"""Export paper tables from preserved numerical results; no new simulations."""
import csv
import hashlib
import json
from pathlib import Path

ROOT = Path(__file__).resolve().parent
OUT = ROOT.parent / 'figures'
HERE = ROOT / 'exports'
HERE.mkdir(exist_ok=True)
sources = {}

def read(rel):
    p = ROOT / rel
    sources[rel] = hashlib.sha256(p.read_bytes()).hexdigest()
    return json.loads(p.read_text())

def write(name, columns, rows):
    for base in [HERE, OUT]:
        with (base / name).open('w', newline='') as f:
            w = csv.writer(f)
            w.writerow(columns)
            w.writerows(rows)

d = read('numerics/E01_gaussian_intensity/results.json')
write('gaussian_mesh.csv', ['h', 'quadrature', 'mc', 'se', 'twose', 'rice'],
      [[r['mesh'], r['crossing_probability_divided_by_mesh'],
        r['conditional_monte_carlo_mean'], r['standard_error'],
        2*r['standard_error'], d['rice_constant']] for r in d['rows']])

d = read('numerics/E681_actual_quadratic/plot_data.json')
p = d['panels'][0]
assert p['delta'] == .5
write('quadratic_cdf.csv', ['x', 'positive', 'negative', 'random', 'se'],
      [[x, p['cdf']['D > 0'][j], p['cdf']['D < 0'][j],
        p['cdf']['Random finite P'][j],
        (p['cdf']['Random finite P'][j]*(1-p['cdf']['Random finite P'][j])/d['random_n'])**.5]
       for j, x in enumerate(p['x'])])

d = read('numerics/E681_actual_quadratic/arithmetic_summary.json')
pos = d['root_groups']['X=100000;sign=1']
neg = d['root_groups']['X=100000;sign=-1']
write('quadratic_counts.csv', ['roots', 'positive', 'negative', 'n_positive', 'n_negative'],
      [[k, pos['counts'].get(str(k), 0)/pos['n'], neg['counts'].get(str(k), 0)/neg['n'],
        pos['counts'].get(str(k), 0), neg['counts'].get(str(k), 0)] for k in range(3)])

d = read('numerics/E1571_actual_modular_twists/full_D17.json')
write('delta_twist17.csv', ['s', 'L', 'Lprime'],
      [[r['s'], r['L'], r['Lprime']] for r in d['refined_grid']])

d = read('numerics/E1577_eight_point_covariance_and_quantile/results.json')
a, b = [r['levels'][-1]['eigenvalues_ascending'] for r in d['matrices']]
write('eight_anchor_gram.csv', ['index', 'central', 'bridge'],
      [[j+1, a[j], b[j]] for j in range(8)])

manifest = {'purpose': 'Pure extraction; original files are not changed.',
            'source_sha256': sources,
            'output_sha256': {p.name: hashlib.sha256(p.read_bytes()).hexdigest()
                              for p in sorted(HERE.glob('*.csv'))},
            'sampling': 'Only Gaussian Monte Carlo and finite Euler-model CDF have sampling SE; all other data are deterministic or finite censuses.',
            'limitations': 'Finite-precision arithmetic and mesh counts are not interval-certified; plots do not validate arithmetic asymptotic constants.'}
(HERE/'manifest.json').write_text(json.dumps(manifest, indent=2)+'\n')
(OUT/'data_manifest.json').write_text(json.dumps(manifest, indent=2)+'\n')
print('Exported five tables from five preserved numerical result files.')

# Materialize genuine TeX tables so figures do not depend on ancillary files at build time.
TEX_TABLES = ROOT.parent.parent / 'figures' / 'numerical_data.tex'
TABLE_MACROS = [('gaussian_mesh.csv', 'BMgaussianmesh'), ('quadratic_cdf.csv', 'BMquadraticcdf'), ('quadratic_counts.csv', 'BMquadraticcounts'), ('delta_twist17.csv', 'BMdeltatwist'), ('eight_anchor_gram.csv', 'BMeightanchorgram')]
tex_lines = ['% Generated from the preserved numerical CSV files; do not round values.']
for filename, macro in TABLE_MACROS:
    tex_lines.append(r'\pgfplotstableread[col sep=comma,row sep=\\]{')
    tex_lines.extend(line + r'\\' for line in (OUT / filename).read_text().splitlines())
    tex_lines.append('}' + chr(92) + macro)
    tex_lines.append('')
TEX_TABLES.write_text('\n'.join(tex_lines) + '\n')
print('Exported TeX-native figure tables.')