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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

scripts_3/README.md

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+# 激振频率逆向预测 (Forced-Vibration Frequency Prediction)
+
+## 项目简介
+本目录用于完成强迫振动频率逆向预测任务。脚本基于建筑顶部响应传感器 `WSMS00007` 的 Z 向数据，从稳态振动段中识别主频，反推出地震台输入的简谐波频率，并输出频率误差与可视化结果。
+
+## 技术路线
+本目录的分析流程覆盖稳态段提取、主频精细识别与结果校验三个部分：
+
+1. **稳态中段截取**
+   对强迫振动时程执行滑动窗口搜索，从中间区域选出波动最稳定的响应片段，用于频谱分析。
+2. **顶部响应主频识别**
+   读取 `WSMS00007` 的 `value3` 列，对稳态段进行加窗 FFT 与补零计算，并结合抛物线插值输出高精度激振频率。
+3. **文件名真值校验**
+   从样本文件名中提取频率标签，计算预测值与真值之间的绝对误差。
+
+## 核心模块说明
+
+- **`task4_predict_freq.py`**
+  任务主脚本。负责稳态段截取、频谱分析、抛物线插值、误差计算与频域图像生成。
+
+## 使用指南
+
+**1. 运行任务脚本**
+对单个强迫振动样本预测激振频率：
+```bash
+python scripts_3\task4_predict_freq.py --file downloads\Non_TMD\val\harmonic_5mm_0.75Hz.csv
+```
+
+**2. 指定输出目录**
+将图像保存到自定义目录：
+```bash
+python scripts_3\task4_predict_freq.py --file downloads\Non_TMD\val\harmonic_5mm_0.75Hz.csv --output-dir evaluation_outputs\task4_custom
+```

scripts_3/__init__.py

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+"""Signal-processing scripts for forced-vibration frequency prediction."""

scripts_3/task4_predict_freq.py

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