DataRosatRMF

class sherpa.astro.data.DataRosatRMF(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.DataRMF

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
ui_name
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
ui_name = 'ROSAT RMF'
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) [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) [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) [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() [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() [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) [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]