Result for A781A4CE10F160864F0F82F2FC2D954EFA0AF064

Query result

Key Value
FileName./usr/lib/python3/dist-packages/emcee/mpi_pool.py
FileSize8568
MD56AFFBD15B5FACABE6A7C939875948BAA
SHA-1A781A4CE10F160864F0F82F2FC2D954EFA0AF064
SHA-2569191A9493ED9A5B17D0F4E61A986767E175D3A6B65625B7A572930E279D3B699
SSDEEP96:ps5RY+/uONDut3Nm9bvL7AVI7nNJduBbl9MG53FL5gUU/DyCqQ8iLpjYanQ5U+U:MnDCsBw6nandzCL38iLhk8
TLSHT1BA02731BA8122A70F647493DD4CBA2736F387C3B679D242A74BC81B81F35175C1A9DD8
hashlookup:parent-total2
hashlookup:trust60

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Parents (Total: 2)

The searched file hash is included in 2 parent files which include package known and seen by metalookup. A sample is included below:

Key Value
FileSize20950
MD59154029D2D6E37E077740FEFD94A3E45
PackageDescriptionAffine-invariant ensemble MCMC sampling for Python 3 emcee is an extensible, pure-Python implementation of Goodman & Weare's Affine Invariant Markov chain Monte Carlo (MCMC) Ensemble sampler. It's designed for Bayesian parameter estimation. . This is the Python 3 package.
PackageMaintainerUbuntu Developers <ubuntu-devel-discuss@lists.ubuntu.com>
PackageNamepython3-emcee
PackageSectionpython
PackageVersion2.1.0-5
SHA-181B325B0B94D7FAB52443297373A512798696D19
SHA-256C5C120686BB4A2034D7F8A2D87FCFA5BC247F6CA7BA084FAD41E1C759A76B5F2
Key Value
FileSize20878
MD5DB5C74813F73B5D3FB76B4D17EA27E8E
PackageDescriptionAffine-invariant ensemble MCMC sampling for Python emcee is an extensible, pure-Python implementation of Goodman & Weare's Affine Invariant Markov chain Monte Carlo (MCMC) Ensemble sampler. It's designed for Bayesian parameter estimation. . This is the Python 2 package.
PackageMaintainerUbuntu Developers <ubuntu-devel-discuss@lists.ubuntu.com>
PackageNamepython-emcee
PackageSectionpython
PackageVersion2.1.0-5
SHA-107A6AD42D90C8AE4C36D89D7AFC8772F00F4D050
SHA-2566BE3C88F3F83E4B872ABE37ADEFA8D939C882C6619C0246AAA8F9DBF627C3AF1