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.

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

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]) Return the dependent axis of a data set.
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() Return total error in dependent variable as an image
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]) Return linear view of independent axis/axes
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, 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
to_component_plot([yfunc, staterrfunc])
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]

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)
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

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_filter(format='%.4f', delim=':')
get_filter_expr()
get_img(yfunc=None)

Return 1D dependent variable as a 1 x N image

get_imgerr()

Return total error in dependent variable as an image

get_indep(filter=False)[source]

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)

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)

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)

Return linear view of independent axis/axes

get_x0(filter=False)

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=False)

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=False, model=None)

Return linear view of bin size in independent axis/axes

get_xlabel()[source]

Return label for linear view of independent axis/axes

get_y(filter=False, yfunc=None, use_evaluation_space=False)

Return dependent axis in N-D view of dependent variable

get_yerr(filter=False, staterrfunc=None)

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

get_ylabel()[source]

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

ignore(*args, **kwargs)
notice(noticed_chans=None)[source]
set_dep(val)

Set the dependent variable values

to_component_plot(yfunc=None, staterrfunc=None)
to_contour(yfunc=None)
to_fit(staterrfunc=None)
to_guess()
to_plot(yfunc=None, staterrfunc=None)