First Version of etl_region_movAverage.py eingefügt

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_movAverage as etl_rmA
 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
@@ -80,3 +81,7 @@ def region_capacities_data(id_1: int, id_2: int):
 	capacities = etl_rcc.region_capacities_comparison(id_1, id_2)
 	return capacities
 
+@app.get("/region/{id}/movingAverage/{startDate}")
+def region_capacities_data(id: int, startDate: str):
+	result = etl_rmA.region_movingAverage(id, startDate)
+	return result

etl/src/data/etl_region_movAverage.py

@@ -0,0 +1,114 @@
+import polars as pl
+from io import StringIO
+from datetime import datetime, timedelta, date
+import matplotlib.pyplot as plt
+
+import data
+
+d = data.load()
+
+def region_movingAverage(id: int, scrape_date_start_min: str):
+    # Settings
+    # Offset between actual and predict ScrapeDate
+    timeOffset = 30
+
+    # Calculation Frame
+    calcFrame = 180
+
+    # Filter Setting
+    windowSize = 7
+
+    # String to Date
+    scrape_date_start_min = datetime.strptime(scrape_date_start_min, '%Y-%m-%d')
+
+    # Get end date of start search-window
+    scrape_date_start_max = scrape_date_start_min + timedelta(days=1)
+
+    # Get start and end date of End search-window
+    scrape_date_end_min = scrape_date_start_min + timedelta(days=timeOffset)
+    scrape_date_end_max = scrape_date_end_min + timedelta(days=1)
+
+    final_end_date = scrape_date_end_min + timedelta(days=calcFrame)
+
+    ex_start = d.singleScrape_of_region(id, scrape_date_start_min, scrape_date_start_max)
+    ex_start_count = ex_start.shape[0]
+
+    ex_end = d.singleScrape_of_region(id, scrape_date_end_min, scrape_date_end_max)
+    ex_end_count = ex_end.shape[0]
+
+    num_properties = [ex_start_count, ex_end_count]
+    start_end = [ex_start, ex_end]
+    outDFList = []
+
+    for df in start_end:
+        df = df.pl()
+        firstExe = True
+        counter = 1
+        outDF = pl.DataFrame(schema={"0": int, "dates": date})
+        for row in df.rows(named=True):
+            if row['calendarBody']:
+                calDF = pl.read_json(StringIO(row['calendarBody']))
+                columnTitles = calDF.columns
+                calDF = calDF.transpose()
+                calDF = calDF.with_columns(pl.Series(name="dates", values=columnTitles))
+                calDF = calDF.with_columns((pl.col("dates").str.to_date()))
+
+                # Filter out all Data that's in the calculation frame
+                calDF = calDF.filter((pl.col("dates") >= scrape_date_end_min))
+                calDF = calDF.filter((pl.col("dates") < final_end_date))
+
+                # Join all information into one Dataframe
+                if firstExe:
+                    outDF = calDF
+                    firstExe = False
+                else:
+                    outDF = outDF.join(calDF, on='dates')
+                    outDF = outDF.rename({'column_0': str(counter)})
+                    counter += 1
+
+        outDF = outDF.sort('dates')
+        outDFList.append(outDF)
+
+    # Calculate the horizontal Sum for all Dates
+    arrayCunter = 0
+    tempDFList = []
+    for df in outDFList:
+        dates = df.select(pl.col("dates"))
+        values = df.select(pl.exclude("dates"))
+        sum_hor = values.sum_horizontal()
+
+        sum_hor = sum_hor / num_properties[arrayCunter] / 2
+        arrayCunter += 1
+
+        newDF = dates.with_columns(sum_hor=pl.Series(sum_hor))
+        tempDFList.append(newDF)
+
+    # Join actual and predict Values
+    outDF = tempDFList[1].join(tempDFList[0], on='dates', how='outer')
+
+    # Rename Columns for clarity
+    outDF = outDF.drop_nulls()
+    outDF = outDF.drop('dates_right')
+
+    # sum_hor_predict is the data from the earlier ScrapeDate
+    outDF = outDF.rename({'sum_hor': 'sum_hor_actual', 'sum_hor_right': 'sum_hor_predict'})
+
+    # Calculate Moving average from Start
+    baseValues = outDF.get_column('sum_hor_predict').to_list()
+    i = 0
+    moving_averages = []
+    while i < len(baseValues) - windowSize + 1:
+        window = baseValues[i: i + windowSize]
+        window_average = sum(window) / windowSize
+        moving_averages.append(window_average)
+        i += 1
+
+    # Add empty values back to the front and end of moving_averages
+    num_empty = int(windowSize / 2)
+    moving_averages = [None] *num_empty + moving_averages + [None] * num_empty
+
+    # Add moving_averages to df
+    outDF = outDF.with_columns(moving_averages=pl.Series(moving_averages))
+
+    result = {'dates':outDF.get_column('dates').to_list(), 'cap_earlierTimeframe':outDF.get_column('sum_hor_predict').to_list(), 'cap_laterTimeframe':outDF.get_column('sum_hor_actual').to_list(), 'movAvg':outDF.get_column('moving_averages').to_list(),}
+    return result