def __init__(self,
binnedData,
srcModel=None,
optimizer='Drmngb',
use_bl2=False,
verbosity=0,
psfcorr=True,
convolve=True,
resample=True,
resamp_fact=2,
minbinsz=0.1,
wmap=None):
AnalysisBase.__init__(self)
if srcModel is None:
srcModel, optimizer = self._srcDialog()
self.binnedData = binnedData
self.srcModel = srcModel
self.optimizer = optimizer
if use_bl2:
self.logLike = pyLike.BinnedLikelihood2(binnedData.countsMap,
binnedData.observation,
binnedData.srcMaps, True,
psfcorr, convolve,
resample, resamp_fact,
minbinsz)
else:
if wmap is None or wmap == "none":
self.logLike = pyLike.BinnedLikelihood(binnedData.countsMap,
binnedData.observation,
binnedData.srcMaps,
True, psfcorr, convolve,
resample, resamp_fact,
minbinsz)
self._wmap = None
else:
self._wmap = pyLike.WcsMapLibrary.instance().wcsmap(
wmap, "SKYMAP")
self._wmap.setInterpolation(False)
self._wmap.setExtrapolation(True)
self.logLike = pyLike.BinnedLikelihood(
binnedData.countsMap, self._wmap, binnedData.observation,
binnedData.srcMaps, True, psfcorr, convolve, resample,
resamp_fact, minbinsz)
self.verbosity = verbosity
self.logLike.initOutputStreams()
self.logLike.readXml(srcModel, _funcFactory, False, True, False)
self.model = SourceModel(self.logLike, srcModel)
self.energies = np.array(self.logLike.energies())
self.e_vals = np.sqrt(self.energies[:-1] * self.energies[1:])
self.nobs = self.logLike.countsSpectrum()
self.sourceFitPlots = []
self.sourceFitResids = []
def __init__(self,
binnedData,
srcModel=None,
optimizer='Drmngb',
use_bl2=False,
verbosity=0,
psfcorr=True,
convolve=True,
resample=True,
resamp_fact=2,
minbinsz=0.1,
wmap=None,
edisp_bins=-1):
AnalysisBase.__init__(self)
if srcModel is None:
srcModel, optimizer = self._srcDialog()
self.binnedData = binnedData
self.srcModel = srcModel
self.optimizer = optimizer
if use_bl2:
raise ValueError("BinnedLikelilihood2 is not longer supported")
if wmap is None or wmap == "none":
self._wmap = None
else:
self._wmap = pyLike.WcsMapLibrary.instance().wcsmap(wmap, "")
self._wmap.setInterpolation(False)
self._wmap.setExtrapolation(True)
try:
self.binned_config = ba.BinnedConfig(use_edisp=edisp_bins != 0,
applyPsfCorrections=psfcorr,
performConvolutiion=convolve,
resample=resample,
resamp_factor=resamp_fact,
minbinsz=minbinsz,
verbose=(verbosity > 0),
edisp_bins=edisp_bins)
self.logLike = pyLike.BinnedLikelihood(binnedData.countsMap,
binnedData.observation,
self.binned_config,
binnedData.srcMaps,
self._wmap)
except NotImplementedError as msg:
print(
"Failed to construct a BinnedLikelihood object because of a mismatch between fermipy and fermitools versions"
)
raise NotImplementedError(msg)
self.verbosity = verbosity
self.logLike.initOutputStreams()
self.logLike.readXml(srcModel, _funcFactory, False, True, False)
self.model = SourceModel(self.logLike, srcModel)
self.energies = np.array(self.logLike.energies())
self.e_vals = np.sqrt(self.energies[:-1] * self.energies[1:])
self.nobs = self.logLike.countsSpectrum()
self.sourceFitPlots = []
self.sourceFitResids = []
def __init__(self,
binnedData,
srcModel=None,
optimizer='Drmngb',
use_bl2=False,
verbosity=0,
psfcorr=True,
wmap=None,
config=None,
delete_local_fixed=False):
AnalysisBase.__init__(self)
if srcModel is None:
srcModel, optimizer = self._srcDialog()
self.binnedData = binnedData
self.srcModel = srcModel
self.optimizer = optimizer
if wmap and wmap != "none":
self.wmap = pyLike.WcsMapLibrary.instance().wcsmap(wmap, "")
self.wmap.setInterpolation(False)
self.wmap.setExtrapolation(True)
else:
self.wmap = None
if use_bl2:
raise ValueError("BinnedLikelihood2 is no longer supported")
if config is None:
config = BinnedConfig(applyPsfCorrections=psfcorr,
delete_local_fixed=delete_local_fixed)
self.logLike = pyLike.BinnedLikelihood(binnedData.countsMap,
binnedData.observation, config,
binnedData.srcMaps, self.wmap)
self.verbosity = verbosity
self.logLike.initOutputStreams()
self.logLike.readXml(srcModel, _funcFactory, False, True, False)
self.model = SourceModel(self.logLike, srcModel)
self.energies = num.array(self.logLike.energies())
self.e_vals = num.sqrt(self.energies[:-1] * self.energies[1:])
self.nobs = self.logLike.countsSpectrum()
self.nobs_wt = self.logLike.countsSpectrum(True)
self.sourceFitPlots = []
self.sourceFitResids = []