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] [edit on github]

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.

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. If a check fails then a warning message is logged.

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

dep Left for compatibility with older versions
indep Return the grid of the data space associated with this data set.
mask Mask array for dependent variable
x Used for compatibility, in particular for __str__ and __repr__
xhi Property kept for compatibility
xlo Property kept for compatibility

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]) Return the dependent axis of a data set.
get_dims([filter]) Return the dimensions of this data space as a tuple of tuples.
get_error([filter, staterrfunc]) Return the total error on the dependent variable.
get_evaluation_indep([filter, model, …])
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]) Return the independent axes of a data set.
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, model, use_evaluation_space])
get_xerr([filter, model]) Return linear view of bin size in independent axis/axes”
get_xlabel() Return label for linear view of independent axis/axes
get_y([filter, yfunc, use_evaluation_space]) 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() Return label for dependent axis in N-D view of dependent variable”
ignore(*args, **kwargs)
notice([noticed_chans])
set_dep(val) Set the dependent variable values”
set_indep(val)
to_component_plot([yfunc, staterrfunc])
to_fit([staterrfunc])
to_guess()
to_plot([yfunc, staterrfunc])

Attributes Documentation

dep

Left for compatibility with older versions

indep

Return the grid of the data space associated with this data set. :returns: :rtype: tuple of array_like

mask

Mask array for dependent variable

Returns:mask
Return type:bool or numpy.ndarray
x

Used for compatibility, in particular for __str__ and __repr__

xhi

Property kept for compatibility

xlo

Property kept for compatibility

Methods Documentation

apply_filter(data) [edit on github]
apply_rmf(src, *args, **kwargs)[source] [edit on github]

Fold the source array src through the RMF and return the result

eval_model(modelfunc) [edit on github]
eval_model_to_fit(modelfunc) [edit on github]
get_bounding_mask() [edit on github]
get_dep(filter=False)[source] [edit on github]

Return the dependent axis of a data set.

Parameters:filter (bool, optional) – Should the filter attached to the data set be applied to the return value or not. The default is False.
Returns:axis – The dependent axis values for the data set. This gives the value of each point in the data set.
Return type:array

See also

get_indep()
Return the independent axis of a data set.
get_error()
Return the errors on the dependent axis of a data set.
get_staterror()
Return the statistical errors on the dependent axis of a data set.
get_syserror()
Return the systematic errors on the dependent axis of a data set.
get_dims(filter=False) [edit on github]

Return the dimensions of this data space as a tuple of tuples. The first element in the tuple is a tuple with the dimensions of the data space, while the second element provides the size of the dependent array. :returns: :rtype: tuple

get_error(filter=False, staterrfunc=None) [edit on github]

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

See also

get_dep()
Return the independent axis of a data set.
get_staterror()
Return the statistical errors on the dependent axis of a data set.
get_syserror()
Return the systematic errors on the dependent axis of a data set.
get_evaluation_indep(filter=False, model=None, use_evaluation_space=False) [edit on github]
get_filter(format='%.4f', delim=':') [edit on github]
get_filter_expr() [edit on github]
get_img(yfunc=None) [edit on github]

Return 1D dependent variable as a 1 x N image

Parameters:yfunc
get_imgerr() [edit on github]
get_indep(filter=False)[source] [edit on github]

Return the independent axes of a data set.

Parameters:filter (bool, optional) – Should the filter attached to the data set be applied to the return value or not. The default is False.
Returns:axis – The independent axis values for the data set. This gives the coordinates of each point in the data set.
Return type:tuple of arrays

See also

get_dep()
Return the dependent axis of a data set.
get_staterror(filter=False, staterrfunc=None) [edit on github]

Return the statistical error on the dependent axis of a data set.

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 statistical error for each data point. A value of None is returned if the data set has no statistical error array and staterrfunc is None.

Return type:

array or None

See also

get_error()
Return the errors on the dependent axis of a data set.
get_indep()
Return the independent axis of a data set.
get_syserror()
Return the systematic errors on the dependent axis of a data set.
get_syserror(filter=False) [edit on github]

Return the statistical error on the dependent axis of a data set.

Parameters:filter (bool, optional) – Should the filter attached to the data set be applied to the return value or not. The default is False.
Returns:axis – The systematic error for each data point. A value of None is returned if the data set has no systematic errors.
Return type:array or None

See also

get_error()
Return the errors on the dependent axis of a data set.
get_indep()
Return the independent axis of a data set.
get_staterror()
Return the statistical errors on the dependent axis of a data set.
get_x(filter=False, model=None, use_evaluation_space=False) [edit on github]
get_xerr(filter=False, model=None) [edit on github]

Return linear view of bin size in independent axis/axes”

Parameters:
  • filter
  • yfunc
get_xlabel()[source] [edit on github]

Return label for linear view of independent axis/axes

get_y(filter=False, yfunc=None, use_evaluation_space=False) [edit on github]

Return dependent axis in N-D view of dependent variable”

Parameters:
  • filter
  • yfunc
  • use_evaluation_space
get_yerr(filter=False, staterrfunc=None) [edit on github]

Return errors in dependent axis in N-D view of dependent variable

Parameters:
  • filter
  • staterrfunc
get_ylabel()[source] [edit on github]

Return label for dependent axis in N-D view of dependent variable”

Parameters:yfunc
ignore(*args, **kwargs) [edit on github]
notice(noticed_chans=None)[source] [edit on github]
set_dep(val) [edit on github]

Set the dependent variable values”

Parameters:val
set_indep(val) [edit on github]
to_component_plot(yfunc=None, staterrfunc=None) [edit on github]
to_fit(staterrfunc=None) [edit on github]
to_guess() [edit on github]
to_plot(yfunc=None, staterrfunc=None) [edit on github]