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# Licensed under a 3-clause BSD style license - see LICENSE.rst |
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import numpy as np |
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from .observation import SpectrumObservation |
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from .utils import SpectrumEvaluator |
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from ..utils.fitting import Dataset, Parameters |
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from ..stats import wstat |
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__all__ = ["SpectrumDatasetOnOff"] |
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class SpectrumDatasetOnOff(Dataset): |
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"""Compute spectral model fit statistic on a ON OFF Spectrum. |
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Parameters |
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---------- |
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model : `~gammapy.spectrum.models.SpectralModel` |
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Fit model |
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counts_on : `~gammapy.spectrum.PHACountsSpectrum` |
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ON Counts spectrum |
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counts_off : `~gammapy.spectrum.PHACountsSpectrum` |
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OFF Counts spectrum |
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livetime : `~astropy.units.Quantity` |
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Livetime |
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mask : numpy.array |
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Mask to apply to the likelihood. |
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aeff : `~gammapy.irf.EffectiveAreaTable` |
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Effective area |
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edisp : `~gammapy.irf.EnergyDispersion` |
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Energy dispersion |
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""" |
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def __init__( |
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self, |
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model=None, |
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counts_on=None, |
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counts_off=None, |
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livetime=None, |
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mask=None, |
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aeff=None, |
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edisp=None, |
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): |
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if mask is not None and mask.dtype != np.dtype("bool"): |
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raise ValueError("mask data must have dtype bool") |
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self.counts_on = counts_on |
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self.counts_off = counts_off |
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self.livetime = livetime |
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self.mask = mask |
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self.aeff = aeff |
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self.edisp = edisp |
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self.model = model |
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@property |
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def alpha(self): |
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"""Exposure ratio between signal and background regions""" |
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return self.counts_on.backscal / self.counts_off.backscal |
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@property |
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def model(self): |
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return self._model |
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@model.setter |
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def model(self, model): |
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self._model = model |
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if model is not None: |
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self._parameters = Parameters(self._model.parameters.parameters) |
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if self.edisp is None: |
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self._predictor = SpectrumEvaluator( |
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model=self.model, |
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livetime=self.livetime, |
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aeff=self.aeff, |
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e_true=self.counts_on.energy.bins, |
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) |
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else: |
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self._predictor = SpectrumEvaluator( |
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model=self.model, |
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aeff=self.aeff, |
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edisp=self.edisp, |
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livetime=self.livetime, |
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) |
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else: |
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self._parameters = None |
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self._predictor = None |
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@property |
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def parameters(self): |
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if self._parameters is None: |
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raise AttributeError("No model set for Dataset") |
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else: |
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return self._parameters |
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@property |
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def data_shape(self): |
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"""Shape of the counts data""" |
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return self.counts_on.data.data.shape |
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def npred(self): |
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"""Returns npred counts vector """ |
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if self._predictor is None: |
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raise AttributeError("No model set for this Dataset") |
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model_npred = self._predictor.compute_npred().data.data |
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return model_npred |
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def likelihood_per_bin(self): |
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"""Likelihood per bin given the current model parameters""" |
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on_stat_ = wstat( |
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n_on=self.counts_on.data.data, |
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n_off=self.counts_off.data.data, |
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alpha=self.alpha, |
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mu_sig=self.npred(), |
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) |
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return np.nan_to_num(on_stat_) |
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View Code Duplication |
def likelihood(self, parameters, mask=None): |
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"""Total likelihood given the current model parameters. |
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Parameters |
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---------- |
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mask : `~numpy.ndarray` |
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Mask to be combined with the dataset mask. |
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""" |
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if self.mask is None and mask is None: |
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stat = self.likelihood_per_bin() |
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elif self.mask is None: |
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stat = self.likelihood_per_bin()[mask] |
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elif mask is None: |
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stat = self.likelihood_per_bin()[self.mask] |
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else: |
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stat = self.likelihood_per_bin()[mask & self.mask] |
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return np.sum(stat, dtype=np.float64) |
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@classmethod |
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def read(cls, filename): |
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"""Read from file |
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For now, filename is assumed to the name of a PHA file where BKG file, ARF, and RMF names |
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must be set in the PHA header and be present in the same folder |
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Parameters |
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---------- |
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filename : str |
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OGIP PHA file to read |
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""" |
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observation = SpectrumObservation.read(filename) |
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return SpectrumDatasetOnOff._from_spectrum_observation(observation) |
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# TODO: check if SpectrumObservation is needed in the long run |
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@classmethod |
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def _from_spectrum_observation(cls, observation): |
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"""Creates a SpectrumDatasetOnOff from a SpectrumObservation object""" |
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# Build mask from quality vector |
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quality = observation.on_vector.quality |
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mask = quality == 0 |
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return cls( |
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counts_on=observation.on_vector, |
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aeff=observation.aeff, |
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counts_off=observation.off_vector, |
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edisp=observation.edisp, |
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livetime=observation.livetime, |
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mask=mask, |
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) |
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gammapy /
gammapy
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