212 lines
7.9 KiB
Python
212 lines
7.9 KiB
Python
from __future__ import annotations
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from io import BytesIO
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from openpyxl import Workbook
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from openpyxl.styles import Alignment, Border, Font, PatternFill, Side
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from openpyxl.utils import get_column_letter
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def _style_header(ws, row: int, ncols: int):
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fill = PatternFill("solid", fgColor="12233D")
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font = Font(color="F4EFE6", bold=True)
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for c in range(1, ncols + 1):
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cell = ws.cell(row, c)
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cell.fill = fill
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cell.font = font
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cell.alignment = Alignment(wrap_text=True, vertical="center")
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def _borders(ws, min_row: int = 1, max_row: int | None = None, max_col: int | None = None):
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thin = Side(style="thin", color="D9D1C3")
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border = Border(left=thin, right=thin, top=thin, bottom=thin)
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max_row = max_row or ws.max_row
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max_col = max_col or ws.max_column
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for row in ws.iter_rows(min_row=min_row, max_row=max_row, min_col=1, max_col=max_col):
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for cell in row:
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if cell.value is not None:
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cell.border = border
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def _group_order(g: str):
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return 0 if g == "物理" else 1 if g == "历史" else 2
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def _safe_title(text: str) -> str:
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return text.replace(":", "-").replace("/", "-").replace("\\", "-").replace("?", "-")[:31]
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def _raw_sheet(wb: Workbook, group: dict, students: list[dict]):
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title = _safe_title(f"{group['group']}方向人员")
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ws = wb.create_sheet(title)
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exams = group["_exams"]
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ws.append(["学号", "姓名", "班级", "方向", "选科", "学籍", "异动"] + exams)
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_style_header(ws, 1, ws.max_column)
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for s in students:
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ws.append(
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[
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s["sid"], s["name"], s["class_name"], s["track"], s.get("subjects") or "",
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s.get("status") or "", s.get("move") or "",
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]
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+ [("缺考" if s["scores"].get(e) is None else s["scores"].get(e)) for e in exams]
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)
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_borders(ws, 2)
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ws.freeze_panes = "A2"
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return ws
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def _probability_sheet(wb: Workbook, group: dict):
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title = _safe_title(f"{group['group']}类概率分析")
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ws = wb.create_sheet(title)
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exams = group["_exams"]
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lines = {x["id"]: x for x in group["targets"]}
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pair_blocks = {x["line"]: x["pairs"] for x in group["pairs"]}
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r = 1
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def pair_for(line_id: str, a: str, b: str):
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line_name = lines[line_id]["name"]
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return next(
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(p for p in pair_blocks[line_name] if p["from_exam"] == a and p["to_exam"] == b),
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None,
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)
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for idx, (a, b) in enumerate(zip(exams, exams[1:]), 1):
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top_pair = pair_for("top", a, b)
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control_pair = pair_for("control", a, b)
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if not top_pair or not control_pair:
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continue
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ws.cell(r, 1, f"情况{idx}·尖子线前{lines['top']['k']}名")
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ws.cell(r, 8, f"情况{idx}·特控线前{lines['control']['k']}名")
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r += 1
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for c0, line_id in ((1, "top"), (8, "control")):
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ws.cell(r, c0, "分层")
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ws.cell(r, c0 + 1, "排名区间")
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ws.cell(r, c0 + 2, a)
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ws.cell(r, c0 + 3, b)
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ws.cell(r, c0 + 4, "概率")
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_style_header(ws, r, 12)
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r += 1
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max_layers = max(len(top_pair["layers"]), len(control_pair["layers"]))
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for i in range(max_layers):
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for c0, pair in ((1, top_pair), (8, control_pair)):
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if i >= len(pair["layers"]):
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continue
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x = pair["layers"][i]
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ws.cell(r, c0, x["layer"])
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ws.cell(r, c0 + 1, x.get("label") or f"第{x['layer']}层")
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ws.cell(r, c0 + 2, x["previous_n"])
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ws.cell(r, c0 + 3, x["hit_n"])
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ws.cell(r, c0 + 4, x["probability"])
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r += 1
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r += 2
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ws.cell(r, 1, f"汇总·尖子线前{lines['top']['k']}名")
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ws.cell(r, 8, f"汇总·特控线前{lines['control']['k']}名")
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r += 1
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for c0, line_id in ((1, "top"), (8, "control")):
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ws.cell(r, c0, "分层")
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ws.cell(r, c0 + 1, "排名区间")
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ws.cell(r, c0 + 2, "汇总样本")
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ws.cell(r, c0 + 3, "汇总命中")
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ws.cell(r, c0 + 4, "合成概率")
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ws.cell(r, c0 + 5, "拟合概率")
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_style_header(ws, r, 13)
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r += 1
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top_layers = group["summary"]["top"]["layers"]
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control_layers = group["summary"]["control"]["layers"]
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for i in range(max(len(top_layers), len(control_layers))):
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for c0, layers in ((1, top_layers), (8, control_layers)):
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if i >= len(layers):
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continue
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x = layers[i]
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ws.cell(r, c0, x["layer"])
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ws.cell(r, c0 + 1, x["label"])
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ws.cell(r, c0 + 2, x["previous_n"])
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ws.cell(r, c0 + 3, x["hit_n"])
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ws.cell(r, c0 + 4, x["probability"])
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ws.cell(r, c0 + 5, x["fitted_probability"])
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r += 1
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r += 1
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note = (
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"计算口径：勾选后的每次相邻两次考试都参与比较，最后一次也进入转移样本；同分同名次，后续名次递增；缺考未排除时按0分排名；"
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"汇总n/k后做加权二项logistic拟合；第1层保留合成概率，首个命中为0的层及其后取0，并强制概率逐层不上升。"
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)
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ws.cell(r, 1, note)
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_borders(ws)
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ws.column_dimensions["A"].width = 10
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ws.column_dimensions["H"].width = 10
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for c in (2, 9):
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ws.column_dimensions[get_column_letter(c)].width = 24
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for c in range(3, 8):
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ws.column_dimensions[get_column_letter(c)].width = 16
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for c in range(10, 14):
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ws.column_dimensions[get_column_letter(c)].width = 16
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return ws
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def _metric_sheet(wb: Workbook, group: dict):
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title = _safe_title(f"{group['group']}指标数")
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ws = wb.create_sheet(title)
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r = 1
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for line in group["targets"]:
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ws.cell(r, 1, f"{group['group']}方向{line['name']}指标分配")
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r += 1
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headers = ["班级"] + [f"{i+1}层人数" for i in range(group["n_layers"])] + ["加权计算", "摊派指标"]
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ws.cell(r, 1, headers[0])
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for i, h in enumerate(headers[1:], 2):
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ws.cell(r, i, h)
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_style_header(ws, r, len(headers))
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r += 1
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ws.cell(r, 1, "排名区间")
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for i, label in enumerate(line["labels"], 2):
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ws.cell(r, i, label)
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r += 1
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ws.cell(r, 1, "拟合概率")
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for i, p in enumerate(line["fitted"], 2):
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ws.cell(r, i, p)
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r += 1
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for row in line["classes"]:
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ws.cell(r, 1, row["class_name"])
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for i, count in enumerate(row["layer_counts"], 2):
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ws.cell(r, i, count)
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ws.cell(r, len(headers) - 1, row["expected"])
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ws.cell(r, len(headers), row["assigned"])
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r += 1
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total_counts = [
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sum(row["layer_counts"][i] for row in line["classes"])
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for i in range(group["n_layers"])
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]
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ws.cell(r, 1, "合计")
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for i, count in enumerate(total_counts, 2):
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ws.cell(r, i, count)
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ws.cell(r, len(headers) - 1, line["sum_expected"])
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ws.cell(r, len(headers), line["sum_assigned"])
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r += 1
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ws.cell(r, 1, "注：加权计算=Σ(各层人数×拟合概率)；摊派指标=加权计算四舍五入。")
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r += 3
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ws.column_dimensions["A"].width = 14
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for c in range(2, ws.max_column + 1):
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ws.column_dimensions[get_column_letter(c)].width = 13
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_borders(ws)
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ws.freeze_panes = "B1"
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return ws
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def build_export(result: dict) -> bytes:
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wb = Workbook()
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wb.remove(wb.active)
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# Keep the same familiar order as basedata.xlsx: 物理 raw/prob/metric, then 历史.
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groups = sorted(result["groups"], key=lambda g: _group_order(g["group"]))
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for i, group in enumerate(groups):
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students = [s for s in result["students"] if s["group"] == group["group"]]
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group["_exams"] = result["exams"]
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_raw_sheet(wb, group, students)
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for group in groups:
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_probability_sheet(wb, group)
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for group in groups:
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_metric_sheet(wb, group)
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bio = BytesIO()
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wb.save(bio)
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return bio.getvalue()
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