closes #11

etl/src/api/main.py

@@ -5,6 +5,7 @@ from data import etl_property_capacities_monthly as etl_pcm
 from data import etl_property_capacities_weekdays as etl_pcw
 from data import etl_property_neighbours as etl_pn
 from data import etl_region_capacities as etl_rc
+from data import etl_region_capacities_weekdays as etl_rcw
 from data import etl_region_movAverage as etl_rmA
 from data import etl_region_properties_capacities as etl_rpc
 from data import etl_region_capacities_comparison as etl_rcc
@@ -77,6 +78,11 @@ def region_capacities_data(id: int):
 	capacities = etl_rc.region_capacities(id)
 	return capacities
 
+@app.get("/region/{id}/capacities/weekdays/{scrapeDate}")
+def region_capacities_data(id: int, scrapeDate: str):
+	capacities = etl_rcw.region_capacities_weekdays(id, scrapeDate)
+	return capacities
+
 @app.get("/region/capacities/comparison/{id_1}/{id_2}")
 def region_capacities_data(id_1: int, id_2: int):
 	capacities = etl_rcc.region_capacities_comparison(id_1, id_2)

etl/src/data/database.py

@@ -473,6 +473,22 @@ class Database:
 				extractions.created_at < '{scrape_date_max}'	
 			""")
 
+	def singleScrape_of_region_scrapDate(self, region_id: int, scrape_date_min: str, scrape_date_max: str):
+		return self.connection.sql(f"""
+			SELECT 
+				JSON_EXTRACT(body, '$.content.days') as calendarBody, 
+				extractions.created_at 
+			FROM 
+				consultancy_d.extractions 
+			LEFT JOIN 
+				consultancy_d.properties ON properties.id = extractions.property_id 
+			WHERE
+				type == 'calendar' AND
+				properties.seed_id = {region_id} AND 
+				extractions.created_at >= '{scrape_date_min}' AND
+				extractions.created_at < '{scrape_date_max}'	
+			""")
+
 	def capacity_global(self):
 		return self.connection.sql(f"""
 			SELECT 

etl/src/data/etl_property_capacities_weekdays.py

@@ -25,7 +25,7 @@ def property_capacities_weekdays(id: int, scrapeDate: str):
 		df_calendar = df_calendar.drop("dates")
 
 	df_calendar = df_calendar.group_by(["weekday", "weekday_num"]).agg(pl.col("column_0").sum())
-	df_calendar = df_calendar.with_columns((pl.col("column_0") / numWeeks * 100).alias("column_0"))
+	df_calendar = df_calendar.with_columns((pl.col("column_0") / numWeeks / 2 * 100).alias("column_0"))
 	df_calendar = df_calendar.sort('weekday_num')
 	df_calendar = df_calendar.drop('weekday_num')
 

etl/src/data/etl_region_capacities_weekdays.py

@@ -0,0 +1,56 @@
+from io import StringIO
+
+import polars as pl
+
+import data
+
+from datetime import datetime, timedelta
+
+d = data.load()
+
+
+def region_capacities_weekdays(id: int, scrapeDate_start: str):
+    # String to Date
+    scrapeDate_start = datetime.strptime(scrapeDate_start, '%Y-%m-%d')
+
+    # Get end date of start search-window
+    scrapeDate_end = scrapeDate_start + timedelta(days=1)
+
+    extractions = d.singleScrape_of_region_scrapDate(id, scrapeDate_start, scrapeDate_end).pl()
+    df_calendar = pl.DataFrame()
+    numWeeks = 0
+    firstExe = True
+    counter = 0
+    for row in extractions.rows(named=True):
+        scrapeDate = row['created_at']
+        if row['calendarBody']:
+            counter += 1
+            df_calendar = pl.read_json(StringIO(row['calendarBody']))
+            columnTitles = df_calendar.columns
+            df_calendar = df_calendar.transpose()
+            df_calendar = df_calendar.with_columns(pl.Series(name="dates", values=columnTitles))
+            df_calendar = df_calendar.with_columns((pl.col("dates").str.to_date()))
+            numWeeks = round((df_calendar.get_column("dates").max() - df_calendar.get_column("dates").min()).days / 7, 0)
+            df_calendar = df_calendar.with_columns(pl.col("dates").dt.weekday().alias("weekday_num"))
+            df_calendar = df_calendar.with_columns(pl.col("dates").dt.strftime("%A").alias("weekday"))
+            df_calendar = df_calendar.drop("dates")
+
+            df_calendar = df_calendar.group_by(["weekday", "weekday_num"]).agg(pl.col("column_0").sum())
+            df_calendar = df_calendar.with_columns((pl.col("column_0") / numWeeks / 2 * 100).alias("column_0"))
+            df_calendar = df_calendar.sort('weekday_num')
+            df_calendar = df_calendar.drop('weekday_num')
+            df_calendar = df_calendar.rename({'column_0': str(counter)})
+
+            if firstExe:
+                outDf = df_calendar
+                firstExe = False
+            else:
+                outDf = outDf.join(df_calendar, on='weekday')
+
+    # Calculate horizontal Mean
+    means = outDf.mean_horizontal()
+    outDf = outDf.insert_column(1, means)
+    outDf = outDf[['weekday', 'mean']]
+
+    result = {"scraping-date": scrapeDate, "weekdays": outDf['weekday'].to_list(),'capacities': outDf['mean'].to_list()}
+    return result