An Introduction to ndcube¶
N-dimensional Data in Astronomy¶
N-dimensional data sets are common in all areas of science and beyond. For example, a series of images taken sequentially with a CCD camera can be stored as a single 3-D array with two spatial axes and one temporal axis. The value in each array-element can represent the reading in a pixel at a given time. In astronomy, the relationship between the array element and the location and time in the Universe being observed is often represented by the World Coordinate System (WCS) framework. WCS’s ability to handle many different physical types (e.g. spatial, temporal, spectral, etc.) and projections (e.g. RA and Dec., helioprojective latitude and longitude, etc.) make it a succinct, standardized and powerful way to relate array axes to the physical properties they represent.
Due of the prevalence of N-D data and the importance of coordinate transformations, there exist mature Python packages that handle them. For example, arrays can be handled by numpy and dask and coordinates by astropy’s WCS and coordinates modules. If you want to treat these components separately, then these existing tools work well. However, they are not suited to treating data and coordinates in a combined way.
What is ndcube?¶
ndcube is a free, open-source, community-developed Python package whose purpose is to link astronomical data and coordinates in single objects. These objects can be manipulated via array-like slicing operations which modify both the data and coordinate systems simultaneously. They also allow coordinate transformations to be performed with reference to the size of the data array and produce visualizations whose axes are automatically described by the coordinates. This coupling of data and coordinates allows users to analyze their data more quickly and accurately, thus helping to boost their scientific output.
In this guide we will introduce you to ndcube’s primary data classes,
NDCollection (ND Objects).
We will then discuss the functionalities they provide including Coordinate Transformations and Visualizing ND Objects.
There are also helpful sections on Installing ndcube, Getting Help and Contributing to ndcube.
Important Concepts: Array, Pixel, and World Axes¶
Throughout this guide we will refer to array axes, pixel axes and world axes, a nomenclature drawn from astropy. To help the reader we will briefly clarify their meaning here.
A WCS object describes any number of physical types.
These are referred to as world axes and the order in which they are stored in the WCS object is referred to as world order (or world axis order).
These physical types are mapped through the WCS to one or more “pixel” axes.
Although in the simplest case, one world axis would uniquely map to one pixel axis, it is possible for multiple world axes can be associated with multiple pixel axes and vice versa.
This is especially common when dealing with projections of the sky onto 2-D image planes.
Take the example of an image of the Sun taken from Earth.
The world axis of helioprojective longitude is dependent on helioprojective latitude, i.e. there is not one pixel axis for longitude and another for latitude.
Both world axes are associated with both pixel axes.
In a WCS object, the mapping between pixel and world axes is described by the
Due to unfortunate convention, WCS orders its pixel axes in the inverse order to numpy. Therefore we use the term “array axes” to refer to pixel axes which have been reversed to reflect the axis order of the numpy data array. Take for example a numpy array with three dimensions. Since the array axes are simply the reverse of the pixel axes, the first axis of the array corresponds to the 3rd pixel axis. And the 2nd array axis corresponds to the 2nd pixel axis. If the array had four axes, the first array axis would correspond to the fourth pixel axis and the second array axis would correspond to the third pixel axis and so on.
In ndcube, inputs and outputs are given in either array axis order or world axis order, depending on the types of information.
Throughout these docs and in the docstrings we will endeavor to highlight which order is relevant.
However a good rule of thumb is that if you are using a sequence of coordinate objects to describe locations in the data cube – for example in the input of
ndcube.NDCube.crop or the output of
ndcube.NDCube.axis_world_coords – they should be in world axis order.
In almost all other cases, array axis order is used.
It’s worth addressing the role ndcube plays within the scientific Python ecosystem and why it exists separately from its most similar package, xarray. The fundamental reason to opt for ndcube is to harness the astronomy-specific World Coordinate System (WCS). The data model of xarray centers on the requirements and conventions of the geosciences. Although very similar to those of astronomy in conception, they are sufficiently different in construction to cause significant friction. Moreover, utilizing the astropy WCS infrastructure enables us to directly read the most common file format in astronomy, FITS. Although the FITS WCS data model is also commonly used outside of FITS files. This data model would require translation of the source data to fit inside an xarray object.
That being said, xarray has a rich feature set and there is nothing beyond a lack of developer time hindering the astronomy and xarray communities from collaborating to provide a common set of tools which would suit everyone’s purposes. See this issue.
Why ndcube 2.0?¶
ndcube 2.0 is a major API-breaking rewrite of ndcube. It has been written to take advantage of many new features not available when ndcube 1.0 was written. Some of these have been made possible by the moving some of the functionalities of ndcube into astropy. Others are due to the fruition of long running projects such as the implementation of astropy’s WCS API (APE 14) and the maturing of the gWCS package. These developments encouraged the reassessment of the state of ndcube, leading to the development of ndcube 2.0.
The main feature of ndcube 2.0 is the removal and moving of almost all specific WCS handling code to astropy and the use of the astropy’s generalised WCS API.
This has the consequence of bringing high-level coordinate objects into the realm of ndcube.
This includes astropy’s
SkyCoord object which combines coordinate and reference frame information to give users a full description of their coordinates.
However users can continue to deal with raw coordinate values without reference frame information if they so choose.
ndcube’s visualization code has been rewritten to exclusively use
WCSAxes, tremendously simplifying it’s implementation, at the expense of some flexibility.
However, it also allows for a more complete and accurate representation of coordinates along plot axes and animations.
extra_coords has been completely re-written to serve as an extra WCS, which can be readily constructed from lookup tables.
This enables users to easily include the extra coordinates when visualizing the data.
Finally, a new
GlobalCoords class can hold coordinates that do not refer to any axis.
This is particularly useful when the dimensionality of an
NDCube is reduced by slicing.
The value of a coordinate at the location along the dropped axis at which the
NDCube was sliced can be retained.
Upgrading from ndcube 1.x to 2.x¶
As discussed above the
ndcube 2.0 package aims to be a framework for data with an APE 14 compliant World Coordinate System object.
This large refactor and associated API changes means that if you are familiar with
ndcube 1.x there is a lot which will be different.
This section aims to cover the main points, if you notice anything we have forgotten please open an issue.
Coordinates and WCS¶
The type of the
ndcube 1.x the
NDCube.wcs property was always an instance of
This is no longer true even if you pass such an instance to NDCube.
The reason for this is that operations like slicing may change the type of the
.wcs object to represent different views into the original WCS.
.wcs property will always be an object which is an instance of
You should therefore adjust any code which needs to work with any
NDCube object to only use this (and associated
Future work in astropy or ndcube may increase the chances the type of the original WCS will be preserved, but it is highly unlikely that it will ever be possible to always carry the type of the WCS through all slicing operations.
As a corollary to the above, there is no longer a
.missing_axes property on
NDCube as all the slicing operations now happen inside the
.wcs property inside astropy.
Dropped dimensions moved from
As another consequence of the WCS slicing, when dimensions are dropped those world coordinates are no longer accessible through the
To overcome this, and also to provide a structured place for future, or custom, cube-wide scalar coordinates the
.global_coords property was added.
.global_coords will automatically be populated by any dimensions dropped via slicing the
NDCube, via functionality in
Scalar coordinates can also be added to the
.global_coords object explicitly using the
The Saga of
As part of the transition to using APE 14 compliant WCS objects everywhere we have transitioned
.extra_coords to use
gWCS underneath to provide a APE-14 compliant API to the extra coords lookup tables.
Due to the extra functionality and therefore complexity of the
ExtraCoords object (over the previous
dict implementation) the
extra_coords= keyword argument has been removed from the
Extra coordinates can be added individually using the
add method on the
There are now three different WCS-like properties on
.wcs: The WCS object passed in through the constructor or a wrapper around it.
.extra_coords: A WCS-like object which can be used in place of a WCS object in
.combined_wcs: A WCS wrapper which combines the coordinates described by
.extra_coordsinto a single APE-14 compliant WCS object.
Various methods on
NDCube now accept a
wcs= keyword argument, which allows the use of any of these attributes, the default is still
In the future the default may change to
.combined_wcs as that is more the desired behaviour, i.e. the integration of extra coords into methods like
However, there are various technical reasons why this hasn’t been done in the initial release, such as a significant performance penalty of using
crop_by_coords is now
NDCube.crop_by_coords method has been replaced with two new methods
The new methods accept high-level (e.g.
SkyCoord) objects and quantities respectively.
The new methods also use a different algorithm to
crop_by_coords, which has been selected to work with data of all dimensionality and coordinates.
Both the crop methods take N points as positional arguments where each point must have an entry for each world axis.
The cube will then be cropped to the smallest pixel box containing the input points.
Note that in this algorithm the input points are not interpreted as corners of a bounding box, although is some cases the result will be equivalent to that interpretation.
For more information see Cropping with Real World Coordinates.
As part of the transition to relying on APE 14 compliant WCS objects
pixel_to_world are now redundant as the APE 14 WCS API specifies that the WCS object must provide these methods with equivalent functionality.
Therefore you should now use
NDCube.wcs.world_to_pixel; in addition to this you can also make use of the
array_index variants of these methods (see
Removed Arithmetic Operations¶
During the rewrite the decision was taken to for
NDCube not inherit the
The primary reason for this is that the operations supported by this mixin are not coordinate aware.
It is intended that in the future,
NDCube will support operations such as add and subtract by first checking for aligned pixel grids.
The final major change in 2.0 is a rework of the built in visualization tooling in ndcube. While the visualization code in 1.x was very powerful, that power came with a very high level of complexity, which made maintaining that functionality difficult. When we were migrating ndcube to use the new WCS APIs we needed to modify large amounts of the existing visualization code, which just became untenable with the amount of time available. We therefore took the decision to significantly reduce the scope of the built in visualization functionality.
The visualization code included in 2.0 only uses
wcsaxes, which means that all plots are made in pixel space with ticks and gridlines overplotted to show world coordinates.
This has dramatically simplified the code in ndcube, as almost all the complexity is now delegated to
In addition to this we have made it easier for users and developers to replace, customize, or disable the built in functionality by use of the
Learn more in Customizing the Visualization.