#!/usr/bin/env python3 """Reproduce this compilation's arithmetic from saved, source-linked inputs. Usage: python compute-warehouse-energy-derived.py [path/to/warehouse-energy-inputs.json] Uses only Python's standard library and performs no network operations. Published EIA estimates are retained: this does not reconstruct their microdata. Outputs JSON to stdout; source discrepancies caused by rounding are preserved. """ from __future__ import annotations import json import math import sys from pathlib import Path from typing import Any def ratio(numerator: float, denominator: float) -> float: if not isinstance(numerator, (int, float)) or not isinstance(denominator, (int, float)): raise ValueError('Ratios require numeric, non-suppressed values.') if denominator == 0: raise ValueError('Ratio denominator must not be zero.') return numerator / denominator def calculate(data: dict[str, Any]) -> dict[str, Any]: rows = data['benchmark_rows'] if len(rows) != 6: raise ValueError('Expected four warehouse leaf categories and two rollups.') total = rows[0][4] count_total, floor_total, energy_total = total[:3] btu_per_kwh = data['btu_per_kwh'] if btu_per_kwh != 3412: raise ValueError('Unexpected electricity conversion; review the source before using it.') comparisons = [] for i, (rid, name, kind, parent, major, electric, gas) in enumerate(rows): pb = data['published_per_building'] out = { 'row_id': rid, 'building_type': name, 'row_kind': kind, 'building_share_percent': 100 * ratio(major[0], count_total), 'floorspace_share_percent': 100 * ratio(major[1], floor_total), 'major_fuel_energy_share_percent': 100 * ratio(major[2], energy_total), 'electricity_using_floorspace_share_percent': 100 * ratio(electric[1], major[1]), 'gas_using_floorspace_share_percent': ( 100 * ratio(gas[1], major[1]) if isinstance(gas[1], (int, float)) else 'Q' ), 'gross_mean_divided_by_building_median': ratio(major[3], major[5]), 'published_average_building_sqft': pb['area_thousand_sqft'][i] * 1000, 'published_energy_per_building_million_btu': pb['energy_million_btu'][i], 'published_energy_per_building_ratio_to_nonrefrigerated_warehouse': ratio( pb['energy_million_btu'][i], pb['energy_million_btu'][2]), 'rounded_total_electricity_share_arithmetic_percent_NOT_OFFICIAL_SHARE': ( 100 * electric[2] * btu_per_kwh / 1000 / major[2]), 'rounding_note': 'Per-building values are EIA published inputs, not quotients of rounded counts. Fuel-share arithmetic from coarse totals is a rounding diagnostic, not an official EIA share.', } comparisons.append(out) leaves = [r for r in rows if r[2] == 'leaf'] end_uses = [] for name, energy, conditional in data['end_use_rows']: end_uses.append({ 'end_use': name, 'eia_modeled_energy_trillion_btu': energy, 'derived_energy_share_percent': 100 * ratio(energy, energy_total), 'derived_gross_intensity_kbtu_sqft_year': 1000 * ratio(energy, floor_total), 'eia_published_conditional_intensity_kbtu_sqft_year': conditional, }) region_recalculations = [ {'division': r[0], 'published_gross_intensity': r[3], 'rounded_input_recalculation': 1000 * ratio(r[1], r[2]), 'recalculation_minus_published': 1000 * ratio(r[1], r[2]) - r[3]} for r in data['regional_rows'] ] hist = data['historical'] historical_change = { k: 100 * (ratio(hist['2018'][k], hist['2012'][k]) - 1) for k in hist['2012'] } leaf_energy = sum(r[4][2] for r in leaves) leaf_floor = sum(r[4][1] for r in leaves) leaf_count = sum(r[4][0] for r in leaves) region_floor = sum(r[2] for r in data['regional_rows']) region_energy = sum(r[1] for r in data['regional_rows']) end_energy = sum(r[1] for r in data['end_use_rows']) converted_electricity = rows[0][5][2] * btu_per_kwh / 1000 refrigerated_rounded_pb = rows[5][4][2] / rows[5][4][0] * 1000 ref_electric_intensity_ratio = 100 * rows[5][5][3] * btu_per_kwh / 1000 / rows[5][4][3] assert leaf_energy == 528 and leaf_floor == 17482 and leaf_count == 1003 assert region_floor == 17483 and region_energy == 529 and end_energy == 529 assert math.isclose(converted_electricity, 324.14) assert len([s for r in data['regional_rows'] for s in r[8].split('|')]) == 51 checks = { '1_component_sums': { 'leaf_energy_trillion_btu': leaf_energy, 'published_total_energy': energy_total, 'leaf_floor_million_sqft': leaf_floor, 'published_total_floor': floor_total, 'leaf_count_thousand': leaf_count, 'published_total_count': count_total, 'interpretation': 'Displayed inputs are independently rounded; retain small discrepancies.', }, '2_electricity_conversion': { 'converted_95_billion_kwh_to_trillion_btu': converted_electricity, 'published_profile_electricity_trillion_btu': 325, 'absolute_difference_percent_of_profile': 100 * abs(converted_electricity - 325) / 325, 'interpretation': 'Rounded-source consistency check, not a replacement of either value.', }, '3_intensity_and_per_building': { 'implied_total_floor_million_sqft': 1000 * energy_total / total[3], 'published_total_floor_million_sqft': floor_total, 'refrigerated_naive_rounded_energy_per_building_million_btu': refrigerated_rounded_pb, 'refrigerated_published_energy_per_building_million_btu': 10144, 'refrigerated_naive_rounded_average_floor_sqft': 327 / 3 * 1000, 'refrigerated_published_average_floor_sqft': 94800, 'interpretation': 'Use the issuer\'s published per-building measures, not naive rounded quotients.', }, '4_regional_and_end_use_sums': { 'regional_floor_million_sqft': region_floor, 'regional_energy_trillion_btu': region_energy, 'modeled_end_use_components_trillion_btu': end_energy, 'published_national_total_energy_trillion_btu': energy_total, 'max_absolute_regional_intensity_rounding_difference': max(abs(r['recalculation_minus_published']) for r in region_recalculations), }, '5_cross_agency_category_ordering': { 'eia_nonrefrigerated_warehouse_median': 18.8, 'epa_combined_warehouse_distribution_median': 22.7, 'eia_distribution_median': 27.6, 'combined_reference_between_subgroups': 18.8 < 22.7 < 27.6, 'interpretation': 'Ordering only; NOT evidence of shared observation year or a validation of combined medians.', }, } derived = { 'distribution_to_nonrefrigerated_average_size_ratio': 47.8 / 12.9, 'warehouse_to_all_commercial_gross_intensity_percent': 100 * 30.2 / 70.4, 'refrigerated_to_nonrefrigerated_gross_intensity_ratio': 107.0 / 26.1, 'heating_plus_lighting_percent': 100 * (208 + 80) / 528, 'heating_rounded_share_times_whole_category_intensity': 0.39 * 30.2, 'conditional_heating_to_derived_gross_heating_ratio': 15.8 / (208 * 1000 / 17483), 'east_north_central_above_national_percent': 100 * (35.6 / 30.2 - 1), 'refrigerated_electric_intensity_ratio_percent_ROUNDING_DIAGNOSTIC': ref_electric_intensity_ratio, 'epa_warehouse_source_median_to_site_median_ratio_NOT_BUILDING_CONVERSION': 52.9 / 22.7, 'epa_refrigerated_source_median_to_site_median_ratio_NOT_BUILDING_CONVERSION': 235.6 / 84.1, 'epa_refrigerated_median_below_eia_percent': 100 * (113.5 - 84.1) / 113.5, 'eia_refrigerated_median_above_epa_percent': 100 * (113.5 - 84.1) / 84.1, 'warehouse_floor_not_using_electricity_percent': 100 * (1 - 16335 / 17483), 'illustrative_50000_sqft_at_5_8_kwh_sqft_NOT_A_MEASUREMENT': 50000 * 5.8, } return {'version': data['version'], 'last_verified': data['verified_on'], 'building_comparisons': comparisons, 'modeled_end_use_calculations': end_uses, 'regional_rounding_checks': region_recalculations, 'historical_percent_changes': historical_change, 'other_derived_arithmetic': derived, 'five_reconciliation_checks': checks, 'validation': 'passed', 'warning': 'Arithmetic on rounded, sample-survey estimates does not create new measurement precision or ratio confidence intervals.'} def main() -> int: path = Path(sys.argv[1]) if len(sys.argv) > 1 else Path(__file__).with_name('warehouse-energy-inputs.json') try: with path.open(encoding='utf-8') as handle: data = json.load(handle) result = calculate(data) except (OSError, ValueError, KeyError, TypeError, AssertionError) as exc: print(f'Input or validation error: {exc}', file=sys.stderr) return 1 print(json.dumps(result, ensure_ascii=False, indent=2, allow_nan=False)) return 0 if __name__ == '__main__': raise SystemExit(main())