import os import sys import glob import numpy as np import pandas as pd import matplotlib matplotlib.use('Agg') import matplotlib.pyplot as plt sys.stdout.reconfigure(encoding='utf-8') BASE_DIR = r"c:\Users\ahmed\.gemini\antigravity\scratch\Allignments\PHONEME_TIMINGS_EACH_RECITER" def sanitize_filename(name): replacements = { ':': '_colon_', '/': '_slash_', '\\': '_bslash_', '*': '_star_', '?': '_qmark_', '"': '_quote_', '<': '_lt_', '>': '_gt_', '|': '_pipe_' } s = str(name) for char, rep in replacements.items(): s = s.replace(char, rep) return s def main(): print("=" * 60) print("Updating Phase 3 with Custom Rules for Long Vowels (a:, i:, u:)") print("=" * 60) pooled_csv_path = os.path.join(BASE_DIR, "all_reciters_phase3_pooled_tokens.csv") master_df = pd.read_csv(pooled_csv_path) unique_phones = sorted(master_df['phone'].dropna().unique()) summary_rows = [] plots_dir = os.path.join(BASE_DIR, "Phase3_Unified_Plots") os.makedirs(plots_dir, exist_ok=True) for phone in unique_phones: phone_str = str(phone) sub = master_df[master_df['phone'] == phone] t_ms = sub['duration_norm_ms'].values n_samples = len(t_ms) if n_samples == 0: continue p1_ms = np.percentile(t_ms, 1.0) p2_5_ms = np.percentile(t_ms, 2.5) q1_ms = np.percentile(t_ms, 25.0) med_ms = np.median(t_ms) q3_ms = np.percentile(t_ms, 75.0) p97_5_ms = np.percentile(t_ms, 97.5) p99_ms = np.percentile(t_ms, 99.0) mean_ms = np.mean(t_ms) std_ms = np.std(t_ms) raw_min_ms = np.min(t_ms) raw_max_ms = np.max(t_ms) # APPLY CUSTOM USER RULES (Option A): # 1. For a: no upper outlier filtering (use raw_max_ms) if phone_str == 'a:': p99_ms = raw_max_ms p97_5_ms = raw_max_ms # 2. For i: stop at P90 (3726.6 ms) elif phone_str == 'i:': p99_ms = np.percentile(t_ms, 90.0) p97_5_ms = np.percentile(t_ms, 88.0) # 3. For u: stop at P97 (3828.0 ms) elif phone_str == 'u:': p99_ms = np.percentile(t_ms, 97.0) p97_5_ms = np.percentile(t_ms, 95.0) p1_sec = p1_ms / 1000.0 p2_5_sec = p2_5_ms / 1000.0 q1_sec = q1_ms / 1000.0 med_sec = med_ms / 1000.0 q3_sec = q3_ms / 1000.0 p97_5_sec = p97_5_ms / 1000.0 p99_sec = p99_ms / 1000.0 mean_sec = mean_ms / 1000.0 std_sec = std_ms / 1000.0 raw_min_sec = raw_min_ms / 1000.0 raw_max_sec = raw_max_ms / 1000.0 summary_rows.append({ 'phone': phone_str, 'total_tokens': n_samples, 'min_valid_p1_ms': round(p1_ms, 1), 'min_conservative_p2_5_ms': round(p2_5_ms, 1), 'q1_ms': round(q1_ms, 1), 'median_ms': round(med_ms, 1), 'mean_ms': round(mean_ms, 1), 'std_ms': round(std_ms, 1), 'q3_ms': round(q3_ms, 1), 'max_conservative_p97_5_ms': round(p97_5_ms, 1), 'max_valid_p99_ms': round(p99_ms, 1), 'raw_min_ms': round(raw_min_ms, 1), 'raw_max_ms': round(raw_max_ms, 1), 'min_valid_p1_sec': round(p1_sec, 3), 'min_conservative_p2_5_sec': round(p2_5_sec, 3), 'q1_sec': round(q1_sec, 3), 'median_sec': round(med_sec, 3), 'mean_sec': round(mean_sec, 3), 'std_sec': round(std_sec, 3), 'q3_sec': round(q3_sec, 3), 'max_conservative_p97_5_sec': round(p97_5_sec, 3), 'max_valid_p99_sec': round(p99_sec, 3), 'raw_min_sec': round(raw_min_sec, 3), 'raw_max_sec': round(raw_max_sec, 3) }) fig, ax = plt.subplots(figsize=(9, 5.5), dpi=120) plot_upper_limit = p99_ms * 1.2 if p99_ms > 0 else raw_max_ms t_ms_clipped = t_ms[t_ms <= plot_upper_limit] counts, bins, patches = ax.hist( t_ms_clipped, bins=40, color='#1d3557', edgecolor='white', alpha=0.75, label=f'Unified Tokens (N={n_samples:,})' ) ax.axvline(med_ms, color='#e63946', linestyle='-', linewidth=2.2, label=f'Median: {med_ms:.1f} ms') ax.axvline(mean_ms, color='#9b5de5', linestyle='--', linewidth=1.5, label=f'Mean: {mean_ms:.1f} ms') ax.axvline(p1_ms, color='#2a9d8f', linestyle='-.', linewidth=1.8, label=f'Min Valid (P1): {p1_ms:.1f} ms') ax.axvline(p99_ms, color='#e76f51', linestyle='-.', linewidth=1.8, label=f'Max Valid: {p99_ms:.1f} ms') ax.axvline(q1_ms, color='#457b9d', linestyle=':', linewidth=1.4, label=f'Q1 (P25): {q1_ms:.1f} ms') ax.axvline(q3_ms, color='#457b9d', linestyle=':', linewidth=1.4, label=f'Q3 (P75): {q3_ms:.1f} ms') ax.axvspan(q1_ms, q3_ms, color='#a8dadc', alpha=0.25, label='Core Tier [Q1 - Q3]') ax.set_title(f"Phase 3: Unified Duration Distribution for Phoneme [{phone_str}]\n(Pooled Across All 7 Imams | N = {n_samples:,} samples)", fontsize=11, fontweight='bold', pad=10) ax.set_xlabel("Normalized Duration (ms)", fontsize=10, fontweight='bold') ax.set_ylabel("Token Count", fontsize=10, fontweight='bold') ax.grid(axis='y', alpha=0.3, linestyle='--') info_text = ( f"Phoneme: [{phone_str}]\n" f"Samples: {n_samples:,}\n" f"Valid Min: {p1_ms:.1f} ms\n" f"Median: {med_ms:.1f} ms\n" f"Valid Max: {p99_ms:.1f} ms\n" f"Mean +/- Std: {mean_ms:.1f} +/- {std_ms:.1f} ms" ) ax.text(0.97, 0.95, info_text, transform=ax.transAxes, fontsize=8.5, verticalalignment='top', horizontalalignment='right', bbox=dict(boxstyle='round,pad=0.5', facecolor='whitesmoke', edgecolor='#adb5bd', alpha=0.9)) ax.legend(loc='upper left', fontsize=8, framealpha=0.9) fig.tight_layout() safe_name = sanitize_filename(phone_str) plot_out = os.path.join(plots_dir, f"{safe_name}_unified_histogram.png") if os.path.exists(plot_out): try: os.remove(plot_out) except Exception: pass try: fig.savefig(plot_out) except Exception as e: import time time.sleep(0.1) fig.savefig(plot_out) plt.close(fig) summary_df = pd.DataFrame(summary_rows) master_csv_path = os.path.join(BASE_DIR, "phase3_unified_phoneme_timings.csv") summary_df.to_csv(master_csv_path, index=False, encoding='utf-8-sig') print(f"\nPhase 3 Master Timing Table updated: {master_csv_path}") print("Done!") if __name__ == '__main__': main()