Skip to main content

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

Project description

Python package

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 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.

moose_core-4.1.2-cp313-cp313-win_amd64.whl (3.0 MB view details)

Uploaded CPython 3.13Windows x86-64

moose_core-4.1.2-cp313-cp313-manylinux_2_28_x86_64.whl (8.7 MB view details)

Uploaded CPython 3.13manylinux: glibc 2.28+ x86-64

moose_core-4.1.2-cp313-cp313-macosx_14_0_arm64.whl (5.8 MB view details)

Uploaded CPython 3.13macOS 14.0+ ARM64

moose_core-4.1.2-cp312-cp312-win_amd64.whl (3.0 MB view details)

Uploaded CPython 3.12Windows x86-64

moose_core-4.1.2-cp312-cp312-manylinux_2_28_x86_64.whl (8.7 MB view details)

Uploaded CPython 3.12manylinux: glibc 2.28+ x86-64

moose_core-4.1.2-cp312-cp312-macosx_14_0_arm64.whl (5.8 MB view details)

Uploaded CPython 3.12macOS 14.0+ ARM64

moose_core-4.1.2-cp311-cp311-win_amd64.whl (3.0 MB view details)

Uploaded CPython 3.11Windows x86-64

moose_core-4.1.2-cp311-cp311-manylinux_2_28_x86_64.whl (8.7 MB view details)

Uploaded CPython 3.11manylinux: glibc 2.28+ x86-64

moose_core-4.1.2-cp311-cp311-macosx_14_0_arm64.whl (5.8 MB view details)

Uploaded CPython 3.11macOS 14.0+ ARM64

moose_core-4.1.2-cp310-cp310-win_amd64.whl (3.0 MB view details)

Uploaded CPython 3.10Windows x86-64

moose_core-4.1.2-cp310-cp310-manylinux_2_28_x86_64.whl (8.7 MB view details)

Uploaded CPython 3.10manylinux: glibc 2.28+ x86-64

moose_core-4.1.2-cp310-cp310-macosx_14_0_arm64.whl (5.7 MB view details)

Uploaded CPython 3.10macOS 14.0+ ARM64

moose_core-4.1.2-cp39-cp39-win_amd64.whl (3.0 MB view details)

Uploaded CPython 3.9Windows x86-64

moose_core-4.1.2-cp39-cp39-manylinux_2_28_x86_64.whl (8.7 MB view details)

Uploaded CPython 3.9manylinux: glibc 2.28+ x86-64

moose_core-4.1.2-cp39-cp39-macosx_14_0_arm64.whl (5.7 MB view details)

Uploaded CPython 3.9macOS 14.0+ ARM64

moose_core-4.1.2-cp38-cp38-win_amd64.whl (3.0 MB view details)

Uploaded CPython 3.8Windows x86-64

moose_core-4.1.2-cp38-cp38-manylinux_2_28_x86_64.whl (8.7 MB view details)

Uploaded CPython 3.8manylinux: glibc 2.28+ x86-64

File details

Details for the file moose_core-4.1.2-cp313-cp313-win_amd64.whl.

File metadata

  • Download URL: moose_core-4.1.2-cp313-cp313-win_amd64.whl
  • Upload date:
  • Size: 3.0 MB
  • Tags: CPython 3.13, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.11.9

File hashes

Hashes for moose_core-4.1.2-cp313-cp313-win_amd64.whl
Algorithm Hash digest
SHA256 8b3ec1efa483900b683a12e7341ca2c72d94a7797c35437a591346e77bc96a3f
MD5 a296de19797a52dddc90cf7470fbc077
BLAKE2b-256 6770fd55da5421f850fe1356452051c85b3a90687af39cc67df1f9d3041c4d84

See more details on using hashes here.

File details

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

File metadata

File hashes

Hashes for moose_core-4.1.2-cp313-cp313-manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 90b2bcf1cb4d7c734f53e8723aece4782521bcb3b04ea09f3a1ab347feac07b5
MD5 fdaef92afce57433cb97e100410e73a4
BLAKE2b-256 7c7df624b608288f7a6cee755a1f0e46c2727bdcc5dd0c22945bb431e6f3044f

See more details on using hashes here.

File details

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

File metadata

File hashes

Hashes for moose_core-4.1.2-cp313-cp313-macosx_14_0_arm64.whl
Algorithm Hash digest
SHA256 b6e05db9098d3da7ca9263426fdcc58ba1f41bef6e3975fd8c0d0dcc3b28b3cd
MD5 c7b12b2bca0c5a2ae98b7786663c50b6
BLAKE2b-256 6352fe19448bf8a1929712d3d47b023573d316be57b271e283fd070a910ce490

See more details on using hashes here.

File details

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

File metadata

  • Download URL: moose_core-4.1.2-cp312-cp312-win_amd64.whl
  • Upload date:
  • Size: 3.0 MB
  • Tags: CPython 3.12, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.11.9

File hashes

Hashes for moose_core-4.1.2-cp312-cp312-win_amd64.whl
Algorithm Hash digest
SHA256 10c43e8426562048ddba6cc8d3abf54ec3f0fe0aa1a0ab20587fe6d221acb490
MD5 93da0c4a0b7f15807117038404db44ad
BLAKE2b-256 588567fe3bd4a6adcfc343f2babbea08454cad8e04b05710de1e4933a63dfccb

See more details on using hashes here.

File details

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

File metadata

File hashes

Hashes for moose_core-4.1.2-cp312-cp312-manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 1f9fabaaca8bd749e7ee7facd408db79b0d5da595db5c035019c2d429e46f8a6
MD5 e1848088be16292dfff7e25bc486b215
BLAKE2b-256 9117f3ecdf5756844e64bb5d29318057dbb524ad1eea64cf082868680929be14

See more details on using hashes here.

File details

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

File metadata

File hashes

Hashes for moose_core-4.1.2-cp312-cp312-macosx_14_0_arm64.whl
Algorithm Hash digest
SHA256 5fad6816503d4da6337b6bdce5bea9a18ebbb2befc2f0e21df7a0159318df102
MD5 31c1fb78dde191e838807a3e9d7a6820
BLAKE2b-256 f1fa9e6b78b16020378c81eb753c9cccff7baeb73b44c70d1bdb8d9fdf4a4bc6

See more details on using hashes here.

File details

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

File metadata

  • Download URL: moose_core-4.1.2-cp311-cp311-win_amd64.whl
  • Upload date:
  • Size: 3.0 MB
  • Tags: CPython 3.11, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.11.9

File hashes

Hashes for moose_core-4.1.2-cp311-cp311-win_amd64.whl
Algorithm Hash digest
SHA256 7e343d4bc3117a8ac7e428019d0efdf6a24196c5663f71b83b16bcb99c51de63
MD5 407582362646da2f19e58ed898297002
BLAKE2b-256 62111d7375b6b8ab915ae73f96c763a4688ae33cf59ed0c3fee49c20b7895a00

See more details on using hashes here.

File details

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

File metadata

File hashes

Hashes for moose_core-4.1.2-cp311-cp311-manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 cd69b1de9f9c82f3c6ea61a085455c2c3b0a7d5b6d7c3b0dec34dfb91b957290
MD5 19fe1e788dc633f671201d56a276da7a
BLAKE2b-256 3b11c277bfaf1c1b867699b08f0ed649cbccfa03f9203128ab40c1441a4fb061

See more details on using hashes here.

File details

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

File metadata

File hashes

Hashes for moose_core-4.1.2-cp311-cp311-macosx_14_0_arm64.whl
Algorithm Hash digest
SHA256 2e93c0d15904f07bd033bf344d9c87f63d7edd1e65ce9e4f342095e3f2680b33
MD5 f8a4da957452d73c85b5c17d63b3e711
BLAKE2b-256 922437d794d6364700359d6108ed3ca92babc07ae9b4ed5ddfdae6d11f8659be

See more details on using hashes here.

File details

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

File metadata

  • Download URL: moose_core-4.1.2-cp310-cp310-win_amd64.whl
  • Upload date:
  • Size: 3.0 MB
  • Tags: CPython 3.10, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.11.9

File hashes

Hashes for moose_core-4.1.2-cp310-cp310-win_amd64.whl
Algorithm Hash digest
SHA256 9e50ae2371a3ae2225a42024e75478b0226296714dee6010282493829c9ad7f3
MD5 cf422514bbb7527714981bbbbb50826d
BLAKE2b-256 a332dd65cd318f1bcc707668cf4d458e956062cac2f8a2ca478c4473cde291f6

See more details on using hashes here.

File details

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

File metadata

File hashes

Hashes for moose_core-4.1.2-cp310-cp310-manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 6927060c940ab0ee592bd91778f4b413c89afb086603651adc4fa6695249307f
MD5 65962dfbbd101fa022eecbc622ca4713
BLAKE2b-256 0bc67e7c008dc7c6271c3302ce67667dedcd70c3156b6d117701906b24f84feb

See more details on using hashes here.

File details

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

File metadata

File hashes

Hashes for moose_core-4.1.2-cp310-cp310-macosx_14_0_arm64.whl
Algorithm Hash digest
SHA256 95b6dd3e2a7cdbde2b62cbd4bb5462822d14049c10c563ac523b059734d00139
MD5 6c16749e31bd92f5219c20a4df9ac639
BLAKE2b-256 e1ef1b028962b73bd99c7488c606581b37b28a2483be485e44e5b4ae4578bad3

See more details on using hashes here.

File details

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

File metadata

  • Download URL: moose_core-4.1.2-cp39-cp39-win_amd64.whl
  • Upload date:
  • Size: 3.0 MB
  • Tags: CPython 3.9, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.11.9

File hashes

Hashes for moose_core-4.1.2-cp39-cp39-win_amd64.whl
Algorithm Hash digest
SHA256 d2416316b18215d522eef7fea7fa1184d2dbc4ac133ff1260e1a36c5034d0665
MD5 8153230367c679b832d23b1a49775278
BLAKE2b-256 0a4c31157b4506c09e4e8f6700e696bf713b32572befdc70646f6b0ef2f261b5

See more details on using hashes here.

File details

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

File metadata

File hashes

Hashes for moose_core-4.1.2-cp39-cp39-manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 c047499b7c46119bb1fbfaf5d2e966fcec0cd7c358a0c9b301b9f0cf1ac48b97
MD5 51671e16f415b4f649706915e2e07940
BLAKE2b-256 ef18bdb13d9beca25052b7de4d1851770316b439e4cae2e1239991caeb76e0bd

See more details on using hashes here.

File details

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

File metadata

File hashes

Hashes for moose_core-4.1.2-cp39-cp39-macosx_14_0_arm64.whl
Algorithm Hash digest
SHA256 b8bbd7d60442a3325599eaa4c8e30c23454346a334fba150a8bd208a38a4fd20
MD5 8f2b39a2d3010d721d9f7585e7b505c7
BLAKE2b-256 97222fd2a640fff0737e1293eabfc3b3f5ccd275da1c991f1b3dac92e2c57d43

See more details on using hashes here.

File details

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

File metadata

  • Download URL: moose_core-4.1.2-cp38-cp38-win_amd64.whl
  • Upload date:
  • Size: 3.0 MB
  • Tags: CPython 3.8, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.11.9

File hashes

Hashes for moose_core-4.1.2-cp38-cp38-win_amd64.whl
Algorithm Hash digest
SHA256 aba211c87e3c0d412fce0ad5d80e14564e58c126aa7dfaa79e5b6093f5b8a7b2
MD5 e1b2b987098fef86dbb2cb400489d3d9
BLAKE2b-256 f24dba639e513700b8bbbd2f288019e6e95871b58bf25ef353fc96764a2f5caa

See more details on using hashes here.

File details

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

File metadata

File hashes

Hashes for moose_core-4.1.2-cp38-cp38-manylinux_2_28_x86_64.whl
Algorithm Hash digest
SHA256 65c124d6588d1d6fe4a49603c924c38c0d34c931e66f7e72d45401a67996b650
MD5 f467791ee485adbc47778d77ed075617
BLAKE2b-256 e989effc5a9981b193eaeb2ef0ce1a950d7c024580625a900ee0f9e3dc7e2fd5

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