2568fd836c
Replace single-dimension Pareto dropdown with 6 simultaneous Pareto charts (不良原因, PACKAGE, TYPE, WORKFLOW, 站點, 機台) in a responsive 3-column grid. Clicking items in one Pareto cross-filters the other 5 (exclude-self logic), and the detail table applies all dimension selections with AND logic. Backend: - Add batch-pareto endpoint (cache-only, no Oracle queries) - Add _apply_cross_filter() with exclude-self pattern - Extend view/export endpoints for multi-dimension sel_* params Frontend: - New ParetoGrid.vue wrapping 6 ParetoSection instances - Simplify ParetoSection: remove dimension dropdown, keep TOP20 toggle - Replace single-dimension state with paretoSelections reactive object - Adaptive x-axis labels (font size, rotation, hideOverlap) for compact grid - Responsive grid: 3-col desktop, 2-col tablet, 1-col mobile Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
295 lines
9.9 KiB
Python
295 lines
9.9 KiB
Python
# -*- coding: utf-8 -*-
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"""Unit tests for reject_dataset_cache helpers."""
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from __future__ import annotations
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import pandas as pd
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import pytest
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from mes_dashboard.services import reject_dataset_cache as cache_svc
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def test_compute_dimension_pareto_applies_policy_filters_before_grouping(monkeypatch):
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"""Cached pareto should honor the same policy toggles as view/query paths."""
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df = pd.DataFrame(
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[
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{
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"CONTAINERID": "C1",
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"LOSSREASONNAME": "001_A",
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"LOSSREASON_CODE": "001_A",
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"SCRAP_OBJECTTYPE": "MATERIAL",
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"PRODUCTLINENAME": "(NA)",
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"WORKCENTER_GROUP": "WB",
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"REJECT_TOTAL_QTY": 100,
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"DEFECT_QTY": 0,
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"MOVEIN_QTY": 1000,
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},
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{
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"CONTAINERID": "C2",
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"LOSSREASONNAME": "001_A",
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"LOSSREASON_CODE": "001_A",
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"SCRAP_OBJECTTYPE": "LOT",
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"PRODUCTLINENAME": "PKG-A",
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"WORKCENTER_GROUP": "WB",
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"REJECT_TOTAL_QTY": 50,
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"DEFECT_QTY": 0,
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"MOVEIN_QTY": 900,
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},
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]
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)
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monkeypatch.setattr(cache_svc, "_get_cached_df", lambda _query_id: df)
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monkeypatch.setattr(
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"mes_dashboard.services.scrap_reason_exclusion_cache.get_excluded_reasons",
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lambda: [],
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)
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excluded_material = cache_svc.compute_dimension_pareto(
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query_id="qid-1",
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dimension="package",
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pareto_scope="all",
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include_excluded_scrap=False,
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exclude_material_scrap=True,
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exclude_pb_diode=True,
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)
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kept_all = cache_svc.compute_dimension_pareto(
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query_id="qid-1",
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dimension="package",
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pareto_scope="all",
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include_excluded_scrap=False,
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exclude_material_scrap=False,
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exclude_pb_diode=True,
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)
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excluded_labels = {item.get("reason") for item in excluded_material.get("items", [])}
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all_labels = {item.get("reason") for item in kept_all.get("items", [])}
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assert "PKG-A" in excluded_labels
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assert "(NA)" not in excluded_labels
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assert "(NA)" in all_labels
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def _build_detail_filter_df():
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return pd.DataFrame(
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[
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{
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"CONTAINERID": "C1",
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"CONTAINERNAME": "LOT-001",
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"TXN_DAY": pd.Timestamp("2026-02-01"),
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"TXN_TIME": pd.Timestamp("2026-02-01 08:00:00"),
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"WORKCENTERSEQUENCE_GROUP": 1,
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"WORKCENTER_GROUP": "WB",
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"WORKCENTERNAME": "WB-A",
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"SPECNAME": "SPEC-A",
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"WORKFLOWNAME": "WF-A",
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"PRIMARY_EQUIPMENTNAME": "EQ-1",
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"EQUIPMENTNAME": "EQ-1",
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"PRODUCTLINENAME": "PKG-A",
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"PJ_TYPE": "TYPE-A",
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"LOSSREASONNAME": "001_A",
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"LOSSREASON_CODE": "001_A",
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"SCRAP_OBJECTTYPE": "LOT",
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"MOVEIN_QTY": 100,
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"REJECT_TOTAL_QTY": 30,
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"DEFECT_QTY": 0,
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},
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{
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"CONTAINERID": "C2",
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"CONTAINERNAME": "LOT-002",
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"TXN_DAY": pd.Timestamp("2026-02-01"),
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"TXN_TIME": pd.Timestamp("2026-02-01 09:00:00"),
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"WORKCENTERSEQUENCE_GROUP": 1,
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"WORKCENTER_GROUP": "WB",
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"WORKCENTERNAME": "WB-B",
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"SPECNAME": "SPEC-B",
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"WORKFLOWNAME": "WF-B",
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"PRIMARY_EQUIPMENTNAME": "EQ-2",
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"EQUIPMENTNAME": "EQ-2",
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"PRODUCTLINENAME": "PKG-B",
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"PJ_TYPE": "TYPE-B",
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"LOSSREASONNAME": "001_A",
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"LOSSREASON_CODE": "001_A",
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"SCRAP_OBJECTTYPE": "LOT",
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"MOVEIN_QTY": 100,
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"REJECT_TOTAL_QTY": 20,
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"DEFECT_QTY": 0,
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},
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{
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"CONTAINERID": "C3",
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"CONTAINERNAME": "LOT-003",
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"TXN_DAY": pd.Timestamp("2026-02-01"),
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"TXN_TIME": pd.Timestamp("2026-02-01 10:00:00"),
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"WORKCENTERSEQUENCE_GROUP": 1,
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"WORKCENTER_GROUP": "WB",
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"WORKCENTERNAME": "WB-C",
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"SPECNAME": "SPEC-C",
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"WORKFLOWNAME": "WF-C",
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"PRIMARY_EQUIPMENTNAME": "EQ-3",
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"EQUIPMENTNAME": "EQ-3",
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"PRODUCTLINENAME": "PKG-C",
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"PJ_TYPE": "TYPE-C",
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"LOSSREASONNAME": "002_B",
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"LOSSREASON_CODE": "002_B",
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"SCRAP_OBJECTTYPE": "LOT",
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"MOVEIN_QTY": 100,
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"REJECT_TOTAL_QTY": 10,
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"DEFECT_QTY": 0,
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},
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]
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)
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def test_apply_view_and_export_share_same_pareto_multi_select_filter(monkeypatch):
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df = _build_detail_filter_df()
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monkeypatch.setattr(cache_svc, "_get_cached_df", lambda _query_id: df)
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monkeypatch.setattr(
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"mes_dashboard.services.scrap_reason_exclusion_cache.get_excluded_reasons",
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lambda: [],
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)
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view_result = cache_svc.apply_view(
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query_id="qid-2",
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pareto_dimension="type",
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pareto_values=["TYPE-A", "TYPE-C"],
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)
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export_rows = cache_svc.export_csv_from_cache(
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query_id="qid-2",
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pareto_dimension="type",
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pareto_values=["TYPE-A", "TYPE-C"],
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)
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detail_items = view_result["detail"]["items"]
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detail_types = {item["PJ_TYPE"] for item in detail_items}
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exported_types = {row["TYPE"] for row in export_rows}
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assert view_result["detail"]["pagination"]["total"] == 2
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assert detail_types == {"TYPE-A", "TYPE-C"}
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assert exported_types == {"TYPE-A", "TYPE-C"}
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assert len(export_rows) == 2
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def test_apply_view_rejects_invalid_pareto_dimension(monkeypatch):
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df = _build_detail_filter_df()
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monkeypatch.setattr(cache_svc, "_get_cached_df", lambda _query_id: df)
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with pytest.raises(ValueError, match="不支援的 pareto_dimension"):
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cache_svc.apply_view(
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query_id="qid-3",
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pareto_dimension="invalid-dimension",
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pareto_values=["X"],
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)
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with pytest.raises(ValueError, match="不支援的 pareto_dimension"):
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cache_svc.export_csv_from_cache(
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query_id="qid-3",
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pareto_dimension="invalid-dimension",
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pareto_values=["X"],
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)
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def test_compute_batch_pareto_applies_cross_filter_exclude_self(monkeypatch):
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df = pd.DataFrame(
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[
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{
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"CONTAINERID": "C1",
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"TXN_DAY": pd.Timestamp("2026-02-01"),
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"LOSSREASONNAME": "R-A",
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"PRODUCTLINENAME": "PKG-1",
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"PJ_TYPE": "TYPE-1",
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"WORKFLOWNAME": "WF-1",
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"WORKCENTER_GROUP": "WB-1",
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"PRIMARY_EQUIPMENTNAME": "EQ-1",
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"SCRAP_OBJECTTYPE": "LOT",
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"LOSSREASON_CODE": "001_A",
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"MOVEIN_QTY": 100,
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"REJECT_TOTAL_QTY": 100,
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"DEFECT_QTY": 0,
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},
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{
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"CONTAINERID": "C2",
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"TXN_DAY": pd.Timestamp("2026-02-01"),
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"LOSSREASONNAME": "R-A",
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"PRODUCTLINENAME": "PKG-2",
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"PJ_TYPE": "TYPE-2",
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"WORKFLOWNAME": "WF-2",
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"WORKCENTER_GROUP": "WB-2",
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"PRIMARY_EQUIPMENTNAME": "EQ-2",
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"SCRAP_OBJECTTYPE": "LOT",
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"LOSSREASON_CODE": "001_A",
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"MOVEIN_QTY": 100,
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"REJECT_TOTAL_QTY": 50,
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"DEFECT_QTY": 0,
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},
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{
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"CONTAINERID": "C3",
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"TXN_DAY": pd.Timestamp("2026-02-01"),
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"LOSSREASONNAME": "R-B",
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"PRODUCTLINENAME": "PKG-1",
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"PJ_TYPE": "TYPE-2",
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"WORKFLOWNAME": "WF-2",
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"WORKCENTER_GROUP": "WB-1",
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"PRIMARY_EQUIPMENTNAME": "EQ-1",
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"SCRAP_OBJECTTYPE": "LOT",
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"LOSSREASON_CODE": "002_B",
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"MOVEIN_QTY": 100,
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"REJECT_TOTAL_QTY": 40,
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"DEFECT_QTY": 0,
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},
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{
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"CONTAINERID": "C4",
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"TXN_DAY": pd.Timestamp("2026-02-01"),
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"LOSSREASONNAME": "R-B",
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"PRODUCTLINENAME": "PKG-3",
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"PJ_TYPE": "TYPE-3",
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"WORKFLOWNAME": "WF-3",
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"WORKCENTER_GROUP": "WB-3",
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"PRIMARY_EQUIPMENTNAME": "EQ-3",
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"SCRAP_OBJECTTYPE": "LOT",
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"LOSSREASON_CODE": "002_B",
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"MOVEIN_QTY": 100,
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"REJECT_TOTAL_QTY": 30,
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"DEFECT_QTY": 0,
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},
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]
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)
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monkeypatch.setattr(cache_svc, "_get_cached_df", lambda _query_id: df)
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monkeypatch.setattr(
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"mes_dashboard.services.scrap_reason_exclusion_cache.get_excluded_reasons",
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lambda: [],
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)
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result = cache_svc.compute_batch_pareto(
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query_id="qid-batch-1",
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metric_mode="reject_total",
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pareto_scope="all",
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include_excluded_scrap=True,
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pareto_selections={
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"reason": ["R-A"],
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"type": ["TYPE-2"],
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},
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)
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reason_items = result["dimensions"]["reason"]["items"]
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type_items = result["dimensions"]["type"]["items"]
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package_items = result["dimensions"]["package"]["items"]
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assert {item["reason"] for item in reason_items} == {"R-A", "R-B"}
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assert {item["reason"] for item in type_items} == {"TYPE-1", "TYPE-2"}
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assert [item["reason"] for item in package_items] == ["PKG-2"]
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def test_apply_pareto_selection_filter_supports_multi_dimension_and_logic():
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df = _build_detail_filter_df()
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filtered = cache_svc._apply_pareto_selection_filter(
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df,
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pareto_selections={
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"reason": ["001_A"],
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"type": ["TYPE-B"],
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},
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)
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assert len(filtered) == 1
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assert set(filtered["CONTAINERNAME"].tolist()) == {"LOT-002"}
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