Deep Learning in ARIANNA

Setting up the environment

The steps listed below are how to get a similar environment to mine when conducting this research. There are however many ways to set up similar environments so use this as only one reference.

• Download and install Anaconda

• Create conda environment with a user "chosen_name" and then list all environments

  $ conda create --name chosen_name
  $ conda env list
  

• Activate Environment

  $ conda activate chosen_name
  

• Deactivate Environment

  $ conda deactivate
  

When the conda environment is activated, packages will be installed only in this environment. Activate the environment once again then install the pip package manager and other relevant python packages. I use tensorflow 2 and python 3.8.5.

  $ conda install pip
  $ pip install matplotlib numpy scipy tensorflow keras, parameter-sherpa 

Try importing some of these packages to determine if the packages were downloaded sucessfully. If there are any dependencies missing from this list, just pip install them the same way as above.

  $ python
  >>> from matplotlib import pyplot as plt
  >>> import numpy as np
  >>> import scipy 
  >>> import keras
  >>> import tensorflow

Next to download this repository to a local computer, use cd to go into your chosen directory for this analysis then

  $ git clone https://github.com/a-anker/ARIANNA_Experiment.git

All of these scripts use the os.environ['ARIANNA_Experiment'] variable to locate this github repository and its scripts, so add your specified local path as an environment variable. To do this, locate and open your .bashrc or .bash_profile, which is usually in your home directory (nano ~/.bash_profile). Next input your github repository location as an environment variable. For example, if this downloaded github repository is located in /home/aanker, you will need to add the text below to the .bash_profile file:

  export ARIANNA_Experiment="/home/aanker/ARIANNA_Experiment"      

This will ensure that the script in this repository will be found by your local computer.

For more tips on installing other analysis tools used within ARIANNA such as ROOT, snowShovel, and additional requirements, refer to the README in Geoffrey Gaswint's ARIANNA repository.

Plots and their corresponding scripts

Refer to template_study/:

Refer to interpretability/:

Refer to 5-fold_CV/:

Refer to cnn_train_test_efficiency.py:

Refer to train_cnn_with_acc_loss_plot.py:

Refer to correlation/:

Refer to compare_similar_distributions/:

Refer to fft.py: