Version 0.17.1

meson.build

@@ -1,7 +1,7 @@
 project(
     'scikit-digital-health',
     'c',
-    version: '0.17.0',
+    version: '0.17.1',
     license: 'MIT',
     meson_version: '>=1.1',
 )

src/skdh/activity/core.py

@@ -944,6 +944,11 @@ def predict(self, *, time, accel, fs=None, wear=None, tz_name=None, **kwargs):
                     day_stop,
                 )
 
+                res['N wear wake hours'][iday] = around(
+                    sum(dwear_stops - dwear_starts) / fs / 3600, 1
+                )
+                res['Wear Wake Minutes'][iday] = sum(time[dwear_stops] - time[dwear_starts]) / 60
+
             self._compute_wear_wake_activity(
                 res,
                 accel,

src/skdh/gait/substeps/s1_preprocessing.py

@@ -8,6 +8,7 @@
 from numpy import (
     mean,
     nanmedian,
+    nanmean,
     argmax,
     sign,
     abs,
@@ -148,9 +149,18 @@ def get_ap_axis_sign(fs, accel, ap_axis):
         idx = searchsorted(mn, mx[mask])
         # make sure that we dont go out of bounds by adding a nan to the end
         mn = append(mn, nan)
-        left_med = nanmedian(mx[mask] - mn[idx - 1])
-        right_med = nanmedian(mn[idx] - mx[mask])
-
+        left_bases = mx[mask] - mn[idx - 1]
+        right_bases = mn[idx] - mx[mask]
+
+        left_med = nanmedian(left_bases)
+        right_med = nanmedian(right_bases)
+
+        # adjust if both the same
+        if left_med == right_med:
+            pk_dist = nanmedian(diff(mx[mask]))
+            left_med = nanmean(left_bases[left_bases < (1.25 * pk_dist)])
+            right_med = nanmean(right_bases[right_bases < (1.25 * pk_dist)])
+
         sign = -1 if (left_med < right_med) else 1
 
         return sign