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)