modified: .gitignore
modified: README.md new file: requirement.txt new file: scripts_2/README.md new file: scripts_2/__init__.py new file: scripts_2/__pycache__/__init__.cpython-310.pyc new file: scripts_2/__pycache__/signal_utils.cpython-310.pyc new file: scripts_2/__pycache__/task3_identify.cpython-310.pyc new file: scripts_2/signal_utils.py new file: scripts_2/task3_identify.py new file: scripts_3/README.md new file: scripts_3/__init__.py new file: scripts_3/__pycache__/__init__.cpython-310.pyc new file: scripts_3/__pycache__/task4_predict_freq.cpython-310.pyc new file: scripts_3/task4_predict_freq.py
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scripts_3/task4_predict_freq.py
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scripts_3/task4_predict_freq.py
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from __future__ import annotations
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import argparse
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from pathlib import Path
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import sys
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import matplotlib
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matplotlib.use("Agg")
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import matplotlib.pyplot as plt
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import numpy as np
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if __package__ is None or __package__ == "":
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sys.path.append(str(Path(__file__).resolve().parents[1]))
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from scripts_2.signal_utils import (
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TOP_RESPONSE_AXIS,
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TOP_RESPONSE_SENSOR,
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dominant_frequency_in_band,
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ensure_parent_dir,
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estimate_sampling_rate,
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get_middle_segment,
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parse_frequency_from_filename,
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read_sensor_signal,
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)
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def parse_args() -> argparse.Namespace:
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project_root = Path(__file__).resolve().parents[1]
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parser = argparse.ArgumentParser(description="Predict the harmonic input frequency from top-response steady-state data only.")
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parser.add_argument(
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"--file",
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type=str,
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default=str(project_root / "downloads" / "Non_TMD" / "val" / "harmonic_5mm_0.75Hz.csv"),
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help="Path to a forced-vibration CSV such as data from Non_TMD val/test.",
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)
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parser.add_argument(
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"--output-dir",
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type=str,
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default=str(project_root / "evaluation_outputs" / "task4_predict_freq"),
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help="Directory for plots and optional exported diagnostics.",
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)
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parser.add_argument(
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"--show",
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action="store_true",
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help="Display the generated figure in addition to saving it.",
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)
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return parser.parse_args()
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def save_task4_figure(
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file_path: Path,
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output_dir: Path,
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time_segment: np.ndarray,
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signal_segment: np.ndarray,
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freqs: np.ndarray,
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magnitudes: np.ndarray,
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predicted_frequency_hz: float,
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true_frequency_hz: float | None,
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) -> Path:
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output_dir = ensure_parent_dir(output_dir)
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fig, axes = plt.subplots(2, 1, figsize=(12, 8))
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fig.suptitle(f"Task 4 | Forced-Vibration Frequency Prediction | {file_path.name}")
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axes[0].plot(time_segment, signal_segment, color="tab:blue", linewidth=1.0)
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axes[0].set_title(f"Steady middle segment from {TOP_RESPONSE_SENSOR} / {TOP_RESPONSE_AXIS}")
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axes[0].set_xlabel("Time (s)")
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axes[0].set_ylabel("Acceleration")
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axes[0].grid(True, alpha=0.25)
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band_mask = (freqs >= 0.1) & (freqs <= 5.0)
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axes[1].plot(freqs[band_mask], magnitudes[band_mask], color="tab:purple", linewidth=1.1, label="Windowed FFT")
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axes[1].axvline(
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predicted_frequency_hz,
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color="tab:red",
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linestyle="--",
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linewidth=1.6,
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label=f"Predicted = {predicted_frequency_hz:.5f} Hz",
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)
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if true_frequency_hz is not None:
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axes[1].axvline(
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true_frequency_hz,
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color="tab:green",
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linestyle=":",
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linewidth=1.6,
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label=f"Filename truth = {true_frequency_hz:.5f} Hz",
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)
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axes[1].set_title("High-resolution spectrum from top steady-state response")
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axes[1].set_xlabel("Frequency (Hz)")
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axes[1].set_ylabel("Magnitude")
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axes[1].grid(True, alpha=0.25)
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axes[1].legend()
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plt.tight_layout()
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figure_path = output_dir / f"{file_path.stem}_task4_predict_freq.png"
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plt.savefig(figure_path, dpi=180, bbox_inches="tight")
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return figure_path
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def analyze_forced_vibration(
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file_path: Path,
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output_dir: Path,
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show: bool = False,
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) -> dict[str, float | int | str]:
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file_path = Path(file_path).resolve()
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time_values, raw_signal = read_sensor_signal(file_path, sensor_code=TOP_RESPONSE_SENSOR, value_column=TOP_RESPONSE_AXIS)
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time_segment, signal_segment = get_middle_segment(time_values, raw_signal)
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sampling_rate = estimate_sampling_rate(time_segment)
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predicted_frequency_hz, freqs, magnitudes, peak_index = dominant_frequency_in_band(
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signal_segment,
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sampling_rate=sampling_rate,
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min_hz=0.1,
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max_hz=5.0,
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zero_padding_factor=32,
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)
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true_frequency_hz = parse_frequency_from_filename(file_path.name)
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absolute_error_hz = None if true_frequency_hz is None else abs(predicted_frequency_hz - true_frequency_hz)
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figure_path = save_task4_figure(
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file_path=file_path,
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output_dir=output_dir,
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time_segment=time_segment,
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signal_segment=signal_segment,
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freqs=freqs,
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magnitudes=magnitudes,
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predicted_frequency_hz=predicted_frequency_hz,
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true_frequency_hz=true_frequency_hz,
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)
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if show:
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plt.show()
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plt.close("all")
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return {
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"file_path": str(file_path),
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"sensor_code": TOP_RESPONSE_SENSOR,
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"axis": TOP_RESPONSE_AXIS,
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"sampling_rate_hz": float(sampling_rate),
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"segment_sample_count": int(signal_segment.size),
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"segment_start_time": float(time_segment[0]),
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"segment_end_time": float(time_segment[-1]),
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"predicted_frequency_hz": float(predicted_frequency_hz),
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"fft_peak_bin_hz": float(freqs[peak_index]),
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"fft_peak_magnitude": float(magnitudes[peak_index]),
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"true_frequency_hz": None if true_frequency_hz is None else float(true_frequency_hz),
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"absolute_error_hz": None if absolute_error_hz is None else float(absolute_error_hz),
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"figure_path": str(figure_path),
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"note": "Prediction uses only the top response sensor WSMS00007/value3. Base sensor WSMS00012/value1 is not used to infer the answer.",
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}
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def print_report(result: dict[str, float | int | str]) -> None:
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print("=== Task 4: Excitation Frequency Reverse Prediction ===")
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print(f"Input file: {result['file_path']}")
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print(f"Top response sensor: {result['sensor_code']} / {result['axis']}")
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print(f"Sampling rate: {result['sampling_rate_hz']:.4f} Hz")
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print(f"Steady segment samples: {result['segment_sample_count']}")
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print(f"Steady segment time range: {result['segment_start_time']:.4f} s to {result['segment_end_time']:.4f} s")
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print(f"Predicted excitation frequency: {result['predicted_frequency_hz']:.6f} Hz")
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if result["true_frequency_hz"] is not None:
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print(f"Filename frequency truth: {result['true_frequency_hz']:.6f} Hz")
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print(f"Absolute error: {result['absolute_error_hz']:.6f} Hz")
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else:
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print("Filename frequency truth: unavailable")
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print(f"Figure saved to: {result['figure_path']}")
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print(result["note"])
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def main() -> None:
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args = parse_args()
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result = analyze_forced_vibration(
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file_path=Path(args.file),
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output_dir=Path(args.output_dir),
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show=bool(args.show),
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)
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print_report(result)
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if __name__ == "__main__":
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main()
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