DataIMGInt¶

class sherpa.astro.data.DataIMGInt(name, x0lo, x1lo, x0hi, x1hi, y, shape=None, staterror=None, syserror=None, sky=None, eqpos=None, coord='logical', header=None)[source]¶

Bases: sherpa.astro.data.DataIMG

Attributes Summary

`coord`	Coordinate system of independent axes
`filter`	Filter for dependent variable
`mask`	Mask array for dependent variable

Methods Summary

`apply_filter`(data)
`eval_model`(modelfunc)
`eval_model_to_fit`(modelfunc)
`filter_region`(data)
`get_axes`()
`get_bounding_mask`()
`get_dep`([filter])
`get_dims`([filter])
`get_error`([filter, staterrfunc])	Return the total error on the dependent variable.
`get_filter`()
`get_filter_expr`()
`get_image`()
`get_img`([yfunc])
`get_imgerr`()
`get_indep`([filter])
`get_logical`()
`get_max_pos`([dep])
`get_physical`()
`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_wcs`()
`get_world`()
`get_x`([filter])	Return linear view of independent axis/axes
`get_x0`([filter])
`get_x0label`()	Return label for first dimension in 2-D view of independent axis/axes
`get_x1`([filter])
`get_x1label`()	Return label for second dimension in 2-D view of independent axis/axes
`get_xerr`([filter])	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])	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`([x0lo, x0hi, x1lo, x1hi, ignore])
`notice2d`([val, ignore])
`set_coord`(coord)
`set_dep`(val)	Set the dependent variable values
`to_contour`([yfunc])
`to_fit`([staterrfunc])
`to_guess`()
`to_plot`([yfunc, staterrfunc])

Attributes Documentation

coord¶: Coordinate system of independent axes

filter¶: Filter for dependent variable

mask¶: Mask array for dependent variable

Methods Documentation

apply_filter(data)¶

eval_model(modelfunc)¶

eval_model_to_fit(modelfunc)¶

filter_region(data)¶

get_axes()[source]¶

get_bounding_mask()¶

get_dep(filter=False)¶

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