IntegratedDataSpace1D

class sherpa.data.IntegratedDataSpace1D(filter, xlo, xhi)[source] [edit on github]

Bases: EvaluationSpace1D

Same as DataSpace1D, but for supporting integrated data sets.

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 and the data space will be joined together and the model will be evaluated over the joined domain. This makes sure that when the models are rebinned back to the data space the evaluation does not have to be extrapolated from the model’s evaluation space alone.

Parameters:

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

Returns:

IntegratedDataSpace1D: 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]