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
section) to reduce our memory requirements, which
becomes important when reducing data (e.g., stacking).
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
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
mask, in two ways:
To deal with the fact that our class is storing
FitsLazyLoadableinstances, not arrays, as
NDDatawould 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
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
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.