RDDAC notebooks
Six end-to-end Jupyter notebooks that companion the online documentation. 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 simulation dataset, and the rddac package mirrors the ddacs API 1:1 — these notebooks mirror the DDACS notebook series the same way.
Prerequisites
# 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:
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 |
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 |
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 |
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 |
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 |
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 |
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 so we can fix the discrepancy with the docs.