IntegratedDataSpace1D¶

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

Bases: sherpa.models.regrid.EvaluationSpace1D

Same as DataSpace1D, but for supporting integrated data sets.

Attributes Summary

`end`	The end of the dataset.
`grid`	Return the grid representation of this dataset.
`is_ascending`	Is the dataset ascending?
`is_empty`	Is the dataset empty?
`is_integrated`	Is the grid integrated?
`midpoint_grid`	Return a single array representing the dataset.
`start`	The start of the dataset.

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 :param other: :type other: EvaluationSpace1D
`zeros_like`()	Utility function that returns an array of zeros that has the same shape as the dataset.

Attributes Documentation

end¶

The end of the dataset.

Returns: The end of the x axis array
Return type: number

grid¶

Return the grid representation of this dataset. The grid is always a tuple, even if the dataset is 1-D and not integrated. This is due to the existing architecture of Sherpa’s model classes and the fact that there is no signature difference among 1-D and 2-D models, as 1-D models can receive 1 or 2 arrays and 2-D models can receive 2 or 4 arrays.

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

is_ascending¶

Is the dataset ascending?

Returns: True if the x axis is ascending, False otherwise.
Return type: bool

is_empty¶

Is the dataset empty?

Returns: True if the x axis is empty, False otherwise
Return type: bool

is_integrated¶

Is the grid integrated?

Returns: True if the x axis is integrated, False otherwise.
Return type: bool

midpoint_grid¶

Return a single array representing the dataset.

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

start¶

The start of the dataset.

Returns: The start of the x axis array
Return type: number

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: model (The model whose evaluation space needs to be joined with the dataset's data space.) –
Returns: 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.
Return type: IntegratedDataSpace1D

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

Get a filtered representation of this data set. If filter is False this object is returned.

Parameters: filter (bool) – whether the data set should be filtered before being returned
Returns
Return type: IntegratedDataSpace1D

overlaps(other) [edit on github]¶

Check if this evaluation space overlaps with another :param other: :type other: EvaluationSpace1D

Returns: True if they overlap, False if not
Return type: bool

zeros_like() [edit on github]¶

Utility function that returns an array of zeros that has the same shape as the dataset.

Returns
Return type: array