scikit_clarans-0.3.4.dist-info/RECORD,,
scikit_clarans-0.3.4.dist-info/WHEEL,sha256=rFEbl7VGpdcTiWMLyPHKRieKvxwiToMCxVXFuj50F_A,91
scikit_clarans-0.3.4.dist-info/METADATA,sha256=oXrLsxbGNHGEH2oAYJM-iXFBNNS_z092RzuW33BRLNo,9669
clarans/_clarans.py,sha256=pKarPRmjpw8ZXPurh_SpNB7EkBwvjyyyjfHwR3d6Dw4,53102
clarans/_core.pyi,sha256=wRmSd8f9D6bzTIBZzFADwqBgb7cDCs9e6R9TrT6ogaY,1276
clarans/_fast_clarans.py,sha256=egYSVFo-abmSkNUVA84tf5xnyoTpAK89EsqExovwpws,13184
clarans/__init__.py,sha256=GiPedH7YD7cEgPHuWgJ-f3K-NFdsksKCVoA4dAbohLU,306
clarans/utils.py,sha256=a7QpxzA4EnVDrLunVA6XAFAKTLFmdFmyQU2aIkQVTRg,6422
clarans/_core.cpython-39-darwin.so,sha256=smDGzXVZDmLOZnUHSb9nQWDMZp3aP7gDb-aOU-KEBI0,319800
clarans/py.typed,sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU,0
clarans/_core.pyx,sha256=ALSMHifnOpbWkrDOz7uejWva7112YIUJL35kv3npJYI,9151
clarans/_initialization.py,sha256=hqNDdHyYAd85-VWw8HR_3ld0xPyGSokBrEVKOKmMQGo,13009
clarans/tests/test_clarans.py,sha256=RDq-I1K4XoDaq9K_-GarJi981I7WwaBe98O4s2Is-Ew,40931
clarans/tests/test_common.py,sha256=pMgb48UMDCOlA-EnqTiRFsic3DLTa1s_FA4_CXWucNI,252
clarans/tests/test_fast_clarans.py,sha256=j8NhMQAdnpr6W19Dw8t17meLBUe1x-RaepGpGRvH-kM,18395
clarans/tests/test_robustness.py,sha256=7UrAmBEb39yfKea9XxuLMM75CQ8LQVtbRKDAwdViCBY,48651
clarans/tests/test_core.py,sha256=KAD3ZOiRFMKdESVKNShpEaUjRTNS2JaR9B0STkNwOGc,13139
clarans/tests/test_initialization.py,sha256=MYzwQiExC5KaGCyRm-u_WkjZpFdv6482FynTMGMF-jE,8047
clarans/tests/__init__.py,sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU,0
clarans/tests/test_ecosystem.py,sha256=qCKmtDT2SWNqWgiuMynb7FdErRfMomqizIOx5S_ibxw,12395
clarans/tests/test_regressions.py,sha256=0N1W-42ywolh7ve31vP4-EEVQI3eFx2aflEqY_pxSh8,39656
