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2f5f844018
* Migrate from Flask to FastAPI
* FastAPI migration:
- Use pydantic model classes as input parameters to the
data/calculation classes.
- Interface field names changed to constructor parameter names (for
simplicity only during transition, should be updated in a followup
PR).
- Add basic interface requirements (e.g. some values > 0, etc.).
* Update tests for new data format.
* Python requirement down to 3.9 (TypeGuard no longer needed)
* Makefile: Add helpful targets (e.g. development server with reload)
* Move API doc from README to pydantic model classes (swagger)
* Link to swagger.io with own openapi.yml.
* Commit openapi.json and check with pytest for changes so the
documentation is always up-to-date.
* Streamline docker
* FastAPI: Run startup action on dev server
* Fix config for /strompreis, endpoint still broken however.
* test_openapi: Compare against docs/.../openapi.json
* Move fastapi to server/ submodule
* See #187 for new repository structure.
65 lines
2.5 KiB
Python
65 lines
2.5 KiB
Python
import numpy as np
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from pydantic import BaseModel, Field
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class HaushaltsgeraetParameters(BaseModel):
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verbrauch_wh: int = Field(
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gt=0,
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description="An integer representing the energy consumption of a household device in watt-hours.",
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)
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dauer_h: int = Field(
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gt=0,
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description="An integer representing the usage duration of a household device in hours.",
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)
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class Haushaltsgeraet:
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def __init__(self, parameters: HaushaltsgeraetParameters, hours=24):
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self.hours = hours # Total duration for which the planning is done
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self.verbrauch_wh = (
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parameters.verbrauch_wh # Total energy consumption of the device in kWh
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)
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self.dauer_h = parameters.dauer_h # Duration of use in hours
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self.lastkurve = np.zeros(self.hours) # Initialize the load curve with zeros
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def set_startzeitpunkt(self, start_hour, global_start_hour=0):
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"""Sets the start time of the device and generates the corresponding load curve.
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:param start_hour: The hour at which the device should start.
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"""
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self.reset()
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# Check if the duration of use is within the available time frame
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if start_hour + self.dauer_h > self.hours:
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raise ValueError("The duration of use exceeds the available time frame.")
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if start_hour < global_start_hour:
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raise ValueError("The start time is earlier than the available time frame.")
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# Calculate power per hour based on total consumption and duration
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leistung_pro_stunde = self.verbrauch_wh / self.dauer_h # Convert to watt-hours
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# Set the power for the duration of use in the load curve array
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self.lastkurve[start_hour : start_hour + self.dauer_h] = leistung_pro_stunde
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def reset(self):
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"""Resets the load curve."""
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self.lastkurve = np.zeros(self.hours)
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def get_lastkurve(self):
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"""Returns the current load curve."""
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return self.lastkurve
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def get_last_fuer_stunde(self, hour):
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"""Returns the load for a specific hour.
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:param hour: The hour for which the load is queried.
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:return: The load in watts for the specified hour.
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"""
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if hour < 0 or hour >= self.hours:
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raise ValueError("The specified hour is outside the available time frame.")
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return self.lastkurve[hour]
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def spaetestmoeglicher_startzeitpunkt(self):
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"""Returns the latest possible start time at which the device can still run completely."""
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return self.hours - self.dauer_h
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