def test_random_models():
# Get model and TOAs
m, t = get_model_and_toas(os.path.join(datadir, "NGC6440E.par"),
os.path.join(datadir, "NGC6440E.tim"))
f = fitter.WLSFitter(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=100)
# 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_ftest_nb():
"""Test for narrowband fitter class F-test."""
m = tm.get_model(os.path.join(datadir, "J0023+0923_ell1_simple.par"))
t = toa.make_fake_toas(56000.0, 56001.0, 10, m, freq=1400.0, obs="AO")
f = fitter.WLSFitter(toas=t, model=m)
f.fit_toas()
# Test adding parameters
F2 = param.prefixParameter(
parameter_type="float",
name="F2",
value=0.0,
units=u.Hz / u.s / u.s,
frozen=False,
)
ft = f.ftest(F2, "Spindown", remove=False)
assert isinstance(ft["ft"], float) or isinstance(ft["ft"], bool)
# Test return the full output
Ftest_dict = f.ftest(F2, "Spindown", remove=False, full_output=True)
assert isinstance(Ftest_dict["ft"], float) or isinstance(Ftest_dict["ft"], bool)
# Test removing parameter
F1 = param.prefixParameter(
parameter_type="float", name="F1", value=0.0, units=u.Hz / u.s, frozen=False
)
ft = f.ftest(F1, "Spindown", remove=True)
assert isinstance(ft["ft"], float) or isinstance(ft["ft"], bool)
Ftest_dict = f.ftest(F1, "Spindown", remove=True, full_output=True)
assert isinstance(Ftest_dict["ft"], float) or isinstance(Ftest_dict["ft"], bool)
def test_ftest_nb():
"""Test for narrowband fitter class F-test."""
m = tm.get_model(os.path.join(datadir, "B1855+09_NANOGrav_9yv1.gls.par"))
t = toa.get_TOAs(os.path.join(datadir, "B1855+09_NANOGrav_9yv1.tim"))
f = fitter.WLSFitter(toas=t, model=m)
f.fit_toas()
# Test adding parameters
FD4 = param.prefixParameter(parameter_type="float",
name="FD4",
value=0.0,
units=u.s,
frozen=False)
ft = f.ftest(FD4, "FD", remove=False)
assert isinstance(ft["ft"], float) or isinstance(ft["ft"], bool)
# Test return the full output
Ftest_dict = f.ftest(FD4, "FD", remove=False, full_output=True)
assert isinstance(Ftest_dict["ft"], float) or isinstance(
Ftest_dict["ft"], bool)
# Test removing parameter
FD3 = param.prefixParameter(parameter_type="float",
name="FD3",
value=0.0,
units=u.s,
frozen=False)
ft = f.ftest(FD3, "FD", remove=True)
assert isinstance(ft["ft"], float) or isinstance(ft["ft"], bool)
Ftest_dict = f.ftest(FD3, "FD", remove=True, full_output=True)
assert isinstance(Ftest_dict["ft"], float) or isinstance(
Ftest_dict["ft"], bool)
def test_zero_H3(toasJ0613):
H3_zero_model = get_model(StringIO(simple_par))
H3_zero_model.H3.value = 0.0
H3_zero_model.H3.frozen = False
assert H3_zero_model.H4.value != 0.0
H3_zero_model.H4.frozen = False
test_toas = toasJ0613[::20]
with pytest.raises(ValueError):
ff.WLSFitter(test_toas, H3_zero_model)
def test_zero_H4(toasJ0613):
H4_zero_model = get_model(StringIO(simple_par))
H4_zero_model.H4.value = 0.0
H4_zero_model.H4.frozen = False
assert H4_zero_model.H3.value != 0.0
H4_zero_model.H3.frozen = False
test_toas = toasJ0613[::20]
f = ff.WLSFitter(test_toas, H4_zero_model)
f.fit_toas()
def test_H3_and_H4_non_zero(toasJ0613):
"""Testing if the different H3, H4 combination breaks the fitting. the
fitting result will not be checked here.
"""
simple_model = get_model(StringIO(simple_par))
test_toas = toasJ0613[::20]
f = ff.WLSFitter(test_toas, simple_model)
f.fit_toas()
def test_CHI2():
"""Should be present after fit in PINT, not in TEMPO/TEMPO2"""
m, t = get_model_and_toas(os.path.join(datadir, "NGC6440E.par"),
os.path.join(datadir, "NGC6440E.tim"))
f = fitter.WLSFitter(toas=t, model=m)
assert "CHI2" in f.model.as_parfile()
assert not ("CHI2" in f.model.as_parfile(format="tempo2"))
assert not ("CHI2" in f.model.as_parfile(format="tempo"))
def test_no_H3_H4(toasJ0613):
"""Test no H3 and H4 in model."""
no_H3_H4 = simple_par.replace("H4 2.0262048E-7 1 1.1276173E-7", "")
no_H3_H4 = no_H3_H4.replace("H3 2.7507208E-7 1 1.5114416E-7", "")
no_H3_H4_model = get_model(StringIO(no_H3_H4))
assert no_H3_H4_model.H3.value is None
assert no_H3_H4_model.H4.value is None
test_toas = toasJ0613[::20]
f = ff.WLSFitter(test_toas, no_H3_H4_model)
f.fit_toas()
def test_fitter_basic():
m = tm.get_model(os.path.join(datadir, "NGC6440E.par"))
m.fit_params = ["F0", "F1"]
e = 1 * u.us
t = simulation.make_fake_toas_uniform(56000, 59000, 16, m, error=e)
T = (t.last_MJD - t.first_MJD).to(u.s)
dF0 = (e * m.F0.quantity / T).to(u.Hz)
f_1 = fitter.WLSFitter(toas=t, model=m)
f_1.fit_toas()
assert abs(f_1.model.F0 - m.F0) < dF0
m_2 = deepcopy(m)
m_2.F0.quantity += 2 * dF0
assert abs(m_2.F0 - m.F0) > dF0
f_2 = fitter.WLSFitter(toas=t, model=m_2)
f_2.fit_toas()
assert abs(f_2.model.F0 - m.F0) < dF0
def test_zero_H3_H4_fit_H3(toasJ0613):
H3H4_zero2_model = get_model(StringIO(simple_par))
H3H4_zero2_model.H3.value = 0.0
H3H4_zero2_model.H3.frozen = False
H3H4_zero2_model.H4.value = 0.0
H3H4_zero2_model.H4.frozen = True
test_toas = toasJ0613[::20]
f = ff.WLSFitter(test_toas, H3H4_zero2_model)
# This should work
f.fit_toas()
assert f.model.H3.value > 0.0
def test_fitsummary_binary():
"""Test fitter print_summary() when an ELL1 binary is fit"""
par = os.path.join(datadir, "B1855+09_NANOGrav_12yv3.wb.gls.par")
tim = os.path.join(datadir, "B1855+09_NANOGrav_dfg+12.tim")
m, t = get_model_and_toas(par, tim)
f = fitter.WLSFitter(t, m)
f.model.free_params = ["PB", "A1", "SINI"]
f.fit_toas()
f.print_summary()
def test_dmxparse():
"""Test for dmxparse function."""
m = tm.get_model(os.path.join(datadir, "B1855+09_NANOGrav_9yv1.gls.par"))
t = toa.get_TOAs(os.path.join(datadir, "B1855+09_NANOGrav_9yv1.tim"))
f = fitter.GLSFitter(toas=t, model=m)
f.fit_toas()
dmx = dmxparse(f, save=False)
# Check exception handling
m = tm.get_model(os.path.join(datadir, "B1855+09_NANOGrav_dfg+12_DMX.par"))
t = toa.get_TOAs(os.path.join(datadir, "B1855+09_NANOGrav_dfg+12.tim"))
f = fitter.WLSFitter(toas=t, model=m)
f.fit_toas()
dmx = dmxparse(f, save=False)
def test_fitter():
# Get model
m = tm.get_model(os.path.join(datadir, "NGC6440E.par"))
# Get TOAs
t = toa.TOAs(os.path.join(datadir, "NGC6440E.tim"))
t.apply_clock_corrections(include_bipm=False)
t.compute_TDBs()
try:
planet_ephems = m.PLANET_SHAPIRO.value
except AttributeError:
planet_ephems = False
t.compute_posvels(planets=planet_ephems)
f = fitter.WLSFitter(toas=t, model=m)
# Print initial chi2
print("chi^2 is initially %0.2f" % f.resids.chi2)
# Plot initial residuals
xt = f.resids.toas.get_mjds().value
yerr = t.get_errors() * 1e-6
plt.close()
p1 = plt.errorbar(
xt,
f.resids.toas.get_mjds().value,
f.resids.time_resids.value,
yerr.value,
fmt="bo",
)
# Do a 4-parameter fit
f.set_fitparams("F0", "F1", "RA", "DEC")
f.fit_toas()
print("chi^2 is %0.2f after 4-param fit" % f.resids.chi2)
p2 = plt.errorbar(xt, f.resids.time_resids.value, yerr.value, fmt="go")
# Now perturb F1 and fit only that. This doesn't work, though tempo2 easily fits
# it.
f.model.F1.value = 1.1 * f.model.F1.value
f.fit_toas()
print("chi^2 is %0.2f after perturbing F1" % f.resids.chi2)
p3 = plt.errorbar(xt, f.resids.time_resids.value, yerr.value, fmt="ms")
f.set_fitparams("F1")
f.fit_toas()
print('chi^2 is %0.2f after fitting just F1 with default method="Powell"' %
f.resids.chi2)
p4 = plt.errorbar(xt, f.resids.time_resids.value, yerr.value, fmt="k^")
def test_zero_H3_H4_fit_H3_H4(toasJ0613):
H3H4_zero_model = get_model(StringIO(simple_par))
H3H4_zero_model.H3.value = 0.0
H3H4_zero_model.H4.value = 0.0
H3H4_zero_model.H3.frozen = False
H3H4_zero_model.H4.frozen = False
# Make sure H3's derivative does not return zero.
test_toas = toasJ0613[::20]
d_h3 = H3H4_zero_model.d_delay_d_param(test_toas, "H3")
assert d_h3.mean().value != 0.0
assert d_h3.std().value != 0.0
f = ff.WLSFitter(test_toas, H3H4_zero_model)
f.fit_toas()
assert f.model.H3.value > 0.0
for i in range(MAXIT):
start = timeit.default_timer()
toa.get_TOAs(tim,
planets=use_planets,
ephem=model_ephem,
usepickle=False)
end = timeit.default_timer()
total = total + (end - start)
getTOAs_nopickle.append(total / MAXIT)
t = toa.get_TOAs(tim,
planets=use_planets,
ephem=model_ephem,
usepickle=True) # to use in timing fitter
f = fitter.WLSFitter(t, m)
print("timing fitter...")
total = 0
for i in range(MAXIT):
start = timeit.default_timer()
f.fit_toas()
end = timeit.default_timer()
total = total + (end - start)
fittimes.append(total / MAXIT)
# pickle time of get_TOAs
print("timing get_TOAs w/ pickling...")
total = 0
for i in range(MAXIT):
start = timeit.default_timer()
linewidth=0.5,
color='red')
ax3.vlines(57575,
np.min(prefit_residuals.time_resids.to(units.us)),
np.max(prefit_residuals.time_resids.to(units.us)),
linewidth=0.5,
color='red')
ax3.set_title("%s Pre-fit Timing Residuals" % model.PSR.value)
ax3.set_xlabel("MJD")
ax3.set_ylabel("Residual ($\mu$s)")
plt.legend()
plt.savefig('Pre_fit_residuals_Graham.pdf')
plt.close()
# Perform the fit
WLS_fit = fitter.WLSFitter(TOAs, model)
WLS_fit.set_fitparams('F0', 'F1', 'RAJ', 'DECJ', 'JUMP1', 'JUMP2', 'JUMP3')
WLS_fit.fit_toas()
fig4 = plt.figure()
ax4 = plt.gca()
# Plot the post-fit residuals
ax4.errorbar(TOAs.get_mjds().value,
WLS_fit.resids.time_resids.to(units.us),
yerr=TOAs.get_errors().to(units.us),
color='black',
elinewidth=0.05,
capsize=0,
marker='o',
markersize=0.8,
modelin.WAVE1.quantity = (0.0 * u.s, 0.0 * u.s)
modelin.WAVE2.quantity = (0.0 * u.s, 0.0 * u.s)
modelin.WAVE3.quantity = (0.0 * u.s, 0.0 * u.s)
modelin.WAVE4.quantity = (0.0 * u.s, 0.0 * u.s)
modelin.WAVE5.quantity = (0.0 * u.s, 0.0 * u.s)
modelin.WAVE1.frozen = False
modelin.WAVE2.frozen = False
modelin.WAVE3.frozen = False
modelin.WAVE4.frozen = False
modelin.WAVE5.frozen = False
prefitresids = Residuals(realtoas, modelin)
try:
f = fit.WLSFitter(realtoas, modelin)
f.fit_toas()
fig, ax = plt.subplots(figsize=(16, 9))
ax.set_title("Residuals using PN from compute_pulse_numbers")
ax.errorbar(
prefitresids.toas.get_mjds(),
prefitresids.time_resids,
yerr=prefitresids.toas.get_errors(),
fmt=".",
label="Prefit",
)
ax.errorbar(
f.resids.toas.get_mjds(),
f.resids.time_resids,
yerr=f.resids.toas.get_errors(),
fmt=".",
def test_fitter():
# Get model
m = tm.get_model(os.path.join(datadir, "NGC6440E.par"))
# Get TOAs
t = toa.TOAs(os.path.join(datadir, "NGC6440E.tim"))
t.apply_clock_corrections(include_bipm=False)
t.compute_TDBs()
try:
planet_ephems = m.PLANET_SHAPIRO.value
except AttributeError:
planet_ephems = False
t.compute_posvels(planets=planet_ephems)
f = fitter.WLSFitter(toas=t, model=m)
# Print initial chi2
print("chi^2 is initially %0.2f" % f.resids.chi2)
# Plot initial residuals
xt = f.resids.toas.get_mjds().value
yerr = t.get_errors() * 1e-6
plt.close()
p1 = plt.errorbar(
xt,
f.resids.toas.get_mjds().value,
f.resids.time_resids.value,
yerr.value,
fmt="bo",
)
# Do a 4-parameter fit
f.model.free_params = ("F0", "F1", "RAJ", "DECJ")
f.fit_toas()
# Check the number of degrees of freedom in the fit.
# Fitting the 4 params above, plus the 1 implicit global offset = 5 free parameters.
# NTOA = 62, so DOF = 62 - 5 = 57
assert f.resids.dof == 57
print("chi^2 is %0.2f after 4-param fit" % f.resids.chi2)
p2 = plt.errorbar(xt, f.resids.time_resids.value, yerr.value, fmt="go")
# Make sure the summary printing works
f.print_summary()
# Try a few utils
# Now perturb F1 and fit only that. This doesn't work, though tempo2 easily fits
# it.
f.model.F1.value = 1.1 * f.model.F1.value
f.fit_toas()
print("chi^2 is %0.2f after perturbing F1" % f.resids.chi2)
p3 = plt.errorbar(xt, f.resids.time_resids.value, yerr.value, fmt="ms")
f.model.free_params = ["F1"]
f.fit_toas()
print('chi^2 is %0.2f after fitting just F1 with default method="Powell"' %
f.resids.chi2)
p4 = plt.errorbar(xt, f.resids.time_resids.value, yerr.value, fmt="k^")
def test_fitting_2comp(self):
f_1 = fitter.WLSFitter(toas=self.t, model=self.m2)
f_1.fit_toas()