Creating/reading spectral cubes


The SpectralCube class is used to represent 3-dimensional datasets (two positional dimensions and one spectral dimension) with a World Coordinate System (WCS) projection that describes the mapping from pixel to world coordinates and vice-versa. The class is imported with:

>>> from spectral_cube import SpectralCube

Reading from a file

In most cases, you are likely to read in an existing spectral cube from a file. The reader is designed to be able to deal with any arbitrary axis order and always return a consistently oriented spectral cube (see Accessing data). To read in a file, use the read() method as follows:

>>> cube ='L1448_13CO.fits')  

This will always read the Stokes I parameter in the file. For information on accessing other Stokes parameters, see Stokes components.


In most cases, the FITS reader should be able to open the file in memory-mapped mode, which means that the data is not immediately read, but is instead read as needed when data is accessed. This allows large files (including larger than memory) to be accessed. However, note that certain FITS files cannot be opened in memory-mapped mode, in particular compressed (e.g. .fits.gz) files. See the Handling large datasets page for more details about dealing with large data sets.

Reading images from file

While spectral-cube is designed for cube analysis, in the course of normal analysis procedures you are likely to need to load up one- and two-dimensional subsets or views of the data.

You can load Projection objects from 2D FITS HDU objects with from_hdu(). Only FITS reading is currently supported:

>>> from import fits  
>>> hdul ='file.fits')
>>> projection = Projection.from_hdu(hdul)

Note that if you pass in a object, by default the data will be loaded from the first HDU. To load a different HDU in the list, the index can be passed to the ext keyword (e.g., ext=1 to load the second HDU in the list).

The resulting Projection object will have .unit, .wcs, and (if available) .beam attributes.

If you are working with two dimensional data that have “dummy” third dimensions, you may load them using the normal read() method. This case is common as such files are normally output from CASA using exportfits with no additional keywords. To get a 2D slice, you simply index the result:

>>> flat_cube ='casa_exported_file.fits')  
>>> image = flat_cube[0]

Reading spectra from file

Similar to 2D objects (images), you may want to load 1D slices - spectra - from disk.

You can load OneDSpectrum objects from FITS HDU objects with from_hdu(). As with projections, only FITS reading is supported:

>>> from import fits  
>>> hdul ='file.fits')
>>> projection = OneDSpectrum.from_hdu(hdul)

The spectrum loader only works for 1D spectra with valid FITS WCS in their headers. For other types of spectra, you may want to use specutils instead.

Direct Initialization

If you are interested in directly creating a SpectralCube instance, you can do so using a 3-d Numpy-like array with a 3-d WCS object:

>>> cube = SpectralCube(data=data, wcs=wcs)  

Here data can be any Numpy-like array, including memory-mapped Numpy arrays (as mentioned in Reading from a file, memory-mapping is a technique that avoids reading the whole file into memory and instead accessing it from the disk as needed).

Hacks for simulated data

If you’re working on synthetic images or simulated data, where a location on the sky is not relevant (but the frequency/wavelength axis still is!), a hack is required to set up the world coordinate system. The header should be set up such that the projection is cartesian, i.e.:

CRVAL1 = 0
CRVAL2 = 0
CDELT1 = 1.0e-4 //degrees
CDELT2 = 1.0e-4 //degrees
CUNIT1 = 'deg'
CUNIT2 = 'deg'

Note that the x/y axes must always have angular units (i.e., degrees). If your data are really in physical units, you should note that in the header in other comments, but spectral-cube doesn’t care about this.

If the frequency axis is irrelevant, spectral-cube is probably not the right tool to use; instead you should use or some other file reader directly.