Metadata-Version: 1.1
Name: moose
Version: 3.0
Summary: MOOSE is the Multiscale Object-Oriented Simulation Environment. It is the base and numerical core for large, detailed simulations including Computational Neuroscience and Systems Biology.
Home-page: http://moose.ncbs.res.in/
Author: Dilawar Singh
Author-email: dilawars@ncbs.res.in
License: LGPL
Description: # MOOSE
        
        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
        processes.
        
        
        # LICENSE
        
        MOOSE is released under GPLv3.
        
        
        # HOMEPAGE 
        
        http://moose.ncbs.res.in/
        
        
        # SOURCE REPOSITORY
        
        https://sourceforge.net/projects/moose/
        
        
        # REQUIREMENTS
        
            ## 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
        
        
            ## PyMOOSE                              OPTIONAL
        
            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
        
                    - SBML          (5.9.x)                 OPTIONAL
                      ---------------------
                      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
        
        
        # AUTHORS
        
        - 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 -
        https://lists.sourceforge.net/lists/listinfo/moose-generic
        
        
        # Bugs:
          
        You can file bug reports and feature requets at the sourceforge tracker -
        http://sourceforge.net/p/moose/bugs/
        
        # 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
            help(moose)
        
        
        MOOSE classes have built-in documentation that can be accessed via
        the `doc()` function -
        
            moose.doc(classname)
        
        This will give the full documentation for the class including the fields
        available.
        
        `moose.doc(classname.fieldname)` 
        will give you information about a particular field in a class.
        
        Complete MOOSE Documentation can be found at -
        http://moose.ncbs.res.in/content/view/5/6/
Keywords: neural simulation
Platform: UNKNOWN
Classifier: Intended Audience :: Science/Research
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python
Classifier: Programming Language :: C++
