def test_derived_tuple_fit():
parfile = pint.config.examplefile("NGC6440E.par")
timfile = pint.config.examplefile("NGC6440E.tim")
m, t = get_model_and_toas(parfile, timfile)
f = WLSFitter(t, m)
# find the best-fit
f.fit_toas()
bestfit = f.resids.chi2
F0 = np.linspace(
f.model.F0.quantity - 3 * f.model.F0.uncertainty,
f.model.F0.quantity + 3 * f.model.F0.uncertainty,
53,
)
tau = np.linspace(8.1, 8.3, 53) * 100 * u.Myr
parvalues = list(zip(F0, tau))
chi2_tau, params, _ = pint.gridutils.tuple_chisq_derived(
f,
("F0", "F1"),
(lambda x, y: x, lambda x, y: -x / 2 / y),
parvalues, # ncpu=1,
)
assert np.isclose(bestfit, chi2_tau.min(), atol=3)
def test_grid_extraparams_singleprocessor():
parfile = pint.config.examplefile("NGC6440E.par")
timfile = pint.config.examplefile("NGC6440E.tim")
m, t = get_model_and_toas(parfile, timfile)
f = WLSFitter(t, m)
f.fit_toas()
bestfit = f.resids.chi2
F0 = np.linspace(
f.model.F0.quantity - 3 * f.model.F0.uncertainty,
f.model.F0.quantity + 3 * f.model.F0.uncertainty,
5,
)
F1 = np.linspace(
f.model.F1.quantity - 3 * f.model.F1.uncertainty,
f.model.F1.quantity + 3 * f.model.F1.uncertainty,
7,
)
chi2grid, extraparams = pint.gridutils.grid_chisq(f, ("F0", "F1"),
(F0, F1), ("DM", ),
ncpu=1)
assert np.isclose(bestfit, chi2grid.min())
def test_grid_derived_extraparam_existingexecutor():
parfile = pint.config.examplefile("NGC6440E.par")
timfile = pint.config.examplefile("NGC6440E.tim")
m, t = get_model_and_toas(parfile, timfile)
f = WLSFitter(t, m)
f.fit_toas()
bestfit = f.resids.chi2
F0 = np.linspace(
f.model.F0.quantity - 3 * f.model.F0.uncertainty,
f.model.F0.quantity + 3 * f.model.F0.uncertainty,
15,
)
tau = np.linspace(8.1, 8.3, 13) * 100 * u.Myr
with concurrent.futures.ProcessPoolExecutor(max_workers=ncpu) as executor:
chi2grid_tau, params, extraparams = pint.gridutils.grid_chisq_derived(
f,
("F0", "F1"),
(lambda x, y: x, lambda x, y: -x / 2 / y),
(F0, tau),
("DM", ),
executor=executor,
)
assert np.isclose(bestfit, chi2grid_tau.min(), atol=1)
def wls():
m = get_model(join(datadir, "NGC6440E.par"))
t = get_TOAs(join(datadir, "NGC6440E.tim"), ephem="DE421")
wls = WLSFitter(t, m)
wls.fit_toas()
return wls
def test_ECL_to_ICRS_uncertainties():
# start with ECL model, fit with both models
# compare parameter values and uncertainties
model_ECL = get_model(io.StringIO(modelstring_ECL))
toas = pint.simulation.make_fake_toas_uniform(MJDStart,
MJDStop,
NTOA,
model=model_ECL,
error=1 * u.us,
add_noise=True)
fit_ECL = WLSFitter(toas, model_ECL)
fit_ICRS = WLSFitter(toas, model_ECL.as_ICRS())
fit_ECL.fit_toas()
fit_ICRS.fit_toas()
m1 = fit_ECL.model
m2 = fit_ICRS.model.as_ECL()
for p in ("ELONG", "ELAT", "PMELONG", "PMELAT"):
assert np.isclose(m1.__getitem__(p).value, m2.__getitem__(p).value)
# do a generous test here since the uncertainties could change
assert np.isclose(m1.__getitem__(p).uncertainty,
m2.__getitem__(p).uncertainty,
rtol=0.5)
def test_residuals_wls_chi2():
model = get_model(
StringIO("""
PSRJ J1234+5678
ELAT 0
ELONG 0
DM 10
F0 1
PEPOCH 58000
"""))
toas = make_fake_toas(57000, 59000, 20, model=model, error=1 * u.us)
np.random.seed(0)
toas.adjust_TOAs(TimeDelta(np.random.randn(len(toas)) * u.us))
r = Residuals(toas, model)
f = WLSFitter(toas, model)
assert f.fit_toas() == r.chi2
def setUpClass(cls):
os.chdir(datadir)
cls.par = "B1855+09_NANOGrav_dfg+12_TAI_FB90.par"
cls.tim = "B1855+09_NANOGrav_dfg+12.tim"
cls.m = get_model(cls.par)
cls.t = toa.get_TOAs(cls.tim, ephem="DE405")
cls.f = WLSFitter(cls.t, cls.m)
# set perturb parameter step
cls.per_param = {
"A1": 1e-05,
"DECJ": 1e-06,
"DMX_0003": 120,
"ECC": 0.2,
"F0": 1e-12,
"F1": 0.001,
"JUMP3": 10.0,
"M2": 10.0,
"OM": 1e-06,
"PB": 1e-08,
"PMDEC": 0.1,
"PMRA": 0.1,
"PX": 100,
"RAJ": 1e-08,
"SINI": -0.004075,
"T0": 1e-10,
}
def test_grid_oneparam():
parfile = pint.config.examplefile("NGC6440E.par")
timfile = pint.config.examplefile("NGC6440E.tim")
m, t = get_model_and_toas(parfile, timfile)
f = WLSFitter(t, m)
f.fit_toas()
bestfit = f.resids.chi2
F0 = np.linspace(
f.model.F0.quantity - 3 * f.model.F0.uncertainty,
f.model.F0.quantity + 3 * f.model.F0.uncertainty,
5,
)
chi2grid, _ = pint.gridutils.grid_chisq(f, ("F0", ), (F0, ), ncpu=ncpu)
assert np.isclose(bestfit, chi2grid.min())
def setUpClass(cls):
os.chdir(datadir)
cls.par = "NGC6440E.par"
cls.tim = "NGC6440E.tim"
cls.m = get_model(cls.par)
cls.t = toa.get_TOAs(cls.tim, ephem="DE421")
cls.wls = WLSFitter(cls.t, cls.m)
cls.gls = GLSFitter(cls.t, cls.m)
cls.gls_full = GLSFitter(cls.t, cls.m)
def test_grid_3param_prefix_multiprocessor():
parfile = pint.config.examplefile("NGC6440E.par")
timfile = pint.config.examplefile("NGC6440E.tim")
m, t = get_model_and_toas(parfile, timfile)
# add a F2 to the model
modelcomponent = m.components["Spindown"]
p = param.prefixParameter(
parameter_type="float",
name="F2",
value=0,
units=modelcomponent.F_unit(2),
uncertainty=0,
description=modelcomponent.F_description(2),
longdouble=True,
frozen=False,
)
modelcomponent.add_param(p, setup=True)
m.validate()
f = WLSFitter(t, m)
f.fit_toas()
bestfit = f.resids.chi2
F0 = np.linspace(
f.model.F0.quantity - 3 * f.model.F0.uncertainty,
f.model.F0.quantity + 3 * f.model.F0.uncertainty,
3,
)
F1 = np.linspace(
f.model.F1.quantity - 3 * f.model.F1.uncertainty,
f.model.F1.quantity + 3 * f.model.F1.uncertainty,
7,
)
F2 = np.linspace(
f.model.F2.quantity - 3 * f.model.F2.uncertainty,
f.model.F2.quantity + 3 * f.model.F2.uncertainty,
5,
)
chi2grid, _ = pint.gridutils.grid_chisq(f, ("F0", "F1", "F2"),
(F0, F1, F2),
ncpu=ncpu)
assert np.isclose(bestfit, chi2grid.min())
def test_grid_derived_multiprocessor():
parfile = pint.config.examplefile("NGC6440E.par")
timfile = pint.config.examplefile("NGC6440E.tim")
m, t = get_model_and_toas(parfile, timfile)
f = WLSFitter(t, m)
f.fit_toas()
bestfit = f.resids.chi2
F0 = np.linspace(
f.model.F0.quantity - 3 * f.model.F0.uncertainty,
f.model.F0.quantity + 3 * f.model.F0.uncertainty,
15,
)
tau = np.linspace(8.1, 8.3, 13) * 100 * u.Myr
chi2grid_tau, params, _ = pint.gridutils.grid_chisq_derived(
f, ("F0", "F1"), (lambda x, y: x, lambda x, y: -x / 2 / y), (F0, tau),
ncpu=ncpu)
assert np.isclose(bestfit, chi2grid_tau.min(), atol=1)
def test_grid_oneparam_existingexecutor():
parfile = pint.config.examplefile("NGC6440E.par")
timfile = pint.config.examplefile("NGC6440E.tim")
m, t = get_model_and_toas(parfile, timfile)
f = WLSFitter(t, m)
f.fit_toas()
bestfit = f.resids.chi2
F0 = np.linspace(
f.model.F0.quantity - 5 * f.model.F0.uncertainty,
f.model.F0.quantity + 5 * f.model.F0.uncertainty,
21,
)
with concurrent.futures.ProcessPoolExecutor(
max_workers=ncpu, ) as executor:
chi2grid, _ = pint.gridutils.grid_chisq(f, ("F0", ), (F0, ),
executor=executor)
assert np.isclose(bestfit, chi2grid.min())
def test_tuple_fit():
parfile = pint.config.examplefile("NGC6440E.par")
timfile = pint.config.examplefile("NGC6440E.tim")
m, t = get_model_and_toas(parfile, timfile)
f = WLSFitter(t, m)
# find the best-fit
f.fit_toas()
bestfit = f.resids.chi2
F0 = np.linspace(
f.model.F0.quantity - 3 * f.model.F0.uncertainty,
f.model.F0.quantity + 3 * f.model.F0.uncertainty,
25,
)
F1 = np.ones(len(F0)) * f.model.F1.quantity
parnames = ("F0", "F1")
parvalues = list(zip(F0, F1))
chi2, extra = pint.gridutils.tuple_chisq(f,
parnames,
parvalues,
extraparnames=("DM", ))
f.model.F0.quantity = F0[3]
f.model.F1.quantity = F1[3]
f.model.F0.frozen = True
f.model.F1.frozen = True
f.fit_toas()
assert np.isclose(chi2[3], f.resids.calc_chi2())
assert np.isclose(chi2.min(), bestfit)
assert np.isclose(extra["DM"][3], f.model.DM.quantity)
from pint.toa import get_TOAs
import matplotlib.pyplot as plt
from astropy.visualization import quantity_support
quantity_support()
import scipy.stats
# %matplotlib inline
# %%
# Load in a basic dataset
parfile = pint.config.examplefile("NGC6440E.par")
timfile = pint.config.examplefile("NGC6440E.tim")
m, t = get_model_and_toas(parfile, timfile)
f = WLSFitter(t, m)
# find the best-fit
f.fit_toas()
bestfit = f.resids.chi2
# %% [markdown]
# What are the free parameters?
# %%
f.model.free_params
# %% [markdown]
# So we were fitting for RA, Dec, $F0$, $F1$, and $DM$.
#
# We'll do something like 3-sigma around the best-fit value of $F0$, fitting for RA, Dec, $F1$ and $DM$ at each grid point.
)
print("Is 'P0' in the timing model: ", "P0" in model.params)
print("Is 'P1' in the timing model: ", "P1" in model.params)
print("Is 'F0' in the timing model: ", "F0" in model.params)
print("Is 'F1' in the timing model: ", "F1" in model.params)
# %% [markdown]
# ### Load TOAs and prepare for fitting
# %%
from pint.fitter import WLSFitter
from pint.toa import get_TOAs
# %%
toas = get_TOAs("NGC6440E.tim", ephem="DE421")
f = WLSFitter(toas, model)
# %% [markdown]
# ### Plot the residuals
# %%
import matplotlib.pyplot as plt
# %% [markdown]
# ### Plot the prefit residuals.
# %%
plt.errorbar(
toas.get_mjds().value,
f.resids_init.time_resids.to_value(u.us),
yerr=toas.get_errors().to_value(u.us),
