48 Stunden Predcition & Optimierung

Ein paar Zeitfunktionen korrigiert (24h / 48h)
Strompreis Cache stündlich leeren
Strompreis bei nur 24h Daten, wird verdoppelt (Prognose fehlt noch)

modules/class_strompreis.py

@@ -14,20 +14,34 @@ utc_time = utc_time.replace(tzinfo=pytz.utc)
 local_time = utc_time.astimezone(pytz.timezone('Europe/Berlin'))
 print(local_time)
 
+def repeat_to_shape(array, target_shape):
+    # Prüfen , ob das Array in die Zielgröße passt
+    if len(target_shape) != array.ndim:
+        raise ValueError("Array and target shape must have the same number of dimensions")
+
+    # die Anzahl der Wiederholungen pro Dimension
+    repeats = tuple(target_shape[i] // array.shape[i] for i in range(array.ndim))
+
+    #  np.tile, um das Array zu erweitern
+    expanded_array = np.tile(array, repeats)
+    return expanded_array
+
 
 
 class HourlyElectricityPriceForecast:
-    def __init__(self, source, cache_dir='cache', abgaben=0.00019):
+    def __init__(self, source, cache_dir='cache', abgaben=0.000, prediction_hours=24): #228
         self.cache_dir = cache_dir
         if not os.path.exists(self.cache_dir):
             os.makedirs(self.cache_dir)
+        self.cache_time_file = os.path.join(self.cache_dir, 'cache_timestamp.txt')
         self.prices = self.load_data(source)
         self.abgaben = abgaben
+        self.prediction_hours = prediction_hours
     
     def load_data(self, source):
+        cache_filename = self.get_cache_filename(source)
         if source.startswith('http'):
-            cache_filename = self.get_cache_filename(source)
-            if os.path.exists(cache_filename):
+            if os.path.exists(cache_filename) and not self.is_cache_expired():
                 print("Lade Daten aus dem Cache...")
                 with open(cache_filename, 'r') as file:
                     data = json.load(file)
@@ -38,6 +52,7 @@ class HourlyElectricityPriceForecast:
                     data = response.json()
                     with open(cache_filename, 'w') as file:
                         json.dump(data, file)
+                    self.update_cache_timestamp()
                 else:
                     raise Exception(f"Fehler beim Abrufen der Daten: {response.status_code}")
         else:
@@ -49,6 +64,18 @@ class HourlyElectricityPriceForecast:
         hash_object = hashlib.sha256(url.encode())
         hex_dig = hash_object.hexdigest()
         return os.path.join(self.cache_dir, f"cache_{hex_dig}.json")
+    
+    def is_cache_expired(self):
+        if not os.path.exists(self.cache_time_file):
+            return True
+        with open(self.cache_time_file, 'r') as file:
+            timestamp_str = file.read()
+            last_cache_time = datetime.strptime(timestamp_str, '%Y-%m-%d %H:%M:%S')
+        return datetime.now() - last_cache_time > timedelta(hours=1)
+    
+    def update_cache_timestamp(self):
+        with open(self.cache_time_file, 'w') as file:
+            file.write(datetime.now().strftime('%Y-%m-%d %H:%M:%S'))
 
 
     
@@ -68,13 +95,17 @@ class HourlyElectricityPriceForecast:
         
         # Extrahieren aller Preise für das spezifizierte Datum
         date_prices = [entry["marketpriceEurocentPerKWh"]+self.abgaben for entry in self.prices if date_str in entry['end']]
+        print("getPRice:",len(date_prices))
         
         # Hinzufügen des letzten Preises des vorherigen Tages am Anfang der Liste
-        date_prices.insert(0, last_price_of_previous_day)
+        if len(date_prices) == 23:
+                date_prices.insert(0, last_price_of_previous_day)
 
         return np.array(date_prices)/(1000.0*100.0) + self.abgaben
     
     def get_price_for_daterange(self, start_date_str, end_date_str):
+        print(start_date_str)
+        print(end_date_str)
         """Gibt alle Preise zwischen dem Start- und Enddatum zurück."""
         start_date_utc = datetime.strptime(start_date_str, "%Y-%m-%d").replace(tzinfo=pytz.utc)
         end_date_utc = datetime.strptime(end_date_str, "%Y-%m-%d").replace(tzinfo=pytz.utc)
@@ -84,13 +115,16 @@ class HourlyElectricityPriceForecast:
         
         price_list = []
 
-        while start_date <= end_date:
+        while start_date < end_date:
             date_str = start_date.strftime("%Y-%m-%d")
             daily_prices = self.get_price_for_date(date_str)
-            #print(len(self.get_price_for_date(date_str)))
+            print(date_str," ",daily_prices)
+            print(len(self.get_price_for_date(date_str)))
+            
             if daily_prices.size ==24:
                 price_list.extend(daily_prices)
             start_date += timedelta(days=1)
         
-        return np.array(price_list)
-
+        price_list = repeat_to_shape(np.array(price_list),(self.prediction_hours,))
+        
+        return price_list