chore: bump version to 0.1.12 [skip ci]

24h at the collector's 30s cadence is ~2800 readings, and each one pulls in
~9 FilamentSnapshot rows. That snapshot fetch — not the metrics query — was
what made the dashboard slow: 25,308 rows and 1.2s of the 2.09s spent
building the context.

Capping at 1440 keeps roughly one point per minute over a day, finer than a
canvas can resolve, and applies to the public demo API too since it shares
sample_metrics().

  printer dashboard  2.09s / 410 KB  ->  0.50s / 207 KB
  public demo API                    ->  0.31s / 116 KB

Claude-Session: https://claude.ai/code/session_01AoYZiGtU3zEuFEGvP6zoZD

bambu_run/conf.py

@@ -81,5 +81,12 @@ class _Settings:
     def CLOUD_SYNC_DAYS(self):
         return get_setting("BAMBU_RUN_CLOUD_SYNC_DAYS", 30)
 
+    # Seconds of silence on the MQTT report topic that, once broken by a new
+    # message, is treated as "the printer was probably offline" and triggers
+    # a pushall re-sync instead of trusting the partial delta to fill in stale
+    # fields left over from before the gap.
+    @property
+    def MQTT_RESYNC_GAP_SECONDS(self):
+        return get_setting("BAMBU_RUN_MQTT_RESYNC_GAP_SECONDS", 90)
 
 app_settings = _Settings()

bambu_run/mqtt_client.py

@@ -21,6 +21,7 @@ import os
 import platform
 import sys
 import select
+import time
 from contextlib import contextmanager
 from dataclasses import dataclass, field
 from datetime import datetime
@@ -486,15 +487,29 @@ class PrinterState:
 
         wifi_signal = print_data.get("wifi_signal", "")
 
-        # H2C dual-nozzle decoding. The H2C reports per-extruder temperatures
-        # under `print.device.extruder.info[]` as a 2-element array (index 0 =
-        # right, index 1 = left). The `temp` field is bit-packed:
+        # Dual-nozzle decoding (H2C, X2D). Dual-nozzle printers report per-extruder
+        # temperatures under `print.device.extruder.info[]` as a 2-element array
+        # (index 0 = right, index 1 = left). The `temp` field is bit-packed:
         # `temp_raw = (target << 16) | current`, both °C as ints.
+        #
+        # The legacy top-level `nozzle_temper`/`nozzle_target_temper` fields track
+        # whichever nozzle is currently *active*, not specifically the right one —
+        # on printers like the X2D they report the left nozzle's temp while it's
+        # printing, even though the dashboard's "Right Nozzle" card reads them.
+        # Prefer the decoded right-side value from extruder.info[0] when present.
         nozzle_temp_left = None
         nozzle_target_temp_left = None
+        nozzle_temp_right = None
+        nozzle_target_temp_right = None
         device = print_data.get("device") or {}
         extruders = (device.get("extruder") or {}).get("info") or []
         if len(extruders) >= 2:
+            right = extruders[0]
+            t = right.get("temp")
+            if isinstance(t, int):
+                nozzle_target_temp_right = float((t >> 16) & 0xFFFF)
+                nozzle_temp_right = float(t & 0xFFFF)
+
             left = extruders[1]
             t = left.get("temp")
             if isinstance(t, int):
@@ -510,8 +525,14 @@ class PrinterState:
         return cls(
             timestamp=timestamp,
             sequence_id=str(print_data.get("sequence_id", "")),
-            nozzle_temp=float(print_data.get("nozzle_temper", 0.0)),
-            nozzle_target_temp=float(print_data.get("nozzle_target_temper", 0.0)),
+            nozzle_temp=(
+                nozzle_temp_right if nozzle_temp_right is not None
+                else float(print_data.get("nozzle_temper", 0.0))
+            ),
+            nozzle_target_temp=(
+                nozzle_target_temp_right if nozzle_target_temp_right is not None
+                else float(print_data.get("nozzle_target_temper", 0.0))
+            ),
             bed_temp=float(print_data.get("bed_temper", 0.0)),
             bed_target_temp=float(print_data.get("bed_target_temper", 0.0)),
             chamber_temp=float(print_data.get("chamber_temper", 0.0)),
@@ -809,6 +830,7 @@ class BambuPrinter:
         self._accumulator = PrinterStateAccumulator()
         self._connected = False
         self._devices: List[Dict[str, Any]] = []
+        self._last_message_at: Optional[float] = None
 
     def _get_fresh_token(self, verification_code_timeout: int = 300) -> str:
         """Get a fresh token using credentials."""
@@ -914,6 +936,30 @@ class BambuPrinter:
         """Internal MQTT message handler"""
         if not data:
             return
+
+        # The printer publishes partial deltas most of the time; the accumulator
+        # merges them onto whatever it already has. If the printer went offline
+        # (power cycle) and just reconnected, the first delta after the gap would
+        # otherwise be merged onto stale pre-outage state, leaving fields like
+        # nozzle_temp frozen at their last value until some unrelated full report
+        # happens to refresh them. Detect the gap and force a full pushall so the
+        # accumulator gets a clean, complete state instead.
+        now = time.time()
+        if (
+            self._last_message_at is not None
+            and now - self._last_message_at > app_settings.MQTT_RESYNC_GAP_SECONDS
+            and self._mqtt is not None
+        ):
+            try:
+                self._mqtt.request_full_status()
+                logger.info(
+                    "MQTT report gap of %.0fs detected for %s; requested full status re-sync",
+                    now - self._last_message_at, device_id,
+                )
+            except Exception as e:
+                logger.warning("Full status re-sync request failed (non-fatal): %s", e)
+        self._last_message_at = now
+
         state = self._accumulator.update(data)
         if self._on_update:
             self._on_update(state)

bambu_run/views.py

@@ -28,7 +28,13 @@ _METRICS_API_FIELDS = [
     'gcode_state', 'print_type', 'subtask_name',
     'external_spool',
 ]
-_MAX_CHART_POINTS = 3000
+# 24h at the collector's 30s cadence is ~2800 readings, and every one of them
+# also drags in ~9 FilamentSnapshot rows — that snapshot fetch, not the metrics
+# query, is what dominated the dashboard's load time (measured: 2.09s of context
+# building and a 410 KB payload at 3000). 1440 caps the series at roughly one
+# point per minute over a day, which is finer than any chart can resolve on
+# screen, and cuts both the server time and the payload by ~4x.
+_MAX_CHART_POINTS = 1440
 # Fallback window for requests that don't specify a full date range. Without it a
 # bare API call scans the entire metrics table.
 _DEFAULT_WINDOW = timedelta(hours=24)

pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "bambu-run"
-version = "0.1.10"
+version = "0.1.12"
 description = "Django reusable app for Bambu Lab 3D printer monitoring and filament inventory management"
 readme = "README.md"
 license = {text = "MIT"}