python -m venv .venv
source .venv/bin/activate
pip install m3u8downloader
downloadm3u8 -o foo.mp4 spaces_m3u8_url
ffmpeg -i foo.mp4 -b:a 192K -vn bar.mp3
python -m venv .venv
source .venv/bin/activate
pip install m3u8downloader
downloadm3u8 -o foo.mp4 spaces_m3u8_url
ffmpeg -i foo.mp4 -b:a 192K -vn bar.mp3
| # Scan for CVE-2017-0143 MS17-010 | |
| # The vulnerability used by WannaCry Ransomware | |
| # | |
| # 1. Use @calderpwn's script | |
| # http://seclists.org/nmap-dev/2017/q2/79 | |
| # | |
| # 2. Save it to Nmap NSE script directory | |
| # Linux - /usr/share/nmap/scripts/ or /usr/local/share/nmap/scripts/ | |
| # OSX - /opt/local/share/nmap/scripts/ | |
| # |
I currently use Python for nearly all of my data science and wrangling work these days but usually find myself switching to R to visualize data using ggplot2. This is due in part to ggplot2's general excellence, but also because I had a lot of trouble learning Python's most popular viz library, matplotlib on my own...its homepage is decent enough...but its variety of plotting APIs (--pylab? OOP? %matplotlib???) has led to widely differing examples and best practices among the many online matplotlib guides (not dissimilar to the general problem of trying to practice either Python 2.x or 3.x).
That changed yesterday when I stumbled across [Nicolas P. Rougier's beautifully designed and com
| //var csv is the CSV file with headers | |
| function csvJSON(csv){ | |
| var lines=csv.split("\n"); | |
| var result = []; | |
| var headers=lines[0].split(","); | |
| for(var i=1;i<lines.length;i++){ |