def __init__(self, r_s=None, rho_s=1 * u.Unit("GeV / cm3")):
r_s = self.DEFAULT_SCALE_RADIUS if r_s is None else r_s
self.parameters = Parameters([
Parameter("r_s", u.Quantity(r_s)),
Parameter("rho_s", u.Quantity(rho_s))
])
def __init__(self, index, amplitude, reference, mean, width):
self.parameters = Parameters([
Parameter("index", index, min=0),
Parameter("amplitude", amplitude, min=0),
Parameter("reference", reference, frozen=True),
Parameter("mean", mean, min=0),
Parameter("width", width, min=0, frozen=True),
])
def __init__(self, r_s=None, alpha=None, rho_s=1 * u.Unit("GeV / cm3")):
alpha = self.DEFAULT_ALPHA if alpha is None else alpha
r_s = self.DEFAULT_SCALE_RADIUS if r_s is None else r_s
self.parameters = Parameters([
Parameter("r_s", u.Quantity(r_s)),
Parameter("alpha", u.Quantity(alpha)),
Parameter("rho_s", u.Quantity(rho_s)),
])
def parameters(self):
"""List of parameters (`~gammapy.utils.fitting.Parameters`)"""
parameters = []
if self.model:
parameters += self.model.parameters.parameters
if self.background_model:
parameters += self.background_model.parameters.parameters
return Parameters(parameters)
def __init__(self,
amplitude=1E-12 * u.Unit('cm-2 s-1 TeV-1'),
reference=10 * u.TeV,
alpha=2,
beta=1):
self.parameters = Parameters([
Parameter('amplitude', amplitude),
Parameter('reference', reference, frozen=True),
Parameter('alpha', alpha),
Parameter('beta', beta)
])
def __init__(
self,
amplitude=1e-12 * u.Unit("cm-2 s-1 TeV-1"),
reference=10 * u.TeV,
alpha=2,
beta=1,
):
self.parameters = Parameters([
Parameter("amplitude", amplitude),
Parameter("reference", reference, frozen=True),
Parameter("alpha", alpha),
Parameter("beta", beta),
])
def __init__(self):
self.parameters = Parameters(
[
Parameter("amplitude", 3e-12, unit="cm-2 s-1 TeV-1"),
Parameter("reference", 1, unit="TeV", frozen=True),
Parameter("alpha", 2.4, min=1, max=5),
Parameter("beta", 0.2, min=0.001, max=1),
Parameter("z_fermi", 0),
Parameter("z_magic", 0),
Parameter("z_veritas", 0),
Parameter("z_fact", 0),
Parameter("z_hess", 0),
]
)
def model(self, model):
self._model = model
if model is not None:
self._parameters = Parameters(self._model.parameters.parameters)
self._predictor = SpectrumEvaluator(
model=self.model,
livetime=self.livetime,
aeff=self.aeff,
e_true=self._energy_axis.edges,
edisp=self.edisp,
)
else:
self._parameters = None
self._predictor = None
def pars():
x = Parameter("x", 2.1)
y = Parameter("y", 3.1, scale=1e5)
z = Parameter("z", 4.1, scale=1e-5)
return Parameters([x, y, z])
def __init__(self):
self.parameters = Parameters(
[Parameter("x", 2),
Parameter("y", 3e2),
Parameter("z", 4e-2)])
def parameters(self):
parameters = []
for skymodel in self.skymodels:
for p in skymodel.parameters:
parameters.append(p)
return Parameters(parameters)
def spectral_model(self, model):
"""`~gammapy.spectrum.models.SpectralModel`"""
self._spectral_model = model
self._parameters = Parameters(
self.spatial_model.parameters.parameters +
self.spectral_model.parameters.parameters)
def __init__(self):
self.parameters = Parameters(
[Parameter("x", 2),
Parameter("y", 3e2),
Parameter("z", 4e-2)])
self.data_shape = (1, )
def pars():
return Parameters(
[Parameter("spam", 42, "deg"),
Parameter("ham", 99, "TeV")])
def test_parameters_autoscale():
pars = Parameters([Parameter("", 20)])
pars.autoscale()
assert_allclose(pars[0].factor, 2)
assert_allclose(pars[0].scale, 10)