Result for 3C84D763B5B05D3E18D92217D94F47C51A4F6E45

Query result

Key Value
FileName./usr/lib/python3/dist-packages/emcee/mh.py
FileSize5475
MD54C4595F462F2D664B7FB346677903F22
RDS:package_id182052
SHA-13C84D763B5B05D3E18D92217D94F47C51A4F6E45
SHA-2564EF15647C28D7450015E0E5884EC3048C8B3FB15153BF35C8BCF5106A55D6AE2
SSDEEP96:+VhXy0/KKjjrAdXeA4n58o2TfrWCNyYIuU:e5bktIxqO
TLSHT100B17476B54007A34347083160DF91626F3D442BF5D69008386F9D94EF0A932D7E9EE4
insert-timestamp1679428009.845026
sourceRDS.db
hashlookup:parent-total7
hashlookup:trust85

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Parents (Total: 7)

The searched file hash is included in 7 parent files which include package known and seen by metalookup. A sample is included below:

Key Value
MD521B1EB2ED7479B1CE67C41226CF7AD53
PackageArchnoarch
PackageDescriptionEmcee is a stable, well tested Python implementation of the affine-invariant ensemble sampler for Markov chain Monte Carlo (MCMC) proposed by Goodman & Weare (2010). The code is open source and has already been used in several published projects in the Astrophysics literature. This is the Python 2 package.
PackageMaintainershlomif <shlomif>
PackageNamepython2-emcee
PackageRelease5.mga7
PackageVersion2.2.1
SHA-1A7D59258BE02B5C9360FB58A3A24920450CA117B
SHA-256C618F6B02D1CEEE35E3822EDDF32C910B2CA699E8B645DD681365B1D6161987E
Key Value
FileName16140
FileSize22112
MD5785C4105A2AFC6F41C4841FE291591D2
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
PackageVersion2.2.1-1
RDS:package_id182052
SHA-1E07F005CF49DAE37B70012DF93312CDCAEF56FB5
SHA-256D171D53B4A5F05EA23476333F6694791445BF6A96885EA34C3CE9D2D1D58A168
insert-timestamp1679408376.2257276
sourceRDS.db
Key Value
FileSize23276
MD5466EE0312B08EA363A6A7430A14E6677
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.
PackageMaintainerDebian Astronomy Team <debian-astro-maintainers@lists.alioth.debian.org>
PackageNamepython3-emcee
PackageSectionpython
PackageVersion2.2.1-1
SHA-139F35B4B300A54CFFBC89E8494E16D88BAC838F1
SHA-256E94D4A4AD2D80BB1B8E4E8125F1D3D5A23CBF54B9F055552748747E2DF863346
Key Value
FileSize22784
MD575AFC54195A431E4439DB9CA5CD2C7E3
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.
PackageMaintainerUbuntu Developers <ubuntu-devel-discuss@lists.ubuntu.com>
PackageNamepython3-emcee
PackageSectionpython
PackageVersion2.2.1-2ubuntu1
SHA-1F87CBA865E7BE394E32F766C9FF196BE0EF5D80D
SHA-256EAB98044EC02A54B9241C032EA9192A746E4309A2240C893DC210988F6607E22
Key Value
FileName16141
FileSize22198
MD53F38654D22AEC622EA797B3B8D242845
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
PackageVersion2.2.1-1
RDS:package_id182052
SHA-109D1E0A13584F01DED0DF4A3753B4A2FFB2529F3
SHA-256BB393D77536CFBC3AC14A446434381E726960D5D5210428C4906ABEEA9B29588
insert-timestamp1679408376.223349
sourceRDS.db
Key Value
FileSize23192
MD5D4D228D080C02A9076088AC2D4DB9C0E
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.
PackageMaintainerDebian Astronomy Team <debian-astro-maintainers@lists.alioth.debian.org>
PackageNamepython-emcee
PackageSectionpython
PackageVersion2.2.1-1
SHA-1CF1410183EEB370B1FE4176BAACD5101A12456FD
SHA-256DBB51A9BA209CEB326EA1F93B17364A06C3C2AB4076A1CEEC5CE41BFA9CE84F8
Key Value
MD57AC2F057BD5F7D65B91F896B4DBB0A47
PackageArchnoarch
PackageDescriptionEmcee is a stable, well tested Python implementation of the affine-invariant ensemble sampler for Markov chain Monte Carlo (MCMC) proposed by Goodman & Weare (2010). The code is open source and has already been used in several published projects in the Astrophysics literature. This is the Python 3 package.
PackageMaintainershlomif <shlomif>
PackageNamepython3-emcee
PackageRelease5.mga7
PackageVersion2.2.1
SHA-1C7FF5CE76A1E9C601FDD2C269C3D876428E52AA1
SHA-256C18CA2949B4E623B69A380CDFC7ECEBFF33B0D4F092EDE80051153F839B936A1