GraphingIEEE/WaveToCsvWithCalFile.py at main · DeepeshPatidar/GraphingIEEE · GitHub

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import numpy as np
import pandas as pd
import os
import re
from scipy.io import wavfile

# ================= USER SETTINGS =================
cal_wav = r"F:\ONOSOKKI\LA7000\DATA\000001\REC\CAL_94dB_1kHz_TEST.wav"
test_wav_folder = r"F:\ONOSOKKI\LA7000\DATA\000001\REC\input"
output_folder   = r"F:\ONOSOKKI\LA7000\DATA\000001\REC\output"

fs_expected = 64000     # Hz (confirmed from FAMOS)
dt_block    = 0.01      # 10 ms
p_ref       = 20e-6     # Pa
cal_skip    = 0.5       # seconds to skip at start of cal file
cal_len     = 3.0       # seconds used for calibration
eps         = 1e-20
# =================================================

os.makedirs(output_folder, exist_ok=True)

# ---------- READ CALIBRATION WAV ----------
fs_cal, cal_sig = wavfile.read(cal_wav)
cal_sig = cal_sig.astype(np.float64)

if fs_cal != fs_expected:
    print(f"⚠ Calibration fs = {fs_cal} Hz (expected {fs_expected})")

if cal_sig.ndim > 1:
    cal_sig = cal_sig[:, 0]

i0 = int(cal_skip * fs_cal)
i1 = int((cal_skip + cal_len) * fs_cal)

cal_block = cal_sig[i0:i1]
Vrms_cal = np.sqrt(np.mean(cal_block * cal_block))

Pa_per_V = 1.0 / Vrms_cal   # 94 dB = 1 Pa

print(f"Calibration applied: 1 Pa = {Vrms_cal:.6e} V")

# ---------- PROCESS TEST WAV FILES ----------
for fname in os.listdir(test_wav_folder):
    if not fname.lower().endswith(".wav"):
        continue

    fs, sig = wavfile.read(os.path.join(test_wav_folder, fname))
    sig = sig.astype(np.float64)

    if fs != fs_expected:
        print(f"⚠ Sample rate mismatch in {fname}: {fs} Hz")

    if sig.ndim > 1:
        sig = sig[:, 0]

    sig_pa = sig * Pa_per_V

    samples_per_block = int(fs * dt_block)
    n_blocks = len(sig_pa) // samples_per_block

    time = np.arange(n_blocks) * dt_block
    dBA  = []

    for i in range(n_blocks):
        block = sig_pa[i*samples_per_block:(i+1)*samples_per_block]
        rms = np.sqrt(np.mean(block * block))
        spl = 20 * np.log10(rms / p_ref + eps)
        dBA.append(spl)

    m = re.search(r"(A\d+)", fname)
    run_id = m.group(1) if m else "UNK"

    df = pd.DataFrame({
        "Time": time,
        f"dB_{run_id}": dBA
    })

    out_csv = fname.replace(".wav", "_10ms_dBA.csv")
    df.to_csv(os.path.join(output_folder, out_csv), index=False)

    print(f"Processed: {fname}")

print("✅ Calibrated WAV → dB(A) CSV conversion complete.")