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

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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.

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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.

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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.

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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.

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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.

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## Blog about math, programming, and data.