Installation

If you’re using a pre-installed version of Pippin - like the one on Midway, ignore this.

If you’re not, installing Pippin is simple.

1. Checkout Pippin git clone git@github.com:dessn/Pippin.git

2. Ensure you have the dependencies isntalled pip install -r requirements.txt and that your python version is 3.7+

3. Celebrate!

There is no need to attempt to install Pippin like a package (no python setup.py install), just run from the clone.

Now, Pippin also interfaces with other tasks: SNANA and machine learning classifiers mostly. I’d highly recommend running on a high performance computer with SNANA already installed, but if you want to take a crack at installing it, you can find the docoumentation here.

I won’t cover installing SNANA here, hopefully you already have it. But to install the classifiers, we’ll take SuperNNova as an example. To install that, find a good place for it and:

1. Checkout SuperNNova: git clone git@github.com:supernnova/SuperNNova.git

2. Create a GPU conda env for it: conda create --name snn_gpu --file env/conda_env_gpu_linux64.txt

3. Activate environment and install natsort: conda activate snn_gpu and conda install --yes natsort

Then, in the Pippin global configuration file cfg.yml in the top level directory, ensure that the SuperNNova path in Pippin is pointing to where you just cloned SuperNNova into. You will need to install the other external software packages if you want to use them, and you do not need to install any package you do not explicitly request in a config file.