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.

Testing git repo

Dry Run Testing for moose dev

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 demos here:
https://github.com/BhallaLab/moose-examples.

Build

To build pymoose, follow the instructions given in
INSTALL.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-4.1.2-cp313-cp313-win_amd64.whl (2.9 MB view details)

Uploaded CPython 3.13Windows x86-64

pymoose_development_jayesh-4.1.2-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-4.1.2-cp313-cp313-macosx_14_0_arm64.whl (4.5 MB view details)

Uploaded CPython 3.13macOS 14.0+ ARM64

pymoose_development_jayesh-4.1.2-cp312-cp312-win_amd64.whl (2.9 MB view details)

Uploaded CPython 3.12Windows x86-64

pymoose_development_jayesh-4.1.2-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-4.1.2-cp312-cp312-macosx_14_0_arm64.whl (4.5 MB view details)

Uploaded CPython 3.12macOS 14.0+ ARM64

pymoose_development_jayesh-4.1.2-cp311-cp311-win_amd64.whl (2.9 MB view details)

Uploaded CPython 3.11Windows x86-64

pymoose_development_jayesh-4.1.2-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-4.1.2-cp311-cp311-macosx_14_0_arm64.whl (4.4 MB view details)

Uploaded CPython 3.11macOS 14.0+ ARM64

pymoose_development_jayesh-4.1.2-cp310-cp310-win_amd64.whl (2.9 MB view details)

Uploaded CPython 3.10Windows x86-64

pymoose_development_jayesh-4.1.2-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-4.1.2-cp310-cp310-macosx_14_0_arm64.whl (4.4 MB view details)

Uploaded CPython 3.10macOS 14.0+ ARM64

pymoose_development_jayesh-4.1.2-cp39-cp39-win_amd64.whl (2.9 MB view details)

Uploaded CPython 3.9Windows x86-64

pymoose_development_jayesh-4.1.2-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-4.1.2-cp39-cp39-macosx_14_0_arm64.whl (4.4 MB view details)

Uploaded CPython 3.9macOS 14.0+ ARM64

pymoose_development_jayesh-4.1.2-cp38-cp38-win_amd64.whl (2.9 MB view details)

Uploaded CPython 3.8Windows x86-64

pymoose_development_jayesh-4.1.2-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-4.1.2-cp313-cp313-win_amd64.whl.

File metadata

File hashes

Hashes for pymoose_development_jayesh-4.1.2-cp313-cp313-win_amd64.whl
Algorithm Hash digest
SHA256 7a739181403186ce0135f9410ebdefd7fe7ffadab71b7cae09f10e634d622474
MD5 ee190d804c9f642396a25bc59278803f
BLAKE2b-256 e27e546f3200f0a8b344683a7f03aadcec0606c238ba88ea7e3d7e4c9e6b66fd

See more details on using hashes here.

File details

Details for the file pymoose_development_jayesh-4.1.2-cp313-cp313-manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for pymoose_development_jayesh-4.1.2-cp313-cp313-manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 6f57f92063e81d86c92e977d33f3c666f900ff2a40557aac1a27518bc42a98ea
MD5 2b498266cd79d964884d21d0d8bbd51d
BLAKE2b-256 efbc26752a4ce446f6e10e9d7094046d8b8de2c0c67aff0eec16b85595e54985

See more details on using hashes here.

File details

Details for the file pymoose_development_jayesh-4.1.2-cp313-cp313-macosx_14_0_arm64.whl.

File metadata

File hashes

Hashes for pymoose_development_jayesh-4.1.2-cp313-cp313-macosx_14_0_arm64.whl
Algorithm Hash digest
SHA256 104b70e9a6d1dfa59f568e9aa1df5c77539ba84561cedd061449472155bd18d2
MD5 0d19307ed210c53875998fe8d64a9af5
BLAKE2b-256 3cb7f20d6889d7293219a3b43cce608d4f4059b1c7715deda4bded6b232818db

See more details on using hashes here.

File details

Details for the file pymoose_development_jayesh-4.1.2-cp312-cp312-win_amd64.whl.

File metadata

File hashes

Hashes for pymoose_development_jayesh-4.1.2-cp312-cp312-win_amd64.whl
Algorithm Hash digest
SHA256 4cd1233ddb1886cffeca4908605b5dc46a34919437eb86b0209366d9f7536bfd
MD5 55fecede0c5a0a1ccdd5e0dbe2c6cc29
BLAKE2b-256 30831abdb21bc6c4255044699cd8fbc196b3f8d88360f6c7e715711eb71a5d8b

See more details on using hashes here.

File details

Details for the file pymoose_development_jayesh-4.1.2-cp312-cp312-manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for pymoose_development_jayesh-4.1.2-cp312-cp312-manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 4906060ffc8a48363043dc2546da5bd46d659c5c28b1c97e56aa69824d1c63cb
MD5 55ed2db2e2738350ca3f516c5a0869f8
BLAKE2b-256 6b04429783a1a4730face0b4ef1987992dff5936ba7eeb6638dc03cf391144c9

See more details on using hashes here.

File details

Details for the file pymoose_development_jayesh-4.1.2-cp312-cp312-macosx_14_0_arm64.whl.

File metadata

File hashes

Hashes for pymoose_development_jayesh-4.1.2-cp312-cp312-macosx_14_0_arm64.whl
Algorithm Hash digest
SHA256 879c605c51286cc07d62fa7445547592c21d197faffc3220bcda742e7299ec61
MD5 5ffd72cd87e9cc4b1d1011a97ef75fcb
BLAKE2b-256 0969da324dc88dc981e18c08a8709f0318613ee88aff5377a30fb3694745bbb4

See more details on using hashes here.

File details

Details for the file pymoose_development_jayesh-4.1.2-cp311-cp311-win_amd64.whl.

File metadata

File hashes

Hashes for pymoose_development_jayesh-4.1.2-cp311-cp311-win_amd64.whl
Algorithm Hash digest
SHA256 a322c48f064a339ca8f0fc8bf9fa718a6ab2a93ac993b025e2aa547599cc81d7
MD5 4e51a3e1c2f58f7c7ae0b9e902330ffd
BLAKE2b-256 137b28c34c7ec94c37f6cc7e79159da8a4c02c960ffaceadc217bfcbf0f1be8d

See more details on using hashes here.

File details

Details for the file pymoose_development_jayesh-4.1.2-cp311-cp311-manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for pymoose_development_jayesh-4.1.2-cp311-cp311-manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 6734c72fb962a241c4aabebb4f3aa060849eede2b3682c4fd843e5ae5111b8d2
MD5 1b8ad3156a1f802c6dacad266879733e
BLAKE2b-256 d52a47f37dfc437b0a3c74db8b7c011b8fdef3fd4d4c931ea86ade17a0268d56

See more details on using hashes here.

File details

Details for the file pymoose_development_jayesh-4.1.2-cp311-cp311-macosx_14_0_arm64.whl.

File metadata

File hashes

Hashes for pymoose_development_jayesh-4.1.2-cp311-cp311-macosx_14_0_arm64.whl
Algorithm Hash digest
SHA256 6d087ee01c92e722e6e99b90327b06ad540c5852d3e361dbe2b7db000eb12ad0
MD5 31258d28cd69dcf16acf11932452ef75
BLAKE2b-256 f5154be997fbe5271cc86175f509445925eed4eb1281af72f8f3965f3ebaf0bd

See more details on using hashes here.

File details

Details for the file pymoose_development_jayesh-4.1.2-cp310-cp310-win_amd64.whl.

File metadata

File hashes

Hashes for pymoose_development_jayesh-4.1.2-cp310-cp310-win_amd64.whl
Algorithm Hash digest
SHA256 e49205fe1138dbe4718b2da3e0bc48348b485156c1054472e74c42bf3ec49b04
MD5 e1c2e9dd0ad4d8f3e869ac97778e7f27
BLAKE2b-256 45ec061f7bab5ad341a3a4d7de211bd4ca198a5afcefc4411c1ade853f7ae0d9

See more details on using hashes here.

File details

Details for the file pymoose_development_jayesh-4.1.2-cp310-cp310-manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for pymoose_development_jayesh-4.1.2-cp310-cp310-manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 cef8009f7f1c09225aa66a45155076da96107f38f91a89aa6e55af08019fe153
MD5 3688c8e52d93a8bafed057e2bc3a2340
BLAKE2b-256 01be3eddb051e61aac9cb56dcad01ca67842974cd42bc64b55cbb1fa366951c1

See more details on using hashes here.

File details

Details for the file pymoose_development_jayesh-4.1.2-cp310-cp310-macosx_14_0_arm64.whl.

File metadata

File hashes

Hashes for pymoose_development_jayesh-4.1.2-cp310-cp310-macosx_14_0_arm64.whl
Algorithm Hash digest
SHA256 642e819c59a96e9b4c3986de528768e0c3ea90e117d41f6a37f71c871bb00f1b
MD5 a9a2190679d5f64d7beac81f12b45002
BLAKE2b-256 c09da63a7dc29d98a6009b402492eac0b1404d0b6c8453d5aa0f082899d39b8c

See more details on using hashes here.

File details

Details for the file pymoose_development_jayesh-4.1.2-cp39-cp39-win_amd64.whl.

File metadata

File hashes

Hashes for pymoose_development_jayesh-4.1.2-cp39-cp39-win_amd64.whl
Algorithm Hash digest
SHA256 91540c972a7dcc8841507530bdb10c017d544b19da87b5f5cc2ef6bfd078d56c
MD5 67a94b3cea265e69f8afa6e199474288
BLAKE2b-256 44ace9331a82e5eb8f619bf42bac19a0544880e169ad57d8ce38b449d7d3da6e

See more details on using hashes here.

File details

Details for the file pymoose_development_jayesh-4.1.2-cp39-cp39-manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for pymoose_development_jayesh-4.1.2-cp39-cp39-manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 8d29e50db3109000ace6aeee71f90f5d0047a736ef8fff724fba548db6c13424
MD5 49d77d36132e589f09e8b0a98f794b67
BLAKE2b-256 6df060404670071a5ae4ff0b61ffe8bd6abb4752fe0f1a4c6daff6da8703cde5

See more details on using hashes here.

File details

Details for the file pymoose_development_jayesh-4.1.2-cp39-cp39-macosx_14_0_arm64.whl.

File metadata

File hashes

Hashes for pymoose_development_jayesh-4.1.2-cp39-cp39-macosx_14_0_arm64.whl
Algorithm Hash digest
SHA256 f9d880ed5d7de0b6d27e181ce42df1229099f2abfee584651c7282fddaa5e372
MD5 b8f1873f07d8813e44415a09b3caf184
BLAKE2b-256 cdb5a7e5e53aec4ce652d917c23756437b0efbdb9e4a8506bf448af93383c3d5

See more details on using hashes here.

File details

Details for the file pymoose_development_jayesh-4.1.2-cp38-cp38-win_amd64.whl.

File metadata

File hashes

Hashes for pymoose_development_jayesh-4.1.2-cp38-cp38-win_amd64.whl
Algorithm Hash digest
SHA256 28f23ae93368cbe643a6e2e8b840512acf9b40a6ab5b42607e34ed72537a3250
MD5 0a9bd17f1daf6a1973fd49e499fc839f
BLAKE2b-256 7ae342826a32df972e3ee96b084f3958d2d78fd6c7f98da4b9d8b33aa0b30f1a

See more details on using hashes here.

File details

Details for the file pymoose_development_jayesh-4.1.2-cp38-cp38-manylinux_2_28_x86_64.whl.

File metadata

File hashes

Hashes for pymoose_development_jayesh-4.1.2-cp38-cp38-manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 712d406546e2ada7e720685d5085a8579e5c20fce5f03c761fcc9139817bc175
MD5 2a4c22aa98bfb63af8c95932ee869463
BLAKE2b-256 29723e1fbba6ba40c91763a8c514b4ca01f945019594c3e8ebcf67ff95eeec24

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