259 lines
14 KiB
Python
259 lines
14 KiB
Python
"""Bounded table reads and operation plans for the existing xlsx writer."""
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from __future__ import annotations
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import csv
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import hashlib
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import json
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import math
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import os
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import tempfile
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from pathlib import Path
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from typing import Any
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import numpy as np
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import pandas as pd
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from _xlsx_common import (
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EXCEL_INPUT_SUFFIXES, input_file, output_file, publish_file,
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normalize_formula_error, validate_cell_range, workbook_has_external_links,
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)
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MAX_DATA_CELLS = 500_000
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SOURCE_ROW = "__source_row"
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def require_columns(frame: pd.DataFrame, names: list[str]) -> list[str]:
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if not isinstance(names, list) or not names or len(set(names)) != len(names):
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raise ValueError("字段必须是非空、无重复的名称列表")
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missing = [name for name in names if name not in frame.columns]
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if missing:
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raise ValueError(f"字段不存在：{missing}；可选：{list(frame.columns)}")
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return names
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def numeric(frame: pd.DataFrame, names: list[str], *, allow_missing: bool = False) -> pd.DataFrame:
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result = frame[require_columns(frame, names)].apply(pd.to_numeric, errors="raise")
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values = result.to_numpy(dtype=float, na_value=np.nan)
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if np.isinf(values).any() or (not allow_missing and np.isnan(values).any()):
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raise ValueError("数值字段含缺失值或无穷值；请先明确处理口径")
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return result.astype(float)
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def scalar(value: Any) -> Any:
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if isinstance(value, np.generic):
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value = value.item()
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if value is None or value is pd.NA or value is pd.NaT:
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return None
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if isinstance(value, float):
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if math.isnan(value):
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return None
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if not math.isfinite(value):
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raise ValueError("结果含无穷值")
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if hasattr(value, "isoformat"):
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return value.isoformat()
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if isinstance(value, (str, int, float, bool)):
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return value
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return str(value)
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def read_dataset(path_value: str, spec: dict[str, Any] | None = None) -> tuple[pd.DataFrame, dict]:
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from openpyxl import load_workbook
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from openpyxl.utils.cell import column_index_from_string, get_column_letter, range_boundaries
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spec = spec or {}
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allowed = {"sheet", "range", "header_row", "columns", "exclude_rows", "numeric", "dates", "encoding", "path"}
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if set(spec) - allowed:
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raise ValueError(f"source 包含未知字段：{sorted(set(spec) - allowed)}")
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source = input_file(path_value, EXCEL_INPUT_SUFFIXES | {".csv", ".tsv"})
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if source.stat().st_size > 25 * 1024 * 1024:
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raise ValueError("分析输入上限为 25 MiB；请先分割文件")
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if workbook_has_external_links(source):
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raise ValueError("分析输入含外部链接，需先确认并固化其数据")
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header_row = int(spec.get("header_row", 1))
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if header_row < 1:
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raise ValueError("header_row 从 1 开始")
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bounds = range_boundaries(validate_cell_range(spec["range"])) if spec.get("range") else None
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if bounds and not bounds[1] <= header_row <= bounds[3]:
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raise ValueError("header_row 必须位于 range 内")
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records = []
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cached_wb = formula_wb = None
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formula_count = 0
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try:
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if source.suffix.lower() in EXCEL_INPUT_SUFFIXES:
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formula_wb = load_workbook(source, read_only=True, data_only=False, keep_links=False)
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cached_wb = load_workbook(source, read_only=True, data_only=True, keep_links=False)
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sheet_name = spec.get("sheet") or formula_wb.active.title
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if sheet_name not in formula_wb.sheetnames:
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raise ValueError(f"工作表不存在：{sheet_name}")
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ws, cached = formula_wb[sheet_name], cached_wb[sheet_name]
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min_col, _, max_col, end_row = bounds or (1, header_row, ws.max_column, ws.max_row)
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if max_col < min_col or end_row < header_row or (end_row - header_row + 1) * (max_col - min_col + 1) > MAX_DATA_CELLS:
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raise ValueError("读取区域超过 500000 单元格或无效；请指定较小 range")
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rows = ws.iter_rows(min_row=header_row, max_row=end_row, min_col=min_col, max_col=max_col)
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values = cached.iter_rows(min_row=header_row, max_row=end_row, min_col=min_col, max_col=max_col)
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for row_number, (formula_row, cached_row) in enumerate(zip(rows, values), header_row):
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record = []
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for cell, cached_cell in zip(formula_row, cached_row):
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is_formula = cell.data_type == "f"
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value = cached_cell.value if is_formula else cell.value
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if is_formula:
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formula_count += 1
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if value is None or cached_cell.data_type == "e":
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raise ValueError(f"{sheet_name}!{cell.coordinate} 的公式缓存缺失或错误；先重算并核对")
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if cell.data_type == "e":
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raise ValueError(f"{sheet_name}!{cell.coordinate} 含 Excel 错误值")
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record.append(value)
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records.append((row_number, record))
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else:
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if spec.get("sheet"):
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raise ValueError("CSV/TSV 没有工作表")
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sheet_name = None
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min_col, _, max_col, end_row = bounds or (1, header_row, 0, 1_048_576)
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with source.open(encoding=spec.get("encoding", "utf-8-sig"), newline="") as handle:
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reader = csv.reader(handle, delimiter="\t" if source.suffix.lower() == ".tsv" else ",")
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cells = 0
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for row_number, row in enumerate(reader, 1):
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if row_number > end_row:
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break
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if row_number < header_row:
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continue
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max_col = max_col or len(row)
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cells += max_col - min_col + 1
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if cells > MAX_DATA_CELLS:
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raise ValueError("读取区域超过 500000 单元格；请指定较小 range")
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if not bounds and len(row) > max_col:
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raise ValueError(f"CSV 第 {row_number} 行比表头多列，请先校正结构")
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record = row[min_col - 1:max_col]
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record += [None] * (max_col - min_col + 1 - len(record))
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records.append((row_number, [None if v == "" else v for v in record]))
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if not records:
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raise ValueError("指定区域没有表头和数据")
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aliases = spec.get("columns")
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if aliases:
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if not isinstance(aliases, dict) or not all(isinstance(k, str) and k for k in aliases):
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raise ValueError("columns 必须为名称到 Excel 列字母的映射")
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names = list(aliases)
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offsets = [column_index_from_string(str(aliases[name]).upper()) - min_col for name in names]
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if len(set(offsets)) != len(offsets) or any(i < 0 or i > max_col - min_col for i in offsets):
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raise ValueError("columns 必须指向区域内不同的列")
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else:
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names = [str(v).strip() if v is not None else "" for v in records[0][1]]
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offsets = list(range(len(names)))
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if not all(names) or len(set(names)) != len(names):
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raise ValueError("表头为空或重复；请用 columns 显式指定名称到列字母的映射")
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if SOURCE_ROW in names:
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raise ValueError(f"{SOURCE_ROW} 为保留字段")
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excluded = set(spec.get("exclude_rows", []))
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if any(not isinstance(v, int) or v <= header_row for v in excluded):
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raise ValueError("exclude_rows 必须是表头之后的原始行号")
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data = [[row_number, *[row[i] for i in offsets]] for row_number, row in records[1:] if row_number not in excluded]
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frame = pd.DataFrame(data, columns=[SOURCE_ROW, *names], dtype=object)
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for name in spec.get("numeric", []):
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frame[name] = numeric(frame, [name], allow_missing=True)[name]
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for name, fmt in spec.get("dates", {}).items():
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require_columns(frame, [name])
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frame[name] = pd.to_datetime(frame[name], format=fmt, errors="raise")
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metadata = {
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"path": str(source), "sha256": hashlib.sha256(source.read_bytes()).hexdigest(),
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"sheet": sheet_name, "header_row": header_row,
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"columns": {name: get_column_letter(min_col + offset) for name, offset in zip(names, offsets)},
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"rows_read": len(records) - 1, "rows_used": len(frame),
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"excluded_rows": sorted(excluded), "formula_cache_cells": formula_count,
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}
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return frame, metadata
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finally:
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if cached_wb is not None:
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cached_wb.close()
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if formula_wb is not None:
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formula_wb.close()
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def save_plan(tables: list[tuple[str, pd.DataFrame]], metadata: dict, destination: str,
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*, overwrite: bool = False, chart: dict | None = None) -> dict:
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from openpyxl.utils import get_column_letter
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target = output_file(destination, {".json"}, overwrite=overwrite)
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names = [name for name, _ in tables]
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if len(set(names)) != len(names) or "分析说明" in names:
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raise ValueError("结果工作表名称重复，或占用了保留名称 分析说明")
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note_rows = []
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def add_note(key: str, value: Any) -> None:
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if isinstance(value, dict) and value:
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for child, item in value.items():
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add_note(f"{key}.{child}", item)
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return
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text = value if isinstance(value, str) else json.dumps(value, ensure_ascii=False, default=scalar)
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for start in range(0, max(1, len(text)), 80):
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note_rows.append([key if start == 0 else f"{key}（续）", text[start:start + 80]])
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for key, value in metadata.items():
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add_note(key, value)
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notes = pd.DataFrame(note_rows, columns=["项目", "说明"])
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tables = [*tables, ("分析说明", notes)]
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operations = []
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cell_count = 0
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for name, table in tables:
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if not name or len(name) > 31 or any(ch in name for ch in '[]:*?/\\'):
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raise ValueError(f"无效的工作表名称：{name}")
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if table.empty and len(table.columns) == 0:
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raise ValueError("结果表没有字段")
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rows = [[str(col) for col in table.columns], *[[scalar(v) for v in row] for row in table.itertuples(index=False, name=None)]]
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# Explicit value objects keep untrusted text out of Excel's formula parser.
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rows = [[{"value": v} if isinstance(v, str) else v for v in row] for row in rows]
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last_col, last_row = get_column_letter(len(table.columns)), len(rows)
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area = f"A1:{last_col}{last_row}"
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cell_count += len(table.columns) * len(rows) * 3 + len(table.columns)
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operations += [
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{"type": "add_sheet", "name": name},
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{"type": "write_rows", "sheet": name, "rows": rows},
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{"type": "style_range", "sheet": name, "range": area,
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"style": {"font": {"name": "Noto Sans CJK SC", "size": 11}, "alignment": {"vertical": "center"}}},
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{"type": "style_range", "sheet": name, "range": f"A1:{last_col}1",
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"style": {"font": {"bold": True, "color": "FFFFFF"}, "fill": {"color": "1F4E78"}}},
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{"type": "auto_fit", "sheet": name, "range": area, "min_width": 12},
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{"type": "freeze_panes", "sheet": name, "cell": "A2"},
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{"type": "set_print", "sheet": name, "print_area": area, "orientation": "landscape", "fit_to_width": 1, "fit_to_height": 0, "repeat_rows": "1:1"},
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]
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for column_index, column in enumerate(table.columns, 1):
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values = table[column].dropna()
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if len(values) and all(isinstance(v, (int, float, np.number)) and not isinstance(v, (bool, np.bool_)) for v in values):
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letter = get_column_letter(column_index)
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number_format = "#,##0" if all(float(v).is_integer() for v in values) else "#,##0.####"
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operations.append({"type": "style_range", "sheet": name, "range": f"{letter}2:{letter}{last_row}",
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"style": {"number_format": number_format, "alignment": {"horizontal": "right", "indent": 1}}})
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cell_count += len(table)
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if cell_count > 100_000:
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raise ValueError("结果超过 apply_workbook 的单次处理上限；请缩小结果范围或分组处理")
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if chart:
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name, table = tables[0]
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category = chart["category"]
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values = chart["values"]
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require_columns(table, [category, *values])
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indexes = [table.columns.get_loc(value) + 1 for value in values]
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if indexes != list(range(min(indexes), max(indexes) + 1)):
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raise ValueError("图表 values 需按顺序选择相邻的结果列")
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c = get_column_letter(table.columns.get_loc(category) + 1)
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end = len(table) + 1
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if end < 2:
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raise ValueError("没有数据可用于图表")
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operations += [{"type": "add_chart", "sheet": name, "chart_type": chart.get("type", "column"),
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"title": chart.get("title", name), "data_range": f"{get_column_letter(min(indexes))}1:{get_column_letter(max(indexes))}{end}",
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"categories_range": f"{c}2:{c}{end}", "anchor": f"A{end+3}", "width": 20, "height": 10,
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"legend_position": "b", "x_axis_title": category, "y_axis_title": chart.get("value_title", "数值")},
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{"type": "set_print", "sheet": name, "print_area": f"A1:{get_column_letter(max(16, len(table.columns)))}{end+27}", "orientation": "landscape", "fit_to_width": 1, "fit_to_height": 0}]
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plan = {"operations": operations, "active_sheet": tables[0][0]}
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raw = json.dumps(plan, ensure_ascii=False, allow_nan=False, default=scalar).encode()
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if len(raw) > 2 * 1024 * 1024:
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raise ValueError("结果操作说明超过 2 MiB；请缩小结果或分批输出")
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descriptor, temp_name = tempfile.mkstemp(dir=target.parent, suffix=".json")
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temp = Path(temp_name)
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try:
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with os.fdopen(descriptor, "wb") as handle:
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handle.write(raw)
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publish_file(temp, target, overwrite=overwrite)
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finally:
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temp.unlink(missing_ok=True)
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return {"spec_path": str(target), "tables": [{"sheet": n, "rows": len(t), "columns": len(t.columns)} for n, t in tables],
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"next_script": "scripts/apply_workbook.py", "metadata": metadata}
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