Filtering
Project description
microfilter
Some ad-hoc approaches to filtering noisy data that don't appear in textbooks
Usage example
Train filter on simulated noisy data
from microfilter.univariate.expnormdist import ExpNormDist
from microfilter.univariate.noisysim import sim_lagged_values_and_times
lagged_values, lagged_times = sim_lagged_values_and_times
dist = ExpNormDist()
dist.hyper_params['max_evals']=500
dist.fit(lagged_values=lagged_values, lagged_times=lagged_times)
pprint(dist.params)
new_value = 17.0
dist.update(value=new_value, dt=1.0)
pprint(dist.state)
See https://github.com/microprediction/microfilter/blob/master/examples/plot_expnorm.py
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
microfilter-0.0.6.tar.gz
(7.3 kB
view hashes)
Built Distribution
Close
Hashes for microfilter-0.0.6-py3-none-any.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 625730ab0c7cbd71b235176b231ad4656ba344afc77abd9ffb2524c6c4b43418 |
|
MD5 | 53d89f182378a606a81bea0ddd3dbc73 |
|
BLAKE2b-256 | b562bc405865608ff5cc8d75c630435d0d3388911bcebe6a9950a42288d8bba3 |