hardware-counters

# Pinned to what was actually used to produce the results in results/ and docs/.
# Versions are the installed ones on ARCHER2 under cray-python/3.10.10.
#
# numpy and pandas come from cray-python via `python -m venv --system-site-packages`
# and are pinned here for the record rather than for pip to install: installing a
# newer numpy shadows and breaks cray-python's pandas, which is why the venv must
# be created with --system-site-packages. Install with:
#     python3 -m venv --system-site-packages $DISS_ROOT/venv
#     source $DISS_ROOT/venv/bin/activate
#     pip install -r requirements.txt
#
# Python 3.10.10

# core numeric stack (provided by cray-python; pinned for reproducibility)
numpy==1.26.4
pandas==2.3.3

# retrain2.py, ablation2.py, crossplatform.py, weak_analysis.py,
# importance_artefact.py, advanced_nn2.py
scikit-learn==1.7.2
scipy==1.10.0

# importance_artefact.py figures (the current analyses are otherwise text output)
matplotlib==3.10.9

# advanced_nn2.py only: PLR embeddings are hand-written in torch, RealMLP-TD
# comes from pytabkit. Neither is needed for the main results, and both are the
# heaviest dependencies, so they are listed last and can be skipped if the
# neural-network negative result is not being reproduced.
torch==2.13.0
pytabkit==1.7.3