def _updateGtlikeModel(self):
"""
#Slow! But no other options at the moment
self.like.writeXml(self.xmlModel)
self.like.logLike.reReadXml(self.xmlModel)
"""
energies = self.lmc.energiesKeV
if self._source_name is None:
for id, srcName in enumerate(
self.likelihoodModel.point_sources.keys()):
values = self.likelihoodModel.get_point_source_fluxes(
id, energies, tag=self._tag)
# on the second iteration, self.like doesn't have the second srcName defined so that needs to be carried from flags
gtlikeSrcModel = self.like[srcName]
my_function = gtlikeSrcModel.getSrcFuncs()["Spectrum"]
my_file_function = pyLike.FileFunction_cast(my_function)
my_file_function.setParam("Normalization", 1)
# Cap the values to avoid numerical errors
capped_values = numpy.minimum(
numpy.maximum(values * 1000, 1e-25), 1e5)
my_file_function.setSpectrum(energies / 1000.0, capped_values)
gtlikeSrcModel.setSpectrum(my_file_function)
# TODO: extended sources
else:
srcName = self._source_name
id = self.likelihoodModel.point_sources.keys().index(srcName)
values = self.likelihoodModel.get_point_source_fluxes(
id, energies, tag=self._tag)
# on the second iteration, self.like doesn't have the second srcName defined so that needs to be carried from flags
gtlikeSrcModel = self.like[srcName]
my_function = gtlikeSrcModel.getSrcFuncs()["Spectrum"]
my_file_function = pyLike.FileFunction_cast(my_function)
my_file_function.setParam("Normalization", 1)
# Cap the values to avoid numerical errors
capped_values = numpy.minimum(numpy.maximum(values * 1000, 1e-25),
1e5)
my_file_function.setSpectrum(energies / 1000.0, capped_values)
gtlikeSrcModel.setSpectrum(my_file_function)
self.like.syncSrcParams()
def _updateGtlikeModel(self):
"""
#Slow! But no other options at the moment
self.like.writeXml(self.xmlModel)
self.like.logLike.reReadXml(self.xmlModel)
"""
energies = self.lmc.energiesKeV
for id, srcName in enumerate(
self.likelihoodModel.point_sources.keys()):
values = self.likelihoodModel.get_point_source_fluxes(
id, energies, tag=self._tag)
gtlikeSrcModel = self.like[srcName]
my_function = gtlikeSrcModel.getSrcFuncs()["Spectrum"]
my_file_function = pyLike.FileFunction_cast(my_function)
my_file_function.setParam("Normalization", 1)
# Cap the values to avoid numerical errors
capped_values = numpy.minimum(numpy.maximum(values * 1000, 1e-25),
1e5)
my_file_function.setSpectrum(energies / 1000.0, capped_values)
gtlikeSrcModel.setSpectrum(my_file_function)
# TODO: extended sources
self.like.syncSrcParams()
def gtlike_spectrum_to_dict(spectrum, errors=False):
""" Convert a pyLikelihood object to a python
dictionary which can be easily saved to a file. """
parameters=ParameterVector()
spectrum.getParams(parameters)
d = dict(name = spectrum.genericName(), method='gtlike')
for p in parameters:
d[p.getName()]= p.getTrueValue()
if errors:
d['%s_err' % p.getName()]= p.error()*p.getScale() if p.isFree() else np.nan
if d['name'] == 'FileFunction':
ff=pyLikelihood.FileFunction_cast(spectrum)
d['file']=ff.filename()
return d
def gtlike_spectrum_to_dict(spectrum):
""" Convert a pyLikelihood object to a python dictionary which can
be easily saved to a file."""
parameters = pyLike.ParameterVector()
spectrum.getParams(parameters)
d = dict(spectrum_type=spectrum.genericName())
for p in parameters:
pname = p.getName()
pval = p.getTrueValue()
perr = abs(p.error() * p.getScale()) if p.isFree() else np.nan
d[pname] = np.array([pval, perr])
if d['spectrum_type'] == 'FileFunction':
ff = pyLike.FileFunction_cast(spectrum)
d['file'] = ff.filename()
return d
parnames = pyLike.StringVector()
self.func.getFreeParamNames(parnames)
print "Parameter : ", i, parnames[i]
print "%.3e " * len(num_derivs) % tuple(num_derivs)
print "%.3e " * len(derivs) % tuple(derivs) + "\n"
return tuple(params)
def __getattr__(self, attrname):
return getattr(self.func, attrname)
if __name__ == '__main__':
funcs = ('AbsEdge', 'BrokenPowerLaw', 'BrokenPowerLawExpCutoff',
'BrokenPowerLaw2', 'ConstantValue', 'ExpCutoff', 'Gaussian',
'LogParabola', 'PowerLaw', 'PowerLaw2', 'FileFunction')
for func in funcs:
tester = DerivTester(func)
print "\n" + func
verbose = 0
if (func == 'PowerLaw2'):
tester.func.parameter('Index').setValue(-1)
if (func == 'FileFunction'):
tester.func = pyLike.FileFunction_cast(tester.func)
tester.func.readFunction('foo.dat')
# verbose = 1
tester(100, verbose)
tester(200, verbose)
tester(1001, verbose)
tester(2000, verbose)
def build_pointlike_model(spectrum):
""" Convert a gtlike model object to a pointlike
model object.
>>> spectrum = _funcFactory.create('PowerLaw')
>>> param=spectrum.getParam('Prefactor')
>>> param.setScale(10)
>>> param.setTrueValue(1e-9)
>>> param.setBounds(1e-11,1e-9)
>>> param.setError(3e-11)
>>> param.setFree(True)
>>> param=spectrum.getParam('Index')
>>> param.setScale(2)
>>> param.setBounds(-10,5)
>>> param.setTrueValue(-3)
>>> param.setError(0.125)
>>> param.setFree(False)
Check spectral values:
>>> model = build_pointlike_model(spectrum)
>>> energies = np.logspace(1, 6, 10000)
>>> from uw.darkmatter.spectral import DMFitFunction
>>> np.allclose(DMFitFunction.call_pylike_spectrum(spectrum, energies),
... model(energies), rtol=1e-20, atol=1e-20)
True
Check prefactor:
>>> model.get_scale('norm')
10.0
>>> np.allclose(model.get_limits('norm'),[1e-10, 1e-08])
True
>>> np.allclose(model.getp('norm'),1e-9)
True
>>> np.allclose(model.error('norm'),1e-10)
True
>>> model.get_free('norm')
True
Check index params:
>>> model.get_scale('index')
-2.0
>>> model.get_limits('index')
[-10.0, 20.0]
>>> model.getp('index')
3.0
>>> np.allclose(model.error('index'),0.25)
True
>>> model.get_free('index')
False
Example creating a FileFunction object:
First, create file out of old model:
>>> from tempfile import NamedTemporaryFile
>>> temp = NamedTemporaryFile()
>>> filename = temp.name
>>> model.save_profile(filename, emin=1, emax=1e6)
Now, make FileFunction:
>>> spectrum = pyLikelihood.FileFunction()
>>> spectrum.readFunction(filename)
Set param values:
>>> param=spectrum.getParam('Normalization')
>>> param.setScale(2)
>>> param.setTrueValue(4)
>>> param.setBounds(.1,10)
>>> model = build_pointlike_model(spectrum)
Test spectral points:
>>> np.allclose(DMFitFunction.call_pylike_spectrum(spectrum, energies),
... model(energies), rtol=1e-20, atol=1e-20)
True
Test param values:
>>> model.get_scale('Normalization')
2.0
>>> model.getp('Normalization')
4.0
>>> model.get_limits('Normalization')
[0.2, 20.0]
"""
gtlike_name = spectrum.genericName()
if gtlike_name == 'FileFunction':
ff = pyLikelihood.FileFunction_cast(spectrum)
filename = ff.filename()
model = FileFunction(file=filename)
else:
model = XML_to_Model.modict[gtlike_name]()
param_names = pyLikelihood.StringVector()
spectrum.getParamNames(param_names)
for gtlike_name in param_names:
pointlike_name = model.get_pointlike_name(gtlike_name)
param = spectrum.getParam(gtlike_name)
if pointlike_name in model.default_extra_params.keys():
# no mapping for extra params
model.setp(pointlike_name, param.getTrueValue())
else:
model.setp_gtlike(pointlike_name, param.getTrueValue())
if pointlike_name in model.param_names:
model.set_mapper(pointlike_name, LinearMapper)
if param.getBounds()[0] < -3.4e+38 and param.getBounds(
)[1] > 3.4e+38:
# No effective bound on parameters
pass
else:
model.set_limits_gtlike(
pointlike_name,
lower=param.getBounds()[0] * param.getScale(),
upper=param.getBounds()[1] * param.getScale(),
scale=param.getScale())
model.set_error(pointlike_name,
abs(param.error() * param.getScale()))
model.set_free(pointlike_name, param.isFree())
return model