In this post I will discuss an implementation of sequential Gaussian simulation (SGS) from the field of geostatistics. Geostatistics is simply a statistical consideration of spatially distributed data. Sequential Gaussian simulation is a technique used to “fill in” a grid representing the area of interest using a smattering of observations, and a model of the observed trend. The basic workflow incorporates three steps:
- Modeling the measured variation using a semivariogram
- Using the semivariogram to perform interpolation by kriging
- Running simulations to estimate the spatial distribution of the variable(s) of interest
Continue reading Two Dimensional Sequential Gaussian Simulation in Python
In this post, I’ll describe how to change the color of an anode RGB LED with a potentiometer. I’ll be using an Arduino UNO, and components from this RadioShack components kit. The motivation for this post was to have an LED change color in response to the reading from a thermistor next to my stove, but when I read about how I’d first need to calibrate the thermistor with some kind of thermometer, my motivation scurried under the sofa like a terrier in a thunderstorm. As a compromise I substituted the thermistor with a trim-pot, reasoning that a variable resistance was a variable resistance.
Continue reading Controlling an RGB LED with a Potentiometer
In this post I’ll describe how to get started using gonum/matrix package for using matrices for math and stats applications. (Documentation here.) I’ll begin with a bit about setting up the Go environment drawn from the How to Write Code page on the Go website. (I highly recommend reading this if you’re unfamiliar with Go.) Next I’ll provide a commented usage case.
Continue reading Using Matrices in Go(lang)
In this post I’ll present the z-score forward and backward transforms used in Sequential Gaussian Simulation, to be discussed at a later date. Some geostatistical algorithms assume that data is distributed normally, but interesting data is generally never normally distributed? Solution: force normality, or quasi-normality. All of this is loosely based on Clayton V. Deutsche’s work on the GSLIB library, and his books.
Continue reading Z-score Transform for Geostatistics
In this post I’ll discuss the basics of walking through a directory tree in Python and Go. If you are dealing with a smaller directory, it may be more convenient to use Python. If you are dealing with a larger directory containing hundreds of subdirectories and thousands of files, you may want to look into using Go, or another compiled language. I enjoy using Go because it compiles quickly, and it doesn’t use pointer arithmetic.
Continue reading Traversing a Directory Tree in Python and Go(lang)
In this post I’ll demonstrate an iterative closest point (ICP) algorithm that works reasonably well. An ICP algorithm seeks to find a transformation between two sets of points that minimizes the error between them, i.e., you are trying to find a transformation that will lay one set of points exactly on top of another.
Continue reading An Iterative Closest Point Algorithm