load_data

sherpa.astro.ui.load_data(id, filename=None, *args, **kwargs) → None

Load a data set from a file.

This loads a data set from the file, trying in order load_pha, load_image, load_table, then load_ascii.

Changed in version 4.13.1: The id argument is now used to define the first identifier when loading in a PHA2 file to match load_pha (previously the range always started at 1).

Parameters:

  • id (int or str, optional) – The identifier for the data set to use. For multi-dataset files, currently only PHA2, the id value indicates the first dataset: if it is an integer then the numbering starts at id, and if a string then a suffix of 1 to n is added. If not given then the default identifier is used, as returned by get_default_id.

  • filename – A file name or a data structure representing the data to use, as used by the I/O backend in use by Sherpa: e.g. a PHACrateDataset, TABLECrate, or IMAGECrate for crates, as used by CIAO, or a list of AstroPy HDU objects.

  • args – The arguments supported by load_pha, load_image, load_table, and load_ascii.

  • kwargs – The keyword arguments supported by load_pha, load_image, load_table, and load_ascii.

See also

load_arrays

Create a data set from array values.

load_ascii

Load an ASCII file as a data set.

load_image

Load an image as a data set.

load_pha

Load a PHA data set.

load_table

Load a FITS binary file as a data set.

set_data

Set a data set.

unpack_data

Create a sherpa data object from a file.

Notes

The function does not follow the normal Python standards for parameter use, since it is designed for easy interactive use. When called with a single un-named argument, it is taken to be the filename parameter. If given two un-named arguments, then they are interpreted as the id and filename parameters, respectively. The remaining parameters are expected to be given as named arguments.

Examples

>>> load_data('tbl.dat')

>>> load_data('hist.dat', dstype=Data1DInt)

>>> load_data('img', 'img.fits')
>>> load_data('bg', 'img_bg.fits')

>>> cols = ['rmid', 'sur_bri', 'sur_bri_err']
>>> load_data(2, 'profile.fits', colkeys=cols)