change(butter_filter): interpolated NaNs

Better to interpolate NaNs than to drop them, which changes
spacing between time points.

src/wela/butterworth_filter.py

 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