etl/src/api/main.py

@@ -5,8 +5,6 @@ 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 data import etl_region_properties_capacities as etl_rpc
 from fastapi import FastAPI, Response
@@ -72,6 +70,10 @@ def region_property_capacities_data(id: int):
 	capacities = etl_rpc.region_properties_capacities(id)
 	return capacities
 
+@app.get("/region/{id}/base")
+def region_base_data(id: int):
+	return d.region_base_data(id).pl().to_dicts()
+
 @app.get("/region/{id}/capacities")
 def region_capacities_data(id: int):
 	capacities = etl_rc.region_capacities(id)

etl/src/data/etl_region_movAverage.py

@@ -1,114 +0,0 @@
-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