Tag: Python

iNatural Image Download Python Script
I started working on a small project to replicate the results found in Justen et al. 2021 Identification of Public Submitted Tick Images: A Neural Network Approach. https://doi.org/10.1371/journal.pone.0260622. One of the sources of tick images used in the paper is from iNaturalist.org. iNaturalist has a publicly accessible data set with millions of observations of thousands…

Export SQL Server Stored Procedures using Python
The following short Python 2.7 script creates an export of all the stored procedures and functions within a SQL Server database.

Rijndael / AES (128 bit) in VB.net, PHP, Python
Being able to transport encrypted data is important in some of my projects at work. Oneway hashes using MD5 usually suffice for most encryption purposes but Symmetric Encryption algorithms are important for encrypting and then decrypting data. For this, we use the Rijndael and AES algorithm in a few different languages.

Indepth details of Particle Swarm Optimization
I explain and show code to construct the Particle Swarm Optimization in Python. I conclude by optimizing on the Rastrigin function, a function that researchers use to test optimization algorithms on.

Estimating Lyapunov Spectra of ODEs using Python
Python code is shown that estimates the Lyapunov spectra for the Rossler and Lorenz systems. This code is written in a way that makes it adaptable for other continuoustime systems.

Modelling Chaotic Systems using Support Vector Regression and Python
Support Vector Regression is a technique in machine learning that can be used to model chaotic data. A program is shown to work on Delayed Henon Map data.

Lyapunov spectra of inverted discrete dynamical systems
One can estimate the lyapunov spectrum of dynamical systems and their inverted counterparts using local Jacobian matrices and Wolf’s algorithm.

Generating Poincaré Sections and Return Maps
Using the iterative 4th order RungeKutta method as described here, we can create low dimensional slices of the system’s attractor known as Poincare Sections, Return maps, or Recurrence maps. We will use the Rossler attractor for this example, with a, b, and c set to 0.2, 0.2, and 5.7, respectively. Poincare sections are important for visualizing…

Generating time series for Ordinary Differential Equations
We can produce a time series from ordinary differential equations by solving the equations using the iterative 4th order RungeKutta method and plugging each of the solutions back into the equations.