Lars
f0e6fd04fb
- Introduced new routes and API endpoints for managing personal reference values. - Updated the SettingsPage to include a section for reference values with navigation to manage them. - Enhanced the backend to support reference values in the data layer and versioning. - Added necessary imports and UI components for a seamless user experience.
53 lines
1.4 KiB
Python
53 lines
1.4 KiB
Python
"""
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Example training base profiles — point to default templates and optional dimension filters.
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Future: DB-backed training types may reference profile keys; this registry is code-only.
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"""
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from __future__ import annotations
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from typing import Dict, Optional
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from data_layer.training_profile.models import TrainingBaseProfile
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_PROFILES: Dict[str, TrainingBaseProfile] = {}
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def _register(p: TrainingBaseProfile) -> None:
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if p.key in _PROFILES:
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raise ValueError(f"Duplicate training base profile key: {p.key}")
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_PROFILES[p.key] = p
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def get_training_base_profile(key: str) -> TrainingBaseProfile:
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if key not in _PROFILES:
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raise KeyError(f"Unknown training base profile: {key}")
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return _PROFILES[key]
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def try_get_training_base_profile(key: str) -> Optional[TrainingBaseProfile]:
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return _PROFILES.get(key)
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def list_training_base_profile_keys() -> tuple[str, ...]:
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return tuple(sorted(_PROFILES.keys()))
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_register(
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TrainingBaseProfile(
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key="scaffold_aerobic_base",
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label="Scaffold: aerobic base profile",
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default_template_id="scaffold_example_aerobic_v1",
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allowed_dimension_keys=None,
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)
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)
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_register(
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TrainingBaseProfile(
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key="scaffold_strength_base",
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label="Scaffold: strength base profile",
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default_template_id="scaffold_example_strength_v1",
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allowed_dimension_keys=frozenset({"effort"}),
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)
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)
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