TemplateSpatialModel#

class gammapy.modeling.models.TemplateSpatialModel(map, meta=None, normalize=True, interp_kwargs=None, filename=None, copy_data=True, **kwargs)[source]#

Bases: SpatialModel

Spatial sky map template model.

For more information see Template spatial model. By default, the position of the model is fixed at the center of the map. The position can be fitted by unfreezing the lon_0 and lat_0 parameters. In that case, the coordinate of every pixel is shifted in lon and lat in the frame of the map. NOTE: planar distances are calculated, so the results are correct only when the fitted position is close to the map center.

Parameters:

mapMap: Map template.
metadict, optional: Meta information, meta[‘filename’] will be used for serialisation.
normalizebool: Normalize the input map so that it integrates to unity.
interp_kwargsdict: Interpolation keyword arguments passed to gammapy.maps.Map.interp_by_coord. Default arguments are {‘method’: ‘linear’, ‘fill_value’: 0, “values_scale”: “log”}.
filenamestr: Name of the map file.
copy_databool: Create a deepcopy of the map data or directly use the original. Default is True. Use False to save memory in case of large maps.
**kwargsdict: Keyword arguments forwarded to SpatialModel.__init__.

Attributes Summary

`covariance`
`default_parameters`
`evaluation_bin_size_min`
`evaluation_radius`	Evaluation radius as an `Angle`.
`evaluation_region`	Evaluation region.
`frozen`	Frozen status of a model, True if all parameters are frozen.
`is_energy_dependent`
`lat_0`	A model parameter.
`lon_0`	A model parameter.
`map`	Template map as a `Map` object.
`map_center`
`parameters`	Parameters as a `Parameters` object.
`parameters_unique_names`
`phi_0`
`position`	Spatial model center position as a `SkyCoord`.
`position_error`	Get 95% containment position error as `EllipseSkyRegion`.
`position_lonlat`	Spatial model center position `(lon, lat)` in radians and frame of the model.
`tag`
`type`

Methods Summary

`__call__`(lon, lat[, energy])	Call evaluate method.
`copy`([copy_data])	Copy model.
`evaluate`(lon, lat[, energy, lon_0, lat_0])	Evaluate the model at given coordinates.
`evaluate_geom`(geom)	Evaluate model on `Geom`.
`freeze`()	Freeze all parameters.
`from_dict`(data)	Create a spatial model from a dictionary.
`from_parameters`(parameters, **kwargs)	Create model from parameter list.
`from_position`(position, **kwargs)	Define the position of the model using a `SkyCoord`.
`integrate_geom`(geom[, oversampling_factor])	Integrate model on `Geom` or `RegionGeom`.
`plot`([ax, geom])	Plot spatial model.
`plot_error`([ax, which, kwargs_position, ...])	Plot the errors of the spatial model.
`plot_grid`([geom])	Plot spatial model energy slices in a grid.
`plot_interactive`([ax, geom])	Plot spatial model.
`plot_position_error`([ax])	Plot position error.
`read`(filename[, normalize])	Read spatial template model from FITS image.
`reassign`(datasets_names, new_datasets_names)	Reassign a model from one dataset to another.
`to_dict`([full_output])	Create dictionary for YAML serilisation.
`to_region`(**kwargs)	Model outline from template map boundary as a `RectangleSkyRegion`.
`unfreeze`()	Restore parameters frozen status to default.
`write`([overwrite, filename])	Write the map.

Attributes Documentation

covariance#

default_parameters = <gammapy.modeling.parameter.Parameters object>#

evaluation_bin_size_min#

evaluation_radius#

Evaluation radius as an Angle.

Set to half of the maximal dimension of the map.

evaluation_region#: Evaluation region.

frozen#: Frozen status of a model, True if all parameters are frozen.

is_energy_dependent#

lat_0#

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, quantity or min and max properties and consider the fact that there is a factor` and scale an 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_min and factor_max properties, i.e. the optimiser “sees” the well-scaled problem.

Parameters:

namestr: Name.
valuefloat or Quantity: Value.
scalefloat, optional: Scale (sometimes used in fitting).
unitUnit or str, optional: Unit.
minfloat, optional: Minimum (sometimes used in fitting).
maxfloat, optional: Maximum (sometimes used in fitting).
frozenbool, optional: Frozen (used in fitting).
errorfloat: Parameter error.
scan_minfloat: Minimum value for the parameter scan. Overwrites scan_n_sigma.
scan_maxfloat: Minimum value for the parameter scan. Overwrites scan_n_sigma.
scan_n_values: int: Number of values to be used for the parameter scan.
scan_n_sigmaint: Number of sigmas to scan.
scan_values: `numpy.array`: Scan values. Overwrites all the scan keywords before.
scale_method{‘scale10’, ‘factor1’, None}: Method used to set factor and scale.
interp{“lin”, “sqrt”, “log”}: Parameter scaling to use for the scan.
priorPrior: Prior set on the parameter.

lon_0#

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, quantity or min and max properties and consider the fact that there is a factor` and scale an 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_min and factor_max properties, i.e. the optimiser “sees” the well-scaled problem.

Parameters:

namestr: Name.
valuefloat or Quantity: Value.
scalefloat, optional: Scale (sometimes used in fitting).
unitUnit or str, optional: Unit.
minfloat, optional: Minimum (sometimes used in fitting).
maxfloat, optional: Maximum (sometimes used in fitting).
frozenbool, optional: Frozen (used in fitting).
errorfloat: Parameter error.
scan_minfloat: Minimum value for the parameter scan. Overwrites scan_n_sigma.
scan_maxfloat: Minimum value for the parameter scan. Overwrites scan_n_sigma.
scan_n_values: int: Number of values to be used for the parameter scan.
scan_n_sigmaint: Number of sigmas to scan.
scan_values: `numpy.array`: Scan values. Overwrites all the scan keywords before.
scale_method{‘scale10’, ‘factor1’, None}: Method used to set factor and scale.
interp{“lin”, “sqrt”, “log”}: Parameter scaling to use for the scan.
priorPrior: Prior set on the parameter.

map#: Template map as a Map object.

map_center#

parameters#: Parameters as a Parameters object.

parameters_unique_names#

phi_0#

position#: Spatial model center position as a SkyCoord.

position_error#: Get 95% containment position error as EllipseSkyRegion.

position_lonlat#: Spatial model center position (lon, lat) in radians and frame of the model.

tag = ['TemplateSpatialModel', 'template']#

type#

Methods Documentation

__call__(lon, lat, energy=None)#: Call evaluate method.

copy(copy_data=False, **kwargs)[source]#

Copy model.

Parameters:

copy_databool: Whether to copy the data. Default is False.
**kwargsdict: Keyword arguments forwarded to TemplateSpatialModel.

Returns:

modelTemplateSpatialModel: Copied template spatial model.

evaluate(lon, lat, energy=None, lon_0=None, lat_0=None)[source]#

Evaluate the model at given coordinates.

Note that, if the map data assume negative values, these are clipped to zero.

evaluate_geom(geom)#

Evaluate model on Geom.

Parameters:

geomWcsGeom: Map geometry.

Returns:

mapMap: Map containing the value in each spatial bin.

freeze()#: Freeze all parameters.

classmethod from_dict(data)[source]#

Create a spatial model from a dictionary.

Parameters:

datadict: Dictionary containing model parameters.
kwargsdict: Keyword arguments passed to from_parameters.

classmethod from_parameters(parameters, **kwargs)#

Create model from parameter list.

Parameters:

parametersParameters: Parameters for init.

Returns:

modelModel: Model instance.

classmethod from_position(position, **kwargs)#

Define the position of the model using a SkyCoord.

The model will be created in the frame of the SkyCoord.

Parameters:

positionSkyCoord: Position.

Returns:

modelSpatialModel: Spatial model.

integrate_geom(geom, oversampling_factor=None)#

Integrate model on Geom or RegionGeom.

Integration is performed by simple rectangle approximation, the pixel center model value is multiplied by the pixel solid angle. An oversampling factor can be used for precision. By default, this parameter is set to None and an oversampling factor is automatically estimated based on the model estimation maximal bin width.

For a RegionGeom, the model is integrated on a tangent WCS projection in the region.

Parameters:

geomWcsGeom or RegionGeom: The geom on which the integration is performed.
oversampling_factorint or None: The oversampling factor to use for integration. Default is None: the factor is estimated from the model minimal bin size.

Returns:

mapMap or gammapy.maps.RegionNDMap: Map containing the integral value in each spatial bin.

plot(ax=None, geom=None, **kwargs)[source]#

Plot spatial model.

Parameters:

axAxes, optional: Matplotlib axes. Default is None.
geomWcsGeom, optional: Geometry to use for plotting. Default is None.
**kwargsdict: Keyword arguments passed to plot().

Returns:

axAxes, optional: Matplotlib axes.

plot_error(ax=None, which='position', kwargs_position=None, kwargs_extension=None)#

Plot the errors of the spatial model.

Parameters:

axAxes, optional

Matplotlib axes to plot the errors on. Default is None.

which: list of str

Which errors to plot. Available options are:

“all”: all the optional steps are plotted

“position”: plot the position error of the spatial model

“extension”: plot the extension error of the spatial model

kwargs_positiondict, optional

Keyword arguments passed to plot_position_error. Default is None.

kwargs_extensiondict, optional

Keyword arguments passed to plot_extension_error. Default is None.

Returns:

axAxes, optional: Matplotlib axes.

plot_grid(geom=None, **kwargs)#

Plot spatial model energy slices in a grid.

Parameters:

geomWcsGeom, optional: Geometry to use for plotting. Default is None.
**kwargsdict: Keyword arguments passed to plot().

Returns:

axAxes, optional: Matplotlib axes.

plot_interactive(ax=None, geom=None, **kwargs)[source]#

Plot spatial model.

Parameters:

axAxes, optional: Matplotlib axes. Default is None.
geomWcsGeom, optional: Geom to use for plotting. Default is None.
**kwargsdict: Keyword arguments passed to plot().

Returns:

axAxes, optional: Matplotlib axes.

plot_position_error(ax=None, **kwargs)#

Plot position error.

Parameters:

axAxes, optional: Matplotlib axes to plot the position error on. Default is None.
**kwargsdict: Keyword arguments passed to plot().

Returns:

axAxes, optional: Matplotlib axes.

classmethod read(filename, normalize=True, **kwargs)[source]#

Read spatial template model from FITS image.

If unit is not given in the FITS header the default is sr-1.

Parameters:

filenamestr: FITS image filename.
normalizebool: Normalize the input map so that it integrates to unity.
kwargsdict: Keyword arguments passed to Map.read().

reassign(datasets_names, new_datasets_names)#

Reassign a model from one dataset to another.

Parameters:

datasets_namesstr or list: Name of the datasets where the model is currently defined.
new_datasets_namesstr or list: Name of the datasets where the model should be defined instead. If multiple names are given the two list must have the save length, as the reassignment is element-wise.

Returns:

modelModel: Reassigned model.

to_dict(full_output=False)[source]#: Create dictionary for YAML serilisation.

to_region(**kwargs)[source]#: Model outline from template map boundary as a RectangleSkyRegion.

unfreeze()#: Restore parameters frozen status to default.

write(overwrite=False, filename=None)[source]#

Write the map.

Parameters:

overwrite: bool, optional: Overwrite existing file. Default is False, which will raise a warning if the template file exists already.
filename: str, optional: Filename of the template model. By default, the template model will be saved with the TemplateSpatialModel.filename attribute, if filename is provided this attribute will be updated.