Result for 1E3536D6A8F3FE123E289D9A3205258DDB374E1D

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Parent (14)

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Key Value
FileName./usr/lib/python3/dist-packages/emcee/tests/unit/test_blobs.py
FileSize1364
MD56EDEB7B444EBA08B93D7026688B1086F
SHA-11E3536D6A8F3FE123E289D9A3205258DDB374E1D
SHA-2563343912D2B795E951B3E8E1863832D8C2FE701BE781DA3B210D1ABA68F58B4BF
SSDEEP24:lQZerYA4CEQChCEA7iCph+GeKGEZHcC4fX1L4y8LvDE9:UecBCEQChCEsiCX+1Ex45ADE9
TLSHT1F6210219D852909563D3D47A20E6A637EB78AD3FE7801CDEB06CA5009F2562C5472B5C
hashlookup:parent-total14
hashlookup:trust100

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Parents (Total: 14)

The searched file hash is included in 14 parent files which include package known and seen by metalookup. A sample is included below:

Key Value
MD569525F72C264ED3364524A4DD6C076E4
PackageArchnoarch
PackageDescription emcee 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.
PackageMaintainerFedora Project
PackageNamepython3-emcee
PackageRelease3.fc34
PackageVersion3.0.2
SHA-1D14214BEC8F5FB43DC807F96139F89047A5D9F2A
SHA-256EDA60DBD48FFA056D3571BED486F34F12D735C6C553E048A55C65F5EA3075D19
Key Value
FileSize28804
MD56BF3059A1F478DE005F39794366850C4
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.
PackageMaintainerDebian Astronomy Team <debian-astro-maintainers@lists.alioth.debian.org>
PackageNamepython3-emcee
PackageSectionpython
PackageVersion3.0.2-2
SHA-117986342F25FF116DC62559DE43D1877E085A237
SHA-25676562CE931F850D729942D31AF72434BE3B8D8D1A0731162D16041F018A25B43
Key Value
MD5BBDC17C6B5F997BABC737139766B846E
PackageArchnoarch
PackageDescriptionEmcee is a Python implementation of the affine-invariant ensemble sampler for Markov chain Monte Carlo (MCMC) proposed by Goodman & Weare (2010) http://cims.nyu.edu/~weare/papers/d13.pdf
PackageNamepython3-emcee
PackageReleaselp152.3.2
PackageVersion3.0.2
SHA-113D212065A9E7F7478C7530CECFB28D4A7D109EF
SHA-2569BD30DB35FF52D39974D1E279860E68C33873E1CB09E10FD0C252B0A1BB5AC9D
Key Value
MD5576D0BF963CF00BC6D9A35169B3DC2D6
PackageArchnoarch
PackageDescriptionEmcee is a Python implementation of the affine-invariant ensemble sampler for Markov chain Monte Carlo (MCMC) proposed by Goodman & Weare (2010) http://cims.nyu.edu/~weare/papers/d13.pdf
PackageNamepython2-emcee
PackageRelease2.1
PackageVersion3.0.2
SHA-10804327E988AC64A392B51184765EA0D79F01010
SHA-256D8A45DCD577FF84FE07CE4BBFFC324531C3045C248E5AF78B367AAC1ED208321
Key Value
MD5978A14FC19EF5BE3AF65740F5C65C3E1
PackageArchnoarch
PackageDescriptionEmcee is a Python implementation of the affine-invariant ensemble sampler for Markov chain Monte Carlo (MCMC) proposed by Goodman & Weare (2010) http://cims.nyu.edu/~weare/papers/d13.pdf
PackageNamepython3-emcee
PackageReleaselp151.3.1
PackageVersion3.0.2
SHA-192E8846173FC09ECFAD65DDA4BF50EC4921A17BD
SHA-256092D3114EE7014628ABA82F24ABEC7F62486855B931CCFF07D7B8D2AB2FF284A
Key Value
MD50C274E7C2E841E52350DE0A1DCA9FD4E
PackageArchnoarch
PackageDescriptionEmcee is a Python implementation of the affine-invariant ensemble sampler for Markov chain Monte Carlo (MCMC) proposed by Goodman & Weare (2010) http://cims.nyu.edu/~weare/papers/d13.pdf
PackageNamepython2-emcee
PackageReleaselp152.3.2
PackageVersion3.0.2
SHA-1C7ACE371E1413E9AC3941A7ADC2A0D729D769AE6
SHA-25602CDB715DF9CD6DF5E3C62250016E39BE406D09C4044E0B89DF0D8D844080F58
Key Value
MD5E78E55C5EDE1471548A524D292B51F87
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.
PackageMaintainerguillomovitch <guillomovitch>
PackageNamepython3-emcee
PackageRelease1.mga8
PackageVersion3.0.2
SHA-1477420828217654F6CE9F89C0E9143E515C37AB5
SHA-25644C92350AFBC55FC12E895A626B988EA02BED472AA8F4FD469285AD70D59CF7C
Key Value
MD5FBB41A0A4556E19EAF375EA81A3CFA00
PackageArchnoarch
PackageDescriptionEmcee is a Python implementation of the affine-invariant ensemble sampler for Markov chain Monte Carlo (MCMC) proposed by Goodman & Weare (2010) http://cims.nyu.edu/~weare/papers/d13.pdf
PackageNamepython2-emcee
PackageReleaselp152.6.2
PackageVersion3.0.2
SHA-1AA9A49700805DDDD4ADC1F1FFBF301631F9B3231
SHA-256628974EC83221F60001C003D4AF2169CA370859CFC53C0A266F40D2A731B12E7
Key Value
MD55B92B8099A78D915F82E46419937FC6F
PackageArchnoarch
PackageDescriptionEmcee is a Python implementation of the affine-invariant ensemble sampler for Markov chain Monte Carlo (MCMC) proposed by Goodman & Weare (2010) http://cims.nyu.edu/~weare/papers/d13.pdf
PackageNamepython3-emcee
PackageRelease2.1
PackageVersion3.0.2
SHA-1C617AED464800CD59CCD3C6EEDFEF579C271A322
SHA-256E39AA527A4DEFF5B0BD8EE97061FD53AE741C36D568E7AFF63F0BFF6D957D1D4
Key Value
MD540A3472B72D05F0769078C7C48F74E73
PackageArchnoarch
PackageDescriptionEmcee is a Python implementation of the affine-invariant ensemble sampler for Markov chain Monte Carlo (MCMC) proposed by Goodman & Weare (2010) http://cims.nyu.edu/~weare/papers/d13.pdf
PackageNamepython2-emcee
PackageRelease2.1
PackageVersion3.0.2
SHA-13ACF38AB75C4D5F339EA7D001AB3CD1CC5B48395
SHA-25654E3D78F5FEB758019D390E4FA2613531EE042EF17947C845BA5BF7361894644
Key Value
MD5899B1AC66621B9056AE2392AA68A429E
PackageArchnoarch
PackageDescriptionEmcee is a Python implementation of the affine-invariant ensemble sampler for Markov chain Monte Carlo (MCMC) proposed by Goodman & Weare (2010) http://cims.nyu.edu/~weare/papers/d13.pdf
PackageNamepython2-emcee
PackageReleaselp151.3.1
PackageVersion3.0.2
SHA-15CF61A11854C3F7E987564344026F1931D1EBE91
SHA-25665996CDCDDAD8BE399F458695F1516AF2E88555B5BBDD1835E9C84B5A7857300
Key Value
FileSize27284
MD5841C69E63AC761E7318305521AF6E1D8
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
PackageVersion3.0.2-2
SHA-1006FF24CE3B43A21DC9C55BD791E81C55650B6A1
SHA-25602B993A48F195D43CFC444060880A87C4A62D68EA343A816F53ECE0FBC0F1482
Key Value
MD5B25B09EF346529363BCA193F3FE6588E
PackageArchnoarch
PackageDescriptionEmcee is a Python implementation of the affine-invariant ensemble sampler for Markov chain Monte Carlo (MCMC) proposed by Goodman & Weare (2010) http://cims.nyu.edu/~weare/papers/d13.pdf
PackageNamepython3-emcee
PackageReleaselp152.6.2
PackageVersion3.0.2
SHA-1AC7EAD51DE8F59EA358DD158F43E8E55811136BB
SHA-25689F91D0B94E009C40989D5A097A7ABD362B4F32A5871BFF66F9B5BFE1828E741
Key Value
MD5779B7A5B3CAD971A706B24BE100092AB
PackageArchnoarch
PackageDescriptionEmcee is a Python implementation of the affine-invariant ensemble sampler for Markov chain Monte Carlo (MCMC) proposed by Goodman & Weare (2010) http://cims.nyu.edu/~weare/papers/d13.pdf
PackageNamepython3-emcee
PackageRelease2.1
PackageVersion3.0.2
SHA-19785B00441EC9BBA70733C105B4AF6D2ED30C20C
SHA-25631C084730EB6754E9CBF7011235E6979BA1A45C686B3DD19B7A1D53675EC46A5





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