microfilter

Some ad-hoc approaches to filtering noisy data that don't appear in textbooks (and maybe shouldn't)

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