Python code to display precipitation globally using GPM dataset.
Information on volcanoes is retrieved from Global Volcanism Program webservice.
- Create destination folder from terminal and clone the repo:
cd $HOME
mkdir -p $HOME/code/Precip
git clone [email protected]:geodesymiami/Precip.git $HOME/code/Precip
- Set environment variables (temp):
export PRECIP_HOME=$HOME/code/Precip
- Prepend to your
$PATH
export PATH=${PRECIP_HOME}/src/Precip/cli:$PATH
export PYTHONPATH=${PRECIP_HOME}/src:$PYTHONPATH
while read requirement; do conda install --yes $requirement; done < requirements.txt
In order to be able to download GPM data locally, you need to have an active EarthData account.
To create one, follow the steps below:
Otherwise you can use a mockup account, just copy paste the following code in your terminal (Mac/Linux, Windows):
cd $HOME
touch .netrc
echo "machine urs.earthdata.nasa.gov login emrehavazli password 4302749" >> .netrc
chmod 0600 .netrc
touch $HOME/.urs_cookies
touch $HOME/.dodsrc
echo "HTTP.NETRC=$HOME/.netrc" >> $HOME/.dodsrc
echo "HTTP.COOKIEJAR=$HOME/.urs_cookies" >> $HOME/.dodsrc
- Open Notepad
- Enter (without quotes):
machine urs.earthdata.nasa.gov login emrehavazli password 4302749
Save as C:.netrc
From terminal (Win
+ R, type cmd )
cd %USERPROFILE%
NUL > .urs_cookies
cd %USERPROFILE%
NUL > .dodsrc
echo "HTTP.NETRC=%USERPROFILE%/.netrc" >> %USERPROFILE%\.dodsrc
echo "HTTP.COOKIEJAR=%USERPROFILE%/.urs_cookies" >> %USERPROFILE%\.dodsrc
You can run the code through command line by simply runnig the following command:
plot_precipitation.py Merapi --style bar --period=20060101:20070101
This line will show the precipitation over Merapi volcano from 01 January 2006 to 2007 as a bar plot, with vertical lines representing the eruptions.
For more examples run:
plot_precipitation.py --h
If You want to show (almost) all the available types of plot in one single command, run:
plot_precipitation_all.py Merapi --period=20060101:20070101
You can add some of the arguments from get_precipitation_lalo.py
, like:
--roll
--bins
--log
--save
For visual examples, refer to the following Jupyter Notebook.
If you have special access to 149.165.154.65
, you can try to connect to JetStream and use the data uploaded there instead of downloading them locally with --use-ssh
argument.