| |
| """Plot scientific characterization statistics for SeismicX-Cont monitoring regimes. |
| |
| The script derives all quantities from the released annotation JSON and SQLite |
| waveform index. Distance, magnitude, picks-per-event, and inter-event-time |
| statistics use catalog/annotation fields; overlap fractions and the plotted |
| arrival distributions use coverage-qualified P/S labels, where coverage is |
| checked against the SQLite waveform index. |
| """ |
|
|
| from __future__ import annotations |
|
|
| import argparse |
| import json |
| import math |
| import sys |
| from collections import Counter, defaultdict |
| from dataclasses import dataclass |
| from datetime import datetime, timezone |
| from pathlib import Path |
| from typing import Any, Sequence |
|
|
| import matplotlib.pyplot as plt |
| import numpy as np |
|
|
|
|
| ROOT = Path(__file__).resolve().parents[1] |
| SCRIPTS_DIR = ROOT / "scripts" |
| if str(SCRIPTS_DIR) not in sys.path: |
| sys.path.insert(0, str(SCRIPTS_DIR)) |
|
|
| from evaluate_picks import WaveformCoverageIndex |
|
|
| DEFAULT_LABEL_JSON = ROOT / "data" / "label" / "annotations_for_continuous_hdf5.json" |
| DEFAULT_MINI_LABEL_JSON = ROOT / "data" / "label" / "annotations_mini_two_hours.json" |
| DEFAULT_WAVEFORM_DB = ROOT / "data" / "index" / "waveform_index.sqlite" |
| DEFAULT_OUT = ROOT / "figures" / "monitoring_regime_characterization.pdf" |
| DEFAULT_SUMMARY_JSON = ROOT / "essd_scripts" / "outputs" / "monitoring_regime_characterization_summary.json" |
| DEFAULT_SUMMARY_TXT = ROOT / "essd_scripts" / "outputs" / "monitoring_regime_characterization_summary.txt" |
|
|
| PERIOD_LABELS = { |
| "2019": "2019 Ridgecrest week", |
| "2021": "2021 lower-rate monitoring week", |
| } |
| PERIOD_COLORS = { |
| "2019": "#9E3D22", |
| "2021": "#2C6AA6", |
| } |
| PHASE_STYLES = { |
| "P": "-", |
| "S": "--", |
| } |
| OVERLAP_TOLERANCES_S = (10, 15, 30) |
|
|
|
|
| @dataclass |
| class PickRecord: |
| period: str |
| event_id: str |
| event_time_epoch: float |
| magnitude: float | None |
| station_id: str |
| phase: str |
| status: str |
| pick_time_epoch: float |
| distance_km: float |
| covered: bool |
|
|
|
|
| def parse_epoch(value: str) -> float: |
| text = str(value).strip() |
| if text.endswith("Z"): |
| text = text[:-1] |
| return datetime.fromisoformat(text).replace(tzinfo=timezone.utc).timestamp() |
|
|
|
|
| def resolve_input_path(path: Path, alternatives: Sequence[Path]) -> Path: |
| """Prefer the full-release file name, but allow the mini package layout.""" |
| if path.exists(): |
| return path |
| for candidate in alternatives: |
| if candidate.exists(): |
| return candidate |
| return path |
|
|
|
|
| def period_from_day(day_key: str) -> str: |
| if day_key.startswith("2019"): |
| return "2019" |
| if day_key.startswith("2021"): |
| return "2021" |
| return "other" |
|
|
|
|
| def load_annotation(path: Path) -> dict[str, Any]: |
| with path.open("r", encoding="utf-8") as f: |
| return json.load(f) |
|
|
|
|
| def iter_events(annotation: dict[str, Any]): |
| for year in annotation["years"].values(): |
| for day_key, day in year["days"].items(): |
| period = period_from_day(day_key) |
| for event_id, event in day["events"].items(): |
| yield period, day_key, event_id, event |
|
|
|
|
| def collect_records(annotation: dict[str, Any], coverage: WaveformCoverageIndex): |
| picks: list[PickRecord] = [] |
| event_times: dict[str, list[float]] = defaultdict(list) |
| events_by_period: dict[str, set[str]] = defaultdict(set) |
| magnitudes_by_event: dict[str, float | None] = {} |
|
|
| for period, _day_key, event_id, event in iter_events(annotation): |
| if period not in PERIOD_LABELS: |
| continue |
| ev = event.get("event", {}) |
| event_time = parse_epoch(ev.get("event_time")) |
| magnitude = ev.get("magnitude") |
| magnitude_value = float(magnitude) if magnitude is not None else None |
| event_times[period].append(event_time) |
| events_by_period[period].add(event_id) |
| magnitudes_by_event[event_id] = magnitude_value |
|
|
| for station_id, station in event.get("stations", {}).items(): |
| for pick in station.get("picks", []): |
| phase = str(pick.get("phase", "")).upper() |
| if phase not in {"P", "S"}: |
| continue |
| distance = pick.get("distance_km") |
| pick_time = pick.get("time") |
| if distance is None or pick_time is None: |
| continue |
| pick_epoch = parse_epoch(pick_time) |
| picks.append( |
| PickRecord( |
| period=period, |
| event_id=event_id, |
| event_time_epoch=event_time, |
| magnitude=magnitude_value, |
| station_id=str(pick.get("station_id") or station_id), |
| phase=phase, |
| status=str(pick.get("status", "")), |
| pick_time_epoch=pick_epoch, |
| distance_km=float(distance), |
| covered=coverage.has_coverage( |
| str(pick.get("station_id") or station_id), pick_epoch |
| ), |
| ) |
| ) |
|
|
| return picks, event_times, events_by_period, magnitudes_by_event |
|
|
|
|
| def percentile(values: list[float] | np.ndarray, q: float) -> float | None: |
| arr = np.asarray(values, dtype=float) |
| arr = arr[np.isfinite(arr)] |
| if arr.size == 0: |
| return None |
| return float(np.percentile(arr, q)) |
|
|
|
|
| def distribution_summary(values: list[float] | np.ndarray) -> dict[str, float | int | None]: |
| arr = np.asarray(values, dtype=float) |
| arr = arr[np.isfinite(arr)] |
| if arr.size == 0: |
| return {"n": 0, "p10": None, "median": None, "p90": None, "max": None} |
| return { |
| "n": int(arr.size), |
| "p10": float(np.percentile(arr, 10)), |
| "median": float(np.percentile(arr, 50)), |
| "p90": float(np.percentile(arr, 90)), |
| "max": float(np.max(arr)), |
| } |
|
|
|
|
| def picks_per_event(picks: list[PickRecord], events_by_period: dict[str, set[str]]): |
| counts: dict[str, Counter] = {period: Counter() for period in PERIOD_LABELS} |
| for pick in picks: |
| if pick.covered: |
| counts[pick.period][pick.event_id] += 1 |
| out: dict[str, list[int]] = {} |
| for period, event_ids in events_by_period.items(): |
| out[period] = [int(counts[period].get(event_id, 0)) for event_id in sorted(event_ids)] |
| return out |
|
|
|
|
| def inter_event_times(event_times: dict[str, list[float]]) -> dict[str, list[float]]: |
| out = {} |
| for period, times in event_times.items(): |
| times = sorted(times) |
| out[period] = [b - a for a, b in zip(times, times[1:]) if b > a] |
| return out |
|
|
|
|
| def overlap_stats(picks: list[PickRecord]) -> dict[str, Any]: |
| grouped: dict[tuple[str, str, str], list[float]] = defaultdict(list) |
| for pick in picks: |
| if pick.covered: |
| grouped[(pick.period, pick.station_id, pick.phase)].append(pick.pick_time_epoch) |
|
|
| totals: dict[str, Counter] = defaultdict(Counter) |
| by_phase: dict[str, dict[str, Counter]] = defaultdict(lambda: defaultdict(Counter)) |
|
|
| for (period, _station_id, phase), times in grouped.items(): |
| times = sorted(times) |
| n = len(times) |
| for i, value in enumerate(times): |
| prev_dt = value - times[i - 1] if i > 0 else math.inf |
| next_dt = times[i + 1] - value if i < n - 1 else math.inf |
| nearest = min(prev_dt, next_dt) |
| totals[period]["n"] += 1 |
| by_phase[period][phase]["n"] += 1 |
| for tol in OVERLAP_TOLERANCES_S: |
| if nearest <= tol: |
| totals[period][f"within_{tol}s"] += 1 |
| by_phase[period][phase][f"within_{tol}s"] += 1 |
|
|
| result: dict[str, Any] = {} |
| for period in PERIOD_LABELS: |
| n = totals[period]["n"] |
| result[period] = { |
| "all": { |
| "n": int(n), |
| **{ |
| f"within_{tol}s_fraction": (float(totals[period][f"within_{tol}s"]) / n if n else None) |
| for tol in OVERLAP_TOLERANCES_S |
| }, |
| }, |
| "by_phase": {}, |
| } |
| for phase in ("P", "S"): |
| phase_n = by_phase[period][phase]["n"] |
| result[period]["by_phase"][phase] = { |
| "n": int(phase_n), |
| **{ |
| f"within_{tol}s_fraction": ( |
| float(by_phase[period][phase][f"within_{tol}s"]) / phase_n |
| if phase_n else None |
| ) |
| for tol in OVERLAP_TOLERANCES_S |
| }, |
| } |
| return result |
|
|
|
|
| def build_summary( |
| picks: list[PickRecord], |
| event_times: dict[str, list[float]], |
| events_by_period: dict[str, set[str]], |
| ) -> dict[str, Any]: |
| covered = [p for p in picks if p.covered] |
| picks_event = picks_per_event(picks, events_by_period) |
| inter_times = inter_event_times(event_times) |
|
|
| distance_by_period_phase = {} |
| for period in PERIOD_LABELS: |
| distance_by_period_phase[period] = {} |
| for phase in ("P", "S"): |
| distance_by_period_phase[period][phase] = distribution_summary( |
| [p.distance_km for p in covered if p.period == period and p.phase == phase] |
| ) |
|
|
| magnitude_distance = {} |
| for period in PERIOD_LABELS: |
| period_picks = [p for p in covered if p.period == period and p.magnitude is not None] |
| small = [p.distance_km for p in period_picks if p.magnitude is not None and p.magnitude < 2.0] |
| moderate = [p.distance_km for p in period_picks if p.magnitude is not None and p.magnitude >= 3.0] |
| magnitude_distance[period] = { |
| "arrival_points": len(period_picks), |
| "magnitude_min": percentile([p.magnitude for p in period_picks if p.magnitude is not None], 0), |
| "magnitude_median": percentile([p.magnitude for p in period_picks if p.magnitude is not None], 50), |
| "magnitude_max": percentile([p.magnitude for p in period_picks if p.magnitude is not None], 100), |
| "distance_p90_for_m_lt_2": percentile(small, 90), |
| "distance_p90_for_m_ge_3": percentile(moderate, 90), |
| } |
|
|
| status_counts = defaultdict(Counter) |
| phase_counts = defaultdict(Counter) |
| for p in covered: |
| status_counts[p.period][p.status] += 1 |
| phase_counts[p.period][p.phase] += 1 |
|
|
| return { |
| "source_definition": { |
| "distance_and_overlap_labels": "coverage-qualified P/S labels from annotations_for_continuous_hdf5.json with station-time coverage checked in waveform_index.sqlite", |
| "inter_event_times": "all cataloged events in the annotation JSON, separated by monitoring period", |
| "picks_per_event": "coverage-qualified P/S labels counted per cataloged event; events with zero covered arrivals are retained", |
| }, |
| "events": {period: len(events_by_period.get(period, set())) for period in PERIOD_LABELS}, |
| "coverage_qualified_arrivals": { |
| period: { |
| "total": int(sum(phase_counts[period].values())), |
| "by_phase": dict(phase_counts[period]), |
| "by_status": dict(status_counts[period]), |
| } |
| for period in PERIOD_LABELS |
| }, |
| "distance_km": distance_by_period_phase, |
| "magnitude_distance_sampling": magnitude_distance, |
| "picks_per_event": { |
| period: distribution_summary(values) |
| for period, values in picks_event.items() |
| }, |
| "inter_event_time_s": { |
| period: { |
| **distribution_summary(values), |
| "fraction_lt_60s": float(np.mean(np.asarray(values) < 60.0)) if values else None, |
| "fraction_lt_300s": float(np.mean(np.asarray(values) < 300.0)) if values else None, |
| } |
| for period, values in inter_times.items() |
| }, |
| "station_phase_overlap": overlap_stats(picks), |
| } |
|
|
|
|
| def format_value(value: float | int | None, digits: int = 1) -> str: |
| if value is None: |
| return "NA" |
| if isinstance(value, int): |
| return f"{value:,}" |
| return f"{value:.{digits}f}" |
|
|
|
|
| def format_percent(fraction: float | None) -> str: |
| if fraction is None: |
| return "NA" |
| return f"{100.0 * fraction:.1f}%" |
|
|
|
|
| def write_summary_text(summary: dict[str, Any], path: Path) -> None: |
| lines = ["Monitoring-regime characterization summary", ""] |
| for period, label in PERIOD_LABELS.items(): |
| lines.append(label) |
| lines.append(f" events: {summary['events'][period]:,}") |
| cov = summary["coverage_qualified_arrivals"][period] |
| lines.append( |
| f" coverage-qualified arrivals: {cov['total']:,} " |
| f"(P={cov['by_phase'].get('P', 0):,}, S={cov['by_phase'].get('S', 0):,})" |
| ) |
| for phase in ("P", "S"): |
| dist = summary["distance_km"][period][phase] |
| lines.append( |
| f" {phase} distance km: n={dist['n']:,}, " |
| f"median={format_value(dist['median'])}, p90={format_value(dist['p90'])}" |
| ) |
| ppe = summary["picks_per_event"][period] |
| iet = summary["inter_event_time_s"][period] |
| ov = summary["station_phase_overlap"][period]["all"] |
| lines.append( |
| f" picks/event: median={format_value(ppe['median'])}, " |
| f"p90={format_value(ppe['p90'])}, max={format_value(ppe['max'], 0)}" |
| ) |
| lines.append( |
| f" inter-event time s: median={format_value(iet['median'])}, " |
| f"p10={format_value(iet['p10'])}, " |
| f"lt60={format_percent(iet['fraction_lt_60s'])}, " |
| f"lt300={format_percent(iet['fraction_lt_300s'])}" |
| ) |
| lines.append( |
| " station-phase overlap: " |
| + ", ".join( |
| f"within {tol}s={format_percent(ov[f'within_{tol}s_fraction'])}" |
| for tol in OVERLAP_TOLERANCES_S |
| ) |
| ) |
| lines.append("") |
| path.write_text("\n".join(lines), encoding="utf-8") |
|
|
|
|
| def positive_values(values: list[int] | list[float]) -> np.ndarray: |
| arr = np.asarray(values, dtype=float) |
| return arr[np.isfinite(arr) & (arr > 0)] |
|
|
|
|
| def plot_figure( |
| picks: list[PickRecord], |
| event_times: dict[str, list[float]], |
| events_by_period: dict[str, set[str]], |
| summary: dict[str, Any], |
| out: Path, |
| png_out: Path | None = None, |
| ) -> None: |
| plt.rcParams.update( |
| { |
| "font.family": "DejaVu Sans", |
| "font.size": 8, |
| "axes.labelsize": 8, |
| "axes.titlesize": 9, |
| "legend.fontsize": 7, |
| "xtick.labelsize": 7, |
| "ytick.labelsize": 7, |
| "pdf.fonttype": 42, |
| "ps.fonttype": 42, |
| } |
| ) |
| fig = plt.figure(figsize=(7.4, 5.4), constrained_layout=True) |
| gs = fig.add_gridspec(2, 3, width_ratios=[1.0, 1.15, 1.0]) |
| ax_a = fig.add_subplot(gs[0, 0]) |
| ax_b = fig.add_subplot(gs[0, 1:]) |
| ax_c = fig.add_subplot(gs[1, 0]) |
| ax_d = fig.add_subplot(gs[1, 1]) |
| ax_e = fig.add_subplot(gs[1, 2]) |
|
|
| covered = [p for p in picks if p.covered] |
| distances_all = np.asarray([p.distance_km for p in covered], dtype=float) |
| x_max = max(100.0, float(np.nanpercentile(distances_all, 99.2))) |
| bins_dist = np.linspace(0.0, x_max, 50) |
| for period in PERIOD_LABELS: |
| for phase in ("P", "S"): |
| values = np.asarray( |
| [p.distance_km for p in covered if p.period == period and p.phase == phase], |
| dtype=float, |
| ) |
| values = values[np.isfinite(values) & (values <= x_max)] |
| if values.size: |
| ax_a.hist( |
| values, |
| bins=bins_dist, |
| density=True, |
| histtype="step", |
| linewidth=1.35, |
| color=PERIOD_COLORS[period], |
| linestyle=PHASE_STYLES[phase], |
| label=f"{period} {phase}", |
| ) |
| ax_a.set_title("(a) Arrival distance") |
| ax_a.set_xlabel("Event-station distance (km)") |
| ax_a.set_ylabel("Density") |
| ax_a.set_xlim(0, x_max) |
| ax_a.legend(frameon=False, ncol=1, loc="upper right") |
|
|
| rng = np.random.default_rng(20260531) |
| for period in PERIOD_LABELS: |
| period_points = [ |
| p for p in covered |
| if p.period == period and p.magnitude is not None and p.distance_km > 0 |
| ] |
| if len(period_points) > 60000: |
| idx = rng.choice(len(period_points), size=60000, replace=False) |
| period_points = [period_points[i] for i in idx] |
| ax_b.scatter( |
| [p.distance_km for p in period_points], |
| [p.magnitude for p in period_points], |
| s=4, |
| alpha=0.18 if period == "2019" else 0.35, |
| color=PERIOD_COLORS[period], |
| edgecolors="none", |
| rasterized=True, |
| label=period, |
| ) |
| ax_b.set_xscale("log") |
| ax_b.set_title("(b) Magnitude-distance sampling") |
| ax_b.set_xlabel("Event-station distance (km, log scale)") |
| ax_b.set_ylabel("Event magnitude") |
| ax_b.grid(True, which="major", color="#d9d9d9", linewidth=0.5) |
| ax_b.legend( |
| frameon=True, |
| facecolor="white", |
| edgecolor="none", |
| framealpha=0.88, |
| loc="upper left", |
| ) |
|
|
| ppe = picks_per_event(picks, events_by_period) |
| max_ppe = max(max(values) for values in ppe.values() if values) |
| bins_ppe = np.unique(np.logspace(0, math.log10(max(2, max_ppe)), 32).astype(int)) |
| for period in PERIOD_LABELS: |
| values = positive_values(ppe.get(period, [])) |
| ax_c.hist( |
| values, |
| bins=bins_ppe, |
| histtype="stepfilled", |
| alpha=0.22, |
| color=PERIOD_COLORS[period], |
| ) |
| ax_c.hist( |
| values, |
| bins=bins_ppe, |
| histtype="step", |
| linewidth=1.2, |
| color=PERIOD_COLORS[period], |
| label=period, |
| ) |
| ax_c.set_xscale("log") |
| ax_c.set_yscale("log") |
| ax_c.set_title("(c) Arrivals per event") |
| ax_c.set_xlabel("Coverage-qualified P/S arrivals") |
| ax_c.set_ylabel("Events") |
| ax_c.legend( |
| frameon=True, |
| facecolor="white", |
| edgecolor="none", |
| framealpha=0.88, |
| loc="upper left", |
| ) |
|
|
| iet = inter_event_times(event_times) |
| all_dt_min = positive_values([dt / 60.0 for values in iet.values() for dt in values]) |
| bins_dt = np.logspace( |
| math.log10(max(1e-2, np.nanmin(all_dt_min))), |
| math.log10(max(1.0, np.nanmax(all_dt_min))), |
| 36, |
| ) |
| for period in PERIOD_LABELS: |
| values = positive_values([dt / 60.0 for dt in iet.get(period, [])]) |
| ax_d.hist( |
| values, |
| bins=bins_dt, |
| histtype="stepfilled", |
| alpha=0.22, |
| color=PERIOD_COLORS[period], |
| ) |
| ax_d.hist( |
| values, |
| bins=bins_dt, |
| histtype="step", |
| linewidth=1.2, |
| color=PERIOD_COLORS[period], |
| label=period, |
| ) |
| ax_d.set_xscale("log") |
| ax_d.set_yscale("log") |
| ax_d.set_title("(d) Inter-event time") |
| ax_d.set_xlabel("Time to next event (min, log scale)") |
| ax_d.set_ylabel("Event pairs") |
|
|
| x = np.arange(len(OVERLAP_TOLERANCES_S)) |
| width = 0.36 |
| for offset, period in zip((-width / 2, width / 2), PERIOD_LABELS): |
| values = [ |
| 100.0 * (summary["station_phase_overlap"][period]["all"][f"within_{tol}s_fraction"] or 0.0) |
| for tol in OVERLAP_TOLERANCES_S |
| ] |
| ax_e.bar( |
| x + offset, |
| values, |
| width=width, |
| color=PERIOD_COLORS[period], |
| label=period, |
| alpha=0.86, |
| ) |
| ax_e.set_xticks(x, [f"+/-{tol} s" for tol in OVERLAP_TOLERANCES_S]) |
| ax_e.set_ylim(0, 100) |
| ax_e.set_title("(e) Station-phase overlap") |
| ax_e.set_xlabel("Time window") |
| ax_e.set_ylabel("Arrivals with neighbor (%)") |
| ax_e.legend(frameon=False, loc="upper left") |
|
|
| for ax in (ax_a, ax_c, ax_d, ax_e): |
| ax.grid(True, axis="y", color="#e1e1e1", linewidth=0.45) |
| for ax in (ax_a, ax_b, ax_c, ax_d, ax_e): |
| for spine in ("top", "right"): |
| ax.spines[spine].set_visible(False) |
|
|
| out.parent.mkdir(parents=True, exist_ok=True) |
| fig.savefig(out, bbox_inches="tight") |
| if png_out is not None: |
| fig.savefig(png_out, dpi=240, bbox_inches="tight") |
| plt.close(fig) |
|
|
|
|
| def main() -> None: |
| parser = argparse.ArgumentParser(description=__doc__) |
| parser.add_argument("--label-json", type=Path, default=DEFAULT_LABEL_JSON) |
| parser.add_argument("--waveform-db", type=Path, default=DEFAULT_WAVEFORM_DB) |
| parser.add_argument("--out", type=Path, default=DEFAULT_OUT) |
| parser.add_argument("--png-out", type=Path, default=None) |
| parser.add_argument("--summary-json", type=Path, default=DEFAULT_SUMMARY_JSON) |
| parser.add_argument("--summary-txt", type=Path, default=DEFAULT_SUMMARY_TXT) |
| args = parser.parse_args() |
|
|
| args.label_json = resolve_input_path(args.label_json, [DEFAULT_MINI_LABEL_JSON]) |
| annotation = load_annotation(args.label_json) |
| coverage = WaveformCoverageIndex( |
| args.waveform_db, channel_families=("HH", "BH", "EH", "HN") |
| ) |
| picks, event_times, events_by_period, _magnitudes = collect_records(annotation, coverage) |
| summary = build_summary(picks, event_times, events_by_period) |
|
|
| args.summary_json.parent.mkdir(parents=True, exist_ok=True) |
| args.summary_json.write_text(json.dumps(summary, indent=2, sort_keys=True), encoding="utf-8") |
| write_summary_text(summary, args.summary_txt) |
|
|
| png_out = args.png_out |
| if png_out is None and args.out.suffix.lower() == ".pdf": |
| png_out = args.out.with_suffix(".png") |
| plot_figure(picks, event_times, events_by_period, summary, args.out, png_out=png_out) |
| print(f"[OK] wrote {args.out}") |
| print(f"[OK] wrote {args.summary_json}") |
| print(f"[OK] wrote {args.summary_txt}") |
|
|
|
|
| if __name__ == "__main__": |
| main() |
|
|
