DataSpace1D¶

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

Bases: sherpa.models.regrid.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 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 shall be done at the user’s request space only and set to 0 every where else.

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: DataSpace1D

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: DataSpace1D

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