Data2DInt

class sherpa.data.Data2DInt(name: str, x0lo: Sequence[float] | ndarray | None, x1lo: Sequence[float] | ndarray | None, x0hi: Sequence[float] | ndarray | None, x1hi: Sequence[float] | ndarray | None, y: Sequence[float] | ndarray | None, shape: Sequence[int] | None = None, staterror: Sequence[float] | ndarray | None = None, syserror: Sequence[float] | ndarray | None = None)

Bases: Data2D

2-D integrated data set

This class represents a 2D data set. It is desigend to work with flattened arrays for coordinates and independent variables, which makes it easy to deal with filters and sparse or irregularly-placed grids.

Of course, the same structure can also be used for regularly-gridded data, it just has to be passed in as a flattened array. However, in this case the more specialiazed DataIMGInt class might be more appropriate.

Parameters:

• name (string) – name of this dataset

• x0lo (array-like) – Lower bounds of the bins in the first dimension of the independent coordinate

• x1lo (array-like) – Lower bound of the bins in the second dimension of the independent coordinate

• x0hi (array-like) – Upper bound of the bins in the first dimension of the independent coordinate

• x1hi (array-like) – Upper bound of the bins in the second dimension of the independent coordinate

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

• shape (tuple) – Shape of the data grid for regularly gridded data (optional). This is used return the data as an image e.g. for display, but it not needed for fitting and modelling. For irregularly gridded data, shape must be None.

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

• syserror (array-like) – the systematic error associated with the data

Examples

Create an irregularly-gridded 2D dataset that has measurements in three areas of different size. The first is in a box of size 10 centered on (-15, -20), the second is a small rectangle centered on (0, 0), and the third another rectangle. The measured data values in these areas are 12, 15 and 23.

>>> x0lo = [-20, -1, 10]
>>> x1lo = [-25, -1, -10]
>>> x0hi = [-10, 1, 15]
>>> x1hi = [-10, 1, -8]
>>> y = [12, 15, 23]
>>> three_rect = Data2DInt("irregular2d_binned", x0lo=x0lo, x1lo=x1lo,
...                        x0hi=x0hi, x1hi=x1hi, y=y)

The regular version of this might represent a binned image, here with square pixels. In this example, we create x1, x0 arrays that represent the pixel center, but need to pass in the pixel edges to make the data object:

>>> import numpy as np
>>> x1, x0 = np.mgrid[20:30, 5:20]
>>> datashape = x0.shape
>>> y = np.sqrt((x0 - 10)**2 + (x1 - 31)**2)
>>> x0 = x0.flatten()
>>> x1 = x1.flatten()
>>> y = y.flatten()
>>> image = Data2DInt("binned_image", x0lo=x0 - 0.5, x1lo=x1 - 0.5,
...                   x0hi=x0 + 0.5, x1hi=x1 + 0.5, y=y, shape=datashape)

Note

Sherpa provides the DataIMGInt class to make it easier to work with regularly-gridded 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.
x0	kept for compatibility
x0hi	Property kept for compatibility
x0lo	Property kept for compatibility
x1	kept for compatibility
x1hi	Property kept for compatibility
x1lo	Property kept for compatibility
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_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 systematic 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)
set_x0label(label)	Set the label for the first independent axis.
set_x1label(label)	Set the label for the second independent axis.
set_ylabel(label)	Set the label for the dependent axis.
to_contour([yfunc])
to_fit([staterrfunc])
to_guess()

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 = 2

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.

x0

kept for compatibility

x0hi

Property kept for compatibility

x0lo

Property kept for compatibility

x1

kept for compatibility

x1hi

Property kept for compatibility

x1lo

Property kept for compatibility

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_axes() → tuple[ndarray, ndarray]

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_filter() → str

get_filter_expr() → str

get_img(yfunc=None)

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()

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_max_pos(dep: ndarray | None = None) → tuple[float, float] | list[tuple[float, float]]

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: 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_x0(filter: bool = False) → ndarray | None

get_x0label() → str

Return label for first dimension in 2-D view of independent axis/axes.

Returns:

label

Return type:

str

See also

get_x1label, set_x0label

get_x1(filter=False) → ndarray | None

get_x1label() → str

Return label for second dimension in 2-D view of independent axis/axes. :returns: label :rtype: str

See also

get_x0label, set_x1label

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

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(x0lo: float | None = None, x0hi: float | None = None, x1lo: float | None = None, x1hi: float | None = None, ignore: bool = False) → None

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_x0label(label: str) → None

Set the label for the first independent axis.

Added in version 4.17.0.

Parameters:

label (str)

See also

get_x0label, set_x1label

set_x1label(label: str) → None

Set the label for the second independent axis.

Added in version 4.17.0.

Parameters:

label (str)

See also

get_x1label, set_x0label

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_contour(yfunc=None)

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

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