jensandClaude Sonnet 4.6
bd08833df5
Feeds real temp_ist/temp_soll readings from a run log back into a fresh TempController (Normal mode) tick by tick, then plots a 3-panel comparison of heat rate and normalized power against the original logged values. Any PID gain can be overridden via --<section>-<gain> flags so candidate params can be evaluated against a past run without touching hardware. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01EY449HByk6UpKhDN2HTnfK
206 lines
8.0 KiB
Python
206 lines
8.0 KiB
Python
#!/usr/bin/env python3
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"""Replay a BrewPi log through the temp controller with candidate PID params.
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Feeds real temp_ist readings from a log back into a fresh TempController
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(Normal mode) tick by tick and compares the resulting power output to the
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original logged values. Because the real sensor measurements are injected at
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each tick, no plant model is needed: the replay shows exactly how a different
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set of PID gains would have responded to the same temperature trajectory.
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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> --config alt_config.json
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By default reads PID params from config.json in the repo root. Any --<section>-<gain>
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flag overrides just that one value; the rest come from the config.
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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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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 — used to normalise power_set."""
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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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"""Heat-rate setpoint from the log: max rate_soll seen while power > 0.
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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 (full-power heating) the two are equal, so the maximum
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over all heating samples is a good estimate of heatrate_soll_set."""
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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, dt):
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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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out = []
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for s in samples:
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tc.set_theta_ist(s['temp_ist'])
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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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out.append({
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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), fontsize='small')
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# Temperature: identical in both runs (real sensor readings are injected)
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ax_temp.plot(t, [s['temp_ist'] for s in samples], '-b', linewidth=1, label='theta_ist')
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ax_temp.plot(t, [s['temp_soll'] for s in samples], '-r', linewidth=1, label='theta_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 (log)')
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ax_rate.plot(t, [r['heatrate_ist'] for r in replay], '--', color='C1', linewidth=1, label='rate_ist (replay)')
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ax_rate.plot(t, [s['rate_soll'] for s in samples], '-r', linewidth=1, alpha=0.4, label='rate_soll (log)')
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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 (both normalised to 0–1)
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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 (log, norm.)')
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ax_power.plot(t, replay_pwr, '-', color='C1', linewidth=1, label='power ({})'.format(params_label))
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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] fed to the replay controller '
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'(default: inferred from log as max rate_soll while heating)')
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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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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('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, 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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