Data1DInt

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

Bases: sherpa.data.Data1D

1-D integrated data set

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

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)

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])

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([yfunc])

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

ignore(*args, **kwargs)

notice([xlo, xhi, ignore])

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

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

Return type

tuple of array_like

mask

Mask array for dependent variable

Returns

mask

Return type

bool or numpy.ndarray

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]
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.

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_evaluation_indep(filter=False, model=None, use_evaluation_space=False) [edit on github]
get_filter(format='%.4f', delim=':')[source] [edit on github]

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

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() [edit on github]

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) [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)[source] [edit on github]
get_xerr(filter=False, model=None)[source] [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(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(xlo=None, xhi=None, ignore=False)[source] [edit on github]

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.

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) [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]