chuck-sys

#!/usr/bin/env python
'''
Perform a O(n^2) code comparison on a singular file with n lines to find duplicate code structures.

Strips all spacings before and after each line for comparison.

Usually checks for 100% similarity, but you can adjust the sensitivity (switch to levenshtein distance).

Requires Python 3.6 or higher. tqdm is optional but would enhance the experience.

chuck-sys

diff --git a/mediaplayer/mediaplayer b/mediaplayer/mediaplayer
index f78bd78..e86959b 100755
--- a/mediaplayer/mediaplayer
+++ b/mediaplayer/mediaplayer
@@ -27,6 +27,7 @@ use Env qw(BLOCK_INSTANCE);
 
 use constant DELAY => 50; # Delay in ms to let network-based players (spotify) reflect new data.
 use constant SPOTIFY_STR => 'spotify';
+use constant MAX_WIDTH => 80;
 

chuck-sys

// g++ face-detect.cc `pkg-config --cflags --libs opencv4` -o face-detect
// Remember to install vtk libraries or nothing will link.
#include <opencv2/imgcodecs.hpp>
#include <opencv2/objdetect.hpp>
#include <opencv2/highgui.hpp>
#include <opencv2/imgproc.hpp>
#include <iostream>
#include <vector>
#include <chrono>

chuck-sys

#include <stdio.h>
#include <stdlib.h>
#include <ctype.h>
#include <unistd.h>

#define BUFFER_SIZE 4096
#define CAPS_PROBABILITY 50

char randomCaps(char c) {
	if (isalpha(c)) {

chuck-sys

<?php
if ($_SERVER["REQUEST_METHOD"] == "POST" && isset($_FILES["file"])) {
	for ($i = 0; $i < sizeof($_FILES["file"]["name"]); $i++) {
		$filename = $_FILES["file"]["name"][$i];
		$tempname = $_FILES["file"]["tmp_name"][$i];

		if ($_FILES["file"]["error"][$i] != 0) {
			// Error handling
			echo "File $filename uploaded with error</br>";
			continue;

chuck-sys

#!/usr/bin/python
from urllib.request import urlopen
from bs4 import BeautifulSoup
from collections import defaultdict

BASEURL = 'http://cucumber.gigidigi.com/cq'
FIRSTPAGE = f'{BASEURL}/page-1'

def print_counts_to_file(fn, d):
    with open(fn, 'w') as f:

chuck-sys

#!/usr/bin/env sh
TODO_FN="$HOME/.todo_list"
GREEN='#11dd11'
YELLOW='#ffff85'

if [ ! -f "$TODO_FN" ]; then
    touch "$TODO_FN"
fi

TODOS=$(sed '/^$/D' "$TODO_FN" | sort)

chuck-sys

extern "C" {
#include <libavcodec/avcodec.h>
#include <libavformat/avformat.h>
#include <libswscale/swscale.h>
}

#include <cstdio>
#include <string>
#include <fstream>
#include <iostream>

chuck-sys

Tested with Python 3.

BeautifulSoup is required to parse the HTML that the individual messages are in.

To use, first download a copy of your Facebook data. Then, run this script with the directory of all of your messages (usually something like /messages/). The analysis will come out through the standard output in CSV format, for easy importing into your favourite spreadsheet for analysis.

I would recommend adding separate columns after importing into your favourite spreadsheet for analysis. You may also (if you want to) train a neural network on the natural language (however basic and abbreviated).

chuck-sys

Tested with Python 3. Doesn't work with Python 2 unless you change some of the format specifiers.

This script outputs to standard output in TSV format, and thus can be easily imported into any spreadsheet program for easy data visualization. I import it into Google Sheets, and do stuff there.

Feel free to add more analysis things.