Understanding Data Axes using Image manipulation with Python

I have recently been working on several projects which allowed me to dive into image analysis. What I found was a lot of people still using nested loops to process pixel data. This is fine for small data sets. Once you have amassed several thousand images to process though, you need a better way. For many new data scientists, one of the most confusing aspects of manipulating image data programmatically seems to be understanding the axes layout many libraries (like NumPy and Pandas) rely on. Understanding how the data is laid out, and how you can efficiently process it, can save you hours of waiting for your code to execute. In this post I will explain this layout and how you can leverage it to your programmatic advantage.

The Art Gallery Problem: Mathematical analysis of area coverage

Often times while doing a security analysis of a physical area, I am interested in the efficient placement of assets to monitor and secure an area. In mathematics the placement of security resources within an area is often modeled as a geometric area coverage problem. We can restate our problem as "What is the minimum number of resources required to cover the inside of a given polygon".  Today I will discuss a software for analyzing and planning physical security layouts based off of the insights revealed by studying this problem.

Abusing Git Hooks with Python

Git hooks is one of the lesser talked about features of git. Most Developers get a basic introduction to hooks, maybe once early on when there is so much to take in that the beauty of these little gems goes almost unseen. Today I would like to discuss some ways to use, then abuse, this often forgotten facet. Then conclude with a discussion of detection.