###############################
# Author: O. Bronchain        #
# Date: June 2018             #
# Institute: UCL CRYPTO GROUP #
###############################
TCHES 2019 paper link: https://tches.iacr.org/index.php/TCHES/article/view/7394

REQUIREMENTS:
 This code is written in Python3 and is mainly based on Numpy1.14 and Scipy1.1
 running on a Linux distribution. The required packages are:
    numpy
    scipy
    matplotlib
    tqdm
 These can be installed by running:
    # sudo pip install numpy matplotlib tqdm scipy

USAGE:
 The code generates all the simulation-based figures of the paper and saves
 two files for each figure. The .pdf is the actual figure and the .pkl contains
 informations to generate the .pdf without running the simulation again. The code
 is used as:
    # python3 figures.py option
 where option can be:
    -fast  - reduced number of samples (no averaging)
    -paper - plots from the paper
    -file  - uses .pkl files and only generates plots
 i.e:
    # python3 figures.py fast

FILES DESCRIPTION
 benchmark.py measures the CPU time for detection algorithms.
 beta_compute.py computes analytical \beta for detection methods.
 figures.py contains all the code to generate the plots of the paper.
 methods.py contains the actual detection methods.
 sampled_experiments.py outputs the sampled experiments mainly for dependent noise issue.
 settings.py initiates the plots settings.
 utils.py contains some useful functions.