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When I was working on the student experiments for the book Signal and Noise in Geosciences (Springer 2021), there was an inexpensive 3D scanner available – right up until that moment when I got the money to actually buy it. The company Hewlett Packard bought the smaller manufacturing company and stopped the production of the cheap scanner. HP was selling much more expensive 3D scanners at that time. Now there is a inexpensive 3D scanner again, the Revopoint MINI, and I just got one. Here are the first results, scanning with the company’s software, then importing, processing and displaying the point cloud with texture with MATLAB. Continue reading “Calculating 3D Point Clouds From 3D Scanning Outputs”

Ed Hawkins, climatologist at U Reading, published a visualization graphics for climate data to display global warming. Here’s a script to display the warming stripes with MATLAB.

Continue reading “Ed Hawkins’ Warming Stripes with MATLAB”

This week scientists from the Chew Bahir project will meet in the Virtual Space to discuss the latest results of the 290 m long ICDP cores from S Ethiopia. Continue reading “Seventh Chew Bahir Drilling Project Workshop in the Virtual Space”

This is the Errata File for Python Recipes for Earth Sciences (Springer 2022). The authors apologize for these errors, which were unintentional. The file is constantly updated on this webpage.

Please let me know if you find any mistakes, misunderstandings in the text, or general suggestions for additions and extensions. No book is as good as its next edition!

Continue reading “New Book “Python Recipes for Earth Sciences” (Springer 2022)”

Here you find the data and MATLAB code and data of our paper “Pleistocene climate variability in eastern Africa influenced hominin evolution” published in Nature Geoscience. Continue reading “Data and MATLAB Code of “Pleistocene climate variability in eastern Africa influenced hominin evolution” (Foerster et al., Nature Geoscience 2021)”

In many projects it is necessary to share the result of programming work with other colleagues – or to work together on these projects. In the MRDAES book, Chapter 2.8.5 describes how to collaborate on MATLAB projects with collaborators with four different levels of MATLAB skills: (1) no software installed and no experience with MATLAB, (2) software installed but only limited MATLAB skills, and (3) software installed and good MATLAB skills. Below you find an update of this chapter, which now also includes using (4) MATLAB Projects to organize and collaborate on large programming projects together with Git, the most popular version control system for programming projects.

Continue reading “Collaborative Coding with Git and MATLAB Projects”

The Principal Component Analysis (PCA) is equivalent to fitting an n-dimensional ellipsoid to the data, where the eigenvectors of the covariance matrix of the data set are the axes of the ellipsoid. The eigenvalues represent the distribution of the variance among each of the eigenvectors. To understand the method, it is helpful to know something about matrix algebra, eigenvectors, and eigenvalues. Here is a n=2 dimensional example to perform a PCA without the use of the NumPy function pca, but with the function of eig for the calculation of eigenvectors and eigenvalues. Continue reading “Principal Component Analysis in 6 Steps – the Python Version”