MapEvaluator¶

class gammapy.cube.MapEvaluator(model=None, exposure=None, psf=None, edisp=None, evaluation_mode='local')[source]¶

Bases: object

Sky model evaluation on maps.

This evaluates a sky model on a 3D map and convolves with the IRFs, and returns a map of the predicted counts. Note that background counts are not added.

For now, we only make it work for 3D WCS maps with an energy axis. No HPX, no other axes, those can be added later here or via new separate model evaluator classes.

Parameters:	model : `SkyModel` Sky model exposure : `Map` Exposure map psf : `PSFKernel` PSF kernel edisp : `EnergyDispersion` Energy dispersion evaluation_mode : {“local”, “global”} Model evaluation mode.

Attributes Summary

`bin_volume`	Map pixel bin volume (solid angle times energy bin width).
`coords`	Return evaluator coords
`energy_bin_width`	Energy axis bin widths (`astropy.units.Quantity`)
`energy_center`	True energy axis bin centers (`Quantity`)
`energy_edges`	True energy axis bin edges (`Quantity`)
`geom`	True energy map geometry (`MapGeom`)
`geom_image`	Image map geometry (`MapGeom`)
`geom_reco`	Reco energy map geometry (`MapGeom`)
`lat`
`lon`
`lon_lat`	Spatial coordinate pixel centers (`lon, lat` tuple of `Quantity`).
`needs_update`	Check whether the model component has drifted away from its support.
`solid_angle`	Solid angle per pixel

Methods Summary

`apply_edisp`(self, npred)	Convolve map data with energy dispersion.
`apply_exposure`(self, flux)	Compute npred cube
`apply_psf`(self, npred)	Convolve npred cube with PSF
`compute_dnde`(self)	Compute model differential flux at map pixel centers.
`compute_flux`(self)	Compute model integral flux over map pixel volumes.
`compute_npred`(self)	Evaluate model predicted counts.
`update`(self, exposure, psf, edisp, geom)	Update MapEvaluator, based on the current position of the model component.

Attributes Documentation

bin_volume¶: Map pixel bin volume (solid angle times energy bin width).

coords¶: Return evaluator coords

energy_bin_width¶: Energy axis bin widths (astropy.units.Quantity)

energy_center¶: True energy axis bin centers (Quantity)

energy_edges¶: True energy axis bin edges (Quantity)

geom¶: True energy map geometry (MapGeom)

geom_image¶: Image map geometry (MapGeom)

geom_reco¶: Reco energy map geometry (MapGeom)

lat¶

lon¶

lon_lat¶: Spatial coordinate pixel centers (lon, lat tuple of Quantity).

needs_update¶: Check whether the model component has drifted away from its support.

solid_angle¶: Solid angle per pixel

Methods Documentation

apply_edisp(self, npred)[source]¶

Convolve map data with energy dispersion.

Parameters:	npred : `Map` Predicted counts in true energy bins
Returns:	npred_reco : `Map` Predicted counts in reco energy bins

apply_exposure(self, flux)[source]¶

Compute npred cube

For now just divide flux cube by exposure

apply_psf(self, npred)[source]¶: Convolve npred cube with PSF

compute_dnde(self)[source]¶

Compute model differential flux at map pixel centers.

Returns:	model_map : `Map` Sky cube with data filled with evaluated model values. Units: `cm-2 s-1 TeV-1 deg-2`

compute_flux(self)[source]¶

Compute model integral flux over map pixel volumes.

For now, we simply multiply dnde with bin volume.

compute_npred(self)[source]¶

Evaluate model predicted counts.

Returns:	npred : `Map` Predicted counts on the map (in reco energy bins)

update(self, exposure, psf, edisp, geom)[source]¶

Update MapEvaluator, based on the current position of the model component.

Parameters:	exposure : `Map` Exposure map. psf : `gammapy.cube.PSFMap` PSF map. edisp : `gammapy.cube.EDispMap` Edisp map. geom : `gammapy.maps.MapGeom` Reference geometry of the data.