Metadata-Version: 2.4
Name: thermax
Version: 0.1.0
Summary: Thermax: trajectory and sequence data for multistrand simulations
Requires-Python: ==3.13.*
Description-Content-Type: text/markdown
Requires-Dist: beartype
Requires-Dist: chex
Requires-Dist: matplotlib>=3.5
Requires-Dist: numpy
Requires-Dist: nvidia-ml-py
Requires-Dist: omegaconf>=2.0
Requires-Dist: optax
Requires-Dist: pyarrow
Requires-Dist: scikit-learn
Requires-Dist: scipy
Requires-Dist: hydra-core==1.3.2
Requires-Dist: hydra-colorlog==1.2.0
Requires-Dist: hydra-submitit-launcher>=1.2.0
Requires-Dist: rootutils
Requires-Dist: tqdm
Requires-Dist: openpyxl>=3.1.5
Requires-Dist: mdtraj
Provides-Extra: cuda12
Requires-Dist: jax[cuda12]>=0.9.0; extra == "cuda12"
Requires-Dist: openmm[cuda12]>=8.5.0b0; platform_machine != "aarch64" and extra == "cuda12"
Provides-Extra: cuda13
Requires-Dist: jax[cuda13]>=0.9.0; extra == "cuda13"
Requires-Dist: openmm[cuda13]>=8.5.0b0; platform_machine != "aarch64" and extra == "cuda13"
Provides-Extra: dev
Requires-Dist: pytest; extra == "dev"
Provides-Extra: qc
Requires-Dist: deeptime; extra == "qc"
Requires-Dist: joblib; extra == "qc"
Requires-Dist: hdbscan; extra == "qc"
Requires-Dist: rdkit; extra == "qc"
Provides-Extra: training
Requires-Dist: wandb; extra == "training"
Requires-Dist: datasets; extra == "training"
Requires-Dist: huggingface_hub; extra == "training"

# We run a forward simulation with multistrand
# Sample one of three tasks: loop (k=1), helix (k=2), toehold (k=3)
# Sample lengths for the k task uniformly from [2, n_max = 10]
# Sample a mismatch count m uniformly from [0, n_of_shortest_strand]
# Sample a random ssDNA sequence for the shortest of the strands
# Sample ssDNA for the remaining strands, constrained by the mismatch count m
# Run Gillespie simulation with Kawasaki rates and the updated parameters from
# Store the simulation results for archival purposes as Apache Arrow Column format
# Extract binarised and memory-mapped stream of tokens for training
# Train a L2R and R2L and sentinel-based infilling AR MoE model

