jensandClaude Sonnet 4.6
2dfaf8dee6
Replaces the open-loop (feed real temp_ist) approach with a proper closed-loop simulation: a Pot plant model is driven by the controller's power output each tick, generating sim_temp_ist that diverges from the real log's real_temp_ist whenever the PID params produce different behaviour. Temperature panel now shows real_temp_ist vs sim_temp_ist vs temp_soll so the effect of candidate gains is immediately visible. Plant params (M, C, L, Td) default to 20 kg water / L=0.2 / Td=30 s and can be overridden via --plant-* flags. Ambient defaults from config. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01EY449HByk6UpKhDN2HTnfK
252 lines
10 KiB
Python
252 lines
10 KiB
Python
#!/usr/bin/env python3
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"""Closed-loop replay simulation for the BrewPi temp controller.
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Loads a real run log and drives a simulated Pot plant with a fresh
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TempController (Normal mode) using the same setpoints (temp_soll,
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heatrate_soll_set) as the original run. The controller's power output
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feeds back into the plant model each tick, generating a simulated
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temperature trajectory (sim_temp_ist) that can be compared against the
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real one from the log (real_temp_ist).
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Use this to verify PID params: tweak gains and see whether the simulated
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plant reaches setpoint faster, overshoots less, or oscillates less than
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the original logged run, all without touching real hardware.
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Usage:
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python utils/replay_sim.py <date_time> <sud_name>
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python utils/replay_sim.py <date_time> <sud_name> \\
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--heat-kp 0.1 --heat-ki 0.03 --heat-kt 1.0
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python utils/replay_sim.py <date_time> <sud_name> \\
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--plant-M 25 --plant-L 0.15 --ambient 18
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Plant params default to water (C=4190 J/kg·K), M=20 kg, L=0.2 W/kg·K,
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Td=30 s — pass --plant-* to match the actual batch. Max heater power is
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inferred from the highest power_eff step in the log.
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By default reads PID params from config.json in the repo root. Any
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--<section>-<gain> flag overrides just that one value.
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"""
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import argparse
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import copy
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import json
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import sys
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from pathlib import Path
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import matplotlib
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matplotlib.use("TkAgg")
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import matplotlib.pyplot as plt
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from matplotlib.ticker import AutoMinorLocator
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sys.path.insert(0, str(Path(__file__).parent.parent))
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from components.pid.temp_controller import TempController
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from components.plant.pot import Pot
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def _apply_gain_overrides(params, args):
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p = copy.deepcopy(params)
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for section, prefix in (('Hold', 'hold'), ('Heat', 'heat'), ('Cool', 'cool')):
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for gain in ('kp', 'ki', 'kd', 'kt'):
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val = getattr(args, '{}_{}'.format(prefix, gain))
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if val is not None:
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p[section][gain] = val
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return p
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def _infer_max_power(samples):
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"""Largest discrete heater step seen in the log — heater's effective max."""
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return max((s['power_eff'] for s in samples), default=1.0) or 1.0
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def _infer_heatrate_soll_set(samples):
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"""Estimate heatrate_soll_set from the log.
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rate_soll = heatrate_soll_set * pid_hold.get_y(); when the hold PID is
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saturated at 1.0 during active heating the two are equal, so the max
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rate_soll seen while the heater is on is a good upper bound."""
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candidates = [s['rate_soll'] for s in samples if s['power_set'] > 0]
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return max(candidates, default=1.0) or 1.0
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def run_replay(samples, pid_params, heatrate_soll_set, plant_params, ambient, max_power, dt):
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"""Closed-loop simulation: controller drives plant, plant feeds temp back.
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The plant is seeded at the real starting temperature from the log so the
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simulation begins at a realistic operating point. Setpoints follow the
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real run's temp_soll schedule sample by sample."""
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tc = TempController(dt)
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tc.set_params(pid_params)
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tc.set_enabled(True)
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plant = Pot(dt)
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plant.set_plant_params(plant_params)
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plant.set_ambient_temperature(ambient)
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plant.initial(samples[0]['temp_ist'])
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out = []
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for s in samples:
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plant.process()
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sim_temp = plant.get_temperature()
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tc.set_theta_ist(sim_temp)
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tc.set_theta_soll(s['temp_soll'])
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tc.set_heatrate_soll(heatrate_soll_set)
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tc.process()
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power_watts = max(0.0, tc.get_power() * max_power)
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plant.set_power(power_watts)
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out.append({
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'sim_temp_ist': sim_temp,
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'power': tc.get_power(),
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'state': tc.state.name,
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'heatrate_ist': tc.get_heatrate_ist(),
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'heatrate_soll': tc.get_heatrate_soll(),
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})
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return out
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def _style_axis(ax):
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ax.set_facecolor('white')
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ax.spines['top'].set_visible(False)
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ax.spines['right'].set_visible(False)
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ax.xaxis.set_minor_locator(AutoMinorLocator())
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ax.yaxis.set_minor_locator(AutoMinorLocator())
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ax.tick_params(which='major', direction='out', labelsize='small')
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ax.tick_params(which='minor', direction='out', length=2)
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ax.grid(which='major', linestyle='-', linewidth=0.5, alpha=0.3)
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ax.grid(which='minor', linestyle=':', linewidth=0.5, alpha=0.15)
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def plot_replay(samples, replay, name, max_power, params_label):
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t = [s['t'] for s in samples]
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fig, (ax_temp, ax_rate, ax_power) = plt.subplots(3, 1, sharex=True, facecolor='white')
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fig.suptitle('{} — replay simulation ({})'.format(name, params_label), fontsize='small')
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# Temperature: real log vs simulated plant trajectory
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ax_temp.plot(t, [s['temp_ist'] for s in samples], '-b', linewidth=1, label='real_temp_ist')
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ax_temp.plot(t, [r['sim_temp_ist'] for r in replay], '-', color='C1', linewidth=1, label='sim_temp_ist')
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ax_temp.plot(t, [s['temp_soll'] for s in samples], '-r', linewidth=1, alpha=0.5, label='temp_soll')
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ax_temp.set_ylabel('Temp [°C]')
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ax_temp.legend(loc='upper left', fontsize='small')
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# Heat rate
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ax_rate.plot(t, [s['rate_ist'] for s in samples], '-b', linewidth=1, alpha=0.6, label='rate_ist (real)')
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ax_rate.plot(t, [r['heatrate_ist'] for r in replay], '-', color='C1', linewidth=1, label='rate_ist (sim)')
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ax_rate.plot(t, [s['rate_soll'] for s in samples], '-r', linewidth=1, alpha=0.4, label='rate_soll (real)')
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ax_rate.set_ylabel('Rate [K/min]')
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ax_rate.legend(loc='upper left', fontsize='small')
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# Power: log (normalised) vs sim controller output
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log_norm = [s['power_set'] / max_power for s in samples]
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replay_pwr = [r['power'] for r in replay]
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ax_power.plot(t, log_norm, '-b', linewidth=1, label='power (real, norm.)')
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ax_power.plot(t, replay_pwr, '-', color='C1', linewidth=1, label='power (sim)')
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ax_power.set_ylabel('Power [0–1]')
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ax_power.set_xlabel('t [s]')
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ax_power.legend(loc='upper left', fontsize='small')
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for ax in (ax_temp, ax_rate, ax_power):
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_style_axis(ax)
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ax_temp.label_outer()
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ax_rate.label_outer()
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fig.tight_layout()
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return fig
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def main():
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repo_root = Path(__file__).parent.parent
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default_config = repo_root / 'config.json'
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default_log_dir = repo_root / 'logs'
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parser = argparse.ArgumentParser(
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description=__doc__,
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formatter_class=argparse.RawDescriptionHelpFormatter,
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)
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parser.add_argument('date_time', help='Timestamp part of log filename, e.g. 20260628T184903')
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parser.add_argument('sud_name', help='Sud name part of log filename, e.g. Sud-0010')
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parser.add_argument('--log-dir', default=str(default_log_dir),
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help='Log directory (default: %(default)s)')
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parser.add_argument('--config', default=str(default_config),
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help='Config file for base PID params (default: %(default)s)')
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parser.add_argument('--rate-soll', type=float, default=None,
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help='Heat-rate setpoint [K/min] (default: inferred from log)')
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parser.add_argument('--ambient', type=float, default=None,
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help='Ambient temperature [°C] (default: from config ambient_temperature)')
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# Plant model params
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parser.add_argument('--plant-M', type=float, default=20.0, metavar='kg',
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help='Plant mass [kg] (default: %(default)s)')
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parser.add_argument('--plant-C', type=float, default=4190.0, metavar='J/kgK',
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help='Specific heat capacity [J/(kg·K)] (default: %(default)s — water)')
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parser.add_argument('--plant-L', type=float, default=0.2, metavar='W/kgK',
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help='Heat loss coefficient [W/(kg·K)] (default: %(default)s)')
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parser.add_argument('--plant-Td', type=float, default=30.0, metavar='s',
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help='Transport delay [s] (default: %(default)s)')
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for section, prefix in (('Hold', 'hold'), ('Heat', 'heat'), ('Cool', 'cool')):
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for gain in ('kp', 'ki', 'kd', 'kt'):
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parser.add_argument(
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'--{}-{}'.format(prefix, gain),
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type=float, default=None,
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dest='{}_{}'.format(prefix, gain),
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metavar='VAL',
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help='Override TempCtrl.{}.{}'.format(section, gain),
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)
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args = parser.parse_args()
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log_path = Path(args.log_dir) / 'log_{}_{}.json'.format(args.date_time, args.sud_name)
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if not log_path.exists():
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print('Error: log file not found: {}'.format(log_path), file=sys.stderr)
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sys.exit(1)
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config_path = Path(args.config)
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if not config_path.exists():
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print('Error: config file not found: {}'.format(config_path), file=sys.stderr)
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sys.exit(1)
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with open(log_path) as f:
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log = json.load(f)
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with open(config_path) as f:
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config = json.load(f)
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samples = log['Samples']
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name = log.get('Name', '{}_{}'.format(args.date_time, args.sud_name))
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pid_params = _apply_gain_overrides(config['TempCtrl'], args)
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dt = (samples[1]['t'] - samples[0]['t']) if len(samples) > 1 else 1.0
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max_power = _infer_max_power(samples)
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rate_soll = args.rate_soll if args.rate_soll is not None else _infer_heatrate_soll_set(samples)
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ambient = args.ambient if args.ambient is not None else config.get('ambient_temperature', 20.0)
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plant_params = {'M': args.plant_M, 'C': args.plant_C, 'L': args.plant_L, 'Td': args.plant_Td}
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any_override = any(
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getattr(args, '{}_{}'.format(p, g)) is not None
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for p in ('hold', 'heat', 'cool')
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for g in ('kp', 'ki', 'kd', 'kt')
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)
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params_label = 'candidate' if any_override else 'config'
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print('Log: {} ({} samples, dt={:.1f} s)'.format(name, len(samples), dt))
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print('Power: max={:.0f} W'.format(max_power))
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print('Rate: heatrate_soll_set={:.2f} K/min'.format(rate_soll))
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print('Plant: M={M} kg C={C} J/kgK L={L} W/kgK Td={Td} s ambient={amb} °C'.format(
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amb=ambient, **plant_params))
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print('Params: {}'.format('overridden' if any_override else 'from config'))
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print()
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for section in ('Hold', 'Heat', 'Cool'):
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p = pid_params[section]
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print(' {}: kp={kp} ki={ki} kd={kd} kt={kt}'.format(section, **p))
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replay = run_replay(samples, pid_params, rate_soll, plant_params, ambient, max_power, dt)
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plot_replay(samples, replay, name, max_power, params_label)
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plt.show()
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if __name__ == '__main__':
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main()
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