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Apply auto smoothing to a time series data.

Project description

Auto Smooth

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Apply data smoothing/filtering to a time series by automatically selecting parameters.

Currently available smoothing/filtering techniques in the package:

  • Savitzky–Golay filter

Quickstart

from auto_smooth import auto_savgol

# apply savgol filter
data_filtered = auto_savgol(data)

>>> wl_best=7, po_best=2

original_vs_smooth

Savitzky-Golay Filtering

Savitzky–Golay (Abraham Savitzky and Marcel J. E. Golay) filter is a type of low-pass filter used for smoothing noisy data.^1 It is based on local least-squares fitting.^2

auto_savgol method applies a Savitzky–Golay filter using the scipy savgol_filter() method.

from auto_smooth import auto_savgol

# apply savgol filter
data_filtered = auto_savgol(data)

# pass window-length and polynomial-order arguments
data_filtered = auto_savgol(data, wl_min=10, wl_max=30, po_min=2, po_max=10)

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