0.5
cls.whitenoise = 2.0
cls.priors = {'x_0_0': cls.x_0_0, 'x_0_1': cls.x_0_1,
'x_0_2': cls.x_0_2, 'amplitude_0': cls.amplitude_0,
'amplitude_1': cls.amplitude_1,
'amplitude_2': cls.amplitude_2, 'fwhm_0': cls.fwhm_0,
'fwhm_1': cls.fwhm_1, 'fwhm_2': cls.fwhm_2,
'whitenoise': cls.whitenoise}
cls.model = models.Lorentz1D(cls.amplitude_0, cls.x_0_0, cls.fwhm_0) +\
models.Lorentz1D(cls.amplitude_1, cls.x_0_1, cls.fwhm_1) + \
models.Lorentz1D(cls.amplitude_2, cls.x_0_2, cls.fwhm_2) + \
models.Const1D(cls.whitenoise)
freq = np.linspace(0.01, 10.0, 10.0 / 0.01)
p = cls.model(freq)
noise = np.random.exponential(size=len(freq))
power = p * noise
cls.ps = Powerspectrum()
cls.ps.freq = freq
cls.ps.power = power
cls.ps.power_err = np.array([0.]*len(power))
cls.ps.df = cls.ps.freq[1] - cls.ps.freq[0]
cls.ps.m = 1
cls.cs = Crossspectrum()
cls.cs.freq = freq
cls.cs.power = power
cls.cs.power_err = np.array([0.]*len(power))
cls.cs.df = cls.cs.freq[1] - cls.cs.freq[0]
cls.cs.m = 1
cls.t0 = np.asarray([200.0, 0.5, 0.1, 100.0, 2.0, 1.0,
50.0, 7.5, 0.5, 2.0])
cls.parest, cls.res = fit_lorentzians(cls.ps, cls.nlor, cls.t0)
def setup_class(cls):
np.random.seed(150)
cls.nlor = 3
cls.x_0_0 = 0.5
cls.x_0_1 = 2.0
cls.x_0_2 = 7.5
cls.amplitude_0 = 200.0
cls.amplitude_1 = 100.0
cls.amplitude_2 = 50.0
cls.fwhm_0 = 0.1
cls.fwhm_1 = 1.0
cls.fwhm_2 = 0.5
cls.whitenoise = 2.0
cls.model = models.Lorentz1D(cls.amplitude_0, cls.x_0_0, cls.fwhm_0) + \
models.Lorentz1D(cls.amplitude_1, cls.x_0_1, cls.fwhm_1) + \
models.Lorentz1D(cls.amplitude_2, cls.x_0_2, cls.fwhm_2) + \
models.Const1D(cls.whitenoise)
freq = np.linspace(0.01, 10.0, 10.0 / 0.01)
p = cls.model(freq)
noise = np.random.exponential(size=len(freq))
power = p * noise
cls.ps = Powerspectrum()
cls.ps.freq = freq
cls.ps.power = power
cls.ps.df = cls.ps.freq[1] - cls.ps.freq[0]
cls.ps.m = 1
cls.t0 = [200.0, 0.5, 0.1, 100.0, 2.0, 1.0, 50.0, 7.5, 0.5, 2.0]
cls.parest, cls.res = fit_lorentzians(cls.ps, cls.nlor, cls.t0)
def test_correct_parameters_without_whitenoise(self):
parest, res = fit_lorentzians(self.ps, self.nlor, self.t0[:-1],
fit_whitenoise=False)
assert len(parest.lpost.model.parameters) / 3 == self.nlor
