closes #7: etl_region_capacities erstellt

!! Wie im Issue beschrieben wurde etl_region_capacities zu etl_region_properties_capacities angepasst und die Endpoints ebenfalls.!!

!!Die Abfrage der globalen Daten ist implementiert und funktioniert, braucht aber recht lange!!

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_properties_capacities as etl_rpc
 from data import etl_region_capacities_comparison as etl_rcc
 from fastapi import FastAPI, Response
 
@@ -64,6 +65,11 @@ def property_capacities_data(id: int, scrapeDate: str):
 def property_base_data(id: int):
 	return d.property_base_data(id).pl().to_dicts()
 
+@app.get("/region/{id}/properties/capacities")
+def region_property_capacities_data(id: int):
+	capacities = etl_rpc.region_properties_capacities(id)
+	return capacities
+
 @app.get("/region/{id}/capacities")
 def region_capacities_data(id: int):
 	capacities = etl_rc.region_capacities(id)

etl/src/data/etl_region_capacities.py

@@ -1,61 +1,53 @@
-import data
-import polars as pl
 from io import StringIO
-import numpy as np
+from datetime import date
 
+import polars as pl
+
+import data
 
 d = data.load()
 
 def region_capacities(id: int):
+
 	# Get Data
 	if id == -1:
-        df = d.capacity_global().pl()
+		extractions = d.capacity_global().pl()
 	else:
-        df = d.capacity_of_region(id).pl()
+		extractions = d.capacity_of_region(id).pl()
 	# turn PropertyIDs to ints for sorting
-    df = df.cast({"property_id": int})
+	extractions = extractions.cast({"property_id": int})
 
-    # Get uniques for dates and propIDs and sort them
-    listOfDates = df.get_column("ScrapeDate").unique().sort()
-    listOfPropertyIDs = df.get_column("property_id").unique().sort()
+	extractions.drop('property_id')
+	df_dates = pl.DataFrame()
 
-    # Create DFs from lists to merge later
-    datesDF = pl.DataFrame(listOfDates).with_row_index("date_index")
-    propIdDF = pl.DataFrame(listOfPropertyIDs).with_row_index("prop_index")
-
-    # Merge Dataframe to generate indices
-    df = df.join(datesDF, on='ScrapeDate')
-    df = df.join(propIdDF, on='property_id')
-    # Drop now useless columns ScrapeDate and property_id
-    df = df[['ScrapeDate', 'calendarBody', 'date_index', 'prop_index']]
-    # Calculate grid values
+	# Get Data from JSON
 	gridData = []
-    for row in df.rows(named=True):
+	dayCounts = []
+	for row in extractions.rows(named=True):
 		# Return 0 for sum if calendar is null
 		if row['calendarBody']:
 			calDF = pl.read_json(StringIO(row['calendarBody']))
 			sum_hor = calDF.sum_horizontal()[0]
 		else:
 			sum_hor = 0
-        # With Index
-        # gridData.append([row['prop_index'], row['date_index'], sum_hor])
-        # With ScrapeDate
-        gridData.append([row['ScrapeDate'], row['date_index'], sum_hor])
+		gridData.append([row['ScrapeDate'], sum_hor, calDF.width])
 
-    gridData = np.array(gridData)
-    # get all values to calculate Max
-    allValues = gridData[:, 2].astype(int)
-    maxValue = np.max(allValues)
-    gridData[:, 2] = (allValues*100)/maxValue
+	# Create Aggregates of values
+	df = pl.DataFrame(gridData)
+	df_count = df.group_by("column_0").agg(pl.col("column_1").count())
+	df_sum = df.group_by("column_0").agg(pl.col("column_1").sum())
+	df_numDays = df.group_by("column_0").agg(pl.col("column_2").max())
 
-    # Return back to list
-    gridData = gridData.tolist()
+	# Join and rename DF's
+	df = df_sum.join(df_count, on= 'column_0').join(df_numDays, on= 'column_0')
+	df = df.rename({"column_0": "ScrapeDate", "column_1": "Sum", "column_1_right": "num_properties", "column_2": "max_value", })
 
-    # Cast listOfDates to datetime
-    listOfDates = listOfDates.cast(pl.Date).to_list()
-    listOfPropertyIDs = listOfPropertyIDs.to_list()
+	# Calculate normed capacities for each scrapeDate
+	df = df.with_columns((pl.col("Sum") / pl.col("num_properties") / (pl.col("max_value")*2) * 100).alias("capacity"))
 
-    # Create JSON
-    outDict = {'scrapeDates': listOfDates, 'property_ids': listOfPropertyIDs, 'values': gridData}
+	# Sort the date column
+	df = df.cast({"ScrapeDate": date})
+	df = df.sort('ScrapeDate')
 
-    return outDict
+	result = {"capacities": df['capacity'].to_list(), "dates": df['ScrapeDate'].to_list()}
+	return result

etl/src/data/etl_region_properties_capacities.py

@@ -0,0 +1,61 @@
+import data
+import polars as pl
+from io import StringIO
+import numpy as np
+
+
+d = data.load()
+
+def region_properties_capacities(id: int):
+    # Get Data
+    if id == -1:
+        df = d.capacity_global().pl()
+    else:
+        df = d.capacity_of_region(id).pl()
+    # turn PropertyIDs to ints for sorting
+    df = df.cast({"property_id": int})
+
+    # Get uniques for dates and propIDs and sort them
+    listOfDates = df.get_column("ScrapeDate").unique().sort()
+    listOfPropertyIDs = df.get_column("property_id").unique().sort()
+
+    # Create DFs from lists to merge later
+    datesDF = pl.DataFrame(listOfDates).with_row_index("date_index")
+    propIdDF = pl.DataFrame(listOfPropertyIDs).with_row_index("prop_index")
+
+    # Merge Dataframe to generate indices
+    df = df.join(datesDF, on='ScrapeDate')
+    df = df.join(propIdDF, on='property_id')
+    # Drop now useless columns ScrapeDate and property_id
+    df = df[['ScrapeDate', 'calendarBody', 'date_index', 'prop_index']]
+    # Calculate grid values
+    gridData = []
+    for row in df.rows(named=True):
+        # Return 0 for sum if calendar is null
+        if row['calendarBody']:
+            calDF = pl.read_json(StringIO(row['calendarBody']))
+            sum_hor = calDF.sum_horizontal()[0]
+        else:
+            sum_hor = 0
+        # With Index
+        # gridData.append([row['prop_index'], row['date_index'], sum_hor])
+        # With ScrapeDate
+        gridData.append([row['ScrapeDate'], row['date_index'], sum_hor])
+
+    gridData = np.array(gridData)
+    # get all values to calculate Max
+    allValues = gridData[:, 2].astype(int)
+    maxValue = np.max(allValues)
+    gridData[:, 2] = (allValues*100)/maxValue
+
+    # Return back to list
+    gridData = gridData.tolist()
+
+    # Cast listOfDates to datetime
+    listOfDates = listOfDates.cast(pl.Date).to_list()
+    listOfPropertyIDs = listOfPropertyIDs.to_list()
+
+    # Create JSON
+    outDict = {'scrapeDates': listOfDates, 'property_ids': listOfPropertyIDs, 'values': gridData}
+
+    return outDict