Result for 08A0D16BA302D1FD2B40DDE0B6427627938DA2D0

Query result

Key Value
FileName./usr/lib/python3.10/site-packages/tables/tests/__pycache__/test_tablesMD.cpython-310.pyc
FileSize49968
MD53DA9D7945A9E8D060C4564607BE6700C
SHA-108A0D16BA302D1FD2B40DDE0B6427627938DA2D0
SHA-256463E6F8AC1483662E62D1A3E6751E0E102B1EA7853F8E1DB3B14C6C1B2D2B74A
SSDEEP1536:hXBV0rt942rkSR2Ppy/0y6iVxFCwIsKkg3p6cdP2r1CVPpPlITMkJ3J7JEJZL7:ZAsc0yvVxFCwIsKkg3p6cder1CVPgTMp
TLSHT1C7230B8D36332E46FF38F8F644B5075ADA1192C587C1F352A425A28A7F046DCDEB689C
hashlookup:parent-total1
hashlookup:trust55

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Parents (Total: 1)

The searched file hash is included in 1 parent files which include package known and seen by metalookup. A sample is included below:

Key Value
MD594271992B0A49546B72F07AD0741194D
PackageArcharmv7hl
PackageDescriptionPyTables is a Python 3 package for managing hierarchical datasets designed to efficiently and easily cope with extremely large amounts of data. It is built on top of the HDF5 library and the NumPy package (numarray and Numeric are also supported). PyTables features an object-oriented interface and performance-critical extensions coded in C (generated using Pyrex) that make it a fast yet extremely easy-to-use tool for interactively processing and searching through very large amounts of data. PyTables also optimizes memory and disk resources so that data occupies much less space than with other solutions such as relational or object-oriented databases (especially when compression is used).
PackageMaintainerneoclust <neoclust>
PackageNamepython3-tables
PackageRelease1.mga9
PackageVersion3.7.0
SHA-1FC8B064172799D28DD12EF3C1BFE8EF0F35919F5
SHA-2563CBADBA3E551754A9F106E1F2E77F609CE4F42835E3D9D677CA6CC18027C49CB