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Updated README file for jalebi release
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README.md

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[![Build Status - master](https://travis-ci.org/BhallaLab/moose-core.svg?branch=master)](https://travis-ci.org/BhallaLab/moose-core) | [![PyPI version](https://badge.fury.io/py/pymoose.svg)](https://badge.fury.io/py/pymoose)
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This is the core computational engine of [MOOSE simulator](https://github.com/BhallaLab/moose). This repository contains
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C++ codebase and python interface called `pymoose`. For more details about MOOSE simulator, visit https://moose.ncbs.res.in .
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[![Python package](https://github.com/BhallaLab/moose-core/actions/workflows/pymoose.yml/badge.svg)](https://github.com/BhallaLab/moose-core/actions/workflows/pymoose.yml)
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# Installation
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# Build
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To build `pymoose`, follow instructions given here at https://github.com/BhallaLab/moose-core/blob/master/INSTALL.md
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----------
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# MOOSE
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MOOSE is the Multiscale Object-Oriented Simulation Environment. It is designed
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to simulate neural systems ranging from subcellular components and biochemical
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reactions to complex models of single neurons, circuits, and large networks.
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MOOSE can operate at many levels of detail, from stochastic chemical
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computations, to multicompartment single-neuron models, to spiking neuron
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network models.
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MOOSE is multiscale: It can do all these calculations together. For example
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it handles interactions seamlessly between electrical and chemical signaling.
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MOOSE is object-oriented. Biological concepts are mapped into classes, and
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a model is built by creating instances of these classes and connecting them
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by messages. MOOSE also has classes whose job is to take over difficult
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computations in a certain domain, and do them fast. There are such solver
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classes for stochastic and deterministic chemistry, for diffusion, and for
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multicompartment neuronal models.
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MOOSE is a simulation environment, not just a numerical engine: It provides
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data representations and solvers (of course!), but also a scripting interface
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with Python, graphical displays with Matplotlib, PyQt, and VPython, and
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support for many model formats. These include SBML, NeuroML, GENESIS kkit
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and cell.p formats, HDF5 and NSDF for data writing.
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This is the core computational engine of [MOOSE simulator](https://github.com/BhallaLab/moose). This repository contains
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C++ codebase and python interface called `pymoose`. For more details about MOOSE simulator, visit https://moose.ncbs.res.in .
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# ABOUT VERSION 4.0.0, Jalebi
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Jalebi is an Indian sweet involving a golden twisting tube like a hyper-pretzel,
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of crunchy batter soaked in sugar syrup lightly flavoured with spices and
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sometimes lemon.
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This release has the following major changes:
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1. A major under-the-hood change to numerics for chemical calculations,
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eliminating the use of 'zombie' objects for the solvers. This simplifies
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and cleans up the code and object access, but doesn't alter runtimes.
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2. Another major under-the-hood change to use pybind11 as a much cleaner
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way to interface the parser with the C++ numerical code.
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3. Addition of a thread-safe and faster parser based on ExprTK
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4. Resurrected objects for handling simulation output saving using HDF5
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format. There is an HDFWriter class, an NSDFWriter, and a new NSDFWriter2.
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The latter two implement storage in NSDF, Neuronal Simulation Data Format,
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Ray et al Neuroinformatics 2016. NSDF is built on HDF5 and builds up a
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specification designed to ensure ready replicability as well as self-
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description of model output.
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5. Multiple enhancements to rdesigneur, including vastly improved 3-D
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graphics output using VPython.
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6. Various bugfixes
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# LICENSE
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MOOSE is released under GPLv3.
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