DataSpace1D

class sherpa.data.DataSpace1D(filter, x)[source] [edit on github]

Bases: EvaluationSpace1D

Class for representing 1-D Data Space. Data Spaces are spaces that describe the data domain. As models can be evaluated over data spaces, data spaces can be considered evaluation spaces themselves. However this “is-a” relationship is in the code mostly for convenience and could be removed in future versions.

Attributes:

end: The end (highest value) of the space.
grid: The grid representation of the space.
is_ascending: Is the space ascending?
is_empty: Is the space empty (the x axis has no elements)?
is_integrated: Is the space integrated?
midpoint_grid: The mid-points of the space.
start: The start (lowest value) of the space.

Methods

`for_model`(model)	Models can be defined over arbitrary evaluation spaces.
`get`([filter])	Get a filtered representation of this data set.
`overlaps`(other)	Check if this evaluation space overlaps with another.
`zeros_like`()	Returns zeroes for each element of the space.

Attributes Summary

`end`	The end (highest value) of the space.
`grid`	The grid representation of the space.
`is_ascending`	Is the space ascending?
`is_empty`	Is the space empty (the x axis has no elements)?
`is_integrated`	Is the space integrated?
`midpoint_grid`	The mid-points of the space.
`start`	The start (lowest value) of the space.

Methods Summary

`for_model`(model)	Models can be defined over arbitrary evaluation spaces.
`get`([filter])	Get a filtered representation of this data set.
`overlaps`(other)	Check if this evaluation space overlaps with another.
`zeros_like`()	Returns zeroes for each element of the space.

Attributes Documentation

end: The end (highest value) of the space.

grid

The grid representation of the space.

Returns:

tuple: A tuple representing the x axis. The tuple will contain two arrays if the dataset is integrated, one otherwise.

is_ascending: Is the space ascending?

is_empty: Is the space empty (the x axis has no elements)?

is_integrated: Is the space integrated?

midpoint_grid

The mid-points of the space.

For non-integrated spaces this returns the X axis.

Returns:

array: Return the average point of the bins of integrated axes, for each bin, or the non-integrated x axis array.

start: The start (lowest value) of the space.

Methods Documentation

for_model(model)[source] [edit on github]

Models can be defined over arbitrary evaluation spaces. However, at evaluation time during a fit, the model’s evaluation space shall be done at the user’s request space only and set to 0 every where else.

Parameters:

modelThe model whose evaluation space needs to be joined with the dataset’s data space.

Returns:

DataSpace1D: A data space that joins this data space with the model’s evaluation space. if the model does not have an evaluation space assigned to itself then self is returned.

get(filter=False)[source] [edit on github]