Worldwide coverage: grade any point on Earth (#32)

Remove the US+Canada bounding box so every endpoint accepts any lat/lon.
The grading pipeline was already global-ready (ERA5 archive, timezone=auto,
day-of-year climatology), so opening it up is mostly deleting the guard —
plus the edge cases that only exist once the whole globe is in play:

- grid.py: snap() wraps longitude into [-180, 180) and clamps latitude, and
  cell centers are normalized so the polar row and the cells straddling the
  antimeridian always report valid coordinates to the weather/geocoding APIs.
  snap() and from_id() now share one _cell() builder, making id round-trips
  exact by construction (verified with a 300k-point global sweep).
- nav.js: neighbor-cell prefetch skips rows past the poles and wraps
  longitudes across the dateline instead of sending out-of-range queries.
- Nominatim reverse geocoding requests accept-language=en so place labels
  render in one script worldwide (matching the forward geocoder).
- mappicker: search suggestions are no longer filtered to US/CA, the
  placeholder and default map view are worldwide.
- calendar: season filter labels flip for southern-hemisphere locations
  (Dec-Feb shows as Summer); the underlying month groups are unchanged, so
  saved filter selections keep meaning the same months.

Verified end-to-end on a scratch server: Tokyo and Sydney grade with real
labels, a Fiji cell on the antimeridian's east edge builds and serves warm
hits from the derived store, and prefetch=1 on a cold cell still answers
204 without spending weather-API quota.

app.py

@@ -350,12 +350,6 @@ def api_grade(
                        description="days to grade after the target (observed, or forecast when future; "
                                    "the forecast reaches ~7 days out so future days cap there)"),
 ):
-    if not grid.in_north_america(lat, lon):
-        raise HTTPException(
-            status_code=400,
-            detail="Location is outside the supported US + Canada region.",
-        )
-
     target = (
         pd.Timestamp(date).normalize()
         if date
@@ -417,11 +411,6 @@ def api_calendar(
     a database read — across restarts — until new archive days arrive. Returns a
     compact per-day shape (see grading.grade_range). New in API v2.
     """
-    if not grid.in_north_america(lat, lon):
-        raise HTTPException(
-            status_code=400,
-            detail="Location is outside the supported US + Canada region.",
-        )
     cell = grid.snap(lat, lon)
 
     with audit.RunAudit(
@@ -480,11 +469,6 @@ def api_day(
     (those payloads expire hourly — the recent bundle's own cadence — while fully
     archived days stay valid until the record itself advances). New in API v2.
     """
-    if not grid.in_north_america(lat, lon):
-        raise HTTPException(
-            status_code=400,
-            detail="Location is outside the supported US + Canada region.",
-        )
     cell = grid.snap(lat, lon)
 
     with audit.RunAudit(
@@ -538,11 +522,6 @@ def api_forecast(
     updates; the graded payload's validity is tied to that fetch stamp, so it
     expires exactly when a new forecast lands. New in API v2.
     """
-    if not grid.in_north_america(lat, lon):
-        raise HTTPException(
-            status_code=400,
-            detail="Location is outside the supported US + Canada region.",
-        )
     cell = grid.snap(lat, lon)
 
     with audit.RunAudit(endpoint="forecast", lat=round(lat, 4), lon=round(lon, 4),
@@ -600,11 +579,6 @@ def api_cell(
     Reverse geocoding makes at most one Nominatim call for a never-labeled cell;
     the client staggers neighbor prefetches to respect that service. New in API v2.
     """
-    if not grid.in_north_america(lat, lon):
-        raise HTTPException(
-            status_code=400,
-            detail="Location is outside the supported US + Canada region.",
-        )
     cell = grid.snap(lat, lon)
     today = pd.Timestamp(datetime.date.today())
 

climate.py

@@ -549,8 +549,10 @@ def reverse_geocode(lat: float, lon: float) -> str | None:
                 "https://nominatim.openstreetmap.org/reverse",
                 # zoom 14 resolves to the suburb/neighbourhood level so we can lead
                 # with it when OSM has one (zoom 10 only ever returns the city).
+                # accept-language=en keeps labels in one script worldwide (matches
+                # the forward geocoder's language=en).
                 {"lat": lat, "lon": lon, "format": "jsonv2", "zoom": 14,
-                 "addressdetails": 1},
+                 "addressdetails": 1, "accept-language": "en"},
                 15,
                 phase="reverse_geocode",
                 headers={"User-Agent": "Thermograph/0.1 (local weather grading app)"},
@@ -575,7 +577,7 @@ def reverse_geocode(lat: float, lon: float) -> str | None:
 
 
 def geocode(name: str, count: int = 5) -> list[dict]:
-    """Look up places by name (US/Canada biased) via Open-Meteo's geocoder."""
+    """Look up places by name worldwide via Open-Meteo's geocoder."""
     r = _request(
         "https://geocoding-api.open-meteo.com/v1/search",
         {"name": name, "count": count, "language": "en", "format": "json"},

grid.py

@@ -4,6 +4,8 @@ The grid is defined by fixed latitude rows (~2 miles tall). Within each row the
 longitude step is scaled by cos(latitude) so cells stay roughly square (~4 sq mi)
 at every latitude instead of getting skinny toward the poles. Cell ids are
 deterministic, so the same physical location always maps to the same cache file.
+Coverage is worldwide: any lat in [-90, 90] and any longitude (wrapped into
+[-180, 180)) maps to a cell.
 """
 import math
 
@@ -18,13 +20,20 @@ def _lon_step(center_lat: float) -> float:
     return LAT_STEP / c
 
 
-def snap(lat: float, lon: float) -> dict:
+def _cell(i: int, j: int) -> dict:
-    """Return the grid cell (id + center + span) containing (lat, lon)."""
+    """Build the cell dict for grid indices (i, j). Shared by snap()/from_id()
-    i = math.floor(lat / LAT_STEP)
+    so an id always rebuilds to the exact same cell."""
     center_lat = (i + 0.5) * LAT_STEP
     lon_step = _lon_step(center_lat)
-    j = math.floor(lon / lon_step)
     center_lon = (j + 0.5) * lon_step
+    # Keep the reported center a valid coordinate for the upstream weather and
+    # geocoding APIs: the topmost row's center overshoots the pole, and a row's
+    # outermost cells can have centers just past the antimeridian on either side.
+    center_lat = min(max(center_lat, -90.0), 90.0)
+    if center_lon > 180.0:
+        center_lon -= 360.0
+    elif center_lon < -180.0:
+        center_lon += 360.0
 
     # Approximate cell dimensions in miles for display.
     height_mi = LAT_STEP * 69.0
@@ -46,16 +55,18 @@ def snap(lat: float, lon: float) -> dict:
     }
 
 
+def snap(lat: float, lon: float) -> dict:
+    """Return the grid cell (id + center + span) containing (lat, lon)."""
+    lat = min(max(lat, -90.0), 90.0)
+    lon = ((lon + 180.0) % 360.0) - 180.0  # wrap into [-180, 180)
+    i = math.floor(lat / LAT_STEP)
+    j = math.floor(lon / _lon_step((i + 0.5) * LAT_STEP))
+    return _cell(i, j)
+
+
 def from_id(cell_id: str) -> dict:
     """Rebuild the full cell dict from a cache id ("i_j") — the inverse of snap().
     Lets offline tooling (the migrate script) recover a cell from its parquet
     filename alone. Raises ValueError on a malformed id."""
     i, j = (int(p) for p in cell_id.split("_"))
-    center_lat = (i + 0.5) * LAT_STEP
+    return _cell(i, j)
-    center_lon = (j + 0.5) * _lon_step(center_lat)
-    return snap(center_lat, center_lon)
-
-
-def in_north_america(lat: float, lon: float) -> bool:
-    """Rough bounding box for the US (incl. Alaska/Hawaii) and Canada."""
-    return 14.0 <= lat <= 84.0 and -172.0 <= lon <= -52.0