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] [edit on github]

Bases: DataIMG

Attributes Summary

`coord`	Return the coordinate setting.
`dep`	Left for compatibility with older versions
`eqpos`	The optional WCS object that converts to the world coordinate system.
`indep`	The grid of the data space associated with this data set.
`mask`	Mask array for dependent variable
`ndim`	The dimensionality of the dataset, if defined, or None.
`size`	The number of elements in the data set.
`sky`	The optional WCS object that converts to the physical coordinate system.
`staterror`	The statistical error on the dependent axis, if set.
`syserror`	The systematic error on the dependent axis, if set.
`x0`	kept for compatibility
`x0hi`	Property kept for compatibility
`x0lo`	Property kept for compatibility
`x1`	kept for compatibility
`x1hi`	Property kept for compatibility
`x1lo`	Property kept for compatibility
`y`	The dependent axis.

Methods Summary

`apply_filter`(data)
`eval_model`(modelfunc)	Evaluate the model on the independent axis.
`eval_model_to_fit`(modelfunc)	Evaluate the model on the independent axis after filtering.
`filter_region`(data)
`get_axes`()
`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_filter`()
`get_filter_expr`()
`get_image`()
`get_img`([yfunc])	Return the dependent axis as a 2D array.
`get_imgerr`()
`get_indep`([filter])	Return the independent axes of a data set.
`get_logical`()
`get_max_pos`([dep])	Return the coordinates of the maximum value.
`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_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_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`([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])	Apply a 2D filter.
`set_coord`(coord)	Change the `coord` attribute.
`set_dep`(val)	Set the dependent variable values.
`set_indep`(val)
`to_contour`([yfunc])
`to_fit`([staterrfunc])
`to_guess`()

Attributes Documentation

coord

Return the coordinate setting.

The attribute is one of ‘logical’, ‘physical’, or ‘world’. Use set_coord to change the setting.

dep: Left for compatibility with older versions

eqpos = None: The optional WCS object that converts to the world coordinate system.

indep

The grid of the data space associated with this data set.

When set, the field must be set to a tuple, even for a one-dimensional data set. The “related” fields such as the dependent axis and the error fields are set to None if their size does not match.

Changed in version 4.14.1: The filter created by notice and ignore is now cleared when the independent axis is changed.

Return type: tuple of array_like

mask

Mask array for dependent variable

Returns: mask
Return type: bool or numpy.ndarray

ndim = 2: The dimensionality of the dataset, if defined, or None.

size

The number of elements in the data set.

Returns: size – If the size has not been set then None is returned.
Return type: int or None

sky = None: The optional WCS object that converts to the physical coordinate system.

staterror

The statistical error on the dependent axis, if set.

This must match the size of the independent axis.

syserror

The systematic error on the dependent axis, if set.

This must match the size of the independent axis.

x0: kept for compatibility

x0hi: Property kept for compatibility

x0lo: Property kept for compatibility

x1: kept for compatibility

x1hi: Property kept for compatibility

x1lo: Property kept for compatibility

y

The dependent axis.

If set, it must match the size of the independent axes.

Methods Documentation

apply_filter(data) [edit on github]

eval_model(modelfunc) [edit on github]: Evaluate the model on the independent axis.

eval_model_to_fit(modelfunc) [edit on github]: Evaluate the model on the independent axis after filtering.

filter_region(data) [edit on github]

get_axes()[source] [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.

Return type: 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_filter() [edit on github]

get_filter_expr() [edit on github]

get_image() [edit on github]

get_img(yfunc=None) [edit on github]

Return the dependent axis as a 2D array.

The data is not filtered.

Parameters: yfunc (sherpa.models.model.Model instance or None, optional) – If set then it is a model that is evaluated on the data grid and returned along with the dependent axis.
Returns: img – The data as a 2D array or a pair of 2D arrays when yfunc is set.
Return type: ndarray or (ndarray, ndarray)

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_logical()[source] [edit on github]

get_max_pos(dep=None) [edit on github]

Return the coordinates of the maximum value.

Parameters: dep (ndarray or None, optional) – The data to search and it must match the current data filter. If not given then the dependent axis is used.
Returns: coords – The coordinates of the maximum location. The data values match the values returned by get_x0 and get_x1. If there is only one maximum pixel then a pair is returned otherwise a list of pairs is returned.
Return type: pair or list of pairs

See also

get_dep, get_x0, get_x1

get_physical()[source] [edit on github]

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_wcs() [edit on github]

get_world()[source] [edit on github]

get_x0(filter=False)[source] [edit on github]

get_x0label() [edit on github]: Return label for first dimension in 2-D view of independent axis/axes

get_x1(filter=False)[source] [edit on github]

get_x1label() [edit on github]: Return label for second dimension in 2-D 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(yfunc=None) [edit on github]

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

Parameters: yfunc –

ignore(*args, **kwargs) [edit on github]

notice(x0lo=None, x0hi=None, x1lo=None, x1hi=None, ignore=False) [edit on github]

notice2d(val=None, ignore=False) [edit on github]

Apply a 2D filter.

Parameters

val (str or None, optional) – The filter to apply. It can be a region string or a filename.
ignore (bool, optional) – If set then the filter should be ignored, not noticed.

set_coord(coord) [edit on github]

Change the coord attribute.

Changed in version 4.14.1: The filter created by notice2d is now cleared when the coordinate system is changed.

Parameters: coord ({'logical', 'image', 'physical', 'world', 'wcs'}) – The coordinate system to use. Note that “image” is a synomym for “logical” and “wcs” is a synomyn for “world”.

set_dep(val) [edit on github]

Set the dependent variable values.

Parameters: val (sequence or number) – If a number then it is used for each element.

set_indep(val) [edit on github]

to_contour(yfunc=None) [edit on github]

to_fit(staterrfunc=None) [edit on github]

to_guess() [edit on github]