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FAstAPI is an async framework. Data may be imported and exported, load and save, set and get asynchronously. Prevent interleaving data operations to corrupt the data. In the previous design sync and async data access was intermixed leading to data corruption. The basic data classes DataSequence and DataContainer and the derived classes like Provider and Measurement now are async. Data access is protected by several async locks. To support the async design of the data classes the database interface became async. The energy management is also adapted to the new async design. Optimization is still off-loaded to another thread, but the prepration for the optimization and the post optimization actions now follow the async design. Adapter operations are now also protected by async locks. Tests were adapted to the async design and new tests were created. Besides this major fix several other improvements and fixes are included in this PR. * fix: key_to_dict/list/array only regard data records with key value set. Before the exclusion of no value data records was only done if the dropna flag was set. * fix: test for visual result pdf generation Due to updates in the library the generated charts text was a little bit different. Adapt the test to create the comaprison pdf in the test data durectory and update the reference pdf. * chore: Remove MutableMapping from DataSequence and DataContainer. Mutable Mapping does not fit to the now async design. * chore: Add NoDB database backend This backend implements the full database backend interface but performs no actual persistence. It is intended for configurations where database persistence is disabled (`provider=None`). * chore: Improve measurement data import testing with real world scenarios. Added two new endpoints to support testing. * chore: Add mermaid to supported documentation tools * chore: Add documentation about async design * chore: Add documentation about generic data handling Covers the basics of measurement and prediction time series data handling. * chore: Add empty lines around markdown lists. * chore: sync pre-commit config to updated package versions Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com>
230 lines
6.8 KiB
Python
230 lines
6.8 KiB
Python
#!/usr/bin/env python3
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import argparse
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import asyncio
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import cProfile
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import pstats
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import sys
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import time
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from akkudoktoreos.core.coreabc import get_config, get_prediction
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config_eos = get_config()
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prediction_eos = get_prediction()
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def config_pvforecast() -> dict:
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"""Configure settings for PV forecast."""
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settings = {
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"general": {
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"latitude": 52.52,
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"longitude": 13.405,
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},
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"prediction": {
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"hours": 48,
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"historic_hours": 24,
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},
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"pvforecast": {
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"provider": "PVForecastAkkudoktor",
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"planes": [
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{
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"peakpower": 5.0,
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"surface_azimuth": -10,
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"surface_tilt": 7,
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"userhorizon": [20, 27, 22, 20],
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"inverter_paco": 10000,
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},
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{
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"peakpower": 4.8,
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"surface_azimuth": -90,
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"surface_tilt": 7,
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"userhorizon": [30, 30, 30, 50],
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"inverter_paco": 10000,
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},
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{
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"peakpower": 1.4,
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"surface_azimuth": -40,
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"surface_tilt": 60,
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"userhorizon": [60, 30, 0, 30],
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"inverter_paco": 2000,
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},
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{
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"peakpower": 1.6,
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"surface_azimuth": 5,
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"surface_tilt": 45,
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"userhorizon": [45, 25, 30, 60],
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"inverter_paco": 1400,
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},
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],
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},
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}
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return settings
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def config_weather() -> dict:
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"""Configure settings for weather forecast."""
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settings = {
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"general": {
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"latitude": 52.52,
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"longitude": 13.405,
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},
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"prediction": {
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"hours": 48,
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"historic_hours": 24,
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},
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"weather": dict(),
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}
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return settings
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def config_elecprice() -> dict:
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"""Configure settings for electricity price forecast."""
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settings = {
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"general": {
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"latitude": 52.52,
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"longitude": 13.405,
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},
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"prediction": {
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"hours": 48,
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"historic_hours": 24,
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},
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"elecprice": dict(),
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}
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return settings
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def config_feedintarifffixed() -> dict:
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"""Configure settings for feed in tariff forecast."""
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settings = {
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"general": {
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"latitude": 52.52,
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"longitude": 13.405,
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},
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"prediction": {
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"hours": 48,
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"historic_hours": 24,
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},
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"feedintariff": dict(),
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}
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return settings
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def config_load() -> dict:
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"""Configure settings for load forecast."""
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settings = {
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"general": {
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"latitude": 52.52,
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"longitude": 13.405,
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},
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"prediction": {
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"hours": 48,
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"historic_hours": 24,
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},
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}
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return settings
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def run_prediction(provider_id: str, verbose: bool = False) -> str:
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"""Run the prediction.
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Args:
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provider_id (str): ID of prediction provider.
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verbose (bool, optional): Whether to print verbose output. Defaults to False.
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Returns:
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dict: Prediction result as a dictionary
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"""
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# Initialize the oprediction
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config_eos = get_config()
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prediction_eos = get_prediction()
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if provider_id in ("PVForecastAkkudoktor",):
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settings = config_pvforecast()
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forecast = "pvforecast"
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elif provider_id in ("BrightSky", "ClearOutside"):
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settings = config_weather()
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forecast = "weather"
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elif provider_id in ("ElecPriceAkkudoktor",):
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settings = config_elecprice()
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forecast = "elecprice"
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elif provider_id in ("FeedInTariffFixed",):
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settings = config_feedintarifffixed()
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forecast = "feedintariff"
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elif provider_id in ("LoadAkkudoktor",):
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settings = config_load()
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forecast = "loadforecast"
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settings["load"]["LoadAkkudoktor"]["loadakkudoktor_year_energy_wh"] = 1000
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else:
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raise ValueError(f"Unknown provider '{provider_id}'.")
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settings[forecast]["provider"] = provider_id
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config_eos.merge_settings_from_dict(settings)
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provider = prediction_eos.provider_by_id(provider_id)
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asyncio.run(prediction_eos.update_data())
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# Return result of prediction
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if verbose:
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print(f"\nProvider ID: {provider.provider_id()}")
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print("----------")
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print("\nSettings\n----------")
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print(settings)
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print("\nProvider\n----------")
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print(f"elecprice.provider: {config_eos.elecprice.provider}")
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print(f"feedintariff.provider: {config_eos.feedintariff.provider}")
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print(f"load.provider: {config_eos.load.provider}")
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print(f"pvforecast.provider: {config_eos.pvforecast.provider}")
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print(f"weather.provider: {config_eos.weather.provider}")
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print(f"enabled: {provider.enabled()}")
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for key in provider.record_keys:
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print(f"\n{key}\n----------")
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print(f"Array: {asyncio.run(provider.key_to_array(key))}")
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return provider.model_dump_json(indent=4)
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def main():
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"""Main function to run the optimization script with optional profiling."""
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parser = argparse.ArgumentParser(description="Run Prediction")
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parser.add_argument("--profile", action="store_true", help="Enable performance profiling")
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parser.add_argument(
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"--verbose", action="store_true", help="Enable verbose output during prediction"
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)
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parser.add_argument("--provider-id", type=str, default=0, help="Provider ID of prediction")
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args = parser.parse_args()
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if args.profile:
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# Run with profiling
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profiler = cProfile.Profile()
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try:
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result = profiler.runcall(
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run_prediction, provider_id=args.provider_id, verbose=args.verbose
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)
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# Print profiling statistics
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stats = pstats.Stats(profiler)
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stats.strip_dirs().sort_stats("cumulative").print_stats(200)
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# Print result
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print("\nPrediction Result:")
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print(result)
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except Exception as e:
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print(f"Error during prediction: {e}", file=sys.stderr)
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sys.exit(1)
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else:
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# Run without profiling
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try:
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start_time = time.time()
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result = run_prediction(provider_id=args.provider_id, verbose=args.verbose)
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end_time = time.time()
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elapsed_time = end_time - start_time
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print(f"\nElapsed time: {elapsed_time:.4f} seconds.")
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print("\nPrediction Result:")
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print(result)
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except Exception as e:
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print(f"Error during prediction: {e}", file=sys.stderr)
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sys.exit(1)
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if __name__ == "__main__":
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asyncio.run(main())
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