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111 lines
4.0 KiB
Python
111 lines
4.0 KiB
Python
from datetime import datetime
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import numpy as np
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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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self.filepath = filepath
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self.data = None
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self.data_year_energy = None
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self.year_energy = year_energy
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self.load_data()
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def get_daily_stats(self, date_str):
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"""
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Returns the 24-hour profile with mean and standard deviation for a given date.
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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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# Convert the date string into a datetime object
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date = self._convert_to_datetime(date_str)
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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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# Extract the 24-hour profile for the given date
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daily_stats = self.data_year_energy[
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day_of_year - 1
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] # -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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Returns the mean and standard deviation for a specific hour of a given date.
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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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# Convert the date string into a datetime object
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date = self._convert_to_datetime(date_str)
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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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# Extract mean and standard deviation for the given hour
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hourly_stats = self.data_year_energy[
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day_of_year - 1, :, hour
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] # 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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Returns the means and standard deviations for a date range.
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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 = 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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# Note that in leap years, the day of the year may need adjustment
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stats_for_range = self.data_year_energy[
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start_day_of_year:end_day_of_year
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] # -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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return stats_for_range
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def load_data(self):
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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(
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list(zip(data["yearly_profiles"], data["yearly_profiles_std"]))
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)
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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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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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"""Returns price data (currently not implemented)."""
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return self.price_data
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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" # Adjust the path to the .npz file
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lf = LoadForecast(filepath=filepath, year_energy=2000)
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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(
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"2024-02-16", 12
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) # Adjust date and hour as needed
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print(specific_hour_stats)
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