def _setup(self):
self._gau = Gaussian(F=100.0, mu=40, sigma=10)
# + Gaussian(F=50.0, mu=60, sigma=5)
for i in range(3):
self._gau += Gaussian(F=100.0 / (i + 1),
mu=10 + (i * 25),
sigma=old_div(5, (i + 1)))
self._returned_values = []
self._traversed_points = []
self._track = False
def _setup(self):
self._gau = Gaussian(F=100.0, mu=40, sigma=10) # type: Gaussian
self._returned_values = []
self._traversed_points = []
self._track = False
def xy_model_and_datalist():
y = np.array(poiss_sig)
xy = XYLike("test", x, y, poisson_data=True)
fitfun = Line() + Gaussian()
fitfun.b_1.bounds = (-10, 10.0)
fitfun.a_1.bounds = (-100, 100.0)
fitfun.F_2 = 60.0
fitfun.F_2.bounds = (1e-3, 200.0)
fitfun.mu_2 = 5.0
fitfun.mu_2.bounds = (0.0, 100.0)
fitfun.sigma_2.bounds = (1e-3, 10.0)
model = Model(PointSource("fake", 0.0, 0.0, fitfun))
data = DataList(xy)
return model, data
def unbinned_polyfit(events: Iterable[float], grade: int, t_start: float, t_stop: float, exposure: float, bayes: bool) -> Tuple[Polynomial, float]:
"""
function to fit a polynomial to unbinned event data.
not a member to allow parallel computation
:param events: the events to fit
:param grade: the polynomical order or grade
:param t_start: the start time to fit over
:param t_stop: the end time to fit over
:param expousure: the exposure of the interval
:param bayes: to do a bayesian fit or not
"""
log.debug(f"starting unbinned_polyfit with grade {grade}")
log.debug(f"have {len(events)} events with {exposure} exposure")
# create 3ML plugins and fit them with 3ML!
# should eventuallly allow better config
# select the model based on the grade
if threeML_config.time_series.default_fit_method is not None:
bayes = threeML_config.time_series.default_fit_method
log.debug("using a default poly fit method")
if len(events) == 0:
log.debug("no events! returning zero")
return Polynomial([0] * (grade + 1)), 0
shape = _grade_model_lookup[grade]()
with silence_console_log():
ps = PointSource("dummy", 0, 0, spectral_shape=shape)
model = Model(ps)
observation = EventObservation(events, exposure, t_start, t_stop)
xy = UnbinnedPoissonLike("series", observation=observation)
if not bayes:
# make sure the model is positive
for i, (k, v) in enumerate(model.free_parameters.items()):
if i == 0:
v.bounds = (0, None)
v.value = 10
else:
v.value = 0.0
# we actually use a line here
# because a constant is returns a
# single number
if grade == 0:
shape.b = 0
shape.b.fix = True
jl: JointLikelihood = JointLikelihood(model, DataList(xy))
grid_minimizer = GlobalMinimization("grid")
local_minimizer = LocalMinimization("minuit")
my_grid = {
model.dummy.spectrum.main.shape.a: np.logspace(0, 3, 10)}
grid_minimizer.setup(
second_minimization=local_minimizer, grid=my_grid)
jl.set_minimizer(grid_minimizer)
# if the fit falis, retry and then just accept
try:
jl.fit(quiet=True)
except(FitFailed, BadCovariance, AllFitFailed, CannotComputeCovariance):
try:
jl.fit(quiet=True)
except(FitFailed, BadCovariance, AllFitFailed, CannotComputeCovariance):
log.debug("all MLE fits failed, returning zero")
return Polynomial([0]*(grade + 1)), 0
coeff = [v.value for _, v in model.free_parameters.items()]
log.debug(f"got coeff: {coeff}")
final_polynomial = Polynomial(coeff)
final_polynomial.set_covariace_matrix(jl.results.covariance_matrix)
min_log_likelihood = xy.get_log_like()
else:
# set smart priors
for i, (k, v) in enumerate(model.free_parameters.items()):
if i == 0:
v.bounds = (0, None)
v.prior = Log_normal(mu=np.log(5), sigma=np.log(5))
v.value = 1
else:
v.prior = Gaussian(mu=0, sigma=.5)
v.value = 0.1
# we actually use a line here
# because a constant is returns a
# single number
if grade == 0:
shape.b = 0
shape.b.fix = True
ba: BayesianAnalysis = BayesianAnalysis(model, DataList(xy))
ba.set_sampler("emcee")
ba.sampler.setup(n_iterations=500, n_burn_in=200, n_walkers=20)
ba.sample(quiet=True)
ba.restore_median_fit()
coeff = [v.value for _, v in model.free_parameters.items()]
log.debug(f"got coeff: {coeff}")
final_polynomial = Polynomial(coeff)
final_polynomial.set_covariace_matrix(
ba.results.estimate_covariance_matrix())
min_log_likelihood = xy.get_log_like()
log.debug(f"-min loglike: {-min_log_likelihood}")
return final_polynomial, -min_log_likelihood
def polyfit(x: Iterable[float], y: Iterable[float], grade: int, exposure: Iterable[float], bayes: bool = False) -> Tuple[Polynomial, float]:
"""
function to fit a polynomial to data.
not a member to allow parallel computation
:param x: the x coord of the data
:param y: teh y coord of the data
:param grade: the polynomical order or grade
:param expousure: the exposure of the interval
:param bayes: to do a bayesian fit or not
"""
# Check that we have enough counts to perform the fit, otherwise
# return a "zero polynomial"
log.debug(f"starting polyfit with grade {grade} ")
if threeML_config.time_series.default_fit_method is not None:
bayes = threeML_config.time_series.default_fit_method
log.debug("using a default poly fit method")
nan_mask = np.isnan(y)
y = y[~nan_mask]
x = x[~nan_mask]
exposure = exposure[~nan_mask]
non_zero_mask = y > 0
n_non_zero = non_zero_mask.sum()
if n_non_zero == 0:
log.debug("no counts, return 0")
# No data, nothing to do!
return Polynomial([0.0]*(grade+1)), 0.0
# create 3ML plugins and fit them with 3ML!
# should eventuallly allow better config
# seelct the model based on the grade
shape = _grade_model_lookup[grade]()
ps = PointSource("_dummy", 0, 0, spectral_shape=shape)
model = Model(ps)
avg = np.mean(y/exposure)
log.debug(f"starting polyfit with avg norm {avg}")
with silence_console_log():
xy = XYLike("series", x=x, y=y, exposure=exposure,
poisson_data=True, quiet=True)
if not bayes:
# make sure the model is positive
for i, (k, v) in enumerate(model.free_parameters.items()):
if i == 0:
v.bounds = (0, None)
v.value = avg
else:
v.value = 0.0
# we actually use a line here
# because a constant is returns a
# single number
if grade == 0:
shape.b = 0
shape.b.fix = True
jl: JointLikelihood = JointLikelihood(model, DataList(xy))
jl.set_minimizer("minuit")
# if the fit falis, retry and then just accept
try:
jl.fit(quiet=True)
except(FitFailed, BadCovariance, AllFitFailed, CannotComputeCovariance):
log.debug("1st fit failed")
try:
jl.fit(quiet=True)
except(FitFailed, BadCovariance, AllFitFailed, CannotComputeCovariance):
log.debug("all MLE fits failed")
pass
coeff = [v.value for _, v in model.free_parameters.items()]
log.debug(f"got coeff: {coeff}")
final_polynomial = Polynomial(coeff)
try:
final_polynomial.set_covariace_matrix(
jl.results.covariance_matrix)
except:
log.exception(f"Fit failed in channel")
raise FitFailed()
min_log_likelihood = xy.get_log_like()
else:
# set smart priors
for i, (k, v) in enumerate(model.free_parameters.items()):
if i == 0:
v.bounds = (0, None)
v.prior = Log_normal(
mu=np.log(avg), sigma=np.max([np.log(avg/2), 1]))
v.value = 1
else:
v.prior = Gaussian(mu=0, sigma=2)
v.value = 1e-2
# we actually use a line here
# because a constant is returns a
# single number
if grade == 0:
shape.b = 0
shape.b.fix = True
ba: BayesianAnalysis = BayesianAnalysis(model, DataList(xy))
ba.set_sampler("emcee")
ba.sampler.setup(n_iterations=500, n_burn_in=200, n_walkers=20)
ba.sample(quiet=True)
ba.restore_median_fit()
coeff = [v.value for _, v in model.free_parameters.items()]
log.debug(f"got coeff: {coeff}")
final_polynomial = Polynomial(coeff)
final_polynomial.set_covariace_matrix(
ba.results.estimate_covariance_matrix())
min_log_likelihood = xy.get_log_like()
log.debug(f"-min loglike: {-min_log_likelihood}")
return final_polynomial, -min_log_likelihood
def test_ubinned_poisson_full(event_observation_contiguous, event_observation_split):
s = Line()
ps = PointSource("s", 0, 0, spectral_shape=s)
s.a.bounds = (0, None)
s.a.value = .1
s.b.value = .1
s.a.prior = Log_normal(mu=np.log(10), sigma=1)
s.b.prior = Gaussian(mu=0, sigma=1)
m = Model(ps)
######
######
######
ub1 = UnbinnedPoissonLike("test", observation=event_observation_contiguous)
jl = JointLikelihood(m, DataList(ub1))
jl.fit(quiet=True)
np.testing.assert_allclose([s.a.value, s.b.value], [6.11, 1.45], rtol=.5)
ba = BayesianAnalysis(m, DataList(ub1))
ba.set_sampler("emcee")
ba.sampler.setup(n_burn_in=100, n_walkers=20, n_iterations=500)
ba.sample(quiet=True)
ba.restore_median_fit()
np.testing.assert_allclose([s.a.value, s.b.value], [6.11, 1.45], rtol=.5)
######
######
######
ub2 = UnbinnedPoissonLike("test", observation=event_observation_split)
jl = JointLikelihood(m, DataList(ub2))
jl.fit(quiet=True)
np.testing.assert_allclose([s.a.value, s.b.value], [2., .2], rtol=.5)
ba = BayesianAnalysis(m, DataList(ub2))
ba.set_sampler("emcee")
ba.sampler.setup(n_burn_in=100, n_walkers=20, n_iterations=500)
ba.sample(quiet=True)
ba.restore_median_fit()
np.testing.assert_allclose([s.a.value, s.b.value], [2., .2], rtol=.5)