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        "author": "Roy Gonzalez-Aleman",
        "author_email": "roy.gonzalez.aleman@gmail.com",
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            "License :: OSI Approved :: GNU General Public License v3 (GPLv3)",
            "Programming Language :: Python :: 3"
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        "description": "# BitClust: Fast and memory efficient clustering of long Molecular Dynamics\n\n\n# Home Page\n\n\nBitClust\u00b4s latest documentation is available [here](https://bitclust.readthedocs.io/en/latest/) \n\n\n# Description\n\n\n**BitClust** is a Python command line interface (CLI) conceived for fast\nclustering of relatively long Molecular Dynamics trajectories following\nDaura's algorithm [1]. Retrieved clusters are roughly equivalent to those\nreported by **VMD's** internal command **measure cluster** but they are computed in a\nmuch faster way (see benchmark section for more details).\n\n\n# Motivation\n\nNowadays very long simulations are carried on routinely. Enhanced sampling\nmethods like metadynamics, REMD and accelerated dynamics allow escaping from\npotential energy minima, returning trajectories that are conformationally sparsed\nand where every cluster can be potentially important to detect and analyze. Improvements\non software designed to address this task is an important field of research.\n\n**BitClust** offer is a classical tradeoff; RAM for speed. It is able to\ncalculate all pairwise distances between frames to run a clustering job and\nthen store them in memory instead of recalculating them whenever a cluster is found.\nIt is worth noting that used memory has been deeply optimized by encoding similarity distances\nas bits (0 if the distance is less equal than a specified threshold, 1 otherwise).\nThis encoding result in a storage reduction as high as 16X compared to similar algorithms\nthat saves the same information as single precision float values.\n\n\n# Main Dependencies\n\n**BitClust** is built on the shoulders of two giants:\n\n *  [MDTraj software](http://mdtraj.org/1.9.0/)  that allows a very fast\n    calculation of RMSD pairwise distances between all frames of trajectories in\n    a parallelized fashion **and**\n\n * [bitarray third-party python library](https://pypi.org/project/bitarray/) \n   which offers a memory efficient data structure of bit vectors (bit arrays)\n   and a set of bitwise operations that are the very heart of our clustering\n   implementation.\n\n\n# Citation\n\nIf you make use of **BitClust** in your scientific work, **BeCool** and cite it ;)\n\nThe BibTeX reference is:\n\n.. todo::\n  insert once published.\n\n\n# Licence\n\n**BitClust** is licensed under GNU General Public License v3.0.\n\n\n# Reference\n\n[1] Daura, X.; van Gunsteren, W. F.; Jaun, B.; Mark, A. E.; Gademann, K.; Seebach, D. Peptide Folding: When Simulation Meets Experiment. Angew. Chemie Int. Ed. 1999, 38 (1/2), 236\u2013240.\n\n\n\n",
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        "name": "bitclust",
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        "summary": "Fast and memory efficient clustering of long Molecular Dynamics",
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