Skip to main content

Python scripting interface of MOOSE Simulator (https://moose.ncbs.res.in)

Project description

Python package

Repo for testing git workflow

This is a repo for testing workflow using git.

This is another test.

This is the third commit entry.

MOOSE

MOOSE is the Multiscale Object-Oriented Simulation Environment. It is designed to simulate neural systems ranging from subcellular components and biochemical reactions to complex models of single neurons, circuits, and large networks. MOOSE can operate at many levels of detail, from stochastic chemical computations, to multicompartment single-neuron models, to spiking neuron network models.

MOOSE is multiscale: It can do all these calculations together. For example it handles interactions seamlessly between electrical and chemical signaling. MOOSE is object-oriented. Biological concepts are mapped into classes, and a model is built by creating instances of these classes and connecting them by messages. MOOSE also has classes whose job is to take over difficult computations in a certain domain, and do them fast. There are such solver classes for stochastic and deterministic chemistry, for diffusion, and for multicompartment neuronal models.

MOOSE is a simulation environment, not just a numerical engine: It provides data representations and solvers (of course!), but also a scripting interface with Python, graphical displays with Matplotlib, PyQt, and VPython, and support for many model formats. These include SBML, NeuroML, GENESIS kkit and cell.p formats, HDF5 and NSDF for data writing.

This is the core computational engine of MOOSE simulator. This repository contains C++ codebase and python interface called pymoose. For more details about MOOSE simulator, visit https://moose.ncbs.res.in .


Installation

See INSTALL.md for instructions on installation.

Have a look at examples, tutorials and demo here https://github.com/BhallaLab/moose-examples.

Build

To build pymoose, follow instructions given in INSTALLATION.md and for platform specific information see:

ABOUT VERSION 4.1.0, Jhangri

Jhangri is an Indian sweet in the shape of a flower. It is made of white-lentil (Vigna mungo) batter, deep-fried in ornamental shape to form the crunchy, golden body, which is then soaked in sugar syrup lightly flavoured with spices.

This release has the following major changes:

  1. Improved support for reading NeuroML2 models
  2. HHGate2D: separate xminA, xminB, etc. for A and B tables replaced by single xmin, xmax, xdivs, ymin, ymax, and ydivs fields for both tables.
  3. Build system switched from cmake to meson
  4. Native binaries for Windows
  5. Updated to conform to c/c++-17 standard
  6. Various bugfixes.

LICENSE

MOOSE is released under GPLv3.

Project details


Download files

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

Source Distributions

No source distribution files available for this release.See tutorial on generating distribution archives.

Built Distributions

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

pymoose_development_jayesh-4.1.0-cp313-cp313-win_amd64.whl (2.9 MB view details)

Uploaded CPython 3.13Windows x86-64

pymoose_development_jayesh-4.1.0-cp313-cp313-manylinux_2_28_x86_64.whl (6.8 MB view details)

Uploaded CPython 3.13manylinux: glibc 2.28+ x86-64

pymoose_development_jayesh-4.1.0-cp312-cp312-win_amd64.whl (2.9 MB view details)

Uploaded CPython 3.12Windows x86-64

pymoose_development_jayesh-4.1.0-cp312-cp312-manylinux_2_28_x86_64.whl (6.8 MB view details)

Uploaded CPython 3.12manylinux: glibc 2.28+ x86-64

pymoose_development_jayesh-4.1.0-cp311-cp311-win_amd64.whl (2.9 MB view details)

Uploaded CPython 3.11Windows x86-64

pymoose_development_jayesh-4.1.0-cp311-cp311-manylinux_2_28_x86_64.whl (6.8 MB view details)

Uploaded CPython 3.11manylinux: glibc 2.28+ x86-64

pymoose_development_jayesh-4.1.0-cp310-cp310-win_amd64.whl (2.9 MB view details)

Uploaded CPython 3.10Windows x86-64

pymoose_development_jayesh-4.1.0-cp310-cp310-manylinux_2_28_x86_64.whl (6.8 MB view details)

Uploaded CPython 3.10manylinux: glibc 2.28+ x86-64

pymoose_development_jayesh-4.1.0-cp39-cp39-win_amd64.whl (2.9 MB view details)

Uploaded CPython 3.9Windows x86-64

pymoose_development_jayesh-4.1.0-cp39-cp39-manylinux_2_28_x86_64.whl (6.8 MB view details)

Uploaded CPython 3.9manylinux: glibc 2.28+ x86-64

pymoose_development_jayesh-4.1.0-cp38-cp38-win_amd64.whl (2.9 MB view details)

Uploaded CPython 3.8Windows x86-64

pymoose_development_jayesh-4.1.0-cp38-cp38-manylinux_2_28_x86_64.whl (6.8 MB view details)

Uploaded CPython 3.8manylinux: glibc 2.28+ x86-64

pymoose_development_jayesh-4.1-cp313-cp313-win_amd64.whl (2.9 MB view details)

Uploaded CPython 3.13Windows x86-64

pymoose_development_jayesh-4.1-cp313-cp313-manylinux_2_28_x86_64.whl (6.8 MB view details)

Uploaded CPython 3.13manylinux: glibc 2.28+ x86-64

pymoose_development_jayesh-4.1-cp313-cp313-macosx_14_0_arm64.whl (4.5 MB view details)

Uploaded CPython 3.13macOS 14.0+ ARM64

pymoose_development_jayesh-4.1-cp312-cp312-win_amd64.whl (2.9 MB view details)

Uploaded CPython 3.12Windows x86-64

pymoose_development_jayesh-4.1-cp312-cp312-manylinux_2_28_x86_64.whl (6.8 MB view details)

Uploaded CPython 3.12manylinux: glibc 2.28+ x86-64

pymoose_development_jayesh-4.1-cp312-cp312-macosx_14_0_arm64.whl (4.5 MB view details)

Uploaded CPython 3.12macOS 14.0+ ARM64

pymoose_development_jayesh-4.1-cp311-cp311-win_amd64.whl (2.9 MB view details)

Uploaded CPython 3.11Windows x86-64

pymoose_development_jayesh-4.1-cp311-cp311-manylinux_2_28_x86_64.whl (6.8 MB view details)

Uploaded CPython 3.11manylinux: glibc 2.28+ x86-64

pymoose_development_jayesh-4.1-cp311-cp311-macosx_14_0_arm64.whl (4.4 MB view details)

Uploaded CPython 3.11macOS 14.0+ ARM64

pymoose_development_jayesh-4.1-cp310-cp310-win_amd64.whl (2.9 MB view details)

Uploaded CPython 3.10Windows x86-64

pymoose_development_jayesh-4.1-cp310-cp310-manylinux_2_28_x86_64.whl (6.8 MB view details)

Uploaded CPython 3.10manylinux: glibc 2.28+ x86-64

pymoose_development_jayesh-4.1-cp310-cp310-macosx_14_0_arm64.whl (4.4 MB view details)

Uploaded CPython 3.10macOS 14.0+ ARM64

pymoose_development_jayesh-4.1-cp39-cp39-win_amd64.whl (2.9 MB view details)

Uploaded CPython 3.9Windows x86-64

pymoose_development_jayesh-4.1-cp39-cp39-manylinux_2_28_x86_64.whl (6.8 MB view details)

Uploaded CPython 3.9manylinux: glibc 2.28+ x86-64

pymoose_development_jayesh-4.1-cp39-cp39-macosx_14_0_arm64.whl (4.4 MB view details)

Uploaded CPython 3.9macOS 14.0+ ARM64

pymoose_development_jayesh-4.1-cp38-cp38-win_amd64.whl (2.9 MB view details)

Uploaded CPython 3.8Windows x86-64

pymoose_development_jayesh-4.1-cp38-cp38-manylinux_2_28_x86_64.whl (6.8 MB view details)

Uploaded CPython 3.8manylinux: glibc 2.28+ x86-64

File details

Details for the file pymoose_development_jayesh-4.1.0-cp313-cp313-win_amd64.whl.

File metadata

File hashes

Hashes for pymoose_development_jayesh-4.1.0-cp313-cp313-win_amd64.whl
Algorithm Hash digest
SHA256 77c57fb22c8897401cc1fa77dd0287ba386b84cdbd3ff79f2b81020b9d1e840e
MD5 b3e1f3e2da12293439d146559724f465
BLAKE2b-256 2e902e99b3ec479a66f2ce1abc90c7de6c756d6336f6ac568e14a51f149cbc9b

See more details on using hashes here.

File details

Details for the file pymoose_development_jayesh-4.1.0-cp313-cp313-manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for pymoose_development_jayesh-4.1.0-cp313-cp313-manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 5ee14371a92288a4cf6164ce8e07a111878fd5564102eb457e5980d5cfe6def3
MD5 097199e8c6c9acd84afc7e5c144abdef
BLAKE2b-256 17134d6ff01b8fb59666cd95e513b8f371b29d6dbcdfa773960d2cfc94eb949c

See more details on using hashes here.

File details

Details for the file pymoose_development_jayesh-4.1.0-cp312-cp312-win_amd64.whl.

File metadata

File hashes

Hashes for pymoose_development_jayesh-4.1.0-cp312-cp312-win_amd64.whl
Algorithm Hash digest
SHA256 4096cd2fdf43a957a60d3d60048cecc614e2e361ec5e4486e50cbc5f7ae7d07a
MD5 cc7ef87d096988a1e90b24140c05c1de
BLAKE2b-256 0cf8a1f712f243e7e1cdd8664910a134c647e2398dbcd21e2292212c70fbf024

See more details on using hashes here.

File details

Details for the file pymoose_development_jayesh-4.1.0-cp312-cp312-manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for pymoose_development_jayesh-4.1.0-cp312-cp312-manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 abf2d89da564ce85efb9989cffe36ed549abad008278f78c98d0731fad70f867
MD5 f01f73d3e9503abe5ff700b5f2b78dfa
BLAKE2b-256 b63e0e37a0740e82c3268db9e877ebc5803814396a5af855ff4fa7db370a8879

See more details on using hashes here.

File details

Details for the file pymoose_development_jayesh-4.1.0-cp311-cp311-win_amd64.whl.

File metadata

File hashes

Hashes for pymoose_development_jayesh-4.1.0-cp311-cp311-win_amd64.whl
Algorithm Hash digest
SHA256 73bec49f98f6e9519eda9591aa93d25e3db15649637b742749c1b1f2640d8395
MD5 4a9ca3a8119ed7c1edd4a9ceb7d4f453
BLAKE2b-256 ee413cd8bd207a184668658799095d0c63ac38678ea8d9c2a046e4c291ed34e9

See more details on using hashes here.

File details

Details for the file pymoose_development_jayesh-4.1.0-cp311-cp311-manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for pymoose_development_jayesh-4.1.0-cp311-cp311-manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 a746c94833bdae92e716bcee483c56ef8e2b64fb1d57539fefd9517bd0983916
MD5 e6cf647e5a4be1e14f997abcabf7b27c
BLAKE2b-256 b0567c83cb43c0c3587bd2f80e85719aee890f2d692d768a06265066189a10e0

See more details on using hashes here.

File details

Details for the file pymoose_development_jayesh-4.1.0-cp310-cp310-win_amd64.whl.

File metadata

File hashes

Hashes for pymoose_development_jayesh-4.1.0-cp310-cp310-win_amd64.whl
Algorithm Hash digest
SHA256 0fe6d338ccaf309acaa62ebd230d23e9632074cf41aa4ca9a1e56bbc2c4d8e10
MD5 f55f1aa88aaf1e43a7e3fd386a396d05
BLAKE2b-256 2a21fa601f5153af41c9d9db472f9a566781a66d126f2c8a2a7dc888540215e5

See more details on using hashes here.

File details

Details for the file pymoose_development_jayesh-4.1.0-cp310-cp310-manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for pymoose_development_jayesh-4.1.0-cp310-cp310-manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 f288484035b1fa83671d73f85d41572b33d74d2d60cc312167d13eac09f89005
MD5 33ff16c3f179e2fc1917d8da515ad201
BLAKE2b-256 c79787f00d535c4d1edf9a44a01ea3abb6fa4e7e2893403cab741a8f83474112

See more details on using hashes here.

File details

Details for the file pymoose_development_jayesh-4.1.0-cp39-cp39-win_amd64.whl.

File metadata

File hashes

Hashes for pymoose_development_jayesh-4.1.0-cp39-cp39-win_amd64.whl
Algorithm Hash digest
SHA256 5f739fc5cc11688d1ab3bf24251e1290d47f5badcb708fe7e4587b73891a89dc
MD5 4c07bff0a507fbd5e7098fd2a825ec1e
BLAKE2b-256 561c2300ea1f2c153e50766da886667d6065afc02b515f38d682d09b91deb3b9

See more details on using hashes here.

File details

Details for the file pymoose_development_jayesh-4.1.0-cp39-cp39-manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for pymoose_development_jayesh-4.1.0-cp39-cp39-manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 3eaabef8eb7434f08978f080965e0612385ef520fa2787195c19a1c6a6f94605
MD5 e8c607fa52457884208bc9b712fdab02
BLAKE2b-256 128b5e56cbd9563968b2ae48c65313b7b40b190258292c52e0ac6bc29fbce6eb

See more details on using hashes here.

File details

Details for the file pymoose_development_jayesh-4.1.0-cp38-cp38-win_amd64.whl.

File metadata

File hashes

Hashes for pymoose_development_jayesh-4.1.0-cp38-cp38-win_amd64.whl
Algorithm Hash digest
SHA256 417048f45a774ae7ee4e1d2a819e698db9c1455593728d686374718ee0ebeca0
MD5 948d898e6b37f5575a8806adb99e11c2
BLAKE2b-256 b093ae2cd4b397db152d5edf17b51195d2a9d9ea641821ccbf6a1190e62f73fb

See more details on using hashes here.

File details

Details for the file pymoose_development_jayesh-4.1.0-cp38-cp38-manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for pymoose_development_jayesh-4.1.0-cp38-cp38-manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 ba63a2eab2378f091820f5e9962dfd45e7f33b0a87ef0be1f9a7d7e25155dd3e
MD5 a6a38626df494ab66f963438624212e2
BLAKE2b-256 fd7f7bfb844a0bd52934b005111786d5974c0c9fd08aa2f6040f8df25653916a

See more details on using hashes here.

File details

Details for the file pymoose_development_jayesh-4.1-cp313-cp313-win_amd64.whl.

File metadata

File hashes

Hashes for pymoose_development_jayesh-4.1-cp313-cp313-win_amd64.whl
Algorithm Hash digest
SHA256 0517c166fb5fe0ea0cc61afa857868c5f3528ce8f58a992067a7849fde0d2c5e
MD5 5e505cdba08db0c962aac8f83c816c41
BLAKE2b-256 ed9f0ebb727d2b01738012691dab060b002074f4b79553a34b7ba427ef594a98

See more details on using hashes here.

File details

Details for the file pymoose_development_jayesh-4.1-cp313-cp313-manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for pymoose_development_jayesh-4.1-cp313-cp313-manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 80eff9ab2b1fa7531ad86c1d870c501dd8a29677e1dc8f7d036eb2f6773a31d7
MD5 ce488494130cfcf283c1f401b4d5b7d4
BLAKE2b-256 5aa1e50dc9b9182e5cb50011c0facdab9c9140ca85e3157fb822aaa8d2e28df4

See more details on using hashes here.

File details

Details for the file pymoose_development_jayesh-4.1-cp313-cp313-macosx_14_0_arm64.whl.

File metadata

File hashes

Hashes for pymoose_development_jayesh-4.1-cp313-cp313-macosx_14_0_arm64.whl
Algorithm Hash digest
SHA256 5cd339598b30c1d65004c65cb718ddf77058c526eb843c19048e3284f2d42739
MD5 af8bddda37db7eee6f51be8a0d074812
BLAKE2b-256 c34538725616783d10f7658f13c7d0ad5ca9410fb04f3cbc30e170966e77f58a

See more details on using hashes here.

File details

Details for the file pymoose_development_jayesh-4.1-cp312-cp312-win_amd64.whl.

File metadata

File hashes

Hashes for pymoose_development_jayesh-4.1-cp312-cp312-win_amd64.whl
Algorithm Hash digest
SHA256 b69e06e233babccfb9e8f9c5c3c5b5e8bb4f8bc2148356c9a055397c07b2fd57
MD5 d0b2fc3dac5a25ca113e6e1cc17cc0b5
BLAKE2b-256 87d1624ca33299c5a9dce7f87d7277b332833e0eaeec46ba67b9f5b87d79c8d5

See more details on using hashes here.

File details

Details for the file pymoose_development_jayesh-4.1-cp312-cp312-manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for pymoose_development_jayesh-4.1-cp312-cp312-manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 c8a7b1bb555964920d96c147c3db2342290b5d134b86a6b03e378050bd0403b9
MD5 38a3809064f22b910d933d216698c9ee
BLAKE2b-256 63d1779646bcdd4d3043af8b28032d9fd5d6c4289c113e81953255f248d481b9

See more details on using hashes here.

File details

Details for the file pymoose_development_jayesh-4.1-cp312-cp312-macosx_14_0_arm64.whl.

File metadata

File hashes

Hashes for pymoose_development_jayesh-4.1-cp312-cp312-macosx_14_0_arm64.whl
Algorithm Hash digest
SHA256 6a771bac536b7bdb2361907787821efd6a9b633a2c7941f04969af2df5dcc060
MD5 e26e05a6bb28aca05d05147896bf4c2e
BLAKE2b-256 06e8f7f70b665dc13aa0ffd69308690351299d09983f0a8fcf0a74df3fd9afaf

See more details on using hashes here.

File details

Details for the file pymoose_development_jayesh-4.1-cp311-cp311-win_amd64.whl.

File metadata

File hashes

Hashes for pymoose_development_jayesh-4.1-cp311-cp311-win_amd64.whl
Algorithm Hash digest
SHA256 3720b2714c7dcacd38ad9b739487b1b941a51d6135fe6cced387a8c3ea462783
MD5 7aac63767b71c6522d5157fb66d08ee8
BLAKE2b-256 8855af197a8a1f7863462732a18d423462e9f14f4f14e65828a5088ac67484b1

See more details on using hashes here.

File details

Details for the file pymoose_development_jayesh-4.1-cp311-cp311-manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for pymoose_development_jayesh-4.1-cp311-cp311-manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 24d900dc72289b2bd23a6f68b4c6aecb7eb185975a56c54b8705210a2f470c31
MD5 3e8e115d8221e3236c07522401b01f5a
BLAKE2b-256 ecc4dc855ec28c02535ef34ea192a3e1a484a93e202c7158f92bc3a7f3619238

See more details on using hashes here.

File details

Details for the file pymoose_development_jayesh-4.1-cp311-cp311-macosx_14_0_arm64.whl.

File metadata

File hashes

Hashes for pymoose_development_jayesh-4.1-cp311-cp311-macosx_14_0_arm64.whl
Algorithm Hash digest
SHA256 f4b9d127c9510ac75999ded60f0fddf86f719831ef6c632e830a0ace202b1168
MD5 848e93bcf9f17632b6911225d9192207
BLAKE2b-256 6068c11b6a7f9500952d211e6b7e8df7c1a13c73dafca6c4fcc8891761e65544

See more details on using hashes here.

File details

Details for the file pymoose_development_jayesh-4.1-cp310-cp310-win_amd64.whl.

File metadata

File hashes

Hashes for pymoose_development_jayesh-4.1-cp310-cp310-win_amd64.whl
Algorithm Hash digest
SHA256 8343d44987827c96df90a01c5e1ce0528ec50a96ab4465af3ccc0bda9fea7608
MD5 407999440c29e0fedd872050a1881a65
BLAKE2b-256 5bfa3ea29ddab0b25c636f5dbfeecb8bbcfbe5527c76b8d8e5b01fafa2251657

See more details on using hashes here.

File details

Details for the file pymoose_development_jayesh-4.1-cp310-cp310-manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for pymoose_development_jayesh-4.1-cp310-cp310-manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 2ecf04f4a9986983ce1fc84bf47c6473a8a4a8eacef2fd333e7e438f6cc1692e
MD5 53be3bfae49e9ccddf9c6b867e23dcd1
BLAKE2b-256 9e79d4f71b88ec411d67ddfa38bb6df964cdce737e4e2bbbe6257c4bb9e6f12d

See more details on using hashes here.

File details

Details for the file pymoose_development_jayesh-4.1-cp310-cp310-macosx_14_0_arm64.whl.

File metadata

File hashes

Hashes for pymoose_development_jayesh-4.1-cp310-cp310-macosx_14_0_arm64.whl
Algorithm Hash digest
SHA256 35ef8369895410f206a81308f4356ee5df75466160e050269c280ee9bbeb74c5
MD5 006365a037a4aee5f9d7c46b33357ed6
BLAKE2b-256 6366042e2968040d3a4c446c8d1ae4ab9c3bf0af0aa4f2b21ac8e7eb9d1cb4c7

See more details on using hashes here.

File details

Details for the file pymoose_development_jayesh-4.1-cp39-cp39-win_amd64.whl.

File metadata

File hashes

Hashes for pymoose_development_jayesh-4.1-cp39-cp39-win_amd64.whl
Algorithm Hash digest
SHA256 1aab207bbebdf9a6f4f309da7baf2b53b304c86412f8d2e41577df9c658a6113
MD5 eb8e90781b8a8a2713014c1e6971337b
BLAKE2b-256 7170e02d92120e60f9d71dffbdd04f583e5cf3bae402b867fca710b4251fdc61

See more details on using hashes here.

File details

Details for the file pymoose_development_jayesh-4.1-cp39-cp39-manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for pymoose_development_jayesh-4.1-cp39-cp39-manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 8743528c71fd549f4cb146f44e6b45b66169fd245c0e1b8f35546a110ebdacdb
MD5 9639cead37bf597f77845a24cbb8c3a0
BLAKE2b-256 885904692facb089419b8d6a8d8dc5b243dbe2e4744df06944c663a76ab97382

See more details on using hashes here.

File details

Details for the file pymoose_development_jayesh-4.1-cp39-cp39-macosx_14_0_arm64.whl.

File metadata

File hashes

Hashes for pymoose_development_jayesh-4.1-cp39-cp39-macosx_14_0_arm64.whl
Algorithm Hash digest
SHA256 289903ea10d4d7188a59533ad6c7ac87ae73582b0467a8cd7306f8dcd1937619
MD5 53e6a0c2d1541ae9bab049cc6d68b490
BLAKE2b-256 4012ca4e8d0536c23beab3eec95c589ba88ec8a2c157b2efc08d9c6c8deeb932

See more details on using hashes here.

File details

Details for the file pymoose_development_jayesh-4.1-cp38-cp38-win_amd64.whl.

File metadata

File hashes

Hashes for pymoose_development_jayesh-4.1-cp38-cp38-win_amd64.whl
Algorithm Hash digest
SHA256 9a098ff317fd65da95131fcf91221d86f542230c872d34389ce87a844ff1b985
MD5 47b4065a0044603d89fc209af2ce8b56
BLAKE2b-256 6d2355391e4225313beacfe995422aa6952280d23e024dd3481a1e69de6c4f9a

See more details on using hashes here.

File details

Details for the file pymoose_development_jayesh-4.1-cp38-cp38-manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for pymoose_development_jayesh-4.1-cp38-cp38-manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 4edefc10d1b92a22fdde0098ab6cfd7f3dbc728292065315b7d5012b8c2207a7
MD5 923a6ea4c54f609786b5617bd6b08a75
BLAKE2b-256 bee95bd804808d07d1a33808dbe212e74fc8758b26e1618c53b1cf24e3d59d10

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