Cambridge University Spaceflight landing predictor - a web-based tool for predicting the flight path and landing location of latex meteorological sounding balloons.
The source for the predictor itself is in pred_src/ and instructions for building it can be found there.
The following items need to be executable (chmod +x ./predict.py) by the user under which the predictor runs:
predict.pypred_src/pred(once compiled)cron/clear-pydap-cache-cronjob.shcron/purge-predictions-cronjob.sh
The predict/preds/ and gfs/ directories need to have rwx access by the PHP interpreter and the predict.py python script. You will need to install the python dependencies listed in requirements.txt. In the case of PyDAP, the exact version is important; the easiest way is:
$ pip install -r requirements.txt
Other than that, just clone this repo to a non web-accessible folder and create symlinks to the predict/ directory in the repo.
There are useful configuration options in predict/includes/config.inc.php.
The two shell scripts in the cron/ directory should both be run daily. clear-pydap-cache-cronjob.sh clears the cache used by pydap so that old data does not build up. purge-predictions-cronjob.sh deletes scenarios and predictions not accessed or modified within the last 7 days. Re-running a prediction for a scenario will therefore reset its time to live to 7 more days.
The directory names are UUIDs comprised of an SHA1 hash of the launch parameters, and re-running predictions will overwrite data in the existing directory, rather than create a new one.
We use GFS data provided by the NOAA, accessed via NDAP and their NOMADS distribution system. The 1.0x1.0 degree data (26 vertical pressure levels) is used for standard predictions, and the 0.5x0.5 degree data (47 vertical pressure levels) is used for the high definition (HD) predictions.
This work is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 2 of the License, or any later version. This work is distributed in the hope that it will be useful, but without any warranty; without even the implied warranty of merchantability or fitness for a particular purpose.
Credit as detailed in individual files, but notably:
- Rich Wareham - The new predictor and the hourly predictor system
- Fergus Noble, Ed Moore and many others
Adam Greig - http://www.randomskk.net - [email protected]
Jon Sowman - http://www.hexoc.com - [email protected]
Copyright Cambridge University Spaceflight 2009-2011 - All Rights Reserved
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent