refactor: adjust overall estimates using weekdaily

Adjust overall estimates based on representative week by converting to
"weekdaily" first.

src/stepcount/stepcount.py

@@ -158,20 +158,34 @@ def _sum(x):
     total = np.round(Y.agg(_sum))  # total steps
     hourly = Y.resample('H').agg(_sum).round().rename('Steps')  # steps, hourly
     daily = Y.resample('D').agg(_sum).round().rename('Steps')  # steps, daily
-    daily_avg = np.round(daily.mean())
-    daily_med = np.round(daily.median())
-    daily_min = np.round(daily.min())
-    daily_max = np.round(daily.max())
+    if not adjust_estimates:
+        daily_avg = np.round(daily.mean())
+        daily_med = np.round(daily.median())
+        daily_min = np.round(daily.min())
+        daily_max = np.round(daily.max())
+    else:
+        weekdaily = daily.groupby(daily.index.weekday).mean()
+        daily_avg = np.round(weekdaily.mean())
+        daily_med = np.round(weekdaily.median())
+        daily_min = np.round(weekdaily.min())
+        daily_max = np.round(weekdaily.max())
 
     # walking
     dt = pd.Timedelta(infer_freq(Y.index)).seconds
     W = Y.mask(~Y.isna(), Y >= steptol)
     total_walk = np.round(W.agg(_sum) * dt / 60)
     daily_walk = (W.resample('D').agg(_sum) * dt / 60).round().rename('Walk(mins)')
-    daily_walk_avg = np.round(daily_walk.mean())
-    daily_walk_med = np.round(daily_walk.median())
-    daily_walk_min = np.round(daily_walk.min())
-    daily_walk_max = np.round(daily_walk.max())
+    if not adjust_estimates:
+        daily_walk_avg = np.round(daily_walk.mean())
+        daily_walk_med = np.round(daily_walk.median())
+        daily_walk_min = np.round(daily_walk.min())
+        daily_walk_max = np.round(daily_walk.max())
+    else:
+        weekdaily_walk = daily_walk.groupby(daily_walk.index.weekday).mean()
+        daily_walk_avg = np.round(weekdaily_walk.mean())
+        daily_walk_med = np.round(weekdaily_walk.median())
+        daily_walk_min = np.round(weekdaily_walk.min())
+        daily_walk_max = np.round(weekdaily_walk.max())
 
     def _max(x, n=1):
         return x.nlargest(n, keep='all').mean()