Result for 33902601A700B63BCFE7985BDD00C8BEDBB46E07

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Parent (42)

Query result

Key Value
FileNameusr/lib/python3.11/site-packages/emcee/moves/de.py
FileSize1581
MD595D791D63407DACDECA7E026E304A1A2
SHA-133902601A700B63BCFE7985BDD00C8BEDBB46E07
SHA-256968ADD3901E19C6F9FB49512098B3EB02DB1A523B302BEBEA970AC2D73964190
SHA-5128A5083FFD16C370E7C13C60F3DF8DCDCBDF43B5546C9A38404E02E80FAACA48DAEAFFB03DCF26FC0BE92837F0EEEA1DD42FE5F789D18FCD64527E9D105C01447
SSDEEP24:lQOOV7xX6LJM3TZ25gTF4NquHhAWjS0kf8slgfh68rfzV/ViJ80rPelD0/WwmEp:9OV7xX6L24LBbu07s+lb1Vy8GelAyEp
TLSHT100317226A61B217383C7CC220CEB7A03BB6F3DAB2B540895387D31582F248774362BC1
insert-timestamp1703903952.8618462
mimetypetext/x-python
sourcesnap:S3VpJTklG9hO6cKHP7KfqdPS5dhSsdYt_12
tar:gnameroot
tar:unameroot
hashlookup:parent-total42
hashlookup:trust100

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Parents (Total: 42)

The searched file hash is included in 42 parent files which include package known and seen by metalookup. A sample is included below:

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
MD503E1F0C041D1CB4B738CE9656FDB92EB
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.
PackageMaintainerghibo <ghibo>
PackageNamepython3-emcee
PackageRelease1.mga9
PackageVersion3.1.3
SHA-100D6403585F9B1EF62326C20CDFADAF86894DC67
SHA-2569AA97DFBED62C986C7BF782C4B4DF4B600D5D4F31B2E03377FA29ECD90661255
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
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
MD5387069683E9154BE807FBC9446C67658
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
PackageRelease1.1
PackageVersion3.1.0
SHA-112298657739E619DF5C7E0337ACFD9FAF0093138
SHA-2566DA021854063D785C086F782084E011877FDDAF69968E5C004626E3F4901E778
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
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
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





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