Edit School 2023

This repository sets up the exercise for the Edit School 2023. The data exercise requires two pieces:

a docker image with a working environment
actual data

Getting started

This assumes you are working in a linux environment. The same should be executable also in a windows or Mac OSX environments. Prerequisites:

You need docker downloaded. All the other dependencies live in the image
You need sufficient free space on your hard drive. BMX data are big: 28G per day.

Steps:

Download the relevant docker image:

docker pull slosar/edit23

Create a directory and download some data from https://www.cosmo.bnl.gov/www/bmx/edit2023/.

You want to do something like:

cd ~
mkdir bmx; cd bmx
mkdir data; cd data
wget https://www.cosmo.bnl.gov/www/bmx/edit2023/edit_230924_2200.tgz
tar zxvf edit_230924_2200.tgz && rm edit_230924_2200.tgz
cd ..

Pull this repository

git clone https://github.com/bmxdemo/edit23.git
cd edit23

Run the docker image, pointing the data to the right place:

docker run -p 2023:2023 -v $HOME/bmx/data:/bmxdata -v $PWD:$PWD -w$PWD --user $(id -u):$(id -g)  slosar/edit23

Different pieces do the following:

-p 2023:2023 maps port 2023 running inside the container: we have jupyter lab running on this port
-v $HOME/bmx/data:/bmxdata makes the data downloaded under point 2 above appear in /bmxdata inside the container
-v $PWD:$PWD makes your current directory appear inside the container
-w $PWD makes the above directory the current directory where jupyter lab lives
--user $(id -u):$(id -g) makes this container run as a current user, as a basic safety precaution

Run the jupyter notebook. Open your browser and go to http://localhost:2023. Jupyter should open. Open example.ipynb and try to run it to confirm basic functionality.