diff --git a/.DS_Store b/.DS_Store index c9da38d..7216fb1 100644 Binary files a/.DS_Store and b/.DS_Store differ diff --git a/.gitignore b/.gitignore index 74e2025..5f7cb0d 100644 --- a/.gitignore +++ b/.gitignore @@ -2,4 +2,6 @@ .vscode/ scripts/__pycache__/ old/ -evaluation_outputs/ \ No newline at end of file +evaluation_outputs/ +.studio_cache/ +.gitignore \ No newline at end of file diff --git a/__pycache__/convert_teacher_csv.cpython-314.pyc b/__pycache__/convert_teacher_csv.cpython-314.pyc new file mode 100644 index 0000000..a2aaba1 Binary files /dev/null and b/__pycache__/convert_teacher_csv.cpython-314.pyc differ diff --git a/__pycache__/evaluation_studio.cpython-314.pyc b/__pycache__/evaluation_studio.cpython-314.pyc new file mode 100644 index 0000000..dd109a0 Binary files /dev/null and b/__pycache__/evaluation_studio.cpython-314.pyc differ diff --git a/convert_teacher_csv.py b/convert_teacher_csv.py new file mode 100644 index 0000000..686b92b --- /dev/null +++ b/convert_teacher_csv.py @@ -0,0 +1,200 @@ +from __future__ import annotations + +import argparse +from pathlib import Path +import re + +import numpy as np +import pandas as pd + + +VIB_COLUMN_RE = re.compile(r"^(WSMS\d+)\.Acc([XYZ])$", flags=re.IGNORECASE) +SGD_COLUMN_RE = re.compile(r"^(WSGD\d+)\.chdata$", flags=re.IGNORECASE) +OUTPUT_COLUMNS = ["code", "type", "time", "value1", "value2", "value3"] + + +def parse_args() -> argparse.Namespace: + parser = argparse.ArgumentParser( + description="Convert teacher wide-format CSV into downloads-compatible long-format CSV." + ) + parser.add_argument("--input", type=str, required=True, help="Input wide-format CSV path.") + parser.add_argument("--output", type=str, required=True, help="Output long-format CSV path.") + parser.add_argument( + "--time-column", + type=str, + default="time", + help="Time column name in the input CSV (default: time).", + ) + parser.add_argument( + "--timezone-offset-hours", + type=float, + default=8.0, + help="Offset used when parsing naive datetime strings to epoch seconds (default: 8).", + ) + return parser.parse_args() + + +def discover_sensor_columns(columns: list[str]) -> tuple[dict[str, dict[str, str]], dict[str, str]]: + vibr_map: dict[str, dict[str, str]] = {} + sgd_map: dict[str, str] = {} + for column in columns: + vibr_match = VIB_COLUMN_RE.match(column) + if vibr_match is not None: + sensor_code = vibr_match.group(1).upper() + axis = vibr_match.group(2).upper() + vibr_map.setdefault(sensor_code, {})[axis] = column + continue + sgd_match = SGD_COLUMN_RE.match(column) + if sgd_match is not None: + sensor_code = sgd_match.group(1).upper() + sgd_map[sensor_code] = column + return vibr_map, sgd_map + + +def _strip_excel_datetime_wrapper(values: pd.Series) -> pd.Series: + text = values.astype(str).str.strip() + text = text.str.replace(r'^="(.*)"$', r"\1", regex=True) + text = text.str.replace(r'^"(.*)"$', r"\1", regex=True) + return text + + +def to_epoch_seconds(time_values: pd.Series, timezone_offset_hours: float) -> pd.Series: + numeric_time = pd.to_numeric(time_values, errors="coerce") + if numeric_time.notna().mean() > 0.8: + return numeric_time.astype("float64") + + text_time = _strip_excel_datetime_wrapper(time_values) + dt_values = pd.to_datetime(text_time, errors="coerce") + epoch = (dt_values - pd.Timestamp("1970-01-01")) / pd.Timedelta(seconds=1) + epoch = epoch - timezone_offset_hours * 3600.0 + epoch = pd.to_numeric(epoch, errors="coerce") + + # Fall back to numeric parsing for rows that were not parsed as datetime. + fallback_numeric = pd.to_numeric(text_time, errors="coerce") + epoch = epoch.where(epoch.notna(), fallback_numeric) + return epoch.astype("float64") + + +def _empty_output_frame() -> pd.DataFrame: + return pd.DataFrame(columns=OUTPUT_COLUMNS) + + +def build_vibr_rows(df: pd.DataFrame, time_column: str, vibr_map: dict[str, dict[str, str]]) -> list[pd.DataFrame]: + rows: list[pd.DataFrame] = [] + for sensor_code in sorted(vibr_map.keys()): + axis_columns = vibr_map[sensor_code] + use_columns = [time_column] + if "X" in axis_columns: + use_columns.append(axis_columns["X"]) + if "Y" in axis_columns: + use_columns.append(axis_columns["Y"]) + if "Z" in axis_columns: + use_columns.append(axis_columns["Z"]) + if len(use_columns) == 1: + continue + + sensor_df = df.loc[:, use_columns].copy() + sensor_df = sensor_df.rename( + columns={ + axis_columns.get("X", ""): "value1", + axis_columns.get("Y", ""): "value2", + axis_columns.get("Z", ""): "value3", + } + ) + for column in ["value1", "value2", "value3"]: + if column not in sensor_df.columns: + sensor_df[column] = np.nan + sensor_df["value1"] = pd.to_numeric(sensor_df["value1"], errors="coerce") + sensor_df["value2"] = pd.to_numeric(sensor_df["value2"], errors="coerce") + sensor_df["value3"] = pd.to_numeric(sensor_df["value3"], errors="coerce") + sensor_df = sensor_df[sensor_df[["value1", "value2", "value3"]].notna().any(axis=1)] + if sensor_df.empty: + continue + + sensor_df.insert(0, "type", "vibr") + sensor_df.insert(0, "code", sensor_code) + rows.append(sensor_df[OUTPUT_COLUMNS]) + return rows + + +def build_sgd_rows(df: pd.DataFrame, time_column: str, sgd_map: dict[str, str]) -> list[pd.DataFrame]: + rows: list[pd.DataFrame] = [] + for sensor_code in sorted(sgd_map.keys()): + sensor_column = sgd_map[sensor_code] + sensor_df = df.loc[:, [time_column, sensor_column]].copy() + sensor_df = sensor_df.rename(columns={sensor_column: "value1"}) + sensor_df["value1"] = pd.to_numeric(sensor_df["value1"], errors="coerce") + sensor_df = sensor_df[sensor_df["value1"].notna()] + if sensor_df.empty: + continue + + sensor_df["value2"] = np.nan + sensor_df["value3"] = np.nan + sensor_df.insert(0, "type", "sgd") + sensor_df.insert(0, "code", sensor_code) + rows.append(sensor_df[OUTPUT_COLUMNS]) + return rows + + +def convert_dataframe(input_df: pd.DataFrame, time_column: str, timezone_offset_hours: float) -> pd.DataFrame: + if time_column not in input_df.columns: + raise ValueError(f"Time column '{time_column}' not found in input CSV.") + + vibr_map, sgd_map = discover_sensor_columns(input_df.columns.tolist()) + if not vibr_map and not sgd_map: + raise ValueError("No sensor columns detected. Expected WSMSxxxx.AccX/Y/Z or WSGDxxxx.chdata columns.") + + df = input_df.copy() + df[time_column] = to_epoch_seconds(df[time_column], timezone_offset_hours=timezone_offset_hours) + df = df[df[time_column].notna()] + if df.empty: + return _empty_output_frame() + + rows: list[pd.DataFrame] = [] + rows.extend(build_vibr_rows(df, time_column=time_column, vibr_map=vibr_map)) + rows.extend(build_sgd_rows(df, time_column=time_column, sgd_map=sgd_map)) + if not rows: + return _empty_output_frame() + + output_df = pd.concat(rows, ignore_index=True) + output_df["time"] = pd.to_numeric(output_df["time"], errors="coerce").astype("float64") + output_df = output_df[output_df["time"].notna()] + output_df = output_df.sort_values(["time", "code"], kind="mergesort").reset_index(drop=True) + return output_df.loc[:, OUTPUT_COLUMNS] + + +def print_summary(df: pd.DataFrame, output_path: Path) -> None: + print(f"Output saved to: {output_path}") + print(f"Rows: {len(df)}") + if df.empty: + print("No data rows were generated.") + return + print(f"Sensors: {df['code'].nunique()}") + print("Rows by sensor:") + counts = df.groupby(["code", "type"]).size().sort_index() + for (code, sensor_type), count in counts.items(): + print(f" - {code} ({sensor_type}): {int(count)}") + + +def main() -> None: + args = parse_args() + input_path = Path(args.input).resolve() + output_path = Path(args.output).resolve() + + if not input_path.exists(): + raise FileNotFoundError(f"Input file not found: {input_path}") + + input_df = pd.read_csv(input_path) + output_df = convert_dataframe( + input_df=input_df, + time_column=args.time_column, + timezone_offset_hours=float(args.timezone_offset_hours), + ) + + output_path.parent.mkdir(parents=True, exist_ok=True) + output_df.to_csv(output_path, index=False) + print_summary(output_df, output_path=output_path) + + +if __name__ == "__main__": + main() diff --git a/downloads/Non_TMD/free_vib/data_converted_free.csv b/downloads/Non_TMD/free_vib/data_converted_free.csv new file mode 100644 index 0000000..a92900f --- /dev/null +++ b/downloads/Non_TMD/free_vib/data_converted_free.csv @@ -0,0 +1,63151 @@ +code,type,time,value1,value2,value3 +WSMS00008,vibr,1775115646.36,0.06091,0.24748,-0.54352 +WSMS00008,vibr,1775115646.38,-0.15091,-0.25364,-0.281 +WSMS00008,vibr,1775115646.4,0.24165,-0.01025,-0.46116 +WSMS00008,vibr,1775115646.42,-0.23314,0.10205,-0.52981 +WSMS00008,vibr,1775115646.44,0.14801,-0.24187,-0.04514 +WSMS00008,vibr,1775115646.46,-0.04718,0.24093,-0.17675 +WSMS00008,vibr,1775115646.48,-0.15384,-0.16986,0.32382 +WSMS00008,vibr,1775115646.5,0.29791,-0.06817,0.37035 +WSMS00008,vibr,1775115646.52,-0.21548,0.09897,0.3657 +WSMS00008,vibr,1775115646.54,0.18026,-0.20213,0.60516 +WSMS00008,vibr,1775115646.56,0.06175,0.14761,0.2138 +WSMS00008,vibr,1775115646.58,-0.10188,-0.06447,0.26676 +WSMS00008,vibr,1775115646.6,0.28129,-0.15735,0.12415 +WSMS00008,vibr,1775115646.62,-0.10527,0.04737,-0.17172 +WSMS00008,vibr,1775115646.64,0.16356,-0.15344,-0.06351 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+WSMS00009,vibr,1775115805.3,0.03326,0.00927,-0.00216 +WSMS00007,vibr,1775115805.32,0.03209,0.00115,-0.04352 +WSMS00009,vibr,1775115805.32,0.02962,0.00192,-0.01858 +WSMS00007,vibr,1775115805.34,0.04317,-0.00286,-0.06581 +WSMS00009,vibr,1775115805.34,0.02011,0.0011,0.0052 diff --git a/evaluation_studio.py b/evaluation_studio.py new file mode 100644 index 0000000..00f782e --- /dev/null +++ b/evaluation_studio.py @@ -0,0 +1,712 @@ +from __future__ import annotations + +import argparse +from datetime import datetime +import math +import pickle +from pathlib import Path +import shutil +import threading +import traceback +import tkinter as tk +from tkinter import filedialog, messagebox, ttk +from tkinter.scrolledtext import ScrolledText + +import matplotlib + +matplotlib.use("Agg") + +import matplotlib.pyplot as plt +import numpy as np +import pandas as pd + + +PROJECT_ROOT = Path(__file__).resolve().parent +CHECKPOINT_DEFAULT = PROJECT_ROOT / "checkpoints_final" / "task1_final" / "task1_final_model.pkl" +ADAPTER_DEFAULT = PROJECT_ROOT / "checkpoints_final" / "task1_final" / "task1_tmd_adapter.pkl" +APP_OUTPUT_DIR = PROJECT_ROOT / "evaluation_outputs" / "studio_outputs" +APP_CACHE_DIR = PROJECT_ROOT / ".studio_cache" + +TASK1_LABEL = "任务1：顶部RMS预测（Non_TMD）" +TASK2_LABEL = "任务2：自由衰减识别自振频率和阻尼比" +TASK3_LABEL = "任务3：顶部响应反推激振频率" +TASK4_LABEL = "挑战任务：TMD RMS预测（基础模型+适配器）" + +TASK_CHOICES = [TASK1_LABEL, TASK2_LABEL, TASK3_LABEL, TASK4_LABEL] + +HAS_DND = False +TkRootClass: type[tk.Tk] = tk.Tk +try: + from tkinterdnd2 import DND_FILES, TkinterDnD # type: ignore + + HAS_DND = True + TkRootClass = TkinterDnD.Tk +except Exception: + DND_FILES = "DND_Files" # type: ignore + + +def pretty_json_like_lines(payload: dict) -> str: + lines: list[str] = [] + for key in sorted(payload.keys()): + value = payload[key] + if isinstance(value, float): + lines.append(f"- {key}: {value:.6f}") + else: + lines.append(f"- {key}: {value}") + return "\n".join(lines) + + +def ensure_long_format_csv(input_path: Path, force_convert: bool, tz_offset_hours: float) -> tuple[Path, bool]: + from convert_teacher_csv import convert_dataframe + + df_head = pd.read_csv(input_path, nrows=5) + lower_columns = {column.lower() for column in df_head.columns} + long_required = {"code", "type", "time", "value1", "value2", "value3"} + is_long = long_required.issubset(lower_columns) + if is_long and not force_convert: + return input_path, False + + APP_CACHE_DIR.mkdir(parents=True, exist_ok=True) + full_df = pd.read_csv(input_path) + converted_df = convert_dataframe( + input_df=full_df, + time_column="time", + timezone_offset_hours=tz_offset_hours, + ) + stamp = datetime.now().strftime("%Y%m%d_%H%M%S") + output_path = APP_CACHE_DIR / f"{input_path.stem}_converted_{stamp}.csv" + converted_df.to_csv(output_path, index=False) + return output_path, True + + +def _load_sensor_frame(df: pd.DataFrame, sensor_code: str, value_col: str, time_col: str = "time") -> pd.DataFrame: + sensor_df = df.loc[df["code"] == sensor_code, [time_col, value_col]].copy() + if sensor_df.empty: + return sensor_df + sensor_df = sensor_df.sort_values(time_col) + sensor_df = sensor_df.drop_duplicates(subset=time_col, keep="first") + sensor_df[time_col] = pd.to_numeric(sensor_df[time_col], errors="coerce") + sensor_df[value_col] = pd.to_numeric(sensor_df[value_col], errors="coerce") + sensor_df = sensor_df[sensor_df[time_col].notna() & sensor_df[value_col].notna()] + return sensor_df + + +def build_prediction_input(csv_path: Path, for_tmd: bool) -> dict: + from scripts.config import make_experiment_config + from scripts.dataset import ( + calculate_rms, + estimate_sampling_rate, + extract_core_features, + extract_frequency_hz, + get_dominant_frequency, + get_middle_segment, + ) + + config = make_experiment_config().data + if for_tmd: + config.data_dir = "downloads/TMD" + config.__post_init__() + + df = pd.read_csv(csv_path) + base_df = _load_sensor_frame(df, sensor_code=config.base_sensor_code, value_col=config.base_axis, time_col=config.time_column) + if base_df.empty: + raise ValueError( + f"缺少基底传感器 {config.base_sensor_code}/{config.base_axis}，" + "无法执行RMS预测任务。" + ) + + response_df = _load_sensor_frame( + df, + sensor_code=config.response_sensor_code, + value_col=config.response_axis, + time_col=config.time_column, + ) + + if response_df.empty: + time_values = base_df[config.time_column].to_numpy(dtype=np.float64) + x_values = base_df[config.base_axis].to_numpy(dtype=np.float32) + time_middle, x_middle, _ = get_middle_segment(time_values, x_values, x_values, config) + y_middle = None + has_truth = False + else: + aligned = base_df.rename(columns={config.base_axis: "base_signal"}).merge( + response_df.rename(columns={config.response_axis: "response_signal"}), + on=config.time_column, + how="left", + ) + aligned["response_signal"] = aligned["response_signal"].interpolate(method=config.interpolation_method, limit_direction="both") + aligned["response_signal"] = aligned["response_signal"].ffill().bfill() + time_values = aligned[config.time_column].to_numpy(dtype=np.float64) + x_values = aligned["base_signal"].to_numpy(dtype=np.float32) + y_values = aligned["response_signal"].to_numpy(dtype=np.float32) + time_middle, x_middle, y_middle = get_middle_segment(time_values, x_values, y_values, config) + has_truth = True + + sampling_rate = estimate_sampling_rate(np.asarray(time_middle, dtype=np.float64)) + frequency_hz = extract_frequency_hz(csv_path.name) + if frequency_hz is None: + frequency_hz = get_dominant_frequency(np.asarray(x_middle, dtype=np.float32), sampling_rate) + features = extract_core_features(np.asarray(x_middle, dtype=np.float32), sampling_rate, config, known_frequency_hz=frequency_hz) + x_rms = float(calculate_rms(np.asarray(x_middle, dtype=np.float32))) + + true_y_rms = None + if has_truth and y_middle is not None: + true_y_rms = float(calculate_rms(np.asarray(y_middle, dtype=np.float32))) + + return { + "config": config, + "time_middle": np.asarray(time_middle, dtype=np.float64), + "x_middle": np.asarray(x_middle, dtype=np.float32), + "y_middle": None if y_middle is None else np.asarray(y_middle, dtype=np.float32), + "sampling_rate": float(sampling_rate), + "frequency_hz": float(frequency_hz), + "features": np.asarray(features, dtype=np.float32), + "x_rms": x_rms, + "true_y_rms": true_y_rms, + "has_truth": has_truth, + } + + +def save_prediction_overview_figure( + *, + file_path: Path, + output_dir: Path, + title_prefix: str, + time_middle: np.ndarray, + x_middle: np.ndarray, + sampling_rate: float, + frequency_hz: float, + pred_rms: float, + true_rms: float | None, +) -> Path: + output_dir.mkdir(parents=True, exist_ok=True) + fft_values = np.fft.rfft(x_middle - np.mean(x_middle)) + freqs = np.fft.rfftfreq(x_middle.size, d=1.0 / sampling_rate) + amplitudes = np.abs(fft_values) + + fig, axes = plt.subplots(3, 1, figsize=(10, 8)) + fig.suptitle(f"{title_prefix} | {file_path.name}") + + axes[0].plot(time_middle, x_middle, color="tab:blue") + axes[0].set_title("Input Base Signal (Middle Segment)") + axes[0].set_xlabel("Time") + axes[0].set_ylabel("Acceleration") + axes[0].grid(True, alpha=0.25) + + axes[1].plot(freqs, amplitudes, color="tab:purple") + axes[1].axvline(frequency_hz, color="tab:red", linestyle="--", linewidth=1.2, label=f"f={frequency_hz:.4f}Hz") + axes[1].set_xlim(0.0, 5.0) + axes[1].set_title("Input Spectrum") + axes[1].set_xlabel("Frequency (Hz)") + axes[1].set_ylabel("Amplitude") + axes[1].grid(True, alpha=0.25) + axes[1].legend() + + if true_rms is None: + axes[2].bar(["Pred RMS"], [pred_rms], color=["tab:orange"]) + axes[2].set_title(f"Pred RMS = {pred_rms:.6f} (truth sensor not provided)") + else: + axes[2].bar(["True RMS", "Pred RMS"], [true_rms, pred_rms], color=["tab:green", "tab:orange"]) + err_pct = abs(pred_rms - true_rms) / max(abs(true_rms), 1e-12) * 100.0 + axes[2].set_title(f"True={true_rms:.6f} | Pred={pred_rms:.6f} | Error={err_pct:.2f}%") + axes[2].set_ylabel("RMS") + axes[2].grid(True, axis="y", alpha=0.25) + + plt.tight_layout() + figure_path = output_dir / f"{file_path.stem}_{title_prefix.replace(' ', '_').lower()}.png" + plt.savefig(figure_path, dpi=170, bbox_inches="tight") + plt.close(fig) + return figure_path + + +def run_task1(csv_path: Path) -> tuple[dict, Path]: + from scripts.config import CORE_FEATURE_NAMES + + with CHECKPOINT_DEFAULT.open("rb") as handle: + payload = pickle.load(handle) + model = payload["model"] + pred_input = build_prediction_input(csv_path=csv_path, for_tmd=False) + pred_tr = max(float(model.predict([pred_input["features"].tolist()])[0]), pred_input["config"].normalization_eps) + pred_rms = pred_tr * float(pred_input["x_rms"]) + true_rms = pred_input["true_y_rms"] + rel_err_pct = None if true_rms is None else abs(pred_rms - true_rms) / max(abs(true_rms), 1e-12) * 100.0 + + out_dir = APP_OUTPUT_DIR / "task1" + fig_path = save_prediction_overview_figure( + file_path=csv_path, + output_dir=out_dir, + title_prefix="Task1 Prediction", + time_middle=pred_input["time_middle"], + x_middle=pred_input["x_middle"], + sampling_rate=float(pred_input["sampling_rate"]), + frequency_hz=float(pred_input["frequency_hz"]), + pred_rms=float(pred_rms), + true_rms=true_rms, + ) + result = { + "task": "task1_rms_prediction", + "file_path": str(csv_path), + "frequency_hz": float(pred_input["frequency_hz"]), + "x_rms": float(pred_input["x_rms"]), + "true_y_rms": None if true_rms is None else float(true_rms), + "pred_tr": float(pred_tr), + "pred_y_rms": float(pred_rms), + "relative_error_percent": None if rel_err_pct is None else float(rel_err_pct), + "truth_available": bool(pred_input["has_truth"]), + "model_name": str(payload.get("model_name", "unknown")), + "feature_names": ",".join(CORE_FEATURE_NAMES), + } + return result, Path(fig_path) + + +def run_task2(csv_path: Path) -> tuple[dict, Path]: + from scripts_2.task3_identify import analyze_free_vibration + + real_csv = csv_path + if "free_vib" not in {part.lower() for part in csv_path.parts}: + temp_dir = APP_CACHE_DIR / "free_vib" + temp_dir.mkdir(parents=True, exist_ok=True) + real_csv = temp_dir / csv_path.name + shutil.copyfile(csv_path, real_csv) + + output_dir = APP_OUTPUT_DIR / "task2" + result = analyze_free_vibration( + file_path=real_csv, + output_dir=output_dir, + bandwidth_hz=0.3, + show=False, + ) + fig_path = Path(str(result["figure_path"])) + return result, fig_path + + +def run_task3(csv_path: Path) -> tuple[dict, Path]: + from scripts.config import make_experiment_config + from scripts_2.signal_utils import ( + TOP_RESPONSE_AXIS, + TOP_RESPONSE_SENSOR, + calculate_rms, + get_middle_segment, + read_sensor_signal, + ) + from scripts_3.task4_predict_freq import analyze_forced_vibration + + output_dir = APP_OUTPUT_DIR / "task3" + result = analyze_forced_vibration( + file_path=csv_path, + output_dir=output_dir, + show=False, + ) + + # Optional inverse estimation: + # infer base RMS from top-response RMS with fixed-point iteration: + # x = y_rms / TR_model(f, x) + try: + config = make_experiment_config() + with CHECKPOINT_DEFAULT.open("rb") as handle: + payload = pickle.load(handle) + model = payload["model"] + + time_values, raw_signal = read_sensor_signal( + csv_path, + sensor_code=TOP_RESPONSE_SENSOR, + value_column=TOP_RESPONSE_AXIS, + ) + _, signal_segment = get_middle_segment(time_values, raw_signal) + top_rms = float(calculate_rms(signal_segment)) + freq_hz = float(result["predicted_frequency_hz"]) + + min_x = config.data.normalization_eps + x_est = max(top_rms, min_x) + converged = False + rel_change = float("inf") + iter_count = 0 + + for i in range(1, 41): + feat = np.asarray( + [ + [ + freq_hz, + freq_hz * freq_hz, + x_est, + (2.0 * np.pi * freq_hz) ** 2 * config.data.harmonic_amplitude_m, + ] + ], + dtype=np.float64, + ) + tr_est = max(float(model.predict(feat)[0]), config.data.normalization_eps) + x_new = max(top_rms / max(tr_est, config.data.normalization_eps), min_x) + if not np.isfinite(x_new): + break + rel_change = abs(x_new - x_est) / max(abs(x_est), min_x) + # Damping update to improve stability in fixed-point iteration. + x_est = 0.6 * x_est + 0.4 * x_new + iter_count = i + if rel_change < 1e-4: + converged = True + break + + feat_final = np.asarray( + [ + [ + freq_hz, + freq_hz * freq_hz, + x_est, + (2.0 * np.pi * freq_hz) ** 2 * config.data.harmonic_amplitude_m, + ] + ], + dtype=np.float64, + ) + tr_final = max(float(model.predict(feat_final)[0]), config.data.normalization_eps) + y_reconstructed = tr_final * x_est + residual_pct = abs(y_reconstructed - top_rms) / max(abs(top_rms), min_x) * 100.0 + + result["top_response_rms"] = float(top_rms) + result["estimated_base_rms"] = float(x_est) + result["estimated_tr_from_model"] = float(tr_final) + result["inversion_converged"] = bool(converged) + result["inversion_iterations"] = int(iter_count) + result["inversion_relative_change"] = float(rel_change) + result["inversion_residual_percent"] = float(residual_pct) + result["inversion_note"] = "Estimated by fixed-point inversion using task1 TR model; use as an estimate only." + except Exception as error: + result["inversion_note"] = f"Base RMS inversion unavailable: {error}" + + fig_path = Path(str(result["figure_path"])) + return result, fig_path + + +def run_task4_tmd(csv_path: Path) -> tuple[dict, Path]: + from scripts_4.adapter import load_adapter + + with CHECKPOINT_DEFAULT.open("rb") as handle: + payload = pickle.load(handle) + model = payload["model"] + adapter, adapter_extra = load_adapter(ADAPTER_DEFAULT) + pred_input = build_prediction_input(csv_path=csv_path, for_tmd=True) + pred_tr = max(float(model.predict([pred_input["features"].tolist()])[0]), pred_input["config"].normalization_eps) + pred_base = pred_tr * float(pred_input["x_rms"]) + pred_tmd = adapter.predict(pred_base, float(pred_input["frequency_hz"])) + true_rms = pred_input["true_y_rms"] + rel_err_pct = None if true_rms is None else abs(pred_tmd - true_rms) / max(abs(true_rms), 1e-12) * 100.0 + + out_dir = APP_OUTPUT_DIR / "task4" + fig_path = save_prediction_overview_figure( + file_path=csv_path, + output_dir=out_dir, + title_prefix="Task4 TMD Prediction", + time_middle=pred_input["time_middle"], + x_middle=pred_input["x_middle"], + sampling_rate=float(pred_input["sampling_rate"]), + frequency_hz=float(pred_input["frequency_hz"]), + pred_rms=float(pred_tmd), + true_rms=true_rms, + ) + result = { + "task": "task4_tmd_rms_prediction", + "file_path": str(csv_path), + "frequency_hz": float(pred_input["frequency_hz"]), + "x_rms": float(pred_input["x_rms"]), + "true_y_rms": None if true_rms is None else float(true_rms), + "pred_base_y_rms": float(pred_base), + "pred_tmd_y_rms": float(pred_tmd), + "adapter_scale_k": float(adapter.scale_at(float(pred_input["frequency_hz"]))), + "relative_error_percent": None if rel_err_pct is None else float(rel_err_pct), + "truth_available": bool(pred_input["has_truth"]), + "model_name": str(payload.get("model_name", "unknown")), + "adapter_extra": str(adapter_extra), + } + return result, Path(fig_path) + + +def build_key_metrics(task_label: str, result: dict) -> str: + if task_label == TASK1_LABEL: + lines = [ + f"预测RMS: {result.get('pred_y_rms')}", + f"预测TR: {result.get('pred_tr')}", + f"激振频率(Hz): {result.get('frequency_hz')}", + f"输入RMS(x): {result.get('x_rms')}", + f"是否有真值: {'是' if result.get('truth_available') else '否'}", + ] + if result.get("truth_available"): + lines.append(f"真值RMS(y): {result.get('true_y_rms')}") + lines.append(f"误差(%): {result.get('relative_error_percent')}") + return "\n".join(lines) + if task_label == TASK2_LABEL: + lines = [ + f"自振频率f_n(Hz): {result.get('natural_frequency_hz')}", + f"阻尼比zeta: {result.get('damping_ratio')}", + f"精确阻尼比: {result.get('damping_ratio_exact')}", + f"包络R²: {result.get('envelope_r2')}", + f"采样率(Hz): {result.get('sampling_rate_hz')}", + ] + return "\n".join(lines) + if task_label == TASK3_LABEL: + lines = [ + f"预测激振频率(Hz): {result.get('predicted_frequency_hz')}", + f"稳态采样点数: {result.get('segment_sample_count')}", + f"采样率(Hz): {result.get('sampling_rate_hz')}", + ] + if result.get("top_response_rms") is not None: + lines.append(f"顶部RMS(y): {result.get('top_response_rms')}") + if result.get("estimated_base_rms") is not None: + lines.append(f"反演底部RMS(x): {result.get('estimated_base_rms')}") + lines.append(f"反演TR: {result.get('estimated_tr_from_model')}") + lines.append(f"反演残差(%): {result.get('inversion_residual_percent')}") + lines.append(f"反演收敛: {result.get('inversion_converged')}") + if result.get("true_frequency_hz") is not None: + lines.append(f"文件名真值(Hz): {result.get('true_frequency_hz')}") + lines.append(f"绝对误差(Hz): {result.get('absolute_error_hz')}") + return "\n".join(lines) + if task_label == TASK4_LABEL: + lines = [ + f"挑战任务预测RMS: {result.get('pred_tmd_y_rms')}", + f"基础模型RMS: {result.get('pred_base_y_rms')}", + f"适配系数k(f): {result.get('adapter_scale_k')}", + f"激振频率(Hz): {result.get('frequency_hz')}", + f"是否有真值: {'是' if result.get('truth_available') else '否'}", + ] + if result.get("truth_available"): + lines.append(f"真值RMS(y): {result.get('true_y_rms')}") + lines.append(f"误差(%): {result.get('relative_error_percent')}") + return "\n".join(lines) + return pretty_json_like_lines(result) + + +class EvaluationStudio: + def __init__(self) -> None: + self.root = TkRootClass() + self.root.title("建筑评测一体式测试系统") + self.root.geometry("1180x760") + self.root.minsize(980, 620) + + self.task_var = tk.StringVar(value=TASK1_LABEL) + self.file_var = tk.StringVar(value="") + self.force_convert_var = tk.BooleanVar(value=False) + self.timezone_var = tk.StringVar(value="8") + self.status_var = tk.StringVar(value="就绪") + self.preview_image: tk.PhotoImage | None = None + self.original_image: tk.PhotoImage | None = None + self.current_image_path: Path | None = None + + self._build_ui() + + def _build_ui(self) -> None: + main = ttk.Frame(self.root, padding=10) + main.pack(fill="both", expand=True) + main.columnconfigure(1, weight=1) + main.rowconfigure(4, weight=1) + + ttk.Label(main, text="评测任务").grid(row=0, column=0, sticky="w", padx=(0, 8), pady=5) + task_box = ttk.Combobox(main, textvariable=self.task_var, values=TASK_CHOICES, state="readonly") + task_box.grid(row=0, column=1, sticky="ew", pady=5) + + ttk.Label(main, text="CSV 文件").grid(row=1, column=0, sticky="w", padx=(0, 8), pady=5) + file_entry = ttk.Entry(main, textvariable=self.file_var) + file_entry.grid(row=1, column=1, sticky="ew", pady=5) + ttk.Button(main, text="选择文件", command=self._choose_file).grid(row=1, column=2, padx=(8, 0), pady=5) + + self.drop_label = ttk.Label( + main, + text="将 CSV 拖到这里（若无拖拽支持，可用“选择文件”）", + relief="groove", + anchor="center", + ) + self.drop_label.grid(row=2, column=0, columnspan=3, sticky="ew", pady=(4, 8), ipady=12) + if HAS_DND: + self.drop_label.drop_target_register(DND_FILES) + self.drop_label.dnd_bind("<>", self._on_drop) + + config_line = ttk.Frame(main) + config_line.grid(row=3, column=0, columnspan=3, sticky="ew", pady=5) + config_line.columnconfigure(6, weight=1) + ttk.Checkbutton( + config_line, + text="强制按老师宽表格式转换", + variable=self.force_convert_var, + ).grid(row=0, column=0, sticky="w") + ttk.Label(config_line, text="字符串时间时区偏移(小时)").grid(row=0, column=1, sticky="w", padx=(12, 4)) + ttk.Entry(config_line, textvariable=self.timezone_var, width=7).grid(row=0, column=2, sticky="w") + ttk.Button(config_line, text="开始评测", command=self._run_clicked).grid(row=0, column=3, padx=(12, 0)) + ttk.Label(config_line, textvariable=self.status_var).grid(row=0, column=4, sticky="w", padx=(12, 0)) + if not HAS_DND: + ttk.Label( + config_line, + text="提示：未检测到 tkinterdnd2，拖拽不可用；可先 pip install tkinterdnd2", + foreground="#aa5500", + ).grid(row=0, column=5, sticky="w", padx=(12, 0)) + + paned = ttk.Panedwindow(main, orient="horizontal") + paned.grid(row=4, column=0, columnspan=3, sticky="nsew", pady=(6, 0)) + + left = ttk.Frame(paned, padding=8) + right = ttk.Frame(paned, padding=8) + paned.add(left, weight=3) + paned.add(right, weight=2) + + ttk.Label(left, text="关键结果").pack(anchor="w") + self.key_text = ScrolledText(left, wrap="word", height=8) + self.key_text.pack(fill="x", expand=False, pady=(0, 6)) + + ttk.Label(left, text="详细结果").pack(anchor="w") + self.result_text = ScrolledText(left, wrap="word", height=16) + self.result_text.pack(fill="both", expand=True) + + ttk.Label(right, text="图像预览（最新结果）").pack(anchor="w") + self.image_label = ttk.Label(right, relief="sunken", anchor="center") + self.image_label.pack(fill="both", expand=True) + self.image_label.bind("", self._on_image_area_resize) + + def _choose_file(self) -> None: + file_path = filedialog.askopenfilename( + title="选择CSV文件", + filetypes=[("CSV Files", "*.csv"), ("All Files", "*.*")], + initialdir=str(PROJECT_ROOT), + ) + if file_path: + self.file_var.set(file_path) + + def _on_drop(self, event) -> None: # noqa: ANN001 + raw = event.data + if not raw: + return + paths = self.root.tk.splitlist(raw) + if not paths: + return + self.file_var.set(str(paths[0]).strip("{}")) + + def _append_result(self, text: str) -> None: + self.result_text.insert("end", text + "\n") + self.result_text.see("end") + + def _on_image_area_resize(self, _event) -> None: # noqa: ANN001 + self._refresh_image_fit() + + def _refresh_image_fit(self) -> None: + if self.original_image is None: + return + width = max(self.image_label.winfo_width(), 1) + height = max(self.image_label.winfo_height(), 1) + orig_w = max(self.original_image.width(), 1) + orig_h = max(self.original_image.height(), 1) + ratio_w = math.ceil(orig_w / width) + ratio_h = math.ceil(orig_h / height) + ratio = max(1, ratio_w, ratio_h) + scaled = self.original_image.subsample(ratio, ratio) + self.preview_image = scaled + self.image_label.configure(image=scaled, text="") + + def _set_image(self, image_path: Path | None) -> None: + if image_path is None or not image_path.exists(): + self.image_label.configure(image="", text="暂无图像") + self.preview_image = None + self.original_image = None + self.current_image_path = None + return + try: + self.original_image = tk.PhotoImage(file=str(image_path)) + self.current_image_path = image_path + self._refresh_image_fit() + except Exception: + self.image_label.configure(text=f"图像已生成：\n{image_path}", image="") + self.preview_image = None + self.original_image = None + self.current_image_path = image_path + + def _run_clicked(self) -> None: + csv_text = self.file_var.get().strip() + if not csv_text: + messagebox.showerror("缺少输入", "请先选择或拖入CSV文件。") + return + csv_path = Path(csv_text).expanduser().resolve() + if not csv_path.exists(): + messagebox.showerror("文件不存在", f"找不到文件：\n{csv_path}") + return + + try: + tz_offset = float(self.timezone_var.get().strip()) + except ValueError: + messagebox.showerror("参数错误", "时区偏移必须是数字。") + return + + self.status_var.set("运行中...") + self.key_text.delete("1.0", "end") + self.result_text.delete("1.0", "end") + self._set_image(None) + + thread = threading.Thread( + target=self._run_pipeline, + args=(csv_path, self.task_var.get(), self.force_convert_var.get(), tz_offset), + daemon=True, + ) + thread.start() + + def _run_pipeline(self, csv_path: Path, task_label: str, force_convert: bool, tz_offset: float) -> None: + try: + APP_OUTPUT_DIR.mkdir(parents=True, exist_ok=True) + + use_csv, converted = ensure_long_format_csv(csv_path, force_convert=force_convert, tz_offset_hours=tz_offset) + notes = [ + f"输入文件: {csv_path}", + f"实际评测文件: {use_csv}", + f"是否执行宽转长: {'是' if converted else '否'}", + f"任务: {task_label}", + "", + ] + + if task_label == TASK1_LABEL: + result, fig_path = run_task1(use_csv) + elif task_label == TASK2_LABEL: + result, fig_path = run_task2(use_csv) + elif task_label == TASK3_LABEL: + result, fig_path = run_task3(use_csv) + elif task_label == TASK4_LABEL: + result, fig_path = run_task4_tmd(use_csv) + else: + raise ValueError(f"不支持的任务：{task_label}") + + output_text = "\n".join(notes) + pretty_json_like_lines(result) + key_text = build_key_metrics(task_label, result) + self.root.after(0, lambda: self.key_text.insert("end", key_text + "\n")) + self.root.after(0, lambda: self.result_text.insert("end", output_text + "\n")) + self.root.after(0, lambda: self._set_image(fig_path)) + self.root.after(0, lambda: self.status_var.set("完成")) + except Exception as error: + stack = traceback.format_exc() + message = f"运行失败：{error}\n\n{stack}" + self.root.after(0, lambda: self.key_text.insert("end", "运行失败，请查看详细错误。\n")) + self.root.after(0, lambda: self.result_text.insert("end", message + "\n")) + self.root.after(0, lambda: self.status_var.set("失败")) + + def run(self) -> None: + self.root.mainloop() + + +def parse_args() -> argparse.Namespace: + parser = argparse.ArgumentParser(description="Integrated visual evaluation studio for tasks 1~4.") + parser.add_argument("--task", type=str, default=None, choices=TASK_CHOICES) + parser.add_argument("--file", type=str, default=None, help="Optional CSV file path to preload.") + parser.add_argument( + "--force-convert", + action="store_true", + help="Force teacher wide-format conversion before running tasks.", + ) + parser.add_argument("--timezone-offset-hours", type=float, default=8.0) + return parser.parse_args() + + +def main() -> None: + args = parse_args() + app = EvaluationStudio() + if args.task: + app.task_var.set(args.task) + if args.file: + app.file_var.set(str(Path(args.file).expanduser().resolve())) + if args.force_convert: + app.force_convert_var.set(True) + app.timezone_var.set(str(args.timezone_offset_hours)) + app.run() + + +if __name__ == "__main__": + main() diff --git a/scripts_2/__pycache__/__init__.cpython-314.pyc b/scripts_2/__pycache__/__init__.cpython-314.pyc new file mode 100644 index 0000000..e939b37 Binary files /dev/null and b/scripts_2/__pycache__/__init__.cpython-314.pyc differ diff --git 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--git a/scripts_3/task4_predict_freq.py b/scripts_3/task4_predict_freq.py index 869e953..d43aaa2 100644 --- a/scripts_3/task4_predict_freq.py +++ b/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) diff --git a/scripts_4/__pycache__/__init__.cpython-314.pyc b/scripts_4/__pycache__/__init__.cpython-314.pyc new file mode 100644 index 0000000..1a91653 Binary files /dev/null and b/scripts_4/__pycache__/__init__.cpython-314.pyc differ diff --git a/scripts_4/__pycache__/adapter.cpython-314.pyc b/scripts_4/__pycache__/adapter.cpython-314.pyc index b565b2a..4bd059d 100644 Binary files a/scripts_4/__pycache__/adapter.cpython-314.pyc and b/scripts_4/__pycache__/adapter.cpython-314.pyc differ