CWTData¶

class gammapy.detect.CWTData(counts, background, n_scale)[source]¶

Bases: object

Images for CWT algorithm.

Contains also input counts and background.

Parameters:

counts : WcsNDMap

2D counts image.

background : WcsNDMap

2D background image.

n_scale : int

Number of scales.

Examples

>>> from gammapy.maps import Map
>>> from gammapy.detect import CWTData
>>> filename = '$GAMMAPY_DATA/fermi_survey/all.fits.gz'
>>> image = Map.read(filename, hdu='COUNTS')
>>> background = Map.read(filename, hdu='BACKGROUND')
>>> data = CWTData(counts=image, background=background, n_scale=2)

Attributes Summary

`approx`	2D approx ??? image (`WcsNDMap`).
`approx_bkg`	2D approx bkg image (`WcsNDMap`).
`background`	2D background input image (`WcsNDMap`).
`counts`	2D counts input image (`WcsNDMap`).
`error`	3D error cube (`WcsNDMap`).
`info_table`	Information about all the images and cubes.
`max_scale_image`	Maximum scale image (`WcsNDMap`).
`model`	2D model image (`WcsNDMap`).
`model_plus_approx`	TODO: document what this is.
`residual`	2D residual image (`WcsNDMap`).
`support_2d`	2D cube exclusion mask (`WcsNDMap`).
`support_3d`	3D support (exclusion) cube (`WcsNDMap`).
`transform_2d`	2D transform ??? image (`WcsNDMap`).
`transform_3d`	3D transform ??? cube (`WcsNDMap`).

Methods Summary

`cube_info`(name[, per_scale])	Compute cube info.
`cubes`()	All the cubes in a dict.
`image_info`(name)	Compute image info.
`images`()	All the images in a dict.
`write`(filename[, overwrite])	Save results to FITS file.

Attributes Documentation

approx¶

2D approx ??? image (WcsNDMap).

In the course of iterations updated by convolution of counts - model - background with kern_approx Primordial initialized by zero array.

approx_bkg¶

2D approx bkg image (WcsNDMap).

In the course of iterations updated by convolution of background with kern_approx. Primordial initialized by zero array.

background¶: 2D background input image (WcsNDMap).

counts¶: 2D counts input image (WcsNDMap).

error¶

3D error cube (WcsNDMap).

Primordial initialized by zero array. In the course of iterations updated by convolution of total_background with kernel^2 for each scale.

info_table¶

Information about all the images and cubes.

Returns:

table : Table

Information about the object.

max_scale_image¶: Maximum scale image (WcsNDMap).

model¶

2D model image (WcsNDMap).

Positive version of transform_2d image. Primordial initialized by zero array.

model_plus_approx¶: TODO: document what this is.

residual¶

2D residual image (WcsNDMap).

Calculate as counts - model - approx.

support_2d¶

2D cube exclusion mask (WcsNDMap).

Created from support_3d by OR-operation per 0 axis.

support_3d¶

3D support (exclusion) cube (WcsNDMap).

Primordial initialized by zero array.

transform_2d¶

2D transform ??? image (WcsNDMap).

Created from transform_3d by summarize values per 0 axes. Primordial initialized by zero array.

transform_3d¶

3D transform ??? cube (WcsNDMap).

Primordial initialized by zero array. In the course of iterations updated by convolution of counts - total_background with kernel for each scale (total_background = model + background + approx).

Methods Documentation

cube_info(name, per_scale=False)[source]¶

Compute cube info.

Compute variance, mean, find max and min values and compute sum for image with given name. Return that information about the image.

Parameters:

name : string

Name of the image. Name can be as one of the follow: {‘transform_3d’, ‘error’, ‘support’}

per_scale : boolean, optional (default False)

If True, return information about the cube per all the scales.

Returns:

table : Table

Information about the object.

cubes()[source]¶

All the cubes in a dict.

Returns:

cubes : dict

Dictionary with keys {‘transform_3d’, ‘error’, ‘support_3d’} and 3D ndarray cubes as values.

image_info(name)[source]¶

Compute image info.

Compute variance, mean, find max and min values and compute sum for image with given name. Return that information about the image.

Parameters:

name : string

Name of the image. Name can be as one of the follow: {‘counts’, ‘background’, ‘model’, ‘approx’, ‘approx_bkg’, ‘transform_2d’, ‘model_plus_approx’, ‘residual’, ‘maximal’, ‘support_2d’}

Returns:

table : Table

Information about the object.

images()[source]¶

All the images in a dict.

Returns:

images : dict

Dictionary with keys {‘counts’, ‘background’, ‘model’, ‘approx’, ‘approx_bkg’, ‘transform_2d’, ‘maximal’, ‘support_2d’} and 2D ndarray images as values.

write(filename, overwrite=False)[source]¶

Save results to FITS file.

Parameters:

filename : str

Fits file name.

overwrite : bool, optional (default False)

If True, overwrite file with name as filename.