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Project Description

MOOSE is the Multiscale Object-Oriented Simulation Environment. It is the
core of a modern software platform for the simulation of neural
systems ranging from subcellular components and biochemical reactions
to complex models of single neurons, large networks, and systems-level


MOOSE is released under GPLv3.




## Core MOOSE

- g++ (>= 4.6.x) REQUIRED
For building the C++ MOOSE core.

- GSL (1.16.x) OPTIONAL
For core moose numerical computation

- OpenMPI (1.8.x) OPTIONAL
For running moose in parallel on clusters


Python interface for core MOOSE API

- Python2 ( >= 2.7.x) REQUIRED
For building the MOOSE Python bindings

- Python-dev ( >= 2.7.x) REQUIRED
Python development headers and libraries

- NumPy ( >= 1.6.x) REQUIRED
For numerical computation in PyMOOSE

- H5py (2.3.x) REQUIRED
For reading and writing data to HDF5 files

### Chemical Kinetics Network Simulations OPTIONAL

- GSL (1.16.x) REQUIRED
For core moose numerical computation

- PyQt4 (4.8.x) REQUIRED
For Python GUI

- Matplotlib ( >= 1.1.x) REQUIRED
For plotting simulation results

For reading and writing signalling models to SBML files

### Compartmental Model Viusalization OPTIONAL
- GSL (1.16.x) REQUIRED
For core moose numerical computation

- OSG (3.2.x) REQUIRED
For 3D rendering and simulation of neuronal models

- Qt4 (4.8.x) REQUIRED
For C++ GUI of Moogli

- PyQt4 (4.8.x) REQUIRED
For Python GUI

- Matplotlib ( >= 1.1.x) REQUIRED
For plotting simulation results


- Upinder S. Bhalla - Primary Architect, Chemical kinetic solvers
- Niraj Dudani - Neuronal solver
- Subhasis Ray - PyMOOSE Design and Documentation, Python Plugin Interface, NSDF Format
- G.V.HarshaRani - Web page design, SBML support, Kinetikit Plugin Development
- Aditya Gilra - NeuroML reader development
- Aviral Goel - Moogli/Neurokit Development
- Dilawar Singh - Packaging

# Support:

You can join the MOOSE generic mailing list for your queries -

# Bugs:

You can file bug reports and feature requets at the sourceforge tracker -

# Getting started:

MOOSE can be used as a python module. Look into the Demos directory
for sample code. A starting point can be Demos/snippets with useful
python code snippets that can be used as building blocks.

MOOSE also comes with a NeuroML reader. Demos/neuroml has some
python scripts showing how to load NeuroML models.

MOOSE is backward compatible with GENESIS
kinetikit. Demos/Genesis_files has some examples. You can load a
kinetikit model with the loadModel function:

moose.loadModel(kkit_file_path, target_model_path)

You can also load GENESIS prototype files. The same loadModel
function can be used for this (but you need to have all the channels
used in the prototype preloaded in /library):

moose.loadModel(prototype_file_path, prototype_model_path)

Top level moose documentation can be accessed in the Python
interpreter the usual way:

import moose

MOOSE classes have built-in documentation that can be accessed via
the `doc()` function -


This will give the full documentation for the class including the fields

will give you information about a particular field in a class.

Complete MOOSE Documentation can be found at -
Release History

Release History


This version

History Node

TODO: Figure out how to actually get changelog content.

Changelog content for this version goes here.

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Download Files

Download Files

TODO: Brief introduction on what you do with files - including link to relevant help section.

File Name & Checksum SHA256 Checksum Help Version File Type Upload Date
moose-3.0.tar.gz (4.2 MB) Copy SHA256 Checksum SHA256 Source Aug 3, 2014

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