SkyDiffuseCube¶
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class
gammapy.modeling.models.SkyDiffuseCube(map, norm=<Quantity 1.>, tilt=<Quantity 0.>, reference=<Quantity 1. TeV>, meta=None, interp_kwargs=None, name='diffuse', filename=None)[source]¶ Bases:
gammapy.modeling.models.SkyModelBaseCube sky map template model (3D).
This is for a 3D map with an energy axis. Use
TemplateSpatialModelfor 2D maps.- Parameters
- map
Map Map template
- normfloat
Norm parameter (multiplied with map values)
- tiltfloat
Additional tilt in the spectrum
- reference
Quantity Reference energy of the tilt.
- metadict, optional
Meta information, meta[‘filename’] will be used for serialization
- interp_kwargsdict
Interpolation keyword arguments passed to
gammapy.maps.Map.interp_by_coord. Default arguments are {‘interp’: ‘linear’, ‘fill_value’: 0}.
- map
Attributes Summary
A model parameter.
Parameters (
Parameters)A model parameter.
A model parameter.
Methods Summary
__call__(self, lon, lat, energy)Call self as a function.
copy(self)A shallow copy
create(tag, \*args, \*\*kwargs)Create a model instance.
evaluate(self, lon, lat, energy)Evaluate model.
evaluate_geom(self, geom)from_dict(data)read(filename, \*\*kwargs)Read map from FITS file.
to_dict(self)Create dict for YAML serialisation
Attributes Documentation
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default_parameters= <gammapy.modeling.parameter.Parameters object>¶
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frame¶
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norm¶ A model parameter.
Note that the parameter value has been split into a factor and scale like this:
value = factor x scale
Users should interact with the
value,quantityorminandmaxproperties and consider the fact that there is afactor`andscalean implementation detail.That was introduced for numerical stability in parameter and error estimation methods, only in the Gammapy optimiser interface do we interact with the
factor,factor_minandfactor_maxproperties, i.e. the optimiser “sees” the well-scaled problem.
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parameters¶ Parameters (
Parameters)
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reference¶ A model parameter.
Note that the parameter value has been split into a factor and scale like this:
value = factor x scale
Users should interact with the
value,quantityorminandmaxproperties and consider the fact that there is afactor`andscalean implementation detail.That was introduced for numerical stability in parameter and error estimation methods, only in the Gammapy optimiser interface do we interact with the
factor,factor_minandfactor_maxproperties, i.e. the optimiser “sees” the well-scaled problem.
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tag= 'SkyDiffuseCube'¶
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tilt¶ A model parameter.
Note that the parameter value has been split into a factor and scale like this:
value = factor x scale
Users should interact with the
value,quantityorminandmaxproperties and consider the fact that there is afactor`andscalean implementation detail.That was introduced for numerical stability in parameter and error estimation methods, only in the Gammapy optimiser interface do we interact with the
factor,factor_minandfactor_maxproperties, i.e. the optimiser “sees” the well-scaled problem.
Methods Documentation
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__call__(self, lon, lat, energy)¶ Call self as a function.
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static
create(tag, *args, **kwargs)¶ Create a model instance.
Examples
>>> from gammapy.modeling import Model >>> spectral_model = Model.create("PowerLaw2SpectralModel", amplitude="1e-10 cm-2 s-1", index=3) >>> type(spectral_model) gammapy.modeling.models.spectral.PowerLaw2SpectralModel
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evaluate_geom(self, geom)¶