modified: .DS_Store

modified:   .gitignore
	new file:   __pycache__/convert_teacher_csv.cpython-314.pyc
	new file:   __pycache__/evaluation_studio.cpython-314.pyc
	new file:   convert_teacher_csv.py
	new file:   downloads/Non_TMD/free_vib/data_converted_free.csv
	new file:   downloads/TMD/val/data_converted_tmd.csv
	new file:   evaluation_studio.py
	new file:   scripts_2/__pycache__/__init__.cpython-314.pyc
	new file:   scripts_2/__pycache__/signal_utils.cpython-314.pyc
	new file:   scripts_2/__pycache__/task3_identify.cpython-314.pyc
	new file:   scripts_3/__pycache__/__init__.cpython-314.pyc
	new file:   scripts_3/__pycache__/task4_predict_freq.cpython-314.pyc
	modified:   scripts_3/task4_predict_freq.py
	new file:   scripts_4/__pycache__/__init__.cpython-314.pyc
	modified:   scripts_4/__pycache__/adapter.cpython-314.pyc

scripts_3/task4_predict_freq.py

@@ -10,6 +10,7 @@ matplotlib.use("Agg")
 
 import matplotlib.pyplot as plt
 import numpy as np
+from scipy.signal import find_peaks
 
 if __package__ is None or __package__ == "":
     sys.path.append(str(Path(__file__).resolve().parents[1]))
@@ -98,6 +99,37 @@ def save_task4_figure(
     return figure_path
 
 
+def pick_fundamental_peak_index(
+    freqs: np.ndarray,
+    magnitudes: np.ndarray,
+    min_hz: float = 0.1,
+    max_hz: float = 5.0,
+    relative_threshold: float = 0.20,
+    relative_prominence: float = 0.08,
+) -> int:
+    band_mask = (freqs >= min_hz) & (freqs <= max_hz)
+    band_indices = np.where(band_mask)[0]
+    if band_indices.size == 0:
+        raise ValueError("No FFT bins fall inside the requested frequency band.")
+
+    band_magnitudes = magnitudes[band_mask]
+    max_magnitude = float(np.max(band_magnitudes))
+    height_threshold = relative_threshold * max_magnitude
+    prominence_threshold = relative_prominence * max_magnitude
+    local_peaks, _ = find_peaks(
+        band_magnitudes,
+        height=height_threshold,
+        prominence=prominence_threshold,
+    )
+
+    if local_peaks.size == 0:
+        # Fallback for very smooth spectra: choose the strongest bin in band.
+        return int(band_indices[int(np.argmax(band_magnitudes))])
+
+    # Fundamental-first rule: choose the left-most valid peak.
+    return int(band_indices[int(local_peaks[0])])
+
+
 def analyze_forced_vibration(
     file_path: Path,
     output_dir: Path,
@@ -107,13 +139,22 @@ def analyze_forced_vibration(
     time_values, raw_signal = read_sensor_signal(file_path, sensor_code=TOP_RESPONSE_SENSOR, value_column=TOP_RESPONSE_AXIS)
     time_segment, signal_segment = get_middle_segment(time_values, raw_signal)
     sampling_rate = estimate_sampling_rate(time_segment)
-    predicted_frequency_hz, freqs, magnitudes, peak_index = dominant_frequency_in_band(
+    _, freqs, magnitudes, _ = dominant_frequency_in_band(
         signal_segment,
         sampling_rate=sampling_rate,
         min_hz=0.1,
         max_hz=5.0,
         zero_padding_factor=32,
     )
+    peak_index = pick_fundamental_peak_index(
+        freqs=freqs,
+        magnitudes=magnitudes,
+        min_hz=0.1,
+        max_hz=5.0,
+        relative_threshold=0.20,
+        relative_prominence=0.08,
+    )
+    predicted_frequency_hz = float(freqs[peak_index])
     true_frequency_hz = parse_frequency_from_filename(file_path.name)
     absolute_error_hz = None if true_frequency_hz is None else abs(predicted_frequency_hz - true_frequency_hz)