def __init__(self,
name,
diffuse_sources,
file,
emin,
emax,
skydir,
scaling_factor=1):
""" Approximates a linear combination of
diffuse sources as a single isotropic
spectrum.
Note, probably does not work for extended
sources. """
self.name = name
self.scaling_factor = scaling_factor
for ds in diffuse_sources:
if len(ds.dmodel) != 1:
raise Exception("dmodels must have length 1")
self.file = open(file, 'w')
self.file.write(
self._make_file(diffuse_sources, skydir, emin, emax,
self.scaling_factor))
self.file.close()
self.smodel = FileFunction(file=file)
self.dmodel = [IsotropicConstant()]
def pointlike_dict_to_spectrum(d):
if d['name'] == 'FileFunction':
model = FileFunction(file=d['file'])
elif d['name'] == 'DMFitFunction':
model = DMFitFunction()
else:
model = uw.like.Models.__dict__[d['name']]()
for k,v in d.items():
if k not in ['name','method','file','covariance_matrix']:
if len(k) > 10 and k[-10:] in ['_upper_err','_lower_err']:
pass
elif len(k) > 4 and k[-4:] == '_err':
model.set_error(k[:-4],v)
else:
model[k]=v
return model
def test_ff(self):
""" Simulate from a filefunction object and test that the best
fit flux
is consistent with the simulated flux. """
name = 'ff'
model = PowerLaw(index=2)
model.set_flux(1e-6)
simdir = path.expand('$SIMDIR/%s' % name)
if not os.path.exists(simdir):
os.makedirs(simdir)
filename = abspath(join(simdir, 'file_function.txt'))
model.save_profile(filename, 10, 1e6)
ff = FileFunction(file=filename)
center = SkyDir(0, 0)
ps = PointSource(name='source', skydir=center, model=ff)
point_sources = [ps]
diffuse_sources = None
roi = PointlikeTest.get_roi(name,
center,
point_sources,
diffuse_sources,
emin=1e2,
emax=1e5,
binsperdec=4)
if PointlikeTest.VERBOSE:
roi.print_summary()
print roi
roi.fit(use_gradient=PointlikeTest.USE_GRADIENT)
if PointlikeTest.VERBOSE:
roi.print_summary()
print roi
fit, error = ff.i_flux(1e2, 1e5, error=True)
true = model.i_flux(1e2, 1e5, error=False)
self.assertPull(fit, true, error, 'flux')
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
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
def get_diffuse_source(spatialModel='ConstantValue',
spatialModelFile=None,
spectralModel='PowerLaw',
spectralModelFile=None,
name=None,
diffdir = None):
""" Return a DiffuseSource instance suitable for
instantiating a child of ROIDiffuseModel.
NB -- don't support front/back distinction atm.
The list of supported models is currently very short, but covers
the usual cases for modeling diffuse backgrounds. Additional
use cases can be developed on an ad hoc basis.
Arguments:
spatialModel -- an XML-style keyword. Valid options are
1) ConstantValue (isotropic)
2) MapCubeFunction (from a FITS file)
spatialModelFile -- if a mapcube is specified, its location
spectralModel -- This can be either an XML-style keyword or an
instance of Model.
If an XML-style keyword, valid options are
1) FileFunction
2) PowerLaw
3) Constant
spectralModelFile -- if a tabular function is specified,
its location
name -- a name for the ol' model
diffdir -- if the XML files specify paths relative to some
directory, set this variable appropriately
"""
if (diffdir is not None):
if spatialModelFile is not None:
spatialModelFile = os.path.join(diffdir,spatialModelFile)
if spectralModelFile is not None:
spectralModelFile = os.path.join(diffdir,spectralModelFile)
# check input sanity
if not isinstance(spectralModel,Model):
if (spectralModelFile is not None):
if not os.path.exists(path.expand(spectralModelFile)):
raise Exception('Could not find the ASCII file specified for FileFunction')
elif not (spectralModel == 'PowerLaw' or spectralModel == 'Constant'):
raise NotImplementedError,'Must provide one of the understood spectral models.'
else:
pass
if spatialModel=='MapCubeFunction':
if (spatialModelFile is None) or (not os.path.exists(path.expand(spatialModelFile))):
raise Exception('Could not find the FITS file specified for MapCubeFunction (file = %s).' % spatialModelFile)
elif spatialModel != 'ConstantValue':
raise NotImplementedError,'Must provide one of the understood spatial models.'
else:
pass
ston = Singleton2()
dmodel = None; smodel = None
# deal with isotropic models
if spatialModel=='ConstantValue':
if isinstance(spectralModel,Model):
smodel=spectralModel
dmodel=IsotropicConstant()
elif spectralModelFile is not None:
smodel = FileFunction(normalization=1, file=spectralModelFile)
dmodel = IsotropicConstant()
elif spectralModel == 'PowerLaw':
# use Sreekumar-like defaults
smodel = PowerLaw(index=2.1)
smodel.set_flux(1.5e-5, emin=100, emax=N.inf)
dmodel = IsotropicConstant()
else:
raise Exception("Unable to parse input.")
# deal with mapcubes
else:
if spectralModel == 'FileFunction':
dmodel1 = IsotropicSpectrum(spectralModelFile)
dmodel2 = ston.add(DiffuseFunction,spatialModelFile,spatialModelFile)
dmodel = CompositeSkySpectrum(dmodel1,dmodel2)
dmodel.saveme1 = dmodel1; dmodel.saveme2 = dmodel2
smodel = Constant()
else:
dmodel = ston.add(DiffuseFunction,path.expand(spatialModelFile),path.expand(spatialModelFile))
dmodel.filename=spatialModelFile
if spectralModel == 'PowerLaw':
smodel = ScalingPowerLaw()
elif spectralModel == 'Constant':
smodel = Constant()
else:
smodel = spectralModel
if (dmodel is None) or (smodel is None):
raise Exception('Was unable to parse input.')
return DiffuseSource(dmodel,smodel,name)