def test_dm_jumps(self):
# First get the toas for jump
toa_backends, valid_flags = self.toas.get_flag_value("fe")
toa_backends = np.array(toa_backends)
all_backends = list(set(toa_backends))
dm_jump_value = self.model.jump_dm(self.toas)
dm_jump_params = [
getattr(self.model, x) for x in self.model.params
if (x.startswith("DMJUMP"))
]
dm_jump_map = {}
for dmj in dm_jump_params:
dm_jump_map[dmj.key_value[0]] = dmj
for be in all_backends:
assert all(
dm_jump_value[toa_backends == be] == -dm_jump_map[be].quantity)
r = WidebandTOAResiduals(self.toas,
self.model,
dm_resid_args=dict(subtract_mean=False))
model2 = deepcopy(self.model)
for i, be in enumerate(all_backends):
dm_jump_map[be].value += i + 1
r2 = WidebandTOAResiduals(self.toas,
model2,
dm_resid_args=dict(subtract_mean=False))
delta_dm = r2.dm.resids_value - r.dm.resids_value
delta_dm_intended = np.zeros_like(delta_dm)
for i, be in enumerate(all_backends):
delta_dm_intended[toa_backends == be] = -(i + 1)
assert np.allclose(delta_dm, delta_dm_intended)
def test_dm_jumps(self):
# First get the toas for jump
all_backends = list(set(self.toa_backends))
dm_jump_value = self.model.jump_dm(self.toas)
dm_jump_map = {}
for dmj in self.dm_jump_params:
dm_jump_map[dmj.key_value[0]] = dmj
for be in all_backends:
assert all(
dm_jump_value[self.toa_backends == be] == -dm_jump_map[be].quantity
)
r = WidebandTOAResiduals(
self.toas, self.model, dm_resid_args=dict(subtract_mean=False)
)
model2 = deepcopy(self.model)
for i, be in enumerate(all_backends):
dm_jump_map[be].value += i + 1
r2 = WidebandTOAResiduals(
self.toas, model2, dm_resid_args=dict(subtract_mean=False)
)
delta_dm = r2.dm.resids_value - r.dm.resids_value
delta_dm_intended = np.zeros_like(delta_dm)
for i, be in enumerate(all_backends):
delta_dm_intended[self.toa_backends == be] = -(i + 1)
assert np.allclose(delta_dm, delta_dm_intended)
def test_wideband_chi2_null_updating(wideband_fake):
toas, model = wideband_fake
model.free_params = ["F0"]
f = WidebandTOAFitter(toas, model)
assert abs(f.fit_toas() - WidebandTOAResiduals(toas, model).chi2) > 1
c2 = WidebandTOAResiduals(toas, f.model).chi2
assert_allclose(f.fit_toas(), c2)
c2 = WidebandTOAResiduals(toas, f.model).chi2
assert_allclose(f.fit_toas(), c2)
def test_wideband_residuals_dmjump(wb_model, wb_toas):
r = WidebandTOAResiduals(wb_toas, wb_model, dm_resid_args=dict(subtract_mean=False))
model = deepcopy(wb_model)
assert wb_model.DMJUMP1.value == 0
model.DMJUMP1.value = 10
assert model.DMJUMP1.value == 10
with pytest.raises(AttributeError):
model.DMJUMP0
with pytest.raises(AttributeError):
model.DMJUMP2
r2 = WidebandTOAResiduals(wb_toas, model, dm_resid_args=dict(subtract_mean=False))
assert 0 < np.sum(r.dm.resids_value != r2.dm.resids_value) < len(r.dm.resids_value)
def test_wideband_chi2_updating(wideband_fake):
toas, model = wideband_fake
model.free_params = ["F0"]
model.F0.value += 1e-6
c2 = WidebandTOAResiduals(
toas, model, toa_resid_args=dict(track_mode="use_pulse_numbers")
).chi2
f2 = WidebandTOAFitter(
toas, model, additional_args=dict(toa=dict(track_mode="use_pulse_numbers"))
)
ftc2 = f2.fit_toas()
assert abs(ftc2 - c2) > 100
assert_allclose(f2.model.F0.value, 1)
assert 1e-3 > abs(WidebandTOAResiduals(toas, f2.model).chi2 - ftc2) > 1e-5
ftc2 = f2.fit_toas(maxiter=10)
assert_allclose(WidebandTOAResiduals(toas, f2.model).chi2, ftc2)
def test_residuals_wideband_chi2(wideband_fake):
toas, model = wideband_fake
r = WidebandTOAResiduals(toas, model)
rn = Residuals(toas, model)
f = WidebandTOAFitter(toas, model)
assert_allclose(f.fit_toas(), r.chi2)
assert f.fit_toas() >= rn.chi2
def test_wideband_residuals_dof(wb_model, wb_toas_all):
wb_model.free_params = ["DMJUMP1"]
r = WidebandTOAResiduals(
wb_toas_all, wb_model, dm_resid_args=dict(subtract_mean=False)
)
assert r.dof == 12 - 2
assert_allclose(r.chi2, 2e14)
assert_allclose(r.reduced_chi2, r.chi2 / r.dof)
def test_residuals_fake_wideband():
model = get_model(
StringIO("""
PSRJ J1234+5678
ELAT 0
ELONG 0
DM 10
F0 1
PEPOCH 58000
EFAC mjd 57000 58000 2
"""))
toas = make_fake_toas(57000, 59000, 20, model=model, error=1 * u.us, dm=10)
r = WidebandTOAResiduals(toas, model)
e = r.toa.get_data_error(scaled=True)
assert np.all(e != 0)
assert 0 < np.sum(e > 1.5 * u.us) < len(toas)
with pytest.raises(ValueError):
model.as_parfile().index("EQUAD")
def test_wideband_residuals(wb_model, wb_toas):
r = WidebandTOAResiduals(wb_toas, wb_model, dm_resid_args=dict(subtract_mean=False))
assert len(r.toa.time_resids) == len(wb_toas)
assert len(r.dm.dm_data) < len(wb_toas)
def test_wideband_residuals(self):
wb_res = WidebandTOAResiduals(toas=self.toa, model=self.model)
assert wb_res.dof == 419
# Make sure the model object are shared by all individual residual class
assert wb_res.model is wb_res.toa.model
assert wb_res.model is wb_res.dm.model