The python-tutorials project is basically a random assortment of some of my tutorials on python. These are tutorials (some of which) I've placed on my blog at www.wandering3ngineer.com. The files for each tutorial are placed in seperate folders. Some of these I've used to provide short courses for students and colleagues
Are you one of those paranoid people who is terrified that an insane calamity is just around the corner? Are you obsessively checking the world news waiting for the moment that you can spring into action? Well, why not harness that obsessiveness, be productive at the same time and check out if your country’s power system is about to come crashing down. Here I’ll show you a little bit of Python coding and analysis I did to discover some information about Taiwan’s Power Grid.For further details: https://www.wandering3ngineer.com/learning-about-the-taiwan-power-grid-by-extracting-zip-and-json-data-using-python/
My goal is to try and do some data-scraping using google to grab some basic weather data. Though of-course this could also be applicable to any webpage and expanded quite easily beyond weather data. I’ll show two different approaches using a direct approach and another approach that controls a browser (via Selenium) to get access to the data after all dynamic HTML data has been rendered.For further details: https://www.wandering3ngineer.com/data-scraping-weather-data-from-web-search/
Want to make your own AI voice assistant. Grab this code and use google speech recognition, speech synthesis and openai to make your own simple voice assistant. Give it a go. And check out the video on youtube for a more detailed tutorial. Make sure you have your own API key for OpenAI - replace the existing key with your version. The key included is a dummy.For further details: https://youtu.be/bGKlWl9fif4
This code is demonstration code for a workshop done in the Summer of 2023 to teach professionals and tech students how to rapidly build and deploy (in 40 hours) their own LLM pre-trained model based voice chat system. The code provides all the foundations to build a microservice deployment of TTS, STT, LLM and API servcies on the backend and Flet / Flutter frontend. Deployment also require some docker container builds along with some shell scripts for testing and configuration. This code is demonstration code for analyzing some sample weather and electricity meter data from a particular region in the world. The code first visualizes the existing data and produces html graphs. It then goes ahead and uses kmeans clustering to determine how meter energy consumption patterns over a year may be related to establish distinct clusters of meters that can be grouped together as similar. An html graph is used to map this data into a map and identify differences in clusters. It then brings weather information into the mix to determine the multivariate correlation (linear) between various weather parameters such as GHI, Wind, Irradiance, etc. and between meter energy consumption. This is then used with autoencoders to determine distinct possible load patterns existing among meters relative to weather. Highly correlated meteers would exhibit high correlation suggesting use of air conditioning to higher levels.