def test_unused_noise_model_parameter(param, value):
model = get_model(io.StringIO("\n".join([par_base, f"{param} TEL ao {value}"])))
toas = make_fake_toas(58000, 58900, 10, model, obs="barycenter", freq=np.inf)
model.free_params = ["F0"]
fitter = pint.fitter.GLSFitter(toas, model)
with pytest.warns(UserWarning, match=param):
fitter.fit_toas()
def test_dm_barycentered():
model = get_model(io.StringIO(par_base))
toas = make_fake_toas(57000, 57900, 10, model, obs="@", freq=np.inf)
model.free_params = ["F0", "DM"]
fitter = pint.fitter.WLSFitter(toas, model)
with pytest.warns(pint.fitter.DegeneracyWarning, match=".*degeneracy.*DM.*"):
fitter.fit_toas()
def test_update_model(Fitter):
model = get_model(
io.StringIO("\n".join([par_base, "JUMP TEL barycenter 0"])))
model.INFO.value = "-f"
model.ECL.value = "IERS2010"
model.TIMEEPH.value = "FB90"
model.T2CMETHOD.value = "IERS2000B"
toas = make_fake_toas(58000,
59000,
10,
model,
obs="barycenter",
freq=np.inf)
fitter = Fitter(toas, model)
fitter.fit_toas()
par_out = fitter.model.as_parfile()
assert re.search(r"CLOCK *TT\(TAI\)", par_out)
assert re.search(r"TIMEEPH *FB90", par_out)
assert re.search(r"T2CMETHOD *IERS2000B", par_out)
assert re.search(r"NE_SW *0.0", par_out)
assert re.search(r"ECL *IERS2010", par_out)
assert re.search(r"DILATEFREQ *N", par_out)
assert re.search(r"INFO *-f", par_out)
assert re.search(r"NTOA *10.0", par_out)
assert re.search(r"CHI2 *\d+.\d+", par_out)
assert re.search(r"EPHEM *DE421", par_out)
assert re.search(r"DMDATA *0.0", par_out)
assert re.search(r"START *58000.0", par_out)
assert re.search(r"FINISH *59000.0", par_out)
def test_jump_everything_wideband():
model = get_model(
io.StringIO("\n".join([par_base, "JUMP TEL barycenter 0"])))
toas = make_fake_toas(58000,
58900,
10,
model,
obs="barycenter",
freq=np.inf)
for f in toas.table["flags"]:
f["pp_dm"] = 15.0
f["pp_dme"] = 1e-4
model.free_params = ["JUMP1", "F0", "DM"]
fitter = pint.fitter.WidebandTOAFitter(toas, model)
with pytest.warns(pint.fitter.DegeneracyWarning,
match=r".*degeneracy.*Offset\b"):
fitter.fit_toas(threshold=1e-14)
for p in fitter.model.free_params:
assert not np.isnan(fitter.model[p].value)
fitter = pint.fitter.WidebandTOAFitter(toas, model)
with pytest.warns(pint.fitter.DegeneracyWarning,
match=r".*degeneracy.*JUMP1\b"):
fitter.fit_toas(threshold=1e-14)
for p in fitter.model.free_params:
assert not np.isnan(fitter.model[p].value)
def fit(self, iters=1):
'''
Run a fit using the specified fitter
'''
if self.fitted:
self.prefit_model = self.postfit_model
self.prefit_resids = self.postfit_resids
if self.fitter == Fitters.POWELL:
fitter = pint.fitter.PowellFitter(self.toas, self.prefit_model)
elif self.fitter == Fitters.WLS:
fitter = pint.fitter.WlsFitter(self.toas, self.prefit_model)
elif self.fitter == Fitters.GLS:
fitter = pint.fitter.GLSFitter(self.toas, self.prefit_model)
chi2 = self.prefit_resids.chi2
wrms = np.sqrt(chi2 / self.toas.ntoas)
print('Pre-Fit Chi2:\t\t%.8g us^2' % chi2)
print('Pre-Fit Weighted RMS:\t%.8g us' % wrms)
fitter.fit_toas(maxiter=1)
self.postfit_model = fitter.model
self.postfit_resids = pint.residuals.resids(self.toas,
self.postfit_model)
self.fitted = True
self.write_fit_summary()
def test_dmx_barycentered(Fitter):
model = get_model(
io.StringIO(
"\n".join(
[
par_base,
"DMX 15",
"DMX_0001 16",
"DMXR1_0001 58000",
"DMXR2_0001 59000",
]
)
)
)
toas = make_fake_toas(58000, 58900, 10, model, obs="@", freq=np.inf)
model.free_params = ["F0", "DM", "DMX_0001"]
fitter = Fitter(toas, model)
with pytest.warns(pint.fitter.DegeneracyWarning, match=r".*degeneracy.*DM\b"):
fitter.fit_toas()
for p in fitter.model.free_params:
assert not np.isnan(fitter.model[p].value)
fitter = Fitter(toas, model)
with pytest.warns(pint.fitter.DegeneracyWarning, match=r".*degeneracy.*DMX_0001\b"):
fitter.fit_toas()
for p in fitter.model.free_params:
assert not np.isnan(fitter.model[p].value)
def test_update_model_sets_things(Fitter):
model = get_model(io.StringIO("\n".join([par_base, "JUMP TEL barycenter 0"])))
model.INFO.value = "-f"
model.ECL.value = "IERS2010"
model.TIMEEPH.value = "IF99"
model.DILATEFREQ.value = True
model.T2CMETHOD.value = "TEMPO"
model.use_aliases(reset_to_default=True)
toas = make_fake_toas_uniform(
58000, 59000, 10, model, obs="barycenter", freq=np.inf
)
fitter = Fitter(toas, model)
fitter.fit_toas()
par_out = fitter.model.as_parfile()
assert re.search(r"CLOCK *TT\(TAI\)", par_out)
assert re.search(r"TIMEEPH *FB90", par_out)
assert re.search(r"T2CMETHOD *IAU2000B", par_out)
assert re.search(r"NE_SW *0.0", par_out)
assert re.search(r"ECL *IERS2010", par_out)
assert re.search(r"DILATEFREQ *N", par_out)
assert re.search(r"INFO *-f", par_out)
assert re.search(r"NTOA *10", par_out)
assert re.search(r"EPHEM *DE421", par_out)
assert re.search(r"DMDATA *N", par_out)
assert re.search(r"START *58000.0", par_out)
assert re.search(r"FINISH *59000.0", par_out)
def test_jump_no_toas():
model = get_model(io.StringIO("\n".join([par_base, "JUMP -fe L_wide 0"])))
toas = make_fake_toas(57000, 57900, 10, model)
assert len(model.JUMP1.select_toa_mask(toas)) == 0
model.JUMP1.frozen = True
model.validate_toas(toas)
model.JUMP1.frozen = False
with pytest.raises(ValueError):
model.validate_toas(toas)
model.JUMP1.frozen = False
fitter = pint.fitter.WLSFitter(toas, model)
with pytest.raises(ValueError):
fitter.fit_toas()
def test_jump_everything(Fitter):
model = get_model(io.StringIO("\n".join([par_base, "JUMP TEL barycenter 0"])))
toas = make_fake_toas(58000, 58900, 10, model, obs="barycenter", freq=np.inf)
model.free_params = ["JUMP1", "F0"]
fitter = Fitter(toas, model)
with pytest.warns(pint.fitter.DegeneracyWarning, match=r".*degeneracy.*Offset\b"):
fitter.fit_toas(threshold=1e-14)
for p in fitter.model.free_params:
assert not np.isnan(fitter.model[p].value)
fitter = Fitter(toas, model)
with pytest.warns(pint.fitter.DegeneracyWarning, match=r".*degeneracy.*JUMP1\b"):
fitter.fit_toas(threshold=1e-14)
for p in fitter.model.free_params:
assert not np.isnan(fitter.model[p].value)
def test_dmx_no_toas():
model = get_model(
io.StringIO("\n".join([
par_base,
"DMX 15",
"DMX_0001 16 1",
"DMXR1_0001 58000",
"DMXR2_0001 59000",
])))
toas = make_fake_toas(57000, 57900, 10, model)
with pytest.raises(MissingTOAs) as e:
model.validate_toas(toas)
assert e.value.parameter_names == ["DMX_0001"]
fitter = pint.fitter.WLSFitter(toas, model)
with pytest.raises(MissingTOAs):
fitter.fit_toas()
def test_START_FINISH_in_par(self):
"""
Check that START/FINISH parameters set up/operate properly when
from input file.
"""
m1 = self.m
t1 = get_TOAs("B1855+09_NANOGrav_dfg+12.tim")
start_preval = 53358.726464889485214
finish_preval = 55108.922917417192366
start_postval = 53358.7274648895 # from Tempo2
finish_postval = 55108.9219174172 # from Tempo2
# check parameter initialization
assert hasattr(m1, "START")
assert type(m1.START) == type(MJDParameter())
assert hasattr(m1, "FINISH")
assert type(m1.FINISH) == type(MJDParameter())
self.assertEqual(m1.START.value, start_preval)
self.assertEqual(m1.FINISH.value, finish_preval)
self.assertEqual(m1.START.frozen, True)
self.assertEqual(m1.FINISH.frozen, True)
# fit toas and compare with expected/Tempo2 (for WLS) values
fitters = [
pint.fitter.PowellFitter(toas=t1, model=m1),
pint.fitter.WLSFitter(toas=t1, model=m1),
pint.fitter.GLSFitter(toas=t1, model=m1),
]
for fitter in fitters:
fitter.fit_toas()
self.assertEqual(m1.START.frozen, True)
self.assertEqual(m1.FINISH.frozen, True)
if fitter.method == "weighted_least_square":
self.assertAlmostEqual(
fitter.model.START.value, start_postval, places=9
)
self.assertAlmostEqual(
fitter.model.FINISH.value, finish_postval, places=9
)
self.assertAlmostEqual(
fitter.model.START.value, fitter.toas.first_MJD.value, places=9
)
self.assertAlmostEqual(
fitter.model.FINISH.value, fitter.toas.last_MJD.value, places=9
)
def test_null_vector(Fitter):
model = get_model(io.StringIO("\n".join([par_base])))
model.free_params = ["ELONG", "ELAT"]
toas = make_fake_toas(
58000,
58900,
10,
model,
obs="barycenter",
freq=1400.0,
dm=15.0 * u.pc / u.cm ** 3,
dm_error=1e-4 * u.pc * u.cm ** -3,
)
fitter = Fitter(toas, model)
with pytest.warns(
pint.fitter.DegeneracyWarning,
match=r".*degeneracy.*following parameter.*ELONG\b",
):
fitter.fit_toas(threshold=1e-14)
for p in fitter.model.free_params:
assert not np.isnan(fitter.model[p].value)
def test_START_FINISH_not_in_par(self):
"""
Check that START/FINISH parameters are added and set up when not
in input file.
"""
# check initialization after fitting for .par file without START/FINISH
m = get_model("NGC6440E.par")
t = get_TOAs("NGC6440E.tim")
start_postval = 53478.2858714192 # from Tempo2
finish_postval = 54187.5873241699 # from Tempo2
self.assertTrue(hasattr(m, "START"))
self.assertTrue(hasattr(m, "FINISH"))
# fit toas and compare with expected/Tempo2 (for WLS) values
fitters = [
pint.fitter.PowellFitter(toas=t, model=m),
pint.fitter.WLSFitter(toas=t, model=m),
pint.fitter.GLSFitter(toas=t, model=m),
]
for fitter in fitters:
fitter.fit_toas()
self.assertTrue(hasattr(fitter.model, "START"))
self.assertTrue(hasattr(fitter.model, "FINISH"))
self.assertEqual(fitter.model.START.frozen, True)
self.assertEqual(fitter.model.FINISH.frozen, True)
if fitter.method == "weighted_least_square":
self.assertAlmostEqual(
fitter.model.START.value, start_postval, places=9
)
self.assertAlmostEqual(
fitter.model.FINISH.value, finish_postval, places=9
)
self.assertAlmostEqual(
fitter.model.START.value, fitter.toas.first_MJD.value, places=9
)
self.assertAlmostEqual(
fitter.model.FINISH.value, fitter.toas.last_MJD.value, places=9
)
def fit(self, iters=1):
'''
Run a fit using the specified fitter
'''
if self.fitted:
self.prefit_model = self.postfit_model
self.prefit_resids = self.postfit_resids
if self.fitter == Fitters.POWELL:
fitter = pint.fitter.PowellFitter(self.toas, self.prefit_model)
elif self.fitter == Fitters.WLS:
fitter = pint.fitter.WlsFitter(self.toas, self.prefit_model)
elif self.fitter == Fitters.GLS:
fitter = pint.fitter.GLSFitter(self.toas, self.prefit_model)
chi2 = self.prefit_resids.chi2
wrms = np.sqrt(chi2 / self.toas.ntoas)
print('Pre-Fit Chi2:\t\t%.8g us^2' % chi2)
print('Pre-Fit Weighted RMS:\t%.8g us' % wrms)
fitter.fit_toas(maxiter=1)
self.postfit_model = fitter.model
self.postfit_resids = pint.residuals.resids(self.toas, self.postfit_model)
self.fitted = True
self.write_fit_summary()
def fit(self, selected, iters=1):
"""
Run a fit using the specified fitter
"""
# Select all the TOAs if none are explicitly set
if not any(selected):
selected = ~selected
if self.fitted:
self.prefit_model = self.postfit_model
self.prefit_resids = self.postfit_resids
if self.fitter == Fitters.POWELL:
fitter = pint.fitter.PowellFitter(self.selected_toas, self.prefit_model)
elif self.fitter == Fitters.WLS:
fitter = pint.fitter.WLSFitter(self.selected_toas, self.prefit_model)
elif self.fitter == Fitters.GLS:
fitter = pint.fitter.GLSFitter(self.selected_toas, self.prefit_model)
chi2 = self.prefit_resids.chi2
wrms = self.prefit_resids.rms_weighted()
print("Pre-Fit Chi2:\t\t%.8g" % chi2)
print("Pre-Fit Weighted RMS:\t%.8g us" % wrms.to(u.us).value)
fitter.fit_toas(maxiter=1)
self.postfit_model = fitter.model
self.postfit_resids = Residuals(self.all_toas, self.postfit_model)
self.fitted = True
self.write_fit_summary()
# TODO: delta_pulse_numbers need some work. They serve both for PHASE and -padd functions from the TOAs
# as well as for phase jumps added manually in the GUI. They really should not be zeroed out here because
# that will wipe out preexisting values
self.all_toas.table["delta_pulse_numbers"] = np.zeros(self.all_toas.ntoas)
self.selected_toas.table["delta_pulse_number"] = np.zeros(
self.selected_toas.ntoas
)
# plot the prefit without jumps
pm_no_jumps = copy.deepcopy(self.prefit_model)
for param in pm_no_jumps.params:
if param.startswith("JUMP"):
getattr(pm_no_jumps, param).value = 0.0
getattr(pm_no_jumps, param).frozen = True
self.prefit_resids_no_jumps = Residuals(self.selected_toas, pm_no_jumps)
f = copy.deepcopy(fitter)
no_jumps = [
False if "jump" in dict.keys() else True for dict in f.toas.table["flags"]
]
f.toas.select(no_jumps)
selectedMJDs = self.selected_toas.get_mjds()
if all(no_jumps):
q = list(self.all_toas.get_mjds())
index = q.index(
[i for i in self.all_toas.get_mjds() if i > selectedMJDs.min()][0]
)
rs_mean = (
Residuals(self.all_toas, f.model)
.phase_resids[index : index + len(selectedMJDs)]
.mean()
)
else:
rs_mean = self.prefit_resids_no_jumps.phase_resids[no_jumps].mean()
# determines how far on either side fake toas go
# TODO: hard limit on how far fake toas can go --> can get clkcorr
# errors if go before GBT existed, etc.
minMJD, maxMJD = selectedMJDs.min(), selectedMJDs.max()
spanMJDs = maxMJD - minMJD
if spanMJDs < 30 * u.d:
redge = ledge = 4
npoints = 400
elif spanMJDs < 90 * u.d:
redge = ledge = 2
npoints = 300
elif spanMJDs < 200 * u.d:
redge = ledge = 1
npoints = 300
elif spanMJDs < 400 * u.d:
redge = ledge = 0.5
npoints = 200
else:
redge = ledge = 1.0
npoints = 250
# Check to see if too recent
nowish = (Time.now().mjd - 40) * u.d
if maxMJD + spanMJDs * redge > nowish:
redge = (nowish - maxMJD) / spanMJDs
if redge < 0.0:
redge = 0.0
f_toas = make_fake_toas_uniform(
minMJD - spanMJDs * ledge, maxMJD + spanMJDs * redge, npoints, f.model
)
rs = calculate_random_models(
f, f_toas, Nmodels=10, keep_models=False, return_time=True
)
# subtract the mean residual of each random model from the respective residual set
# based ONLY on the mean of the random residuals in the real data range
start_index = np.where(abs(f_toas.get_mjds() - minMJD) < 1 * u.d)
end_index = np.where(abs(f_toas.get_mjds() - maxMJD) < 1 * u.d)
for i in range(len(rs)):
# use start_index[0][0] since np.where returns np.array([], dtype), extract index from list in array
rs_mean = rs[i][start_index[0][0] : end_index[0][0]].mean()
rs[i][:] = [resid - rs_mean for resid in rs[i]]
self.random_resids = rs
self.fake_toas = f_toas
def fit(self, selected, iters=1):
"""
Run a fit using the specified fitter
"""
# Select all the TOAs if none are explicitly set
if not any(selected):
selected = ~selected
"""JUMP check, TODO: put in fitter?"""
if "PhaseJump" in self.prefit_model.components:
# if attempted fit (selected)
# A) contains only jumps, don't do the fit and return an error
# B) excludes a jump, turn that jump off
# C) partially contains a jump, redefine that jump only with the overlap
fit_jumps = []
for param in self.prefit_model.params:
if getattr(self.prefit_model,
param).frozen == False and param.startswith("JUMP"):
fit_jumps.append(int(param[4:]))
numjumps = self.prefit_model.components[
"PhaseJump"].get_number_of_jumps()
if numjumps == 0:
log.warn(
"There are no jumps (maskParameter objects) in PhaseJump. Please delete the PhaseJump object and try again. "
)
return None
# boolean array to determine if all selected toas are jumped
jumps = [
True if "jump" in dict.keys() and dict["jump"] in fit_jumps
else False for dict in self.all_toas.table["flags"][selected]
]
# check if par file jumps in PhaseJump object
if not any(jumps):
# for every jump, set appropriate flag for TOAs it jumps
for jump_par in self.prefit_model.components[
"PhaseJump"].get_jump_param_objects():
# find TOAs jump applies to
mask = jump_par.select_toa_mask(self.all_toas)
# apply to dictionaries for future use
for dict in self.all_toas.table["flags"][mask]:
dict["jump"] = jump_par.index
jumps = [
True if "jump" in dict.keys() and dict["jump"] in fit_jumps
else False
for dict in self.all_toas.table["flags"][selected]
]
if all(jumps):
log.warn(
"toas being fit must not all be jumped. Remove or uncheck at least one jump in the selected toas before fitting."
)
return None
# numerical array of selected jump flags
sel_jump_nums = [
dict["jump"] if "jump" in dict.keys() else np.nan
for dict in self.all_toas.table["flags"][selected]
]
# numerical array of all jump flags
full_jump_nums = [
dict["jump"] if "jump" in dict.keys() else np.nan
for dict in self.all_toas.table["flags"]
]
for num in range(1, numjumps + 1):
num = int(num)
if num not in sel_jump_nums:
getattr(self.prefit_model, "JUMP" + str(num)).frozen = True
continue
jump_select = [num == jump_num for jump_num in full_jump_nums]
overlap = [a and b for a, b in zip(jump_select, selected)]
# remove the jump flags for that num
for dict in self.all_toas.table["flags"]:
if "jump" in dict.keys() and dict["jump"] == num:
del dict["jump"]
# re-add the jump using overlap as 'selected'
for dict in self.all_toas.table["flags"][overlap]:
dict["jump"] = num
if self.fitted:
self.prefit_model = self.postfit_model
self.prefit_resids = self.postfit_resids
if self.fitter == Fitters.POWELL:
fitter = pint.fitter.PowellFitter(self.selected_toas,
self.prefit_model)
elif self.fitter == Fitters.WLS:
fitter = pint.fitter.WLSFitter(self.selected_toas,
self.prefit_model)
elif self.fitter == Fitters.GLS:
fitter = pint.fitter.GLSFitter(self.selected_toas,
self.prefit_model)
chi2 = self.prefit_resids.chi2
wrms = np.sqrt(chi2 / self.selected_toas.ntoas)
print("Pre-Fit Chi2:\t\t%.8g us^2" % chi2)
print("Pre-Fit Weighted RMS:\t%.8g us" % wrms)
fitter.fit_toas(maxiter=1)
self.postfit_model = fitter.model
self.postfit_resids = Residuals(self.all_toas, self.postfit_model)
self.fitted = True
self.write_fit_summary()
# TODO: delta_pulse_numbers need some work. They serve both for PHASE and -padd functions from the TOAs
# as well as for phase jumps added manually in the GUI. They really should not be zeroed out here because
# that will wipe out preexisting values
self.fulltoas.table["delta_pulse_numbers"] = np.zeros(
self.fulltoas.ntoas)
self.selected_toas.table["delta_pulse_number"] = np.zeros(
self.selected_toas.ntoas)
# plot the prefit without jumps
pm_no_jumps = copy.deepcopy(self.postfit_model)
for param in pm_no_jumps.params:
if param.startswith("JUMP"):
getattr(pm_no_jumps, param).value = 0.0
getattr(pm_no_jumps, param).frozen = True
self.prefit_resids_no_jumps = Residuals(self.selected_toas,
pm_no_jumps)
f = copy.deepcopy(fitter)
no_jumps = [
False if "jump" in dict.keys() else True
for dict in f.toas.table["flags"]
]
f.toas.select(no_jumps)
selectedMJDs = self.selected_toas.get_mjds()
if all(no_jumps):
q = list(self.all_toas.get_mjds())
index = q.index([
i for i in self.all_toas.get_mjds() if i > selectedMJDs.min()
][0])
rs_mean = (Residuals(
self.all_toas,
f.model).phase_resids[index:index + len(selectedMJDs)].mean())
else:
rs_mean = self.prefit_resids_no_jumps.phase_resids[no_jumps].mean()
# determines how far on either side fake toas go
# TODO: hard limit on how far fake toas can go --> can get clkcorr
# errors if go before GBT existed, etc.
minMJD, maxMJD = selectedMJDs.min(), selectedMJDs.max()
spanMJDs = maxMJD - minMJD
if spanMJDs < 30 * u.d:
redge = ledge = 4
npoints = 400
elif spanMJDs < 90 * u.d:
redge = ledge = 2
npoints = 300
elif spanMJDs < 200 * u.d:
redge = ledge = 1
npoints = 300
elif spanMJDs < 400 * u.d:
redge = ledge = 0.5
npoints = 200
else:
redge = ledge = 0.2
npoints = 150
# Check to see if too recent
nowish = (Time.now().mjd - 40) * u.d
if maxMJD + spanMJDs * redge > nowish:
redge = (nowish - maxMJD) / spanMJDs
if redge < 0.0:
redge = 0.0
f_toas, rs, mrands = random_models(
f,
rs_mean=rs_mean,
redge_multiplier=redge,
ledge_multiplier=ledge,
npoints=npoints,
iter=10,
)
self.random_resids = rs
self.fake_toas = f_toas
def fit(self, selected, iters=1):
"""
Run a fit using the specified fitter
"""
# Select all the TOAs if none are explicitly set
if not any(selected):
selected = ~selected
"""JUMP check, TODO: put in fitter?"""
if "PhaseJump" in self.prefit_model.components:
# if attempted fit (selected)
# A) contains only jumps, don't do the fit and return an error
# B) excludes a jump, turn that jump off
# C) partially contains a jump, redefine that jump only with the overlap
fit_jumps = []
for param in self.prefit_model.params:
if getattr(self.prefit_model,
param).frozen == False and param.startswith("JUMP"):
fit_jumps.append(int(param[4:]))
jumps = [
True if "jump" in dict.keys() and dict["jump"] in fit_jumps
else False for dict in self.selected_toas.table["flags"]
]
if all(jumps):
log.warn(
"toas being fit must not all be jumped. Remove or uncheck at least one jump in the selected toas before fitting."
)
return None
sel_jump_nums = [
dict["jump"] if "jump" in dict.keys() else np.nan
for dict in self.selected_toas.table["flags"]
]
full_jump_nums = [
dict["jump"] if "jump" in dict.keys() else np.nan
for dict in self.all_toas.table["flags"]
]
for num in range(1, int(np.nanmax(full_jump_nums) + 1)):
num = int(num)
if num not in sel_jump_nums:
getattr(self.prefit_model, "JUMP" + str(num)).frozen = True
continue
jump_select = [num == jump_num for jump_num in full_jump_nums]
overlap = [a and b for a, b in zip(jump_select, selected)]
# remove the jump flags for that num
for dict in self.all_toas.table["flags"]:
if "jump" in dict.keys() and dict["jump"] == num:
del dict["jump"]
# re-add the jump using overlap as 'selected'
for dict in self.all_toas.table["flags"][overlap]:
dict["jump"] = num
if self.fitted:
self.prefit_model = self.postfit_model
self.prefit_resids = self.postfit_resids
if self.fitter == Fitters.POWELL:
fitter = pint.fitter.PowellFitter(self.selected_toas,
self.prefit_model)
elif self.fitter == Fitters.WLS:
fitter = pint.fitter.WLSFitter(self.selected_toas,
self.prefit_model)
elif self.fitter == Fitters.GLS:
fitter = pint.fitter.GLSFitter(self.selected_toas,
self.prefit_model)
chi2 = self.prefit_resids.chi2
wrms = np.sqrt(chi2 / self.selected_toas.ntoas)
print("Pre-Fit Chi2:\t\t%.8g us^2" % chi2)
print("Pre-Fit Weighted RMS:\t%.8g us" % wrms)
fitter.fit_toas(maxiter=1)
self.postfit_model = fitter.model
self.postfit_resids = Residuals(self.all_toas,
self.postfit_model,
set_pulse_nums=True)
self.fitted = True
self.write_fit_summary()
# TODO: set pulse nums above not working to reset delta pulse nums, have to force it here
# self.fulltoas.table['delta_pulse_numbers'] = np.zeros(self.fulltoas.ntoas)
self.selected_toas.table["delta_pulse_number"] = np.zeros(
self.selected_toas.ntoas)
# plot the prefit without jumps
pm_no_jumps = copy.deepcopy(self.postfit_model)
for param in pm_no_jumps.params:
if param.startswith("JUMP"):
getattr(pm_no_jumps, param).value = 0.0
getattr(pm_no_jumps, param).frozen = True
self.prefit_resids_no_jumps = Residuals(self.all_toas,
pm_no_jumps,
set_pulse_nums=True)
f = copy.deepcopy(fitter)
no_jumps = [
False if "jump" in dict.keys() else True
for dict in f.toas.table["flags"]
]
f.toas.select(no_jumps)
selectedMJDs = self.selected_toas.get_mjds()
if all(no_jumps):
q = list(self.all_toas.get_mjds())
index = q.index([
i for i in self.all_toas.get_mjds() if i > selectedMJDs.min()
][0])
rs_mean = (Residuals(
self.all_toas, f.model,
set_pulse_nums=True).phase_resids[index:index +
len(selectedMJDs)].mean())
else:
rs_mean = self.prefit_resids_no_jumps.phase_resids[no_jumps].mean()
# determines how far on either side fake toas go
# TODO: hard limit on how far fake toas can go --> can get clkcorr
# errors if go before GBT existed, etc.
minMJD, maxMJD = selectedMJDs.min(), selectedMJDs.max()
spanMJDs = maxMJD - minMJD
if spanMJDs < 30 * u.d:
redge = ledge = 4
npoints = 400
elif spanMJDs < 90 * u.d:
redge = ledge = 2
npoints = 300
elif spanMJDs < 200 * u.d:
redge = ledge = 1
npoints = 300
elif spanMJDs < 400 * u.d:
redge = ledge = 0.5
npoints = 200
else:
redge = ledge = 0.2
npoints = 150
# Check to see if too recent
nowish = (Time.now().mjd - 40) * u.d
if maxMJD + spanMJDs * redge > nowish:
redge = (nowish - maxMJD) / spanMJDs
if redge < 0.0:
redge = 0.0
f_toas, rs = random_models(
f,
rs_mean=rs_mean,
redge_multiplier=redge,
ledge_multiplier=ledge,
npoints=npoints,
iter=10,
)
self.random_resids = rs
self.fake_toas = f_toas
