Result for 640EF5BAE6AA9A87F2AAA2000EC51AFDD377BC5C

Query result

Key Value
FileName./usr/lib/python3.9/site-packages/__pycache__/lazyarray.cpython-39.opt-1.pyc
FileSize17658
MD5F73D7A7FD9BEE5CEC7513E6A5CABF786
SHA-1640EF5BAE6AA9A87F2AAA2000EC51AFDD377BC5C
SHA-2565A3ECB3CD0964D0598CAF1AE42780DBE5252F2AB2C40BF8A23D871B3B9E2BEA5
SSDEEP384:QqGfVF6auCBj8Llek0nfFszIP+tYYsgJAM10y6JP:QqGfVF6auCBwLlN8szk8sgJ51sJ
TLSHT18182FAD597800F7DFD46F3FEC04A1A54AA60B176138D9253F00F89AA2F4DAD81A719DC
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
MD5ED032EFF2A70EAE35B94B835DE503F6B
PackageArchnoarch
PackageDescription lazyarray is a Python package that provides a lazily-evaluated numerical array class, ``larray``, based on and compatible with NumPy arrays. Lazy evaluation means that any operations on the array (potentially including array construction) are not performed immediately, but are delayed until evaluation is specifically requested. Evaluation of only parts of the array is also possible. Use of an ``larray`` can potentially save considerable computation time and memory in cases where: * arrays are used conditionally (i.e. there are cases in which the array is never used) * only parts of an array are used (for example in distributed computation, in which each MPI node operates on a subset of the elements of the array) Documentation: http://lazyarray.readthedocs.org
PackageMaintainerFedora Project
PackageNamepython3-lazyarray
PackageRelease8.fc33
PackageVersion0.3.2
SHA-17C7D722BE9E67A5238C92EACABA12E8C0C0C6B7E
SHA-25684159442335CC3B75B2EB3A0ABB44E0AD399EF507E894095F61A1E0B463AB0CD