MFC
High-fidelity multiphase flow simulation
Loading...
Searching...
No Matches
Testing

To run MFC's test suite, run

./mfc.sh test -j <thread count>

It will generate and run test cases, comparing their output to previous runs from versions of MFC considered accurate. golden files, stored in the tests/ directory contain this data, aggregating .dat files generated when running MFC. A test is considered passing when our error tolerances are met in order to maintain a high level of stability and accuracy. Run ./mfc.sh test -h for a full list of accepted arguments.

Most notably, you can consult the full list of tests by running

./mfc.sh test -l

To restrict to a given range, use the --from (-f) and --to (-t) options. To run a (non-contiguous) subset of tests, use the --only (-o) option instead. To specify a computer, pass the -c flag to ./mfc.sh run like so:

./mfc.sh test -j <thread count> -- -c <computer name>

where <computer name> could be phoenix or any of the others in the templates). You can create new templates with the appropriate run commands or omit this option. The use of -- in the above command passes options to the ./mfc.sh run command underlying the ./mfc.sh test.

Creating Tests

To (re)generate golden files, append the --generate option:

./mfc.sh test --generate -j 8

It is recommended that a range be specified when generating golden files for new test cases, as described in the previous section, in an effort not to regenerate the golden files of existing test cases.

Note: If you output new variables and want to update the golden files to include these without modifying the original data, use the --add-new-variables option instead.

Adding a new test case can be done by modifying cases.py. The function list_cases is responsible for generating the list of test cases. Loops and conditionals are used to vary parameters, whose defaults can be found in the BASE_CFG case object within case.py. The function operates on two variables:

  • stack: A stack that holds the variations to the default case parameters. By pushing and popping the stack inside loops and conditionals, it is easier to nest test case descriptions, as it holds the variations that are common to all future test cases within the same indentation level (in most scenarios).
  • cases: A list that holds fully-formed Case objects, that will be returned at the end of the function.

Internally a test case is described as:

@dataclasses.dataclass(init=False)
class Case:
trace: str
params: dict
ppn: int

where:

  • The trace is a string that contains a human-readable description of what parameters were varied, or more generally what the case is meant to test. Each trace must be distinct.
  • params is the fully resolved case dictionary, as would appear in a Python case input file.
  • ppn is the number of processes per node to use when running the case.

To illustrate, consider the following excerpt from list_cases:

for weno_order in [3, 5]:
stack.push(f"weno_order={weno_order}", {'weno_order': weno_order})
for mapped_weno, mp_weno in [('F', 'F'), ('T', 'F'), ('F', 'T')]:
stack.push(f"(mapped_weno={mapped_weno},mp_weno={mp_weno})", {
'mapped_weno': mapped_weno,
'mp_weno': mp_weno
})
if not (mp_weno == 'T' and weno_order != 5):
cases.append(define_case_d(stack, '', {}))
stack.pop()
stack.pop()

When pushing to the stack or creating a new case with the define_case_d function, you must specify:

  • stack: The current stack.
  • trace: A human-readable string describing what you are currently varying.
  • variations: A Python dictionary with case parameter variations.
  • (Optional) ppn: The number of processes per node to use (default is 1).

If a trace is empty (that is, the empty string ""), it will not appear in the final trace, but any case parameter variations associated with it will still be applied.

Finally, the case is appended to the cases list, which will be returned by the list_cases function.

Testing Post Process

To test the post-processing code, append the -a or --test-all option:

./mfc.sh test -a -j 8

This argument will re-run the test stack with ‘parallel_io='T’, which generates silo_hdf5 files. It will also turn most write parameters (*_wrt) on. Then, it searches through the silo files usingh5dumpto ensure that there are noNaNs orInfinitys. Although adding this option does not guarantee that accurate.silo` files are generated, it does ensure that the post-process code does not fail or produce malformed data.