Data1DInt

Bases: Data1D

1-D integrated data set.

This class is designed to handle non-consecutive bins.

Parameters:

namestr: name of this dataset
xloarray_like: lower bounds of the bins of the independent coordinates
xhiarray_like: Upper bound of the bins of the independent coordinates
yarray_like: The values of the dependent observable. If this is a numpy masked array, the mask will used to initialize a mask.
staterrorarray_like: the statistical error associated with the data
syserrorarray_like: the systematic error associated with the data

Attributes:

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
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__
xhi: Property kept for compatibility
xlo: Property kept for compatibility
y: The dependent axis.

Methods

`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_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`([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_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_xerr`([filter, model])	Returns an X "error".
`get_xlabel`()	Return label for linear view of independent axis/axes
`get_y`([filter, yfunc, use_evaluation_space])	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.
`notice`([xlo, xhi, ignore])	Notice or ignore the given range.
`set_dep`(val)	Set the dependent variable values.
`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_plot`([yfunc, staterrfunc])	Get arrays required to make plots

apply_filter
get_bounding_mask
get_evaluation_indep
get_imgerr
get_x
ignore
set_indep
to_fit
to_guess

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__
`xhi`	Property kept for compatibility
`xlo`	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_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, model])	Returns an X "error".
`get_xlabel`()	Return label for linear view of independent axis/axes
`get_y`([filter, yfunc, use_evaluation_space])	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])	Get arrays required to make plots

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.

Returns:

tuple of array_like or None

mask

Mask array for dependent variable

Returns:

maskbool 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:

sizeint or None: If the size has not been set then None is returned.

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__

xhi: Property kept for compatibility

xlo: Property kept for compatibility

y

The dependent axis.

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

Methods Documentation

apply_filter(data: Sequence[float] | ndarray | None) → ndarray | None [edit on github]

eval_model(modelfunc: Callable[[...], Sequence[float] | ndarray]) → Sequence[float] | ndarray [edit on github]: Evaluate the model on the independent axis.

eval_model_to_fit(modelfunc: Callable[[...], Sequence[float] | ndarray]) → Sequence[float] | ndarray [edit on github]: Evaluate the model on the independent axis after filtering.

get_bounding_mask() → tuple[ndarray, tuple[int]] | tuple[bool, None] [edit on github]

get_dep(filter: bool = False) → ndarray | None [edit on github]

Return the dependent axis of a data set.

Parameters:

filterbool, optional: Should the filter attached to the data set be applied to the return value or not. The default is False.

Returns:

axis: array: The dependent axis values for the data set. This gives the value of each point in the data set.

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, ...] [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.

Returns:

tuple

get_error(filter=False, staterrfunc=None) [edit on github]

Return the total error on the dependent variable.

Parameters:

filterbool, optional: Should the filter attached to the data set be applied to the return value or not. The default is False.
staterrfuncfunction: 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:

axisarray or None: The error for each data point, formed by adding the statistical and systematic errors in quadrature.

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 [edit on github]

get_filter(format='%.4f', delim=':') → str[source] [edit on github]

Return the data filter as a string.

For each noticed range the filter is reported as starting at the low edge of the first bin and ends at the upper edge of the last bin in the range.

Changed in version 4.14.0: Prior to 4.14.0 the filter used the mid-point of the bin, not its low or high value.

Parameters:

formatstr, optional: The formatting of the numeric values.
delimstr, optional: The string used to mark the low-to-high range.

Returns:

filterstr: The filter, represented as a collection of ranges separated by commas.

See also

get_filter_expr, ignore, notice

Examples

>>> import numpy as np
>>> xlo = np.asarray([1, 2, 3, 5, 6])
>>> xhi = xlo + 1
>>> y = np.ones(5)
>>> d = Data1DInt('example', xlo, xhi, y)
>>> d.get_filter()
'1.0000:7.0000'
>>> d.ignore(2.5, 4.5)
>>> d.get_filter()
'1.0000:2.0000,5.0000:7.0000'

>>> d.get_filter(format='%i', delim='-')
'1-2,5-7'

get_filter_expr() → str [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:

filterstr: The filter, represented as a collection of single values or ranges, separated by commas.

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) [edit on github]

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

Parameters:

yfunc

Returns:

get_imgerr() [edit on github]

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

Return the independent axes of a data set.

Parameters:

filterbool, optional: Should the filter attached to the data set be applied to the return value or not. The default is False.

Returns:

axis: tuple of arrays: The independent axis values for the data set. This gives the coordinates of each point in the data set.

See also

get_dep: Return the dependent axis of a data set.

Return the statistical error on the dependent axis of a data set.

Parameters:

filterbool, optional: Should the filter attached to the data set be applied to the return value or not. The default is False.
staterrfuncfunction: 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:

axisarray or None: 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.

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 [edit on github]

Return the systematic error on the dependent axis of a data set.

Parameters:

filterbool, optional: Should the filter attached to the data set be applied to the return value or not. The default is False.

Returns:

axisarray or None: The systematic error for each data point. A value of None is returned if the data set has no systematic errors.

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[source] [edit on github]

get_xerr(filter: bool = False, model: Callable[[...], Sequence[float] | ndarray] | None = None) → ndarray | None[source] [edit on github]

Returns an X “error”.

The error value for the independent axis is not well defined in Sherpa.

Changed in version 4.16.1: The return value is now half the bin width instead of the full bin width.

Parameters:

filterbool, optional: Should the values be filtered to the current notice range?
modelModel or None, optional

Returns:

xerrndarray: The half-width of each bin. Although the types suggest it can be None, it will always be an array, even if empty.

get_xlabel() → str [edit on github]

Return label for linear view of independent axis/axes

Returns:

labelstr

See also

set_xlabel

get_y(filter=False, yfunc=None, use_evaluation_space=False) [edit on github]

Return the dependent axis.

Parameters:

filter
yfunc
use_evaluation_space

Returns:

yarray or (array, array) or None: 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.

get_yerr(filter=False, staterrfunc=None) [edit on github]

Return errors in dependent axis in N-D view of dependent variable.

Parameters:

filter
staterrfunc

Returns:

get_ylabel(yfunc=None) → str [edit on github]

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

Parameters:

yfunc: Unused.

Returns:

label: str: The label.

See also

set_ylabel

ignore(*args, **kwargs) → None [edit on github]

notice(xlo: float | None = None, xhi: float | None = None, ignore: bool = False) → None[source] [edit on github]

Notice or ignore the given range.

Ranges are inclusive for the lower limit and exclusive for the upper limit.

Changed in version 4.14.0: Filtering Data1DInt datasets has been improved to fix a number of corner cases. As part of this the upper limit has been changed to be exclusive whereas previously it was not obvious what the filter was doing.

Parameters:

xlo, xhinumber or None, optional: The range to change. A value of None means the minimum or maximum permitted value.
ignorebool, 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
>>> edges = np.arange(0.4, 2.6, 0.2)
>>> xlo, xhi = edges[:-1], edges[1:]
>>> y = np.ones_like(xlo)
>>> d = Data1DInt('example', xlo, xhi, y)
>>> print(d.xlo[0], d.xhi[-1])
0.4 2.400000000000001
>>> d.get_filter(format='%.1f')
'0.4:2.4'
>>> d.notice(0.8, 1.9)
>>> d.get_filter(format='%.1f')
'0.8:2.0'

>>> d.notice()
>>> d.get_filter(format='%.1f')
'0.4:2.4'

set_dep(val: Sequence[float] | ndarray | float) → None [edit on github]

Set the dependent variable values.

Parameters:

valsequence or number: If a number then it is used for each element.

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

set_xlabel(label: str) → None [edit on github]

Set the label for the independent axis.

Added in version 4.17.0.

Parameters:

labelstr: The new label.

See also

set_xlabel

set_ylabel(label: str) → None [edit on github]

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], Sequence[float] | ndarray] | None = None) [edit on github]

to_fit(staterrfunc: Callable[[Sequence[float] | ndarray], Sequence[float] | ndarray] | None = None) → tuple[ndarray | None, Sequence[float] | ndarray | None, ndarray | None] [edit on github]

to_guess() → tuple[ndarray | None, ...] [edit on github]

Get arrays required to make plots

This method returns all the ingredients to make a plot of the data. It’s used internally in Sherpa in the plotting functions, but can also be called by users to obtain just the arrays to perform the plotting themselves, for example with a different plotting package than supported by Sherpa.

Parameters:

yfunccallable() or None, optional: If None the binned and filtered data values are returned. If yfunc is a function, this function is called with the bin positions and the return values of that function are also returned. The purpose of this is to obtain model values evaluated on the data grid.
staterrfunccallable() or None, optional: 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:

xndarray: Bin mid-points after applying the filter
yndarray or tuple of two arrays: Values in each bin for data. If yfunc is given, then this is a tuple of two arrays, where the first array holds the data and the second array the evaluated yfunc.
yerrndarray: Uncertainties for the data values
xerrndarray: Half-width of each bin
xlabelstr: Label for the x axis
ylabelstr: Label for the y axis