EvaluationSpace2D¶

class sherpa.models.regrid.EvaluationSpace2D(x=None, y=None, xhi=None, yhi=None)[source] [edit on github]¶

Bases: object

Class for 2D Evaluation Spaces. An Evaluation Space is a set of data axes representing the data space over which a model can be evaluated.

A 2D Evaluation Space has two axes, x and y.

Attributes Summary

`end`	The enf 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?
`shape`
`start`	The start of the dataset.

Methods Summary

`overlaps`(other)	Check if this evaluation space overlaps with another Note that this is more stringent for 2D, as the boundaries need to coincide in this case.
`zeros_like`()	Utility function that returns an array of zeros that has the same shape as the dataset.

Attributes Documentation

end¶

The enf of the dataset.

Returns: The end of the x and y axis arrays, respectively
Return type: tuple

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.

The x and y arrays in the grid are one-dimentional representations of the meshgrid obtained from the x and y axis arrays, as in numpy.meshgrid(x, y)[0].ravel()

Returns: A tuple representing the x and y axes. The tuple will contain four arrays if the dataset is integrated, two otherwise.
Return type: tuple

is_ascending¶

Is the dataset ascending?

Returns: True if the axis is ascending, False otherwise, for the x and y axes respectively
Return type: tuple(bool)

is_empty¶

Is the dataset empty?

Returns: True if the x axis or y axis are empty, False otherwise
Return type: bool

is_integrated¶

Is the grid integrated?

Returns: True if the axes are integrated, False otherwise.
Return type: bool

shape¶

start¶

The start of the dataset.

Returns: The start of the x and y axis arrays, respectively
Return type: tuple

Methods Documentation

overlaps(other)[source] [edit on github]¶

Check if this evaluation space overlaps with another Note that this is more stringent for 2D, as the boundaries need to coincide in this case.

Parameters: other (EvaluationSpace2D) –
Returns: True if they overlap, False if not
Return type: bool

zeros_like()[source] [edit on github]¶

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

Returns
Return type: array