DataSpace1D

class sherpa.data.DataSpace1D(filter, x)

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)

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)

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)

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()

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

Returns: Return type:array