Data1D

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

Bases: Data

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.

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__

y

The dependent axis.

Methods Summary

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, 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

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

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__

y

The dependent axis.

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

Methods Documentation

apply_filter(data) [edit on github]
eval_model(modelfunc) [edit on github]

Evaluate the model on the independent axis.

eval_model_to_fit(modelfunc) [edit on github]

Evaluate the model on the independent axis after filtering.

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

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]