Selecting a random element from an array of length n is easy: simply
generate a random integer i, with 0 <= i < n, and use the array
element at that index position. But what if the length of the array is
not known beforehand, or is, in fact, infinite (i.e. a stream)? And what
if we don’t just want a single element, but a set of m samples,
without replacement?
When writing programs that do computations, my overwhelming preference is to simply write results to standard output, and to use shell redirection to capture the output in a file. In this way, I am leveraging the shell’s full functionality, in particular filename completion, in the most convenient way possible. For the file format itself, I prefer simple, column-oriented, delimiter-separated flat files. They are completely portable, and can be read and understood by most tools. (They also play well with the usual Unix toolset.)
But this simple approach breaks down, once a program has to write more than one output stream: for example in the case of a simulation run, I may want to capture periodic snapshots of the simulation itself, but also track various calculated metrics as well. These two streams will not fit comfortable into a single flat file. One option is to use a structured file format, the other option is to write to multiple files simultaneously.
I have started to get interested in Hidden Markov Models (HMM). As a warm-up, I prepared a pure Python implementation of the relevant algorithms (github).
I occasionally see references to the HDF5 file format, but I have never encountered it in the wild. But a recent project generated multiple data sets simultaneously, in addition to metadata. Was there a better way than maintaining a collection of flat files? This prompted me to look at HDF5.
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