Past-Toto overlay: re-enable, capped at last Ecowitt actual

Bring the translucent Toto p50 overlay back on the historical side of
the chart but strictly cap target_ts <= last_actual_ts so it can't bleed
into the forecast window.

historical_predictions() now takes an explicit until_unix parameter
(default = now) and includes it in the WHERE clause.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

app.py

@@ -180,12 +180,31 @@ def _build_view(view: dict, log_conn, log_to_scoreboard: bool) -> dict:
     visible_steps = int(round(hours / step_hours))
     visible_history = history.tail(visible_steps)
 
+    # Past Toto forecasts: for each past hour visible on the chart, the
+    # most-recent forecast we issued *before* that hour. Strictly capped at
+    # the most recent Ecowitt actual so the overlay never bleeds into the
+    # future portion of the chart.
+    since_unix = (
+        int(visible_history.index.min().timestamp()) if not visible_history.empty else None
+    )
+    until_unix = int(last_actual.timestamp()) if last_actual is not None else None
+    past_toto: dict[str, pd.DataFrame] = {}
+    for m in METRICS:
+        col = m["col"]
+        pt = forecast_log.historical_predictions(
+            log_conn, "toto", col,
+            since_unix=since_unix, until_unix=until_unix,
+        )
+        if not pt.empty:
+            past_toto[col] = pt
+
     fig = combined_figure(
         history=visible_history,
         totos=totos,
         nws_df=nws_future,
         metrics=METRICS,
         now=now,
+        past_toto=past_toto,
     )
     return {
         "fig": fig,

src/forecast_log.py

@@ -165,14 +165,18 @@ def historical_predictions(
     source: str,
     metric: str,
     since_unix: int | None = None,
+    until_unix: int | None = None,
 ) -> pd.DataFrame:
-    """For each past target_ts, return the most-recent forecast issued for it.
+    """For each target_ts in [since, until], return the most-recent forecast
+    issued *before* that hour.
 
-    Useful for overlaying 'what we predicted vs what actually happened' on the
-    historical portion of the chart. Result is a DataFrame indexed by UTC
-    timestamp with columns p10, p50, p90 (NWS rows have NULL p10/p90).
+    `until_unix` defaults to now — pass it to cap the overlay so it doesn't
+    bleed into the future portion of the chart.
     """
-    params: list = [source, metric]
+    import time as _time  # noqa: PLC0415
+    if until_unix is None:
+        until_unix = int(_time.time())
+    params: list = [source, metric, until_unix]
     where_extra = ""
     if since_unix is not None:
         where_extra = " AND target_ts >= ?"
@@ -182,8 +186,10 @@ def historical_predictions(
         SELECT source, target_ts, metric,
                MAX(forecast_made_at) AS forecast_made_at
         FROM forecast_snapshots
-        WHERE source = ? AND metric = ? AND forecast_made_at <= target_ts
-        {where_extra}
+        WHERE source = ? AND metric = ?
+          AND forecast_made_at <= target_ts
+          AND target_ts <= ?
+          {where_extra}
         GROUP BY source, target_ts, metric
     )
     SELECT f.target_ts, f.p10, f.p50, f.p90

src/weather_ui.py

@@ -187,6 +187,7 @@ def combined_figure(
     nws_df: pd.DataFrame | None,
     metrics: list[dict],
     now: pd.Timestamp | None = None,
+    past_toto: dict[str, pd.DataFrame] | None = None,
 ) -> go.Figure:
     """Three stacked subplots sharing the x-axis."""
     fig = make_subplots(
@@ -212,6 +213,18 @@ def combined_figure(
             ),
             row=i, col=1,
         )
+        # Past Toto forecasts overlaid on actuals (historical side only).
+        if past_toto and col in past_toto:
+            pt = past_toto[col]
+            fig.add_trace(
+                go.Scatter(
+                    x=pt.index, y=pt["p50"].values,
+                    name="🤖 Toto (past forecasts)", mode="lines",
+                    line=dict(color="rgba(31,119,180,0.55)", width=1.5),
+                    showlegend=showlegend, legendgroup="toto-past",
+                ),
+                row=i, col=1,
+            )
         if toto is not None:
             fig.add_trace(
                 go.Scatter(