scikit_clarans-0.3.4.dist-info/METADATA,sha256=oXrLsxbGNHGEH2oAYJM-iXFBNNS_z092RzuW33BRLNo,9669
scikit_clarans-0.3.4.dist-info/WHEEL,sha256=suq8ARrxbiI7iLH3BgK-82uzxQ-4Hm-m8w01oCokrtA,85
clarans/_core.cp313-win_amd64.pyd,sha256=9jn9UJVIizpTF9TcP671TRAoRL58dxtNqh5WYvl_kCI,258560
clarans/_core.cp313-win_amd64.lib,sha256=x6LKQ06qUu8RkSHvSyqwaeKGc6BJhnzZzc9wtW74UPE,1976
clarans/__init__.py,sha256=yL7KuYHjOv3cn1nfzxLf7q6-VG4YV6cvsCP5zQw23ZI,319
clarans/_clarans.py,sha256=kupzhh2fvKHQI73752W6H1Vv2FoPrlIfuStm2-x904c,54410
clarans/_fast_clarans.py,sha256=0_d0MsTDxD0GDWE1EcZz97RiPDhMlwep8bsPWDPQjPw,13524
clarans/_initialization.py,sha256=lOvndgEDkXt3DhVAEj0ENoFFX533w5jIJM9q1sRt8cg,13382
clarans/utils.py,sha256=WMdBAxNsNb4LP4TFvYutMawm9V3sNaxb7BFKR5p-aRk,6614
clarans/py.typed,sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU,0
clarans/_core.pyi,sha256=w3LxXImqqNKBENMLZS-Tna0vWXAUvbsNf8s6LJKrgAA,1331
clarans/_core.pyx,sha256=21F1mU38UGxfpdzLYrFz0cRHbHDO8NPEukUkbPPoKZ0,9453
clarans/tests/__init__.py,sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU,0
clarans/tests/test_clarans.py,sha256=fWQhT7R2DwcQr7jVCkPxR0lwYutZgrXmy8uldbykg-s,41921
clarans/tests/test_common.py,sha256=Z_J_4iqlFto1FNi2MJKm6gBZJjfLx667Iz4MxjReRHM,260
clarans/tests/test_core.py,sha256=2-Gvwb4A1fzH7TAU0BwRsOLukzujU62OfYMkVz1rHeY,13435
clarans/tests/test_ecosystem.py,sha256=Lbt1EpPwDgniY29mkjS7TDXGCVhePVXlvhTOVdvS264,12685
clarans/tests/test_fast_clarans.py,sha256=RUR3H2Xw4KhOl9H1qYxmLkOfw9jbya3Z7ONEBmD4Mt4,18843
clarans/tests/test_initialization.py,sha256=YV9mBDrsGdf4fOx0lRKPW6Hkx9Rx0wxETJM4wO3rB5U,8233
clarans/tests/test_regressions.py,sha256=nnGv3zzN8pIqCKResc2sNvNt8UaukO_abUh_l-VfVes,40686
clarans/tests/test_robustness.py,sha256=CJVh9-EUn8EpVYaSac9PppS_hwAdR-j-Maq4lBisaf0,49756
scikit_clarans-0.3.4.dist-info/RECORD,,
