Data1D

class sherpa.data.Data1D(name, x, y, staterror=None, syserror=None)[source] [edit on github]

Bases: sherpa.data.Data

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__

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, yfunc])

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__

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()[source] [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)[source] [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)[source] [edit on github]
get_filter(format='%.4f', delim=':')[source] [edit on github]

Return the data filter as a string.

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 single values or ranges, separated by commas.

Return type

str

Examples

>>> x = np.asarray([1, 2, 3, 5, 6])
>>> y = np.ones(5)
>>> d = Data1D('example', x, y)
>>> d.get_filter()
'1.0000:6.0000'
>>> d.ignore(2.5, 4.5)
>>> d.get_filter()
'1.0000:2.0000,5.0000:6.0000'
>>> d.get_filter(format='%i', delim='-')
'1-2,5-6'
get_filter_expr()[source] [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)[source] [edit on github]

Return 1D dependent variable as a 1 x N image

Parameters

yfunc

get_imgerr()[source] [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, yfunc=None)[source] [edit on github]

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

Parameters
  • filter

  • yfunc

get_xlabel()[source] [edit on github]

Return label for linear view of independent axis/axes

get_y(filter=False, yfunc=None, use_evaluation_space=False)[source] [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 both the lower and upper limits.

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

>>> x = np.arange(0.4, 2.6, 0.2)
>>> y = np.ones_like(x)
>>> d = Data1D('example', x, y)
>>> d.x[0], d.x[-1]
(0.4, 2.4000000000000004)
>>> d.notice()
>>> d.get_filter(format='%.1f')
'0.4:2.4'
>>> d.notice(0.8, 1.2)
>>> d.get_filter(format='%.1f')
'0.8:1.2'
>>> d.notice(1.5, 2.1)
>>> d.get_filter(format='%.1f')
'0.8:1.2,1.6:2.0'
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)[source] [edit on github]
to_fit(staterrfunc=None) [edit on github]
to_guess() [edit on github]
to_plot(yfunc=None, staterrfunc=None)[source] [edit on github]