| |
| """ |
| Example: train a BatteryMHM state-of-health predictor on your own cells. |
| |
| Replace `load_my_cells()` with your real data: for each cell you need an |
| early-cycle capacity curve (a 1-D array of per-cycle discharge capacities) |
| and a target SOH label (e.g. capacity retention at end of life). |
| |
| Run: python examples/predict_soh.py |
| """ |
|
|
| import numpy as np |
|
|
| from batterymhm import MHMEnsemble, compute_metrics, mhm_full_features, seq_to_harmonics |
|
|
|
|
| def load_my_cells(): |
| """ |
| Stand-in data loader — returns (curves, soh_labels). |
| Swap this out for your measured cells (e.g. parsed from a cycler export). |
| """ |
| rng = np.random.default_rng(7) |
| curves, labels = [], [] |
| for _ in range(120): |
| rate = rng.uniform(0.001, 0.004) |
| cap = 1.0 - rate * np.sqrt(np.arange(1, 61)) + rng.normal(0, 0.0015, 60) |
| curves.append(np.clip(cap, 0, 1.05)) |
| labels.append(float(cap[-1])) |
| return curves, np.asarray(labels) |
|
|
|
|
| def featurize(curves): |
| """Quantise each curve to HINs and build the MHM descriptor matrix.""" |
| dicts = [mhm_full_features(seq_to_harmonics(list(c), bins=9)) for c in curves] |
| keys = sorted(dicts[0]) |
| X = np.array([[d[k] for k in keys] for d in dicts], dtype=float) |
| return X, keys |
|
|
|
|
| def main(): |
| curves, y = load_my_cells() |
| X, keys = featurize(curves) |
|
|
| n = len(y) |
| cut = int(0.75 * n) |
| model = MHMEnsemble().fit(X[:cut], y[:cut], feature_names=keys) |
| pred = model.predict(X[cut:]) |
|
|
| print("Held-out performance:", compute_metrics(y[cut:], pred)) |
| print("\nMost informative MHM features:") |
| for name, imp in model.top_features(8): |
| print(f" {imp:6.4f} {name}") |
|
|
|
|
| if __name__ == "__main__": |
| main() |
|
|
