feat: update prediction workbook output
This commit is contained in:
+117
-20
@@ -58,6 +58,39 @@ FEATURES = [
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ID_COLUMN = "管道编号"
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PIPE_AGE_COLUMN = "管龄"
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MATERIAL_COLUMN = "管材"
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MATERIAL_CODE_OPTIONS = [
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(1, "镀锌"),
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(2, "钢塑"),
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(3, "铝塑"),
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(4, "PPR"),
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(5, "PE"),
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(6, "UPVC"),
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(7, "铸铁"),
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(8, "预应力"),
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(9, "自应力"),
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(10, "玻璃钢夹砂"),
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(11, "钢管"),
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(12, "钢套混凝土管"),
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(13, "球墨铸铁"),
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(14, "其他"),
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]
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MATERIAL_ALIAS_TO_CODE = {
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str(code): code
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for code, _ in MATERIAL_CODE_OPTIONS
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}
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MATERIAL_ALIAS_TO_CODE.update(
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{
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name.casefold(): code
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for code, name in MATERIAL_CODE_OPTIONS
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}
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)
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MATERIAL_ALIAS_TO_CODE.update(
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{
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f"{code}-{name}".casefold(): code
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for code, name in MATERIAL_CODE_OPTIONS
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}
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)
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SUPPORTED_EXTENSIONS = {".csv", ".xls", ".xlsx"}
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CHINESE_FONT_PROP = None
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@@ -150,13 +183,21 @@ def secure_upload_name(original_filename: str, run_id: str) -> tuple[str, str]:
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def read_input_file(path: Path, suffix: str) -> pd.DataFrame:
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try:
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if suffix == ".csv":
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return pd.read_csv(path)
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return pd.read_excel(path)
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return pd.read_csv(path, dtype={ID_COLUMN: "string"})
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return pd.read_excel(path, dtype={ID_COLUMN: "string"})
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except Exception as exc:
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logging.exception("文件解析失败: %s", exc)
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raise PredictionError("文件解析失败，请检查编码或表格格式。")
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def normalize_id_value(value: Any, fallback: str) -> str:
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if pd.isna(value):
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return fallback
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if isinstance(value, str):
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return value.strip()
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return str(value)
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def validate_input_frame(df: pd.DataFrame) -> None:
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if df.empty:
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raise PredictionError("上传文件没有可预测的数据。")
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@@ -166,6 +207,28 @@ def validate_input_frame(df: pd.DataFrame) -> None:
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raise PredictionError(f"缺少必要字段: {', '.join(missing)}")
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def normalize_material_code(value: Any) -> int:
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if pd.isna(value):
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raise PredictionError("管材不能为空。")
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if isinstance(value, str):
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key = value.strip().casefold()
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else:
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numeric_value = float(value)
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if not numeric_value.is_integer():
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raise PredictionError(f"管材编码无效: {value}")
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key = str(int(numeric_value))
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code = MATERIAL_ALIAS_TO_CODE.get(key)
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if code is None:
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raise PredictionError(f"管材编码无效: {value}")
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return code
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def prepare_model_features(df: pd.DataFrame) -> pd.DataFrame:
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x_test = df[FEATURES].copy()
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x_test[MATERIAL_COLUMN] = x_test[MATERIAL_COLUMN].map(normalize_material_code)
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return x_test
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def grade_info(probability: float) -> tuple[str, str, str]:
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if probability <= 0.2:
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return ("I级", "管道安全风险十分严重，需立刻进行抢修或更新改造", "bg-dangerSoft text-dangerText")
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@@ -318,7 +381,7 @@ def run_prediction(uploaded: FileStorage, user_id: int, model) -> PredictionArti
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try:
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df = read_input_file(original_path, suffix)
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validate_input_frame(df)
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x_test = df[FEATURES].copy()
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x_test = prepare_model_features(df)
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try:
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curves = model.predict_survival_function(x_test)
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except Exception as exc:
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@@ -365,7 +428,7 @@ def render_survival_chart(df: pd.DataFrame, curves, image_path: Path) -> tuple[l
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for i, curve in enumerate(curves):
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times = [float(x) for x in list(curve.x)]
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probs = [float(y) for y in list(curve.y)]
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pipe_id = str(df.iloc[i][ID_COLUMN]) if pd.notna(df.iloc[i][ID_COLUMN]) else f"Pipe_{i+1:03d}"
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pipe_id = normalize_id_value(df.iloc[i][ID_COLUMN], f"Pipe_{i+1:03d}")
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pipe_age = f"{df.iloc[i][PIPE_AGE_COLUMN]} 年" if PIPE_AGE_COLUMN in df.columns and pd.notna(df.iloc[i][PIPE_AGE_COLUMN]) else "-"
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health_probability = interpolate_probability(times, probs, 10)
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remaining_life = estimate_remaining_life(times, probs)
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@@ -386,15 +449,13 @@ def render_survival_chart(df: pd.DataFrame, curves, image_path: Path) -> tuple[l
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{
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"管道编号": pipe_id,
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"管龄": pipe_age,
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"健康概率": health_probability,
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"预计剩余寿命": remaining_life,
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"健康等级": grade_label,
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}
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)
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plt.step(times, probs, where="post", linewidth=2, label=pipe_id)
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font_kwargs = chinese_font_kwargs()
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plt.xlabel("预测时间轴（年）", **font_kwargs)
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plt.xlabel("管龄（年）", **font_kwargs)
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plt.ylabel("生存概率", **font_kwargs)
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plt.title("预测分析图", **font_kwargs)
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plt.grid(alpha=0.18)
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@@ -416,22 +477,58 @@ def write_prediction_workbook(
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summary_rows: list[dict[str, Any]],
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summary_sheet_rows: list[dict[str, Any]],
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) -> None:
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sample_data_rows: list[dict[str, Any]] = []
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chart_times: set[float] = set()
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chart_series: list[tuple[str, dict[float, float]]] = []
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for i, curve in enumerate(curves):
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times = [float(x) for x in list(curve.x)]
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probs = [float(y) for y in list(curve.y)]
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pipe_id = summary_rows[i]["pipe_id"]
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for time, probability in zip(times, probs):
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sample_data_rows.append(
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{
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"管道编号": pipe_id,
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"样本序号": i + 1,
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"时间（年）": time,
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"生存概率": probability,
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"风险概率": 1 - probability,
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}
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)
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chart_times.update(times)
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chart_series.append((pipe_id, dict(zip(times, probs))))
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sorted_times = sorted(chart_times)
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sample_columns = ["管龄（年）", *[pipe_id for pipe_id, _ in chart_series]]
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sample_data_rows = [
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[time, *[series.get(time) for _, series in chart_series]]
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for time in sorted_times
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]
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with pd.ExcelWriter(excel_path, engine="openpyxl") as writer:
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pd.DataFrame(summary_sheet_rows).to_excel(writer, sheet_name="结果摘要", index=False)
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pd.DataFrame(sample_data_rows).to_excel(writer, sheet_name="样本数据", index=False)
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summary_df = pd.DataFrame(summary_sheet_rows, columns=[ID_COLUMN, PIPE_AGE_COLUMN, "健康等级"])
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sample_df = pd.DataFrame(sample_data_rows, columns=sample_columns)
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if ID_COLUMN in summary_df.columns:
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summary_df[ID_COLUMN] = summary_df[ID_COLUMN].astype("string")
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summary_df.to_excel(writer, sheet_name="结果摘要", index=False)
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sample_df.to_excel(writer, sheet_name="样本数据", index=False)
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sample_worksheet = writer.book["样本数据"]
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sample_worksheet.freeze_panes = "A2"
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sample_worksheet.auto_filter.ref = sample_worksheet.dimensions
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sample_worksheet.column_dimensions["A"].width = 12
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for column_cells in sample_worksheet.iter_cols(min_col=2, max_col=sample_worksheet.max_column):
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header_cell = column_cells[0]
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sample_worksheet.column_dimensions[header_cell.column_letter].width = max(12, len(str(header_cell.value)) + 2)
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for cell in column_cells[1:]:
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cell.number_format = "0.0%"
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for sheet_name in ("结果摘要", "样本数据"):
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worksheet = writer.book[sheet_name]
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header_cells = next(worksheet.iter_rows(min_row=1, max_row=1), [])
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id_column_index = None
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for cell in header_cells:
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if cell.value == ID_COLUMN:
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id_column_index = cell.column
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break
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if id_column_index is None:
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continue
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for cell in worksheet.iter_cols(
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min_col=id_column_index,
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max_col=id_column_index,
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min_row=2,
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max_row=worksheet.max_row,
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):
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for item in cell:
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if item.value is not None:
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item.value = str(item.value)
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item.number_format = "@"
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+2
-1
@@ -30,6 +30,7 @@ from .time_utils import format_datetime_for_timezone
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bp = Blueprint("main", __name__)
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REFERENCE_PDF_NAME = "20260630标准文本——供水管道健康状态与剩余寿命评估技术导则.pdf"
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TEMPLATE_EXCEL_NAME = "管道预测数据模板.xlsx"
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REGISTRATION_SETTING_KEY = "allow_registration"
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RECORDS_PER_PAGE = 10
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@@ -375,7 +376,7 @@ def download_template():
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template_path = BASE_DIR / "example.xlsx"
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if not template_path.exists():
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abort(404)
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return send_file(template_path, as_attachment=True, download_name="example.xlsx")
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return send_file(template_path, as_attachment=True, download_name=TEMPLATE_EXCEL_NAME)
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@bp.route("/reference_pdf")
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Binary file not shown.
@@ -19,7 +19,6 @@ const resultImportanceWrap = document.getElementById('resultImportanceWrap');
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const resultImportanceImage = document.getElementById('resultImportanceImage');
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const excelBtn = document.getElementById('excelBtn');
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const resultPageBtn = document.getElementById('resultPageBtn');
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const homeStateKey = 'pipelineLifetime.homeState';
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let selectedFile = null;
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let alertTimer = null;
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let alertHideTimer = null;
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@@ -66,24 +65,6 @@ function updateSelectedFile(file) {
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selectedFile = file;
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selectedFileName.textContent = '已选择文件：' + file.name;
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selectedFileName.classList.remove('hidden');
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saveHomeState({ selectedFilename: file.name });
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}
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function readHomeState() {
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try {
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return JSON.parse(sessionStorage.getItem(homeStateKey)) || {};
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} catch (err) {
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return {};
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}
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}
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function saveHomeState(nextState) {
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try {
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const currentState = readHomeState();
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sessionStorage.setItem(homeStateKey, JSON.stringify({ ...currentState, ...nextState }));
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} catch (err) {
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// Ignore storage failures; prediction still works without client-side restore.
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}
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}
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function renderResult(data) {
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@@ -102,17 +83,6 @@ function renderResult(data) {
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resultContent.classList.add('flex');
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}
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function restoreHomeState() {
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const savedState = readHomeState();
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if (savedState.selectedFilename) {
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selectedFileName.textContent = '上次选择文件：' + savedState.selectedFilename + '（需重新选择）';
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selectedFileName.classList.remove('hidden');
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}
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if (savedState.result) {
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renderResult(savedState.result);
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}
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}
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fileInput.addEventListener('change', () => {
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const file = fileInput.files[0];
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if (!file) return;
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@@ -176,15 +146,6 @@ form.addEventListener('submit', async (e) => {
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return;
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}
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renderResult(data);
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saveHomeState({
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selectedFilename: data.original_filename || selectedFile.name,
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result: {
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image_url: data.image_url,
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importance_url: data.importance_url || '',
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excel_url: data.excel_url,
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result_url: data.result_url
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}
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});
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showAlert('预测完成，已生成图表与电子表格报告。', 'success');
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inlineResult.scrollIntoView({ behavior: 'smooth', block: 'nearest' });
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} catch (err) {
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@@ -197,5 +158,3 @@ form.addEventListener('submit', async (e) => {
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submitIcon = restoredIcon;
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}
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});
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restoreHomeState();
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@@ -142,15 +142,6 @@
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showAppNotification(message, category === 'error' ? 'error' : 'info');
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}
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document.querySelectorAll('[data-clear-home-state]').forEach((form) => {
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form.addEventListener('submit', () => {
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try {
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sessionStorage.removeItem('pipelineLifetime.homeState');
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} catch (err) {
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// Ignore storage failures during logout.
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}
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});
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});
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})();
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</script>
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{% block scripts %}{% endblock %}
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@@ -51,12 +51,13 @@
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</section>
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<section class="flex flex-1 flex-col rounded-lg border border-line bg-white p-5 shadow-panel">
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<h2 class="text-lg font-extrabold">样本预览</h2>
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<h2 class="text-lg font-extrabold">结果展示</h2>
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<div class="mt-4 overflow-hidden rounded-md border border-line">
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<table class="w-full text-sm">
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<thead class="bg-slate-50 text-xs font-bold text-textSub">
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<tr>
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<th class="px-3 py-3 text-left">管道编号</th>
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<th class="px-3 py-3 text-left">编号</th>
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<th class="px-3 py-3 text-left">管龄</th>
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<th class="px-3 py-3 text-left">等级</th>
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</tr>
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</thead>
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@@ -64,6 +65,7 @@
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{% for item in result.summary_rows %}
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<tr class="border-t border-line">
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<td class="px-3 py-3 font-semibold">{{ item.pipe_id }}</td>
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<td class="px-3 py-3 text-textSub">{{ item.pipe_age }}</td>
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<td class="px-3 py-3"><span class="inline-flex rounded-full px-2.5 py-1 text-xs font-bold {{ item.grade_class }}">{{ item.grade_label }}</span></td>
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</tr>
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{% endfor %}
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@@ -130,6 +130,19 @@ class RegistrationRoutesTest(unittest.TestCase):
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self.assertIn('name="captcha"', html)
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self.assertIn(captcha, html)
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def test_template_download_uses_chinese_filename(self) -> None:
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with TemporaryDirectory() as temp_dir:
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app = self.create_test_app(temp_dir, allow_registration=True)
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response = app.test_client().get("/download_template")
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self.assertEqual(response.status_code, 200)
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self.assertIn("attachment", response.headers["Content-Disposition"])
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self.assertIn(
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"filename*=UTF-8''%E7%AE%A1%E9%81%93%E9%A2%84%E6%B5%8B%E6%95%B0%E6%8D%AE%E6%A8%A1%E6%9D%BF.xlsx",
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response.headers["Content-Disposition"],
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)
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def test_register_post_does_not_create_user_when_registration_is_closed(self) -> None:
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with TemporaryDirectory() as temp_dir:
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app = self.create_test_app(temp_dir, allow_registration=False)
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@@ -3,16 +3,22 @@ from __future__ import annotations
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import unittest
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from pathlib import Path
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from tempfile import TemporaryDirectory
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from unittest.mock import patch
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import pandas as pd
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from openpyxl import load_workbook
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from app.prediction import (
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FEATURES,
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ID_COLUMN,
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PIPE_AGE_COLUMN,
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PredictionError,
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estimate_remaining_life,
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grade_info,
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interpolate_probability,
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prepare_model_features,
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read_input_file,
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render_survival_chart,
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secure_upload_name,
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validate_input_frame,
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write_prediction_workbook,
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@@ -53,6 +59,37 @@ class PredictionHelpersTest(unittest.TestCase):
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self.assertIn("缺少必要字段", ctx.exception.message)
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def test_read_input_file_preserves_text_pipe_ids(self) -> None:
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with TemporaryDirectory() as temp_dir:
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output_path = Path(temp_dir) / "input.xlsx"
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pd.DataFrame({ID_COLUMN: ["00123"], FEATURES[0]: [1]}).to_excel(output_path, index=False)
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df = read_input_file(output_path, ".xlsx")
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self.assertEqual(df[ID_COLUMN].tolist(), ["00123"])
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def test_prepare_model_features_maps_material_aliases_to_codes(self) -> None:
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rows = []
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for value in ["镀锌", "2-钢塑", 13]:
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row = {feature: 1 for feature in FEATURES}
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row["管材"] = value
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rows.append(row)
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df = pd.DataFrame(rows)
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x_test = prepare_model_features(df)
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self.assertEqual(x_test["管材"].tolist(), [1, 2, 13])
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def test_prepare_model_features_rejects_invalid_material_alias(self) -> None:
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row = {feature: 1 for feature in FEATURES}
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row["管材"] = "未知管材"
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df = pd.DataFrame([row])
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with self.assertRaises(PredictionError) as ctx:
|
||||
prepare_model_features(df)
|
||||
|
||||
self.assertIn("管材编码无效", ctx.exception.message)
|
||||
|
||||
def test_prediction_workbook_keeps_sample_data_in_one_sheet(self) -> None:
|
||||
curves = [
|
||||
DummyCurve([1, 2], [0.9, 0.7]),
|
||||
@@ -60,8 +97,8 @@ class PredictionHelpersTest(unittest.TestCase):
|
||||
]
|
||||
summary_rows = [{"pipe_id": "P001"}, {"pipe_id": "P002"}]
|
||||
summary_sheet_rows = [
|
||||
{"管道编号": "P001", "健康概率": 0.7},
|
||||
{"管道编号": "P002", "健康概率": 0.6},
|
||||
{"管道编号": "P001", "管龄": "10 年", "健康概率": 0.7, "预计剩余寿命": 2.0, "健康等级": "IV级"},
|
||||
{"管道编号": "P002", "管龄": "12 年", "健康概率": 0.6, "预计剩余寿命": 1.5, "健康等级": "III级"},
|
||||
]
|
||||
|
||||
with TemporaryDirectory() as temp_dir:
|
||||
@@ -71,10 +108,51 @@ class PredictionHelpersTest(unittest.TestCase):
|
||||
workbook = pd.ExcelFile(output_path)
|
||||
self.assertEqual(workbook.sheet_names, ["结果摘要", "样本数据"])
|
||||
|
||||
summary_data = pd.read_excel(output_path, sheet_name="结果摘要")
|
||||
self.assertEqual(summary_data.columns.tolist(), [ID_COLUMN, PIPE_AGE_COLUMN, "健康等级"])
|
||||
|
||||
sample_data = pd.read_excel(output_path, sheet_name="样本数据")
|
||||
self.assertEqual(len(sample_data), 4)
|
||||
self.assertEqual(sample_data["管道编号"].tolist(), ["P001", "P001", "P002", "P002"])
|
||||
self.assertIn("风险概率", sample_data.columns)
|
||||
self.assertEqual(sample_data.columns.tolist(), ["管龄（年）", "P001", "P002"])
|
||||
self.assertEqual(len(sample_data), 2)
|
||||
self.assertEqual(sample_data["管龄（年）"].tolist(), [1, 2])
|
||||
self.assertEqual(sample_data["P001"].tolist(), [0.9, 0.7])
|
||||
self.assertEqual(sample_data["P002"].tolist(), [0.8, 0.6])
|
||||
|
||||
openpyxl_workbook = load_workbook(output_path)
|
||||
sample_worksheet = openpyxl_workbook["样本数据"]
|
||||
self.assertEqual(sample_worksheet.freeze_panes, "A2")
|
||||
self.assertEqual(sample_worksheet.auto_filter.ref, "A1:C3")
|
||||
self.assertEqual(sample_worksheet["B2"].number_format, "0.0%")
|
||||
|
||||
def test_prediction_workbook_writes_pipe_ids_as_excel_text(self) -> None:
|
||||
curves = [DummyCurve([1], [0.9])]
|
||||
summary_rows = [{"pipe_id": "00123"}]
|
||||
summary_sheet_rows = [{ID_COLUMN: "00123", PIPE_AGE_COLUMN: "10 年", "健康等级": "V级"}]
|
||||
|
||||
with TemporaryDirectory() as temp_dir:
|
||||
output_path = Path(temp_dir) / "prediction.xlsx"
|
||||
write_prediction_workbook(output_path, curves, summary_rows, summary_sheet_rows)
|
||||
|
||||
workbook = load_workbook(output_path)
|
||||
summary_worksheet = workbook["结果摘要"]
|
||||
self.assertEqual(summary_worksheet["A2"].value, "00123")
|
||||
self.assertEqual(summary_worksheet["A2"].data_type, "s")
|
||||
self.assertEqual(summary_worksheet["A2"].number_format, "@")
|
||||
|
||||
sample_worksheet = workbook["样本数据"]
|
||||
self.assertEqual(sample_worksheet["B1"].value, "00123")
|
||||
self.assertEqual(sample_worksheet["B1"].data_type, "s")
|
||||
|
||||
def test_survival_chart_uses_pipe_age_x_axis_label(self) -> None:
|
||||
df = pd.DataFrame({ID_COLUMN: ["P001"], PIPE_AGE_COLUMN: [12]})
|
||||
|
||||
with TemporaryDirectory() as temp_dir:
|
||||
output_path = Path(temp_dir) / "chart.png"
|
||||
with patch("app.prediction.plt.xlabel") as xlabel:
|
||||
render_survival_chart(df, [DummyCurve([1, 2], [0.9, 0.7])], output_path)
|
||||
|
||||
xlabel.assert_called_once()
|
||||
self.assertEqual(xlabel.call_args.args[0], "管龄（年）")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
|
||||
Reference in New Issue
Block a user