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scripts/extract_iot_10min.py

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+"""Extract real 10-minute IoT snapshots for the Sukawarna Kandang 2 cycle window.
+
+The public IoT API (dashboard.cpsp.id/api/iot/flocks/) polls the coop every 10
+minutes, so a full cycle day holds up to 144 rows. This script scans the API's
+history for flock 686df69f407b21002da8750c (Kandang 2 Lantai 1) inside the
+seeded cycle window 2026-05-22 .. 2026-07-09 and writes every snapshot to
+``iot_panel_10min_extract.json``. The Django seed command reads this cache so
+Kandang 2 Lantai 1 is seeded from real sensor readings; the other floors and
+Kandang 1 are generated from it.
+"""
+import json
+import sys
+import urllib.request
+from datetime import datetime, timedelta, timezone
+from pathlib import Path
+
+SCRIPT_DIR = Path(__file__).resolve().parent
+sys.path.insert(0, str(SCRIPT_DIR))
+from experience_temperature import LANTAI_1_IOT_FLOCK_ID, panel_fields_from_payload
+
+BASE = "https://dashboard.cpsp.id/api/iot/flocks/"
+FLOCK_ID = LANTAI_1_IOT_FLOCK_ID
+CYCLE_START = datetime(2026, 5, 22).date()
+CYCLE_END = datetime(2026, 7, 9).date()
+TZ = timezone(timedelta(hours=7))
+OUT = SCRIPT_DIR / "iot_panel_10min_extract.json"
+
+
+def parse_dt(value: str) -> datetime:
+    return datetime.fromisoformat(value.replace("Z", "+00:00"))
+
+
+def fetch_page(page: int) -> dict:
+    with urllib.request.urlopen(f"{BASE}?page={page}", timeout=60) as resp:
+        return json.loads(resp.read())
+
+
+def main() -> None:
+    p1 = fetch_page(1)
+    last_page = (p1["count"] + len(p1["results"]) - 1) // len(p1["results"])
+    hi = min(last_page, 320)
+    print(f"Scanning pages 1..{hi} (of {last_page}) for flock {FLOCK_ID}", flush=True)
+
+    # key: (date, slot_index) -> mapped row (keep the latest snapshot per 10-min slot)
+    rows_by_slot: dict[tuple[str, int], dict] = {}
+    in_window = 0
+    for page in range(1, hi + 1):
+        for r in fetch_page(page)["results"]:
+            if r["flock_id"] != FLOCK_ID:
+                continue
+            fetched = r.get("fetched_at")
+            if not fetched:
+                continue
+            ts = parse_dt(fetched).astimezone(TZ)
+            cal = ts.date()
+            if not (CYCLE_START <= cal <= CYCLE_END):
+                continue
+            in_window += 1
+            d = r["payload"]["data"]
+            age = (cal - CYCLE_START).days + 1
+            fields = panel_fields_from_payload(d, age)
+            slot = (ts.hour * 60 + ts.minute) // 10
+            key = (str(cal), slot)
+            rows_by_slot[key] = {
+                "timestamp": ts.isoformat(),
+                "date": str(cal),
+                "age": age,
+                **fields,
+            }
+        if page % 50 == 0:
+            print(f"  page {page}/{hi} — in-window so far: {in_window}", flush=True)
+
+    rows = [rows_by_slot[k] for k in sorted(rows_by_slot, key=lambda k: (k[0], k[1]))]
+
+    out = {
+        "flock_id": FLOCK_ID,
+        "cycle_start": str(CYCLE_START),
+        "cycle_end": str(CYCLE_END),
+        "count": len(rows),
+        "rows": rows,
+    }
+    OUT.write_text(json.dumps(out, indent=2), encoding="utf-8")
+
+    per_date: dict[str, int] = {}
+    for row in rows:
+        per_date[row["date"]] = per_date.get(row["date"], 0) + 1
+    print(f"\nSnapshots in window: {in_window}; unique slots written: {len(rows)}")
+    print(f"Unique dates: {len(per_date)} (expect 49)")
+    counts = sorted(set(per_date.values()))
+    print(f"Rows-per-date distribution: {counts}")
+    low = [d for d, c in per_date.items() if c < 144]
+    print(f"Dates with fewer than 144 rows ({len(low)}): {low[:20]}")
+    print(f"Wrote {OUT}")
+
+
+if __name__ == "__main__":
+    main()