Gitea Issue 1 Beispiel 2

#1
etl_region_capacities_comparison eingefügt

etl/src/api/main.py

@@ -2,6 +2,7 @@ import data
 import polars as pl
 from data import etl_property_capacities as etl_pc
 from data import etl_region_capacities as etl_rc
+from data import etl_region_capacities_comparison as etl_rcc
 from fastapi import FastAPI, Response
 
 d = data.load()
@@ -50,5 +51,8 @@ def region_capacities_data(id: int):
 	capacities = etl_rc.region_capacities(id)
 	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)
+	return capacities
 

etl/src/data/database.py

@@ -430,5 +430,20 @@ class Database:
 				type == 'calendar'
 			""")
 
-
+	def capacity_comparison_of_region(self, region_id_1, region_id_2):
+		return self.connection.sql(f"""
+			SELECT 
+				JSON_EXTRACT(body, '$.content.days') as calendarBody, 
+				strftime(extractions.created_at, '%Y-%m-%d') AS ScrapeDate,
+				extractions.property_id, 
+				properties.seed_id
+			FROM 
+				consultancy_d.extractions 
+			LEFT JOIN 
+				consultancy_d.properties ON properties.id = extractions.property_id 
+			WHERE
+				type == 'calendar' AND
+				(properties.seed_id = {region_id_1} OR 
+				properties.seed_id = {region_id_2})		
+			""")
 

etl/src/data/etl_region_capacities.py

@@ -45,9 +45,7 @@ def region_capacities(id: int):
     gridData = np.array(gridData)
     # get all values to calculate Max
     allValues = gridData[:, 2].astype(int)
-    print(allValues)
     maxValue = np.max(allValues)
-    print(maxValue)
     gridData[:, 2] = (allValues*100)/maxValue
 
     # Return back to list

etl/src/data/etl_region_capacities_comparison.py

@@ -0,0 +1,68 @@
+import data
+import polars as pl
+from io import StringIO
+import numpy as np
+
+
+d = data.load()
+
+def region_capacities_comparison(id_1: int, id_2: int):
+    fulldf = d.capacity_comparison_of_region(id_1, id_2).pl()
+    # turn PropertyIDs and seedIDs to ints for sorting and filtering
+    fulldf = fulldf.cast({"property_id": int})
+    fulldf = fulldf.cast({"seed_id": int})
+    df_region1 = fulldf.filter(pl.col("seed_id") == id_1)
+    df_region2 = fulldf.filter(pl.col("seed_id") == id_2)
+    df_list = [df_region1, df_region2]
+    outDictList = []
+
+    for df in df_list:
+        # 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
+        tempDict = {'scrapeDates': listOfDates, 'property_ids': listOfPropertyIDs, 'values': gridData}
+        outDictList.append(tempDict)
+
+    outDict = {'region1': outDictList[0], 'region2': outDictList[1],}
+    return outDict
+
+out = region_capacities_comparison(1,2)
+print(out)