FOSS effort to bring live pixel art to microcontroller-driven displays, such ESP32 projects with LED matrices or LCD/OLED modules.
This is for:
- Makers: Get art that looks awesome on low-res, download firmware
- Pixel artists: Reach real-world displays, gain points and badges
- Everyone: Enjoy free art & community, even if you don't own a display
At high level, the project seeks to combine an MQTT broker for live updates (new posts), a lightweight free & ad-free social media experience (comments and likes only), a self-hosted approach to storing the artworks, and MCU firmware to turn any display into a web-connected pixel art player.
Join us! It is a lot to build and this is all just for fun & love.
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[Sorry for reposting, the Discord link was broken! Now it's fixed!]
FOSS effort to bring live pixel art to microcontroller-driven displays, such ESP32 projects with LED matrices or LCD/OLED modules.
This is for:
- Makers: Get art that looks awesome on low-res, download firmware
- Pixel artists: Reach real-world displays, gain points and badges
- Everyone: Enjoy free art & community, even if you don't own a display
At high level, the project seeks to combine an MQTT broker for live updates (new posts), a lightweight free & ad-free social media experience (comments and likes only), a self-hosted approach to storing the artworks, and MCU firmware to turn any display into a web-connected pixel art player.
Join us! It is a lot to build and this is all just for fun & love.
I am yet to find a replacement for R's dbplyr in Python. Read: Python-to-SQL code generation (dbplyr performs R-to-SQL). This is a very powerful package that alone can make me stay in R.
Ibis would be the equivalent of unified frontend for tabular data. It was created by the Wes McKinney, also Pandas creator, that now works in Posit (old RStudio).
I wrote an R script to help me generate my own workouts.
What it actually does is schedule (distribute) the desired muscle groups across days according to your specification of:
- volume: # sets to do on each workout day of that muscle group
- frequency: # days between workout days of that muscle group
The goal is to avoid having some days at the gym be huge (many exercises) while others are small (fewer exercises). The script uses simulated annealing to try to even out the schedule as much as possible.
I am afraid I do not have a website or write-up but the code is here: https://github.com/fabkury/caltre. I have been actually using this script for years and it has made my gym scheduling effortless. I can just focus on the "meta parameters", e.g.:
INPUT:
muscle group,`X sets per training day`,`train every X
days`
back,9,4
chest,9,4
quads,8,4
biceps,7,3
shoulder,6,4
abdominal,4,2
calf,4,5
anterior forearm,5,6
posterior forearm,5,6
OUTPUT:
It tells you what to do on each training day, as many days you want (it is an "infinite roll"). Here are 9 days to exemplify:
day 1:
chest 9
biceps 7
day 2:
quads 8
abdominal 4
day 3:
back 9
calf 4
anterior forearm 5
day 4:
biceps 7
shoulder 6
abdominal 4
day 5:
chest 9
posterior forearm 5
day 6:
quads 8
abdominal 4
day 7:
back 9
biceps 7
day 8:
shoulder 6
abdominal 4
calf 4
day 9:
chest 9
anterior forearm 5
day X:
...
Nice to see the localhost solution! I (my team) did very similar when I was a postdoc. In 2015 we used the HERE API (www.here.com) to map geographical access to hospitals in the entirety of two countries, USA and Brazil.
As starting point, I used the lat/lon of the population center of each census block group as provided by Census.gov. In Brazil the closest geographical equivalent is the setor censitário (census sector). I used Haversine distance (great circle distance between lat/lon's) to narrow the search space to the 5 closest hospitals, from the hospital database by the American Hospital Association, and the Brazilian CNES registry (Cadastro Nacional de Estabelecimentos de Saúde). From those 5 closest candidates, I then asked the HERE API for the true driving time to their address. Many many API calls.
We plotted average driving time against variables from the census block group provided by the American Community Survey to show how values change as driving distances change. Mostly just to demonstrate the kind of analyses possible. I presented the work at the NLM Informatics Training confereces, e.g. with many pictures of maps: https://github.com/fabkury/drithop/raw/master/Kury%20-%20dri... . Source code (but not data) are in the GitHub repo `drithop`. The maps are self-contained HTML files and interactive using OpenStreetMap.
Healthcare data scientist. Has comprehensive background in manipulating health data, and building analyses, datasets, and other assets. Also experienced in publishing as a researcher in Biomedical Informatics.
Open-source ESP32-P4-based wi-fi connected pixel art player, part of the Makapix Club project (https://makapix.club/).
Link to firmware repo: https://github.com/fabkury/p3a