def test_model_units_match(data, t_unit, frequency_unit, y_unit): t, y, dy = data t_fit = t[:5] frequency = 1.0 t = t * t_unit t_fit = t_fit * t_unit y = y * y_unit dy = dy * y_unit frequency = frequency * frequency_unit ls = LombScargle(t, y, dy) y_fit = ls.model(t_fit, frequency) assert y_fit.unit == y_unit
def test_model(fit_mean, with_units, freq): rand = np.random.RandomState(0) t = 10 * rand.rand(40) params = 10 * rand.rand(3) y = np.zeros_like(t) if fit_mean: y += params[0] y += params[1] * np.sin(2 * np.pi * freq * (t - params[2])) if with_units: t = t * units.day y = y * units.mag freq = freq / units.day ls = LombScargle(t, y, center_data=False, fit_mean=fit_mean) y_fit = ls.model(t, freq) assert_quantity_allclose(y_fit, y)
def test_model(fit_mean, with_units, freq): rand = np.random.RandomState(0) t = 10 * rand.rand(40) params = 10 * rand.rand(3) y = np.zeros_like(t) if fit_mean: y += params[0] y += params[1] * np.sin(2 * np.pi * freq * (t - params[2])) if with_units: t = t * u.day y = y * u.mag freq = freq / u.day ls = LombScargle(t, y, center_data=False, fit_mean=fit_mean) y_fit = ls.model(t, freq) assert_quantity_allclose(y_fit, y)
def test_model_parameters(data, nterms, fit_mean, center_data, errors, with_units): if nterms == 0 and not fit_mean: return t, y, dy = data frequency = 1.5 if with_units: t = t * units.day y = y * units.mag dy = dy * units.mag frequency = frequency / t.unit if errors == 'none': dy = None elif errors == 'partial': dy = dy[0] elif errors == 'full': pass else: raise ValueError("Unrecognized error type: '{0}'".format(errors)) ls = LombScargle(t, y, dy, nterms=nterms, fit_mean=fit_mean, center_data=center_data) tfit = np.linspace(0, 20, 10) if with_units: tfit = tfit * units.day model = ls.model(tfit, frequency) params = ls.model_parameters(frequency) design = ls.design_matrix(frequency, t=tfit) offset = ls.offset() assert len(params) == int(fit_mean) + 2 * nterms assert_quantity_allclose(offset + design.dot(params), model)
def test_model_parameters(data, nterms, fit_mean, center_data, errors, with_units): if nterms == 0 and not fit_mean: return t, y, dy = data frequency = 1.5 if with_units: t = t * u.day y = y * u.mag dy = dy * u.mag frequency = frequency / t.unit if errors == 'none': dy = None elif errors == 'partial': dy = dy[0] elif errors == 'full': pass else: raise ValueError("Unrecognized error type: '{0}'".format(errors)) ls = LombScargle(t, y, dy, nterms=nterms, fit_mean=fit_mean, center_data=center_data) tfit = np.linspace(0, 20, 10) if with_units: tfit = tfit * u.day model = ls.model(tfit, frequency) params = ls.model_parameters(frequency) design = ls.design_matrix(frequency, t=tfit) offset = ls.offset() assert len(params) == int(fit_mean) + 2 * nterms assert_quantity_allclose(offset + design.dot(params), model)
def test_absolute_times(data, timedelta): # Make sure that we handle absolute times correctly. We also check that # TimeDelta works properly when timedelta is True. # The example data uses relative times t, y, dy = data # FIXME: There seems to be a numerical stability issue in that if we run # the algorithm with the same values but offset in time, the transit_time # is not offset by a fixed amount. To avoid this issue in this test, we # make sure the first time is also the smallest so that internally the # values of the relative time should be the same. t[0] = 0. # Add units t = t * u.day y = y * u.mag dy = dy * u.mag # We now construct a set of absolute times but keeping the rest the same start = Time('2019-05-04T12:34:56') trel = TimeDelta(t) if timedelta else t t = trel + start # and we set up two instances of LombScargle, one with absolute and one # with relative times. ls1 = LombScargle(t, y, dy) ls2 = LombScargle(trel, y, dy) kwargs = dict(samples_per_peak=6, nyquist_factor=2, minimum_frequency=2 / u.day, maximum_frequency=None) freq1 = ls1.autofrequency(**kwargs) freq2 = ls2.autofrequency(**kwargs) assert_quantity_allclose(freq1, freq2) power1 = ls1.power(freq1) power2 = ls2.power(freq2) assert_quantity_allclose(power1, power2) freq1, power1 = ls1.autopower(**kwargs) freq2, power2 = ls2.autopower(**kwargs) assert_quantity_allclose(freq1, freq2) assert_quantity_allclose(power1, power2) model1 = ls1.model(t, 2 / u.day) model2 = ls2.model(trel, 2 / u.day) assert_quantity_allclose(model1, model2) # Check model validation with pytest.raises(TypeError) as exc: ls1.model(trel, 2 / u.day) assert exc.value.args[0] == ('t was provided as a relative time but the ' 'LombScargle class was initialized with ' 'absolute times.') with pytest.raises(TypeError) as exc: ls2.model(t, 2 / u.day) assert exc.value.args[0] == ('t was provided as an absolute time but the ' 'LombScargle class was initialized with ' 'relative times.') # Check design matrix design1 = ls1.design_matrix(2 / u.day, t=t) design2 = ls2.design_matrix(2 / u.day, t=trel) assert_quantity_allclose(design1, design2) # Check design matrix validation with pytest.raises(TypeError) as exc: ls1.design_matrix(2 / u.day, t=trel) assert exc.value.args[0] == ('t was provided as a relative time but the ' 'LombScargle class was initialized with ' 'absolute times.') with pytest.raises(TypeError) as exc: ls2.design_matrix(2 / u.day, t=t) assert exc.value.args[0] == ('t was provided as an absolute time but the ' 'LombScargle class was initialized with ' 'relative times.')