This is a semi-fork of the CUSF Standalone Predictor, which provides a Python wrapper around the predictor binary, and provides a means of gathering the requisite wind data.
2018-02 Update: Wind downloader updated to use the NOMADS GRIB Filter, as the OpenDAP interface stopped working. As such, we no longer require PyDAP, but we do now require GDAL to read in the GRIB2 files.
2021-03 Update: We have dropped GDAL in favour of cfgrib.
On a Raspbian/Ubuntu/Debian system, you can get most of the required dependencies using:
$ sudo apt-get install git cmake build-essential libglib2.0-dev python3-numpy python3-requests python3-dateutil python3-pip libeccodes-data libeccodes0 libgeos-dev libatlas-base-dev
Clone this repository with:
$ git clone https://github.com/darksidelemm/cusf_predictor_wrapper.git
The easiest way to install the python wrapper is via pip:
$ sudo pip3 install cusfpredict
(Replace pip3 with pip if necessary on your system, however note that Python 2 is not supported!)
If you couldn't install from pip for whatever reason, then the cusfpredict python package can then be installed in the usual Python way:
$ cd cusf_predictor_wrapper
$ sudo python3 setup.py install
This should grab the other required Python dependencies, but if not, they are available in requirements.txt and can be preinstalled using
$ sudo pip3 install -r requirements.txt
Note that as pip3 installes shapely, it may throw some errors about not finding geos_c.h. These can be ignored.
The predictor itself is a binary ('pred'), which we (currently) build seperately, using CMake.
From within the cusf_predictor_wrapper directory, run the following to build the predictor binary:
$ cd src
$ mkdir build
$ cd build
$ cmake ../
$ make
The pred binary then needs to be copied into the 'apps' directory, or somewhere else useful, i.e.
$ cp pred ../../apps/
If you are building this utility for use with chasemapper, then you should copy pred into the chasemapper directory:
$ cp pred ~/chasemapper/
A pre-compiled Windows binary of the predictor is available here: http://rfhead.net/horus/cusf_standalone_predictor.zip Use at your own risk!
The predictor binary uses a custom wind data format, extracted from NOAA's Global Forecast System wind models. The cusfpredict.gfs Python module pulls down and formats the relevant data from NOAA's NOMADS server.
If you are using this library with ChaseMapper, you will need to adjust the download command in the chasemapper configuration file.
An example of running it is as follows:
$ python3 -m cusfpredict.gfs --lat=-33 --lon=139 --latdelta=10 --londelta=10 -f 24 -m 0p50 -o gfs
The command line arguments are as follows:
Area of interest:
--lat Latitude (Decimal Degrees) of the centre of the area to gather.
--lon Longitude (Decimal Degrees) of the centre of the area to gather.
--latdelta Gather data from lat+/-latdelta
--londelta Gather data from lon+/-londelta
Time of interest:
-f X Gather data up to X hours into the future, from the start of the most recent model. (Note that this can be up to 8 hours in the past.) Make sure you get enough for the flight!
GFS Model Choice:
-m <model> Choose between either:
0p50 - 0.5 Degree Spatial, 3-hour Time Resolution
0p25_1hr - 0.25 Degree Spatial, 1-hour Time Resolution (default)
Other settings:
-v Verbose output
-o output_dir (Where to save the gfs data to, defaults to ./gfs/)
The higher resolution wind model you choose, the larger the amount of data to download, and the longer it will take. It also increases the prediction calculation time (though not significantly).
wind_grabber.sh is an example script to automatically grab wind data first to a temporary directory, and then to the final gfs directory. This could be run from a cronjob to keep the wind data up-to-date.
New wind models become available approximately every 6 hours, approximately 4 hours after the model's nominal time (i.e. the 00Z model becomes available around 04Z). Information on the status of the GFS model generation is available here: http://www.nco.ncep.noaa.gov/pmb/nwprod/prodstat_new/
(Note: This section is intended for users within to run predictions from within their own software. If you are just installing this library for use with chasemapper, you can skip all of this!)
The basic usage of the predictor from within Python is as follows:
import datetime
from cusfpredict.predict import Predictor
pred = Predictor(bin_path='./pred', gfs_path='./gfs')
flight_path = pred.predict(
launch_lat=-34.9499,
launch_lon=138.5194,
launch_alt=0.0,
ascent_rate=5.0,
descent_rate=5.0,
burst_alt=30000,
launch_time=datetime.datetime.utcnow()
)
Note that the launch time is a datetime object interpreted as UTC, so make sure you convert your launch time as appropriate.
The output is a list-of-lists, containing entries of [utctimestamp, lat, lon, alt], i.e.:
>>> flight_path
[[1516702953, -34.9471, 138.517, 250.0], [1516703003, -34.9436, 138.514, 500.0], <etc>, [1516703053, -34.9415, 138.513, 750.0]]
There is also a command-line utility, predict.py, which allows performing predictions with launch parameter variations:
usage: predict.py [-h] [-a ASCENTRATE] [-d DESCENTRATE] [-b BURSTALT]
[--launchalt LAUNCHALT] [--latitude LATITUDE]
[--longitude LONGITUDE] [--time TIME] [-o OUTPUT]
[--altitude_deltas ALTITUDE_DELTAS]
[--time_deltas TIME_DELTAS] [--absolute]
optional arguments:
-h, --help show this help message and exit
-a ASCENTRATE, --ascentrate ASCENTRATE
Ascent Rate (m/s). Default 5m/s
-d DESCENTRATE, --descentrate DESCENTRATE
Descent Rate (m/s). Default 5m/s
-b BURSTALT, --burstalt BURSTALT
Burst Altitude (m). Default 30000m
--launchalt LAUNCHALT
Launch Altitude (m). Default 0m
--latitude LATITUDE Launch Latitude (dd.dddd)
--longitude LONGITUDE
Launch Longitude (dd.dddd)
--time TIME Launch Time (string, UTC). Default = Now
-o OUTPUT, --output OUTPUT
Output KML File. Default = prediction.kml
--altitude_deltas ALTITUDE_DELTAS
Comma-delimited list of altitude deltas. (metres).
--time_deltas TIME_DELTAS
Comma-delimited list of time deltas. (hours)
--absolute Show absolute altitudes for tracks and placemarks.
For example, to predict a radiosonde launch from Adelaide Airport (5m/s ascent, 26km burst, 7.5m/s descent), but to look at what happens if the burst altitude is higher or lower than usual:
$ python3 predict.py --latitude=-34.9499 --longitude=138.5194 -a 5 -d 7.5 -b 26000 --time "2018-01-27 11:15Z" --altitude_deltas="-2000,0,2000"
Running using GFS Model: gfs20180127-00z
2018-01-27T11:15:00+00:00 5.0/24000.0/7.5 - Landing: -34.8585, 138.9600 at 2018-01-27T13:03:33
2018-01-27T11:15:00+00:00 5.0/26000.0/7.5 - Landing: -34.8587, 138.8870 at 2018-01-27T13:11:01
2018-01-27T11:15:00+00:00 5.0/28000.0/7.5 - Landing: -34.8598, 138.7990 at 2018-01-27T13:18:22
KML written to prediction.kml
A few other example scripts are located in the 'apps' directory:
- basic_usage.py - Example showing how to write a predicted flight path out to a KML file
- sonde_predict.py - A more complex example, where predictions for the next week's of radiosonde flights are run and written to a KML file.