3.4. Data Containers

A third, and very important part of the AstroData core package is the data container. We have chosen to extend Astropy’s NDData with our own requirements, particularly lazy-loading of data using by opening the FITS files in read-only, memory-mapping mode, and exploiting the windowing capability of astropy.io.fits (using section) to reduce our memory requirements, which becomes important when reducing data (e.g., stacking).

We’ll describe here how we depart from NDData, and how do we integrate the data containers with the rest of the package. Please refer to NDData for the full interface.

Our main data container is astrodata.NDAstroData. Fundamentally, it is a derivative of astropy.nddata.NDData, plus a number of mixins to add functionality:

class NDAstroData(NDArithmeticMixin, NDSlicingMixin, NDData):

This allows us out of the box to have proper arithmetic with error propagation, and slicing the data with the array syntax.

Our first customization is NDAstroData.__init__. It relies mostly on the upstream initialization, but customizes it because our class is initialized with lazy-loaded data wrapped around a custom class (astrodata.fits.FitsLazyLoadable) that mimics a astropy.io.fits HDU instance just enough to play along with NDData’s initialization code.

FitsLazyLoadable is an integral part of our memory-mapping scheme, and among other things it will scale data on the fly, as memory-mapped FITS data can only be read unscaled. Our NDAstroData redefines the properties data, uncertainty, and mask, in two ways:

  • To deal with the fact that our class is storing FitsLazyLoadable instances, not arrays, as NDData would expect. This is to keep data out of memory as long as possible.

  • To replace lazy-loaded data with a real in-memory array, under certain conditions (e.g., if the data is modified, as we won’t apply the changes to the original file!)

Our obsession with lazy-loading and discarding data is directed to reduce memory fragmentation as much as possible. This is a real problem that can hit applications dealing with large arrays, particularly when using Python. Given the choice to optimize for speed or for memory consumption, we’ve chosen the latter, which is the more pressing issue.

Another addition of as is the variance property as a convenience for the user. Astropy, so far, only provides a standard deviation class for storing uncertainties and the code to propagate errors stored this way already exists. However, our coding elsewhere is greatly simplified if we are able to access and set the variance directly.

Lastly, we’ve added another new property, window, that can be used to explicitly exploit the astropy.io.fits’s section property, to (again) avoid loading unneeded data to memory. This property returns an instance of NDWindowing which, when sliced, in turn produces an instance of NDWindowingAstroData, itself a proxy of NDAstroData. This scheme may seem complex, but it was deemed the easiest and cleanest way to achieve the result that we were looking for.


We expect to make changes to NDAstroData in future releases. In particular, we plan to make use of the wcs and unit attributes provided by the NDData class and increase the use of memory-mapping by default. These changes mostly represent increased functionality and we anticipate a high (and possibly full) degree of backward compatibility.