In the previous post, we generated images with circles and squares in Python with the package Pillow. The code was simple and the results are not that compelling. So, in this post I’m looking to step it up a bit. A while back Numberphile posted a video of Matt Henderson explaining how maze generation and breadth-first search can be used to generate a compelling animation of lightning. We are going to use a similar approach to generate images of lightning. ...
This is the first of what we’ll likely be a series of blog posts on generative art, specifically images. At the start, everything will be very simple. This is mainly because the topic is new to me and the library we’re going to use to generate images is also new to me. Speaking of, we’re going to be using Python and Pillow to generate images; at least at the start, we’ll see where we go. To start, I want to break this post into two parts: (1) pseudo-random and (2) generating a simple image with circles and squares. ...
In this post, I want to show how to implement a simple web-based typing game. A version of the final product is online; the code is on Github. The game has a menu screen where users can select whether or not to allow capitals letters and press a button to start the game. When the player begins the game, they’ll see characters—not necessarily a valid word, more an assortment of letters—to type. After the user has finished typing, the next value will be longer than the previous one. The game continues until the player mistypes. At which point, the user can see how well they did and restart. ...
If it were up to me, every website would be required to have a theme option of light or dark. Unfortunately, it is not. It is uncommon to find websites that include both options or, at least, default to dark mode. Recently FaceBook included a dark mode option and slack now has it built into their app. It is a wonderful thing to see it become more popular. Still, there is no guarantee. As a result, I have turned to Dark Reader. A great chrome extension that will automatically run on all websites but gives you the option to turn off it completely or turn off for certain websites. ...
Let’s start by examining the simplest case of joint probability with single word probability via a uni-gram. If we want to know the probability of a word, without any context appearing before or after, then we could take a corpus of text and convert it into a dictionary with the key for the word and the count as the value: { "a": 1000, "an": 3, "animal": 12, ... }. Say we want to calculate P("a"), meaning the likelihood of “a.” We would take the count of occurrences for the word “a” and divide it by the total number of words in the corpus: P("a") ≈ count("a") / corpus_word_count. This is called maximum likelihood estimation (MLE) and we use the “≈” sign because our corpus is not a perfect representation of the actual likelihood of the word. ...
In my last post I discussed n-grams and gave an example of them being used on the text of Harry Potter but I didn’t cover the implementation and instead linked the source. Today, I want to go over a key data structure used in my implementation: ring buffers (also known as a circular buffer). A ring buffer needs a max size, N, that represents its max capacity. Until the buffer has reached its max capacity, it is exactly like a list. However, once the max capacity is reached the buffer will drop elements when new ones are added resulting in a first in first out (FIFO) behavior. An example of this data structure in action can be seen below. We initialize a ring buffer of size three. At first the ring buffer acts like a list but stops when the fourth element is added. On this add, the ring buffer drops the 0 because it was the first element added and the buffer has reached its max capacity of three. Say, for example, we ran this again and added a four. The buffer would then be [2,3,4] because the one would be the next element to be dropped. ...