Merge branch 'master' of github.com:BhallaLab/moose

INSTALL.md

-# Building and installing moose
+# Pre-built packages
 
-## Download the latest source code of moose from github or sourceforge.
+Use our repositories hosted at [Open Build Service](http://build.opensuse.org).
+We have packages for Debian, Ubuntu, CentOS, Fedora, OpenSUSE/SUSE, RHEL,
+Scientific Linux.  Visit the following page and follow the instructions there. 
+
+https://software.opensuse.org/download.html?project=home:moose&package=moose
+
+
+# Building from source
+
+First, Download the latest source code of moose from github.
 
     $ git clone https://github.com/BhallaLab/moose
     $ cd moose
@@ -46,16 +55,13 @@ On Ubuntu-120.4 or higher, these can be installed with:
 
     $ mkdir _build
     $ cd _build 
-    $ cmake -DBUILD_MOOGLI=OFF -DWITH_DOC=OFF ..
+    $ cmake -DWITH_DOC=OFF ..
     $ make 
     $ ctest --output-on-failure
 
 This will build moose and its python extentions, `ctest` will run few tests to
 check if build process was successful.
 
-If you also want MOOGLI then pass `-DBUILD_MOOGLI=ON`.  Also see the section
-__Building and installing moogli__ for more details about its dependencies.
-
 To install MOOSE into non-standard directory, pass additional argument
 `-DCMAKE_INSTALL_PREFIX=path/to/install/dir` to cmake.
 
@@ -92,7 +98,7 @@ On Ubuntu, following packages should suffice:
     libqt4-dev 
 
 
-### Travis
+## Travis
 
 We use `Travis-CI` to build MOOSE after every commit. You can see `.travis.yml`
 file in our repository. It has all instructions to build MOOSE on `Ubuntu-12.04

README.md

-[![Build Status - master](https://travis-ci.org/BhallaLab/moose.svg?branch=master)](https://travis-ci.org/BhallaLab/moose)
+[![Build Status - master](https://travis-ci.org/BhallaLab/moose.svg?branch=master)](https://travis-ci.org/BhallaLab/moose)   [![Documentation Status](https://readthedocs.org/projects/moose-core/badge/?version=latest)](https://readthedocs.org/projects/moose-core/?badge=latest)
 
 # About
 
+URL : http://moose.ncbs.res.in/
+
 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.
@@ -29,11 +31,7 @@ source stable MOOSE code.
 
 # VERSION
 
-This is MOOSE 3.0.2pre "Ghevar"
-
-# ABOUT VERSION 3.0.2, Ghevar
-
-The Ghevar release is the third of series 3 of MOOSE releases.
+This is MOOSE 3.0.2pre "Ghevar". The Ghevar release is the third of series 3 of MOOSE releases.
 
 Ghevar is a Rajasthani sweet with a stiff porous body soaked in sugar syrup.
 
@@ -49,27 +47,54 @@ MOOSE is released under the GNU General Public License as published by
 the Free Software Foundation, either version 3 of the License, or (at
 your option) any later version.
 
-# HOMEPAGE 
+# Building and installing
+
+See the file `INSTALL.md`.
 
-http://moose.ncbs.res.in/
+# AUTHORS
 
-# SOURCE REPOSITORY
+- 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, integrate-and-fire neurons/networks, STDP
+- Aviral Goel           -   Moogli/Neurokit Development
+- Dilawar Singh         -   Packaging
 
-Old [SourceForge repository](https://sourceforge.net/projects/moose/) is no
-longer maintained. Current source repository is hosted on
-[github](https://github.com/BhallaLab/moose-core) with almost all revision
-history.
+# Examples, tutorials and Demos: 
 
-# Building and installing
+Look in the [moose-examples repository](https://github.com/BhallaLab/moose-examples) for sample code. 
 
-See the file `INSTALL.md`.
+- [tutorials](https://github.com/BhallaLab/moose-examples/tree/master/tutorials): Standalone scripts meant for teaching. Students are expected
+  to modify the scripts to learn the principles of the models.
+- [squid](https://github.com/BhallaLab/moose-examples/tree/master/squid): The Hodkin-Huxley squid model, fully graphical interface.
+- [Genesis_files](https://github.com/BhallaLab/moose-examples/tree/master/Genesis_files): A number of kinetics models used in MOOSE demos.
+- [neuroml](https://github.com/BhallaLab/moose-examples/tree/master/neuroml): A number of NeuroML models used in MOOSE demos
+- [traub_2005](https://github.com/BhallaLab/moose-examples/tree/master/traub_2005): Example scripts for each of the individual cell models from
+  the Traub 2005 thalamocortical model.
+- [snippets](https://github.com/BhallaLab/moose-examples/tree/master/snippets): Code snippets that can be used as building blocks and to
+  illustrate how to use certain kinds of objects in MOOSE. These snippets are
+  all meant to run as individual files.
+
+
+# Supported file formats.
+
+MOOSE 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, modelname )
+
+MOOSE is backward compatible with GENESIS <model>.p files used for neuronal
+model specification. The same loadModel function can be used for this but you
+need to have all the channels used in the .p file preloaded in /library:
+
+    moose.loadModel(prototype_file_path, modelname )
 
-# MOOSE repositories
+MOOSE can also read .swc files from NeuroMorpho.org.
 
-You can find detailed description of each MOOSE component in respective
-`README.md` in  their repositories below:
+# Documentation
 
-- [MOOSE with python support](https://github.com/BhallaLab/moose-core)
-- [GUI](https://github.com/BhallaLab/moose-gui)
-- [Examples and Demos](https://github.com/BhallaLab/moose-examples)
-- [MOOGLI](https://github.com/BhallaLab/moogli) 
+Complete MOOSE Documentation can be found at -  http://moose.ncbs.res.in/content/view/5/6/