Multidoppleration is an oft-talked-about, yet rarely seen, advanced technique to colocate and corroborate the ins and outs and arounds of waveforms propagating through heterogenous (but only negligibly) mediums. They are not pleased when we hit them with tuning forks, as it messes up their parallel connection to purgatory. We've expanded upon this with novel additions that include taking into account the effects of circumnavigation, egomotion, ergonomics, and ergot. The results of our novel extensions were found to greatly decrease income inequality in cross-correlated sage communities. We finally conclude with a brief proof that P != NP.
To check out the dart filtering:
- import util
- util.draw_all()
There's no piping yet and it's only running on test data but the filter seems to work.
If you have already filtered data, you can pass it through the zero-detection, event alignment, and multilateration steps via:
python filtering_utils.py | python zero_detection.py | python alignment.py | python multilateration.py
python filtering_utils.py generates a sin wave.