fix(flag_nonwear): account for NA to calculate wear time stats

Previous calculation does not account for potential NA in the data. Also recalculate and return NumInterrupts and Covers24hOK after nonwear segmentes are flagged.

src/actipy/processing.py

@@ -259,22 +259,33 @@ def flag_nonwear(data, patience='90m', window='10s', stdtol=15 / 1000):
 
     nonwear_segments = find_nonwear_segments(data, patience=patience, window=window, stdtol=stdtol)
 
+    # Num nonwear episodes and total nonwear time
     count_nonwear = len(nonwear_segments)
     total_nonwear = nonwear_segments.sum().total_seconds()
-    total_wear = (
-        data.index.to_series().diff()
-        .pipe(lambda x: x[x < pd.Timedelta('1s')].sum())
-        .total_seconds()
-    ) - total_nonwear
-
-    info['WearTime(days)'] = total_wear / (60 * 60 * 24)
-    info['NonwearTime(days)'] = total_nonwear / (60 * 60 * 24)
-    info['NumNonwearEpisodes'] = count_nonwear
 
     # Flag nonwear segments
     data = data.copy(deep=True)  # copy to avoid modifying original data
     for start_time, length in nonwear_segments.items():
-        data.loc[start_time:start_time + length] = np.nan
+        data.loc[start_time : start_time + length] = np.nan
+    del nonwear_segments
+
+    # Calculate total wear time and interrupts (data gaps)
+    tol = pd.Timedelta('1s')
+    tdiff = data.dropna().index.to_series().diff()  # Note: Index.diff() was only added in pandas 2.1
+    total_time = tdiff[tdiff < tol].sum().total_seconds()
+    num_interrupts = (tdiff > tol).sum()
+    del tdiff
+
+    # Check if data covers all 24 hours of the day
+    coverage = data.notna().any(axis=1).groupby(data.index.hour).mean()
+    covers24hok = int(len(coverage) == 24 and np.min(coverage) >= 0.01)
+    del coverage
+
+    info['NonwearTime(days)'] = total_nonwear / (60 * 60 * 24)
+    info['NumNonwearEpisodes'] = count_nonwear
+    info['WearTime(days)'] = total_time / (60 * 60 * 24)
+    info['NumInterrupts'] = num_interrupts
+    info['Covers24hOK'] = covers24hok
 
     return data, info
 
@@ -553,9 +564,10 @@ def find_nonwear_segments(data, patience='90m', window='10s', stdtol=15 / 1000):
     """
 
     stationary_indicator = (  # this is more memory friendly than data[['x', 'y', 'z']].std()
-        data['x'].resample(window, origin='start').std().lt(stdtol)
-        & data['y'].resample(window, origin='start').std().lt(stdtol)
-        & data['z'].resample(window, origin='start').std().lt(stdtol)
+        # use ffill() so that NA also counted as stationary
+        data['x'].ffill().resample(window, origin='start').std().lt(stdtol)
+        & data['y'].ffill().resample(window, origin='start').std().lt(stdtol)
+        & data['z'].ffill().resample(window, origin='start').std().lt(stdtol)
     )
 
     segment_edges = (stationary_indicator != stationary_indicator.shift(1))

tests/test_all.py

@@ -104,9 +104,11 @@ def test_detect_nonwear():
     data, info_nonwear = P.flag_nonwear(data, patience='1m')
 
     info_nonwear_ref = {
-        'WearTime(days)': 0.1203330787037037,
         'NonwearTime(days)': 0.0008101851851851852,
-        'NumNonwearEpisodes': 1
+        'NumNonwearEpisodes': 1,
+        'WearTime(days)': 0.1203330787037037,
+        'NumInterrupts': 2,
+        'Covers24hOK': 0,
     }
     assert_dict_equal(info_nonwear, info_nonwear_ref, rel=0.01)