Introduction

The project is separated into three parts:

• the predictor: provides an API over which requests for a prediction can be made. This API is public, and can be used by main predictor web UI, as a live predictor by mapping software, and potential future uses we haven’t thought of.
• the web UI
• the dataset downloader: runs as a single standalone separate process, watches for new datasets published by the NOAA and retrieves them.

Setup & Installation

Predictor

…is written for Python 3 (though is compatible with Python 2) and needs Cython:

$ virtualenv venv
$ source venv/bin/activate
$ pip install cython
$ python setup.py build_ext --inplace

The last line (re-)builds the Cython extensions, and needs to be run again after modifying any .pyx files.

Downloader

The downloader uses gevent, so we are (disappointingly) restricted to running it under Python 2 for now.

At the time of writing, pygrib head did not work (in contrast to an earlier version), and both have a broken setup.py. Therefore, we need to install numpy first, and pyproj separately:

$ sudo aptitude install libevent-dev libgrib-api-dev
$ virtualenv -p python2 venv
$ source venv/bin/activate
$ pip install numpy
$ pip install pygrib==1.9.6 pyproj 'gevent<1.0'

Web API

The web API may be run in a development web-server using the tawhiri-webapp script. If necessary, you can use the TAWHIRI_SETTINGS environment variable to load configuration from a file:

$ cat > devel-settings.txt <<EOL
ELEVATION_DATASET = '/path/to/ruaumoko-dataset'
WIND_DATASET_DIR = '/path/to/tawhiri-datasets'
EOL
$ tawhiri-webapp runserver -rd

See the output of tawhiri-webapp -? and tawhiri-webapp runserver -? for more information.