def test_fitter_respects_pulse_numbers(fitter, model, fake_toas):
t = fake_toas
delta_f = (1 / (t.last_MJD - t.first_MJD)).to(u.Hz)
# Unchanged model, fitting should be trivial
f_0 = fitter(t, model, track_mode="use_pulse_numbers")
f_0.fit_toas()
assert abs(f_0.model.F0.quantity - model.F0.quantity) < 0.01 * delta_f
m_2 = deepcopy(model)
m_2.F0.quantity += 2 * delta_f
m_2.free_params = ["F0"]
# Check fitter with and without tracking
with pytest.raises(ValueError):
fitter(t, m_2, track_mode="capybara")
f_1 = fitter(t, m_2, track_mode="nearest")
try:
f_1.fit_toas()
assert abs(f_1.model.F0.quantity - model.F0.quantity) > 0.1 * delta_f
except ValueError:
# convergence fails for Downhill fitters
pass
f_2 = fitter(t, m_2, track_mode="use_pulse_numbers")
f_2.fit_toas()
assert abs(f_2.model.F0.quantity - model.F0.quantity) < 0.01 * delta_f
def test_random_models(fitter):
# Get model and TOAs
m, t = get_model_and_toas(os.path.join(datadir, "NGC6440E.par"),
os.path.join(datadir, "NGC6440E.tim"))
f = fitter(toas=t, model=m)
# Do a 4-parameter fit
f.model.free_params = ("F0", "F1", "RAJ", "DECJ")
f.fit_toas()
# this contains TOAs up through 54200
# make new ones starting there
tnew = simulation.make_fake_toas_uniform(54200, 59000, 59000 - 54200,
f.model)
dphase, mrand = simulation.calculate_random_models(f, tnew, Nmodels=30)
# this is a bit stochastic, but I see typically < 0.14 cycles
# for the uncertainty at 59000
assert np.all(dphase.std(axis=0) < 0.2)
# redo it with only F0 free
dphase_F, mrand_F = simulation.calculate_random_models(f,
tnew,
Nmodels=100,
params=["F0"])
# this should be less than the fully free version
assert dphase_F.std(axis=0).max() < dphase.std(axis=0).max()
def test_random_models_wb(fitter):
model = get_model(
os.path.join(datadir, "J1614-2230_NANOGrav_12yv3.wb.gls.par"))
toas = get_TOAs(
os.path.join(datadir, "J1614-2230_NANOGrav_12yv3.wb.tim"),
ephem="DE436",
bipm_version="BIPM2015",
)
f = fitter(toas, model)
# Do a 4-parameter fit
f.model.free_params = ("F0", "F1", "ELONG", "ELAT")
f.fit_toas()
tnew = simulation.make_fake_toas_uniform(54200, 59000,
(59000 - 54200) // 10, f.model)
dphase, mrand = simulation.calculate_random_models(f, tnew, Nmodels=30)
# this is a bit stochastic, but I see typically < 1e-4 cycles for this
# for the uncertainty at 59000
assert np.all(dphase.std(axis=0) < 1e-4)