#!/usr/bin/env python3 """ African Judicial Access Indicators — Synthetic Dataset Generator ================================================================ Electric Sheep Africa | electricsheepafrica on HuggingFace Generates synthetic judicial access indicator data for sub-Saharan African countries at the subnational (region) level, parameterized from peer-reviewed literature, WJP indices, Afrobarometer surveys, and national reports. PHASE 2 — Causal & Correlation Structure (DAG) ================================================ ROOT NODES (sampled independently): ├── country → Categorical (weighted by population) ├── year → Uniform [2018–2025] └── region_type → Categorical {urban, peri_urban, rural, remote_rural} INTERMEDIATE NODES (sampled conditionally): ├── population → Conditional on country, year ├── lawyer_density → Conditional on country tier, region_type [Ref 16] ├── court_density → Conditional on country tier, region_type ├── distance_to_court → Conditional on region_type, court_density [Ref 1] ├── travel_time_hours → Conditional on distance, region_type ├── legal_aid_coverage→ Conditional on country tier, region_type [Refs 6,7] ├── legal_aid_per_cap → Conditional on country tier [Ref 10] ├── paralegal_density → Conditional on country, region_type [Ref 4] ├── cost_pct_income → Conditional on region_type, country tier [Ref 2] ├── bribery_rate → Conditional on country tier [Ref 9] └── gender_access_ratio → Conditional on country tier, region_type [Ref 12,15] LEAF NODES (derived / looked up): ├── court_contact_rate → Conditional on distance, cost, trust [Ref 9] ├── trust_in_courts → Conditional on bribery, country tier [Ref 9] ├── customary_justice_use → Conditional on region_type, legal_aid [Ref 6] ├── wjp_score → Looked up by country with noise [Ref 5] └── access_level → Classification: high, moderate, low, very_low CORRELATION TARGET MATRIX (lower triangle): lawyer_d court_d distance legal_aid cost bribery trust lawyer_dens 1.00 court_dens 0.50 1.00 distance -0.40 -0.60 1.00 legal_aid 0.45 0.35 -0.35 1.00 cost_pct -0.30 -0.25 0.40 -0.35 1.00 bribery -0.35 -0.30 0.25 -0.40 0.30 1.00 trust 0.25 0.30 -0.20 0.35 -0.25 -0.60 1.00 Sources: Afrobarometer R6 [Ref 9] for trust-bribery r≈-0.6; distance-contact inverse from Benyawa 2023 [Ref 1]; Soboka 2019 [Ref 2] for cost barriers. References ---------- [1] Benyawa (2023), "How Far Are Kenya's Courts?", J. of African Law [2] Soboka (2019), "What does justice cost in South Africa?", SA J. Human Rights [3] Bilchitz & Williams (2020), PER/PELJ [4] Cambridge UP (2018), Community Paralegals and the Pursuit of Justice [5] World Justice Project, Rule of Law Index 2024/2025 [6] UNODC (2011), Access to Legal Aid in Africa [7] UNDP (2014), Legal Aid Service Provision in Africa [8] UNODC (2014), Handbook on Legal Aid in Africa [9] Afrobarometer (2017), Policy Paper No. 39 [10] Legal Aid South Africa, Annual Report 2022/23 [11] HiiL (2023), Justice Needs Survey: Nigeria [12] UN Women (2021), Access to Justice for Women ESA [13] World Bank (2019), Cost-Benefit Analysis of Legal Aid [14] ACCORD (2012), Abunzi Mediation in Rwanda [15] Brookings (2022), Women and Access to Justice in Africa [16] World Population Review (2026), Lawyers per Capita [17] NYU CIC (2023), Front-line Justice Services Policy Brief """ import argparse import os import sys import numpy as np import pandas as pd # ============================================================================ # SECTION 1: LITERATURE-INFORMED PARAMETERS # ============================================================================ # --- 1A. Country metadata --- # WJP scores [Ref 5], lawyer density [Ref 16], access tier COUNTRIES = { "South Africa": {"pop_2024": 62.0, "wjp": 0.56, "tier": "high", "growth": 0.008, "lawyer_density": 37.04, "legal_aid_pc": 2.25}, "Botswana": {"pop_2024": 2.6, "wjp": 0.60, "tier": "high", "growth": 0.015, "lawyer_density": 25.0, "legal_aid_pc": 1.50}, "Mauritius": {"pop_2024": 1.3, "wjp": 0.60, "tier": "high", "growth": 0.001, "lawyer_density": 45.21, "legal_aid_pc": 1.80}, "Rwanda": {"pop_2024": 14.0, "wjp": 0.63, "tier": "high", "growth": 0.024, "lawyer_density": 10.0, "legal_aid_pc": 0.40}, "Kenya": {"pop_2024": 55.0, "wjp": 0.45, "tier": "moderate", "growth": 0.021, "lawyer_density": 15.82, "legal_aid_pc": 0.02}, "Ghana": {"pop_2024": 34.0, "wjp": 0.54, "tier": "moderate", "growth": 0.021, "lawyer_density": 7.26, "legal_aid_pc": 0.03}, "Tanzania": {"pop_2024": 67.0, "wjp": 0.46, "tier": "moderate", "growth": 0.030, "lawyer_density": 4.0, "legal_aid_pc": 0.05}, "Senegal": {"pop_2024": 18.0, "wjp": 0.56, "tier": "moderate", "growth": 0.027, "lawyer_density": 6.5, "legal_aid_pc": 0.04}, "Nigeria": {"pop_2024": 230.0,"wjp": 0.41, "tier": "low", "growth": 0.025, "lawyer_density": 11.0, "legal_aid_pc": 0.01}, "DRC": {"pop_2024": 105.0,"wjp": 0.34, "tier": "low", "growth": 0.032, "lawyer_density": 13.93, "legal_aid_pc": 0.005}, "Uganda": {"pop_2024": 49.0, "wjp": 0.38, "tier": "low", "growth": 0.032, "lawyer_density": 5.0, "legal_aid_pc": 0.02}, "Malawi": {"pop_2024": 21.0, "wjp": 0.52, "tier": "low", "growth": 0.026, "lawyer_density": 2.5, "legal_aid_pc": 0.02}, "Mozambique": {"pop_2024": 33.0, "wjp": 0.37, "tier": "low", "growth": 0.028, "lawyer_density": 2.0, "legal_aid_pc": 0.005}, } # --- 1B. Region type distribution --- # SSA: ~35-40% urban, 60-65% rural [World Bank] REGION_TYPES = { "urban": 0.20, "peri_urban": 0.18, "rural": 0.42, "remote_rural": 0.20, } # --- 1C. Distance to court by region type (km) [Ref 1: Kenya avg 22km] --- DISTANCE_KM = { "urban": {"mean": 5, "sd": 3, "min": 0.5, "max": 20}, "peri_urban": {"mean": 15, "sd": 8, "min": 3, "max": 40}, "rural": {"mean": 40, "sd": 20, "min": 10, "max": 100}, "remote_rural": {"mean": 90, "sd": 40, "min": 30, "max": 250}, } # Tier multiplier for distance [low-access countries have worse infrastructure] DISTANCE_TIER_MULT = {"high": 0.7, "moderate": 1.0, "low": 1.4} # --- 1D. Travel time model: time = distance / speed + overhead --- # Speed varies by region type and infrastructure quality TRAVEL_SPEED_KPH = { "urban": {"mean": 20, "sd": 5}, # congested, public transport "peri_urban": {"mean": 25, "sd": 8}, "rural": {"mean": 15, "sd": 5}, # poor roads, mixed transport "remote_rural": {"mean": 10, "sd": 4}, # walking, motorbike, boat } TRAVEL_OVERHEAD_HOURS = { "urban": 0.2, "peri_urban": 0.3, "rural": 0.5, "remote_rural": 1.0, } # --- 1E. Lawyer density by region type [Ref 16: >80% concentrated in capitals] --- # Multiplier applied to country's national lawyer_density LAWYER_REGION_MULT = { "urban": 2.5, # Capital/city concentration [Ref 16] "peri_urban": 1.0, "rural": 0.2, "remote_rural": 0.05, # Near zero in remote areas } # --- 1F. Court density per 100K by tier [Refs from Phase 1] --- # SA: ~1.17; Tanzania: ~1.2-1.5; Rwanda: ~3.0 (formal); Kenya: ~0.22; DRC: <0.10 COURT_DENSITY = { "high": {"mean": 2.0, "sd": 0.8, "min": 0.5, "max": 5.0}, "moderate": {"mean": 0.8, "sd": 0.4, "min": 0.1, "max": 2.0}, "low": {"mean": 0.3, "sd": 0.2, "min": 0.05, "max": 1.0}, } COURT_REGION_MULT = { "urban": 2.0, "peri_urban": 1.2, "rural": 0.5, "remote_rural": 0.15, } # --- 1G. Legal aid coverage % [Refs 6, 7, 10] --- LEGAL_AID_COVERAGE = { "high": {"mean": 20, "sd": 8, "min": 5, "max": 40}, "moderate": {"mean": 7, "sd": 3, "min": 2, "max": 15}, "low": {"mean": 3, "sd": 2, "min": 0.5,"max": 8}, } LEGAL_AID_REGION_MULT = { "urban": 2.0, "peri_urban": 1.0, "rural": 0.4, "remote_rural": 0.1, } # --- 1H. Paralegal / community justice density per 100K [Refs 4, 14, 17] --- # Rwanda: 270/100K (Abunzi); SA: ~1-2; Uganda: ~1-2; Kenya: ~1-3 PARALEGAL_DENSITY = { "high": {"mean": 30, "sd": 60, "min": 1, "max": 300}, # Rwanda pulls mean up "moderate": {"mean": 3, "sd": 2, "min": 0.5, "max": 10}, "low": {"mean": 1, "sd": 1, "min": 0.1, "max": 5}, } # --- 1I. Cost of justice as % monthly income [Ref 2: SA 250% for poor] --- COST_PCT_INCOME = { "urban": {"mean": 60, "sd": 40, "min": 10, "max": 250}, "peri_urban": {"mean": 100, "sd": 50, "min": 15, "max": 350}, "rural": {"mean": 150, "sd": 70, "min": 25, "max": 450}, "remote_rural": {"mean": 200, "sd": 90, "min": 40, "max": 550}, } COST_TIER_MULT = {"high": 0.6, "moderate": 1.0, "low": 1.4} # --- 1J. Bribery rate % [Ref 9: Afrobarometer 30% SSA avg] --- BRIBERY_RATE = { "high": {"mean": 15, "sd": 5, "min": 5, "max": 30}, "moderate": {"mean": 28, "sd": 8, "min": 10, "max": 45}, "low": {"mean": 40, "sd": 10, "min": 15, "max": 60}, } # --- 1K. Trust in courts % [Ref 9: SSA avg 53%; Tanzania 88%; Southern Africa 25%] --- TRUST_COURTS = { "high": {"mean": 70, "sd": 12, "min": 40, "max": 95}, "moderate": {"mean": 50, "sd": 12, "min": 25, "max": 75}, "low": {"mean": 35, "sd": 10, "min": 15, "max": 55}, } # --- 1L. Gender access ratio (women/men) [Refs 12, 15: 64% globally] --- GENDER_ACCESS = { "high": {"mean": 0.85, "sd": 0.06, "min": 0.65, "max": 0.98}, "moderate": {"mean": 0.72, "sd": 0.08, "min": 0.50, "max": 0.90}, "low": {"mean": 0.58, "sd": 0.08, "min": 0.40, "max": 0.78}, } GENDER_REGION_MULT = { "urban": 1.10, "peri_urban": 1.0, "rural": 0.90, "remote_rural": 0.80, } # --- 1M. Customary justice use % [Ref 6: Malawi 80-90% rural] --- CUSTOMARY_USE = { "urban": {"mean": 15, "sd": 8, "min": 2, "max": 35}, "peri_urban": {"mean": 30, "sd": 12, "min": 10, "max": 55}, "rural": {"mean": 60, "sd": 15, "min": 25, "max": 85}, "remote_rural": {"mean": 80, "sd": 10, "min": 50, "max": 98}, } # --- 1N. Access level classification --- # Composite score = weighted combination of indicators # Thresholds calibrated so baseline produces ~15-25% each tier ACCESS_LEVELS = { "high": (0.55, 1.01), "moderate": (0.38, 0.55), "low": (0.22, 0.38), "very_low": (0.00, 0.22), } # --- 1O. Scenario definitions --- SCENARIOS = { "baseline": { "description": "Current SSA judicial access landscape (2018-2025)", "distance_mult": 1.0, "lawyer_mult": 1.0, "legal_aid_mult": 1.0, "cost_mult": 1.0, "paralegal_mult": 1.0, }, "improved_access": { "description": "Countries investing in justice reform and legal aid expansion", "distance_mult": 0.8, "lawyer_mult": 1.3, "legal_aid_mult": 1.5, "cost_mult": 0.7, "paralegal_mult": 2.0, }, "constrained": { "description": "Fiscal austerity / conflict reducing access to justice", "distance_mult": 1.2, "lawyer_mult": 0.8, "legal_aid_mult": 0.5, "cost_mult": 1.5, "paralegal_mult": 0.6, }, } # WJP annual noise [Ref 5] WJP_ANNUAL_NOISE_SD = 0.015 # ============================================================================ # SECTION 2: UTILITY FUNCTIONS # ============================================================================ def trunc_normal(rng, mean, sd, low, high, size=1): """Sample from truncated normal distribution.""" from scipy.stats import truncnorm if sd <= 0: return np.full(size, mean) a = (low - mean) / sd b = (high - mean) / sd return truncnorm.rvs(a, b, loc=mean, scale=sd, size=size, random_state=rng) def sample_categorical(rng, categories, probabilities, size=1): """Weighted categorical sampling.""" probs = np.array(probabilities, dtype=float) probs /= probs.sum() return rng.choice(categories, size=size, p=probs) def classify_access(score): """Classify access level from composite score.""" for label, (lo, hi) in ACCESS_LEVELS.items(): if lo <= score < hi: return label return "very_low" # ============================================================================ # SECTION 3: MAIN GENERATOR FUNCTION # ============================================================================ def generate_judicial_access_data(scenario_name="baseline", n=10000, seed=42): """ Generate synthetic African judicial access indicator records. SAMPLING ORDER (topological sort of DAG): ───────────────────────────────────────── 1. [Root] → sample country 2. [Root] → sample year 3. [Root] → sample region_type 4. [Intermediate] → sample lawyer_density | country, region 5. [Intermediate] → sample court_density | tier, region 6. [Intermediate] → sample distance_to_court | region, tier 7. [Intermediate] → derive travel_time | distance, region 8. [Intermediate] → sample legal_aid_coverage | tier, region 9. [Intermediate] → lookup legal_aid_per_capita | country 10. [Intermediate] → sample paralegal_density | tier, country 11. [Intermediate] → sample cost_pct_income | region, tier 12. [Intermediate] → sample bribery_rate | tier 13. [Intermediate] → sample gender_access_ratio | tier, region 14. [Intermediate] → sample customary_justice_use | region 15. [Leaf] → derive trust_in_courts | bribery, tier 16. [Leaf] → derive court_contact_rate | distance, cost, trust 17. [Leaf] → lookup wjp_score | country, year 18. [Leaf] → derive access_score & classify access_level ───────────────────────────────────────── """ rng = np.random.default_rng(seed) scenario = SCENARIOS[scenario_name] # --- Root nodes --- country_names = list(COUNTRIES.keys()) country_pops = np.array([COUNTRIES[c]["pop_2024"] for c in country_names]) countries = sample_categorical(rng, country_names, country_pops / country_pops.sum(), size=n) years = rng.integers(2018, 2026, size=n) region_names = list(REGION_TYPES.keys()) region_probs = list(REGION_TYPES.values()) regions = sample_categorical(rng, region_names, region_probs, size=n) # Pre-allocate cols = {} lawyer_density = np.zeros(n) court_density = np.zeros(n) distance_km = np.zeros(n) travel_time = np.zeros(n) legal_aid_cov = np.zeros(n) legal_aid_pc = np.zeros(n) paralegal_dens = np.zeros(n) cost_pct = np.zeros(n) bribery = np.zeros(n) gender_ratio = np.zeros(n) customary_use = np.zeros(n) trust = np.zeros(n) contact_rate = np.zeros(n) wjp = np.zeros(n) access_score = np.zeros(n) access_level = np.empty(n, dtype=object) for i in range(n): c = countries[i] r = regions[i] meta = COUNTRIES[c] tier = meta["tier"] # Step 4: Lawyer density | country, region [Ref 16] base_ld = meta["lawyer_density"] * scenario["lawyer_mult"] ld = base_ld * LAWYER_REGION_MULT[r] ld += rng.normal(0, ld * 0.2) # 20% noise lawyer_density[i] = round(np.clip(ld, 0.01, 150), 2) # Step 5: Court density | tier, region cd_params = COURT_DENSITY[tier] cd = trunc_normal(rng, cd_params["mean"], cd_params["sd"], cd_params["min"], cd_params["max"])[0] cd *= COURT_REGION_MULT[r] court_density[i] = round(np.clip(cd, 0.01, 15), 3) # Step 6: Distance to court | region, tier [Ref 1] d_params = DISTANCE_KM[r] d = trunc_normal(rng, d_params["mean"] * DISTANCE_TIER_MULT[tier] * scenario["distance_mult"], d_params["sd"], d_params["min"], d_params["max"])[0] # Inverse relationship with court density if court_density[i] > 0.5: d *= 0.8 distance_km[i] = round(np.clip(d, 0.5, 300), 1) # Step 7: Travel time | distance, region spd_params = TRAVEL_SPEED_KPH[r] speed = max(rng.normal(spd_params["mean"], spd_params["sd"]), 2) overhead = TRAVEL_OVERHEAD_HOURS[r] tt = distance_km[i] / speed + overhead travel_time[i] = round(np.clip(tt, 0.1, 48), 2) # Step 8: Legal aid coverage | tier, region [Refs 6, 7] la_params = LEGAL_AID_COVERAGE[tier] la = trunc_normal(rng, la_params["mean"] * scenario["legal_aid_mult"], la_params["sd"], la_params["min"], la_params["max"] * scenario["legal_aid_mult"])[0] la *= LEGAL_AID_REGION_MULT[r] legal_aid_cov[i] = round(np.clip(la, 0.1, 60), 1) # Step 9: Legal aid per capita | country [Ref 10] la_pc = meta["legal_aid_pc"] * scenario["legal_aid_mult"] la_pc *= rng.lognormal(0, 0.3) legal_aid_pc[i] = round(np.clip(la_pc, 0.001, 10.0), 3) # Step 10: Paralegal density | tier [Refs 4, 14, 17] pl_params = PARALEGAL_DENSITY[tier] # Rwanda special case: Abunzi system [Ref 14] if c == "Rwanda": pl = trunc_normal(rng, 270 * scenario["paralegal_mult"], 50, 100, 400)[0] else: pl = trunc_normal(rng, pl_params["mean"] * scenario["paralegal_mult"], pl_params["sd"], pl_params["min"], pl_params["max"])[0] paralegal_dens[i] = round(np.clip(pl, 0.05, 400), 1) # Step 11: Cost as % monthly income | region, tier [Ref 2] cost_params = COST_PCT_INCOME[r] co = trunc_normal(rng, cost_params["mean"] * COST_TIER_MULT[tier] * scenario["cost_mult"], cost_params["sd"], cost_params["min"], cost_params["max"])[0] cost_pct[i] = round(np.clip(co, 5, 700), 0) # Step 12: Bribery rate | tier [Ref 9] br_params = BRIBERY_RATE[tier] br = trunc_normal(rng, br_params["mean"], br_params["sd"], br_params["min"], br_params["max"])[0] bribery[i] = round(np.clip(br, 1, 70), 1) # Step 13: Gender access ratio | tier, region [Refs 12, 15] ga_params = GENDER_ACCESS[tier] ga = trunc_normal(rng, ga_params["mean"], ga_params["sd"], ga_params["min"], ga_params["max"])[0] ga *= GENDER_REGION_MULT[r] gender_ratio[i] = round(np.clip(ga, 0.30, 1.0), 3) # Step 14: Customary justice use | region [Ref 6] cj_params = CUSTOMARY_USE[r] cj = trunc_normal(rng, cj_params["mean"], cj_params["sd"], cj_params["min"], cj_params["max"])[0] customary_use[i] = round(np.clip(cj, 1, 99), 1) # Step 15: Trust in courts | bribery, tier [Ref 9] # Trust inversely related to bribery: r ≈ -0.6 tr_params = TRUST_COURTS[tier] tr_base = trunc_normal(rng, tr_params["mean"], tr_params["sd"], tr_params["min"], tr_params["max"])[0] # Inject bribery effect: higher bribery → lower trust bribery_effect = -0.6 * (bribery[i] - 30) # centered on SSA avg 30% tr = tr_base + bribery_effect trust[i] = round(np.clip(tr, 10, 98), 1) # Step 16: Court contact rate | distance, cost, trust [Ref 9] # SSA avg: 13%; range 4-28%. Weaker effects + more noise to avoid # overly mechanical correlations. base_contact = 13.0 # Distance penalty: each 10km above 20 reduces by 0.3pp dist_effect = -0.3 * max(0, (distance_km[i] - 20)) / 10 # Cost penalty: each 50% above 100% reduces by 0.5pp cost_effect = -0.5 * max(0, (cost_pct[i] - 100)) / 50 # Trust bonus: each 10pp above 50% adds 0.6pp trust_effect = 0.6 * (trust[i] - 50) / 10 cr = base_contact + dist_effect + cost_effect + trust_effect cr += rng.normal(0, 5) # larger noise to decouple contact_rate[i] = round(np.clip(cr, 1, 40), 1) # Step 17: WJP score | country, year [Ref 5] base_wjp = meta["wjp"] years_to_2025 = 2025 - years[i] wjp_noise = rng.normal(0, WJP_ANNUAL_NOISE_SD * np.sqrt(years_to_2025 + 1)) wjp[i] = round(np.clip(base_wjp + wjp_noise, 0.15, 0.90), 3) # Step 18: Access score & classification # Composite: normalize each indicator to [0,1] and weight ld_norm = np.clip(lawyer_density[i] / 50, 0, 1) # 50/100K = best cd_norm = np.clip(court_density[i] / 3.0, 0, 1) # 3/100K = best dist_norm = 1 - np.clip(distance_km[i] / 150, 0, 1) # 0km = best la_norm = np.clip(legal_aid_cov[i] / 30, 0, 1) # 30% = best cost_norm = 1 - np.clip(cost_pct[i] / 500, 0, 1) # 0% = best trust_norm = np.clip(trust[i] / 100, 0, 1) # 100% = best bribe_norm = 1 - np.clip(bribery[i] / 60, 0, 1) # 0% = best gender_norm = np.clip(gender_ratio[i], 0, 1) score = (0.15 * ld_norm + 0.15 * cd_norm + 0.15 * dist_norm + 0.15 * la_norm + 0.10 * cost_norm + 0.10 * trust_norm + 0.10 * bribe_norm + 0.10 * gender_norm) access_score[i] = round(score, 3) access_level[i] = classify_access(score) # --- Assemble DataFrame --- df = pd.DataFrame({ "record_id": np.arange(1, n + 1), "country": countries, "year": years, "region_type": regions, "lawyer_density_per_100k": lawyer_density, "court_density_per_100k": court_density, "distance_to_court_km": distance_km, "travel_time_hours": travel_time, "legal_aid_coverage_pct": legal_aid_cov, "legal_aid_per_capita_usd": legal_aid_pc, "paralegal_density_per_100k": paralegal_dens, "cost_pct_monthly_income": cost_pct, "bribery_rate_pct": bribery, "trust_in_courts_pct": trust, "gender_access_ratio": gender_ratio, "customary_justice_use_pct": customary_use, "court_contact_rate_pct": contact_rate, "wjp_rule_of_law_score": wjp, "access_score": access_score, "access_level": access_level, }) # --- Print summary --- print(f"\n{'='*70}") print(f"AFRICAN JUDICIAL ACCESS INDICATORS — GENERATION SUMMARY") print(f"Scenario: {scenario_name} | N: {n:,} | Seed: {seed}") print(f"{'='*70}") print(f"\nCountry distribution:") print(df["country"].value_counts().to_string()) print(f"\nRegion type distribution:") print(df["region_type"].value_counts(normalize=True).mul(100).round(1).to_string()) print(f"\nAccess level distribution:") print(df["access_level"].value_counts(normalize=True).mul(100).round(1).to_string()) print(f"\nKey statistics:") for col in ["lawyer_density_per_100k", "distance_to_court_km", "travel_time_hours", "legal_aid_coverage_pct", "cost_pct_monthly_income", "bribery_rate_pct", "trust_in_courts_pct", "gender_access_ratio", "court_contact_rate_pct", "access_score"]: print(f" {col}: mean={df[col].mean():.2f}, sd={df[col].std():.2f}, " f"min={df[col].min():.2f}, max={df[col].max():.2f}") print(f"{'='*70}\n") return df # ============================================================================ # SECTION 4: CLI ENTRY POINT # ============================================================================ def main(): parser = argparse.ArgumentParser( description="Generate synthetic African judicial access indicators dataset" ) parser.add_argument("--scenario", choices=list(SCENARIOS.keys()), default="baseline") parser.add_argument("--n", type=int, default=10000) parser.add_argument("--seed", type=int, default=42) parser.add_argument("--output", type=str, default=None) args = parser.parse_args() df = generate_judicial_access_data(args.scenario, args.n, args.seed) if args.output is None: script_dir = os.path.dirname(os.path.abspath(__file__)) output_dir = os.path.join(script_dir, "data") os.makedirs(output_dir, exist_ok=True) output_path = os.path.join(output_dir, f"{args.scenario}.csv") else: output_path = args.output os.makedirs(os.path.dirname(output_path) or ".", exist_ok=True) df.to_csv(output_path, index=False) print(f"Dataset saved to: {output_path}") print(f"Shape: {df.shape}") if __name__ == "__main__": main()