parallel weekly ported to indexed db

py/main.py

@@ -133,25 +133,35 @@ def parallel_weekly():
     monday = date.fromisocalendar(yea, wee, 1)
     weekday_names = ['Monday','Tuesday','Wednesday','Thursday','Friday','Saturday','Sunday']
     weekdays = {str(monday + timedelta(days=x)):weekday_names[x] for x in range(7)}
-    print(weekdays)
+    #print(weekdays)
 
     # Get the right sensors for the family
     table_name, col_names = get_table_cols(hus, fam, typ)
     sql_col_names = ','.join(f'"{x}"' for x in col_names)
 
-    conn = sqlite3.connect(DB_NAMES[hus])
-    cur = conn.execute(f'SELECT {sql_col_names} FROM "{table_name}" WHERE "{date_col}" IN (?,?,?,?,?,?,?);', list(weekdays.keys()))
+    # Complex query to get each day of the week
+    query = f'SELECT {sql_col_names} FROM {table_name} WHERE'
+    query += 'OR'.join('(Year=? AND Month=? AND Day=?)' for _ in range(len(weekdays)))
+    query += ' GROUP BY Day;'
+
+    # Break each date into three components then merge them all (in a flat list)
+    params = [y for d in list(weekdays.keys()) for y in d.split('-')]
+
+    conn = sqlite3.connect(DB_NAMES[hus])    
+    cur = conn.execute(query, params)
     res_all = cur.fetchall()
 
-    sample = [["pos1", *[x*10+10 for x in range(len(col_names)-2)]]]
+    sample = [["pos1", *[x*10+10 for x in range(len(col_names)-6)]]]
 
     myxs = {}
     # group by day; take first of each group
-    for k, g in itertools.groupby(res_all, lambda x: x[0]):
-        data_point = next(g)
-        point_id = f'{weekdays[data_point[0]]} ({data_point[0]})'
+    #for k, g in itertools.groupby(res_all, lambda x: x[0]):
+    for i, data_point in enumerate(res_all):
+        #data_point = next(g)
+        wd = list(weekdays.items())[i]
+        point_id = f'{wd[1]} ({wd[0]})'
         myxs[point_id] = 'pos1'
-        data_point = [point_id, *data_point[2:]]
+        data_point = [point_id, *data_point[6:]]
         sample.append(data_point)
 
     return dict(sample=sample, myxs=myxs)