Metadata-Version: 2.1
Name: metawards
Version: 0.4.0
Summary: MetaWards disease metapopulation modelling
Home-page: https://github.com/metawards/metawards
Author: Leon Danon (original C code), Christopher Woods (Python port)
Author-email: l.danon@bristol.ac.uk, Christopher.Woods@bristol.ac.uk
License: GPL3
Project-URL: Documentation, https://github.com/metawards/metawards
Project-URL: Code, https://github.com/metawards/metawards
Project-URL: Issue tracker, https://github.com/metawards/metawards/issues
Description: # MetaWards
        
        [![Build status](https://github.com/metawards/MetaWards/workflows/Build/badge.svg)](https://github.com/metawards/MetaWards/actions?query=workflow%3ABuild)
        [![PyPI version](https://badge.fury.io/py/metawards.svg)](https://pypi.python.org/pypi/metawards)
        
        This is a Python port of the [MetaWards](https://github.com/ldanon/MetaWards)
        package originally written by Leon Danon. This is currently a work in progress
        and is not intended yet for production use. The port is kept in sync with
        the original C code, with checks in place to ensure that the two codes
        give identical results. This improves the robustness of both codes, as
        it minimises the footprint to bugs that can evade both C and Python.
        
        The aim of this port is to make it easier for others to contribute to the
        program, to improve robustness by adding in unit and integration test,
        and to also open up scope for further optimisation and parallelisation.
        
        The package makes heavy use of [cython](https://cython.org) which is used
        to compile bottleneck parts of the code to C. This enables this Python port
        to run at approximately the same speed as the original C program.
        
        There is still a lot of work to do, so please bear with us as we work to
        make this package into a well-documented, easily usable application that
        will help you to reproduce the model runs and analysis. Apologies in
        advance if we are slow to respond to any issues - we are working at high
        speed to get everything ready and aim to have most things completed by
        the beginning of Easter. We appreciate people want to help. At this stage
        adding more people won't speed things up, and we now have a team of
        professional [Research Software Engineers](https://www.bristol.ac.uk/acrc/research-software-engineering/) working on this package.
        
        ## Data
        
        The data and input parameters needed to use this package are stored in
        the [MetaWardsData](https://github.com/metawards/MetaWardsData)
        repository. Please make sure that you clone this repository to your
        computer and supply the full path to that repository to the program
        when it runs. There are three ways to do this;
        
        1. Set the `METAWARDSDATA` environment variable to point to this directory,
           e.g. `export METAWARDSDATA=$HOME/GitHub/MetaWards`
        
        2. Pass the `repository` variable to the input data classes
           [Disease](https://github.com/metawards/MetaWards/blob/devel/src/metawards/_disease.py), [InputFiles](https://github.com/metawards/MetaWards/blob/devel/src/metawards/_inputfiles.py) and [Parameters](https://github.com/metawards/MetaWards/blob/devel/src/metawards/_parameters.py)
        
        3. Or simply make sure you clone into the directory `$HOME/GitHub/MetaWardsData`
           as this is the default path.
        
        ## References
        
        These are the references behind the
        [original C code](https://github.com/ldanon/MetaWards) are;
        
        - _"Individual identity and movement networks for disease metapopulations"_
        Matt J. Keeling, Leon Danon, Matthew C. Vernon, Thomas A. House
        Proceedings of the National Academy of Sciences May 2010, 107 (19) 8866-8870; DOI: [10.1073/pnas.1000416107](https://doi.org/10.1073/pnas.1000416107)
        
        - _"A spatial model of CoVID-19 transmission in England and Wales: early spread and peak timing"_
        Leon Danon, Ellen Brooks-Pollock, Mick Bailey, Matt J Keeling
        medRxiv 2020.02.12.20022566; doi: [10.1101/2020.02.12.20022566](https://doi.org/10.1101/2020.02.12.20022566)
        
        ## Dependencies
        
        The code requires Python 3.7 or above and depends on
        [pygsl](http://pygsl.sourceforge.net) and [cython](https://cython.org).
        
        You will also need to install the [GNU Scientific Library](https://www.gnu.org/software/gsl/doc/html)
        (GSL). On macOS this can be installed using [Homebrew](https://brew.sh):
        
        ```sh
        brew install gsl
        ```
        
        On Linux, you can install the `libgsl` package, e.g. for Ubuntu:
        
        ```sh
        apt-get install libgsl-dev
        ```
        
        The `pygsl` Python package also requires `numpy`, which isn't installed
        automatically as a dependency if you install `pygsl` using [pip](https://pypi.org/project/pip).
        
        Only the `gsl_rng.binomial` function is used from GSL, so it is likely
        that this requirement will soon be dropped. The link to GSL forces us
        to use a GPL3 license for this Python package. Once we have replaced
        the binomial function we will re-evaluate the license under which this
        code is distributed.
        
        ## Installation
        
        You can install the code from source by typing;
        
        ```
        git clone https://github.com/metawards/MetaWards
        cd MetaWards
        CYTHONIZE=1 python setup.py install
        ```
        
        (assuming that `python` is version 3.7 or above)
        
        You can run tests using pytest, e.g.
        
        ```
        METAWARDSDATA="/path/to/MetaWardsData" pytest tests
        ```
        
        You can also install via `pip` using
        
        ```
        pip install metawards
        ```
        
        ## Running
        
        You can either load and use the Python classes directly, or you can
        run the `metawards` front-end command line program that is automatically installed.
        
        ```
        metawards --help
        ```
        
        will print out all of the help for the program. For example;
        
        ```
        metawards --input tests/data/ncovparams.csv --seed 15324 --nsteps 30
        ```
        
        This will duplicate the run of the MetaWards C program that is bundled
        in this repository that was run using;
        
        ```
        ./original/metawards 15324 tests/data/ncovparams.csv 0 1.0
        ```
        
        The original C code, command line and expected output are in the
        [original](https://github.com/metawards/MetaWards/tree/devel/original)
        directory that is bundled in this repo.
        
        ### Running an ensemble
        
        We will be integrating this Python port with
        [multiprocessing](https://docs.python.org/3.7/library/multiprocessing.html) and
        [dask](https://dask.org) to enable multiple models and seeds
        to be run in parallel over distributed resources. The aim is to have
        this parallel code working by the beginning of Easter.
        
Platform: UNKNOWN
Classifier: Development Status :: 1 - Planning
Classifier: License :: OSI Approved :: GNU General Public License v3 (GPLv3)
Classifier: Intended Audience :: Education
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Mathematics
Classifier: Natural Language :: English
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: C
Classifier: Programming Language :: Cython
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3 :: Only
Classifier: Programming Language :: Python :: Implementation :: CPython
Requires-Python: >=3.7
Description-Content-Type: text/markdown
Provides-Extra: dev
Provides-Extra: docs
