Data2D

class sherpa.data.Data2D(name, x0, x1, y, shape=None, 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

x0

kept for compatibility

x1

kept for compatibility

Methods Summary

apply_filter(data)

eval_model(modelfunc)

eval_model_to_fit(modelfunc)

get_axes()

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_img([yfunc])

Return the dependent axis as a 2D array.

get_imgerr()

get_indep([filter])

Return the independent axes of a data set.

get_max_pos([dep])

Return the coordinates of the maximum value.

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_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])

set_dep(val)

Set the dependent variable values.

set_indep(val)

to_contour([yfunc])

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.

Return type

tuple of array_like

mask

Mask array for dependent variable

Returns

mask

Return type

bool or numpy.ndarray

x0

kept for compatibility

x1

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_axes()[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_filter()[source] [edit on github]
get_filter_expr()[source] [edit on github]
get_img(yfunc=None)[source] [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()[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_max_pos(dep=None)[source] [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_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_x0(filter=False)[source] [edit on github]
get_x0label()[source] [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()[source] [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)[source] [edit on github]
set_dep(val) [edit on github]

Set the dependent variable values.

Parameters

val

set_indep(val) [edit on github]
to_contour(yfunc=None)[source] [edit on github]
to_fit(staterrfunc=None) [edit on github]
to_guess() [edit on github]