Пример #1
0
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)
Пример #2
0
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)
Пример #3
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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)
Пример #4
0
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)