def get_scale_model(self, models):
"""Set scale model
Parameters
----------
models : `Models`
Models
Return
------
model : `ScaleSpectralModel`
Scale spectral model
"""
ref_model = models[self.source].spectral_model
scale_model = ScaleSpectralModel(ref_model)
scale_model.norm.value = 1.0
scale_model.norm.frozen = False
if hasattr(ref_model, "amplitude"):
scaled_parameter = ref_model.amplitude
else:
scaled_parameter = ref_model.norm
scale_model.norm.min = scaled_parameter.min / scaled_parameter.value
scale_model.norm.max = scaled_parameter.max / scaled_parameter.value
scale_model.norm.interp = scaled_parameter.interp
scale_model.norm.scan_values = self.norm_values
scale_model.norm.scan_min = self.norm_min
scale_model.norm.scan_max = self.norm_max
scale_model.norm.scan_n_values = self.norm_n_values
return scale_model
def run(self, datasets, steps="all"):
"""Estimate flux for a given energy range.
The fit is performed in the energy range provided by the dataset masks.
The input energy range is used only to compute the flux normalization.
Parameters
----------
datasets : list of `~gammapy.spectrum.SpectrumDataset`
Spectrum datasets.
steps : list of str
Which steps to execute. Available options are:
* "norm-err": estimate symmetric error.
* "errn-errp": estimate asymmetric errors.
* "ul": estimate upper limits.
* "ts": estimate ts and sqrt(ts) values.
* "norm-scan": estimate fit statistic profiles.
By default all steps are executed.
Returns
-------
result : dict
Dict with results for the flux point.
"""
datasets = self._check_datasets(datasets)
if not datasets.is_all_same_type or not datasets.is_all_same_energy_shape:
raise ValueError(
"Flux point estimation requires a list of datasets"
" of the same type and data shape.")
dataset = datasets[0]
ref_model = dataset.models[self.source].spectral_model
scale_model = ScaleSpectralModel(ref_model)
scale_model.norm.min = 0
scale_model.norm.max = 1e5
self._set_model(datasets, scale_model)
steps = self._prepare_steps(steps)
result = self._prepare_result(scale_model.model)
scale_model.norm.value = 1.0
scale_model.norm.frozen = False
result.update(super().run(
datasets,
scale_model.norm,
steps,
null_value=0,
scan_values=self.norm_values,
))
self._set_model(datasets, ref_model)
return result
def __init__(
self,
datasets,
time_intervals=None,
source="",
norm_min=0.2,
norm_max=5,
norm_n_values=11,
norm_values=None,
sigma=1,
sigma_ul=2,
reoptimize=False,
):
self.datasets = Datasets(datasets)
if not self.datasets.is_all_same_type and self.datasets.is_all_same_shape:
raise ValueError(
"Light Curve estimation requires a list of datasets"
" of the same type and data shape.")
if time_intervals is None:
time_intervals = [
Time([d.gti.time_start[0], d.gti.time_stop[-1]])
for d in self.datasets
]
self._check_and_sort_time_intervals(time_intervals)
dataset = self.datasets[0]
if isinstance(dataset, SpectrumDatasetOnOff):
model = dataset.model
else:
model = dataset.model[source].spectral_model
self.model = ScaleSpectralModel(model)
self.model.norm.min = 0
self.model.norm.max = 1e5
if norm_values is None:
norm_values = np.logspace(np.log10(norm_min), np.log10(norm_max),
norm_n_values)
self.norm_values = norm_values
self.sigma = sigma
self.sigma_ul = sigma_ul
self.reoptimize = reoptimize
self.source = source
self.group_table_info = None
def get_scale_model(self, models):
"""Set scale model
Parameters
----------
models : `Models`
Models
Returns
-------
model : `ScaleSpectralModel`
Scale spectral model
"""
ref_model = models[self.source].spectral_model
scale_model = ScaleSpectralModel(ref_model)
if "amplitude" in ref_model.parameters.names:
scaled_parameter = ref_model.parameters["amplitude"]
else:
scaled_parameter = ref_model.parameters["norm"]
scale_model.norm = self._set_norm_parameter(scale_model.norm,
scaled_parameter)
return scale_model
def __init__(
self,
datasets,
source="",
norm_min=0.2,
norm_max=5,
norm_n_values=11,
norm_values=None,
sigma=1,
sigma_ul=2,
reoptimize=False,
):
if not isinstance(datasets, Datasets):
datasets = Datasets(datasets)
self.datasets = datasets
if not datasets.is_all_same_type and datasets.is_all_same_shape:
raise ValueError(
"Light Curve estimation requires a list of datasets"
" of the same type and data shape."
)
dataset = self.datasets.datasets[0]
if isinstance(dataset, SpectrumDatasetOnOff):
model = dataset.model
else:
model = dataset.model[source].spectral_model
self.model = ScaleSpectralModel(model)
self.model.norm.min = 0
self.model.norm.max = 1e5
if norm_values is None:
norm_values = np.logspace(
np.log10(norm_min), np.log10(norm_max), norm_n_values
)
self.norm_values = norm_values
self.sigma = sigma
self.sigma_ul = sigma_ul
self.reoptimize = reoptimize
self.source = source
self._set_scale_model()
def __init__(
self,
datasets,
source,
energy_range,
norm_min=0.2,
norm_max=5,
norm_n_values=11,
norm_values=None,
sigma=1,
sigma_ul=3,
reoptimize=True,
):
# make a copy to not modify the input datasets
datasets = self._check_datasets(datasets)
if not datasets.is_all_same_type or not datasets.is_all_same_energy_shape:
raise ValueError(
"Flux point estimation requires a list of datasets"
" of the same type and data shape.")
datasets = datasets.copy()
dataset = datasets[0]
model = dataset.models[source].spectral_model
self.model = ScaleSpectralModel(model)
self.model.norm.min = 0
self.model.norm.max = 1e5
if norm_values is None:
norm_values = np.logspace(np.log10(norm_min), np.log10(norm_max),
norm_n_values)
self.norm_values = norm_values
self.source = source
self.energy_range = energy_range
super().__init__(
datasets,
sigma,
sigma_ul,
reoptimize,
)
self._set_scale_model()
def __init__(
self,
datasets,
e_edges,
source=0,
norm_min=0.2,
norm_max=5,
norm_n_values=11,
norm_values=None,
sigma=1,
sigma_ul=2,
reoptimize=False,
):
# make a copy to not modify the input datasets
if not isinstance(datasets, Datasets):
datasets = Datasets(datasets)
if not datasets.is_all_same_type and datasets.is_all_same_shape:
raise ValueError(
"Flux point estimation requires a list of datasets"
" of the same type and data shape.")
self.datasets = datasets.copy()
self.e_edges = e_edges
dataset = self.datasets[0]
model = dataset.models[source].spectral_model
self.model = ScaleSpectralModel(model)
self.model.norm.min = 0
self.model.norm.max = 1e3
if norm_values is None:
norm_values = np.logspace(np.log10(norm_min), np.log10(norm_max),
norm_n_values)
self.norm_values = norm_values
self.sigma = sigma
self.sigma_ul = sigma_ul
self.reoptimize = reoptimize
self.source = source
self.fit = Fit(self.datasets)
self._set_scale_model()
self._contribute_to_stat = False
def get_scale_model(self, models):
"""Set scale model
Parameters
----------
models : `Models`
Models
Return
------
model : `ScaleSpectralModel`
Scale spectral model
"""
ref_model = models[self.source].spectral_model
scale_model = ScaleSpectralModel(ref_model)
scale_model.norm.min = -1e5
scale_model.norm.max = 1e5
scale_model.norm.value = 1.0
scale_model.norm.frozen = False
return scale_model
def get_scale_model(self, models):
"""Set scale model
Parameters
----------
models : `Models`
Models
Return
------
model : `ScaleSpectralModel`
Scale spectral model
"""
ref_model = models[self.source].spectral_model
scale_model = ScaleSpectralModel(ref_model)
scale_model.norm.value = 1.0
scale_model.norm.frozen = False
scale_model.norm.interp = "log"
scale_model.norm.scan_values = self.norm_values
scale_model.norm.scan_min = self.norm_min
scale_model.norm.scan_max = self.norm_max
scale_model.norm.scan_n_values = self.norm_n_values
return scale_model
