MATLAB and Python Recipes for Earth Sciences
Martin H. Trauth, University of Potsdam, Germany
About four years ago, I published a post on reading meteorological data from a Netatmo weather station. Since then, Netatmo has changed the authentication process. Here is an update of my MATLAB scripts for the weather station. Continue reading “ThingSpeak IoT Weather Station Update”
The German version of the 1th edition of Python Recipes for Earth Sciences is out. Thanks to the subject of SpringerSpektrum for the professional support in the realization of the project.
Continue reading “German Translation of Python Recipes for Earth Sciences Published”
In my book Signal and Noise, I use LEGO MINDSTORMS components alongside smartphones and other technical devices to demonstrate methods of data acquisition in the geosciences. Here is an update. Continue reading “MATLAB/LEGO MINDSTORMS Practical Update”
The Community tab of the MathWorks website now offers the new AI Chat Playground. This allows you to transfer programming tasks to an AI – and try them out directly. Continue reading “The New MATLAB AI Chat Playground”
After 30 years of teaching statistical methods in the geosciences, I would like to give a few tips for our next generation. Back then, I was in awe of a term like chi-square test and kept my hands off it for a long time. However, I could not avoid complicated methods such as spectral analysis and filtering, because my doctoral project was about signal processing of paleoceanographic time series. Here we go. Continue reading “Statistics in Earth Sciences in 6 Steps”
The book “Python Recipes for Earth Sciences” (Springer 2022) is ranked 7th (Winter 2023/2024) in the Top 100 out of more than 4,600 books in Earth Sciences & Environment of Springer. The book “MATLAB Recipes for Earth Sciences” (Springer 2021) is ranked 24th. Thanks to all readers for buying the books!
Most methods of time series analysis require evenly spaced time axes, which is why we have to convert unevenly spaced time series into a time series with an evenly spaced time axis using interpolation. Continue reading “Interpolating Unevenly Spaced Data With MATLAB”
On-screen digitizing is a widely-used image processing technique. While practical digitizer tablets exist in all formats and sizes, most people prefer digitizing vector data from the screen. Examples of this type of application include the digitizing of river networks and catchment areas on topographic maps, of the outlines of lithologic units on geological maps, of landslide distributions on satellite images, and of mineral grain distributions in microscopic images. This chapter was not included in the first edition of PRES because of problems developing cross-platform Python code for digitizing images. These problems seem to be solved now, so a Python variant of minput can be presented here.
Continue reading “Digitizing from the Screen – the Python Version”