From 8291b4e3dd125b4cb22802a5e857a733f1d8ad20 Mon Sep 17 00:00:00 2001
From: pswain <peter.swain@ed.ac.uk>
Date: Thu, 9 May 2024 18:13:12 +0100
Subject: [PATCH] change(butter_filter): interpolated NaNs
Better to interpolate NaNs than to drop them, which changes
spacing between time points.
---
src/wela/butterworth_filter.py | 23 ++++++++++++-----------
1 file changed, 12 insertions(+), 11 deletions(-)
diff --git a/src/wela/butterworth_filter.py b/src/wela/butterworth_filter.py
index a9e100b..3c8a903 100644
--- a/src/wela/butterworth_filter.py
+++ b/src/wela/butterworth_filter.py
@@ -1,8 +1,9 @@
import numpy as np
from scipy import signal
+import pandas as pd
-def butterworth_filter(timeseries, params=None):
+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
@@ -20,18 +21,18 @@ def butterworth_filter(timeseries, params=None):
fs=params["sampling_freq"],
output="sos",
)
+ # fill NaNs between data points
+ data = pd.DataFrame(data).interpolate(axis=1).to_numpy()
# subtract time series by mean
- timeseries_norm = timeseries - np.nanmean(timeseries, axis=1).reshape(
- timeseries.shape[0], 1
- )
- if np.any(np.isnan(timeseries_norm)):
+ data_norm = data - np.nanmean(data, axis=1).reshape(data.shape[0], 1)
+ if np.any(np.isnan(data_norm)):
# filter one at a time because of NaNs at different times
- filtered_timeseries = np.nan * np.ones(timeseries_norm.shape)
- for i in range(timeseries_norm.shape[0]):
- data = timeseries_norm[i, :]
- filtered = signal.sosfiltfilt(sos, data[~np.isnan(data)])
- filtered_timeseries[i, ~np.isnan(data)] = filtered
+ filtered_timeseries = np.nan * np.ones(data_norm.shape)
+ for i in range(data_norm.shape[0]):
+ s_data = data_norm[i, :]
+ filtered = signal.sosfiltfilt(sos, s_data[~np.isnan(s_data)])
+ filtered_timeseries[i, ~np.isnan(s_data)] = filtered
else:
# filter all at once
- filtered_timeseries = signal.sosfiltfilt(sos, timeseries_norm, axis=1)
+ filtered_timeseries = signal.sosfiltfilt(sos, data_norm, axis=1)
return filtered_timeseries
--
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