CryoPARES Documentation ======================= **CryoPARES** is a software package for assigning poses to 2D cryo-electron microscopy (cryo-EM) particle images using supervised deep learning. The key idea is to train a neural network on a high-quality reference reconstruction, and then reuse this trained model to rapidly estimate particle poses in other, similar datasets. .. toctree:: :maxdepth: 2 :caption: User Guide training_guide configuration_guide cli scripts_guide troubleshooting .. toctree:: :maxdepth: 2 :caption: API Reference api/index .. toctree:: :maxdepth: 1 :caption: Additional Resources GitHub Repository Paper Quick Start ----------- Installation ~~~~~~~~~~~~ .. code-block:: bash conda create -n cryopares python=3.12 conda activate cryopares pip install git+https://github.com/rsanchezgarc/cryoPARES.git Training ~~~~~~~~ .. code-block:: bash ulimit -n 65536 # Increase file descriptor limit cryopares_train \ --symmetry C1 \ --particles_star_fname /path/to/aligned_particles.star \ --train_save_dir /path/to/output \ --n_epochs 20 Inference ~~~~~~~~~ .. code-block:: bash cryopares_infer \ --particles_star_fname /path/to/new_particles.star \ --checkpoint_dir /path/to/output/version_0 \ --results_dir /path/to/results Indices and tables ================== * :ref:`genindex` * :ref:`modindex` * :ref:`search`