def test_evaluate_psd(self):
tm = QuadraticModel(interpolate=False)
tm.set_data(self.time)
flux = tm.evaluate(0.1, [0.2, 0.3], 0.0, 1.0, 3.0, 0.5*pi)
assert flux.ndim == 1
assert flux.size == self.time.size
def test_evaluate_2d(self):
tm = QuadraticModel(interpolate=False)
tm.set_data(self.time, self.lcids, self.pbids)
flux = tm.evaluate(self.radius_ratios[0, 0], self.ldc[0], self.zero_epochs[0], self.periods[0], self.smas[0], self.inclinations[0])
assert flux.ndim == 1
assert flux.size == self.time.size
a_Rs = a_au / (R_s * 0.00465047)
# TODO start with rp_rs that causes twice depth than curve RMS
curve_rms = np.std(flux)
min_depth = 2 * curve_rms
initial_rp = (min_depth * (R_s**2))**(1 / 2)
rp_rs = initial_rp / R_s
from pytransit import QuadraticModel
tm = QuadraticModel()
time_model = np.arange(0, 1, 0.0001)
tm.set_data(time_model)
# k is the radius ratio, ldc is the limb darkening coefficient vector, t0 the zero epoch, p the orbital period, a the
# semi-major axis divided by the stellar radius, i the inclination in radians, e the eccentricity, and w the argument
# of periastron. Eccentricity and argument of periastron are optional, and omitting them defaults to a circular orbit.
model = tm.evaluate(k=rp_rs,
ldc=ld_coefficients,
t0=0.5,
p=1.0,
a=a_Rs,
i=0.5 * np.pi)
model = model[model < 1]
baseline_model = np.full(len(model), 1)
model = np.append(baseline_model, model)
model = np.append(model, baseline_model)
def downsample(array, npts: int):
interpolated = interp1d(np.arange(len(array)),
array,
axis=0,
fill_value='extrapolate')
downsampled = interpolated(np.linspace(0, len(array), npts))
return downsampled