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New /api/usage/live endpoint derives instantaneous-ish kW from the most recent interval reading, shown as a solar -> house/grid flow diagram that updates every 30s and flips direction/color between exporting and importing. /api/usage/summary now also reports net revenue for the selected period, reusing the existing costing engine against the current plan. Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
125 lines
4.5 KiB
Python
125 lines
4.5 KiB
Python
from __future__ import annotations
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from collections import defaultdict
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from datetime import datetime, timedelta, timezone
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from zoneinfo import ZoneInfo
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from fastapi import APIRouter, HTTPException, Query
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from app.costing.engine import IntervalReading, evaluate
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from app.db.repositories import (
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get_current_plan,
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get_earliest_reading_ts,
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get_latest_reading,
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get_usage_series,
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)
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from app.db.session import get_session
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router = APIRouter(prefix="/api/usage", tags=["usage"])
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BNE = ZoneInfo("Australia/Brisbane")
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def _resolve_start(session, days: str, end: datetime) -> datetime:
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if days == "all":
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return get_earliest_reading_ts(session) or end
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try:
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n = int(days)
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except ValueError:
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raise HTTPException(400, "days must be an integer or 'all'")
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if n < 1:
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raise HTTPException(400, "days must be >= 1")
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return end - timedelta(days=n)
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def _bucket_key(local_date, granularity: str) -> str:
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if granularity == "month":
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return local_date.replace(day=1).isoformat()
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if granularity == "week":
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return (local_date - timedelta(days=local_date.weekday())).isoformat()
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return local_date.isoformat()
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@router.get("/daily")
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def daily_usage(days: str = Query("30")) -> list[dict]:
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end = datetime.now(timezone.utc)
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with get_session() as session:
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start = _resolve_start(session, days, end)
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rows = get_usage_series(session, start, end)
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# Plotting one bar per day is unreadable (and slow) once the range
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# stretches into years, so roll up to weekly/monthly buckets for long
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# ranges -- this is a display concern only, the underlying stored data
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# stays at full 5-minute resolution.
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span_days = (end - start).days
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granularity = "month" if span_days > 550 else "week" if span_days > 120 else "day"
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by_bucket: dict[str, dict[str, float]] = defaultdict(lambda: {"generation_kwh": 0.0, "load_kwh": 0.0, "import_kwh": 0.0, "export_kwh": 0.0})
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for row in rows:
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key = _bucket_key(row.ts.astimezone(BNE).date(), granularity)
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bucket = by_bucket[key]
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bucket["generation_kwh"] += row.generation_kwh
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bucket["load_kwh"] += row.load_kwh
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bucket["import_kwh"] += row.import_kwh
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bucket["export_kwh"] += row.export_kwh
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return [{"date": day, **{k: round(v, 3) for k, v in totals.items()}} for day, totals in sorted(by_bucket.items())]
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@router.get("/summary")
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def usage_summary(days: str = Query("30")) -> dict:
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end = datetime.now(timezone.utc)
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with get_session() as session:
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start = _resolve_start(session, days, end)
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rows = get_usage_series(session, start, end)
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current_plan = get_current_plan(session)
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if not rows:
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return {"days_with_data": 0}
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dates_seen: set[str] = set()
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totals = {"generation_kwh": 0.0, "load_kwh": 0.0, "import_kwh": 0.0, "export_kwh": 0.0}
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for row in rows:
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dates_seen.add(row.ts.astimezone(BNE).date().isoformat())
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totals["generation_kwh"] += row.generation_kwh
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totals["load_kwh"] += row.load_kwh
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totals["import_kwh"] += row.import_kwh
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totals["export_kwh"] += row.export_kwh
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net_revenue_dollars = None
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if current_plan is not None:
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# Reuses the same costing engine the plan comparison runs on --
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# "net revenue" is just the negative of what the current plan would
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# bill for this period (positive when export credit outweighs the
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# cost of imports).
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readings = [IntervalReading(ts=row.ts, import_kwh=row.import_kwh, export_kwh=row.export_kwh) for row in rows]
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net_revenue_dollars = round(-evaluate(current_plan, readings).total_dollars, 2)
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return {
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"days_with_data": len(dates_seen),
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"first_day": min(dates_seen),
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"last_day": max(dates_seen),
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"net_revenue_dollars": net_revenue_dollars,
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**{k: round(v, 2) for k, v in totals.items()},
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}
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@router.get("/live")
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def live_status() -> dict:
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with get_session() as session:
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row = get_latest_reading(session)
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if row is None:
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return {"available": False}
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step_hours = row.resolution_minutes / 60 if row.resolution_minutes else None
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def _kw(kwh: float) -> float | None:
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return round(kwh / step_hours, 3) if step_hours else None
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return {
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"available": True,
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"ts": row.ts.isoformat(),
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"generation_kw": _kw(row.generation_kwh),
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"load_kw": _kw(row.load_kwh),
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"import_kw": _kw(row.import_kwh),
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"export_kw": _kw(row.export_kwh),
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}
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