sensor: model real temp sensor noise in TempSensorSim

Replaces the opaque variance/√12 formula with two explicit noise components:

  sigma (default 0.05 °C) — white Gaussian noise, calibrated against the
  20260628T184903 Sud-0010 log (detrended hold-phase tick-to-tick std ≈ 0.053 °C).

  stirrer_sigma / stirrer_tau — optional AR(1) low-frequency component for
  stirrer-induced fluctuations (off by default); steady-state std equals
  stirrer_sigma, correlation time equals stirrer_tau ticks.

plant_factory.py updated to use sigma=0.05 instead of the old variance=0.01
(which gave std ≈ 0.003 °C — far too quiet).

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

components/sensor/tempSensorSim.py

@@ -3,17 +3,43 @@ import numpy as np
 
 
 class TempSensorSim(ATemperatureSensor):
+	"""Simulated temperature sensor with two independent noise components.
+
+	sigma         -- white Gaussian noise std [°C]; calibrated against the
+	                 20260628T184903 Sud-0010 log: detrended hold-phase
+	                 tick-to-tick std ≈ 0.053 °C.
+
+	stirrer_sigma -- steady-state std [°C] of a slow AR(1) process that
+	                 models stirrer-induced low-frequency temperature
+	                 fluctuations at the sensor.  Zero by default (off).
+	stirrer_tau   -- correlation time [ticks] of the AR(1) process;
+	                 stirrer_sigma * sqrt(2/stirrer_tau) is the per-tick
+	                 innovation std so that the steady-state variance equals
+	                 stirrer_sigma².
+	"""
+
 	def name(self):
 		return "FakeTemp"
 
-	def __init__(self, temp_offset=0.0, variance=0.0, **kwargs):
+	def __init__(self, sigma=0.05, stirrer_sigma=0.0, stirrer_tau=20.0,
+	             temp_offset=0.0, **kwargs):
 		ATemperatureSensor.__init__(self)
 		self.temp_set = 19.99
 		self.offset = temp_offset
-		self.variance = variance
+		self.sigma = sigma
+		self.stirrer_sigma = stirrer_sigma
+		self.stirrer_tau = stirrer_tau
+		self._stirrer_state = 0.0
 
 	def set_fake_temp(self, temp):
 		self.temp_set = temp
 
 	def temperature(self):
-		return self.temp_set + self.offset + self.variance * np.random.normal(0, 1) / np.sqrt(12.0)
+		white = self.sigma * np.random.normal() if self.sigma > 0 else 0.0
+
+		if self.stirrer_sigma > 0:
+			alpha = 1.0 / self.stirrer_tau
+			innovation = self.stirrer_sigma * np.sqrt(2.0 * alpha) * np.random.normal()
+			self._stirrer_state = (1.0 - alpha) * self._stirrer_state + innovation
+
+		return self.temp_set + self.offset + white + self._stirrer_state