331 lines
13 KiB
Python
331 lines
13 KiB
Python
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"""系数表是参数版本的编辑视图，不保留第二套工资公式或独立系数数据。"""
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from datetime import date
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from decimal import ROUND_HALF_UP, Decimal
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from fractions import Fraction
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from io import BytesIO
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from zipfile import ZipFile
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from openpyxl import Workbook, load_workbook
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from pydantic import ValidationError
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from sqlalchemy import select
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from app.core.exceptions import BadRequestError
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from app.schemas.coefficient import CoefficientRowIn
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from app.schemas.param import ParamChangeIn, ParamChangeItemIn
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from app.services import param_service
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from app.services.param_registry import get_spec, validate_and_normalize
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from app.services.rate_rules import decide, personal_values
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from app.services.tplus.workbench import digest
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CATEGORIES = ("municipal_water", "rail_transit")
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HEADERS = ("product_category", "customer_type", "stage", "value", "slope", "boundary")
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def _coefficient(rate):
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return str(1 / rate) if rate else None
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def _average_text(value):
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if value is None:
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return None
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return format(
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param_service.to_decimal(value).quantize(Decimal("0.000001"), rounding=ROUND_HALF_UP), "f"
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)
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def _revision(session, lock=False):
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if session is None:
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return []
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from app.models import ParamChangeBatch
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query = select(
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ParamChangeBatch.id, ParamChangeBatch.effective_date, ParamChangeBatch.version_no
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).order_by(ParamChangeBatch.id)
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if lock:
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query = query.with_for_update()
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return [tuple(row) for row in session.execute(query)]
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def catalog(session, as_of=None, revision=None):
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day = as_of or date.today()
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snap = param_service.resolve_snapshot(session, day)
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rows = []
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for category in CATEGORIES:
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values = personal_values(snap, category)
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params = {**values, "enterprise": snap.get("enterprise.rate", product_category=category)}
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rates = [
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decide(
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params, category=category, customer_type="personal", role_code="salesperson", days=d
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)[0]
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for d in range(1, 361)
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]
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average = sum((1 / r for r in rates if r), Fraction(0)) / 360 if all(rates) else None
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for stage, field, days in (
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("first", "first", 1),
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("linear", "start", 2),
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("after", "floor", int(Fraction(values["boundary"])) + 1),
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):
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rate = decide(
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params,
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category=category,
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customer_type="personal",
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role_code="salesperson",
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days=days,
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)[0]
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rows.append(
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{
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"product_category": category,
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"customer_type": "personal",
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"stage": stage,
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"value": values[field],
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"slope": values["slope"] if stage == "linear" else None,
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"boundary": int(Fraction(values["boundary"])) if stage == "linear" else None,
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"rate": str(rate),
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"system_coefficient": _coefficient(rate),
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"average_coefficient_360": _average_text(average),
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}
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)
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rate = Fraction(params["enterprise"]) / 1000
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rows.append(
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{
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"product_category": category,
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"customer_type": "enterprise",
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"stage": "fixed",
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"value": params["enterprise"],
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"slope": None,
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"boundary": None,
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"rate": str(rate),
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"system_coefficient": _coefficient(rate),
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"average_coefficient_360": _average_text(1 / rate if rate else None),
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}
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)
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rows.append(
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{
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"product_category": category,
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"customer_type": "manager_personal",
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"stage": "excluded",
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"value": "0",
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"rate": "0",
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"system_coefficient": None,
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"average_coefficient_360": None,
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"readonly": True,
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}
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)
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return {
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"as_of": str(day),
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"rows": rows,
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"fingerprint": digest(
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{
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"as_of": day,
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"rows": rows,
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"revision": _revision(session) if revision is None else revision,
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}
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),
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"average_rule": "第1—360天每日系统系数的算术平均，展示保留6位小数；存在零计提日时不适用。经理个人户不单笔计提。",
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}
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def items_for(rows):
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items = []
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seen = set()
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for row in rows:
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key = (row.product_category, row.customer_type, row.stage)
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if key in seen:
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raise BadRequestError("存在重复的产品／户型／阶段")
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seen.add(key)
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if row.customer_type == "enterprise":
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if row.stage != "fixed":
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raise BadRequestError("企业户使用固定阶段")
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fields = [("enterprise.rate", row.value)]
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elif row.stage == "fixed":
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raise BadRequestError("个人户请选择首日、线性或超期阶段")
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elif row.stage == "linear":
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if row.slope is None or row.boundary is None:
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raise BadRequestError("线性阶段须填写日斜率和截止天数")
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try:
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endpoint = Fraction(row.value) - row.boundary * Fraction(row.slope)
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except (ValueError, ZeroDivisionError) as exc:
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raise BadRequestError("比例及日斜率须为有效数值或分数") from exc
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if endpoint < 0:
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raise BadRequestError("线性阶段末比例不能为负数")
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fields = [
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("coefficient.personal.start", row.value),
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("coefficient.personal.slope", row.slope),
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("coefficient.personal.boundary", str(row.boundary)),
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]
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else:
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fields = [
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(
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"coefficient.personal.first"
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if row.stage == "first"
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else "coefficient.personal.floor",
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row.value,
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)
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]
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for code, value in fields:
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normalized = validate_and_normalize(get_spec(code), value)
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items.append(
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ParamChangeItemIn(
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param_code=code, dimension_value=row.product_category, value=normalized
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)
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)
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return items
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def save(session, payload, operator, meta=None):
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from app.models import ParamDefinition
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list(session.scalars(select(ParamDefinition).order_by(ParamDefinition.id).with_for_update()))
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revision = _revision(session, lock=True)
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if payload.fingerprint != catalog(session, payload.base_date, revision)["fingerprint"]:
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raise BadRequestError("系数表已变化，请重新查询或重新导入预览")
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return param_service.submit_change(
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session,
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ParamChangeIn(
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change_type=2,
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effective_date=payload.effective_date,
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remark=payload.remark,
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items=items_for(payload.rows),
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),
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operator=operator,
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meta=meta,
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)
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def workbook(session, as_of=None):
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data = catalog(session, as_of)
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book = Workbook()
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sheet = book.active
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sheet.title = "系数参数"
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sheet.append([*HEADERS, "system_coefficient", "average_coefficient_360"])
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for row in data["rows"]:
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if row.get("readonly"):
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continue
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sheet.append(
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[row.get(k) for k in (*HEADERS, "system_coefficient", "average_coefficient_360")]
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)
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note = book.create_sheet("填写说明")
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note.append(["基准日期", data["as_of"]])
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for line in (
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"value为‰数；线性阶段value为公式起点，slope为每日递减‰数，可填写分数。",
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"产品：municipal_water市政水利／rail_transit轨道交通；户型personal个人户／enterprise企业户。",
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"阶段：first首日／linear线性／after超期／fixed企业固定；经理个人户不单笔计提，不导入。",
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data["average_rule"],
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"系统系数及平均系数是只读派生列，导入时可留空，填写则必须与参数一致。",
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):
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note.append([line])
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for column in sheet.columns:
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sheet.column_dimensions[column[0].column_letter].width = 28
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sheet.freeze_panes = "A2"
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output = BytesIO()
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book.save(output)
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return output.getvalue()
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def import_preview(session, content):
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if len(content) > 2 * 1024 * 1024:
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raise BadRequestError("系数文件不能超过2MB")
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try:
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with ZipFile(BytesIO(content)) as archive:
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if sum(item.file_size for item in archive.infolist()) > 20 * 1024 * 1024:
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raise BadRequestError("工作簿解压后超过20MB，请使用系数模板")
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book = load_workbook(BytesIO(content), read_only=True, data_only=False)
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sheet = book["系数参数"]
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if sheet.max_row > 30 or sheet.max_column > 8:
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raise BadRequestError("系数表最多16条数据")
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values = list(sheet.iter_rows(values_only=True))
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if tuple(values[0][:6]) != HEADERS:
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raise BadRequestError("请使用系数表模板，保留前六列列名")
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rows = []
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errors = []
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for number, value in enumerate(values[1:], 2):
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if not any(v is not None for v in value):
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continue
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try:
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raw = {
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k: str(v) if v is not None else None
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for k, v in zip(HEADERS, value[:6], strict=True)
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}
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raw["boundary"] = int(raw["boundary"]) if raw["boundary"] else None
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row = CoefficientRowIn.model_validate(raw)
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items_for([row])
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rows.append(row)
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except (ValidationError, ValueError, BadRequestError) as exc:
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errors.append({"row": number, "message": str(exc)[:180]})
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if not rows and not errors:
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errors.append({"row": 2, "message": "文件没有可导入数据"})
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if rows:
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items_for(rows)
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# 派生列只做校验，不作为第二套输入。用临时参数快照复算。
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base = date.fromisoformat(str(book["填写说明"].cell(1, 2).value))
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snap = param_service.resolve_snapshot(session, base)
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temporary = {code: dict(dims) for code, dims in snap._values.items()}
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for item in items_for(rows):
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temporary.setdefault(item.param_code, {})[item.dimension_value] = item.value
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candidate = param_service.ParamSnapshot(base, temporary, {})
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for number, raw in enumerate(values[1:], 2):
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if len(raw) < 7:
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continue
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matching = next(
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(
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r
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for r in rows
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if (r.product_category, r.customer_type, r.stage) == tuple(raw[:3])
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),
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None,
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)
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if matching is None:
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continue
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pv = personal_values(candidate, matching.product_category)
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day = {"first": 1, "linear": 2, "after": int(Fraction(pv["boundary"])) + 1, "fixed": 1}[
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matching.stage
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]
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rate = decide(
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{
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**pv,
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"enterprise": candidate.get(
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"enterprise.rate", product_category=matching.product_category
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),
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},
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category=matching.product_category,
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customer_type=matching.customer_type,
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role_code="salesperson",
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days=day,
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)[0]
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if raw[6] not in (None, "") and (rate == 0 or Fraction(str(raw[6])) != 1 / rate):
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errors.append(
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{"row": number, "message": "系统系数与公式参数不一致，请清空派生列或重新导出"}
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)
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if len(raw) > 7 and raw[7] not in (None, ""):
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if matching.customer_type == "enterprise":
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average = 1 / rate if rate else None
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else:
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daily = [
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decide(
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{**pv, "enterprise": "0"},
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category=matching.product_category,
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customer_type="personal",
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role_code="salesperson",
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days=d,
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)[0]
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for d in range(1, 361)
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]
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average = sum((1 / r for r in daily), Fraction(0)) / 360 if all(daily) else None
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if average is None or Decimal(str(raw[7])) != Decimal(_average_text(average)):
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errors.append({"row": number, "message": "360天平均系数与公式参数不一致"})
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return {
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"rows": [r.model_dump() for r in rows],
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"errors": errors,
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"can_apply": not errors,
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"fingerprint": catalog(session, base)["fingerprint"],
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"base_date": str(base),
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}
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except BadRequestError:
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raise
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except Exception as exc:
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raise BadRequestError("无法读取系数表，请检查工作表名称和文件格式") from exc
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finally:
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if "book" in locals():
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book.close()
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