INTERACTIVE GPS DATA VISUALIZATIONS IN PYTHON/JUPYTER
Did you know you can plot your geographic data on interactive maps embedded directly in your Python notebooks? Check it out, as we play with and analyze some GPS tracking data. A database of tracked walking routes data available on a health/fitness website provides a convenient trove of data not only to play with, but also to explore the geometric interference effects of downtown buildings upon GPS track solutions.
SEVERAL PROJECTS USING RASPBERRY PI ZERO
The tiny version of the Raspberry Pi computer, the Pi Zero, is barely larger than a stick of gum yet is a full Linux-based computer. It's a bit less powerful than the larger Pi 2 & 3 models, but it uses much less power than they do. It's a great field sensor platform for some applications, and super quick to implement in all sorts of projects. Projects here include: 1.) adding a canned sensor attachment and running a Jupyter server on the Pi to analyze/view the sensor data in a web-based Python notebook; 2.) attaching a USB mic and audio amplifier/speaker add-on and implementing the Jasper speech/voice-control agent; 3.) attaching a near-infrared camera and taking nighttime photos in our back yard after dark.
MULTI-PHASE LINEAR REGRESSION
Code to fit multiple co-joined straight lines to a set of data points. Note the popular name (above, or sometimes "segmented linear regression") for this topic may be a bit misleading, as this is in fact a nonlinear regression problem due to solving for the intersection points of the co-joined lines.
PREDICTIVE FILTERS COMPARISON
A classic textbook for predictive (tracking) filters is Applied Optimal Estimation, edited by Gelb (1974). In section 6.1 of that book are two simple radar tracking examples (6.1-2 and 6.1-3) which demonstrate several nonlinear filters. I've programmed up those examples into a Matlab script and added a few additional filters to compare and contrast them in both linear and nonlinear cases.
InvGN: GAUSS-NEWTON NONLINEAR INVERSION CODE
Calculate Tikhonov-regularized, Gauss-Newton nonlinear iterated inversion to solve the damped nonlinear least squares problem, using the InvGN toolkit for Matlab/Octave. While Matlab's optimization toolbox contains
lsqnonlin, it does not inherently include the Tikhonov regularization and is only the optimization component, whereas inversion also requires uncertainty quantification. My InvGN package is for weakly nonlinear, Tikhonov-regularized, inverse problems (so includes solution uncertainties and resolution quantification), handles both frequentist and Bayesian frameworks, and also works in Octave.
MINISIM SIMULATION CLIENT GUI
I wasn't quite sure where in this website to stick this project honestly, so it's here. It's not really physics or data science, more GUI programming, but what the heck...