bpsci
pgmpy
| bpsci | pgmpy | |
|---|---|---|
| 5 | 2 | |
| 27 | 2,621 | |
| - | 0.8% | |
| 4.7 | 8.0 | |
| 3 months ago | 5 days ago | |
| Python | Python | |
| GNU General Public License v3.0 only | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
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For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
bpsci
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I visualized the telemetry from the last lap of the 2021 Abu Dhabi Grand Prix
If you’re interested (I’ve posted about this on r/Python) I’m developing a visualization library in Blender and I’ve done some orbital dynamics and F1 visualization with it on GitHub
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bpsci - A visualization library for Blender (example)
Hey everyone! A while back I posted about a visualization library I was working on called bpsci. I recently created a professional(ish) render of a simulated racing line for a Formula 1 car at the Abu Dhabi F1 circuit - YouTube link using bpsci for all of the animation.
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Abu Dhabi F1 Racing Line Visualized with bpsci for Blender
Links: bpsci
- 3D Technical Visualization Library for Blender with Python Scripting
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A proof of concept technical orbital simulation created with BlenderPython (a little glitchy at the end)
For those interested, I did the actual simulation in Python, exported a csv with all of the data and then read it into Blender’s Python API using pandas. I make many of these visualizations so I’ve started an abstraction library for animating technical visualizations in Blender: you can check it out at https://github.com/jerryvarghese1/bpsci It’s still in progress so some functionality is not fully supported but the core functions seem to be working well so far
pgmpy
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Use data from tables generated in python console,
No need to post the help, here is the DiscreteFactor class https://github.com/pgmpy/pgmpy/blob/eb65f40d2b32bf2ad971181333bb9ed7aefde907/pgmpy/factors/discrete/DiscreteFactor.py
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[D] Python toolboxes for probabilistic graphical model inference
I do know of a few promising toolboxes such as pgmpy, pymc3, and pyro, but have not used either of them (for this purpose) and am at a bit of a loss picking one to start with.
What are some alternatives?
Fast-F1 - FastF1 is a python package for accessing and analyzing Formula 1 results, schedules, timing data and telemetry
causalnex - A Python library that helps data scientists to infer causation rather than observing correlation.
f1-circuits - A repository of Formula 1™ circuits in GeoJSON format.
statsmodels - Statsmodels: statistical modeling and econometrics in Python
global_racetrajectory_optimization - This repository contains multiple approaches for generating global racetrajectories.
scikit-learn - scikit-learn: machine learning in Python
CausalPy - A Python package for causal inference in quasi-experimental settings
rustworkx - A high performance Python graph library implemented in Rust.
pyhf - pure-Python HistFactory implementation with tensors and autodiff
Lottery-Simulation - This program can simulate a number of drawings in the Lottery (6 out of 49). The guesses and the draws are chosen randomly and the user can choose how many right guesses there should be (0-6). Then the program will run through the simulation as many times as it takes to get the exact number of correct guesses the user chose. The user can also choose how many times this should be repeated (the higher the number, the more accurate the result will be). Then the program will automatically calculate the average number of tries it took to get the chosen number of correct guesses and tell the user the chance of getting this certain number of correct guesses.
dodiscover - [Experimental] Global causal discovery algorithms
generalized-additive-models - Generalized Additive Models in Python.