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from __future__ import annotations
import argparse
import json
from pathlib import Path
from typing import Any
import h5py
import numpy as np
import pandas as pd
import torch
from scipy.signal import find_peaks
from seisbench.models import EQTransformer
from torch.utils.data import DataLoader, Dataset
from tqdm import tqdm
def normalize_std(waveform: np.ndarray) -> np.ndarray:
if waveform.ndim != 2:
raise ValueError(f"Expected 2D waveform, got {waveform.shape}")
if waveform.shape[-1] != 3 and waveform.shape[0] == 3:
waveform = waveform.T
if waveform.shape[-1] != 3:
raise ValueError(f"Expected waveform as (T, 3) or (3, T), got {waveform.shape}")
mean = waveform.mean(axis=0, keepdims=True)
std = waveform.std(axis=0, keepdims=True)
std[std < 1e-12] = 1.0
return ((waveform - mean) / std).T.astype(np.float32, copy=False)
class H5TestDataset(Dataset):
def __init__(self, h5_path: Path, data_key: str = "data", max_samples: int = 0, cache_waveforms: bool = False) -> None:
self.h5_path = h5_path
self.data_key = data_key
self._h5: h5py.File | None = None
self._cached_waveforms: np.ndarray | None = None
with h5py.File(h5_path, "r") as f:
self.n = int(f[data_key].shape[0])
self.npts = int(f[data_key].shape[1] if f[data_key].shape[-1] == 3 else f[data_key].shape[-1])
if max_samples > 0:
self.n = min(self.n, int(max_samples))
if cache_waveforms:
waves = np.asarray(f[data_key][: self.n], dtype=np.float32)
if waves.ndim != 3:
raise ValueError(f"Expected waveform data with 3 dims, got {waves.shape}")
if waves.shape[1] == 3:
mean = waves.mean(axis=2, keepdims=True)
std = waves.std(axis=2, keepdims=True)
std[std < 1e-12] = 1.0
waves = (waves - mean) / std
elif waves.shape[-1] == 3:
mean = waves.mean(axis=1, keepdims=True)
std = waves.std(axis=1, keepdims=True)
std[std < 1e-12] = 1.0
waves = ((waves - mean) / std).transpose(0, 2, 1)
else:
raise ValueError(f"Expected waveform shape (N, 3, T) or (N, T, 3), got {waves.shape}")
self._cached_waveforms = np.ascontiguousarray(waves, dtype=np.float32)
if max_samples > 0:
self.n = min(self.n, int(max_samples))
def __len__(self) -> int:
return self.n
def _ensure_h5(self) -> h5py.File:
if self._h5 is None:
self._h5 = h5py.File(self.h5_path, "r")
return self._h5
def __getitem__(self, idx: int) -> dict[str, torch.Tensor | int]:
if self._cached_waveforms is not None:
waveform = self._cached_waveforms[idx]
else:
h5 = self._ensure_h5()
waveform = np.asarray(h5[self.data_key][idx], dtype=np.float32)
waveform = normalize_std(waveform)
return {"waveform": torch.from_numpy(waveform), "index": int(idx)}
def read_h5_phase_labels(h5_path: Path, n: int) -> tuple[np.ndarray, np.ndarray] | None:
with h5py.File(h5_path, "r") as f:
if "labels" not in f:
return None
labels = np.asarray(f["labels"][:n], dtype=np.float64)
if labels.ndim != 2 or labels.shape[1] < 3:
return None
return labels[:, 1], labels[:, 2]
def validate_meta_against_h5_labels(h5_path: Path, meta: pd.DataFrame, n: int) -> None:
h5_labels = read_h5_phase_labels(h5_path, n)
if h5_labels is None or not {"p_index", "s_index"}.issubset(meta.columns):
return
h5_p, h5_s = h5_labels
meta_p = pd.to_numeric(meta["p_index"], errors="coerce").to_numpy(dtype=float)
meta_s = pd.to_numeric(meta["s_index"], errors="coerce").to_numpy(dtype=float)
p_ok = np.isclose(h5_p, meta_p, atol=0.5, equal_nan=False)
s_ok = np.isclose(h5_s, meta_s, atol=0.5, equal_nan=False)
mismatch = int(np.sum(~(p_ok & s_ok)))
if mismatch:
raise ValueError(
f"test_meta is not aligned with HDF5 labels: {mismatch}/{n} rows differ. "
"Use the metadata generated with this HDF5 file, or add an explicit source_index mapping before evaluation."
)
def phase_peak_summary(
prob: np.ndarray,
true_sample: int,
threshold: float,
distance: int,
tolerance_samples: int,
) -> dict[str, Any]:
peaks, props = find_peaks(prob, height=threshold, distance=distance)
if peaks.size == 0:
peak_idx = int(np.argmax(prob)) if prob.size else -1
return {
"pred": peak_idx,
"score": float(prob[peak_idx]) if peak_idx >= 0 else float("nan"),
"detected": False,
"correct": False,
"delta": float(peak_idx - true_sample) if peak_idx >= 0 and true_sample >= 0 else float("nan"),
"delta_recalled": float("nan"),
"peak_count": 0,
"correct_peak_count": 0,
"peak_samples": "",
"peak_scores": "",
"matched_peak_samples": "",
}
heights = props["peak_heights"]
deltas = peaks.astype(np.int64) - int(true_sample)
correct_mask = np.abs(deltas) <= int(tolerance_samples) if true_sample >= 0 else np.zeros(peaks.shape, dtype=bool)
matched_count = 1 if np.any(correct_mask) else 0
if np.any(correct_mask):
correct_indices = np.where(correct_mask)[0]
best = int(correct_indices[np.argmin(np.abs(deltas[correct_indices]))])
else:
best = int(np.argmax(heights))
return {
"pred": int(peaks[best]),
"score": float(heights[best]),
"detected": True,
"correct": bool(np.any(correct_mask)),
"delta": float(deltas[best]) if true_sample >= 0 else float("nan"),
"delta_recalled": float(deltas[best]) if np.any(correct_mask) else float("nan"),
"peak_count": int(peaks.size),
"correct_peak_count": int(matched_count),
"peak_samples": ";".join(str(int(value)) for value in peaks),
"peak_scores": ";".join(f"{float(value):.4f}" for value in heights),
"matched_peak_samples": str(int(peaks[best])) if matched_count else "",
}
def summarize_predictions(df: pd.DataFrame, tolerance_samples: int) -> dict[str, Any]:
summary: dict[str, Any] = {"n": int(len(df)), "tolerance_samples": int(tolerance_samples)}
for phase in ["p", "s"]:
correct_col = f"{phase}_correct"
peak_col = f"{phase}_num_peaks"
correct_peak_col = f"{phase}_correct_peak_count"
delta_col = f"{phase}_delta_recalled_samples"
if correct_col not in df.columns:
continue
correct = df[correct_col].fillna(False).astype(bool).to_numpy()
recall = float(correct.mean()) if len(correct) else np.nan
correct_peak_sum = pd.to_numeric(df[correct_peak_col], errors="coerce").fillna(0).sum()
peak_sum = pd.to_numeric(df[peak_col], errors="coerce").fillna(0).sum()
precision = float(correct_peak_sum / max(float(peak_sum), 1.0))
f1 = 2.0 * recall * precision / (recall + precision) if np.isfinite(recall) and np.isfinite(precision) and (recall + precision) > 0 else np.nan
err = pd.to_numeric(df[delta_col], errors="coerce").to_numpy(dtype=float)
summary[f"{phase}_recall"] = float(recall)
summary[f"{phase}_precision"] = float(precision)
summary[f"{phase}_f1"] = float(f1)
summary[f"{phase}_mae_samples"] = float(np.nanmean(np.abs(err)))
summary[f"{phase}_bias_samples"] = float(np.nanmean(err))
summary[f"{phase}_bias_sd_samples"] = float(np.nanstd(err, ddof=1)) if np.sum(np.isfinite(err)) > 1 else float("nan")
return summary
def print_summary(summary: dict[str, Any], out_dir: Path) -> None:
parts = []
for phase in ["p", "s"]:
if f"{phase}_recall" not in summary:
continue
parts.append(
f"{phase.upper()} R={summary.get(f'{phase}_recall', float('nan')):.3f} "
f"Prec={summary.get(f'{phase}_precision', float('nan')):.3f} "
f"F1={summary.get(f'{phase}_f1', float('nan')):.3f} "
f"bias={summary.get(f'{phase}_bias_samples', float('nan')):.1f}+/-{summary.get(f'{phase}_bias_sd_samples', float('nan')):.1f}"
)
print(f"n={summary.get('n', 0)} | " + " | ".join(parts) + f" | out={out_dir}")
def parse_args() -> argparse.Namespace:
parser = argparse.ArgumentParser(description="Predict Iquique test set with a trained EQTransformer checkpoint.")
parser.add_argument("--checkpoint", default="work/EQT/eqtransformer_original_state_dict.pt", help="Path to best_val_state_dict.pt or another EQT state dict.")
parser.add_argument("--test-h5", default="data/processed/iquique_decay_arias90/test/stead_100hz_60s_test.h5")
parser.add_argument("--test-meta", default="data/processed/iquique_decay_arias90/test/stead_100hz_60s_test_meta.csv")
parser.add_argument("--output-dir", required=True)
parser.add_argument("--batch-size", type=int, default=128)
parser.add_argument("--workers", type=int, default=8)
parser.add_argument("--device", default="cuda")
parser.add_argument("--threshold", type=float, default=0.1)
parser.add_argument("--peak-distance-samples", type=int, default=100)
parser.add_argument("--tolerance-samples", type=int, default=50)
parser.add_argument("--max-samples", type=int, default=0)
parser.add_argument("--phase", choices=["both", "p", "s"], default="both", help="Which phase picks to decode and report.")
parser.add_argument("--save-probs", action="store_true", help="Save detection/P/S probability curves as float16 npz.")
parser.add_argument("--cache-waveforms", action="store_true", help="Load and standardize the whole test HDF5 into memory before prediction.")
return parser.parse_args()
def build_eqtransformer_for_state(npts: int, state: dict[str, torch.Tensor]) -> EQTransformer:
uses_pretrained_lstm = any(".lstm.cell_f." in key or ".lstm.cell_b." in key for key in state)
if uses_pretrained_lstm:
model = EQTransformer.from_pretrained("original")
if getattr(model, "in_samples", npts) != npts:
print(f"WARNING: pretrained original in_samples={getattr(model, 'in_samples', None)}, data npts={npts}.")
return model
return EQTransformer(in_samples=npts)
def main() -> None:
args = parse_args()
checkpoint = Path(args.checkpoint).expanduser().resolve()
test_h5 = Path(args.test_h5).expanduser().resolve()
test_meta = Path(args.test_meta).expanduser().resolve()
out_dir = Path(args.output_dir).expanduser().resolve()
out_dir.mkdir(parents=True, exist_ok=True)
ds = H5TestDataset(test_h5, max_samples=int(args.max_samples), cache_waveforms=bool(args.cache_waveforms))
if args.cache_waveforms:
print(f"Cached test waveforms in memory: n={len(ds)}, npts={ds.npts}")
loader = DataLoader(
ds,
batch_size=int(args.batch_size),
shuffle=False,
num_workers=int(args.workers),
pin_memory=torch.cuda.is_available() and str(args.device).startswith("cuda"),
persistent_workers=int(args.workers) > 0,
)
device = torch.device(str(args.device) if torch.cuda.is_available() else "cpu")
state = torch.load(checkpoint, map_location=device)
model = build_eqtransformer_for_state(ds.npts, state).to(device)
model.load_state_dict(state)
model.eval()
meta = pd.read_csv(test_meta).iloc[: len(ds)].reset_index(drop=True) if test_meta.exists() else pd.DataFrame(index=np.arange(len(ds)))
validate_meta_against_h5_labels(test_h5, meta, len(ds))
rows: list[dict[str, Any]] = []
prob_chunks: list[np.ndarray] = []
with torch.no_grad():
for batch in tqdm(loader, desc="predict", unit="batch"):
x = batch["waveform"].to(device, non_blocking=True)
det, p, s = model(x, logits=True)
probs = torch.stack([torch.sigmoid(det), torch.sigmoid(p), torch.sigmoid(s)], dim=1).cpu().numpy()
indices = batch["index"].cpu().numpy()
if args.save_probs:
prob_chunks.append(probs.astype(np.float16))
for j, idx in enumerate(indices):
det_i, p_i, s_i = probs[j]
p_true = int(round(float(meta.loc[int(idx), "p_index"]))) if "p_index" in meta.columns else -1
s_true = int(round(float(meta.loc[int(idx), "s_index"]))) if "s_index" in meta.columns else -1
row: dict[str, Any] = {
"index": int(idx),
"det_max": float(np.max(det_i)),
}
if args.phase in {"both", "p"}:
p_pick = phase_peak_summary(p_i, p_true, float(args.threshold), int(args.peak_distance_samples), int(args.tolerance_samples))
row.update(
{
"p_pred": int(p_pick["pred"]),
"p_score": float(p_pick["score"]),
"p_num_peaks": int(p_pick["peak_count"]),
"p_correct_peak_count": int(p_pick["correct_peak_count"]),
"p_peak_samples": str(p_pick["peak_samples"]),
"p_peak_scores": str(p_pick["peak_scores"]),
"p_matched_peak_samples": str(p_pick["matched_peak_samples"]),
"p_correct": bool(p_pick["correct"]),
"p_delta_samples": float(p_pick["delta"]),
"p_delta_recalled_samples": float(p_pick["delta_recalled"]),
}
)
if args.phase in {"both", "s"}:
s_pick = phase_peak_summary(s_i, s_true, float(args.threshold), int(args.peak_distance_samples), int(args.tolerance_samples))
row.update(
{
"s_pred": int(s_pick["pred"]),
"s_score": float(s_pick["score"]),
"s_num_peaks": int(s_pick["peak_count"]),
"s_correct_peak_count": int(s_pick["correct_peak_count"]),
"s_peak_samples": str(s_pick["peak_samples"]),
"s_peak_scores": str(s_pick["peak_scores"]),
"s_matched_peak_samples": str(s_pick["matched_peak_samples"]),
"s_correct": bool(s_pick["correct"]),
"s_delta_samples": float(s_pick["delta"]),
"s_delta_recalled_samples": float(s_pick["delta_recalled"]),
}
)
if "sample_id" in meta.columns:
row["sample_id"] = meta.loc[int(idx), "sample_id"]
if "trace_name" in meta.columns:
row["trace_name"] = meta.loc[int(idx), "trace_name"]
if "p_index" in meta.columns and args.phase in {"both", "p"}:
row["p_true"] = p_true
row["p_error_samples"] = row["p_delta_samples"]
if "s_index" in meta.columns and args.phase in {"both", "s"}:
row["s_true"] = s_true
row["s_error_samples"] = row["s_delta_samples"]
rows.append(row)
pred_df = pd.DataFrame(rows)
pred_df.to_csv(out_dir / "eqt_test_predictions.csv", index=False)
summary = summarize_predictions(pred_df, int(args.tolerance_samples))
summary.update(
{
"checkpoint": str(checkpoint),
"test_h5": str(test_h5),
"test_meta": str(test_meta),
"threshold": float(args.threshold),
"peak_distance_samples": int(args.peak_distance_samples),
"phase": args.phase,
}
)
with (out_dir / "eqt_test_summary.json").open("w", encoding="utf-8") as f:
json.dump(summary, f, indent=2)
if args.save_probs and prob_chunks:
np.savez_compressed(out_dir / "eqt_test_probabilities_float16.npz", probs=np.concatenate(prob_chunks, axis=0))
print_summary(summary, out_dir)
if __name__ == "__main__":
main()
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