Open AlexanderGeiger opened 5 years ago
Thanks for the proposal @AlexanderGeiger
Some thoughts and comments:
scipy.signal.butter.ba.json
, which points at scipy.signal.butter
, has N
and Wn
and btype
as tunable Hyperparameters and output=ba
and analog=False
as fixed hyperparameter and which inputs nothing but returns b
and a
, which will be set as context variables.scipy.signal.filtfilt.json
, which points at scipy.signal.filtfilt
, has axis
as fixed hyperparameter and padtype
and padlen
as tunable hyperparameters and which inputs a
, b
and X
and returns X
.Optionally, we would add scipy.signal.butter.zpk
in the future if needed.
Doing this, no python code is needed and both primitives can be freely combined with other options.
timeseries_preprocessing.time_series_aggregation
, which outputs X and the time index as two different variables.Thanks for the feedback @csala I like the first approach and will try to implement the primitive this way.
Description
A low pass filter (in this example a butterworth filter) for the preprocessing of time series data.
The outcome of such a filter should be similar to the moving aggregations, but the number of samples will not be decreased and therefore might improve the performance of the pipeline.
It takes an array of the data, that should be filtered, as input and returns another filtered array.
What I Did
I started implementing this primitive for testing purposes in the butterworth branch on my fork, which you can check out.
Concretely, I added a Primitive JSON file and a custom function in timeseries_preprocessing.py.
Any feedback on the primitive itself and the implementation would be highly appreciated.