Data

class sherpa.data.Data(name, indep, y, staterror=None, syserror=None)

Bases: sherpa.utils.NoNewAttributesAfterInit, sherpa.data.BaseData

Data class for generic, N-Dimensional data sets, where N depends on the number of independent axes passed during initialization.

A data class is the collection of a data space and a number of data array for the dependent variable and associated errors.

This class can be extended by classes definining data sets of specific dimensionality. Extending classes should override the _init_data_space method.

This classe provides most of the infrastructure for extending classes for free.

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

Methods Summary

apply_filter(data)
eval_model(modelfunc)
eval_model_to_fit(modelfunc)
get_dep([filter])	Return the dependent axis of a data set.
get_dims()	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_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_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(mins, maxes[, ignore])
set_dep(val)	Set the dependent variable values”
set_indep(val)
to_fit([staterrfunc])
to_guess()

Attributes Documentation

dep

Left for compatibility with older versions

indep

Return the grid of the data space associated with this data set. :returns: :rtype: tuple of array_like

mask

Mask array for dependent variable

Returns:mask Return type:bool or numpy.ndarray

Methods Documentation

apply_filter(data)

eval_model(modelfunc)

eval_model_to_fit(modelfunc)

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()

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. :returns: :rtype: 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_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_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(yfunc=None)

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

Parameters:yfunc –

ignore(*args, **kwargs)

notice(mins, maxes, ignore=False)

set_dep(val)

Set the dependent variable values”

Parameters:val –

set_indep(val)

to_fit(staterrfunc=None)

to_guess()