TemplateSpatialModel

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

Bases: gammapy.modeling.models.spatial.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 fittted 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.
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])	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.

is_normbool

Whether the parameter represents the flux norm of the model.

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.

is_normbool

Whether the parameter represents the flux norm of the model.

priorPrior

Prior set on the parameter.

map

Template map as a Map object.

map_center

parameters

Parameters as a Parameters object.

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)

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)

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)

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)

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)

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)

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)

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=False)

Write the map.

Parameters

overwrite: bool, optional

Overwrite existing file. Default is False, which will raise a warning if the template file exists already.