peakBreaker

################################ MY ARCH INSTALL #################################
# Official install guide: https://wiki.archlinux.org/index.php/installation_guide
#################### NOTE: Dual booting with Windows 10 UEFI: ####################
## - Use the existing EFI partition made by Windows instead of creating a new one
## - Partition up empty space for Linux install in Windows
## - Configure GRUB to choose between Windows or Arch boot
##################################################################################
# Pre Install:
## Download arch from https://www.archlinux.org/download/
## Flash to USB drive: 

peakBreaker

#!/usr/bin/env bash

# GET ARGS
while getopts ":r:c:o:w:hs:hd" o; do case "${o}" in
	h) 
    echo -e "Optional arguments for custom use:"
    echo -e "  -r: Repository (local file or url)"
    echo -e "  -c: Config file"
    echo -e "  -o: Output file"
    echo -e "  -w: Worker program to call"

peakBreaker

# Program to enable avahi service discovery of printers and CUPS
# In Arch Linux
#
## First install the needed programs
sudo pacman -S cups nss-mdns
## Add user to cups group
sudo usermod -a -G cups <USER>

## Alter the hosts line to be like this
# hosts: ... mdns_minimal [NOTFOUND=return] resolve [!UNAVAIL=return] dns ...

peakBreaker

%% 
%% Full page table test
%% 

\documentclass[12pt]{article}
\pagenumbering{gobble}
\usepackage{tabularx}
\newcolumntype{C}{>{\hsize=.5\hsize}X}
\usepackage[left=1cm, right=1cm, top=1cm, bottom=1cm]{geometry}

peakBreaker

import datetime

# Set the config for the date iterator
start_date = datetime.datetime(2018, 9, 10)
end_date = datetime.datetime(2018, 11, 20)
d = start_date
delta = datetime.timedelta(days=1)

# Iterate from start date, adding delta with every iteration
while d <= end_date:

peakBreaker

# Get filename, whcih can be used for script identification
filename_no_ext = path.splitext(path.basename(__file__))[0]

peakBreaker

Based on talk by James Powell - https://www.youtube.com/watch?v=mr2SE_drU5o

Static Analysis

1. cloc
2. find -iname '.' | xargs cat |sed -e 's/^[ \t]//' | sort | uniq -c | sort -nr
3. Python:

from subprocess import check_output
files = check_output('find -iname *.<type>'.split())\

peakBreaker

def ecdf(data):
    """Compute ECDF for a one-dimensional array of measurements.
    
    Very useful for graphical EDA
    """
    # Number of data points: n
    n = len(data)

    # x-data for the ECDF: x
    x = np.sort(data)

peakBreaker

# Pred and prob arrays are numpy array outputs from a sklearn model:
#  - pred_array = model.predict(X).astype(int)
#  - prob_arr = model.predict_proba(X)
#
# Here we run the inital data through multiple models and structure the
# model output into a multilevel dataframe for probabilities and predictions
# 
# Typically the next stage would be to enhance the labels of numerical results
# to string/categories or similar basaed on whatever we want, aswell as providing
# the results to a database or something like that

peakBreaker

# Import TSNE
from sklearn.manifold import TSNE


def run_tsne(samples):
    # Create a TSNE instance: model
    model = TSNE(learning_rate=200)

    # Apply fit_transform to samples: tsne_features
    tsne_features = model.fit_transform(samples)