Add new daemons and debug scripts for Sigenergy and Oracle functionalities

- Implement `sigen_daemon.py` to poll Sigenergy plant metrics and store snapshots. - Create `web_daemon.py` for serving a web interface with various endpoints. - Add debug scripts: - `debug_duplicates.py` to find duplicate target times in forecast data. - `debug_energy_forecast.py` to print baseline energy forecast curves. - `debug_oracle_evaluations.py` to run the oracle evaluator. - `debug_sigen.py` to inspect stored Sigenergy plant snapshots. - `debug_weather.py` to trace resolved truth data. - `modbus_test.py` for exploring Sigenergy plants or inverters over Modbus TCP. - Introduce `oracle_evaluator.py` for evaluating stored oracle predictions against actuals. - Add TCN training scripts in `tcn` directory for training usage sequence models.
2026-04-28 08:14:00 +02:00
parent ff0c65a794
commit c8e3016fd6
55 changed files with 6385 additions and 633 deletions
@@ -0,0 +1,152 @@
+from __future__ import annotations
+
+import json
+from datetime import timedelta
+
+from gibil.classes.oracle.store import OracleStore
+
+
+class OracleQualityDisplay:
+    """Renders oracle prediction quality tables."""
+
+    def render(self) -> str:
+        return """
+        <section class="panel oracle-quality-panel" data-module="oracle-quality-display">
+          <div class="panel-heading">
+            <div>
+              <h2>Oracle Quality</h2>
+              <p>Prediction error by model and horizon</p>
+            </div>
+            <div class="control-row">
+              <label>
+                Window
+                <select id="quality-lookback">
+                  <option value="24">24 hours</option>
+                  <option value="168" selected>7 days</option>
+                  <option value="720">30 days</option>
+                </select>
+              </label>
+            </div>
+          </div>
+          <div class="table-shell">
+            <table>
+              <thead>
+                <tr>
+                  <th>Kind</th>
+                  <th>Model</th>
+                  <th>Horizon</th>
+                  <th>Samples</th>
+                  <th>Bias</th>
+                  <th>MAE</th>
+                  <th>Median AE</th>
+                  <th>MAPE</th>
+                  <th>Coverage</th>
+                </tr>
+              </thead>
+              <tbody id="quality-rows"></tbody>
+            </table>
+          </div>
+        </section>
+        <script>
+        window.astrapeModules = window.astrapeModules || {};
+        window.astrapeModules.oracleQualityDisplay = (() => {
+          function init() {
+            document.getElementById("quality-lookback").addEventListener("change", refresh);
+            refresh();
+            setInterval(refresh, 10000);
+          }
+
+          async function refresh() {
+            const lookback = document.getElementById("quality-lookback").value;
+            const response = await fetch(`/api/oracle-quality?lookback_hours=${lookback}`, { cache: "no-store" });
+            const payload = await response.json();
+            render(payload.rows || []);
+          }
+
+          function render(rows) {
+            const tbody = document.getElementById("quality-rows");
+            if (!rows.length) {
+              tbody.innerHTML = `<tr><td colspan="9">No evaluated oracle predictions yet.</td></tr>`;
+              return;
+            }
+            tbody.innerHTML = rows.map((row) => `
+              <tr>
+                <td>${escapeHtml(row.kind)}</td>
+                <td>${escapeHtml(row.model_version)}</td>
+                <td>${formatHorizon(row)}</td>
+                <td>${row.evaluated_count}</td>
+                <td class="${biasClass(row.mean_error_w)}">${formatW(row.mean_error_w)}</td>
+                <td>${formatW(row.mean_absolute_error_w)}</td>
+                <td>${formatW(row.median_absolute_error_w)}</td>
+                <td>${formatPct(row.mean_absolute_pct_error)}</td>
+                <td>${formatPct(row.interval_coverage)}</td>
+              </tr>
+            `).join("");
+          }
+
+          function formatHorizon(row) {
+            if (row.horizon_label) return row.horizon_label;
+            return `${row.min_horizon_minutes}-${row.max_horizon_minutes}m`;
+          }
+
+          function formatW(value) {
+            if (value === null || value === undefined) return "n/a";
+            return `${Math.round(Number(value))} W`;
+          }
+
+          function formatPct(value) {
+            if (value === null || value === undefined) return "n/a";
+            return `${(Number(value) * 100).toFixed(1)}%`;
+          }
+
+          function biasClass(value) {
+            if (value === null || value === undefined) return "";
+            const absolute = Math.abs(Number(value));
+            if (absolute < 250) return "metric-good";
+            if (absolute < 1000) return "metric-warn";
+            return "metric-bad";
+          }
+
+          function escapeHtml(value) {
+            return String(value ?? "")
+              .replace(/&/g, "&amp;")
+              .replace(/</g, "&lt;")
+              .replace(/>/g, "&gt;")
+              .replace(/"/g, "&quot;")
+              .replace(/'/g, "&#039;");
+          }
+
+          return { init };
+        })();
+        window.astrapeModules.oracleQualityDisplay.init();
+        </script>
+        """
+
+    def data_payload(self, lookback_hours: float = 168) -> str:
+        try:
+            rows = OracleStore.from_env().load_evaluation_summary(
+                lookback=timedelta(hours=lookback_hours)
+            )
+        except Exception:
+            rows = []
+
+        return json.dumps(
+            {
+                "lookback_hours": lookback_hours,
+                "rows": [self._row(row) for row in rows],
+            }
+        )
+
+    def _row(self, row: dict[str, object]) -> dict[str, object]:
+        return {
+            key: self._json_value(value)
+            for key, value in row.items()
+        }
+
+    def _json_value(self, value: object) -> object:
+        if value is None or isinstance(value, (str, int, float, bool)):
+            return value
+        try:
+            return float(value)
+        except (TypeError, ValueError):
+            return str(value)