get_axes¶

sherpa.astro.ui.get_axes(id=None, bkg_id=None)¶

Return information about the independent axes of a data set.

Parameters:	id (int or str, optional) – The identifier for the data set to use. If not given then the default identifier is used, as returned by get_default_id. bkg_id (int or str, optional) – Set if the values returned should be from the given background component, instead of the source data set.
Returns:	axis – The independent axis values. The difference to get_dep is that this represents the “alternate grid” for the axis. For PHA data, this is the energy grid (E_MIN and E_MAX). For image data it is an array for each axis, of the length of the axis, using the current coordinate system for the data set.
Return type:	tuple of arrays
Raises:	`sherpa.utils.err.IdentifierErr` – If the data set does not exist.

See also

get_indep(): Return the independent axis of a data set.
list_data_ids(): List the identifiers for the loaded data sets.

Examples

For 1D data sets, the “alternate” view is the same as the independent axis:

>>> load_arrays(1, [10,15,19], [4,5,9], Data1D)
>>> get_indep()
array([10, 15, 19])
>>> get_axes()
array([10, 15, 19])

For a PHA data set, the approximate energy grid of the channels is returned (this is determined by the EBOUNDS extension of the RMF).

>>> load_pha('core', 'src.pi')
read ARF file src.arf
read RMF file src.rmf
read background file src_bkg.pi
>>> (chans,) = get_indep()
>>> (elo, ehi) = get_axes()
>>> chans[0:5]
array([ 1.,  2.,  3.,  4.,  5.])
>>> elo[0:5]
array([ 0.0073,  0.0146,  0.0292,  0.0438,  0.0584])
>>> ehi[0:5]
array([ 0.0146,  0.0292,  0.0438,  0.0584,  0.073 ])

The image has 101 columns by 108 rows. The get_indep function returns one-dimensional arrays, for the full dataset, where as get_axes returns values for the individual axis:

>>> load_image('img', 'img.fits')
>>> get_data('img').shape
(108, 101)
>>> set_coord('img', 'physical')
>>> (x0, x1) = get_indep('img')
>>> (a0, a1) = get_axes('img')
>>> (x0.size, x1.size)
(10908, 10908)
>>> (a0.size, a1.size)
(101, 108)
>>> np.all(x0[:101] == a0)
True
>>> np.all(x1[::101] == a1)
True