| # RDDAC notebooks |
|
|
| Six end-to-end Jupyter notebooks that companion the [online documentation](https://rddac.readthedocs.io). Each notebook is self-contained: it opens with a Walkthrough list and the Assumptions it relies on, then walks through the topic step by step. Reading top to bottom is the intended flow. |
|
|
| RDDAC is the experimental counterpart to the [DDACS](https://ddacs.readthedocs.io) simulation dataset, and the `rddac` package mirrors the `ddacs` API 1:1 — these notebooks mirror the DDACS notebook series the same way. |
|
|
| ## Prerequisites |
|
|
| ```bash |
| # Install the package + the PyTorch extra (needed for 03_pytorch.ipynb). |
| pip install 'rddac[torch]' |
| |
| # Fetch the small sample bundle once. Writes metadata.json, |
| # process_parameters.csv, and one sample zip with 18 experiments |
| # (one per category) into ./data. |
| rddac download --small -y |
| ``` |
|
|
| The full release (`rddac download`, ~87 GB) additionally fetches the matching DDACS simulations into `./data/simulation/`; the notebooks only need the small bundle. Notebooks 05 and 06 write throwaway artifacts into the system temp directory and clean up after themselves, so the data directory stays untouched. |
|
|
| ## Run |
|
|
| From the repository root: |
|
|
| ```bash |
| jupyter lab notebooks/ |
| ``` |
|
|
| Inside each notebook the data directory is hard-coded as `DATA_DIR = '../data'` because the notebooks live in `notebooks/` while the data sits at the repo root (adjust to `'../data'` if you downloaded with the default `rddac download` output directory). |
|
|
| ## Index |
|
|
| | Notebook | What it covers | |
| |----------|----------------| |
| | [`01_getting_started.ipynb`](01_getting_started.ipynb) | Install, download, `rddac.load`, iterate one record of the experiment index, open one HDF5 file with `rddac.open_h5` + `inspect_h5`, render the OP10 laser scan as a height image. | |
| | [`02_views.ipynb`](02_views.ipynb) | The `field-map` lookup table, appending a custom `RecordSet` with `rddac.add_view` (whole fields, index slicing, `process-parameters` CSV columns), inspecting the resolved transforms, streaming with `rddac.streaming.iter_view`. | |
| | [`03_pytorch.ipynb`](03_pytorch.ipynb) | Build an `RDDACDataset`, stream ragged records at `batch_size=1`, batch properly via a fixed-shape custom view + `dataset=`, filter by manifest columns and `sim_ids`, canonical train/val/test splits, shuffle with `set_epoch`, the metadata-column limitation and its workarounds, and a minimal training-loop skeleton. | |
| | [`04_visualization.ipynb`](04_visualization.ipynb) | OP10 vs OP20 height scans with `plot_scan`, the luminescence channel, `scan_to_pointcloud` + `plot_point_cloud`, press signals with `plot_force`, and the sheet-thickness / oil-film traverses with `plot_traverse` (including masking the raw sensor spikes). | |
| | [`05_loose_h5.ipynb`](05_loose_h5.ipynb) | The post-`--extract --remove-zip` workflow: inspect the loose layout, filter `process_parameters.csv` with pandas, open loose `.h5` files directly with `h5py`, and stream the loose layout with `rddac.streaming.iter_view`. | |
| | [`06_streaming.ipynb`](06_streaming.ipynb) | `rddac.streaming.iter_view` in depth (`where=`, `sim_ids=`, `dataset=`), `export_to_numpy` with `transforms` + `record_transform` on a fixed-shape scan-profile view, `load_export` + `by_sim_id`, back-to-back timing of the streaming vs memmap paths, and `export_to_numpy_per_sim` for ragged fields. | |
|
|
| If any of these fail to run end to end after `rddac download --small`, please open an [issue](https://github.com/BaumSebastian/RDDAC/issues) so we can fix the discrepancy with the docs. |
|
|
