Creating/reading spectral cubes¶
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
>>> 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 = SpectralCube.read('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.
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
objects from 2D FITS HDU objects with
FITS reading is currently supported:
>>> from astropy.io import fits >>> hdul = fits.open('file.fits') >>> projection = Projection.from_hdu(hdul)
Note that if you pass in a
astropy.io.fits.HDUList 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).
object will have
.wcs, and (if available)
If you are working with two dimensional data that have “dummy” third dimensions,
you may load them using the normal
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
>>> flat_cube = SpectralCube.read('casa_exported_file.fits') >>> image = flat_cube
Reading spectra from file¶
Similar to 2D objects (images), you may want to load 1D slices - spectra - from disk.
You can load
objects from FITS HDU objects with
from_hdu(). As with
FITS reading is supported:
>>> from astropy.io import fits >>> hdul = fits.open('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.
>>> cube = SpectralCube(data=data, wcs=wcs)
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 CTYPE1 = 'RA---CAR' CRVAL2 = 0 CTYPE2 = 'DEC--CAR' 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
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 astropy.io.fits or some other file reader