cbb943dfe5
Trace pipeline pool isolation: - Switch event_fetcher and lineage_engine to read_sql_df_slow (non-pooled) - Reduce EVENT_FETCHER_MAX_WORKERS 4→2, TRACE_EVENTS_MAX_WORKERS 4→2 - Add 60s timeout per batch query, cache skip for CID>10K - Early del raw_domain_results + gc.collect() for large queries - Increase DB_SLOW_MAX_CONCURRENT: base 3→5, dev 2→3, prod 3→5 Test fixes (51 pre-existing failures → 0): - reject_history: WORKFLOW CSV header, strict bool validation, pareto mock path - portal shell: remove non-existent /tmtt-defect route from tests - conftest: add --run-stress option to skip stress/load tests by default - migration tests: skipif baseline directory missing - performance test: update Vite asset assertion - wip hold: add firstname/waferdesc mock params - template integration: add /reject-history canonical route Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
1044 lines
38 KiB
Python
1044 lines
38 KiB
Python
# -*- coding: utf-8 -*-
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"""Service layer for reject-history page APIs."""
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from __future__ import annotations
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import csv
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import io
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import logging
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from datetime import date, datetime
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from typing import Any, Dict, Generator, Iterable, Optional
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import pandas as pd
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from mes_dashboard.core.database import read_sql_df_slow as read_sql_df
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from mes_dashboard.services.scrap_reason_exclusion_cache import get_excluded_reasons
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from mes_dashboard.sql import QueryBuilder, SQLLoader
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logger = logging.getLogger("mes_dashboard.reject_history_service")
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MAX_QUERY_DAYS = 730
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VALID_GRANULARITY = {"day", "week", "month"}
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VALID_METRIC_MODE = {"reject_total", "defect"}
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MATERIAL_REASON_OPTION = "原物料報廢"
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def _parse_date(value: str) -> date:
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return datetime.strptime(value, "%Y-%m-%d").date()
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def _validate_range(start_date: str, end_date: str) -> None:
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start = _parse_date(start_date)
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end = _parse_date(end_date)
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if end < start:
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raise ValueError("end_date 不可早於 start_date")
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if (end - start).days > MAX_QUERY_DAYS:
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raise ValueError(f"日期範圍不可超過 {MAX_QUERY_DAYS} 天")
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def _normalize_text(value: Any) -> str:
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return str(value or "").strip()
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def _as_int(value: Any) -> int:
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if value is None:
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return 0
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try:
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if pd.isna(value):
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return 0
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except Exception:
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pass
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try:
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return int(float(value))
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except (TypeError, ValueError):
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return 0
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def _as_float(value: Any) -> float:
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if value is None:
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return 0.0
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try:
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if pd.isna(value):
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return 0.0
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except Exception:
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pass
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try:
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return float(value)
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except (TypeError, ValueError):
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return 0.0
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def _to_date_str(value: Any) -> str:
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if isinstance(value, datetime):
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return value.strftime("%Y-%m-%d")
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if isinstance(value, date):
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return value.strftime("%Y-%m-%d")
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if isinstance(value, pd.Timestamp):
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return value.to_pydatetime().strftime("%Y-%m-%d")
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text = _normalize_text(value)
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if not text:
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return ""
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try:
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return pd.to_datetime(text).strftime("%Y-%m-%d")
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except Exception:
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return text
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def _to_datetime_str(value: Any) -> str:
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if isinstance(value, datetime):
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return value.strftime("%Y-%m-%d %H:%M:%S")
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if isinstance(value, pd.Timestamp):
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return value.to_pydatetime().strftime("%Y-%m-%d %H:%M:%S")
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if isinstance(value, date):
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return value.strftime("%Y-%m-%d")
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text = _normalize_text(value)
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if not text:
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return ""
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try:
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return pd.to_datetime(text).strftime("%Y-%m-%d %H:%M:%S")
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except Exception:
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return text
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def _date_bucket_expr(granularity: str) -> str:
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if granularity == "week":
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return "TRUNC(b.TXN_DAY, 'IW')"
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if granularity == "month":
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return "TRUNC(b.TXN_DAY, 'MM')"
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return "TRUNC(b.TXN_DAY)"
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def _metric_column(metric_mode: str) -> str:
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if metric_mode == "defect":
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return "b.DEFECT_QTY"
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return "b.REJECT_TOTAL_QTY"
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def _load_sql(name: str) -> str:
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return SQLLoader.load(f"reject_history/{name}")
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def _base_query_sql(variant: str = "") -> str:
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sql_name = "performance_daily_lot" if variant == "lot" else "performance_daily"
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sql = _load_sql(sql_name).strip().rstrip(";")
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# Strip leading comment/blank lines so WITH parsing can detect the first SQL token.
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lines = sql.splitlines()
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first_sql_line = 0
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for index, line in enumerate(lines):
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token = line.strip()
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if not token or token.startswith("--"):
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continue
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first_sql_line = index
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break
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return "\n".join(lines[first_sql_line:]).strip()
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def _split_with_query(sql: str) -> tuple[str, str] | None:
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"""Split a top-level WITH query into (cte_segment, final_select)."""
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text = (sql or "").strip()
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if not text.lower().startswith("with "):
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return None
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depth = 0
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in_string = False
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i = 0
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while i < len(text):
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ch = text[i]
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if ch == "'":
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if in_string and i + 1 < len(text) and text[i + 1] == "'":
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i += 2
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continue
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in_string = not in_string
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i += 1
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continue
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if in_string:
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i += 1
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continue
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if ch == "(":
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depth += 1
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elif ch == ")":
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depth = max(depth - 1, 0)
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elif depth == 0:
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head = text[i : i + 6]
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if head.lower() == "select":
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prev_ok = i == 0 or not (text[i - 1].isalnum() or text[i - 1] == "_")
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next_idx = i + 6
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next_ok = next_idx >= len(text) or not (
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text[next_idx].isalnum() or text[next_idx] == "_"
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)
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if prev_ok and next_ok:
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cte_segment = text[5:i].strip().rstrip(",")
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final_select = text[i:].strip()
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if cte_segment and final_select:
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return cte_segment, final_select
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return None
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i += 1
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return None
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def _base_with_cte_sql(alias: str = "base", variant: str = "") -> str:
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base_sql = _base_query_sql(variant)
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split = _split_with_query(base_sql)
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if split is None:
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return f"WITH {alias} AS (\n{base_sql}\n)"
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cte_segment, final_select = split
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return f"WITH {cte_segment},\n{alias} AS (\n{final_select}\n)"
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def _build_where_clause(
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*,
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workcenter_groups: Optional[list[str]] = None,
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packages: Optional[list[str]] = None,
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reasons: Optional[list[str]] = None,
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categories: Optional[list[str]] = None,
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include_excluded_scrap: bool = False,
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exclude_material_scrap: bool = True,
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exclude_pb_diode: bool = True,
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) -> tuple[str, dict[str, Any], dict[str, Any]]:
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builder = QueryBuilder()
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normalized_wc_groups = sorted({_normalize_text(v) for v in (workcenter_groups or []) if _normalize_text(v)})
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normalized_packages = sorted({_normalize_text(v) for v in (packages or []) if _normalize_text(v)})
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normalized_reasons = sorted({_normalize_text(v) for v in (reasons or []) if _normalize_text(v)})
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material_reason_selected = MATERIAL_REASON_OPTION in normalized_reasons
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reason_name_filters = [value for value in normalized_reasons if value != MATERIAL_REASON_OPTION]
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normalized_categories = sorted({_normalize_text(v) for v in (categories or []) if _normalize_text(v)})
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if normalized_wc_groups:
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from mes_dashboard.services.filter_cache import get_specs_for_groups
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specs_in_groups = get_specs_for_groups(normalized_wc_groups)
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if specs_in_groups:
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# Specs in cache are uppercase; use UPPER() for case-insensitive match
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builder.add_in_condition("UPPER(b.SPECNAME)", specs_in_groups)
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else:
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# Fallback: cache not ready or no specs found for these groups
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builder.add_in_condition("b.WORKCENTER_GROUP", normalized_wc_groups)
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if normalized_packages:
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builder.add_in_condition("b.PRODUCTLINENAME", normalized_packages)
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if reason_name_filters:
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builder.add_in_condition("b.LOSSREASONNAME", reason_name_filters)
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if material_reason_selected:
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builder.add_condition("UPPER(NVL(TRIM(b.SCRAP_OBJECTTYPE), '-')) = 'MATERIAL'")
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material_exclusion_applied = False
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if exclude_material_scrap and not material_reason_selected:
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builder.add_condition("UPPER(NVL(TRIM(b.SCRAP_OBJECTTYPE), '-')) <> 'MATERIAL'")
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material_exclusion_applied = True
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pb_diode_exclusion_applied = False
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if exclude_pb_diode and not any(p.startswith("PB_") for p in normalized_packages):
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builder.add_condition("b.PRODUCTLINENAME NOT LIKE 'PB\\_%' ESCAPE '\\'")
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pb_diode_exclusion_applied = True
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if normalized_categories:
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builder.add_in_condition("b.REJECTCATEGORYNAME", normalized_categories)
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exclusions_applied = False
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excluded_reason_codes = []
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reason_name_prefix_policy_applied = False
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if not include_excluded_scrap:
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excluded_reason_codes = sorted(get_excluded_reasons())
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reason_name_prefix_policy_applied = True
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if excluded_reason_codes:
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exclusions_applied = True
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# Support exclusion matching by both normalized reason code and full reason text.
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builder.add_not_in_condition(
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"UPPER(NVL(TRIM(b.LOSSREASON_CODE), '-'))",
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excluded_reason_codes,
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)
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builder.add_not_in_condition(
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"UPPER(NVL(TRIM(b.LOSSREASONNAME), '-'))",
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excluded_reason_codes,
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)
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# Exclude reason labels that are not "NNN_*", and always exclude XXX_/ZZZ_ prefixes.
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builder.add_condition(
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"REGEXP_LIKE(UPPER(NVL(TRIM(b.LOSSREASONNAME), '')), '^[0-9]{3}_')"
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)
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builder.add_condition(
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"NOT REGEXP_LIKE(UPPER(NVL(TRIM(b.LOSSREASONNAME), '')), '^(XXX|ZZZ)_')"
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)
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exclusions_applied = True
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where_clause, params = builder.build_where_only()
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meta = {
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"include_excluded_scrap": bool(include_excluded_scrap),
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"exclusion_applied": exclusions_applied,
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"reason_name_prefix_policy_applied": reason_name_prefix_policy_applied,
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"exclude_material_scrap": bool(exclude_material_scrap),
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"material_exclusion_applied": material_exclusion_applied,
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"excluded_reason_count": len(excluded_reason_codes),
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"workcenter_group_count": len(normalized_wc_groups),
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"package_filter_count": len(normalized_packages),
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"reason_filter_count": len(reason_name_filters),
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"material_reason_selected": material_reason_selected,
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"exclude_pb_diode": bool(exclude_pb_diode),
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"pb_diode_exclusion_applied": pb_diode_exclusion_applied,
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}
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return where_clause, params, meta
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_DEFAULT_BASE_WHERE = (
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"r.TXNDATE >= TO_DATE(:start_date, 'YYYY-MM-DD')"
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" AND r.TXNDATE < TO_DATE(:end_date, 'YYYY-MM-DD') + 1"
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)
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def _prepare_sql(
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name: str,
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*,
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where_clause: str = "",
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bucket_expr: str = "",
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metric_column: str = "",
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base_variant: str = "",
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base_where: str = "",
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dimension_column: str = "",
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) -> str:
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sql = _load_sql(name)
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sql = sql.replace("{{ BASE_QUERY }}", _base_query_sql(base_variant))
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sql = sql.replace("{{ BASE_WITH_CTE }}", _base_with_cte_sql("base", base_variant))
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sql = sql.replace("{{ BASE_WHERE }}", base_where or _DEFAULT_BASE_WHERE)
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sql = sql.replace("{{ WHERE_CLAUSE }}", where_clause or "")
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sql = sql.replace("{{ BUCKET_EXPR }}", bucket_expr or "TRUNC(b.TXN_DAY)")
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sql = sql.replace("{{ METRIC_COLUMN }}", metric_column or "b.REJECT_TOTAL_QTY")
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sql = sql.replace("{{ DIMENSION_COLUMN }}", dimension_column or "b.LOSSREASONNAME")
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return sql
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def _common_params(start_date: str, end_date: str, extra: Optional[dict[str, Any]] = None) -> dict[str, Any]:
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params = {"start_date": start_date, "end_date": end_date}
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if extra:
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params.update(extra)
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return params
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def _list_to_csv(
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rows: Iterable[dict[str, Any]],
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headers: list[str],
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) -> Generator[str, None, None]:
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# BOM for UTF-8 so Excel opens the CSV with correct encoding
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yield "\ufeff"
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buffer = io.StringIO()
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writer = csv.DictWriter(buffer, fieldnames=headers)
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writer.writeheader()
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yield buffer.getvalue()
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buffer.seek(0)
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buffer.truncate(0)
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for row in rows:
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writer.writerow(row)
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yield buffer.getvalue()
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buffer.seek(0)
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buffer.truncate(0)
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def _extract_distinct_text_values(df: pd.DataFrame, column: str) -> list[str]:
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if df is None or df.empty or column not in df.columns:
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return []
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return sorted({
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_normalize_text(value)
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for value in df[column].dropna()
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if _normalize_text(value)
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})
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def _extract_workcenter_group_options(df: pd.DataFrame) -> list[dict[str, Any]]:
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if df is None or df.empty or "WORKCENTER_GROUP" not in df.columns:
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return []
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sequence_by_name: dict[str, int] = {}
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for _, row in df.iterrows():
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name = _normalize_text(row.get("WORKCENTER_GROUP"))
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if not name:
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continue
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sequence = _as_int(row.get("WORKCENTERSEQUENCE_GROUP"))
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if name not in sequence_by_name:
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sequence_by_name[name] = sequence
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continue
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sequence_by_name[name] = min(sequence_by_name[name], sequence)
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ordered_names = sorted(
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sequence_by_name.keys(),
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key=lambda item: (sequence_by_name[item], item),
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)
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return [{"name": name, "sequence": sequence_by_name[name]} for name in ordered_names]
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def _has_material_scrap(df: pd.DataFrame) -> bool:
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if df is None or df.empty or "SCRAP_OBJECTTYPE" not in df.columns:
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return False
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return (
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df["SCRAP_OBJECTTYPE"]
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.map(lambda value: _normalize_text(value).upper())
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.eq("MATERIAL")
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.any()
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)
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def get_filter_options(
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*,
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start_date: str,
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end_date: str,
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workcenter_groups: Optional[list[str]] = None,
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packages: Optional[list[str]] = None,
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reasons: Optional[list[str]] = None,
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categories: Optional[list[str]] = None,
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include_excluded_scrap: bool = False,
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exclude_material_scrap: bool = True,
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exclude_pb_diode: bool = True,
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) -> dict[str, Any]:
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"""Return options under current draft filter context with one SQL round-trip."""
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_validate_range(start_date, end_date)
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where_clause, params, meta = _build_where_clause(
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workcenter_groups=workcenter_groups,
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packages=packages,
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reasons=reasons,
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categories=categories,
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include_excluded_scrap=include_excluded_scrap,
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exclude_material_scrap=exclude_material_scrap,
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exclude_pb_diode=exclude_pb_diode,
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)
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sql = _prepare_sql("filter_options", where_clause=where_clause)
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df = read_sql_df(sql, _common_params(start_date, end_date, params))
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if df is None:
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df = pd.DataFrame()
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reason_options = _extract_distinct_text_values(df, "REASON")
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if _has_material_scrap(df) and MATERIAL_REASON_OPTION not in reason_options:
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reason_options.append(MATERIAL_REASON_OPTION)
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return {
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"workcenter_groups": _extract_workcenter_group_options(df),
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"packages": _extract_distinct_text_values(df, "PACKAGE"),
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"reasons": reason_options,
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"meta": meta,
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}
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|
|
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def query_summary(
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*,
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start_date: str,
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end_date: str,
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workcenter_groups: Optional[list[str]] = None,
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|
packages: Optional[list[str]] = None,
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|
reasons: Optional[list[str]] = None,
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categories: Optional[list[str]] = None,
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|
include_excluded_scrap: bool = False,
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exclude_material_scrap: bool = True,
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|
exclude_pb_diode: bool = True,
|
|
) -> dict[str, Any]:
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_validate_range(start_date, end_date)
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where_clause, params, meta = _build_where_clause(
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workcenter_groups=workcenter_groups,
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packages=packages,
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reasons=reasons,
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categories=categories,
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include_excluded_scrap=include_excluded_scrap,
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exclude_material_scrap=exclude_material_scrap,
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exclude_pb_diode=exclude_pb_diode,
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)
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sql = _prepare_sql("summary", where_clause=where_clause)
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df = read_sql_df(sql, _common_params(start_date, end_date, params))
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row = (df.iloc[0] if df is not None and not df.empty else {})
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return {
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"MOVEIN_QTY": _as_int(row.get("MOVEIN_QTY")),
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"REJECT_TOTAL_QTY": _as_int(row.get("REJECT_TOTAL_QTY")),
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"DEFECT_QTY": _as_int(row.get("DEFECT_QTY")),
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"REJECT_RATE_PCT": round(_as_float(row.get("REJECT_RATE_PCT")), 4),
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"DEFECT_RATE_PCT": round(_as_float(row.get("DEFECT_RATE_PCT")), 4),
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"REJECT_SHARE_PCT": round(_as_float(row.get("REJECT_SHARE_PCT")), 4),
|
|
"AFFECTED_LOT_COUNT": _as_int(row.get("AFFECTED_LOT_COUNT")),
|
|
"AFFECTED_WORKORDER_COUNT": _as_int(row.get("AFFECTED_WORKORDER_COUNT")),
|
|
"meta": meta,
|
|
}
|
|
|
|
|
|
def query_trend(
|
|
*,
|
|
start_date: str,
|
|
end_date: str,
|
|
granularity: str = "day",
|
|
workcenter_groups: Optional[list[str]] = None,
|
|
packages: Optional[list[str]] = None,
|
|
reasons: Optional[list[str]] = None,
|
|
categories: Optional[list[str]] = None,
|
|
include_excluded_scrap: bool = False,
|
|
exclude_material_scrap: bool = True,
|
|
exclude_pb_diode: bool = True,
|
|
) -> dict[str, Any]:
|
|
_validate_range(start_date, end_date)
|
|
normalized_granularity = _normalize_text(granularity).lower() or "day"
|
|
if normalized_granularity not in VALID_GRANULARITY:
|
|
raise ValueError("Invalid granularity. Use day, week, or month")
|
|
|
|
where_clause, params, meta = _build_where_clause(
|
|
workcenter_groups=workcenter_groups,
|
|
packages=packages,
|
|
reasons=reasons,
|
|
categories=categories,
|
|
include_excluded_scrap=include_excluded_scrap,
|
|
exclude_material_scrap=exclude_material_scrap,
|
|
exclude_pb_diode=exclude_pb_diode,
|
|
)
|
|
sql = _prepare_sql(
|
|
"trend",
|
|
where_clause=where_clause,
|
|
bucket_expr=_date_bucket_expr(normalized_granularity),
|
|
)
|
|
df = read_sql_df(sql, _common_params(start_date, end_date, params))
|
|
items = []
|
|
if df is not None and not df.empty:
|
|
for _, row in df.iterrows():
|
|
items.append(
|
|
{
|
|
"bucket_date": _to_date_str(row.get("BUCKET_DATE")),
|
|
"MOVEIN_QTY": _as_int(row.get("MOVEIN_QTY")),
|
|
"REJECT_TOTAL_QTY": _as_int(row.get("REJECT_TOTAL_QTY")),
|
|
"DEFECT_QTY": _as_int(row.get("DEFECT_QTY")),
|
|
"REJECT_RATE_PCT": round(_as_float(row.get("REJECT_RATE_PCT")), 4),
|
|
"DEFECT_RATE_PCT": round(_as_float(row.get("DEFECT_RATE_PCT")), 4),
|
|
}
|
|
)
|
|
|
|
return {
|
|
"items": items,
|
|
"granularity": normalized_granularity,
|
|
"meta": meta,
|
|
}
|
|
|
|
|
|
def query_reason_pareto(
|
|
*,
|
|
start_date: str,
|
|
end_date: str,
|
|
metric_mode: str = "reject_total",
|
|
pareto_scope: str = "top80",
|
|
workcenter_groups: Optional[list[str]] = None,
|
|
packages: Optional[list[str]] = None,
|
|
reasons: Optional[list[str]] = None,
|
|
categories: Optional[list[str]] = None,
|
|
include_excluded_scrap: bool = False,
|
|
exclude_material_scrap: bool = True,
|
|
exclude_pb_diode: bool = True,
|
|
) -> dict[str, Any]:
|
|
_validate_range(start_date, end_date)
|
|
normalized_metric = _normalize_text(metric_mode).lower() or "reject_total"
|
|
if normalized_metric not in VALID_METRIC_MODE:
|
|
raise ValueError("Invalid metric_mode. Use reject_total or defect")
|
|
|
|
normalized_scope = _normalize_text(pareto_scope).lower() or "top80"
|
|
if normalized_scope not in {"top80", "all"}:
|
|
raise ValueError("Invalid pareto_scope. Use top80 or all")
|
|
|
|
where_clause, params, meta = _build_where_clause(
|
|
workcenter_groups=workcenter_groups,
|
|
packages=packages,
|
|
reasons=reasons,
|
|
categories=categories,
|
|
include_excluded_scrap=include_excluded_scrap,
|
|
exclude_material_scrap=exclude_material_scrap,
|
|
exclude_pb_diode=exclude_pb_diode,
|
|
)
|
|
sql = _prepare_sql(
|
|
"reason_pareto",
|
|
where_clause=where_clause,
|
|
metric_column=_metric_column(normalized_metric),
|
|
)
|
|
df = read_sql_df(sql, _common_params(start_date, end_date, params))
|
|
all_items = []
|
|
if df is not None and not df.empty:
|
|
for _, row in df.iterrows():
|
|
all_items.append(
|
|
{
|
|
"reason": _normalize_text(row.get("REASON")) or "(未填寫)",
|
|
"metric_value": _as_float(row.get("METRIC_VALUE")),
|
|
"MOVEIN_QTY": _as_int(row.get("MOVEIN_QTY")),
|
|
"REJECT_TOTAL_QTY": _as_int(row.get("REJECT_TOTAL_QTY")),
|
|
"DEFECT_QTY": _as_int(row.get("DEFECT_QTY")),
|
|
"count": _as_int(row.get("AFFECTED_LOT_COUNT")),
|
|
"pct": round(_as_float(row.get("PCT")), 4),
|
|
"cumPct": round(_as_float(row.get("CUM_PCT")), 4),
|
|
}
|
|
)
|
|
|
|
items = list(all_items)
|
|
if normalized_scope == "top80" and items:
|
|
top_items = [item for item in items if _as_float(item.get("cumPct")) <= 80.0]
|
|
# Keep strict top-80% behavior, but still return one row when first item already exceeds 80%.
|
|
if not top_items:
|
|
top_items = [items[0]]
|
|
items = top_items
|
|
|
|
return {
|
|
"items": items,
|
|
"metric_mode": normalized_metric,
|
|
"pareto_scope": normalized_scope,
|
|
"meta": {
|
|
**meta,
|
|
"total_items_after_filter": len(all_items),
|
|
"displayed_items": len(items),
|
|
},
|
|
}
|
|
|
|
|
|
# Allowed dimension → SQL column mapping for dimension_pareto
|
|
_DIMENSION_COLUMN_MAP = {
|
|
"reason": "b.LOSSREASONNAME",
|
|
"package": "b.PRODUCTLINENAME",
|
|
"type": "b.PJ_TYPE",
|
|
"workflow": "b.WORKFLOWNAME",
|
|
"workcenter": "b.WORKCENTER_GROUP",
|
|
"equipment": "b.PRIMARY_EQUIPMENTNAME",
|
|
}
|
|
|
|
|
|
def query_dimension_pareto(
|
|
*,
|
|
start_date: str,
|
|
end_date: str,
|
|
dimension: str = "reason",
|
|
metric_mode: str = "reject_total",
|
|
pareto_scope: str = "top80",
|
|
workcenter_groups: Optional[list[str]] = None,
|
|
packages: Optional[list[str]] = None,
|
|
reasons: Optional[list[str]] = None,
|
|
categories: Optional[list[str]] = None,
|
|
include_excluded_scrap: bool = False,
|
|
exclude_material_scrap: bool = True,
|
|
exclude_pb_diode: bool = True,
|
|
) -> dict[str, Any]:
|
|
"""Pareto chart grouped by an arbitrary dimension (reason, package, type, workcenter, equipment)."""
|
|
_validate_range(start_date, end_date)
|
|
|
|
normalized_dim = _normalize_text(dimension).lower() or "reason"
|
|
if normalized_dim not in _DIMENSION_COLUMN_MAP:
|
|
raise ValueError(f"Invalid dimension '{dimension}'. Use: {', '.join(_DIMENSION_COLUMN_MAP)}")
|
|
|
|
# For reason dimension, delegate to existing optimized function
|
|
if normalized_dim == "reason":
|
|
return query_reason_pareto(
|
|
start_date=start_date, end_date=end_date,
|
|
metric_mode=metric_mode, pareto_scope=pareto_scope,
|
|
workcenter_groups=workcenter_groups, packages=packages,
|
|
reasons=reasons, categories=categories,
|
|
include_excluded_scrap=include_excluded_scrap,
|
|
exclude_material_scrap=exclude_material_scrap,
|
|
exclude_pb_diode=exclude_pb_diode,
|
|
)
|
|
|
|
normalized_metric = _normalize_text(metric_mode).lower() or "reject_total"
|
|
if normalized_metric not in VALID_METRIC_MODE:
|
|
raise ValueError("Invalid metric_mode. Use reject_total or defect")
|
|
|
|
normalized_scope = _normalize_text(pareto_scope).lower() or "top80"
|
|
if normalized_scope not in {"top80", "all"}:
|
|
raise ValueError("Invalid pareto_scope. Use top80 or all")
|
|
|
|
dim_col = _DIMENSION_COLUMN_MAP[normalized_dim]
|
|
|
|
where_clause, params, meta = _build_where_clause(
|
|
workcenter_groups=workcenter_groups,
|
|
packages=packages,
|
|
reasons=reasons,
|
|
categories=categories,
|
|
include_excluded_scrap=include_excluded_scrap,
|
|
exclude_material_scrap=exclude_material_scrap,
|
|
exclude_pb_diode=exclude_pb_diode,
|
|
)
|
|
sql = _prepare_sql(
|
|
"dimension_pareto",
|
|
where_clause=where_clause,
|
|
metric_column=_metric_column(normalized_metric),
|
|
dimension_column=dim_col,
|
|
)
|
|
df = read_sql_df(sql, _common_params(start_date, end_date, params))
|
|
all_items = []
|
|
if df is not None and not df.empty:
|
|
for _, row in df.iterrows():
|
|
all_items.append({
|
|
"reason": _normalize_text(row.get("DIMENSION_VALUE")) or "(未知)",
|
|
"metric_value": _as_float(row.get("METRIC_VALUE")),
|
|
"MOVEIN_QTY": _as_int(row.get("MOVEIN_QTY")),
|
|
"REJECT_TOTAL_QTY": _as_int(row.get("REJECT_TOTAL_QTY")),
|
|
"DEFECT_QTY": _as_int(row.get("DEFECT_QTY")),
|
|
"count": _as_int(row.get("AFFECTED_LOT_COUNT")),
|
|
"pct": round(_as_float(row.get("PCT")), 4),
|
|
"cumPct": round(_as_float(row.get("CUM_PCT")), 4),
|
|
})
|
|
|
|
items = list(all_items)
|
|
if normalized_scope == "top80" and items:
|
|
top_items = [item for item in items if _as_float(item.get("cumPct")) <= 80.0]
|
|
if not top_items:
|
|
top_items = [items[0]]
|
|
items = top_items
|
|
|
|
return {
|
|
"items": items,
|
|
"dimension": normalized_dim,
|
|
"metric_mode": normalized_metric,
|
|
"pareto_scope": normalized_scope,
|
|
"meta": {
|
|
**meta,
|
|
"total_items_after_filter": len(all_items),
|
|
"displayed_items": len(items),
|
|
},
|
|
}
|
|
|
|
|
|
def _apply_metric_filter(where_clause: str, metric_filter: str) -> str:
|
|
"""Append metric-type filter (reject / defect) to an existing WHERE clause."""
|
|
if metric_filter == "reject":
|
|
cond = "b.REJECT_TOTAL_QTY > 0"
|
|
elif metric_filter == "defect":
|
|
cond = "b.DEFECT_QTY > 0"
|
|
else:
|
|
return where_clause
|
|
if where_clause.strip():
|
|
return f"{where_clause} AND {cond}"
|
|
return f"WHERE {cond}"
|
|
|
|
|
|
def query_list(
|
|
*,
|
|
start_date: str,
|
|
end_date: str,
|
|
page: int = 1,
|
|
per_page: int = 50,
|
|
workcenter_groups: Optional[list[str]] = None,
|
|
packages: Optional[list[str]] = None,
|
|
reasons: Optional[list[str]] = None,
|
|
categories: Optional[list[str]] = None,
|
|
include_excluded_scrap: bool = False,
|
|
exclude_material_scrap: bool = True,
|
|
exclude_pb_diode: bool = True,
|
|
metric_filter: str = "all",
|
|
) -> dict[str, Any]:
|
|
_validate_range(start_date, end_date)
|
|
|
|
page = max(int(page or 1), 1)
|
|
per_page = min(max(int(per_page or 50), 1), 200)
|
|
offset = (page - 1) * per_page
|
|
|
|
where_clause, params, meta = _build_where_clause(
|
|
workcenter_groups=workcenter_groups,
|
|
packages=packages,
|
|
reasons=reasons,
|
|
categories=categories,
|
|
include_excluded_scrap=include_excluded_scrap,
|
|
exclude_material_scrap=exclude_material_scrap,
|
|
exclude_pb_diode=exclude_pb_diode,
|
|
)
|
|
where_clause = _apply_metric_filter(where_clause, metric_filter)
|
|
sql = _prepare_sql("list", where_clause=where_clause, base_variant="lot")
|
|
query_params = _common_params(
|
|
start_date,
|
|
end_date,
|
|
{
|
|
**params,
|
|
"offset": offset,
|
|
"limit": per_page,
|
|
},
|
|
)
|
|
df = read_sql_df(sql, query_params)
|
|
|
|
items = []
|
|
total = 0
|
|
if df is not None and not df.empty:
|
|
total = _as_int(df.iloc[0].get("TOTAL_COUNT"))
|
|
for _, row in df.iterrows():
|
|
items.append(
|
|
{
|
|
"TXN_TIME": _to_datetime_str(row.get("TXN_TIME")),
|
|
"TXN_DAY": _to_date_str(row.get("TXN_DAY")),
|
|
"TXN_MONTH": _normalize_text(row.get("TXN_MONTH")),
|
|
"WORKCENTER_GROUP": _normalize_text(row.get("WORKCENTER_GROUP")),
|
|
"WORKCENTERNAME": _normalize_text(row.get("WORKCENTERNAME")),
|
|
"SPECNAME": _normalize_text(row.get("SPECNAME")),
|
|
"WORKFLOWNAME": _normalize_text(row.get("WORKFLOWNAME")),
|
|
"EQUIPMENTNAME": _normalize_text(row.get("EQUIPMENTNAME")),
|
|
"PRODUCTLINENAME": _normalize_text(row.get("PRODUCTLINENAME")),
|
|
"PJ_TYPE": _normalize_text(row.get("PJ_TYPE")),
|
|
"CONTAINERNAME": _normalize_text(row.get("CONTAINERNAME")),
|
|
"PJ_FUNCTION": _normalize_text(row.get("PJ_FUNCTION")),
|
|
"PRODUCTNAME": _normalize_text(row.get("PRODUCTNAME")),
|
|
"LOSSREASONNAME": _normalize_text(row.get("LOSSREASONNAME")),
|
|
"LOSSREASON_CODE": _normalize_text(row.get("LOSSREASON_CODE")),
|
|
"REJECTCOMMENT": _normalize_text(row.get("REJECTCOMMENT")),
|
|
"MOVEIN_QTY": _as_int(row.get("MOVEIN_QTY")),
|
|
"REJECT_QTY": _as_int(row.get("REJECT_QTY")),
|
|
"STANDBY_QTY": _as_int(row.get("STANDBY_QTY")),
|
|
"QTYTOPROCESS_QTY": _as_int(row.get("QTYTOPROCESS_QTY")),
|
|
"INPROCESS_QTY": _as_int(row.get("INPROCESS_QTY")),
|
|
"PROCESSED_QTY": _as_int(row.get("PROCESSED_QTY")),
|
|
"REJECT_TOTAL_QTY": _as_int(row.get("REJECT_TOTAL_QTY")),
|
|
"DEFECT_QTY": _as_int(row.get("DEFECT_QTY")),
|
|
"REJECT_RATE_PCT": round(_as_float(row.get("REJECT_RATE_PCT")), 4),
|
|
"DEFECT_RATE_PCT": round(_as_float(row.get("DEFECT_RATE_PCT")), 4),
|
|
"REJECT_SHARE_PCT": round(_as_float(row.get("REJECT_SHARE_PCT")), 4),
|
|
"AFFECTED_WORKORDER_COUNT": _as_int(row.get("AFFECTED_WORKORDER_COUNT")),
|
|
}
|
|
)
|
|
|
|
total_pages = max((total + per_page - 1) // per_page, 1) if total else 1
|
|
return {
|
|
"items": items,
|
|
"pagination": {
|
|
"page": page,
|
|
"perPage": per_page,
|
|
"total": total,
|
|
"totalPages": total_pages,
|
|
},
|
|
"meta": meta,
|
|
}
|
|
|
|
|
|
def export_csv(
|
|
*,
|
|
start_date: str,
|
|
end_date: str,
|
|
workcenter_groups: Optional[list[str]] = None,
|
|
packages: Optional[list[str]] = None,
|
|
reasons: Optional[list[str]] = None,
|
|
categories: Optional[list[str]] = None,
|
|
include_excluded_scrap: bool = False,
|
|
exclude_material_scrap: bool = True,
|
|
exclude_pb_diode: bool = True,
|
|
metric_filter: str = "all",
|
|
) -> Generator[str, None, None]:
|
|
_validate_range(start_date, end_date)
|
|
|
|
where_clause, params, _meta = _build_where_clause(
|
|
workcenter_groups=workcenter_groups,
|
|
packages=packages,
|
|
reasons=reasons,
|
|
categories=categories,
|
|
include_excluded_scrap=include_excluded_scrap,
|
|
exclude_material_scrap=exclude_material_scrap,
|
|
exclude_pb_diode=exclude_pb_diode,
|
|
)
|
|
where_clause = _apply_metric_filter(where_clause, metric_filter)
|
|
sql = _prepare_sql("export", where_clause=where_clause, base_variant="lot")
|
|
df = read_sql_df(sql, _common_params(start_date, end_date, params))
|
|
|
|
rows = []
|
|
if df is not None and not df.empty:
|
|
for _, row in df.iterrows():
|
|
rows.append(
|
|
{
|
|
"LOT": _normalize_text(row.get("CONTAINERNAME")),
|
|
"WORKCENTER": _normalize_text(row.get("WORKCENTERNAME")),
|
|
"WORKCENTER_GROUP": _normalize_text(row.get("WORKCENTER_GROUP")),
|
|
"Package": _normalize_text(row.get("PRODUCTLINENAME")),
|
|
"FUNCTION": _normalize_text(row.get("PJ_FUNCTION")),
|
|
"TYPE": _normalize_text(row.get("PJ_TYPE")),
|
|
"WORKFLOW": _normalize_text(row.get("WORKFLOWNAME")),
|
|
"PRODUCT": _normalize_text(row.get("PRODUCTNAME")),
|
|
"原因": _normalize_text(row.get("LOSSREASONNAME")),
|
|
"EQUIPMENT": _normalize_text(row.get("EQUIPMENTNAME")),
|
|
"COMMENT": _normalize_text(row.get("REJECTCOMMENT")),
|
|
"SPEC": _normalize_text(row.get("SPECNAME")),
|
|
"REJECT_QTY": _as_int(row.get("REJECT_QTY")),
|
|
"STANDBY_QTY": _as_int(row.get("STANDBY_QTY")),
|
|
"QTYTOPROCESS_QTY": _as_int(row.get("QTYTOPROCESS_QTY")),
|
|
"INPROCESS_QTY": _as_int(row.get("INPROCESS_QTY")),
|
|
"PROCESSED_QTY": _as_int(row.get("PROCESSED_QTY")),
|
|
"扣帳報廢量": _as_int(row.get("REJECT_TOTAL_QTY")),
|
|
"不扣帳報廢量": _as_int(row.get("DEFECT_QTY")),
|
|
"MOVEIN_QTY": _as_int(row.get("MOVEIN_QTY")),
|
|
"報廢時間": _to_datetime_str(row.get("TXN_TIME")),
|
|
"日期": _to_date_str(row.get("TXN_DAY")),
|
|
}
|
|
)
|
|
|
|
headers = [
|
|
"LOT",
|
|
"WORKCENTER",
|
|
"WORKCENTER_GROUP",
|
|
"Package",
|
|
"FUNCTION",
|
|
"TYPE",
|
|
"WORKFLOW",
|
|
"PRODUCT",
|
|
"原因",
|
|
"EQUIPMENT",
|
|
"COMMENT",
|
|
"SPEC",
|
|
"REJECT_QTY",
|
|
"STANDBY_QTY",
|
|
"QTYTOPROCESS_QTY",
|
|
"INPROCESS_QTY",
|
|
"PROCESSED_QTY",
|
|
"扣帳報廢量",
|
|
"不扣帳報廢量",
|
|
"MOVEIN_QTY",
|
|
"報廢時間",
|
|
"日期",
|
|
]
|
|
return _list_to_csv(rows, headers=headers)
|
|
|
|
|
|
def _derive_summary(df: pd.DataFrame) -> dict[str, Any]:
|
|
"""Aggregate analytics rows into a single summary dict."""
|
|
if df is None or df.empty:
|
|
return {
|
|
"MOVEIN_QTY": 0,
|
|
"REJECT_TOTAL_QTY": 0,
|
|
"DEFECT_QTY": 0,
|
|
"REJECT_RATE_PCT": 0,
|
|
"DEFECT_RATE_PCT": 0,
|
|
"REJECT_SHARE_PCT": 0,
|
|
"AFFECTED_LOT_COUNT": 0,
|
|
"AFFECTED_WORKORDER_COUNT": 0,
|
|
}
|
|
|
|
movein = _as_int(df["MOVEIN_QTY"].sum())
|
|
reject_total = _as_int(df["REJECT_TOTAL_QTY"].sum())
|
|
defect = _as_int(df["DEFECT_QTY"].sum())
|
|
affected_lot = _as_int(df["AFFECTED_LOT_COUNT"].sum())
|
|
affected_wo = _as_int(df["AFFECTED_WORKORDER_COUNT"].sum())
|
|
|
|
total_scrap = reject_total + defect
|
|
reject_rate = round((reject_total / movein * 100) if movein else 0, 4)
|
|
defect_rate = round((defect / movein * 100) if movein else 0, 4)
|
|
reject_share = round((reject_total / total_scrap * 100) if total_scrap else 0, 4)
|
|
|
|
return {
|
|
"MOVEIN_QTY": movein,
|
|
"REJECT_TOTAL_QTY": reject_total,
|
|
"DEFECT_QTY": defect,
|
|
"REJECT_RATE_PCT": reject_rate,
|
|
"DEFECT_RATE_PCT": defect_rate,
|
|
"REJECT_SHARE_PCT": reject_share,
|
|
"AFFECTED_LOT_COUNT": affected_lot,
|
|
"AFFECTED_WORKORDER_COUNT": affected_wo,
|
|
}
|
|
|
|
|
|
def _derive_trend(df: pd.DataFrame) -> list[dict[str, Any]]:
|
|
"""Group analytics rows by BUCKET_DATE into daily trend items."""
|
|
if df is None or df.empty:
|
|
return []
|
|
|
|
grouped = df.groupby("BUCKET_DATE", sort=True).agg(
|
|
MOVEIN_QTY=("MOVEIN_QTY", "sum"),
|
|
REJECT_TOTAL_QTY=("REJECT_TOTAL_QTY", "sum"),
|
|
DEFECT_QTY=("DEFECT_QTY", "sum"),
|
|
).reset_index()
|
|
|
|
items = []
|
|
for _, row in grouped.iterrows():
|
|
movein = _as_int(row["MOVEIN_QTY"])
|
|
reject_total = _as_int(row["REJECT_TOTAL_QTY"])
|
|
defect = _as_int(row["DEFECT_QTY"])
|
|
items.append({
|
|
"bucket_date": _to_date_str(row["BUCKET_DATE"]),
|
|
"MOVEIN_QTY": movein,
|
|
"REJECT_TOTAL_QTY": reject_total,
|
|
"DEFECT_QTY": defect,
|
|
"REJECT_RATE_PCT": round((reject_total / movein * 100) if movein else 0, 4),
|
|
"DEFECT_RATE_PCT": round((defect / movein * 100) if movein else 0, 4),
|
|
})
|
|
return items
|
|
|
|
|
|
def _derive_pareto(df: pd.DataFrame, metric_mode: str = "reject_total") -> list[dict[str, Any]]:
|
|
"""Group analytics rows by REASON into pareto items with PCT/CUM_PCT."""
|
|
if df is None or df.empty:
|
|
return []
|
|
|
|
metric_col = "REJECT_TOTAL_QTY" if metric_mode == "reject_total" else "DEFECT_QTY"
|
|
|
|
grouped = df.groupby("REASON", sort=False).agg(
|
|
MOVEIN_QTY=("MOVEIN_QTY", "sum"),
|
|
REJECT_TOTAL_QTY=("REJECT_TOTAL_QTY", "sum"),
|
|
DEFECT_QTY=("DEFECT_QTY", "sum"),
|
|
AFFECTED_LOT_COUNT=("AFFECTED_LOT_COUNT", "sum"),
|
|
).reset_index()
|
|
|
|
grouped = grouped.sort_values(metric_col, ascending=False).reset_index(drop=True)
|
|
total_metric = _as_float(grouped[metric_col].sum())
|
|
|
|
items = []
|
|
cum = 0.0
|
|
for _, row in grouped.iterrows():
|
|
metric_value = _as_float(row[metric_col])
|
|
pct = round((metric_value / total_metric * 100) if total_metric else 0, 4)
|
|
cum += pct
|
|
reason_text = _normalize_text(row["REASON"]) or "(未填寫)"
|
|
items.append({
|
|
"reason": reason_text,
|
|
"metric_value": metric_value,
|
|
"MOVEIN_QTY": _as_int(row["MOVEIN_QTY"]),
|
|
"REJECT_TOTAL_QTY": _as_int(row["REJECT_TOTAL_QTY"]),
|
|
"DEFECT_QTY": _as_int(row["DEFECT_QTY"]),
|
|
"count": _as_int(row["AFFECTED_LOT_COUNT"]),
|
|
"pct": pct,
|
|
"cumPct": round(cum, 4),
|
|
})
|
|
return items
|
|
|
|
|
|
def _derive_raw_items(df: pd.DataFrame) -> list[dict[str, Any]]:
|
|
"""Return per-(date, reason) rows for client-side re-derivation."""
|
|
if df is None or df.empty:
|
|
return []
|
|
items = []
|
|
for _, row in df.iterrows():
|
|
items.append({
|
|
"bucket_date": _to_date_str(row["BUCKET_DATE"]),
|
|
"reason": _normalize_text(row["REASON"]) or "(未填寫)",
|
|
"MOVEIN_QTY": _as_int(row["MOVEIN_QTY"]),
|
|
"REJECT_TOTAL_QTY": _as_int(row["REJECT_TOTAL_QTY"]),
|
|
"DEFECT_QTY": _as_int(row["DEFECT_QTY"]),
|
|
"AFFECTED_LOT_COUNT": _as_int(row["AFFECTED_LOT_COUNT"]),
|
|
"AFFECTED_WORKORDER_COUNT": _as_int(row["AFFECTED_WORKORDER_COUNT"]),
|
|
})
|
|
return items
|
|
|
|
|
|
def query_analytics(
|
|
*,
|
|
start_date: str,
|
|
end_date: str,
|
|
metric_mode: str = "reject_total",
|
|
workcenter_groups: Optional[list[str]] = None,
|
|
packages: Optional[list[str]] = None,
|
|
reasons: Optional[list[str]] = None,
|
|
categories: Optional[list[str]] = None,
|
|
include_excluded_scrap: bool = False,
|
|
exclude_material_scrap: bool = True,
|
|
exclude_pb_diode: bool = True,
|
|
) -> dict[str, Any]:
|
|
"""Single DB query → summary + trend + pareto (replaces 3 separate queries)."""
|
|
_validate_range(start_date, end_date)
|
|
normalized_metric = _normalize_text(metric_mode).lower() or "reject_total"
|
|
if normalized_metric not in VALID_METRIC_MODE:
|
|
raise ValueError("Invalid metric_mode. Use reject_total or defect")
|
|
|
|
where_clause, params, meta = _build_where_clause(
|
|
workcenter_groups=workcenter_groups,
|
|
packages=packages,
|
|
reasons=reasons,
|
|
categories=categories,
|
|
include_excluded_scrap=include_excluded_scrap,
|
|
exclude_material_scrap=exclude_material_scrap,
|
|
exclude_pb_diode=exclude_pb_diode,
|
|
)
|
|
sql = _prepare_sql("analytics", where_clause=where_clause)
|
|
df = read_sql_df(sql, _common_params(start_date, end_date, params))
|
|
|
|
return {
|
|
"summary": _derive_summary(df),
|
|
"trend": {
|
|
"items": _derive_trend(df),
|
|
"granularity": "day",
|
|
},
|
|
"pareto": {
|
|
"items": _derive_pareto(df, normalized_metric),
|
|
"metric_mode": normalized_metric,
|
|
},
|
|
"raw_items": _derive_raw_items(df),
|
|
"meta": meta,
|
|
}
|