Implement sleep_efficiency and sleep_onset_latency in sleep diaries

Author: djmarques

circStudio/analysis/sleep/diary.py

@@ -242,7 +242,7 @@ def sleep_efficiency(self, data):
         Returns
         -------
         float
-            Sleep efficiency.
+            Sleep efficiency (decimal)
 
         """
         # Calculate average total sleep time (within the main sleep bout)

circStudio/analysis/sleep/sleep.py

@@ -1564,15 +1564,18 @@ def main_sleep_bouts(data, report='major'):
     minor_sleep = sleep_events.drop(main_sleep.index)
 
     if report == 'major':
-        # Calculate mean duration (in minutes) of the main sleep episode
-        mean = main_sleep['duration'].mean().total_seconds() / 60
+        # Calculate mean duration of the main sleep episode
+        #mean = main_sleep['duration'].mean().total_seconds() / 60
+        mean = np.mean(main_sleep['duration'])
 
         # Return dataframe with major sleep events and summary stats
         return main_sleep, mean
 
     elif report == 'minor':
-        # Calculate mean duration (in minutes) of the main sleep episode
-        mean = minor_sleep['duration'].mean().total_seconds() / 60
+        # Calculate mean duration of the main sleep episode
+        #mean = minor_sleep['duration'].mean().total_seconds() / 60
+        mean = np.mean(minor_sleep['duration'])
+
 
         # Return dataframe with major sleep events and summary stats
         return minor_sleep, mean

docs/source/tutorial_3.ipynb

@@ -34,7 +34,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 98,
    "id": "ba14333118837a04",
    "metadata": {
     "ExecuteTime": {
@@ -59,7 +59,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 99,
    "id": "8c2a425e-42bb-4b3c-a397-a998cea80ff6",
    "metadata": {},
    "outputs": [],
@@ -71,6 +71,27 @@
     "raw = circStudio.io.read_atr(os.path.join(fpath, 'data', 'test_sample_atr.txt'))"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 102,
+   "id": "b5808c98-90d1-467e-8b3d-66d99f76f3f6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.75"
+      ]
+     },
+     "execution_count": 102,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.Timedelta(hours=6)/pd.Timedelta(hours=8)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "a88e0e70-27b0-4983-be19-d68cb186b291",