Add Scripts 39b/c/d/e + 40b + 43: anchor-based FAR diagnostics
Spike checkpoint in response to codex rounds 28-30 review:
- 39b/c: signature-level dip test on Big-4 and non-Big-4 marginals
- 39d: dHash discrete-value robustness (raw vs jittered + histogram
valleys + firm residualization); confirms within-firm dHash dip
rejection is integer-mass-point artefact
- 39e: dHash firm-residualized + jittered 2x2 factorial decomposition;
confirms Big-4 pooled dh "multimodality" is composition + integer
artefact (centered + jittered p=0.35, 0/5 seeds reject)
- 40b: inter-CPA per-pair FAR sweep (cos + dh marginal + joint +
conditional); replicates v3 cos>0.95 FAR=0.0006 and provides
v4-new dh FAR curve
- 43: pool-normalized per-signature FAR (codex round-30 fix for
per-pair vs per-signature conflation); per-sig FAR for deployed
any-pair rule = 11.02%, per-firm structure shows Firm A 20% vs
B/C/D <1%
These scripts replace the distributional path (K=3 mixture / dip /
antimode) with anchor-based threshold derivation. Companion
artefacts in reports/v4_big4/{signature_level_diptest,
midsmall_signature_diptest, dhash_discrete_robustness,
inter_cpa_far_sweep, pool_normalized_far}/.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
This commit is contained in:
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#!/usr/bin/env python3
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"""
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Script 39b: Signature-Level Dip Test (multimodality at the signature cloud)
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============================================================================
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Phase 5 pre-emptive evidence. Script 34 / 36 already report Hartigan
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dip tests on the 437 accountant-level (cos_mean, dh_mean) means and
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both marginals reject unimodality at p < 5e-4. Reviewers may ask
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whether the same multimodality is detectable at the signature level
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itself (n = 150,442 Big-4 signatures) and whether the multimodality
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is a within-firm or only a between-firm phenomenon.
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This script supplies the missing dip evidence on the raw signature
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cloud. It is a *diagnostic* in the same role as Scripts 34/36 dip
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tests: it does not derive an operational threshold; it characterises
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the marginal distributions of (cos, dh_indep) at the signature level.
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Outputs:
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reports/v4_big4/signature_level_diptest/
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sig_diptest_results.json
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sig_diptest_report.md
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Tests performed:
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A. Pooled Big-4 marginals (cos, dh_indep), n = 150,442
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B. Per-firm marginals (Firm A / B / C / D separately)
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"""
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import json
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import sqlite3
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import numpy as np
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import diptest
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from pathlib import Path
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from datetime import datetime
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from scipy import stats
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from scipy.signal import find_peaks
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DB = '/Volumes/NV2/PDF-Processing/signature-analysis/signature_analysis.db'
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OUT = Path('/Volumes/NV2/PDF-Processing/signature-analysis/reports/'
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'v4_big4/signature_level_diptest')
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OUT.mkdir(parents=True, exist_ok=True)
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BIG4 = ('勤業眾信聯合', '安侯建業聯合', '資誠聯合', '安永聯合')
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ALIAS = {'勤業眾信聯合': 'Firm A',
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'安侯建業聯合': 'Firm B',
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'資誠聯合': 'Firm C',
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'安永聯合': 'Firm D'}
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N_BOOT = 2000
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def load_big4_signatures():
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conn = sqlite3.connect(DB)
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cur = conn.cursor()
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cur.execute('''
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SELECT s.assigned_accountant, a.firm,
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s.max_similarity_to_same_accountant,
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CAST(s.min_dhash_independent AS REAL)
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FROM signatures s
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JOIN accountants a ON s.assigned_accountant = a.name
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WHERE s.assigned_accountant IS NOT NULL
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AND s.max_similarity_to_same_accountant IS NOT NULL
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AND s.min_dhash_independent IS NOT NULL
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AND a.firm IN (?, ?, ?, ?)
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''', BIG4)
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rows = cur.fetchall()
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conn.close()
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return rows
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def kde_dip(values, n_boot=N_BOOT):
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arr = np.asarray(values, dtype=float)
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arr = arr[np.isfinite(arr)]
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dip, pval = diptest.diptest(arr, boot_pval=True, n_boot=n_boot)
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kde = stats.gaussian_kde(arr, bw_method='silverman')
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xs = np.linspace(arr.min(), arr.max(), 2000)
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density = kde(xs)
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peaks, _ = find_peaks(density, prominence=density.max() * 0.02)
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antimodes = []
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for i in range(len(peaks) - 1):
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seg = density[peaks[i]:peaks[i + 1]]
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if not len(seg):
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continue
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local = peaks[i] + int(np.argmin(seg))
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antimodes.append(float(xs[local]))
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return {
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'n': int(len(arr)),
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'dip': float(dip),
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'dip_pvalue': float(pval),
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'unimodal_alpha05': bool(pval > 0.05),
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'n_modes': int(len(peaks)),
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'mode_locations': [float(xs[p]) for p in peaks],
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'antimodes': antimodes,
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'n_boot': int(n_boot),
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}
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def _fmt_p(p):
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if p == 0.0:
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return '< 5e-4 (no bootstrap replicate exceeded observed dip)'
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return f'{p:.4g}'
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def main():
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print('=' * 72)
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print('Script 39b: Signature-Level Dip Test')
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print('=' * 72)
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rows = load_big4_signatures()
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cos_all = np.array([r[2] for r in rows], dtype=float)
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dh_all = np.array([r[3] for r in rows], dtype=float)
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firms = np.array([ALIAS[r[1]] for r in rows])
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print(f'\nLoaded {len(rows):,} Big-4 signatures')
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for f in sorted(set(firms)):
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print(f' {f}: {(firms == f).sum():,}')
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results = {
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'meta': {
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'script': '39b',
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'timestamp': datetime.now().isoformat(timespec='seconds'),
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'n_total': int(len(rows)),
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'n_boot': N_BOOT,
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'note': ('Signature-level Hartigan dip test on Big-4 '
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'(cos, dh_indep) marginals; pooled and per-firm.'),
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},
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'pooled': {},
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'per_firm': {},
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}
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# A. Pooled
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print('\n[A] Pooled Big-4')
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for desc, arr in [('cos', cos_all), ('dh_indep', dh_all)]:
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r = kde_dip(arr)
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results['pooled'][desc] = r
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print(f' {desc}: n={r["n"]:,}, dip={r["dip"]:.5f}, '
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f'p={_fmt_p(r["dip_pvalue"])}, n_modes={r["n_modes"]}')
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# B. Per-firm
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print('\n[B] Per-firm')
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for f in sorted(set(firms)):
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mask = firms == f
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results['per_firm'][f] = {}
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for desc, arr in [('cos', cos_all[mask]), ('dh_indep', dh_all[mask])]:
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r = kde_dip(arr)
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results['per_firm'][f][desc] = r
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print(f' {f} {desc}: n={r["n"]:,}, dip={r["dip"]:.5f}, '
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f'p={_fmt_p(r["dip_pvalue"])}, n_modes={r["n_modes"]}')
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json_path = OUT / 'sig_diptest_results.json'
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json_path.write_text(json.dumps(results, indent=2, ensure_ascii=False),
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encoding='utf-8')
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print(f'\n[json] {json_path}')
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md = ['# Signature-Level Dip Test (Script 39b)',
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'',
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f'Generated: {results["meta"]["timestamp"]}',
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f'Bootstrap replicates: {N_BOOT}',
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'',
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'## A. Pooled Big-4 signature cloud',
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'',
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f'n = {results["meta"]["n_total"]:,} signatures',
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'',
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'| Marginal | dip | p (boot) | n_modes | unimodal @0.05 |',
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'|---|---|---|---|---|']
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for desc in ['cos', 'dh_indep']:
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r = results['pooled'][desc]
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md.append(f'| {desc} | {r["dip"]:.5f} | {_fmt_p(r["dip_pvalue"])} | '
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f'{r["n_modes"]} | {r["unimodal_alpha05"]} |')
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md += ['', '## B. Per-firm signature-level dip tests', '',
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'| Firm | Marginal | n | dip | p (boot) | n_modes | unimodal @0.05 |',
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'|---|---|---|---|---|---|---|']
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for f in sorted(results['per_firm']):
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for desc in ['cos', 'dh_indep']:
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r = results['per_firm'][f][desc]
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md.append(f'| {f} | {desc} | {r["n"]:,} | {r["dip"]:.5f} | '
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f'{_fmt_p(r["dip_pvalue"])} | {r["n_modes"]} | '
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f'{r["unimodal_alpha05"]} |')
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md += ['',
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'## Reading guide',
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'',
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('A unimodality rejection at the signature level confirms '
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'multimodal structure independent of accountant-level '
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'aggregation. A within-firm rejection further indicates the '
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'multimodality is not solely a between-firm artefact. A '
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'within-firm non-rejection (e.g., Firm A) is consistent with '
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'that firm being concentrated in a single mechanism corner.'),
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'',
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('All thresholds and operational classifiers remain those of '
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'v3.x §III-K and v4.0 §III-J; this script supplies diagnostic '
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'evidence only.'),
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'']
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md_path = OUT / 'sig_diptest_report.md'
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md_path.write_text('\n'.join(md), encoding='utf-8')
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print(f'[md ] {md_path}')
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if __name__ == '__main__':
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main()
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