def test_cstat_rsppha(): """What does CSTAT calculate when there is an RSP+PHA instrument model. This includes the AREASCAL when evaluating the model. See Also -------- test_cstat_nophamodel, test_cstat_arfpha, test_cstat_rmfpha """ dset, mdl, expected = setup_likelihood(scale=True) # use the full channel grid; the energy grid has to be # "the same" as the channel values since the model # has a dependency on the independent axis # egrid = 1.0 * np.concatenate((dset.channel, [dset.channel.max() + 1])) arf = make_arf(energ_lo=egrid[:-1], energ_hi=egrid[1:]) rmf = make_ideal_rmf(e_min=egrid[:-1], e_max=egrid[1:]) mdl_ascal = RSPModelPHA(arf, rmf, dset, mdl) stat = CStat() sval_ascal = stat.calc_stat(dset, mdl_ascal) assert_allclose(sval_ascal[0], expected)
def test_cstat_rsppha(): """What does CSTAT calculate when there is an RSP+PHA instrument model. This includes the AREASCAL when evaluating the model. See Also -------- test_cstat_nophamodel, test_cstat_arfpha, test_cstat_rmfpha """ dset, mdl, expected = setup_likelihood(scale=True) # use the full channel grid; the energy grid has to be # "the same" as the channel values since the model # has a dependency on the independent axis # egrid = 1.0 * np.concatenate((dset.channel, [dset.channel.max() + 1])) arf = make_arf(energ_lo=egrid[:-1], energ_hi=egrid[1:]) rmf = make_ideal_rmf(e_min=egrid[:-1], e_max=egrid[1:]) mdl_ascal = RSPModelPHA(arf, rmf, dset, mdl) stat = CStat() sval_ascal = stat.calc_stat(dset, mdl_ascal) assert_allclose(sval_ascal[0], expected)
def test_cstat_rmfpha(): """What does CSTAT calculate when there is an RMF+PHA instrument model. This includes the AREASCAL when evaluating the model. See Also -------- test_cstat_nophamodel, test_cstat_arfpha, test_cstat_rsppha """ dset, mdl, expected = setup_likelihood(scale=True) # use the full channel grid; the energy grid has to be # "the same" as the channel values since the model # has a dependency on the independent axis # egrid = 1.0 * np.concatenate((dset.channel, [dset.channel.max() + 1])) rmf = create_delta_rmf(egrid[:-1], egrid[1:]) mdl_ascal = RMFModelPHA(rmf, dset, mdl) stat = CStat() sval_ascal = stat.calc_stat(dset, mdl_ascal) assert sval_ascal[0] == pytest.approx(expected)
def setup(make_data_path): from sherpa.astro.io import read_pha from sherpa.astro.xspec import XSwabs, XSpowerlaw old_level = logger.getEffectiveLevel() logger.setLevel(logging.CRITICAL) pha = make_data_path("refake_0934_1_21_1e4.fak") simarf = make_data_path("aref_sample.fits") pcaarf = make_data_path("aref_Cedge.fits") data = read_pha(pha) data.ignore(None, 0.3) data.ignore(7.0, None) rsp = Response1D(data) abs1 = XSwabs('abs1') p1 = XSpowerlaw('p1') model = rsp(abs1 * p1) abs1.nh = 0.092886 p1.phoindex = 0.994544 p1.norm = 9.26369 fit = Fit(data, model, CStat(), NelderMead(), Covariance()) yield {'simarf': simarf, 'pcaarf': pcaarf, 'niter': 10, 'fit': fit} # Reset the logger logger.setLevel(old_level)
def setUp(self): try: from sherpa.astro.io import read_pha from sherpa.astro.xspec import XSwabs, XSpowerlaw except: return # self.startdir = os.getcwd() self.old_level = logger.getEffectiveLevel() logger.setLevel(logging.CRITICAL) pha = self.make_path("refake_0934_1_21_1e4.fak") # rmf = self.make_path("ccdid7_default.rmf") # arf = self.make_path("quiet_0934.arf") self.simarf = self.make_path("aref_sample.fits") self.pcaarf = self.make_path("aref_Cedge.fits") data = read_pha(pha) data.ignore(None, 0.3) data.ignore(7.0, None) rsp = Response1D(data) self.abs1 = XSwabs('abs1') self.p1 = XSpowerlaw('p1') model = rsp(self.abs1 * self.p1) self.fit = Fit(data, model, CStat(), NelderMead(), Covariance())
def test_cstat_nophamodel(): """What does CSTAT calculate when there is no PHA instrument model. The value here is technically wrong, in that the AREASCAL value is not being included in the calculation, but is included as a test to validate the current approach. See Also -------- test_cstat_arfpha, test_cstat_rmfpha, test_cstat_rsppha """ dset, mdl, expected = setup_likelihood(scale=False) stat = CStat() sval_noascal = stat.calc_stat(dset, mdl) assert_allclose(sval_noascal[0], expected)
def test_cstat_nophamodel(): """What does CSTAT calculate when there is no PHA instrument model. The value here is technically wrong, in that the AREASCAL value is not being included in the calculation, but is included as a test to validate the current approach. See Also -------- test_cstat_arfpha, test_cstat_rmfpha, test_cstat_rsppha """ dset, mdl, expected = setup_likelihood(scale=False) stat = CStat() sval_noascal = stat.calc_stat(dset, mdl) assert_allclose(sval_noascal[0], expected)
def test_cstat_arfpha(): """What does CSTAT calculate when there is an ARF+PHA instrument model. The value here is technically wrong, in that the AREASCAL value is not being included in the calculation, but is included as a test to validate this use case. See Also -------- test_cstat_nophamodel, test_cstat_rmfpha, test_cstat_rsppha """ dset, mdl, expected = setup_likelihood(scale=True) # Use the channel grid as the "energy axis". # arf = make_arf(energ_lo=dset.channel, energ_hi=dset.channel + 1) mdl_ascal = ARFModelPHA(arf, dset, mdl) stat = CStat() sval_ascal = stat.calc_stat(dset, mdl_ascal) assert_allclose(sval_ascal[0], expected)
def test_cstat_arfpha(): """What does CSTAT calculate when there is an ARF+PHA instrument model. The value here is technically wrong, in that the AREASCAL value is not being included in the calculation, but is included as a test to validate this use case. See Also -------- test_cstat_nophamodel, test_cstat_rmfpha, test_cstat_rsppha """ dset, mdl, expected = setup_likelihood(scale=True) # Use the channel grid as the "energy axis". # arf = make_arf(energ_lo=dset.channel, energ_hi=dset.channel + 1) mdl_ascal = ARFModelPHA(arf, dset, mdl) stat = CStat() sval_ascal = stat.calc_stat(dset, mdl_ascal) assert_allclose(sval_ascal[0], expected)
def test_metropolisMH(self): self.fit.method = NelderMead() self.fit.stat = CStat() results = self.fit.fit() results = self.fit.est_errors() cov = results.extra_output mcmc = MCMC() mcmc.set_sampler('MetropolisMH') #mcmc.set_sampler_opt('verbose', True) log = logging.getLogger("sherpa") level = log.level log.setLevel(logging.ERROR) stats, accept, params = mcmc.get_draws(self.fit, cov, niter=1e2) log.setLevel(level)
def test_cstat_stat(hide_logging, reset_xspec, setup): fit = Fit(setup['data'], setup['model'], CStat(), NelderMead()) results = fit.fit() _fit_cstat_results_bench = { 'succeeded': 1, 'numpoints': 460, 'dof': 457, 'istatval': 21647.62293983995, 'statval': 472.6585691450068, 'parvals': numpy.array([1.75021021282262, 5.474614304244775, -1.9985761873334102]) } compare_results(_fit_cstat_results_bench, results)
def test_cstat_stat(self): fit = Fit(self.data, self.model, CStat(), NelderMead()) results = fit.fit() self.compare_results(self._fit_cstat_results_bench, results)
def run(fit, null_comp, alt_comp, conv_mdl=None, stat=None, method=None, niter=500, numcores=None): if stat is None: stat = CStat() if method is None: method = NelderMead() if not isinstance(stat, (Cash, CStat)): raise TypeError("Sherpa fit statistic must be Cash or CStat" + " for likelihood ratio test") niter = int(niter) alt = alt_comp null = null_comp oldaltvals = numpy.array(alt.thawedpars) oldnullvals = numpy.array(null.thawedpars) data = fit.data if conv_mdl is not None: # Copy the PSF null_conv_mdl = deepcopy(conv_mdl) alt = conv_mdl(alt_comp) if hasattr(conv_mdl, 'fold'): conv_mdl.fold(data) # Convolve the null model null = null_conv_mdl(null_comp) if hasattr(null_conv_mdl, 'fold'): null_conv_mdl.fold(data) nullfit = Fit(data, null, stat, method, Covariance()) # Fit with null model nullfit_results = nullfit.fit() debug(nullfit_results.format()) null_stat = nullfit_results.statval null_vals = nullfit_results.parvals # Calculate niter samples using null best-fit and covariance sampler = NormalParameterSampleFromScaleMatrix() samples = sampler.get_sample(nullfit, None, niter) # Fit with alt model, null component starts at null's best fit params. altfit = Fit(data, alt, stat, method, Covariance()) altfit_results = altfit.fit() debug(altfit_results.format()) alt_stat = altfit_results.statval alt_vals = altfit_results.parvals LR = -(alt_stat - null_stat) def worker(proposal, *args, **kwargs): return LikelihoodRatioTest.calculate(nullfit, altfit, proposal, null_vals, alt_vals) olddep = data.get_dep(filter=False) try: #statistics = map(worker, samples) statistics = parallel_map(worker, samples, numcores) finally: data.set_dep(olddep) alt.thawedpars = list(oldaltvals) null.thawedpars = list(oldnullvals) debug("statistic null = " + repr(null_stat)) debug("statistic alt = " + repr(alt_stat)) debug("LR = " + repr(LR)) statistics = numpy.asarray(statistics) pppvalue = numpy.sum(statistics[:, 2] > LR) / (1.0 * niter) debug('ppp value = ' + str(pppvalue)) return LikelihoodRatioResults(statistics[:, 2], statistics[:, 0:2], samples, LR, pppvalue, null_stat, alt_stat)