Key | Value |
---|---|
FileName | ./usr/lib/python3/dist-packages/emcee/tests/integration/test_kde.py |
FileSize | 697 |
MD5 | 9F0972FA82A48F67145B3730001D7CB6 |
SHA-1 | 3BE4B320BE7C31B6B3E99F66B19DB9FDC008B683 |
SHA-256 | 2EA3773CA2F129C1AB4993D087B7D2FB1697F5205C1431938008AE7DD4036345 |
SSDEEP | 12:icKyEx2RwUbRFB568s6GO65qRJPoVABhwke2GUFqs6GRvKwke2GUXs6TnvKwke2y:l/wU1kvOV3bzRuUFpvMRuUctRuUKuve |
TLSH | T1C101DBC088F76C72C7D698404BAFD7627BA4FCD91D2801FB4D95CA53E78552863D1701 |
hashlookup:parent-total | 24 |
hashlookup:trust | 100 |
The searched file hash is included in 24 parent files which include package known and seen by metalookup. A sample is included below:
Key | Value |
---|---|
MD5 | 03E1F0C041D1CB4B738CE9656FDB92EB |
PackageArch | noarch |
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. |
PackageMaintainer | ghibo <ghibo> |
PackageName | python3-emcee |
PackageRelease | 1.mga9 |
PackageVersion | 3.1.3 |
SHA-1 | 00D6403585F9B1EF62326C20CDFADAF86894DC67 |
SHA-256 | 9AA97DFBED62C986C7BF782C4B4DF4B600D5D4F31B2E03377FA29ECD90661255 |
Key | Value |
---|---|
MD5 | 06A7C768308BB5F4EA3A2BDA9960946D |
PackageArch | noarch |
PackageDescription | Emcee 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 |
PackageName | python38-emcee |
PackageRelease | 7.10 |
PackageVersion | 3.1.1 |
SHA-1 | 0B83C84B987968ADFE53508D88E3B920807BCD9D |
SHA-256 | 1BA7BFDF31C878A77335B985A224122CE37151EE16B3443B5203628B3ED7D07A |
Key | Value |
---|---|
MD5 | 387069683E9154BE807FBC9446C67658 |
PackageArch | noarch |
PackageDescription | Emcee 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 |
PackageName | python39-emcee |
PackageRelease | 1.1 |
PackageVersion | 3.1.0 |
SHA-1 | 12298657739E619DF5C7E0337ACFD9FAF0093138 |
SHA-256 | 6DA021854063D785C086F782084E011877FDDAF69968E5C004626E3F4901E778 |
Key | Value |
---|---|
MD5 | DFA9691B2D38D4CF783A0476876D5D8B |
PackageArch | noarch |
PackageDescription | Emcee 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 |
PackageMaintainer | https://bugs.opensuse.org |
PackageName | python38-emcee |
PackageRelease | 1.2 |
PackageVersion | 3.1.1 |
SHA-1 | 1ADB56B575FD15742109771FE6619FC0510B4F46 |
SHA-256 | E12BAA04B4EF0A16620DF2BE87562912304FE4203EC67141C483C73C6C210476 |
Key | Value |
---|---|
MD5 | AD964B64831F212D3B01875019EB9C81 |
PackageArch | noarch |
PackageDescription | Emcee 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 |
PackageName | python39-emcee |
PackageRelease | 7.12 |
PackageVersion | 3.1.1 |
SHA-1 | 254A60D826B0452AA9EE299DB3156CED3849758A |
SHA-256 | 8F9A0B28EC754E28D63E893C940B4FB63EC39C2EBE6B1711281EDC71E87B1E98 |
Key | Value |
---|---|
MD5 | 3E4B6178816B41DB46D3EA46EF2AB1D5 |
PackageArch | noarch |
PackageDescription | Emcee 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 |
PackageName | python39-emcee |
PackageRelease | 7.11 |
PackageVersion | 3.1.1 |
SHA-1 | 2A680AEC6E675EC28CB68BBAE4F855C3B6F808FC |
SHA-256 | B138C47B80E6F83A1E4B88D01FF296F7FB16AF563814919589B56418177746CB |
Key | Value |
---|---|
MD5 | 73C4AF9D87621DE9B5DF374835C9388A |
PackageArch | noarch |
PackageDescription | Emcee 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 |
PackageName | python310-emcee |
PackageRelease | 7.11 |
PackageVersion | 3.1.1 |
SHA-1 | 3495D3E0FC0B9B1B577BBCF4FE72F9BF9F3F917D |
SHA-256 | 4A5B2A9709D3106DE0D352AC95684BE1D62CD3D981CFD141062CA8AC6F00B16A |
Key | Value |
---|---|
FileSize | 31808 |
MD5 | F85B1730C3B9F17ECC7C89F100BD57D8 |
PackageDescription | Affine-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. |
PackageMaintainer | Debian Astronomy Team <debian-astro-maintainers@lists.alioth.debian.org> |
PackageName | python3-emcee |
PackageSection | python |
PackageVersion | 3.1.1-1 |
SHA-1 | 35012F2F5C0CB0BA4AD250FC2E6C0A8AAB365B13 |
SHA-256 | 964D2D9E8D8C805555FDD44BDF69F1E66E9D85E534B121935462E19314A4359A |
Key | Value |
---|---|
MD5 | 8BB9C19AF1E7D1F7C6AC1A638A2F90A5 |
PackageArch | noarch |
PackageDescription | Emcee 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 |
PackageName | python3-emcee |
PackageRelease | 2.4 |
PackageVersion | 3.1.1 |
SHA-1 | 39298F9D84B6D35467F3052EAEF97DFD2FA1DFA7 |
SHA-256 | 72A7E780B39DD6C272F7AEF220307E831831E259A384C7DB6E16B07ACB7F1899 |
Key | Value |
---|---|
MD5 | 52D86774823AF30231A30C358FC7ACB3 |
PackageArch | noarch |
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. |
PackageMaintainer | umeabot <umeabot> |
PackageName | python3-emcee |
PackageRelease | 3.mga9 |
PackageVersion | 3.1.1 |
SHA-1 | 3E2BCA13E99F20D16BF5D81B414DD0950B0D02BA |
SHA-256 | D3EF1E9BC2A171DFF8F47848917991DFD1C999CDEA9F9EE8CA1B948159786487 |