def mwl_fit_low_level():
"""Use high-level Sherpa API.
Low-level = no session, classes.
Example: http://python4astronomers.github.io/fitting/low-level.html
"""
fermi_data = FermiData().sherpa_data
hess_data = IACTData().sherpa_data
# spec_model = PowLaw1D('spec_model')
spec_model = LogParabola('spec_model')
spec_model.c1 = 0.5
spec_model.c2 = 0.2
spec_model.ampl = 5e-11
data = DataSimulFit(name='global_data', datasets=[fermi_data, hess_data])
# TODO: Figure out how to notice using the low-level API
# data.notice(mins=1e-3, maxes=None, axislist=None)
model = SimulFitModel(name='global_model', parts=[spec_model, spec_model])
stat = FermiStat()
method = LevMar()
fit = Fit(data=data, model=model, stat=stat, method=method)
result = fit.fit()
# IPython.embed()
return Bunch(results=result, model=spec_model)
def test():
"""Check that the model parametrizations are identical now."""
amplitude = 1E-12 * u.Unit("cm-2 s-1 TeV-1")
reference = 2 * u.TeV
alpha = 2.3
beta = 0.1
model_gammapy = Log10Parabola(amplitude, reference, alpha, beta)
print(model_gammapy)
from sherpa.models import LogParabola
model_sherpa = LogParabola()
model_sherpa.ampl = amplitude.value
model_sherpa.c1 = alpha
model_sherpa.c2 = beta
model_sherpa.ref = reference.value
print(model_sherpa)
# compare
energy = 4.2 * u.TeV
dnde_gammapy = model_gammapy(energy)
print(dnde_gammapy)
dnde_sherpa = model_sherpa(energy.value)
print(dnde_sherpa)
from numpy.testing import assert_allclose
assert_allclose(dnde_gammapy.value, dnde_sherpa)
def mwl_fit_low_level_calling_fermi():
"""Example how to do a Sherpa model fit,
but use the Fermi ScienceTools to evaluate
the likelihood for the Fermi dataset.
"""
spec_model = LogParabola('spec_model')
spec_model.c1 = 0.5
spec_model.c2 = 0.2
spec_model.ampl = 5e-11
model = spec_model
data = FermiDataShim()
stat = FermiStatShim()
method = LevMar()
fit = Fit(data=data, model=model, stat=stat, method=method)
result = fit.fit()
return dict(results=result, model=spec_model)
def mwl_fit_low_level_calling_fermi():
"""Example how to do a Sherpa model fit,
but use the Fermi ScienceTools to evaluate
the likelihood for the Fermi dataset.
"""
spec_model = LogParabola('spec_model')
spec_model.c1 = 0.5
spec_model.c2 = 0.2
spec_model.ampl = 5e-11
model = spec_model
data = FermiDataShim()
stat = FermiStatShim()
method = LevMar()
fit = Fit(data=data, model=model, stat=stat, method=method)
result = fit.fit()
# IPython.embed()
return Bunch(results=result, model=spec_model)