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)
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 test_fake_DMfit():
""" fit only for DM with fake TOAs
compare the variance of that result against the uncertainties
"""
parfile = pint.config.examplefile("NGC6440E.par")
timfile = pint.config.examplefile("NGC6440E.tim")
m, t = get_model_and_toas(parfile, timfile)
# pick only data for a DM fit
t = t[-8:]
m.RAJ.frozen = True
m.DECJ.frozen = True
m.F0.frozen = True
m.F1.frozen = True
f = GLSFitter(t, m)
f.fit_toas()
N = 30
DMs = np.zeros(N) * u.pc / u.cm**3
for iter in range(N):
t_fake = pint.simulation.make_fake_toas(t, m, add_noise=True)
f_fake = GLSFitter(t_fake, m)
f_fake.fit_toas()
DMs[iter] = f_fake.model.DM.quantity.astype(np.float64)
assert np.isclose(DMs.std(), f.model.DM.uncertainty, rtol=0.5)
assert np.abs(DMs.mean() -
f.model.DM.quantity) < 5 * f.model.DM.uncertainty
def test_grid_fitters_multiprocessor(fitter):
parfile = pint.config.examplefile("NGC6440E.par")
timfile = pint.config.examplefile("NGC6440E.tim")
m, t = get_model_and_toas(parfile, timfile)
f = fitter(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,
7,
)
chi2grid, _ = pint.gridutils.grid_chisq(f, ("F0", ), (F0, ),
printprogress=False,
ncpu=ncpu)
assert np.isclose(bestfit, chi2grid.min())
def test_fake_from_timfile():
m, t = get_model_and_toas(
pint.config.examplefile("B1855+09_NANOGrav_9yv1.gls.par"),
pint.config.examplefile("B1855+09_NANOGrav_9yv1.tim"),
)
# refit the data to get rid of a trend
f = GLSFitter(t, m)
f.fit_toas()
r = pint.residuals.Residuals(t, f.model)
t_sim = pint.simulation.make_fake_toas(t, f.model, add_noise=True)
r_sim = pint.residuals.Residuals(t_sim, f.model)
# need a generous rtol because of the small statistics
assert np.isclose(r.calc_time_resids().std(),
r_sim.calc_time_resids().std(),
rtol=2)
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 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())
from pint.fitter import WLSFitter
from pint.models.model_builder import get_model, get_model_and_toas
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 $DM$.
#
