Source code for threeML.plugins.UnresolvedExtendedXYLike

import copy

import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from astromodels import Model, PointSource

from threeML.classicMLE.goodness_of_fit import GoodnessOfFit
from threeML.classicMLE.joint_likelihood import JointLikelihood
from threeML.data_list import DataList
from threeML.exceptions.custom_exceptions import custom_warnings
from import get_path_of_data_file
from threeML.plugin_prototype import PluginPrototype
from threeML.plugins.XYLike import XYLike
from threeML.utils.statistics.likelihood_functions import (
    half_chi2, poisson_log_likelihood_ideal_bkg)

__instrument_name = "n.a.""threeml.mplstyle")))

[docs]class UnresolvedExtendedXYLike(XYLike): def __init__( self, name, x, y, yerr=None, exposure=None, poisson_data=False, quiet=False, source_name=None, ): super(UnresolvedExtendedXYLike, self).__init__( name=name, x=x, y=y, yerr=yerr, exposure=exposure, poisson_data=poisson_data, quiet=quiet, source_name=source_name, )
[docs] def assign_to_source(self, source_name): """ Assign these data to the given source (instead of to the sum of all sources, which is the default) :param source_name: name of the source (must be contained in the likelihood model) :return: none """ if self._likelihood_model is not None and source_name is not None: assert source_name in self._likelihood_model.sources, ( "Source %s is not contained in " "the likelihood model" % source_name ) self._source_name = source_name
[docs] def set_model(self, likelihood_model_instance): """ Set the model to be used in the joint minimization. Must be a LikelihoodModel instance. :param likelihood_model_instance: instance of Model :type likelihood_model_instance: astromodels.Model """ if likelihood_model_instance is None: return if self._source_name is not None: # Make sure that the source is in the model assert self._source_name in likelihood_model_instance.sources, ( "This XYLike plugin refers to the source %s, " "but that source is not in the likelihood model" % ( self._source_name) ) self._likelihood_model = likelihood_model_instance
def _get_total_expectation(self): if self._source_name is None: n_point_sources = self._likelihood_model.get_number_of_point_sources() n_ext_sources = self._likelihood_model.get_number_of_extended_sources() assert ( n_point_sources + n_ext_sources > 0 ), "You need to have at least one source defined" # Make a function which will stack all point sources (XYLike do not support spatial dimension) expectation_point = np.sum( [ source(self._x, tag=self._tag) for source in list(self._likelihood_model.point_sources.values()) ], axis=0, ) expectation_ext = np.sum( [ source.get_spatially_integrated_flux(self._x) for source in list(self._likelihood_model.extended_sources.values()) ], axis=0, ) expectation = expectation_point + expectation_ext else: # This XYLike dataset refers to a specific source # Note that we checked that self._source_name is in the model when the model was set if self._source_name in self._likelihood_model.point_sources: expectation = self._likelihood_model.point_sources[self._source_name]( self._x ) elif self._source_name in self._likelihood_model.extended_sources: expectation = self._likelihood_model.extended_sources[ self._source_name ].get_spatially_integrated_flux(self._x) else: raise KeyError( "This XYLike plugin has been assigned to source %s, " "which is neither a point soure not an extended source in the current model" % self._source_name ) return expectation
[docs] def plot(self, x_label="x", y_label="y", x_scale="linear", y_scale="linear"): fig, sub = plt.subplots(1, 1) sub.errorbar(self.x, self.y, yerr=self.yerr, fmt=".", label="data") sub.set_xscale(x_scale) sub.set_yscale(y_scale) sub.set_xlabel(x_label) sub.set_ylabel(y_label) if self._likelihood_model is not None: flux = self._get_total_expectation() label = ( "model" if self._source_name is None else "model (%s)" % self._source_name ) sub.plot(self.x, flux, "--", label=label) sub.legend(loc=0) return fig