def Ts2(self, srcName, reoptimize=False, approx=True,
tol=None, MaxIterations=10, verbosity=0):
srcName = str(srcName)
if verbosity > 0:
print("*** Start Ts_dl ***")
source_attributes = self.components[0].getExtraSourceAttributes()
self.syncSrcParams()
freeParams = pyLike.DoubleVector()
self.components[0].logLike.getFreeParamValues(freeParams)
logLike1 = -self()
for comp in self.components:
comp.scaleSource(srcName, 1E-10)
comp._ts_src = comp.logLike.getSource(srcName)
free_flag = comp._ts_src.spectrum().normPar().isFree()
if reoptimize:
comp._ts_src.spectrum().normPar().setFree(False)
self.syncSrcParams()
logLike0 = -self()
if tol is None:
tol = self.tol
if reoptimize:
if verbosity > 0:
print("** Do reoptimize")
optFactory = pyLike.OptimizerFactory_instance()
myOpt = optFactory.create(self.optimizer, self.composite)
Niter = 1
while Niter <= MaxIterations:
try:
myOpt.find_min(0, tol)
break
except RuntimeError as e:
print(e)
if verbosity > 0:
print("** Iteration :", Niter)
Niter += 1
else:
if approx:
try:
self._renorm()
except ZeroDivisionError:
pass
self.syncSrcParams()
logLike0 = max(-self(), logLike0)
Ts_value = 2 * (logLike1 - logLike0)
for comp in self.components:
comp.scaleSource(srcName, 1E10)
if reoptimize:
comp._ts_src.spectrum().normPar().setFree(free_flag)
self.syncSrcParams(srcName)
comp.logLike.setFreeParamValues(freeParams)
comp.model = SourceModel(comp.logLike)
for src in source_attributes:
comp.model[src].__dict__.update(source_attributes[src])
self.model = self.components[0].model
return Ts_value
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 _set_errors(self, errors):
source_attributes = self.components[0].getExtraSourceAttributes()
my_errors = list(errors)
self.composite.setErrors(my_errors)
for component in self.components:
component.model = SourceModel(component.logLike)
for src in source_attributes:
component.model[src].__dict__.update(source_attributes[src])
def Ts2(self, srcName, reoptimize=False, approx=True,
tol=None, MaxIterations=10, verbosity=0):
"""Computes the TS value for a source indicated by "srcName."
If "reoptimize=True" is selected this function will reoptimize
the model up to "MaxIterations" given the tolerance "tol"
(default is the tolerance selected for the overall fit). If
"appox=True" is selected (the default) it will renormalize the
model (see _renorm).
"""
saved_state = LikelihoodState(self)
if verbosity > 0:
print("*** Start Ts_dl ***")
source_attributes = self.getExtraSourceAttributes()
self.logLike.syncParams()
src = self.logLike.getSource(srcName)
self._ts_src = src
freeParams = pyLike.DoubleVector()
self.logLike.getFreeParamValues(freeParams)
logLike1 = self.logLike.value()
self.scaleSource(srcName, 1E-10)
logLike0 = self.logLike.value()
if tol is None:
tol = self.tol
if reoptimize:
if verbosity > 0:
print("** Do reoptimize")
optFactory = pyLike.OptimizerFactory_instance()
myOpt = optFactory.create(self.optimizer, self.logLike)
Niter = 1
while Niter <= MaxIterations:
try:
myOpt.find_min(0, tol)
break
except RuntimeError as e:
print(e)
if verbosity > 0:
print("** Iteration :", Niter)
Niter += 1
else:
if approx:
try:
self._renorm()
except ZeroDivisionError:
pass
self.logLike.syncParams()
logLike0 = max(self.logLike.value(), logLike0)
Ts_value = 2 * (logLike1 - logLike0)
self.scaleSource(srcName, 1E10)
self.logLike.setFreeParamValues(freeParams)
self.model = SourceModel(self.logLike)
for src in source_attributes:
self.model[src].__dict__.update(source_attributes[src])
saved_state.restore()
self.logLike.value()
return Ts_value
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, observation, srcModel=None, optimizer='Minuit'):
if srcModel is None:
srcModel, optimizer = self._srcDialog()
self._inputs = srcModel, observation, optimizer
self.observation = observation
self.optimizer = optimizer
self.events = self.observation.eventCont().events();
self.logLike = pyLike.LogLike(self.observation())
self.logLike.readXml(srcModel, _funcFactory)
self.logLike.computeEventResponses()
self.model = SourceModel(self.logLike)
eMin, eMax = self.observation.roiCuts().getEnergyCuts()
nee = 21
estep = num.log(eMax/eMin)/(nee-1)
self.energies = eMin*num.exp(estep*num.arange(nee, type=num.Float))
self.disp = None
self.resids = None
self.tolType = pyLike.ABSOLUTE
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 __init__(self, observation, srcModel=None, optimizer='Drmngb', nee=21):
AnalysisBase.__init__(self)
if srcModel is None:
srcModel, optimizer = self._srcDialog()
self.observation = observation
self.srcModel = srcModel
self.optimizer = optimizer
self.logLike = pyLike.LogLike(self.observation.observation)
self.logLike.initOutputStreams()
self.logLike.readXml(srcModel, _funcFactory, True, True, False)
self.logLike.computeEventResponses()
self.model = SourceModel(self.logLike, srcModel)
eMin, eMax = self.observation.roiCuts().getEnergyCuts()
estep = num.log(eMax/eMin)/(nee-1)
self.energies = eMin*num.exp(estep*num.arange(nee, dtype=num.float))
self.energies[-1] = eMax
self.e_vals = num.sqrt(self.energies[:-1]*self.energies[1:])
self.nobs = self._Nobs()
self.nobs_wt = self.nobs
self.disp = None
self.resids = None
self.sourceFitPlots = []
self.sourceFitResids = []
def _set_errors(self, errors):
my_errors = list(errors)
self.composite.setErrors(my_errors)
for component in self.components:
component.model = SourceModel(component.logLike)
if verbosity > 0:
print "** Iteration :", Niter
Niter += 1
else:
if approx and n_free_base > 0:
try:
self._renorm()
except ZeroDivisionError:
pass
self.syncSrcParams()
logLike0 = max(-self(), logLike0)
Ts_value = 2 * (logLike1 - logLike0)
for ts_src, comp in zip(self._ts_src, self.components):
comp.logLike.addSource(ts_src)
comp.logLike.setFreeParamValues(freeParams)
comp.model = SourceModel(comp.logLike)
for src in source_attributes:
comp.model[src].__dict__.update(source_attributes[src])
self.model = self.components[0].model
return Ts_value
def _renorm(self, factor=None):
if factor is None:
freeNpred, totalNpred = self._npredValues()
deficit = self.total_nobs() - totalNpred
self.renormFactor = 1. + deficit / freeNpred
else:
self.renormFactor = factor
if self.renormFactor < 1:
self.renormFactor = 1
srcNames = self.sourceNames()
