From e8c9556297d2a535e05405114680eee519fe739c Mon Sep 17 00:00:00 2001
From: Peter Swain <pswain@Home-iMac.local>
Date: Tue, 14 May 2024 10:43:30 +0100
Subject: [PATCH] change: rename butterworth_filter ; lowpass_filter
Renamed butterworth_filter to flavin_filter.
---
src/wela/fft_filtering.py | 30 ++++++++++++------------------
1 file changed, 12 insertions(+), 18 deletions(-)
diff --git a/src/wela/fft_filtering.py b/src/wela/fft_filtering.py
index 7469f01..f0c0842 100644
--- a/src/wela/fft_filtering.py
+++ b/src/wela/fft_filtering.py
@@ -105,24 +105,18 @@ def snr(fft_freqs, fft_power, cutoff_freq):
return snr
-def butterworth_filter(data, params=None):
- """Apply Butterworth filter to time series arranged in rows."""
- # second-order-sections output
- # by default, using a digital filter
- if params is None:
- params = {
- "order": 2,
- "critical_freqs": (1 / 350),
- "filter_type": "highpass",
- "sampling_freq": 1 / 3,
- }
- sos = signal.butter(
- N=params["order"],
- Wn=params["critical_freqs"],
- btype=params["filter_type"],
- fs=params["sampling_freq"],
- output="sos",
- )
+def lowpass_filter(data, cutoff, fs, N):
+ """Apply low-pass filter to a single time-series."""
+ data = data - np.mean(data)
+ sos = signal.butter(N, cutoff, btype="lowpass", fs=fs, output="sos")
+ filtered_data = signal.sosfiltfilt(sos, data)
+ return filtered_data + np.mean(data)
+
+
+def flavin_filter(data, cutoff=1 / 350, fs=1 / 5, N=2):
+ """Apply high-pass Butterworth filter to time series arranged in rows."""
+ # use a digital filter assuming regularly spaced sampling
+ sos = signal.butter(N=N, Wn=cutoff, btype="highpass", fs=fs, output="sos")
# fill NaNs between data points
data = pd.DataFrame(data).interpolate(axis=1).to_numpy()
# subtract time series by mean
--
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