Result for 3E66CED35584071FEF53D7DE3D460E9899135E90

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Parent (23)

Query result

Key Value
FileName./usr/lib/python3.10/site-packages/emcee/emcee_version.py
FileSize22
MD5E3D3368A731285C84E7A46C055F50962
SHA-13E66CED35584071FEF53D7DE3D460E9899135E90
SHA-256D7878898208DC51878A563087E429EE21E4E092D4809F4631D28F601F8045DAD
SSDEEP3:cvyUCv:8yHv
TLSH
hashlookup:parent-total23
hashlookup:trust100

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Parents (Total: 23)

The searched file hash is included in 23 parent files which include package known and seen by metalookup. A sample is included below:

Key Value
MD506A7C768308BB5F4EA3A2BDA9960946D
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
PackageNamepython38-emcee
PackageRelease7.10
PackageVersion3.1.1
SHA-10B83C84B987968ADFE53508D88E3B920807BCD9D
SHA-2561BA7BFDF31C878A77335B985A224122CE37151EE16B3443B5203628B3ED7D07A
Key Value
MD5928DCAC5BCA45C8C0EC435413A74B30A
PackageArchnoarch
PackageDescriptionA py.test fixture for benchmarking code. It will group the tests into rounds that are calibrated to the chosen timer.
PackageMaintainerhttps://bugs.opensuse.org
PackageNamepython3-pytest-benchmark
PackageReleaselp151.1.1
PackageVersion3.1.1
SHA-10F3C5185AC6DEBA3475AE9A57406C8CE316FF74C
SHA-2567251C797BBC64F86CDF989E7D77D1AF715D1011E1865C1C0DC9E80ACE269C97D
Key Value
MD5DFA9691B2D38D4CF783A0476876D5D8B
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
PackageMaintainerhttps://bugs.opensuse.org
PackageNamepython38-emcee
PackageRelease1.2
PackageVersion3.1.1
SHA-11ADB56B575FD15742109771FE6619FC0510B4F46
SHA-256E12BAA04B4EF0A16620DF2BE87562912304FE4203EC67141C483C73C6C210476
Key Value
MD5AD964B64831F212D3B01875019EB9C81
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
PackageNamepython39-emcee
PackageRelease7.12
PackageVersion3.1.1
SHA-1254A60D826B0452AA9EE299DB3156CED3849758A
SHA-2568F9A0B28EC754E28D63E893C940B4FB63EC39C2EBE6B1711281EDC71E87B1E98
Key Value
MD53E4B6178816B41DB46D3EA46EF2AB1D5
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
PackageNamepython39-emcee
PackageRelease7.11
PackageVersion3.1.1
SHA-12A680AEC6E675EC28CB68BBAE4F855C3B6F808FC
SHA-256B138C47B80E6F83A1E4B88D01FF296F7FB16AF563814919589B56418177746CB
Key Value
MD573C4AF9D87621DE9B5DF374835C9388A
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
PackageNamepython310-emcee
PackageRelease7.11
PackageVersion3.1.1
SHA-13495D3E0FC0B9B1B577BBCF4FE72F9BF9F3F917D
SHA-2564A5B2A9709D3106DE0D352AC95684BE1D62CD3D981CFD141062CA8AC6F00B16A
Key Value
FileSize31808
MD5F85B1730C3B9F17ECC7C89F100BD57D8
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
PackageVersion3.1.1-1
SHA-135012F2F5C0CB0BA4AD250FC2E6C0A8AAB365B13
SHA-256964D2D9E8D8C805555FDD44BDF69F1E66E9D85E534B121935462E19314A4359A
Key Value
MD58BB9C19AF1E7D1F7C6AC1A638A2F90A5
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
PackageRelease2.4
PackageVersion3.1.1
SHA-139298F9D84B6D35467F3052EAEF97DFD2FA1DFA7
SHA-25672A7E780B39DD6C272F7AEF220307E831831E259A384C7DB6E16B07ACB7F1899
Key Value
MD552D86774823AF30231A30C358FC7ACB3
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.
PackageMaintainerumeabot <umeabot>
PackageNamepython3-emcee
PackageRelease3.mga9
PackageVersion3.1.1
SHA-13E2BCA13E99F20D16BF5D81B414DD0950B0D02BA
SHA-256D3EF1E9BC2A171DFF8F47848917991DFD1C999CDEA9F9EE8CA1B948159786487
Key Value
MD5681A3BDA4BFF5577C9D379B19382BF4D
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
PackageNamepython310-emcee
PackageRelease7.10
PackageVersion3.1.1
SHA-146DAF951D75EA3F5F6958A57E762BF022B22E836
SHA-2568B4588B16129AAAD4D325FD282CE17B754EC3F9168C769757338422B09778CE7





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