paulknewton/fritzbox-monitor

Stars
6
Rank 2,539,965 (Top 51 %)
Language
Python
Created over 5 years ago
Updated 8 months ago

paulknewton/fritzbox-monitor

paulknewton

There are no reviews yet. Be the first to send feedback to the community and the maintainers!

Monitors internet health of a FRITZ!Box router and plots graphs of internet outage.

pi-cloud-frame

An icloud-powered digital frame running on a Raspberry Pi. Downloads a random sample of photos from your icloud account, crops them to the correct aspect ratio and displays them. Supports parallel slideshows, interactive menus, GPS/EXIF lookup and auto rotation via a MPU-6050 accelerometer.

minecraft-skin-generator

Python tool to convert a photo to a Minecraft skin.

pi-tank-watcher

Raspberry Pi project to monitor water depth in a rainwater tank using a HC-SR04 ultrasound sensor, and cross reference these to weather conditions. Logging and trend analysis via the ThingSpeak IoT platform and the python data libraries (pandas, numpy, matplotlib).

covid-ml

Analyses publicly available data on the COVID-19 pandemic and identifies trends and patterns using a Jupyter live notebook and the pandas data analysis framework. Shows how python can be used to analyse data sets and present results in ways that can be easily understood.

minecraft-stat-checker

Tools to extract Minecraft player names from screenshots and retrieve player statistics. Used to assess the level of your opponents before entering the game. Uses optical character recognition via Google Tesseract to extract usernames from images. Statistics retrieved from online Minecraft servers. Data is summarised using pandas dataframes and Seaborn heatmaps.

kindle-collection-generator

Copy files to a Kindle and automatically create Collections that correspond to the filesystem structure. Turns your Kindle into a walking data storage brick!

twitter-ml

Project to analyse text streams (tweets or docs) using big data and machine learning. Uses Apache Spark to built textual metrics, then processes the text via various classification models to evaluate the sentiment (models via SciKit-Learn).