def get_log_like(self):
"""
Return the value of the log-likelihood with the current values for the
parameters
"""
expectation = self._get_total_expectation()
if self._is_poisson:
# Poisson log-likelihood
return np.sum(
poisson_log_likelihood_ideal_bkg(self._y,
np.zeros_like(self._y),
expectation))
else:
# Chi squared
chi2_ = half_chi2(self._y, self._yerr, expectation)
assert np.all(np.isfinite(chi2_))
return np.sum(chi2_) * (-1)
def get_current_value(self):
# In this likelihood the background becomes part of the model, which means that
# the uncertainty in the background is completely neglected
model_counts = self._spectrum_plugin.get_model()
loglike, _ = poisson_log_likelihood_ideal_bkg(self._spectrum_plugin.current_observed_counts,
self._spectrum_plugin.current_scaled_background_counts,
model_counts)
return np.sum(loglike), None
def get_current_value(self):
# In this likelihood the background becomes part of the model, which means that
# the uncertainty in the background is completely neglected
model_counts = self._spectrum_plugin.get_model()
loglike, _ = poisson_log_likelihood_ideal_bkg(self._spectrum_plugin.current_observed_counts,
self._spectrum_plugin.current_scaled_background_counts,
model_counts)
return np.sum(loglike), None
def get_current_value(self, precalc_fluxes: Optional[np.array]=None):
# In this likelihood the background becomes part of the model, which means that
# the uncertainty in the background is completely neglected
model_counts = self._spectrum_plugin.get_model(precalc_fluxes=precalc_fluxes)
loglike, _ = poisson_log_likelihood_ideal_bkg(
self._spectrum_plugin.current_observed_counts,
self._spectrum_plugin.current_scaled_background_counts,
model_counts,
)
return nb_sum(loglike), None
def get_current_value(self):
# In this likelihood the background becomes part of the model, which means that
# the uncertainty in the background is completely neglected
model_counts = self._spectrum_plugin.get_model()
# we scale the background model to the observation
background_model_counts = self._spectrum_plugin.get_background_model() * self._spectrum_plugin.scale_factor
loglike, _ = poisson_log_likelihood_ideal_bkg(self._spectrum_plugin.current_observed_counts,
background_model_counts,
model_counts)
bkg_log_like = self._spectrum_plugin.background_plugin.get_log_like()
total_log_like = np.sum(loglike) + bkg_log_like
return total_log_like, None
def get_current_value(self):
# In this likelihood the background becomes part of the model, which means that
# the uncertainty in the background is completely neglected
model_counts = self._spectrum_plugin.get_model()
# we scale the background model to the observation
background_model_counts = self._spectrum_plugin.get_background_model() * self._spectrum_plugin.scale_factor
loglike, _ = poisson_log_likelihood_ideal_bkg(self._spectrum_plugin.current_observed_counts,
background_model_counts,
model_counts)
bkg_log_like = self._spectrum_plugin.background_plugin.get_log_like()
total_log_like = np.sum(loglike) + bkg_log_like
return total_log_like, None
def get_log_like(self):
"""
Return the value of the log-likelihood with the current values for the
parameters
"""
expectation = self._get_total_expectation()
if self._is_poisson:
# Poisson log-likelihood
return np.sum(poisson_log_likelihood_ideal_bkg(self._y, np.zeros_like(self._y), expectation))
else:
# Chi squared
chi2_ = half_chi2(self._y, self._yerr, expectation)
assert np.all(np.isfinite(chi2_))
return np.sum(chi2_) * (-1)
def _poisson_like(y, zeros, expectation):
return np.sum(
poisson_log_likelihood_ideal_bkg(
y, zeros, expectation
)[0]
)
