A blog of Python-related topics and code.
Quadtrees #2: Implementation in Python
Posted by: christian in Featured on frontpage on 19 Apr 2020
The following code implements a Quadtree in Python (see the previous blog post). There are three classes: Point represents a point in two-dimensional space, with an optional "payload" (data structure associating the Point with more information, for example the identity of an object). The Rect class represents a rectangle in two-dimensional space through its centre, width and height. There are methods to determine if a given Point object is inside the Rect and to determine if the Rect intersects another Rect.
The double compound pendulum
Posted by: christian in Featured on frontpage on 15 Apr 2020
Following on from this post about the simple double pendulum, (two bobs connected by light, rigid rods), this post animates the double compound pendulum (also called a double complex or physical pendulum): two rods connected to each other, with their mass distributed along their length. The analysis on Wikipedia provides the dynamical equations for the case of equal-mass and equal-length rods. Here, the more general case of rods with lengths $l_1$ and $l_2$ and masses $m_1$ and $m_2$ is considered.
COVID-19 Quarantine House: Hollywood Walk of Fame Edition
Posted by: christian on 9 Apr 2020
To find your Hollywood Walk of Fame Quarantine house, use this script on the command line, providing your latitude and longitude:
Illustrating the Padovan sequence
Posted by: christian on 8 Apr 2020
The Padovan sequence is a cousin of the more famous Fibonacci sequence defined by the initial values $P(1)=P(2)=P(3)=1$ and the recurrence relation $P(n) = P(n-2) + P(n-3)$. That is, the next value is obtained not by summing the previous two values but by summing the two values before the last one. The first few values are:
Plotting COVID-19 case growth charts
Posted by: christian on 29 Mar 2020
Following on from this previous post, here is a short tutorial on creating this and similar charts using pandas by automatically downloading data from Johns Hopkins University's CSSE GitHub repository (the "JHU/CSSE dataset").