Script Reference¶

The reproduction scripts are intentionally thin orchestration layers around the documented LION APIs. Their filenames are stable command-line entry points; output manifests record the exact expanded commands.

Pipeline¶

pipeline/PaDIS_run_pipeline.sh

Top-level dispatcher for GCP, Slurm, smoke, and fast-smoke modes.

pipeline/PaDIS_finalise_pipeline.sh

Runs unconditional generation, verification, timing extraction, tables, and figures after reconstruction.

pipeline/PaDIS_run_fast_smoke.sh

Hardware-bounded end-to-end validation using representative models and methods without changing final settings.

Training¶

training/PaDIS_LIDC_256.py

Patch/whole-image 256-square prior training with caching, W&B resumption, timed checkpoints, and validation-intensive selection.

training/PaDIS_LIDC_512.py

Native-512 patch-prior training.

training/PaDIS_LIDC_PnP_denoiser.py

Fixed-noise and noise-conditioned DRUNet training for PnP-ADMM.

Reconstruction¶

reconstruction/PaDIS_LIDC_reconstruction.py

Single-method reconstruction driver shared by matrix and tuning jobs.

reconstruction/PaDIS_run_reconstruction_matrix.py

Declarative 109-job matrix expansion, ordering, tuned-default application, and command generation.

reconstruction/PaDIS_verify_reconstruction_matrix.py

Completeness, settings, finite-metric, bootstrap, and optional quality-gate verification.

reconstruction/PaDIS_reconcile_reconstruction_manifest.py

Semantic migration of runner markers when a manifest is rebuilt.

reconstruction/PaDIS_LIDC_generation.py

Seeded unconditional prior generation.

Tuning and reporting¶

tuning/PaDIS_run_reproduction_tuning.py and tuning/PaDIS_tune_reconstruction_hyperparameters.py

Reproduce the documented validation-only candidate design.

tuning/PaDIS_hparam_defaults.py

Select and export model-safe final hyperparameter defaults.

reporting/PaDIS_make_tables.py

Decode verification metrics, extract GCP/Colab or Slurm timings, and write CSV/LaTeX tables.

reporting/PaDIS_make_paper_figures.py

Render the complete figure set with canonical labels and legacy path compatibility.

Platform launchers¶

platforms/gcp contains restartable spot-instance training and manual multi-GPU reconstruction runners. platforms/slurm contains matching A100 array jobs, submission helpers, preflight checks, and finalisation jobs. Both backends call the same Python training/reconstruction programs and use the same hyperparameter registry; only scheduling, cache staging, and hardware-safe batch limits differ.

Developer diagnostics¶

The scripts under scripts/dev check public-repository equivalence, short- run determinism, login-node cache visibility, machine readiness, training checkpoint resumption, and reconstruction quality. They emit structured JSON where possible so failures can be archived alongside run logs.