"""Gather the analysis's numbers into the declared results the claims name, each value at the precision its uncertainty supports. python collect.py Reads the registered analysis (results/*.json), the same analysis without the events whose SAP flux moved the opposite way (results/corrected/*.json), the development set (results/dev/), the sign check, the audit, and the exploratory analyses. R1 to R10 and R13 come from the corrected analysis; R0 keeps the registered one's primary numbers. """ import json import os import sys from math import log10 out = sys.argv[1] cor = os.path.join(out, 'corrected') dev = os.path.join(out, 'dev') def load(d, name): p = os.path.join(d, name + '.json') return json.load(open(p)) if os.path.exists(p) else None def r(x, nd=2): return None if x is None else round(float(x), nd) def rate_block(t, nd=2): """The numbers of one comparison with the Kepler rate.""" return { 'stars': t['stars'], 'light_curves': t['light_curves'], 'star_years': r(t['star_years'], 0), 'observed': t['observed'], 'expected_kepler': r(t['expected_vasilyev'], 1), 'expected_earlier': r(t['expected_earlier'], 2), 'ratio': r(t['ratio'], nd), 'ratio_low': r(t['ratio_ci95'][0], nd), 'ratio_high': r(t['ratio_ci95'][1], nd), 'p_at_most_observed_if_kepler': r(t['p_at_most_observed_if_vasilyev'], 4), 'log10_p_at_least_observed_if_earlier': r(log10(max(t['p_at_least_observed_if_earlier'], 1e-300)), 1), } def sensitivity(t): return {'observed': t['observed'], 'expected_kepler': r(t['expected_vasilyev'], 1), 'ratio': r(t['ratio']), 'ratio_low': r(t['ratio_ci95'][0]), 'ratio_high': r(t['ratio_ci95'][1])} def write(name, obj): with open(os.path.join(out, name + '.json'), 'w') as fh: json.dump(obj, fh, indent=1, sort_keys=True) def main(): write('R0', rate_block(load(out, 'primary'))) p = load(cor, 'primary') block = rate_block(p) f34 = p['vasilyev_f34'] block['kepler_rate_per_year'] = r(f34, 5) block['earlier_years_per_superflare'] = 2500 block['rate_per_year'] = r(p['rate_above_1e34_per_year'], 4) block['rate_low'] = r(p['rate_above_1e34_ci95'][0], 4) block['rate_high'] = r(p['rate_above_1e34_ci95'][1], 4) block['years_per_superflare'] = r(p['years_per_superflare'], 0) block['years_low'] = r(1 / p['rate_above_1e34_ci95'][1], 0) block['years_high'] = r(1 / p['rate_above_1e34_ci95'][0], 0) write('R1', block) write('R2', rate_block(load(cor, 'sunlike'))) write('R3', {'low': rate_block(load(cor, 'low_activity')), 'high': rate_block(load(cor, 'high_activity')), 'median_index_nm': r(load(cor, 'sample')['activity_median_nm'], 4)}) write('R4', {'bright': rate_block(load(cor, 'tmag_0_8.5')), 'middle': rate_block(load(cor, 'tmag_8.5_9.5')), 'faint': rate_block(load(cor, 'tmag_9.5_10.5'))}) write('R5', {'above_e34_5': rate_block(load(cor, 'above_10^34.5')), 'above_e35': rate_block(load(cor, 'above_10^35'))}) b = load(cor, 'background_subtracted') s = load(out, 'sap_sign') flares_before = s['flares_checked'] - s['flares_opposite_sign'] dips_before = s['dips_checked'] - s['dips_opposite_sign'] write('R6', {'dips': b['dips'], 'dips_before_eclipser_rule': dips_before, 'flares_before_eclipser_rule': flares_before, 'ratio_less_every_dip_before_rule': r((p['observed'] - dips_before) / p['expected_vasilyev'])}) d = load(cor, 'durations') write('R7', {'light_curves': d['light_curves'], 'short': sensitivity(d['short'] | {'observed': p['observed']}), 'long': sensitivity(d['long'] | {'observed': p['observed']})}) t = load(cor, 'flare_temperature') write('R8', {f't{k}': sensitivity(v) for k, v in t.items()}) write('R9', rate_block(load(cor, 'localized'))) write('R10', rate_block(load(cor, 'strict_single'))) sm = load(cor, 'sample') sel = load(out, 'selection') write('R11', {'stars_selected': sel['main_stars'], 'light_curves_selected': sel['main_light_curves'], 'stars_fast_rotators': sm['stars_tess_fast_rotators'], 'stars_eclipsers': sm['stars_eclipsers'], 'stars_used': sm['stars_used'], 'light_curves_used': sm['light_curves_used'], 'star_years': r(sm['star_years'], 0), 'median_noise_ppm': r(sm['median_sigma_ppm'], -1), 'selection_reproduced': sel['sample_stars']['identical'] and sel['sample_lcs']['identical']}) a = load(out, 'audit_all') write('R12', {'light_curves': a['light_curves'], 'light_curves_agree': a['light_curves_agree'], 'fields_compared': a['fields_compared'], 'fields_mismatched': a['fields_mismatched'], 'all_but_fits_agree': a['all_but_fits_agree'], 'fit_differences_on_vetted': a['fit_differences_on_vetted'], 'max_fit_relative_difference': float(f"{a['max_fit_relative_difference']:.1g}"), 'fits_within_one_percent': a['fits_within_one_percent']}) write('R13', rate_block(load(dev, 'primary'))) w = load(out, 'width_check') write('R15', w) write('R17', {'flares_opposite_sign': s['counted_flares_opposite_sign'], 'dips_opposite_sign': s['dips_opposite_sign'], 'injection_light_curves': s['injection_light_curves'], 'injections_recovered_above_1e34': s['injections_recovered_above_1e34'], 'injections_changed_by_sign': s['injections_changed_by_sign']}) c = load(out, 'cadence') sig = lambda x: float(f'{x:.3g}') # noqa: E731 shape = lambda v: {'factor_low_bin': r(v['factor_low_bin']), 'factor_high_bin': r(v['factor_high_bin'])} # noqa: E731 fig, rates, fit = c['figure'], c['rates'], c['calibration'] write('R18', {name: shape(c[name]) for name in ('two_point', 'short', 'base', 'long', 'base_steeper')} | { 'energy_bins': fit['bins'], 'fit_rms': r(fit['rms'], 3), 'fit_difference_max': r(fit['max_difference'], 3), 'beta': r(c['beta']), 'beta_steeper': r(c['beta_steeper']), 'applied_exceeds_figure_from_log_e': r(fig['applied_exceeds_from_log_e']), 'log_e_near_1e35': r(fig['log_e_near_1e35']), 'applied_r_near_1e35': r(fig['applied_r_near_1e35']), 'figure_max_r_near_1e35': r(fig['figure_max_r_near_1e35']), 'figure_rate_low_bin': sig(rates['figure_low_bin']), 'figure_rate_high_bin': sig(rates['figure_high_bin']), 'table_rate_low_bin': rates['table_low_bin'], 'table_rate_high_bin': rates['table_high_bin']}) if __name__ == '__main__': main()