Skip to main content

Cython bindings and Python interface to the MEME suite.

Project description

🐍Ⓜ️ PyMEMEsuite Stars

Cython bindings and Python interface to the MEME suite, a collection of tools for the analysis of sequence motifs.

Actions Coverage PyPI Wheel Python Versions Python Implementations License Source GitHub issues Docs Changelog Downloads

🗺️ Overview

The MEME suite is a collection of tools used for discovery and analysis of biological sequence motifs.

pymemesuite is a Python module, implemented using the Cython language, that provides bindings to the MEME suite. It directly interacts with the MEME internals, which has the following advantages over CLI wrappers:

  • single dependency: If your software or your analysis pipeline is distributed as a Python package, you can add pymemesuite as a dependency to your project, and stop worrying about the MEME binaries being properly setup on the end-user machine.
  • no intermediate files: Everything happens in memory, in Python objects you have control on, making it easier to pass your inputs to MEME without needing to write them to a temporary file. Output retrieval is also done in memory.

This library is still a work-in-progress, and in an experimental stage, but it should already pack enough features to run biological analyses or workflows involving FIMO.

🔧 Installing

pymemesuite can be installed from PyPI, which hosts some pre-built CPython wheels for x86-64 Linux, as well as the code required to compile from source with Cython:

$ pip install pymemesuite

💡 Example

Use MotifFile to load a motif from a MEME motif file, and display the consensus motif sequence followed by the letter frequencies:

from pymemesuite.common import MotifFile

with MotifFile("tests/data/fimo/prodoric_mx000001_meme.txt") as motif_file:
    motif = motif_file.read()

print(motif.name.decode())
print(motif.consensus)

for row in motif.frequencies:
    print(" ".join(f'{freq:.2f}' for freq in row))

Then use FIMO to find occurences of this particular motif in a collection of sequences, and show coordinates of matches:

import Bio.SeqIO
from pymemesuite.common import Sequence
from pymemesuite.fimo import FIMO

sequences = [
    Sequence(str(record.seq), name=record.id.encode())
    for record in Bio.SeqIO.parse("tests/data/fimo/mibig-genes.fna", "fasta")
]

fimo = FIMO(both_strands=False)
pattern = fimo.score_motif(motif, sequences, motif_file.background)

for m in pattern.matched_elements:
    print(
        m.source.accession.decode(),
        m.start,
        m.stop,
        m.strand,
        m.score,
        m.pvalue,
        m.qvalue
    )

You should then get a single matched element on the forward strand:

BGC0002035.1_3425_15590 6700 6714 + 9.328571428571422 1.1024163606971822e-05 0.6174858127445146

📋 Features

🧶 Thread-safety

FIMO objects are thread-safe, and the FIMO.score_motif and FIMO.score_pssm methods are re-entrant. This means you can search occurences of several motifs in parallel with a ThreadPool and a single FIMO instance:

from multiprocessing.pool import ThreadPool
from pymemesuite.fimo import FIMO

fimo = FIMO()
with ThreadPool() as pool:
    patterns = pool.map(
        lambda motif: fimo.score_motif(motif, sequences, background),
        motifs
    )

📌 Roadmap

  • error management: Make sure to catch exceptions raised by the MEME core without exiting forcefully.
  • transfac: Support for TRANSFAC motifs in addition to MEME motifs.
  • meme: Motif discovery through enrichment analysis between two collections of sequences.

💭 Feedback

⚠️ Issue Tracker

Found a bug ? Have an enhancement request ? Head over to the GitHub issue tracker if you need to report or ask something. If you are filing in on a bug, please include as much information as you can about the issue, and try to recreate the same bug in a simple, easily reproducible situation.

🏗️ Contributing

Contributions are more than welcome! See CONTRIBUTING.md for more details.

⚖️ License

This library is provided under the MIT License. The MEME suite code is available under an academic license which allows distribution and non-commercial usage. See vendor/meme/COPYING for more information.

Test sequence data were obtained from MIBiG and are distributed under the CC BY 4.0 license. Test motifs were obtained from PRODORIC and are distributed under the CC BY-NC 4.0 license.

This project is in no way affiliated, sponsored, or otherwise endorsed by the original MEME suite authors. It was developed by Martin Larralde during his PhD project at the European Molecular Biology Laboratory in the Zeller team.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

pymemesuite-0.1.0a2.tar.gz (3.6 MB view details)

Uploaded Source

Built Distributions

If you're not sure about the file name format, learn more about wheel file names.

pymemesuite-0.1.0a2-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (2.6 MB view details)

Uploaded PyPymanylinux: glibc 2.17+ x86-64

pymemesuite-0.1.0a2-pp39-pypy39_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (2.5 MB view details)

Uploaded PyPymanylinux: glibc 2.17+ ARM64

pymemesuite-0.1.0a2-pp39-pypy39_pp73-macosx_10_9_x86_64.whl (2.4 MB view details)

Uploaded PyPymacOS 10.9+ x86-64

pymemesuite-0.1.0a2-pp38-pypy38_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (2.6 MB view details)

Uploaded PyPymanylinux: glibc 2.17+ x86-64

pymemesuite-0.1.0a2-pp38-pypy38_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (2.5 MB view details)

Uploaded PyPymanylinux: glibc 2.17+ ARM64

pymemesuite-0.1.0a2-pp38-pypy38_pp73-macosx_10_9_x86_64.whl (2.4 MB view details)

Uploaded PyPymacOS 10.9+ x86-64

pymemesuite-0.1.0a2-pp37-pypy37_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (2.6 MB view details)

Uploaded PyPymanylinux: glibc 2.17+ x86-64

pymemesuite-0.1.0a2-pp37-pypy37_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (2.6 MB view details)

Uploaded PyPymanylinux: glibc 2.17+ ARM64

pymemesuite-0.1.0a2-pp37-pypy37_pp73-macosx_10_9_x86_64.whl (2.4 MB view details)

Uploaded PyPymacOS 10.9+ x86-64

pymemesuite-0.1.0a2-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (10.3 MB view details)

Uploaded CPython 3.11manylinux: glibc 2.17+ x86-64

pymemesuite-0.1.0a2-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (10.2 MB view details)

Uploaded CPython 3.11manylinux: glibc 2.17+ ARM64

pymemesuite-0.1.0a2-cp311-cp311-macosx_11_0_arm64.whl (2.5 MB view details)

Uploaded CPython 3.11macOS 11.0+ ARM64

pymemesuite-0.1.0a2-cp311-cp311-macosx_10_9_x86_64.whl (2.7 MB view details)

Uploaded CPython 3.11macOS 10.9+ x86-64

pymemesuite-0.1.0a2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (10.2 MB view details)

Uploaded CPython 3.10manylinux: glibc 2.17+ x86-64

pymemesuite-0.1.0a2-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (10.1 MB view details)

Uploaded CPython 3.10manylinux: glibc 2.17+ ARM64

pymemesuite-0.1.0a2-cp310-cp310-macosx_11_0_arm64.whl (2.5 MB view details)

Uploaded CPython 3.10macOS 11.0+ ARM64

pymemesuite-0.1.0a2-cp310-cp310-macosx_10_9_x86_64.whl (2.7 MB view details)

Uploaded CPython 3.10macOS 10.9+ x86-64

pymemesuite-0.1.0a2-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (10.2 MB view details)

Uploaded CPython 3.9manylinux: glibc 2.17+ x86-64

pymemesuite-0.1.0a2-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (10.1 MB view details)

Uploaded CPython 3.9manylinux: glibc 2.17+ ARM64

pymemesuite-0.1.0a2-cp39-cp39-macosx_11_0_arm64.whl (2.5 MB view details)

Uploaded CPython 3.9macOS 11.0+ ARM64

pymemesuite-0.1.0a2-cp39-cp39-macosx_10_9_x86_64.whl (2.7 MB view details)

Uploaded CPython 3.9macOS 10.9+ x86-64

pymemesuite-0.1.0a2-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (10.2 MB view details)

Uploaded CPython 3.8manylinux: glibc 2.17+ x86-64

pymemesuite-0.1.0a2-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (10.1 MB view details)

Uploaded CPython 3.8manylinux: glibc 2.17+ ARM64

pymemesuite-0.1.0a2-cp38-cp38-macosx_11_0_arm64.whl (2.5 MB view details)

Uploaded CPython 3.8macOS 11.0+ ARM64

pymemesuite-0.1.0a2-cp38-cp38-macosx_10_9_x86_64.whl (2.7 MB view details)

Uploaded CPython 3.8macOS 10.9+ x86-64

pymemesuite-0.1.0a2-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (10.1 MB view details)

Uploaded CPython 3.7mmanylinux: glibc 2.17+ x86-64

pymemesuite-0.1.0a2-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (10.0 MB view details)

Uploaded CPython 3.7mmanylinux: glibc 2.17+ ARM64

pymemesuite-0.1.0a2-cp37-cp37m-macosx_10_9_x86_64.whl (2.7 MB view details)

Uploaded CPython 3.7mmacOS 10.9+ x86-64

pymemesuite-0.1.0a2-cp36-cp36m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (10.1 MB view details)

Uploaded CPython 3.6mmanylinux: glibc 2.17+ x86-64

pymemesuite-0.1.0a2-cp36-cp36m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (10.0 MB view details)

Uploaded CPython 3.6mmanylinux: glibc 2.17+ ARM64

pymemesuite-0.1.0a2-cp36-cp36m-macosx_10_9_x86_64.whl (2.7 MB view details)

Uploaded CPython 3.6mmacOS 10.9+ x86-64

File details

Details for the file pymemesuite-0.1.0a2.tar.gz.

File metadata

  • Download URL: pymemesuite-0.1.0a2.tar.gz
  • Upload date:
  • Size: 3.6 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.9.16

File hashes

Hashes for pymemesuite-0.1.0a2.tar.gz
Algorithm Hash digest
SHA256 e594693d379b35b68048ee090bc8c6c876f99a0059000c826202cd129e5a13c8
MD5 1d2925286eaffd4e275c4f3350b5b9d6
BLAKE2b-256 25ab6d9954f825b3997d30b448f32f2c5683e2a4e20209443b8f0f985e324456

See more details on using hashes here.

File details

Details for the file pymemesuite-0.1.0a2-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for pymemesuite-0.1.0a2-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 ddf968c6a32969674fe1a1c2678106eb96b169f4c8d6379f54a0d8fa87371d51
MD5 64d68301ca8b90c1c1a24eeac464df8a
BLAKE2b-256 51473e6dac0c0cb3fb5f2952e043ead01af8be60cfeba36a53fe8f6c54b26dad

See more details on using hashes here.

File details

Details for the file pymemesuite-0.1.0a2-pp39-pypy39_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for pymemesuite-0.1.0a2-pp39-pypy39_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 2ca84f5afc99402d659e0dc3ad5ebeb32b6b7fe87194a8005da420a6e1d5a313
MD5 6642801c1ef8cc21110fac333b32caec
BLAKE2b-256 308d327d4b4ed040f3fea76236e567481ce62438ac2a486e7feb12faeb3b2f0c

See more details on using hashes here.

File details

Details for the file pymemesuite-0.1.0a2-pp39-pypy39_pp73-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for pymemesuite-0.1.0a2-pp39-pypy39_pp73-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 9c8f86dee1767e22aab4bac718f3c161b2c6bdae4d0d624b4f61feb94227248a
MD5 1426ca108e9ae0530ba13fd9fb8583ae
BLAKE2b-256 0bf2a34cc4595938779e1e16a787ab45f111f7a2e109811a399b4a3f1d5f94f4

See more details on using hashes here.

File details

Details for the file pymemesuite-0.1.0a2-pp38-pypy38_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for pymemesuite-0.1.0a2-pp38-pypy38_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 5416b42be8a4e0d51f95324eaf2b2c45e80d0dfa4a04d71ffcc20e46d81f9a41
MD5 37162f3167dab6ea38553ae0d4f36b10
BLAKE2b-256 9103aab589610aeea4a3e6387dec866c8d3eb19211c453c6999a1827176ac3e5

See more details on using hashes here.

File details

Details for the file pymemesuite-0.1.0a2-pp38-pypy38_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for pymemesuite-0.1.0a2-pp38-pypy38_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 146eae99190cc880fa35a262f8df0f004c140e790b532b03a1ed524320de1370
MD5 ee65bcdb594e2a7b914831bfd3bf7d0e
BLAKE2b-256 3fb21104b2c6fc41ecdcb0fb57fa6f996567932afad9f7e2a6c1208e031d76c6

See more details on using hashes here.

File details

Details for the file pymemesuite-0.1.0a2-pp38-pypy38_pp73-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for pymemesuite-0.1.0a2-pp38-pypy38_pp73-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 f285e82e66b64d480a903ac5479ab83887d888c34c3a1cdc9008ddda351bed1b
MD5 fa73117a3c247a38309cfe549b038fe8
BLAKE2b-256 ab54f49dc4c903e5a759a26b01df37729adea8fdf15786ecd4039eecc22878e5

See more details on using hashes here.

File details

Details for the file pymemesuite-0.1.0a2-pp37-pypy37_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for pymemesuite-0.1.0a2-pp37-pypy37_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 9b1627fc2ff5e29f3be051868a49b61505cff69da580ae4c0237d6bfd0b9e2ee
MD5 dc47a6250a46d043b6bd588ecbdaa091
BLAKE2b-256 2b65411ac947d019144aa70416530d0b30e211fae4c94b49111d7984ffa6bac5

See more details on using hashes here.

File details

Details for the file pymemesuite-0.1.0a2-pp37-pypy37_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for pymemesuite-0.1.0a2-pp37-pypy37_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 ced0ff7786df56d63de4f5b83dfde53c5d2a22eead52ae012158ea37b7b9dd01
MD5 acc270c34d00855a10f0b8f2ce017804
BLAKE2b-256 1029690f65fd1afa9cc683a4c895cc3597fe214299bb06578172a67662afdb3b

See more details on using hashes here.

File details

Details for the file pymemesuite-0.1.0a2-pp37-pypy37_pp73-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for pymemesuite-0.1.0a2-pp37-pypy37_pp73-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 26f62e60a4d2ffe6fb4b9242fa0dbeee108aa2adf8c595e3737a9073b583ca45
MD5 8a72b1ed74f6a7216000981a4205cd0e
BLAKE2b-256 9a321c344af93bdbf04722fab31c15ad7042d7253959ec8c169bb58226b479e5

See more details on using hashes here.

File details

Details for the file pymemesuite-0.1.0a2-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for pymemesuite-0.1.0a2-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 7ce66628ffe4eb9a6d0b55224bb263794c9b78840ba8408e5526b6f6b96b86bd
MD5 712f9b36fd27573780b84dd443a16b9f
BLAKE2b-256 b0d339961f169490cb400f3ad12fcc3ac23f0d62835cf367b8ede68e6a2556ca

See more details on using hashes here.

File details

Details for the file pymemesuite-0.1.0a2-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for pymemesuite-0.1.0a2-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 00070627daa7675406d4bf29f7cf85107f1fc864c75233fb5d6bde39b203c453
MD5 389c491187cd452cb3fc8759fcdd15e0
BLAKE2b-256 23c08d7899d8054449fc23a7c85c2b33f5b3ef3b445116a6b9f253b3d21c5c57

See more details on using hashes here.

File details

Details for the file pymemesuite-0.1.0a2-cp311-cp311-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for pymemesuite-0.1.0a2-cp311-cp311-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 1bf573df571349ecedf45b2a5d1f151468f155ca22d49634e46961294aa39fd4
MD5 e3d2bf1a58c298a4b5c64888e9ba64b9
BLAKE2b-256 8b60409c4009848a2cd34b4450cee255c20863bca9af7ba15b63535ae3bc9c04

See more details on using hashes here.

File details

Details for the file pymemesuite-0.1.0a2-cp311-cp311-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for pymemesuite-0.1.0a2-cp311-cp311-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 dba3a900c07fabefad78fdefccbb9c7f44a57e63707dcb505d8cbd127102aa8a
MD5 86240354fa0e62e2d80d8914e6ac524a
BLAKE2b-256 936fb2c0094ee2c7635e32b87ae06d5b7f381d17b835c4b74d7b2403555154a5

See more details on using hashes here.

File details

Details for the file pymemesuite-0.1.0a2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for pymemesuite-0.1.0a2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 ceadd63f4c3b0e80f58d0fabdc2c3084bc1cfe38f297cf77ee81aad4d158ce8f
MD5 688279a0847b0ba2298e1e21e502d336
BLAKE2b-256 299c4d3e7e2ab7539f6470734dfdb1f162d5b2e194e50b1ef8104b870393cc65

See more details on using hashes here.

File details

Details for the file pymemesuite-0.1.0a2-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for pymemesuite-0.1.0a2-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 fd128b8467c45a4cfdac9bf671eb5a41d0389166bb89b643b3216064e729207f
MD5 2f044c63333a9fd25e83dc3a0f97aaf5
BLAKE2b-256 85ae4b34c76d71ae5af402e1f5f8fb7b02fb88f240fb67e726c6cd6dc6a9d155

See more details on using hashes here.

File details

Details for the file pymemesuite-0.1.0a2-cp310-cp310-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for pymemesuite-0.1.0a2-cp310-cp310-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 8165bd2d128db8d4b1ca449af51d4caa0fee07a740fa8c96be6a2ccf864daceb
MD5 a243440354fcb1b59636880135e7b342
BLAKE2b-256 bf930e3d23b091bf9b14d78d72c9e3f6b8faa257e69b545f8acf019456885313

See more details on using hashes here.

File details

Details for the file pymemesuite-0.1.0a2-cp310-cp310-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for pymemesuite-0.1.0a2-cp310-cp310-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 ecfce01fb279205f03c55d8b1fa076c48318ac43670e52a83b566f36f558cd5b
MD5 ed427f1e078f6179e65f1625521e9ae2
BLAKE2b-256 b35874959c12f92df08df44db63837e6a2a3bda89bf49337cb8b26ce95983537

See more details on using hashes here.

File details

Details for the file pymemesuite-0.1.0a2-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for pymemesuite-0.1.0a2-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 dd025d65a3e03f4417008ec15d9e5c5490ccc5528fe35057a089c9391946031d
MD5 8b9855657f4c74b3a87b9f59cc23c15a
BLAKE2b-256 e8374935e19d238c8fda058e3232f60e7a3408854f63bda99345ea11fac69d6a

See more details on using hashes here.

File details

Details for the file pymemesuite-0.1.0a2-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for pymemesuite-0.1.0a2-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 bbda26bd3f724e029b03411529a62160b9dfdeaf792792c56d7885406fae3da6
MD5 7036e326299bed8a8cfa341b29ec82c9
BLAKE2b-256 88dcc85adc8cd98e221197bc09be537c25ba7c338786f84c4a947eedea73022c

See more details on using hashes here.

File details

Details for the file pymemesuite-0.1.0a2-cp39-cp39-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for pymemesuite-0.1.0a2-cp39-cp39-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 23774df11b5e89fea04614c9ef96b3f6b01bb8381b498f13cdb61ee6b5a141c4
MD5 a73d89e22279f32aa1477746a5aacd39
BLAKE2b-256 937610c638bc4093e5420e288f71a702e35ea9f3f382394f54f319c3f7862118

See more details on using hashes here.

File details

Details for the file pymemesuite-0.1.0a2-cp39-cp39-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for pymemesuite-0.1.0a2-cp39-cp39-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 4921dc046d111d4a1ec64fa384431f70078eda4721163f0f7c1f64f4f68a0d65
MD5 998183469baf36faf2ffab9c1193932e
BLAKE2b-256 5ba3c991fff4557ed3617b71674ca5693f3f277d43b28df36d4c57720e7bc0c9

See more details on using hashes here.

File details

Details for the file pymemesuite-0.1.0a2-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for pymemesuite-0.1.0a2-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 fb08197f9777b99ddf05875b1dbc49dcb7f61bf5f4d65a649049cfedd8c1e5a5
MD5 ddc86f00c6a6132477ad7153b1ac7916
BLAKE2b-256 15d4ca7b0d5cc7c97e88200df57741a205b47ba537d75d375d04fb4e7d9b4aa0

See more details on using hashes here.

File details

Details for the file pymemesuite-0.1.0a2-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for pymemesuite-0.1.0a2-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 ceddea74f2ca1b85145f954b401cbddeedf69462aebd156ee890dcb10c40fbb9
MD5 f73751bba3ae2cb78cbff6811a86a9ae
BLAKE2b-256 f590775e0d1372dd6c1e440ac09220c66c713b2ff48f2d6fad1a2f26746fbae0

See more details on using hashes here.

File details

Details for the file pymemesuite-0.1.0a2-cp38-cp38-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for pymemesuite-0.1.0a2-cp38-cp38-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 73d9ab93585b482c41b5f14805cc21cb69d6fb5a7814ca2f9f95bff953e9e92b
MD5 42373a7b5caff48cb16ddd11d73195ad
BLAKE2b-256 83e04a13865a396d1ec8ce40ebf83773c0c9a235c791adc8ad1f6910aa1fc37d

See more details on using hashes here.

File details

Details for the file pymemesuite-0.1.0a2-cp38-cp38-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for pymemesuite-0.1.0a2-cp38-cp38-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 178dd46552d66d2fe358993f384991f2302cb9f5dd26a76e99903553de626b16
MD5 030a823ff48a369657d9f3c6e22723f3
BLAKE2b-256 21dde8cbf25345ccde6f3cdd0b64bbc26e0593422e4de4215b9cfe9bbf5b0104

See more details on using hashes here.

File details

Details for the file pymemesuite-0.1.0a2-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for pymemesuite-0.1.0a2-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 5f4775d8fab8e97244fcfcc2fb86ac04a43b8ff5a4b241418e19e7ca5a6e0a49
MD5 f4bb67c19ba71332bb4f1b9defeb84a6
BLAKE2b-256 fa8782af59bf58e999e4a1890149170d8e53d6c1ca7ef02c3bd982edec0f9f3e

See more details on using hashes here.

File details

Details for the file pymemesuite-0.1.0a2-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for pymemesuite-0.1.0a2-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 b634fb816c31221fca2ed451b53a6b81c3d2738a6db318d08da6075302c3008c
MD5 5602e016fa7abb14d9d95db523de2fbb
BLAKE2b-256 a49c2d40a3cc1da96adc621b8ac783082666cd0f03223b024fd401819d3e36a4

See more details on using hashes here.

File details

Details for the file pymemesuite-0.1.0a2-cp37-cp37m-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for pymemesuite-0.1.0a2-cp37-cp37m-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 8672c2686c4a126286bc4412c15e75ddcaf443e75b01fdc211e04e4f97b66354
MD5 a94e9cb05a7acf99cd6aef12e2993a9c
BLAKE2b-256 473ba308123a3f3031d8bf2e5751ccbef5298dbb33ba5a8f681589b9e60644e8

See more details on using hashes here.

File details

Details for the file pymemesuite-0.1.0a2-cp36-cp36m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for pymemesuite-0.1.0a2-cp36-cp36m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 a90c0e562867ae51ed6d1820389d2b3f1fdf381f7805eeee4a29d2ae726bd103
MD5 a6d42c0ed20dd6e835655f6c1cb274a6
BLAKE2b-256 3965e3b24497d3270b5a6142fef6f658e8807de751670594db2e5bb7d71d9da0

See more details on using hashes here.

File details

Details for the file pymemesuite-0.1.0a2-cp36-cp36m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for pymemesuite-0.1.0a2-cp36-cp36m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 2159c535139237908fe5068a4f241c1553576e76464d49a4f368aea740aab5a0
MD5 0c5961b53d980b6aa39e190d1a55ad2d
BLAKE2b-256 7e1978388e4648f46bc1e05e8f576fdb5df1343c1444383bbc09dddfee703849

See more details on using hashes here.

File details

Details for the file pymemesuite-0.1.0a2-cp36-cp36m-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for pymemesuite-0.1.0a2-cp36-cp36m-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 ea21721dc708848394f4ae6cfbbf837aa1e68f4963931de57b0cfd6aebc1c484
MD5 5b3d206d3f2a5efad96f10163dbfca77
BLAKE2b-256 baf8e2db4a2b6124c42955b7571c5260ce9dc3de8d3e81bca14378598a87cd10

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page