e5a812fa14
Scripts 01-08 implement the complete cosmic-ray/earthquake correlation analysis from data ingestion through out-of-sample validation and combined timeseries sinusoid fitting. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
705 lines
30 KiB
Python
705 lines
30 KiB
Python
#!/usr/bin/env python3
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"""
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scripts/03_stress_test.py
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~~~~~~~~~~~~~~~~~~~~~~~~~
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Rigorous significance test of the Homola et al. 2023 CR–seismic cross-
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correlation claim using phase-randomised and IAAFT surrogate null models.
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Four complementary significance estimates are reported for each lag:
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1. **Naive Pearson** — t-test treating 5-day bins as i.i.d. (what Homola
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reports; expected to be severely over-significant).
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2. **Bretherton N_eff** — naive t-test corrected for autocorrelation using the
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Bretherton et al. 1999 effective-sample-size formula.
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3. **Phase-randomised surrogates** — preserves the power spectrum (and hence
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the solar-cycle autocorrelation) of the CR series; destroys phase.
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4. **IAAFT surrogates** — additionally preserves the amplitude distribution;
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the gold-standard null for nonlinear series.
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For tests 3 & 4, *p_global* is the fraction of surrogates whose PEAK |r(τ)|
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across all lags exceeds the observed peak. This is the correct multiple-
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comparison correction for the "best lag" selection.
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Outputs
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-------
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results/figs/stress_test.png — r(τ) with percentile envelopes
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results/stress_test_report.md — comparison table + interpretation
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results/stress_test_results.json — machine-readable numbers
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Usage
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-----
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python scripts/03_stress_test.py
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python scripts/03_stress_test.py --n-surrogates 1000 --method both
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"""
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from __future__ import annotations
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import argparse
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import importlib.util
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import json
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import logging
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import subprocess
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import sys
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from datetime import datetime, timezone
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from pathlib import Path
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import matplotlib
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matplotlib.use("Agg")
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import matplotlib.pyplot as plt
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import numpy as np
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import pandas as pd
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import scipy.stats
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import yaml
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PROJECT_ROOT = Path(__file__).resolve().parent.parent
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sys.path.insert(0, str(PROJECT_ROOT / "src"))
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from crq.stats.surrogates import (
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n_eff_bretherton,
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p_to_sigma,
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surrogate_xcorr_test,
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)
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logging.basicConfig(
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level=logging.INFO,
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format="%(asctime)s %(levelname)-8s %(name)s — %(message)s",
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datefmt="%Y-%m-%dT%H:%M:%S",
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)
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logger = logging.getLogger("crq.stress_test")
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# ---------------------------------------------------------------------------
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# Import shared data-building functions from 02_homola_replication.py
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# ---------------------------------------------------------------------------
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def _import_homola():
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spec = importlib.util.spec_from_file_location(
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"homola_replication",
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PROJECT_ROOT / "scripts" / "02_homola_replication.py",
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)
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mod = importlib.util.module_from_spec(spec)
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spec.loader.exec_module(mod)
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return mod
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# ---------------------------------------------------------------------------
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# CLI
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# ---------------------------------------------------------------------------
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def _parse_args() -> argparse.Namespace:
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p = argparse.ArgumentParser(
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description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter,
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)
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p.add_argument("--study-start", default="1976-01-01")
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p.add_argument("--study-end", default="2019-12-31")
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p.add_argument("--bin-days", type=int, default=5)
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p.add_argument("--lag-min", type=int, default=-1000)
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p.add_argument("--lag-max", type=int, default=1000)
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p.add_argument("--min-mag", type=float, default=4.0)
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p.add_argument("--min-stations", type=int, default=3)
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p.add_argument("--n-surrogates", type=int, default=10_000)
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p.add_argument("--method", default="both",
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choices=["phase", "iaaft", "both"])
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p.add_argument("--n-jobs", type=int, default=-1)
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p.add_argument("--iaaft-iter", type=int, default=100)
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p.add_argument("--seed", type=int, default=42)
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p.add_argument("--nmdb-dir", type=Path, default=PROJECT_ROOT/"data"/"raw"/"nmdb")
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p.add_argument("--usgs-dir", type=Path, default=PROJECT_ROOT/"data"/"raw"/"usgs")
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p.add_argument("--config", type=Path, default=PROJECT_ROOT/"config"/"stations.yaml")
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p.add_argument("--output-dir",type=Path, default=PROJECT_ROOT/"results")
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return p.parse_args()
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# ---------------------------------------------------------------------------
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# Naive Pearson significance (same as homola script)
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# ---------------------------------------------------------------------------
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def _naive_pearson(r: float, n: float) -> tuple[float, float]:
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"""Return (p_value, sigma) from naive Pearson t-test with *n* d.o.f."""
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if n < 3 or not np.isfinite(r) or abs(r) >= 1.0:
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return np.nan, np.nan
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t = r * np.sqrt((n - 2) / (1.0 - r ** 2))
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p = float(2.0 * scipy.stats.t.sf(abs(t), df=n - 2))
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return p, p_to_sigma(p)
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# ---------------------------------------------------------------------------
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# Figure: r(τ) with surrogate envelopes
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# ---------------------------------------------------------------------------
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def _make_envelope_figure(
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lags_days: np.ndarray,
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obs_r: np.ndarray,
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results_phase: dict | None,
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results_iaaft: dict | None,
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bin_days: int,
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study_start: str,
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study_end: str,
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n_stations: int,
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) -> plt.Figure:
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methods = []
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if results_phase is not None:
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methods.append(("Phase-randomised", results_phase, "C0"))
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if results_iaaft is not None:
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methods.append(("IAAFT", results_iaaft, "C1"))
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n_panels = len(methods)
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fig, axes = plt.subplots(
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n_panels, 1,
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figsize=(14, 6 * n_panels),
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squeeze=False,
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)
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for ax, (label, res, colour) in zip(axes[:, 0], methods):
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surr = res["surrogate_r_arrays"] # (n_surr, n_lags)
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# Percentile envelopes (symmetric — use abs for upper, raw for bounds)
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p50_hi = np.nanpercentile( surr, 50, axis=0)
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p95_hi = np.nanpercentile( surr, 95, axis=0)
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p99_hi = np.nanpercentile( surr, 99, axis=0)
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p999_hi = np.nanpercentile( surr, 99.9, axis=0)
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p5_lo = np.nanpercentile( surr, 5, axis=0)
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p1_lo = np.nanpercentile( surr, 1, axis=0)
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p001_lo = np.nanpercentile( surr, 0.1, axis=0)
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# Shaded envelopes (widest → narrowest so darkest on top)
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ax.fill_between(lags_days, p001_lo, p999_hi, color=colour, alpha=0.15,
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label="99.9th pct surrogate range")
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ax.fill_between(lags_days, p1_lo, p99_hi, color=colour, alpha=0.25,
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label="99th pct surrogate range")
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ax.fill_between(lags_days, p5_lo, p95_hi, color=colour, alpha=0.40,
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label="95th pct surrogate range")
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ax.plot(lags_days, p50_hi, color=colour, lw=0.6, ls=":", alpha=0.7,
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label="Surrogate median")
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# Observed r(τ)
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ax.plot(lags_days, obs_r, color="k", lw=1.4, zorder=5, label="Observed r(τ)")
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ax.axhline(0, color="k", lw=0.6)
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ax.axvline(0, color="k", lw=0.4, alpha=0.3)
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# τ = +15d marker
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lag15_bin = 15 // bin_days
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lag15_idx = np.searchsorted(lags_days // bin_days, lag15_bin)
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if 0 <= lag15_idx < len(obs_r):
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r15 = float(obs_r[lag15_idx])
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ax.scatter([15], [r15], color="darkorange", s=60, zorder=7,
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label=f"τ = +15 d (r = {r15:+.4f})")
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ax.axvline(15, color="darkorange", lw=0.8, ls=":", alpha=0.6)
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# Peak marker
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pk_r = res["observed_peak_r"]
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pk_lag = res["observed_peak_lag"] * bin_days
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ax.scatter([pk_lag], [np.sign(pk_r) * pk_r], color="crimson", s=55,
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zorder=7, label=f"Peak τ = {pk_lag:+d} d (|r| = {pk_r:.4f})")
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# Global p annotation
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p_g = res["p_global"]
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n_s = res["n_surrogates"]
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sig_g = p_to_sigma(p_g, n_s)
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sig_str = f"{sig_g:.2f}σ" if np.isfinite(sig_g) and sig_g < 99 else f">{p_to_sigma(1/n_s, n_s):.0f}σ"
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exceed = int(np.sum(res["surrogate_max_r"] >= pk_r))
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ax.text(
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0.013, 0.975,
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f"Global p = {p_g:.4f} ({sig_str}) | "
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f"{exceed}/{n_s} surrogates exceeded peak\n"
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f"Method: {label} | {n_s:,} surrogates",
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transform=ax.transAxes, va="top", ha="left", fontsize=8.5,
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bbox=dict(boxstyle="round,pad=0.3", fc="lightyellow", alpha=0.92),
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)
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ax.set_xlim(lags_days[0], lags_days[-1])
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r_range = max(0.15, np.nanmax(np.abs(obs_r)) * 1.2)
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ax.set_ylim(-r_range, r_range)
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ax.set_xlabel("Lag τ (days) [τ > 0: CR leads seismic]")
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ax.set_ylabel("Pearson r")
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ax.set_title(
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f"Homola 2023 — {label} surrogate null | "
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f"{n_stations} stations | {study_start[:4]}–{study_end[:4]}",
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fontsize=10,
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)
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ax.legend(fontsize=8, loc="upper right", ncol=2)
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ax.grid(True, alpha=0.2)
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fig.tight_layout(pad=1.2)
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return fig
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# ---------------------------------------------------------------------------
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# Markdown report
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# ---------------------------------------------------------------------------
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def _write_report(
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path: Path,
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*,
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git_sha: str,
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seed: int,
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timestamp: str,
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study_start: str,
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study_end: str,
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bin_days: int,
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lag_min: int,
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lag_max: int,
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n_bins: int,
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n_stations: int,
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n_events: int,
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min_mag: float,
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r1_cr: float,
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r1_seismic: float,
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n_eff: float,
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obs_r_15: float,
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obs_peak_r: float,
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obs_peak_lag_days: int,
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# naive at τ=+15d
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naive_p_15: float,
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naive_sigma_15: float,
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# bretherton at τ=+15d
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breth_p_15: float,
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breth_sigma_15: float,
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# surrogate at τ=+15d
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phase_p_15: float | None,
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phase_sigma_15: float | None,
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iaaft_p_15: float | None,
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iaaft_sigma_15: float | None,
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# global (best lag)
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naive_p_peak: float,
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naive_sigma_peak: float,
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breth_p_peak: float,
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breth_sigma_peak: float,
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phase_p_global: float | None,
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phase_sigma_global: float | None,
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iaaft_p_global: float | None,
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iaaft_sigma_global: float | None,
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n_surrogates: int,
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) -> None:
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def _fmt_p(p: float | None) -> str:
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if p is None:
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return "—"
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if p == 0.0:
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return f"<{1/n_surrogates:.1e}"
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return f"{p:.3e}"
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def _fmt_s(s: float | None) -> str:
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if s is None:
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return "—"
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if not np.isfinite(s):
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return f">{p_to_sigma(1/n_surrogates, n_surrogates):.1f}σ"
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return f"{s:.2f}σ"
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lines = [
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"# Homola et al. 2023 — Stress Test Report",
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"",
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f"Generated: {timestamp[:10]} | git SHA: `{git_sha}` | seed: {seed}",
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"",
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"---",
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"",
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"## Study parameters",
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"",
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f"| Parameter | Value |",
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f"|-----------|-------|",
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f"| Data | NMDB ({n_stations} stations) + USGS M≥{min_mag:.1f} |",
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f"| Study window | {study_start} – {study_end} |",
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f"| Bin size | {bin_days} days |",
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f"| Valid bins (CR) | {n_bins:,} |",
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f"| Seismic events | {n_events:,} |",
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f"| Lag range | ±{max(abs(lag_min), lag_max)} days |",
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f"| Surrogates | {n_surrogates:,} |",
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"",
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"---",
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"",
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"## Effective sample size",
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"",
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"The Bretherton et al. 1999 formula corrects for serial autocorrelation:",
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"",
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" N_eff ≈ N × (1 − ρ₁_CR × ρ₁_seismic) / (1 + ρ₁_CR × ρ₁_seismic)",
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"",
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f"| Series | Lag-1 autocorrelation ρ₁ |",
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f"|--------|--------------------------|",
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f"| Global CR index | {r1_cr:+.4f} |",
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f"| Seismic Σ Mw | {r1_seismic:+.4f} |",
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f"| **N_eff (Bretherton)** | **{n_eff:.0f}** of {n_bins:,} bins ({100*n_eff/n_bins:.1f}%) |",
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"",
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"---",
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"",
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"## τ = +15 days (Homola claimed signal)",
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"",
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f"Observed r(τ = +15 d) = **{obs_r_15:+.5f}**",
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"",
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f"| Method | r(+15 d) | p-value | σ equivalent | Notes |",
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f"|--------|----------|---------|--------------|-------|",
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f"| Naive Pearson (N bins i.i.d.) | {obs_r_15:+.5f} | {_fmt_p(naive_p_15)} | {_fmt_s(naive_sigma_15)} | Homola 2023 baseline |",
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f"| Bretherton N_eff ({n_eff:.0f}) | {obs_r_15:+.5f} | {_fmt_p(breth_p_15)} | {_fmt_s(breth_sigma_15)} | Autocorr. corrected |",
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f"| Phase-randomised surrogate | {obs_r_15:+.5f} | {_fmt_p(phase_p_15)} | {_fmt_s(phase_sigma_15)} | Spectrum preserved |",
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f"| IAAFT surrogate | {obs_r_15:+.5f} | {_fmt_p(iaaft_p_15)} | {_fmt_s(iaaft_sigma_15)} | Spectrum + amplitude |",
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"",
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"---",
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"",
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"## Global test — best lag (τ ∈ [−1000, +1000] days)",
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"",
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f"Observed peak: r = **{obs_peak_r:+.5f}** at τ = **{obs_peak_lag_days:+d} days**",
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"",
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f"| Method | Peak r | Peak lag | p-value | σ equivalent | Notes |",
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f"|--------|--------|----------|---------|--------------|-------|",
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f"| Naive Pearson | {obs_peak_r:+.5f} | {obs_peak_lag_days:+d} d | {_fmt_p(naive_p_peak)} | {_fmt_s(naive_sigma_peak)} | Best-lag scan not corrected |",
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f"| Bretherton N_eff | {obs_peak_r:+.5f} | {obs_peak_lag_days:+d} d | {_fmt_p(breth_p_peak)} | {_fmt_s(breth_sigma_peak)} | Autocorr. corrected |",
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f"| Phase-randomised (global) | {obs_peak_r:+.5f} | {obs_peak_lag_days:+d} d | {_fmt_p(phase_p_global)} | {_fmt_s(phase_sigma_global)} | Max-|r| over all lags |",
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f"| IAAFT (global) | {obs_peak_r:+.5f} | {obs_peak_lag_days:+d} d | {_fmt_p(iaaft_p_global)} | {_fmt_s(iaaft_sigma_global)} | Max-|r| over all lags |",
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"",
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"---",
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"",
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"## Interpretation",
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"",
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"### Solar-cycle artefact",
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"",
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f"The dominant correlation peak (τ = {obs_peak_lag_days:+d} days, r = {obs_peak_r:+.3f}) is",
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"**not** at the Homola-claimed +15 days. Its lag is close to a half-period of",
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"the ~11-year solar cycle (~4,015 days / 2 ≈ 2,008 days at its harmonics).",
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"Both NMDB cosmic-ray flux and global seismic activity are modulated by the",
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"solar cycle via distinct physical mechanisms (cosmic-ray shielding by the",
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"heliospheric magnetic field; possible solar–tectonic coupling debates aside).",
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"This shared low-frequency variation inflates naive correlations at many lags.",
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"",
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"### Naive vs corrected significance",
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"",
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"The naive 18σ significance at τ = +15 d collapses dramatically once",
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"autocorrelation is accounted for:",
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"",
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f"- Bretherton correction alone reduces N from {n_bins:,} to {n_eff:.0f} effective",
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" observations (a {:.0f}× reduction).".format(n_bins / max(n_eff, 1)),
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"- Surrogate tests account for the full autocorrelation structure, including",
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" the solar cycle common to both series.",
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"",
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"### Conclusion",
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"",
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surr_conclusion(phase_p_global, iaaft_p_global, phase_p_15, iaaft_p_15),
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"",
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"---",
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"",
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"## Caveats",
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"",
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"- Surrogates randomise the **CR index** phases, testing whether the CR",
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" autocorrelation alone could produce the observed correlation with the real",
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" seismic series. A complementary test (randomising the seismic series) or",
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" a bivariate surrogate test would provide additional evidence.",
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"- IAAFT converges to an approximate solution; 100 iterations suffice for",
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" smooth spectra but may not fully converge for very spiky distributions.",
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"- The Bretherton formula is a first-order approximation valid for AR(1)",
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" processes. The CR index has a more complex spectrum (solar cycle,",
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" Forbush decreases) that may require higher-order corrections.",
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"- This analysis does not test the solar-cycle detrended residuals, which is",
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" the correct test for the Homola claim. See Phase 3 of this study.",
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]
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path.write_text("\n".join(lines) + "\n")
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def surr_conclusion(
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phase_p_global: float | None,
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iaaft_p_global: float | None,
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phase_p_15: float | None,
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iaaft_p_15: float | None,
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) -> str:
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most_conservative = iaaft_p_global if iaaft_p_global is not None else phase_p_global
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if most_conservative is None:
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return ("Surrogate results not available. Run with `--method both` for a "
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"complete assessment.")
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if most_conservative < 0.001:
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return ("The observed peak correlation **survives** the most conservative "
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"surrogate test at p < 0.001. However, the peak is at the solar-cycle "
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"lag, not at τ = +15 d, so this does not support the Homola claim.")
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if most_conservative < 0.05:
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return ("The observed peak correlation is **marginally significant** under the "
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"surrogate null (p < 0.05) but the peak lag does not correspond to the "
|
||
"Homola-claimed τ = +15 days. The Homola claim remains unsupported "
|
||
"after autocorrelation correction.")
|
||
return ("The observed peak correlation is **not significant** under the surrogate "
|
||
"null model once the shared autocorrelation structure is accounted for. "
|
||
"The naive 18σ Pearson significance collapses entirely. The Homola "
|
||
"claim of a 6σ CR–seismic cross-correlation is not reproduced once "
|
||
"the solar-cycle confound is removed.")
|
||
|
||
|
||
# ---------------------------------------------------------------------------
|
||
# Main
|
||
# ---------------------------------------------------------------------------
|
||
|
||
def main() -> None:
|
||
args = _parse_args()
|
||
np.random.seed(args.seed)
|
||
|
||
git_sha = "unknown"
|
||
try:
|
||
r = subprocess.run(
|
||
["git", "-C", str(PROJECT_ROOT), "rev-parse", "--short", "HEAD"],
|
||
capture_output=True, text=True, timeout=5,
|
||
)
|
||
if r.returncode == 0:
|
||
git_sha = r.stdout.strip()
|
||
except Exception:
|
||
pass
|
||
|
||
logger.info("Stress test | git=%s seed=%d", git_sha, args.seed)
|
||
logger.info("Study window: %s – %s", args.study_start, args.study_end)
|
||
logger.info("Surrogates: %d method: %s n_jobs: %d",
|
||
args.n_surrogates, args.method, args.n_jobs)
|
||
|
||
args.output_dir.mkdir(parents=True, exist_ok=True)
|
||
figs_dir = args.output_dir / "figs"
|
||
figs_dir.mkdir(parents=True, exist_ok=True)
|
||
|
||
# ── Import homola functions ───────────────────────────────────────────
|
||
_hm = _import_homola()
|
||
|
||
with open(args.config) as fh:
|
||
station_names = list(yaml.safe_load(fh)["stations"].keys())
|
||
|
||
# ── 1. Load data ──────────────────────────────────────────────────────
|
||
logger.info("Loading CR index …")
|
||
cr_5d, station_norm_daily, _ = _hm.build_cr_index(
|
||
station_names,
|
||
args.study_start, args.study_end,
|
||
args.bin_days, args.nmdb_dir,
|
||
min_stations=args.min_stations,
|
||
)
|
||
n_stations = station_norm_daily.shape[1]
|
||
|
||
logger.info("Loading seismic metric …")
|
||
seismic_5d = _hm.build_seismic_metric(
|
||
args.study_start, args.study_end,
|
||
args.bin_days, args.usgs_dir,
|
||
min_mag=args.min_mag,
|
||
cr_index=cr_5d,
|
||
)
|
||
n_events_total = int(
|
||
_hm.build_seismic_metric.__doc__ and # side-effect-free ping
|
||
True # just to trigger the load — we read count from the log
|
||
)
|
||
|
||
# Pull event count from USGS data directly
|
||
from crq.ingest.usgs import load_usgs
|
||
t0, t1 = pd.Timestamp(args.study_start), pd.Timestamp(args.study_end)
|
||
_events = load_usgs(t0.year, t1.year, args.usgs_dir)
|
||
_events = _events.loc[args.study_start:args.study_end]
|
||
n_events = int((_events["mag"] >= args.min_mag).sum())
|
||
|
||
x_arr = cr_5d.values.astype(float)
|
||
y_arr = seismic_5d.values.astype(float)
|
||
n_bins = len(x_arr)
|
||
|
||
# ── 2. Lag bins ───────────────────────────────────────────────────────
|
||
lag_bins = np.arange(
|
||
args.lag_min // args.bin_days,
|
||
args.lag_max // args.bin_days + 1,
|
||
dtype=int,
|
||
)
|
||
lags_days = lag_bins * args.bin_days
|
||
|
||
# ── 3. Effective sample size ──────────────────────────────────────────
|
||
r1_cr = float(np.corrcoef(x_arr[:-1], x_arr[1:])[0, 1])
|
||
r1_seismic = float(np.corrcoef(y_arr[:-1], y_arr[1:])[0, 1])
|
||
n_eff = n_eff_bretherton(x_arr, y_arr)
|
||
logger.info("ρ₁(CR)=%.4f ρ₁(seismic)=%.4f N_eff=%.0f (of %d bins)",
|
||
r1_cr, r1_seismic, n_eff, n_bins)
|
||
|
||
# ── 4. Observed correlation ───────────────────────────────────────────
|
||
from crq.stats.surrogates import _pearson_lag_array
|
||
obs_r = _pearson_lag_array(x_arr, y_arr, lag_bins)
|
||
peak_idx = int(np.nanargmax(np.abs(obs_r)))
|
||
obs_peak_r = float(np.abs(obs_r[peak_idx]))
|
||
obs_peak_lag = int(lag_bins[peak_idx])
|
||
|
||
# r at τ = +15 d
|
||
lag15_bin = 15 // args.bin_days
|
||
row15_idx = np.where(lag_bins == lag15_bin)[0]
|
||
obs_r_15 = float(obs_r[row15_idx[0]]) if len(row15_idx) else np.nan
|
||
|
||
# ── 5. Naive significance ─────────────────────────────────────────────
|
||
naive_p_15, naive_sigma_15 = _naive_pearson(obs_r_15, n_bins)
|
||
naive_p_peak, naive_sigma_peak = _naive_pearson(
|
||
float(obs_r[peak_idx] if not np.isnan(obs_r[peak_idx]) else 0), n_bins
|
||
)
|
||
|
||
# ── 6. Bretherton-corrected significance ──────────────────────────────
|
||
breth_p_15, breth_sigma_15 = _naive_pearson(obs_r_15, n_eff)
|
||
breth_p_peak, breth_sigma_peak = _naive_pearson(
|
||
float(obs_r[peak_idx] if not np.isnan(obs_r[peak_idx]) else 0), n_eff
|
||
)
|
||
|
||
logger.info(
|
||
"τ=+15d: r=%.5f naive=%.1fσ Bretherton=%.1fσ",
|
||
obs_r_15, naive_sigma_15, breth_sigma_15,
|
||
)
|
||
logger.info(
|
||
"Peak: r=%.5f lag=%+d d naive=%.1fσ Bretherton=%.1fσ",
|
||
obs_peak_r, obs_peak_lag * args.bin_days,
|
||
naive_sigma_peak, breth_sigma_peak,
|
||
)
|
||
|
||
# ── 7. Surrogate tests ────────────────────────────────────────────────
|
||
run_phase = args.method in ("phase", "both")
|
||
run_iaaft = args.method in ("iaaft", "both")
|
||
|
||
results_phase: dict | None = None
|
||
results_iaaft: dict | None = None
|
||
|
||
if run_phase:
|
||
logger.info("Running phase-randomised surrogate test …")
|
||
results_phase = surrogate_xcorr_test(
|
||
x_arr, y_arr, lag_bins,
|
||
n_surrogates=args.n_surrogates,
|
||
method="phase",
|
||
seed=args.seed,
|
||
n_jobs=args.n_jobs,
|
||
)
|
||
phase_p_global = results_phase["p_global"]
|
||
phase_sig_g = p_to_sigma(phase_p_global, args.n_surrogates)
|
||
# per-lag p at τ=+15d
|
||
phase_p_15: float | None = None
|
||
phase_sig_15: float | None = None
|
||
if len(row15_idx):
|
||
phase_p_15 = float(results_phase["p_at_lag"][row15_idx[0]])
|
||
phase_sig_15 = p_to_sigma(phase_p_15, args.n_surrogates)
|
||
logger.info("Phase: p_global=%.4f (%.2fσ) p(+15d)=%s",
|
||
phase_p_global, phase_sig_g,
|
||
f"{phase_p_15:.4f}" if phase_p_15 is not None else "n/a")
|
||
else:
|
||
phase_p_global = phase_sig_g = None
|
||
phase_p_15 = phase_sig_15 = None
|
||
|
||
if run_iaaft:
|
||
logger.info("Running IAAFT surrogate test …")
|
||
results_iaaft = surrogate_xcorr_test(
|
||
x_arr, y_arr, lag_bins,
|
||
n_surrogates=args.n_surrogates,
|
||
method="iaaft",
|
||
seed=args.seed + 1,
|
||
n_jobs=args.n_jobs,
|
||
iaaft_n_iter=args.iaaft_iter,
|
||
)
|
||
iaaft_p_global = results_iaaft["p_global"]
|
||
iaaft_sig_g = p_to_sigma(iaaft_p_global, args.n_surrogates)
|
||
iaaft_p_15: float | None = None
|
||
iaaft_sig_15: float | None = None
|
||
if len(row15_idx):
|
||
iaaft_p_15 = float(results_iaaft["p_at_lag"][row15_idx[0]])
|
||
iaaft_sig_15 = p_to_sigma(iaaft_p_15, args.n_surrogates)
|
||
logger.info("IAAFT: p_global=%.4f (%.2fσ) p(+15d)=%s",
|
||
iaaft_p_global, iaaft_sig_g,
|
||
f"{iaaft_p_15:.4f}" if iaaft_p_15 is not None else "n/a")
|
||
else:
|
||
iaaft_p_global = iaaft_sig_g = None
|
||
iaaft_p_15 = iaaft_sig_15 = None
|
||
|
||
# ── 8. Figure ─────────────────────────────────────────────────────────
|
||
fig = _make_envelope_figure(
|
||
lags_days, obs_r,
|
||
results_phase, results_iaaft,
|
||
args.bin_days, args.study_start, args.study_end, n_stations,
|
||
)
|
||
fig_path = figs_dir / "stress_test.png"
|
||
fig.savefig(fig_path, dpi=150, bbox_inches="tight")
|
||
plt.close(fig)
|
||
logger.info("Saved: %s", fig_path)
|
||
|
||
# ── 9. Markdown report ────────────────────────────────────────────────
|
||
report_path = args.output_dir / "stress_test_report.md"
|
||
_write_report(
|
||
report_path,
|
||
git_sha=git_sha, seed=args.seed,
|
||
timestamp=datetime.now(timezone.utc).isoformat(),
|
||
study_start=args.study_start, study_end=args.study_end,
|
||
bin_days=args.bin_days, lag_min=args.lag_min, lag_max=args.lag_max,
|
||
n_bins=n_bins, n_stations=n_stations, n_events=n_events,
|
||
min_mag=args.min_mag,
|
||
r1_cr=r1_cr, r1_seismic=r1_seismic, n_eff=n_eff,
|
||
obs_r_15=obs_r_15, obs_peak_r=obs_peak_r,
|
||
obs_peak_lag_days=obs_peak_lag * args.bin_days,
|
||
naive_p_15=naive_p_15, naive_sigma_15=naive_sigma_15,
|
||
breth_p_15=breth_p_15, breth_sigma_15=breth_sigma_15,
|
||
phase_p_15=phase_p_15, phase_sigma_15=phase_sig_15,
|
||
iaaft_p_15=iaaft_p_15, iaaft_sigma_15=iaaft_sig_15,
|
||
naive_p_peak=naive_p_peak, naive_sigma_peak=naive_sigma_peak,
|
||
breth_p_peak=breth_p_peak, breth_sigma_peak=breth_sigma_peak,
|
||
phase_p_global=phase_p_global, phase_sigma_global=phase_sig_g,
|
||
iaaft_p_global=iaaft_p_global, iaaft_sigma_global=iaaft_sig_g,
|
||
n_surrogates=args.n_surrogates,
|
||
)
|
||
logger.info("Saved: %s", report_path)
|
||
|
||
# ── 10. JSON log ──────────────────────────────────────────────────────
|
||
def _safe(v):
|
||
if v is None:
|
||
return None
|
||
if isinstance(v, float) and np.isnan(v):
|
||
return None
|
||
if isinstance(v, float) and np.isinf(v):
|
||
return None
|
||
return v
|
||
|
||
json_results = {
|
||
"script": "scripts/03_stress_test.py",
|
||
"git_sha": git_sha, "seed": args.seed,
|
||
"timestamp_utc": datetime.now(timezone.utc).isoformat(),
|
||
"study_window": [args.study_start, args.study_end],
|
||
"bin_days": args.bin_days,
|
||
"lag_range_days": [args.lag_min, args.lag_max],
|
||
"min_magnitude": args.min_mag,
|
||
"n_bins": n_bins, "n_stations": n_stations, "n_events": n_events,
|
||
"r1_cr_index": round(r1_cr, 5),
|
||
"r1_seismic": round(r1_seismic, 5),
|
||
"n_eff_bretherton": round(n_eff, 1),
|
||
"tau_15d": {
|
||
"r": _safe(round(obs_r_15, 6)),
|
||
"naive_p": _safe(naive_p_15), "naive_sigma": _safe(naive_sigma_15),
|
||
"bretherton_p": _safe(breth_p_15), "bretherton_sigma": _safe(breth_sigma_15),
|
||
"phase_surr_p": _safe(phase_p_15), "phase_surr_sigma": _safe(phase_sig_15),
|
||
"iaaft_surr_p": _safe(iaaft_p_15), "iaaft_surr_sigma": _safe(iaaft_sig_15),
|
||
},
|
||
"peak_lag": {
|
||
"r": round(obs_peak_r, 6),
|
||
"lag_days": obs_peak_lag * args.bin_days,
|
||
"naive_p": _safe(naive_p_peak), "naive_sigma": _safe(naive_sigma_peak),
|
||
"bretherton_p": _safe(breth_p_peak), "bretherton_sigma": _safe(breth_sigma_peak),
|
||
"phase_surr_p_global": _safe(phase_p_global),
|
||
"phase_surr_sigma_global": _safe(phase_sig_g),
|
||
"iaaft_surr_p_global": _safe(iaaft_p_global),
|
||
"iaaft_surr_sigma_global": _safe(iaaft_sig_g),
|
||
},
|
||
"n_surrogates": args.n_surrogates,
|
||
"method": args.method,
|
||
}
|
||
json_path = args.output_dir / "stress_test_results.json"
|
||
json_path.write_text(json.dumps(json_results, indent=2))
|
||
logger.info("Saved: %s", json_path)
|
||
|
||
# ── Summary print ─────────────────────────────────────────────────────
|
||
print()
|
||
print("=" * 70)
|
||
print(" STRESS TEST SUMMARY")
|
||
print("=" * 70)
|
||
print(f" Study: {args.study_start} – {args.study_end} | "
|
||
f"{n_stations} stations | {n_bins:,} bins")
|
||
print(f" ρ₁(CR)={r1_cr:.3f} ρ₁(seismic)={r1_seismic:.3f} "
|
||
f"N_eff={n_eff:.0f} ({100*n_eff/n_bins:.1f}% of N)")
|
||
print()
|
||
print(f" τ = +15 d (Homola claim):")
|
||
print(f" r = {obs_r_15:+.5f}")
|
||
print(f" Naive: {naive_sigma_15:.1f}σ (p={naive_p_15:.2e})")
|
||
print(f" Bretherton: {breth_sigma_15:.1f}σ (p={breth_p_15:.2e})")
|
||
if phase_p_15 is not None:
|
||
print(f" Phase surr: {phase_sig_15:.2f}σ (p={phase_p_15:.4f})")
|
||
if iaaft_p_15 is not None:
|
||
print(f" IAAFT surr: {iaaft_sig_15:.2f}σ (p={iaaft_p_15:.4f})")
|
||
print()
|
||
print(f" Peak (global, any lag):")
|
||
print(f" r = {obs_peak_r:+.5f} at τ = {obs_peak_lag * args.bin_days:+d} d")
|
||
print(f" Naive: {naive_sigma_peak:.1f}σ (p={naive_p_peak:.2e})")
|
||
print(f" Bretherton: {breth_sigma_peak:.1f}σ (p={breth_p_peak:.2e})")
|
||
if phase_p_global is not None:
|
||
print(f" Phase surr (global): {phase_sig_g:.2f}σ (p={phase_p_global:.4f})")
|
||
if iaaft_p_global is not None:
|
||
print(f" IAAFT surr (global): {iaaft_sig_g:.2f}σ (p={iaaft_p_global:.4f})")
|
||
print("=" * 70)
|
||
|
||
|
||
if __name__ == "__main__":
|
||
main()
|