import os
from typing import Dict, List
import matplotlib.pyplot as plt
def _ensure_dir(path: str):
os.makedirs(os.path.dirname(path), exist_ok=True)
def plot_timeseries(log: Dict[str, List[float]], out_path: str):
''' Plot the time series of temperatures and thresholds.
log: A dictionary containing time series data with keys:
- "t": time points
- "T_true": true temperature
- "T_meas": measured/filtered temperature
- "T_out": outdoor temperature
- "lower": lower threshold (optional value)
- "upper": upper threshold (optional value)
- "setpoint": setpoint temperature
out_path: Path to save the output plot image.
'''
_ensure_dir(out_path)
t = log["t"]
T_true = log["T_true"]
T_meas = log["T_meas"]
T_out = log["T_out"]
lower = log["lower"]
upper = log["upper"]
setpoint = log["setpoint"]
fig, ax = plt.subplots()
ax.plot(t, T_true, label="True T")
ax.plot(t, T_meas, label="Measured/Filtered T")
ax.plot(t, setpoint, label="Setpoint")
ax.plot(t, T_out, label="Outdoor T", linestyle="--")
if lower and upper:
ax.plot(t, lower, linestyle=":", label="Lower threshold")
ax.plot(t, upper, linestyle=":", label="Upper threshold")
ax.fill_between(t, lower, upper, color="gray", alpha=0.2, label="Deadband region")
ax.set_xlabel("Time (s)")
ax.set_ylabel("Temperature (°C)")
ax.set_title("Temperatures vs Time")
ax.legend(loc="best")
fig.tight_layout()
fig.savefig(out_path)
plt.close(fig)
def plot_heater(log: Dict[str, List[float]], out_path: str):
''' Plot the heater state over time.
log: A dictionary containing time series data with keys:
- "t": time points
- "heater": heater state (0=OFF, 1=ON)
out_path: Path to save the output plot image.
'''
_ensure_dir(out_path)
t = log["t"]
heater = log["heater"]
fig, ax = plt.subplots()
ax.step(t, heater, where="post")
ax.set_xlabel("Time (s)")
ax.set_ylabel("Heater (0=OFF, 1=ON)")
ax.set_ylim(-0.1, 1.1)
ax.set_title("Heater State Timeline")
fig.tight_layout()
fig.savefig(out_path)
plt.close(fig)
def plot_error(log: Dict[str, List[float]], out_path: str):
''' Plot the tracking error (setpoint - measured temperature) over time.
log: A dictionary containing time series data with keys:
- "t": time points
- "error": tracking error (setpoint - measured temperature)
out_path: Path to save the output plot image.
'''
_ensure_dir(out_path)
t = log["t"]
error = log["error"]
fig, ax = plt.subplots()
ax.plot(t, error)
ax.set_xlabel("Time (s)")
ax.set_ylabel("Error (setpoint - measured)")
ax.set_title("Tracking Error")
fig.tight_layout()
fig.savefig(out_path)
plt.close(fig)
def plot_duty(log: Dict[str, List[float]], out_path: str, window:int=60):
''' Plot the rolling duty cycle of the heater over time.
log: A dictionary containing time series data with keys:
- "t": time points
- "heater": heater state (0=OFF, 1=ON)
out_path: Path to save the output plot image.
window: Size of the rolling window in seconds for duty cycle calculation.
'''
_ensure_dir(out_path)
heater = log["heater"]
t = log["t"]
duty = []
s = 0.0
buf = []
for b in heater:
buf.append(b)
s += b
if len(buf) > window:
s -= buf.pop(0)
duty.append(s / len(buf))
fig, ax = plt.subplots()
ax.plot(t, duty)
ax.set_xlabel("Time (s)")
ax.set_ylabel(f"Heater Duty (rolling, window={window})")
ax.set_title("Heater Duty Cycle (Rolling)")
fig.tight_layout()
fig.savefig(out_path)
plt.close(fig)
def plot_predictive(log: Dict[str, List[float]], out_path: str):
''' Plot the predictive control diagnostics including measured temperature,
predicted temperature, and thresholds over time.
log: A dictionary containing time series data with keys:
- "t": time points
- "T_meas": measured/filtered temperature
- "T_pred": predicted temperature (optional)
- "lower": lower threshold (optional)
- "upper": upper threshold (optional)
out_path: Path to save the output plot image.
'''
_ensure_dir(out_path)
t = log["t"]
T_pred = log.get("T_pred", [])
lower = log.get("lower", [])
upper = log.get("upper", [])
T_meas = log["T_meas"]
fig, ax = plt.subplots()
ax.plot(t, T_meas, label="Measured/Filtered T")
if T_pred:
ax.plot(t, T_pred, label="Predicted T (lookahead)")
if lower and upper:
ax.plot(t, lower, linestyle=":", label="Lower threshold")
ax.plot(t, upper, linestyle=":", label="Upper threshold")
ax.fill_between(t, lower, upper, alpha=0.1)
ax.set_xlabel("Time (s)")
ax.set_ylabel("Temperature (°C)")
ax.set_title("Predictive Control Diagnostics")
ax.legend(loc="best")
fig.tight_layout()
fig.savefig(out_path)
plt.close(fig)
