Ejemplo n.º 1
0
def test_wstat_corner_cases():
    """test WSTAT formulae for corner cases"""
    n_on = 0
    n_off = 5
    mu_sig = 2.3
    alpha = 0.5

    actual = stats.wstat(n_on=n_on, mu_sig=mu_sig, n_off=n_off, alpha=alpha)
    desired = 2 * (mu_sig + n_off * np.log(1 + alpha))
    assert_allclose(actual, desired)

    actual = stats.get_wstat_mu_bkg(n_on=n_on,
                                    mu_sig=mu_sig,
                                    n_off=n_off,
                                    alpha=alpha)
    desired = n_off / (alpha + 1)
    assert_allclose(actual, desired)

    # n_off = 0 and mu_sig < n_on * (alpha / alpha + 1)
    n_on = 9
    n_off = 0
    mu_sig = 2.3
    alpha = 0.5

    actual = stats.wstat(n_on=n_on, mu_sig=mu_sig, n_off=n_off, alpha=alpha)
    desired = -2 * (mu_sig * (1.0 / alpha) + n_on * np.log(alpha /
                                                           (1 + alpha)))
    assert_allclose(actual, desired)

    actual = stats.get_wstat_mu_bkg(n_on=n_on,
                                    mu_sig=mu_sig,
                                    n_off=n_off,
                                    alpha=alpha)
    desired = n_on / (1 + alpha) - (mu_sig / alpha)
    assert_allclose(actual, desired)

    # n_off = 0 and mu_sig > n_on * (alpha / alpha + 1)
    n_on = 5
    n_off = 0
    mu_sig = 5.3
    alpha = 0.5

    actual = stats.wstat(n_on=n_on, mu_sig=mu_sig, n_off=n_off, alpha=alpha)
    desired = 2 * (mu_sig + n_on * (np.log(n_on) - np.log(mu_sig) - 1))
    assert_allclose(actual, desired)

    actual = stats.get_wstat_mu_bkg(n_on=n_on,
                                    mu_sig=mu_sig,
                                    n_off=n_off,
                                    alpha=alpha)
    assert_allclose(actual, 0)
Ejemplo n.º 2
0
 def background(self):
     """Background counts estimated from the marginalized likelihood estimate.
      See :ref:`wstat`
      """
     mu_bkg = self.alpha.data * get_wstat_mu_bkg(
         n_on=self.counts.data,
         n_off=self.counts_off.data,
         alpha=self.alpha.data,
         mu_sig=self.npred_sig().data,
     )
     return RegionNDMap.from_geom(geom=self._geom, data=mu_bkg)
Ejemplo n.º 3
0
def calc_wstat_gammapy(mu_sig, n_on, n_off, alpha):
    from gammapy.stats import wstat, get_wstat_mu_bkg, get_wstat_gof_terms

    # background estimate
    bkg = get_wstat_mu_bkg(mu_sig=mu_sig, n_on=n_on, n_off=n_off, alpha=alpha)
    print("Gammapy mu_bkg: {}".format(bkg))

#    # without extra terms
#    statsvec = wstat(extra_terms=False, mu_sig=mu_sig, n_on=n_on, n_off=n_off,
#                     alpha = alpha)
#
#    print("Gammapy stat: {}".format(np.sum(statsvec)))
#    print("Gammapy statsvec: {}".format(statsvec))
#
#    print("---> with extra terms")
#    extra_terms = _get_wstat_extra_terms(n_on = n_on,
#                                         n_off = n_off)
#    print("Gammapy extra terms: {}".format(extra_terms))

    statsvec = wstat(extra_terms=True, mu_sig=mu_sig, n_on=n_on, n_off=n_off,
                     alpha=alpha)

    print("Gammapy stat: {}".format(np.sum(statsvec)))
    print("Gammapy statsvec: {}".format(statsvec))