Result for 060A53281CC2C47DF825FBE3AB276B539E0ADAC1

Query result

Key Value
FileName./usr/lib64/python3.10/site-packages/tables/nodes/tests/__pycache__/test_filenode.cpython-310.pyc
FileSize27425
MD5C86DC126F2C1BE4B15D1B91F8B90CB54
SHA-1060A53281CC2C47DF825FBE3AB276B539E0ADAC1
SHA-256EB90795BE16C6ACA38D7F0802A88C4901914E52F5922707F380FBFE96D3D1A82
SSDEEP768:TOd08yOzT/xBdiiJzcVTODwDyLsbVuE4NW9ypcnkQeKIW/0GSU7fHgSFwyMw4kRw:TObLgNUwroUHhsTU7AlV9XBb
TLSHT16DC283CA8E475E6BFFA0FBFC86690364E650C23E13255B07B41C911B3F495A869F58C8
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
MD5469BA2DA942B6AB78CB705F3F7D1BAC0
PackageArchaarch64
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-18B4E8FE0D62B1914E90B0EB706DB818C4D25FFEE
SHA-2562E709179F16BC5E82AF4802DEAC8C9BA05FEE517D653EFE90F25C91E145813D5