Linking SciML Julia helicopter challenge to Microprediction.Org
Project description
helicopter
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Tiny repo that creates a helicopter data stream at Microprediction.Org
It also contains some snippets of code showing how to:
- Retrive and interpret bivariate stream data at Microprediction.Org
- Fit a copula function
- Make a prediction submission
See article for more explanation.
Background information
Yes this is a Python package but it was inspired by the Helicopter Julia Challenge created by Chris Rackausckas of the SciML group at MIT. In that challenge contestants are provided incomplete data from a helicopter and asked to infer the latent dynamics.
- Video of the helicopter https://www.youtube.com/watch?v=2g1-sDZ3BVw
- Home page of challenge https://github.com/SciML/HelicopterSciML.jl
- Chris will be teaching how to do automated discovery of missing physical equations from a first principle model at at workshop on July 26th, 2020. Sign up for JuliaCon at https://juliacon.org/2020/
The goal of the Julia challenge is to utilize automated tools to discover a physcially-explainable model that accurately predicts the dynamics of the system.
A quick peek at the helicopter data
import pandas as pd
data = pd.read_csv('https://raw.githubusercontent.com/SciML/HelicopterSciML.jl/master/data/Lab-Helicopter_Experimental-data.csv').plot()
Fitting implied helicopter data
See helicopter/exploratory/helicopula.py
If you prefer notebooks:
https://github.com/microprediction/PDCI/blob/master/helicopula.ipynb
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