added simple caching for etl

.gitignore

@@ -66,4 +66,8 @@ env3.*/
 # duckdb
 *.duckdb
 
+# cache
+*.obj
+
 /src/mauro/dok/
+

etl/src/data/etl_cache.py

@@ -0,0 +1,18 @@
+from pathlib import Path
+from pickle import dump, load
+
+Path('cache').mkdir(parents=True, exist_ok=True)
+
+# load pickle obj
+def openObj(file):
+	filepath = Path(f"cache/{file}")
+	if filepath.is_file():
+		with open(filepath, 'rb') as f:
+			return load(f)
+	return False
+
+# save pickle obj
+def saveObj(file, result):
+	filepath = Path(f"cache/{file}")
+	with open(filepath, 'wb') as f:
+		dump(result, f)

etl/src/data/etl_property_capacities.py

@@ -3,11 +3,17 @@ from io import StringIO
 import polars as pl
 
 import data
+from data import etl_cache
 
 d = data.load()
 
 def property_capacities(id: int):
 
+	file = f"etl_property_capacities_{id}.obj"
+	obj = etl_cache.openObj(file)
+	if obj:
+		return obj
+
 	extractions = d.extractions_for(id).pl()
 	df_dates = pl.DataFrame()
 
@@ -35,4 +41,6 @@ def property_capacities(id: int):
 		max_capacity_perc = 100 / max_capacity
 		result['capacities'].append(round(max_capacity_perc * row['sum'], 2))
 	result['capacities'].reverse()
+
+	etl_cache.saveObj(file, result)
 	return result

etl/src/data/etl_property_capacities_monthly.py

@@ -3,10 +3,17 @@ from io import StringIO
 import polars as pl
 
 import data
+from data import etl_cache
 
 d = data.load()
 
 def property_capacities_monthly(id: int, scrapeDate: str):
+
+	file = f"etl_property_capacities_monthly_{id}.obj"
+	obj = etl_cache.openObj(file)
+	if obj:
+		return obj
+
 	extractions = d.extractions_propId_scrapeDate(id, scrapeDate).pl()
 	df_calendar = pl.DataFrame()
 
@@ -24,4 +31,5 @@ def property_capacities_monthly(id: int, scrapeDate: str):
 	df_calendar = df_calendar.sort('dates')
 	df_calendar = df_calendar.drop('dates')
 	result = {"scraping-date": scrapeDate, "months": df_calendar['date_short'].to_list(), 'capacities': df_calendar['column_0'].to_list()}
-	return result
+	etl_cache.saveObj(file, result)
+	return result

etl/src/data/etl_property_capacities_weekdays.py

@@ -3,10 +3,17 @@ from io import StringIO
 import polars as pl
 
 import data
+from data import etl_cache
 
 d = data.load()
 
 def property_capacities_weekdays(id: int, scrapeDate: str):
+	
+	file = f"etl_property_capacities_weekdays_{id}.obj"
+	obj = etl_cache.openObj(file)
+	if obj:
+		return obj
+
 	extractions = d.extractions_propId_scrapeDate(id, scrapeDate).pl()
 	weekdays = ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday']
 	df_calendar = pl.DataFrame()
@@ -30,4 +37,5 @@ def property_capacities_weekdays(id: int, scrapeDate: str):
 	df_calendar = df_calendar.drop('weekday_num')
 
 	result = {"scraping-date": scrapeDate, "weekdays": df_calendar['weekday'].to_list(), 'capacities': df_calendar['column_0'].to_list()}
+	etl_cache.saveObj(file, result)
 	return result

etl/src/data/etl_property_neighbours.py

@@ -1,7 +1,9 @@
+from math import asin, atan2, cos, degrees, radians, sin, sqrt
+
 import polars as pl
-from math import radians, cos, sin, asin, sqrt, degrees, atan2
 
 import data
+from data import etl_cache
 
 d = data.load()
 
@@ -23,6 +25,12 @@ def calcHaversinDistance(latMain, lonMain, lat, lon):
     return d
 
 def property_neighbours(id: int):
+
+    file = f"etl_property_neighbours_{id}.obj"
+    obj = etl_cache.openObj(file)
+    if obj:
+        return obj
+
     extractions = d.properties_geo_seeds().pl()
 
     # Get lat, long and region from main property
@@ -61,6 +69,6 @@ def property_neighbours(id: int):
 
     #result = {"ids": extractions['id'].to_list(), "lat": extractions['lat'].to_list(), "lon": extractions['lon'].to_list()}
     result = extractions.to_dicts()
-
+    etl_cache.saveObj(file, result)
 
     return result

etl/src/data/etl_region_capacities.py

@@ -4,11 +4,17 @@ from io import StringIO
 import polars as pl
 
 import data
+from data import etl_cache
 
 d = data.load()
 
 def region_capacities(id: int):
 
+	file = f"etl_region_capacities_{id}.obj"
+	obj = etl_cache.openObj(file)
+	if obj:
+		return obj
+
 	# Get Data
 	if id == -1:
 		extractions = d.capacity_global().pl()
@@ -47,4 +53,6 @@ def region_capacities(id: int):
 	df = df.cast({"scrape_date": date}).sort('scrape_date')
 
 	result = {"capacities": df['capacity'].to_list(), "dates": df['scrape_date'].to_list()}
+
+	etl_cache.saveObj(file, result)
 	return result

etl/src/data/etl_region_capacities_monthly.py

@@ -1,15 +1,21 @@
+from datetime import datetime, timedelta
 from io import StringIO
 
 import polars as pl
 
 import data
-
+from data import etl_cache
-from datetime import datetime, timedelta
 
 d = data.load()
 
 
 def region_capacities_monthly(id: int, scrapeDate_start: str):
+
+    file = f"etl_region_capacities_monthly_{id}.obj"
+    obj = etl_cache.openObj(file)
+    if obj:
+        return obj
+
     # String to Date
     scrapeDate_start = datetime.strptime(scrapeDate_start, '%Y-%m-%d')
 
@@ -50,4 +56,5 @@ def region_capacities_monthly(id: int, scrapeDate_start: str):
     outDf = outDf[['date_short', 'mean']]
 
     result = {"scraping-date": scrapeDate, "months": outDf['date_short'].to_list(),'capacities': outDf['mean'].to_list()}
+    etl_cache.saveObj(file, result)
     return result

etl/src/data/etl_region_capacities_weekdays.py

@@ -1,15 +1,20 @@
+from datetime import datetime, timedelta
 from io import StringIO
 
 import polars as pl
 
 import data
-
+from data import etl_cache
-from datetime import datetime, timedelta
 
 d = data.load()
 
-
 def region_capacities_weekdays(id: int, scrapeDate_start: str):
+
+    file = f"etl_region_capacities_weekdays_{id}.obj"
+    obj = etl_cache.openObj(file)
+    if obj:
+        return obj
+
     # String to Date
     scrapeDate_start = datetime.strptime(scrapeDate_start, '%Y-%m-%d')
 
@@ -53,4 +58,5 @@ def region_capacities_weekdays(id: int, scrapeDate_start: str):
     outDf = outDf[['weekday', 'mean']]
 
     result = {"scraping-date": scrapeDate, "weekdays": outDf['weekday'].to_list(),'capacities': outDf['mean'].to_list()}
+    etl_cache.saveObj(file, result)
     return result

etl/src/data/etl_region_movAverage.py

@@ -4,10 +4,17 @@ from io import StringIO
 import polars as pl
 
 import data
+from data import etl_cache
 
 d = data.load()
 
 def region_movingAverage(id: int, scrape_date_start_min: str):
+
+    file = f"etl_region_movingAverage_{id}.obj"
+    obj = etl_cache.openObj(file)
+    if obj:
+        return obj
+
     # Settings
     # Offset between actual and predict ScrapeDate
     timeOffset = 30
@@ -119,4 +126,5 @@ def region_movingAverage(id: int, scrape_date_start_min: str):
     # 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(),}
+    etl_cache.saveObj(file, result)
     return result

etl/src/data/etl_region_properties_capacities.py

@@ -3,10 +3,17 @@ from io import StringIO
 import polars as pl
 
 import data
+from data import etl_cache
 
 d = data.load()
 
 def region_properties_capacities(id: int):
+
+    file = f"etl_region_properties_capacities_{id}.obj"
+    obj = etl_cache.openObj(file)
+    if obj:
+        return obj
+
     # Get Data
     if id == -1:
         df = d.capacity_global().pl()
@@ -53,5 +60,5 @@ def region_properties_capacities(id: int):
 
     # Create JSON
     outDict = {'scrapeDates': listOfDates, 'property_ids': listOfPropertyIDs, 'values': values}
-
+    etl_cache.saveObj(file, outDict)
     return outDict