SQM Calibration

SQM = α + β × log₁₀(DVNL_agg + ε) + γ × (elevation / 1000)

SQMCalibrationModel(epsilon: float = 1e-3)

fit(
    aggregated_dvnl: np.ndarray,
    elevation_m: np.ndarray,
    target_sqm: np.ndarray
) -> SQMCalibrationModel

predict(
    aggregated_dvnl: np.ndarray,
    elevation_m: np.ndarray
) -> np.ndarray

save(filepath: str) -> None

load(filepath: str) -> SQMCalibrationModel

import numpy as np
from light_pollution.calibration import SQMCalibrationModel

# Initialize model
model = SQMCalibrationModel(epsilon=1e-3)

# Prepare training data
agg_dvnl = np.array([0.5, 1.2, 2.3, 0.8])
elevation = np.array([100, 250, 50, 300])
sqm_measurements = np.array([21.5, 20.8, 19.5, 21.9])

# Fit the model
model.fit(agg_dvnl, elevation, sqm_measurements)

# Make predictions
new_dvnl = np.array([0.6, 1.5])
new_elevation = np.array([150, 200])
predicted_sqm = model.predict(new_dvnl, new_elevation)

# Save for later use
model.save("sqm_calibration.pkl")

# Load the model
loaded_model = SQMCalibrationModel().load("sqm_calibration.pkl")