DataARF

class sherpa.astro.data.DataARF(name, energ_lo, energ_hi, specresp, bin_lo=None, bin_hi=None, exposure=None, header=None, ethresh=None)

Bases: DataOgipResponse

ARF data set.

The ARF format is described in OGIP documents [CAL_92_002] and [CAL_92_002a].

Parameters:

• name (str) – The name of the data set; often set to the name of the file containing the data.

• energ_lo (numpy.ndarray) – 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.

• energ_hi (numpy.ndarray) – 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.

• specresp (numpy.ndarray) – 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.

• bin_lo (array or None, optional) –

• bin_hi (array or None, optional) –

• exposure (number or None, optional) – The exposure time for the ARF, in seconds.

• 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.

Raises:

sherpa.utils.err.DataErr – This is raised if the energy arrays do not follow some of the OGIP standards.

Notes

There is limited checking that the ARF 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.

Attributes Summary

dep	Left for compatibility with older versions
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.
specresp
staterror	The statistical error on the dependent axis, if set.
syserror	The systematic error on the dependent axis, if set.
x	Used for compatibility, in particular for __str__ and __repr__
xhi	Property kept for compatibility
xlo	Property kept for compatibility
y	The dependent axis.

Methods Summary

apply_arf(src, *args, **kwargs)	Fold the source array src through the ARF and return the result
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.
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])	Return the data filter as a string.
get_filter_expr()	Return the data filter as a string along with the units.
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([bin_mask])	Notice or ignore the given range.
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

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 = 1

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

specresp

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.

x

Used for compatibility, in particular for __str__ and __repr__

xhi

Property kept for compatibility

xlo

Property kept for compatibility

y

The dependent axis.

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

Methods Documentation

apply_arf(src, *args, **kwargs)

Fold the source array src through the ARF and return the result

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.

get_bounding_mask()

get_dep(filter=False)

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)

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)

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)

get_filter(format='%.4f', delim=':')

Return the data filter as a string.

For each noticed range the filter is reported as starting at the low edge of the first bin and ends at the upper edge of the last bin in the range.

Changed in version 4.14.0: Prior to 4.14.0 the filter used the mid-point of the bin, not its low or high value.

Parameters:

• format (str, optional) – The formatting of the numeric values.

• delim (str, optional) – The string used to mark the low-to-high range.

Returns:

filter – The filter, represented as a collection of ranges separated by commas.

Return type:

str

See also

get_filter_expr, ignore, notice

Examples

>>> xlo = np.asarray([1, 2, 3, 5, 6])
>>> xhi = xlo + 1
>>> y = np.ones(5)
>>> d = Data1DInt('example', xlo, xhi, y)
>>> d.get_filter()
'1.0000:7.0000'
>>> d.ignore(2.5, 4.5)
>>> d.get_filter()
'1.0000:2.0000,5.0000:7.0000'

>>> d.get_filter(format='%i', delim='-')
'1-2,5-7'

get_filter_expr()

Return the data filter as a string along with the units.

This is a specialised version of get_filter which adds the axis units.

Returns:

filter – The filter, represented as a collection of single values or ranges, separated by commas.

Return type:

str

See also

get_filter

Examples

>>> d.get_filter_expr()
'1.0000-2.0000,5.0000-6.0000 x'

get_img(yfunc=None)

Return 1D dependent variable as a 1 x N image

Parameters:

yfunc –

get_imgerr()

get_indep(filter=False)

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)

get_xerr(filter=False, model=None)

Return linear view of bin size in independent axis/axes”

Parameters:

• filter –

• yfunc –

get_xlabel()

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”

Parameters:

• filter –

• yfunc –

• use_evaluation_space –

get_yerr(filter=False, staterrfunc=None)

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

Parameters:

• filter –

• staterrfunc –

get_ylabel()

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

Parameters:

yfunc –

ignore(*args, **kwargs)

notice(bin_mask=None)

Notice or ignore the given range.

Ranges are inclusive for the lower limit and exclusive for the upper limit.

Changed in version 4.14.0: Filtering Data1DInt datasets has been improved to fix a number of corner cases. As part of this the upper limit has been changed to be exclusive whereas previously it was not obvious what the filter was doing.

Parameters:

• xlo (number or None, optional) – The range to change. A value of None means the minimum or maximum permitted value.

• xhi (number or None, optional) – The range to change. A value of None means the minimum or maximum permitted value.

• ignore (bool, optional) – Set to True if the range should be ignored. The default is to notice the range.

See also

get_filter, get_filter_expr

Notes

If no ranges have been ignored then a call to notice with ignore=False will select just the lo to hi range, and exclude any bins outside this range. If there has been a filter applied then the range lo to hi will be added to the range of noticed data (when ignore=False).

Examples

>>> edges = np.arange(0.4, 2.6, 0.2)
>>> xlo, xhi = edges[:-1], edges[1:]
>>> y = np.ones_like(xlo)
>>> d = Data1DInt('example', xlo, xhi, y)
>>> d.xlo[0], d.xhi[-1]
(0.4, 2.400000000000001)
>>> d.get_filter(format='%.1f')
'0.4:2.4'
>>> d.notice(0.8, 1.9)
>>> d.get_filter(format='%.1f')
'0.8:2.0'

>>> d.notice()
>>> d.get_filter(format='%.1f')
'0.4:2.4'

set_dep(val)

Set the dependent variable values.

Parameters:

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

set_indep(val)

to_component_plot(yfunc=None, staterrfunc=None)

to_fit(staterrfunc=None)

to_guess()

to_plot(yfunc=None, staterrfunc=None)