def test_toa_merge_again():
filenames = ["NGC6440E.tim", "testtimes.tim", "parkes.toa"]
toas = [toa.get_TOAs(ff) for ff in filenames]
ntoas = sum([tt.ntoas for tt in toas])
nt = toa.merge_TOAs(toas)
# The following tests merging with and already merged TOAs
other = toa.get_TOAs("test1.tim")
nt2 = toa.merge_TOAs([nt, other])
assert len(nt2.filename) == 5
assert nt2.ntoas == ntoas + 9
check_indices_contiguous(nt2)
def test_toa_merge_again():
filenames = ["NGC6440E.tim", "testtimes.tim", "parkes.toa"]
toas = [toa.get_TOAs(ff) for ff in filenames]
ntoas = sum([tt.ntoas for tt in toas])
other = toa.get_TOAs("test1.tim")
# check consecutive merging
nt = toa.merge_TOAs(toas[:2])
nt = toa.merge_TOAs([nt, toas[2]])
nt = toa.merge_TOAs([nt, other])
assert len(nt.filename) == 5
assert nt.ntoas == ntoas + 9
check_indices_contiguous(nt)
assert nt.check_hashes()
def test_merge_indices():
m = get_model(StringIO(simplepar))
fakes = [
toa.make_fake_toas(55000, 55500, 5, model=m, obs="ao"),
toa.make_fake_toas(56000, 56500, 10, model=m, obs="gbt"),
toa.make_fake_toas(57000, 57500, 15, model=m, obs="@"),
]
fakes_excised = [f[1:-1] for f in fakes]
toas = toa.merge_TOAs(fakes)
toas_excised = toa.merge_TOAs(fakes_excised)
for i in range(len(toas_excised)):
ix = toas_excised.table["index"][i]
match = toas.table[toas.table["index"] == ix]
assert len(match) == 1
assert match[0]["tdbld"] == toas_excised.table["tdbld"][i]
def model_eccentric_toas_wb():
g = np.random.default_rng(0)
model_eccentric = get_model(
io.StringIO("\n".join([par_eccentric, "ECORR tel @ 2"])))
toas = merge_TOAs([
make_fake_toas(
57000,
57001,
20,
model_eccentric,
freq=1000,
obs="@",
dm=10 * u.pc / u.cm**3,
),
make_fake_toas(
57000,
57001,
20,
model_eccentric,
freq=2000,
obs="@",
dm=10 * u.pc / u.cm**3,
),
])
toas.adjust_TOAs(
TimeDelta(g.standard_normal(len(toas)) * toas.table["error"]))
return model_eccentric, toas
def test_tim_writing_order():
m = get_model(StringIO(simplepar))
fakes = [
simulation.make_fake_toas_uniform(55000, 56000, 5, model=m, obs="ao"),
simulation.make_fake_toas_uniform(55000, 56000, 5, model=m, obs="gbt"),
simulation.make_fake_toas_uniform(55000, 56000, 5, model=m, obs="@"),
]
toas = toa.merge_TOAs(fakes)
toas.table["index"][np.argsort(toas.table["tdbld"])] = np.arange(len(toas))
o = StringIO()
toas.write_TOA_file(o, order_by_index=False)
toas.write_TOA_file("test.tim", order_by_index=False)
obs = [
ln.split()[4] for ln in o.getvalue().split("\n")[1:]
if (ln and not ln.startswith("C "))
]
assert obs[0] == obs[1] == obs[2] == obs[3]
o = StringIO()
toas.write_TOA_file(o, order_by_index=True)
obs = [
ln.split()[4] for ln in o.getvalue().split("\n")[1:]
if (ln and not ln.startswith("C "))
]
assert obs[0] == obs[3] == obs[6] == obs[9]
assert obs[0] != obs[1]
def test_get_highest_density_range(ndays):
par_base = """
PSR J1234+5678
F0 1 0
ELAT 0 0
ELONG 0 0
PEPOCH 57000
DM 10 0
SOLARN0 0
"""
model = get_model(io.StringIO(par_base))
toas_1 = simulation.make_fake_toas_uniform(57000,
58000,
1000,
model,
obs="@")
toas_2 = simulation.make_fake_toas_uniform(57500,
57500 + ndays.value,
100,
model,
obs="@")
merged = toa.merge_TOAs([toas_1, toas_2])
if ndays == 7 * u.d:
x1 = merged.get_highest_density_range()
x2 = merged.get_highest_density_range(ndays)
assert abs(x2[0].value - 57500) <= 1e-5
assert abs(x2[1].value - (57500 + ndays.value)) <= 1e-5
if ndays == 7 * u.d:
assert abs(x2[0].value - x1[0].value) <= 1e-5
assert abs(x2[1].value - x1[1].value) <= 1e-5
def test_merge_indices():
m = get_model(StringIO(simplepar))
fakes = [
toa.make_fake_toas(55000, 55500, 5, model=m, obs="ao"),
toa.make_fake_toas(56000, 56500, 10, model=m, obs="gbt"),
toa.make_fake_toas(57000, 57500, 15, model=m, obs="@"),
]
toas = toa.merge_TOAs(fakes)
check_indices_contiguous(toas)
def test_merge_indices():
m = get_model(StringIO(simplepar))
fakes = [
toa.make_fake_toas(55000, 55500, 5, model=m, obs="ao"),
toa.make_fake_toas(56000, 56500, 10, model=m, obs="gbt"),
toa.make_fake_toas(57000, 57500, 15, model=m, obs="@"),
]
toas = toa.merge_TOAs(fakes)
assert len(set(toas.table["index"])) == len(toas)
def test_toa_merge():
filenames = ["NGC6440E.tim", "testtimes.tim", "parkes.toa"]
toas = [toa.get_TOAs(ff) for ff in filenames]
ntoas = sum([tt.ntoas for tt in toas])
nt = toa.merge_TOAs(toas)
assert len(nt.observatories) == 3
assert nt.table.meta["filename"] == filenames
assert nt.ntoas == ntoas
check_indices_contiguous(nt)
assert nt.check_hashes()
def test_renumber_subset_reordered():
m = get_model(StringIO(simplepar))
fakes = [
toa.make_fake_toas(55000, 55500, 5, model=m, obs="ao"),
toa.make_fake_toas(56000, 56500, 10, model=m, obs="gbt"),
toa.make_fake_toas(57000, 57500, 15, model=m, obs="@"),
]
fakes_excised = [f[1:-1] for f in fakes]
toas_excised = toa.merge_TOAs(fakes_excised)
assert 0 not in toas_excised.table["index"]
toas_excised.renumber()
assert set(toas_excised.table["index"]) == set(range(len(toas_excised)))
toas_excised.renumber(index_order=False)
assert np.all(toas_excised.table["index"] == np.arange(len(toas_excised)))
def test_load_multiple(tmpdir):
m = get_model(StringIO(simplepar))
fakes = [
toa.make_fake_toas(55000, 55500, 10, model=m, obs="ao"),
toa.make_fake_toas(56000, 56500, 10, model=m, obs="gbt"),
toa.make_fake_toas(57000, 57500, 10, model=m, obs="@"),
]
filenames = [os.path.join(tmpdir, f"t{i+1}.tim") for i in range(len(fakes))]
for t, f in zip(fakes, filenames):
t.write_TOA_file(f, format="tempo2")
merged = toa.merge_TOAs([toa.get_TOAs(f, model=m) for f in filenames])
assert merged == toa.get_TOAs(filenames, model=m)
def test_toa_merge():
filenames = ["NGC6440E.tim", "testtimes.tim", "parkes.toa"]
toas = [toa.get_TOAs(ff) for ff in filenames]
ntoas = sum([tt.ntoas for tt in toas])
nt = toa.merge_TOAs(toas)
assert len(nt.observatories) == 3
assert nt.table.meta["filename"] == filenames
assert nt.ntoas == ntoas
# The following tests merging with and already merged TOAs
other = toa.get_TOAs("test1.tim")
nt2 = toa.merge_TOAs([nt, other])
assert len(nt2.filename) == 5
assert nt2.ntoas == ntoas + 9
# check consecutive merging
nt = toa.merge_TOAs(toas[:2])
nt = toa.merge_TOAs([nt, toas[2]])
nt = toa.merge_TOAs([nt, other])
assert len(nt.filename) == 5
assert nt.ntoas == ntoas + 9
# now test a failure if ephems are different
toas[0].ephem = "DE436"
with pytest.raises(TypeError):
nt = toa.merge_TOAs(toas)
def random_models(self, selected):
"""Compute and plot random models
"""
log.info("Computing random models based on parameter covariance matrix.")
if [p for p in self.postfit_model.free_params if p.startswith("DM")]:
log.warning(
"Fitting for DM while using random models can cause strange behavior."
)
# These are the currently selected TOAs in the fit
sim_sel = copy.deepcopy(self.selected_toas)
# These are single TOAs from each cluster of TOAs
inds = np.zeros(sim_sel.ntoas, dtype=bool)
inds[np.unique(sim_sel.get_clusters(), return_index=True)[1]] |= True
sim_sel = sim_sel[inds]
# Get the range of MJDs we are using in the fit
mjds = sim_sel.get_mjds().value
minselMJD, maxselMJD = mjds.min(), mjds.max()
extra = 0.1 # Fraction beyond TOAs to plot or calculate random models
if self.faketoas1 is None:
mjds = self.all_toas.get_mjds().value
minallMJD, maxallMJD = mjds.min(), mjds.max()
spanMJD = maxallMJD - minallMJD
# want roughly 1 per day up to 3 years
if spanMJD < 1000:
Ntoas = min(400, int(spanMJD))
elif spanMJD < 4000:
Ntoas = min(750, int(spanMJD) // 2)
else:
Ntoas = min(1500, int(spanMJD) // 4)
log.debug(f"Generating {Ntoas} fake TOAs for the random models")
# By default we will use TOAs from the TopoCenter. This gets done only once.
self.faketoas1 = make_fake_toas_uniform(
minallMJD - extra * spanMJD,
maxallMJD + extra * spanMJD,
Ntoas,
self.postfit_model,
obs="coe",
freq=1 * u.THz, # effectively infinite frequency
include_bipm=sim_sel.clock_corr_info["include_bipm"],
include_gps=sim_sel.clock_corr_info["include_gps"],
)
self.faketoas1.compute_pulse_numbers(self.postfit_model)
# Combine our TOAs
toas = merge_TOAs([sim_sel, self.faketoas1])
zero_residuals(toas, self.postfit_model)
toas.table.sort("tdbld") # for plotting
# Get a selection array to select the non-fake TOAs
refs = np.asarray(toas.get_flag_value("name")[0]) != "fake"
# Compute the new random timing models
rs = calculate_random_models(
self.fitter, toas, Nmodels=15, keep_models=False, return_time=True,
)
# Get a selection array for the fake TOAs that covers the fit TOAs (plus extra)
mjds = toas.get_mjds().value
spanMJD = maxselMJD - minselMJD
toplot = np.bitwise_and(
mjds > (minselMJD - extra * spanMJD), mjds < (maxselMJD + extra * spanMJD)
)
# This is the mean of the reference resids for the selected TOAs
if selected.sum(): # shorthand for having some selected
ref_mean = self.postfit_resids.time_resids[selected][inds].mean()
else:
ref_mean = self.postfit_resids.time_resids[inds].mean()
# This is the means of the corresponding resids from the random models
ran_mean = rs[:, refs].mean(axis=1)
# Now adjust each set of random resids so that the ran_mean == ref_mean
rs -= ran_mean[:, np.newaxis]
rs += ref_mean
# And store the key things for plotting
self.faketoas = toas
self.random_resids = rs
def test_toa_merge_different_ephem():
filenames = ["NGC6440E.tim", "testtimes.tim", "parkes.toa"]
toas = [toa.get_TOAs(ff) for ff in filenames]
toas[0].ephem = "DE436"
with pytest.raises(TypeError):
nt = toa.merge_TOAs(toas)
