| 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) |
| |
| |
| |
| if phone_str == 'a:': |
| p99_ms = raw_max_ms |
| p97_5_ms = raw_max_ms |
| |
| elif phone_str == 'i:': |
| p99_ms = np.percentile(t_ms, 90.0) |
| p97_5_ms = np.percentile(t_ms, 88.0) |
| |
| 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() |
