DataRMF¶

class sherpa.astro.data.DataRMF(name, detchans, energ_lo, energ_hi, n_grp, f_chan, n_chan, matrix, offset=1, e_min=None, e_max=None, header=None, ethresh=None)[source]¶

Bases: sherpa.astro.data.DataOgipResponse

RMF data set.

The RMF format is described in OGIP documents [1] and [2].

Parameters:

name (str) – The name of the data set; often set to the name of the file containing the data.
detchans (int) –
energ_hi (energ_lo,) – The values of the ENERG_LO, ENERG_HI, and SPECRESP columns for the ARF. The ENERG_HI values must be greater than the ENERG_LO values for each bin, and the energy arrays must be in increasing or decreasing order.
f_chan, n_chan, matrix (n_grp,) –
offset (int, optional) –
e_max (e_min,) –
header (dict or None, optional) –
ethresh (number or None, optional) – If set it must be greater than 0 and is the replacement value to use if the lowest-energy value is 0.0.

Raises:

sherpa.utils.err.DataErr – This is raised if the energy arrays do not follow some of the OGIP standards.

Notes

There is limited checking that the RMF matches the OGIP standard, but as there are cases of released data products that do not follow the standard, these checks can not cover all cases.

References

[1]	“The Calibration Requirements for Spectral Analysis (Definition of RMF and ARF file formats)”, https://heasarc.gsfc.nasa.gov/docs/heasarc/caldb/docs/memos/cal_gen_92_002/cal_gen_92_002.html

[2]	“The Calibration Requirements for Spectral Analysis Addendum: Changes log”, https://heasarc.gsfc.nasa.gov/docs/heasarc/caldb/docs/memos/cal_gen_92_002a/cal_gen_92_002a.html

Attributes Summary

`filter`	Filter for dependent variable
`mask`	Mask array for dependent variable

Methods Summary

`apply_filter`(data)
`apply_rmf`(src, \args, \\*kwargs)	Fold the source array src through the RMF and return the result
`eval_model`(modelfunc)
`eval_model_to_fit`(modelfunc)
`get_bounding_mask`()
`get_dep`([filter])
`get_dims`([filter])
`get_error`([filter, staterrfunc])	Return the total error on the dependent variable.
`get_filter`([format, delim])
`get_filter_expr`()
`get_img`([yfunc])	Return 1D dependent variable as a 1 x N image
`get_imgerr`()
`get_indep`([filter])
`get_staterror`([filter, staterrfunc])	Return the statistical error on the dependent axis of a data set.
`get_syserror`([filter])	Return the statistical error on the dependent axis of a data set.
`get_x`([filter])
`get_x0`([filter])	Return first dimension in 2-D view of independent axis/axes
`get_x0label`()	Return label for first dimension in 2-D view of independent axis/axes
`get_x1`([filter])	Return second dimension in 2-D view of independent axis/axes
`get_x1label`()	Return label for second dimension in 2-D view of independent axis/axes
`get_xerr`([filter])
`get_xlabel`()
`get_y`([filter, yfunc])	Return dependent axis in N-D view of dependent variable
`get_yerr`([filter, staterrfunc])	Return errors in dependent axis in N-D view of dependent variable
`get_ylabel`()
`ignore`(\args, \\*kwargs)
`notice`([noticed_chans])
`set_dep`(val)	Set the dependent variable values
`to_contour`([yfunc])
`to_fit`([staterrfunc])
`to_guess`()
`to_plot`([yfunc, staterrfunc])

Attributes Documentation

filter¶: Filter for dependent variable

mask¶: Mask array for dependent variable

Methods Documentation

apply_filter(data)¶

apply_rmf(src, *args, **kwargs)[source]¶: Fold the source array src through the RMF and return the result

eval_model(modelfunc)¶

eval_model_to_fit(modelfunc)¶

get_bounding_mask()¶

get_dep(filter=False)[source]¶

get_dims(filter=False)¶

get_error(filter=False, staterrfunc=None)¶

Return the total error on the dependent variable.

Parameters:	filter (bool, optional) – Should the filter attached to the data set be applied to the return value or not. The default is False. staterrfunc (function) – If no statistical error has been set, the errors will be calculated by applying this function to the dependent axis of the data set.
Returns:	axis – The error for each data point, formed by adding the statistical and systematic errors in quadrature.
Return type:	array or None