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Update class_load.py
initial clean up, unused imports removed, translations, minor error handling for file added
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NormannK
Andreas
parent
9b439c9228
commit
c2af6cc1b3
@ -1,9 +1,8 @@
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import json
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from datetime import datetime, timedelta, timezone
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import numpy as np
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from datetime import datetime
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from pprint import pprint
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# Lade die .npz-Datei beim Start der Anwendung
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# Load the .npz file when the application starts
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class LoadForecast:
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def __init__(self, filepath=None, year_energy=None):
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@ -15,96 +14,85 @@ class LoadForecast:
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def get_daily_stats(self, date_str):
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"""
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Gibt den 24-Stunden-Verlauf mit Erwartungswert und Standardabweichung für ein gegebenes Datum zurück.
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Returns the 24-hour profile with mean and standard deviation for a given date.
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:param data: NumPy Array mit Shape (365, 2, 24), repräsentiert Daten für ein Jahr
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:param date_str: Datum als String im Format "YYYY-MM-DD"
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:return: Ein Array mit Shape (2, 24), enthält Erwartungswerte und Standardabweichungen
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:param date_str: Date as a string in the format "YYYY-MM-DD"
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:return: An array with shape (2, 24), contains means and standard deviations
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"""
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# Umwandlung des Datums-Strings in ein datetime-Objekt
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date = datetime.strptime(date_str, "%Y-%m-%d")
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# Convert the date string into a datetime object
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date = self._convert_to_datetime(date_str)
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# Berechnung des Tages des Jahres (1 bis 365)
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# Calculate the day of the year (1 to 365)
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day_of_year = date.timetuple().tm_yday
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# Extraktion des 24-Stunden-Verlaufs für das gegebene Datum
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daily_stats = self.data_year_energy[day_of_year - 1] # -1, da die Indizierung bei 0 beginnt
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# Extract the 24-hour profile for the given date
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daily_stats = self.data_year_energy[day_of_year - 1] # -1 because indexing starts at 0
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return daily_stats
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def get_hourly_stats(self, date_str, hour):
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"""
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Gibt Erwartungswert und Standardabweichung für eine spezifische Stunde eines gegebenen Datums zurück.
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Returns the mean and standard deviation for a specific hour of a given date.
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:param data: NumPy Array mit Shape (365, 2, 24), repräsentiert Daten für ein Jahr
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:param date_str: Datum als String im Format "YYYY-MM-DD"
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:param hour: Spezifische Stunde (0 bis 23)
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:return: Ein Array mit Shape (2,), enthält Erwartungswert und Standardabweichung für die spezifizierte Stunde
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:param date_str: Date as a string in the format "YYYY-MM-DD"
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:param hour: Specific hour (0 to 23)
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:return: An array with shape (2,), contains mean and standard deviation for the specified hour
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"""
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# Umwandlung des Datums-Strings in ein datetime-Objekt
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date = datetime.strptime(date_str, "%Y-%m-%d")
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# Convert the date string into a datetime object
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date = self._convert_to_datetime(date_str)
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# Berechnung des Tages des Jahres (1 bis 365)
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# Calculate the day of the year (1 to 365)
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day_of_year = date.timetuple().tm_yday
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# Extraktion von Erwartungswert und Standardabweichung für die gegebene Stunde
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hourly_stats = self.data_year_energy[day_of_year - 1, :, hour] # Zugriff auf die spezifische Stunde
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# Extract mean and standard deviation for the given hour
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hourly_stats = self.data_year_energy[day_of_year - 1, :, hour] # Access the specific hour
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return hourly_stats
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def get_stats_for_date_range(self, start_date_str, end_date_str):
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"""
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Gibt die Erwartungswerte und Standardabweichungen für einen Zeitraum zurück.
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Returns the means and standard deviations for a date range.
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:param start_date_str: Startdatum als String im Format "YYYY-MM-DD"
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:param end_date_str: Enddatum als String im Format "YYYY-MM-DD"
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:return: Ein Array mit den aggregierten Daten für den Zeitraum
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:param start_date_str: Start date as a string in the format "YYYY-MM-DD"
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:param end_date_str: End date as a string in the format "YYYY-MM-DD"
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:return: An array with aggregated data for the date range
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"""
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start_date = datetime.strptime(start_date_str, "%Y-%m-%d")
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end_date = datetime.strptime(end_date_str, "%Y-%m-%d")
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start_date = self._convert_to_datetime(start_date_str)
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end_date = self._convert_to_datetime(end_date_str)
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start_day_of_year = start_date.timetuple().tm_yday
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end_day_of_year = end_date.timetuple().tm_yday
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# Beachten, dass bei Schaltjahren der Tag des Jahres angepasst werden muss
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stats_for_range = self.data_year_energy[start_day_of_year:end_day_of_year] # -1 da die Indizierung bei 0 beginnt
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# print(start_day_of_year,"-",end_day_of_year)
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# print(stats_for_range.shape)
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stats_for_range =stats_for_range.swapaxes(1, 0)
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# Note that in leap years, the day of the year may need adjustment
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stats_for_range = self.data_year_energy[start_day_of_year:end_day_of_year] # -1 because indexing starts at 0
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stats_for_range = stats_for_range.swapaxes(1, 0)
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stats_for_range = stats_for_range.reshape(stats_for_range.shape[0],-1)
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# print(stats_for_range.shape)
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# print(stats_for_range)
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# print()
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# print(stats_for_range)
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# print(start_day_of_year, " ",end_day_of_year)
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# Hier kannst du entscheiden, wie du die Daten über den Zeitraum aggregieren möchtest
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# Zum Beispiel könntest du Mittelwerte, Summen oder andere Statistiken über diesen Zeitraum berechnen
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stats_for_range = stats_for_range.reshape(stats_for_range.shape[0], -1)
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return stats_for_range
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def load_data(self):
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with open(self.filepath, 'r') as file:
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"""Loads data from the specified file."""
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try:
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data = np.load(self.filepath)
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self.data = np.array(list(zip(data["yearly_profiles"],data["yearly_profiles_std"])))
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self.data = np.array(list(zip(data["yearly_profiles"], data["yearly_profiles_std"])))
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self.data_year_energy = self.data * self.year_energy
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#pprint(self.data_year_energy)
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# pprint(self.data_year_energy)
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except FileNotFoundError:
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print(f"Error: File {self.filepath} not found.")
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except Exception as e:
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print(f"An error occurred while loading data: {e}")
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def get_price_data(self):
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# load_profiles_exp_l = load_profiles_exp*year_energy
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# load_profiles_std_l = load_profiles_std*year_energy
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"""Returns price data (currently not implemented)."""
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return self.price_data
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# Beispiel für die Verwendung der Klasse
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def _convert_to_datetime(self, date_str):
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"""Converts a date string to a datetime object."""
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return datetime.strptime(date_str, "%Y-%m-%d")
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# Example usage of the class
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if __name__ == '__main__':
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filepath = r'..\load_profiles.npz' # Pfad zur JSON-Datei anpassen
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filepath = r'..\load_profiles.npz' # Adjust the path to the .npz file
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lf = LoadForecast(filepath=filepath, year_energy=2000)
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#load_forecast = lf.get_price_data
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#
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#price_forecast = HourlyElectricityPriceForecast(filepath)
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specific_date_prices = lf.get_daily_stats('2024-02-16') # Datum anpassen
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specific_date_prices = lf.get_hourly_stats('2024-02-16', 12) # Datum anpassen
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print(specific_date_prices)
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#for price in price_forecast.get_price_data():
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# print(price.get_starts_at(), price.get_total(), price.get_currency())
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specific_date_prices = lf.get_daily_stats('2024-02-16') # Adjust date as needed
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specific_hour_stats = lf.get_hourly_stats('2024-02-16', 12) # Adjust date and hour as needed
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print(specific_hour_stats)
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