Data1D

class sherpa.data.Data1D(name: str, x: Sequence[float] | ndarray | None, y: Sequence[float] | ndarray | None, staterror: Sequence[float] | ndarray | None = None, syserror: Sequence[float] | ndarray | None = None)

Bases: Data

Dateset for 1D data.

Parameters:

• name (string) – name of this dataset

• x (array-like) – Coordinates of the independent variable

• y (array-like) – The values of the dependent observable. If this is a numpy masked array, the mask will used to initialize a mask.

• staterror (array-like) – the statistical error associated with the data

• syserror (array-like) – the systematic error associated with the 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 systematic 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()	Return the dependent axis.
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)
set_xlabel(label)	Set the label for the independent axis.
set_ylabel(label)	Set the label for the dependent axis.
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 or None

mask

Mask array for dependent variable

Returns:

mask

Return type:

bool or numpy.ndarray

ndim: int | None = 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)

eval_model(modelfunc: Callable[[...], Sequence[float] | ndarray]) → Sequence[float] | ndarray

Evaluate the model on the independent axis.

eval_model_to_fit(modelfunc: Callable[[...], Sequence[float] | ndarray]) → Sequence[float] | ndarray

Evaluate the model on the independent axis after filtering.

get_bounding_mask() → tuple[bool, None]

get_bounding_mask() → tuple[ndarray, tuple[int]]

get_dep(filter: bool = False) → ndarray | None

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: bool = False) → tuple[int, ...]

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)

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: bool = False, model: Callable[[...], Sequence[float] | ndarray] | None = None, use_evaluation_space: bool = False) → ndarray | None

get_filter(format: str = '%.4f', delim: str = ':') → str

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

See also

get_filter_expr, ignore, notice

Examples

>>> import numpy as np
>>> 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() → str

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 = Data1D('example', [1., 2., 3., 5., 6., 7.], [0, .4, .5, .6, .7, .8])
>>> d.notice(1., 6.)
>>> d.ignore(2.5, 4.)
>>> d.get_filter_expr()
'1.0000-2.0000,5.0000-6.0000 x'

Note that the expression lists the valid data points. While we ignore only the range 2.5-4.0, there is no data point between 4. and 5., so the second part of the valid range is 5.0 to 6.0.

get_img(yfunc=None)

Return 1D dependent variable as a 1 x N image.

Parameters:

yfunc

get_imgerr()

get_indep(filter: bool = False) → tuple[ndarray, ...] | tuple[None, ...]

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: bool = False, staterrfunc: Callable[[...], Sequence[float] | ndarray] | None = None) → Sequence[float] | ndarray | 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: bool = False) → ndarray | None

Return the systematic 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: bool = False, model: Callable[[...], Sequence[float] | ndarray] | None = None, use_evaluation_space: bool = False) → ndarray | None

get_xerr(filter: bool = False, yfunc=None) → ndarray | None

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

Parameters:

• filter

• yfunc

Returns:

xerr

Return type:

array or None

get_xlabel() → str

Return label for linear view of independent axis/axes

Returns:

label

Return type:

str

See also

set_xlabel

get_y(filter: bool, yfunc: None = None, use_evaluation_space: bool = False) → ndarray

get_y(filter: bool, yfunc: Callable[[...], Sequence[float] | ndarray], use_evaluation_space: bool = False) → tuple[ndarray, Sequence[float] | ndarray]

Return the dependent axis.

Parameters:

• filter

• yfunc

• use_evaluation_space

Returns:

y – If yfunc is not None and the dependent axis is set then the return value is (y, y2) where y2 is yfunc evaluated on the independent axis.

Return type:

array or (array, array) or None

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) → str

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

Parameters:

yfunc – Unused.

Returns:

label – The label.

Return type:

str

See also

set_ylabel

ignore(*args, **kwargs) → None

notice(xlo: float | None = None, xhi: float | None = None, ignore: bool = False) → None

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.

See also

get_filter, get_filter_expr

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

>>> import numpy as np
>>> 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: Sequence[float] | ndarray | float) → None

Set the dependent variable values.

Parameters:

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

set_indep(val: tuple[Sequence[float] | ndarray, ...] | tuple[None, ...]) → None

set_xlabel(label: str) → None

Set the label for the independent axis.

Added in version 4.17.0.

Parameters:

label (str) – The new label.

See also

set_xlabel

set_ylabel(label: str) → None

Set the label for the dependent axis.

Added in version 4.17.0.

Parameters:

label (str) – The new label.

See also

get_ylabel

to_component_plot(yfunc: Callable[[...], Sequence[float] | ndarray] | None = None, staterrfunc: Callable[[...], Sequence[float] | ndarray] | None = None)

to_fit(staterrfunc: Callable[[...], Sequence[float] | ndarray] | None = None) → tuple[ndarray | None, Sequence[float] | ndarray | None, ndarray | None]

to_guess() → tuple[ndarray | None, ...]

to_plot(yfunc: Callable[[...], Sequence[float] | ndarray] | None = None, staterrfunc: Callable[[...], Sequence[float] | ndarray] | None = None)