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 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
