rpotter6298
c8e3016fd6
- Implement `sigen_daemon.py` to poll Sigenergy plant metrics and store snapshots. - Create `web_daemon.py` for serving a web interface with various endpoints. - Add debug scripts: - `debug_duplicates.py` to find duplicate target times in forecast data. - `debug_energy_forecast.py` to print baseline energy forecast curves. - `debug_oracle_evaluations.py` to run the oracle evaluator. - `debug_sigen.py` to inspect stored Sigenergy plant snapshots. - `debug_weather.py` to trace resolved truth data. - `modbus_test.py` for exploring Sigenergy plants or inverters over Modbus TCP. - Introduce `oracle_evaluator.py` for evaluating stored oracle predictions against actuals. - Add TCN training scripts in `tcn` directory for training usage sequence models.
36 lines
1.0 KiB
Python
36 lines
1.0 KiB
Python
from __future__ import annotations
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from dataclasses import dataclass
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from gibil.classes.predictors.usage_sequence_dataset import (
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UsageSequenceDatasetBuilder,
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UsageSequenceScaleConfig,
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)
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@dataclass(frozen=True)
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class UsageHybridModelShape:
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"""Describes the fixed-plus-token sequence model input contract."""
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past_scales: tuple[UsageSequenceScaleConfig, ...]
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past_fixed_features: tuple[str, ...]
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future_fixed_features: tuple[str, ...]
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future_steps: int
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quantiles: tuple[float, ...] = (0.10, 0.50, 0.90)
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@classmethod
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def from_dataset_builder(
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cls,
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builder: UsageSequenceDatasetBuilder,
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) -> "UsageHybridModelShape":
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return cls(
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past_scales=builder.config.past_scales,
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past_fixed_features=tuple(builder.past_feature_names),
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future_fixed_features=tuple(builder.future_feature_names),
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future_steps=builder.future_steps,
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)
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@property
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def output_width(self) -> int:
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return self.future_steps * len(self.quantiles)
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