86984cfeb1
Reject History: - Compute dimension pareto (package/type/workflow/workcenter/equipment) from cached DataFrame instead of re-querying Oracle per dimension change - Propagate supplementary filters and trend date selection to dimension pareto - Add staleness tracking to prevent race conditions on rapid dimension switches - Add WORKFLOWNAME to detail and export outputs - Fix button hover visibility with CSS specificity MSD (製程不良追溯分析): - Separate raw events caching from aggregation computation so changing loss_reasons uses EventFetcher per-domain cache (fast) and recomputes aggregation with current filters instead of returning stale cached results - Exclude loss_reasons from MSD seed cache key since seed resolution does not use it, avoiding unnecessary Oracle re-queries - Add suspect context panel, analysis summary, upstream station/spec filters - Add machine bar click drill-down and filtered attribution charts Query Tool: - Support batch container_ids in lot CSV export (history/materials/rejects/holds) Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
365 lines
12 KiB
Python
365 lines
12 KiB
Python
# -*- coding: utf-8 -*-
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"""Service tests for mid-section defect analysis."""
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from __future__ import annotations
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from unittest.mock import patch
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import pandas as pd
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from mes_dashboard.services.mid_section_defect_service import (
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_attribute_materials,
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_attribute_wafer_roots,
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build_trace_aggregation_from_events,
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query_analysis,
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query_analysis_detail,
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query_all_loss_reasons,
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query_station_options,
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)
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def test_query_analysis_invalid_date_format_returns_error():
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result = query_analysis('2025/01/01', '2025-01-31')
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assert 'error' in result
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assert 'YYYY-MM-DD' in result['error']
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def test_query_analysis_start_after_end_returns_error():
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result = query_analysis('2025-02-01', '2025-01-31')
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assert 'error' in result
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assert '起始日期不能晚於結束日期' in result['error']
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def test_query_analysis_exceeds_max_days_returns_error():
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result = query_analysis('2025-01-01', '2025-12-31')
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assert 'error' in result
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assert '180' in result['error']
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@patch('mes_dashboard.services.mid_section_defect_service.query_analysis')
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def test_query_analysis_detail_returns_sorted_first_page(mock_query_analysis):
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mock_query_analysis.return_value = {
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'detail': [
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{'CONTAINERNAME': 'C', 'DEFECT_RATE': 0.3},
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{'CONTAINERNAME': 'A', 'DEFECT_RATE': 5.2},
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{'CONTAINERNAME': 'B', 'DEFECT_RATE': 3.1},
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]
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}
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result = query_analysis_detail('2025-01-01', '2025-01-31', page=1, page_size=2)
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assert [row['CONTAINERNAME'] for row in result['detail']] == ['A', 'B']
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assert result['pagination'] == {
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'page': 1,
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'page_size': 2,
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'total_count': 3,
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'total_pages': 2,
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}
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@patch('mes_dashboard.services.mid_section_defect_service.query_analysis')
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def test_query_analysis_detail_clamps_page_to_last_page(mock_query_analysis):
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mock_query_analysis.return_value = {
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'detail': [
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{'CONTAINERNAME': 'A', 'DEFECT_RATE': 9.9},
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{'CONTAINERNAME': 'B', 'DEFECT_RATE': 8.8},
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{'CONTAINERNAME': 'C', 'DEFECT_RATE': 7.7},
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]
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}
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result = query_analysis_detail('2025-01-01', '2025-01-31', page=10, page_size=2)
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assert result['pagination']['page'] == 2
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assert result['pagination']['total_pages'] == 2
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assert len(result['detail']) == 1
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assert result['detail'][0]['CONTAINERNAME'] == 'C'
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@patch('mes_dashboard.services.mid_section_defect_service.query_analysis')
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def test_query_analysis_detail_returns_error_passthrough(mock_query_analysis):
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mock_query_analysis.return_value = {'error': '日期格式無效'}
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result = query_analysis_detail('2025-01-01', '2025-01-31', page=1, page_size=200)
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assert result == {'error': '日期格式無效'}
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@patch('mes_dashboard.services.mid_section_defect_service.query_analysis')
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def test_query_analysis_detail_returns_none_on_service_failure(mock_query_analysis):
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mock_query_analysis.return_value = None
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result = query_analysis_detail('2025-01-01', '2025-01-31', page=1, page_size=200)
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assert result is None
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@patch('mes_dashboard.services.mid_section_defect_service.cache_get')
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@patch('mes_dashboard.services.mid_section_defect_service.read_sql_df')
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def test_query_all_loss_reasons_cache_hit_skips_query(mock_read_sql_df, mock_cache_get):
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mock_cache_get.return_value = {'loss_reasons': ['Cached_A', 'Cached_B']}
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result = query_all_loss_reasons()
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assert result == {'loss_reasons': ['Cached_A', 'Cached_B']}
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mock_read_sql_df.assert_not_called()
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@patch('mes_dashboard.services.mid_section_defect_service.cache_get', return_value=None)
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@patch('mes_dashboard.services.mid_section_defect_service.cache_set')
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@patch('mes_dashboard.services.mid_section_defect_service.read_sql_df')
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@patch('mes_dashboard.services.mid_section_defect_service.SQLLoader.load')
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def test_query_all_loss_reasons_cache_miss_queries_and_caches_sorted_values(
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mock_sql_load,
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mock_read_sql_df,
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mock_cache_set,
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_mock_cache_get,
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):
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mock_sql_load.return_value = 'SELECT ...'
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mock_read_sql_df.return_value = pd.DataFrame(
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{'LOSSREASONNAME': ['B_REASON', None, 'A_REASON', 'B_REASON']}
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)
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result = query_all_loss_reasons()
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assert result == {'loss_reasons': ['A_REASON', 'B_REASON']}
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mock_cache_set.assert_called_once_with(
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'mid_section_loss_reasons:None:',
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{'loss_reasons': ['A_REASON', 'B_REASON']},
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ttl=86400,
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)
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@patch('mes_dashboard.services.mid_section_defect_service.cache_set')
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@patch('mes_dashboard.services.mid_section_defect_service.cache_get', return_value=None)
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@patch('mes_dashboard.services.mid_section_defect_service.release_lock')
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@patch('mes_dashboard.services.mid_section_defect_service.try_acquire_lock', return_value=True)
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@patch('mes_dashboard.services.mid_section_defect_service._fetch_upstream_history')
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@patch('mes_dashboard.services.mid_section_defect_service._resolve_full_genealogy')
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@patch('mes_dashboard.services.mid_section_defect_service._fetch_station_detection_data')
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def test_trace_aggregation_matches_query_analysis_summary(
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mock_fetch_detection_data,
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mock_resolve_genealogy,
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mock_fetch_upstream_history,
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_mock_lock,
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_mock_release_lock,
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_mock_cache_get,
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_mock_cache_set,
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):
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detection_df = pd.DataFrame([
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{
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'CONTAINERID': 'CID-001',
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'CONTAINERNAME': 'LOT-001',
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'TRACKINQTY': 100,
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'REJECTQTY': 5,
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'LOSSREASONNAME': 'R1',
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'WORKFLOW': 'WF-A',
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'PRODUCTLINENAME': 'PKG-A',
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'PJ_TYPE': 'TYPE-A',
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'DETECTION_EQUIPMENTNAME': 'EQ-01',
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'TRACKINTIMESTAMP': '2025-01-10 10:00:00',
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'FINISHEDRUNCARD': 'FR-001',
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},
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{
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'CONTAINERID': 'CID-002',
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'CONTAINERNAME': 'LOT-002',
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'TRACKINQTY': 120,
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'REJECTQTY': 6,
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'LOSSREASONNAME': 'R2',
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'WORKFLOW': 'WF-B',
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'PRODUCTLINENAME': 'PKG-B',
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'PJ_TYPE': 'TYPE-B',
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'DETECTION_EQUIPMENTNAME': 'EQ-02',
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'TRACKINTIMESTAMP': '2025-01-11 10:00:00',
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'FINISHEDRUNCARD': 'FR-002',
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},
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])
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ancestors = {
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'CID-001': {'CID-101'},
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'CID-002': set(),
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}
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upstream_normalized = {
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'CID-101': [{
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'workcenter_group': '中段',
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'equipment_id': 'EQ-01',
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'equipment_name': 'EQ-01',
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'spec_name': 'SPEC-A',
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'track_in_time': '2025-01-09 08:00:00',
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}],
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'CID-002': [{
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'workcenter_group': '中段',
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'equipment_id': 'EQ-02',
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'equipment_name': 'EQ-02',
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'spec_name': 'SPEC-B',
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'track_in_time': '2025-01-11 08:00:00',
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}],
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}
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upstream_events = {
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'CID-101': [{
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'WORKCENTER_GROUP': '中段',
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'EQUIPMENTID': 'EQ-01',
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'EQUIPMENTNAME': 'EQ-01',
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'SPECNAME': 'SPEC-A',
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'TRACKINTIMESTAMP': '2025-01-09 08:00:00',
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}],
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'CID-002': [{
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'WORKCENTER_GROUP': '中段',
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'EQUIPMENTID': 'EQ-02',
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'EQUIPMENTNAME': 'EQ-02',
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'SPECNAME': 'SPEC-B',
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'TRACKINTIMESTAMP': '2025-01-11 08:00:00',
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}],
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}
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mock_fetch_detection_data.return_value = detection_df
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mock_resolve_genealogy.return_value = ancestors
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mock_fetch_upstream_history.return_value = upstream_normalized
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summary = query_analysis('2025-01-01', '2025-01-31')
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staged_summary = build_trace_aggregation_from_events(
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'2025-01-01',
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'2025-01-31',
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seed_container_ids=['CID-001', 'CID-002'],
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lineage_ancestors={
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'CID-001': ['CID-101'],
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'CID-002': [],
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},
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upstream_events_by_cid=upstream_events,
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)
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assert staged_summary['available_loss_reasons'] == summary['available_loss_reasons']
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assert staged_summary['genealogy_status'] == summary['genealogy_status']
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assert staged_summary['detail_total_count'] == len(summary['detail'])
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assert staged_summary['kpi']['total_input'] == summary['kpi']['total_input']
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assert staged_summary['kpi']['lot_count'] == summary['kpi']['lot_count']
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assert staged_summary['kpi']['total_defect_qty'] == summary['kpi']['total_defect_qty']
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assert abs(
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staged_summary['kpi']['total_defect_rate'] - summary['kpi']['total_defect_rate']
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) <= 0.01
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assert staged_summary['daily_trend'] == summary['daily_trend']
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assert staged_summary['charts'].keys() == summary['charts'].keys()
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def test_query_station_options_returns_ordered_list():
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result = query_station_options()
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assert isinstance(result, list)
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assert len(result) == 12
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assert result[0]['name'] == '切割'
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assert result[0]['order'] == 0
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assert result[-1]['name'] == '測試'
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assert result[-1]['order'] == 11
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# --- _attribute_materials tests ---
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def _make_detection_data(entries):
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"""Helper: build detection_data dict from simplified entries."""
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data = {}
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for e in entries:
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data[e['cid']] = {
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'containername': e.get('name', e['cid']),
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'trackinqty': e['trackinqty'],
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'rejectqty_by_reason': e.get('reasons', {}),
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}
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return data
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def test_attribute_materials_basic_rate_calculation():
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detection_data = _make_detection_data([
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{'cid': 'C1', 'trackinqty': 100, 'reasons': {'R1': 5}},
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{'cid': 'C2', 'trackinqty': 200, 'reasons': {'R1': 10}},
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])
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ancestors = {'C1': {'A1'}, 'C2': {'A1'}}
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materials_by_cid = {
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'A1': [{'MATERIALPARTNAME': 'PART-A', 'MATERIALLOTNAME': 'LOT-X'}],
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}
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result = _attribute_materials(detection_data, ancestors, materials_by_cid)
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assert len(result) == 1
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assert result[0]['MATERIAL_KEY'] == 'PART-A (LOT-X)'
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assert result[0]['INPUT_QTY'] == 300
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assert result[0]['DEFECT_QTY'] == 15
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assert abs(result[0]['DEFECT_RATE'] - 5.0) < 0.01
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assert result[0]['DETECTION_LOT_COUNT'] == 2
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def test_attribute_materials_null_lot_name():
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detection_data = _make_detection_data([
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{'cid': 'C1', 'trackinqty': 100, 'reasons': {'R1': 3}},
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])
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ancestors = {'C1': {'A1'}}
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materials_by_cid = {
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'A1': [{'MATERIALPARTNAME': 'PART-B', 'MATERIALLOTNAME': None}],
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}
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result = _attribute_materials(detection_data, ancestors, materials_by_cid)
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assert len(result) == 1
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assert result[0]['MATERIAL_KEY'] == 'PART-B'
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assert result[0]['MATERIAL_LOT_NAME'] == ''
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def test_attribute_materials_with_loss_reason_filter():
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detection_data = _make_detection_data([
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{'cid': 'C1', 'trackinqty': 100, 'reasons': {'R1': 5, 'R2': 3}},
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])
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ancestors = {'C1': {'A1'}}
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materials_by_cid = {
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'A1': [{'MATERIALPARTNAME': 'P', 'MATERIALLOTNAME': 'L'}],
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}
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result = _attribute_materials(detection_data, ancestors, materials_by_cid, loss_reasons=['R1'])
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assert result[0]['DEFECT_QTY'] == 5
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# --- _attribute_wafer_roots tests ---
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def test_attribute_wafer_roots_basic():
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detection_data = _make_detection_data([
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{'cid': 'C1', 'name': 'LOT-1', 'trackinqty': 100, 'reasons': {'R1': 5}},
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{'cid': 'C2', 'name': 'LOT-2', 'trackinqty': 200, 'reasons': {'R1': 10}},
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])
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roots = {'C1': 'ROOT-A', 'C2': 'ROOT-A'}
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result = _attribute_wafer_roots(detection_data, roots)
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assert len(result) == 1
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assert result[0]['ROOT_CONTAINER_NAME'] == 'ROOT-A'
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assert result[0]['INPUT_QTY'] == 300
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assert result[0]['DEFECT_QTY'] == 15
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def test_attribute_wafer_roots_self_root():
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"""LOTs with no root mapping should use their own container name."""
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detection_data = _make_detection_data([
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{'cid': 'C1', 'name': 'LOT-SELF', 'trackinqty': 100, 'reasons': {'R1': 2}},
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])
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roots = {} # No root for C1
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result = _attribute_wafer_roots(detection_data, roots)
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assert len(result) == 1
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assert result[0]['ROOT_CONTAINER_NAME'] == 'LOT-SELF'
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def test_attribute_wafer_roots_multiple_roots():
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detection_data = _make_detection_data([
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{'cid': 'C1', 'name': 'L1', 'trackinqty': 100, 'reasons': {'R1': 5}},
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{'cid': 'C2', 'name': 'L2', 'trackinqty': 200, 'reasons': {'R1': 20}},
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])
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roots = {'C1': 'ROOT-A', 'C2': 'ROOT-B'}
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result = _attribute_wafer_roots(detection_data, roots)
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assert len(result) == 2
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# Sorted by DEFECT_RATE desc
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assert result[0]['ROOT_CONTAINER_NAME'] == 'ROOT-B'
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assert result[1]['ROOT_CONTAINER_NAME'] == 'ROOT-A'
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