def test_fit_powerspectrum(self):
model = self.model.copy()
t0 = model.parameters
_, res1 = fit_powerspectrum(self.ps, model)
_, res2 = fit_powerspectrum(self.ps, model, t0)
_, res3 = fit_crossspectrum(self.ps, model, t0, priors=self.priors)
assert np.all(np.isclose(t0, res1.p_opt, rtol=0.5, atol=0.5))
assert np.all(np.isclose(t0, res2.p_opt, rtol=0.5, atol=0.5))
assert np.all(np.isclose(t0, res3.p_opt, rtol=0.5, atol=0.5))
def test_fit_powerspectrum(self):
model = self.model.copy()
t0 = model.parameters
_, res1 = fit_powerspectrum(self.ps, model)
_, res2 = fit_powerspectrum(self.ps, model, t0)
_, res3 = fit_crossspectrum(self.ps, model, t0, priors=self.priors)
assert np.all(np.isclose(t0, res1.p_opt, rtol=0.5, atol=0.5))
assert np.all(np.isclose(t0, res2.p_opt, rtol=0.5, atol=0.5))
assert np.all(np.isclose(t0, res3.p_opt, rtol=0.5, atol=0.5))
def test_fitting_with_tied_pars(self):
double_f = lambda model: model.x_0_0 * 4
triple_f = lambda model: model.x_0_0 * 15
model = self.model.copy()
model.x_0_1.tied = double_f
model.x_0_2.tied = triple_f
model.amplitude_0 = self.t0[0]
# model.bounds = {}
t0 = np.array([self.amplitude_0, self.x_0_0, self.fwhm_0,
self.amplitude_1, self.fwhm_1,
self.amplitude_2, self.fwhm_2,
self.whitenoise])
parest, res = fit_powerspectrum(self.ps, model,
np.random.normal(t0,
t0 / 10))
true_pars = [self.amplitude_0,
self.x_0_0, self.fwhm_0,
self.amplitude_1, self.fwhm_1,
self.amplitude_2, self.fwhm_2,
self.whitenoise]
assert np.all(np.isclose(true_pars, res.p_opt, rtol=0.5))
def test_fitting_with_fixed_pars(self):
model = self.model.copy()
model.amplitude_0 = self.t0[0]
model.amplitude_0.fixed = True
# model.bounds = {}
parest, res = fit_powerspectrum(
self.ps, model, np.random.normal(self.t0, self.t0 / 10))
true_pars = [
self.x_0_0, self.fwhm_0, self.amplitude_1, self.x_0_1, self.fwhm_1,
self.amplitude_2, self.x_0_2, self.fwhm_2, self.whitenoise
]
assert np.all(np.isclose(true_pars, res.p_opt, rtol=0.5))
def test_fitting_with_tied_pars(self):
double_f = lambda model: model.x_0_0 * 4
triple_f = lambda model: model.x_0_0 * 15
model = self.model.copy()
model.x_0_1.tied = double_f
model.x_0_2.tied = triple_f
model.amplitude_0 = self.t0[0]
# model.bounds = {}
parest, res = fit_powerspectrum(
self.ps, model, np.random.normal(self.t0, self.t0 / 10))
true_pars = [
self.amplitude_0, self.x_0_0, self.fwhm_0, self.amplitude_1,
self.fwhm_1, self.amplitude_2, self.fwhm_2, self.whitenoise
]
assert np.all(np.isclose(true_pars, res.p_opt, rtol=0.5))
def test_fitting_with_fixed_pars(self):
model = self.model.copy()
model.amplitude_0 = self.t0[0]
model.amplitude_0.fixed = True
# model.bounds = {}
t0 = np.array([self.x_0_0, self.fwhm_0,
self.amplitude_1, self.x_0_1, self.fwhm_1,
self.amplitude_2, self.x_0_2, self.fwhm_2,
self.whitenoise])
parest, res = fit_powerspectrum(self.ps, model,
np.random.normal(t0,
t0 / 10))
true_pars = [self.x_0_0, self.fwhm_0,
self.amplitude_1, self.x_0_1, self.fwhm_1,
self.amplitude_2, self.x_0_2, self.fwhm_2,
self.whitenoise]
assert np.all(np.isclose(true_pars, res.p_opt, rtol=0.5))
