hermess.tests.baselines.inverter_baseline

Shared configuration + runner for the inverter byte-identical baselines.

Single source of truth for the IEEE39_bus_inverter baseline runs, imported by both create_baseline_inverter.py (which pickles the references) and the test_recur_sim_inverter.py gates (which compare against them). Keeping the config here prevents drift between baseline-creation and the tests. Two cases:

sim_ld – SIM, line_dyn=True (ODE; cvodes): 2 GridForming + 3 GridFollowing + 5 SGs through the t=4.0 bus fault. Run at the production output step ts=1e-4 but stored decimated (every SIM_LD_STRIDE-th sample) so the pickle stays ~1 MB; a byte-identical refactor matches at every sample.
sim_alg – SIM, line_dyn=False (algebraic network / DAE; idas).

Baselines store only the x_full numpy arrays, not the whole DaeSim objects: under pytest’s pythonpath="hermess" + --import-mode=importlib, system.DaeSim is reachable under two module paths, so whole-object pickling raises “it’s not the same object”. The arrays are all the gates compare.

Attributes

`FIXTURE_ROOT`
`_BASE`
`SIM_LD_STRIDE`
`T_END`
`BASELINES`
`_COMMON`
`_INT_OPTS`

Functions

`make_inverter_baseline_config`(case)	Return the config for one baseline `case` (`sim_ld` / `sim_alg`).
`run_inverter_case`(→ numpy.ndarray)	Run one baseline `case` and return the simulated trajectory

Module Contents

hermess.tests.baselines.inverter_baseline.FIXTURE_ROOT

hermess.tests.baselines.inverter_baseline._BASE

hermess.tests.baselines.inverter_baseline.SIM_LD_STRIDE = 50

hermess.tests.baselines.inverter_baseline.T_END = 5.0

hermess.tests.baselines.inverter_baseline.BASELINES

hermess.tests.baselines.inverter_baseline._COMMON

hermess.tests.baselines.inverter_baseline._INT_OPTS

hermess.tests.baselines.inverter_baseline.make_inverter_baseline_config(case: str)[source]

Return the config for one baseline case (sim_ld / sim_alg).

Parameters:: case (str)

hermess.tests.baselines.inverter_baseline.run_inverter_case(case: str) → numpy.ndarray[source]

Run one baseline case and return the simulated trajectory (decimated for sim_ld).

Parameters:: case (str)
Return type:: numpy.ndarray