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Update class_load.py

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initial clean up, unused imports removed, translations, minor error handling for file added
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NormannK 2024-09-20 12:43:28 +02:00 committed by Andreas
parent 9b439c9228
commit c2af6cc1b3
1 changed files with 47 additions and 59 deletions
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modules/class_load.py
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@ -1,9 +1,8 @@
import json
from datetime import datetime, timedelta, timezone
import numpy as np import numpy as np
from datetime import datetime
from pprint import pprint from pprint import pprint
# Lade die .npz-Datei beim Start der Anwendung # Load the .npz file when the application starts
class LoadForecast: class LoadForecast:
def __init__(self, filepath=None, year_energy=None): def __init__(self, filepath=None, year_energy=None):
@ -15,96 +14,85 @@ class LoadForecast:
def get_daily_stats(self, date_str): def get_daily_stats(self, date_str):
""" """
Gibt den 24-Stunden-Verlauf mit Erwartungswert und Standardabweichung für ein gegebenes Datum zurück. Returns the 24-hour profile with mean and standard deviation for a given date.
:param data: NumPy Array mit Shape (365, 2, 24), repräsentiert Daten für ein Jahr :param date_str: Date as a string in the format "YYYY-MM-DD"
:param date_str: Datum als String im Format "YYYY-MM-DD" :return: An array with shape (2, 24), contains means and standard deviations
:return: Ein Array mit Shape (2, 24), enthält Erwartungswerte und Standardabweichungen
""" """
# Umwandlung des Datums-Strings in ein datetime-Objekt # Convert the date string into a datetime object
date = datetime.strptime(date_str, "%Y-%m-%d") date = self._convert_to_datetime(date_str)
# Berechnung des Tages des Jahres (1 bis 365) # Calculate the day of the year (1 to 365)
day_of_year = date.timetuple().tm_yday day_of_year = date.timetuple().tm_yday
# Extraktion des 24-Stunden-Verlaufs für das gegebene Datum # Extract the 24-hour profile for the given date
daily_stats = self.data_year_energy[day_of_year - 1] # -1, da die Indizierung bei 0 beginnt daily_stats = self.data_year_energy[day_of_year - 1] # -1 because indexing starts at 0
return daily_stats return daily_stats
def get_hourly_stats(self, date_str, hour): def get_hourly_stats(self, date_str, hour):
""" """
Gibt Erwartungswert und Standardabweichung für eine spezifische Stunde eines gegebenen Datums zurück. Returns the mean and standard deviation for a specific hour of a given date.
:param data: NumPy Array mit Shape (365, 2, 24), repräsentiert Daten für ein Jahr :param date_str: Date as a string in the format "YYYY-MM-DD"
:param date_str: Datum als String im Format "YYYY-MM-DD" :param hour: Specific hour (0 to 23)
:param hour: Spezifische Stunde (0 bis 23) :return: An array with shape (2,), contains mean and standard deviation for the specified hour
:return: Ein Array mit Shape (2,), enthält Erwartungswert und Standardabweichung für die spezifizierte Stunde
""" """
# Umwandlung des Datums-Strings in ein datetime-Objekt # Convert the date string into a datetime object
date = datetime.strptime(date_str, "%Y-%m-%d") date = self._convert_to_datetime(date_str)
# Berechnung des Tages des Jahres (1 bis 365) # Calculate the day of the year (1 to 365)
day_of_year = date.timetuple().tm_yday day_of_year = date.timetuple().tm_yday
# Extraktion von Erwartungswert und Standardabweichung für die gegebene Stunde # Extract mean and standard deviation for the given hour
hourly_stats = self.data_year_energy[day_of_year - 1, :, hour] # Zugriff auf die spezifische Stunde hourly_stats = self.data_year_energy[day_of_year - 1, :, hour] # Access the specific hour
return hourly_stats return hourly_stats
def get_stats_for_date_range(self, start_date_str, end_date_str): def get_stats_for_date_range(self, start_date_str, end_date_str):
""" """
Gibt die Erwartungswerte und Standardabweichungen für einen Zeitraum zurück. Returns the means and standard deviations for a date range.
:param start_date_str: Startdatum als String im Format "YYYY-MM-DD" :param start_date_str: Start date as a string in the format "YYYY-MM-DD"
:param end_date_str: Enddatum als String im Format "YYYY-MM-DD" :param end_date_str: End date as a string in the format "YYYY-MM-DD"
:return: Ein Array mit den aggregierten Daten für den Zeitraum :return: An array with aggregated data for the date range
""" """
start_date = datetime.strptime(start_date_str, "%Y-%m-%d") start_date = self._convert_to_datetime(start_date_str)
end_date = datetime.strptime(end_date_str, "%Y-%m-%d") end_date = self._convert_to_datetime(end_date_str)
start_day_of_year = start_date.timetuple().tm_yday start_day_of_year = start_date.timetuple().tm_yday
end_day_of_year = end_date.timetuple().tm_yday end_day_of_year = end_date.timetuple().tm_yday
# Beachten, dass bei Schaltjahren der Tag des Jahres angepasst werden muss # Note that in leap years, the day of the year may need adjustment
stats_for_range = self.data_year_energy[start_day_of_year:end_day_of_year] # -1 da die Indizierung bei 0 beginnt stats_for_range = self.data_year_energy[start_day_of_year:end_day_of_year] # -1 because indexing starts at 0
# print(start_day_of_year,"-",end_day_of_year)
# print(stats_for_range.shape)
stats_for_range = stats_for_range.swapaxes(1, 0) stats_for_range = stats_for_range.swapaxes(1, 0)
stats_for_range = stats_for_range.reshape(stats_for_range.shape[0], -1) stats_for_range = stats_for_range.reshape(stats_for_range.shape[0], -1)
# print(stats_for_range.shape)
# print(stats_for_range)
# print()
# print(stats_for_range)
# print(start_day_of_year, " ",end_day_of_year)
# Hier kannst du entscheiden, wie du die Daten über den Zeitraum aggregieren möchtest
# Zum Beispiel könntest du Mittelwerte, Summen oder andere Statistiken über diesen Zeitraum berechnen
return stats_for_range return stats_for_range
def load_data(self): def load_data(self):
with open(self.filepath, 'r') as file: """Loads data from the specified file."""
try:
data = np.load(self.filepath) data = np.load(self.filepath)
self.data = np.array(list(zip(data["yearly_profiles"], data["yearly_profiles_std"]))) self.data = np.array(list(zip(data["yearly_profiles"], data["yearly_profiles_std"])))
self.data_year_energy = self.data * self.year_energy self.data_year_energy = self.data * self.year_energy
# pprint(self.data_year_energy) # pprint(self.data_year_energy)
except FileNotFoundError:
print(f"Error: File {self.filepath} not found.")
except Exception as e:
print(f"An error occurred while loading data: {e}")
def get_price_data(self): def get_price_data(self):
# load_profiles_exp_l = load_profiles_exp*year_energy """Returns price data (currently not implemented)."""
# load_profiles_std_l = load_profiles_std*year_energy
return self.price_data return self.price_data
# Beispiel für die Verwendung der Klasse def _convert_to_datetime(self, date_str):
"""Converts a date string to a datetime object."""
return datetime.strptime(date_str, "%Y-%m-%d")
# Example usage of the class
if __name__ == '__main__': if __name__ == '__main__':
filepath = r'..\load_profiles.npz' # Pfad zur JSON-Datei anpassen filepath = r'..\load_profiles.npz' # Adjust the path to the .npz file
lf = LoadForecast(filepath=filepath, year_energy=2000) lf = LoadForecast(filepath=filepath, year_energy=2000)
#load_forecast = lf.get_price_data specific_date_prices = lf.get_daily_stats('2024-02-16') # Adjust date as needed
# specific_hour_stats = lf.get_hourly_stats('2024-02-16', 12) # Adjust date and hour as needed
#price_forecast = HourlyElectricityPriceForecast(filepath) print(specific_hour_stats)
specific_date_prices = lf.get_daily_stats('2024-02-16') # Datum anpassen
specific_date_prices = lf.get_hourly_stats('2024-02-16', 12) # Datum anpassen
print(specific_date_prices)
#for price in price_forecast.get_price_data():
# print(price.get_starts_at(), price.get_total(), price.get_currency())