def plotSourceFit(self, srcName, color='black'):
self._importPlotter()
nobs = num.array(self.logLike.countsSpectrum(srcName, False))
errors = []
for ntilde, nsq in zip(nobs, num.sqrt(self.nobs)):
if nsq == 0:
errors.append(0)
else:
errors.append(ntilde/nsq)
errors = num.array(errors)
model = self._srcCnts(srcName)
self.sourceFitPlots.append(self._plotData(nobs))
self._plotSource(srcName, color=color, display=self.sourceFitPlots[-1])
self.sourceFitPlots[-1].setTitle(srcName)
resid = (nobs - model)/model
resid_err = errors/model
self.sourceFitResids.append(self.plotter(self.e_vals, resid,
dy=resid_err,
xtitle='Energy (MeV)',
ytitle='(counts-model)/model',
xlog=1, color=color,
symbol='plus',
xrange=self._xrange()))
zeros = num.zeros(len(self.e_vals))
self.sourceFitResids[-1].overlay(self.e_vals, zeros, symbol='dotted')
self.sourceFitResids[-1].setTitle(srcName)
def reset_ebounds(self, new_energies):
eMin, eMax = self.observation.roiCuts().getEnergyCuts()
if eMin != min(new_energies) or eMax != max(new_energies):
self.logLike.set_ebounds(min(new_energies), max(new_energies))
elist = [x for x in new_energies]
elist.sort()
self.energies = num.array(elist)
self.e_vals = num.sqrt(self.energies[:-1] * self.energies[1:])
self.nobs = self._Nobs()
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 = []
def _srcCnts(self, srcName):
cnts = num.array(self.logLike.modelCountsSpectrum(srcName))
return cnts
def _srcCnts(self, srcName, weighted=False):
cnts = num.array(self.logLike.modelCountsSpectrum(srcName, weighted))
return cnts
def _srcCnts(self, srcName):
source = self.logLike.getSource(srcName)
cnts = []
for emin, emax in zip(self.energies[:-1], self.energies[1:]):
cnts.append(source.Npred(emin, emax))
return num.array(cnts)
def _Nobs(self):
return num.array(self.observation.eventCont().nobs(self.energies))