valska.external_tools.bayeseor.submit

Submission helpers for BayesEoR prepared run directories.

Functions

`build_submit_plan`(run_dir)	Create a submission plan from an existing prepared run_dir.
`load_jobs`(run_dir)
`load_manifest`(run_dir)	Load and parse manifest.json from a prepared run directory.
`submit_bayeseor_run`(run_dir, *[, stage, ...])	Submit BayesEoR prepared scripts for a run_dir.
`write_jobs`(run_dir, jobs)

Classes

SubmitPlan(run_dir, manifest_path, ...)

Resolved paths needed to submit a prepared BayesEoR run.

Exceptions

`InvalidArgumentError`	Raised when CLI arguments are invalid for submission.
`MissingDependencyError`	Raised when required inputs or artefacts are missing.
`SbatchError`	Raised when sbatch fails or returns unparseable output.
`SubmissionError`	Raised when submission cannot proceed safely or sbatch fails.

exception valska.external_tools.bayeseor.submit.InvalidArgumentError: Raised when CLI arguments are invalid for submission.

exception valska.external_tools.bayeseor.submit.MissingDependencyError: Raised when required inputs or artefacts are missing.

exception valska.external_tools.bayeseor.submit.SbatchError: Raised when sbatch fails or returns unparseable output.

exception valska.external_tools.bayeseor.submit.SubmissionError: Raised when submission cannot proceed safely or sbatch fails.

class valska.external_tools.bayeseor.submit.SubmitPlan(run_dir: Path, manifest_path: Path, cpu_script: Path, gpu_signal_fit_script: Path | None, gpu_no_signal_script: Path | None, cpu_precompute_driver_hypothesis: str | None)

Resolved paths needed to submit a prepared BayesEoR run.

cpu_precompute_driver_hypothesis: str | None

cpu_script: Path

gpu_no_signal_script: Path | None

gpu_signal_fit_script: Path | None

manifest_path: Path

run_dir: Path

valska.external_tools.bayeseor.submit._ensure_script_exists(path: Path, label: str) → None

valska.external_tools.bayeseor.submit._extract_cpu_jobid_from_existing(existing: dict[str, Any] | None) → str | None: Try to locate a CPU job id from an existing jobs.json structure.

valska.external_tools.bayeseor.submit._find_completed_cpu_precompute_matrix_dir(run_dir: Path) → Path | None

Return a matrix-stack directory when CPU precompute outputs appear complete.

BayesEoR GPU runs require the CPU-built matrix stack, most importantly the Ninv and T_Ninv_T artefacts. For the standard ValSKA-generated configs these are written beneath run_dir/matrices/....

We treat CPU precompute as reusable only when both markers are present in the same matrix directory.

valska.external_tools.bayeseor.submit._jobs_path(run_dir: Path) → Path

valska.external_tools.bayeseor.submit._merge_jobs_record(existing: dict[str, Any] | None, new_result: dict[str, Any]) → dict[str, Any]

Merge a new submission result into an existing jobs.json record.

Keeps stable top-level metadata (run_dir, manifest)
Updates “jobs” by stage (cpu_precompute, gpu)
Appends to “history” so we don’t lose what happened

valska.external_tools.bayeseor.submit._run_sbatch(script: Path, *, dependency_afterok: str | None = None, sbatch_exe: str = 'sbatch', cwd: Path | None = None, dry_run: bool = False) → tuple[str | None, str]: Returns (job_id, command_str). If dry_run=True, job_id is None.

valska.external_tools.bayeseor.submit._safe_int_jobid(x: Any) → str | None

valska.external_tools.bayeseor.submit._utc_now_iso() → str

valska.external_tools.bayeseor.submit.build_submit_plan(run_dir: Path) → SubmitPlan

Create a submission plan from an existing prepared run_dir.

This reads manifest.json and uses manifest[‘artefacts’] paths rather than guessing filenames.

valska.external_tools.bayeseor.submit.load_jobs(run_dir: Path) → dict[str, Any] | None

valska.external_tools.bayeseor.submit.load_manifest(run_dir: Path) → dict[str, Any]

Load and parse manifest.json from a prepared run directory.

Parameters

run_dir: Prepared run directory containing manifest.json.

Returns

dict: Parsed manifest content.

valska.external_tools.bayeseor.submit.submit_bayeseor_run(run_dir: Path, *, stage: Literal['cpu', 'gpu', 'all'] = 'all', hypothesis: Literal['signal_fit', 'no_signal', 'both'] = 'both', depend_afterok: str | None = None, sbatch_exe: str = 'sbatch', dry_run: bool = False, force: bool = False, record: Literal['jobs.json', 'manifest'] = 'jobs.json') → dict[str, Any]

Submit BayesEoR prepared scripts for a run_dir.

Parameters

run_dir: Prepared run directory.
stage: Which stage(s) to submit: “cpu”, “gpu”, or “all”.
hypothesis: Which GPU hypothesis to run: “signal_fit”, “no_signal”, or “both”.
depend_afterok: Optional sbatch job id to depend on for GPU submissions.
sbatch_exe: sbatch executable to invoke.
dry_run: If True, do not submit jobs; return the commands that would run.
force: If True, allow resubmission even if jobs.json indicates prior submissions.
record: Where to record submission metadata. Currently only “jobs.json” is supported.

Returns

dict: A jobs.json-style record of the submission (merged if not dry_run).

Notes on jobs.json recording

jobs.json is treated as a durable record that may be updated across invocations:

stage=cpu creates/updates jobs.cpu_precompute
stage=gpu appends/updates jobs.gpu
stage=all updates both

We also keep a submission ‘history’ list so previous job ids are not lost.

valska.external_tools.bayeseor.submit.write_jobs(run_dir: Path, jobs: dict[str, Any]) → Path