Testing mcasopt

In order to ensure that this package functions correctly, we will build a suite of test cases.

The first step will be to extract the job-spawning logic into a single, self-contained class, and define a simple interface for the function that is being optimised.

We can then implement this interface for a range of functions and types of computations, in order from simplest to most complex:

  1. A simple mathematical function implemented in Python. For example:

    lambda x, y: abs((x - 2)**3) + (y - 4)**2

  2. A simple mathematical function (e.g., as above) but implemented as a Python script that will be run in a separate process.

  3. As per 2, but where the script delays the computation for, say, 2 seconds.

  4. As per 2, but where we terminate the optimiser after every \(N\) function evaluations, to simulate a long-running slurm job being terminated, and to test that the cache works correctly.

  5. A simple mathematical function, implemented as a Python script, that reads parameter values from an MCAS input file and writes an MCAS output file, where for \(N\) parameters it introduces an error in each of the first \(N\) compound states. This will ensure that the error metric is being evaluated correctly.