Data1DInt¶

class sherpa.data.Data1DInt(name, xlo, xhi, y, staterror=None, syserror=None)[source]¶

1-D integrated data set

Attributes Summary

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

Methods Summary

`apply_filter`(data)
`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`()	Return label for linear view of independent axis/axes
`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`([yfunc])	Return label for dependent axis in N-D view of dependent variable
`ignore`(\args, \\*kwargs)
`notice`([xlo, xhi, ignore])
`set_dep`(val)	Set the dependent variable values
`to_contour`([yfunc])
`to_fit`([staterrfunc])
`to_guess`()
`to_plot`([yfunc, staterrfunc])

Attributes Documentation

Methods Documentation

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