NDDataArray¶

class gammapy.utils.nddata.NDDataArray(axes, data=None, meta=None, interp_kwargs=None)[source]¶

Bases: object

ND Data Array Base class

for usage examples see nddata_demo.html

Parameters:

axes : list

List of DataAxis

data : Quantity

Data

meta : dict

Meta info

interp_kwargs : dict

TODO

Attributes Summary

`axes`	Array holding the axes in correct order
`data`	Array holding the n-dimensional data.
`default_interp_kwargs`	Default interpolation kwargs used to initialize the `scipy.interpolate.RegularGridInterpolator`.
`dim`	Dimension (number of axes)

Methods Summary

`axis`(name)	Return axis by name
`evaluate`([method])	Evaluate NDData Array
`evaluate_at_coord`(points[, method])	Evaluate NDData Array on set of points.
`find_node`(**kwargs)	Find next node

Attributes Documentation

axes¶: Array holding the axes in correct order

data¶: Array holding the n-dimensional data.

default_interp_kwargs = {'bounds_error': False, 'fill_value': None}¶: Default interpolation kwargs used to initialize the scipy.interpolate.RegularGridInterpolator. The interpolation behaviour of an individual axis (‘log’, ‘linear’) can be passed to the axis on initialization.

dim¶: Dimension (number of axes)

Methods Documentation

axis(name)[source]¶: Return axis by name

evaluate(method=None, **kwargs)[source]¶

Evaluate NDData Array

This function provides a uniform interface to several interpolators. The evaluation nodes are given as kwargs.

Currently available: RegularGridInterpolator, methods: linear, nearest

Parameters:

method : str {‘linear’, ‘nearest’}, optional

Interpolation method

kwargs : dict

Keys are the axis names, Values the evaluation points

Returns:

array : Quantity

Interpolated values, axis order is the same as for the NDData array

evaluate_at_coord(points, method='linear', **kwargs)[source]¶

Evaluate NDData Array on set of points.

TODO: merge with evaluate? This method was added to support evaluating on arbitrary arrays of coordinates, not just on the outer product like evaluate.

Parameters:

points: dict

contains the coordinates on which you want to interpolate (axis_name: value)

method : str {‘linear’, ‘nearest’}, optional

Interpolation method

kwargs : dict

Keys are the axis names, Values the evaluation points

Returns:

array : Quantity

Interpolated values, axis order is the same as for the NDData array

find_node(**kwargs)[source]¶

Find next node

Parameters:

kwargs : dict

Keys are the axis names, Values the evaluation points