Py.Cafe
Create Interactive Greetings with Panel
- folderCompressed_Images/
- CM-Transfer_Project-Overview.xlsx
- app.py
- requirements.txt
app.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
836
837
838
839
840
841
842
843
844
845
846
847
848
849
850
851
852
853
854
855
856
857
858
859
860
861
862
863
864
865
866
867
868
869
870
871
872
873
874
875
876
877
878
879
880
881
882
883
884
885
886
887
888
889
890
891
892
893
894
895
896
897
898
899
900
901
902
903
904
905
906
907
908
909
910
911
912
913
914
915
916
917
918
919
920
921
922
923
924
925
926
927
928
929
930
931
932
933
934
935
936
937
938
939
940
941
942
943
944
945
946
947
948
949
950
951
952
953
954
955
956
957
958
959
960
961
962
963
964
965
966
967
968
969
970
971
972
973
974
975
976
977
978
979
980
981
982
983
984
985
986
987
988
989
990
991
992
993
994
995
996
997
998
999
1000
1001
1002
1003
1004
1005
1006
1007
1008
1009
1010
1011
1012
1013
1014
1015
1016
1017
1018
1019
1020
1021
1022
1023
1024
1025
1026
1027
1028
1029
1030
1031
1032
1033
1034
1035
1036
1037
1038
1039
1040
1041
1042
1043
1044
1045
1046
1047
1048
1049
1050
1051
1052
1053
1054
1055
1056
1057
1058
1059
1060
1061
1062
1063
1064
1065
1066
1067
1068
1069
1070
1071
1072
1073
1074
1075
1076
1077
1078
1079
1080
1081
1082
1083
1084
1085
1086
1087
1088
1089
1090
1091
1092
1093
1094
1095
1096
1097
1098
1099
1100
1101
1102
1103
1104
1105
1106
1107
1108
1109
1110
1111
1112
1113
1114
1115
1116
1117
1118
1119
1120
1121
1122
1123
1124
1125
1126
1127
1128
1129
1130
1131
1132
1133
1134
1135
1136
1137
1138
1139
1140
1141
1142
1143
1144
1145
1146
1147
1148
1149
1150
1151
1152
1153
1154
1155
1156
1157
1158
1159
1160
1161
1162
1163
1164
1165
1166
1167
1168
1169
1170
1171
1172
1173
1174
1175
1176
1177
1178
1179
1180
1181
1182
1183
1184
1185
1186
1187
1188
1189
1190
1191
1192
1193
1194
1195
1196
1197
1198
1199
1200
1201
1202
1203
1204
1205
1206
1207
1208
1209
1210
1211
1212
1213
1214
1215
1216
1217
1218
1219
1220
1221
1222
1223
1224
1225
1226
1227
1228
1229
1230
1231
1232
1233
1234
1235
1236
1237
1238
1239
1240
1241
1242
1243
1244
1245
1246
1247
1248
1249
1250
1251
1252
1253
1254
1255
1256
1257
1258
1259
1260
1261
1262
1263
1264
1265
1266
1267
1268
1269
1270
1271
1272
1273
1274
1275
1276
1277
1278
1279
1280
1281
1282
1283
1284
1285
1286
1287
1288
1289
1290
1291
1292
1293
1294
1295
1296
1297
1298
1299
1300
1301
1302
1303
1304
1305
1306
1307
1308
1309
1310
1311
1312
1313
1314
1315
1316
1317
1318
1319
1320
1321
1322
1323
1324
1325
1326
1327
1328
1329
1330
1331
1332
1333
1334
1335
1336
1337
1338
1339
1340
1341
1342
1343
1344
1345
1346
1347
1348
1349
1350
1351
1352
1353
1354
1355
1356
1357
1358
1359
1360
1361
1362
1363
1364
1365
1366
1367
1368
1369
1370
1371
1372
1373
1374
1375
1376
1377
1378
1379
1380
1381
1382
1383
1384
1385
1386
1387
1388
1389
1390
1391
1392
1393
1394
1395
1396
1397
1398
1399
1400
1401
1402
1403
1404
1405
1406
1407
1408
1409
1410
1411
1412
1413
1414
1415
1416
1417
1418
1419
1420
1421
1422
1423
1424
1425
1426
1427
1428
1429
1430
1431
1432
1433
1434
1435
1436
1437
1438
1439
1440
1441
1442
1443
1444
1445
1446
1447
1448
1449
1450
1451
1452
1453
1454
1455
1456
1457
1458
1459
1460
1461
1462
1463
1464
1465
1466
1467
1468
1469
1470
1471
1472
1473
1474
1475
1476
1477
1478
1479
1480
1481
1482
1483
1484
1485
1486
1487
1488
1489
1490
1491
1492
1493
1494
1495
1496
1497
1498
1499
1500
1501
1502
1503
1504
1505
1506
1507
1508
1509
1510
1511
1512
1513
1514
1515
1516
1517
1518
1519
1520
1521
1522
1523
1524
1525
1526
1527
1528
1529
1530
1531
1532
1533
1534
1535
1536
1537
1538
1539
1540
1541
1542
1543
1544
1545
1546
1547
1548
1549
1550
1551
1552
1553
1554
1555
1556
1557
1558
1559
1560
1561
1562
1563
1564
1565
1566
1567
1568
1569
1570
1571
1572
1573
1574
1575
1576
1577
1578
1579
1580
1581
1582
1583
1584
1585
1586
1587
1588
1589
1590
1591
1592
1593
1594
1595
1596
1597
1598
1599
1600
1601
1602
1603
1604
1605
1606
1607
1608
1609
1610
1611
1612
1613
1614
1615
1616
1617
1618
1619
1620
1621
1622
1623
1624
1625
1626
1627
1628
1629
1630
1631
1632
1633
1634
1635
1636
1637
1638
1639
1640
1641
1642
1643
1644
1645
1646
1647
1648
1649
1650
1651
1652
1653
1654
1655
1656
1657
1658
1659
1660
1661
1662
1663
1664
1665
1666
1667
1668
1669
1670
1671
1672
1673
1674
1675
1676
1677
1678
1679
1680
1681
1682
1683
1684
1685
1686
1687
1688
1689
1690
1691
1692
1693
1694
1695
1696
1697
1698
1699
1700
1701
1702
1703
1704
1705
1706
1707
1708
1709
1710
1711
1712
1713
1714
1715
1716
1717
1718
1719
1720
1721
1722
1723
1724
1725
1726
1727
1728
1729
1730
1731
1732
1733
1734
1735
1736
1737
1738
1739
1740
1741
1742
1743
1744
1745
1746
1747
1748
1749
1750
1751
1752
1753
1754
1755
1756
1757
1758
1759
1760
1761
1762
1763
1764
1765
1766
1767
1768
1769
1770
1771
1772
1773
1774
1775
1776
1777
1778
1779
1780
1781
1782
1783
1784
1785
1786
1787
1788
1789
1790
1791
1792
1793
1794
1795
1796
1797
1798
1799
1800
1801
1802
1803
1804
1805
1806
1807
1808
1809
1810
1811
1812
1813
1814
1815
1816
1817
1818
1819
1820
1821
1822
1823
1824
1825
1826
1827
1828
1829
1830
1831
1832
1833
1834
1835
1836
1837
1838
1839
1840
1841
1842
1843
1844
1845
1846
1847
1848
1849
1850
1851
1852
1853
1854
1855
1856
1857
1858
1859
1860
1861
1862
1863
1864
1865
1866
1867
1868
1869
1870
1871
1872
1873
1874
1875
1876
1877
1878
1879
1880
1881
1882
1883
1884
1885
1886
1887
1888
1889
1890
1891
1892
1893
1894
1895
1896
1897
1898
1899
1900
1901
1902
1903
1904
1905
1906
1907
1908
1909
1910
1911
1912
1913
1914
1915
1916
1917
1918
1919
1920
1921
1922
1923
1924
1925
1926
1927
1928
1929
1930
1931
1932
1933
1934
1935
1936
1937
1938
1939
1940
1941
1942
1943
1944
1945
1946
1947
1948
1949
1950
1951
1952
1953
1954
1955
1956
1957
1958
1959
1960
1961
1962
1963
1964
1965
1966
1967
1968
1969
1970
1971
1972
1973
1974
1975
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
2021
2022
2023
2024
2025
2026
2027
2028
2029
2030
2031
2032
2033
2034
2035
2036
2037
2038
2039
2040
2041
2042
2043
2044
2045
2046
2047
2048
2049
2050
2051
2052
2053
2054
2055
2056
2057
2058
2059
2060
2061
2062
2063
2064
2065
2066
2067
2068
2069
2070
2071
2072
2073
2074
2075
2076
2077
2078
2079
2080
2081
2082
2083
2084
2085
2086
2087
2088
2089
2090
2091
2092
2093
2094
2095
2096
2097
2098
2099
2100
2101
2102
2103
2104
2105
2106
2107
2108
2109
2110
2111
2112
2113
2114
2115
2116
2117
2118
2119
2120
2121
2122
2123
2124
2125
2126
2127
2128
2129
2130
2131
2132
2133
2134
2135
2136
2137
2138
2139
2140
2141
2142
2143
2144
2145
2146
2147
2148
2149
2150
2151
2152
2153
2154
2155
2156
2157
2158
2159
2160
2161
2162
2163
2164
2165
2166
2167
2168
2169
2170
2171
2172
2173
2174
2175
2176
2177
2178
2179
2180
2181
2182
2183
2184
2185
2186
2187
2188
2189
2190
2191
2192
2193
2194
2195
2196
2197
2198
2199
2200
2201
2202
2203
2204
2205
2206
2207
2208
2209
2210
2211
2212
2213
2214
2215
2216
2217
2218
2219
2220
2221
2222
2223
2224
2225
2226
2227
2228
2229
2230
2231
2232
2233
2234
2235
2236
2237
2238
2239
2240
2241
2242
2243
2244
2245
2246
2247
2248
2249
2250
2251
2252
2253
2254
2255
2256
2257
2258
2259
2260
2261
2262
2263
2264
2265
2266
2267
2268
2269
2270
2271
2272
2273
2274
2275
2276
2277
2278
2279
2280
2281
2282
2283
2284
2285
2286
2287
2288
2289
2290
2291
2292
2293
2294
2295
2296
2297
2298
2299
2300
2301
2302
2303
2304
2305
2306
2307
2308
2309
2310
2311
2312
2313
2314
2315
2316
2317
2318
2319
2320
2321
2322
2323
2324
2325
2326
2327
2328
2329
2330
2331
2332
2333
2334
2335
2336
2337
2338
2339
2340
2341
2342
2343
2344
2345
2346
2347
2348
2349
2350
2351
2352
2353
2354
2355
2356
2357
2358
2359
2360
2361
2362
2363
2364
2365
2366
2367
2368
2369
2370
2371
2372
2373
2374
2375
2376
2377
2378
2379
2380
2381
2382
2383
2384
2385
2386
2387
2388
2389
2390
2391
2392
2393
2394
2395
2396
2397
2398
2399
2400
2401
2402
2403
2404
2405
2406
2407
2408
2409
2410
2411
2412
2413
2414
2415
2416
2417
2418
2419
2420
2421
2422
2423
2424
2425
2426
2427
2428
2429
2430
2431
2432
2433
2434
2435
2436
2437
2438
2439
2440
2441
2442
2443
2444
2445
2446
2447
2448
2449
2450
2451
2452
2453
2454
2455
2456
2457
2458
2459
2460
2461
2462
2463
2464
2465
2466
2467
2468
2469
2470
2471
2472
2473
2474
2475
2476
2477
2478
2479
2480
2481
2482
2483
2484
2485
2486
2487
2488
2489
2490
2491
2492
2493
2494
2495
2496
2497
2498
2499
2500
2501
2502
2503
2504
2505
2506
2507
2508
2509
2510
2511
2512
2513
2514
2515
2516
2517
2518
2519
2520
2521
2522
2523
2524
2525
2526
2527
2528
2529
2530
2531
2532
2533
2534
2535
2536
2537
2538
2539
2540
2541
2542
2543
2544
2545
2546
2547
2548
2549
2550
2551
2552
2553
2554
2555
2556
2557
2558
2559
2560
2561
2562
2563
2564
2565
2566
2567
2568
2569
2570
2571
2572
2573
2574
2575
2576
2577
2578
2579
2580
2581
2582
2583
2584
2585
2586
2587
2588
2589
2590
2591
2592
2593
2594
2595
2596
2597
2598
2599
2600
2601
2602
2603
2604
2605
2606
2607
2608
2609
2610
2611
2612
2613
2614
2615
2616
2617
2618
2619
2620
2621
2622
2623
2624
2625
2626
2627
2628
2629
2630
2631
2632
2633
2634
2635
2636
2637
2638
2639
2640
2641
2642
2643
2644
2645
2646
2647
2648
2649
2650
2651
2652
2653
2654
2655
2656
2657
2658
2659
2660
2661
2662
2663
2664
2665
2666
2667
2668
2669
2670
2671
2672
2673
2674
2675
2676
2677
2678
2679
2680
2681
2682
2683
2684
2685
2686
2687
2688
2689
2690
2691
2692
2693
2694
2695
2696
2697
2698
2699
2700
2701
2702
2703
2704
2705
2706
2707
2708
2709
2710
2711
2712
2713
2714
2715
2716
2717
2718
2719
2720
2721
2722
2723
2724
2725
2726
2727
2728
2729
2730
2731
2732
2733
2734
2735
2736
2737
2738
2739
2740
2741
2742
2743
2744
2745
2746
2747
2748
2749
2750
2751
2752
2753
2754
2755
2756
2757
2758
2759
2760
2761
2762
2763
2764
2765
2766
2767
2768
2769
2770
2771
2772
2773
2774
2775
2776
2777
2778
2779
2780
2781
2782
2783
2784
2785
2786
2787
2788
2789
2790
2791
2792
2793
2794
2795
2796
2797
2798
2799
2800
2801
2802
2803
2804
2805
2806
2807
2808
2809
2810
2811
2812
2813
2814
2815
2816
2817
2818
2819
2820
2821
2822
2823
2824
2825
2826
2827
2828
2829
2830
2831
2832
2833
2834
2835
2836
2837
2838
2839
2840
2841
2842
2843
2844
2845
2846
2847
2848
2849
2850
2851
2852
2853
2854
2855
2856
2857
2858
2859
2860
2861
2862
2863
2864
2865
2866
2867
2868
2869
2870
2871
2872
2873
2874
2875
2876
2877
2878
2879
2880
2881
2882
2883
2884
2885
2886
2887
2888
2889
2890
2891
2892
2893
2894
2895
2896
2897
2898
2899
2900
2901
2902
2903
2904
2905
2906
2907
2908
2909
2910
2911
2912
2913
2914
2915
2916
2917
2918
2919
2920
2921
2922
2923
2924
2925
2926
2927
2928
2929
2930
2931
2932
2933
2934
2935
2936
2937
2938
2939
2940
2941
2942
2943
2944
2945
2946
2947
2948
2949
2950
2951
2952
2953
2954
2955
2956
2957
2958
2959
2960
2961
2962
2963
2964
2965
2966
2967
2968
2969
2970
2971
2972
2973
2974
2975
2976
2977
2978
2979
2980
2981
2982
2983
2984
2985
2986
2987
2988
2989
2990
2991
2992
2993
2994
2995
2996
2997
2998
2999
3000
3001
3002
3003
3004
3005
3006
3007
3008
3009
3010
3011
3012
3013
3014
3015
3016
3017
3018
3019
3020
3021
3022
3023
3024
3025
3026
3027
3028
3029
3030
3031
3032
3033
3034
3035
3036
3037
3038
3039
3040
3041
3042
3043
3044
3045
3046
3047
3048
3049
3050
3051
3052
3053
3054
3055
3056
3057
3058
3059
3060
3061
3062
3063
3064
3065
3066
3067
3068
3069
3070
3071
3072
3073
3074
3075
3076
3077
3078
3079
3080
3081
3082
3083
3084
3085
3086
3087
3088
3089
3090
3091
3092
3093
3094
3095
3096
3097
3098
3099
3100
3101
3102
3103
3104
3105
3106
3107
3108
3109
3110
3111
3112
3113
3114
3115
3116
3117
3118
3119
3120
3121
3122
3123
3124
3125
3126
3127
3128
3129
3130
3131
3132
3133
3134
3135
3136
3137
3138
3139
3140
3141
3142
3143
3144
3145
3146
3147
3148
3149
3150
3151
3152
3153
3154
3155
3156
3157
3158
3159
3160
3161
3162
3163
3164
3165
3166
3167
3168
3169
3170
3171
3172
3173
3174
3175
3176
3177
3178
3179
3180
3181
3182
3183
3184
3185
3186
3187
3188
3189
3190
3191
3192
3193
3194
3195
3196
3197
3198
3199
3200
3201
3202
3203
3204
3205
3206
3207
3208
3209
3210
3211
3212
3213
3214
3215
3216
3217
3218
3219
3220
3221
3222
3223
3224
3225
3226
3227
3228
3229
3230
3231
3232
3233
3234
3235
3236
3237
3238
3239
3240
3241
3242
3243
3244
3245
3246
3247
3248
3249
3250
3251
3252
3253
3254
3255
3256
3257
3258
3259
3260
3261
3262
3263
3264
3265
3266
3267
3268
3269
3270
3271
3272
3273
3274
3275
3276
3277
3278
3279
3280
3281
3282
3283
3284
3285
3286
3287
3288
3289
3290
3291
3292
3293
3294
3295
3296
3297
3298
3299
3300
3301
3302
3303
3304
3305
3306
3307
3308
3309
3310
3311
3312
3313
3314
3315
3316
3317
3318
3319
3320
3321
3322
3323
3324
3325
3326
3327
3328
3329
3330
3331
3332
3333
3334
3335
3336
3337
3338
3339
3340
3341
3342
3343
3344
3345
3346
3347
3348
3349
3350
3351
3352
3353
3354
3355
3356
3357
3358
3359
3360
3361
3362
3363
3364
3365
3366
3367
3368
3369
3370
3371
3372
3373
3374
3375
3376
3377
3378
3379
3380
3381
3382
3383
3384
3385
3386
3387
3388
3389
3390
3391
3392
3393
3394
3395
3396
3397
3398
3399
3400
3401
3402
3403
3404
3405
3406
3407
3408
3409
3410
3411
3412
3413
3414
3415
3416
3417
3418
3419
3420
3421
3422
3423
3424
3425
3426
3427
3428
3429
3430
3431
3432
3433
3434
3435
3436
3437
3438
3439
3440
3441
3442
3443
3444
3445
3446
3447
3448
3449
3450
3451
3452
3453
3454
3455
3456
3457
3458
3459
3460
3461
3462
3463
3464
3465
3466
3467
3468
3469
3470
3471
3472
3473
3474
3475
3476
3477
3478
3479
3480
3481
3482
3483
3484
3485
3486
3487
3488
3489
3490
3491
3492
3493
3494
3495
3496
3497
3498
3499
3500
3501
3502
3503
3504
3505
3506
3507
3508
3509
3510
3511
3512
3513
3514
3515
3516
3517
3518
3519
3520
3521
3522
3523
3524
3525
3526
3527
3528
3529
3530
3531
3532
3533
3534
3535
3536
3537
3538
3539
3540
3541
3542
3543
3544
3545
3546
3547
3548
3549
3550
3551
3552
3553
3554
3555
3556
3557
3558
3559
3560
3561
3562
3563
3564
3565
3566
3567
3568
3569
3570
3571
3572
3573
3574
3575
3576
3577
3578
3579
3580
3581
3582
3583
3584
3585
3586
3587
3588
3589
3590
3591
3592
3593
3594
3595
3596
3597
3598
3599
3600
3601
3602
3603
3604
3605
3606
3607
3608
3609
3610
3611
3612
3613
3614
3615
3616
3617
3618
3619
3620
3621
3622
3623
3624
3625
3626
3627
3628
3629
3630
3631
3632
3633
3634
3635
3636
3637
3638
3639
3640
3641
3642
3643
3644
3645
3646
3647
3648
3649
3650
3651
3652
3653
3654
3655
3656
3657
3658
3659
3660
3661
3662
3663
3664
3665
3666
3667
3668
3669
3670
3671
3672
3673
3674
3675
3676
3677
3678
3679
3680
3681
3682
3683
3684
3685
3686
3687
3688
3689
3690
3691
3692
3693
3694
3695
3696
3697
3698
3699
3700
3701
3702
3703
3704
3705
3706
3707
3708
3709
3710
3711
3712
3713
3714
3715
3716
3717
3718
3719
3720
3721
3722
3723
3724
3725
3726
3727
3728
3729
3730
3731
3732
3733
3734
3735
3736
3737
3738
3739
3740
3741
3742
3743
3744
3745
3746
3747
3748
3749
3750
3751
3752
3753
3754
3755
3756
3757
3758
3759
3760
3761
3762
3763
3764
3765
3766
3767
3768
3769
3770
3771
3772
3773
3774
3775
3776
3777
3778
3779
3780
3781
3782
3783
3784
3785
3786
3787
3788
3789
3790
3791
3792
3793
3794
3795
3796
3797
3798
3799
3800
3801
3802
3803
3804
3805
3806
3807
3808
3809
3810
3811
3812
3813
3814
3815
3816
3817
3818
3819
3820
3821
3822
3823
3824
3825
3826
3827
3828
3829
3830
3831
3832
3833
3834
3835
3836
3837
3838
3839
3840
3841
3842
3843
3844
3845
3846
3847
3848
3849
3850
3851
3852
3853
3854
3855
3856
3857
3858
3859
3860
3861
3862
3863
3864
3865
3866
3867
3868
3869
3870
3871
3872
3873
3874
3875
3876
3877
3878
3879
3880
3881
3882
3883
3884
3885
3886
3887
3888
3889
3890
3891
3892
3893
3894
3895
3896
3897
3898
3899
3900
3901
3902
3903
3904
3905
3906
3907
3908
3909
3910
3911
3912
3913
3914
3915
3916
3917
3918
3919
3920
3921
3922
3923
3924
3925
3926
3927
3928
3929
3930
3931
3932
3933
3934
3935
3936
3937
3938
3939
3940
3941
3942
3943
3944
3945
3946
3947
3948
3949
3950
3951
3952
3953
3954
3955
3956
3957
3958
3959
3960
3961
3962
3963
3964
3965
3966
3967
3968
3969
3970
3971
3972
3973
3974
3975
3976
3977
3978
3979
3980
3981
3982
3983
3984
3985
3986
3987
3988
3989
3990
3991
3992
3993
3994
3995
3996
3997
3998
3999
4000
4001
4002
4003
4004
4005
4006
4007
4008
4009
4010
4011
4012
4013
4014
4015
4016
4017
4018
4019
4020
4021
4022
4023
4024
4025
4026
4027
4028
4029
4030
4031
4032
4033
4034
4035
4036
4037
4038
4039
4040
4041
4042
4043
4044
4045
4046
4047
4048
4049
4050
4051
4052
4053
4054
4055
4056
4057
4058
4059
4060
4061
4062
4063
4064
4065
4066
4067
4068
4069
4070
4071
4072
4073
4074
4075
4076
4077
4078
4079
4080
4081
4082
4083
4084
4085
4086
4087
4088
4089
4090
4091
4092
4093
4094
4095
4096
4097
4098
4099
4100
4101
4102
4103
4104
4105
4106
4107
4108
4109
4110
4111
4112
4113
4114
4115
4116
4117
4118
4119
4120
4121
4122
4123
4124
4125
4126
4127
4128
4129
4130
4131
4132
4133
4134
4135
4136
4137
4138
4139
4140
4141
4142
4143
4144
4145
4146
4147
4148
4149
4150
4151
4152
4153
4154
4155
4156
4157
4158
4159
4160
4161
4162
4163
4164
4165
4166
4167
4168
4169
4170
4171
4172
4173
4174
4175
4176
4177
4178
4179
4180
4181
4182
4183
4184
4185
4186
4187
4188
4189
4190
4191
4192
4193
4194
4195
4196
4197
4198
4199
4200
4201
4202
4203
4204
4205
4206
4207
4208
4209
4210
4211
4212
4213
4214
4215
4216
4217
4218
4219
4220
4221
4222
4223
4224
4225
4226
4227
4228
4229
4230
4231
4232
4233
4234
4235
4236
4237
4238
4239
4240
4241
4242
4243
4244
4245
4246
4247
4248
4249
4250
4251
4252
4253
4254
4255
4256
4257
4258
4259
4260
4261
4262
4263
4264
4265
4266
4267
4268
4269
4270
4271
4272
4273
4274
4275
4276
4277
4278
4279
4280
4281
4282
4283
4284
4285
4286
4287
4288
4289
4290
4291
4292
4293
4294
4295
4296
4297
4298
4299
4300
4301
4302
4303
4304
4305
4306
4307
4308
4309
4310
4311
4312
4313
4314
4315
4316
4317
4318
4319
4320
4321
4322
4323
4324
4325
4326
4327
4328
4329
4330
4331
4332
4333
4334
4335
4336
4337
4338
4339
4340
4341
4342
4343
4344
4345
4346
4347
4348
4349
4350
4351
4352
4353
4354
4355
4356
4357
4358
4359
4360
4361
4362
4363
4364
4365
4366
4367
4368
4369
4370
4371
4372
4373
4374
4375
4376
4377
4378
4379
4380
4381
4382
4383
4384
4385
4386
4387
4388
4389
4390
4391
4392
4393
4394
4395
4396
4397
4398
4399
4400
4401
4402
4403
4404
4405
4406
4407
4408
4409
4410
4411
4412
4413
4414
4415
4416
4417
4418
4419
4420
4421
4422
4423
4424
4425
4426
4427
4428
4429
4430
4431
4432
4433
4434
4435
4436
4437
4438
4439
4440
4441
4442
4443
4444
4445
4446
4447
4448
4449
4450
4451
4452
4453
4454
4455
4456
4457
4458
4459
4460
4461
4462
4463
4464
4465
4466
4467
4468
4469
4470
4471
4472
4473
4474
4475
4476
4477
4478
4479
4480
4481
4482
4483
4484
4485
4486
4487
4488
4489
4490
4491
4492
4493
4494
4495
4496
4497
4498
4499
4500
4501
4502
4503
4504
4505
4506
4507
4508
4509
4510
4511
4512
4513
4514
4515
4516
4517
4518
4519
4520
4521
4522
4523
4524
4525
4526
4527
4528
4529
4530
4531
4532
4533
4534
4535
4536
4537
4538
4539
4540
4541
4542
4543
4544
4545
4546
4547
4548
4549
4550
4551
4552
4553
4554
4555
4556
4557
4558
4559
4560
4561
4562
4563
4564
4565
4566
4567
4568
4569
4570
4571
4572
4573
4574
4575
4576
4577
4578
4579
4580
4581
4582
4583
4584
4585
4586
4587
4588
4589
4590
4591
4592
4593
4594
4595
4596
4597
4598
4599
4600
4601
4602
4603
4604
4605
4606
4607
4608
4609
4610
4611
4612
4613
4614
4615
4616
4617
4618
4619
4620
4621
4622
4623
4624
4625
4626
4627
4628
4629
4630
4631
4632
4633
4634
4635
4636
4637
4638
4639
4640
4641
4642
4643
4644
4645
4646
4647
4648
4649
4650
4651
4652
4653
4654
4655
4656
4657
4658
4659
4660
4661
4662
4663
4664
4665
4666
4667
4668
4669
4670
4671
4672
4673
4674
4675
4676
4677
4678
4679
4680
4681
4682
4683
4684
4685
4686
4687
4688
4689
4690
4691
4692
4693
4694
4695
4696
4697
4698
4699
4700
4701
4702
4703
4704
4705
4706
4707
4708
4709
4710
4711
4712
4713
4714
4715
4716
4717
4718
4719
4720
4721
4722
4723
4724
4725
4726
4727
4728
4729
4730
4731
4732
4733
4734
4735
4736
4737
4738
4739
4740
4741
4742
4743
4744
4745
4746
4747
4748
4749
4750
4751
4752
4753
4754
4755
4756
4757
4758
4759
4760
4761
4762
4763
4764
4765
4766
4767
4768
4769
4770
4771
4772
4773
4774
4775
4776
4777
4778
4779
4780
4781
4782
4783
4784
4785
4786
4787
4788
4789
4790
4791
4792
4793
4794
4795
4796
4797
4798
4799
4800
4801
4802
4803
4804
4805
4806
4807
4808
4809
4810
4811
4812
4813
4814
4815
4816
4817
4818
4819
4820
4821
4822
4823
4824
4825
4826
4827
4828
4829
4830
4831
4832
4833
4834
4835
4836
4837
4838
4839
4840
4841
4842
4843
4844
4845
4846
4847
4848
4849
4850
4851
4852
4853
4854
4855
4856
4857
4858
4859
4860
4861
4862
4863
4864
4865
4866
4867
4868
4869
4870
4871
4872
4873
4874
4875
4876
4877
4878
4879
4880
4881
4882
4883
4884
4885
4886
4887
4888
4889
4890
4891
4892
4893
4894
4895
4896
4897
4898
4899
4900
4901
4902
4903
4904
4905
4906
4907
4908
4909
4910
4911
4912
4913
4914
4915
4916
4917
4918
4919
4920
4921
4922
4923
4924
4925
4926
4927
4928
4929
4930
4931
4932
4933
4934
4935
4936
4937
4938
4939
4940
4941
4942
4943
4944
4945
4946
4947
4948
4949
4950
4951
4952
4953
4954
4955
4956
4957
4958
4959
4960
4961
4962
4963
4964
4965
4966
4967
4968
4969
4970
4971
4972
4973
4974
4975
4976
4977
4978
4979
4980
4981
4982
4983
4984
4985
4986
4987
4988
4989
4990
4991
4992
4993
4994
4995
4996
4997
4998
4999
5000
5001
5002
5003
5004
5005
5006
5007
5008
5009
5010
5011
5012
5013
5014
5015
5016
5017
5018
5019
5020
5021
5022
5023
5024
5025
5026
5027
5028
5029
5030
5031
5032
5033
5034
5035
5036
5037
5038
5039
5040
5041
5042
5043
5044
5045
5046
5047
5048
5049
5050
5051
5052
5053
5054
5055
5056
5057
5058
5059
5060
5061
5062
5063
5064
5065
5066
5067
5068
5069
5070
5071
5072
5073
5074
5075
5076
5077
5078
5079
5080
5081
5082
5083
5084
5085
5086
5087
5088
5089
5090
5091
5092
5093
5094
5095
5096
5097
5098
5099
5100
5101
5102
5103
5104
5105
5106
5107
5108
5109
5110
5111
5112
5113
5114
5115
5116
5117
5118
5119
5120
5121
5122
5123
5124
5125
5126
5127
5128
5129
5130
5131
5132
5133
5134
5135
5136
5137
5138
5139
5140
5141
5142
5143
5144
5145
5146
5147
5148
5149
5150
5151
5152
5153
5154
5155
5156
5157
5158
5159
5160
5161
5162
5163
5164
5165
5166
5167
5168
5169
5170
5171
5172
5173
5174
5175
5176
5177
5178
5179
5180
5181
5182
5183
5184
5185
5186
5187
5188
5189
5190
5191
5192
5193
5194
5195
5196
5197
5198
5199
5200
5201
5202
5203
5204
5205
5206
5207
5208
5209
5210
5211
5212
5213
5214
5215
5216
5217
5218
5219
5220
5221
5222
5223
5224
5225
5226
5227
5228
5229
5230
5231
5232
5233
5234
5235
5236
5237
5238
5239
5240
5241
5242
5243
5244
5245
5246
5247
5248
5249
5250
5251
5252
5253
5254
5255
5256
5257
5258
5259
5260
5261
5262
5263
5264
5265
5266
5267
5268
5269
5270
5271
5272
5273
5274
5275
5276
5277
5278
5279
5280
5281
5282
5283
5284
5285
5286
5287
5288
5289
5290
5291
5292
5293
5294
5295
5296
5297
5298
5299
5300
5301
5302
5303
5304
5305
5306
5307
5308
5309
5310
5311
5312
5313
5314
5315
5316
5317
5318
5319
5320
5321
5322
5323
5324
5325
5326
5327
5328
5329
5330
5331
5332
5333
5334
5335
5336
5337
5338
5339
5340
5341
5342
5343
5344
5345
5346
5347
5348
5349
5350
5351
5352
5353
5354
5355
5356
5357
5358
5359
5360
5361
5362
5363
5364
5365
5366
5367
5368
5369
5370
5371
5372
5373
5374
5375
5376
5377
5378
5379
5380
5381
5382
5383
5384
5385
5386
5387
5388
5389
5390
5391
5392
5393
5394
5395
5396
5397
5398
5399
5400
5401
5402
5403
5404
5405
5406
5407
5408
5409
5410
5411
5412
5413
5414
5415
5416
5417
5418
5419
5420
5421
5422
5423
5424
5425
5426
5427
5428
5429
5430
5431
5432
5433
5434
5435
5436
5437
5438
5439
5440
5441
5442
5443
5444
5445
5446
5447
5448
5449
5450
5451
5452
5453
5454
5455
5456
5457
5458
5459
5460
5461
5462
5463
5464
5465
5466
5467
5468
5469
5470
5471
5472
5473
5474
5475
5476
5477
5478
5479
5480
5481
5482
5483
5484
5485
5486
5487
5488
5489
5490
5491
5492
5493
5494
5495
5496
5497
5498
5499
5500
5501
5502
5503
5504
5505
5506
5507
5508
5509
5510
5511
5512
5513
5514
5515
5516
5517
5518
5519
5520
5521
5522
5523
5524
5525
5526
5527
5528
5529
5530
5531
5532
5533
5534
5535
5536
5537
5538
5539
5540
5541
5542
5543
5544
5545
5546
5547
5548
5549
5550
5551
5552
5553
5554
5555
5556
5557
5558
5559
5560
5561
5562
5563
5564
5565
5566
5567
5568
5569
5570
5571
5572
5573
5574
5575
5576
5577
5578
5579
5580
5581
5582
5583
5584
5585
5586
5587
5588
5589
5590
5591
5592
5593
5594
5595
5596
5597
5598
5599
5600
5601
5602
5603
5604
5605
5606
5607
5608
5609
5610
5611
5612
5613
5614
5615
5616
5617
5618
5619
5620
5621
5622
5623
5624
5625
5626
5627
5628
5629
5630
5631
5632
5633
5634
5635
5636
5637
5638
5639
5640
5641
5642
5643
5644
5645
5646
5647
5648
5649
5650
5651
5652
5653
5654
5655
5656
5657
5658
5659
5660
5661
5662
5663
5664
5665
5666
5667
5668
5669
5670
5671
5672
5673
5674
5675
5676
5677
5678
5679
5680
5681
5682
5683
5684
5685
5686
5687
5688
5689
5690
5691
5692
5693
5694
5695
5696
5697
5698
5699
5700
5701
5702
5703
5704
5705
5706
5707
5708
5709
5710
5711
5712
5713
5714
5715
5716
5717
5718
5719
5720
5721
5722
5723
5724
5725
5726
5727
5728
5729
5730
5731
5732
5733
5734
5735
5736
5737
5738
5739
5740
5741
5742
5743
5744
5745
5746
5747
5748
5749
5750
5751
5752
5753
5754
5755
5756
5757
5758
5759
5760
5761
5762
5763
5764
5765
5766
5767
5768
5769
5770
5771
5772
5773
5774
5775
5776
5777
5778
5779
5780
5781
5782
5783
5784
5785
5786
5787
5788
5789
5790
5791
5792
5793
5794
5795
5796
5797
5798
5799
5800
5801
5802
5803
5804
5805
5806
5807
5808
5809
5810
5811
5812
5813
5814
5815
5816
5817
5818
5819
5820
5821
5822
5823
5824
5825
5826
5827
5828
5829
5830
5831
5832
5833
5834
5835
5836
5837
5838
5839
5840
5841
5842
5843
5844
5845
5846
5847
5848
5849
5850
5851
5852
5853
5854
5855
5856
5857
5858
5859
5860
5861
5862
5863
5864
5865
5866
5867
5868
5869
5870
5871
5872
5873
5874
5875
5876
5877
5878
5879
5880
5881
5882
5883
5884
5885
5886
5887
5888
5889
5890
5891
5892
5893
5894
5895
5896
5897
5898
5899
5900
5901
5902
5903
5904
5905
5906
5907
5908
5909
5910
5911
5912
5913
5914
5915
5916
5917
5918
5919
5920
5921
5922
5923
5924
5925
5926
5927
5928
5929
5930
5931
5932
5933
5934
5935
5936
5937
5938
5939
5940
5941
5942
5943
5944
5945
5946
5947
5948
5949
5950
5951
5952
5953
5954
5955
5956
5957
5958
5959
5960
5961
5962
5963
5964
5965
5966
5967
5968
5969
5970
5971
5972
5973
5974
5975
5976
5977
5978
5979
5980
5981
5982
5983
5984
5985
5986
5987
5988
5989
5990
5991
5992
5993
5994
5995
5996
5997
5998
5999
6000
6001
6002
6003
6004
6005
6006
6007
6008
6009
6010
6011
6012
6013
6014
6015
6016
6017
6018
6019
6020
6021
6022
6023
6024
6025
6026
6027
6028
6029
6030
6031
6032
6033
6034
6035
6036
6037
6038
6039
6040
6041
6042
6043
6044
6045
6046
6047
6048
6049
6050
6051
6052
6053
6054
6055
6056
6057
6058
6059
6060
6061
6062
6063
6064
6065
6066
6067
6068
6069
6070
6071
6072
6073
6074
6075
6076
6077
6078
6079
6080
6081
6082
6083
6084
6085
6086
6087
6088
6089
6090
6091
6092
6093
6094
6095
6096
6097
6098
6099
6100
6101
6102
6103
6104
6105
6106
6107
6108
6109
6110
6111
6112
6113
6114
6115
6116
6117
6118
6119
6120
6121
6122
6123
6124
6125
6126
6127
6128
6129
6130
6131
6132
6133
6134
6135
6136
6137
6138
6139
6140
6141
6142
6143
6144
6145
6146
6147
6148
6149
6150
6151
6152
6153
6154
6155
6156
6157
6158
6159
6160
6161
6162
6163
6164
6165
6166
6167
6168
6169
6170
6171
6172
6173
6174
6175
6176
6177
6178
6179
6180
6181
6182
6183
6184
6185
6186
6187
6188
6189
6190
6191
6192
6193
6194
6195
6196
6197
6198
6199
6200
6201
6202
6203
6204
6205
6206
6207
6208
6209
6210
6211
6212
6213
6214
6215
6216
6217
6218
6219
6220
6221
6222
6223
6224
6225
6226
6227
6228
6229
6230
6231
6232
6233
6234
6235
6236
6237
6238
6239
6240
6241
6242
6243
6244
6245
6246
6247
6248
6249
6250
6251
6252
6253
6254
6255
6256
6257
6258
6259
6260
6261
6262
6263
6264
6265
6266
6267
6268
6269
6270
6271
6272
6273
6274
6275
6276
6277
6278
6279
6280
6281
6282
6283
6284
6285
6286
6287
6288
6289
6290
6291
6292
6293
6294
6295
6296
6297
6298
6299
6300
6301
6302
6303
6304
6305
6306
6307
6308
6309
6310
6311
6312
6313
6314
6315
6316
6317
6318
6319
6320
6321
6322
6323
6324
6325
6326
6327
6328
6329
6330
6331
6332
6333
6334
6335
6336
6337
6338
6339
6340
6341
6342
6343
6344
6345
6346
6347
6348
6349
6350
6351
6352
6353
6354
6355
6356
6357
6358
6359
6360
6361
6362
6363
6364
6365
6366
6367
6368
6369
6370
6371
6372
6373
6374
6375
6376
6377
6378
6379
6380
6381
6382
6383
6384
6385
6386
6387
6388
6389
6390
6391
6392
6393
6394
6395
6396
6397
6398
6399
6400
6401
6402
6403
6404
6405
6406
6407
6408
6409
6410
6411
6412
6413
6414
6415
6416
6417
6418
6419
6420
6421
6422
6423
6424
6425
6426
6427
6428
6429
6430
6431
6432
6433
6434
6435
6436
6437
6438
6439
6440
6441
6442
6443
6444
6445
6446
6447
6448
6449
6450
6451
6452
6453
6454
6455
6456
6457
6458
6459
6460
6461
6462
6463
6464
6465
6466
6467
6468
6469
6470
6471
6472
6473
6474
6475
6476
6477
6478
6479
6480
6481
6482
6483
6484
6485
6486
6487
6488
6489
6490
6491
6492
6493
6494
6495
6496
6497
6498
6499
6500
6501
6502
6503
6504
6505
6506
6507
6508
6509
6510
6511
6512
6513
6514
6515
6516
6517
6518
6519
6520
6521
6522
6523
6524
6525
6526
6527
6528
6529
6530
6531
6532
6533
6534
6535
6536
6537
6538
6539
6540
6541
6542
6543
6544
6545
6546
6547
6548
6549
6550
6551
6552
6553
6554
6555
6556
6557
6558
6559
6560
6561
6562
6563
6564
6565
6566
6567
6568
6569
6570
6571
6572
6573
6574
6575
6576
6577
6578
6579
6580
6581
6582
6583
6584
6585
6586
6587
6588
6589
6590
6591
6592
6593
6594
6595
6596
6597
6598
6599
6600
6601
6602
6603
6604
6605
6606
6607
6608
6609
6610
6611
6612
6613
6614
6615
6616
6617
6618
6619
6620
6621
6622
6623
6624
6625
6626
6627
6628
6629
6630
6631
6632
6633
6634
6635
6636
6637
6638
6639
6640
6641
6642
6643
6644
6645
6646
6647
6648
6649
6650
6651
6652
6653
6654
6655
6656
6657
6658
6659
6660
6661
6662
6663
6664
6665
6666
6667
6668
6669
6670
6671
6672
6673
6674
6675
6676
6677
6678
6679
6680
6681
6682
6683
6684
6685
6686
6687
6688
6689
6690
6691
6692
6693
6694
6695
6696
6697
6698
6699
6700
6701
6702
6703
6704
6705
6706
6707
6708
6709
6710
6711
6712
6713
6714
6715
6716
6717
6718
6719
6720
6721
6722
6723
6724
6725
6726
6727
6728
6729
6730
6731
6732
6733
6734
6735
6736
6737
6738
6739
6740
6741
6742
6743
6744
6745
6746
6747
6748
6749
6750
6751
6752
6753
6754
6755
6756
6757
6758
6759
6760
6761
6762
6763
6764
6765
6766
6767
6768
6769
6770
6771
6772
6773
6774
6775
6776
6777
6778
6779
6780
6781
6782
6783
6784
6785
6786
6787
6788
6789
6790
6791
6792
6793
6794
6795
6796
6797
6798
6799
6800
6801
6802
6803
6804
6805
6806
6807
6808
6809
6810
6811
6812
6813
6814
6815
6816
6817
6818
6819
6820
6821
6822
6823
6824
6825
6826
6827
6828
6829
6830
6831
6832
6833
6834
6835
6836
6837
6838
6839
6840
6841
6842
6843
6844
6845
6846
6847
6848
6849
6850
6851
6852
6853
6854
6855
6856
6857
6858
6859
6860
6861
6862
6863
6864
6865
6866
6867
6868
6869
6870
6871
6872
6873
6874
6875
6876
6877
6878
6879
6880
6881
6882
6883
6884
6885
6886
6887
6888
6889
6890
6891
6892
6893
6894
6895
6896
6897
6898
6899
6900
6901
6902
6903
6904
6905
6906
6907
6908
6909
6910
6911
6912
6913
6914
6915
6916
6917
6918
6919
6920
6921
6922
6923
6924
6925
6926
6927
6928
6929
6930
6931
6932
6933
6934
6935
6936
6937
6938
6939
6940
6941
6942
6943
6944
6945
6946
6947
6948
6949
6950
6951
6952
6953
6954
6955
6956
6957
6958
6959
6960
6961
6962
6963
6964
6965
6966
6967
6968
6969
6970
6971
6972
6973
6974
6975
6976
6977
6978
6979
6980
6981
6982
6983
6984
6985
6986
6987
6988
6989
6990
6991
6992
6993
6994
6995
6996
6997
6998
6999
7000
7001
7002
7003
7004
7005
7006
7007
7008
7009
7010
7011
7012
7013
7014
7015
7016
7017
7018
7019
7020
7021
7022
7023
7024
7025
7026
7027
7028
7029
7030
7031
7032
7033
7034
7035
7036
7037
7038
7039
7040
7041
7042
7043
7044
7045
7046
7047
7048
7049
7050
7051
7052
7053
7054
7055
7056
7057
7058
7059
7060
7061
7062
7063
7064
7065
7066
7067
7068
7069
7070
7071
7072
7073
7074
7075
7076
7077
7078
7079
7080
7081
7082
7083
7084
7085
7086
7087
7088
7089
7090
7091
7092
7093
7094
7095
7096
7097
7098
7099
7100
7101
7102
7103
7104
7105
7106
7107
7108
7109
7110
7111
7112
7113
7114
7115
7116
7117
7118
7119
7120
7121
7122
7123
7124
7125
7126
7127
7128
7129
7130
7131
7132
7133
7134
7135
7136
7137
7138
7139
7140
7141
7142
7143
7144
7145
7146
7147
7148
7149
7150
7151
7152
7153
7154
7155
7156
7157
7158
7159
7160
7161
7162
7163
7164
7165
7166
7167
7168
7169
7170
7171
7172
7173
7174
7175
7176
7177
7178
7179
7180
7181
7182
7183
7184
7185
7186
7187
7188
7189
7190
7191
7192
7193
7194
7195
7196
7197
7198
7199
7200
7201
7202
7203
7204
7205
7206
7207
7208
7209
7210
7211
7212
7213
7214
7215
7216
7217
7218
7219
7220
7221
7222
7223
7224
7225
7226
7227
7228
7229
7230
7231
7232
7233
7234
7235
7236
7237
7238
7239
7240
7241
7242
7243
7244
7245
7246
7247
7248
7249
7250
7251
7252
7253
7254
7255
7256
7257
7258
7259
7260
7261
7262
7263
7264
7265
7266
7267
7268
7269
7270
7271
7272
7273
7274
7275
7276
7277
7278
7279
7280
7281
7282
7283
7284
7285
7286
7287
7288
7289
7290
7291
7292
7293
7294
7295
7296
7297
7298
7299
7300
7301
7302
7303
7304
7305
7306
7307
7308
7309
7310
7311
7312
7313
7314
7315
7316
7317
7318
7319
7320
7321
7322
7323
7324
7325
7326
7327
7328
7329
7330
7331
7332
7333
7334
7335
7336
7337
7338
7339
7340
7341
7342
7343
7344
7345
7346
7347
7348
7349
7350
7351
7352
7353
7354
7355
7356
7357
7358
7359
7360
7361
7362
7363
7364
7365
7366
7367
7368
7369
7370
7371
7372
7373
7374
7375
7376
7377
7378
7379
7380
7381
7382
7383
7384
7385
7386
7387
7388
7389
7390
7391
7392
7393
7394
7395
7396
7397
7398
7399
7400
7401
7402
7403
7404
7405
7406
7407
7408
7409
7410
7411
7412
7413
7414
7415
7416
7417
7418
7419
7420
7421
7422
7423
7424
7425
7426
7427
7428
7429
7430
7431
7432
7433
7434
7435
7436
7437
7438
7439
7440
7441
7442
7443
7444
7445
7446
7447
7448
7449
7450
7451
7452
7453
7454
7455
7456
7457
7458
7459
7460
7461
7462
7463
7464
7465
7466
7467
7468
7469
7470
7471
7472
7473
7474
7475
7476
7477
7478
7479
7480
7481
7482
7483
7484
7485
7486
7487
7488
7489
7490
7491
7492
7493
7494
7495
7496
7497
7498
7499
7500
7501
7502
7503
7504
7505
7506
7507
7508
7509
7510
7511
7512
7513
7514
7515
7516
7517
7518
7519
7520
7521
7522
7523
7524
7525
7526
7527
7528
7529
7530
7531
7532
7533
7534
7535
7536
7537
7538
7539
7540
7541
7542
7543
7544
7545
7546
7547
7548
7549
7550
7551
7552
7553
7554
7555
7556
7557
7558
7559
7560
7561
7562
7563
7564
7565
7566
7567
7568
7569
7570
7571
7572
7573
7574
7575
7576
7577
7578
7579
7580
7581
7582
7583
7584
7585
7586
7587
7588
7589
7590
7591
7592
7593
7594
7595
7596
7597
7598
7599
7600
7601
7602
7603
7604
7605
7606
7607
7608
7609
7610
7611
7612
7613
7614
7615
7616
7617
7618
7619
7620
7621
7622
7623
7624
7625
7626
7627
7628
7629
7630
7631
7632
7633
7634
7635
7636
7637
7638
7639
7640
7641
7642
7643
7644
7645
7646
7647
7648
7649
7650
7651
7652
7653
7654
7655
7656
7657
7658
7659
7660
7661
7662
7663
7664
7665
7666
7667
7668
7669
7670
7671
7672
7673
7674
7675
7676
7677
7678
7679
7680
7681
7682
7683
7684
7685
7686
7687
7688
7689
7690
7691
7692
7693
7694
7695
7696
7697
7698
7699
7700
7701
7702
7703
7704
7705
7706
7707
7708
7709
7710
7711
7712
7713
7714
7715
7716
7717
7718
7719
7720
7721
7722
7723
7724
7725
7726
7727
7728
7729
7730
7731
7732
7733
7734
7735
7736
7737
7738
7739
7740
7741
7742
7743
7744
7745
7746
7747
7748
7749
7750
7751
7752
7753
7754
7755
7756
7757
7758
7759
7760
7761
7762
7763
7764
7765
7766
7767
7768
7769
7770
7771
7772
7773
7774
7775
7776
7777
7778
7779
7780
7781
7782
7783
7784
7785
7786
7787
7788
7789
7790
7791
7792
7793
7794
7795
7796
7797
7798
7799
7800
7801
7802
7803
7804
7805
7806
7807
7808
7809
7810
7811
7812
7813
7814
7815
7816
7817
7818
7819
7820
7821
7822
7823
7824
7825
7826
7827
7828
7829
7830
7831
7832
7833
7834
7835
7836
7837
7838
7839
7840
7841
7842
7843
7844
7845
7846
7847
7848
7849
7850
7851
7852
7853
7854
7855
7856
7857
7858
7859
7860
7861
7862
7863
7864
7865
7866
7867
7868
7869
7870
7871
7872
7873
7874
7875
7876
7877
7878
7879
7880
7881
7882
7883
7884
7885
7886
7887
7888
7889
7890
7891
7892
7893
7894
7895
7896
7897
7898
7899
7900
7901
7902
7903
7904
7905
7906
7907
7908
7909
7910
7911
7912
7913
7914
7915
7916
7917
7918
7919
7920
7921
7922
7923
7924
7925
7926
7927
7928
7929
7930
7931
7932
7933
7934
7935
7936
7937
7938
7939
7940
7941
7942
7943
7944
7945
7946
7947
7948
7949
7950
7951
7952
7953
7954
7955
7956
7957
7958
7959
7960
7961
7962
7963
7964
7965
7966
7967
7968
7969
7970
7971
7972
7973
7974
7975
7976
7977
7978
7979
7980
7981
7982
7983
7984
7985
7986
7987
7988
7989
7990
7991
7992
7993
7994
7995
7996
7997
7998
7999
8000
8001
8002
8003
8004
8005
8006
8007
8008
8009
8010
8011
8012
8013
8014
8015
8016
8017
8018
8019
8020
8021
8022
8023
8024
8025
8026
8027
8028
8029
8030
8031
8032
8033
8034
8035
8036
8037
8038
8039
8040
8041
8042
8043
8044
8045
8046
8047
8048
8049
8050
8051
8052
8053
8054
8055
8056
8057
8058
8059
8060
8061
8062
8063
8064
8065
8066
8067
8068
8069
8070
8071
8072
8073
8074
8075
8076
8077
8078
8079
8080
8081
8082
8083
8084
8085
8086
8087
8088
8089
8090
8091
8092
8093
8094
8095
8096
8097
8098
8099
8100
8101
8102
8103
8104
8105
8106
8107
8108
8109
8110
8111
8112
8113
8114
8115
8116
8117
8118
8119
8120
8121
8122
8123
8124
8125
8126
8127
8128
8129
8130
8131
8132
8133
8134
8135
8136
8137
8138
8139
8140
8141
8142
8143
8144
8145
8146
8147
8148
8149
8150
8151
8152
8153
8154
8155
8156
8157
8158
8159
8160
8161
8162
8163
8164
8165
8166
8167
8168
8169
8170
8171
8172
8173
8174
8175
8176
8177
8178
8179
8180
8181
8182
8183
8184
8185
8186
8187
8188
8189
8190
8191
8192
8193
8194
8195
8196
8197
8198
8199
8200
8201
8202
8203
8204
8205
8206
8207
8208
8209
8210
8211
8212
8213
8214
8215
8216
8217
8218
8219
8220
8221
8222
8223
8224
8225
8226
8227
8228
8229
8230
8231
8232
8233
8234
8235
8236
8237
8238
8239
8240
8241
8242
8243
8244
8245
8246
8247
8248
8249
8250
8251
8252
8253
8254
8255
8256
8257
8258
8259
8260
8261
8262
8263
8264
8265
8266
8267
8268
8269
8270
8271
8272
8273
8274
8275
8276
8277
8278
8279
8280
8281
8282
8283
8284
8285
8286
8287
8288
8289
8290
8291
8292
8293
8294
8295
8296
8297
8298
8299
8300
8301
8302
8303
8304
8305
8306
8307
8308
8309
8310
8311
8312
8313
8314
8315
8316
8317
8318
8319
8320
8321
8322
8323
8324
8325
8326
8327
8328
8329
8330
8331
8332
8333
8334
8335
8336
8337
8338
8339
8340
8341
8342
8343
8344
8345
8346
8347
8348
8349
8350
8351
8352
8353
8354
8355
8356
8357
8358
8359
8360
8361
8362
8363
8364
8365
8366
8367
8368
8369
8370
8371
8372
8373
8374
8375
8376
8377
8378
8379
8380
8381
8382
8383
8384
8385
8386
8387
8388
8389
8390
8391
8392
8393
8394
8395
8396
8397
8398
8399
8400
8401
8402
8403
8404
8405
8406
8407
8408
8409
8410
8411
8412
8413
8414
8415
8416
8417
8418
8419
8420
8421
8422
8423
8424
8425
8426
8427
8428
8429
8430
8431
8432
8433
8434
8435
8436
8437
8438
8439
8440
8441
8442
8443
8444
8445
8446
8447
8448
8449
8450
8451
8452
8453
8454
8455
8456
8457
8458
8459
8460
8461
8462
8463
8464
8465
8466
8467
8468
8469
8470
8471
8472
8473
8474
8475
8476
8477
8478
8479
8480
8481
8482
8483
8484
8485
8486
8487
8488
8489
8490
8491
8492
8493
8494
8495
8496
8497
8498
8499
8500
8501
8502
8503
8504
8505
8506
8507
8508
8509
8510
8511
8512
8513
8514
8515
8516
8517
8518
8519
8520
8521
8522
8523
8524
8525
8526
8527
8528
8529
8530
8531
8532
8533
8534
8535
8536
8537
8538
8539
8540
8541
8542
8543
8544
8545
8546
8547
8548
8549
8550
8551
8552
8553
8554
8555
8556
8557
8558
8559
8560
8561
8562
8563
8564
8565
8566
8567
8568
8569
8570
8571
8572
8573
8574
8575
8576
8577
8578
8579
8580
8581
8582
8583
8584
8585
8586
8587
8588
8589
8590
8591
8592
8593
8594
8595
8596
8597
8598
8599
8600
8601
8602
8603
8604
8605
8606
8607
8608
8609
8610
8611
8612
8613
8614
8615
8616
8617
8618
8619
8620
8621
8622
8623
8624
8625
8626
8627
8628
8629
8630
8631
8632
8633
8634
8635
8636
8637
8638
8639
8640
8641
8642
8643
8644
8645
8646
8647
8648
8649
8650
8651
8652
8653
8654
8655
8656
8657
8658
8659
8660
8661
8662
8663
8664
8665
8666
8667
8668
8669
8670
8671
8672
8673
8674
8675
8676
8677
8678
8679
8680
8681
8682
8683
8684
8685
8686
8687
8688
8689
8690
8691
8692
8693
8694
8695
8696
8697
8698
8699
8700
8701
8702
8703
8704
8705
8706
8707
8708
8709
8710
8711
8712
8713
8714
8715
8716
8717
8718
8719
8720
8721
8722
8723
8724
8725
8726
8727
8728
8729
8730
8731
8732
8733
8734
8735
8736
8737
8738
8739
8740
8741
8742
8743
8744
8745
8746
8747
8748
8749
8750
8751
8752
8753
8754
8755
8756
8757
8758
8759
8760
8761
8762
8763
8764
8765
8766
8767
8768
8769
8770
8771
8772
8773
8774
8775
8776
8777
8778
8779
8780
8781
8782
8783
8784
8785
8786
8787
8788
8789
8790
8791
8792
8793
8794
8795
8796
8797
8798
8799
8800
8801
8802
8803
8804
8805
8806
8807
8808
8809
8810
8811
8812
8813
8814
8815
8816
8817
8818
8819
8820
8821
8822
8823
8824
8825
8826
8827
8828
8829
8830
8831
8832
8833
8834
8835
8836
8837
8838
8839
8840
8841
8842
8843
8844
8845
8846
8847
8848
8849
8850
8851
8852
8853
8854
8855
8856
8857
8858
8859
8860
8861
8862
8863
8864
8865
8866
8867
8868
8869
8870
8871
8872
8873
8874
8875
8876
8877
8878
8879
8880
8881
8882
8883
8884
8885
8886
8887
8888
8889
8890
8891
8892
8893
8894
8895
8896
8897
8898
8899
8900
8901
8902
8903
8904
8905
8906
8907
8908
8909
8910
8911
8912
8913
8914
8915
8916
8917
8918
8919
8920
8921
8922
8923
8924
8925
8926
8927
8928
8929
8930
8931
8932
8933
8934
8935
8936
8937
8938
8939
8940
8941
8942
8943
8944
8945
8946
8947
8948
8949
8950
8951
8952
8953
8954
8955
8956
8957
8958
8959
8960
8961
8962
8963
8964
8965
8966
8967
8968
8969
8970
8971
8972
8973
8974
8975
8976
8977
8978
8979
8980
8981
8982
8983
8984
8985
8986
8987
8988
8989
8990
8991
8992
8993
8994
8995
8996
8997
8998
8999
9000
9001
9002
9003
9004
9005
9006
9007
9008
9009
9010
9011
9012
9013
9014
9015
9016
9017
9018
9019
9020
9021
9022
9023
9024
9025
9026
9027
9028
9029
9030
9031
9032
9033
9034
9035
9036
9037
9038
9039
9040
9041
9042
9043
9044
9045
9046
9047
9048
9049
9050
9051
9052
9053
9054
9055
9056
9057
9058
9059
9060
9061
9062
9063
9064
9065
9066
9067
9068
9069
9070
9071
9072
9073
9074
9075
9076
9077
9078
9079
9080
9081
9082
9083
9084
9085
9086
9087
9088
9089
9090
9091
9092
9093
9094
9095
9096
9097
9098
9099
9100
9101
9102
9103
9104
9105
9106
9107
9108
9109
9110
9111
9112
9113
9114
9115
9116
9117
9118
9119
9120
9121
9122
9123
9124
9125
9126
9127
9128
9129
9130
9131
9132
9133
9134
9135
9136
9137
9138
9139
9140
9141
9142
9143
9144
9145
9146
9147
9148
9149
9150
9151
9152
9153
9154
9155
9156
9157
9158
9159
9160
9161
9162
9163
9164
9165
9166
9167
9168
9169
9170
9171
9172
9173
9174
9175
9176
9177
9178
9179
9180
9181
9182
9183
9184
9185
9186
9187
9188
9189
9190
9191
9192
9193
9194
9195
9196
9197
9198
9199
9200
9201
9202
9203
9204
9205
9206
9207
9208
9209
9210
9211
9212
9213
9214
9215
9216
9217
9218
9219
9220
9221
9222
9223
9224
9225
9226
9227
9228
9229
9230
9231
9232
9233
9234
9235
9236
9237
9238
9239
9240
9241
9242
9243
9244
9245
9246
9247
9248
9249
9250
9251
9252
9253
9254
9255
9256
9257
9258
9259
9260
9261
9262
9263
9264
9265
9266
9267
9268
9269
9270
9271
9272
9273
9274
9275
9276
9277
9278
9279
9280
9281
9282
9283
9284
9285
9286
9287
9288
9289
9290
9291
9292
9293
9294
9295
9296
9297
9298
9299
9300
9301
9302
9303
9304
9305
9306
9307
9308
9309
9310
9311
9312
9313
9314
9315
9316
9317
9318
9319
9320
9321
9322
9323
9324
9325
9326
9327
9328
9329
9330
9331
9332
9333
9334
9335
9336
9337
9338
9339
9340
9341
9342
9343
9344
9345
9346
9347
9348
9349
9350
9351
9352
9353
9354
9355
9356
9357
9358
9359
9360
9361
9362
9363
9364
9365
9366
9367
9368
9369
9370
9371
9372
9373
9374
9375
9376
9377
9378
9379
9380
9381
9382
9383
9384
9385
9386
9387
9388
9389
9390
9391
9392
9393
9394
9395
9396
9397
9398
9399
9400
9401
9402
9403
9404
9405
9406
9407
9408
9409
9410
9411
9412
9413
9414
9415
9416
9417
9418
9419
9420
9421
9422
9423
9424
9425
9426
9427
9428
9429
9430
9431
9432
9433
9434
9435
9436
9437
9438
9439
9440
9441
9442
9443
9444
9445
9446
9447
9448
9449
9450
9451
9452
9453
9454
9455
9456
9457
9458
9459
9460
9461
9462
9463
9464
9465
9466
9467
9468
9469
9470
9471
9472
9473
9474
9475
9476
9477
9478
9479
9480
9481
9482
9483
9484
9485
9486
9487
9488
9489
9490
9491
9492
9493
9494
9495
9496
9497
9498
9499
9500
9501
9502
9503
9504
9505
9506
9507
9508
9509
9510
9511
9512
9513
9514
9515
9516
9517
9518
9519
9520
9521
9522
9523
9524
9525
9526
9527
9528
9529
9530
9531
9532
9533
9534
9535
9536
9537
9538
9539
9540
9541
9542
9543
9544
9545
9546
9547
9548
9549
9550
9551
9552
9553
9554
9555
9556
9557
9558
9559
9560
9561
9562
9563
9564
9565
9566
9567
9568
9569
9570
9571
9572
9573
9574
9575
9576
9577
9578
9579
9580
9581
9582
9583
9584
9585
9586
9587
9588
9589
9590
9591
9592
9593
9594
9595
9596
9597
9598
9599
9600
9601
9602
9603
9604
9605
9606
9607
9608
9609
9610
9611
9612
9613
9614
9615
9616
9617
9618
9619
9620
9621
9622
9623
9624
9625
9626
9627
9628
9629
9630
9631
9632
9633
9634
9635
9636
9637
9638
9639
9640
9641
9642
9643
9644
9645
9646
9647
9648
9649
9650
9651
9652
9653
9654
9655
9656
9657
9658
9659
9660
9661
9662
9663
9664
9665
9666
9667
9668
9669
9670
9671
9672
9673
9674
9675
9676
9677
9678
9679
9680
9681
9682
9683
9684
9685
9686
9687
9688
9689
9690
9691
9692
9693
9694
9695
9696
9697
9698
9699
9700
9701
9702
9703
9704
9705
9706
9707
9708
9709
9710
9711
9712
9713
9714
9715
9716
9717
9718
9719
9720
9721
9722
9723
9724
9725
9726
9727
9728
9729
9730
9731
9732
9733
9734
9735
9736
9737
9738
9739
9740
9741
9742
9743
9744
9745
9746
9747
9748
9749
9750
9751
9752
9753
9754
9755
9756
9757
9758
9759
9760
9761
9762
9763
9764
9765
9766
9767
9768
9769
9770
9771
9772
9773
9774
9775
9776
9777
9778
9779
9780
9781
9782
9783
9784
9785
9786
9787
9788
9789
9790
9791
9792
9793
9794
9795
9796
9797
9798
9799
9800
9801
9802
9803
9804
9805
9806
9807
9808
9809
9810
9811
9812
9813
9814
9815
9816
9817
9818
9819
9820
9821
9822
9823
9824
9825
9826
9827
9828
9829
9830
9831
9832
9833
9834
9835
9836
9837
9838
9839
9840
9841
9842
9843
9844
9845
9846
9847
9848
9849
9850
9851
9852
9853
9854
9855
9856
9857
9858
9859
9860
9861
9862
9863
9864
9865
9866
9867
9868
9869
9870
9871
9872
9873
9874
9875
9876
9877
9878
9879
9880
9881
9882
9883
9884
9885
9886
9887
9888
9889
9890
9891
9892
9893
9894
9895
9896
9897
9898
9899
9900
9901
9902
9903
9904
9905
9906
9907
9908
9909
9910
9911
9912
9913
9914
9915
9916
9917
9918
9919
9920
9921
9922
9923
9924
9925
9926
9927
9928
9929
9930
9931
9932
9933
9934
9935
9936
9937
9938
9939
9940
9941
9942
9943
9944
9945
9946
9947
9948
9949
9950
9951
9952
9953
9954
9955
9956
9957
9958
9959
9960
9961
9962
9963
9964
9965
9966
9967
9968
9969
9970
9971
9972
9973
9974
9975
9976
9977
9978
9979
9980
9981
9982
9983
9984
9985
9986
9987
9988
9989
9990
9991
9992
9993
9994
9995
9996
9997
9998
9999
10000
10001
10002
10003
10004
10005
10006
10007
10008
10009
10010
10011
10012
10013
10014
10015
10016
10017
10018
10019
10020
10021
10022
10023
10024
10025
10026
10027
10028
10029
10030
10031
10032
10033
10034
10035
10036
10037
10038
10039
10040
10041
10042
10043
10044
10045
10046
10047
10048
10049
10050
10051
10052
10053
10054
10055
10056
10057
10058
10059
10060
10061
10062
10063
10064
10065
10066
10067
10068
10069
10070
10071
10072
10073
10074
10075
10076
10077
10078
10079
10080
10081
10082
10083
10084
10085
10086
10087
10088
10089
10090
10091
10092
10093
10094
10095
10096
10097
10098
10099
10100
10101
10102
10103
10104
10105
10106
10107
10108
10109
10110
10111
10112
10113
10114
10115
10116
10117
10118
10119
10120
10121
10122
10123
10124
10125
10126
10127
10128
10129
10130
10131
10132
10133
10134
10135
10136
10137
10138
10139
10140
10141
10142
10143
10144
10145
10146
10147
10148
10149
10150
10151
10152
10153
10154
10155
10156
10157
10158
10159
10160
10161
10162
10163
10164
10165
10166
10167
10168
10169
10170
10171
10172
10173
10174
10175
10176
10177
10178
10179
10180
10181
10182
10183
10184
10185
10186
10187
10188
10189
10190
10191
10192
10193
10194
10195
10196
10197
10198
10199
10200
10201
10202
10203
10204
10205
10206
10207
10208
10209
10210
10211
10212
10213
10214
10215
10216
10217
10218
10219
10220
10221
10222
10223
10224
10225
10226
10227
10228
10229
10230
10231
10232
10233
10234
10235
10236
10237
10238
10239
10240
10241
10242
10243
10244
10245
10246
10247
10248
10249
10250
10251
10252
10253
10254
10255
10256
10257
10258
10259
10260
10261
10262
10263
10264
10265
10266
10267
10268
10269
10270
10271
10272
10273
10274
10275
10276
10277
10278
10279
10280
10281
10282
10283
10284
10285
10286
10287
10288
10289
10290
10291
10292
10293
10294
10295
10296
10297
10298
10299
10300
10301
10302
10303
10304
10305
10306
10307
10308
10309
10310
10311
10312
10313
10314
10315
10316
10317
10318
10319
10320
10321
10322
10323
10324
10325
10326
10327
10328
10329
10330
10331
10332
10333
10334
10335
10336
10337
10338
10339
10340
10341
10342
10343
10344
10345
10346
10347
10348
10349
10350
10351
10352
10353
10354
10355
10356
10357
10358
10359
10360
10361
10362
10363
10364
10365
10366
10367
10368
10369
10370
10371
10372
10373
10374
10375
10376
10377
10378
10379
10380
10381
10382
10383
10384
10385
10386
10387
10388
10389
10390
10391
10392
10393
10394
10395
10396
10397
10398
10399
10400
10401
10402
10403
10404
10405
10406
10407
10408
10409
10410
10411
10412
10413
10414
10415
10416
10417
10418
10419
10420
10421
10422
10423
10424
10425
10426
10427
10428
10429
10430
10431
10432
10433
10434
10435
10436
10437
10438
10439
10440
10441
10442
10443
10444
10445
10446
10447
10448
10449
10450
10451
10452
10453
10454
10455
10456
10457
10458
10459
10460
10461
10462
10463
10464
10465
10466
10467
10468
10469
10470
10471
10472
10473
10474
10475
10476
10477
10478
10479
10480
10481
10482
10483
10484
10485
10486
10487
10488
10489
10490
10491
10492
10493
10494
10495
10496
10497
10498
10499
10500
10501
10502
10503
10504
10505
10506
10507
10508
10509
10510
10511
10512
10513
10514
10515
10516
10517
10518
10519
10520
10521
10522
10523
10524
10525
10526
10527
10528
10529
10530
10531
10532
10533
10534
10535
10536
10537
10538
10539
10540
10541
10542
10543
10544
10545
10546
10547
10548
10549
10550
10551
10552
10553
10554
10555
10556
10557
10558
10559
10560
10561
10562
10563
10564
10565
10566
10567
10568
10569
10570
10571
10572
10573
10574
10575
10576
10577
10578
10579
10580
10581
10582
10583
10584
10585
10586
10587
10588
10589
10590
10591
10592
10593
10594
10595
10596
10597
10598
10599
10600
10601
10602
10603
10604
10605
10606
10607
10608
10609
10610
10611
10612
10613
10614
10615
10616
10617
10618
10619
10620
10621
10622
10623
10624
10625
10626
10627
10628
10629
10630
10631
10632
10633
10634
10635
10636
10637
10638
10639
10640
10641
10642
10643
10644
10645
10646
10647
10648
10649
10650
10651
10652
10653
10654
10655
10656
10657
10658
10659
10660
10661
10662
10663
10664
10665
10666
10667
10668
10669
10670
10671
10672
10673
10674
10675
10676
10677
10678
10679
10680
10681
10682
10683
10684
10685
10686
10687
10688
10689
10690
10691
10692
10693
10694
10695
10696
10697
10698
10699
10700
10701
10702
10703
10704
10705
10706
10707
10708
10709
10710
10711
10712
10713
10714
10715
10716
10717
10718
10719
10720
10721
10722
10723
10724
10725
10726
10727
10728
10729
10730
10731
10732
10733
10734
10735
10736
10737
10738
10739
10740
10741
10742
10743
10744
10745
10746
10747
10748
10749
10750
10751
10752
10753
10754
10755
10756
10757
10758
10759
10760
10761
10762
10763
10764
10765
10766
10767
10768
10769
10770
10771
10772
10773
10774
10775
10776
10777
10778
10779
10780
10781
10782
10783
10784
10785
10786
10787
10788
10789
10790
10791
10792
10793
10794
10795
10796
10797
10798
10799
10800
10801
10802
10803
10804
10805
10806
10807
10808
10809
10810
10811
10812
10813
10814
10815
10816
10817
10818
10819
10820
10821
10822
10823
10824
10825
10826
10827
10828
10829
10830
10831
10832
10833
10834
10835
10836
10837
10838
10839
10840
10841
10842
10843
10844
10845
10846
10847
10848
10849
10850
10851
10852
10853
10854
10855
10856
10857
10858
10859
10860
10861
10862
10863
10864
10865
10866
10867
10868
10869
10870
10871
10872
10873
10874
10875
10876
10877
10878
10879
10880
10881
10882
10883
10884
10885
10886
10887
10888
10889
10890
10891
10892
10893
10894
10895
10896
10897
10898
10899
10900
10901
10902
10903
10904
10905
10906
10907
10908
10909
10910
10911
10912
10913
10914
10915
10916
10917
10918
10919
10920
10921
10922
10923
10924
10925
10926
10927
10928
10929
10930
10931
10932
10933
10934
10935
10936
10937
10938
10939
10940
10941
10942
10943
10944
10945
10946
10947
10948
10949
10950
10951
10952
10953
10954
10955
10956
10957
10958
10959
10960
10961
10962
10963
10964
10965
10966
10967
10968
10969
10970
10971
10972
10973
10974
10975
10976
10977
10978
10979
10980
10981
10982
10983
10984
10985
10986
10987
10988
10989
10990
10991
10992
10993
10994
10995
10996
10997
10998
10999
11000
11001
11002
11003
11004
11005
11006
11007
11008
11009
11010
11011
11012
11013
11014
11015
11016
11017
11018
11019
11020
11021
11022
11023
11024
11025
11026
11027
11028
11029
11030
11031
11032
11033
11034
11035
11036
11037
11038
11039
11040
11041
11042
11043
11044
11045
11046
11047
11048
11049
11050
11051
11052
11053
11054
11055
11056
11057
11058
11059
11060
11061
11062
11063
11064
11065
11066
11067
11068
11069
11070
11071
11072
11073
11074
11075
11076
11077
11078
11079
11080
11081
11082
11083
11084
11085
11086
11087
11088
11089
11090
11091
11092
11093
11094
11095
11096
11097
11098
11099
11100
11101
11102
11103
11104
11105
11106
11107
11108
11109
11110
11111
11112
11113
11114
11115
11116
11117
11118
11119
11120
11121
11122
11123
11124
11125
11126
11127
11128
11129
11130
11131
11132
11133
11134
11135
11136
11137
11138
11139
11140
11141
11142
11143
11144
11145
11146
11147
11148
11149
11150
11151
11152
11153
11154
11155
11156
11157
11158
11159
11160
11161
11162
11163
11164
11165
11166
11167
11168
11169
11170
11171
11172
11173
11174
11175
11176
11177
11178
11179
11180
11181
11182
11183
11184
11185
11186
11187
11188
11189
11190
11191
11192
11193
11194
11195
11196
11197
11198
11199
11200
11201
11202
11203
11204
11205
11206
11207
11208
11209
11210
11211
11212
11213
11214
11215
11216
11217
11218
11219
11220
11221
11222
11223
11224
11225
11226
11227
11228
11229
11230
11231
11232
11233
11234
11235
11236
11237
11238
11239
11240
11241
11242
11243
11244
11245
11246
11247
11248
11249
11250
11251
11252
11253
11254
11255
11256
11257
11258
11259
11260
11261
11262
11263
11264
11265
11266
11267
11268
11269
11270
11271
11272
11273
11274
11275
11276
11277
11278
11279
11280
11281
11282
11283
11284
11285
11286
11287
11288
11289
11290
11291
11292
11293
11294
11295
11296
11297
11298
11299
11300
11301
11302
11303
11304
11305
11306
11307
11308
11309
11310
11311
11312
11313
11314
11315
11316
11317
11318
11319
11320
11321
11322
11323
11324
11325
11326
11327
11328
11329
11330
11331
11332
11333
11334
11335
11336
11337
11338
11339
11340
11341
11342
11343
11344
11345
11346
11347
11348
11349
11350
11351
11352
11353
11354
11355
11356
11357
11358
11359
11360
11361
11362
11363
11364
11365
11366
11367
11368
11369
11370
11371
11372
11373
11374
11375
11376
11377
11378
11379
11380
11381
11382
11383
11384
11385
11386
11387
11388
11389
11390
11391
11392
11393
11394
11395
11396
11397
11398
11399
11400
11401
11402
11403
11404
11405
11406
11407
11408
11409
11410
11411
11412
11413
11414
11415
11416
11417
11418
11419
11420
11421
11422
11423
11424
11425
11426
11427
11428
11429
11430
11431
11432
11433
11434
11435
11436
11437
11438
11439
11440
11441
11442
11443
11444
11445
11446
11447
11448
11449
11450
11451
11452
11453
11454
11455
11456
11457
11458
11459
11460
11461
11462
11463
11464
11465
11466
11467
11468
11469
11470
11471
11472
11473
11474
11475
11476
11477
11478
11479
11480
11481
11482
11483
11484
11485
11486
11487
11488
11489
11490
11491
11492
11493
11494
11495
11496
11497
11498
11499
11500
11501
11502
11503
11504
11505
11506
11507
11508
11509
11510
11511
11512
11513
11514
11515
11516
11517
11518
11519
11520
11521
11522
11523
11524
11525
11526
11527
11528
11529
11530
11531
11532
11533
11534
11535
11536
11537
11538
11539
11540
11541
11542
11543
11544
11545
11546
11547
11548
11549
11550
11551
11552
11553
11554
11555
11556
11557
11558
11559
11560
11561
11562
11563
11564
11565
11566
11567
11568
11569
11570
11571
11572
11573
11574
11575
11576
11577
11578
11579
11580
11581
11582
11583
11584
11585
11586
11587
11588
11589
11590
11591
11592
11593
11594
11595
11596
11597
11598
11599
11600
11601
11602
11603
11604
11605
11606
11607
11608
11609
11610
11611
11612
11613
11614
11615
11616
11617
11618
11619
11620
11621
11622
11623
11624
11625
11626
11627
11628
11629
11630
11631
11632
11633
11634
11635
11636
11637
11638
11639
11640
11641
11642
11643
11644
11645
11646
11647
11648
11649
11650
11651
11652
11653
11654
11655
11656
11657
11658
11659
11660
11661
11662
11663
11664
11665
11666
11667
11668
11669
11670
11671
11672
11673
11674
11675
11676
11677
11678
11679
11680
11681
11682
11683
11684
11685
11686
11687
11688
11689
11690
11691
11692
11693
11694
11695
11696
11697
11698
11699
11700
11701
11702
11703
11704
11705
11706
11707
11708
11709
11710
11711
11712
11713
11714
11715
11716
11717
11718
11719
11720
11721
11722
11723
11724
11725
11726
11727
11728
11729
11730
11731
11732
11733
11734
11735
11736
11737
11738
11739
11740
11741
11742
11743
11744
11745
11746
11747
11748
11749
11750
11751
11752
11753
11754
11755
11756
11757
11758
11759
11760
11761
11762
11763
11764
11765
11766
11767
11768
11769
11770
11771
11772
11773
11774
11775
11776
11777
11778
11779
11780
11781
11782
11783
11784
11785
11786
11787
11788
11789
11790
11791
11792
11793
11794
11795
11796
11797
11798
11799
11800
11801
11802
11803
11804
11805
11806
11807
11808
11809
11810
11811
11812
11813
11814
11815
11816
11817
11818
11819
11820
11821
11822
11823
11824
11825
11826
11827
11828
11829
11830
11831
11832
11833
11834
11835
11836
11837
11838
11839
11840
11841
11842
11843
11844
11845
11846
11847
11848
11849
11850
11851
11852
11853
11854
11855
11856
11857
11858
11859
11860
11861
11862
11863
11864
11865
11866
11867
11868
11869
11870
11871
11872
11873
11874
11875
11876
11877
11878
11879
11880
11881
11882
11883
11884
11885
11886
11887
11888
11889
11890
11891
11892
11893
11894
11895
11896
11897
11898
11899
11900
11901
11902
11903
11904
11905
11906
11907
11908
11909
11910
11911
11912
11913
11914
11915
11916
11917
11918
11919
11920
11921
11922
11923
11924
11925
11926
11927
11928
11929
11930
11931
11932
11933
11934
11935
11936
11937
11938
11939
11940
11941
11942
11943
11944
11945
11946
11947
11948
11949
11950
11951
11952
11953
11954
11955
11956
11957
11958
11959
11960
11961
11962
11963
11964
11965
11966
11967
11968
11969
11970
11971
11972
11973
11974
11975
11976
11977
11978
11979
11980
11981
11982
11983
11984
11985
11986
11987
11988
11989
11990
11991
11992
11993
11994
11995
11996
11997
11998
11999
12000
12001
12002
12003
12004
12005
12006
12007
12008
12009
12010
12011
12012
12013
12014
12015
12016
12017
12018
12019
12020
12021
12022
12023
12024
12025
12026
12027
12028
12029
12030
12031
12032
12033
12034
12035
12036
12037
12038
12039
12040
12041
12042
12043
12044
12045
12046
12047
12048
12049
12050
12051
12052
12053
12054
12055
12056
12057
12058
12059
12060
12061
12062
12063
12064
12065
12066
12067
12068
12069
12070
12071
12072
12073
12074
12075
12076
12077
12078
12079
12080
12081
12082
12083
12084
12085
12086
12087
12088
12089
12090
12091
12092
12093
12094
12095
12096
12097
12098
12099
12100
12101
12102
12103
12104
12105
12106
12107
12108
12109
12110
12111
12112
12113
12114
12115
12116
12117
12118
12119
12120
12121
12122
12123
12124
12125
12126
12127
12128
12129
12130
12131
12132
12133
12134
12135
12136
12137
12138
12139
12140
12141
12142
12143
12144
12145
12146
12147
12148
12149
12150
12151
12152
12153
12154
12155
12156
12157
12158
12159
12160
12161
12162
12163
12164
12165
12166
12167
12168
12169
12170
12171
12172
12173
12174
12175
12176
12177
12178
12179
12180
12181
12182
12183
12184
12185
12186
12187
12188
12189
12190
12191
12192
12193
12194
12195
12196
12197
12198
12199
12200
12201
12202
12203
12204
12205
12206
12207
12208
12209
12210
12211
12212
12213
12214
12215
12216
12217
12218
12219
12220
12221
12222
12223
12224
12225
12226
12227
12228
12229
12230
12231
12232
12233
12234
12235
12236
12237
12238
12239
12240
12241
12242
12243
12244
12245
12246
12247
12248
12249
12250
12251
12252
12253
12254
12255
12256
12257
12258
12259
12260
12261
12262
12263
12264
12265
12266
12267
12268
12269
12270
12271
12272
12273
12274
12275
12276
12277
12278
12279
12280
12281
12282
12283
12284
12285
12286
12287
12288
12289
12290
12291
12292
12293
12294
12295
12296
12297
12298
12299
12300
12301
12302
12303
12304
12305
12306
12307
12308
12309
12310
12311
12312
12313
12314
12315
12316
12317
12318
12319
12320
12321
12322
12323
12324
12325
12326
12327
12328
12329
12330
12331
12332
12333
12334
12335
12336
12337
12338
12339
12340
12341
12342
12343
12344
12345
12346
12347
12348
12349
12350
12351
12352
12353
12354
12355
12356
12357
12358
12359
12360
12361
12362
12363
12364
12365
12366
12367
12368
12369
12370
12371
12372
12373
12374
12375
12376
12377
12378
12379
12380
12381
12382
12383
12384
12385
12386
12387
12388
12389
12390
12391
12392
12393
12394
12395
12396
12397
12398
12399
12400
12401
12402
12403
12404
12405
12406
12407
12408
12409
12410
12411
12412
12413
12414
12415
12416
12417
12418
12419
12420
12421
12422
12423
12424
12425
12426
12427
12428
12429
12430
12431
12432
12433
12434
12435
12436
12437
12438
12439
12440
12441
12442
12443
12444
12445
12446
12447
12448
12449
12450
12451
12452
12453
12454
12455
12456
12457
12458
12459
12460
12461
12462
12463
12464
12465
12466
12467
12468
12469
12470
12471
12472
12473
12474
12475
12476
12477
12478
12479
12480
12481
12482
12483
12484
12485
12486
12487
12488
12489
12490
12491
12492
12493
12494
12495
12496
12497
12498
12499
12500
12501
12502
12503
12504
12505
12506
12507
12508
12509
12510
12511
12512
12513
12514
12515
12516
12517
12518
12519
12520
12521
12522
12523
12524
12525
12526
12527
12528
12529
12530
12531
12532
12533
12534
12535
12536
12537
12538
12539
12540
12541
12542
12543
12544
12545
12546
12547
12548
12549
12550
12551
12552
12553
12554
12555
12556
12557
12558
12559
12560
12561
12562
12563
12564
12565
12566
12567
12568
12569
12570
12571
12572
12573
12574
12575
12576
12577
12578
12579
12580
12581
12582
12583
12584
12585
12586
12587
12588
12589
12590
12591
12592
12593
12594
12595
12596
12597
12598
12599
12600
12601
12602
12603
12604
12605
12606
12607
12608
12609
12610
12611
12612
12613
12614
12615
12616
12617
12618
12619
12620
12621
12622
12623
12624
12625
12626
12627
12628
12629
12630
12631
12632
12633
12634
12635
12636
12637
12638
12639
12640
12641
12642
12643
12644
12645
12646
12647
12648
12649
12650
12651
12652
12653
12654
12655
12656
12657
12658
12659
12660
12661
12662
12663
12664
12665
12666
12667
12668
12669
12670
12671
12672
12673
12674
12675
12676
12677
12678
12679
12680
12681
12682
12683
12684
12685
12686
12687
12688
12689
12690
12691
12692
12693
12694
12695
12696
12697
12698
12699
12700
12701
12702
12703
12704
12705
12706
12707
12708
12709
12710
12711
12712
12713
12714
12715
12716
12717
12718
12719
12720
12721
12722
12723
12724
12725
12726
12727
12728
12729
12730
12731
12732
12733
12734
12735
12736
12737
12738
12739
12740
12741
12742
12743
12744
12745
12746
12747
12748
12749
12750
12751
12752
12753
12754
12755
12756
12757
12758
12759
12760
12761
12762
12763
12764
12765
12766
12767
12768
12769
12770
12771
12772
12773
12774
12775
12776
12777
12778
12779
12780
12781
12782
12783
12784
12785
12786
12787
12788
12789
12790
12791
12792
12793
12794
12795
12796
12797
12798
12799
12800
12801
12802
12803
12804
12805
12806
12807
12808
12809
12810
12811
12812
12813
12814
12815
12816
12817
12818
12819
12820
12821
12822
12823
12824
12825
12826
12827
12828
12829
12830
12831
12832
12833
12834
12835
12836
12837
12838
12839
12840
12841
12842
12843
12844
12845
12846
12847
12848
12849
12850
12851
12852
12853
12854
12855
12856
12857
12858
12859
12860
12861
12862
12863
12864
12865
12866
12867
12868
12869
12870
12871
12872
12873
12874
12875
12876
12877
12878
12879
12880
12881
12882
12883
12884
12885
12886
12887
12888
12889
12890
12891
12892
12893
12894
12895
12896
12897
12898
12899
12900
12901
12902
12903
12904
12905
12906
12907
12908
12909
12910
12911
12912
12913
12914
12915
12916
12917
12918
12919
12920
12921
12922
12923
12924
12925
12926
12927
12928
12929
12930
12931
12932
12933
12934
12935
12936
12937
12938
12939
12940
12941
12942
12943
12944
12945
12946
12947
12948
12949
12950
12951
12952
12953
12954
12955
12956
12957
12958
12959
12960
12961
12962
12963
12964
12965
12966
12967
12968
12969
12970
12971
12972
12973
12974
12975
12976
12977
12978
12979
12980
12981
12982
12983
12984
12985
12986
12987
12988
12989
12990
12991
12992
12993
12994
12995
12996
12997
12998
12999
13000
13001
13002
13003
13004
13005
13006
13007
13008
13009
13010
13011
13012
13013
13014
13015
13016
13017
13018
13019
13020
13021
13022
13023
13024
13025
13026
13027
13028
13029
13030
13031
13032
13033
13034
13035
13036
13037
13038
13039
13040
13041
13042
13043
13044
13045
13046
13047
13048
13049
13050
13051
13052
13053
13054
13055
13056
13057
13058
13059
13060
13061
13062
13063
13064
13065
13066
13067
13068
13069
13070
13071
13072
13073
13074
13075
13076
13077
13078
13079
13080
13081
13082
13083
13084
13085
13086
13087
13088
13089
13090
13091
13092
13093
13094
13095
13096
13097
13098
13099
13100
13101
13102
13103
13104
13105
13106
13107
13108
13109
13110
13111
13112
13113
13114
13115
13116
13117
13118
13119
13120
13121
13122
13123
13124
13125
13126
13127
13128
13129
13130
13131
13132
13133
13134
13135
13136
13137
13138
13139
13140
13141
13142
13143
13144
13145
13146
13147
13148
13149
13150
13151
13152
13153
13154
13155
13156
13157
13158
13159
13160
13161
13162
13163
13164
13165
13166
13167
13168
13169
13170
13171
13172
13173
13174
13175
13176
13177
13178
13179
13180
13181
13182
13183
13184
13185
13186
13187
13188
13189
13190
13191
13192
13193
13194
13195
13196
13197
13198
13199
13200
13201
13202
13203
13204
13205
13206
13207
13208
13209
13210
13211
13212
13213
13214
13215
13216
13217
13218
13219
13220
13221
13222
13223
13224
13225
13226
13227
13228
13229
13230
13231
13232
13233
13234
13235
13236
13237
13238
13239
13240
13241
13242
13243
13244
13245
13246
13247
13248
13249
13250
13251
13252
13253
13254
13255
13256
13257
13258
13259
13260
13261
13262
13263
13264
13265
13266
13267
13268
13269
13270
13271
13272
13273
13274
13275
13276
13277
13278
13279
13280
13281
13282
13283
13284
13285
13286
13287
13288
13289
13290
13291
13292
13293
13294
13295
13296
13297
13298
13299
13300
13301
13302
13303
13304
13305
13306
13307
13308
13309
13310
13311
13312
13313
13314
13315
13316
13317
13318
13319
13320
13321
13322
13323
13324
13325
13326
13327
13328
13329
13330
13331
13332
13333
13334
13335
13336
13337
13338
13339
13340
13341
13342
13343
13344
13345
13346
13347
13348
13349
13350
13351
13352
13353
13354
13355
13356
13357
13358
13359
13360
13361
13362
13363
13364
13365
13366
13367
13368
13369
13370
13371
13372
13373
13374
13375
13376
13377
13378
13379
13380
13381
13382
13383
13384
13385
13386
13387
13388
13389
13390
13391
13392
13393
13394
13395
13396
13397
13398
13399
13400
13401
13402
13403
13404
13405
13406
13407
13408
13409
13410
13411
13412
13413
13414
13415
13416
13417
13418
13419
13420
13421
13422
13423
13424
13425
13426
13427
13428
13429
13430
13431
13432
13433
13434
13435
13436
13437
13438
13439
13440
13441
13442
13443
13444
13445
13446
13447
13448
13449
13450
13451
13452
13453
13454
13455
13456
13457
13458
13459
13460
13461
13462
13463
13464
13465
13466
13467
13468
13469
13470
13471
13472
13473
13474
13475
13476
13477
13478
13479
13480
13481
13482
13483
13484
13485
13486
13487
13488
13489
13490
13491
13492
13493
13494
13495
13496
13497
13498
13499
13500
13501
13502
13503
13504
13505
13506
13507
13508
13509
13510
13511
13512
13513
13514
13515
13516
13517
13518
13519
13520
13521
13522
13523
13524
13525
13526
13527
13528
13529
13530
13531
13532
13533
13534
13535
13536
13537
13538
13539
13540
13541
13542
13543
13544
13545
13546
13547
13548
13549
13550
13551
13552
13553
13554
13555
13556
13557
13558
13559
13560
13561
13562
13563
13564
13565
13566
13567
13568
13569
13570
13571
13572
13573
13574
13575
13576
13577
13578
13579
13580
13581
13582
13583
13584
13585
13586
13587
13588
13589
13590
13591
13592
13593
13594
13595
13596
13597
13598
13599
13600
13601
13602
13603
13604
13605
13606
13607
13608
13609
13610
13611
13612
13613
13614
13615
13616
13617
13618
13619
13620
13621
13622
13623
13624
13625
13626
13627
13628
13629
13630
13631
13632
13633
13634
13635
13636
13637
13638
13639
13640
13641
13642
13643
13644
13645
13646
13647
13648
13649
13650
13651
13652
13653
13654
13655
13656
13657
13658
13659
13660
13661
13662
13663
13664
13665
13666
13667
13668
13669
13670
13671
13672
13673
13674
13675
13676
13677
13678
13679
13680
13681
13682
13683
13684
13685
13686
13687
13688
13689
13690
13691
13692
13693
13694
13695
13696
13697
13698
13699
13700
13701
13702
13703
13704
13705
13706
13707
13708
13709
13710
13711
13712
13713
13714
13715
13716
13717
13718
13719
13720
13721
13722
13723
13724
13725
13726
13727
13728
13729
13730
13731
13732
13733
13734
13735
13736
13737
13738
13739
13740
13741
13742
13743
13744
13745
13746
13747
13748
13749
13750
13751
13752
13753
13754
13755
13756
13757
13758
13759
13760
13761
13762
13763
13764
13765
13766
13767
13768
13769
13770
13771
13772
13773
13774
13775
13776
13777
13778
13779
13780
13781
13782
13783
13784
13785
13786
13787
13788
13789
13790
13791
13792
13793
13794
13795
13796
13797
13798
13799
13800
13801
13802
13803
13804
13805
13806
13807
13808
13809
13810
13811
13812
13813
13814
13815
13816
13817
13818
13819
13820
13821
13822
13823
13824
13825
13826
13827
13828
13829
13830
13831
13832
13833
13834
13835
13836
13837
13838
13839
13840
13841
13842
13843
13844
13845
13846
13847
13848
13849
13850
13851
13852
13853
13854
13855
13856
13857
13858
13859
13860
13861
13862
13863
13864
13865
13866
13867
13868
13869
13870
13871
13872
13873
13874
13875
13876
13877
13878
13879
13880
13881
13882
13883
13884
13885
13886
13887
13888
13889
13890
13891
13892
13893
13894
13895
13896
13897
13898
13899
13900
13901
13902
13903
13904
13905
13906
13907
13908
13909
13910
13911
13912
13913
13914
13915
13916
13917
13918
13919
13920
13921
13922
13923
13924
13925
13926
13927
13928
13929
13930
13931
13932
13933
13934
13935
13936
13937
13938
13939
13940
13941
13942
13943
13944
13945
13946
13947
13948
13949
13950
13951
13952
13953
13954
13955
13956
13957
13958
13959
13960
13961
13962
13963
13964
13965
13966
13967
13968
13969
13970
13971
13972
13973
13974
13975
13976
13977
13978
13979
13980
13981
13982
13983
13984
13985
13986
13987
13988
13989
13990
13991
13992
13993
13994
13995
13996
13997
13998
13999
14000
14001
14002
14003
14004
14005
14006
14007
14008
14009
14010
14011
14012
14013
14014
14015
14016
14017
14018
14019
14020
14021
14022
14023
14024
14025
14026
14027
14028
14029
14030
14031
14032
14033
14034
14035
14036
14037
14038
14039
14040
14041
14042
14043
14044
14045
14046
14047
14048
14049
14050
14051
14052
14053
14054
14055
14056
14057
14058
14059
14060
14061
14062
14063
14064
14065
14066
14067
14068
14069
14070
14071
14072
14073
14074
14075
14076
14077
14078
14079
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
import panel as pn
import re
from openpyxl import load_workbook
from io import BytesIO
from datetime import datetime
import param
import hvplot.pandas
from io import StringIO
import html
import holoviews as hv
from holoviews import opts
from bokeh.plotting import figure, show
from bokeh.models import TextInput, HoverTool, WheelZoomTool, LinearAxis, Range1d, ColumnDataSource, NumeralTickFormatter, LabelSet, Legend, LegendItem, CategoricalColorMapper, FactorRange, Title, DatetimeTickFormatter, CustomJS, CustomJSHover, CDSView, BooleanFilter, HTMLTemplateFormatter, ImageURL, Div, CustomJSTickFormatter
from bokeh.palettes import Category10, Category20
from bokeh.layouts import column, row
import warnings
import bokeh.plotting as bkp
import base64
import io
import os
from PIL import Image
from bokeh.transform import factor_cmap, dodge # 03/27
from bokeh.models.glyphs import VBar
from bokeh.palettes import Category20c
#from bokeh.transform import cumsum
import numpy as np
import calendar # 03/04
from bokeh.palettes import Category20c
import numpy as np
from bokeh.models import Label, Span #04/01
from bokeh.models import LinearColorMapper, ColorBar, BasicTicker #04/07
from bokeh.palettes import Viridis256 #04/07
from bokeh.transform import transform #04/07
#from bokeh.models import TableColumn, NumberFormatter, DataTable #04/08
#------ 04/08 --------------------
from openpyxl import Workbook
from openpyxl.utils.dataframe import dataframe_to_rows
from openpyxl.styles import Alignment, PatternFill, Font, Border, Side
from openpyxl.utils import get_column_letter
###################################################################################################################################
# Enable the Panel extension
pn.extension()
# Suppress all warnings
warnings.filterwarnings("ignore")
# Load the Tabulator extension
pn.extension('tabulator')
# Initialize HoloViews and Panel extensions
hv.extension('bokeh')
##############################################################################################################################
# Define date and path
##############################################################################################################################
# Define paths and file names
input_file_formatted = 'CM-Transfer_Project-Overview.xlsx'
##############################################################################################################################
# Load workbook
##############################################################################################################################
# Load the Excel files into pandas DataFrames
try:
df_Summary = pd.read_excel(input_file_formatted, sheet_name='Summary', index_col=False)
df_Priority = pd.read_excel(input_file_formatted, sheet_name='CM-Priority', index_col=False)
df_Snapshot = pd.read_excel(input_file_formatted, sheet_name='Snapshot', index_col=False)
df_TurnoverReport = pd.read_excel(input_file_formatted, sheet_name='CM-TurnoverReport', index_col=False)
df_Backlog = pd.read_excel(input_file_formatted, sheet_name='CM-Backlog', index_col=False)
df_WIP = pd.read_excel(input_file_formatted, sheet_name='CM-WIP', index_col=False)
df_PendingReport = pd.read_excel(input_file_formatted, sheet_name='PendingReport', index_col=False)
df_ADCNReport = pd.read_excel(input_file_formatted, sheet_name='CM-ADCNReport', index_col=False)
df_Historic = pd.read_excel(input_file_formatted, sheet_name='CM-Historic', index_col=False)
df_Timeline = pd.read_excel(input_file_formatted, sheet_name='Timeline', index_col=False) # 05/19
print("Input files loaded successfully.")
except FileNotFoundError as e:
print(f"File not found: {e}")
exit()
# Load the workbook
try:
workbook = load_workbook(input_file_formatted)
print(f"Successfully loaded '{input_file_formatted}'")
except FileNotFoundError as e:
print(f"File not found: {e}")
exit()
#--------------------------------------------------
# Define 'file date' based on the value od W2 in df_Backlog
#--------------------------------------------------
# Open the workbook
workbook = load_workbook(input_file_formatted, data_only=True) # 'data_only' ensures formulas are evaluated
backlog_sheet = workbook["CM-Backlog"] # Load the CM-Backlog sheet
# Extract file date from cell W2
file_date = backlog_sheet["W2"].value
if file_date:
print("File Date:", file_date)
else:
print("Could not retrieve File Date from W2.")
# Close the workbook
workbook.close()
################################################################
# Filter out "Canceled" or "To be transferred" from df_priority
################################################################
# Filter out rows where 'Production Status' is 'Canceled' or 'To be transferred' or 'Officially transferred'
if 'Production Status' in df_Priority.columns:
df_Priority = df_Priority[~df_Priority['Production Status'].isin(['Canceled', 'To be transferred', 'Officially transferred'])]
#-------------------- 03/11 - Convert 'Priority' column to int ------------------
# Ensure 'Priority' is numeric, converting errors to NaN
df_Priority['Priority'] = pd.to_numeric(df_Priority['Priority'], errors='coerce')
# Remove NaN values
df_Priority.dropna(subset=['Priority'], inplace=True) #Remove rows with NaN in 'Priority'
# Convert to integer
df_Priority['Priority'] = df_Priority['Priority'].astype(int)
#-----------------------------------------------------------------------------------
#----------------------------------------------------------
# 02/11 - Change 'Phase 4' or 'Phase 5' with 'Phase 4-5'
#----------------------------------------------------------
# For df_Priority
if 'Program' in df_Priority.columns and 'Pty Indice' in df_Priority.columns:
mask = (
df_Priority['Program'].isin(['Phase 4', 'Phase 5']) &
~df_Priority['Pty Indice'].str.contains('Phase5', na=False)
)
df_Priority.loc[mask, 'Program'] = 'Phase 4-5'
# For df_Backlog
if 'Program' in df_Backlog.columns and 'Pty Indice' in df_Backlog.columns:
mask = (
df_Backlog['Program'].isin(['Phase 4', 'Phase 5']) &
~df_Backlog['Pty Indice'].str.contains('Phase5', na=False)
)
df_Backlog.loc[mask, 'Program'] = 'Phase 4-5'
# For df_TurnoverReport
# Check if the DataFrame is empty
if df_TurnoverReport.empty:
print("The DataFrame is empty. Skipping operations.")
else:
# Ensure 'Pty Indice' is a string column and handle missing values
df_TurnoverReport['Pty Indice'] = df_TurnoverReport['Pty Indice'].astype(str).fillna('')
# Apply the condition to update the 'Program' column
if 'Program' in df_TurnoverReport.columns and 'Pty Indice' in df_TurnoverReport.columns:
mask = (
df_TurnoverReport['Program'].isin(['Phase 4', 'Phase 5']) &
~df_TurnoverReport['Pty Indice'].str.contains('Phase5', case=False, na=False)
)
df_TurnoverReport.loc[mask, 'Program'] = 'Phase 4-5'
else:
print("Required columns ('Program' or 'Pty Indice') are missing in the DataFrame.")
# For df_Historic
if 'Program' in df_Historic.columns and 'Pty Indice' in df_Historic.columns:
mask = (
df_Historic['Program'].isin(['Phase 4', 'Phase 5']) &
~df_Historic['Pty Indice'].str.contains('Phase5', na=False)
)
df_Historic.loc[mask, 'Program'] = 'Phase 4-5'
# For df_Snapshot (new addition)
if 'Program' in df_Snapshot.columns and 'Pty Indice' in df_Snapshot.columns:
mask = (
df_Snapshot['Program'].isin(['Phase 4', 'Phase 5']) &
~df_Snapshot['Pty Indice'].str.contains('Phase5', na=False)
)
df_Snapshot.loc[mask, 'Program'] = 'Phase 4-5'
#----------------------------------------------------------
#------------------------------------------------------------------------------------------
# 03/19 - Only keep 'Program' = 'Phase 4-5', 'EMBRAER', SIKORSKY', '1stTB', '2ndTB'
#------------------------------------------------------------------------------------------
# Define the allowed values for the 'Program' column
allowed_programs = ['Phase 4-5', 'EMBRAER', 'SIKORSKY', 'COMAC', '1stTB', '2ndTB']
# Apply filtering to each DataFrame
df_Priority = df_Priority[df_Priority['Program'].isin(allowed_programs)]
df_Backlog = df_Backlog[df_Backlog['Program'].isin(allowed_programs)]
df_TurnoverReport = df_TurnoverReport[df_TurnoverReport['Program'].isin(allowed_programs)]
df_Historic = df_Historic[df_Historic['Program'].isin(allowed_programs)]
df_Snapshot = df_Snapshot[df_Snapshot['Program'].isin(allowed_programs)]
#------------------------------------------------------------------------------------------
#*****************************************************************************************************************************
# |General Overview| - Table creation
#*****************************************************************************************************************************
#--------------------------------------------------------------------------
# Create pivot_table_combined
#--------------------------------------------------------------------------
# Create a new column 'Industrialization'
df_Snapshot['Industrialization'] = df_Snapshot['Production Status'].apply(
lambda x: 'Industrialized' if x.strip() in ['Industrialized', 'Completed'] else 'Not Industrialized'
)
# Fill empty 'Qty WIP' with 0
df_Snapshot['Qty WIP'] = df_Snapshot['Qty WIP'].fillna(0)
#######################################################
# Assuming df_Priority has the columns 'Pty Indice' and 'Priority'
priority_mapping = df_Priority.set_index('Pty Indice')['Priority'].to_dict()
# Create the pivot table without 'Priority'
pivot_table_13 = pd.pivot_table(df_Snapshot,
index=['Top-Level Status', 'Industrialization', 'Product Category', 'Pty Indice'],
values=['IDD Backlog Qty', 'Remain. crit. Qty', 'Qty clear to build', 'Qty WIP', 'Shipped', 'Critical Qty'],
aggfunc='sum',
fill_value=0).reset_index()
# Add the 'Priority' column to pivot_table_13 using the mapping
pivot_table_13['Priority'] = pivot_table_13['Pty Indice'].map(priority_mapping)
###################### New 08/12 #########################################
# Add the 'Program' column to pivot_table_13 using the mapping
program_mapping = df_Priority.set_index('Pty Indice')['Program'].to_dict()
pivot_table_13['Program'] = pivot_table_13['Pty Indice'].map(program_mapping)
#########################################################
# Merge df_Backlog with df_Snapshot to get additional columns
merged_df = pd.merge(df_Backlog, df_Snapshot[['Pty Indice', 'Qty clear to build', 'Top-Level Status', 'Qty WIP', 'Industrialization', 'Product Category', 'Shipped', 'Critical Qty', 'Priority']], on='Pty Indice', how='left')
# Rename 'Backlog row Qty' to 'IDD Backlog Qty'
merged_df.rename(columns={'Backlog row Qty': 'IDD Backlog Qty'}, inplace=True)
# Create 'Order Type' column
merged_df['Order Type'] = merged_df['Order'].apply(lambda x: 'DX/DO' if str(x).startswith('D') else ('Standard' if 'NC' not in str(x) else None))
# Aggregate IDD Backlog Qty by 'Pty Indice', 'Order Type', and other relevant columns
unique_merged_df = merged_df.groupby(
['Pty Indice', 'Top-Level Status', 'Industrialization', 'Product Category', 'Order Type']
).agg({
'IDD Backlog Qty': 'sum', # Sum IDD Backlog Qty for unique combinations
'Qty clear to build': 'sum',
'Remain. crit. Qty': 'sum',
'Qty WIP': 'sum',
}).reset_index()
# Create a pivot table to separate IDD Backlog Qty by Order Type
pivot_order_type = unique_merged_df.pivot_table(
index=['Pty Indice', 'Top-Level Status', 'Industrialization', 'Product Category'],
columns='Order Type',
values='IDD Backlog Qty',
aggfunc='sum', # Sum values to ensure accurate totals
fill_value=0 # Fill NaNs with 0
).reset_index()
# Flatten MultiIndex columns
pivot_order_type.columns = [f'{col[0]}_{col[1]}' if col[1] != '' else col[0] for col in pivot_order_type.columns]
# Rename columns to be more descriptive
pivot_order_type.columns = ['Pty Indice', 'Top-Level Status', 'Industrialization', 'Product Category', 'DX_Order_Type', 'Standard_Order_Type']
# Merge the two pivot tables
pivot_table_combined = pd.merge(
pivot_table_13,
pivot_order_type,
on=['Pty Indice', 'Top-Level Status', 'Industrialization', 'Product Category'],
how='left'
)
# Rename the remaining columns for clarity
pivot_table_combined.rename(columns={
'Top-Level Status_x': 'Top-Level Status',
'Industrialization_x': 'Industrialization',
'Product Category_x': 'Product Category',
'DX_Order_Type': 'DPAS Order',
'Standard_Order_Type':'Standard Order',
'Shipped':'Qty Shipped',
'Critical Qty': 'Total Critical Qty'
}, inplace=True)
#Ordering pivot_table_combined to get the column in the relevant order of relevance for the bar chart
# Define the desired column order
desired_column_order = [
'Industrialization',
'Top-Level Status',
'Product Category',
'Priority',
'Pty Indice',
'Standard Order',
'DPAS Order',
'Qty WIP',
'Qty clear to build',
'Total Critical Qty',
'Qty Shipped',
'Remain. crit. Qty',
'IDD Backlog Qty'
]
# Reorder the columns in pivot_table_combined
pivot_table_combined = pivot_table_combined[desired_column_order]
# Ensure 'Priority' is in the correct data type (int or float)
# Convert 'Priority' column to numeric, coercing errors (non-numeric entries will become NaN)
pivot_table_combined['Priority'] = pd.to_numeric(pivot_table_combined['Priority'], errors='coerce')
pivot_table_combined['Priority'].fillna(999, inplace=True)
# Fill all other than column then 'Priotity' containing NaN values with 0 in the entire DataFrame
pivot_table_combined.fillna(0, inplace=True)
# Define the custom sort orders, including the additional categories
industrialization_order = pivot_table_combined['Industrialization'].unique().tolist()
top_level_status_order = pivot_table_combined['Top-Level Status'].unique().tolist() + ['All Top-Level Status']
product_category_order = pivot_table_combined['Product Category'].unique().tolist() + ['All Product Categories']
# Set the categories and order for sorting
pivot_table_combined['Industrialization'] = pd.Categorical(
pivot_table_combined['Industrialization'],
categories=pivot_table_combined['Industrialization'].unique().tolist(),
ordered=True
)
pivot_table_combined['Top-Level Status'] = pd.Categorical(
pivot_table_combined['Top-Level Status'],
categories=pivot_table_combined['Top-Level Status'].unique().tolist(),
ordered=True
)
pivot_table_combined['Product Category'] = pd.Categorical(
pivot_table_combined['Product Category'],
categories=pivot_table_combined['Product Category'].unique().tolist(),
ordered=True
)
# Sort by Priority; specify na_position if needed (e.g., na_position='last')
pivot_table_combined.sort_values(by=['Priority', 'Pty Indice'], inplace=True, na_position='last')
#--------------------------------------------------------------------------
# Create pivot_table_14
#--------------------------------------------------------------------------
##############################################################################################################################
# Financial KPI datafram to be update with df_Historic
# --> To be update 09/23 to use df_Historic instead of df_Snapshot to calculate the 'Realized sales' and 'Realized Margin'. The calculation should be based on the real data from the df_Historic trunover Report including the change of price over time
### Need to create 'IDD Current Sales (Total)' and ['IDD Current Margin (Total)'] should be based on df_Historic --> AVG of the sales and margin should work
# --> # Need to use 'IDD AVG realized sales price [USD]' & ['IDD AVG realized Margin[%]'] instead of the 'IDD Current Sales (Total)' & ['IDD Current Margin (Total)']
# New columns introduced in df_Snapshot:
# df_snapshot['IDD AVG realized sales price [USD]']
# df_snapshot['IDD AVG realized Margin Standard [USD]']
# df_snapshot['IDD AVG realized Margin [%]']
##############################################################################################################################
# Creating Graph#14 [IDD Expected Total Sales & IDD Marge per Pty Indice by Top-Level Status, Production Status & Product Category]
##############################################################################################################################
# Calculate 'IDD Expected Total Sales'
df_Snapshot['IDD Expected Total Sales'] = df_Snapshot['IDD Backlog Qty'] * df_Snapshot['IDD Sale Price']
# Calculate 'IDD Current Total Sales'
df_Snapshot['IDD Current Sales (Total)'] = df_Snapshot['Shipped'] * df_Snapshot['IDD Sale Price']
# Calculate 'IDD Expected Total Marge'
df_Snapshot['IDD Expected Total Margin'] = df_Snapshot['IDD Backlog Qty'] * df_Snapshot['IDD Marge Standard (unit)']
# Calculate 'IDD Current Total Marge'
df_Snapshot['IDD Current Margin (Total)'] = df_Snapshot['Shipped'] * df_Snapshot['IDD Marge Standard (unit)']
df_Snapshot['Industrialization'] = df_Snapshot['Production Status'].apply(
lambda x: 'Industrialized' if x.strip() in ['Industrialized', 'Completed'] else 'Not Industrialized'
)
##############################################################################################################################
# Assuming df_Priority has the columns 'Pty Indice' and 'Priority'
priority_mapping = df_Priority.set_index('Pty Indice')['Priority'].to_dict()
# Create the pivot table without 'Priority'
pivot_table_14 = pd.pivot_table(df_Snapshot,
index=['Top-Level Status', 'Industrialization', 'Product Category', 'Pty Indice'],
values=['IDD Expected Total Sales', 'IDD Expected Total Margin', 'IDD Current Margin (%)', 'Priority', 'Critical Qty', 'Shipped', 'IDD Backlog Qty', 'IDD Current Sales (Total)', 'IDD Current Margin (Total)', 'IDD Expected ROI (Total)', 'IDD AVG realized sales price [USD]', 'IDD AVG realized Margin Standard [USD]', 'IDD AVG realized Margin [%]'],
aggfunc='sum',
fill_value=0).reset_index()
# Add the 'Priority' column to pivot_table_13 using the mapping
pivot_table_14['Priority'] = pivot_table_14['Pty Indice'].map(priority_mapping)
# Add the 'Program' column to pivot_table_14 using the mapping
pivot_table_14['Program'] = pivot_table_14['Pty Indice'].map(program_mapping)
# Map 'DPAS Order' from pivot_table_combined on 'Pty Indice' column
pivot_table_14 = pivot_table_14.merge(pivot_table_combined[['Pty Indice', 'DPAS Order']], on='Pty Indice', how='left')
# Calculate '% Completion' and round to one decimal place
pivot_table_14['% Completion'] = round((pivot_table_14['Shipped'] / pivot_table_14['Critical Qty']) * 100, 1)
pivot_table_14['% Completion Total Backlog'] = round((pivot_table_14['Shipped'] / (pivot_table_14['IDD Backlog Qty'] + pivot_table_14['Shipped'])) * 100, 1) # New 09/26, updated 10/08 because Total Backlog should be 'IDD Backlog Qty' + 'Shipped' to consider the initial backlog
# Calculate '% DPAS Order' and round to one decimal place
pivot_table_14['% DPAS Order'] = round((pivot_table_14['DPAS Order'] / pivot_table_14['IDD Backlog Qty']) * 100, 1)
# Define the sort order for both columns
industrialization_order = ['Industrialized', 'Not Industrialized']
top_level_status_order = ['Clear-to-Build', 'Short', 'Completed - No Backlog'] # Update 09/16
# Set the categories and order for sorting
pivot_table_14['Industrialization'] = pd.Categorical(pivot_table_14['Industrialization'], categories=industrialization_order, ordered=True)
pivot_table_14['Top-Level Status'] = pd.Categorical(pivot_table_14['Top-Level Status'], categories=top_level_status_order, ordered=True)
# Sort by Industrialization, then by Top-Level Status, and finally by Product Category
pivot_table_14.sort_values(by=['Industrialization', 'Top-Level Status', 'Product Category'], inplace=True)
# delete '%' from 'IDD Current Margin (%)'
pivot_table_14['IDD Current Margin (%)'] = pivot_table_14['IDD Current Margin (%)'].str.replace('%', '').astype(float)
# Round the values to one decimal place
pivot_table_14['IDD Current Margin (%)'] = pivot_table_14['IDD Current Margin (%)'].round()
# Convert 'IDD Expected ROI (Total)' to string, replacing NaN with '0%'
pivot_table_14['IDD Expected ROI (Total)'] = pivot_table_14['IDD Expected ROI (Total)'].fillna('0%').astype(str)
# Remove '%' and convert to float
pivot_table_14['IDD Expected ROI (Total)'] = pivot_table_14['IDD Expected ROI (Total)'].str.replace('%', '').astype(float)
# Round the values to one decimal place
pivot_table_14['IDD Expected ROI (Total)'] = pivot_table_14['IDD Expected ROI (Total)'].round()
# Round the values to one decimal place
pivot_table_14['IDD Expected Total Sales'] = pivot_table_14['IDD Expected Total Sales'].round()
pivot_table_14['IDD Expected Total Margin'] = pivot_table_14['IDD Expected Total Margin'].round()
pivot_table_14['IDD Current Sales (Total)'] = pivot_table_14['IDD Current Sales (Total)'].round()
pivot_table_14['IDD Current Margin (Total)'] = pivot_table_14['IDD Current Margin (Total)'].round()
#######################################################################
# Step 1: Clean and convert 'IDD AVG realized Margin [%]'
pivot_table_14['IDD AVG realized Margin [%]'] = pivot_table_14['IDD AVG realized Margin [%]'].str.replace('%', '').astype(float)
# Round the margin to 1 decimal place
pivot_table_14['IDD AVG realized Margin [%]'] = pivot_table_14['IDD AVG realized Margin [%]'].round(1)
# Step 2: Clean and convert 'IDD AVG realized Margin Standard [USD]'
# Remove '$' and ',' before conversion
pivot_table_14['IDD AVG realized Margin Standard [USD]'] = (
pivot_table_14['IDD AVG realized Margin Standard [USD]']
.replace({'\\$': '', ',': ''}, regex=True)
)
# Use pd.to_numeric to handle conversion and coercion
pivot_table_14['IDD AVG realized Margin Standard [USD]'] = pd.to_numeric(pivot_table_14['IDD AVG realized Margin Standard [USD]'], errors='coerce').round(2)
# Step 3: Clean and convert 'IDD AVG realized sales price [USD]'
pivot_table_14['IDD AVG realized sales price [USD]'] = (
pivot_table_14['IDD AVG realized sales price [USD]']
.replace({'\\$': '', ',': ''}, regex=True)
)
# Use pd.to_numeric to handle conversion and coercion
pivot_table_14['IDD AVG realized sales price [USD]'] = pd.to_numeric(pivot_table_14['IDD AVG realized sales price [USD]'], errors='coerce').round(2)
# Step 4: Calculate 'IDD Realized Sales'
pivot_table_14['IDD Realized Sales'] = pivot_table_14['IDD AVG realized sales price [USD]'] * pivot_table_14['Shipped']
# Step 5: Calculate 'IDD Realized Margin'
pivot_table_14['IDD Realized Margin'] = pivot_table_14['IDD AVG realized Margin Standard [USD]'] * pivot_table_14['Shipped']
# Step 6: Format 'IDD AVG realized sales price [USD]' as currency and replace NaN with 0
pivot_table_14['IDD AVG realized sales price [USD]'] = pivot_table_14['IDD AVG realized sales price [USD]'].apply(lambda x: f"${x:,.2f}" if pd.notna(x) else '$0.00')
# Step 7: Format 'IDD AVG realized Margin Standard [USD]' as currency and replace NaN with 0
pivot_table_14['IDD AVG realized Margin Standard [USD]'] = pivot_table_14['IDD AVG realized Margin Standard [USD]'].apply(lambda x: f"${x:,.2f}" if pd.notna(x) else '$0.00')
# Step 8: Format 'IDD AVG realized Margin [%]' as percentage and replace NaN with 0
pivot_table_14['IDD AVG realized Margin [%]'] = pivot_table_14['IDD AVG realized Margin [%]'].apply(lambda x: f"{x:.1f}%" if pd.notna(x) else '0.0%')
# Ensure 'Priority' is in the correct data type (int or float)
pivot_table_14['Priority'] = pd.to_numeric(pivot_table_14['Priority'], errors='coerce')
# Sort the DataFrame by 'Priority' in ascending order (use ascending=False for descending order)
#pivot_table_14 = pivot_table_14.sort_values(by='Priority', ascending=True)
pivot_table_14.sort_values(by=['Priority', 'Pty Indice'], inplace=True)
#--------------------------------------------------------------------------
# Create pivot_table_15 - Production metrics Expected Time, Actual Time, Standard Deviation
#--------------------------------------------------------------------------
# Create df_MaekArchi = |CM-MakeArchitecture|
df_MakeArchi = pd.read_excel(input_file_formatted, sheet_name='CM-MakeArchitecture', index_col=False)
# df_Production = df_Snapshot['Priority', 'Pty Indice', 'Program', 'Expected Time [hour]', 'Actual Time [hour]', 'Standard Deviation [hour]']
df_Production = df_Snapshot.copy()
#Selected relevant columns
df_Production = df_Production[['Top-Level Status', 'Priority', 'Pty Indice', 'IDD Top Level', 'SEDA Top Level', 'Production Status', 'Product Category', 'Program', 'Max Expected Time (full ASSY)[hour]', 'Avg Actual Time (full ASSY)[hour]', 'Max Standard Deviation [hour]', 'Total WO Count']]
# Rename the selected columns
df_Production = df_Production.rename(columns={
'Max Expected Time (full ASSY)[hour]': 'Expected Time',
'Avg Actual Time (full ASSY)[hour]': 'Actual Time',
'Max Standard Deviation [hour]': 'Standard Deviation',
})
df_Production['Industrialization'] = df_Production['Production Status'].apply(
lambda x: 'Industrialized' if x.strip() in ['Industrialized', 'Completed'] else 'Not Industrialized'
)
# Ensure 'Priority' is in the correct data type (int or float)
df_Production['Priority'] = pd.to_numeric(df_Production['Priority'], errors='coerce')
# Define sort orders
top_level_status_order_prod = sorted(df_Production['Top-Level Status'].unique().tolist()) # Ensure it's unique and sorted
product_category_order_prod = sorted(df_Production['Product Category'].unique().tolist()) # Ensure it's unique and sorted
# Optionally, you might want to fill or drop NaNs depending on your requirement
# For example, fill NaNs with a default value 999:
df_Production['Priority'].fillna(999, inplace=True)
# Fill all other than column then 'Priotity' containing NaN values with 0 in the entire DataFrame
df_Production.fillna(0, inplace=True)
# Convert 'Priority' column to integers
df_Production['Priority'] = df_Production['Priority'].astype(int)
# Set categorical data type for 'Industrialization' column
df_Production['Industrialization'] = pd.Categorical(
df_Production['Industrialization'],
categories=['Industrialized', 'Not Industrialized'],
ordered=False
)
# Set categorical data types with the specified sort orders for other columns
df_Production['Top-Level Status'] = pd.Categorical(
df_Production['Top-Level Status'],
categories=top_level_status_order_prod,
ordered=False
)
df_Production['Product Category'] = pd.Categorical(
df_Production['Product Category'],
categories=product_category_order_prod,
ordered=False
)
# Sort by Priority; specify na_position if needed (e.g., na_position='last')
df_Production.sort_values(by=['Priority', 'Pty Indice'], inplace=True, na_position='last')
###########################################################################################################################################
## Map 'WO_Count' from Top-Level (Level = 0) from |CM-MakeArchitecture| (df_MakeArchi) on 'Pty Indice' and rename 'Top-Level WO Count'
## Map 'Avg Actual Time (unit)[hour]' from Top-Level (Level = 0) from |CM-MakeArchitecture| (df_MakeArchi) on 'Pty Indice' and rename 'Actual Time (Top-Level only)'
###########################################################################################################################################
# Include 'Total Top-Level Qty' and 'Total Components Qty' in he table 'Top-Level WO Count'*'Qty per WO' and 'Total WO Count'*'Total Components Qty'
# Filter df_MakeArchi where Level == 0 to get Top-Level
top_level_df = df_MakeArchi[df_MakeArchi['Level'] == 0]
# Filter df_MakeArchi where Level != 0 to get sub-Level
sub_level_df = df_MakeArchi[df_MakeArchi['Level'] != 0]
# Create a dictionary mapping Pty Indice to WO_Count and Avg Actual Time (unit)[hour]
wo_count_top_level_mapping = dict(zip(top_level_df['Pty Indice'], top_level_df['WO_Count']))
avg_actual_time_mapping = dict(zip(top_level_df['Pty Indice'], top_level_df['Avg Actual Time (unit)[hour]']))
# Map 'Top-Level WO Count' to df_Production using the mapping dictionary
df_Production['Top-Level WO Count'] = df_Production['Pty Indice'].map(wo_count_top_level_mapping)
df_Production['Actual Time (Top-Level only)'] = df_Production['Pty Indice'].map(avg_actual_time_mapping)
#New 09/10 --> Include 'Total Top-Level Qty' and 'Total Components Qty' in he table 'Top-Level WO Count'*'Qty per WO' and 'Total WO Count'*'Total Components Qty'
qty_top_level_count_mapping = dict(zip(top_level_df['Pty Indice'], top_level_df['Qty_Count']))
qty_sub_level_count_mapping = dict(zip(sub_level_df['Pty Indice'], sub_level_df['Qty_Count']))
df_Production['Total Top-Level Qty'] = df_Production['Pty Indice'].map(qty_top_level_count_mapping)
df_Production['Total sub-Level Qty'] = df_Production['Pty Indice'].map(qty_sub_level_count_mapping)
# Fill NaN values in specific columns with 0
df_Production[['Total WO Count', 'Top-Level WO Count', 'Total Top-Level Qty', 'Total sub-Level Qty']] = df_Production[['Total WO Count', 'Top-Level WO Count', 'Total Top-Level Qty', 'Total sub-Level Qty']].fillna(0)
# Ensure there are no infinite values (inf) in the columns
df_Production[['Total WO Count', 'Top-Level WO Count', 'Total Top-Level Qty', 'Total sub-Level Qty']] = df_Production[['Total WO Count', 'Top-Level WO Count', 'Total Top-Level Qty', 'Total sub-Level Qty']].replace([np.inf, -np.inf], 0)
# Convert 'Total WO Count' and 'Top-Level WO Count' to integer
#------------------- 03/24 ------------------------
# 1. Replace the problematic string pattern with 0
df_Production['Total WO Count'] = df_Production['Total WO Count'].replace(
r'0 \(similar to \d+-\d+-\d+\)',
'0',
regex=True
)
# 2. Convert to numeric (non-numeric becomes NaN), then fill NaN with 0
df_Production['Total WO Count'] = pd.to_numeric(
df_Production['Total WO Count'],
errors='coerce'
).fillna(0)
# 3. Finally convert to integer
df_Production['Total WO Count'] = df_Production['Total WO Count'].astype(int)
#---------------------------------------------------
df_Production['Top-Level WO Count'] = df_Production['Top-Level WO Count'].astype(int)
df_Production['Total Top-Level Qty'] = df_Production['Total Top-Level Qty'].astype(int)
df_Production['Total sub-Level Qty'] = df_Production['Total sub-Level Qty'].astype(int)
# Optionally fill NaNs in 'Actual Time (Top-Level only)' with 0 or another value if necessary
df_Production['Actual Time (Top-Level only)'] = df_Production['Actual Time (Top-Level only)'].fillna(0)
# Round(1) 'Expected Time', 'Actual Time, 'Standard Deviation', 'Actual Time (Top-Level only)'
df_Production['Expected Time'] = df_Production['Expected Time'].round(1)
df_Production['Actual Time'] = df_Production['Actual Time'].round(1)
df_Production['Standard Deviation'] = df_Production['Standard Deviation'].round(1)
df_Production['Actual Time (Top-Level only)'] = df_Production['Actual Time (Top-Level only)'].round(1)
#Rename 'Expected Time' to 'Standard Time' for better clarifity on the graph
df_Production.rename(columns={'Expected Time': 'Standard Time (Routing, full ASSY)'}, inplace=True)
df_Production.rename(columns={'Actual Time': 'Actual Time (AVG Prod, full ASSY)'}, inplace=True)
df_Production.rename(columns={'Standard Deviation': 'Standard Deviation (on Actual Time, full ASSY)'}, inplace=True)
df_Production.rename(columns={'Actual Time (Top-Level only)': 'Actual Time (AVG Prod, Top-Level only)'}, inplace=True)
#Create Pivot_table_15 base on df_Production only for |General Overview| & Only keep Pivot_table_15['Program'] = 'Phase 4'
pivot_table_15 = df_Production.copy()
#||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
#||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
#*****************************************************************************************************************************
##############################################################################################################################
# |4 cadrans|
##############################################################################################################################
#*****************************************************************************************************************************
#||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
#||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
# Create titles for each quadrant
engineering_title = "Engineering"
sales_title = "Sales"
supply_chain_title = "Supply Chain"
production_title = "Production"
# Create a mapping dictionary from df_Priority
indice_to_program = dict(zip(df_Priority['Pty Indice'], df_Priority['Program']))
indice_to_priority = dict(zip(df_Priority['Pty Indice'], df_Priority['Priority']))
# Apply mapping to create 'Program' column in df_Summary and df_Backlog
df_Summary['Program'] = df_Summary['Pty Indice'].map(indice_to_program)
df_Backlog['Program'] = df_Backlog['Pty Indice'].map(indice_to_program)
df_TurnoverReport['Program'] = df_TurnoverReport['Pty Indice'].map(indice_to_program)
df_Snapshot['Program'] = df_Snapshot['Pty Indice'].map(indice_to_program)
df_WIP['Program'] = df_WIP['Pty Indice'].map(indice_to_program)
df_PendingReport['Priority'] = df_PendingReport['Pty Indice'].map(indice_to_priority)
###################################################
# Renaming
###################################################
# Rename certain columns and assign back to df_WIP
df_WIP = df_WIP.rename(columns={
'Qty Ordered': 'WO Qty',
})
# Rename certain columns and assign back to df_Backlog
df_Backlog = df_Backlog.rename(columns={
#'Backlog row Qty': 'Qty',
'Backlog row Qty': 'Backlog Qty',
'Remain. crit. Qty': 'Rem. Qty'
})
# Rename certain columns
df_TurnoverReport = df_TurnoverReport.rename(columns={
'TurnoverReport row Qty': 'Qty',
})
# Rename certain columns
df_Summary = df_Summary.rename(columns={
'Remain. crit. Qty': 'Rem. Qty',
'Max Qty (GS)': 'Qty (GS/BOM)'
})
#################################################################################################################
# Widgets initialization
################################################################################################################
#-------------------------------------- UPDATE 04/18 -----------------------------
default_program = 'Phase 4-5'
default_priority = 6
default_indice = 'P6'
# Function to filter priorities based on program selection
def filter_priorities(program):
if program == 'All':
priorities = ['All'] + df_Priority['Priority'].unique().tolist()
else:
priorities = df_Priority[df_Priority['Program'] == program]['Priority'].unique().tolist()
return priorities
# Function to filter indices based on priority selection
def filter_indices(priority):
if priority == 'All':
indices = df_Priority['Pty Indice'].unique().tolist() # Remove 'All' option here
else:
# Filter indices based on the selected priority
indices = df_Priority[df_Priority['Priority'] == priority]['Pty Indice'].unique().tolist()
return indices
''' Updated 03/28
# Initialize program widget, excluding NaN values
unique_programs = df_Priority['Program'].dropna().unique().tolist()
program_widget = pn.widgets.Select(name='Select Program', options=unique_programs, value=default_program)
'''
#---------------- 03/28 ------------------------------
# Explicit list of allowed programs
allowed_programs_4quadrants = ['Phase 4-5', 'EMBRAER', 'SIKORSKY', 'COMAC', '1stTB', 'Space-X'] # 04/17
# Validate default_program - ensure it's never empty/invalid
if (not isinstance(default_program, str) or
default_program.strip() == "" or
default_program not in allowed_programs_4quadrants):
default_program = allowed_programs_4quadrants[0] # Fallback to first valid program
# Create widget with guaranteed non-empty selection
program_widget = pn.widgets.Select(
name='Select Program',
options=allowed_programs_4quadrants,
value=default_program,
)
#----------------------------------------------------
# Initialize priority widgets
filtered_priorities = filter_priorities(default_program)
priority_widget = pn.widgets.Select(name='Select Priority', options=filtered_priorities, value=default_priority)
# Initialize indice widgets
filtered_indices = filter_indices(default_priority)
indice_widget = pn.widgets.Select(name='Select Pty Indice', options=filtered_indices, value=default_indice)
#---------------------------------------------------------------------------------------------
######################################################################################
# Widgets callback functions
########################################################################################
# Callback function to update priority_widget and indice_widget when the program changes
def update_program(event):
selected_program = program_widget.value
# Update priorities based on selected program
priority_widget.options = filter_priorities(selected_program)
# Ensure the selected priority is valid
if priority_widget.value not in priority_widget.options:
priority_widget.value = priority_widget.options[0] if priority_widget.options else None
# Update indices based on the updated priority
update_priorities(event)
# Function to update priorities based on program selection
def update_priorities(event):
selected_program = program_widget.value
updated_priorities = df_Priority[df_Priority['Program'] == selected_program]['Priority'].unique().tolist()
# Update priorities widget options
priority_widget.options = updated_priorities
# Ensure the selected priority is valid
if priority_widget.value not in updated_priorities:
priority_widget.value = updated_priorities[0] if updated_priorities else None
# Update indices based on the updated priority
update_indices(event)
# Function to update indices based on priority selection
def update_indices(event):
selected_priority = priority_widget.value
indice_widget.options = filter_indices(selected_priority)
# Set to default value if it's valid; otherwise, choose the first available
if default_indice in indice_widget.options:
indice_widget.value = default_indice
else:
indice_widget.value = indice_widget.options[0] if indice_widget.options else None
#--------------------------------------------------------------
# Add filter widgets for IDD Top Level and SEDA Top Level
#--------------------------------------------------------------
# Define filtering widgets for 'IDD Top Level' and 'SEDA Top Level'
label_idd_top_level = pn.pane.HTML('<b style="color:#2B70B3;">IDD Top Level Filter</b>')
label_seda_top_level = pn.pane.HTML('<b style="color:#2B70B3;">SEDA Top Level Filter</b>')
# 05/16 update
filters_top_level_4_cadran = {
'IDD Top Level': pn.widgets.TextInput(name='', placeholder='Enter IDD Top Level'),
'SEDA Top Level': pn.widgets.TextInput(name='', placeholder='Enter SEDA Top Level'),
}
# Create buttons for applying filters
filters_top_level_button_4_cadran = pn.widgets.Button(name='Apply Filters', button_type='primary')
# Set default value to None for all filter widgets
for widget in filters_top_level_4_cadran.values():
widget.value = ''
# 05/16 - New code
#--------------------------------------------------------------
# Add Reset widgets for IDD Top Level and SEDA Top Level
#--------------------------------------------------------------
# Create Reset button
reset_button_4_cadran = pn.widgets.Button(name='Reset filters', button_type='danger')
# Define reset callback
def reset_filters_4_cadran(event):
"""
Resets all widgets, including filters_Prod (with DatePickers), to their default values and triggers updates.
"""
# Reset main selection widgets to default values
program_widget.value = default_program
priority_widget.value = default_priority
indice_widget.value = default_indice
# Reset IDD Top Level and SEDA Top Level filters
filters_top_level_4_cadran['IDD Top Level'].value = ''
filters_top_level_4_cadran['SEDA Top Level'].value = ''
# Reset filters_Prod widgets based on their type
for widget in filters_Prod.values():
if isinstance(widget, pn.widgets.TextInput):
widget.value = ''
elif isinstance(widget, pn.widgets.MultiChoice):
widget.value = []
elif isinstance(widget, pn.widgets.Select):
widget.value = widget.options[0] if widget.options else None
elif isinstance(widget, pn.widgets.Checkbox):
widget.value = False
elif isinstance(widget, pn.widgets.DatePicker):
widget.value = None # Clear date fields
# Add other widget types as needed
# Trigger updates to refresh widget options and dashboard
try:
update_program(None) # Update priorities and indices based on default program
update_priorities(None) # Ensure priority options are updated
update_indices(None) # Ensure indice options are updated
update_supply_table_fullArchi(None) # Update Supply Chain tables
update_wip_selected_top(None) # Update Production Dashboard (e.g., wip_table)
except Exception as e:
print(f"Error in updates: {e}")
# Attach callback to reset button
reset_button_4_cadran.on_click(reset_filters_4_cadran)
#--------------------------------------------------------------
# 05/16 - Update with Reset button
# Create the layout with labels, filter widgets, and buttons
filter_widgets_top_level_4_cadran = pn.Row(
pn.Column(label_idd_top_level, filters_top_level_4_cadran['IDD Top Level']),
pn.Column(label_seda_top_level, filters_top_level_4_cadran['SEDA Top Level']),
pn.Column(
pn.Spacer(height=25), # Spacer before the buttons
pn.Row(
filters_top_level_button_4_cadran,
pn.Spacer(width=10), # Space between Apply Filters and Reset buttons
reset_button_4_cadran
)
)
)
#--------------------------------------------------------------
# Create a mapping between IDD Top Level/SEDA Top Level and other widget values
#--------------------------------------------------------------
def create_mapping(df):
mapping = {}
for _, row in df.iterrows():
idd_top_level = row['IDD Top Level']
seda_top_level = row['SEDA Top Level']
program = row['Program']
priority = row['Priority']
pty_indice = row['Pty Indice']
# Map IDD Top Level to Program, Priority, and Pty Indice
mapping[idd_top_level] = {'Program': program, 'Priority': priority, 'Pty Indice': pty_indice}
# Map SEDA Top Level to Program, Priority, and Pty Indice
mapping[seda_top_level] = {'Program': program, 'Priority': priority, 'Pty Indice': pty_indice}
return mapping
# Create the mapping
mapping = create_mapping(df_Priority)
#--------------------------------------------------------------
# Update Widgets Based on IDD Top Level or SEDA Top Level
#--------------------------------------------------------------
# This function will be executed when the "Apply Filters" button is clicked
def apply_filters_button_click(event):
# Get the current widget values
selected_program = program_widget.value
selected_priority = priority_widget.value
selected_indice = indice_widget.value
idd_top_level_filter = filters_top_level_4_cadran['IDD Top Level'].value
seda_top_level_filter = filters_top_level_4_cadran['SEDA Top Level'].value
# Update widgets based on IDD Top Level or SEDA Top Level filters
if idd_top_level_filter and idd_top_level_filter in mapping:
program_widget.value = mapping[idd_top_level_filter]['Program']
priority_widget.value = mapping[idd_top_level_filter]['Priority']
indice_widget.value = mapping[idd_top_level_filter]['Pty Indice']
elif seda_top_level_filter and seda_top_level_filter in mapping:
program_widget.value = mapping[seda_top_level_filter]['Program']
priority_widget.value = mapping[seda_top_level_filter]['Priority']
indice_widget.value = mapping[seda_top_level_filter]['Pty Indice']
else:
print("No valid input, resetting widgets to default.") # Debugging
program_widget.value = 'All'
priority_widget.value = 'All'
indice_widget.value = 'All'
# Initialize a boolean mask with all True values
mask = pd.Series(True, index=df_Priority.index)
# Apply filters based on selections
if selected_program != 'All':
mask &= (df_Priority['Program'] == selected_program)
if selected_priority != 'All':
mask &= (df_Priority['Priority'] == selected_priority)
if selected_indice != 'All':
mask &= (df_Priority['Pty Indice'] == selected_indice)
if idd_top_level_filter:
mask &= (df_Priority['IDD Top Level'].str.contains(idd_top_level_filter, case=False, na=False))
if seda_top_level_filter:
mask &= (df_Priority['SEDA Top Level'].str.contains(seda_top_level_filter, case=False, na=False))
# Apply the mask and return the filtered DataFrame
filtered_df = df_Priority[mask]
# Display or print the filtered DataFrame (for testing)
print(filtered_df) # Or update a display component with the filtered DataFrame
# Link the button click event to the apply_filters_button_click function
filters_top_level_button_4_cadran.on_click(apply_filters_button_click)
#--------------------------------------------------------------
##############################################################################################################################
# --->>>> ENGINEERING <<<---
##############################################################################################################################
# --> Pending Report
##############################################################################################################################
#Get date from the Pending Report - ['Last Update'] in [T2]
date_pendingreport = df_PendingReport['Last Update'].iloc[0] # Get the first date in the 'Last Update' column
# Fill NaN values with empty strings
df_PendingReport['Comment'].fillna('', inplace=True)
# Define the initial empty DataFrame for changes_table
initial_changes_df = pd.DataFrame(columns=['Pty Indice', 'IDD Top Level', 'SEDA Top Level', 'Item Number', 'Action Needed', 'Rel Date', 'Comment'])
# Define column widths dictionary
column_widths = {
'Pty Indice': 60
}
# Create a Tabulator widget for sales_table
changes_table = pn.widgets.Tabulator(
initial_changes_df,
layout='fit_data_table', # Adjust columns to fit data (excluding header)
sizing_mode='stretch_width',
show_index=False, # This hides the index column
widths=column_widths # Set column widths
)
# Create a Markdown pane for messages in the Pending Report
pending_message_pane = pn.pane.Markdown("")
def update_changes_table(event):
selected_program = program_widget.value
selected_priority = priority_widget.value
selected_indice = indice_widget.value
# Filter df_PendingReport based on selected_program
if selected_program == 'All':
filtered_df_PendingReport = df_PendingReport.copy()
else:
filtered_df_PendingReport = df_PendingReport[df_PendingReport['Program'] == selected_program]
# Apply additional filters
if selected_priority != 'All':
filtered_df_PendingReport = filtered_df_PendingReport[filtered_df_PendingReport['Priority'] == selected_priority]
if selected_indice != 'All':
filtered_df_PendingReport = filtered_df_PendingReport[filtered_df_PendingReport['Pty Indice'] == selected_indice]
# Check if the filtered DataFrame is empty
if filtered_df_PendingReport.empty:
pending_message_pane.object = "**No open changes related to this PN**" # Simple message
changes_table.visible = False # Hide the changes table
else:
changes_table.value = filtered_df_PendingReport[['Pty Indice', 'IDD Top Level', 'SEDA Top Level', 'Item Number', 'Action Needed', 'Rel Date', 'Comment']]
pending_message_pane.object = "" # Clear the message
changes_table.visible = True # Show the changes table
# Initialize the changes table with default values
update_changes_table(None)
##############################################################################################################################
# --> ADCN Report
##############################################################################################################################
#Get date from the Pending Report - ['Last Update'] is column [S] wiht the date in S2
date_adcngreport = df_ADCNReport['Last Update'].iloc[0] # Get the first date in the 'Last Update' column
# Reformatting of 'Pty Indice', erase the '[' and ']' and only keep the values inside or the values separated with ';'
df_ADCNReport['Pty Indice'] = df_ADCNReport['Pty Indice'].str.strip('[]').str.split(';')
# Explode the DataFrame to create a new row for each index
df_exploded = df_ADCNReport.explode('Pty Indice')
# Mapping Priority based on 'Pty Indice'
df_exploded['Priority'] = df_exploded['Pty Indice'].map(indice_to_priority)
# Reset the index if needed
df_exploded.reset_index(drop=True, inplace=True)
# Display the final DataFrame
#print('df_ADCNReport after transformation:')
#display(df_exploded[['Pty Indice', 'ADCN#', 'ESR#', 'Created', 'Release Date', 'Drawing Number', 'Status', 'ADCN Rev', 'Change Description', 'Priority', 'Program']])
# Update df_ADCNReport with the exploded DataFrame
df_ADCNReport = df_exploded
#Create a panda datafram with the columns 'Pty Indice', 'ADCN#' , 'ESR#', 'Created', 'Release Date', 'Drawing Number', 'Status', 'ADCN Rev', 'Change Description', 'Priority', 'Program'
df_ADCNReport = df_ADCNReport[['Pty Indice', 'ADCN#', 'ESR#', 'Created', 'Release Date', 'Drawing Number', 'Status', 'ADCN Rev', 'Change Description', 'Priority', 'Program']]
#Replace 'Status'and 'ADCN Rev' with empty space
df_ADCNReport['Status'].fillna('', inplace=True) # Replace NaN with empty string for 'Status'
df_ADCNReport['ADCN Rev'].fillna('', inplace=True) # Replace NaN with empty string for 'ADCN Rev'
df_ADCNReport['Change Description'].fillna('', inplace=True) # Replace NaN with empty string for 'ADCN Rev'
# Convert 'Created' and 'Release Date' to datetime format, stripping time, and formatting as MM/DD/YYYY
df_ADCNReport['Created'] = pd.to_datetime(df_ADCNReport['Created'], errors='coerce')
df_ADCNReport['Release Date'] = pd.to_datetime(df_ADCNReport['Release Date'], errors='coerce')
# Format dates to MM/DD/YYYY
df_ADCNReport['Created'] = df_ADCNReport['Created'].dt.strftime('%m/%d/%Y')
df_ADCNReport['Release Date'] = df_ADCNReport['Release Date'].dt.strftime('%m/%d/%Y')
# Write 'ADCN not created' where 'Created' is empty
df_ADCNReport['Created'].replace('', 'ADCN not created', inplace=True)
df_ADCNReport['Created'].replace(pd.NaT, 'ADCN not created', inplace=True)
# Replace NaN in 'Release Date' with 'Not released'
df_ADCNReport['Release Date'].replace(pd.NaT, 'ADCN not released', inplace=True)
#Display dataframe
#print('df_ADCNReport')
#display(df_ADCNReport)
# Function to apply font color formatting based on 'Status'
def font_color_status(val):
if val == 'ADCN not created':
return 'color: red;' # Return red font color for "ADCN not created"
else:
return 'color: black;' # Default to black font color
# Function to apply font color formatting based on 'Release Date'
def font_color_release_date(val):
if val == 'ADCN not released':
return 'color: red;' # Return red font color for "Not released"
else:
return 'color: black;' # Default to black font color
# Initialize the DataFrame pane and message pane
#ADCN_pane = pn.pane.DataFrame(pd.DataFrame(), sizing_mode='stretch_width')
ADCN_pane = pn.pane.DataFrame(pd.DataFrame(),
sizing_mode='stretch_width', # Keep it responsive
height=600) # Set max height
adcn_message_pane = pn.pane.Markdown("", sizing_mode='stretch_width')
# Update function to handle table updates with color formatting
def update_ADCN_table(event):
selected_program = program_widget.value
selected_priority = priority_widget.value
selected_indice = indice_widget.value
# Filter the DataFrame based on selected values
mask = pd.Series(True, index=df_ADCNReport.index)
if selected_priority != 'All':
mask &= (df_ADCNReport['Priority'] == selected_priority)
if selected_program != 'All':
mask &= (df_ADCNReport['Program'] == selected_program)
if selected_indice != 'All':
mask &= df_ADCNReport['Pty Indice'].str.contains(selected_indice)
# Apply the mask to filter the DataFrame
filtered_df = df_ADCNReport[mask]
# Check if the filtered DataFrame is empty
if filtered_df.empty:
ADCN_pane.object = pd.DataFrame() # Clear the DataFrame pane
ADCN_pane.visible = False # Hide the DataFrame pane
adcn_message_pane.object = '**No ADCN related to this PN**' # Show no data message
else:
# Select relevant columns to display and hide 'Priority' and 'Program'
displayed_df = filtered_df[['Pty Indice', 'ADCN#', 'ESR#', 'Created', 'Release Date',
'Drawing Number', 'Status', 'ADCN Rev', 'Change Description']]
# Apply color formatting using 'applymap' for the 'Status' column
#styled_df = displayed_df.style.applymap(font_color_status, subset=['Status'])
# Apply color formatting using 'applymap' for the 'Status' and 'Release Date' columns
styled_df = displayed_df.style.applymap(font_color_status, subset=['Created']) \
.applymap(font_color_release_date, subset=['Release Date'])
# Update the ADCN_pane with the styled DataFrame
ADCN_pane.object = styled_df.hide(axis='index')
ADCN_pane.visible = True # Show the DataFrame pane
adcn_message_pane.object = "" # Clear the message
# Call the update function initially to populate the table
update_ADCN_table(None)
# Attach the update function to widget value changes
program_widget.param.watch(update_ADCN_table, 'value')
priority_widget.param.watch(update_ADCN_table, 'value')
indice_widget.param.watch(update_ADCN_table, 'value')
###############################################################################################
# Initial call to update_widgets_and_table to populate the table based on default selections
#############################################################################################
# Define supply dashboard
changes_dashboard = pn.Column(
pn.pane.HTML(f"""
<div style="text-align: left;">
<style>
h2 {{ margin-bottom: 0; color: #305496; }} /* Set title color here */
p {{ margin-top: 0; }}
</style>
<h2>Engineering</h2>
<p>{f"|PendingReport| - <b>{date_pendingreport}</b>: IDD's internal changes based on Agile (PLM) | Pending Report from Change Analyst | [weekly update]"}</p>
</div>
"""),
pending_message_pane,
changes_table,
pn.Spacer(height=20),
pn.pane.HTML(f"""
<div style="text-align: left;">
<style>
p {{ margin-top: 0; }}
</style>
<p>{f"|ADCN Report| - <b>{date_adcngreport}</b>: IDD's external changes based on SEDA's ADCN Report | Since beginning of the current year | [weekly update]"}</p>
</div>
"""),
adcn_message_pane, # Add ADCN message pane here
ADCN_pane,
sizing_mode='stretch_width' # Adjust sizing mode
)
##############################################################################################################################
# --->>>> SALES <<<--- updated 08/22
##############################################################################################################################
# Backlog
#######################################################
# Define the initial empty DataFrame for sales_table
#initial_sales_df = pd.DataFrame(columns=['Pty Indice', 'IDD Top Level', 'SEDA Top Level', 'Qty','Rem. Qty', 'Order', 'Due Date'])
initial_sales_df = pd.DataFrame(columns=['Pty Indice', 'IDD Top Level', 'SEDA Top Level', 'Backlog Qty', 'Rem. Qty', 'Order', 'Due Date'])
# Define column widths dictionary
column_widths = {
'Pty Indice': 60,
#'Qty': 60,
'Backlog Qty': 60,
'Rem. Qty': 80,
'Order': 80
}
# Create widget for sales_table
sales_table = pn.pane.DataFrame(
initial_sales_df,
min_height=50,
height=None,
sizing_mode='stretch_width',
index=False # Hides the index column
)
# Create a Markdown pane for messages in the Sales table
message_pane = pn.pane.Markdown("")
def update_sales_table(event):
selected_program = program_widget.value
selected_priority = priority_widget.value
selected_indice = indice_widget.value
# Filter df_Backlog based on selected_program
if selected_program == 'All':
filtered_df_backlog = df_Backlog.copy() # Use a copy of the entire DataFrame
else:
filtered_df_backlog = df_Backlog[df_Backlog['Program'] == selected_program]
# Apply additional filters based on selected_priority and selected_indice
if selected_priority != 'All':
filtered_df_backlog = filtered_df_backlog[filtered_df_backlog['Priority'] == selected_priority]
if selected_indice != 'All':
filtered_df_backlog = filtered_df_backlog[filtered_df_backlog['Pty Indice'] == selected_indice]
# Group by 'Order' and aggregate the 'Qty' (sum) and 'Rem. Qty' (first)
aggregated_df = filtered_df_backlog.groupby('Order').agg({
'Pty Indice': 'first',
'IDD Top Level': 'first',
'SEDA Top Level': 'first',
#'Qty': 'sum',
'Backlog Qty': 'sum',
'Rem. Qty': 'first', # Use the first non-null value
'Due Date': 'first',
}).reset_index()
if aggregated_df.empty:
sales_table.object = pd.DataFrame() # Empty DataFrame
message_pane.object = "No data available"
else:
sales_table.object = aggregated_df # Assign new DataFrame
message_pane.object = ""
# Initialize the sales table with default values
update_sales_table(None)
# define color for important text
important_text_color = '#002060' # dark bleu
########################################################
# Create a pane for displaying dynamic text for sales
########################################################
sales_summary = pn.pane.Str(sizing_mode='stretch_width')
def update_sales_summary(event):
selected_program = program_widget.value
selected_priority = priority_widget.value
selected_indice = indice_widget.value
# Initialize a boolean mask with all True values
mask = pd.Series(True, index=df_Backlog.index)
# Apply filters based on selections
if selected_program != 'All':
mask &= (df_Backlog['Program'] == selected_program)
if selected_priority != 'All':
mask &= (df_Backlog['Priority'] == selected_priority)
if selected_indice != 'All':
mask &= (df_Backlog['Pty Indice'] == selected_indice)
# Filter df_Backlog using the constructed mask
filtered_df_backlog = df_Backlog[mask]
# Check if filtered_df_backlog is empty or not
if filtered_df_backlog.empty:
sales_summary.object = 'No data available'
else:
# Initialize a dictionary to sum quantities for each 'Pty Indice'
summary_dict = {}
# Iterate over each row in filtered_df_backlog
for idx, row in filtered_df_backlog.iterrows():
pty_indice = row['Pty Indice']
order = row['Order']
#qty = row['Qty'] 08/22
qty = row['Backlog Qty']
if pty_indice not in summary_dict:
summary_dict[pty_indice] = {
'total_qty': 0,
'orders': set(),
'shipped': 0,
'critical_qty': 0
} # Initialize orders as a set
summary_dict[pty_indice]['total_qty'] += qty
summary_dict[pty_indice]['orders'].add(order) # Use set to avoid duplicates
# Retrieve shipment data from df_Priority
shipment_data = df_Priority[df_Priority['Pty Indice'].isin(summary_dict.keys())]
# Get details for the current 'Pty Indice'
details = filtered_df_backlog[filtered_df_backlog['Pty Indice'] == pty_indice].iloc[0]
for pty_indice in summary_dict.keys():
# Fetch the shipment data for the current 'Pty Indice'
shipment_info = shipment_data[shipment_data['Pty Indice'] == pty_indice]
# Calculate shipped quantities
shipped = shipment_info['Shipped'].sum() if not shipment_info['Shipped'].isna().all() else 0
#Convert 'shipped' to int - 08/14
shipped = int(shipped)
# Handle and convert 'Critical Qty'
critical_qty = shipment_info['Critical Qty']
if not critical_qty.isna().all():
try:
critical_qty = critical_qty.astype(int).sum()
except ValueError:
critical_qty = 0
else:
critical_qty = 0
# Handle non-numeric values for 'Critical Qty'
if isinstance(critical_qty, str) and critical_qty.strip().lower() == 'completed':
shipment = "Critical quantity fulfilled"
else:
shipment = f"Total quantity (<b>{shipped}</b>) shipped over (<b>{critical_qty}</b>) total critical quantity"
# Add shipment information to the summary
summary_dict[pty_indice]['shipment'] = shipment
# Format the output
lines = []
for pty_indice, data in summary_dict.items():
orders_concat = ', '.join(data['orders']) # Convert set to a sorted list for display
shipment_info = data.get('shipment', 'No shipment information available')
line = (
#f"<u>Pty Indice</u>: <b>{pty_indice}</b> - <b>{details['IDD Top Level']}</b> ({details['SEDA Top Level']})<br>"
f"<u>Pty Indice</u>: <span style='color:{important_text_color};'><b>{pty_indice}</b> - <b>{details['IDD Top Level']}</b></span> ({details['SEDA Top Level']})<br>"
f"▷ IDD Backlog for {pty_indice} is <b>{data['total_qty']}</b> Top-Level within the following SO: {orders_concat}<br>"
f"▷ {shipment_info}<br>"
)
lines.append(line)
# Join all lines into a single string
display_text = '\n'.join(lines)
sales_summary.object = display_text
# Define an initial call to populate the table when the app starts
update_sales_summary(None)
###############################################################
# Turnover Report
###############################################################
# Convert 'Tracking#' to string
df_TurnoverReport['Tracking#'] = df_TurnoverReport['Tracking#'].astype(str)
# Replace 'nan' with an empty string
df_TurnoverReport['Tracking#'] = df_TurnoverReport['Tracking#'].replace('nan', '')
# Remove '.0' from the string values
df_TurnoverReport['Tracking#'] = df_TurnoverReport['Tracking#'].str.replace('.0', '', regex=False)
#Define the initial empty DataFrame for turnover_table
initial_turnover_df = pd.DataFrame(columns=['Pty Indice', 'IDD Top Level', 'SEDA Top Level', 'Qty', 'Invoice date', 'Order', 'Tracking#'])
# Ensure 'Invoice date' is in datetime format
df_TurnoverReport['Invoice date'] = pd.to_datetime(df_TurnoverReport['Invoice date'])
# Format 'Invoice date' to short date format
df_TurnoverReport['Invoice date'] = df_TurnoverReport['Invoice date'].dt.strftime('%m/%d/%Y')
# Define column widths dictionary
column_widths = {
'Pty Indice': 60,
'Qty': 60,
'Rem. Qty': 80,
'Order': 80
}
###############################################
# Create a widget for turnover_table
################################################
turnover_table = pn.pane.DataFrame(
initial_turnover_df,
min_height=50,
height=None,
sizing_mode='stretch_width',
index=False # Hides the index column
)
# Define the turnover_message_pane
turnover_message_pane = pn.pane.HTML('')
def update_turnover_table(event):
selected_program = program_widget.value
selected_priority = priority_widget.value
selected_indice = indice_widget.value
# Filter df_TurnoverReport based on selected_program
if selected_program == 'All':
filtered_df_TurnoverReport = df_TurnoverReport.copy() # Make a copy of the entire DataFrame
else:
filtered_df_TurnoverReport = df_TurnoverReport[df_TurnoverReport['Program'] == selected_program]
# Apply additional filters based on selected_priority and selected_indice
if selected_priority != 'All':
filtered_df_TurnoverReport = filtered_df_TurnoverReport[filtered_df_TurnoverReport['Priority'] == selected_priority]
if selected_indice != 'All':
filtered_df_TurnoverReport = filtered_df_TurnoverReport[filtered_df_TurnoverReport['Pty Indice'] == selected_indice]
# Check if the filtered DataFrame is empty
if filtered_df_TurnoverReport.empty:
turnover_table.object = pd.DataFrame()
turnover_message_pane.object = f"No shipment or NC received within the period for {selected_priority if selected_indice == 'All' else selected_indice}"
else:
turnover_table.object = filtered_df_TurnoverReport[['Pty Indice', 'IDD Top Level', 'SEDA Top Level', 'Qty', 'Invoice date', 'Order', 'Tracking#']]
turnover_message_pane.object = ""
# Initialize the turnover table with default values
update_turnover_table(None)
###############################################
# Create a Markdown pane for turnover summary
################################################
turnover_summary = pn.pane.Str(sizing_mode='stretch_width')
# Convert 'Invoice date' to datetime format
df_TurnoverReport['Invoice date'] = pd.to_datetime(df_TurnoverReport['Invoice date'])
# Calculate the span period of the Turnover Report span_TurnoverReport
start_date = df_TurnoverReport['Invoice date'].min()
end_date = df_TurnoverReport['Invoice date'].max()
span_TurnoverReport = f"{start_date.date()} to {end_date.date()}" # Format dates as needed
def update_turnover_summary(event):
selected_program = program_widget.value
selected_priority = priority_widget.value
selected_indice = indice_widget.value
# Filter df_TurnoverReport based on selected_program
if selected_program == 'All':
filtered_df_TurnoverReport = df_TurnoverReport.copy() # Make a copy of the entire DataFrame
else:
filtered_df_TurnoverReport = df_TurnoverReport[df_TurnoverReport['Program'] == selected_program]
# Apply additional filters based on selected_priority and selected_indice
if selected_priority != 'All':
filtered_df_TurnoverReport = filtered_df_TurnoverReport[filtered_df_TurnoverReport['Priority'] == selected_priority]
if selected_indice != 'All':
filtered_df_TurnoverReport = filtered_df_TurnoverReport[filtered_df_TurnoverReport['Pty Indice'] == selected_indice]
# Check if filtered_df_TurnoverReport is empty or not
if filtered_df_TurnoverReport.empty:
turnover_summary.object = 'No data available'
else:
# Initialize a dictionary to sum quantities for each 'Pty Indice'
summary_dict = {}
# Iterate over each row in filtered_df_TurnoverReport
for idx, row in filtered_df_TurnoverReport.iterrows():
pty_indice = row['Pty Indice']
order = row['Order']
qty = row['Qty']
if pty_indice not in summary_dict:
summary_dict[pty_indice] = {
'total_qty': 0,
'top_level_shipped': 0,
'nc_shipped': 0,
'nc_received': 0,
'orders': set(),
'details': filtered_df_TurnoverReport[filtered_df_TurnoverReport['Pty Indice'] == pty_indice].iloc[0]
} # Initialize orders as a set
summary_dict[pty_indice]['total_qty'] += qty
summary_dict[pty_indice]['orders'].add(order) # Use set to avoid duplicates
# Categorize the quantity
if qty > 0:
if 'NC' in order:
summary_dict[pty_indice]['nc_shipped'] += qty
else:
summary_dict[pty_indice]['top_level_shipped'] += qty
elif qty < 0 and 'NC' in order:
summary_dict[pty_indice]['nc_received'] -= qty # Use '-' to keep positive values for display
# Format the output
lines = []
for pty_indice, data in summary_dict.items():
details = data['details']
orders_concat = ', '.join(data['orders'])
line = (
#f"<u>Pty Indice</u>: <b>{pty_indice}</b> - <b>{details['IDD Top Level']}</b> ({details['SEDA Top Level']}) - Repport's span (<b>{span_TurnoverReport}</b>)<br>"
f"<u>Pty Indice</u>: <span style='color:{important_text_color};'><b>{pty_indice}</b> - <b>{details['IDD Top Level']}</b></span> ({details['SEDA Top Level']}) - Repport's span (<b>{span_TurnoverReport}</b>)<br>"
f"▷ Qty Top-Level shipped on the period: <b>{data['top_level_shipped']}</b><br>"
f"▷ Qty NC shipped on the period: <b>{data['nc_shipped']}</b><br>"
f"▷ Qty NC received on the period: <b>{data['nc_received']}</b><br>"
)
lines.append(line)
# Join all lines into a single string
display_text = '<br>'.join(lines)
turnover_summary.object = display_text
# Define an initial call to populate the table when the app starts
update_turnover_summary(None)
###############################################################################################
# Initial call to update_widgets_and_table to populate the table based on default selections
#############################################################################################
sales_dashboard = pn.Column(
pn.pane.HTML(f"""
<div style='text-align: left;'>
<style>
h2 {{ margin-bottom: 0; color: #305496; }} /* Set title color here */
p {{ margin-top: 0; }}
</style>
<h2>Backlog & recent shipment</h2>
<p>{f"|CM-Backlog| - <b>{file_date}</b>: IDD's backlog based on QAD (ERP) | [Daily update]"}</p>
</div>
"""),
pn.Row(sales_summary, sizing_mode='stretch_both'), # Row to stretch content
sales_table,
pn.pane.HTML(f"""
<div style='text-align: left;'>
<p>{f"|CM-Turnover Report| - <b>{file_date}</b>: Top-Level shipped/Received at IDD based on QAD (ERP) | [Daily update, limited span (starts the 1st of current month)]"}</p>
</div>
"""),
turnover_message_pane, # Add the turnover message pane here
turnover_summary,
turnover_table,
sizing_mode='stretch_width', # Adjust sizing mode
height=600 # Set a fixed height to enforce the maximum height
)
##############################################################################################################################
# --->>>> SUPPLY CHAIN <<<---
##############################################################################################################################
# Filter out rows where 'Qty On Hand' is NaN
df_Summary = df_Summary[df_Summary['Qty On Hand'].notna()]
# Round 'Qty On Hand' and 'Qty (GS/BOM)' to integers
df_Summary['Qty On Hand'] = df_Summary['Qty On Hand'].round().astype(int)
#df_Summary['Qty (GS/BOM)'] = df_Summary['Qty (GS/BOM)'].round().astype(int) # saved 02/03
### Update 02/03 #################
# Convert to numeric type first (invalid values become NaN)
df_Summary['Qty (GS/BOM)'] = pd.to_numeric(df_Summary['Qty (GS/BOM)'], errors='coerce')
df_Summary['Max Qty Top-Level'] = pd.to_numeric(df_Summary['Max Qty Top-Level'], errors='coerce') # 03/06
# Handle missing values (fill with 0 or appropriate value)
df_Summary['Qty (GS/BOM)'] = df_Summary['Qty (GS/BOM)'].fillna(0)
# Now perform rounding and conversion to integers
df_Summary['Qty (GS/BOM)'] = df_Summary['Qty (GS/BOM)'].round().astype(int)
####################################
#Display selected_indice, with related 'IDD Top Level', 'SEDA Top Level', 'Top-Level Status' and 'Max Qty Top-Level' from df_Summary above the table
#Apply mapping to create 'Program' column in df_Summary
df_Summary['Program'] = df_Summary['Pty Indice'].map(indice_to_program)
# Fill NaN values with empty strings
df_Summary['Top Level sharing Components'].fillna('', inplace=True)
df_Summary['Comment'].fillna('', inplace=True)
# Replace 'SAFRAN ELEC & DEFENSE(S9412)' with 'Safran EDA' in the 'Supplier' column
df_Summary['Supplier'].replace('SAFRAN ELEC & DEFENSE(S9412)', 'Safran EDA', inplace=True)
# 09/20 update
# Define a list of acronyms to preserve in uppercase
acronyms = ['EDA', 'PCB', 'PWB', 'CPA', 'CPSL', 'ISP', 'TBD'] # Add more acronyms as needed
# Function to capitalize while preserving acronyms
def title_with_acronyms(text, acronyms):
# Convert the text to title case (first letter capitalized, rest lowercase)
text = text.lower().title()
# Use regex to replace the acronyms in uppercase
for acronym in acronyms:
text = re.sub(rf'\b{acronym.title()}\b', acronym, text)
return text
# Apply the function to the 'Supplier' and 'Description' columns
df_Summary['Supplier'] = df_Summary['Supplier'].astype(str) # Added 02/03
df_Summary['Supplier'] = df_Summary['Supplier'].apply(lambda x: title_with_acronyms(x, acronyms))
df_Summary['Description'] = df_Summary['Description'].astype(str) # Added 02/03
df_Summary['Description'] = df_Summary['Description'].apply(lambda x: title_with_acronyms(x, acronyms))
##################################################################
# Create a supply_table with Panel for Purchased architecture
##################################################################
# Color formating of wip_table - ['Level']
###########################################
# Color of 'Level'
#Level == 0: '63BE7B' # Green
#Level == 1: 'A2C075' # Lighter Green
#Level == 2: 'FFEB84' # Yellow
#Level == 3: 'FFD166' # Orange
#Level == 4: 'F88E5B' # Darker Orange
#Level == 5: 'F8696B' # Red
#Level == 6: '8B0000' # Darker Red
# Define color mapping for 'Level'
color_mapping_Level = {
0: '#63BE7B',
1: '#A2C075',
2: '#FFEB84',
3: '#FFD166',
4: '#F88E5B',
5: '#F8696B',
6: '#8B0000'
}
def apply_color_formatting(df):
# Create a DataFrame for styles, initializing with empty strings
styles = pd.DataFrame('', index=df.index, columns=df.columns)
# Fill missing values in 'Level' with a default value or handle them separately
if 'Level' in df.columns:
df['Level'] = df['Level'].fillna(-1) # Using -1 or any other default value that does not conflict with valid levels
for idx, value in df['Level'].items():
if pd.isna(value) or value not in color_mapping_Level:
styles.at[idx, 'Level'] = 'background-color: #FFFFFF' # Default color for missing values
else:
styles.at[idx, 'Level'] = f'background-color: {color_mapping_Level[value]}'
# Apply font and fill styling for 'Qty (GS/BOM)' column - New 09/18
# Apply font, fill, and border styling for 'Qty (GS/BOM)' column
if 'Qty (GS/BOM)' in df.columns:
for idx, value in df['Qty (GS/BOM)'].items():
if value == 0:
styles.at[idx, 'Qty (GS/BOM)'] = (
'color: #C00000; '
'background-color: #FFC7CE; '
'border: 1px dashed #C00000'
)
return styles
# Function to apply color formatting to 'Level' column
def color_levels(val):
color = color_mapping_Level.get(val, '#FFFFFF') # Default to white if no mapping exists
return f'background-color: {color}'
# Update function to handle table updates with color formatting
def update_supply_table(event):
selected_program = program_widget.value
selected_priority = priority_widget.value
selected_indice = indice_widget.value
# Filter df_Summary based on selected_program
if selected_program == 'All':
filtered_df_summary = df_Summary.copy() # Make a copy of the entire DataFrame
else:
filtered_df_summary = df_Summary[df_Summary['Program'] == selected_program]
# Apply additional filters based on selected_priority and selected_indice
if selected_priority != 'All':
filtered_df_summary = filtered_df_summary[filtered_df_summary['Priority'] == selected_priority]
if selected_indice != 'All':
filtered_df_summary = filtered_df_summary[filtered_df_summary['Pty Indice'] == selected_indice]
# Filter out rows where 'Supplier' == 'Make Part'
#filtered_df_summary = filtered_df_summary[filtered_df_summary['Supplier'] != 'Make Part']
filtered_df_summary = filtered_df_summary[~filtered_df_summary['Supplier'].str.contains(r'Make Part( CUU)?( \(Phantom\))?', case=False, na=False)] # 03/06
#------------- 03/06 ----------------
# Ensure the 'Critical' column is boolean
# If the column contains strings like 'TRUE' or 'FALSE', convert them to boolean values
if 'Critical' in filtered_df_summary.columns:
filtered_df_summary['Critical'] = filtered_df_summary['Critical'].astype(str).str.strip().str.upper() == 'TRUE'
# Filter out rows where 'Critical' is not True
filtered_df_summary = filtered_df_summary[filtered_df_summary['Critical']]
#----------------------------------------
# Convert 'Level' and 'Rem. Qty' to integer
filtered_df_summary['Level'] = pd.to_numeric(filtered_df_summary['Level'], errors='coerce').fillna(-1).astype(int)
filtered_df_summary['Rem. Qty'] = pd.to_numeric(filtered_df_summary['Rem. Qty'], errors='coerce').fillna(0).astype(int)
# Filter out rows where 'Qty (GS/BOM)' > 'Remain. crit. Qty'
#filtered_df_summary = filtered_df_summary[filtered_df_summary['Qty (GS/BOM)'] <= filtered_df_summary['Rem. Qty']] # 03/06 display only component where 'Qty (GS/BOM)' < 'Max Qty Top-Level' (from 'Level' == 0)
#filtered_df_summary = filtered_df_summary[filtered_df_summary['Qty (GS/BOM)'] <= max_qty_top_level] # 03/06WIP display only component where 'Qty (GS/BOM)' < 'Max Qty Top-Level' (from 'Level' == 0)
# Exclude rows where 'Level' == 0 # 03/06
filtered_df_summary = filtered_df_summary[filtered_df_summary['Level'] != 0]
# Sort by 'Pty Indice' and 'BOM Index'
#filtered_df_summary = filtered_df_summary.sort_values(by=['Pty Indice', 'BOM Index']) #saved 02/03
#filtered_df_summary = filtered_df_summary.sort_values(by=['Pty Indice', 'BOM_Index'])
filtered_df_summary = filtered_df_summary.sort_values(by=['BOM_Index']) # 03/06
# Check if the filtered DataFrame is empty # 02/26
if filtered_df_summary.empty:
supply_table.object = pd.DataFrame({
'Pty Indice': ['No Data'],
'IDD Component': [''],
'Level': [''],
'Description': [''],
'Qty (GS/BOM)': [''],
'Supplier': [''],
#'Top Level sharing Components': [''],
#'Comment': [''],
#'Qty On Hand': [''],
'Max Qty Top-Level': [''], # 03/06
'Rem. Qty': ['']
})
message_pane.object = 'No data available'
else:
#supply_table_df = filtered_df_summary[['Pty Indice', 'IDD Component', 'Level', 'Description', 'Qty (GS/BOM)', 'Supplier', 'Top Level sharing Components', 'Comment', 'Qty On Hand', 'Rem. Qty']]
supply_table_df = filtered_df_summary[['Pty Indice', 'IDD Component', 'Level', 'Description', 'Qty (GS/BOM)', 'Supplier', 'Max Qty Top-Level', 'Rem. Qty']] # 03/06
# Apply color formatting using 'applymap' for the 'Level' column
styled_df = supply_table_df.style.applymap(color_levels, subset=['Level'])
# Update the supply_table with the styled DataFrame
supply_table.object = styled_df.hide(axis='index')
message_pane.object = "" # Clear the message
# Initialize the supply_table pane
supply_table = pn.pane.DataFrame(pd.DataFrame(), sizing_mode='stretch_width')
message_pane = pn.pane.Markdown("", sizing_mode='stretch_width')
def on_widget_change_supply(event):
update_supply_table(event) # Simply call the update function
# Call the update initially to trigger the first load
update_supply_table(None)
############## Update 09/16 ################
# Filters for update_supply_table_fullArchi
#########################################
# Define filtering widgets using HTML panes for labels
label_idd_component = pn.pane.HTML('<b style="color:#2B70B3;">IDD Component Filter</b>')
label_supplier = pn.pane.HTML('<b style="color:#2B70B3;">Supplier Filter</b>')
# Define filtering widgets
filters_fullArchi = {
'IDD Component': pn.widgets.TextInput(name=''),
'Supplier': pn.widgets.TextInput(name=''),
}
# Create a button to trigger filtering
filters_fullArchi_button = pn.widgets.Button(name='Apply Filters', button_type='primary')
reset_fullArchi_button = pn.widgets.Button(name='Reset Filters', button_type='default')
############## Update 09/16 ################
# filters_fullArchi for update_supply_table_fullArchi
#########################################
# Set default value to None for all filter widgets
for widget in filters_fullArchi.values():
widget.value = None
# Create the layout with labels and widgets
filter_widgets_fullArchi = pn.Row(
pn.Column(label_idd_component, filters_fullArchi['IDD Component']),
pn.Column(label_supplier, filters_fullArchi['Supplier']),
pn.Column(
pn.Spacer(height=25), # Spacer before the buttons
pn.Row(filters_fullArchi_button, reset_fullArchi_button)
)
)
# Initial call to populate the table based on default selections
#update_supply_table_fullArchi(None)
#########################################
# Update 09/16
########################################################################################
# Create a supply_table with Panel for full architecture Make and Purchased part
#######################################################################################
# Update WIP 09/27 to apply color mapping when widget is updated
def update_supply_table_fullArchi(event):
selected_program = program_widget.value
selected_priority = priority_widget.value
selected_indice = indice_widget.value
idd_component_filter = filters_fullArchi['IDD Component'].value
supplier_filter = filters_fullArchi['Supplier'].value
# Filter df_Summary based on selected_program
if selected_program == 'All':
filtered_df_summary = df_Summary.copy() # Make a copy of the entire DataFrame
else:
filtered_df_summary = df_Summary[df_Summary['Program'] == selected_program]
# Apply additional filters based on selected_priority and selected_indice
if selected_priority != 'All':
filtered_df_summary = filtered_df_summary[filtered_df_summary['Priority'] == selected_priority]
if selected_indice != 'All':
filtered_df_summary = filtered_df_summary[filtered_df_summary['Pty Indice'] == selected_indice]
# Apply filters from the new filter widgets for 'IDD Component' and 'Supplier'
if idd_component_filter: # Only apply if a filter value is provided
filtered_df_summary = filtered_df_summary[filtered_df_summary['IDD Component'].str.contains(idd_component_filter, case=False, na=False)]
if supplier_filter: # Only apply if a filter value is provided
filtered_df_summary = filtered_df_summary[filtered_df_summary['Supplier'].str.contains(supplier_filter, case=False, na=False)]
#------------- 03/06 ----------------
# Ensure the 'Critical' column is boolean
# If the column contains strings like 'TRUE' or 'FALSE', convert them to boolean values
if 'Critical' in filtered_df_summary.columns:
filtered_df_summary['Critical'] = filtered_df_summary['Critical'].astype(str).str.strip().str.upper() == 'TRUE'
# Filter out rows where 'Critical' is not True
filtered_df_summary = filtered_df_summary[filtered_df_summary['Critical']]
#----------------------------------------
# Convert 'Level' and 'Rem. Qty' to integer, handling formatting issues
filtered_df_summary['Level'] = pd.to_numeric(filtered_df_summary['Level'], errors='coerce').fillna(-1).astype(int)
filtered_df_summary['Rem. Qty'] = pd.to_numeric(filtered_df_summary['Rem. Qty'], errors='coerce').fillna(0).astype(int)
# Print unique values in 'Level' to debug
#print("Unique values in 'Level' after conversion:", filtered_df_summary['Level'].unique())
# Ensure that -1 is in color_mapping_Level or handle it
if -1 not in color_mapping_Level:
color_mapping_Level[-1] = 'background-color: #FFFFFF' # Default color for -1
# Filter out rows where 'Qty (GS/BOM)' > 'Rem. Qty'
#filtered_df_summary = filtered_df_summary[filtered_df_summary['Qty (GS/BOM)'] <= filtered_df_summary['Rem. Qty']] # 03/06 display only component where 'Qty (GS/BOM)' < 'Max Qty Top-Level'
#filtered_df_summary = filtered_df_summary[filtered_df_summary['Qty (GS/BOM)'] <= filtered_df_summary['Max Qty Top-Level']] # 03/06WIP display only component where 'Qty (GS/BOM)' < 'Max Qty Top-Level'
# Exclude rows where 'Level' == 0 # 03/06
filtered_df_summary = filtered_df_summary[filtered_df_summary['Level'] != 0]
# Sort by 'Pty Indice' and 'BOM Index'
#filtered_df_summary = filtered_df_summary.sort_values(by=['Pty Indice', 'BOM Index']) # saved 02/03
#filtered_df_summary = filtered_df_summary.sort_values(by=['Pty Indice', 'BOM_Index'])
filtered_df_summary = filtered_df_summary.sort_values(by=['BOM_Index']) # 03/06
# Check if the filtered DataFrame is empty # 02/26
if filtered_df_summary.empty:
supply_table_fullArchi.object = pd.DataFrame({
'Pty Indice': ['No Data'],
'IDD Component': [''],
'Level': [''],
'Description': [''],
'Qty (GS/BOM)': [''],
'Supplier': [''],
#'Top Level sharing Components': [''],
#'Comment': [''],
#'Qty On Hand': [''],
'Max Qty Top-Level': [''], # 03/06
'Rem. Qty': ['']
})
message_pane_fullArchi.object = 'No data available' # Display a message indicating no data
else:
#supply_table_df = filtered_df_summary[['Pty Indice', 'IDD Component', 'Level', 'Description', 'Qty (GS/BOM)', 'Supplier', 'Top Level sharing Components', 'Comment', 'Qty On Hand', 'Rem. Qty']]
supply_table_df = filtered_df_summary[['Pty Indice', 'IDD Component', 'Level', 'Description', 'Qty (GS/BOM)', 'Supplier', 'Max Qty Top-Level', 'Rem. Qty']] # 03/06
# Apply color formatting to 'Level' column
styles = apply_color_formatting(supply_table_df)
# Update the supply_table with styled DataFrame
supply_table_fullArchi.object = supply_table_df.style.apply(lambda x: styles.loc[x.name], axis=1).hide(axis='index')
message_pane_fullArchi.object = "" # Clear the message
#### New 09/16 ####
# Define callback function for the button
def on_filter_button_click(event):
update_supply_table_fullArchi(event)
# Define callback function for the Reset Filters button
def on_reset_button_click(event):
# Reset filter values
for widget in filters_fullArchi.values():
widget.value = ""
# Update table with no filters applied
update_supply_table_fullArchi(event)
# Link the buttons to their respective update functions
filters_fullArchi_button.on_click(on_filter_button_click)
reset_fullArchi_button.on_click(on_reset_button_click)
####################
# Initialize the supply_table pane
message_pane_title = pn.pane.Str("▷ List of components, full architecture (<b>Make Part & Purchased parts</b>) to reach the critical quantity:", sizing_mode='stretch_width')
supply_table_fullArchi = pn.pane.DataFrame(pd.DataFrame(), sizing_mode='stretch_width')
message_pane_fullArchi = pn.pane.Markdown("", sizing_mode='stretch_width')
def on_widget_change_supply_fullArchi(event):
update_supply_table_fullArchi(event)
# Initial call to populate the table based on default selections
update_supply_table_fullArchi(None)
###########################################
# Create a widget for supply_selected_top
###########################################
# Create a scrollable pane for displaying text
#supply_selected_top = pn.pane.Str(example_text, height_policy='max', max_height=400, sizing_mode='stretch_width')
# Create a pane for displaying dynamic text (supply_selected_top)
supply_selected_top = pn.pane.Str(sizing_mode='stretch_width')
def update_supply_selected_top(event):
selected_program = program_widget.value
selected_priority = priority_widget.value
selected_indice = indice_widget.value
# Initialize a boolean mask with all True values
mask = pd.Series(True, index=df_Snapshot.index)
# Apply filters based on selections
if selected_program != 'All':
mask &= (df_Snapshot['Program'] == selected_program)
if selected_priority != 'All':
mask &= (df_Snapshot['Priority'] == selected_priority)
if selected_indice != 'All':
mask &= (df_Snapshot['Pty Indice'] == selected_indice)
# Filter df_Snapshot using the constructed mask
filtered_df_snapshot = df_Snapshot[mask]
# Check if filtered_df_snapshot is empty or not
if filtered_df_snapshot.empty:
# Handle empty DataFrame scenario, e.g., display a message or return early
supply_selected_top.object = 'No data available'
else:
# Initialize an empty list to store formatted strings
lines = []
# Merge with df_Summary to get 'Qty (GS/BOM)' to get 'Qty (GS/BOM)'
merged_df = filtered_df_snapshot.merge(df_Summary[['Pty Indice', 'Qty (GS/BOM)', 'Rem. Qty']], on='Pty Indice', how='left')
# Ensure 'Rem. Qty' column exists and convert it to integers - 08/14
#merged_df['Rem. Qty'] = merged_df['Rem. Qty'].astype(int)
# Handle missing and infinite values in 'Rem. Qty' - Update 09/03
merged_df['Rem. Qty'] = merged_df['Rem. Qty'].replace([float('inf'), -float('inf')], 0)
merged_df['Rem. Qty'] = merged_df['Rem. Qty'].fillna(0)
# Convert the column to integer
merged_df['Rem. Qty'] = merged_df['Rem. Qty'].astype(int)
# Calculate the minimum value from 'Qty (GS/BOM)' in the merged DataFrame
min_qty_gs = merged_df['Qty (GS/BOM)'].min()
# Drop duplicate rows based on 'Pty Indice'
merged_df = merged_df.drop_duplicates(subset=['Pty Indice'])
# Function to determine the color based on 'Top-Level Status' - Update 09/16 with Top-Level status 'Completed - No Backlog'
def get_status_color(status):
if status == 'Clear-to-Build':
return 'green'
elif status == 'Short':
return 'red'
else:
return 'black'
# Iterate over each row in merged_df
for idx, row in merged_df.iterrows():
status_color = get_status_color(row['Top-Level Status'])
# Format the line for display with color coding
line = (
#f"<u>Pty Indice</u>: <b>{row['Pty Indice']}</b> - <b>{row['IDD Top Level']}</b> ({row['SEDA Top Level']})<br>"
f"<u>Pty Indice</u>: <span style='color:{important_text_color};'><b>{row['Pty Indice']}</b> - <b>{row['IDD Top Level']}</b></span> ({row['SEDA Top Level']})<br>"
f"▷ Top Level Status: <b style='color:{status_color};'>{row['Top-Level Status']}</b><br>"
f"▷ Qty of {row['Pty Indice']} Top-Level clear to build based on Purchased Part: <b>{row['Qty clear to build']}</b><br>"
f"▷ Qty of {row['Pty Indice']} Top-Level clear to be released based on Make Part: <b>{min_qty_gs}</b><br>"
f"▷ List of components (<b>Purchased only</b>) missing at IDD to reach the critical quantity (<b>{row['Rem. Qty']}</b>) of {row['Pty Indice']}: <br>"
)
# Append the formatted line to lines list
lines.append(line)
# Join all lines into a single string with double newlines between entries
display_text = '\n'.join(lines)
# Update supply_selected_top with the formatted display_text
supply_selected_top.object = display_text
# Define an initial call to populate the table when the app starts
update_supply_selected_top(None)
#############################################################################################
# Initial call to update_widgets_and_table to populate the table based on default selections
#############################################################################################
# Define supply dashboard
supply_dashboard = pn.Column(
pn.pane.HTML(f"""
<div style="text-align: left;">
<style>
h2 {{ margin-bottom: 0; color: #305496; }} /* Set title color here */
p {{ margin-top: 0; }}
</style>
<h2>Supply Chain</h2>
<p>{f"|Summary| - <b>{file_date}</b>: IDD's inventory status based on QAD (ERP) | [Daily update]"}</p>
</div>
"""),
supply_selected_top,
supply_table,
pn.Spacer(height=20),
pn.Column(message_pane_title, filter_widgets_fullArchi, supply_table_fullArchi), # Encapsulate title and table
sizing_mode='stretch_width', # Adjust sizing mode
#height=600 # Set a fixed height to enforce the maximum height
)
##############################################################################################################################
# --->>>> PRODUCTION <<<---
##############################################################################################################################
# Apply mapping to create 'Program' column in df_WIP
df_WIP['Program'] = df_WIP['Pty Indice'].map(indice_to_program)
# Convert Priority in df_WIP to numeric, coercing errors to NaN
df_WIP['Priority'] = pd.to_numeric(df_WIP['Priority'], errors='coerce')
###########################################
# Create a widget for wip_selected_top
###########################################
# Create a pane for displaying dynamic text (supply_selected_top)
#wip_selected_top = pn.pane.Markdown(sizing_mode='stretch_width') -- text style is different
# Create a pane for displaying dynamic text (supply_selected_top)
wip_selected_top = pn.pane.Str(sizing_mode='stretch_width')
# Define constants for sizing
default_max_height = 200 # Max height for the text pane
max_table_height = 300 # Max height for the table
total_height = 600 # Total height for the layout
row_height = 20 # Adjust row height as needed
# Create a pane for displaying dynamic text with scrolling
#wip_selected_top = pn.pane.Markdown('', sizing_mode='stretch_width')
###########################################
# Create a function for wip_selected_top
###########################################
# Function to split long text into multiple lines
def split_long_text(text, max_length):
words = text.split(', ')
lines = []
current_line = ''
for word in words:
if len(current_line) + len(word) + 2 > max_length: # +2 for ", "
lines.append(current_line)
current_line = word
else:
if current_line:
current_line += ', '
current_line += word
lines.append(current_line)
return '\n'.join(lines)
def update_wip_selected_top(event):
selected_program = program_widget.value
selected_priority = priority_widget.value
selected_indice = indice_widget.value
# Convert 'Priority' and 'Level' to numeric, coercing errors to NaN
df_WIP['Priority'] = pd.to_numeric(df_WIP['Priority'], errors='coerce')
df_WIP['Level'] = pd.to_numeric(df_WIP['Level'], errors='coerce')
# Apply filters
mask = pd.Series(True, index=df_WIP.index)
if selected_program != 'All':
mask &= (df_WIP['Program'] == selected_program)
if selected_priority != 'All':
mask &= (df_WIP['Priority'] == selected_priority)
if selected_indice != 'All':
mask &= (df_WIP['Pty Indice'] == selected_indice)
# Create a copy of the filtered DataFrame to avoid SettingWithCopyWarning
filtered_df_wip = df_WIP[mask].copy()
# Check if any data is available
if filtered_df_wip.empty:
wip_selected_top.object = 'No data available for the selected filters.'
else:
# Forward fill missing values using .loc
filtered_df_wip['WO'] = filtered_df_wip['WO'].fillna(method='ffill')
# Use .loc for forward filling within groups
change_indices = filtered_df_wip.index.to_series().diff().ne(0).cumsum()
filtered_df_wip['WO'] = filtered_df_wip.groupby(change_indices)['WO'].transform(lambda x: x.ffill())
lines = []
filtered_df_wip = filtered_df_wip.drop_duplicates(subset=['Pty Indice', 'WO'])
# Handle cases where 'qty_top_level' or 'qty_sub_level' might be NaN
qty_top_level = filtered_df_wip[filtered_df_wip['Level'] == 0].groupby('Pty Indice')['WO Qty'].sum()
qty_sub_level = filtered_df_wip[filtered_df_wip['Level'] > 0].groupby('Pty Indice')['WO Qty'].sum()
summary_df = pd.DataFrame({
'qty_top_level': qty_top_level,
'qty_sub_level': qty_sub_level
}).reset_index()
for _, row in summary_df.iterrows():
pty_indice = row['Pty Indice']
# Ensure the values are not NaN before converting to int
qty_top_level_value = int(row['qty_top_level']) if pd.notna(row['qty_top_level']) and str(row['qty_top_level']).isdigit() else 0
qty_sub_level_value = int(row['qty_sub_level']) if pd.notna(row['qty_sub_level']) and str(row['qty_sub_level']).isdigit() else 0
# Get details for the current 'Pty Indice'
details = filtered_df_wip[filtered_df_wip['Pty Indice'] == pty_indice].iloc[0]
# Filter work orders for top and sub levels
top_level_df = filtered_df_wip[filtered_df_wip['Level'] == 0]
sub_level_df = filtered_df_wip[filtered_df_wip['Level'] > 0]
# Get unique work orders for top level and sub level, and filter out NaNs
unique_wo_top = top_level_df[top_level_df['Pty Indice'] == pty_indice]['WO'].astype(str).unique()
unique_wo_sub = sub_level_df[sub_level_df['Pty Indice'] == pty_indice]['WO'].astype(str).unique()
# Convert arrays to lists
list_wo_top = ', '.join(unique_wo_top) if unique_wo_top.size > 0 else 'None'
list_wo_sub = ', '.join(unique_wo_sub) if unique_wo_sub.size > 0 else 'None'
# Split long text into multiple lines
list_wo_top = split_long_text(list_wo_top, max_length=120) # Adjust max_length as needed
list_wo_sub = split_long_text(list_wo_sub, max_length=120) # Adjust max_length as needed
# Calculate the number of unique work orders and components
unique_wo_qty_top = len(unique_wo_top)
unique_wo_qty_sub = len(unique_wo_sub)
unique_sub_PN = sub_level_df[sub_level_df['Pty Indice'] == pty_indice]['IDD Component'].nunique()
# Construct the line for output based on availability
top_level_info = (
f"▷ Quantity <b><span style='color:{important_text_color};'>Top-Level</span></b> {pty_indice} on the floor: <b>{qty_top_level_value}</b> Top-Level within <b>{unique_wo_qty_top}</b> WO:<br> {list_wo_top}"
if qty_top_level_value > 0 else f"▷ Quantity {pty_indice} Top Level on the floor: No Top-Level on the floor"
)
sub_level_info = (
f"▷ Quantity of {pty_indice}'s related <b><span style='color:{important_text_color};'>Sub-Level</span></b> on the floor</b>: Total of <b>{qty_sub_level_value}</b> Sub-Level, including <b>{unique_sub_PN}</b> unique components within <b>{unique_wo_qty_sub}</b> WO:\n{list_wo_sub}"
if qty_sub_level_value > 0 else f"▷ Quantity of related {pty_indice} sub-Level on the floor: No Sub-Level on the floor"
)
line = (
#f"<u>Pty Indice</u>: <b>{pty_indice}</b> - <b>{details['IDD Top Level']}</b> ({details['SEDA Top Level']})<br>"
f"<u>Pty Indice</u>: <span style='color:{important_text_color};'><b>{pty_indice}</b> - <b>{details['IDD Top Level']}</b></span> ({details['SEDA Top Level']})<br>"
f"{top_level_info}<br>"
f"{sub_level_info}<br>"
)
lines.append(line)
display_text = '\n'.join(lines)
wip_selected_top.object = display_text
# Define an initial call to populate the table when the app starts
update_wip_selected_top(None)
#########################################################
# Define filtering widgets for filters_Prod
#########################################################
label_wo = pn.pane.HTML('<b style="color:#2B70B3;">WO Filter</b>')
label_idd_component = pn.pane.HTML('<b style="color:#2B70B3;">IDD Component Filter</b>')
filters_Prod = {
'WO': pn.widgets.TextInput(name=''),
'IDD Component': pn.widgets.TextInput(name=''),
}
# Create buttons for applying and resetting filters
filters_Prod_button = pn.widgets.Button(name='Apply Filters', button_type='primary')
reset_Prod_button = pn.widgets.Button(name='Reset Filters', button_type='default')
# Set default value to None for all filter widgets
for widget in filters_Prod.values():
widget.value = ''
# Create the layout with labels, filter widgets, and buttons
filter_widgets_Prod = pn.Row(
pn.Column(label_wo, filters_Prod['WO']),
pn.Column(label_idd_component, filters_Prod['IDD Component']),
pn.Column(
pn.Spacer(height=25), # Spacer before the buttons
pn.Row(filters_Prod_button, reset_Prod_button)
)
)
############################################
# Create a function for wip_table
############################################
# Update 04/18
def update_wip_table():
selected_program = program_widget.value
selected_priority = priority_widget.value
selected_indice = indice_widget.value
wo_filter = filters_Prod['WO'].value
idd_component_filter = filters_Prod['IDD Component'].value
# Apply filters to df_WIP
mask = pd.Series(True, index=df_WIP.index)
if selected_program != 'All':
mask &= (df_WIP['Program'] == selected_program)
if selected_priority != 'All':
mask &= (df_WIP['Priority'] == selected_priority)
if selected_indice != 'All':
mask &= (df_WIP['Pty Indice'] == selected_indice)
if wo_filter:
mask &= (df_WIP['WO'].str.contains(wo_filter, case=False, na=False))
if idd_component_filter:
mask &= (df_WIP['IDD Component'].str.contains(idd_component_filter, case=False, na=False))
filtered_df_wip = df_WIP.loc[mask].copy()
if filtered_df_wip.empty:
# Display a placeholder message if no data is available
wip_table.object = pd.DataFrame({
'Pty Indice': ['No Data'],
'WO': [''],
'WO Qty': [''],
'Last movement': [''],
'Area': [''],
'IDD Component': [''],
'Level': [''],
'Description Component': [''],
'Release': [''],
'Site': [''], # 04/18
'BOM Index': ['']
})
message_pane.object = 'No data available'
else:
# Date processing
filtered_df_wip['Last movement'] = pd.to_datetime(filtered_df_wip['Last movement'], errors='coerce')
filtered_df_wip['Release'] = pd.to_datetime(filtered_df_wip['Release'], errors='coerce')
filtered_df_wip['Last movement'] = filtered_df_wip['Last movement'].fillna(pd.NaT)
filtered_df_wip['Release'] = filtered_df_wip['Release'].fillna(pd.NaT)
#------------------------ 04/18 ----------------------------------------
#Remove rows where 'Last movement' is NA
#filtered_df_wip = filtered_df_wip.dropna(subset=['Last movement'])
# Ensure there are valid rows to process
if filtered_df_wip.empty:
print("No valid data to display in WIP table.")
return # Or update the UI to show an empty table
#--------------------------- 04/21 ---------------------------------------
# Change 'Site' == 100 with 'RED' and 150 with 'CUU'
filtered_df_wip['Site'] = filtered_df_wip['Site'].replace({100: 'RED', 150: 'CUU'})
#-------------------------------------------------------------------------
# Group by 'WO' and select most recent 'Last movement'
def select_most_recent(group):
if group['Last movement'].notna().any():
return group.loc[group['Last movement'].idxmax()]
else:
return group.iloc[0] # fallback: return the first row if all are NaT
filtered_df_wip = filtered_df_wip.groupby('WO').apply(select_most_recent).reset_index(drop=True)
filtered_df_wip = filtered_df_wip.sort_values(by='Release')
# Format dates: convert NaT to empty string for display
filtered_df_wip['Last movement'] = filtered_df_wip['Last movement'].apply(
lambda x: x.strftime('%m-%d-%Y') if pd.notna(x) else ''
)
filtered_df_wip['Release'] = filtered_df_wip['Release'].apply(
lambda x: x.strftime('%m-%d-%Y') if pd.notna(x) else ''
)
# Sort and clean DataFrame
filtered_df_wip = filtered_df_wip[['Pty Indice', 'WO', 'WO Qty', 'Last movement', 'Area', 'IDD Component', 'Level', 'Description Component', 'Release', 'Site', 'BOM Index']]
filtered_df_wip['Level'] = filtered_df_wip['Level'].fillna(-1)
filtered_df_wip = filtered_df_wip.sort_values(by='BOM Index') # Sort only by BOM Index
filtered_df_wip = filtered_df_wip.drop(columns=['BOM Index'])
# Apply color formatting
styles = apply_color_formatting(filtered_df_wip) # Ensure this function returns a valid DataFrame
styled_df = filtered_df_wip.style.apply(lambda x: styles.loc[x.name], axis=1).hide(axis='index')
wip_table.object = styled_df
message_pane.object = ""
# Initialize the wip_table pane with an empty DataFrame
wip_table = pn.pane.DataFrame(
pd.DataFrame(columns=df_WIP.columns),
sizing_mode='stretch_width',
height=500
)
message_pane = pn.pane.Markdown("", sizing_mode='stretch_width')
# Initial call to populate the table based on default selections
update_wip_table()
##########################################################
# Define callback function for the Apply Filters button
###########################################################
# Define callback functions for the buttons
def on_filter_button_click_Prod(event):
update_wip_table()
def on_reset_button_click_prod(event):
for widget in filters_Prod.values():
widget.value = ""
update_wip_table()
filters_Prod_button.on_click(on_filter_button_click_Prod)
reset_Prod_button.on_click(on_reset_button_click_prod)
# Set up callbacks for all widgets
def widget_change_prod(event):
update_wip_table()
##########################################################
#******#########################*******########################*********************************
#########################################################################################################################
# Create Graph 13bis - Combinaison (side by side) of Graph 13 and 13b from the tab |General Overview|
#******#########################*******########################*******************************##########################
def create_placeholder_plot(message):
# Create an empty figure
p = figure(height=250, width=400, title=message)
p.xaxis.visible = False
p.yaxis.visible = False
p.grid.visible = False
p.add_layout(Title(text=message, align='center', text_font_size='10pt', text_color="#002570"), 'above')
return p
########################################################################################################
#Copying pivot table for graphs 13bis and 13bbis and sort by Pty Indice to get PXA before PXB etc.
pivot_table_combined_2 = pivot_table_combined.copy()
#print('pivot_table_combined_2')
#display(pivot_table_combined_2)
# Sort pivot_table_combined_2 by 'Pty Indice'
pivot_table_combined_2 = pivot_table_combined_2.sort_values(by='Pty Indice')
#print('pivot_table_combined_2')
#display(pivot_table_combined_2)
# Mapping on pivot_table_combined to get program
pivot_table_combined_2['Program'] = pivot_table_combined_2['Pty Indice'].map(indice_to_program)
###################################################
# Palette for color of bars and y labels - Order does not matter
###################################################
custom_palette13bis = {"Standard Order":"#A08EBC",
"DPAS Order": "#E4DFEC",
"Qty clear to build": "#7FDB91",
"Qty WIP": "#DAEEF3"}
custom_palette13bbis = {"Total Critical Qty": "#FFA07A",
"Qty Shipped": "#5AB2CA",
"Remain. crit. Qty": "#778899",
"IDD Backlog Qty": "#cdbedd"}
#//////////////////////////////////////////////////
###################################################
# create_plot_13bis
###################################################
def create_plot_13bis():
selected_program = program_widget.value
selected_priority = priority_widget.value
selected_indice = indice_widget.value
mask = pd.Series(True, index=pivot_table_combined_2.index)
if selected_program != 'All':
mask &= (pivot_table_combined_2['Program'] == selected_program)
if selected_priority != 'All':
mask &= (pivot_table_combined_2['Priority'] == selected_priority)
if selected_indice != 'All':
mask &= (pivot_table_combined_2['Pty Indice'] == selected_indice)
filtered_df = pivot_table_combined_2.loc[mask].copy()
#---------- 03/07 - Handled 'No data' in 'Qty clear to build' -----------------------------------------
# Replace 'No data' with NaN in the 'Qty clear to build' column
filtered_df['Qty clear to build'] = filtered_df['Qty clear to build'].replace('No data', np.nan)
# Convert 'Qty clear to build' to numeric (if not already done)
filtered_df['Qty clear to build'] = pd.to_numeric(filtered_df['Qty clear to build'], errors='coerce')
# Optionally, fill NaN with 0 (or another value)
filtered_df['Qty clear to build'] = filtered_df['Qty clear to build'].fillna(0)
#--------------------------------------------------------------------------------------------------------
#####################################################################
# Dynamic y bounds for create_plot_13bis
#####################################################################
if filtered_df.empty:
return create_placeholder_plot('No data available for the selected filters.')
max_quantity_value13bis = filtered_df[['Qty WIP', 'DPAS Order', 'Standard Order' , 'Qty clear to build']].max().max()
min_quantity_value13bis = filtered_df[['Qty WIP', 'DPAS Order', 'Standard Order', 'Qty clear to build']].min().min()
max_y_bound13bis = max_quantity_value13bis * 2
min_y_bound13bis = min_quantity_value13bis * 2 if min_quantity_value13bis < 0 else 0
#Keep order of pivot_table_combined: 'Standard Order', 'DPAS Order', 'Qty WIP', 'Qty clear to build'
# Melt the dataframe --> Reverse order of the bars on the graph
melted_df = filtered_df.melt(id_vars=['Pty Indice'],
value_vars=['Qty WIP', 'Qty clear to build', 'Standard Order', 'DPAS Order'],
var_name='Quantity Type', value_name='Quantity Value')
#############################################
# Set Order of melted_df --> Order of y label
#############################################
# Define the order of categories for 'Quantity Type'
unique_quantity_types = ['DPAS Order', 'Standard Order', 'Qty clear to build', 'Qty WIP']
# Convert 'Quantity Type' to a categorical type with the defined order
melted_df['Quantity Type'] = pd.Categorical(melted_df['Quantity Type'], categories=unique_quantity_types, ordered=True)
##################################################
# Define unique indices and calculate x_combined
##################################################
# New code 08/07
# Define constants
unique_indices = melted_df['Pty Indice'].astype('category').cat.categories
unique_quantity_types = melted_df['Quantity Type'].astype('category').cat.categories
num_types = len(unique_quantity_types) # Number of bars per 'Pty Indice'
num_indice = len(unique_indices) # Number of selected 'Pty Indice'
# Generate base_positions based on enumerate(unique_indices)
base_positions = {indice: i * (num_indice + 1) for i, indice in enumerate(unique_indices)}
# Define gaps -- The gap is supposed to change based on the number of num_indice
def get_gap(num_indice):
# Define a mapping of num_indice to gap values
gap_map = {
3: 0.75,
4: 0.5,
5: 0.33, # Example value, adjust as needed
6: 0.25, # Example value, adjust as needed
8: 0.175, # Example value, adjust as needed
9: 0.125 # Example value, adjust as needed
}
# Return the gap based on the number of indices
return gap_map.get(num_indice, 0.5) # Default to 0.5 if num_indice is not found
# Get Gap
gap = get_gap(num_indice)
# Create a mapping of 'Pty Indice' to its index, starting from 0
indice_mapping = {indice: i for i, indice in enumerate(unique_indices)}
# Calculate x_combined for the bar positions
def calculate_x_combined(row):
pty_indice = row['Pty Indice']
quantity_type_code = melted_df['Quantity Type'].cat.codes[row.name]
# Get the index of the current 'Pty Indice'
indice = indice_mapping[pty_indice]
# Calculate x_combined
x_combined = (base_positions[pty_indice]
+ quantity_type_code
+ 1 / (num_indice + 1) # Small offset to separate bars
+ gap * indice) # Adjust for the gap
# Optionally, print debug information
# print(f"Pty Indice: {pty_indice}, Quantity Type Code: {quantity_type_code}, base position: {base_positions[pty_indice]}, x_combined: {x_combined}")
return x_combined
# Apply the function to calculate x_combined
melted_df['x_combined'] = melted_df.apply(calculate_x_combined, axis=1)
# To inspect the result
#print(melted_df[['Pty Indice', 'Quantity Type', 'x_combined']])
###################
# Create the plot
##################
plot = melted_df.hvplot.bar(
x='Pty Indice',
y='Quantity Value',
by='Quantity Type',
color='Quantity Type',
cmap=custom_palette13bis,
#title='IDD Type of order (DPAS/Standard), Qty Clear-to-Build & Qty WIP per Pty Indice',
xlabel='Pty Indice',
ylabel='Top-Level [Quantity]',
legend='top_right',
stacked=False,
bar_width=0.6, # Set bar width - 09/12
tools=[],
).opts(
xrotation=90,
)
updated_bokeh_plot = hv.render(plot, backend='bokeh')
updated_bokeh_plot.tools = [tool for tool in updated_bokeh_plot.tools if not isinstance(tool, HoverTool)]
hover = HoverTool()
hover.tooltips = [
("Pty Indice", "@Pty_Indice"),
("KPI", "@color"),
("Value", "@Quantity_Value"),
]
updated_bokeh_plot.add_tools(hover)
# 09/12 - Set wheel woom inactive
updated_bokeh_plot.toolbar.active_scroll = None
# Customizations
updated_bokeh_plot.xaxis.major_label_text_font_size = '0pt'
updated_bokeh_plot.yaxis.major_label_text_font_size = '10pt'
#updated_bokeh_plot.title.text_font_size = '8pt'
#updated_bokeh_plot.title.text_color = "#002570"
updated_bokeh_plot.xaxis.axis_line_width = 2
updated_bokeh_plot.yaxis.axis_line_width = 2
updated_bokeh_plot.xaxis.major_label_orientation = 'vertical'
updated_bokeh_plot.yaxis.major_label_orientation = 'horizontal'
updated_bokeh_plot.yaxis.axis_label_text_font_size = '10pt'
updated_bokeh_plot.xgrid.grid_line_color = None
updated_bokeh_plot.ygrid.grid_line_color = '#F2F2F2'
updated_bokeh_plot.ygrid.grid_line_dash = [6, 4]
updated_bokeh_plot.y_range = Range1d(start=min_y_bound13bis, end=max_y_bound13bis)
updated_bokeh_plot.toolbar.logo = None
updated_bokeh_plot.legend.label_text_font_size = '8pt'
# Add custom formatted title
updated_bokeh_plot.add_layout(Title(
text="Backlog KPI#1",
align='center',
text_font_size='10pt', # Adjust font size for the title
text_color="#002570" # Adjust color for the title
), 'above')
# Add labels on top of the bars
source = ColumnDataSource(melted_df)
labels = LabelSet(
x= 'x_combined',
#x= 'Quantity Type',
y='Quantity Value',
text='Quantity Value',
level='glyph',
source=source,
text_font_size='8pt',
text_font_style='bold', # Set the font style to bold
text_align='center',
text_baseline='bottom', # Place labels above the bars
y_offset= 5, # Dynamically set the offset based on Quantity Value,
text_color={'field': 'Quantity Type', 'transform': CategoricalColorMapper(
factors=unique_quantity_types, palette=[custom_palette13bis[qtype] for qtype in unique_quantity_types]
)}
)
updated_bokeh_plot.add_layout(labels)
# Debugging: Output the calculated x_combined and other columns
#print("Values in melted_df with x_combined:")
#print(melted_df[['Pty Indice', 'Quantity Type', 'Quantity Value', 'x_combined']])
return updated_bokeh_plot
#//////////////////////////////////////////////////
###################################################
# create_plot_13bbis
###################################################
def create_plot_13bbis():
selected_program = program_widget.value
selected_priority = priority_widget.value
selected_indice = indice_widget.value
mask = pd.Series(True, index=pivot_table_combined_2.index)
if selected_program != 'All':
mask &= (pivot_table_combined_2['Program'] == selected_program)
if selected_priority != 'All':
mask &= (pivot_table_combined_2['Priority'] == selected_priority)
if selected_indice != 'All':
mask &= (pivot_table_combined_2['Pty Indice'] == selected_indice)
filtered_df = pivot_table_combined_2.loc[mask].copy()
#####################################################################
# Dynamic y bounds for create_plot_13bbis
#####################################################################
if filtered_df.empty:
return create_placeholder_plot('No data available for the selected filters.')
max_quantity_value13bbis = filtered_df[['Total Critical Qty', 'Qty Shipped', 'Remain. crit. Qty', 'IDD Backlog Qty']].max().max()
min_quantity_value13bbis = filtered_df[['Total Critical Qty', 'Qty Shipped', 'Remain. crit. Qty', 'IDD Backlog Qty']].min().min()
max_y_bound13bbis = max_quantity_value13bbis * 2
min_y_bound13bbis = min_quantity_value13bbis * 2 if min_quantity_value13bbis < 0 else 0
# keep order of pivot_table_combined: 'Total Critical Qty', 'Qty Shipped', 'Remain. crit. Qty', 'IDD Backlog Qty'
melted_df = filtered_df.melt(id_vars=['Pty Indice'],
value_vars=['Total Critical Qty', 'Qty Shipped', 'Remain. crit. Qty', 'IDD Backlog Qty'],
var_name='Quantity Type', value_name='Quantity Value')
#############################################
# Set Order of melted_df --> Order of y label
#############################################
# Define the order of categories for 'Quantity Type'
unique_quantity_types = ['IDD Backlog Qty', 'Remain. crit. Qty', 'Qty Shipped', 'Total Critical Qty']
# Convert 'Quantity Type' to a categorical type with the defined order
melted_df['Quantity Type'] = pd.Categorical(melted_df['Quantity Type'], categories=unique_quantity_types, ordered=True)
####################################################
# Define unique indices and calculate x_combined
##################################################
# New code 08/07
# Define constants
unique_indices = melted_df['Pty Indice'].astype('category').cat.categories
unique_quantity_types = melted_df['Quantity Type'].astype('category').cat.categories
num_types = len(unique_quantity_types) # Number of bars per 'Pty Indice'
num_indice = len(unique_indices) # Number of selected 'Pty Indice'
# Generate base_positions based on enumerate(unique_indices)
base_positions = {indice: i * (num_indice + 1) for i, indice in enumerate(unique_indices)}
#define gaps -- The gap is suppose to change based on the number of num_indice (for num_indice = 4 -- gap = 0.5 works well)
#gap = 0.5
gap = 1/(num_indice/2)
# Create a mapping of 'Pty Indice' to its index, starting from 0
indice_mapping = {indice: i for i, indice in enumerate(unique_indices)}
# Calculate x_combined for the bar positions
def calculate_x_combined(row):
pty_indice = row['Pty Indice']
quantity_type_code = melted_df['Quantity Type'].cat.codes[row.name]
# Get the index of the current 'Pty Indice'
indice = indice_mapping[pty_indice]
x_combined = base_positions[pty_indice] + quantity_type_code + 1/(num_indice + 1) + gap*indice #Calculation of the gap is yet to be refined as it does not work for all cases when Pty indice > 4
return x_combined
# Apply the function to calculate x_combined
melted_df['x_combined'] = melted_df.apply(calculate_x_combined, axis=1)
#####################################################
plot = melted_df.hvplot.bar(
x='Pty Indice',
y='Quantity Value',
by='Quantity Type',
color='Quantity Type',
cmap=custom_palette13bbis,
#title='<div style="text-align: center;">IDD Total Backlog, Total Critical Quantity,<br> Qty Shipped & Remaining Critical Qty per Pty Indice</div>',
xlabel='Pty Indice',
ylabel='Top-Level [Quantity]',
legend='top_right',
stacked=False,
bar_width=0.6, # Set bar width - 09/12
tools=[],
).opts(
xrotation=90,
)
updated_bokeh_plot = hv.render(plot, backend='bokeh')
updated_bokeh_plot.tools = [tool for tool in updated_bokeh_plot.tools if not isinstance(tool, HoverTool)]
hover = HoverTool()
hover.tooltips = [
("Pty Indice", "@Pty_Indice"),
("KPI", "@color"),
("Value", "@Quantity_Value"),
]
updated_bokeh_plot.add_tools(hover)
# 09/12 - Set wheel woom inactive
updated_bokeh_plot.toolbar.active_scroll = None
updated_bokeh_plot.xaxis.major_label_text_font_size = '0pt'
updated_bokeh_plot.yaxis.major_label_text_font_size = '10pt'
#updated_bokeh_plot.title.text_font_size = '8pt'
#updated_bokeh_plot.title.text_color = "#002570"
updated_bokeh_plot.xaxis.axis_line_width = 2
updated_bokeh_plot.yaxis.axis_line_width = 2
updated_bokeh_plot.xaxis.major_label_orientation = 'vertical'
updated_bokeh_plot.yaxis.major_label_orientation = 'horizontal'
updated_bokeh_plot.yaxis.axis_label_text_font_size = '10pt'
updated_bokeh_plot.xgrid.grid_line_color = None
updated_bokeh_plot.ygrid.grid_line_color = '#F2F2F2'
updated_bokeh_plot.ygrid.grid_line_dash = [6, 4]
updated_bokeh_plot.y_range = Range1d(start=min_y_bound13bbis, end=max_y_bound13bbis)
updated_bokeh_plot.toolbar.logo = None
updated_bokeh_plot.legend.label_text_font_size = '8pt' # Set the font size of legend text
# Add custom formatted title
updated_bokeh_plot.add_layout(Title(
text="Backlog KPI#2",
align='center',
text_font_size='10pt', # Adjust font size for the title
text_color="#002570" # Adjust color for the title
), 'above')
# Add labels on top of the bars
source = ColumnDataSource(melted_df)
labels = LabelSet(
x= 'x_combined',
#x= 'Quantity Type',
y='Quantity Value',
text='Quantity Value',
level='glyph',
source=source,
text_font_size='8pt',
text_font_style='bold', # Set the font style to bold
text_align='center',
text_baseline='bottom', # Place labels above the bars
y_offset=5,
text_color={'field': 'Quantity Type', 'transform': CategoricalColorMapper(
factors=unique_quantity_types, palette=[custom_palette13bbis[qtype] for qtype in unique_quantity_types]
)}
)
updated_bokeh_plot.add_layout(labels)
return updated_bokeh_plot
#############
# Inital call
#############
plot_pane_13bis = pn.pane.Bokeh(create_plot_13bis())
plot_pane_13bbis = pn.pane.Bokeh(create_plot_13bbis())
################################################################
# Update methods to include messages when no data is available
###############################################################
def update_plot_13bis(event):
plot_pane_13bis.object = create_plot_13bis()
def update_plot_13bbis(event):
plot_pane_13bbis.object = create_plot_13bbis()
#########################################################################################################################
# Create Graph 14-14b - Combinaison (side by side) of Graph 14 and 14b from the tab |General Overview|
#******#########################*******########################*******************************##########################
#Copying pivot table for graphs 14bis and 14bbis
pivot_table_14_2 = pivot_table_14.copy()
# Sort pivot_table_combined_2 by 'Pty Indice' - Update 08/28
#pivot_table_14_2 = pivot_table_14_2.sort_values(by='Pty Indice')
pivot_table_14_2.sort_values(by=['Priority', 'Pty Indice'], inplace=True)
# Mapping on pivot_table_combined to get program
pivot_table_14_2['Program'] = pivot_table_14_2['Pty Indice'].map(indice_to_program)
custom_palette14bis = {
"IDD Expected Total Sales": "rgba(68, 114, 196, 0.8)", # #4472C4 with alpha 0.8
"IDD Expected Total Margin": "rgba(63, 201, 89, 0.5)", # #3FC959 with alpha 0.5
"IDD Current Sales (Total)": "#4472C3",
"IDD Current Margin (Total)": "#548235",
}
custom_palette14bis_2 = {
"IDD Expected Total Sales": "rgba(68, 114, 196, 0.8)", # #4472C4 with alpha 0.8
"IDD Expected Total Margin": "rgba(63, 201, 89, 0.5)", # #3FC959 with alpha 0.5
"IDD Realized Sales": "#4472C3",
"IDD Realized Margin": "#548235",
}
custom_palette14bbis = {
"IDD Current Margin (%)": "#E2EFDA",
"% Completion": "#7FDB91",
"% DPAS Order": "#E4DFEC",
"IDD Expected ROI (Total)": "#568838",
}
#//////////////////////////////////////////////////
##################################################
# Create graph 14bis and 14bbis ---> Financial KPI
# --> To be update 09/23 to use df_Historic instead of df_Snapshot to calculate the 'Realized sales' and 'Realized Margin'
# The calculation should be based on the real data from the df_Historic trunover Report including the change of price over time
# New columns introduced in df_Snapshot:
# df_snapshot['IDD AVG realized sales price [USD]']
# df_snapshot['IDD AVG realized Margin Standard [USD]']
# df_snapshot['IDD AVG realized Margin [%]']
####################################################
##### New 09/24 to replace Graph 14bis with newlly added column in df_snapshot
def create_plot_14bis_2():
selected_program = program_widget.value
selected_priority = priority_widget.value
selected_indice = indice_widget.value
mask = pd.Series(True, index=pivot_table_14_2.index)
if selected_program != 'All':
mask &= (pivot_table_14_2['Program'] == selected_program)
if selected_priority != 'All':
mask &= (pivot_table_14_2['Priority'] == selected_priority)
if selected_indice != 'All':
mask &= (pivot_table_14_2['Pty Indice'] == selected_indice)
filtered_df = pivot_table_14_2.loc[mask].copy()
#####################################################################
# Dynamic y bounds for create_plot_14bis
#####################################################################
if filtered_df.empty:
return create_placeholder_plot('No data available for the selected filters.')
max_quantity_value14bis_2 = filtered_df[['IDD Expected Total Sales', 'IDD Expected Total Margin', 'IDD Realized Sales', 'IDD Realized Margin']].max().max()
min_quantity_value14bis_2 = filtered_df[['IDD Expected Total Sales', 'IDD Expected Total Margin', 'IDD Realized Sales', 'IDD Realized Margin']].min().min()
max_y_bound14bis_2 = max_quantity_value14bis_2 * 2
min_y_bound14bis_2 = min_quantity_value14bis_2 * 2 if min_quantity_value14bis_2 < 0 else 0
melted_df = filtered_df.melt(id_vars=['Pty Indice'],
value_vars=['IDD Expected Total Sales', 'IDD Expected Total Margin', 'IDD Realized Sales', 'IDD Realized Margin'],
var_name='Quantity Type', value_name='Quantity Value')
# Add a column with formatted values in thousands with '$Xk' prefix and rounded to whole numbers
#melted_df['Formatted Value'] = melted_df['Quantity Value'].apply(lambda x: f"${x / 1000:,.0f}k")
#############################################
# Set Order of melted_df --> Order of y label
#############################################
# Define the order of categories for 'Quantity Type'
unique_quantity_types = ['IDD Realized Margin', 'IDD Realized Sales', 'IDD Expected Total Margin', 'IDD Expected Total Sales']
# Convert 'Quantity Type' to a categorical type with the defined order
melted_df['Quantity Type'] = pd.Categorical(melted_df['Quantity Type'], categories=unique_quantity_types, ordered=True)
####################################################
# Define unique indices and calculate x_combined
##################################################
# New code 08/07
# Define constants
unique_indices = melted_df['Pty Indice'].astype('category').cat.categories
unique_quantity_types = melted_df['Quantity Type'].astype('category').cat.categories
num_types = len(unique_quantity_types) # Number of bars per 'Pty Indice'
num_indice = len(unique_indices) # Number of selected 'Pty Indice'
# Generate base_positions based on enumerate(unique_indices)
base_positions = {indice: i * (num_indice + 1) for i, indice in enumerate(unique_indices)}
#define gaps -- The gap is suppose to change based on the number of num_indice (for num_indice = 4 -- gap = 0.5 works well)
#gap = 0.5
gap = 1/(num_indice/2)
# Create a mapping of 'Pty Indice' to its index, starting from 0
indice_mapping = {indice: i for i, indice in enumerate(unique_indices)}
# Calculate x_combined for the bar positions
def calculate_x_combined(row):
pty_indice = row['Pty Indice']
quantity_type_code = melted_df['Quantity Type'].cat.codes[row.name]
# Get the index of the current 'Pty Indice'
indice = indice_mapping[pty_indice]
x_combined = base_positions[pty_indice] + quantity_type_code + 1/(num_indice + 1) + gap*indice #Calculation of the gap is yet to be refined as it does not work for all cases when Pty indice > 4
return x_combined
# Apply the function to calculate x_combined
melted_df['x_combined'] = melted_df.apply(calculate_x_combined, axis=1)
#####################################################
#New 09/11 - Calculate Y position
# Calculate the y_offset dynamically based on the 'Quantity Value'
#melted_df['y_position'] = melted_df['Quantity Value'] + melted_df['Quantity Value']*0.1
# Compute the maximum value of Quantity Value
max_quantity_value = melted_df['Quantity Value'].max()
# Calculate the 5% offset of the maximum value
offset = max_quantity_value * 0.05
# Define the function to calculate y_position with the conditional offset
def calculate_y_position(quantity_value):
if quantity_value >= 0:
return quantity_value + offset
else:
return offset # Apply offset in the opposite direction for negative values
# Apply the function to the DataFrame
melted_df['y_position'] = melted_df['Quantity Value'].apply(calculate_y_position)
#print('melted_df')
#display(melted_df)
plot = melted_df.hvplot.bar(
x='Pty Indice',
y='Quantity Value',
by='Quantity Type',
color='Quantity Type',
cmap=custom_palette14bis_2,
#title='IDD Total Sales & IDD Marge per Pty Indice by Top-Level Status, Production Status & Product Category',
xlabel='Pty Indice',
ylabel='[K$]',
legend='top_right',
stacked=False,
bar_width=0.6, # Set bar width - 09/12
#padding=1,
tools=[],
).opts(
xrotation=90,
)
updated_bokeh_plot = hv.render(plot, backend='bokeh')
updated_bokeh_plot.tools = [tool for tool in updated_bokeh_plot.tools if not isinstance(tool, HoverTool)]
hover = HoverTool()
hover.tooltips = [
("Pty Indice", "@Pty_Indice"),
("KPI", "@color"),
("Quantity Value", "@Quantity_Value{($0,0k)}") # Format values: thousands with 'K' # Quantity_Value with the '_' otherwise that does not work!
]
updated_bokeh_plot.add_tools(hover)
# Remove wheel zoom from active tools if you want it inactive by default - 08/12
#updated_bokeh_plot.tools = [tool for tool in updated_bokeh_plot.tools if not isinstance(tool, WheelZoomTool)]
# 09/12 - Set wheel woom inactive
updated_bokeh_plot.toolbar.active_scroll = None
updated_bokeh_plot.xaxis.major_label_text_font_size = '0pt'
updated_bokeh_plot.yaxis.major_label_text_font_size = '10pt'
#updated_bokeh_plot.title.text_font_size = '8pt'
#updated_bokeh_plot.title.text_color = "#002570"
updated_bokeh_plot.xaxis.axis_line_width = 2
updated_bokeh_plot.yaxis.axis_line_width = 2
updated_bokeh_plot.xaxis.major_label_orientation = 'vertical'
updated_bokeh_plot.yaxis.major_label_orientation = 'horizontal'
updated_bokeh_plot.yaxis.axis_label_text_font_size = '10pt'
updated_bokeh_plot.xgrid.grid_line_color = None
updated_bokeh_plot.ygrid.grid_line_color = '#F2F2F2'
updated_bokeh_plot.ygrid.grid_line_dash = [6, 4]
updated_bokeh_plot.y_range = Range1d(start=min_y_bound14bis_2, end=max_y_bound14bis_2)
updated_bokeh_plot.toolbar.logo = None
updated_bokeh_plot.legend.label_text_font_size = '8pt' # Set the font size of legend text
# Add custom formatted title
updated_bokeh_plot.add_layout(Title(
text="Financial KPI",
align='center',
text_font_size='10pt', # Adjust font size for the title
text_color="#002570" # Adjust color for the title
), 'above')
# Format the y-axis ticks in thousands with a dollar sign
updated_bokeh_plot.yaxis.formatter =CustomJSTickFormatter(code="""
return '$' + (tick / 1000).toFixed(0) + 'k';
""")
#Format the y-label to display on the graph
melted_df['formatted_labels'] = melted_df['Quantity Value'].apply(lambda x: f"${x / 1000:.0f}k")
# Add labels on top of the bars
source = ColumnDataSource(melted_df)
labels = LabelSet(
x= 'x_combined',
y='y_position',
#x= 'Quantity Type',
#y='Quantity Value',
#text='Quantity Value',
text='formatted_labels',
level='glyph',
source=source,
text_font_size='8pt',
text_font_style='bold', # Set the font style to bold
text_align='center',
#text_baseline='bottom', # Place labels above the bars #09/11
#y_offset=5, #09/11
text_color={'field': 'Quantity Type', 'transform': CategoricalColorMapper(
factors=unique_quantity_types, palette=[custom_palette14bis_2[qtype] for qtype in unique_quantity_types]
)}
)
updated_bokeh_plot.add_layout(labels)
return updated_bokeh_plot
#//////////////////////////////////////////////////
###################################################
# create_plot_14bbis
###################################################
def create_plot_14bbis():
selected_program = program_widget.value
selected_priority = priority_widget.value
selected_indice = indice_widget.value
mask = pd.Series(True, index=pivot_table_14_2.index)
if selected_program != 'All':
mask &= (pivot_table_14_2['Program'] == selected_program)
if selected_priority != 'All':
mask &= (pivot_table_14_2['Priority'] == selected_priority)
if selected_indice != 'All':
mask &= (pivot_table_14_2['Pty Indice'] == selected_indice)
filtered_df = pivot_table_14_2.loc[mask].copy()
#####################################################################
# Dynamic y bounds for create_plot_14bbis
#####################################################################
if filtered_df.empty:
return create_placeholder_plot('No data available for the selected filters.')
max_margin_value14bbis = filtered_df[['IDD Current Margin (%)', '% Completion', '% DPAS Order', 'IDD Expected ROI (Total)']].max().max()
min_margin_value14bbis = filtered_df[['IDD Current Margin (%)', '% Completion', '% DPAS Order', 'IDD Expected ROI (Total)']].min().min()
max_y_bound14bbis = max_margin_value14bbis * 2
# Add 10% to min value if any of the values are negative, otherwise set to 0
min_y_bound14bbis = min_margin_value14bbis * 2 if min_margin_value14bbis < 0 else 0
melted_df = filtered_df.melt(id_vars=['Pty Indice'],
value_vars=['IDD Current Margin (%)', '% Completion', '% DPAS Order', 'IDD Expected ROI (Total)'],
var_name='Quantity Type', value_name='Quantity Value')
#############################################
# Set Order of melted_df --> Order of y label
#############################################
# Define the order of categories for 'Quantity Type'
unique_quantity_types = ['IDD Expected ROI (Total)', '% DPAS Order', '% Completion', 'IDD Current Margin (%)']
# Convert 'Quantity Type' to a categorical type with the defined order
melted_df['Quantity Type'] = pd.Categorical(melted_df['Quantity Type'], categories=unique_quantity_types, ordered=True)
####################################################
# Define unique indices and calculate x_combined
##################################################
# New code 08/07
# Define constants
unique_indices = melted_df['Pty Indice'].astype('category').cat.categories
unique_quantity_types = melted_df['Quantity Type'].astype('category').cat.categories
num_types = len(unique_quantity_types) # Number of bars per 'Pty Indice'
num_indice = len(unique_indices) # Number of selected 'Pty Indice'
# Generate base_positions based on enumerate(unique_indices)
base_positions = {indice: i * (num_indice + 1) for i, indice in enumerate(unique_indices)}
#define gaps -- The gap is suppose to change based on the number of num_indice (for num_indice = 4 -- gap = 0.5 works well)
#gap = 0.5
gap = 1/(num_indice/2)
# Create a mapping of 'Pty Indice' to its index, starting from 0
indice_mapping = {indice: i for i, indice in enumerate(unique_indices)}
# Calculate x_combined for the bar positions
def calculate_x_combined(row):
pty_indice = row['Pty Indice']
quantity_type_code = melted_df['Quantity Type'].cat.codes[row.name]
# Get the index of the current 'Pty Indice'
indice = indice_mapping[pty_indice]
x_combined = base_positions[pty_indice] + quantity_type_code + 1/(num_indice + 1) + gap*indice #Calculation of the gap is yet to be refined as it does not work for all cases when Pty indice > 4
return x_combined
# Apply the function to calculate x_combined
melted_df['x_combined'] = melted_df.apply(calculate_x_combined, axis=1)
#####################################################
#New 09/11 - Calculate Y position
# Compute the maximum value of Quantity Value
max_quantity_value = melted_df['Quantity Value'].max()
# Calculate the 5% offset of the maximum value
offset = max_quantity_value * 0.05
# Define the function to calculate y_position with the conditional offset
def calculate_y_position(quantity_value):
if quantity_value >= 0:
return quantity_value + offset
else:
return offset # Apply offset in the opposite direction for negative values
# Apply the function to the DataFrame
melted_df['y_position'] = melted_df['Quantity Value'].apply(calculate_y_position)
plot = melted_df.hvplot.bar(
x='Pty Indice',
y='Quantity Value',
by='Quantity Type',
color='Quantity Type',
cmap=custom_palette14bbis,
#title='IDD % Margin per Pty Indice by Top-Level Status, Production Status & Product Category',
xlabel='Pty Indice',
ylabel='IDD % Margin',
legend='top_right',
stacked=False,
bar_width=0.6, # Set bar width - 09/12
#padding=0.1,
tools=[],
).opts(
xrotation=90,
)
updated_bokeh_plot = hv.render(plot, backend='bokeh')
updated_bokeh_plot.tools = [tool for tool in updated_bokeh_plot.tools if not isinstance(tool, HoverTool)]
hover = HoverTool()
hover.tooltips = [
("Pty Indice", "@Pty_Indice"),
("KPI", "@color"),
("Value", "@Quantity_Value%"), # 08/09
]
updated_bokeh_plot.add_tools(hover)
updated_bokeh_plot.xaxis.major_label_text_font_size = '0pt'
updated_bokeh_plot.yaxis.major_label_text_font_size = '10pt'
#updated_bokeh_plot.title.text_font_size = '8pt'
#updated_bokeh_plot.title.text_color = "#002570"
updated_bokeh_plot.xaxis.axis_line_width = 2
updated_bokeh_plot.yaxis.axis_line_width = 2
updated_bokeh_plot.xaxis.major_label_orientation = 'vertical'
updated_bokeh_plot.yaxis.major_label_orientation = 'horizontal'
updated_bokeh_plot.yaxis.axis_label_text_font_size = '10pt'
updated_bokeh_plot.xgrid.grid_line_color = None
updated_bokeh_plot.ygrid.grid_line_color = '#F2F2F2'
updated_bokeh_plot.ygrid.grid_line_dash = [6, 4]
updated_bokeh_plot.y_range = Range1d(start=min_y_bound14bbis, end=max_y_bound14bbis)
updated_bokeh_plot.toolbar.logo = None
updated_bokeh_plot.legend.label_text_font_size = '8pt' # Set the font size of legend text
# Add custom formatted title
updated_bokeh_plot.add_layout(Title(
text="IDD % Margin, % Completion, % DPAS Order \n& Expected ROI per Pty Indice",
align='center',
text_font_size='10pt', # Adjust font size for the title
text_color="#002570" # Adjust color for the title
), 'above')
# Format y-axis ticks as percentages
updated_bokeh_plot.yaxis.formatter =CustomJSTickFormatter(code="""
return (tick).toFixed(0) + '%';
""")
# Format labels to include percentage sign
melted_df['formatted_labels'] = melted_df['Quantity Value'].apply(lambda x: f"{x:.0f}%")
# Add labels on top of the bars
source = ColumnDataSource(melted_df)
labels = LabelSet(
x= 'x_combined',
y = 'y_position', # Use y_position for the vertical position of the labels
#x= 'Quantity Type',
#y='Quantity Value',
#text='Quantity Value',
text='formatted_labels',
level='glyph',
source=source,
text_font_size='8pt',
text_font_style='bold', # Set the font style to bold
text_align='center',
#text_baseline='bottom', # Place labels above the bars
#text_baseline='text_baseline', # Use text_baseline for dynamic alignment #09/11 not working
#y_offset=5,
#y_offset='y_offset', #09/11 not working
text_color={'field': 'Quantity Type', 'transform': CategoricalColorMapper(
factors=unique_quantity_types, palette=[custom_palette14bbis[qtype] for qtype in unique_quantity_types]
)}
)
updated_bokeh_plot.add_layout(labels)
return updated_bokeh_plot
###############
# Inital call
###############
plot_pane_14bis_2 = pn.pane.Bokeh(create_plot_14bis_2())
plot_pane_14bbis = pn.pane.Bokeh(create_plot_14bbis())
##############################################################
# Update methods to include messages when no data is available
###############################################################
def update_plot_14bis_2(event):
plot_pane_14bis_2.object = create_plot_14bis_2()
def update_plot_14bbis(event):
plot_pane_14bbis.object = create_plot_14bbis()
#New 08/28
#######################################################################################
# Create plot_15bis base on pivot_table_15 with production_table_pane attacehd to it
#######################################################################################
# Custom color palette for the new plot
custom_palette15bis = {
"Standard Time (Routing, full ASSY)": "#6699FF", # Blue for Standard Time
"Actual Time (AVG Prod, full ASSY)": "#A2C075", # Green for actual time
"Standard Deviation (on Actual Time, full ASSY)": "#FF5733", # Orange for standard deviation
"Actual Time (AVG Prod, Top-Level only)": "#63BE7B", # bleu
}
#//////////////////////////////////////////////////
###################################################
# create_plot_15bis
###################################################
def create_plot_15bis():
selected_program = program_widget.value
selected_priority = priority_widget.value
selected_indice = indice_widget.value
mask = pd.Series(True, index=pivot_table_15.index)
if selected_program != 'All':
mask &= (pivot_table_15['Program'] == selected_program)
if selected_priority != 'All':
mask &= (pivot_table_15['Priority'] == selected_priority)
if selected_indice != 'All':
mask &= (pivot_table_15['Pty Indice'] == selected_indice)
filtered_df = pivot_table_15.loc[mask].copy()
if filtered_df.empty:
return create_placeholder_plot('No data available for the selected filters.')
#####################################################################
# Dynamic y bounds for create_plot_14bbis
#####################################################################
max_time_value15bis = filtered_df[['Standard Time (Routing, full ASSY)', 'Actual Time (AVG Prod, full ASSY)', 'Standard Deviation (on Actual Time, full ASSY)', 'Actual Time (AVG Prod, Top-Level only)']].max().max()
min_time_value15bis = filtered_df[['Standard Time (Routing, full ASSY)', 'Actual Time (AVG Prod, full ASSY)', 'Standard Deviation (on Actual Time, full ASSY)', 'Actual Time (AVG Prod, Top-Level only)']].min().min()
max_y_bound15bis = max_time_value15bis * 2
min_y_bound15bis = min_time_value15bis * 2 if min_time_value15bis < 0 else 0
# Melt the dataframe to reshape it for the plot
melted_df = filtered_df.melt(
id_vars=['Pty Indice'],
#value_vars=['Standard Time (Routing, full ASSY)', 'Actual Time (AVG Prod, full ASSY)', 'Standard Deviation (on Actual Time, full ASSY)', 'Actual Time (AVG Prod, Top-Level only)'],
value_vars=['Standard Time (Routing, full ASSY)', 'Actual Time (AVG Prod, full ASSY)', 'Standard Deviation (on Actual Time, full ASSY)'], # Without 'Actual Time (AVG Prod, Top-Level only)'
var_name='Time Type',
value_name='Time Value'
)
#############################################
# Set Order of melted_df --> Order of y label
#############################################
# Define the order for 'Time Type'
#unique_time_types = ['Actual Time (AVG Prod, Top-Level only)', 'Standard Deviation (on Actual Time, full ASSY)' , 'Actual Time (AVG Prod, full ASSY)', 'Standard Time (Routing, full ASSY)']
unique_time_types = ['Standard Deviation (on Actual Time, full ASSY)' , 'Actual Time (AVG Prod, full ASSY)', 'Standard Time (Routing, full ASSY)'] # Without 'Actual Time (AVG Prod, Top-Level only)'
melted_df['Time Type'] = pd.Categorical(melted_df['Time Type'], categories=unique_time_types, ordered=True)
####################################################
# Define unique indices and calculate x_combined
##################################################
# New code 08/07
# Define constants
unique_indices = melted_df['Pty Indice'].astype('category').cat.categories
unique_time_types = melted_df['Time Type'].astype('category').cat.categories
num_types = len(unique_time_types) # Number of bars per 'Pty Indice'
num_indice = len(unique_indices) # Number of selected 'Pty Indice'
# Generate base_positions based on enumerate(unique_indices)
base_positions = {indice: i * (num_indice + 1) for i, indice in enumerate(unique_indices)}
#define gaps -- The gap is suppose to change based on the number of num_indice (for num_indice = 4 -- gap = 0.5 works well)
#gap = 0.5
gap = 1/(num_indice/2)
# Create a mapping of 'Pty Indice' to its index, starting from 0
indice_mapping = {indice: i for i, indice in enumerate(unique_indices)}
# Calculate x_combined for the bar positions
def calculate_x_combined(row):
pty_indice = row['Pty Indice']
time_type_code = melted_df['Time Type'].cat.codes[row.name]
# Get the index of the current 'Pty Indice'
indice = indice_mapping[pty_indice]
x_combined = base_positions[pty_indice] + time_type_code + 1/(num_indice + 1) + gap*indice #Calculation of the gap is yet to be refined as it does not work for all cases when Pty indice > 4
return x_combined
# Apply the function to calculate x_combined
melted_df['x_combined'] = melted_df.apply(calculate_x_combined, axis=1)
#####################################################
# Create the plot for Graph 15bis
plot_15bis = melted_df.hvplot.bar(
x='Pty Indice',
y='Time Value',
by='Time Type',
color='Time Type',
#title='Standard Time VS Actual Time',
xlabel='Pty Indice',
ylabel='Time [hours]',
cmap=custom_palette15bis,
legend='top_right',
stacked=False,
bar_width=0.6, # Set bar width - 09/12
#padding=1,
tools=[],
).opts(xrotation=90)
# Customize the Bokeh plot
bokeh_plot_15bis = hv.render(plot_15bis, backend='bokeh')
bokeh_plot_15bis.tools = [tool for tool in bokeh_plot_15bis.tools if not isinstance(tool, HoverTool)]
hover = HoverTool()
hover.tooltips = [
("Pty Indice", "@Pty_Indice"),
("Time Type", "@color"),
("Time Value", "@Time_Value{0.0} hours"),
]
bokeh_plot_15bis.add_tools(hover)
# 09/12 - Set wheel woom inactive
bokeh_plot_15bis.toolbar.active_scroll = None
# Further customizations
bokeh_plot_15bis.xaxis.major_label_text_font_size = '0pt'
bokeh_plot_15bis.yaxis.major_label_text_font_size = '10pt'
bokeh_plot_15bis.title.text_font_size = '10pt'
bokeh_plot_15bis.title.text_color = "#002570"
bokeh_plot_15bis.xaxis.axis_line_width = 2
bokeh_plot_15bis.yaxis.axis_line_width = 2
bokeh_plot_15bis.xaxis.major_label_orientation = 'vertical'
bokeh_plot_15bis.yaxis.major_label_orientation = 'horizontal'
bokeh_plot_15bis.yaxis.axis_label_text_font_size = '10pt'
bokeh_plot_15bis.xgrid.grid_line_color = None
bokeh_plot_15bis.ygrid.grid_line_color = '#F2F2F2'
bokeh_plot_15bis.ygrid.grid_line_dash = [6, 4]
bokeh_plot_15bis.toolbar.logo = None
bokeh_plot_15bis.y_range = Range1d(start=min_y_bound15bis, end=max_y_bound15bis)
bokeh_plot_15bis.legend.label_text_font_size = '8pt'
# Add custom formatted title
bokeh_plot_15bis.add_layout(Title(
text="Production KPI",
align='center',
text_font_size='10pt',
text_color="#002570"
), 'above')
# Add labels on top of the bars
source = ColumnDataSource(melted_df)
labels = LabelSet(
x='x_combined',
y='Time Value',
text='Time Value',
level='glyph',
source=source,
text_font_size='8pt',
text_font_style='bold', # Set the font style to bold
text_align='center',
text_baseline='bottom',
y_offset=5,
text_color={'field': 'Time Type', 'transform': CategoricalColorMapper(
factors=unique_time_types, palette=[custom_palette15bis[ttype] for ttype in unique_time_types]
)}
)
bokeh_plot_15bis.add_layout(labels)
return bokeh_plot_15bis
# Initial call
plot_pane_15bis = pn.pane.Bokeh(create_plot_15bis())
# Update method
def update_plot_15bis(event):
plot_pane_15bis.object = create_plot_15bis()
#///////////////////////////////////////
########################################
# Create table related to Graph-15
########################################
#///////////////////////////////////////
# Update 09/10 WIP --> Include 'Total Top-Level Qty' and 'Total Components Qty' in he table 'Top-Level WO Count'*'Qty per WO' and 'Total WO Count'*'Total Components Qty'
# Table containing 'Pty Indice', 'Total WO Count' and 'Top-Level WO Count' based on the widget (Program, Priority, Pty Indice) selection of |Products Status|
pivot_table_15_2 = pivot_table_15.copy()
def create_production_table_by_pty_indice(df):
return df[['Pty Indice', 'Total WO Count', 'Top-Level WO Count', 'Total Top-Level Qty', 'Total sub-Level Qty']]
# Define the function to update the production table based on widget values
def update_production_table_by_pty_indice(event=None):
selected_program = program_widget.value
selected_priority = priority_widget.value
selected_indice = indice_widget.value
mask = pd.Series(True, index=pivot_table_15_2.index)
if selected_program != 'All':
mask &= (pivot_table_15_2['Program'] == selected_program)
if selected_priority != 'All':
mask &= (pivot_table_15_2['Priority'] == selected_priority)
if selected_indice != 'All':
mask &= (pivot_table_15_2['Pty Indice'] == selected_indice)
filtered_df = pivot_table_15_2.loc[mask].copy()
if filtered_df.empty:
updated_table = create_placeholder_plot('No data available for the selected filters.')
else:
updated_table = create_production_table_by_pty_indice(filtered_df)
# Update the production table pane
production_table_by_pty_indice_pane.object = updated_table
# Initialize the production table with default values
initial_production_table_by_pty_indice = create_production_table_by_pty_indice(pivot_table_15_2)
production_table_by_pty_indice_pane = pn.pane.DataFrame(initial_production_table_by_pty_indice, width=420, index=False) #09/10
# Initial table setup
update_production_table_by_pty_indice()
# Attach the update function to widget value changes
program_widget.param.watch(update_production_table_by_pty_indice, 'value')
priority_widget.param.watch(update_production_table_by_pty_indice, 'value')
indice_widget.param.watch(update_production_table_by_pty_indice, 'value')
##################################################################################################################
# 09/25 - Create a second panda datafram related to Graph 15 with data from df_Snapshot related to Production KPI
##################################################################################################################
# The table should contain 'Pty Indice', 'Actual vs Standard time [%]', 'Deviation vs Actual [%]'
# Apply color formating on 'Deviation vs Actual [%]' : green if < 30%, orange if 30 to 50% and red if > 50%
# Step 1: Create a copy of df_Snapshot and rename the columns
df_Snapshot_prod_KPI = df_Snapshot.copy()
df_Snapshot_prod_KPI.rename(columns={
'Actual vs Standard time [%]': 'Standard time to Actual time [%]',
}, inplace=True)
# Need to keep 'Priority', 'Program' for the widget to work
relevant_columns_KPI = ['Pty Indice', 'Priority', 'Program',
'Standard time to Actual time [%]', 'Deviation vs Actual [%]']
df_Snapshot_prod_KPI = df_Snapshot_prod_KPI[relevant_columns_KPI]
# Convert percentage strings to float in numeric DataFrame
def convert_percentage_columns(df, percentage_columns):
for col in percentage_columns:
df[col] = (
df[col]
.str.replace('%', '', regex=False)
.astype(float) / 100
)
return df
# Step 3: Create a numeric DataFrame for percentage calculations
df_Snapshot_KPI_numeric = df_Snapshot_prod_KPI.copy()
percentage_columns_KPI = ['Standard time to Actual time [%]', 'Deviation vs Actual [%]']
df_Snapshot_KPI_numeric = convert_percentage_columns(df_Snapshot_KPI_numeric, percentage_columns_KPI)
# Step 4: Function to create a color dictionary
def create_color_dictionary(df, column):
color_dict = {}
for value in df[column]:
try:
# Handle both string and float types
if isinstance(value, str):
num_value = float(value.replace('%', '')) / 100
else:
num_value = value # Assume it's already a float
# Determine color based on value ranges
if num_value < 0.3:
color_dict[num_value] = 'green'
elif 0.3 <= num_value < 0.5:
color_dict[num_value] = 'orange'
else:
color_dict[num_value] = 'red'
except ValueError:
color_dict[value] = 'black' # Handle non-convertible values
return color_dict
# Create a color dictionary for 'Deviation vs Actual [%]'
color_mapping = create_color_dictionary(df_Snapshot_prod_KPI, 'Deviation vs Actual [%]')
# Function to apply color formatting to the DataFrame
def apply_color_formatting_prod_KPI(df, color_dict, column):
"""Apply conditional color formatting to a specified column in the DataFrame."""
def color_deviation(val):
"""Return the corresponding color based on the value."""
try:
# Handle both string and float types
if isinstance(val, str):
num_value = float(val.replace('%', '')) / 100
else:
num_value = val # Assume it's already a float
color = color_dict.get(num_value, 'black') # Default to black if not found
except ValueError:
color = 'black' # Handle conversion failure
return f'color: {color}'
# Create a styled DataFrame with color formatting
styled_df = df.style.applymap(color_deviation, subset=[column])
# Center text in both header and cells
styled_df.set_table_styles(
[
{'selector': 'th', 'props': [('text-align', 'center')]}, # Center headers
{'selector': 'td', 'props': [('text-align', 'center')]} # Center cells
]
)
return styled_df
# Prepare the display DataFrame
df_display = df_Snapshot_prod_KPI.copy()
# Apply the conditional color formatting to the 'Deviation vs Actual [%]' column
styled_df = apply_color_formatting_prod_KPI(df_display, color_mapping, 'Deviation vs Actual [%]')
# Update the KPI table function
def update_kpi_table_prod(event):
"""Update the KPI table based on selected filters."""
selected_program = program_widget.value
selected_priority = priority_widget.value
selected_indice = indice_widget.value
# Create a mask for filtering
mask = pd.Series(True, index=df_Snapshot_prod_KPI.index)
if selected_program != 'All':
mask &= (df_Snapshot_prod_KPI['Program'] == selected_program)
if selected_priority != 'All':
mask &= (df_Snapshot_prod_KPI['Priority'] == selected_priority)
if selected_indice != 'All':
mask &= (df_Snapshot_prod_KPI['Pty Indice'] == selected_indice)
# Filter the DataFrame
filtered_df = df_Snapshot_prod_KPI.loc[mask].copy().reset_index(drop=True)
# Drop 'Priority' and 'Program' from the filtered DataFrame for display
filtered_df_display = filtered_df.drop(columns=['Priority', 'Program'])
# Apply the conditional color formatting to the 'Deviation vs Actual [%]' column
filtered_styled_df = apply_color_formatting_prod_KPI(filtered_df_display, color_mapping, 'Deviation vs Actual [%]').hide(axis='index')
# Update the KPI table pane
kpi_table_pane_prod.object = filtered_styled_df
# Create the initial styled DataFrame without index
def create_styled_dataframe(df):
"""Create a styled DataFrame without the index."""
return apply_color_formatting_prod_KPI(df, color_mapping, 'Deviation vs Actual [%]').hide(axis='index')
# Initialize the KPI table pane
kpi_table_pane_prod = pn.pane.DataFrame(
create_styled_dataframe(df_Snapshot_prod_KPI.copy().reset_index(drop=True)), # Create a styled DataFrame
width=420,
)
# Initialize update_kpi_table_prod
update_kpi_table_prod(None)
# Attach update function to widget changes
program_widget.param.watch(update_kpi_table_prod, 'value')
priority_widget.param.watch(update_kpi_table_prod, 'value')
indice_widget.param.watch(update_kpi_table_prod, 'value')
#####################################################################################
# 09/24 - Create a panda datafram to summazize the production KPI from df_Snapashot
#####################################################################################
# First rename 'Critical Qty' to 'Critical Qty Initial' and 'IDD Production Cost (unit)' to 'IDD current cost (unit)'
# The table_production_KPI contain: 'Pty Indice', 'IDD Marge Standard (unit)', 'IDD Sale Price', 'IDD Current Margin (%)', 'IDD current cost (unit)', 'IDD AVG realized Margin [%]', 'IDD Corrected Margin [%]', 'Critical Qty Initial'
# Color in darf green font when 'IDD AVG realized Margin [%]', 'IDD Current Margin (%)', 'IDD Corrected Margin [%]' are positive and in red font when negative
# Make sure that the following column are currency (USD): 'IDD Marge Standard (unit)', 'IDD Sale Price', 'IDD current cost (unit)',
# Make sure that the following column are percentage (%): 'IDD AVG realized Margin [%]', 'IDD Current Margin (%)', 'IDD Corrected Margin [%]'
# Step 1: Create a copy of df_Snapshot and rename the columns
df_Snapshot_prod = df_Snapshot.copy()
df_Snapshot_prod.rename(columns={
'Critical Qty': 'Critical Qty (Initial)',
'IDD Production Cost (unit)': 'IDD current cost (per unit)',
'IDD Marge Standard (unit)': 'IDD Margin Standard (per unit)',
'IDD Current Margin (%)': 'IDD Current Margin [%]'
}, inplace=True)
# Step 2: Keep only the necessary columns
relevant_columns = ['Pty Indice', 'Priority', 'Program',
'IDD Margin Standard (per unit)', 'IDD Sale Price',
'IDD Current Margin [%]', 'IDD current cost (per unit)',
'IDD AVG realized Margin [%]', 'IDD Corrected Margin [%]',
'Critical Qty (Initial)']
df_Snapshot_prod = df_Snapshot_prod[relevant_columns]
# Step 3: Create a numeric DataFrame for percentage calculations
df_Snapshot_numeric = df_Snapshot_prod.copy()
percentage_columns = ['IDD Current Margin [%]', 'IDD AVG realized Margin [%]', 'IDD Corrected Margin [%]']
# Convert percentage strings to float in numeric DataFrame
def convert_percentage_columns(df, percentage_columns):
for col in percentage_columns:
df[col] = (
df[col]
.str.replace('%', '', regex=False)
.astype(float) / 100
)
return df
df_Snapshot_numeric = convert_percentage_columns(df_Snapshot_numeric, percentage_columns)
# --> WIP 10/07 <--
# Step 4: Create a color mapping for percentage columns
def create_color_mapping_percentage(df, columns):
"""Create a color mapping for multiple percentage columns based on value ranges."""
color_dict = {}
for column in columns:
for idx, value in df[column].items():
try:
num_value = float(value) # Assume value is already a float
# Define the color based on value ranges
if num_value > 0:
color_dict[(idx, column)] = 'green'
elif num_value < 0:
color_dict[(idx, column)] = 'red'
else:
color_dict[(idx, column)] = 'black'
except ValueError:
color_dict[(idx, column)] = 'black' # Fallback for non-convertible values
return color_dict
# Create color mappings for the relevant percentage columns
percentage_columns = ['IDD Current Margin [%]', 'IDD AVG realized Margin [%]', 'IDD Corrected Margin [%]']
color_mapping_margin = create_color_mapping_percentage(df_Snapshot_numeric, percentage_columns)
# Step 5: Function to apply color formatting
def apply_color_formatting_margin(df, color_dict):
"""Apply conditional color formatting to the percentage columns in the DataFrame."""
def color_deviation(val, idx, col):
"""Return the corresponding color based on the value."""
try:
# Get color for the (index, column) pair
color = color_dict.get((idx, col), 'black')
except ValueError:
color = 'black' # Handle conversion failure
return f'color: {color}'
# Create a styled DataFrame with color formatting for the relevant percentage columns
styled_df = df.style.apply(
lambda x: [color_deviation(x[col], x.name, col) for col in df.columns], axis=1
)
# Center text in both header and cells
styled_df.set_table_styles(
[
{'selector': 'th', 'props': [('text-align', 'center')]}, # Center headers
{'selector': 'td', 'props': [('text-align', 'center')]} # Center cells
]
)
return styled_df
# Step 6: Function to format currency values
def format_currency(value):
"""Format a number as currency with one decimal place."""
return f"${value:,.1f}"
# Update the KPI table function
def update_kpi_table(event):
"""Update the KPI table based on selected filters."""
selected_program = program_widget.value
selected_priority = priority_widget.value
selected_indice = indice_widget.value
# Create a mask for filtering
mask = pd.Series(True, index=df_Snapshot_prod.index)
if selected_program != 'All':
mask &= (df_Snapshot_prod['Program'] == selected_program)
if selected_priority != 'All':
mask &= (df_Snapshot_prod['Priority'] == selected_priority)
if selected_indice != 'All':
mask &= (df_Snapshot_prod['Pty Indice'] == selected_indice)
filtered_df = df_Snapshot_prod.loc[mask].copy().reset_index(drop=True)
filtered_numeric_df = df_Snapshot_numeric.loc[mask].copy().reset_index(drop=True)
# Apply currency formatting to the specified columns
currency_columns = ['IDD Margin Standard (per unit)', 'IDD Sale Price', 'IDD current cost (per unit)']
for col in currency_columns:
filtered_df[col] = filtered_df[col].apply(format_currency)
# Create a new color mapping for the filtered DataFrame
color_mapping_margin = create_color_mapping_percentage(filtered_numeric_df, percentage_columns)
# Apply the conditional color formatting to the relevant percentage columns
filtered_styled_df = apply_color_formatting_margin(filtered_df, color_mapping_margin).hide(axis='index')
# Update the KPI table pane
kpi_table_pane.object = filtered_styled_df
# Step 7: Create the initial styled DataFrame
def create_styled_dataframe(df):
"""Create a styled DataFrame without the index."""
return apply_color_formatting_margin(df, color_mapping_margin).hide(axis='index')
# Initialize the KPI table pane
kpi_table_pane = pn.pane.DataFrame(
create_styled_dataframe(df_Snapshot_prod.copy().reset_index(drop=True)), # Create a styled DataFrame
width=1250,
)
# Initialize and update KPI table
update_kpi_table(None)
# Attach update function to widget changes
program_widget.param.watch(update_kpi_table, 'value')
priority_widget.param.watch(update_kpi_table, 'value')
indice_widget.param.watch(update_kpi_table, 'value')
#####################################################################################
# 09/26 - Create a panda datafram to summazize the backlog KPI from df_Snapashot
#####################################################################################
# Build a table with 5 columns: pivot_table_14['Pty Indice'], , pivot_table_14['Priority'], pivot_table_14['Program'], pivot_table_14['% Completion'], pivot_table_14['% DPAS Order']
# Display only 'Pty Indice', % Completion' and '% DPAS Order'
# Function to format numeric values as percentages with one decimal point
def format_percentage(value):
"""Format a numeric value as a percentage with 1 decimal point."""
return "{:.1f}%".format(value) # No multiplication, directly format the value
def create_backlog_table():
"""Build a backlog table from a copy of pivot_table_14."""
# Create a copy of pivot_table_14 to avoid modifying the original DataFrame
backlog_table = pivot_table_14.copy()
# Select the relevant columns
backlog_table = backlog_table[['Pty Indice', 'Priority', 'Program', '% Completion', '% Completion Total Backlog', '% DPAS Order']]
# Rename '% Completion' to '% Completion Critical Qty'
backlog_table.rename(columns={'% Completion': '% Completion Critical Qty'}, inplace=True)
# Return the backlog table without additional formatting for calculations
return backlog_table
# Initialize the backlog_table globally to be accessible elsewhere
backlog_table = create_backlog_table()
def update_filtered_backlog_table(event):
"""Update the backlog table based on widget filters."""
selected_program = program_widget.value
selected_priority = priority_widget.value
selected_indice = indice_widget.value
# Re-create a copy of the backlog table for filtering
backlog_table = create_backlog_table()
# Apply filters based on the widget values
mask = pd.Series(True, index=backlog_table.index)
if selected_program != 'All':
mask &= (backlog_table['Program'] == selected_program)
if selected_priority != 'All':
mask &= (backlog_table['Priority'] == selected_priority)
if selected_indice != 'All':
mask &= (backlog_table['Pty Indice'] == selected_indice)
# Filter the backlog table based on the mask
filtered_backlog_table = backlog_table.loc[mask].reset_index(drop=True) # Reset index here
# Display only the columns: 'Pty Indice', '% Completion', '% DPAS Order'
display_columns = ['Pty Indice', '% Completion Total Backlog', '% Completion Critical Qty', '% DPAS Order']
filtered_display_table = filtered_backlog_table[display_columns].copy()
# Format the percentage columns for display with one decimal point
filtered_display_table['% Completion Critical Qty'] = filtered_display_table['% Completion Critical Qty'].apply(format_percentage)
filtered_display_table['% Completion Total Backlog'] = filtered_display_table['% Completion Total Backlog'].apply(format_percentage)
filtered_display_table['% DPAS Order'] = filtered_display_table['% DPAS Order'].apply(format_percentage)
# Create and style the DataFrame directly
styled_filtered_table = create_styled_dataframe(filtered_display_table)
# Update the Panel DataFrame pane
backlog_table_pane.object = styled_filtered_table
def create_styled_dataframe(df):
"""Create a styled DataFrame without the index and center values."""
# Create a styled DataFrame
styled_df = df.style.hide(axis='index') # Hide the index
# Set table styles for centering text in both headers and data cells
styled_df.set_table_styles(
[
{
'selector': 'th, td', # Select both headers and data cells
'props': [('text-align', 'center')] # Center text
}
],
axis=0 # Applies the styles to all columns
)
return styled_df
# Initialize the backlog_table_pane with the styled backlog table
backlog_table_pane = pn.pane.DataFrame(
create_styled_dataframe(backlog_table.reset_index(drop=True)), # Create a styled DataFrame
width=750,
)
# Trigger the first update of the pane
update_filtered_backlog_table(None)
# Attach the update function to widget changes to update the backlog table automatically
program_widget.param.watch(update_filtered_backlog_table, 'value')
priority_widget.param.watch(update_filtered_backlog_table, 'value')
indice_widget.param.watch(update_filtered_backlog_table, 'value')
#################################################################################
# New 09/26 - Text boxes related to production KPI - Production KPI tables
###################################################################################
# Styles for the header and the card
header_styles = {
"background_color": "#9EC5E4", # Header color
"font_color": "white" # Text color for header
}
# Custom CSS for the card and header
custom_css = f"""
<style>
.custom-card {{
border: 2px solid #A9A9A9; /* Gray border */
border-radius: 10px; /* Rounded corners */
background-color: #ECF4FA; /* Light background inside the card */
box-shadow: 2px 2px 10px rgba(0, 0, 0, 0.1); /* Subtle shadow */
padding: 15px; /* Padding inside the card */
}}
.custom-card-header {{
background-color: {header_styles["background_color"]}; /* Header background */
color: {header_styles["font_color"]}; /* Header text color */
padding: 10px;
font-size: 18px;
text-align: center;
font-weight: bold;
border-top-left-radius: 10px; /* Rounded top corners */
border-top-right-radius: 10px; /* Rounded top corners */
}}
</style>
"""
# Headers text for the card
header_html_Prod = f"<div class='custom-card-header'>Production KPI overview</div>"
header_html_Finance = f"<div class='custom-card-header'> Overview of Financial KPIs and the influence of Production KPIs</div>"
header_html_Backlog = f"<div class='custom-card-header'>Backlog KPI overview</div>"
#/////////////////////////////////////////////////////////////////////////////////
######################################################
# Create text box 'textbox_production_table_by_pty_indice_pane' under the table production_table_by_pty_indice_pane
######################################################
# 'Qty Shipped' - 'Total Top-Level Qty' Top-Level are filtered-out from the calculation due to related WOs considerede abberant value.
# 'Total Top-Level Qty' Top-Level are thus considered for the calcualtion.
# The calculation is made based on 'Total WO Count' data-point representinf the number of WOs, in which 'Top-Level WO Count' represents Top-Level WOs.
#/////////////////////////////////////////////////////////////////////////////////
# Initialize global variables
top_filtered_out_qty = 0
total_top_level_qty = 0
top_wo_count = 0
total_wo_count = 0
# Function to update the 'Top-Level Qty filtered-out' quantity
def update_top_filtered_out_qty(filtered_df_snapshot, filtered_df):
global top_filtered_out_qty
top_filtered_out_qty = filtered_df_snapshot['Shipped'].sum() - filtered_df['Total Top-Level Qty'].sum()
# Function to update the 'Total Top-Level Qty' based on filtered data
def update_total_top_level_qty(filtered_df):
global total_top_level_qty
total_top_level_qty = filtered_df['Total Top-Level Qty'].sum() if 'Total Top-Level Qty' in filtered_df else 0
# Function to update the 'Total WO Count' based on filtered data
def update_total_wo_count(filtered_df):
global total_wo_count
total_wo_count = filtered_df['Total WO Count'].sum() if 'Total WO Count' in filtered_df else 0
# Function to update the 'Top-Level WO Count' based on filtered data
def update_top_wo_count(filtered_df):
global top_wo_count
top_wo_count = filtered_df['Top-Level WO Count'].sum() if 'Top-Level WO Count' in filtered_df else 0
# Initialize the textbox to display results
textbox_production_table_by_pty_indice_pane = pn.pane.Markdown("", sizing_mode='stretch_width')
# Function to update quantities and text box based on widget selections
def update_textbox(event):
selected_program = program_widget.value
selected_priority = priority_widget.value
selected_indice = indice_widget.value
# Initialize a boolean mask with all True values for pivot_table_15_2
mask = pd.Series(True, index=pivot_table_15_2.index)
# Apply filters based on selections for pivot_table_15_2
if selected_program != 'All':
mask &= (pivot_table_15_2['Program'] == selected_program)
if selected_priority != 'All':
mask &= (pivot_table_15_2['Priority'] == selected_priority)
if selected_indice != 'All':
mask &= (pivot_table_15_2['Pty Indice'] == selected_indice)
# Apply the mask to filter pivot_table_15_2
filtered_df = pivot_table_15_2.loc[mask].copy()
# Initialize a boolean mask for df_Snapshot
mask_snapshot = pd.Series(True, index=df_Snapshot.index)
# Apply filters based on selections for df_Snapshot
if selected_program != 'All':
mask_snapshot &= (df_Snapshot['Program'] == selected_program)
if selected_priority != 'All':
mask_snapshot &= (df_Snapshot['Priority'] == selected_priority)
if selected_indice != 'All':
mask_snapshot &= (df_Snapshot['Pty Indice'] == selected_indice)
# Filter df_Snapshot using the constructed mask
filtered_df_snapshot = df_Snapshot[mask_snapshot].copy()
# Update quantities based on the filtered DataFrames
update_top_filtered_out_qty(filtered_df_snapshot, filtered_df)
update_total_top_level_qty(filtered_df)
update_top_wo_count(filtered_df)
update_total_wo_count(filtered_df)
# Update the text box content with the actual values
textbox_production_table_by_pty_indice_pane.object = f"""
▷ <b>{top_filtered_out_qty}</b> Top-Level are filtered-out from the calculation. <br>
▷ <b>{total_top_level_qty}</b> Top-Level are considered for the calculation. <br>
▷ <b>{total_wo_count}</b> represents the number of WOs included in the calculation, with <b>{top_wo_count}</b> representing Top-Level WOs.
"""
# Attach the update function to widget value changes
program_widget.param.watch(update_textbox, 'value')
priority_widget.param.watch(update_textbox, 'value')
indice_widget.param.watch(update_textbox, 'value')
# Initial text box setup
update_textbox(None) # Call once to initialize with default values
#/////////////////////////////////////////////////////////////////////////////////
##################################################################################
# Create a text box 'textbox_kpi_table_prod' under the table kpi_table_prod
##################################################################################
# Standard deviation represents 'Deviation vs Actual [%]' of the Standard Time.
# Standard Time has to be incresed by 'Standard time to Actual time [%]' in order to reflect the Actual Time.
#/////////////////////////////////////////////////////////////////////////////////
# 'Deviation vs Actual [%]' and 'Standard time to Actual time [%]' are coming from
# Initialize global variables for the dynamic text
deviation_vs_actual = 0
standard_time_to_actual = 0
def convert_percentage_to_numeric(percentage):
"""Convert a percentage string to a numeric value (decimal)."""
if isinstance(percentage, str):
# Handle string input, stripping '%' and converting to decimal
return float(percentage.strip('%')) / 100
elif isinstance(percentage, (float, int)):
# Handle float or int input, already numeric
return percentage
else:
# Handle unexpected types, returning NaN or a default value
return float('nan') # Or return 0.0, depending on your requirement
# Function to update the dynamic values
def update_dynamic_values(filtered_df):
global deviation_vs_actual, standard_time_to_actual
# Convert columns to numeric values
if 'Deviation vs Actual [%]' in filtered_df:
filtered_df['Deviation vs Actual [%]'] = filtered_df['Deviation vs Actual [%]'].apply(convert_percentage_to_numeric)
deviation_vs_actual = filtered_df['Deviation vs Actual [%]'].mean() # Calculate mean
if 'Standard time to Actual time [%]' in filtered_df:
filtered_df['Standard time to Actual time [%]'] = filtered_df['Standard time to Actual time [%]'].apply(convert_percentage_to_numeric)
standard_time_to_actual = filtered_df['Standard time to Actual time [%]'].mean() # Calculate mean
# Create the dynamic text box for the KPI table
def update_textbox_kpi_table_prod(event):
selected_program = program_widget.value
selected_priority = priority_widget.value
selected_indice = indice_widget.value
# Create a mask for filtering df_Snapshot_prod_KPI
mask = pd.Series(True, index=df_Snapshot_prod_KPI.index)
if selected_program != 'All':
mask &= (df_Snapshot_prod_KPI['Program'] == selected_program)
if selected_priority != 'All':
mask &= (df_Snapshot_prod_KPI['Priority'] == selected_priority)
if selected_indice != 'All':
mask &= (df_Snapshot_prod_KPI['Pty Indice'] == selected_indice)
# Filter the DataFrame
filtered_df = df_Snapshot_prod_KPI.loc[mask].copy()
# Update dynamic values based on filtered DataFrame
update_dynamic_values(filtered_df)
# Update the text box content with the actual values
textbox_kpi_table_prod.object = f"""
▷ <b>Standard deviation</b> represents <b>{deviation_vs_actual:.0%}</b> of the Standard Time.
▷ <b>Standard Time</b> has to be increased by <b>{standard_time_to_actual:.0%}</b> to reflect the <b>Actual Time</b>.
"""
# Initialize the textbox to display results
textbox_kpi_table_prod = pn.pane.Markdown("", width=425)
# Attach the update function to widget value changes
program_widget.param.watch(update_textbox_kpi_table_prod, 'value')
priority_widget.param.watch(update_textbox_kpi_table_prod, 'value')
indice_widget.param.watch(update_textbox_kpi_table_prod, 'value')
# Initial text box setup
update_textbox_kpi_table_prod(None) # Call once to initialize with default values
#/////////////////////////////////////////////////////////////////////////////////
###################################################
# Create a text box 'textbox_kpi_table_finance' under the table 'kpi_table_pane'
####################################################
textbox_kpi_table_finance = pn.pane.Markdown(
"""
▷ <b>IDD Current Margin [%]</b>: Margin based on the current Standard Time and Sale Price <br>
▷ <b>IDD AVG realized Margin [%]</b>: Average Margin over time considering any potential change of price <br>
▷ <b>IDD Corrected Margin [%]</b>: Real Margin reflecting the potential difference between Standard Time and Actual Time. Calculated based on the average 2024 labor cost ($79.58/h). <br>
➥ The Corrected Margin is not exact, as it includes only the labor efficiency but does not account for other factors such as labor cost variance and material purchasing variances. However, it is more representative of the real margin than the IDD Current Margin, which does not consider the labor efficiency (Actual Time vs. Stanadrd Time).
""",
width=1270
)
#/////////////////////////////////////////////////////////////////////////////////
###################################################
# Create a text box 'textbox_kpi_table_finance' under the table 'kpi_table_pane'
####################################################
textbox_kpi_table_backlog = pn.pane.Markdown(
"""
▷ <b>% Completion Critical Qty</b>: Based on the critical quantity. It can be > 100%. <br>
▷ <b>% Completion Total</b>: Including follow-up orders.
""",
width=600
)
########################################################################################
#### Create the layout for the production card, including the header and other elements
########################################################################################
def create_kpi_summary_card():
# This includes the custom CSS and the header HTML
card_layout = pn.Column(
pn.pane.HTML(custom_css + header_html_Prod), # Apply custom styles and header
pn.Spacer(height=10), # Spacer for layout
pn.Row(
pn.Spacer(width=20),
pn.Column(
production_table_by_pty_indice_pane, # First KPI Table
pn.Spacer(height=5), # Space between elements
textbox_production_table_by_pty_indice_pane, # First text box
pn.Spacer(height=5), # Space between elements
kpi_table_pane_prod, # Second KPI Table
pn.Spacer(height=5), # Space between elements
textbox_kpi_table_prod, # Second text box
width=425,
),
),
css_classes=["custom-card"] # Apply the custom card styling here
)
return card_layout
# Create and display the KPI summary card
kpi_summary_card = create_kpi_summary_card()
########################################################################################
#### Create the layout for the Financial card, including the header and other elements
########################################################################################
def create_kpi_summary_card():
# This includes the custom CSS and the header HTML
card_layout = pn.Column(
pn.pane.HTML(custom_css + header_html_Finance), # Apply custom styles and header
pn.Spacer(height=10), # Spacer for layout
pn.Row(
pn.Spacer(width=20),
pn.Column(
kpi_table_pane, # Fianance Table
pn.Spacer(height=5), # Space between elements
textbox_kpi_table_finance, # First text box
),
),
css_classes=["custom-card"] # Apply the custom card styling here
)
return card_layout
# Create and display the KPI summary card
kpi_summary_card_finance = create_kpi_summary_card()
########################################################################################
#### Create the layout for the backlog card, including the header and other elements
########################################################################################
def create_kpi_summary_card_backlog():
# This includes the custom CSS and the header HTML
card_layout = pn.Column(
pn.pane.HTML(custom_css + header_html_Backlog), # Apply custom styles and header
pn.Spacer(height=10), # Spacer for layout
pn.Row(
pn.Spacer(width=20),
pn.Spacer(height=5),
pn.Column(
backlog_table_pane, # Backlog Table
pn.Spacer(height=5), # Space between elements
textbox_kpi_table_backlog, # First text box
),
),
css_classes=["custom-card"] # Apply the custom card styling here
)
return card_layout
# Create and display the KPI summary card
kpi_summary_card_backlog = create_kpi_summary_card_backlog()
###################################################################################
#//////////////////////////////////////////////////
########################################
# Create Graph13-13b-14-14b Dashboard
########################################
#//////////////////////////////////////////////////
# Set explicit width and height for each plot # 02/26
plot_pane_13bis.width = 370
plot_pane_13bis.height = 450
plot_pane_13bbis.width = 370
plot_pane_13bbis.height = 450
plot_pane_14bis_2.width = 370
plot_pane_14bis_2.height = 450
plot_pane_15bis.width = 370
plot_pane_15bis.height = 450
# Create vertical divier (gray vertical line for separation)
vertical_divider_med1 = pn.pane.HTML(
'<div style="width: 3px; height: 450px; background-color:#D9D9D9;"></div>',
)
vertical_divider_long = pn.pane.HTML(
'<div style="width: 3px; height: 770px; background-color:#D9D9D9;"></div>',
)
# Updated 09/26 - to integrate kpi_summary_card_backlog
# Define the layout for the plots and tables
combined_plots_layout = pn.Column(
pn.Row( # Main row to contain left and right columns with a vertical divider
pn.Column( # Left column for backlog graphs and backlog table
pn.Row( # First row with the first set of plots
plot_pane_13bis, # First plot
pn.Spacer(width=10), # Spacer
plot_pane_13bbis, # Second plot
),
pn.Spacer(height=50), # 10/07 20 --> 50
kpi_summary_card_backlog, # KPI summary for backlog below the second plot
pn.Spacer(height=10), # Optional vertical spacer between rows
),
vertical_divider_long, # Vertical divider separating the left and right columns
pn.Spacer(width=20),
pn.Column( # Right column for additional plots and KPI summaries
pn.Row( # Row for the third plot and KPI summary
plot_pane_15bis, # Third plot
pn.Spacer(width=10), # Spacer
kpi_summary_card, # KPI summary card
pn.Spacer(width=30), # Spacer
vertical_divider_med1, # Vertical divider for layout
pn.Spacer(width=30), # Spacer
plot_pane_14bis_2, # Last plot in this row
),
pn.Spacer(height=40), # 10/07
pn.Row( # Row for finance KPI card
pn.Spacer(height=20), # Optional spacer for vertical spacing
kpi_summary_card_finance # KPI finance table card
),
),
)
)
# Create a container with a max width constraint using pn.layout
container = pn.Column(
combined_plots_layout,
sizing_mode='stretch_width',
)
# Create a dashboard combining all plots with the title
combined_dashboard = pn.Column(
container,
sizing_mode='stretch_both' # Ensure the column stretches to fill available vertical space
)
##############################################################################################
# Initial call to update_widgets_and_table to populate the table based on default selections
#############################################################################################
production_dashboard = pn.Column(
pn.pane.HTML(f"""
<div style="text-align: left;">
<style>
h2 {{ margin-bottom: 0; color: #305496; }} /* Set title color here */
p {{ margin-top: 0; }}
</style>
<h2>Production</h2>
<p>{f"|CM-WIP| - <b>{file_date}</b>: Open WO at IDD based on QAD (ERP) | [Daily update]"}</p>
</div>
"""),
wip_selected_top,
filter_widgets_Prod,
wip_table,
height=600, # Set a fixed height to Production doashboard stays within the 600
sizing_mode='stretch_width' # Adjust sizing mode
)
#New 09/20
################################################################################
# Display the selected 'Pty Indice' & 'Drawing#' on the dashboard
#################################################################################
# Text widget to display the output
text_widget = pn.pane.HTML()
def trim_seda_top_level(seda_top_level):
"""
Trims the SEDA Top Level value to derive the drawing#.
"""
if pd.isnull(seda_top_level):
return ""
seda_top_level = str(seda_top_level)
# Case 1: Ends with - followed by digits (e.g., 3616-1, 2101-915-2, 851-11622-140)
if "-" in seda_top_level and seda_top_level.split("-")[-1].isdigit():
parts = seda_top_level.split("-")
if len(parts[-1]) == 1: # e.g., 3616-1
return "-".join(parts[:-1])
elif len(parts[-1]) == 2: # e.g., 2101-915-2
return "-".join(parts[:-1])
elif len(parts[-1]) == 3: # e.g., 851-11622-140
return seda_top_level[:-4]
# Case 2: Keep as is (e.g., 590010-1IE032)
return seda_top_level
def generate_display_text():
try:
# Get selected values from widgets
selected_indice = indice_widget.value
# Filter df_Priority based on the selected Pty Indice
if selected_indice == 'All':
filtered_rows = df_Priority # Include all rows if 'All' is selected
else:
if isinstance(selected_indice, list):
filtered_rows = df_Priority[df_Priority['Pty Indice'].isin(selected_indice)]
else:
filtered_rows = df_Priority[df_Priority['Pty Indice'] == selected_indice]
# Handle empty results
if filtered_rows.empty:
return "<span style='font-size: 18px; color: red;'>No matching data found</span>"
# Generate unique formatted strings
display_set = set()
for _, row in filtered_rows.iterrows():
idd_top_level = row['IDD Top Level']
seda_top_level = row['SEDA Top Level']
# Trim SEDA Top Level to derive drawing#
seda_trimmed = trim_seda_top_level(seda_top_level)
pty_indice = row['Pty Indice']
# Capitalize Pty Indice and IDD Top Level
pty_indice = pty_indice.upper()
idd_top_level = idd_top_level.upper()
# Create the display text
display_text = f"<b>{pty_indice} {idd_top_level}</b> (drawing# {seda_trimmed})"
display_set.add(display_text)
# Sort and group the display text
sorted_display_texts = sorted(display_set)
grouped_text = [
"; ".join(sorted_display_texts[i:i+3])
for i in range(0, len(sorted_display_texts), 3)
]
final_display_text = "<br>".join(grouped_text)
# Wrap the final text in the desired styling
return f"<span style='font-size: 18px; color: #32599E;'>{final_display_text}</span>"
except Exception as e:
return f"<span style='font-size: 18px; color: red;'>Error: {str(e)}</span>"
# Callback to update the text when widget values change
def update_text(event):
text_widget.object = generate_display_text()
# Attach the callback to the widgets
indice_widget.param.watch(update_text, 'value')
priority_widget.param.watch(update_text, 'value')
program_widget.param.watch(update_text, 'value')
# Initialize the text widget with default values
text_widget.object = generate_display_text()
#//////////////////////////////////////////////////#//////////////////////////////////////////////////
###*********************#################********************##############*************************
####################################################################################################################
# Creating the complete dashboard
#####################################################################################################################
#//////////////////////////////////////////////////#//////////////////////////////////////////////////
#########################################################
# Watch changes on widget
#########################################################
program_widget.param.watch(update_priorities, 'value')
priority_widget.param.watch(update_indices, 'value')
#########################################################
# Callbaks
#########################################################
# Define callbacks for widget events - update_changes_table
program_widget.param.watch(update_changes_table, 'value')
priority_widget.param.watch(update_changes_table, 'value')
indice_widget.param.watch(update_changes_table, 'value')
# Define callbacks for widget events - update_sales_table
program_widget.param.watch(update_sales_table, 'value')
priority_widget.param.watch(update_sales_table, 'value')
indice_widget.param.watch(update_sales_table, 'value')
# Define callbacks for widget events - update_sales_summary
program_widget.param.watch(update_sales_summary, 'value')
priority_widget.param.watch(update_sales_summary, 'value')
indice_widget.param.watch(update_sales_summary, 'value')
# Define callbacks for widget events - update_supply_table
program_widget.param.watch(update_supply_table, 'value')
priority_widget.param.watch(update_supply_table, 'value')
indice_widget.param.watch(update_supply_table, 'value')
# Define callbacks for widget events - update_turnover_table
program_widget.param.watch(update_turnover_table, 'value')
priority_widget.param.watch(update_turnover_table, 'value')
indice_widget.param.watch(update_turnover_table, 'value')
# Define callbacks for widget events - update_turnover_summary
program_widget.param.watch(update_turnover_summary, 'value')
priority_widget.param.watch(update_turnover_summary, 'value')
indice_widget.param.watch(update_turnover_summary, 'value')
# Define callbacks for widget events - update_supply_selected_top
program_widget.param.watch(update_supply_selected_top, 'value')
priority_widget.param.watch(update_supply_selected_top, 'value')
indice_widget.param.watch(update_supply_selected_top, 'value')
# Define callbacks for widget events - update_wip_selected_top
program_widget.param.watch(update_wip_selected_top, 'value')
priority_widget.param.watch(update_wip_selected_top, 'value')
indice_widget.param.watch(update_wip_selected_top, 'value')
# Update 09/16
# Set up callbacks for all widgets
for widget in [program_widget, priority_widget, indice_widget] + list(filters_Prod.values()):
widget.param.watch(widget_change_prod, 'value')
# New 09/03
# Set up callbacks for supply chain table purchased archi
program_widget.param.watch(on_widget_change_supply, 'value')
priority_widget.param.watch(on_widget_change_supply, 'value')
indice_widget.param.watch(on_widget_change_supply, 'value')
# Update 09/16
# Set up callbacks for supply chain table full archi
for widget in [program_widget, priority_widget, indice_widget] + list(filters_fullArchi.values()):
widget.param.watch(lambda event: update_supply_table_fullArchi(event), 'value')
# Link widget and plot update for Graph 13bis
program_widget.param.watch(update_plot_13bis, 'value')
priority_widget.param.watch(update_plot_13bis, 'value')
indice_widget.param.watch(update_plot_13bis, 'value')
# Link widget and plot update for Graph 13bbis
program_widget.param.watch(update_plot_13bbis, 'value')
priority_widget.param.watch(update_plot_13bbis, 'value')
indice_widget.param.watch(update_plot_13bbis, 'value')
# Link widget and plot update for Graph 14bis_2
program_widget.param.watch(update_plot_14bis_2, 'value')
priority_widget.param.watch(update_plot_14bis_2, 'value')
indice_widget.param.watch(update_plot_14bis_2, 'value')
# Link widget and plot update for Graph 14bbis
program_widget.param.watch(update_plot_14bbis, 'value')
priority_widget.param.watch(update_plot_14bbis, 'value')
indice_widget.param.watch(update_plot_14bbis, 'value')
# Link widget and plot update for Graph 14bbis
program_widget.param.watch(update_plot_15bis, 'value')
priority_widget.param.watch(update_plot_15bis, 'value')
indice_widget.param.watch(update_plot_15bis, 'value')
################################################################
# Define the cadrans_dashboard layout
################################################################
text_above_4cadrans_graphs = (
f"These graphs are based on data from |Snapshot| - <b>{file_date}</b>:<br>"
"▷ <b>Snapshot table</b>: Represents the remaining scope of the Transfer Project for the selected 'Program'.<br>"
"➥ It includes all PNs that have an existing IDD Backlog or for which the 'Critical Quantity,' defined as part of the transfer project, has not yet been reached. This applies even if the PN is not currently listed in the IDD Backlog.<br>"
"➥ Some PNs may not yet have an assigned IDD PN under 'IDD Top-Level'. In such cases, the BOM does not exist, and the given PN won't be present in this table; therefore, no data will be available for the graphs.<br>"
)
# Define your color
line_color = "#4472C4" # Change this to your desired color
font_top_color = "#4472C4"
#subtitle_backgroud_color = "#F2F2F2" #Gray
subtitle_background_color = "#aee0d9"
#------------------------------------------
# Convert the string to a datetime object using the format "%m-%d-%Y" to match the m-d-Y format
file_date_obj = datetime.strptime(file_date, "%m-%d-%Y")
# Format the datetime object into the desired m/d/Y format
formatted_date = file_date_obj.strftime("%m/%d/%Y")
# include within cadran title
cadrans_title = f"Status snapshot & 4 quadrant [{formatted_date}]"
#------------------------------------------
candrans_subtitle = "Selection of the Priority to be displayed on the dashboard"
candrans_subtitle2 = "Snapshot"
candrans_subtitle3 = " 4 quadrant - Engineering / Backlog / Supply Chain / Production"
# Create vertical and horizontal divs to act as colored lines
vertical_line = pn.pane.HTML(f"<div style='width: 6px; height: 800px; background-color: {line_color};'></div>")
horizontal_line = pn.pane.HTML(f"<div style='width: 2600px; height: 6px; background-color: {line_color};'></div>")
title_section = pn.pane.HTML(f"""
<div style='background-color: {font_top_color}; width: 100%; padding: 10px; box-sizing: border-box;'>
<h1 style='font-size: 24px; color: white; text-align: left; margin: 0;'>{cadrans_title}</h1>
</div>
""", sizing_mode='stretch_width')
# Title Layout
title_layout = pn.Column(
title_section,
pn.layout.Divider(margin=(-10, 0, 0, 0)), # Title divider
pn.Column(
pn.layout.Spacer(height=5), # Spacer to add space after subtitle
pn.Row(
program_widget,
priority_widget,
indice_widget,
pn.Column( # Group the spacer and text_widget inside a column for proper vertical spacing
pn.layout.Spacer(height=15), # Spacer to add space before text_widget
text_widget # Your formatted text widget
),
pn.layout.Spacer(height=10), # Spacer to add space after widget selection
sizing_mode='stretch_width' # Ensure the row stretches to fill the width
),
#pn.pane.HTML('<hr style="border: 1px solid #D9D9D9; margin: 10px 0;">'), # Custom horizontal line (break)
pn.Row(
filter_widgets_top_level_4_cadran, # Newly added 02/26, placed in a dedicated row
sizing_mode='stretch_width' # Ensure the row stretches to fill the width
),
sizing_mode='stretch_width' # Ensure the column stretches to fill the width
),
sizing_mode='stretch_width' # Ensure the title layout stretches to fill the width
)
# Define Secondary Layout
secondary_layout = pn.Column(
pn.pane.HTML(f"""
<div style='background-color: {subtitle_background_color};
width: 100%;
padding: 10px;
box-sizing: border-box;
border-radius: 15px;'> <!-- Corrected closing div tag -->
<h1 style='font-size: 22px; color: white; text-align: left; margin: 0;'>
{candrans_subtitle2}
</h1>
</div>
""",sizing_mode='stretch_width'),
#pn.layout.Divider(margin=(-10, 0, 0, 0)), # Title divider
pn.Spacer(height=10), # Spacer before plots
text_above_4cadrans_graphs,
pn.Spacer(height=10), # Spacer before plots
combined_dashboard,
sizing_mode='stretch_width', # Ensure the secondary layout stretches to fill the width
height=1000 # Set a fixed height to prevent overlap # 10/07 920 --> 1000
)
# Define Primary Layout
primary_layout = pn.Column(
pn.Column(
pn.pane.HTML(f"""
<div style='background-color: {subtitle_background_color};
width: 100%;
padding: 10px;
box-sizing: border-box;
border-radius: 15px;'> <!-- Corrected closing div tag -->
<h1 style='font-size: 22px; color: white; text-align: left; margin: 0;'>
{candrans_subtitle3}
</h1>
</div>
""",sizing_mode='stretch_width'),
#pn.layout.Divider(margin=(-10, 0, 0, 0)), # Title divider
pn.Row(
pn.Column(
changes_dashboard,
sizing_mode='stretch_width' # Adjust sizing mode for Engineering quadrant
),
vertical_line, # Add vertical line between columns
pn.Column(
production_dashboard,
sizing_mode='stretch_width' # Adjust sizing mode for Production quadrant
),
sizing_mode='stretch_width', # Adjust sizing mode for the entire row
),
horizontal_line, # Add horizontal line between upper and lower quadrants
pn.Row(
pn.Column(
supply_dashboard,
sizing_mode='stretch_both' # Adjust sizing mode for Supply Chain quadrant
),
vertical_line, # Add vertical line between columns
pn.Column(
sales_dashboard,
sizing_mode='stretch_both' # Adjust sizing mode for Sales quadrant
),
),
sizing_mode='stretch_both', # Adjust sizing mode for the entire primary layout
)
)
# Combine Title, Primary, and Secondary Layouts
cadrans_dashboard = pn.Column(
title_layout,
pn.layout.Divider(margin=(0, 0, -10, 0)), # Add some space between primary and secondary layouts if needed
secondary_layout,
pn.layout.Divider(margin=(0, 0, -10, 0)), # Add some space between primary and secondary layouts if needed
primary_layout,
sizing_mode='stretch_both' # Ensure the final layout stretches to fill available space
)
#||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
#||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
#*****************************************************************************************************************************
##############################################################################################################################
# |Project Overview| - historic_dashboard
##############################################################################################################################
#*****************************************************************************************************************************
#||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
#||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
###############################################################
# Historic datafram
###############################################################
# Ensure 'Invoice date' is in datetime format
df_Historic['Invoice date'] = pd.to_datetime(df_Historic['Invoice date'])
#Updated 09/06 to replace 'Standard amount USD' with 'Currency turnover ex.VAT' which represents the sales
# Rename columns
df_Historic = df_Historic.rename(columns={
'Quantity': 'Quantity shipped',
'Currency turnover ex.VAT': 'Sales',
'Complexity': 'Average Complexity',
})
# Filter to exclude rows where 'Order' contains 'NC'
df_Historic = df_Historic[~df_Historic['Order'].str.contains('NC')]
##########################################
# Sorting df_Historic
###########################################
# Function to check if a value is numeric
def is_numeric(val):
try:
int(val)
return True
except ValueError:
return False
# Separate numeric and non-numeric 'Priority' values
df_numeric_priority = df_Historic[df_Historic['Priority'].apply(is_numeric)]
df_non_numeric_priority = df_Historic[~df_Historic['Priority'].apply(is_numeric)]
# Convert 'Priority' values to integers for numeric priorities
df_numeric_priority['Priority'] = df_numeric_priority['Priority'].astype(int)
# Sort numeric priorities in ascending order
#df_numeric_priority = df_numeric_priority.sort_values(by='Priority', ascending=True) - update 08/28
df_numeric_priority.sort_values(by=['Priority', 'Pty Indice'], inplace=True)
# Combine the DataFrames, placing numeric priorities first and non-numeric priorities at the end
df_Historic_sorted = pd.concat([df_numeric_priority, df_non_numeric_priority])
# Reset index if needed
df_Historic_sorted.reset_index(drop=True, inplace=True)
# Update the original DataFrame
df_Historic = df_Historic_sorted
#print('df_Historic:')
#display(df_Historic)
#####################################
# Group by 'Month' and 'Program'
########################################
monthly_summary = df_Historic.groupby(['Month', 'Program']).agg({
'Quantity shipped': 'sum',
'Sales': 'sum',
'Pty Indice': lambda x: ', '.join(map(str, x)),
'IDD Top Level': lambda x: ', '.join(map(str, x)), # 03/10
'SEDA Top Level': lambda x: ', '.join(map(str, x)), #03/10
'IDD Marge Standard': 'sum',
'Invoice date': 'first', # Keep the 'Invoice date' as the first date in each group
'Average Complexity': 'mean' # Calculate the average complexity
}).reset_index()
# Define a function to format numbers with 1 decimal digit if necessary
def format_complexity(value):
if pd.isna(value): # Handle NaN values
return value
elif value.is_integer():
return int(value) # Return as integer if value is an integer
else:
return round(value, 1) # Round to 1 decimal place otherwise
# Apply the formatting function to 'Complexity' column
monthly_summary['Average Complexity'] = monthly_summary['Average Complexity'].apply(format_complexity)
#Create 'Normalized Complexity'
monthly_summary['Normalized Complexity'] = monthly_summary['Average Complexity']*monthly_summary['Quantity shipped']
###################################
# Fill NaN values appropriately
###################################
# Fill numeric columns with 0
numeric_cols = monthly_summary.select_dtypes(include='number').columns
monthly_summary[numeric_cols] = monthly_summary[numeric_cols].fillna(0)
# Fill string columns with ''
string_cols = monthly_summary.select_dtypes(include='object').columns
monthly_summary[string_cols] = monthly_summary[string_cols].fillna('')
# Ensure 'Invoice date' is in datetime format
df_Historic['Invoice date'] = pd.to_datetime(df_Historic['Invoice date'])
# Sort by 'Invoice date' in descending order
monthly_summary = monthly_summary.sort_values(by='Invoice date', ascending=True)
# Display the updated DataFrame
#print('monthly_summary:')
#display(monthly_summary)
#################################################################################################################
# Widgets initialization
################################################################################################################
# Explicit list of allowed programs
allowed_programs_Historic = ['Phase 4-5', 'EMBRAER', 'SIKORSKY', 'COMAC', '1stTB', 'Space-X'] # 04/17
# Example usage with defaults
default_program_historic = 'Phase 4-5'
# Initialize program widget, excluding NaN values
#unique_programs_historic = df_Priority['Program'].dropna().unique().tolist()
# program_widget_historic = pn.widgets.Select(name='Select Program', options=unique_programs_historic, value=default_program_historic)
#----------------------------------- New 03/25 ------------------------------------------------------
# Create widget with only allowed programs
program_widget_historic = pn.widgets.Select(
name='Select Program',
options=allowed_programs_Historic,
value=default_program_historic
)
#------------------------------------------------------------------------------------------------------
program = program_widget_historic.value
#//////////////////////////////////////////////////
########################################
# Create Graphs Monthly_history
########################################
#//////////////////////////////////////////////////
# To review 09/06
# Custom color palette with alpha transparency
custom_palette = {
'Quantity shipped': '#5AB2CA',
'Sales': '#63BE7B',
'IDD Marge Standard': '#E2EFDA',
'Normalized Complexity': 'rgba(255, 47, 47, 0.7)' # Alpha applied
}
def customize_qty_shipped_plot(bokeh_plot):
""" Apply customizations to the Quantity Shipped plot. """
bokeh_plot.xaxis.major_label_text_font_size = '8pt'
bokeh_plot.yaxis.major_label_text_font_size = '10pt'
bokeh_plot.title.text_font_size = '12pt'
bokeh_plot.title.text_color = "#305496"
bokeh_plot.xaxis.axis_line_width = 2
bokeh_plot.yaxis.axis_line_width = 2
bokeh_plot.xaxis.major_label_orientation = 'vertical' # 10/23 change to vertical
bokeh_plot.yaxis.major_label_orientation = 'horizontal'
bokeh_plot.yaxis.axis_label_text_font_size = '10pt'
bokeh_plot.ygrid.grid_line_color = '#E0E0E0'
bokeh_plot.ygrid.grid_line_dash = [4, 6]
bokeh_plot.toolbar.logo = None
return bokeh_plot
def customize_combined_plot(bokeh_plot):
""" Apply customizations to the Combined plot. """
bokeh_plot.xaxis.major_label_text_font_size = '8pt'
bokeh_plot.yaxis.major_label_text_font_size = '10pt'
bokeh_plot.title.text_font_size = '12pt'
bokeh_plot.title.text_color = "#305496"
bokeh_plot.xaxis.axis_line_width = 2
bokeh_plot.yaxis.axis_line_width = 2
bokeh_plot.xaxis.major_label_orientation = 'vertical' # 10/23 change to vertical
bokeh_plot.yaxis.major_label_orientation = 'horizontal'
bokeh_plot.yaxis.axis_label_text_font_size = '10pt'
bokeh_plot.ygrid.grid_line_color = '#F0F0F0'
bokeh_plot.ygrid.grid_line_dash = [4, 6]
bokeh_plot.toolbar.logo = None
# Format the y-axis ticks in thousands with a dollar sign
bokeh_plot.yaxis.formatter =CustomJSTickFormatter(code="""
return '$' + (tick / 1000).toFixed(0) + 'k';
""")
return bokeh_plot
def customize_total_quantity_plot(bokeh_plot):
""" Apply customizations to the Total Quantity plot. """
bokeh_plot.xaxis.major_label_text_font_size = '8pt'
bokeh_plot.yaxis.major_label_text_font_size = '10pt'
bokeh_plot.title.text_font_size = '12pt'
bokeh_plot.title.text_color = "#305496"
bokeh_plot.xaxis.axis_line_width = 2
bokeh_plot.yaxis.axis_line_width = 2
bokeh_plot.xaxis.major_label_orientation = 'vertical'
bokeh_plot.yaxis.major_label_orientation = 'horizontal'
bokeh_plot.yaxis.axis_label_text_font_size = '10pt'
bokeh_plot.ygrid.grid_line_color = '#E0E0E0'
bokeh_plot.ygrid.grid_line_dash = [4, 6]
bokeh_plot.toolbar.logo = None
return bokeh_plot
def create_total_quantity_plot(df_Historic, default_program_historic):
# Filter data by the default program
filtered_data = df_Historic[df_Historic['Program'] == default_program_historic]
if filtered_data.empty:
print("No data found for the default program.")
return None
# Aggregate data: Sum 'Quantity shipped' for each 'Pty Indice'
aggregated_data = filtered_data.groupby('Pty Indice')['Quantity shipped'].sum().reset_index()
# If the program is 'Phase 4-5', sort by 'Priority'
if default_program_historic == 'Phase 4-5':
# Merge the aggregated data with original to retain 'Priority'
aggregated_data = pd.merge(aggregated_data, filtered_data[['Pty Indice', 'Priority']].drop_duplicates(), on='Pty Indice')
# Convert 'Priority' values to integers for sorting
aggregated_data['Priority'] = aggregated_data['Priority'].astype(int)
# Sort numeric priorities in ascending order
#aggregated_data = aggregated_data.sort_values(by='Priority', ascending=True) - update 08/28
aggregated_data.sort_values(by=['Priority', 'Pty Indice'], inplace=True)
# Define the uniform color - 09/12
uniform_color = '#5AB2CA' # Light blue color
# Create the plot Total Quantity Shipped Monthly
total_quantity_plot = aggregated_data.hvplot.bar(
x='Pty Indice',
y='Quantity shipped',
title="Total Quantity Shipped Monthly",
xlabel='Pty Indice',
ylabel='Total Quantity Shipped',
#cmap=custom_palette,
color=uniform_color, # Apply the same color to all bars
legend='top_left',
height=400,
tools=[]
)
return total_quantity_plot
def create_plots(monthly_summary, default_program_historic, df_Historic):
# Filter data by default program
filtered_data = monthly_summary[monthly_summary['Program'] == default_program_historic]
if filtered_data.empty:
print("No data found for the default program.")
return None, None, None
# Melt the DataFrame to include Normalized Complexity
melted_df = filtered_data.melt(id_vars=['Month'], value_vars=['Quantity shipped', 'Sales', 'IDD Marge Standard', 'Normalized Complexity'],
var_name='Quantity Type', value_name='Quantity Value')
# Create plot for 'Quantity shipped' and 'Normalized Complexity'
qty_shipped_plot = melted_df[melted_df['Quantity Type'].isin(['Quantity shipped', 'Normalized Complexity'])].hvplot.bar(
x='Month',
y='Quantity Value',
color='Quantity Type',
title="Monthly shipment - Quantity Shipped & Normalized Complexity",
xlabel='Month',
ylabel='Top-Level shipped [Quantity]',
cmap=custom_palette,
legend='top_left',
height=400,
bar_width=0.6, # Set bar width - 09/12
tools=[]
)
bokeh_qty_shipped_plot = hv.render(qty_shipped_plot, backend='bokeh')
bokeh_qty_shipped_plot = customize_qty_shipped_plot(bokeh_qty_shipped_plot)
#New 08/09
#####################################################
# Remove existing HoverTools (if any) before adding a new one
bokeh_qty_shipped_plot.tools = [tool for tool in bokeh_qty_shipped_plot.tools if not isinstance(tool, HoverTool)]
# Add HoverTool with custom formatting
hover = HoverTool()
hover.tooltips = [
("Month", "@Month"),
("KPI", "@color"),
("Value", "@Quantity_Value")
]
# Add HoverTool to the plot
bokeh_qty_shipped_plot.add_tools(hover)
# Remove wheel zoom from active tools if you want it inactive by default - 08/12
bokeh_qty_shipped_plot.tools = [tool for tool in bokeh_qty_shipped_plot.tools if not isinstance(tool, WheelZoomTool)]
############################################################
# Create combined plot for 'IDD Marge Standard' and 'Sales'
combined_plot = melted_df[melted_df['Quantity Type'].isin(['IDD Marge Standard', 'Sales'])].hvplot.bar(
x='Month',
y='Quantity Value',
color='Quantity Type',
title="Monthly shipment - IDD Margin & Total Sales",
xlabel='Month',
ylabel='[K$]',
cmap=custom_palette,
legend='top_left',
stacked=True, # Stacking bars
height=400,
bar_width=0.6, # Set bar width - 09/12
tools=[]
)
bokeh_combined_plot = hv.render(combined_plot, backend='bokeh')
bokeh_combined_plot = customize_combined_plot(bokeh_combined_plot)
#New 08/08
#####################################################
# Remove existing HoverTools (if any) before adding a new one
bokeh_combined_plot.tools = [tool for tool in bokeh_combined_plot.tools if not isinstance(tool, HoverTool)]
# Add HoverTool with custom formatting
hover = HoverTool()
hover.tooltips = [
("Month", "@Month"),
("KPI", "@color"),
("Value", "@Quantity_Value{($0,0k)}") # Format values: thousands with 'K' # Quantity_Value with the '_' otherwise that does not work!
]
# Add HoverTool to the plot
bokeh_combined_plot.add_tools(hover)
# Remove wheel zoom from active tools if you want it inactive by default - 08/12
bokeh_combined_plot.tools = [tool for tool in bokeh_combined_plot.tools if not isinstance(tool, WheelZoomTool)]
############################################################
# Create Total Quantity Shipped plot
total_quantity_plot = create_total_quantity_plot(df_Historic, default_program_historic)
if total_quantity_plot:
bokeh_total_quantity_plot = hv.render(total_quantity_plot, backend='bokeh')
bokeh_total_quantity_plot = customize_total_quantity_plot(bokeh_total_quantity_plot)
else:
bokeh_total_quantity_plot = None
# Remove wheel zoom from active tools if you want it inactive by default - 08/12
bokeh_total_quantity_plot.tools = [tool for tool in bokeh_total_quantity_plot.tools if not isinstance(tool, WheelZoomTool)]
return bokeh_qty_shipped_plot, bokeh_combined_plot, bokeh_total_quantity_plot
def update_plots(event):
# Get the selected program from the widget
program = program_widget_historic.value
#print(f"Updating plots for program: {program}")
# Filter data by the selected program
filtered_data = monthly_summary[monthly_summary['Program'] == program]
if filtered_data.empty:
print("No data found for the selected program.")
return
# Melt the DataFrame
melted_df = filtered_data.melt(id_vars=['Month'], value_vars=['Quantity shipped', 'Sales', 'IDD Marge Standard', 'Normalized Complexity'],
var_name='Quantity Type', value_name='Quantity Value')
# Update plots
bokeh_qty_shipped_plot, bokeh_combined_plot, bokeh_total_quantity_plot = create_plots(filtered_data, program, df_Historic)
# Update the plots in the Panel layout
plot_pane1.object = bokeh_qty_shipped_plot
plot_pane2.object = bokeh_combined_plot
plot_pane3.object = bokeh_total_quantity_plot
# Initial setup of the plots
bokeh_qty_shipped_plot, bokeh_combined_plot, bokeh_total_quantity_plot = create_plots(monthly_summary, default_program_historic, df_Historic)
# Convert Bokeh plots to Panel
plot_pane1 = pn.pane.Bokeh(bokeh_qty_shipped_plot, sizing_mode='stretch_width')
plot_pane2 = pn.pane.Bokeh(bokeh_combined_plot, sizing_mode='stretch_width')
plot_pane3 = pn.pane.Bokeh(bokeh_total_quantity_plot, sizing_mode='stretch_width')
# Update plot initially - Needed for the sizing_mode='stretch_width' to be set
update_plots(None)
############################################################################################
# Display the datafram monthly_summary of list of Pty Indice for each Month under Graph 3
#############################################################################################
# Function as to use dataframe df_Historic as monthly_summary does not have enough information
# Function to format Pty Indice with quantities from 'Total Qty Shipped'
def format_pty_indice_with_qty(row, df_Historic):
"""Format Pty Indice with actual quantities shipped per month.
Args:
row (pd.Series): A row from the DataFrame containing 'Month' and 'Program'.
df_Historic (pd.DataFrame): The historical DataFrame containing 'Month', 'Pty Indice', and 'Quantity shipped'.
Returns:
str: Formatted string with Pty Indices and their quantities shipped for the given month.
"""
try:
# Extract the month and program from the row
month = row['Month']
program = row['Program']
# Filter df_Historic for the given month and program
filtered_data = df_Historic[
(df_Historic['Month'] == month) &
(df_Historic['Program'] == program)
]
if filtered_data.empty:
return "No data found for this month and program."
# Group by Pty Indice and sum the quantities shipped
pty_indice_qty = filtered_data.groupby('Pty Indice')['Quantity shipped'].sum().reset_index()
# Format the output as "Pty Indice (Quantity shipped)"
formatted_output = [
f"{pty_indice} ({int(qty)} shipped)"
for pty_indice, qty in zip(pty_indice_qty['Pty Indice'], pty_indice_qty['Quantity shipped'])
]
return ', '.join(formatted_output)
except Exception as e:
print(f"Error processing row: {e}")
return "Format Error"
# Function to remove duplicates in comma-separated strings
def remove_duplicates_from_string(s):
if pd.isnull(s):
return s
items = s.split(', ')
unique_items = sorted(set(items), key=items.index) # Preserve order
return ', '.join(unique_items)
# Filter DataFrame by program - Update 03/11
def filter_dataframe_monthly_summary(program, df_Historic):
# Apply the filter based on the selected program
filtered_df = monthly_summary[monthly_summary['Program'] == program]
if filtered_df.empty:
print("No data found for the specified program.")
return filtered_df
# Apply the formatting function to each row
filtered_df['Pty Indice'] = filtered_df.apply(
lambda row: format_pty_indice_with_qty(row, df_Historic), axis=1
)
# Handle other columns and return the result
filtered_df = filtered_df[['Month', 'Pty Indice', 'Quantity shipped', 'IDD Top Level']]
filtered_df = filtered_df.rename(columns={'Quantity shipped': 'Total Qty Shipped'})
# Convert 'Month' column to datetime format
filtered_df['Month'] = pd.to_datetime(filtered_df['Month'], format='%b %y')
# Sort by 'Month' in descending order (most recent first)
filtered_df = filtered_df.sort_values(by='Month', ascending=False)
# Convert 'Month' back to the original string format (optional)
filtered_df['Month'] = filtered_df['Month'].dt.strftime('%b %y')
# Reorder 'IDD Top Level' based on 'Pty Indice' order
def reorder_idd_top_level(row, df_Historic):
month = row['Month']
pty_indices = [x.split(' ')[0] for x in row['Pty Indice'].split(', ')] # Extract Pty Indices
# Filter df_Historic for the given month and Pty Indices
filtered_data = df_Historic[
(df_Historic['Month'] == month) &
(df_Historic['Pty Indice'].isin(pty_indices))
]
if filtered_data.empty:
return row['IDD Top Level']
# Group by Pty Indice and collect IDD Top Level values
idd_mapping = filtered_data.groupby('Pty Indice')['IDD Top Level'].unique().reset_index()
# Create a dictionary to map Pty Indice to IDD Top Level
idd_dict = dict(zip(idd_mapping['Pty Indice'], idd_mapping['IDD Top Level']))
# Reorder IDD Top Level based on Pty Indice order
ordered_idd = []
for pty in pty_indices:
if pty in idd_dict:
ordered_idd.extend(idd_dict[pty])
# Join the ordered IDD Top Level values
return ', '.join(ordered_idd)
# Apply the reordering function to each row
filtered_df['IDD Top Level'] = filtered_df.apply(
lambda row: reorder_idd_top_level(row, df_Historic), axis=1
)
# Remove duplicates in 'IDD Top Level' column
filtered_df['IDD Top Level'] = filtered_df['IDD Top Level'].apply(remove_duplicates_from_string)
return filtered_df
#####################################################
# Table colored in bleu and white every other rows
#######################################################
def style_dataframe_bleu(df):
"""Apply custom styles to the DataFrame (alternating row colors and centered text).
Args:
df (pd.DataFrame): The DataFrame to style.
Returns:
pd.io.formats.style.Styler: Styled DataFrame.
"""
def row_styles(row):
"""Apply alternating row colors."""
color = '#ADDAE5' if row.name % 2 == 0 else '#ffffff'
return [f'background-color: {color}'] * len(row)
# Reset the index to ensure it is sequential
df = df.reset_index(drop=True)
# Create a Styler object
styled_df = df.style
# Apply alternating row colors
styled_df = styled_df.apply(row_styles, axis=1)
# Center text in specific columns
styled_df = styled_df.set_properties(
subset=['Month', 'Total Qty Shipped'], # Columns to center
**{'text-align': 'center'} # CSS property for centering text
)
# Hide the index
styled_df.hide(axis="index")
return styled_df
# Function to update the DataFrame summary with custom styling
def update_dataframe_monthly_summary(program, df_Historic):
"""Update the DataFrame summary with custom styling.
Args:
program (str): The selected program.
df_Historic (pd.DataFrame): The historical DataFrame.
Returns:
str: HTML representation of the styled DataFrame.
"""
filtered_df = filter_dataframe_monthly_summary(program, df_Historic)
styled_df = style_dataframe_bleu(filtered_df)
styled_html = styled_df.to_html() # Convert styled DataFrame to HTML
# Add CSS for overflow handling directly in the HTML
html_with_overflow = f'<div style="overflow-y: auto; height: 450px;">{styled_html}</div>'
return html_with_overflow
# Callback function to update the table based on widget value
def update_table(event):
"""Callback function to update the table based on the selected program."""
# Get the selected program from the widget
program = program_widget_historic.value
# Update the DataFrame pane with the new styled DataFrame
monthly_summary_table.object = update_dataframe_monthly_summary(program, df_Historic)
# Attach callback to the widget
program_widget_historic.param.watch(update_table, 'value')
# Create initial DataFrame table
monthly_summary_table = pn.pane.HTML(
update_dataframe_monthly_summary(default_program_historic, df_Historic),
width=1000
)
######################################
# Create text bellow graphs
########################################
# Convert 'Invoice date' to datetime format
df_Historic['Invoice date'] = pd.to_datetime(df_Historic['Invoice date'])
# Calculate the span period of the Turnover Report span_TurnoverReport
start_date_historic = df_Historic['Invoice date'].min()
end_date_historic = df_Historic['Invoice date'].max()
span_df_Historic = f"{start_date_historic.date()} to {end_date_historic.date()}" # Format dates as needed
text_below_graph_qty_shipped_plot = (
f"This graph is based on data from |CM-Historic| - Span: <b>{span_df_Historic}</b>:<br>"
f"▷ <b>Quantity shipped</b>: Total quantity of Top-Level related to the selected program shipped since {start_date_historic.date()}<br>"
"▷ <b>Normalized Complexity</b>: Average complexity of the Top-Level shipped normalized on the quantity of each PN shipped on the period.<br>"
"▷ <b>The complexity is define as</b>: Kit, Subs = 0, Lighplate = 1, Rotottelite = 2, CPA = 3, ISP = 4.<br>"
)
text_below_graph_Marge_Sales = (
f"This graph is based on data from |CM-Historic| - Span: <b>{span_df_Historic}</b>:<br>"
"▷ <b>Sales</b>: Sum of the 'Currency turnover ex.VAT' for the PN shipped during the specified month<br>"
"▷ <b>IDD Marge Standard</b>: Sum of the 'IDD Margin Standard' for the PN shipped during the specified month.<br>"
)
text_below_graph_shipped_pty_indice = (
f"This graph is based on data from |CM-Historic| - Span: <b>{span_df_Historic}</b>:<br>"
f"▷ <b>Total Quantity shipped</b>: Total quantity of Top-Level related to the selected pty Indice shipped since {start_date_historic.date()}.<br>"
)
###WORKING CODE
##############################################
# Combine plots into a vertical Panel layout
###############################################
#create short vertical divider
vertical_divider_medium = pn.pane.HTML(
'<div style="width: 1px; height: 500px; background-color:#D9D9D9;"></div>',
)
vertical_divider_medium2 = pn.pane.HTML(
'<div style="width: 1px; height: 500px; background-color:#D9D9D9;"></div>',
)
# Combine the plots and table in the layout
combined_plots_history = pn.Column(
pn.Row(
pn.Column(
plot_pane1,
text_below_graph_qty_shipped_plot
),
pn.Spacer(width=50),
vertical_divider_medium,
pn.Spacer(width=50),
pn.Column(
plot_pane2,
text_below_graph_Marge_Sales
),
sizing_mode='stretch_width'
),
pn.Spacer(height=50), # Spacer before the next row
pn.Row(
pn.Column(
plot_pane3,
text_below_graph_shipped_pty_indice
),
monthly_summary_table, # Add table pane to the right of the text and plot
)
)
########################################
# Call-out on program_widget_historic
########################################
program_widget_historic.param.watch(update_plots, 'value')
#//////////////////////////////////////////////////
###################################################
# Create Graphs Categories of products - 08/09
###################################################
#Load df_Priority as it has been filtered previously on the code
df_Priority = pd.read_excel(input_file_formatted, sheet_name='CM-Priority', index_col=False)
#----------------------------------------------------------
# 02/11 - Change 'Phase 4' or 'Phase 5' with 'Phase 4-5'
#----------------------------------------------------------
# For df_Priority
if 'Program' in df_Priority.columns and 'Pty Indice' in df_Priority.columns:
mask = (
df_Priority['Program'].isin(['Phase 4', 'Phase 5']) &
~df_Priority['Pty Indice'].str.contains('Phase5', na=False)
)
df_Priority.loc[mask, 'Program'] = 'Phase 4-5'
#----------------------------------------------------------
#------------------------------------------------------------------------------------------
# 03/19 - Only keep 'Program' = 'Phase 4-5', 'EMBRAER', SIKORSKY', '1stTB', '2ndTB'
#------------------------------------------------------------------------------------------
# Define the allowed values for the 'Program' column
allowed_programs = ['Phase 4-5', 'EMBRAER', 'SIKORSKY', 'COMAC', '1stTB', '2ndTB']
# Apply filtering to each DataFrame
df_Priority = df_Priority[df_Priority['Program'].isin(allowed_programs)]
#------------------------------------------------------------------------------------------
# Correctly accessing multiple columns
Pivot_table_distribution = df_Snapshot[['Pty Indice', 'Top-Level Status', 'Priority', 'Shipped', 'Remain. crit. Qty', 'Production Status', 'IDD Backlog Qty', 'Product Category', 'Critical Qty']]
# Apply mapping to create 'Program' column in df_WIP
Pivot_table_distribution['Program'] = Pivot_table_distribution['Pty Indice'].map(indice_to_program)
#Include missing Pty from df_Priority and fill 'IDD Backlog Qty' with 0
# Perform a left join, so all rows from Pivot_table_distribution are kept
Pivot_table_distribution = pd.merge(
Pivot_table_distribution,
df_Priority[['Pty Indice']].drop_duplicates(), # Selecting only the 'Pty Indice' column from df_Priority and dropping duplicates
on='Pty Indice',
how='left' # 'left' join to keep all rows from Pivot_table_distribution
)
#Fill 'IDD Backlog Qty' with 0 where missing
Pivot_table_distribution['IDD Backlog Qty'].fillna(0, inplace=True)
# Calculate necessary fields - Update 08/14: 'Total Quantity' should be set as 'IDD Backlog Qty' + 'Shipped' not just 'IDD Backlog Qty'
#Pivot_table_distribution['Total Quantity'] = Pivot_table_distribution[['Critical Qty', 'IDD Backlog Qty']].max(axis=1)
# Ensure that there are no NaN values in 'Shipped' and 'Remain. crit. Qty'
Pivot_table_distribution['Shipped'].fillna(0, inplace=True)
Pivot_table_distribution['Remain. crit. Qty'].fillna(0, inplace=True)
# Compute the sum of 'IDD Backlog Qty' and 'Shipped'
Pivot_table_distribution['Sum IDD Backlog and Shipped'] = Pivot_table_distribution['IDD Backlog Qty'] + Pivot_table_distribution['Shipped']
# Apply conditional logic: Total Quantity' = 'IDD Backlog Qty' + 'Shipped' ONLY if 'Remain. crit. Qty' = 0
Pivot_table_distribution['Total Quantity'] = np.where(
Pivot_table_distribution['Remain. crit. Qty'] == 0,
Pivot_table_distribution['Sum IDD Backlog and Shipped'],
Pivot_table_distribution[['Critical Qty', 'IDD Backlog Qty']].max(axis=1)
)
# Optionally, drop the intermediate column if no longer needed
#Pivot_table_distribution.drop(columns=['Sum IDD Backlog and Shipped'], inplace=True)
#print("Pivot_table_distribution:")
#display(Pivot_table_distribution)
#Saved 08/13 to include redlist in these tables
'''
###################################
# Aggregate data by Program and Product Category
aggregation_by_product_category = Pivot_table_distribution.groupby(['Program', 'Product Category']) \
.agg(Pty_Indice_Count_Product_Category=('Pty Indice', 'nunique')) \
.reset_index()
# Aggregate data by Program and Production Status
aggregation_by_production_status = Pivot_table_distribution.groupby(['Program', 'Production Status']) \
.agg(Pty_Indice_Count_Production_Status=('Pty Indice', 'nunique')) \
.reset_index()
#########################
# Print aggregated data
#########################
#print("Aggregated Data by Product Category:")
#display(aggregation_by_product_category)
#print("Aggregated Data by Production Status:")
#display(aggregation_by_production_status)
'''
#New 08/12
######################################################################################
# Define the % Completion of each Pty Indice in a new dataframe Pivot_table_completion
##########################################################################################
# Copy Pivot_table_distribution
Pivot_table_completion = Pivot_table_distribution.copy()
#print('Pivot_table_completion before adding new rows')
#display(Pivot_table_completion)
######################################################################################################################
# df_Priority is missing ['Top-Level Status', 'IDD Backlog Qty', 'Product Category', 'Total Quantity'] --> delete 'Top-Level Status', 'Product Category' & 'IDD Backlog Qty' from Dataframe
# If PN is not in df_snapshot and therefor not in Pivot_table_completion, it should mean that either:
# --> The PN does not have a BOM: Either not a Top-Level or prep work not completed.
# --> The PN is part of the redlist & 'Top-Level Status' = short or 'IDD Backlog Qty' = 0 or both: Filtered-out from df_Snapshot
# --> The values should be set as: = 'Total Quantity' ='Critical Qty'
####################################################################################################################
# Columns to remove from Pivot_table_completion
columns_to_remove = ['Top-Level Status', 'Production Status', 'IDD Backlog Qty', 'Product Category']
Pivot_table_completion = Pivot_table_completion.drop(columns=columns_to_remove, errors='ignore')
# Create mappings from df_Priority
priority_mapping = df_Priority.set_index('Pty Indice')['Priority']
program_mapping = df_Priority.set_index('Pty Indice')['Program']
shipped_mapping = df_Priority.set_index('Pty Indice')['Shipped']
# Map values to Pivot_table_completion
Pivot_table_completion['Priority'] = Pivot_table_completion['Pty Indice'].map(priority_mapping)
Pivot_table_completion['Program'] = Pivot_table_completion['Pty Indice'].map(program_mapping)
Pivot_table_completion['Qty Shipped'] = Pivot_table_completion['Pty Indice'].map(shipped_mapping)
# Fill missing values for 'Priority' and 'Program'
Pivot_table_completion['Priority'].fillna('Unknown', inplace=True)
Pivot_table_completion['Program'].fillna('Unknown', inplace=True)
# Optionally, filter out rows where 'Qty Shipped' is NaN or 0
Pivot_table_completion = Pivot_table_completion[Pivot_table_completion['Qty Shipped'].notna() & (Pivot_table_completion['Qty Shipped'] > 0)]
# Define criteria for including additional rows from df_Priority
# For example, you might want to include rows where 'Qty Shipped' > a certain threshold
additional_rows_criteria = df_Priority['Shipped'] > 0 # Example condition
additional_rows = df_Priority[additional_rows_criteria]
# Add only the rows that are not already in Pivot_table_completion based on 'Pty Indice'
additional_rows = additional_rows[~additional_rows['Pty Indice'].isin(Pivot_table_completion['Pty Indice'])]
# Select and rename columns to match Pivot_table_completion
additional_rows = additional_rows[['Pty Indice', 'Shipped', 'Priority', 'Program', 'Critical Qty']]
additional_rows.rename(columns={'Shipped': 'Qty Shipped'}, inplace=True)
# Append additional rows to Pivot_table_completion
Pivot_table_completion = pd.concat([Pivot_table_completion, additional_rows], ignore_index=True)
# Optionally, remove duplicates if necessary
Pivot_table_completion = Pivot_table_completion.drop_duplicates(subset='Pty Indice', keep='last')
# Fill 'Total Quantity' with 'Critical Qty' where 'Total Quantity' is NaN -- Update 08/14: When 'IDD Backlog' > 0, the 'Total Quantity' = 'IDD Backlog' + 'Qty Shipped'
Pivot_table_completion['Total Quantity'] = Pivot_table_completion['Total Quantity'].fillna(Pivot_table_completion['Critical Qty'])
#Sort by 'Priority' end place string at the end of the datafram
# Convert 'Priority' column to numeric, coercing errors (non-numeric entries become NaN)
Pivot_table_completion['Priority'] = pd.to_numeric(Pivot_table_completion['Priority'], errors='coerce')
# Fill NaNs with a default value (e.g., 999) for sorting
Pivot_table_completion['Priority'].fillna(999, inplace=True)
# Sort the DataFrame by 'Priority' column in ascending order
#Pivot_table_completion.sort_values(by='Priority', ascending=True, inplace=True) - Update 08/28
Pivot_table_completion.sort_values(by=['Priority', 'Pty Indice'], inplace=True)
# Reset index if needed
Pivot_table_completion.reset_index(drop=True, inplace=True)
#print('Pivot_table_completion with missing rows, no duplicates, and updated columns:')
#display(Pivot_table_completion)
########################################################################################
# Define % Conpletion Critical Qty = Critical Qty / Qty Shipped
Pivot_table_completion['% Completion Critical Qty'] = (Pivot_table_completion['Qty Shipped'] / Pivot_table_completion['Critical Qty']) * 100
# Cap the values at 100%
Pivot_table_completion['% Completion Critical Qty'] = Pivot_table_completion['% Completion Critical Qty'].clip(upper=100)
# Replace NaN values with 0
Pivot_table_completion['% Completion Critical Qty'] = Pivot_table_completion['% Completion Critical Qty'].fillna(0)
# Round to the nearest whole number
Pivot_table_completion['% Completion Critical Qty'] = Pivot_table_completion['% Completion Critical Qty'].round(0).astype(int)
# Replace NaN values with 0
Pivot_table_completion['% Completion Critical Qty'] = Pivot_table_completion['% Completion Critical Qty'].fillna(0)
# Define % Completion Total Qty = Total Qty / Qty Shipped
Pivot_table_completion['% Completion Total Qty'] = (Pivot_table_completion['Qty Shipped'] / Pivot_table_completion['Total Quantity']) * 100
# Replace NaN values with 0
Pivot_table_completion['% Completion Total Qty'] = Pivot_table_completion['% Completion Total Qty'].fillna(0)
# Round to the nearest whole number
#Pivot_table_completion['% Completion Total Qty'] = Pivot_table_completion['% Completion Total Qty'].round(0).astype(int) # saved 02/03
##### 02/03 ####################################
# First handle infinite values and NaNs
Pivot_table_completion['% Completion Total Qty'] = (
Pivot_table_completion['% Completion Total Qty']
.replace([np.inf, -np.inf], np.nan) # Replace infinities with NaN
.fillna(0) # Now fill all NaNs with 0
)
# Then perform rounding and conversion
Pivot_table_completion['% Completion Total Qty'] = (
Pivot_table_completion['% Completion Total Qty']
.round(0)
.astype(int)
)
####################################################
###############################
# Print Pivot_table_completion
################################
# Set options to display the entire DataFrame
pd.set_option('display.max_rows', None)
pd.set_option('display.max_columns', None)
#print('Pivot_table_completion with ALL column')
#display(Pivot_table_completion)
#############################################################################################################
# New 08/13 - Include product from the redlist 'To be transferred' or 'Canceled' or 'Officially transferred' within Pivot_table_completion
##############################################################################################################
Pivot_table_scope = df_Priority.copy()
#print('df_Priority without any filter')
#display(Pivot_table_scope)
#Function to get the 'Product Category' based on 'Description'
# Define the 'Product Category' based on 'Description'
def determine_category(description):
if not isinstance(description, str):
return 'Others'
if description == 'Rototellite':
return 'Rototellite'
elif 'Indicator' in description or 'CPA' in description:
return 'CPA'
elif 'Lightplate' in description:
return 'Lightplate'
elif 'ISP' in description or 'Keyboard' in description:
return 'ISP'
elif 'Module' in description:
return 'CPA'
elif 'optics' in description:
return 'Fiber Optics'
else:
return 'Others'
# Create 'Product Category' column based on the 'Description'
Pivot_table_scope['Product Category'] = Pivot_table_scope['Description'].apply(determine_category)
#Include 'Total Quantity' in Pivot_table_scope from Pivot_table_completion otherwise set 'Total Quantity' = 'Critical Qty'
# Merge with Pivot_table_completion to get 'Total Quantity'
Pivot_table_scope = Pivot_table_scope.merge(
Pivot_table_completion[['Pty Indice', 'Total Quantity']],
on='Pty Indice',
how='left'
)
# Fill in 'Total Quantity' where missing with 'Critical Qty'
Pivot_table_scope['Total Quantity'] = Pivot_table_scope['Total Quantity'].fillna(Pivot_table_scope['Critical Qty'])
#Keep only relevant column
Pivot_table_scope = Pivot_table_scope[['Pty Indice', 'Product Category', 'Critical Qty', 'Production Status', 'Total Quantity', 'Program']]
#print('Pivot_table_scope')
#display(Pivot_table_scope)
#################################################################################################
# Filter-out 'Canceled' PN from aggreagated data to exclude the canceled order from the scope
#################################################################################################
# Filter out rows where 'Production Status' is 'Canceled' OR 'Critical Qty' is 'To be canceled'
Pivot_table_scope_filtered = Pivot_table_scope[
~((Pivot_table_scope['Production Status'] == 'Canceled') |
(Pivot_table_scope['Critical Qty'] == 'To be canceled'))
]
######################################
#Ordering 'Product Category' from Pivot_table_scope_filtered: ISP, CPA, Lightplate, Rototellite, Others, Fiber Optics
#####################################
# Define the desired order for 'Product Category'
category_order = ['ISP', 'CPA', 'Lightplate', 'Rototellite', 'Others', 'Fiber Optics']
# Convert 'Product Category' to a categorical type with the specified order
Pivot_table_scope_filtered['Product Category'] = pd.Categorical(
Pivot_table_scope_filtered['Product Category'],
categories=category_order,
ordered=True
)
# Sort the DataFrame by 'Product Category'
Pivot_table_scope_filtered = Pivot_table_scope_filtered.sort_values(by='Product Category')
#print('Pivot_table_scope_filtered')
#display(Pivot_table_scope_filtered)
#######################################
# Aggregation on 'Product Category' and 'program', summing 'Total Quantity'
Pivot_table_scope_filtered_aggregated = Pivot_table_scope_filtered.groupby(['Product Category', 'Program'])['Total Quantity'].sum().reset_index()
#print('Pivot_table_scope_filtered_aggregated')
#display(Pivot_table_scope_filtered_aggregated)
# Aggregate data by Program and Product Category - Filtered to exclude canceled orders
aggregation_by_product_category = Pivot_table_scope_filtered.groupby(['Program', 'Product Category']) \
.agg(Pty_Indice_Count_Product_Category=('Pty Indice', 'nunique')) \
.reset_index()
# Aggregate data by Program and Production Status - Not filtered to include canceled orders
aggregation_by_production_status = Pivot_table_scope.groupby(['Program', 'Production Status']) \
.agg(Pty_Indice_Count_Production_Status=('Pty Indice', 'nunique')) \
.reset_index()
# Ordering aggregation_by_production_status by 'Production Status': Completed, Industrialized, FTB WIP, Proto WIP, FTB, Proto + FTB, To be transferred, Canceled, 'Officially transferred'
# Define the desired order for 'Production Status'
status_order = ['Completed', 'Industrialized', 'FTB WIP', 'Proto WIP', 'FTB', 'Proto + FTB', 'To be transferred', 'Canceled', 'Officially transferred']
# Convert 'Production Status' to a categorical type with the specified order
aggregation_by_production_status['Production Status'] = pd.Categorical(
aggregation_by_production_status['Production Status'],
categories=status_order,
ordered=True
)
# Sort the DataFrame by 'Production Status'
aggregation_by_production_status = aggregation_by_production_status.sort_values(by='Production Status')
#########################
# Print aggregated data
#########################
#print("Aggregated Data by Product Category - With redlist:")
#display(aggregation_by_product_category)
#print("Aggregated Data by Production Status - With redlist:")
#display(aggregation_by_production_status)
#New 08/14
#####################################################################################################################################
#### Include 'Combined PN' representing the full Transfer Project
## Pivot_table_completion contain only PN shipped, all the other PN are therefor at %0
## Get the other PN from CM-Priority (filtering-out 'Canceled') --> Pivot_table_scope_filtered does not contain the 'Canceled' PN but contain 'To be transferred'
####################################################################################################################################
# Merge Pivot_table_completion with Pivot_table_scope_filtered on 'Pty Indice' to get the '% Completion Total Qty' and '% Completion Critical Qty'
# Select only the relevant columns from Pivot_table_completion
completion_filtered = Pivot_table_completion[['Priority', 'Pty Indice', 'Qty Shipped', '% Completion Total Qty', '% Completion Critical Qty']]
# Merge to keep the column from Pivot_table_scope_filtered and include the missing from Pivot_table_completion
Pivot_table_completion_upated = pd.merge(Pivot_table_scope_filtered, completion_filtered, on='Pty Indice', how='left')
# Set 'NaN' from '% Completion Total Qty' and '% Completion Critical Qty' to 0
Pivot_table_completion_upated['% Completion Total Qty'] = Pivot_table_completion_upated['% Completion Total Qty'].fillna(0)
Pivot_table_completion_upated['% Completion Critical Qty'] = Pivot_table_completion_upated['% Completion Critical Qty'].fillna(0)
Pivot_table_completion_upated['Qty Shipped'] = Pivot_table_completion_upated['Qty Shipped'].fillna(0)
# Create a Priority Mapping from CM-Protity to Pivot_table_completion_upated on 'Pty Indice' for row with 'Pty Indice = NaN
priority_mapping = df_Priority.set_index('Pty Indice')['Priority']
#Identify rows with NaN Priority
missing_priority_mask = Pivot_table_completion_upated['Priority'].isna()
#Apply the Priority Mapping only to rows with NaN Priority
Pivot_table_completion_upated.loc[missing_priority_mask, 'Priority'] = Pivot_table_completion_upated.loc[missing_priority_mask, 'Pty Indice'].map(priority_mapping)
# Identify string values in 'Priority' column
string_mask = Pivot_table_completion_upated['Priority'].apply(lambda x: isinstance(x, str))
# Replace string values with 999
Pivot_table_completion_upated.loc[string_mask, 'Priority'] = 999
# Drop NaN values # 03/11
Pivot_table_completion_upated = Pivot_table_completion_upated.dropna(subset=['Priority'])
# Convert columns from float to integer
Pivot_table_completion_upated['Priority'] = Pivot_table_completion_upated['Priority'].astype(int)
Pivot_table_completion_upated['Total Quantity'] = Pivot_table_completion_upated['Total Quantity'].astype(int)
Pivot_table_completion_upated['Qty Shipped'] = Pivot_table_completion_upated['Qty Shipped'].astype(int)
# Round the percentage columns to one decimal place
Pivot_table_completion_upated['% Completion Total Qty'] = Pivot_table_completion_upated['% Completion Total Qty'].round(1)
Pivot_table_completion_upated['% Completion Critical Qty'] = Pivot_table_completion_upated['% Completion Critical Qty'].round(1)
# Create an empty list to store the new rows
new_rows = []
# Group by 'Program'
grouped = Pivot_table_completion_upated.groupby('Program')
# Calculate aggregated values for each group
for program, group in grouped:
total_quantity_sum = group['Total Quantity'].sum()
total_shipped_sum = group['Qty Shipped'].sum()
#average_completion_total_qty = group['% Completion Total Qty'].mean()
#average_completion_critical_qty = group['% Completion Critical Qty'].mean()
total_critical_qty = group['Critical Qty'].sum()
# Calculate completion percentages
# Handle division by zero cases by checking if total values are greater than zero
completion_total_qty = (total_shipped_sum / total_quantity_sum) * 100 if total_quantity_sum > 0 else 0
completion_critical_qty = (total_shipped_sum / total_critical_qty) * 100 if total_critical_qty > 0 else 0
# Create a new row for each program
new_row = {
'Pty Indice': f'Combined PN {program}',
'Priority': 98,
'Program': program,
'Product Category': 'Made up row, combined PN',
'Qty Shipped': total_shipped_sum,
'Critical Qty': total_critical_qty,
'Total Quantity': total_quantity_sum,
'% Completion Total Qty': completion_total_qty,
'% Completion Critical Qty': completion_critical_qty,
'Production Status': 'Made up row, combined PN'
}
# Append the new row to the list
new_rows.append(new_row)
# Convert the list of new rows into a DataFrame
new_rows_df = pd.DataFrame(new_rows)
# Append the new rows to the existing DataFrame
Pivot_table_completion_upated_combinedPN = pd.concat([Pivot_table_completion_upated, new_rows_df], ignore_index=True)
# Round the percentage columns to one decimal place
Pivot_table_completion_upated_combinedPN['% Completion Total Qty'] = Pivot_table_completion_upated_combinedPN['% Completion Total Qty'].round(1)
Pivot_table_completion_upated_combinedPN['% Completion Critical Qty'] = Pivot_table_completion_upated_combinedPN['% Completion Critical Qty'].round(1)
# Sort the DataFrame by 'Priority' - update 08/28
Pivot_table_completion_upated = Pivot_table_completion_upated.sort_values(by='Priority')
Pivot_table_completion_upated_combinedPN = Pivot_table_completion_upated_combinedPN.sort_values(by='Priority')
Pivot_table_completion_upated = Pivot_table_completion_upated.sort_values(by=['Priority', 'Pty Indice'])
Pivot_table_completion_upated_combinedPN = Pivot_table_completion_upated_combinedPN.sort_values(by=['Priority', 'Pty Indice'])
#print('Pivot_table_completion_upated')
#display(Pivot_table_completion_upated)
#print('Pivot_table_completion_upated_combinedPN')
#display(Pivot_table_completion_upated_combinedPN)
#New 08/14
###############################################################################################################
# Create new datafram Pourcentage_distribution based on Pivot_table_scope_filtered (canceled order excluded)
###############################################################################################################
#Copy Pivot_table_scope_filtered
Pourcentage_distribution = Pivot_table_scope_filtered.copy()
#print('Pourcentage_distribution')
#display(Pourcentage_distribution)
# Function to round numeric columns except the 'Program' column
def round_except_program(df, decimals=0, int_columns=None):
# Select columns to round (exclude 'Program')
numeric_columns = df.columns[df.columns != 'Program']
if int_columns:
# Convert specified columns to integer
df[int_columns] = df[int_columns].astype(int)
# Round remaining columns
df[numeric_columns] = df[numeric_columns].round(decimals)
return df
#########################################
# Group by TOTAL Qty 'Productin Status'
##########################################
# Group by 'Program' and 'Product Status'
status_distribution = Pourcentage_distribution.groupby(['Program', 'Production Status'])['Total Quantity'].sum().reset_index()
# Calculate percentage
status_total = status_distribution.groupby('Program')['Total Quantity'].transform('sum')
status_distribution['Percentage'] = (status_distribution['Total Quantity'] / status_total) * 100
# Pivot the table
status_distribution_pivot = status_distribution.pivot_table(
index='Program',
columns='Production Status',
values='Total Quantity',
aggfunc='sum',
fill_value=0
)
status_percentage_pivot = status_distribution.pivot_table(
index='Program',
columns='Production Status',
values='Percentage',
aggfunc='sum',
fill_value=0
)
# Resetting index for clarity
status_distribution_pivot = status_distribution_pivot.reset_index()
status_percentage_pivot = status_percentage_pivot.reset_index()
# Apply the function
#status_distribution_pivot = round_except_program(status_distribution_pivot, int_columns=['Completed', 'FTB', 'FTB WIP', 'Industrialized', 'Proto + FTB', 'Proto WIP', 'To be transferred', 'Officially transferred'])
status_distribution_pivot = round_except_program(status_distribution_pivot, int_columns=['Completed', 'FTB', 'FTB WIP', 'Industrialized', 'Proto + FTB', 'Proto WIP', 'Officially transferred'])
status_percentage_pivot = round_except_program(status_percentage_pivot, decimals=1)
#print
#print('status_distribution _pivot')
#display(status_distribution_pivot)
#print('status_percentage_pivot')
#display(status_percentage_pivot)
#####################################
# Group by TOTAL Qty Product Category'
#####################################
# Group by 'Program' and 'Product Category'
category_distribution = Pourcentage_distribution.groupby(['Program', 'Product Category'])['Total Quantity'].sum().reset_index()
# Calculate percentage
category_total = category_distribution.groupby('Program')['Total Quantity'].transform('sum')
category_distribution['Percentage'] = (category_distribution['Total Quantity'] / category_total) * 100
# Pivot the table
category_distribution_pivot = category_distribution.pivot_table(
index='Program',
columns='Product Category',
values='Total Quantity',
aggfunc='sum',
fill_value=0
)
category_percentage_pivot = category_distribution.pivot_table(
index='Program',
columns='Product Category',
values='Percentage',
aggfunc='sum',
fill_value=0
)
# Resetting index for clarity
category_distribution_pivot = category_distribution_pivot.reset_index()
category_percentage_pivot = category_percentage_pivot.reset_index()
# Apply the function for rounding
category_distribution_pivot = round_except_program(category_distribution_pivot, int_columns=['ISP', 'CPA', 'Lightplate', 'Rototellite', 'Others', 'Fiber Optics'])
category_percentage_pivot = round_except_program(category_percentage_pivot, decimals=1)
# print
#print('category_distribution_pivot')
#display(category_distribution_pivot)
############################################################################################################################
# Melt category_percentage_pivot & status_percentage_pivot - For Chart 4 - Distribution by TOTAL Quantity
###############################################################################################################################
# Melt the DataFrame to long format
df_melted_status_percentage_pivot = status_percentage_pivot.melt(id_vars=['Program'], var_name='Production Status', value_name='Percentage Status')
df_melted_category_percentage_pivot = category_percentage_pivot.melt(id_vars=['Program'], var_name='Product Category', value_name='Percentage Status')
#print('df_melted_status_percentage_pivot')
#display(df_melted_status_percentage_pivot)
# New 08/16
#########################################################
# - For Chart 5 - Distribution by UNIQUE Top-Level
########################################################
#Copy Pivot_table_scope_filtered
Pourcentage_distribution_Unique = Pivot_table_scope_filtered.copy()
##############################
# Group by UNIQUE 'Production Status'
###############################
# Group by 'Program' and 'Production Status'
status_distribution_unique = Pourcentage_distribution_Unique.groupby(['Program', 'Production Status'])['Pty Indice'].nunique().reset_index()
# Calculate total unique count for each Program
status_total_unique = status_distribution_unique.groupby('Program')['Pty Indice'].transform('sum')
# Calculate percentage
status_distribution_unique['Percentage'] = (status_distribution_unique['Pty Indice'] / status_total_unique) * 100
# Pivot the table
status_distribution_unique_pivot = status_distribution_unique.pivot_table(
index='Program',
columns='Production Status',
values='Pty Indice',
aggfunc='sum',
fill_value=0
)
status_percentage_unique_pivot = status_distribution_unique.pivot_table(
index='Program',
columns='Production Status',
values='Percentage',
aggfunc='sum',
fill_value=0
)
# Resetting index for clarity
status_distribution_unique_pivot = status_distribution_unique_pivot.reset_index()
status_percentage_unique_pivot = status_percentage_unique_pivot.reset_index()
# Apply the function for rounding
status_distribution_unique_pivot = round_except_program(status_distribution_unique_pivot, int_columns=['Completed', 'FTB', 'FTB WIP', 'Industrialized', 'Proto + FTB', 'Proto WIP', 'Officially transferred'])
status_percentage_unique_pivot = round_except_program(status_percentage_unique_pivot, decimals=1)
# print
#print('status_percentage_unique_pivot')
#display(status_percentage_unique_pivot)
##############################
# Group by UNIQUE 'Product Category'
###############################
# Group by 'Program' and 'Product Category'
category_distribution_unique = Pourcentage_distribution_Unique.groupby(['Program', 'Product Category'])['Pty Indice'].nunique().reset_index()
# Calculate total unique count for each Program
category_total_unique = category_distribution_unique.groupby('Program')['Pty Indice'].transform('sum')
# Calculate percentage
category_distribution_unique['Percentage'] = (category_distribution_unique['Pty Indice'] / category_total_unique) * 100
# Pivot the table
category_distribution_unique_pivot = category_distribution_unique.pivot_table(
index='Program',
columns='Product Category',
values='Pty Indice',
aggfunc='sum',
fill_value=0
)
category_percentage_unique_pivot = category_distribution_unique.pivot_table(
index='Program',
columns='Product Category',
values='Percentage',
aggfunc='sum',
fill_value=0
)
# Resetting index for clarity
category_distribution_unique_pivot = category_distribution_unique_pivot.reset_index()
category_percentage_unique_pivot = category_percentage_unique_pivot.reset_index()
# Apply the function for rounding
category_distribution_unique_pivot = round_except_program(category_distribution_unique_pivot, int_columns=['ISP', 'CPA', 'Lightplate', 'Rototellite', 'Others', 'Fiber Optics'])
category_percentage_unique_pivot = round_except_program(category_percentage_unique_pivot, decimals=1)
# print
#print('category_percentage_unique_pivot')
#display(category_percentage_unique_pivot)
############################################################################################################################
# Melt category_percentage_pivot & status_percentage_pivot - For Chart 5 - Distribution by UNIQUE Tpop-Level
###############################################################################################################################
# Melt the DataFrame to long format
#df_melted_status_percentage_pivot_UNIQUE = status_percentage_unique_pivot.melt(id_vars=['Program'], var_name='Production Status Unique', value_name='Percentage Status Unique')
#df_melted_category_percentage_pivot = category_distribution_unique_pivot.melt(id_vars=['Program'], var_name='Product Category Unique', value_name='Percentage Status Unique')
#print('df_melted_status_percentage_pivot_UNIQUE')
#display(df_melted_status_percentage_pivot_UNIQUE)
# Update on 18/16 to include Crossfiltering
#//////////////////////////////////////////////////#//////////////////////////////////////////////////#/////////////////////////////////////
##########################################################################################################################################
# Chart 1 - Percentage distribution of Product Categories --> Based on dataframe 'aggregation_by_product_category'
# Representing of each unique IDD Top-Level for each categories
##########################################################################################################################################
# Chart 2 - Percentage distribution of unique Top-Level by Product Category --> Based on dataframe 'Pivot_table_scope_filtered_aggregated'
#Representing of each category based on of the Total quantity to Build of each Pty Indice including in each categories
##########################################################################################################################################
# Chart 3 - Percentage distribution of Production Status --> Based on dataframe 'aggregation_by_production_status'
#Representing of each Production Status based on each unique Pty Indice
##########################################################################################################################################
#//////////////////////////////////////////////////#//////////////////////////////////////////////////#/////////////////////////////////////
#########################################################################################
#//////////////////////////////////////////////////#////////////////////////////////////
# Bar charts of product categories distribution and production statuses distribution
#########################################################################################
#//////////////////////////////////////////////////#////////////////////////////////////
#-------------------------------
# BEGINNING of WIP Code Update - 04/16
#-------------------------------
#-----------------------------------------------------------------------------------------------------------------------------------------------
# Define colors mapping if used
colors_palette = {
'CPA': '#a2d5d6',
'ISP': '#64179d',
'Lightplate': '#dfddda',
'Others': '#cfa8cf',
'Rototellite': '#233b3f',
'Fiber Optics': '#ea9770',
'Completed': 'green',
'FTB': '#9EC0F6',
'FTB WIP': '#DAEEF3',
'Industrialized': '#a7d0ac',
'Proto + FTB': '#6199ea',
'Proto WIP': '#00d3ff',
'To be transferred': '#F2DCDB',
'Officially transferred': '#FF7A5B',
'Canceled': '#F35757',
}
# Customize distribution plot
def customize_distribution_plot(bokeh_plot):
""" Apply customizations to the Total Quantity plot. """
bokeh_plot.xaxis.major_label_text_font_size = '10pt'
bokeh_plot.yaxis.major_label_text_font_size = '10pt'
bokeh_plot.xaxis.axis_line_width = 2
bokeh_plot.yaxis.axis_line_width = 2
bokeh_plot.xaxis.major_label_orientation = 45
bokeh_plot.yaxis.major_label_orientation = 'horizontal'
bokeh_plot.yaxis.axis_label_text_font_size = '10pt'
bokeh_plot.ygrid.grid_line_color = '#E0E0E0'
bokeh_plot.ygrid.grid_line_dash = [4, 6]
bokeh_plot.toolbar.logo = None
return bokeh_plot
###########################
# Bar chart 1
###########################
def create_product_category(aggregation_by_product_category, program):
# Filter data by the default program
filtered_data = aggregation_by_product_category[aggregation_by_product_category['Program'] == program]
if filtered_data.empty:
print("No data found for the default program.")
return None
# Sort the filtered data by 'Pty_Indice_Count_Product_Category'
filtered_data = filtered_data.sort_values(by='Pty_Indice_Count_Product_Category')
# Create the plot
product_category_plot = filtered_data.hvplot.bar(
x='Product Category',
y='Pty_Indice_Count_Product_Category',
title=None, # Remove the built-in title
xlabel='Product Category',
ylabel='Nb of unique Top-Level',
cmap=colors_palette,
color='Product Category',
legend='top_left',
bar_width=0.6, # Set bar width
tools=[]
)
# Render the plot to Bokeh
bokeh_product_category_plot = hv.render(product_category_plot, backend='bokeh')
# Set explicit dimensions for the Bokeh plot
bokeh_product_category_plot.width = 400
bokeh_product_category_plot.height = 600
# Apply customizations
bokeh_product_category_plot = customize_distribution_plot(bokeh_product_category_plot)
# Remove borders from the bars
for renderer in bokeh_product_category_plot.renderers:
if isinstance(renderer.glyph, VBar):
renderer.glyph.line_color = None
# Create the HTML formatted title using Div
title_text = """<span style="font-size: 16px; color: #305496; margin-left: 60px;">
<b>Unique</b> Top-Level <b>Quantity</b> by Product Category
</span>"""
title_product_category_plot = Div(text=title_text)
# Remove existing HoverTools and add a new one
bokeh_product_category_plot.tools = [tool for tool in bokeh_product_category_plot.tools if not isinstance(tool, HoverTool)]
hover = HoverTool()
hover.tooltips = [
("Product Category", "@Product_Category"),
("Unique PN", "@Pty_Indice_Count_Product_Category")
]
bokeh_product_category_plot.add_tools(hover)
# Remove wheel zoom from active tools
bokeh_product_category_plot.tools = [tool for tool in bokeh_product_category_plot.tools if not isinstance(tool, WheelZoomTool)]
# Combine title and plot into a Panel Column layout
product_category_plot_layout = pn.Column(title_product_category_plot, bokeh_product_category_plot)
return product_category_plot_layout
# Create the initial plot
product_category_plot_layout = create_product_category(aggregation_by_product_category, default_program_historic)
bar_plot_pane1 = pn.panel(product_category_plot_layout, sizing_mode='fixed', height=600, width=400)
# Define the update function
def update_plots_product_category(event):
program = event.new if event else program_widget_historic.value
print(f"Selected Program: {program}")
filtered_data = aggregation_by_product_category[aggregation_by_product_category['Program'] == program]
print(f"Filtered Data: {filtered_data.head()}")
if filtered_data.empty:
print("No data found for the selected program.")
plot1_layout[:] = [pn.pane.HTML("<b>No data available for the selected program.</b>"), pn.Spacer(height=30), text_below_product_category]
return
product_category_plot_layout = create_product_category(filtered_data, program)
plot1_layout[:] = [product_category_plot_layout, pn.Spacer(height=30), text_below_product_category]
print("Plot Updated in Layout")
# Link the widget to the update function
program_widget_historic.param.watch(update_plots_product_category, 'value')
#######################################################
# Bar chart 2 - Pivot_table_scope_filtered_aggregated
########################################################
def create_product_category_total_qty(Pivot_table_scope_filtered_aggregated, program):
# Filter data by the default program
filtered_data = Pivot_table_scope_filtered_aggregated[Pivot_table_scope_filtered_aggregated['Program'] == program]
if filtered_data.empty:
print("No data found for the default program.")
return None
# Sort the filtered data by 'Total Quantity'
filtered_data = filtered_data.sort_values(by='Total Quantity')
# Create the plot
product_category_total_qty = filtered_data.hvplot.bar(
x='Product Category',
y='Total Quantity',
title=None, # Remove the built-in title
xlabel='Product Category',
ylabel='Total quantity of Top-Level',
cmap=colors_palette,
color='Product Category',
legend='top_left',
bar_width=0.6, # Set bar width
tools=[]
)
# Render the plot to Bokeh
bokeh_product_category_total_qty_plot = hv.render(product_category_total_qty, backend='bokeh')
# Set explicit dimensions for the Bokeh plot
bokeh_product_category_total_qty_plot.width = 400
bokeh_product_category_total_qty_plot.height = 600
# Apply customizations
bokeh_product_category_total_qty_plot = customize_distribution_plot(bokeh_product_category_total_qty_plot)
# Remove borders from the bars
for renderer in bokeh_product_category_total_qty_plot.renderers:
if isinstance(renderer.glyph, VBar):
renderer.glyph.line_color = None
# Create the HTML formatted title using Div
title_text = """<span style="font-size: 16px; color: #305496; margin-left: 60px;">
<b>Total</b> Top-Level <b>Quantity</b> by Product Category
</span>"""
title_product_category_total_qty_plot = Div(text=title_text)
# Remove existing HoverTools and add a new one
bokeh_product_category_total_qty_plot.tools = [tool for tool in bokeh_product_category_total_qty_plot.tools if not isinstance(tool, HoverTool)]
hover = HoverTool()
hover.tooltips = [
("Product Category", "@Product_Category"),
("Total Quantity", "@Total_Quantity")
]
bokeh_product_category_total_qty_plot.add_tools(hover)
# Remove wheel zoom from active tools
bokeh_product_category_total_qty_plot.tools = [tool for tool in bokeh_product_category_total_qty_plot.tools if not isinstance(tool, WheelZoomTool)]
# Combine title and plot into a Panel Column layout
product_category_total_qty_layout = pn.Column(title_product_category_total_qty_plot, bokeh_product_category_total_qty_plot)
return product_category_total_qty_layout
#--------------------------------------------------------------------
# TITLE NOT UPDATING
# Bar Chart 1: Unique Top-Level Quantity by Product Category
def update_plots_product_category_total_qty(event):
program = event.new if event else program_widget_historic.value
print(f"Selected Program: {program}")
filtered_data = Pivot_table_scope_filtered_aggregated[Pivot_table_scope_filtered_aggregated['Program'] == program]
print(f"Filtered Data: {filtered_data.head()}")
if filtered_data.empty:
print("No data found for the selected program.")
plot2_layout[:] = [pn.pane.HTML("<b>No data available for the selected program.</b>"), pn.Spacer(height=30), text_below_product_category_total_qty]
return
# Recreate the full layout with title
product_category_total_qty_layout = create_product_category_total_qty(filtered_data, program)
if product_category_total_qty_layout is not None:
plot2_layout[:] = product_category_total_qty_layout.objects + [pn.Spacer(height=30), text_below_product_category_total_qty]
print("Plot and title updated in layout")
else:
print("Failed to create product_category_total_qty_layout")
# Link the widget to the update function
program_widget_historic.param.watch(update_plots_product_category_total_qty, 'value')
####################################################################
# Bar chart 3 - Production Status, Pty_Indice_Count_Production_Status
######################################################################
def create_production_status(aggregation_by_production_status, program):
# Filter data by the default program
filtered_data = aggregation_by_production_status[aggregation_by_production_status['Program'] == program]
if filtered_data.empty:
print("No data found for the default program.")
return pn.pane.HTML("<b>No data available for the selected program.</b>")
# Get all unique production statuses from the entire dataset
all_production_statuses = aggregation_by_production_status['Production Status'].dropna().unique()
# Create a pivot table or aggregate data for the filtered program
status_counts = filtered_data.groupby('Production Status')['Pty_Indice_Count_Production_Status'].sum().reset_index()
# Create a complete DataFrame with all statuses
status_counts_complete = pd.DataFrame({
'Production Status': all_production_statuses
}).merge(status_counts, on='Production Status', how='left')
# Fill missing numeric values with 0, ensuring 'Production Status' remains categorical
status_counts_complete['Pty_Indice_Count_Production_Status'] = status_counts_complete['Pty_Indice_Count_Production_Status'].fillna(0).astype(float)
# Sort by count for better visualization
status_counts_complete = status_counts_complete.sort_values(by='Pty_Indice_Count_Production_Status', ascending=False)
# Create the plot
production_status_plot = status_counts_complete.hvplot.bar(
x='Production Status',
y='Pty_Indice_Count_Production_Status',
title=None,
xlabel='Production Status',
ylabel='Nb of unique Top-Level',
cmap=colors_palette,
color='Production Status',
legend='top_left',
bar_width=0.6,
tools=[]
)
# Render the plot to Bokeh
bokeh_production_status_plot = hv.render(production_status_plot, backend='bokeh')
# Set explicit dimensions for the Bokeh plot
bokeh_production_status_plot.width = 600
bokeh_production_status_plot.height = 600
# Apply customizations
bokeh_production_status_plot = customize_distribution_plot(bokeh_production_status_plot)
# Remove borders from the bars
for renderer in bokeh_production_status_plot.renderers:
if isinstance(renderer.glyph, VBar):
renderer.glyph.line_color = None
# Create the HTML formatted title using Div
title_text = """<span style="font-size: 16px; color: #305496; margin-left: 60px;">
<b>Unique</b> Top-level <b>Quantity</b> by Production Status
</span>"""
title_production_status_plot = Div(text=title_text)
# Remove existing HoverTools and add a new one
bokeh_production_status_plot.tools = [tool for tool in bokeh_production_status_plot.tools if not isinstance(tool, HoverTool)]
hover = HoverTool()
hover.tooltips = [
("Production Status", "@Production_Status"),
("Unique PN", "@Pty_Indice_Count_Production_Status")
]
bokeh_production_status_plot.add_tools(hover)
# Remove wheel zoom from active tools
bokeh_production_status_plot.tools = [tool for tool in bokeh_production_status_plot.tools if not isinstance(tool, WheelZoomTool)]
# Combine title and plot into a Panel Column layout
production_status_layout = pn.Column(title_production_status_plot, bokeh_production_status_plot)
return production_status_layout
#--------------------------------------------------------------------
# TITLE NOT UPDATING
# Bar Chart 3: Unique Top-Level Quantity by Production Status
def update_plots_production_status(event):
program = event.new if event else program_widget_historic.value
print(f"Selected Program: {program}")
filtered_data = aggregation_by_production_status[aggregation_by_production_status['Program'] == program]
print(f"Filtered Data: {filtered_data.head()}")
if filtered_data.empty:
print("No data found for the selected program.")
plot3_layout[:] = [pn.pane.HTML("<b>No data available for the selected program.</b>"), pn.Spacer(height=30), text_below_production_status]
return
# Recreate the full layout with title
production_status_layout = create_production_status(filtered_data, program)
if production_status_layout is not None:
plot3_layout[:] = production_status_layout.objects + [pn.Spacer(height=30), text_below_production_status]
print("Plot and title updated in layout")
else:
print("Failed to create production_status_layout")
# Link the widget to the update function
program_widget_historic.param.watch(update_plots_production_status, 'value')
#######################
# Create initial plots
########################
# Create the initial production status layout
product_category_total_qty_layout = create_product_category_total_qty(Pivot_table_scope_filtered_aggregated, default_program_historic)
production_status_layout = create_production_status(aggregation_by_production_status, default_program_historic)
# Create Bokeh plot panes with fixed dimensions
bar_plot_pane2 = pn.panel(product_category_total_qty_layout, sizing_mode='fixed', height=600, width=400)
bar_plot_pane3 = pn.panel(production_status_layout, sizing_mode='fixed', height=600, width=600)
#-------------------------------
# End of WIP Code Update - 04/16
#-------------------------------
######################################
# Create text bellow graphs
########################################
# Convert 'Last Update' to datetime format
df_Priority['Last Update'] = pd.to_datetime(df_Priority['Last Update'], format='%m/%d/%Y', errors='coerce')
# Format the date as a short date (MM-DD-YYYY)
df_Priority['Last Update'] = df_Priority['Last Update'].dt.strftime('%m-%d-%Y')
# Extract the single date value from the DataFrame
Date_CM_Priority = df_Priority['Last Update'].iloc[0]
text_below_product_category = pn.pane.HTML(
f"This graph is based on data from |CM-Priority| - <b>{Date_CM_Priority}</b>:<br>"
"▷ <b>Unique Top-Level Quantity </b>: Number of unique Part Number for each Category of Product.<br>"
"▷ <b>This graph includes</b>: All PN since the beginning of the transfer for the selected program included the 'redlist'.<br>"
"▷ <b>Redlist</b>: PN 'to be transferred' or 'Officially transferred', not yet 'Canceled' - Canceled order are filtered-out of this graph.<br>",
width=450 # Match the width of bar_plot_pane1 and bar_plot_pane2
)
text_below_product_category_total_qty = pn.pane.HTML(
f"This graph is based on data from |CM-Priority| - <b>{Date_CM_Priority}</b>:<br>"
"▷ <b>Total Quantity Top-Level</b>: Total Quantity of parts either in SEDA's Backlog for each category. The follow-up orders are included in this 'Total Quantity'.<br>"
"▷ <b>This graph includes</b>: All PN since the beginning of the transfer for the selected program included the 'redlist' (not yet canceled).<br>"
"▷ <b>Redlist</b>: PN 'to be transferred' or Officially transferred, not yet 'Canceled' - Canceled order are filtered-out of this graph.<br>",
width=450 # Match the width of bar_plot_pane2
)
text_below_production_status = pn.pane.HTML(
f"This graph is based on data from |CM-Priority| - <b>{Date_CM_Priority}</b>:<br>"
"▷ <b>Unique Quantity Top-Level </b>: Number of unique Part Number for each Category of Product.<br>"
"▷ <b>This graph includes</b>: All PN since the beginning of the transfer for the selected program included the 'redlist' & Canceled order.<br>",
width=600 # Match the width of bar_plot_pane3
)
#New 08/15
###########################################################################################################################################################################################
# Chart 4 - Percentage distribution of Product Category & Production Status --> Base on intial dataframe 'Pourcentage_distribution'
#Representing the % each product category and Proudction status based on TOTAL QTY Top-LeveL --> Dataframes df_melted_status_percentage_pivot & df_melted_category_percentage_pivot
##########################################################################################################################################################################################
# Combinaison of Dataframes df_melted_status_percentage_pivot & df_melted_category_percentage_pivot on create_percentage_product_category_production_status
# Updated 09/17
def create_percentage_product_category_production_status(df_melted_status_percentage_pivot, df_melted_category_percentage_pivot, program, colors_palette):
# Filter data by the default program
filtered_data_status = df_melted_status_percentage_pivot[df_melted_status_percentage_pivot['Program'] == program]
filtered_data_category = df_melted_category_percentage_pivot[df_melted_category_percentage_pivot['Program'] == program]
# Ensure 'Percentage Status' is numeric, fill NaN values with 0
filtered_data_status['Percentage Status'] = pd.to_numeric(filtered_data_status['Percentage Status'], errors='coerce').fillna(0)
filtered_data_category['Percentage Status'] = pd.to_numeric(filtered_data_category['Percentage Status'], errors='coerce').fillna(0)
# Convert categorical columns to categorical type
filtered_data_status['Production Status'] = pd.Categorical(filtered_data_status['Production Status'])
filtered_data_category['Product Category'] = pd.Categorical(filtered_data_category['Product Category'])
# Sort data by 'Percentage Status' by ascending order (smallest to largest)
filtered_data_status = filtered_data_status.sort_values(by='Percentage Status', ascending=False)
filtered_data_category = filtered_data_category.sort_values(by='Percentage Status', ascending=False)
# Extract the categories and corresponding colors
categories_status = filtered_data_status['Production Status'].cat.categories
categories_category = filtered_data_category['Product Category'].cat.categories
# Ensure the palette covers all categories
colors_palette_status = [colors_palette.get(cat, '#808080') for cat in categories_status]
colors_palette_category = [colors_palette.get(cat, '#808080') for cat in categories_category]
# Convert DataFrame to ColumnDataSource
source_status = ColumnDataSource(filtered_data_status)
source_category = ColumnDataSource(filtered_data_category)
# HTML title for Product Category
title_text_status_distribution = """<span style="font-size: 16px; color: #305496; margin-left: 150px;">
Production Status <b>Distribution</b> based on <b>Total Quantity</b>
</span>"""
title_status_plot_distribution = Div(text=title_text_status_distribution)
# Create figure for Production Status
status_plot = figure(
y_range=FactorRange(*filtered_data_status['Production Status']),
x_axis_label='Percentage Production Status',
y_axis_label='Production Status',
x_range=(0, 100), # Set x-axis range from 0 to 100
title=None
)
# Rename columns just before configuring HoverTool
filtered_data_status_renamed = filtered_data_status.rename(columns={
'Production Status': 'Production_Status',
'Percentage Status': 'Percentage_Status'
})
source_status_renamed = ColumnDataSource(filtered_data_status_renamed)
# Remove existing hover tool (if any) and create a new one
status_plot.tools = [tool for tool in status_plot.tools if not isinstance(tool, HoverTool)]
hover_status = HoverTool(
tooltips=[
("Production Status", "@Production_Status"),
("Percentage", "@Percentage_Status{0.0f}%")
]
)
status_plot.add_tools(hover_status)
status_plot.hbar(
y='Production_Status',
right='Percentage_Status',
source=source_status_renamed,
height=0.6, # Thickness of each bar
color=factor_cmap('Production_Status', palette=colors_palette_status, factors=categories_status),
legend_field='Production_Status' # Add legend field
)
# Set fixed dimensions directly here
status_plot.width = 600
status_plot.height = 600
# HTML title for Product Category
title_text_category_distribution = """<span style="font-size: 16px; color: #305496; margin-left: 50px;">
Product Category <b>Distribution</b> based on <b>Total Quantity</b>
</span>"""
title_category_plot_distribution = Div(text=title_text_category_distribution)
# Create figure for Product Category
category_plot = figure(
y_range=FactorRange(*filtered_data_category['Product Category']),
x_axis_label='Percentage Product Category',
y_axis_label='Product Category',
x_range=(0, 100), # Set x-axis range from 0 to 100
title=None
)
# Rename columns just before configuring HoverTool
filtered_data_category_renamed = filtered_data_category.rename(columns={
'Product Category': 'Product_Category',
'Percentage Status': 'Percentage_Status'
})
source_category_renamed = ColumnDataSource(filtered_data_category_renamed)
# Remove existing hover tool (if any) and create a new one
category_plot.tools = [tool for tool in category_plot.tools if not isinstance(tool, HoverTool)]
hover_category = HoverTool(
tooltips=[
("Product Category", "@Product_Category"),
("Percentage", "@Percentage_Status{0.0f}%")
]
)
category_plot.add_tools(hover_category)
category_plot.hbar(
y='Product_Category',
right='Percentage_Status',
source=source_category_renamed,
height=0.6, # Thickness of each bar
color=factor_cmap('Product_Category', palette=colors_palette_category, factors=categories_category),
legend_field='Product_Category' # Add legend field
)
category_plot.legend.location = 'top_right' # Position the legend in the top right
status_plot.legend.location = 'top_right' # Position the legend in the top right
# Apply customization for consistency with other graphs
status_plot = customize_distribution_plot(status_plot)
category_plot = customize_distribution_plot(category_plot)
# Customize grid lines
status_plot.xgrid.grid_line_color = '#E0E0E0'
status_plot.xgrid.grid_line_dash = [4, 6] # Dash style for x-axis grid lines
status_plot.ygrid.grid_line_color = None # Remove y-axis grid lines
category_plot.xgrid.grid_line_color = '#E0E0E0'
category_plot.xgrid.grid_line_dash = [4, 6] # Dash style for x-axis grid lines
category_plot.ygrid.grid_line_color = None # Remove y-axis grid lines
# Set fixed dimensions directly here
category_plot.width = 450
category_plot.height = 600
# Arrange plots and titles in columns
status_layout_distribution = column(title_status_plot_distribution, status_plot)
category_layout_distribution = column(title_category_plot_distribution, category_plot)
#return status_plot_unique, category_plot_unique # Update 10/07
return status_layout_distribution, category_layout_distribution
def update_plot_percentage_product_category_production_status(event):
program = program_widget_historic.value
# Filter data by the selected program
filtered_data_status = df_melted_status_percentage_pivot[df_melted_status_percentage_pivot['Program'] == program]
filtered_data_category = df_melted_category_percentage_pivot[df_melted_category_percentage_pivot['Program'] == program]
if filtered_data_status.empty or filtered_data_category.empty:
print("No data found for the selected program.")
return
# Get the plot layout
status_layout_distribution, category_layout_distribution = create_percentage_product_category_production_status(
df_melted_status_percentage_pivot,
df_melted_category_percentage_pivot,
program,
colors_palette # Make sure colors_palette is passed here
)
# Extract the actual plot (assuming it's the second child in the layout)
bokeh_status_plot = status_layout_distribution.children[1] # Accessing the second child (the plot)
bokeh_category_plot = category_layout_distribution.children[1] # Accessing the second child (the plot)
# Customize grid lines
#bokeh_status_plot.xgrid.grid_line_color = '#E0E0E0'
#bokeh_status_plot.xgrid.grid_line_dash = [4, 6] # Dash style for x-axis grid lines
#bokeh_status_plot.ygrid.grid_line_color = None # Remove y-axis grid lines
#bokeh_category_plot.xgrid.grid_line_color = '#E0E0E0'
#bokeh_category_plot.xgrid.grid_line_dash = [4, 6] # Dash style for x-axis grid lines
#bokeh_category_plot.ygrid.grid_line_color = None # Remove y-axis grid lines
# Set fixed dimensions
bokeh_status_plot.width = 600
bokeh_status_plot.height = 600
bokeh_category_plot.width = 450
bokeh_category_plot.height = 600
# Update Bokeh plot panes
bar_plot_pane_status_percentage.object = bokeh_status_plot
bar_plot_pane_category_percentage.object = bokeh_category_plot
#######################
# Create initial plots
########################
bokeh_status_plot, bokeh_category_plot = create_percentage_product_category_production_status(
df_melted_status_percentage_pivot,
df_melted_category_percentage_pivot,
default_program_historic,
colors_palette # Make sure colors_palette is passed here
)
# Create Bokeh plot panes with fixed dimensions
bar_plot_pane_status_percentage = pn.pane.Bokeh(bokeh_status_plot, sizing_mode='fixed', height=600, width=600)
bar_plot_pane_category_percentage = pn.pane.Bokeh(bokeh_category_plot, sizing_mode='fixed', height=600, width=450)
#update_plot_percentage_product_category_production_status(None)
####################################################
# Watch the widget and update the plot on change
####################################################
program_widget_historic.param.watch(update_plot_percentage_product_category_production_status, 'value')
######################################################################################################################################################################################
# Chart 5 - Percentage distribution of Product Category & Production Status --> Base on intial dataframe 'Pourcentage_distribution'
#Representing the % each product category and Proudction status based on UNIQUE Top-Level --> Dataframes df_melted_status_percentage_pivot & df_melted_category_percentage_pivot
#######################################################################################################################################################################################
# Updated 09/17
def create_percentage_product_category_production_status_UNIQUE(status_percentage_unique_pivot, category_percentage_unique_pivot, program, colors_palette):
#program = default_program_historic
# Filter data by the default program
filtered_data_status = status_percentage_unique_pivot[status_percentage_unique_pivot['Program'] == program]
filtered_data_category = category_percentage_unique_pivot[category_percentage_unique_pivot['Program'] == program]
if filtered_data_status.empty or filtered_data_category.empty:
print("No data found for the default program.")
return None, None
# Transform filtered_data_status into the desired format
transformed_status = filtered_data_status.melt(
id_vars=['Program'],
var_name='Production Status',
value_name='Percentage'
)
# Transform filtered_data_category into the desired format
transformed_category = filtered_data_category.melt(
id_vars=['Program'],
var_name='Product Category',
value_name='Percentage'
)
# Ensure 'Percentage' is numeric, fill NaN values with 0
transformed_status['Percentage'] = pd.to_numeric(transformed_status['Percentage'], errors='coerce').fillna(0)
transformed_category['Percentage'] = pd.to_numeric(transformed_category['Percentage'], errors='coerce').fillna(0)
# Sort data by 'Percentage' in descending order
transformed_status = transformed_status.sort_values(by='Percentage', ascending=False)
transformed_category = transformed_category.sort_values(by='Percentage', ascending=False)
# Convert categorical columns to categorical type
transformed_status['Production Status'] = pd.Categorical(transformed_status['Production Status'])
transformed_category['Product Category'] = pd.Categorical(transformed_category['Product Category'])
# Extract the categories and corresponding colors
categories_status = transformed_status['Production Status'].cat.categories
categories_category = transformed_category['Product Category'].cat.categories
# Ensure the palette covers all categories
colors_palette_status = [colors_palette.get(cat, '#808080') for cat in categories_status]
colors_palette_category = [colors_palette.get(cat, '#808080') for cat in categories_category]
# Convert DataFrame to ColumnDataSource
source_status = ColumnDataSource(transformed_status)
source_category = ColumnDataSource(transformed_category)
# Rename columns for hover tool compatibility
transformed_status_renamed = transformed_status.rename(columns={
'Production Status': 'Production_Status',
'Percentage': 'Percentage_Status'
})
transformed_category_renamed = transformed_category.rename(columns={
'Product Category': 'Product_Category',
'Percentage': 'Percentage_Status'
})
# Convert renamed DataFrames to ColumnDataSource
source_status_renamed = ColumnDataSource(transformed_status_renamed)
source_category_renamed = ColumnDataSource(transformed_category_renamed)
# HTML title for Production Status - margin-left creates a gap before the title
title_text_status = """<span style="font-size: 16px; color: #305496; margin-left: 130px;">
Production Status <b>Distribution</b> based on <b>Unique</b> Top-Level
</span>"""
title_status_plot_unique = Div(text=title_text_status)
# Create figure for Production Status without title
status_plot_unique = figure(
y_range=FactorRange(*transformed_status['Production Status']),
x_axis_label='Percentage Production Status',
y_axis_label='Production Status',
x_range=(0, 100),
title=None
)
# Remove existing hover tool (if any) and create a new one
status_plot_unique.tools = [tool for tool in status_plot_unique.tools if not isinstance(tool, HoverTool)]
hover_status = HoverTool(
tooltips=[
("Production Status", '@Production_Status'),
("Percentage", "@Percentage_Status{0.0f}%")
]
)
status_plot_unique.add_tools(hover_status)
status_plot_unique.hbar(
y='Production_Status',
right='Percentage_Status',
source=source_status_renamed,
height=0.6, # Thickness of each bar
color=factor_cmap('Production_Status', palette=colors_palette_status, factors=categories_status),
legend_field='Production_Status' # Add legend field
)
# Set fixed dimensions directly here
status_plot_unique.width = 600
status_plot_unique.height = 600
# HTML title for Product Category
title_text_category = """<span style="font-size: 16px; color: #305496; margin-left: 20px;">
Product Category <b>Distribution</b> based on <b>Unique</b> Top-Level
</span>"""
title_category_plot_unique = Div(text=title_text_category)
# Create figure for Product Category
category_plot_unique = figure(
y_range=FactorRange(*transformed_category['Product Category']),
x_axis_label='Percentage Product Category',
y_axis_label='Product Category',
x_range=(0, 100),
title=None
)
# Remove existing hover tool (if any) and create a new one
category_plot_unique.tools = [tool for tool in category_plot_unique.tools if not isinstance(tool, HoverTool)]
hover_category = HoverTool(
tooltips=[
("Product Category", '@Product_Category'),
("Percentage", "@Percentage_Status{0.0f}%")
]
)
category_plot_unique.add_tools(hover_category)
category_plot_unique.hbar(
y='Product_Category',
right='Percentage_Status',
source=source_category_renamed,
height=0.6, # Thickness of each bar
color=factor_cmap('Product_Category', palette=colors_palette_category, factors=categories_category),
legend_field='Product_Category' # Add legend field
)
# Set fixed dimensions directly here
category_plot_unique.width = 450
category_plot_unique.height = 600
# Set legend position
category_plot_unique.legend.location = 'top_right' # Position the legend in the top right
status_plot_unique.legend.location = 'top_right' # Position the legend in the top right
# Apply customization for consistency with other graphs
status_plot_unique = customize_distribution_plot(status_plot_unique)
category_plot_unique = customize_distribution_plot(category_plot_unique)
# Customize grid lines
status_plot_unique.xgrid.grid_line_color = '#E0E0E0'
status_plot_unique.xgrid.grid_line_dash = [4, 6] # Dash style for x-axis grid lines
status_plot_unique.ygrid.grid_line_color = None # Remove y-axis grid lines
category_plot_unique.xgrid.grid_line_color = '#E0E0E0'
category_plot_unique.xgrid.grid_line_dash = [4, 6] # Dash style for x-axis grid lines
category_plot_unique.ygrid.grid_line_color = None # Remove y-axis grid lines
# Arrange plots and titles in columns
status_layout_unique = column(title_status_plot_unique, status_plot_unique)
category_layout_unique = column(title_category_plot_unique, category_plot_unique)
#return status_plot_unique, category_plot_unique # Update 10/07
return status_layout_unique, category_layout_unique
# Update function for widget changes
def update_plot_percentage_product_category_production_status_UNIQUE(event):
program = program_widget_historic.value # Fetch selected program
# Filter data
filtered_data_status = status_percentage_unique_pivot[status_percentage_unique_pivot['Program'] == program]
if filtered_data_status.empty:
print(f"No data found for the selected program: {program}")
return
# Create updated plots
bokeh_status_unique_plot, bokeh_category_unique_plot = create_percentage_product_category_production_status_UNIQUE(
status_percentage_unique_pivot,
category_percentage_unique_pivot,
program,
colors_palette
)
# Update Bokeh plot panes
bar_plot_pane_status_unique.object = bokeh_status_unique_plot
bar_plot_pane_category_unique.object = bokeh_category_unique_plot
# Create initial plots
bokeh_status_unique_plot, bokeh_category_unique_plot = create_percentage_product_category_production_status_UNIQUE(
status_percentage_unique_pivot,
category_percentage_unique_pivot,
default_program_historic,
colors_palette
)
#######################
# Create initial plots
######################
# Create initial plots --> 2 distinct plots
bokeh_status_unique_plot, bokeh_category_unique_plot = create_percentage_product_category_production_status_UNIQUE(status_percentage_unique_pivot, category_percentage_unique_pivot, default_program_historic, colors_palette)
# Create Bokeh plots panes with fixed dimensions
bar_plot_pane_status_unique = pn.pane.Bokeh(bokeh_status_unique_plot, sizing_mode='fixed', height=600, width=600)
bar_plot_pane_category_unique = pn.pane.Bokeh(bokeh_category_unique_plot, sizing_mode='fixed', height=600, width=450)
####################################################
# Watch the widget and update the plot on change
####################################################
program_widget_historic.param.watch(update_plot_percentage_product_category_production_status_UNIQUE, 'value')
###############################
# Text bellow Chart 4
#############################
# Define text components with fixed widths to match plot widths
text_below_percentage_product_category = pn.pane.HTML(
f"These graphs are based on data from |CM-Priority| - <b>{Date_CM_Priority}</b>:<br>"
"▷ <b>% Product Category</b>: Percentage of each Product Categories based on the 'Total Quantity' of each PN.<br>"
"▷ <b>These Graphs includes</b>: All PN since the beginning of the transfer for the selected program included the 'redlist'.<br>",
width=450 # Match the width of bar_plot_pane4 which is 2 grpahs of 225
)
# Define text components with fixed widths to match plot widths
text_below_percentage_production_status = pn.pane.HTML(
f"These graphs are based on data from |CM-Priority| - <b>{Date_CM_Priority}</b>:<br>"
"▷ <b>% Production Status</b>: Percentage of each Production Statuses based on the 'Total Quantity' of each PN.<br>"
"▷ <b>These Graphs includes</b>: All PN since the beginning of the transfer for the selected program included the 'redlist'.<br>",
width=620 # Match the width of bar_plot_pane4 which is 2 grpahs of 225
)
####################################################################################
# Text bellow Chart 5 - bar_plot_pane_status_unique & bar_plot_pane_category_unique
#####################################################################################
# Define text components with fixed widths to match plot widths
text_below_percentage_product_category_UNIQUE = pn.pane.HTML(
f"These graphs are based on data from |CM-Priority| - <b>{Date_CM_Priority}</b>:<br>"
"▷ <b>% Product Category</b>: Percentage of each Product Categories based on the number of unique PN.<br>"
"▷ <b>These Graphs includes</b>: All PN since the beginning of the transfer for the selected program included the 'redlist'.<br>",
width=450 # Match the width of bar_plot_pane4 which is 2 grpahs of 225
)
# Define text components with fixed widths to match plot widths
text_below_percentage_production_status_UNIQUE = pn.pane.HTML(
f"These graphs are based on data from |CM-Priority| - <b>{Date_CM_Priority}</b>:<br>"
"▷ <b>% Production Status</b>: Percentage of each Production Statuses based on the number of unique PN.<br>"
"▷ <b>These Graphs includes</b>: All PN since the beginning of the transfer for the selected program included the 'redlist'.<br>",
width=650 # Match the width of bar_plot_pane4 which is 2 grpahs of 225
)
#//////////////////////////////////////////////////#//////////////////////////////////////////////////
#################################################################################################
# LAYOUT - Combine plots into a vertical Panel layout - Chart 1 to 5
####################################################################################################
#//////////////////////////////////////////////////#//////////////////////////////////////////////////
#Create vertical layouts for each plot and its corresponding text
plot1_layout = pn.Column(
bar_plot_pane1,
pn.Spacer(height=30),
text_below_product_category
)
plot2_layout = pn.Column(
bar_plot_pane2,
pn.Spacer(height=30),
text_below_product_category_total_qty
)
plot3_layout = pn.Column(
bar_plot_pane3,
pn.Spacer(height=30),
text_below_production_status
)
plot_category_percentage = pn.Column(
bar_plot_pane_category_percentage,
pn.Spacer(height=30),
text_below_percentage_product_category
)
plot_status_percentage = pn.Column(
bar_plot_pane_status_percentage,
pn.Spacer(height=30),
text_below_percentage_production_status
)
plot_category_unique = pn.Column(
bar_plot_pane_category_unique,
pn.Spacer(height=30),
text_below_percentage_product_category_UNIQUE
)
plot_status_unique = pn.Column(
bar_plot_pane_status_unique,
pn.Spacer(height=30),
text_below_percentage_production_status_UNIQUE
)
###############################################
# Update 09/16
# Create a vertical divider with custom CSS
###############################################
vertical_divider = pn.pane.HTML(
'<div style="width: 1px; height: 800px; background-color:#D9D9D9;"></div>',
)
vertical_divider2 = pn.pane.HTML(
'<div style="width: 1px; height: 800px; background-color:#D9D9D9;"></div>',
)
###############################################################
# Production Status
##############################################################
# Combine plots and content into columns and rows
distribution_dashboard_production_status = pn.Row(
pn.Column(
pn.pane.HTML("<h2 style='font-size: 18px; color: black; text-align: center; font-weight: bold; padding-left: 15px;'> #1 Based on <u>Unique</u> Part Number in the backlog</h2>"),
pn.Row(plot3_layout, pn.Spacer(width=30), plot_status_unique) # Place plots in the same row
),
pn.Row(pn.Spacer(width=30), vertical_divider, pn.Spacer(width=30)),
pn.Column(
pn.pane.HTML("<h2 style='font-size: 18px; color: black; text-align: center; font-weight: bold; padding-left: 15px;'> #2 Based on <u>Total Quantity</u> of Top-Level in the backlog</h2>"),
plot_status_percentage
)
)
########################
# Product Category
########################
# Combine plots in another horizontal row
distribution_dashboard_product_category = pn.Row(
pn.Column(
pn.pane.HTML("<h2 style='font-size: 18px; color: black; text-align: center; font-weight: bold; padding-left: 15px;'> #1 Based on <u>Unique</u> Part Number in the backlog</h2>"),
pn.Row(plot1_layout, pn.Spacer(width=30), plot_category_unique) # Place plots in the same row
),
pn.Row(pn.Spacer(width=30), vertical_divider2, pn.Spacer(width=30)),
pn.Column(
pn.pane.HTML("<h2 style='font-size: 18px; color: black; text-align: center; font-weight: bold; padding-left: 15px;'> #2 Based on <u>Total Quantity</u> of Top-Level in the backlog</h2>"),
pn.Row(plot_category_percentage, pn.Spacer(width=30), plot2_layout) # Place plots in the same row
)
)
# Update 08/14
#//////////////////////////////////////////////////#//////////////////////////////////////////////////
###############################################################################################
# Create a bar chart graphs representing the % completion Critical Qty and % Completion Total
###############################################################################################
#//////////////////////////////////////////////////#//////////////////////////////////////////////////
# Include the Combined PN {Program} by using Pivot_table_completion_upated_combinedPN instead of Pivot_table_completion
# Filter out rows where '% Completion Total Qty' = 0 from Pivot_table_completion_upated_combinedPN
Pivot_table_completion_upated_combinedPN_filtered = Pivot_table_completion_upated_combinedPN[Pivot_table_completion_upated_combinedPN['% Completion Total Qty'] != 0]
# Create a key numbers pane (initially empty)
key_numbers_pane = pn.pane.DataFrame(pd.DataFrame(), width=700, index=False)
def update_data(event):
# Filter out rows where '% Completion Total Qty' = 0
filtered_data = Pivot_table_completion_upated_combinedPN[Pivot_table_completion_upated_combinedPN['% Completion Total Qty'] != 0]
# Define the target value for 'Pty Indice'
target_value = f'Combined PN {program_widget_historic.value}'
# Filter the DataFrame based on the target value
filtered_data_target_value = filtered_data[filtered_data['Pty Indice'] == target_value]
# Extract the desired columns
key_numbers = filtered_data_target_value[['Pty Indice', 'Qty Shipped', 'Critical Qty', 'Total Quantity', '% Completion Critical Qty', '% Completion Total Qty']]
# Update the key numbers display
key_numbers_pane.object = key_numbers # Update the key numbers pane
# Attach the callback to the widget
program_widget_historic.param.watch(update_data, 'value')
#########################################################################################################################
def customize_completion_plot(bokeh_plot):
"""Apply customizations to the % Completion plot."""
bokeh_plot.xaxis.major_label_text_font_size = '8pt'
bokeh_plot.yaxis.major_label_text_font_size = '10pt'
bokeh_plot.title.text_font_size = '12pt'
bokeh_plot.title.text_color = "#305496"
bokeh_plot.xaxis.axis_line_width = 2
bokeh_plot.yaxis.axis_line_width = 2
bokeh_plot.xaxis.major_label_orientation = 'vertical'
bokeh_plot.yaxis.major_label_orientation = 'horizontal'
bokeh_plot.yaxis.axis_label_text_font_size = '10pt'
bokeh_plot.ygrid.grid_line_color = '#F0F0F0'
bokeh_plot.ygrid.grid_line_dash = [4, 6]
bokeh_plot.toolbar.logo = None
# Cap the y-axis at 100%
bokeh_plot.y_range.end = 100
return bokeh_plot
def create_completion_plot(Pivot_table_completion_upated_combinedPN_filtered, default_program_historic):
# Filter data by default program
filtered_data = Pivot_table_completion_upated_combinedPN_filtered[Pivot_table_completion_upated_combinedPN_filtered['Program'] == default_program_historic]
if filtered_data.empty:
print("No data found for the default program.")
return None, None
# Filter rows where either % Completion Critical Qty or % Completion Total Qty is greater than 0
filtered_data = filtered_data[
(filtered_data['% Completion Critical Qty'] > 0) |
(filtered_data['% Completion Total Qty'] > 0)
]
# Melt the DataFrame to long format for plotting
melted_df = filtered_data.melt(
id_vars=['Pty Indice'],
value_vars=['% Completion Critical Qty', '% Completion Total Qty'],
var_name='Completion Type',
value_name='Completion Percentage'
)
# Create a bar chart
completion_plot = melted_df.hvplot.bar(
x='Pty Indice',
y='Completion Percentage',
color='Completion Type',
title="% Completion Critical Qty and % Completion Total Qty per Pty Indice",
xlabel='Pty Indice',
ylabel='% Completion',
cmap='Category20',
legend='top_left',
height=400,
tools=[]
)
# Render and customize the plot
bokeh_completion_plot = hv.render(completion_plot, backend='bokeh')
bokeh_completion_plot = customize_completion_plot(bokeh_completion_plot)
# Remove existing HoverTools (if any) before adding a new one
bokeh_completion_plot.tools = [tool for tool in bokeh_completion_plot.tools if not isinstance(tool, HoverTool)]
# Add HoverTool with custom formatting
hover = HoverTool()
hover.tooltips = [
("Pty Indice", "@Pty_Indice"),
("Completion Type", "@color"),
("Percentage", "@Completion_Percentage%") #"@value{0.1f}%") # Round to 1 decimal
]
bokeh_completion_plot.add_tools(hover)
# Remove wheel zoom from active tools if you want it inactive by default - 08/12
bokeh_completion_plot.tools = [tool for tool in bokeh_completion_plot.tools if not isinstance(tool, WheelZoomTool)]
return bokeh_completion_plot
def update_bar_chart(event):
# Get the selected program from the widget
program = program_widget_historic.value
#print(f"Updating plots for program: {program}")
# Filter data by the selected program
filtered_data = Pivot_table_completion_upated_combinedPN_filtered[Pivot_table_completion_upated_combinedPN_filtered['Program'] == program]
if filtered_data.empty:
print("No data found for the selected program.")
return
# Further filter rows where either % Completion Critical Qty or % Completion Total Qty is greater than 0
filtered_data = filtered_data[
(filtered_data['% Completion Critical Qty'] > 0) |
(filtered_data['% Completion Total Qty'] > 0)
]
# Melt the filtered DataFrame for plotting
melted_df = filtered_data.melt(
id_vars=['Pty Indice'],
value_vars=['% Completion Critical Qty', '% Completion Total Qty'],
var_name='Completion Type',
value_name='Completion Percentage'
)
# Update plots
bokeh_completion_plot = create_completion_plot(filtered_data, program)
# Update the plots in the Panel layout
plot_pane_completion.object = bokeh_completion_plot
# Create initial plot
bokeh_completion_plot = create_completion_plot(Pivot_table_completion_upated_combinedPN_filtered, default_program_historic)
# Convert to Panel/Bokeh
plot_pane_completion = pn.pane.Bokeh(bokeh_completion_plot, sizing_mode='stretch_width')
# Update plot initially - Needed for the sizing_mode='stretch_width' to be set
update_bar_chart(None)
#######################################################
# Watch the widget and update the plot on change
program_widget_historic.param.watch(update_bar_chart, 'value') # moved bellow 10/25
######################################
# Create text bellow graphs
########################################
'''
text_below_completion_plot = (
f"This graph is based on data from |Snapshot| & |CM-Priority|- <b> {file_date} & {Date_CM_Priority}</b>:<br>"
"▷<b>Total Quantity</b>: Is calculated as 'IDD Backlog Qty' + 'Qty Shipped' if 'Remain. crit. Qty' = 0.<br>"
"➥ 'Total Quantity' increases over time as follow-up orders are placed, while the 'Critical Quantity' is defined as part of the project scope.<br>"
"▷ <b>% Completion Critical Qty</b>: Progress based on the defined 'Critical Qty', usually incompassing the DPAS orders.<br>"
"▷ <b>% Completion Total Qty</b>: Progress based on the 'Total Qty' including the potential follow-up orders.<br>"
)
'''
text_below_completion_plot = (
f"<div style='width: 860px;'>"
f"This graph is based on data from |Snapshot| & |CM-Priority|- <b> {file_date} & {Date_CM_Priority}</b>:<br>"
"▷<b>Total Quantity</b>: Is calculated as 'IDD Backlog Qty' + 'Qty Shipped' if 'Remain. crit. Qty' = 0.<br>"
"➥ 'Total Quantity' increases over time as follow-up orders are placed, while the 'Critical Quantity' is defined as part of the project scope.<br>"
"▷ <b>% Completion Critical Qty</b>: Progress based on the defined 'Critical Qty', usually encompassing the DPAS orders.<br>"
"▷ <b>% Completion Total Qty</b>: Progress based on the 'Total Qty' including the potential follow-up orders.<br>"
"</div>"
)
text_above_key_number = (
f"▷ The Data-point <b>'Combined PN'</b>: Represents the full scope of the project for the selected 'Program'.<br>"
"➥ This data-point is a made-up PN representative of the entire scope in term of ' Total Quantity' of the project based on the Priority List.<br>"
"➥ The Canceled orders are filtered-out but the PN 'To be transferred' are still included.<br>"
)
# Arrange text_above_key_number above key_numbers_pane
key_numbers_column = pn.Column(
text_above_key_number,
key_numbers_pane,
sizing_mode='stretch_width'
)
#create short vertical divider
vertical_divider_short = pn.pane.HTML(
'<div style="width: 1px; height: 170px; background-color:#D9D9D9;"></div>',
)
# Arrange text_below_completion_plot and key_numbers_column side-by-side
side_by_side = pn.Row(
text_below_completion_plot,
pn.Spacer(width=100), # Add space between the plot and the text
vertical_divider_short,
pn.Spacer(width=100),
key_numbers_column,
sizing_mode='stretch_width'
)
# Arrange plot_pane_completion on top and side_by_side below it
completion_dashboard = pn.Column(
plot_pane_completion,
pn.Spacer(height=50), # Add space between the plot and the text
side_by_side,
sizing_mode='stretch_width'
)
#08/21
#//////////////////////////////////////////////////#//////////////////////////////////////////////////
#######################################################################################################################
# Backlog Projection --> Quantity of PN to build per Month - Graph combined and Graph PN by PN
#######################################################################################################################
#re-load df_Backlog to erase any potnetial change on the original dataframe
#df_Backlog = pd.read_excel(input_file_formatted, sheet_name='CM-Backlog', index_col=False)
#Rename 'Backlog row Qty' to 'Backlog Qty'
#df_Backlog.rename(columns={'Backlog row Qty': 'Backlog Qty'}, inplace=True)
# 09/19 ---> to be updated with 'Requested Date' & 'Month Requested' <---
#//////////////////////////////////////////////////#//////////////////////////////////////////////////
#Preparation of dataframes backlog_monthly_summary based on df_Backlog
backlog_monthly_summary = df_Backlog.copy()
#Filter relevant column from df_Backlog
backlog_monthly_summary = backlog_monthly_summary[['Priority', 'Pty Indice', 'IDD Top Level', 'SEDA Top Level', 'Backlog Qty', 'Marge standard', 'Site', 'Order', 'Invoice name', 'Requested Date', 'Due Date','Actual amount -standard', 'Month', 'Month Requested', 'Product_Category', 'Complexity', 'Program']]
#print('backlog_monthly_summary')
#display(backlog_monthly_summary)
###############################################################
# backlog_monthly_summary dataframe
###############################################################
# Ensure 'Due Date' is in datetime format
backlog_monthly_summary['Due Date'] = pd.to_datetime(backlog_monthly_summary['Due Date'])
backlog_monthly_summary['Requested Date'] = pd.to_datetime(backlog_monthly_summary['Requested Date'])
# Rename columns
backlog_monthly_summary = backlog_monthly_summary.rename(columns={
'Actual amount -standard': 'Sales',
'Marge standard': 'IDD Marge Standard',
'Complexity': 'Average Complexity',
})
# Filter to exclude rows where 'Order' contains 'NC'
backlog_monthly_summary = backlog_monthly_summary[~backlog_monthly_summary['Order'].str.contains('NC')]
#####################################
# Sorting backlog_monthly_summary
######################################
# Function to check if a value is numeric
def is_numeric(val):
try:
int(val)
return True
except ValueError:
return False
# Separate numeric and non-numeric 'Priority' values
backlog_numeric_priority = backlog_monthly_summary[backlog_monthly_summary['Priority'].apply(is_numeric)]
backlog_non_numeric_priority = backlog_monthly_summary[~backlog_monthly_summary['Priority'].apply(is_numeric)]
# Convert 'Priority' values to integers for numeric priorities
backlog_numeric_priority['Priority'] = backlog_numeric_priority['Priority'].astype(int)
# Sort numeric priorities in ascending order
#backlog_numeric_priority = backlog_numeric_priority.sort_values(by='Priority', ascending=True) #Update 08/28
backlog_numeric_priority.sort_values(by=['Priority', 'Pty Indice'], inplace=True)
# Combine the DataFrames, placing numeric priorities first and non-numeric priorities at the end
backlog_monthly_summary_sorted = pd.concat([backlog_numeric_priority, backlog_non_numeric_priority])
# Reset index if needed
backlog_monthly_summary_sorted.reset_index(drop=True, inplace=True)
# Update the original DataFrame
backlog_monthly_summary = backlog_monthly_summary_sorted
#print('backlog_monthly_summary:')
#display(backlog_monthly_summary)
##########################
########################################################################
# Create datafram for Graph 1 by grouping by 'Month' and 'Program'
#########################################################################
#Update 09/19
# 'Month' is related to the 'Due Date' whcih correspond to the modified PO set to build a more sustainable backlog
# 'Month Requested' is related to the 'Requested Date' whcih correspond to the original PO placed by SEDA
###########################
backlog_monthly_summary_combined = backlog_monthly_summary.groupby(['Month', 'Program']).agg({
'Backlog Qty': 'sum',
'Sales': 'sum',
'Pty Indice': lambda x: ', '.join(map(str, x)),
'IDD Top Level': lambda x: ', '.join(map(str, x)),
'SEDA Top Level': lambda x: ', '.join(map(str, x)),
'IDD Marge Standard': 'sum',
'Due Date': 'first', # Keep the 'Invoice date' as the first date in each group
'Average Complexity': 'mean' # Calculate the average complexity
}).reset_index()
# Define a function to format numbers with 1 decimal digit if necessary
def format_complexity(value):
if pd.isna(value): # Handle NaN values
return value
elif value.is_integer():
return int(value) # Return as integer if value is an integer
else:
return round(value, 1) # Round to 1 decimal place otherwise
# Apply the formatting function to 'Complexity' column
backlog_monthly_summary_combined['Average Complexity'] = backlog_monthly_summary_combined['Average Complexity'].apply(format_complexity)
#Create 'Normalized Complexity'
backlog_monthly_summary_combined['Normalized Complexity'] = backlog_monthly_summary_combined['Average Complexity']*backlog_monthly_summary_combined['Backlog Qty']
#print('backlog_monthly_summary_combined')
#display(backlog_monthly_summary_combined)
###################################
# Fill NaN values appropriately
###################################
# Fill numeric columns with 0
Backlog_numeric_cols = backlog_monthly_summary_combined.select_dtypes(include='number').columns
backlog_monthly_summary_combined[Backlog_numeric_cols] = backlog_monthly_summary_combined[Backlog_numeric_cols].fillna(0)
# Fill string columns with ''
Backlog_string_cols = backlog_monthly_summary_combined.select_dtypes(include='object').columns
backlog_monthly_summary_combined[Backlog_string_cols] = backlog_monthly_summary_combined[Backlog_string_cols].fillna('')
# Sort by 'Invoice date' in descending order
backlog_monthly_summary_combined = backlog_monthly_summary_combined.sort_values(by='Due Date', ascending=True)
# Display the updated DataFrame
#print('backlog_monthly_summary_combined:')
#display(backlog_monthly_summary_combined)
##################################################################################################
# Backlog graph 1 - Combined Quantity of PN to build per months
##################################################################################################
##################################################################################################
# Backlog graph 2 - Quantity of PN to build per month for each given Pty Indice
####################################################################################################
##################################################################################################
# Backlog graph 3 - XXXX
####################################################################################################
##################################################################################################
# Backlog graph 4 - Backlog Projection using 'Month Requested' and Requested Date'
####################################################################################################
# Load backlog
df_Backlog_overview = df_Backlog.copy()
### update 08/23
# Custom color palette with alpha transparency
custom_palette_bkg = {
'Backlog Qty': '#cdbedd',
'Sales': '#63BE7B',
'IDD Marge Standard': '#E2EFDA',
'Normalized Complexity': 'rgba(255, 47, 47, 0.7)' # Alpha applied
}
def customize_qty_backlog_plot(bokeh_plot):
""" Apply customizations to the Quantity backlog plot. """
bokeh_plot.xaxis.major_label_text_font_size = '8pt'
bokeh_plot.yaxis.major_label_text_font_size = '10pt'
bokeh_plot.title.text_font_size = '12pt'
bokeh_plot.title.text_color = "#305496"
bokeh_plot.xaxis.axis_line_width = 2
bokeh_plot.yaxis.axis_line_width = 2
bokeh_plot.xaxis.major_label_orientation = 'vertical'
bokeh_plot.yaxis.major_label_orientation = 'horizontal'
bokeh_plot.yaxis.axis_label_text_font_size = '10pt'
bokeh_plot.ygrid.grid_line_color = '#E0E0E0'
bokeh_plot.ygrid.grid_line_dash = [4, 6]
bokeh_plot.toolbar.logo = None
return bokeh_plot
def customize_total_quantity_backlog_plot(bokeh_plot):
""" Apply customizations to the Total Quantity plot. """
bokeh_plot.xaxis.major_label_text_font_size = '6pt'
bokeh_plot.yaxis.major_label_text_font_size = '10pt'
bokeh_plot.title.text_font_size = '12pt'
bokeh_plot.title.text_color = "#305496"
bokeh_plot.xaxis.axis_line_width = 2
bokeh_plot.yaxis.axis_line_width = 2
bokeh_plot.xaxis.major_label_orientation = 'vertical'
bokeh_plot.yaxis.major_label_orientation = 'horizontal'
bokeh_plot.yaxis.axis_label_text_font_size = '10pt'
bokeh_plot.ygrid.grid_line_color = '#E0E0E0'
bokeh_plot.ygrid.grid_line_dash = [4, 6]
bokeh_plot.toolbar.logo = None
return bokeh_plot
def customize_combined_backlog_plot(bokeh_plot):
""" Apply customizations to the Combined plot. """
bokeh_plot.xaxis.major_label_text_font_size = '8pt'
bokeh_plot.yaxis.major_label_text_font_size = '10pt'
bokeh_plot.title.text_font_size = '12pt'
bokeh_plot.title.text_color = "#305496"
bokeh_plot.xaxis.axis_line_width = 2
bokeh_plot.yaxis.axis_line_width = 2
bokeh_plot.xaxis.major_label_orientation = 'vertical'
bokeh_plot.yaxis.major_label_orientation = 'horizontal'
bokeh_plot.yaxis.axis_label_text_font_size = '10pt'
bokeh_plot.ygrid.grid_line_color = '#F0F0F0'
bokeh_plot.ygrid.grid_line_dash = [4, 6]
bokeh_plot.toolbar.logo = None
# Format the y-axis ticks in thousands with a dollar sign
bokeh_plot.yaxis.formatter =CustomJSTickFormatter(code="""
return '$' + (tick / 1000).toFixed(0) + 'k';
""")
return bokeh_plot
def create_total_quantity_backlog_plot(df_Backlog_overview, default_program_historic):
# Filter data by the default program
filtered_data = df_Backlog_overview[df_Backlog_overview['Program'] == default_program_historic]
if filtered_data.empty:
print("No data found for the default program.")
return None
# Aggregate data: Sum 'Backlog Qty' for each 'Pty Indice'
aggregated_data = filtered_data.groupby('Pty Indice')['Backlog Qty'].sum().reset_index()
# If the program is 'Phase 4-5', sort by 'Priority'
if default_program_historic == 'Phase 4-5':
# Merge the aggregated data with original to retain 'Priority'
aggregated_data = pd.merge(aggregated_data, filtered_data[['Pty Indice', 'Priority']].drop_duplicates(), on='Pty Indice')
# Convert 'Priority' values to integers for sorting
aggregated_data['Priority'] = aggregated_data['Priority'].astype(int)
# Sort numeric priorities in ascending order - Update 08/28
#aggregated_data = aggregated_data.sort_values(by='Priority', ascending=True)
aggregated_data.sort_values(by=['Priority', 'Pty Indice'], inplace=True)
# Define the uniform color
uniform_color = '#cdbedd' # Light blue color
# Create the plot
total_quantity_plot = aggregated_data.hvplot.bar(
x='Pty Indice',
y='Backlog Qty',
title="Total Backlog Qty",
xlabel='Pty Indice',
ylabel='Total Backlog Qty',
#cmap=custom_palette_bkg,
color=uniform_color, # Apply the same color to all bars
legend='top_left',
height=400,
tools=[]
)
return total_quantity_plot
def create_backlog_chart_detailed (backlog_monthly_summary_combined, default_program_historic, df_Backlog_overview):
# Filter data by default program
filtered_data = backlog_monthly_summary_combined[backlog_monthly_summary_combined['Program'] == default_program_historic]
if filtered_data.empty:
print("No data found for the default program.")
return None, None, None
# Melt the DataFrame to include Normalized Complexity
melted_df = filtered_data.melt(id_vars=['Month'], value_vars=['Backlog Qty', 'Sales', 'IDD Marge Standard', 'Normalized Complexity'],
var_name='Quantity Type', value_name='Quantity Value')
# Create plot for 'Backlog Qty' and 'Normalized Complexity'
backlog_qty_plot = melted_df[melted_df['Quantity Type'].isin(['Backlog Qty', 'Normalized Complexity'])].hvplot.bar(
x='Month',
y='Quantity Value',
color='Quantity Type',
title="Monthly Backlog - Backlog Quantity & Normalized Complexity",
xlabel='Month',
ylabel='Backlog Qty & Normalized Complexity',
cmap=custom_palette_bkg,
legend='top_left',
height=400,
bar_width=0.6, # Set bar width - 09/12
tools=[]
)
bokeh_backlog_qty_plot = hv.render(backlog_qty_plot, backend='bokeh')
bokeh_backlog_qty_plot = customize_qty_backlog_plot(bokeh_backlog_qty_plot)
#####################################################
# Remove existing HoverTools (if any) before adding a new one
bokeh_backlog_qty_plot.tools = [tool for tool in bokeh_backlog_qty_plot.tools if not isinstance(tool, HoverTool)]
# Add HoverTool with custom formatting
hover = HoverTool()
hover.tooltips = [
("Month", "@Month"),
("KPI", "@color"),
("Value", "@Quantity_Value")
]
# Add HoverTool to the plot
bokeh_backlog_qty_plot.add_tools(hover)
# Remove wheel zoom from active tools if you want it inactive by default - 08/12
bokeh_backlog_qty_plot.tools = [tool for tool in bokeh_backlog_qty_plot.tools if not isinstance(tool, WheelZoomTool)]
############################################################
# Create combined plot for 'IDD Marge Standard' and 'Sales'
combined_backlog_plot = melted_df[melted_df['Quantity Type'].isin(['IDD Marge Standard', 'Sales'])].hvplot.bar(
x='Month',
y='Quantity Value',
color='Quantity Type',
title="Monthly Backlog - IDD Margin & Total Sales",
xlabel='Month',
ylabel='[K$]',
cmap=custom_palette_bkg,
legend='top_left',
stacked=True, # Stacking bars
height=400,
bar_width=0.6, # Set bar width - 09/12
tools=[]
)
bokeh_combined_backlog_plot = hv.render(combined_backlog_plot, backend='bokeh')
bokeh_combined_backlog_plot = customize_combined_backlog_plot(bokeh_combined_backlog_plot)
#New 08/08
#####################################################
# Remove existing HoverTools (if any) before adding a new one
bokeh_combined_backlog_plot.tools = [tool for tool in bokeh_combined_backlog_plot.tools if not isinstance(tool, HoverTool)]
# Add HoverTool with custom formatting
hover = HoverTool()
hover.tooltips = [
("Month", "@Month"),
("KPI", "@color"),
("Value", "@Quantity_Value{($0,0k)}") # Format values: thousands with 'K' # Quantity_Value with the '_' otherwise that does not work!
]
# Add HoverTool to the plot
bokeh_combined_backlog_plot.add_tools(hover)
# Remove wheel zoom from active tools if you want it inactive by default - 08/12
bokeh_combined_backlog_plot.tools = [tool for tool in bokeh_combined_backlog_plot.tools if not isinstance(tool, WheelZoomTool)]
############################################################
# Create Total Quantity backlog plot
total_quantity_backlog_plot = create_total_quantity_backlog_plot(df_Backlog_overview, default_program_historic)
if total_quantity_backlog_plot:
bokeh_total_quantity_backlog_plot = hv.render(total_quantity_backlog_plot, backend='bokeh')
bokeh_total_quantity_backlog_plot = customize_total_quantity_backlog_plot(bokeh_total_quantity_backlog_plot)
# Remove wheel zoom from active tools if you want it inactive by default - 08/12
bokeh_total_quantity_backlog_plot.tools = [tool for tool in bokeh_total_quantity_backlog_plot.tools if not isinstance(tool, WheelZoomTool)]
else:
bokeh_total_quantity_backlog_plot = None
return bokeh_backlog_qty_plot, bokeh_combined_backlog_plot, bokeh_total_quantity_backlog_plot
def update_backlog_chart_combined(event):
# Get the selected program from the widget
program = program_widget_historic.value
#print(f"Updating plots for program: {program}")
# Filter data by the selected program
filtered_data = backlog_monthly_summary_combined[backlog_monthly_summary_combined['Program'] == program]
if filtered_data.empty:
print("No data found for the selected program.")
return
# Melt the DataFrame
melted_df = filtered_data.melt(id_vars=['Month'], value_vars=['Backlog Qty', 'Sales', 'IDD Marge Standard', 'Normalized Complexity'],
var_name='Quantity Type', value_name='Quantity Value')
# Update plots
bokeh_backlog_qty_plot, bokeh_combined_backlog_plot, bokeh_total_quantity_backlog_plot = create_backlog_chart_detailed(filtered_data, program, df_Backlog_overview)
# Update the plots in the Panel layout
backlog_plot_pane1.object = bokeh_backlog_qty_plot
backlog_plot_pane2.object = bokeh_combined_backlog_plot
backlog_plot_pane3.object = bokeh_total_quantity_backlog_plot
# Create initial bokeh plots
bokeh_backlog_qty_plot, bokeh_combined_backlog_plot, bokeh_total_quantity_backlog_plot = create_backlog_chart_detailed(backlog_monthly_summary_combined, default_program_historic, df_Backlog_overview)
# Convert Bokeh plots to Panel
backlog_plot_pane1 = pn.pane.Bokeh(bokeh_backlog_qty_plot, sizing_mode='stretch_width')
backlog_plot_pane2 = pn.pane.Bokeh(bokeh_combined_backlog_plot, sizing_mode='stretch_width')
backlog_plot_pane3 = pn.pane.Bokeh(bokeh_total_quantity_backlog_plot, sizing_mode='stretch_width')
# Update plot initially - Needed for the sizing_mode='stretch_width' to be set
update_backlog_chart_combined(None)
#######################################################
# Watch the widget and update the plot on change
program_widget_historic.param.watch(update_backlog_chart_combined, 'value')
#//////////////////////////////////////
############################################################################################
# Display the datafram monthly_summary of list of Pty Indice for each Month under Graph 3
#############################################################################################
#///////////////////////////////////////
# Function to remove duplicates in comma-separated strings
def remove_duplicates_from_string(s):
items = s.split(', ')
unique_items = sorted(set(items), key=items.index) # Preserve order
return ', '.join(unique_items)
# Function to filter and sort DataFrame by program and month (for backlog)
def filter_dataframe_monthly_summary_backlog(program):
# Apply the filter based on selected program
filtered_df = backlog_monthly_summary_combined[backlog_monthly_summary_combined['Program'] == program]
# Check if the filtered DataFrame is empty
if filtered_df.empty:
print("No data found for the specified program.") # New check for empty DataFrame
return filtered_df # Return empty DataFrame if no matches found
# Filter columns
filtered_df = filtered_df[['Month', 'Pty Indice', 'Backlog Qty', 'IDD Top Level']]
# Remove duplicates in specified columns
filtered_df['Pty Indice'] = filtered_df['Pty Indice'].apply(remove_duplicates_from_string)
filtered_df['IDD Top Level'] = filtered_df['IDD Top Level'].apply(remove_duplicates_from_string)
# Create a temporary column for sorting by converting 'Month' to datetime
filtered_df['Month_dt'] = pd.to_datetime(filtered_df['Month'], format='%b %y', errors='coerce')
# Check for any invalid dates after conversion
if filtered_df['Month_dt'].isnull().any():
print("Some dates could not be parsed. Please check the 'Month' column for incorrect formats.") # Error handling
return filtered_df # Return DataFrame without sorting
# Sort by the new 'Month_dt' column
filtered_df = filtered_df.sort_values(by='Month_dt') # ascending=False - Do not set by desceding to display older month first
# Reset the index after sorting
filtered_df.reset_index(drop=True, inplace=True)
# Print the DataFrame before deleting the temporary column
#print("Filtered and sorted DataFrame before dropping the temporary column:")
#display(filtered_df) # Displaying the DataFrame for verification
# Drop the 'Month_dt' column
filtered_df = filtered_df.drop(columns=['Month_dt'])
return filtered_df
#####################################################
# Table colored in purple and white every other rows
#######################################################
# Function to apply custom styles to the DataFrame (alternating row colors)
def style_dataframe_purple(df):
def row_styles(row):
# Alternate row colors based on row index
color = '#E4DFEC' if row.name % 2 == 0 else '#ffffff' # Alternate colors
return [f'background-color: {color}'] * len(row) # Apply to all columns
# Apply the style function to the DataFrame rows
styled_df = df.style.apply(row_styles, axis=1)
# Hide the index
styled_df.hide(axis="index") # Hide index
return styled_df
# Function to update DataFrame display with custom styling
def update_dataframe_monthly_summary_backlog(program):
filtered_df = filter_dataframe_monthly_summary_backlog(program)
styled_df = style_dataframe_purple(filtered_df)
styled_html = styled_df.to_html() # New 10/24
# Add CSS for overflow handling directly in the HTML
html_with_overflow = f'<div style="overflow-y: auto; height: 450px;">{styled_html}</div>'
return html_with_overflow
# Initialize the backlog table
monthly_summary_backlog_table = pn.pane.HTML(update_dataframe_monthly_summary_backlog(default_program_historic), width=700)
# Callback function to update the table based on widget value
def update_table_backlog(event):
print(f"Widget value changed to: {event.new}") # Check new value
new_df = filter_dataframe_monthly_summary_backlog(event.new)
# Style the new DataFrame
styled_df = style_dataframe_purple(new_df)
# Convert styled DataFrame to HTML for rendering
styled_html = styled_df.to_html()
html_with_overflow = f'<div style="overflow-y: auto; height: 450px;">{styled_html}</div>'
# Update the object attribute directly
monthly_summary_backlog_table.object = html_with_overflow # Update existing HTML pane
# Attach callback to the widget
program_widget_historic.param.watch(update_table_backlog, 'value')
# Create a backlog high level summary table with 'Backlog Quantity' of PN and 'Sales' related to 'Total Past due backlog',
# 'Total future backlog', 'Total current year remaining backlog'
backlog_highlevelsummary_table = None
######################################
# Create text bellow graphs
########################################
# Convert 'Invoice date' to datetime format
df_Backlog_overview['Due Date'] = pd.to_datetime(df_Backlog_overview['Due Date'])
text_below_graph_backlog_qty_plot = (
f"This graph is based on data from |CM-Backlog|:<br>"
"▷ <b>Backlog Qty</b>: Total quantity of Top-Level related to the selected program in IDD backlog <br>"
"➥ The backlog does not necessarily represent the Master Production Schedule (MPS) as manually entered by the Master Scheduler. <br>"
"▷ <b>Normalized Complexity</b>: Average complexity of the Top-Level in backlog normalized on the quantity of each PN on the period.<br>"
"▷ <b>The complexity is define as</b>: Kit, Subs = 0, Lighplate = 1, Rotottelite = 2, CPA = 3, ISP = 4.<br>"
)
text_below_graph_Marge_Sales_Backlog = (
f"This graph is based on data from |CM-Backlog|:<br>"
"▷ <b>Sales</b>: Sum of the 'Currency turnover ex.VAT' for the PN in backlog during the specified month<br>"
"▷ <b>IDD Marge Standard</b>: Sum of the 'IDD Margin Standard' for the PN in backlog during the specified month.<br>"
"➥ The value is displayed as: Gain (Loss). <br>"
)
text_below_graph_backlog_pty_indice = (
f"This graph is based on data from |CM-Backlog|:<br>"
"▷ <b>Total Backlog Qty </b>: Total quantity of Top-Level related to the selected pty Indice in IDD Backlog.<br>"
)
##############################################
# Combine plots into a vertical Panel layout
###############################################
# Combine the plots and table in the layout
# Create the dashboard layout for backlog overview
Backlogoverview_dashboard = pn.Column(
pn.Row(
# First Row with two columns
pn.Column(
backlog_plot_pane1, # First plot
text_below_graph_backlog_qty_plot # Text below first plot
),
pn.Spacer(width=50), # Spacer between columns
vertical_divider_medium2, # Vertical divider
pn.Spacer(width=50), # Spacer between columns
pn.Column(
backlog_plot_pane2, # Second plot
text_below_graph_Marge_Sales_Backlog # Text below second plot
),
sizing_mode='stretch_width' # Stretch columns to fit width
),
pn.Spacer(height=50), # Spacer before the next row
pn.Row(
# Second Row with another two columns
pn.Column(
backlog_plot_pane3, # Third plot
text_below_graph_backlog_pty_indice # Text below third plot
),
monthly_summary_backlog_table, # Summary table on the right
),
)
#//////////////////////////////////////////////////
#######################################################################################################################
# Fianal Layout of the |Project Overview| tab
#######################################################################################################################
#//////////////////////////////////////////////////
# Define your color
line_color = "#4472C4" # Change this to your desired color
font_top_color = "#4472C4"
subtitle_background_color = "#aee0d9" # "#F2F2F2" #Gray
# Convert end_date_historic to the desired format
formatted_end_date = end_date_historic.strftime("%m/%d/%Y")
# Use the formatted date in your string
Historic_title = f"Transfer Project Overview [{formatted_end_date}]"
Historic_subtitle = "Selection of the program"
Historic_subtitle2 = "Monthly shipments & related Sales"
Historic_subtitle3bis = " Distribution of product category"
Historic_subtitle3 = " Distribution of production status"
Historic_subtitle4 = "Pourcentage Completion of the project"
Historic_subtitle5 = "Backlog Overview"
Historic_subsubtitle1 = "Backlog high level summary"
###########################################
title_section = pn.pane.HTML(f"""
<div style='background-color: {font_top_color}; width: 100%; padding: 10px; box-sizing: border-box;'>
<h1 style='font-size: 24px; color: white; text-align: left; margin: 0;'>{Historic_title}</h1>
</div>
""", sizing_mode='stretch_width')
# Title Layout
title_layout = pn.Column(
title_section,
pn.layout.Divider(margin=(-10, 0, 0, 0)),
pn.Column(
#pn.pane.HTML(f"<h3 style='font-size: 12px; text-align: center; font-weight: normal;'>{Historic_subtitle}</h3>"),
pn.layout.Spacer(height=5),
pn.Row(
program_widget_historic,
sizing_mode='stretch_width'
),
sizing_mode='stretch_width'
),
sizing_mode='stretch_width'
)
# Update 04/10 to conditionnaly display the distribution_dashboard_product_category
# Define Secondary Layout
secondary_layout = pn.Column(
pn.pane.HTML(
f"""
<div style='background-color: {subtitle_background_color};
width: 100%;
padding: 10px;
box-sizing: border-box;
border-radius: 15px;'>
<h1 style='font-size: 22px; color: white; text-align: left; margin: 0;'>
{Historic_subtitle2}
</h1>
</div>
""",
sizing_mode='stretch_width'
),
pn.Spacer(height=50), # Spacer before plots
combined_plots_history,
pn.Spacer(height=50),
pn.layout.Divider(margin=(0, 0, -10, 0)),
# Percentage Completion of the project section
pn.pane.HTML(
f"""
<div style='background-color: {subtitle_background_color};
width: 100%;
padding: 10px;
box-sizing: border-box;
border-radius: 15px;'>
<h1 style='font-size: 22px; color: white; text-align: left; margin: 0;'>
{Historic_subtitle4}
</h1>
</div>
""",
sizing_mode='stretch_width'
),
pn.Spacer(height=50), # Spacer before plots
completion_dashboard,
pn.Spacer(height=50),
pn.layout.Divider(margin=(0, 0, -10, 0)),
# Backlog Overview section
pn.pane.HTML(
f"""
<div style='background-color: {subtitle_background_color};
width: 100%;
padding: 10px;
box-sizing: border-box;
border-radius: 15px;'>
<h1 style='font-size: 22px; color: white; text-align: left; margin: 0;'>
{Historic_subtitle5}
</h1>
</div>
""",
sizing_mode='stretch_width'
),
pn.Spacer(height=50), # Spacer before plots
Backlogoverview_dashboard,
pn.Spacer(height=50), # Spacer before plots
pn.layout.Divider(margin=(0, 0, -10, 0)),
# Distribution of product category section
pn.pane.HTML(
f"""
<div style='background-color: {subtitle_background_color};
width: 100%;
padding: 10px;
box-sizing: border-box;
border-radius: 15px;'>
<h1 style='font-size: 22px; color: white; text-align: left; margin: 0;'>
{Historic_subtitle3bis}
</h1>
</div>
""",
sizing_mode='stretch_width'
),
pn.Spacer(height=50), # Spacer before plots
distribution_dashboard_product_category,
pn.Spacer(height=50), # Spacer before plots
pn.layout.Divider(margin=(0, 0, -10, 0)),
# Distribution of production status section
pn.pane.HTML(
f"""
<div style='background-color: {subtitle_background_color};
width: 100%;
padding: 10px;
box-sizing: border-box;
border-radius: 15px;'>
<h1 style='font-size: 22px; color: white; text-align: left; margin: 0;'>
{Historic_subtitle3}
</h1>
</div>
""",
sizing_mode='stretch_width'
),
pn.Spacer(height=50), # Spacer before plots
distribution_dashboard_production_status,
pn.Spacer(height=50), # Spacer before plots
)
# Combine Title, Primary, and Secondary Layouts
historic_tab = pn.Column(
title_layout,
pn.layout.Divider(margin=(0, 0, -10, 0)), # Add some space between primary and secondary layouts if needed
secondary_layout,
pn.Spacer(height=50), # Spacer before plots
pn.layout.Divider(margin=(0, 0, -10, 0)), # Add some space between primary and secondary layouts if needed
sizing_mode='stretch_width' # Ensure the final layout stretches to fill available space
)
#||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
#||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
#*****************************************************************************************************************************
##############################################################################################################################
# |Priority List|
##############################################################################################################################
#*****************************************************************************************************************************
#||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
#||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
#Load df_Priority as it has been filtered previously on the code
df_Priority_table = pd.read_excel(input_file_formatted, sheet_name='CM-Priority', index_col=False)
#----------------------------------------------------------
# 02/11 - Change 'Phase 4' or 'Phase 5' with 'Phase 4-5'
#----------------------------------------------------------
# For df_Priority
if 'Program' in df_Priority_table.columns and 'Pty Indice' in df_Priority_table.columns:
mask = (
df_Priority_table['Program'].isin(['Phase 4', 'Phase 5']) &
~df_Priority_table['Pty Indice'].str.contains('Phase5', na=False)
)
df_Priority_table.loc[mask, 'Program'] = 'Phase 4-5'
######################
# Create Datafram
######################
#Display a simplified CM-Priorty to have access to the IDD Top-Level and SEDA Top-Level associated with the Pty Indice
List_priority = df_Priority_table[['Priority', 'Pty Indice', 'IDD Top Level', 'SEDA Top Level', 'Description', 'Critical Qty', 'Production Status', 'Program']]
# Include 'Product Category' within List_priority
#List_priority['Product Category'] = List_priority['Description'].apply(determine_category)
# Replace 0 with 'Not yet assigned' in ['IDD Top Level']
List_priority['IDD Top Level'] = List_priority['IDD Top Level'].replace(0, 'Not yet assigned')
#################################################################################################################
# Widgets initialization
################################################################################################################
# Defaults program
default_program_List = 'Phase 4-5'
# Widgets initialization
unique_programs_List = df_Priority_table['Program'].dropna().unique().tolist()
default_program_List = unique_programs_List[0] # Use the first available program if the default is not in the list
program_widget_List = pn.widgets.Select(name='Select Program', options=unique_programs_List, value=default_program_List)
#-------------
# 03/03
def filter_dataframe(df, program, idd_filter, seda_filter):
"""
Filter the DataFrame based on Program, IDD Top Level, and SEDA Top Level.
"""
filtered_df = df[df['Program'] == program]
if 'IDD Top Level' in filtered_df.columns and idd_filter:
filtered_df = filtered_df[filtered_df['IDD Top Level'].str.contains(idd_filter, case=False, na=False)]
if 'SEDA Top Level' in filtered_df.columns and seda_filter:
filtered_df = filtered_df[filtered_df['SEDA Top Level'].str.contains(seda_filter, case=False, na=False)]
return filtered_df
#-------------------------------------------------------------------
# Define filtering widgets for 'IDD Top Level' and 'SEDA Top Level' (Priority List)
#-------------------------------------------------------------------
label_idd_top_level_priority = pn.pane.HTML('<b style="color:#2B70B3;">IDD Top Level Filter</b>')
label_seda_top_level_priority = pn.pane.HTML('<b style="color:#2B70B3;">SEDA Top Level Filter</b>')
filters_top_level_priority = {
'IDD Top Level': pn.widgets.TextInput(name='', placeholder='Enter IDD Top Level'),
'SEDA Top Level': pn.widgets.TextInput(name='', placeholder='Enter SEDA Top Level'),
}
# Create buttons for applying filters (Priority List)
filters_top_level_button_priority = pn.widgets.Button(name='Apply Filters', button_type='primary')
reset_button_priority = pn.widgets.Button(name='Reset Filters', button_type='danger')
# Set default value to None for all filter widgets
for widget in filters_top_level_priority.values():
widget.value = ''
# Create the layout with labels, filter widgets, and buttons (Priority List)
filter_widgets_top_level_priority = pn.Row(
pn.Column(label_idd_top_level_priority, filters_top_level_priority['IDD Top Level']),
pn.Column(label_seda_top_level_priority, filters_top_level_priority['SEDA Top Level']),
pn.Column(
pn.Spacer(height=25), # Spacer before the buttons
pn.Row(filters_top_level_button_priority, reset_button_priority)
)
)
#--- 05/16 update
# Define the filter_dataframe function
def filter_dataframe(df, program, idd_filter, seda_filter):
"""
Filter the DataFrame based on Program, IDD Top Level, and SEDA Top Level.
"""
filtered_df = df[df['Program'] == program]
if idd_filter:
filtered_df = filtered_df[filtered_df['IDD Top Level'].str.contains(idd_filter, case=False, na=False)]
if seda_filter:
filtered_df = filtered_df[filtered_df['SEDA Top Level'].str.contains(seda_filter, case=False, na=False)]
return filtered_df
# Define callback function for the Apply Filters button (Priority List)
def on_top_level_filter_button_click_priority(event):
idd_filter = filters_top_level_priority['IDD Top Level'].value
seda_filter = filters_top_level_priority['SEDA Top Level'].value
selected_program = program_widget_List.value
# Filter the DataFrame using the filter_dataframe function
filtered_df = filter_dataframe(List_priority, selected_program, idd_filter, seda_filter)
# Update the Program widget options based on the filtered DataFrame
unique_programs = filtered_df['Program'].dropna().unique().tolist()
if unique_programs:
program_widget_List.options = sorted(unique_programs)
# Ensure the selected program is still valid
if selected_program not in unique_programs:
program_widget_List.value = unique_programs[0]
else:
program_widget_List.options = [selected_program]
filtered_df = List_priority[List_priority['Program'] == selected_program]
# Update the table
priority_table_pane.object = format_priority_with_colors(filtered_df)
# Define callback function for the Reset Filters button (Priority List)
def on_reset_button_click_priority(event):
# Reset the filter widgets to their default values
filters_top_level_priority['IDD Top Level'].value = ''
filters_top_level_priority['SEDA Top Level'].value = ''
# Reset the Program widget to its default value
program_widget_List.options = sorted(df_Priority_table['Program'].dropna().unique().tolist())
program_widget_List.value = default_program_List
# Reset the table to the original data for the default program
filtered_df = List_priority[List_priority['Program'] == default_program_List]
priority_table_pane.object = format_priority_with_colors(filtered_df)
# Link the buttons to their respective callbacks
filters_top_level_button_priority.on_click(on_top_level_filter_button_click_priority)
reset_button_priority.on_click(on_reset_button_click_priority)
#-------------------------------------------------------------------
#--- 05/16 END update
###################################################################
# Function to update the DataFrame based on the selected program
####################################################################
#######################
# Define color mappings
#########################
color_mapping_production_status = {
'Industrialized': '#D8E4BC', # Light Gren
'FTB WIP': '#DAEEF3', # Light Blue
'Proto WIP': '#DAEEF3', # Light Blue
'Completed': '#75B44A', # # Gray fill '#F2F2F2' or Dark green '##75B44A'
'To be transferred': '#F2DCDB', # Light red
'Officially transferred':'#FF7A5B',
'Canceled': '#F35757' # Dark red
}
font_mapping_production_status = {
'Industrialized': '#375623', # Dark green
'FTB WIP': '#0070C0', # Dark Blue
'Proto WIP': '#0070C0', # Dark Blue
'Completed': '#375623', # Dark green
'To be transferred': '#C00000', # Dark red
'Officially transferred':'#C00000',
'Canceled': '#C00000' # Dark red
}
# Define border and alignment styles
light_gray_border = 'border: 1px solid #D3D3D3;'
centered_text = 'text-align: center;'
def apply_color_and_bold(row):
"""
Apply color and bold formatting to a row based on 'Production Status' and 'Pty Indice' values.
"""
styles = [''] * len(row)
font_colors = [''] * len(row)
production_status = row.get('Production Status', '')
pty_indice = row.get('Pty Indice', '')
# Determine the background color based on 'Production Status'
if production_status in color_mapping_production_status:
color = color_mapping_production_status[production_status]
else:
color = '#FFFFFF' # Default to white if status is not in the mapping
# Determine the font color based on 'Production Status'
if production_status in font_mapping_production_status:
font_color = f'color: {font_mapping_production_status[production_status]};'
else:
font_color = '' # Default to no color if status is not in the mapping
# Apply background color and border to each cell
for i in range(len(row)):
styles[i] = f'background-color: {color}; {light_gray_border}; {centered_text}'
if font_colors[i]: # Apply font color if it's set
styles[i] += f'; {font_colors[i]}'
# Add font color specifically to 'Production Status' and 'Pty Indice' cells
if 'Production Status' in row.index:
production_status_index = row.index.get_loc('Production Status')
styles[production_status_index] += f'; {font_color}; font-weight: bold;'
if 'Pty Indice' in row.index:
pty_indice_index = row.index.get_loc('Pty Indice')
styles[pty_indice_index] += f'; {font_color}; font-weight: bold;'
return styles
def format_priority_with_colors(df):
"""
Format DataFrame with colors and alignment.
"""
# Define header styling to center the text
header_style = {
'selector': 'thead th',
'props': [('text-align', 'center')]
}
# Apply color formatting and header styling
return df.style \
.apply(lambda row: apply_color_and_bold(row), axis=1) \
.set_table_styles([header_style]) \
.hide(axis="index") \
.hide(subset=['Program'], axis="columns") # Use .hide() with subset to hide the 'Program' column
##################
#Create panel pane
#################
# Function to update the DataFrame based on the selected program - 03/04
def update_priority_table(event):
selected_program = event.new # Use event.new to get the new value
filtered_df = List_priority[List_priority['Program'] == selected_program]
# Format the DataFrame only once
priority_table_pane.object = format_priority_with_colors(filtered_df)
# Initial display
initial_filtered_df = List_priority[List_priority['Program'] == default_program_List]
priority_table_pane = pn.pane.DataFrame(format_priority_with_colors(initial_filtered_df), width=1000, index=False)
# Attach the callback to the program_widget_List
program_widget_List.param.watch(update_priority_table, 'value')
#################################
# Layout
#################################
Priority_title = "Priority List"
text_above_Priority = (
f"This table is based on data from |CM-Priority| - <b>{Date_CM_Priority}</b>:<br>"
"▷ <b>Priority List</b>: This table represents the total scope of the Transfer Project for the selected 'Program'.<br>"
"➥ It includes all PNs related to the project, regardless of whether they still have an existing IDD Backlog or if the 'Critical Quantity' defined as part of the transfer project has been reached.<br>"
"➥ Some PNs may not yet have an assigned IDD PN under 'IDD Top-Level'. In such cases, the BOM does not exist, and the given PN won't be present in the |Snapshot| table.<br>"
"➥ The color formatting is based on [<b>'Production Status'</b>].<br>"
)
# Create the title section for the Priority Tab
priority_title_section = pn.pane.HTML(f"""
<div style='background-color: {font_top_color}; width: 100%; padding: 10px; box-sizing: border-box;'>
<h1 style='font-size: 24px; color: white; text-align: left; margin: 0;'>{Priority_title}</h1>
</div>
""", sizing_mode='stretch_width')
# Create the layout for Priority Tab
priority_tab = pn.Column(
priority_title_section,
pn.layout.Divider(margin=(-10, 0, 0, 0)),
pn.Row(program_widget_List, sizing_mode='stretch_width'),
filter_widgets_top_level_priority,
pn.Spacer(height=25),
pn.layout.Divider(margin=(-10, 0, 0, 0)),
pn.Spacer(height=5),
text_above_Priority,
pn.Spacer(height=5),
priority_table_pane,
sizing_mode='stretch_width'
)
#||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
#||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
#*****************************************************************************************************************************
##############################################################################################################################
# |Snapshot|
##############################################################################################################################
#*****************************************************************************************************************************
#||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
#||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
#Load df_Snapshot_table as it has been filtered previously on the code
df_Snapshot_table = pd.read_excel(input_file_formatted, sheet_name='Snapshot', index_col=False)
#----------------------------------------------------------
# 02/11 - Change 'Phase 4' or 'Phase 5' with 'Phase 4-5'
#----------------------------------------------------------
# For df_Snapshot (new addition)
if 'Program' in df_Snapshot_table.columns and 'Pty Indice' in df_Snapshot_table.columns:
mask = (
df_Snapshot_table['Program'].isin(['Phase 4', 'Phase 5']) &
~df_Snapshot_table['Pty Indice'].str.contains('Phase5', na=False)
)
df_Snapshot_table.loc[mask, 'Program'] = 'Phase 4-5'
#----------------------------------------------------------
#Map 'Total Qty' from Pivot_table_completion['Total Quantity'] on 'Pty Indice'
# Create the mapping from 'Pty Indice' to 'Total Quantity'
mapping_total_qty = Pivot_table_completion.set_index('Pty Indice')['Total Quantity']
# Apply the mapping to df_Snapshot based on 'Pty Indice'
df_Snapshot_table['Total Quantity'] = df_Snapshot_table['Pty Indice'].map(mapping_total_qty)
#If df_Snapshot['Total Quantity'] = NaN it means that the PN is not in Pibot_table_completion meaning that IDD never shipped it --> Replace NaN with 'Critical Qty' or 'IDD Backlog Qty' watherver is the biggest
# Replace NaN values in 'Total Quantity' with the maximum of 'Critical Qty' or 'IDD Backlog Qty'
df_Snapshot_table['Total Quantity'] = df_Snapshot_table.apply(
lambda row: max(row['Critical Qty'], row['IDD Backlog Qty']) if pd.isna(row['Total Quantity']) else row['Total Quantity'],
axis=1
)
# Filter-out some column for better visibility - 03/03
df_Snapshot_table = df_Snapshot_table.drop(columns=[
'Engineering Cost', 'Start date target', 'IDD Marge Standard (unit)',
'IDD Current Margin (%)', 'IDD Production Cost (unit)', 'Critical Qty', 'IDD Expected ROI (Total)', 'IDD AVG realized sales price [USD]',
'Total WO Count', 'Max Expected Time (full ASSY)[hour]',
'Gap Actual vs Standard [USD]', 'IDD Corrected Cost [USD]',
'IDD Corrected Margin Standard (unit) [USD]', 'IDD AVG realized Margin Standard [USD]', 'Total Quantity', 'Avg Actual Time (full ASSY)[hour]', 'Max Standard Deviation [hour]'
])
# Rename 'Max Standard Deviation [hour]' to 'Standard Deviation [hour]'
#df_Snapshot_table = df_Snapshot_table.rename(columns={'Max Standard Deviation [hour]': 'Standard Deviation [hour]'})
#################################################################################################################
# Widgets initialization
################################################################################################################
# Defaults program
default_program_snapshot = 'Phase 4-5'
# Widgets initialization
unique_programs_snapshot = df_Priority_table['Program'].dropna().unique().tolist()
default_program_snapshot = unique_programs_List[0] # Use the first available program if the default is not in the snapshot
program_widget_snapshot = pn.widgets.Select(name='Select Program', options=unique_programs_snapshot, value=default_program_snapshot)
##---------
# 03/03
#-------------------------------------------------------------------
# Define filtering widgets for 'IDD Top Level' and 'SEDA Top Level' (Snapshot)
#-------------------------------------------------------------------
label_idd_top_level_snapshot = pn.pane.HTML('<b style="color:#2B70B3;">IDD Top Level Filter</b>')
label_seda_top_level_snapshot = pn.pane.HTML('<b style="color:#2B70B3;">SEDA Top Level Filter</b>')
filters_top_level_snapshot = {
'IDD Top Level': pn.widgets.TextInput(name='', placeholder='Enter IDD Top Level'),
'SEDA Top Level': pn.widgets.TextInput(name='', placeholder='Enter SEDA Top Level'),
}
# Create buttons for applying filters (Snapshot)
filters_top_level_button_snapshot = pn.widgets.Button(name='Apply Filters', button_type='primary')
reset_button_snapshot = pn.widgets.Button(name='Reset Filters', button_type='danger')
# Set default value to None for all filter widgets
for widget in filters_top_level_snapshot.values():
widget.value = ''
# Create the layout with labels, filter widgets, and buttons (Snapshot)
filter_widgets_top_level_snapshot = pn.Row(
pn.Column(label_idd_top_level_snapshot, filters_top_level_snapshot['IDD Top Level']),
pn.Column(label_seda_top_level_snapshot, filters_top_level_snapshot['SEDA Top Level']),
pn.Column(
pn.Spacer(height=25), # Spacer before the buttons
pn.Row(filters_top_level_button_snapshot, reset_button_snapshot)
)
)
# Define callback function for the Apply Filters button (Snapshot)
def on_top_level_filter_button_click_snapshot(event):
idd_filter = filters_top_level_snapshot['IDD Top Level'].value
seda_filter = filters_top_level_snapshot['SEDA Top Level'].value
# Filter the DataFrame based on IDD Top Level and SEDA Top Level
filtered_df = df_Snapshot_table.copy()
if idd_filter:
filtered_df = filtered_df[filtered_df['IDD Top Level'].str.contains(idd_filter, case=False, na=False)]
if seda_filter:
filtered_df = filtered_df[filtered_df['SEDA Top Level'].str.contains(seda_filter, case=False, na=False)]
# Update the Program widget options based on the filtered DataFrame
unique_programs = filtered_df['Program'].dropna().unique().tolist()
program_widget_snapshot.options = sorted(unique_programs)
# Set the Program widget value to the first available program (if any)
if unique_programs:
program_widget_snapshot.value = unique_programs[0]
# Update the table
snapshot_table_pane.object = format_snapshot_with_colors(filtered_df)
# Define callback function for the Reset Filters button (Snapshot)
def on_reset_button_click_snapshot(event):
# Reset the filter widgets to their default values
filters_top_level_snapshot['IDD Top Level'].value = ''
filters_top_level_snapshot['SEDA Top Level'].value = ''
# Reset the Program widget to its default value
program_widget_snapshot.options = sorted(df_Snapshot_table['Program'].dropna().unique().tolist())
program_widget_snapshot.value = default_program_snapshot
# Reset the table to the original data
snapshot_table_pane.object = format_snapshot_with_colors(df_Snapshot_table)
# Link the buttons to their respective callbacks
filters_top_level_button_snapshot.on_click(on_top_level_filter_button_click_snapshot)
reset_button_snapshot.on_click(on_reset_button_click_snapshot)
#-------------------
############################
# Formatting snapshot table
###########################
# Replace NaN with 0
df_Snapshot_table = df_Snapshot_table.fillna(0)
#print(df_Snapshot_table[['Max Expected Time (full ASSY)[hour]', 'Avg Actual Time (full ASSY)[hour]']].dtypes)
# Formatting columns
df_Snapshot_table['Shipped'] = df_Snapshot_table['Shipped'].astype(int)
df_Snapshot_table['Remain. crit. Qty'] = df_Snapshot_table['Remain. crit. Qty'].round().astype(int)
# df_Snapshot_table['IDD Marge Standard (unit)'] = df_Snapshot_table['IDD Marge Standard (unit)'].map('${:,.1f}'.format) # Dropped column
df_Snapshot_table['IDD Sale Price'] = df_Snapshot_table['IDD Sale Price'].map('${:,.1f}'.format)
# df_Snapshot_table['IDD Production Cost (unit)'] = df_Snapshot_table['IDD Production Cost (unit)'].map('${:,.1f}'.format) # Dropped column
# df_Snapshot_table['Critical Qty'] = df_Snapshot_table['Critical Qty'].astype(int) # Dropped column
df_Snapshot_table['Qty WIP'] = df_Snapshot_table['Qty WIP'].astype(int)
#df_Snapshot_table['Total Quantity'] = df_Snapshot_table['Total Quantity'].astype(int)
# df_Snapshot_table['Total WO Count'] = df_Snapshot_table['Total WO Count'].astype(int) # Dropped column
# df_Snapshot_table['Max Expected Time (full ASSY)[hour]'] = df_Snapshot_table['Max Expected Time (full ASSY)[hour]'].apply(lambda x: 'No Data' if x == 0 else '{:.2f}'.format(x)) # Dropped column
#df_Snapshot_table['Avg Actual Time (full ASSY)[hour]'] = df_Snapshot_table['Avg Actual Time (full ASSY)[hour]'].apply(lambda x: 'No Data' if x == 0 else '{:.2f}'.format(x))
# df_Snapshot_table['Max Standard Deviation [hour]'] = df_Snapshot_table['Max Standard Deviation [hour]'].apply(lambda x: 'No Data' if x == 0 else '{:.2f}'.format(x)) # Dropped column
#df_Snapshot_table['Standard Deviation [hour]'] = df_Snapshot_table['Standard Deviation [hour]'].apply(lambda x: 'No Data' if x == 0 else '{:.2f}'.format(x)) # Dropped column
# Column name update in df_Snapshot
# Remove the percentage sign and convert to numeric
df_Snapshot_table['Actual vs Standard time [%]'] = pd.to_numeric(df_Snapshot_table['Actual vs Standard time [%]'].str.rstrip('%'), errors='coerce')
# Define a function to format the values or replace NaN with 'N/A'
def format_percentage(value):
if pd.isna(value):
return 'N/A'
return '{:.0f}%'.format(value)
# Apply the function to the column
df_Snapshot_table['Actual vs Standard time [%]'] = df_Snapshot_table['Actual vs Standard time [%]'].apply(format_percentage)
# Replace 0 with ''
#df_Snapshot_table['Start date target'] = df_Snapshot_table['Start date target'].replace(0, '')
###################################################################
# Function to update the DataFrame based on the selected program
##################################################################
#######################
# Define color mappings - 04/16
#########################
color_mapping_production_status = {
'Industrialized': '#cff2c8', # Light Gren
'FTB WIP': '#DAEEF3', # Light Blue
'Proto WIP': '#DAEEF3', # Light Blue
'Completed': '#F2F2F2', # GRAY
'To be transferred': '#F2DCDB', # Light red
'Officially transferred':'#FF7A5B',
'Canceled': '#FF7A5B' # Dark red
}
color_mapping_top_level_status = {
'Clear-to-Build': '#C6EFCE', # Light Green fill for 'Clear-to-Build'
'Short': '#FFC7CE', # Light Red fill for 'Short'
'Completed - No Backlog': '#F2F2F2', # GRAY
'No data': '#FFFFFF', # WHITE
'Completed - Still some Backlog': '#FEFCA6', # Yellow
'Transferred': '#FF7A5B' # Dark red
}
font_mapping_top_level_status = {
'Clear-to-Build': '#4D7731', # Dark Green font for 'Clear-to-Build'
'Short': '#C00000' # Dark Red font for 'Short'
}
font_mapping_production_status = {
'Industrialized': '#375623', # Dark green
'FTB WIP': '#0070C0', # Dark Blue
'Proto WIP': '#0070C0', # Dark Blue
'Completed': '#375623', # Dark green
'To be transferred': '#C00000', # Dark red
'Officially transferred':'#C00000',
'Canceled': '#C00000' # Dark red
}
def apply_color(row):
# Initialize color list with default (empty) colors
colors = [''] * len(row)
font_colors = [''] * len(row)
# Apply color based on 'Top-Level Status'
top_level_status = row.get('Top-Level Status', '')
if top_level_status in color_mapping_top_level_status:
colors[row.index.get_loc('Top-Level Status')] = color_mapping_top_level_status[top_level_status]
if top_level_status in font_mapping_top_level_status:
font_colors[row.index.get_loc('Top-Level Status')] = f'color: {font_mapping_top_level_status[top_level_status]};'
# Apply color for the rest of the row based on 'Production Status'
production_status = row.get('Production Status', '')
if production_status in color_mapping_production_status:
color = color_mapping_production_status[production_status]
for idx, value in enumerate(row):
if row.index[idx] != 'Top-Level Status':
colors[idx] = color
# Apply font color for 'Pty Indice' and 'Production Status' based on 'Production Status'
if production_status in font_mapping_production_status:
font_color = f'color: {font_mapping_production_status[production_status]};'
if 'Production Status' in row.index:
production_status_index = row.index.get_loc('Production Status')
font_colors[production_status_index] = font_color
if 'Pty Indice' in row.index:
pty_indice_index = row.index.get_loc('Pty Indice')
font_colors[pty_indice_index] = font_color
# Apply border, background color, and center text alignment to each cell
cell_styles = [f'background-color: {color}; {light_gray_border}; {centered_text}' for color in colors]
# Add font color to the cells as needed
for idx, font_color in enumerate(font_colors):
if font_color:
cell_styles[idx] += f'; {font_color}'
# Add bold formatting to the 'Production Status' and 'Pty Indice' cells
if 'Production Status' in row.index:
production_status_index = row.index.get_loc('Production Status')
cell_styles[production_status_index] += 'font-weight: bold;'
if 'Pty Indice' in row.index:
pty_indice_index = row.index.get_loc('Pty Indice')
cell_styles[pty_indice_index] += 'font-weight: bold;'
return cell_styles
# Apply color formatting and header centered - 03/04
def format_snapshot_with_colors(df):
"""
Format DataFrame with colors and alignment, and hide the 'Program' column.
"""
# Define header styling
header_style = {
'selector': 'thead th',
'props': [('text-align', 'center')]
}
# Apply color formatting and header styling
return df.style \
.apply(apply_color, axis=1) \
.set_table_styles([header_style]) \
.hide(axis="index") \
.hide(subset=['Program'], axis="columns") # Use .hide() with subset to hide the 'Program' column
#------------------------------------------------
# Reorder snapshot table - 03/04
#------------------------------------------------
# Define the desired column order
desired_columns_snapshot = [
'Program', 'Top-Level Status', 'Pty Indice', 'Priority', 'IDD Top Level', 'SEDA Top Level',
'Shipped', 'Remain. crit. Qty', 'IDD Backlog Qty', 'Qty clear to build', 'Qty WIP',
'Description', 'Production Status', 'IDD Sale Price',
'Actual vs Standard time [%]', 'Deviation vs Actual [%]',
'IDD Corrected Margin [%]', 'IDD AVG realized Margin [%]'
]
# Reorder the columns in df_Snapshot_table
df_Snapshot_table = df_Snapshot_table[desired_columns_snapshot]
##################
#Create panel pane
#################
# Update function
def update_snapshot_table(event):
selected_program = program_widget_snapshot.value
df_filtered = df_Snapshot_table[df_Snapshot_table['Program'] == selected_program]
# Format the DataFrame
styled_df_snapshot = format_snapshot_with_colors(df_filtered)
snapshot_table_pane.object = styled_df_snapshot
# Create the initial styled DataFrame pane
df_initial_filtered = df_Snapshot_table[df_Snapshot_table['Program'] == default_program_snapshot].drop(columns=['Priority', 'Description'])
styled_df_snapshot = format_snapshot_with_colors(df_initial_filtered)
snapshot_table_pane = pn.pane.DataFrame(styled_df_snapshot)
# Attach the callback to the program_widget_List
program_widget_snapshot.param.watch(update_snapshot_table, 'value')
#################################
# Layout
##################################
Snapshot_title = f"Snapshot [{formatted_date}]"
text_above_snapshot = (
f"This table is based on data from |Snapshot| - <b>{file_date}</b>:<br>"
"▷ <b>Snapshot table</b>: This table represents the remaining scope of the Transfer Project for the selected 'Program'.<br>"
"➥ It includes all PNs that have an existing IDD Backlog or for which the 'Critical Quantity', defined as part of the transfer project, has not yet been reached. This applies even if the PN is not currently listed in the IDD Backlog.<br>"
"➥ Some PNs may not yet have an assigned IDD PN under 'IDD Top-Level'. In such cases, the BOM does not exist, and the given PN won't be present in this table.<br>"
"➥ The color formatting is based on ['Top-Level Status'] & ['Production Status'].<br>"
)
# Create the title section for the Priority Tab
Snapshot_title_section = pn.pane.HTML(f"""
<div style='background-color: {font_top_color}; width: 100%; padding: 10px; box-sizing: border-box;'>
<h1 style='font-size: 24px; color: white; text-align: left; margin: 0;'>{Snapshot_title}</h1>
</div>
""", sizing_mode='stretch_width')
# Create the layout for Priority Tab
Snapshot_tab = pn.Column(
Snapshot_title_section,
pn.layout.Divider(margin=(-10, 0, 0, 0)),
pn.Row(program_widget_snapshot, sizing_mode='stretch_width'),
filter_widgets_top_level_snapshot,
pn.Spacer(height=25),
pn.layout.Divider(margin=(-10, 0, 0, 0)),
pn.Spacer(height=5),
text_above_snapshot,
pn.Spacer(height=5),
snapshot_table_pane,
sizing_mode='stretch_width'
)
#||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
#||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
#*****************************************************************************************************************************
##############################################################################################################################
# |Cover Dashboard|
##############################################################################################################################
#*****************************************************************************************************************************
#||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
#||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
# Loading dataframe used for the |Cover Dashboard|
####################################################
# Create a dictionnay to assigned a 'Product Category' to a 'Pty Indice' based on the function
def determine_category(description):
if not isinstance(description, str):
return 'Others'
if description == 'Rototellite':
return 'Rototellite'
elif 'Indicator' in description or 'CPA' in description:
return 'CPA'
elif 'Lightplate' in description:
return 'Lightplate'
elif 'ISP' in description or 'Keyboard' in description:
return 'ISP'
elif 'Module' in description:
return 'CPA'
elif 'optics' in description:
return 'Fiber Optics'
else:
return 'Others'
################################################################################################
# Sales and shipement progress using panel indicators 'Number', 'Progress' and 'Trend'
################################################################################################
df_Historic_dashboard = pd.read_excel(input_file_formatted, sheet_name='CM-Historic', index_col=False)
df_Priority_dashboard = pd.read_excel(input_file_formatted, sheet_name='CM-Priority', index_col=False)
df_Backlog_dashboard = df_Backlog.copy()
#----------------------------------------------------------
# 02/11 - Change 'Phase 4' or 'Phase 5' with 'Phase 4-5'
#----------------------------------------------------------
# For df_Priority
if 'Program' in df_Priority_dashboard.columns and 'Pty Indice' in df_Priority_dashboard.columns:
mask = (
df_Priority_dashboard['Program'].isin(['Phase 4', 'Phase 5']) &
~df_Priority_dashboard['Pty Indice'].str.contains('Phase5', na=False)
)
df_Priority_dashboard.loc[mask, 'Program'] = 'Phase 4-5'
# For df_Historic
if 'Program' in df_Historic_dashboard.columns and 'Pty Indice' in df_Historic_dashboard.columns:
mask = (
df_Historic_dashboard['Program'].isin(['Phase 4', 'Phase 5']) &
~df_Historic_dashboard['Pty Indice'].str.contains('Phase5', na=False)
)
df_Historic_dashboard.loc[mask, 'Program'] = 'Phase 4-5'
#----------------------------------------------------------
#------------------------------------------------------------------------------------------
# 03/19 - Only keep 'Program' = 'Phase 4-5', 'EMBRAER', SIKORSKY', '1stTB' BUT NOT '2ndTB'
#------------------------------------------------------------------------------------------
# Define the allowed values for the 'Program' column
allowed_programs = ['Phase 4-5', 'EMBRAER', 'SIKORSKY', 'COMAC', '1stTB']
# Apply filtering to each DataFrame
df_Priority_dashboard = df_Priority_dashboard[df_Priority_dashboard['Program'].isin(allowed_programs)]
df_Historic_dashboard = df_Historic_dashboard[df_Historic_dashboard['Program'].isin(allowed_programs)]
#------------------------------------------------------------------------------------------
# Create 'Product_Category' column based on the 'Description' in order to apply the filter if needed
# df_Historic_dashboard already contain ['Product Category']
#df_Backlog_dashboard contain Product_Category
df_Priority_dashboard['Product Category'] = df_Priority_dashboard['Description'].apply(determine_category)
########################################################################################################################################################
######################
# df_Historic_dashboard
######################
# 'Standard amount USD' is not used in the code bellow. Only 'Currency turnover ex.VAT' is used to calculated the sales but 'Standard amount USD' to define the margin if needed later on.
df_Historic_dashboard['Order'] = df_Historic_dashboard['Order'].astype(str)
df_Historic_dashboard = df_Historic_dashboard[~df_Historic_dashboard['Order'].str.contains('NC')]
df_Historic_dashboard = df_Historic_dashboard[['Pty Indice', 'Quantity', 'Invoice date', 'Order', 'Currency turnover ex.VAT', 'Standard amount USD', 'Program', 'IDD Marge Standard', 'Product Category', 'NRE Fee', 'FAI Fee']] # 03/25
df_Historic_dashboard.rename(columns={'Quantity': 'Qty Shipped'}, inplace=True)
df_Historic_dashboard['Qty Shipped'] = df_Historic_dashboard['Qty Shipped'].astype(int)
df_Historic_dashboard['NRE Fee'] = df_Historic_dashboard['NRE Fee'].fillna(0).astype(int) # 03/25
df_Historic_dashboard['FAI Fee'] = df_Historic_dashboard['FAI Fee'].fillna(0).astype(int) # 03/25
df_Historic_dashboard.dropna(inplace=True)
df_Historic_dashboard['Invoice date'] = pd.to_datetime(df_Historic_dashboard['Invoice date'])
df_Historic_dashboard['Year'] = df_Historic_dashboard['Invoice date'].dt.year
df_Historic_dashboard['Month'] = df_Historic_dashboard['Invoice date'].dt.month
df_Historic_dashboard['Week'] = df_Historic_dashboard['Invoice date'].dt.isocalendar().week
#Rename 'Currency turnover ex.VAT' to 'Sales USD'
df_Historic_dashboard.rename(columns={'Currency turnover ex.VAT': 'Sales USD'}, inplace=True)
# 03/08
# Define the span_report_historic_dashboard place holder
span_report_historic_dashboard = "No data available after filtering"
#SAVED 03/08
'''
older_date = df_Historic_dashboard['Invoice date'].min()
recent_date = df_Historic_dashboard['Invoice date'].max()
span_report_historic_dashboard = (older_date, recent_date)
# Format the dates as short dates
older_date_str = older_date.strftime('%m/%d/%Y')
recent_date_str = recent_date.strftime('%m/%d/%Y')
# Create formatted title strings
span_report_historic_dashboard = f"{older_date_str} - {recent_date_str}"
'''
#/////////////////////////////////////////////////////////////////////////////////////////////////////////////
#################################################################################################################
# Widgets initialization and datafram update
################################################################################################################
#/////////////////////////////////////////////////////////////////////////////////////////////////////////////
#03/08 update
# Extract unique programs while ensuring only allowed ones are included
unique_programs_List = list(set(df_Priority['Program'].dropna()) & set(allowed_programs)) or ['No Program Available']
# Set default program
default_program_List = 'Phase 4-5' if 'Phase 4-5' in unique_programs_List else unique_programs_List[0]
'''
# Unique programs list from the dataframe
unique_programs_List = df_Priority['Program'].dropna().unique().tolist()
# Check if 'Phase 4-5' is in unique_programs_List, else fall back to the first item.
default_program_List = 'Phase 4-5' if 'Phase 4-5' in unique_programs_List else unique_programs_List[0]
'''
# Widget initialization
program_widget_List = pn.widgets.Select(name='Select Program', options=unique_programs_List, value=default_program_List)
###################################################################
# Update the DataFrame based on the selected program
###################################################################
# Function to update data based on the selected program
#/////////////////////////////////////////////////////////////////////////////////////////////////////////////
# New 10/01
#######################################################################################################################################################
# Create a filter on the 'Product Category' to filter-out 'Lightplate' and 'Others' from the dashboard when click on a 2 disctincts button with 2 positions: "Included (on)/Excluded (off)" Lightplate, "Included (on)/Excluded (off)" others
# Directly filter the datafram df_Priority_dashboard, df_Historic_dashboard & df_Backlog_dashboard to filter-out the necessary rows based on the buttons
#######################################################################################################################################################
# Function to filter data based on toggle buttons for 'Lightplate' and 'Others' and to modify the original dataframes
# Function to apply filters based on toggle status
#/////////////////////////////////////////////////////////////////////////////////////////////////////////////
# Data Filtering Function
def filter_dashboard(df, include_lightplate, include_others, column_name):
"""Filter the dashboard DataFrame based on the include_lightplate and include_others flags."""
if not include_lightplate:
df = df[df[column_name] != 'Lightplate']
if not include_others:
df = df[df[column_name] != 'Others']
return df
# Function to apply filters based on the toggle buttons #03/21
def apply_filters(event):
global df_Historic_dashboard, df_Priority_dashboard, df_Backlog_dashboard, df_Snapshot_KPI
# Filter each dashboard with the correct column names
filtered_Historic = filter_dashboard(
df_Historic_dashboard, toggle_lightplate.value, toggle_others.value, 'Product Category'
)
filtered_Priority = filter_dashboard(
df_Priority_dashboard, toggle_lightplate.value, toggle_others.value, 'Product Category'
)
filtered_Backlog = filter_dashboard(
df_Backlog_dashboard, toggle_lightplate.value, toggle_others.value, 'Product_Category'
)
# New 03/21
filtered_Snapshot = filter_dashboard(
df_Snapshot_KPI, toggle_lightplate.value, toggle_others.value, 'Product Category'
)
# Display filtered data shapes for debugging
#print("After Filtering:")
#print(f"Historic Dashboard Shape: {filtered_Historic.shape}")
#print(f"Priority Dashboard Shape: {filtered_Priority.shape}")
#print(f"Backlog Dashboard Shape: {filtered_Backlog.shape}")
# Attach this to the toggle events
def on_lightplate_toggle(event):
update_button_styles(toggle_lightplate)
apply_filters(event) # Pass the event argument here
def on_others_toggle(event):
update_button_styles(toggle_others)
apply_filters(event) # Pass the event argument here
# Create Toggle Widgets with default button styles
toggle_lightplate = pn.widgets.Toggle(name='Include Lightplate', value=True, button_type='primary')
toggle_others = pn.widgets.Toggle(name='Include Sub-Levels & Kits', value=True, button_type='primary')
# Update the button type based on its value
def update_button_styles(toggle_widget):
"""Updates the button style to solid (active) or outline (inactive) based on the value."""
if toggle_widget.value:
toggle_widget.button_type = 'primary' # Solid fill when active
else:
toggle_widget.button_type = 'default' # Outline when inactive
# Attach the update function to the toggle widgets
toggle_lightplate.param.watch(lambda event: update_button_styles(toggle_lightplate), 'value')
toggle_others.param.watch(lambda event: update_button_styles(toggle_others), 'value')
# Initial styles application
update_button_styles(toggle_lightplate)
update_button_styles(toggle_others)
################################################################################################################
#/////////////////////////////////////////////////////////////////////////////////////////////////////////////
# Define the YoY Monthly sales as a line graph
# Define the Monthly sales since the beginning of the project as a line graph
# Define the yearly, Monthly and since the beginning of the project sales & shipement
# Define specific colors for years 2022 to 2030 - 04/09
year_color_map = {
2022: '#F4B084', # Salmon
2023: '#c4818e', # Pinkinch
2024: '#a2d5d6', # Green #Alternative #4e9376 Forest Green (earthy, stable)
2025: '#4e9376', #
2026: '#de9cdc', # Lavender Gray (soft, muted)
2027: '#88aee1', # Pastel Purple (playful, light)
2028: '#dec49f', # Pale Lilac (delicate, subtle)
2029: '#adcac9', # Dusty Teal (neutral, balanced)
2030: '#777777', # Medium Gray (solid, endpoint)
}
# Update 05/16
#---------------------------------------------------------------------------------------------------------
##################################################################
#///////////////////////////////////////////////////////////////
# Create sales and shipements Graphs YoY, MoM and total since inception
#///////////////////////////////////////////////////////////////
##################################################################
# Figure 'Year-Over-Year' monthly sales - # Update 05/16
##################################################################
def create_yoy_sales_figure(df_Historic_dashboard_filtered, selected_program, year_color_map):
# Month mapping
month_map = {1: 'Jan', 2: 'Feb', 3: 'Mar', 4: 'Apr', 5: 'May', 6: 'Jun',
7: 'Jul', 8: 'Aug', 9: 'Sep', 10: 'Oct', 11: 'Nov', 12: 'Dec'}
# Get all years and sort them
all_years = sorted(df_Historic_dashboard_filtered['Year'].unique(), reverse=False)
# Create a complete index for all Year-Month combinations
complete_index = pd.MultiIndex.from_product([all_years, range(1, 13)], names=['Year', 'Month'])
# Aggregate data and reindex to include missing months with NaN
df_YoY_sales = df_Historic_dashboard_filtered.groupby(['Year', 'Month'])['Sales USD'].sum().reindex(complete_index).reset_index()
df_YoY_sales['Month Name'] = df_YoY_sales['Month'].map(month_map)
df_YoY_sales['Sales K$'] = df_YoY_sales['Sales USD'] / 1000
# Initially set missing data to NaN
df_YoY_sales.loc[df_YoY_sales['Sales USD'].isna(), 'Sales K$'] = np.nan
# For each year, set 0 for months between two months with values, keep NaN otherwise
for year in all_years:
subset = df_YoY_sales[df_YoY_sales['Year'] == year].copy()
sales = subset['Sales K$'].values
for i in range(len(sales)):
if pd.isna(sales[i]): # If the current month is NaN
# Check if there are non-NaN values before and after
left_value = None
right_value = None
for j in range(i - 1, -1, -1): # Look left
if not pd.isna(sales[j]):
left_value = sales[j]
break
for j in range(i + 1, len(sales)): # Look right
if not pd.isna(sales[j]):
right_value = sales[j]
break
# If there are non-NaN values on both sides, set to 0
if left_value is not None and right_value is not None:
df_YoY_sales.loc[(df_YoY_sales['Year'] == year) & (df_YoY_sales['Month'] == i + 1), 'Sales K$'] = 0
# Determine the x-axis range
if selected_program == '1stTB':
# Find the last month with data
if df_YoY_sales['Sales K$'].isna().all():
last_year, last_month = all_years[-1], 12 # Default to December of the last year
else:
last_data_point = df_YoY_sales.dropna(subset=['Sales K$']).sort_values(by=['Year', 'Month']).iloc[-1]
last_year, last_month = last_data_point['Year'], last_data_point['Month']
# Define the x-axis range: start from the last month and go back 12 months
months_order = []
for i in range(12):
month_idx = (last_month - i) % 12
if month_idx == 0:
month_idx = 12 # Handle the case where modulo gives 0 (December)
months_order.insert(0, month_map[month_idx]) # Insert at the beginning to reverse the order
else:
# For other programs, use the default Jan to Dec order
months_order = list(month_map.values())
# Set the y-axis range
max_sales = df_YoY_sales['Sales K$'].max()
y_range = (0, max_sales * 1.25) if not np.isnan(max_sales) else (0, 100)
# Create the figure with the adjusted x_range
p_YoY_sales = figure(title="Year-Over-Year Monthly Sales [K$]",
x_axis_label='Month',
y_axis_label='Sales [K$]',
x_range=months_order,
y_range=y_range,
tools="pan,wheel_zoom,box_zoom,reset")
all_renderers = []
# Plot data for each year
for year in all_years:
subset = df_YoY_sales[df_YoY_sales['Year'] == year].copy()
line_color = year_color_map.get(year, 'gray')
subset['Year'] = subset['Year'].astype(str) # Ensure year is a string for hover
# Get the color for the year, default to 'gray' if not in the map
line_color = year_color_map.get(year, 'gray')
# For 1stTB program, we need to handle the circular x-axis specially
if selected_program == '1stTB':
# Split the data into two segments: Jun-Dec and Jan-May
first_half = subset[subset['Month'].between(6, 12)] # Jun-Dec
second_half = subset[subset['Month'].between(1, 5)] # Jan-May
# Plot each segment separately
if not first_half.empty:
line1 = p_YoY_sales.line(first_half['Month Name'], first_half['Sales K$'],
line_width=2, color=line_color, legend_label=str(year))
circle1 = p_YoY_sales.circle(first_half['Month Name'], first_half['Sales K$'],
size=8, color=line_color, alpha=0.8)
all_renderers.extend([line1, circle1])
if not second_half.empty:
line2 = p_YoY_sales.line(second_half['Month Name'], second_half['Sales K$'],
line_width=2, color=line_color, legend_label=str(year))
circle2 = p_YoY_sales.circle(second_half['Month Name'], second_half['Sales K$'],
size=8, color=line_color, alpha=0.8)
all_renderers.extend([line2, circle2])
else:
# For other programs, plot normally
line = p_YoY_sales.line(subset['Month Name'], subset['Sales K$'],
line_width=2, color=line_color, legend_label=str(year))
circle = p_YoY_sales.circle(subset['Month Name'], subset['Sales K$'],
size=8, color=line_color, alpha=0.8)
all_renderers.extend([line, circle])
# Add hover tool with Year, Month, and Sales
hover = HoverTool(renderers=all_renderers,
tooltips=[
('Month', '@{x}'),
('Shipments', '@y{0,0}')
],
mode='mouse')
p_YoY_sales.add_tools(hover)
# Customize the plot
p_YoY_sales.ygrid.grid_line_dash = [6, 4]
p_YoY_sales.xgrid.visible = False
p_YoY_sales.toolbar.logo = None
p_YoY_sales.legend.location = "top_left"
p_YoY_sales.legend.click_policy = "hide"
# X-axis font size
p_YoY_sales.xaxis.major_label_text_font_size = "12pt"
p_YoY_sales.xaxis.major_label_text_font_style = "bold"
# Title text font
p_YoY_sales.title.text_font_size = "14pt"
return p_YoY_sales
##################################################################
# Figure 'Year-Over-Year' monthly shipment - #Update 05/16
##################################################################
def create_yoy_shipments_figure(df_Historic_dashboard_filtered, selected_program, year_color_map):
# Month mapping
month_map = {1: 'Jan', 2: 'Feb', 3: 'Mar', 4: 'Apr', 5: 'May', 6: 'Jun',
7: 'Jul', 8: 'Aug', 9: 'Sep', 10: 'Oct', 11: 'Nov', 12: 'Dec'}
# Get all years and sort them
all_years = sorted(df_Historic_dashboard_filtered['Year'].unique(), reverse=False)
# Create a complete index for all Year-Month combinations
complete_index = pd.MultiIndex.from_product([all_years, range(1, 13)], names=['Year', 'Month'])
# Aggregate data and reindex to include missing months with NaN
df_YoY_shipments = df_Historic_dashboard_filtered.groupby(['Year', 'Month'])['Qty Shipped'].sum().reindex(complete_index).reset_index()
df_YoY_shipments['Month Name'] = df_YoY_shipments['Month'].map(month_map)
# Initially set missing data to NaN
df_YoY_shipments.loc[df_YoY_shipments['Qty Shipped'].isna(), 'Qty Shipped'] = np.nan
# For each year, set 0 for months between two months with values, keep NaN otherwise
for year in all_years:
subset = df_YoY_shipments[df_YoY_shipments['Year'] == year].copy()
shipments = subset['Qty Shipped'].values
for i in range(len(shipments)):
if pd.isna(shipments[i]): # If the current month is NaN
# Check if there are non-NaN values before and after
left_value = None
right_value = None
for j in range(i - 1, -1, -1): # Look left
if not pd.isna(shipments[j]):
left_value = shipments[j]
break
for j in range(i + 1, len(shipments)): # Look right
if not pd.isna(shipments[j]):
right_value = shipments[j]
break
# If there are non-NaN values on both sides, set to 0
if left_value is not None and right_value is not None:
df_YoY_shipments.loc[(df_YoY_shipments['Year'] == year) & (df_YoY_shipments['Month'] == i + 1), 'Qty Shipped'] = 0
# Determine the x-axis range
if selected_program == '1stTB':
# Find the last month with data
if df_YoY_shipments['Qty Shipped'].isna().all():
last_year, last_month = all_years[-1], 12 # Default to December of the last year
else:
last_data_point = df_YoY_shipments.dropna(subset=['Qty Shipped']).sort_values(by=['Year', 'Month']).iloc[-1]
last_year, last_month = last_data_point['Year'], last_data_point['Month']
# Define the x-axis range: start from the last month and go back 12 months
months_order = []
for i in range(12):
month_idx = (last_month - i) % 12
if month_idx == 0:
month_idx = 12 # Handle the case where modulo gives 0 (December)
months_order.insert(0, month_map[month_idx]) # Insert at the beginning to reverse the order
else:
# For other programs, use the default Jan to Dec order
months_order = list(month_map.values())
# Set the y-axis range
max_shipments = df_YoY_shipments['Qty Shipped'].max()
y_range = (0, max_shipments * 1.25) if not np.isnan(max_shipments) else (0, 100)
# Create the figure with the adjusted x_range
p_YoY_shipments = figure(title="Year-Over-Year Monthly Shipments [quantity shipped]",
x_axis_label='Month',
y_axis_label='Shipments',
x_range=months_order,
y_range=y_range,
tools="pan,wheel_zoom,box_zoom,reset")
all_renderers = []
# Plot data for each year
for year in all_years:
subset = df_YoY_shipments[df_YoY_shipments['Year'] == year].copy()
subset['Year'] = subset['Year'].astype(str) # Ensure year is a string for hover
# Get the color for the year, default to 'gray' if not in the map
line_color = year_color_map.get(year, 'gray')
# For 1stTB program, split the data into two segments to avoid connecting May to June
if selected_program == '1stTB':
# Split the data into two segments: Jun-Dec and Jan-May
first_half = subset[subset['Month'].between(6, 12)] # Jun-Dec
second_half = subset[subset['Month'].between(1, 5)] # Jan-May
# Plot each segment separately
if not first_half.empty:
line1 = p_YoY_shipments.line(first_half['Month Name'], first_half['Qty Shipped'],
line_width=2, color=line_color, legend_label=str(year))
circle1 = p_YoY_shipments.circle(first_half['Month Name'], first_half['Qty Shipped'],
size=8, color=line_color, alpha=0.8)
all_renderers.extend([line1, circle1])
if not second_half.empty:
line2 = p_YoY_shipments.line(second_half['Month Name'], second_half['Qty Shipped'],
line_width=2, color=line_color)
circle2 = p_YoY_shipments.circle(second_half['Month Name'], second_half['Qty Shipped'],
size=8, color=line_color, alpha=0.8)
all_renderers.extend([line2, circle2])
else:
# For other programs, plot normally
line = p_YoY_shipments.line(subset['Month Name'], subset['Qty Shipped'],
line_width=2, color=line_color, legend_label=str(year))
circle = p_YoY_shipments.circle(subset['Month Name'], subset['Qty Shipped'],
size=8, color=line_color, alpha=0.8)
all_renderers.extend([line, circle])
# Add hover tool with Year, Month, and Shipments
hover = HoverTool(renderers=all_renderers,
tooltips=[
('Month', '@{x}'),
('Shipments', '@y{0,0}')
],
mode='mouse')
p_YoY_shipments.add_tools(hover)
# Customize the plot
p_YoY_shipments.ygrid.grid_line_dash = [6, 4]
p_YoY_shipments.xgrid.visible = False
p_YoY_shipments.toolbar.logo = None
p_YoY_shipments.legend.location = "top_left"
p_YoY_shipments.legend.click_policy = "hide"
# X-axis font size
p_YoY_shipments.xaxis.major_label_text_font_size = "12pt"
p_YoY_shipments.xaxis.major_label_text_font_style = "bold"
# Title text font
p_YoY_shipments.title.text_font_size = "14pt"
return p_YoY_shipments
#---------------------------------------------------------------------------------------------------------
##################################################################
#///////////////////////////////////////////////////////////////
# Create costumers Graph
#///////////////////////////////////////////////////////////////
##################################################################
#######################################################################################
# Define the total shipement and total sales per costumers
#######################################################################################
# Card 'Costumers'
#################################
#////////////////////////////////
################################
# Integrate logo to the graph
###############################
#////////////////////////////////
# Created 09/06
def image_to_base64(image_path):
with open(image_path, "rb") as image_file:
return base64.b64encode(image_file.read()).decode('utf-8')
def scale_image(image_base64, max_width, max_height):
# Decode base64 image
image_data = base64.b64decode(image_base64)
image = Image.open(io.BytesIO(image_data))
# Calculate new dimensions maintaining aspect ratio
width, height = image.size
scaling_factor = min(max_width / width, max_height / height)
new_width = int(width * scaling_factor)
new_height = int(height * scaling_factor)
# Resize image with LANCZOS resampling
image = image.resize((new_width, new_height), Image.Resampling.LANCZOS)
# Encode image back to base64
buffered = io.BytesIO()
image.save(buffered, format="PNG")
new_image_base64 = base64.b64encode(buffered.getvalue()).decode('utf-8')
return f'data:image/png;base64,{new_image_base64}'
def create_logo_mapping(image_directory, max_width=30, max_height=30):
logo_mapping = {}
for image_name in os.listdir(image_directory):
if image_name.endswith(('png', 'jpg', 'jpeg')):
customer_name = os.path.splitext(image_name)[0]
image_path = os.path.join(image_directory, image_name)
base64_image = image_to_base64(image_path)
scaled_image = scale_image(base64_image, max_width, max_height)
logo_mapping[customer_name] = scaled_image
return logo_mapping
####################################
# Adjust the path to your directory
######################################
current_folder = os.getcwd() # Get the current working directory
image_directory = os.path.join(current_folder, 'Compressed_Images')
# Print all files in the directory to ensure the path is correct
#print(os.listdir(image_directory))
# Create logo mapping
logo_mapping = create_logo_mapping(image_directory)
#define the default logo offset on top of the bars
#default_logo_offset = 50
# Add this after creating logo_mapping to inspect
#print(logo_mapping)
######################################
#--------------------------------------------------------------------------------------------------------------------------------------------------------
# 04/07
#--------------------------------------------------------------------------------------------------------------------------------------------------------
# Update the span of the graph.
# Need to use df_Historic_dashboard_filtered to get to the number of unit shipped in a given span and df df_Historic_dashboard_filtered to get to the customer related to a given Pty Indice
# Use a limited span for selected_program == '1stTB'
#04/07
def create_customers_figure(df_priority_dashboard_filtered, logo_mapping, df_Historic_dashboard_filtered, selected_program):
"""
Creates customer shipment and sales figures using:
- Customer info from priority dashboard (df_priority_dashboard_filtered)
- Shipment data from historic dashboard (df_Historic_dashboard_filtered)
- Logo mappings (logo_mapping)
Returns:
- p_shipment: Figure showing shipment quantities by customer
- p_sales: Figure showing sales amounts by customer
"""
# 1. Get customer mapping from priority dashboard
customer_mapping = (
df_priority_dashboard_filtered[['Pty Indice', 'End Costumer']].drop_duplicates()
.rename(columns={'End Costumer': 'End_Customer'})
)
# 2. Aggregate historical shipment data by Pty Indice
historic_shipments = (
df_Historic_dashboard_filtered.groupby('Pty Indice')
.agg({'Qty Shipped': 'sum', 'Sales USD': 'sum'})
.reset_index()
)
# 3. Merge to attach customer names to shipment data
df_customer_summary = pd.merge(
historic_shipments, customer_mapping, on='Pty Indice', how='inner'
)
# 4. Group by customer (summing quantities and sales)
df_customer_summary = df_customer_summary.groupby('End_Customer').agg({
'Qty Shipped': 'sum', 'Sales USD': 'sum'
}).reset_index()
# 5. Prepare plot data (convert sales to K$)
df_customer_summary['Sales_USD'] = df_customer_summary['Sales USD'] / 1000
df_customer_summary['Logo'] = df_customer_summary['End_Customer'].map(logo_mapping)
# Calculate max values for plot scaling
max_qty_shipped = df_customer_summary['Qty Shipped'].max()
max_sales_usd = df_customer_summary['Sales_USD'].max()
# Calculate logo offsets
logo_offset_shipment = max(max_qty_shipped * 0.1, 20)
logo_offset_sale = max(max_sales_usd * 0.1, 10)
df_customer_summary['Logo_Offset_Shipments'] = df_customer_summary['Qty Shipped'] + logo_offset_shipment
df_customer_summary['Logo_Offset_Sales'] = df_customer_summary['Sales_USD'] + logo_offset_sale
# Create color palette
customers = df_customer_summary['End_Customer'].unique()
num_customers = len(customers)
base_palette = Category20[max(3, min(20, num_customers))]
pastel_colors = []
for color in base_palette[:num_customers]:
r = int(color[1:3], 16)
g = int(color[3:5], 16)
b = int(color[5:7], 16)
r = int(r + (255 - r) * 0.6)
g = int(g + (255 - g) * 0.6)
b = int(b + (255 - b) * 0.6)
pastel_colors.append(f'#{r:02x}{g:02x}{b:02x}')
customer_color_map = dict(zip(customers, pastel_colors))
df_customer_summary['Color'] = df_customer_summary['End_Customer'].map(customer_color_map)
# Create data source for plots
source = ColumnDataSource(df_customer_summary)
# Convert 'Invoice date' to datetime
df_Historic_dashboard_filtered['Invoice date'] = pd.to_datetime(df_Historic_dashboard_filtered['Invoice date'], format='%m/%d/%Y')
# Extract the earliest and latest months
earliest_month = df_Historic_dashboard_filtered['Invoice date'].min().strftime('%b %Y')
latest_month = df_Historic_dashboard_filtered['Invoice date'].max().strftime('%b %Y')
#print(f'Earliest Month: {earliest_month}, Latest Month: {latest_month}')
# Create shipment plot with a plain text title
p_shipment = figure(
x_range=df_customer_summary['End_Customer'].tolist(),
title=f"Total Shipment per Customer, Program: {selected_program} - [{earliest_month} to {latest_month}]",
x_axis_label='Customer',
y_axis_label='Shipments',
tools="pan,wheel_zoom,save,reset",
y_range=(0, max_qty_shipped * 1.5),
height=400,
width=800,
min_height=400, # Ensure the plot doesn't shrink below 400px in height
output_backend='svg'
)
p_shipment.vbar(
x='End_Customer',
top='Qty Shipped',
width=0.4,
source=source,
fill_color='Color',
line_color='Color',
alpha=0.6
)
p_shipment.add_tools(HoverTool(
tooltips=[('Customer', '@End_Customer'), ('Qty Shipped', '@{Qty Shipped}{0,0}')]
))
p_shipment.add_glyph(
source,
ImageURL(
url='Logo',
x='End_Customer',
y='Logo_Offset_Shipments',
anchor="center",
)
)
# Create sales plot with a plain text title
p_sales = figure(
x_range=df_customer_summary['End_Customer'].tolist(),
title=f"Total Sales per Customer, Program: {selected_program} - [{earliest_month} to {latest_month}]",
x_axis_label='Customer',
y_axis_label='Sales [K$]',
tools="pan,wheel_zoom,save,reset",
y_range=(0, max_sales_usd * 1.5),
height=400,
width=800,
min_height=400, # Ensure the plot doesn't shrink below 400px in height
output_backend='svg'
)
p_sales.vbar(
x='End_Customer',
top='Sales_USD',
width=0.4,
source=source,
fill_color='Color',
line_color='Color',
alpha=0.6
)
p_sales.add_tools(HoverTool(
tooltips=[('Customer', '@End_Customer'), ('Sales', '@Sales_USD{($0,0.0)}K')]
))
p_sales.add_glyph(
source,
ImageURL(
url='Logo',
x='End_Customer',
y='Logo_Offset_Sales',
anchor="center",
)
)
# Common formatting for both plots
for plot in [p_shipment, p_sales]:
#plot.title.text_color = "#000000"
plot.title.text_font_size = "14pt"
plot.xaxis.major_label_text_font_size = '12pt'
plot.xaxis.major_label_text_font_style = "bold"
plot.xaxis.major_label_orientation = 1.2
plot.ygrid.grid_line_dash = [6, 4]
plot.xgrid.visible = False
plot.toolbar.logo = None
p_shipment.yaxis.formatter = NumeralTickFormatter(format="0,0")
p_sales.yaxis.formatter = CustomJSTickFormatter(code="return '$' + (tick).toFixed(0) + 'k';")
return p_shipment, p_sales
#-----------------------------------------------------------------------------
#-----------------------------------------------------------------------------------------------------------
# 03/04 - Create new datafram for RMA (NC) KPI
#-----------------------------------------------------------------------------------------------------------
def load_and_preprocess_nc_data(input_file_formatted):
"""
Load and preprocess the RMA (NC) data from the Excel file.
Args:
input_file_formatted (str): Path to the formatted Excel file.
Returns:
pd.DataFrame: Preprocessed DataFrame for RMA (NC) data.
"""
try:
# Load data
df_Historic_NC = pd.read_excel(input_file_formatted, sheet_name='CM-Historic', index_col=False)
# Preprocess df_Historic_NC to combine "Phase 4" and "Phase 5" into "Phase 4-5"
if 'Program' in df_Historic_NC.columns and 'Pty Indice' in df_Historic_NC.columns:
mask = (
df_Historic_NC['Program'].isin(['Phase 4', 'Phase 5']) &
~df_Historic_NC['Pty Indice'].str.contains('Phase5', na=False)
)
df_Historic_NC.loc[mask, 'Program'] = 'Phase 4-5'
# Filter rows where 'Order' contains 'NC'
df_Historic_NC = df_Historic_NC[df_Historic_NC['Order'].str.contains('NC', na=False)]
# Ensure 'Quantity' is numeric and filter out rows where 'Quantity' <= 0
df_Historic_NC['Quantity'] = pd.to_numeric(df_Historic_NC['Quantity'], errors='coerce')
df_Historic_NC = df_Historic_NC[df_Historic_NC['Quantity'] > 0]
# Ensure 'Invoice date' is a datetime column
df_Historic_NC['Invoice date'] = pd.to_datetime(df_Historic_NC['Invoice date'], errors='coerce')
# Extract 'Year' and 'Month' from the 'Invoice date' column
df_Historic_NC['Year'] = df_Historic_NC['Invoice date'].dt.year
df_Historic_NC['Month'] = df_Historic_NC['Invoice date'].dt.month
return df_Historic_NC
except Exception as e:
print(f"Error loading or preprocessing RMA (NC) data: {e}")
return pd.DataFrame() # Return an empty DataFrame in case of error
# Load and preprocess the RMA (NC) data
df_Historic_NC = load_and_preprocess_nc_data(input_file_formatted)
#-----------------------------------------------------------------------------------------------------------
# 03/06 - Create bar graph with quantity of NC received Monthly
#-----------------------------------------------------------------------------------------------------------
def create_card_graph_RMA(df_Historic_NC_filtered, year_color_map):
"""
Create a card displaying the monthly RMA graph.
Args:
df_Historic_NC_filtered (pd.DataFrame): Filtered RMA (NC) data.
year_color_map (dict): Mapping of years to colors.
Returns:
figure: Bokeh figure object for the RMA graph.
"""
try:
# Aggregate the data by Year and Month
df_NC_monthly = df_Historic_NC_filtered.groupby(['Year', 'Month']).agg({'Quantity': 'sum'}).reset_index()
# Convert month number to month name and format as "Month Year"
df_NC_monthly['Year-Month'] = df_NC_monthly.apply(
lambda row: f"{calendar.month_abbr[row['Month']]} {row['Year']}", axis=1
)
# Get unique years that have data
available_years = df_NC_monthly['Year'].unique()
# Create the figure
p_NC_monthly = figure(
x_range=df_NC_monthly['Year-Month'].unique().tolist(), # X-axis: Year-Month
title="Monthly Quantity of RMA Received",
x_axis_label='Month',
y_axis_label='Quantity of RMA',
tools="pan,wheel_zoom,box_zoom,reset,save",
width=1200, # 03/25
height=650, #03/25
sizing_mode='fixed', # Explicitly fixed size
)
# Add bars and legend only for available years
for year in available_years:
if year in year_color_map: # Ensure the year has a defined color
df_year = df_NC_monthly[df_NC_monthly['Year'] == year]
source = ColumnDataSource(df_year)
p_NC_monthly.vbar(
x='Year-Month',
top='Quantity',
width=0.9,
source=source,
fill_color=year_color_map[year],
line_color=year_color_map[year],
legend_label=str(year) # Legend entry only for existing years
)
# Add hover tool
hover = HoverTool()
hover.tooltips = [
("Year-Month", "@{Year-Month}"),
("Quantity", "@Quantity")
]
p_NC_monthly.toolbar.logo = None
p_NC_monthly.add_tools(hover)
# Customize the legend
p_NC_monthly.legend.title = "Year"
p_NC_monthly.legend.label_text_font_size = "10pt"
p_NC_monthly.legend.location = "top_left" # Legend in top left
p_NC_monthly.legend.background_fill_alpha = 0.3 # Semi-transparent background
# Customize the plot
p_NC_monthly.xaxis.major_label_orientation = 1.2 # Rotate x-axis labels
p_NC_monthly.xgrid.visible = False # Hide x-axis grid lines
p_NC_monthly.ygrid.grid_line_dash = [6, 4] # Customize y-axis grid lines
# X-axis font size 04/02
p_NC_monthly.xaxis.major_label_text_font_size = "12pt"
p_NC_monthly.xaxis.major_label_text_font_style = "bold"
# Title text font
p_NC_monthly.title.text_font_size = "14pt"
return p_NC_monthly
except Exception as e:
print(f"Error creating monthly RMA graph: {e}")
return None
#Update 03/17
def create_yearly_rma_card(df_Historic_NC_filtered, year_color_map, yearly_sales_shipment_data):
"""
Create a card displaying the total NC items received each year in the format "Year X: Y RMA received, Z% of total units shipped".
Args:
df_Historic_NC_filtered (pd.DataFrame): Filtered RMA (NC) data.
year_color_map (dict): Mapping of years to colors.
yearly_sales_shipment_data (dict): Data containing total units shipped per year.
Returns:
pn.pane.HTML: Panel card displaying yearly RMA metrics.
"""
try:
# Aggregate RMA (NC) data by year
df_NC_yearly = df_Historic_NC_filtered.groupby('Year').agg({'Quantity': 'sum'}).reset_index()
# Generate content for the card
content = """
<div style="font-size: 20px; font-family: Arial, sans-serif; padding: 10px; border: 1px solid #ddd; border-radius: 5px; width: 100%; box-sizing: border-box;">
<h3 style="margin: 0; color: teal;">Yearly RMA</h3>
"""
for _, row in df_NC_yearly.iterrows():
year = row['Year']
quantity = row['Quantity']
total_shipped = yearly_sales_shipment_data.get(year, {}).get('total_shipped', 0)
percentage = (quantity / total_shipped * 100) if total_shipped > 0 else 0
year_color = year_color_map.get(year, 'gray') # Default to gray if year not in map
content += f"""
<p style="margin: 10px 0; font-size: 16px;">
<span style="color: {year_color}; font-weight: bold;">Year {year}:</span>
<strong>{quantity:,.0f}</strong> RMA received, <strong>{percentage:,.1f}%</strong> of total units shipped
</p>
"""
# Close the HTML content
content += "</div>"
# Create a Panel card
yearly_rma_card = pn.pane.HTML(content, sizing_mode='stretch_width')
return yearly_rma_card
except Exception as e:
print(f"Error creating yearly RMA card: {e}")
return pn.pane.HTML("<div>Error loading yearly RMA data.</div>", sizing_mode='stretch_width')
#--------------------------------------------------------------------------
# 03/19 - Create a new card 'Yearly KPI - Backlog' based on df_Backlog_KPI
#--------------------------------------------------------------------------
df_Backlog_KPI = df_Backlog.copy() # Use .copy() to create a copy of the dataframe
# Rename 'Product_Category' to 'Product Category'
df_Backlog_KPI = df_Backlog_KPI.rename(columns={'Product_Category': 'Product Category'})
# Filter out rows where 'Order' contains 'NC'
df_Backlog_KPI = df_Backlog_KPI[~df_Backlog_KPI['Order'].str.contains('NC', na=False)]
# Filter out rows where 'Order' starts with 'E', this is for prototyping
df_Backlog_KPI = df_Backlog_KPI[~df_Backlog_KPI['Order'].str.startswith('E', na=False)]
# Create column 'Year' in df_Backlog_KPI based on ['Due Date']
# Ensure 'Due Date' is in datetime format
df_Backlog_KPI['Due Date'] = pd.to_datetime(df_Backlog_KPI['Due Date'])
# Extract the year from 'Due Date' and create a new 'Year' column
df_Backlog_KPI['Year'] = df_Backlog_KPI['Due Date'].dt.year
#display(df_Backlog_KPI.head()) # Check the first few rows
#print(df_Backlog_KPI.columns) # Verify the columns
#--------------------------------------------------------------------------
# Step 1: Define the function to calculate backlog metrics
def calculate_backlog_metrics(df_Backlog):
# Ensure the required columns exist
required_columns = ['Year', 'Backlog Qty', 'Currency net amount']
for col in required_columns:
if col not in df_Backlog.columns:
raise KeyError(f"The column '{col}' is missing in the backlog dataframe.")
# Group by year and calculate total backlog units and sales
backlog_metrics = df_Backlog.groupby('Year').agg({
'Backlog Qty': 'sum', # Total backlog units
'Currency net amount': 'sum' # Total backlog sales
}).reset_index()
# Convert the metrics to a dictionary for easy access
backlog_metrics_dict = backlog_metrics.set_index('Year').to_dict(orient='index')
return backlog_metrics_dict
# Step 2: Create the HTML content for the card
def create_backlog_card(df_Backlog, year_color_map, file_date):
try:
Report_date = file_date
# Calculate backlog metrics
backlog_metrics = calculate_backlog_metrics(df_Backlog)
# Ensure file_date is a date object
Report_date = pd.to_datetime(Report_date).date()
# Calculate past due backlog
past_due_backlog = df_Backlog[df_Backlog['Due Date'].dt.date < Report_date]
past_due_units = past_due_backlog['Backlog Qty'].sum()
past_due_sales = past_due_backlog['Currency net amount'].sum()
except KeyError as e:
# Handle missing columns
return pn.pane.HTML(f"<div style='color: red;'>Error: {str(e)}</div>")
# Create HTML content for the card (without the title)
html_content = f"""
<div style="font-size: 20px; font-family: Arial, sans-serif; padding: 10px; border: 1px solid #ddd; border-radius: 5px; width: 100%; box-sizing: border-box;">
<p style="margin: 10px 0; font-size: 16px;">
<span style="color: black; font-weight: bold;">Past Due Backlog</span> [{file_date}]:
<strong>{past_due_units:,.0f}</strong> units, representing <strong>${past_due_sales / 1000:,.1f}K</strong> in sales
</p>
<hr style="border: none; height: 0.5px; background-color: #ddd; margin: 10px 0;"> <!-- Thinner gray divider line -->
"""
# Add metrics for each year with color coding
for year, metrics in backlog_metrics.items():
if metrics['Backlog Qty'] == 0 and metrics['Currency net amount'] == 0:
continue # Skip years with zero backlog
# Get the color for the year from the year_color_map
year_color = year_color_map.get(year, 'black') # Default to black if year not in map
html_content += f"""
<p style="margin: 10px 0; font-size: 16px;">
<span style="color: {year_color}; font-weight: bold;">Year {year}:</span>
<strong>{metrics['Backlog Qty']:,.0f}</strong> units in backlog, representing <strong>${metrics['Currency net amount'] / 1000:,.1f}K</strong> in sales
</p>
"""
# Close the HTML content
html_content += "</div>"
# Create a layout with the HTML content
backlog_card_layout = pn.Column(
pn.pane.HTML(html_content, sizing_mode='stretch_width'),
sizing_mode='stretch_width'
)
return backlog_card_layout
#--------------------------------------------------------------------------
#-----------------------------------------------------------------------------------------------------------
# 03/31WIP - Create New Graph based on Sciforma Report
#-----------------------------------------------------------------------------------------------------------
# Load df_AccessDB
df_AccessDB = pd.read_excel(input_file_formatted, sheet_name='AccessReport', index_col=False)
# Combine phase 4 and 5 to Phase 4-5
if 'Program' in df_AccessDB.columns and 'Pty Indice' in df_AccessDB.columns:
mask = (
df_AccessDB['Program'].isin(['Phase 4', 'Phase 5']) &
~df_AccessDB['Pty Indice'].str.contains('Phase5', na=False)
)
df_AccessDB.loc[mask, 'Program'] = 'Phase 4-5'
date_columns = ['Date to ENG', 'Date completed', 'Date Order']
for col in date_columns:
df_AccessDB[col] = pd.to_datetime(df_AccessDB[col], errors='coerce')
def calculate_working_days(start_date, end_date):
if pd.isna(start_date) or pd.isna(end_date):
return np.nan
# If inputs are strings, convert to datetime first
if isinstance(start_date, str):
start_date = pd.to_datetime(start_date, errors='coerce')
if isinstance(end_date, str):
end_date = pd.to_datetime(end_date, errors='coerce')
# If conversion failed, return NaN
if pd.isna(start_date) or pd.isna(end_date):
return np.nan
return np.busday_count(start_date.date(), end_date.date())
# Apply the function to calculate completion time and order to ENG time
df_AccessDB['Completion Time'] = df_AccessDB.apply(
lambda row: calculate_working_days(row['Date to ENG'], row['Date completed']),
axis=1
)
df_AccessDB['Order to ENG Time'] = df_AccessDB.apply(
lambda row: calculate_working_days(row['Date Order'], row['Date to ENG']),
axis=1
)
# Filter the rows where we have valid data (no missing 'Date completed' or 'Date to ENG')
valid_rows = df_AccessDB.dropna(subset=['Completion Time'])
# Create the 'Month' column from 'Date completed'
df_AccessDB['Month_completed'] = df_AccessDB['Date completed'].dt.strftime('%b-%Y')
# Data preparation
#print('df_AccessDB:')
#display(df_AccessDB)
#-----------------------------------------------------------------------------------------------------------
# Load SciformaReport tab to get the NRE data
df_SciformaReport = pd.read_excel(input_file_formatted, sheet_name='SciformaSummary', index_col=False)
#-----------------------------------------------------------------------------------------------------------
# Create a HTMl text bellow the graph create_monthly_nre_fees_graph with:
# Average NRE fees charged monthly:
# Average NRE spent monthly:
# Average ECO released monthly:
# Quantity ECO released on the period: based on 'Change#' from df_AccessDB for a given program (column 'Program'):
# divider gray light
# Theoritical fees to be charged ($5K per ECO): quantity ECO released * $5K
# Fees charged vs. theoritical: {percentage}
# 04/14 - Dashed Line Inconsistency
def create_monthly_nre_fees_graph(df_Historic_dashboard_filtered, df_SciformaReport_filtered, df_AccessDB_filtered, year_color_map, selected_program):
"""
Creates a bar chart showing monthly NRE fees and NRE spending (both in $K) for the selected program,
with horizontal dashed purple bars for average NRE Fees and NRE Spending, and embeds HTML text with metrics below the graph.
Args:
df_Historic_dashboard_filtered: Filtered historical data DataFrame
df_SciformaReport_filtered: DataFrame containing program hours per month
df_AccessDB: DataFrame containing ECO data
year_color_map: Dictionary mapping years to colors
selected_program: Program name (e.g., "1stTB")
Returns:
p: Bokeh layout object (column) containing the figure and HTML text Div
"""
# --- Data Preparation for NRE Fees (from df_Historic_dashboard_filtered) ---
df = df_Historic_dashboard_filtered.copy()
df['YearMonth'] = df['Invoice date'].dt.to_period('M')
# Group by YearMonth and calculate NRE fees
monthly_data = df.groupby('YearMonth').agg({
'NRE Fee': 'sum'
}).reset_index()
# Convert to $K for display
monthly_data['NRE_Fees_K'] = monthly_data['NRE Fee'] / 1000 # Convert to $K
monthly_data['NRE_Display'] = monthly_data['NRE_Fees_K'] # Already in $K
# Get the date span from df_SciformaReport_filtered
month_columns = [col for col in df_SciformaReport_filtered.columns if col.startswith('202')]
earliest_month = pd.to_datetime(month_columns[0], format='%Y-%m').strftime('%b %Y')
latest_month = pd.to_datetime(month_columns[-1], format='%Y-%m').strftime('%b %Y')
# Create a full range of months
start_date = monthly_data['YearMonth'].min()
end_date = monthly_data['YearMonth'].max()
if pd.isna(start_date) or pd.isna(end_date):
start_date = pd.Period(month_columns[0], freq='M')
end_date = pd.Period(month_columns[-1], freq='M')
all_months = pd.period_range(start=start_date, end=end_date, freq='M')
all_months_df = pd.DataFrame({'YearMonth': all_months})
# Merge with all months to ensure no months are missing
monthly_data = all_months_df.merge(monthly_data, on='YearMonth', how='left').fillna({'NRE_Fees_K': 0, 'NRE_Display': 0})
# Create display columns
monthly_data['Date'] = monthly_data['YearMonth'].dt.to_timestamp()
monthly_data['Month_Year'] = monthly_data['Date'].dt.strftime('%b %Y')
monthly_data['Year'] = monthly_data['Date'].dt.year
monthly_data['Color'] = monthly_data['Year'].map(year_color_map).fillna('gray')
# --- Data Preparation for NRE Spending (from df_SciformaReport_filtered) ---
df_SciformaReport_filtered = df_SciformaReport_filtered.copy()
# Melt the DataFrame to get months as rows
spending_data = df_SciformaReport_filtered.melt(id_vars=['Program'], value_vars=month_columns,
var_name='Month', value_name='Hours')
# Calculate NRE Spending as $132 * Hours and convert to $K
spending_data['NRE_Spending'] = spending_data['Hours'] * 132 # Full dollars
spending_data['NRE_Spending_K'] = spending_data['NRE_Spending'] / 1000 # Convert to $K
# Convert Month to datetime and format for consistency
spending_data['Date'] = pd.to_datetime(spending_data['Month'], format='%Y-%m')
spending_data['Month_Year'] = spending_data['Date'].dt.strftime('%b %Y')
spending_data['Year'] = spending_data['Date'].dt.year
spending_data['Color'] = spending_data['Year'].map(year_color_map).fillna('gray')
# Create a full range of months for spending data
spending_data['YearMonth'] = spending_data['Date'].dt.to_period('M')
spending_data = all_months_df.merge(spending_data, on='YearMonth', how='left').fillna({'NRE_Spending_K': 0, 'Hours': 0})
spending_data['Date'] = spending_data['YearMonth'].dt.to_timestamp()
spending_data['Month_Year'] = spending_data['Date'].dt.strftime('%b %Y')
spending_data['Year'] = spending_data['Date'].dt.year
spending_data['Color'] = spending_data['Year'].map(year_color_map).fillna('gray')
# Merge the two datasets on Month_Year to ensure alignment
combined_data = monthly_data.merge(spending_data[['Month_Year', 'NRE_Spending_K', 'Hours']],
on='Month_Year', how='left')
combined_data['NRE_Spending_K'] = combined_data['NRE_Spending_K'].fillna(0)
combined_data['Hours'] = combined_data['Hours'].fillna(0)
# Filter out months where both NRE Fees and NRE Spending are 0
combined_data = combined_data[(combined_data['NRE_Fees_K'] > 0) | (combined_data['NRE_Spending_K'] > 0)]
# If no data remains after filtering, handle the edge case
if combined_data.empty:
raise ValueError("No data to display after filtering. Both NRE Fees and NRE Spending are 0 for all months.")
# Update the earliest and latest months based on the filtered data
earliest_month = combined_data['Date'].min().strftime('%b %Y')
latest_month = combined_data['Date'].max().strftime('%b %Y')
# --- Calculate Averages for Plotting ---
avg_nre_fees = combined_data['NRE_Fees_K'].mean()
avg_nre_spending = combined_data['NRE_Spending_K'].mean()
# --- Create Bokeh Figure ---
p = figure(
x_range=combined_data['Month_Year'],
title=f"Monthly NRE Fees vs. NRE Spending (@$132/h), Program: {selected_program} - [{earliest_month} to {latest_month}]",
width=1200,
height=650,
tools='pan,wheel_zoom,box_zoom,reset,save',
toolbar_location='right',
x_axis_label='Month',
y_axis_label='Amount ($K)',
y_range=(0, max(combined_data['NRE_Fees_K'].max(), combined_data['NRE_Spending_K'].max(), avg_nre_fees, avg_nre_spending) * 1.3),
sizing_mode='fixed',
output_backend='svg'
)
# X-axis font size
p.xaxis.major_label_text_font_size = "12pt"
p.xaxis.major_label_text_font_style = "bold"
# Title text font
p.title.text_font_size = "14pt"
# Customize grid
p.ygrid.grid_line_color = "#E0E0E0"
p.ygrid.grid_line_dash = [6, 4]
p.xgrid.visible = False
# Create ColumnDataSource
source = ColumnDataSource(combined_data)
# Create legend items and renderers
nre_legend_items = []
spending_legend_items = []
renderers = []
# Visualization: NRE Fees bars by year with hatch pattern
for year in sorted(combined_data['Year'].unique()):
year_data = combined_data[combined_data['Year'] == year]
year_source = ColumnDataSource(year_data)
color = year_color_map.get(year, 'gray')
# NRE Fees bars with hatch pattern
nre_bar = p.vbar(
x='Month_Year',
width=0.4,
bottom=0,
top='NRE_Fees_K',
source=year_source,
color=color,
alpha=0.3,
hatch_pattern="/",
hatch_color=color,
hatch_alpha=0.7,
hatch_weight=0.8,
name=f"nre_{year}"
)
nre_legend_items.append((f"NRE Fees {year}", [nre_bar]))
renderers.append(nre_bar)
# NRE Spending bars (solid fill)
spending_bar = p.vbar(
x='Month_Year',
width=0.4,
bottom=0,
top='NRE_Spending_K',
source=year_source,
color=color,
alpha=0.8,
name=f"spending_{year}"
)
spending_legend_items.append((f"NRE Spending {year}", [spending_bar]))
renderers.append(spending_bar)
# Adjust bar positions to avoid overlap
for r in renderers:
if "nre_" in r.name:
r.glyph.x = dodge('Month_Year', -0.2, range=p.x_range) # Shift NRE Fees bars left
elif "spending_" in r.name:
r.glyph.x = dodge('Month_Year', 0.2, range=p.x_range) # Shift NRE Spending bars right
# Add horizontal dashed purple bars for averages using Span
avg_nre_fees_span = Span(
location=avg_nre_fees,
dimension='width',
line_color='purple',
line_dash=[6, 4], # Consistent dash pattern
line_alpha=0.3,
line_width=2,
name='avg_nre_fees'
)
p.add_layout(avg_nre_fees_span)
avg_nre_spending_span = Span(
location=avg_nre_spending,
dimension='width',
line_color='purple',
line_dash=[6, 4], # Consistent dash pattern
line_alpha=0.8,
line_width=2,
name='avg_nre_spending'
)
p.add_layout(avg_nre_spending_span)
# Add a transparent line for hover consistency
hover_line = p.line(
x='Month_Year',
y='NRE_Fees_K',
source=source,
line_alpha=0 # Fully transparent
)
# Attach hover tool
hover = HoverTool(
tooltips=[
("Period", "@Month_Year"),
("NRE Fees", "@NRE_Display{$0,0.0}K"),
("NRE Spending", "@NRE_Spending_K{$0,0.0}K"),
("Hours", "@Hours{0.0}")
],
mode='vline',
renderers=[hover_line]
)
p.add_tools(hover)
# Remove Bokeh logo
p.toolbar.logo = None
# Create legends
nre_legend = Legend(
items=nre_legend_items,
location=(50, 420),
title="NRE Fees ($)",
title_text_font_size="12pt",
label_text_font_size="10pt",
glyph_height=20,
glyph_width=20,
spacing=5,
background_fill_alpha=0.7
)
spending_legend = Legend(
items=spending_legend_items,
location=(200, 420),
title="NRE Spending ($) - @$132/h",
title_text_font_size="12pt",
label_text_font_size="10pt",
glyph_height=20,
glyph_width=20,
spacing=5,
background_fill_alpha=0.7
)
# Dedicated legend for averages
avg_data_for_legend = pd.DataFrame({
'x': [combined_data['Month_Year'].iloc[0], combined_data['Month_Year'].iloc[-1]],
'y_fees': [0, 0], # y-values don’t matter since not rendered
'y_spending': [0, 0]
})
avg_legend_source = ColumnDataSource(avg_data_for_legend)
# Proxy lines for the legend (not rendered on the plot)
avg_nre_fees_line = p.line(
x='x',
y='y_fees',
source=avg_legend_source,
line_color='purple',
line_dash=[6, 4], # Match the dash pattern
line_alpha=0.3, # Match the alpha
line_width=2,
name='avg_nre_fees_legend',
visible=True # Only for legend, not rendered on plot
)
avg_nre_spending_line = p.line(
x='x',
y='y_spending',
source=avg_legend_source,
line_color='purple',
line_dash=[6, 4], # Match the dash pattern
line_alpha=0.8, # Match the alpha
line_width=2,
name='avg_nre_spending_legend',
visible=True # Only for legend, not rendered on plot
)
avg_legend_items = [
(f"Average NRE Fees: ${avg_nre_fees:,.1f}K", [avg_nre_fees_line]),
(f"Average NRE Spending: ${avg_nre_spending:,.1f}K", [avg_nre_spending_line])
]
avg_legend = Legend(
items=avg_legend_items,
location=(500, 420),
title="Averages fees on the period ($)",
title_text_font_size="12pt",
label_text_font_size="10pt",
glyph_height=20,
glyph_width=20,
spacing=5,
background_fill_alpha=0.7
)
p.add_layout(nre_legend)
p.add_layout(spending_legend)
p.add_layout(avg_legend)
# Final styling
p.xaxis.major_label_orientation = 1.2
p.yaxis.formatter = NumeralTickFormatter(format="$0a")
p.legend.click_policy = "hide"
# --- Calculate Metrics for HTML Text ---
# Average NRE Fees Charged Monthly ($K)
total_nre_fees = df_Historic_dashboard_filtered['NRE Fee'].sum() / 1000 # Convert to $K
num_months = len(combined_data['YearMonth'].unique())
avg_nre_fees_monthly = total_nre_fees / num_months if num_months > 0 else 0
# Average NRE Spent Monthly ($K)
total_nre_spending = spending_data['NRE_Spending_K'].sum()
avg_nre_spending_monthly = total_nre_spending / num_months if num_months > 0 else 0
# Initialize ECO-related metrics
avg_eco_monthly = 0
total_eco = 0
theoretical_fees = 0
fees_vs_theoretical = 0
# Process ECO data only if df_AccessDB_filtered is valid
if not df_AccessDB_filtered.empty and 'Program' in df_AccessDB_filtered.columns and 'Date completed' in df_AccessDB_filtered.columns:
try:
eco_data = df_AccessDB_filtered[df_AccessDB_filtered['Program'] == selected_program].copy()
if not eco_data.empty:
eco_data['YearMonth'] = pd.to_datetime(eco_data['Date completed']).dt.to_period('M')
# Average ECO Released Monthly
monthly_eco_counts = eco_data.groupby('YearMonth')['Change#'].nunique()
avg_eco_monthly = monthly_eco_counts.mean() if not monthly_eco_counts.empty else 0
# Total ECOs Released
total_eco = eco_data['Change#'].nunique()
except Exception as e:
print(f"Error processing ECO data: {e}")
# Calculate theoretical fees and fees vs. theoretical
theoretical_fees = total_eco * 5000 / 1000 # Convert to $K
fees_vs_theoretical = (total_nre_fees / theoretical_fees * 100) if theoretical_fees > 0 else 0
# --- Generate HTML Text for Div ---
html_text = f"""
<div style="font-family: Arial, sans-serif; font-size: 14px; width: 1200px; padding: 10px;">
<p><b>Average NRE fees charged monthly:</b> ${avg_nre_fees_monthly:,.1f}K</p>
<p><b>Average NRE spent monthly:</b> ${avg_nre_spending_monthly:,.1f}K</p>
<p><b>Average ECO released monthly:</b> {avg_eco_monthly:.1f}</p>
<p><b>Quantity ECO released on the period:</b> {total_eco}</p>
<hr style="border: 1px solid lightgray;">
<p><b>Theoretical fees to be charged ($5K per ECO):</b> ${theoretical_fees:,.1f}K</p>
<p><b>Fees charged vs. theoretical:</b> {fees_vs_theoretical:.1f}%</p>
</div>
"""
# Create Bokeh Div for HTML text
text_div = Div(text=html_text, width=1200)
# Combine plot and text in a column layout
layout = column(p, text_div, sizing_mode='fixed')
return layout
#-----------------------------------------------------------------------------------------------------------
##################################################################
#///////////////////////////////////////////////////////////////
# Create INDICATORS
#///////////////////////////////////////////////////////////////
##################################################################
# Card 'Yearly metrics - comparison this year vs last year
##################################################################
# --> Comparison of cumulative metrics from the start of the current year up to today against the same period in the previous year
def calculate_yearly_metrics(df_Historic_dashboard_filtered):
today = pd.to_datetime('today')
current_year = today.year
last_year = current_year - 1
current_month = today.month
current_day = today.day
# Calculate start date for the current year and last year
start_of_current_year = pd.Timestamp(year=current_year, month=1, day=1)
start_of_last_year = pd.Timestamp(year=last_year, month=1, day=1)
end_of_last_year = pd.Timestamp(year=last_year, month=current_month, day=current_day)
# Filter and aggregate data for the current year up to today
df_Current_Year = df_Historic_dashboard_filtered[
(df_Historic_dashboard_filtered['Year'] == current_year) &
(df_Historic_dashboard_filtered['Invoice date'] <= today)
].agg({
'Qty Shipped': 'sum',
'Sales USD': 'sum'
}).to_dict()
# Filter and aggregate data for the last year up to the same date
df_Last_Year = df_Historic_dashboard_filtered[
(df_Historic_dashboard_filtered['Year'] == last_year) &
(df_Historic_dashboard_filtered['Invoice date'] <= end_of_last_year)
].agg({
'Qty Shipped': 'sum',
'Sales USD': 'sum'
}).to_dict()
# All values converted to integer
total_shipped_current = int(df_Current_Year['Qty Shipped'])
total_sales_current = int(df_Current_Year['Sales USD'])
total_shipped_last = int(df_Last_Year['Qty Shipped'])
total_sales_last = int(df_Last_Year['Sales USD'])
# Calculate percentage change
if total_shipped_last != 0:
pct_change_shipped = ((total_shipped_current - total_shipped_last) / total_shipped_last) * 100
else:
pct_change_shipped = float('inf') if total_shipped_current > 0 else float('-inf')
if total_sales_last != 0:
pct_change_sales = ((total_sales_current - total_sales_last) / total_sales_last) * 100
else:
pct_change_sales = float('inf') if total_sales_current > 0 else float('-inf')
return {
'total_shipped_current': total_shipped_current,
'total_sales_current': total_sales_current,
'total_shipped_last': total_shipped_last,
'total_sales_last': total_sales_last,
'pct_change_shipped': pct_change_shipped,
'pct_change_sales': pct_change_sales
}
def create_yearly_metrics_indicator(df_Historic_dashboard_filtered):
# Calculate the metrics
metrics = calculate_yearly_metrics(df_Historic_dashboard_filtered)
# Determine trend colors and arrows
trend_color_shipped = "green" if metrics['pct_change_shipped'] >= 0 else "red"
trend_arrow_shipped = "▲" if metrics['pct_change_shipped'] >= 0 else "▼"
trend_color_sales = "green" if metrics['pct_change_sales'] >= 0 else "red"
trend_arrow_sales = "▲" if metrics['pct_change_sales'] >= 0 else "▼"
# Create HTML content with trend information
html_content = f"""
<div style="font-size: 20px; font-family: Arial, sans-serif; padding: 10px; border: 1px solid #ddd; border-radius: 5px; width: 100%; box-sizing: border-box;">
<h3 style="margin: 0; color: teal;"> Yearly comparison - {pd.to_datetime('today').year} to date vs. {pd.to_datetime('today').year - 1}</h3>
<p style="margin: 10px 0; font-size: 16px;">
<strong>Total Quantity Shipped YTD</strong> (vs. {pd.to_datetime('today').year - 1}): <span style="color: black;">{metrics['total_shipped_current']:,.0f}</span>
<span style="font-size: 14px; color: {trend_color_shipped};">({trend_arrow_shipped} {metrics['pct_change_shipped']:+,.1f}%)</span>
</p>
<p style="margin: 10px 0; font-size: 16px;">
<strong>Total realized Sales YTD</strong> (vs. {pd.to_datetime('today').year - 1}): <span style="color: black;">${metrics['total_sales_current']:,.0f}</span>
<span style="font-size: 14px; color: {trend_color_sales};">({trend_arrow_sales} {metrics['pct_change_sales']:+,.1f}%)</span>
</p>
</div>
"""
# Create layout with indicators and HTML content
layout = pn.Column(
pn.pane.HTML(html_content, sizing_mode='stretch_width'),
sizing_mode='stretch_width'
)
return layout
##################################################################
# Card 'Since Inception - Beginning of the project'
##################################################################
# update 09/12 --> Include 'Total backlog Sales (USD)' & 'Total past due Sales (USD)'
# This 2 new variables should come from the datafram 'df_Backlog_dashboard' and be calculated based on the 'Requested Date' (because the 'Due Date' has been changed for some PN, the 'Requested Date' the real due date.
#------------------------------------------------------------------------------------------------------------------
# 03/21 - Update the card with df_Snapshot_table column 'IDD Corrected Margin [%]' and 'IDD AVG realized Margin [%]'
#------------------------------------------------------------------------------------------------------------------
# Include 4 new KPI lines in the card:
# 'AVG Realized Margin' based on 'IDD AVG realized Margin [%]' for the given program
# 'Gross Profit Realized' based on 'IDD AVG realized Margin [%]' * Sum of 'Sales USD' df_Historic_dashboard_filtered from the given program
# 'Current Margin (Corrected based on labor)' based on 'IDD Corrected Margin [%]' for the given program
# 'Gross Profit (Corrected based on labor)' based on 'IDD Corrected Margin [%]' * Sum of 'Sales USD' for the given program
#------------------------------------------------------------------------------------------------------------------
# The overall margin for each program is based on the number of units shipped for each top-level item within the program.
# Thus, is an weighted average of the margins of individual items, where the weights are the quantities shipped.
#------------------------------------------------------------------------------------------------------------------
def clean_and_convert_columns(df, columns_to_clean):
"""
Clean specified columns by removing '%' and '$' symbols and converting them to numeric values.
Parameters:
df (pd.DataFrame): The DataFrame containing the columns to clean.
columns_to_clean (list): List of column names to clean.
Returns:
pd.DataFrame: The DataFrame with cleaned and converted columns.
"""
for column in columns_to_clean:
if column in df.columns:
# Remove '%' and '$' symbols and convert to numeric
df[column] = df[column].replace({'%': '', r'\$': ''}, regex=True)
df[column] = pd.to_numeric(df[column], errors='coerce') # Convert to numeric, set invalid values to NaN
else:
print(f"Warning: Column '{column}' not found in DataFrame.")
return df
# List of columns to clean (replace with your actual column names)
columns_to_clean = [
'IDD AVG realized Margin [%]',
'IDD Corrected Margin [%]',
# Add other columns as needed
]
# df_Snapshot_KPI datafram
df_Snapshot_KPI = df_Snapshot.copy()
# Clean and convert columns in df_Snapshot_KPI
df_Snapshot_KPI = clean_and_convert_columns(df_Snapshot_KPI, columns_to_clean)
#03/31WIP
def calculate_since_inception_metrics(df_Historic_dashboard_filtered, df_Snapshot_KPI):
"""
Calculate cumulative metrics since inception for the selected program.
Parameters:
df_Historic_dashboard_filtered (pd.DataFrame): Filtered DataFrame for the selected program (existing metrics).
df_Snapshot_KPI (pd.DataFrame): Snapshot data for the selected program (new metrics).
Returns:
dict: Dictionary containing cumulative metrics.
"""
# Display datafram
#display(df_Historic_dashboard_filtered.head())
# Existing metrics (from df_Historic_dashboard_filtered)
total_shipped = int(df_Historic_dashboard_filtered['Qty Shipped'].sum())
total_sales = int(df_Historic_dashboard_filtered['Sales USD'].sum())
# Calculate avg_realized_margin using 'IDD Marge Standard' and 'Quantity'
# Total margin in USD = sum of (IDD Marge Standard * Quantity) for each row
total_margin_usd = df_Historic_dashboard_filtered['IDD Marge Standard'].sum()
# Average realized margin (%) = (total margin USD / total sales) * 100
avg_realized_margin = (total_margin_usd / total_sales) * 100 if total_sales != 0 else 0
# Gross profit realized = total margin in USD
gross_profit_realized = total_margin_usd
# From df_Snapshot_KPI
current_margin_corrected = df_Snapshot_KPI['IDD Corrected Margin [%]'].mean() # Current Margin (Corrected based on labor)
gross_profit_corrected = current_margin_corrected * total_sales / 100 # Gross Profit (Corrected based on labor)
# Return metrics as a dictionary
return {
'total_shipped': total_shipped,
'total_sales': total_sales,
'avg_realized_margin': avg_realized_margin, # Updated metric
'gross_profit_realized': gross_profit_realized, # Updated metric
'current_margin_corrected': current_margin_corrected,
'gross_profit_corrected': gross_profit_corrected
}
# Updated 03/28
def create_since_inception_indicator(df_Historic_dashboard_filtered, df_Snapshot_KPI):
"""
Create a card displaying cumulative KPIs since inception, including program name.
"""
# Calculate cumulative metrics
metrics = calculate_since_inception_metrics(df_Historic_dashboard_filtered, df_Snapshot_KPI)
# Get the program name
program_name = df_Snapshot_KPI['Program'].iloc[0] if not df_Snapshot_KPI['Program'].empty else "Unknown Program"
# Special handling for 1stTB program
if program_name == '1stTB':
# Calculate total fees
total_nre_fee = int(df_Historic_dashboard_filtered['NRE Fee'].sum())
total_fai_fee = int(df_Historic_dashboard_filtered['FAI Fee'].sum())
total_fees = total_nre_fee + total_fai_fee
html_content = f"""
<div style="font-size: 20px; font-family: Arial, sans-serif; padding: 10px; border: 1px solid #ddd; border-radius: 5px; width: 100%; box-sizing: border-box;">
<h3 style="margin: 0; color: teal;"> 1 year span ({span_report_historic_dashboard}) - {program_name}</h3>
<p style="margin: 10px 0; font-size: 16px;">
<strong>Total Shipped:</strong> {metrics['total_shipped']:,.0f} units
</p>
<p style="margin: 10px 0; font-size: 16px;">
<strong>Total Sales:</strong> ${metrics['total_sales'] / 1000:,.1f}K
</p>
<hr style="border: none; height: 0.5px; background-color: #ddd; margin: 10px 0;">
<p style="margin: 10px 0; font-size: 16px;">
<strong>Realized Gross Margin</strong> (ERP based):
<span style="color: {'green' if metrics['avg_realized_margin'] >= 0 else 'red'};">{metrics['avg_realized_margin']:.2f}%</span>
</p>
<p style="margin: 10px 0; font-size: 16px;">
<strong>Realized Gross Profit</strong> (ERP based):
<span style="color: {'green' if metrics['gross_profit_realized'] >= 0 else 'red'};">${metrics['gross_profit_realized'] / 1000:,.1f}K</span>
</p>
</div>
"""
else:
# Original content for other programs
html_content = f"""
<div style="font-size: 20px; font-family: Arial, sans-serif; padding: 10px; border: 1px solid #ddd; border-radius: 5px; width: 100%; box-sizing: border-box;">
<h3 style="margin: 0; color: teal;">Cumulative KPIs Since Inception - {program_name}</h3>
<p style="margin: 10px 0; font-size: 16px;">
<strong>Total Shipped:</strong> {metrics['total_shipped']:,.0f} units
</p>
<p style="margin: 10px 0; font-size: 16px;">
<strong>Total Sales:</strong> ${metrics['total_sales'] / 1000:,.1f}K
</p>
<hr style="border: none; height: 0.5px; background-color: #ddd; margin: 10px 0;">
<p style="margin: 10px 0; font-size: 16px;">
<strong>Realized Gross Margin</strong> (ERP based):
<span style="color: {'green' if metrics['avg_realized_margin'] >= 0 else 'red'};">{metrics['avg_realized_margin']:.2f}%</span>
</p>
<p style="margin: 10px 0; font-size: 16px;">
<strong>Realized Gross Profit</strong> (ERP based):
<span style="color: {'green' if metrics['gross_profit_realized'] >= 0 else 'red'};">${metrics['gross_profit_realized'] / 1000:,.1f}K</span>
</p>
</div>
"""
# Create a layout with the HTML content
layout = pn.Column(
pn.pane.HTML(html_content, sizing_mode='stretch_width'),
sizing_mode='stretch_width'
)
return layout
def create_note_since_inception(df_Snapshot_KPI):
"""
Create an HTML note explaining the calculation of profit and margin for the since-inception indicator,
including the program name and unique PN count.
Returns:
str: HTML content for the note.
"""
# Get the program name (assuming 'Program' is a column in df_Snapshot_KPI)
program_name = df_Snapshot_KPI['Program'].iloc[0] if not df_Snapshot_KPI['Program'].empty else "Unknown Program"
# Count unique PN
unique_pn_count = df_Snapshot_KPI['IDD Top Level'].nunique()
note = f"""
<div style="
font-size: 12px;
font-family: Arial, sans-serif;
padding: 10px;
border: 1px solid #ddd;
border-radius: 5px;
width: 1310px;
height: 120px;
box-sizing: border-box;
overflow: auto;
line-height: 1.1; <!-- Reduced line height -->
position: relative; <!-- Required for arrow positioning -->
">
<p style="margin: 10px 0;">
▷ The overall profit and margin for each Program are calculated as a <b>weighted average</b> of the individual top-level margins within the program. The weights are based on the <b>number of units shipped</b> for each top-level item.
</p>
<p style="margin: 10px 0; padding-left: 20px;"> <!-- Indentation using padding -->
➥ <b>Realized</b> profit and margin are derived from the <b>ERP turnover report</b>, which captures price changes over time. However, this calculation does not account for differences between <b>actual labor time</b> and the <b>standard labor time</b> reflected in the pricing.
</p>
<p style="margin: 10px 0; padding-left: 20px;"> <!-- Indentation using padding -->
➥ <b>Corrected</b> profit and margin incorporate data from <b>labor reports</b> for each top-level item. This adjustment provides a <b>more accurate financial picture</b> by accounting for actual labor time variances.
</p>
<p style="margin: 10px 0; padding-left: 20px;">
➥ <b>{program_name}:</b> {unique_pn_count} unique top-level PN accounted for within the calculation.
</p>
</div>
"""
return note
#------------------------------------------------------------------------------------------------------------------
# 03/25 - Create New graph Monthly Sales and Fees
#------------------------------------------------------------------------------------------------------------------
#04/01 to include margin
def create_monthly_sales_fees_graph(df_Historic_dashboard_filtered, year_color_map, selected_program):
"""
Creates a stacked bar chart showing monthly sales and fees with a secondary axis for % margin.
Args:
df_Historic_dashboard_filtered: Filtered historical data
year_color_map: Dictionary mapping years to colors
Returns:
Bokeh figure object: The created visualization figure
"""
# Data Preparation
df = df_Historic_dashboard_filtered.copy()
df['YearMonth'] = df['Invoice date'].dt.to_period('M')
# Group by YearMonth and calculate metrics
monthly_data = df.groupby('YearMonth').agg({
'Sales USD': 'sum',
'NRE Fee': 'sum',
'FAI Fee': 'sum',
'Qty Shipped': 'sum',
'IDD Marge Standard': 'sum'
}).reset_index()
# Calculate total fees and convert to $K
monthly_data['Total Fees'] = monthly_data['NRE Fee'] + monthly_data['FAI Fee']
monthly_data['Sales_K'] = monthly_data['Sales USD']
monthly_data['Fees_K'] = monthly_data['Total Fees']
monthly_data['Total_K'] = monthly_data['Sales_K'] + monthly_data['Fees_K']
# Calculate margin percentage (margin/sales) in decimal form
monthly_data['Margin_Pct'] = monthly_data['IDD Marge Standard'] / monthly_data['Sales USD']
# Create display columns
monthly_data['Date'] = monthly_data['YearMonth'].dt.to_timestamp()
monthly_data['Month_Year'] = monthly_data['Date'].dt.strftime('%b %Y')
monthly_data['Year'] = monthly_data['Date'].dt.year
monthly_data['Color'] = monthly_data['Year'].map(year_color_map).fillna('gray')
monthly_data['Sales_Display'] = monthly_data['Sales_K'] / 1000
monthly_data['Fees_Display'] = monthly_data['Fees_K'] / 1000
monthly_data['Total_Display'] = monthly_data['Total_K'] / 1000
# Add a column for margin circle colors
monthly_data['Margin_Circle_Color'] = monthly_data['Margin_Pct'].apply(lambda x: '#82c372' if x >= 0 else '#ec3a42')
# Create figure with SVG output backend
p = figure(
x_range=monthly_data['Month_Year'],
title=f"Monthly Sales and Fees Breakdown - Program: {selected_program}",
width=1200,
height=650,
tools='pan,wheel_zoom,box_zoom,reset,save',
toolbar_location='right',
x_axis_label='Month',
y_axis_label='Revenue ($K)',
sizing_mode='fixed',
output_backend='svg'
)
# Set the title text color
#p.title.text_color = 'black'
# X-axis font size 04/02
p.xaxis.major_label_text_font_size = "12pt"
p.xaxis.major_label_text_font_style = "bold"
# Title text font
p.title.text_font_size = "14pt"
# Customize grid
p.ygrid.grid_line_color = "#E0E0E0"
p.ygrid.grid_line_dash = [6, 4]
p.xgrid.visible = False
# Create ColumnDataSource
source = ColumnDataSource(monthly_data)
# Create legend items
sales_legend_items = []
fees_legend_items = []
renderers = []
# Set range for primary y-axis (Revenue in $K) with more room at the top
p.y_range = Range1d(0, monthly_data['Total_K'].max() * 1.5) #150% primary Y-Axis (Sales $K)
# Add secondary y-axis for margin percentage with more room at the top
margin_min = monthly_data['Margin_Pct'].min() * 1
margin_max = monthly_data['Margin_Pct'].max() * 2 #200% secondary Y-Axis (Margin %)
p.extra_y_ranges = {"margin": Range1d(start=margin_min, end=margin_max)}
margin_axis = LinearAxis(
y_range_name="margin",
axis_label="Margin (%)",
formatter=NumeralTickFormatter(format="0%"),
major_label_text_color="black",
axis_label_text_color="black"
)
p.add_layout(margin_axis, 'right')
# Visualization code - bars
for year in sorted(monthly_data['Year'].unique()):
year_data = monthly_data[monthly_data['Year'] == year]
year_source = ColumnDataSource(year_data)
color = year_color_map.get(year, 'gray')
# Sales bars (solid color)
sales_bar = p.vbar(
x='Month_Year',
width=0.8,
bottom=0,
top='Sales_K',
source=year_source,
color=color,
alpha=1.0,
name=f"sales_{year}"
)
sales_legend_items.append((f"{year}", [sales_bar]))
renderers.append(sales_bar)
# Fees bars (with pattern)
fees_bar = p.vbar(
x='Month_Year',
width=0.8,
bottom='Sales_K',
top='Total_K',
source=year_source,
color=color,
alpha=0.3,
hatch_pattern="/",
hatch_color=color,
hatch_alpha=0.7,
hatch_weight=0.8,
name=f"fees_{year}"
)
fees_legend_items.append((f"{year}", [fees_bar]))
renderers.append(fees_bar)
# Prepare data for segmented margin line
xs = []
ys = []
colors = []
for i in range(len(monthly_data) - 1):
xs.append([monthly_data['Month_Year'].iloc[i], monthly_data['Month_Year'].iloc[i + 1]])
ys.append([monthly_data['Margin_Pct'].iloc[i], monthly_data['Margin_Pct'].iloc[i + 1]])
colors.append('#82c372' if monthly_data['Margin_Pct'].iloc[i] >= 0 else '#ec3a42')
# Add visible segmented margin line
margin_line = p.multi_line(
xs=xs,
ys=ys,
line_color=colors,
line_width=2,
line_dash='dashed',
y_range_name="margin"
)
# Add empty circles for each margin data point with conditional coloring
margin_circles = p.circle(
x='Month_Year',
y='Margin_Pct',
source=source,
y_range_name="margin",
size=8,
fill_alpha=0,
line_color='Margin_Circle_Color',
line_width=1.5
)
# Create dummy lines for the margin legend
positive_margin_dummy = p.line(
x=[monthly_data['Month_Year'].iloc[0]],
y=[monthly_data['Margin_Pct'].iloc[0]],
line_color='#82c372',
line_width=2,
line_dash='dashed',
y_range_name="margin"
)
negative_margin_dummy = p.line(
x=[monthly_data['Month_Year'].iloc[0]],
y=[monthly_data['Margin_Pct'].iloc[0]],
line_color='#ec3a42',
line_width=2,
line_dash='dashed',
y_range_name="margin"
)
# Add invisible line for hover
invisible_margin_line = p.line(
x='Month_Year',
y='Margin_Pct',
source=source,
y_range_name="margin",
line_width=2,
line_alpha=0,
hover_line_color='black',
hover_alpha=0.5
)
# Update hover tool
hover = HoverTool(
tooltips=[
("Period", "@Month_Year"),
("Sales", "@Sales_Display{$0,0.0}K"),
("Fees", "@Fees_Display{$0,0.0}K"),
("Total", "@Total_Display{$0,0.0}K"),
("Margin", "@Margin_Pct{0.0%}"),
("Quantity Shipped", "@{Qty Shipped}{0,0}")
],
mode='vline',
renderers=[invisible_margin_line]
)
p.add_tools(hover)
# Remove Bokeh logo
p.toolbar.logo = None
# Create legends
sales_legend = Legend(
items=sales_legend_items,
location=(50, 400),
title="Sales",
title_text_font_size="10pt",
label_text_font_size="10pt",
glyph_height=20,
glyph_width=20,
spacing=5,
background_fill_color="white",
background_fill_alpha=0.8,
border_line_color=None,
border_line_width=1
)
fees_legend = Legend(
items=fees_legend_items,
location=(150, 400),
title="Fees",
title_text_font_size="10pt",
label_text_font_size="10pt",
glyph_height=20,
glyph_width=20,
spacing=5,
background_fill_color="white",
background_fill_alpha=0.8,
border_line_color=None,
border_line_width=1
)
# Add a separate legend for margin lines
margin_legend_items = [
("Gross Margin % (> 0)", [positive_margin_dummy]),
("Gross Margin % (< 0)", [negative_margin_dummy])
]
margin_legend = Legend(
items=margin_legend_items,
location=(250, 400),
title="Margin (ERP based)",
title_text_font_size="10pt",
label_text_font_size="10pt",
glyph_height=20,
glyph_width=20,
spacing=5,
background_fill_color="white",
background_fill_alpha=0.8,
border_line_color=None,
border_line_width=1
)
p.add_layout(sales_legend)
p.add_layout(fees_legend)
p.add_layout(margin_legend)
# Final styling
p.xaxis.major_label_orientation = 1.2
p.yaxis.formatter = NumeralTickFormatter(format="$0a")
p.yaxis[1].formatter = NumeralTickFormatter(format="0%")
p.legend.click_policy = "hide"
return p
#------------------------------------------------------------------------------------------------------------------
#------------------------------------------------------------------------------------------------------------------
# 03/27WIP - Define the Function to Select Top PNs
#------------------------------------------------------------------------------------------------------------------
# Updated 04/09
def get_top_critical_assemblies_by_value(df_Backlog_filtered, comparison_date, selected_program, num_pns=5):
"""
Calculate top critical assemblies by backlog dollar value.
Parameters:
df_Backlog_filtered (pd.DataFrame): Filtered DataFrame with backlog data.
comparison_date (datetime): Date for determining past-due status.
selected_program (str): Program name to determine index column.
num_pns (int): Number of top assemblies to return (default: 5).
Returns:
pd.DataFrame: DataFrame with index column, metrics, and additional columns for Phase 4-5.
"""
try:
df = df_Backlog_filtered.copy()
index_column = 'Pty Indice' if selected_program == 'Phase 4-5' else 'IDD Top Level'
# Verify index column exists
if index_column not in df.columns:
raise ValueError(f"Required column '{index_column}' not found in input DataFrame. Available columns: {list(df.columns)}")
# Ensure Due Date is datetime64[ns]
df['Due Date'] = pd.to_datetime(df['Due Date'], errors='coerce')
df['Backlog Value'] = df['Currency net amount']
# Filter past-due items
past_due_df = df[df['Due Date'] < comparison_date]
if past_due_df.empty:
print(f"No past-due assemblies found. Due dates range: {df['Due Date'].min()} to {df['Due Date'].max()}")
return pd.DataFrame({
index_column: [],
'Past Due Value': [],
'Total Value': [],
'Percentage': [],
'IDD Top Level': [] if selected_program == 'Phase 4-5' else [],
'SEDA Top Level': [] if selected_program == 'Phase 4-5' else []
})
# Aggregate past-due values
past_due_summary = (past_due_df.groupby(index_column, as_index=False)
.agg({'Backlog Value': 'sum'})
.rename(columns={'Backlog Value': 'Past Due Value'}))
# Get top N by past-due value
past_due_summary = past_due_summary.nlargest(num_pns, 'Past Due Value')
# Aggregate total values
total_summary = (df.groupby(index_column, as_index=False)
.agg({'Backlog Value': 'sum'})
.rename(columns={'Backlog Value': 'Total Value'}))
# For Phase 4-5, get IDD Top Level and SEDA Top Level
if selected_program == 'Phase 4-5':
# Get unique mappings of Pty Indice to IDD Top Level and SEDA Top Level
mapping_df = df[[index_column, 'IDD Top Level', 'SEDA Top Level']].drop_duplicates(subset=[index_column])
else:
mapping_df = pd.DataFrame({index_column: past_due_summary[index_column]})
# Merge to combine past-due and total values
summary_df = pd.merge(past_due_summary, total_summary, on=index_column, how='left')
# Merge the IDD Top Level and SEDA Top Level mappings
if selected_program == 'Phase 4-5':
summary_df = pd.merge(summary_df, mapping_df, on=index_column, how='left')
# Fill any missing values with 'N/A'
summary_df['IDD Top Level'] = summary_df['IDD Top Level'].fillna('N/A')
summary_df['SEDA Top Level'] = summary_df['SEDA Top Level'].fillna('N/A')
# Calculate percentage of total PAST DUE backlog
total_past_due_value = past_due_df['Backlog Value'].sum()
if total_past_due_value > 0:
summary_df['Percentage'] = (summary_df['Past Due Value'] / total_past_due_value * 100).round(1)
else:
summary_df['Percentage'] = 0.0
return summary_df
except Exception as e:
print(f"Error in get_top_critical_assemblies_by_value: {str(e)}")
return pd.DataFrame({
index_column: [],
'Past Due Value': [],
'Total Value': [],
'Percentage': [],
'IDD Top Level': [] if selected_program == 'Phase 4-5' else [],
'SEDA Top Level': [] if selected_program == 'Phase 4-5' else []
})
# Updated 04/09
def create_critical_assemblies_card(df_Backlog_filtered, file_date, selected_program, num_pns=5):
try:
# Convert file_date to datetime for comparison
comparison_date = pd.to_datetime(file_date)
# Get metrics for top assemblies
metrics_df = get_top_critical_assemblies_by_value(
df_Backlog_filtered,
comparison_date,
selected_program,
num_pns
)
if metrics_df.empty:
return pn.Column(
pn.pane.Alert("No past-due assemblies found", alert_type="warning"),
sizing_mode='stretch_width'
)
# Calculate totals
formatted_date = comparison_date.strftime('%m-%d-%Y')
total_past_due_value = metrics_df['Past Due Value'].sum()
total_value = metrics_df['Total Value'].sum()
# Filter for past due items
past_due_mask = pd.to_datetime(df_Backlog_filtered['Due Date']) < comparison_date
total_past_due_backlog = df_Backlog_filtered.loc[past_due_mask, 'Currency net amount'].sum()
# Create HTML header without inner border
html_content = f"""
<div style="font-family: Arial, sans-serif; padding: 15px;">
<h3 style="margin: 0 0 5px 0; color: teal; font-size: 22px;">
Top {len(metrics_df)} Assemblies by $ value</h3>
<p style="margin: 5px 0; font-size: 14px;">
<strong>Related Backlog [{formatted_date}]:</strong><br>
<span style="margin-left: 20px;">• <strong>Past Due:</strong> ${total_past_due_value/1_000:,.0f}K ({(total_past_due_value/total_past_due_backlog*100 if total_past_due_backlog > 0 else 0):.1f}% of total past due)</span><br>
<span style="margin-left: 20px;">• <strong>Total Backlog:</strong> ${total_value/1_000:,.0f}K</span>
</p>
</div>
"""
# Determine column name based on selected_program
index_column = 'Pty Indice' if selected_program == 'Phase 4-5' else 'IDD Top Level'
# Ensure that index_column is in the DataFrame before renaming
if index_column not in metrics_df.columns:
available_columns = list(metrics_df.columns)
raise ValueError(f"Expected column '{index_column}' not found in metrics_df. Available columns: {available_columns}")
# Prepare chart data (values in original units)
chart_data = metrics_df.rename(columns={
index_column: 'PN',
'Past Due Value': 'Past_Due',
'Total Value': 'Total'
})
chart_data['Percent'] = metrics_df['Percentage'].astype(str) + '%'
# Prepare table data with additional columns for hover when Phase 4-5
table_data = chart_data[['PN', 'Past_Due', 'Total']].copy()
table_data['% Past Due'] = (chart_data['Past_Due'] / total_past_due_backlog * 100).round(1)
# Add IDD Top Level and SEDA Top Level columns if Phase 4-5
if selected_program == 'Phase 4-5':
table_data['IDD Top Level'] = metrics_df['IDD Top Level']
table_data['SEDA Top Level'] = metrics_df['SEDA Top Level']
table_data.rename(columns={
'PN': 'Pty' if selected_program == 'Phase 4-5' else 'IDD PN',
'Past_Due': 'Past Due ($K)',
'Total': 'Total Backlog ($K)'
}, inplace=True)
# Format numeric columns
table_data['Past Due ($K)'] = table_data['Past Due ($K)'].apply(lambda x: f"${x/1_000:,.0f}K")
table_data['Total Backlog ($K)'] = table_data['Total Backlog ($K)'].apply(lambda x: f"${x/1_000:,.0f}K")
table_data['% Past Due'] = table_data['% Past Due'].apply(lambda x: f"{x:.1f}%")
# Create table with proper styling
if selected_program == 'Phase 4-5':
table_html = """
<style>
.custom-table {
text-align: center;
font-size: 12px;
margin: 0 auto;
border-collapse: collapse;
font-family: Arial, sans-serif;
}
.custom-table th {
padding: 8px;
background-color: #f5f5f5;
border-bottom: 1px solid #ddd;
}
.custom-table td {
padding: 8px;
border-bottom: 1px solid #eee;
}
.custom-table td:hover {
background-color: #f9f9f9;
cursor: pointer;
}
</style>
<table class='custom-table'>
"""
table_html += '<tr>' + ''.join(f'<th>{col}</th>' for col in table_data.columns[:4]) + '</tr>'
for _, row in table_data.iterrows():
tooltip = f'IDD Top Level: {row["IDD Top Level"]}\nSEDA Top Level: {row["SEDA Top Level"]}'
table_html += '<tr>'
table_html += f'<td title="{tooltip}">{row[table_data.columns[0]]}</td>'
table_html += ''.join(f'<td>{row[col]}</td>' for col in table_data.columns[1:4])
table_html += '</tr>'
table_html += '</table>'
table_pane = pn.pane.HTML(table_html, sizing_mode='stretch_width')
else:
table_styles = {
'text-align': 'center',
'font-size': '12px',
'margin': '0 auto'
}
table_pane = pn.pane.DataFrame(table_data.iloc[:, :4],
index=False,
sizing_mode='stretch_width',
styles=table_styles)
# Create Bokeh chart with K-formatted Y-axis
source = ColumnDataSource(chart_data)
max_value = max(chart_data['Total'].max(), chart_data['Past_Due'].max())
y_upper = max_value * 1.4
p = figure(
x_range=chart_data['PN'].tolist(),
height=300,
width=400,
title=f"Top {min(num_pns, len(metrics_df))} Assemblies by Backlog Value",
tools="hover,reset",
toolbar_location=None,
background_fill_color='white',
y_range=(0, y_upper)
)
p.ygrid.grid_line_dash = 'dashed'
# Add bars
past_due = p.vbar(
x=dodge('PN', -0.1, range=p.x_range),
top='Past_Due',
width=0.2,
source=source,
fill_color='#e15759',
legend_label='$ Past Due',
alpha=0.8
)
total = p.vbar(
x=dodge('PN', 0.1, range=p.x_range),
top='Total',
width=0.2,
source=source,
fill_color='#4e79a7',
legend_label='$ Total',
alpha=0.8
)
# Configure hover tool with conditional label
hover_label = "Pty" if selected_program == 'Phase 4-5' else "IDD"
p.add_tools(HoverTool(
tooltips=[
(hover_label, "@PN"),
("Past Due", "@Past_Due{$0,0}"),
("Total", "@Total{$0,0}"),
("% of Total Past Due", "@Percent")
],
renderers=[past_due, total]
))
# Format Y-axis to show K suffix
p.yaxis.formatter = NumeralTickFormatter(format="$0,0K")
p.yaxis.axis_label = "Backlog Value ($K)"
p.xaxis.major_label_orientation = 45
p.xgrid.grid_line_color = None
p.legend.location = "top_left"
p.legend.click_policy = "hide"
p.legend.orientation = "horizontal"
# Create the final layout with outer border only
return pn.Column(
pn.pane.HTML(html_content, sizing_mode='stretch_width'),
pn.Spacer(height=20),
table_pane,
pn.Spacer(height=40),
pn.pane.Bokeh(p, sizing_mode='stretch_width'),
styles={
'border': '1px solid #e0e0e0',
'border-radius': '5px',
'padding': '10px'
}
)
except Exception as e:
error_msg = f"Error displaying critical assemblies: {str(e)}"
print(error_msg)
return pn.Column(
pn.pane.Alert(error_msg, alert_type="danger"),
sizing_mode='stretch_width'
)
#------------------------------------------------------------------------------------------------------------------
#------------------------------------------------------------------------------------------------------------------
# 04/01 - Create new Graph 'Monthly Engineering Hours per Team' from df_SciformaReport_Team
#------------------------------------------------------------------------------------------------------------------
df_SciformaReport_Team = pd.read_excel(input_file_formatted, sheet_name='SciformaReport_Team', index_col=False)
# Keep the years in the usual color scheme define in year_color_map and use a pattern for each function define in function_patterns
# Second step to be included later (data is missing currently): Create 2 bars for each month 'Planned' vs 'Realized' in the same pattern an colors with 'Planned' in alpha 0.5
#Combine 'Program Manager' and 'Manager' under 'Manager'
df_SciformaReport_Team['Function'] = df_SciformaReport_Team['Function'].replace({'Program Manager': 'Manager'})
#print('df_SciformaReport_Team:')
#display(df_SciformaReport_Team)
#------------------------------------------------------------------------------------------------------------------
# 04/07
def create_monthly_engineering_hours_graph(df_SciformaReport_Team_filtered, selected_program):
# Define pastel colors for each team
team_colors = {
"Mechanical Engineer": "#debbd4", #
"Electrical Engineer": "#B0E0E6", # Powder Blue
"System Engineer": "#98FB98", # Pale Green
"Mechanical Drafter": "#DDA0DD", # Plum
"Sustaining Engineer": "#93c7b2", #
"Manufacturing Engineer": "#E6E6FA",# Lavender
"Lightplate Engineer": "#FFA07A", # Light Salmon
"Project Engineer": "#B0C4DE", # Light Steel Blue
"Manager": "#F5DEB3" # Wheat
}
# Data Preparation
df = df_SciformaReport_Team_filtered.copy()
# Identify the month columns (all columns that look like 'YYYY-MM')
month_columns = [col for col in df.columns if col.startswith(('2024', '2025'))]
if not month_columns: # Handle case where no month columns are found
month_columns = []
month_columns = sorted(month_columns, key=lambda x: int(x.split('-')[0]), reverse=False)
# Check if required columns exist
if not all(col in df.columns for col in ['Program', 'Function']) or not month_columns:
p = figure(
x_range=["No Data"],
title=f"Monthly Engineering Hours per Team - Program: {selected_program}",
width=1200,
height=650,
tools='pan,wheel_zoom,box_zoom,reset,save',
toolbar_location='right',
x_axis_label='Month',
y_axis_label='Engineering Hours',
output_backend='svg'
)
p.text(
x=[0],
y=[50],
text=["Invalid or missing data in DataFrame"],
text_align="center",
text_font_size="12pt"
)
p.y_range = Range1d(0, 100)
p.xgrid.visible = False
p.ygrid.grid_line_color = "#E0E0E0"
p.ygrid.grid_line_dash = [6, 4]
p.toolbar.logo = None
return p
# Reshape the DataFrame
df = df.melt(
id_vars=['Program', 'Function'],
value_vars=month_columns,
var_name='Month',
value_name='Hours'
)
# Convert 'Month' to datetime format
df['Month'] = pd.to_datetime(df['Month'], format='%Y-%m')
df['YearMonth'] = df['Month'].dt.to_period('M')
# Aggregate data
monthly_data = df.groupby(['YearMonth', 'Function', 'Month'])['Hours'].sum().reset_index()
# Generate display values
monthly_data['Month_Year'] = monthly_data['Month'].dt.strftime('%b %Y')
# Assign colors by team
monthly_data['Color'] = monthly_data['Function'].map(lambda x: team_colors.get(x, '#D3D3D3')) # Default to light gray
monthly_data['BarAlpha'] = monthly_data['Hours'].apply(lambda x: 0.7 if x > 0 else 0.0)
# Sort data by month
monthly_data = monthly_data.sort_values('Month')
# Handle empty data case
unique_functions = sorted(monthly_data['Function'].unique())
if len(unique_functions) == 0:
p = figure(
x_range=["No Data"],
title=f"Monthly Engineering Hours per Team - Program: {selected_program}",
width=1200,
height=650,
tools='pan,wheel_zoom,box_zoom,reset,save',
toolbar_location='right',
x_axis_label='Month',
y_axis_label='Engineering Hours',
output_backend='svg'
)
p.text(
x=[0],
y=[50],
text=["No engineering hours data available for the selected filters"],
text_align="center",
text_font_size="12pt"
)
p.y_range = Range1d(0, 100)
p.xgrid.visible = False
p.ygrid.grid_line_color = "#E0E0E0"
p.ygrid.grid_line_dash = [6, 4]
p.toolbar.logo = None
return p
# Create ColumnDataSource
source = ColumnDataSource(monthly_data)
# Unique months for x-axis
unique_months = sorted(monthly_data['Month_Year'].unique(),
key=lambda x: pd.to_datetime(x, format='%b %Y'))
# Create figure
p = figure(
x_range=unique_months,
title=f"Monthly Engineering Hours per Team - Program: {selected_program}",
width=1200,
height=650,
tools='pan,wheel_zoom,box_zoom,reset,save',
toolbar_location='right',
x_axis_label='Month',
y_axis_label='Engineering Hours',
output_backend='svg'
)
# Set grid and y-range
p.ygrid.grid_line_color = "#E0E0E0"
p.ygrid.grid_line_dash = [6, 4]
p.xgrid.visible = False
# X-axis font size
p.xaxis.major_label_text_font_size = "12pt"
p.xaxis.major_label_text_font_style = "bold"
# Title text font
p.title.text_font_size = "14pt"
if not monthly_data.empty:
max_hours = monthly_data['Hours'].max()
p.y_range = Range1d(0, max_hours * 1.3)
else:
p.y_range = Range1d(0, 100)
# Calculate bar width and offsets
bar_width = 0.8 / len(unique_functions)
offsets = {func: (i - (len(unique_functions) - 1) / 2) * bar_width for i, func in enumerate(unique_functions)}
# Create bars
renderers = []
team_legend_items = []
used_functions = set()
for function in unique_functions:
func_data = monthly_data[monthly_data['Function'] == function]
func_source = ColumnDataSource(func_data)
color = team_colors.get(function, '#D3D3D3')
offset = offsets[function]
bar = p.vbar(
x=dodge('Month_Year', offset, range=p.x_range),
width=bar_width,
top='Hours',
source=func_source,
color=color,
alpha=0.7,
name=function
)
if function not in used_functions:
team_legend_items.append((function, [bar]))
used_functions.add(function)
renderers.append(bar)
# Add hover tool
hover = HoverTool(
tooltips=[
("Month", "@Month_Year"),
("Function", "@Function"),
("Hours", "@Hours{0,0}"),
],
mode='vline',
renderers=renderers
)
p.add_tools(hover)
# Create team legend only
team_legend = Legend(
items=team_legend_items,
title="Team",
location="top_center",
label_text_font_size="10pt",
background_fill_color=None,
background_fill_alpha=0.0,
border_line_color=None,
click_policy="hide",
ncols=3,
spacing=5,
margin=5
)
p.add_layout(team_legend, 'center')
p.min_border_left = 100
p.min_border_right = 100
p.xaxis.major_label_orientation = 1.2
p.yaxis.formatter = NumeralTickFormatter(format="0,0")
p.toolbar.logo = None
return p
#------------------------------------------------------------------------------------------------------------------
#------------------------------------------------------------------------------------------------------------------
# 04/09 - Create new Graph 'create_monthly_engineering_cumulative_hours_graph' from df_SciformaReport and include the nubmer of ECO closed per month from df_AccessDB
#------------------------------------------------------------------------------------------------------------------
# 04/11 - Exclude 'Manager' hours from df_SciformaReport_Team
#--------------------------------------------------------------
#print('df_SciformaReport_Team')
#display(df_SciformaReport_Team)
#print('df_SciformaReport')
#display(df_SciformaReport)
#--------------------------------------------------------------
# Step 1: Calculate Manager hours from df_SciformaReport_Team
df_manager = df_SciformaReport_Team[df_SciformaReport_Team['Function'] == 'Manager'].copy()
month_columns = [col for col in df_manager.columns if col.startswith('202')]
if df_manager.empty or not month_columns:
print("Warning: No Manager data or valid month columns in df_SciformaReport_Team.")
df_manager_grouped = pd.DataFrame(columns=['Program', 'YearMonth', 'Manager_Hours'])
else:
# Melt Manager data
df_manager_melted = df_manager.melt(id_vars=['Program'], value_vars=month_columns,
var_name='YearMonth', value_name='Manager_Hours')
df_manager_melted['YearMonth'] = pd.to_datetime(df_manager_melted['YearMonth'], format='%Y-%m')
# Group by Program and YearMonth to sum Manager hours
df_manager_grouped = df_manager_melted.groupby(['Program', 'YearMonth'])['Manager_Hours'].sum().reset_index()
# Step 2: Prepare df_SciformaReport
df_hours = df_SciformaReport.copy()
month_columns = [col for col in df_hours.columns if col.startswith('202')]
if not month_columns:
print("Warning: No valid month columns in df_SciformaReport.")
elif df_hours[month_columns].isna().all().all():
print("Warning: df_SciformaReport contains only NaN values for hours.")
else:
# Melt df_SciformaReport to get hours per month
df_hours_melted = df_hours.melt(id_vars=['Program'], value_vars=month_columns,
var_name='YearMonth', value_name='Hours')
df_hours_melted['YearMonth'] = pd.to_datetime(df_hours_melted['YearMonth'], format='%Y-%m')
# Remove NaN hours and programs
df_hours_melted = df_hours_melted.dropna(subset=['Hours', 'Program'])
# Group by Program and YearMonth to sum hours (handles duplicates)
df_hours_grouped = df_hours_melted.groupby(['Program', 'YearMonth'])['Hours'].sum().reset_index()
# Step 3: Subtract Manager hours for corresponding Program
df_hours_grouped = df_hours_grouped.merge(df_manager_grouped, on=['Program', 'YearMonth'], how='left').fillna(0)
df_hours_grouped['Hours'] = df_hours_grouped['Hours'] - df_hours_grouped['Manager_Hours']
df_hours_grouped['Hours'] = df_hours_grouped['Hours'].clip(lower=0)
# Step 4: Pivot to wide format
df_hours_pivoted = df_hours_grouped.pivot(index='Program', columns='YearMonth', values='Hours').reset_index()
# Ensure all month columns exist
for col in month_columns:
col_dt = pd.to_datetime(col, format='%Y-%m')
if col_dt not in df_hours_pivoted.columns:
df_hours_pivoted[col_dt] = 0
# Rename columns to YYYY-MM format
df_hours_pivoted.columns = ['Program'] + [col.strftime('%Y-%m') if isinstance(col, pd.Timestamp) else col for col in df_hours_pivoted.columns[1:]]
# Reorder columns to match original
df_SciformaReport = df_hours_pivoted[['Program'] + month_columns]
# Ensure non-negative hours
df_SciformaReport[month_columns] = df_SciformaReport[month_columns].clip(lower=0)
# Warn if output is empty
if df_SciformaReport.empty or df_SciformaReport[month_columns].isna().all().all():
print("Warning: No valid hours remain after processing.")
#print('df_SciformaReport, excluding Manager')
#display(df_SciformaReport)
#----------------------------------------------------------------------------------------
# 04/14
def create_monthly_cumulative_engineering_hours_with_ECO(df_SciformaReport_filtered, df_AccessDB_filtered, selected_program, year_color_map):
# Prepare Engineering Hours data
df_hours = df_SciformaReport_filtered.copy()
month_columns = [col for col in df_hours.columns if col.startswith('202')]
df_hours_melted = df_hours.melt(id_vars=['Program'], value_vars=month_columns,
var_name='YearMonth', value_name='Hours')
df_hours_melted['YearMonth'] = pd.to_datetime(df_hours_melted['YearMonth'], format='%Y-%m')
df_hours_grouped = df_hours_melted.groupby('YearMonth').sum().reset_index()
df_hours_grouped['Month_Year'] = df_hours_grouped['YearMonth'].dt.strftime('%b %Y')
# Filter out months with no hours (empty data) from df_SciformaReport_filtered
df_hours_grouped = df_hours_grouped[df_hours_grouped['Hours'] > 0]
# Prepare ECO data
monthly_metrics = pd.DataFrame(columns=['Month_completed', 'Total_ECOs', 'Month_dt', 'Month_Year'])
if not df_AccessDB_filtered.empty and 'Month_completed' in df_AccessDB_filtered.columns:
try:
monthly_metrics = df_AccessDB_filtered.groupby('Month_completed').agg(
Total_ECOs=('ID', 'count')
).reset_index()
monthly_metrics['Month_dt'] = pd.to_datetime(monthly_metrics['Month_completed'], format='%b-%Y')
monthly_metrics = monthly_metrics.sort_values('Month_dt')
monthly_metrics['Month_Year'] = monthly_metrics['Month_dt'].dt.strftime('%b %Y')
except Exception as e:
print(f"Error processing ECO data: {e}")
# Merge the dataframes, keeping only months present in df_hours_grouped
df_combined = df_hours_grouped.merge(monthly_metrics[['Month_Year', 'Total_ECOs']],
on='Month_Year', how='left').fillna({'Total_ECOs': 0})
# Ensure YearMonth is still datetime after merge
df_combined['YearMonth'] = pd.to_datetime(df_combined['YearMonth'], errors='coerce')
# Sort the combined dataframe chronologically
df_combined['SortDate'] = pd.to_datetime(df_combined['Month_Year'], format='%b %Y')
df_combined = df_combined.sort_values('SortDate')
df_combined = df_combined.drop(columns=['SortDate']) # Clean up temporary column
# Handle case where df_combined is empty
if df_combined.empty:
print(f"No engineering hours data available for program: {selected_program}")
return pn.pane.Markdown(f"No engineering hours data for {selected_program}.")
# Apply existing year_color_map
df_combined['Year'] = df_combined['YearMonth'].dt.year
df_combined['Color'] = df_combined['Year'].map(year_color_map).fillna('gray')
# Calculate average hours
average_hours = df_combined['Hours'].mean()
# Create ColumnDataSource for the main data
source = ColumnDataSource(df_combined)
# Create a separate ColumnDataSource for ECO data, filtering out zero values
eco_data = df_combined[df_combined['Total_ECOs'] > 0]
eco_source = ColumnDataSource(eco_data)
# Create figure with sorted x_range, only including months with data
p = figure(
x_range=df_combined['Month_Year'].tolist(), # Only months with data
title=f"Cumulative Engineering Hours and ECO Released per Month - Program: {selected_program}",
width=1200,
height=700,
tools='pan,wheel_zoom,box_zoom,reset,save,hover',
toolbar_location='right',
y_range=(0, max(df_combined['Hours'].max(), 1) * 1.3), # Ensure y_range handles zero data
output_backend='svg'
)
# Primary axis (Hours) - Bars (render first)
bars = p.vbar(
x='Month_Year',
top='Hours',
width=0.4,
source=source,
color='Color',
y_range_name="default"
)
# Set the axis label for primary Y-axis
p.yaxis.axis_label = "Cumulative Hours Consumed"
# Create legend items for years (render bars for legend)
present_years = sorted(set(df_combined['Year'].unique()))
legend_years = [year for year in present_years if year in year_color_map]
renderers_by_year = {}
legend_items_years = []
for year in legend_years:
year_data = df_combined[df_combined['Year'] == year]
year_source = ColumnDataSource(year_data)
year_bar = p.vbar(
x='Month_Year',
top='Hours',
width=0.4,
source=year_source,
color=year_color_map[year],
visible=True
)
legend_items_years.append((str(year), [year_bar]))
renderers_by_year[year] = year_bar
# Average hours line (render after bars) - Use Span to span full width
average_line = Span(
location=average_hours,
dimension='width',
line_color='#7030A0',
line_dash=[6, 4], # Consistent dash pattern
line_width=2
)
p.add_layout(average_line)
# Proxy line for average hours in legend (not rendered on the plot)
avg_line_data = pd.DataFrame({
'x': [df_combined['Month_Year'].iloc[0], df_combined['Month_Year'].iloc[-1]],
'y': [0, 0] # y-value doesn't matter since it won't be rendered
})
avg_line_source = ColumnDataSource(avg_line_data)
avg_line_renderer = p.line(
x='x',
y='y',
source=avg_line_source,
line_color='#7030A0',
line_dash=[6, 4], # Match the dash pattern
line_width=2,
visible=True # Only used for legend, not rendered on plot
)
# Secondary axis (ECOs) - Only show for '1stTB' or 'Phase 4-5'
eco_line = None
eco_points = None
eco_legend_line = None
if selected_program in ('1stTB', 'Phase 4-5'):
max_ecos = df_combined['Total_ECOs'].max()
p.extra_y_ranges = {"changes": Range1d(start=0, end=max(10, max_ecos * 1.2))}
# Add secondary axis
secondary_axis = LinearAxis(
y_range_name="changes",
axis_label="ECOs Released",
axis_label_text_color="#4472C4",
)
p.add_layout(secondary_axis, 'right')
# Use full data source (no filtering) to ensure data is present
eco_source = ColumnDataSource(df_combined)
# ECO line
eco_line = p.line(
x='Month_Year',
y='Total_ECOs',
y_range_name="changes",
line_width=2,
color='#4472C4',
source=eco_source
)
# ECO points
eco_points = p.scatter(
x='Month_Year',
y='Total_ECOs',
y_range_name="changes",
size=10,
fill_color='white',
line_color='#4472C4',
line_width=2,
source=eco_source
)
# Proxy line for ECOs in legend
eco_legend_line = p.line(
x='Month_Year',
y='Total_ECOs',
y_range_name="changes",
line_width=2,
color='#4472C4',
source=eco_source,
visible=True
)
# Styling
p.ygrid.grid_line_color = "#E0E0E0"
p.ygrid.grid_line_dash = [6, 4]
p.xgrid.visible = False
p.xaxis.major_label_text_font_size = "12pt"
p.xaxis.major_label_text_font_style = "bold"
p.xaxis.major_label_orientation = 1.2
p.title.text_font_size = "14pt"
p.yaxis.axis_label_text_align = "center"
# Structure legend items with sections
final_legend_items = []
# "Years" section
final_legend_items.append(("Years", []))
final_legend_items.extend(legend_items_years)
# Spacer
final_legend_items.append((" " * 10, []))
# "Average hours" section
final_legend_items.append(("Average hours consumed on the period:", []))
final_legend_items.append((f'{average_hours:,.0f} Hours', [avg_line_renderer]))
# Spacer and "ECOs Released" section - only if program is '1stTB' or 'Phase 4-5'
if selected_program in ('1stTB', 'Phase 4-5'):
final_legend_items.append((" " * 10, []))
final_legend_items.append(("ECO Released", []))
final_legend_items.append(("", [eco_legend_line]))
# Add legend inside the plot
if final_legend_items:
legend = Legend(
items=final_legend_items,
label_text_font_size="10pt",
background_fill_alpha=0.7,
glyph_height=20,
glyph_width=20,
spacing=5,
orientation="horizontal",
click_policy="hide",
location=(50, 450),
border_line_color=None,
padding=5,
margin=10
)
p.add_layout(legend, 'center')
# Hover tool
hover = p.select_one(HoverTool)
hover.tooltips = [
("Month", "@Month_Year"),
("Cumulative Hours", "@Hours{0,0}"),
]
# Only include ECOs in hover if program is '1stTB' or 'Phase 4-5'
hover_renderers = [bars]
if selected_program in ('1stTB', 'Phase 4-5'):
hover.tooltips.append(("ECO Released", "@Total_ECOs"))
hover_renderers.extend([eco_line, eco_points]) # Include both line and points
hover.renderers = hover_renderers
p.toolbar.logo = None
return p
#------------------------------------------------------------------------------------------------------------------
#------------------------------------------------------------------------------------------------------------------
# 04/02 - Create new Graph 'create_engineering_monthly_ECO_KPI_Graph' from df_AccessDB_filtered
#------------------------------------------------------------------------------------------------------------------
# update 04/15
def create_engineering_monthly_ECO_KPI_Graph(df_AccessDB_filtered, selected_program='1stTB'):
# Handle empty DataFrame
if df_AccessDB_filtered.empty:
print(f"No data available for program: {selected_program}")
return pn.pane.HTML("<h3>No ECO data available for 1stTB</h3>")
# Verify Month_completed column
if 'Month_completed' not in df_AccessDB_filtered.columns:
raise ValueError("Required column 'Month_completed' not found in df_AccessDB_filtered")
# Filter out NaN Month_completed
df_AccessDB_filtered = df_AccessDB_filtered[df_AccessDB_filtered['Month_completed'].notna()]
if df_AccessDB_filtered.empty:
print("No data after removing NaN Month_completed")
return pn.pane.HTML("<h3>No valid ECO data available for 1stTB</h3>")
# Data preparation
monthly_metrics = df_AccessDB_filtered.groupby('Month_completed').agg(
Avg_Completion_Time=('Completion Time', 'mean'),
Total_Jobs=('ID', 'count')
).reset_index()
# Convert to datetime for proper sorting
monthly_metrics['Month_dt'] = pd.to_datetime(
monthly_metrics['Month_completed'],
format='%b-%Y'
)
# Sort chronologically and format for display
monthly_metrics = monthly_metrics.sort_values('Month_dt')
monthly_metrics['Month'] = monthly_metrics['Month_dt'].dt.strftime('%b-%Y')
monthly_metrics = monthly_metrics.drop(columns=['Month_dt'])
# Data validation
if monthly_metrics.empty:
raise ValueError("No valid data available for plotting")
#--------- New 05/22 -------------
# Determine the dynamic span based on the DataFrame data
start_date = pd.to_datetime(df_AccessDB_filtered['Date completed'].min()).strftime('%m/%d/%Y')
end_date = pd.to_datetime(df_AccessDB_filtered['Date completed'].max()).strftime('%m/%d/%Y')
title_span = f"{start_date} to {end_date}"
#---------------------------------
source = ColumnDataSource(monthly_metrics)
# Create figure
p = figure(
x_range=monthly_metrics['Month'].tolist(),
#width=2400, # 05/16
width=1200, # 05/16
height=700,
tools=["hover", "pan", "box_zoom", "wheel_zoom", "reset", "save"],
toolbar_location="right",
#title="Monthly ECO Performance - ECO released from Engineering vs. 30 days target",
title=f"Monthly ECO Performance - ECO released vs. 30 days target - ECO released from: {title_span}", # 05/22
output_backend='svg'
)
# Formatting
p.title.text_font_size = "14pt"
p.xaxis.major_label_orientation = 45
p.xaxis.major_label_text_font_size = "10pt"
p.ygrid.grid_line_alpha = 0.3
p.xgrid.grid_line_color = None
# X-axis font size 04/02
p.xaxis.major_label_text_font_size = "12pt"
p.xaxis.major_label_text_font_style = "bold"
# Title text font
p.title.text_font_size = "14pt"
# Bars for completion time
bars = p.vbar(
x='Month',
top='Avg_Completion_Time',
width=0.6,
color='#00B050',
alpha=0.7,
legend_label="Avg Completion Time (days)",
source=source,
)
# Primary Y-axis
p.y_range = Range1d(start=0, end=monthly_metrics['Avg_Completion_Time'].max() * 1.2)
p.yaxis.axis_label = "Average Completion Time (days)"
p.yaxis.axis_label_text_color = "#00B050"
# Secondary Y-axis
max_jobs = monthly_metrics['Total_Jobs'].max()
p.extra_y_ranges = {"changes": Range1d(start=0, end=max(10, max_jobs * 1.2))}
secondary_axis = LinearAxis(
y_range_name="changes",
axis_label="Quantity of ECO released",
axis_label_text_color="#4472C4"
)
p.add_layout(secondary_axis, 'right')
# Target line
target_line = Span(
location=30,
dimension='width',
line_color='#7030A0',
line_dash='dashed',
line_width=2
)
p.add_layout(target_line)
# Add a purple "30 days" label on the left y-axis
thirty_days_label = Label(
x=20, # pixels from the left edge
y=32,
x_units='screen', # Position based on screen pixels
y_units='data',
text="30 days",
text_color="#7030A0",
text_font_size="12px",
text_align="left",
angle=90, # Rotate text by 90 degrees
angle_units="deg" # Specify degrees
)
p.add_layout(thirty_days_label)
# Line for number of changes
change_line = p.line(
x='Month',
y='Total_Jobs',
y_range_name="changes",
line_width=2,
color='#4472C4',
legend_label="Quantity of ECO released",
source=source
)
# Points for number of changes
change_points = p.scatter(
x='Month',
y='Total_Jobs',
y_range_name="changes",
marker="circle",
size=10,
fill_color='white',
line_color='#548235',
source=source
)
# Legend
p.legend.location = "top_right"
p.legend.click_policy = "hide"
# Add dahsed line for legend entry
p.add_layout(target_line)
# Create a Line glyph to represent the dashed line in the legend
line_renderer = p.line([0, 1], line_color="#7030A0", line_dash="dashed", line_width=2)
# Create a legend item for the dashed line (90% target line)
legend_item = LegendItem(label="30-Day Target", renderers=[line_renderer])
# Add this legend item to the plot's legend
p.legend.items.append(legend_item)
# Hover tool
hover = p.select_one(HoverTool)
hover.tooltips = [
("Month", "@Month"),
("Avg Completion", "@Avg_Completion_Time{0.0} days"),
("Quantity of ECO released", "@Total_Jobs")
]
hover.formatters = {
'@Avg_Completion_Time': 'numeral',
'@Total_Jobs': 'numeral'
}
p.toolbar.logo = None
return p
#------------------------------------------------------------------------------------------------------------------
#------------------------------------------------------------------------------------------------------------------
# 05/16 - create_FTB_new_per_customer(df_AccessDB_filtered, selected_program)
#------------------------------------------------------------------------------------------------------------------
# Create a graph reprenting the number of ECO released per Customer using the exact same formating than create_customers_figure
# Datafram input is df_AccessDB_filtered 'Change#' represent every unique ECO released, 'Date completed' represent the completion date. 'Costumer' represent the customer
def create_FTB_new_per_customer(df_AccessDB_filtered, selected_program, logo_mapping):
"""
Creates a bar plot representing the number of ECOs released per customer with logos.
Parameters:
- df_AccessDB_filtered: DataFrame containing ECO data with 'Change#', 'Date completed', 'Costumer', and 'Program' columns
- selected_program: String representing the selected program name
- logo_mapping: Dictionary mapping customer names to logo URLs
Returns:
- p_eco: Figure showing ECO counts by customer
"""
# 1. Filter data for the selected program
df_filtered = df_AccessDB_filtered[df_AccessDB_filtered['Program'] == selected_program]
# 2. Aggregate ECO counts by customer
df_eco_summary = (
df_filtered.groupby('End Costumer')
.agg({'Change#': 'nunique'})
.reset_index()
.rename(columns={'Change#': 'ECO_Count', 'End Costumer': 'Customer'})
)
# 3. Calculate max value for plot scaling
max_eco_count = df_eco_summary['ECO_Count'].max()
# 4. Add logo URLs and offset
df_eco_summary['Logo'] = df_eco_summary['Customer'].map(logo_mapping)
logo_offset_eco = max(max_eco_count * 0.02, 2) # Reduced offset for closer logo placement
df_eco_summary['Logo_Offset_ECO'] = df_eco_summary['ECO_Count'] + logo_offset_eco
# 5. Create color palette
customers = df_eco_summary['Customer'].unique()
num_customers = len(customers)
base_palette = Category20[max(3, min(20, num_customers))]
pastel_colors = []
for color in base_palette[:num_customers]:
r = int(color[1:3], 16)
g = int(color[3:5], 16)
b = int(color[5:7], 16)
r = int(r + (255 - r) * 0.6)
g = int(g + (255 - g) * 0.6)
b = int(b + (255 - b) * 0.6)
pastel_colors.append(f'#{r:02x}{g:02x}{b:02x}')
customer_color_map = dict(zip(customers, pastel_colors))
df_eco_summary['Color'] = df_eco_summary['Customer'].map(customer_color_map)
# 6. Create data source for plot
source = ColumnDataSource(df_eco_summary)
# 7. Convert 'Date completed' to datetime and extract date range
df_filtered['Date completed'] = pd.to_datetime(df_filtered['Date completed'])
earliest_date = df_filtered['Date completed'].min().strftime('%b %Y')
latest_date = df_filtered['Date completed'].max().strftime('%b %Y')
# 8. Create ECO count plot
p_eco = figure(
x_range=df_eco_summary['Customer'].tolist(),
title=f"Total ECOs Released per Customer, Program: {selected_program} - [{earliest_date} to {latest_date}]",
x_axis_label='Customer',
y_axis_label='ECO Count',
tools="pan,wheel_zoom,save,reset",
y_range=(0, max_eco_count * 1.5),
height=700,
width=1200,
min_height=400,
output_backend='svg'
)
p_eco.vbar(
x='Customer',
top='ECO_Count',
width=0.4,
source=source,
fill_color='Color',
line_color='Color',
alpha=0.6
)
p_eco.add_tools(HoverTool(
tooltips=[('Customer', '@Customer'), ('ECO Count', '@ECO_Count{0,0}')]
))
# 9. Add logos to the plot
p_eco.add_glyph(
source,
ImageURL(
url='Logo',
x='Customer',
y='Logo_Offset_ECO',
anchor="center",
)
)
# 10. Apply common formatting
p_eco.title.text_font_size = "14pt"
p_eco.xaxis.major_label_text_font_size = '12pt'
p_eco.xaxis.major_label_text_font_style = "bold"
p_eco.xaxis.major_label_orientation = 1.2
p_eco.ygrid.grid_line_dash = [6, 4]
p_eco.xgrid.visible = False
p_eco.toolbar.logo = None
p_eco.yaxis.formatter = NumeralTickFormatter(format="0,0")
return p_eco
#------------------------------------------------------------------------------------------------------------------
# 04/04WIP - Create new Datafram 'create_wip_dataframe_dashboard' from df_Backlog and df_WIP
#------------------------------------------------------------------------------------------------------------------
# Starts with df_WIP and merge info from df_Backlog (sale price, potential NRE and FAI fess)
df_production_wip = df_WIP.copy()
# 04/18 - Keep data only for 'Site' = 100 (Redmond)
df_production_wip = df_production_wip[df_production_wip['Site'] == 100]
#print('df_Backlog:')
#display(df_Backlog.head())
# filtered_df_wip = filtered_df_wip[~filtered_df_wip['WO'].str.contains('NC', na=False)]
#------------------------------------------------------------------------------------------------------------------
# Updated 04/18
def create_wip_dataframe_dashboard(df_production_wip_filtered, df_Backlog_filtered, selected_program, df_Historic_dashboard_filtered):
filtered_df_wip = df_production_wip_filtered.copy()
df_Backlog_wip = df_Backlog_filtered.copy()
# Create date of report
date_report = pd.to_datetime(file_date)
formatted_date = date_report.strftime('%m-%d-%Y')
# Filter data
filtered_df_wip = filtered_df_wip[filtered_df_wip['Level'] == 0] # Filter on 'Level'
df_Backlog_wip = df_Backlog_wip[~df_Backlog_wip['Order'].str.contains('NC', na=False)]
filtered_df_wip = filtered_df_wip[~filtered_df_wip['WO'].str.contains('NC', na=False)]
backlog_first = df_Backlog_wip.groupby('IDD Top Level').first().reset_index()
backlog_first['Sales Price per Unit ($)'] = backlog_first.apply(
lambda row: row['Currency net amount'] / row['Backlog Qty'] if row['Backlog Qty'] != 0 else 0,
axis=1
)
price_map = backlog_first.set_index('IDD Top Level')[['Sales Price per Unit ($)', 'NRE Fee', 'FAI Fee']].to_dict()
filtered_df_wip['Sales Price per Unit ($)'] = filtered_df_wip['IDD Component'].map(price_map['Sales Price per Unit ($)']).fillna(0)
filtered_df_wip['NRE Fee'] = filtered_df_wip['IDD Component'].map(price_map['NRE Fee']).fillna(0)
filtered_df_wip['FAI Fee'] = filtered_df_wip['IDD Component'].map(price_map['FAI Fee']).fillna(0)
filtered_df_wip['Sales value current WO ($)'] = filtered_df_wip['Sales Price per Unit ($)'] * filtered_df_wip['WO Qty']
# Handle empty DataFrame
if filtered_df_wip.empty:
processed_df = pd.DataFrame({
'Pty Indice': ['No Data'], 'WO': [''], 'WO Qty': [''], 'Last movement': [''], 'Work Ctr: ['']'
'Area': [''], 'IDD Component': [''], 'Customer PN': [''],
'Description Component': [''], 'Release': [''], 'Sales Price per Unit ($)': [''],
'Sales value current WO ($)': [''], 'NRE Fee': [''], 'FAI Fee': ['']
})
else:
# Parse dates
filtered_df_wip['Last movement'] = pd.to_datetime(filtered_df_wip['Last movement'], errors='coerce')
filtered_df_wip['Release'] = pd.to_datetime(filtered_df_wip['Release'], errors='coerce')
# Group by WO and get the most recent row
def select_most_recent(group):
return group.loc[group['Last movement'].idxmax()] if group['Last movement'].notna().any() else group.iloc[0]
filtered_df_wip = filtered_df_wip.groupby('WO', group_keys=False).apply(select_most_recent)
filtered_df_wip = filtered_df_wip.sort_values(by='Release')
# Format dates
filtered_df_wip['Last movement'] = filtered_df_wip['Last movement'].apply(
lambda x: x.strftime('%m-%d-%Y') if pd.notna(x) else ''
)
filtered_df_wip['Release'] = filtered_df_wip['Release'].apply(
lambda x: x.strftime('%m-%d-%Y') if pd.notna(x) else ''
)
# Handle missing column
if 'SEDA Top Level' in filtered_df_wip.columns:
filtered_df_wip = filtered_df_wip.rename(columns={'SEDA Top Level': 'Customer PN'})
else:
filtered_df_wip['Customer PN'] = filtered_df_wip['IDD Component']
filtered_df_wip = filtered_df_wip[[
'Pty Indice', 'WO', 'WO Qty', 'Last movement', 'Work Ctr', 'Area', 'IDD Component',
'Customer PN', 'Description Component', 'Release',
'Sales Price per Unit ($)', 'Sales value current WO ($)', 'NRE Fee', 'FAI Fee'
]]
filtered_df_wip['Customer PN'] = filtered_df_wip['Customer PN'].fillna('N/A')
filtered_df_wip = filtered_df_wip.sort_values(by=['Pty Indice'])
cols_to_round = ['Sales Price per Unit ($)', 'Sales value current WO ($)', 'NRE Fee', 'FAI Fee']
for col in cols_to_round:
filtered_df_wip[col] = pd.to_numeric(filtered_df_wip[col], errors='coerce').fillna(0)
filtered_df_wip[cols_to_round] = filtered_df_wip[cols_to_round].round(1)
processed_df = filtered_df_wip
# New Calculations for Summary Metrics
''' SAVED 04/17
# Total sale value on the floor (excluding 'Area' = 'Kitting' - Not case sensitive)
total_sales_excl_kitting = processed_df[processed_df['Area'].str.lower() != 'kitting']['Sales value current WO ($)'].sum()
# Total sale value released (kitting included) - already calculated
total_sales = processed_df['Sales value current WO ($)'].sum()
# Number of WO on the floor (excluding 'Area' = 'Kitting' - Not case sensitive)
unique_wos_excl_kitting = processed_df[processed_df['Area'].str.lower() != 'kitting']['WO'].nunique()
'''
#---- 04/17 -------------------------------------------
# New Calculations for Summary Metrics
# Ensure 'Area' is string and handle missing values
processed_df['Area'] = processed_df['Area'].fillna('').astype(str)
# Total sale value on the floor (excluding 'Area' = 'Kitting' - Not case sensitive)
total_sales_excl_kitting = processed_df[processed_df['Area'].str.lower() != 'kitting']['Sales value current WO ($)'].sum()
# Total sale value released (kitting included) - already calculated
total_sales = processed_df['Sales value current WO ($)'].sum()
# Number of WO on the floor (excluding 'Area' = 'Kitting' - Not case sensitive)
unique_wos_excl_kitting = processed_df[processed_df['Area'].str.lower() != 'kitting']['WO'].nunique()
#-------------------------------------------------------
# Number of WO released (kitting included)
unique_wos = processed_df['WO'].nunique()
# Total fees value
total_fees = processed_df['NRE Fee'].sum() + processed_df['FAI Fee'].sum()
# Number of unique assemblies
unique_assemblies = processed_df['Pty Indice'].nunique()
# Number of First Time Build/Prototype (WO containing 'F')
first_time_builds = processed_df[processed_df['WO'].str.contains('F', na=False)]['WO'].nunique()
# Shipment past couple weeks (2 weeks from file_date)
if df_Historic_dashboard_filtered is not None:
two_weeks_ago = date_report - pd.Timedelta(weeks=2)
recent_shipments = df_Historic_dashboard_filtered[
(pd.to_datetime(df_Historic_dashboard_filtered['Invoice date']) >= two_weeks_ago) &
(pd.to_datetime(df_Historic_dashboard_filtered['Invoice date']) <= date_report)
]['Qty Shipped'].sum()
else:
recent_shipments = 0 # Default if df_Historic_dashboard_filtered is not provided
# Updated Summary HTML
summary_html = f"""
<div style="text-align:left; font-size:16px; line-height:2; padding:30px;">
<b>Total sale value on the floor:</b> ${total_sales_excl_kitting:,.2f}<br>
<b>Total sale value released</b> (kitting included): ${total_sales:,.2f}<br>
<b>Number of WO on the floor:</b> {unique_wos_excl_kitting} WO<br>
<b>Number of WO released </b> (kitting included): {unique_wos} WO<br>
<b>Total fees value on the floor:</b> ${total_fees:,.2f}<br>
<b>Number of unique assemblies:</b> {unique_assemblies}<br>
<b>Number of First Time Build WO/Prototype:</b> {first_time_builds} WO<br>
<hr style="border: 1px solid lightgray;">
<b>Shipment the past couple weeks:</b> {recent_shipments:,} units
</div>
"""
# Style the DataFrame for Panel display (unchanged)
styled_df = processed_df.style.set_properties(**{
'text-align': 'center',
'vertical-align': 'middle'
}).set_table_styles([
{'selector': 'th', 'props': [('text-align', 'center'), ('vertical-align', 'middle')]}
]).format({
'Sales Price per Unit ($)': '{:.1f}',
'Sales value current WO ($)': '{:.1f}',
'NRE Fee': '{:.1f}',
'FAI Fee': '{:.1f}'
}).hide(axis='index')
# Save Button with Excel Formatting (unchanged)
def save_to_excel(event):
save_button.loading = True
try:
if not selected_program:
raise ValueError("No program selected.")
filename = f"{selected_program}_Top-Level_WIP-Redmond_{formatted_date}.xlsx"
save_path = os.path.join(os.getcwd(), filename)
wb = Workbook()
ws = wb.active
ws.title = "WIP Dashboard"
for r in dataframe_to_rows(processed_df, index=False, header=True):
ws.append(r)
font_color_black = '000000'
alignment = Alignment(horizontal="center", vertical="center")
header_fill = PatternFill(start_color='4472C4', end_color='4472C4', fill_type='solid')
header_font = Font(color='FFFFFF', bold=True)
thin_border = Border(left=Side(style="thin"),
right=Side(style="thin"),
top=Side(style="thin"),
bottom=Side("thin"))
for cell in ws[1]:
cell.fill = header_fill
cell.font = header_font
cell.alignment = alignment
cell.border = thin_border
for row in ws.iter_rows(min_row=2, max_row=ws.max_row, min_col=1, max_col=ws.max_column):
for cell in row:
cell.font = Font(color=font_color_black)
cell.alignment = alignment
cell.border = thin_border
for col in ws.columns:
column_letter = get_column_letter(col[0].column)
ws.column_dimensions[column_letter].width = 15
for col_letter in ['D', 'E', 'F', 'I']:
ws.column_dimensions[col_letter].width = 20
ws.column_dimensions['H'].width = 25
ws.auto_filter.ref = ws.dimensions
wb.save(save_path)
print(f"Table saved to {save_path}")
if hasattr(pn.state, 'notifications') and pn.state.notifications:
pn.state.notifications.success(f"Table saved: {filename}")
except Exception as e:
error_msg = f"❌ Save failed: {str(e)}"
print(error_msg)
if hasattr(pn.state, 'notifications') and pn.state.notifications:
pn.state.notifications.error(error_msg)
finally:
save_button.loading = False
save_button = pn.widgets.Button(name="Save to Excel", button_type="primary", width=200)
save_button.on_click(save_to_excel)
# Create Panel panes (unchanged)
table_pane = pn.pane.DataFrame(styled_df, width=1900, height=700)
summary_pane = pn.pane.HTML(summary_html, width=500, height=650)
Production_Dashboard_Dataframe = pn.Row(
table_pane,
pn.Column(
summary_pane,
save_button,
align="start"
),
align="center"
)
return Production_Dashboard_Dataframe
#------------------------------------------------------------------------------------------------------------------
#------------------------------------------------------------------------------------------------------------------
# 04/07 - Heatmap
#------------------------------------------------------------------------------------------------------------------
# uodate 04/11 to exclude 'Manager'
def create_monthly_engineering_hours_heatmap(df_SciformaReport_Team_filtered, selected_program):
# New 04/11 - Filter out rows where 'Function' is 'Manager'
df_SciformaReport_Team_filtered = df_SciformaReport_Team_filtered[df_SciformaReport_Team_filtered['Function'] != 'Manager']
# Data Preparation
df = df_SciformaReport_Team_filtered.copy()
df = df[df['Program'] == selected_program]
# Dynamically identify month columns in the format YYYY-MM using regex
month_columns = [col for col in df.columns if re.match(r'^\d{4}-\d{2}$', col)]
if not month_columns:
month_columns = []
# Sort month columns chronologically
month_columns = sorted(month_columns, key=lambda x: x) # Sorts by string, which works for YYYY-MM format
if not all(col in df.columns for col in ['Program', 'Function']) or not month_columns:
p = figure(
title=f"Percentage of Engineering Hours by Team - Program: {selected_program}",
width=1200,
height=700,
tools='pan,wheel_zoom,box_zoom,reset,save',
toolbar_location='right',
output_backend='svg'
)
p.text(
x=[0], y=[50], text=["Invalid or missing data in DataFrame"],
text_align="center", text_font_size="12pt"
)
p.y_range = Range1d(0, 100)
p.xgrid.visible = False
p.ygrid.grid_line_color = "#E0E0E0"
p.ygrid.grid_line_dash = [6, 4]
p.toolbar.logo = None
return p
# Reshape the DataFrame
df = df.melt(
id_vars=['Program', 'Function'],
value_vars=month_columns,
var_name='Month',
value_name='Hours'
)
df['Month'] = pd.to_datetime(df['Month'], format='%Y-%m')
df['Month_Year'] = df['Month'].dt.strftime('%b %Y')
monthly_data = df.groupby(['Function', 'Month_Year'])['Hours'].sum().reset_index()
monthly_totals = monthly_data.groupby('Month_Year')['Hours'].sum().reset_index()
monthly_totals.rename(columns={'Hours': 'Total_Hours'}, inplace=True)
monthly_data = monthly_data.merge(monthly_totals, on='Month_Year')
monthly_data['Percentage'] = (monthly_data['Hours'] / monthly_data['Total_Hours']) * 100
monthly_data['Percentage'] = monthly_data['Percentage'].fillna(0)
if monthly_data.empty:
p = figure(
title=f"Percentage of Engineering Hours by Team - Program: {selected_program}",
width=1200,
height=700,
tools='pan,wheel_zoom,box_zoom,reset,save',
toolbar_location='right',
output_backend='svg'
)
p.text(
x=[0], y=[50], text=["No engineering hours data available for the selected program"],
text_align="center", text_font_size="12pt"
)
p.y_range = Range1d(0, 100)
p.xgrid.visible = False
p.ygrid.grid_line_color = "#E0E0E0"
p.ygrid.grid_line_dash = [6, 4]
p.toolbar.logo = None
return p
source = ColumnDataSource(monthly_data)
unique_months = sorted(monthly_data['Month_Year'].unique(),
key=lambda x: pd.to_datetime(x, format='%b %Y'))
unique_functions = sorted(monthly_data['Function'].unique())
p = figure(
x_range=unique_months,
y_range=unique_functions,
title=f"Percentage of Engineering Hours Consumed by Team - Program: {selected_program}",
width=1200,
height=700,
tools='pan,wheel_zoom,box_zoom,reset,save,hover',
toolbar_location='right',
x_axis_label='Month',
y_axis_label='Function',
output_backend='svg'
)
p.xgrid.grid_line_color = None
p.ygrid.grid_line_color = "#E0E0E0"
p.ygrid.grid_line_dash = [6, 4]
# X-axis font size
p.xaxis.major_label_text_font_size = "12pt"
p.xaxis.major_label_text_font_style = "bold"
# Title text font
p.title.text_font_size = "14pt"
# Custom pastel Viridis-like gradient (purple → blue → green → yellow)
pastel_viridis = [
"#E6E4EA", # Light pastel purple (0%)
"#D7D9E5", # Slightly lighter purple
"#C8CEE0", # Soft purple-blue
"#B9C3DB", # Muted blue
"#AAC8D6", # Pastel teal
"#BBDED6", # Light teal-green
"#CCE4D1", # Pastel green
"#DDE9CC", # Light green
"#EEEFC7", # Pale yellow-green
"#FFF5C2" # Soft pastel yellow (100%)
]
color_mapper = LinearColorMapper(palette=pastel_viridis, low=0, high=100)
p.rect(
x='Month_Year',
y='Function',
width=1,
height=1,
source=source,
fill_color=transform('Percentage', color_mapper),
line_color=None
)
color_bar = ColorBar(
color_mapper=color_mapper,
ticker=BasicTicker(desired_num_ticks=10),
label_standoff=12,
border_line_color=None,
location=(0, 0),
title="Percentage (%)"
)
p.add_layout(color_bar, 'right')
hover = HoverTool(
tooltips=[
("Month", "@Month_Year"),
("Function", "@Function"),
("Hours", "@Hours{0,0}"),
("Percentage", "@Percentage{0.1f}%")
]
)
p.hover.tooltips = hover.tooltips
p.xaxis.major_label_orientation = 1.2
p.min_border_left = 100
p.min_border_right = 100
p.toolbar.logo = None
return p
#------------------------------------------------------------------------------------------------------------------
#------------------------------------------------------------------------------------------------------------------
# 04/10 - Quality - Dataframe NC Received per Assembly
#------------------------------------------------------------------------------------------------------------------
df_Historic_complete = pd.read_excel(input_file_formatted, sheet_name='CM-Historic', index_col=False)
# Change 'Phase 4' or 'Phase 5' with 'Phase 4-5'
if 'Program' in df_Historic_complete.columns and 'Pty Indice' in df_Historic_complete.columns:
mask = (
df_Historic_complete['Program'].isin(['Phase 4', 'Phase 5']) &
~df_Historic_complete['Pty Indice'].str.contains('Phase5', na=False)
)
df_Historic_complete.loc[mask, 'Program'] = 'Phase 4-5'
#------------------------------------------------------------------------------------------------------------------
# 04/11 - Apply determine_category to create the column 'Product Category' in df_Historic_complete based on 'Description'
df_Historic_complete['Product Category'] = df_Historic_complete['Pty Indice'].apply(determine_category)
#------------------------------------------------------------------------------------------------------------------
def create_NC_received_per_assembly(df_Historic_complete_filtered, selected_program):
# Verify input dataframe
if df_Historic_complete_filtered is None or df_Historic_complete_filtered.empty:
print("Debug: df_Historic_complete_filtered is None or empty")
p = pn.Column(
pn.pane.HTML(f"""
<h3 style='text-align: center; margin-bottom: 15px;'>
Program: {selected_program} - NC Received per Assembly
</h3>
<p style='text-align: center; color: red;'>
No data available for NC Received per Assembly
</p>
"""),
sizing_mode='stretch_width'
)
return p
# Create a copy of the dataframe
df = df_Historic_complete_filtered.copy()
# Check required columns
required_cols = ['Order', 'Quantity', 'Pty Indice', 'IDD Top Level', 'SEDA Top Level']
missing_cols = [col for col in required_cols if col not in df.columns]
if missing_cols:
raise KeyError(f"Missing required columns: {missing_cols}")
# Split data into NC and non-NC orders
df_nc = df[df['Order'].str.contains('NC', na=False)].copy()
df_non_nc = df[~df['Order'].str.contains('NC', na=False)].copy()
#print(f"Debug: NC orders count (before filtering negative quantities): {len(df_nc)}, Non-NC orders count: {len(df_non_nc)}")
# Filter out NC orders with negative quantities
df_nc = df_nc[df_nc['Quantity'] > 0]
#print(f"Debug: NC orders count (after filtering negative quantities): {len(df_nc)}")
# Calculate Qty NC received (only for positive quantities)
nc_received = df_nc.groupby(['Pty Indice', 'IDD Top Level', 'SEDA Top Level'])['Quantity'].sum().reset_index()
nc_received = nc_received.rename(columns={'Quantity': 'Qty NC received'})
# Calculate total Qty shipped from non-NC orders
qty_shipped = df_non_nc.groupby(['Pty Indice', 'IDD Top Level', 'SEDA Top Level'])['Quantity'].sum().reset_index()
qty_shipped = qty_shipped.rename(columns={'Quantity': 'Qty shipped'})
# Merge and calculate percentage
result_df = pd.merge(nc_received, qty_shipped, on=['Pty Indice', 'IDD Top Level', 'SEDA Top Level'], how='outer').fillna(0)
result_df['% NC received vs. Qty shipped'] = (result_df['Qty NC received'] / result_df['Qty shipped'] * 100).fillna(0).round(2)
result_df.loc[result_df['Qty shipped'] == 0, '% NC received vs. Qty shipped'] = 0
# Filter for rows where Qty NC received > 0
result_df = result_df[result_df['Qty NC received'] > 0]
# If no rows have NC > 0, return a "no data" message
if result_df.empty:
print("Debug: No assemblies with NC received > 0 after filtering")
p = pn.Column(
pn.pane.HTML(f"""
<h3 style='text-align: center; margin-bottom: 15px;'>
Program: {selected_program} - NC Received per Assembly
</h3>
<p style='text-align: center; color: red;'>
No assemblies with NC received for this program
</p>
"""),
sizing_mode='stretch_width'
)
return p
# Sort by Pty Indice
result_df = result_df.sort_values('Pty Indice')
# Rename 'Pty Indice' to 'pty'
result_df = result_df.rename(columns={'Pty Indice': 'pty'})
# Style the dataframe
styled_df = result_df.style.set_properties(**{
'text-align': 'center',
'vertical-align': 'middle'
}).set_table_styles([
{'selector': 'th', 'props': [('text-align', 'center'), ('vertical-align', 'middle')]}
]).format({
'Qty NC received': '{:,.0f}',
'Qty shipped': '{:,.0f}',
'% NC received vs. Qty shipped': '{:.2f}%'
}).hide(axis='index')
# Create the Panel layout
p = pn.Column(
pn.pane.HTML(f"""
<h3 style='text-align: center; margin-bottom: 15px;'>
Program: {selected_program} - NC Received per Assembly
</h3>
"""),
pn.pane.DataFrame(styled_df, width=1200, height=550),
sizing_mode='stretch_width'
)
return p
#------------------------------------------------------------------------------------------------------------------
##################################################################
# Card 'Monthly metrics - Previous month vs previous previous month
##################################################################
def calculate_monthly_metrics(df_Historic_dashboard_filtered):
current_date = pd.to_datetime('today')
current_month = current_date.month
current_year = current_date.year
# Calculate the month and year for the previous month
if current_month > 1:
previous_month = current_month - 1
previous_month_year = current_year
else:
previous_month = 12
previous_month_year = current_year - 1
# Calculate the month and year for the month before the previous month
if previous_month > 1:
two_months_ago = previous_month - 1
two_months_ago_year = previous_month_year
else:
two_months_ago = 12
two_months_ago_year = previous_month_year - 1
# Filter and aggregate data for the previous month
df_Previous_Month = df_Historic_dashboard_filtered[
(df_Historic_dashboard_filtered['Month'] == previous_month) &
(df_Historic_dashboard_filtered['Year'] == previous_month_year)
].agg({
'Qty Shipped': 'sum',
'Sales USD': 'sum'
}).to_dict()
# Filter and aggregate data for the month before the previous month
df_Two_Months_Ago = df_Historic_dashboard_filtered[
(df_Historic_dashboard_filtered['Month'] == two_months_ago) &
(df_Historic_dashboard_filtered['Year'] == two_months_ago_year)
].agg({
'Qty Shipped': 'sum',
'Sales USD': 'sum'
}).to_dict()
# Extract aggregated values
total_shipped_previous = int(df_Previous_Month['Qty Shipped'])
total_sales_previous = int(df_Previous_Month['Sales USD'])
total_shipped_two_months_ago = int(df_Two_Months_Ago['Qty Shipped'])
total_sales_two_months_ago = int(df_Two_Months_Ago['Sales USD'])
# Calculate percentage changes
if total_shipped_two_months_ago != 0:
pct_change_shipped = ((total_shipped_previous - total_shipped_two_months_ago) / total_shipped_two_months_ago) * 100
else:
pct_change_shipped = float('inf') if total_shipped_previous > 0 else float('-inf')
if total_sales_two_months_ago != 0:
pct_change_sales = ((total_sales_previous - total_sales_two_months_ago) / total_sales_two_months_ago) * 100
else:
pct_change_sales = float('inf') if total_sales_previous > 0 else float('-inf')
return {
'total_shipped_previous': total_shipped_previous,
'total_sales_previous': total_sales_previous,
'total_shipped_two_months_ago': total_shipped_two_months_ago,
'total_sales_two_months_ago': total_sales_two_months_ago,
'pct_change_shipped': pct_change_shipped,
'pct_change_sales': pct_change_sales
}
# update 03/20
def create_monthly_metrics_indicator(df_Historic_dashboard_filtered):
# Calculate the current date and determine the previous month and year
today = pd.to_datetime('today')
if today.month > 1:
previous_month = today.month - 1
previous_month_year = today.year
else:
previous_month = 12
previous_month_year = today.year - 1
# Calculate the month and year for two months ago
if previous_month > 1:
two_months_ago = previous_month - 1
two_months_ago_year = previous_month_year
else:
two_months_ago = 12
two_months_ago_year = previous_month_year - 1
# Convert month numbers to month names
month_names = ["January", "February", "March", "April", "May", "June", "July", "August", "September", "October", "November", "December"]
previous_month_name = month_names[previous_month - 1]
two_months_ago_name = month_names[two_months_ago - 1]
# Calculate metrics for the previous month compared to two months ago
metrics = calculate_monthly_metrics(df_Historic_dashboard_filtered)
# Determine trend colors and arrows
trend_color_shipped = "green" if metrics['pct_change_shipped'] >= 0 else "red"
trend_arrow_shipped = "▲" if metrics['pct_change_shipped'] >= 0 else "▼"
trend_color_sales = "green" if metrics['pct_change_sales'] >= 0 else "red"
trend_arrow_sales = "▲" if metrics['pct_change_sales'] >= 0 else "▼"
# Create HTML content with trend information
html_content = f"""
<div style="font-size: 20px; font-family: Arial, sans-serif; padding: 10px; border: 1px solid #ddd; border-radius: 5px; width: 100%; box-sizing: border-box;">
<h3 style="margin: 0; color: teal;">Monthly comparison - {previous_month_name} vs. {two_months_ago_name} {two_months_ago_year}</h3>
<p style="margin: 10px 0; font-size: 16px;">
<strong>Total Quantity Shipped {previous_month_name}</strong> (vs. {two_months_ago_name}): <span style="color: black;">{metrics['total_shipped_previous']:,.0f}</span>
<span style="font-size: 14px; color: {trend_color_shipped};">({trend_arrow_shipped} {metrics['pct_change_shipped']:+,.1f}%)</span>
</p>
<p style="margin: 10px 0; font-size: 16px;">
<strong>Total realized Sales {previous_month_name}</strong> (vs. {two_months_ago_name}): <span style="color: black;">${metrics['total_sales_previous']:,.0f}</span>
<span style="font-size: 14px; color: {trend_color_sales};">({trend_arrow_sales} {metrics['pct_change_sales']:+,.1f}%)</span>
</p>
</div>
"""
# Create layout with indicators and HTML content
layout = pn.Column(
pn.pane.HTML(html_content, sizing_mode='stretch_width'),
sizing_mode='stretch_width'
)
return layout
#/////////////////////////////////////////////////////////////
##############################################################
# Creation of Card and Layout
##############################################################
#/////////////////////////////////////////////////////////////
##############################################
# Defining color and dimensions for the cards
###############################################
# Define style properties for each card
# background_color = Header background color
# font_color = Header text font color
# width & height are the dimension of the card, and margin is the margin around the card (horizontal, vertical)
card_styles = {
"YoY Sales": {
"background_color": "#98c6e5",
"font_color": "white",
"width": 1200, # 03/28
"height": 700,
"margin": (5, 5),
"font_weight": "bold"
},
"YoY Shipments": {
"background_color": "#98c6e5",
"font_color": "white",
"width": 1200, # 03/28
"height": 700,
"margin": (5, 5),
"font_weight": "bold"
},
"Total Shipment per customer": {
"background_color": "#aee0d9",
"font_color": "white",
"width": 1200,
"height": 700,
"margin": (5, 5),
"font_weight": "bold"
},
"Total sales per customer": {
"background_color": "#aee0d9",
"font_color": "white",
"width": 1200,
"height": 700,
"margin": (5, 5),
"font_weight": "bold"
},
"Yearly KPI": {
"background_color": "#98c6e5",
"font_color": "white",
"width": 650,
"height": 200,
"margin": (5, 5),
"font_weight": "bold"
},
"Cumulative KPI": {
"background_color": "#aee0d9",
"font_color": "white",
"width": 650,
"height": 200,
"margin": (5, 5),
"font_weight": "bold"
},
"Monthly KPI": {
"background_color": "#a8c1a5",
"font_color": "white",
"width": 650,
"height": 200,
"margin": (5, 5),
"font_weight": "bold"
},
# 02/27 - New card
"Yearly Sales and Shipment": {
"background_color": "#98c6e5",
"font_color": "white",
"width": 650,
"height": 250,
"margin": (5, 5),
"font_weight": "bold"
},
# 03/04 - RMA card
"Yearly RMA": {
"background_color": "#98c6e5",
"font_color": "white",
"width": 650,
"height": 250,
"margin": (5, 5),
"font_weight": "bold"
},
# 03/04 - RMA raph
"Monthly RMA": {
"background_color": "#98c6e5",
"font_color": "white",
"width": 1200,
"height": 700,
"margin": (5, 5),
"font_weight": "bold"
},
# 03/19 - Backlog card
"Yearly KPI - Backlog": {
"background_color": "#98c6e5", # Use the same color as other yearly KPIs
"font_color": "white",
"width": 650,
"height": 250,
"margin": (5, 5),
"font_weight": "bold"
},
# 03/25 - Graph Sales and Fees
"Monthly Sales and Fees": {
"background_color": "#98c6e5",
"font_color": "white",
"width": 1200,
"height": 700,
"margin": (5, 5),
"font_weight": "bold"
},
# 03/27 - Card most critical backlog
"Backlog - Most Critical Assemblies": {
"background_color": "#ff6f61", # Reddish for criticality
"font_color": "white",
"width": 415,
"height": 700, # Increased height to accommodate graph
"margin": (5, 5),
"font_weight": "bold"
},
# 03/28 - Graph create_monthly_nre_fees_graph
"Monthly NRE Fees": {
"background_color": "#98c6e5",
"font_color": "white",
"width": 1200,
"height": 700, # Increased height to accommodate graph
"margin": (5, 5),
"font_weight": "bold"
},
# 04/01 - Graph create_monthly_engineering_hours_graph
"Monthly Engineering Hours Consumed per Team": {
"background_color": "#98c6e5",
"font_color": "white",
"width": 2400,
"height": 700, # Increased height to accommodate graph
"margin": (5, 5),
"font_weight": "bold"
},
# 04/02 - Graph create_monthly_engineering_cumulative_hours_graph
"Monthly Engineering Cumulative Hours Consumed": {
"background_color": "#98c6e5",
"font_color": "white",
"width": 1200,
"height": 700, # Increased height to accommodate graph
"margin": (5, 5),
"font_weight": "bold"
},
# 04/02 - Graph create_engineering_monthly_ECO_KPI_Graph # 05/16 update size
"Monthly ECO KPI": {
"background_color": "#98c6e5",
"font_color": "white",
"width": 1200,
"height": 700, # Increased height to accommodate graph
"margin": (5, 5),
"font_weight": "bold"
},
#04/04 - Table create_wip_dataframe_dashboard
"Production - Assemblies on the floor at IDD in Redmond": {
"background_color": "#98c6e5",
"font_color": "white",
"width": 2400,
"height": 700, # Increased height to accommodate graph
"margin": (5, 5),
"font_weight": "bold"
},
#04/07 - Table create_monthly_engineering_hours_heatmap
"Engineering - Heatmap": {
"background_color": "#98c6e5",
"font_color": "white",
"width": 1200,
"height": 700, # Increased height to accommodate graph
"margin": (5, 5),
"font_weight": "bold"
},
#04/10 - Table create_NC_received_per_assembly
"Quality - Quality - RMA Received per Assembly": {
"background_color": "#98c6e5",
"font_color": "white",
"width": 1200,
"height": 700, # Increased height to accommodate graph
"margin": (5, 5),
"font_weight": "bold"
},
#05/16 - Graph New Product Introduction
"Engineering - New Product Introduction": {
"background_color": "#98c6e5",
"font_color": "white",
"width": 1200,
"height": 700, # Increased height to accommodate graph
"margin": (5, 5),
"font_weight": "bold"
},
}
###############################################
# Defining function
###############################################
def create_card_indicators(card_title, panel_object, styles):
# Ensure the panel object has responsive sizing
panel_object.sizing_mode = 'stretch_both'
# Create inline HTML for the card header and card styles (border, round corners)
card_style = f"""
<style>
.custom-card {{
border: 2px solid {styles["background_color"]}; /* Border matching header background */
border-radius: 10px; /* Rounded corners for the card */
overflow: hidden; /* Ensure content fits within the card */
box-shadow: 2px 2px 10px rgba(0, 0, 0, 0.1); /* Add a subtle shadow for aesthetics */
}}
.custom-card-header {{
background-color: {styles["background_color"]}; /* Header background */
color: {styles["font_color"]}; /* Header font color */
padding: 10px;
font-size: 16px;
text-align: center;
font-weight: bold; /* Make text bold */
width: 100%;
border-top-left-radius: 10px; /* Rounded top corners */
border-top-right-radius: 10px; /* Rounded top corners */
}}
</style>
"""
header_html = f"<div class='custom-card-header'>{card_title}</div>"
# Create the card layout with the header and the panel object
card = pn.Column(
pn.pane.HTML(card_style + header_html), # Header with custom style
panel_object, # The panel object (e.g., plot or indicator)
width=styles["width"],
height=styles["height"],
sizing_mode='fixed',
margin=styles["margin"],
css_classes=['custom-card'] # Applying the custom card style
)
return card
#-----------------------------------------------------------------------------
# 02/27 - Define a function to calculate yearly metrics KPI
#-----------------------------------------------------------------------------
def calculate_yearly_sales_shipment(df_Historic_dashboard_filtered):
# Get the current year
current_year = pd.to_datetime('today').year
# Initialize a dictionary to store yearly metrics
KPI_yearly_metrics = {}
# Loop through the years (e.g., 2022, 2023, 2024)
for year in range(2022, current_year + 1):
# Filter data for the specific year
df_year = df_Historic_dashboard_filtered[df_Historic_dashboard_filtered['Year'] == year]
# Aggregate metrics
total_shipped = int(df_year['Qty Shipped'].sum())
total_sales = int(df_year['Sales USD'].sum())
# Store metrics in the dictionary
KPI_yearly_metrics[year] = {
'total_shipped': total_shipped,
'total_sales': total_sales
}
return KPI_yearly_metrics
def create_yearly_sales_shipment_card(df_Historic_dashboard_filtered):
# Calculate yearly metrics
KPI_yearly_metrics = calculate_yearly_sales_shipment(df_Historic_dashboard_filtered)
# Create HTML content for the card
html_content = """
<div style="font-size: 20px; font-family: Arial, sans-serif; padding: 10px; border: 1px solid #ddd; border-radius: 5px; width: 100%; box-sizing: border-box;">
<h3 style="margin: 0; color: teal;">Yearly Sales and Shipment</h3>
"""
# Add metrics for each year with color coding, excluding zero values
for year, metrics in KPI_yearly_metrics.items():
if metrics['total_shipped'] == 0 and metrics['total_sales'] == 0:
continue # Skip years with zero sales and shipment
# Get the color for the year from the year_color_map
year_color = year_color_map.get(year, 'black') # Default to black if year not in map
html_content += f"""
<p style="margin: 10px 0; font-size: 16px;">
<span style="color: {year_color}; font-weight: bold;">Year {year}:</span>
<strong>{metrics['total_shipped']:,.0f}</strong> PN shipped, <strong>${metrics['total_sales'] / 1000:,.1f}K</strong> of sales
</p>
"""
# Close the HTML content
html_content += "</div>"
# Create a layout with the HTML content
yearly_sales_shipment_layout = pn.Column(
pn.pane.HTML(html_content, sizing_mode='stretch_width'),
sizing_mode='stretch_width'
)
return yearly_sales_shipment_layout # Return the unique layout variable
#-----------------------------------------------------------------------------
#//////////////////////////////////////////////////////////////
################################################################
# Data update & Update cards
################################################################
#//////////////////////////////////////////////////////////////
# Function to update the cards when the data changes
''' SAVED 04/10
def update_cards(event):
# Update data and re-render the layout
new_layout = update_data_dashboard(event, file_date) # Get the updated layout from update_data_dashboard # 03/28 update_data_dashboard
cover_dashboard[-1] = new_layout # Replace the last item in the column with the updated layout
'''
def update_cards(event):
global file_date
# Ensure file_date is set
if 'file_date' not in globals() or file_date is None:
file_date = pd.to_datetime(datetime.now().strftime("%m-%d-%Y"))
print("Warning: file_date was not set in update_cards, defaulting to today:", file_date)
print('file_date:', file_date)
# Update data and re-render the layout
new_layout = update_data_dashboard(event, file_date)
cover_dashboard[-1] = new_layout
# Attach the update_cards function to the program selection widget
program_widget_List.param.watch(update_cards, 'value') # Added 10/21
# Attach update_cards to toggle changes
toggle_lightplate.param.watch(update_cards, 'value') # Watch for changes in toggle_lightplate
toggle_others.param.watch(update_cards, 'value') # Watch for changes in toggle_others
# Data update function - # updated 03/28
def update_data_dashboard(event, file_date):
global span_report_historic_dashboard # Access the global variable
selected_program = program_widget_List.value
#-------- Debugg 04/11 -----------------------
print(f"update_data_dashboard received file_date: {file_date} (type: {type(file_date)})")
#--------------------------------------------
# Ensure file_date is a datetime object --> file_date_datetime
file_date_datetime = pd.to_datetime(file_date)
# Define the 1-year span for '1stTB'
start_date = file_date_datetime - pd.Timedelta(days=365) if selected_program == '1stTB' else None
end_date = file_date_datetime if selected_program == '1stTB' else None
# Create copies of all dataframes to avoid modifying globals
df_Historic_dashboard_filtered = df_Historic_dashboard.copy()
df_Priority_dashboard_filtered = df_Priority_dashboard.copy()
df_Historic_NC_filtered = df_Historic_NC.copy()
df_Backlog_filtered = df_Backlog_KPI.copy()
df_Snapshot_KPI_filtered = df_Snapshot_KPI.copy()
df_SciformaReport_filtered = df_SciformaReport.copy()
df_SciformaReport_Team_filtered = df_SciformaReport_Team.copy()
df_production_wip_filtered = df_production_wip.copy()
df_Historic_complete_filtered = df_Historic_complete.copy()
df_AccessDB_filtered = df_AccessDB.copy()
# Apply 1-year filter for '1stTB' to specified dataframes
if selected_program == '1stTB':
# Filter df_Historic_dashboard_filtered (uses 'Invoice date')
df_Historic_dashboard_filtered = df_Historic_dashboard_filtered[
(df_Historic_dashboard_filtered['Invoice date'] >= start_date) &
(df_Historic_dashboard_filtered['Invoice date'] <= end_date)
]
# Filter df_Priority_dashboard_filtered (assumes 'Invoice date')
if 'Invoice date' in df_Priority_dashboard_filtered.columns:
df_Priority_dashboard_filtered = df_Priority_dashboard_filtered[
(df_Priority_dashboard_filtered['Invoice date'] >= start_date) &
(df_Priority_dashboard_filtered['Invoice date'] <= end_date)
]
else:
print("Warning: 'Invoice date' not found in df_Priority_dashboard_filtered. 1-year filter not applied.")
# Filter df_Historic_NC_filtered (uses 'Invoice date')
df_Historic_NC_filtered = df_Historic_NC_filtered[
(df_Historic_NC_filtered['Invoice date'] >= start_date) &
(df_Historic_NC_filtered['Invoice date'] <= end_date)
]
# Apply program-specific filtering
df_Historic_dashboard_filtered = df_Historic_dashboard_filtered[df_Historic_dashboard_filtered['Program'] == selected_program]
df_Priority_dashboard_filtered = df_Priority_dashboard_filtered[df_Priority_dashboard_filtered['Program'] == selected_program]
df_Historic_NC_filtered = df_Historic_NC_filtered[df_Historic_NC_filtered['Program'] == selected_program]
df_Backlog_filtered = df_Backlog_filtered[df_Backlog_filtered['Program'] == selected_program]
df_Snapshot_KPI_filtered = df_Snapshot_KPI_filtered[
(df_Snapshot_KPI_filtered['Program'] == selected_program) &
(df_Snapshot_KPI_filtered['Shipped'] > 0)
]
df_SciformaReport_filtered = df_SciformaReport_filtered[df_SciformaReport_filtered['Program'] == selected_program]
df_SciformaReport_Team_filtered = df_SciformaReport_Team_filtered[df_SciformaReport_Team_filtered['Program'] == selected_program]
df_production_wip_filtered = df_production_wip_filtered[df_production_wip_filtered['Program'] == selected_program]
df_Historic_complete_filtered = df_Historic_complete_filtered[df_Historic_complete_filtered['Program'] == selected_program]
df_AccessDB_filtered = df_AccessDB_filtered[df_AccessDB_filtered['Program'] == selected_program]
# Handle empty df_AccessDB_filtered case
if df_AccessDB_filtered.empty:
print(f"⚠️ No AccessDB data found for program: {selected_program}. Proceeding with empty DataFrame.")
df_AccessDB_filtered = pd.DataFrame() # or keep as-is if it’s already empty
# Update span_report_historic_dashboard based on filtered historic data
if not df_Historic_dashboard_filtered.empty:
older_date = df_Historic_dashboard_filtered['Invoice date'].min()
recent_date = df_Historic_dashboard_filtered['Invoice date'].max()
older_date_str = older_date.strftime('%m/%d/%Y')
recent_date_str = recent_date.strftime('%m/%d/%Y')
span_report_historic_dashboard = f"{older_date_str} - {recent_date_str}"
else:
span_report_historic_dashboard = "No data available after filtering"
# Merge df_Priority_dashboard_filtered with filtered historic data
df_Priority_dashboard_filtered = pd.merge(
df_Priority_dashboard_filtered,
df_Historic_dashboard_filtered[['Pty Indice', 'Sales USD', 'Qty Shipped']],
on='Pty Indice',
how='left'
)
df_Priority_dashboard_filtered.fillna(0, inplace=True)
df_Priority_dashboard_filtered['Qty Shipped'] = df_Priority_dashboard_filtered['Qty Shipped'].astype(int)
df_Priority_dashboard_filtered['Shipped'] = df_Priority_dashboard_filtered['Shipped'].astype(int)
# Apply toggle filters
df_Historic_dashboard_filtered = filter_dashboard(
df_Historic_dashboard_filtered, toggle_lightplate.value, toggle_others.value, 'Product Category'
)
df_Priority_dashboard_filtered = filter_dashboard(
df_Priority_dashboard_filtered, toggle_lightplate.value, toggle_others.value, 'Product Category'
)
df_Historic_NC_filtered = filter_dashboard(
df_Historic_NC_filtered, toggle_lightplate.value, toggle_others.value, 'Product Category'
)
df_Backlog_filtered = filter_dashboard(
df_Backlog_filtered, toggle_lightplate.value, toggle_others.value, 'Product Category'
)
df_Historic_complete_filtered = filter_dashboard(
df_Historic_complete_filtered, toggle_lightplate.value, toggle_others.value, 'Product Category'
)
# Update plots and indicators
return update_plots_and_indicators(
df_Historic_dashboard_filtered, df_Priority_dashboard_filtered, df_Historic_NC_filtered,
df_Backlog_filtered, df_Snapshot_KPI_filtered, selected_program, df_SciformaReport_filtered, df_SciformaReport_Team_filtered, df_production_wip_filtered, df_Historic_complete_filtered, df_AccessDB_filtered
)
#03/28 updated
# Attach the update function to the program selection widget
program_widget_List.param.watch(update_data_dashboard, 'value')
#-------------------------------------------------------------------------------------------------------------------
# updated 03/28 and 04/01
# Function to update the plots and indicators
# Function to update the plots and indicators
def update_plots_and_indicators(df_Historic_dashboard_filtered, df_Priority_dashboard_filtered, df_Historic_NC_filtered, df_Backlog_filtered, df_Snapshot_KPI_filtered, selected_program, df_SciformaReport_filtered, df_SciformaReport_Team_filtered, df_production_wip_filtered, df_Historic_complete_filtered, df_AccessDB_filtered):
# Create date of report
date_report = pd.to_datetime(file_date)
formatted_date = date_report.strftime('%m-%d-%Y')
# Create figures and indicators
yoy_sales_figure = create_yoy_sales_figure(df_Historic_dashboard_filtered, selected_program, year_color_map)
yoy_shipments_figure = create_yoy_shipments_figure(df_Historic_dashboard_filtered, selected_program, year_color_map)
#customers_shipment, customers_sales = create_customers_figure(df_Priority_dashboard_filtered, logo_mapping, logo_offset) # 10/21
customers_shipment, customers_sales = create_customers_figure(df_Priority_dashboard_filtered, logo_mapping, df_Historic_dashboard_filtered, selected_program)
yearly_metrics_indicator = create_yearly_metrics_indicator(df_Historic_dashboard_filtered)
since_inception_indicator = create_since_inception_indicator(df_Historic_dashboard_filtered, df_Snapshot_KPI_filtered)
monthly_metrics_indicator = create_monthly_metrics_indicator(df_Historic_dashboard_filtered)
# 02/27 - Create the new yearly sales and shipment card
yearly_sales_shipment_layout = create_yearly_sales_shipment_card(df_Historic_dashboard_filtered)
# 03/04 - Create the yearly RMA card
#yearly_rma_card = create_yearly_rma_card(df_Historic_NC_filtered, year_color_map)
yearly_sales_shipment_data = calculate_yearly_sales_shipment(df_Historic_dashboard_filtered) # New 03/17
yearly_rma_card = create_yearly_rma_card(df_Historic_NC_filtered, year_color_map, yearly_sales_shipment_data) # Update 03/17
# 03/04 - Create the monthly RMA graph
monthly_rma_graph = create_card_graph_RMA(df_Historic_NC_filtered, year_color_map)
# 03/19 - Create the new backlog card
backlog_card = create_backlog_card(df_Backlog_filtered, year_color_map, file_date)
# 03/20 - Create the since inception note
note_since_inception = pn.pane.HTML(
create_note_since_inception(df_Snapshot_KPI_filtered), # Call the function to generate the HTML note
#sizing_mode='stretch_width'
)
#03/25 - Graph sales and fees
sales_fees_graph = create_monthly_sales_fees_graph(df_Historic_dashboard_filtered, year_color_map, selected_program)
# 03/27 - Create the new critical assemblies card
critical_assemblies_layout = create_critical_assemblies_card(df_Backlog_filtered, file_date, selected_program)
# 03/28 - Create the Monthly NRE fees Graph
NRE_fees_graph = create_monthly_nre_fees_graph(df_Historic_dashboard_filtered, df_SciformaReport_filtered, df_AccessDB_filtered, year_color_map, selected_program)
# 04/01 - Create the MMonthly Engineering Hours Consumed per Team
Engineering_hours_graph = create_monthly_engineering_hours_graph(df_SciformaReport_Team_filtered, selected_program)
# Update 04/09 - Create the Monthly Engineering Hours
Engineering_cumulative_hours_graph = create_monthly_cumulative_engineering_hours_with_ECO(df_SciformaReport_filtered, df_AccessDB_filtered, selected_program, year_color_map)
#04/15 updated - Create Engineering_monthly_ECO_KPI
#Engineering_monthly_ECO_KPI_Graph = create_engineering_monthly_ECO_KPI_Graph(df_AccessDB_filtered, selected_program)
# Conditionally create ECO KPI graph only for '1stTB'
if selected_program == '1stTB':
Engineering_monthly_ECO_KPI_Graph = create_engineering_monthly_ECO_KPI_Graph(df_AccessDB_filtered, selected_program)
else:
Engineering_monthly_ECO_KPI_Graph = pn.pane.HTML("<h3>ECO KPI Graph only available for 1stTB</h3>")
#04/04 - Create Production_wip_dataframe
Production_wip_dataframe = create_wip_dataframe_dashboard(df_production_wip_filtered, df_Backlog_filtered, selected_program, df_Historic_dashboard_filtered)
#04/04 - Create Heatmap
Sciforma_Heatmap = create_monthly_engineering_hours_heatmap(df_SciformaReport_Team_filtered, selected_program)
#04/10 - Create create_NC_received_per_assembly dataframe
NC_received_per_assembly = create_NC_received_per_assembly(df_Historic_complete_filtered, selected_program)
#05/16 - create_FTB_new_per_customer
FTB_new_per_customer = create_FTB_new_per_customer(df_AccessDB_filtered, selected_program, logo_mapping)
# Create and display the cards with specified styles
card_yoy_sales = create_card_indicators("Sales - YoY Sales", yoy_sales_figure, card_styles["YoY Sales"])
card_yoy_shipments = create_card_indicators("Sales - YoY Shipments", yoy_shipments_figure, card_styles["YoY Shipments"])
card_customers_shipment = create_card_indicators(f"Sales - Total Shipment per customer - [{span_report_historic_dashboard}]", customers_shipment, card_styles["Total Shipment per customer"])
card_customers_sales = create_card_indicators(f"Sales - Total sales per customer - [{span_report_historic_dashboard}]", customers_sales, card_styles["Total sales per customer"])
card_yearly_metrics = create_card_indicators("Sales - Beginning current year up to today vs. same period year prior", yearly_metrics_indicator, card_styles["Yearly KPI"])
card_since_inception = create_card_indicators(f"Sales - Cumulative KPI - [{span_report_historic_dashboard}]", since_inception_indicator, card_styles["Cumulative KPI"])
card_monthly_metrics = create_card_indicators("Sales - Monthly KPI, Previous month vs. two month prior", monthly_metrics_indicator, card_styles["Monthly KPI"])
# 02/27 - Create the new yearly sales and shipment card
card_yearly_sales_shipment = create_card_indicators("Sales - Yearly KPI, Sales and Shipment", yearly_sales_shipment_layout, card_styles["Yearly Sales and Shipment"])
# 03/04 - Create the yearly RMA card
card_yearly_RMA = create_card_indicators("Quality - Yearly KPI", yearly_rma_card, card_styles["Yearly RMA"])
# 03/04 - Create the yearly RMA graph
card_monthly_RMA = create_card_indicators("Quality - Monthly KPI", monthly_rma_graph, card_styles["Monthly RMA"])
# 03/19
card_backlog = create_card_indicators("Backlog - Yearly KPI", backlog_card, card_styles["Yearly KPI - Backlog"])
# 03/25 - Create the card using your defined styles
sales_fees_card = create_card_indicators("Sales - Monthly Sales and Fees", sales_fees_graph, card_styles["Monthly Sales and Fees"])
# 03/27 - Create the new critical assemblies card
card_critical_assemblies = create_card_indicators("Backlog - Most Critical Assemblies", critical_assemblies_layout, card_styles["Backlog - Most Critical Assemblies"])
# 03/28 - Create the card for create_monthly_nre_fees_graph
card_NRE_Grah = create_card_indicators("Engineering - Monthly NRE Fees", NRE_fees_graph, card_styles["Monthly NRE Fees"])
# 04/01 - Create the card for create_monthly_engineering_hours_graph
card_EngineeringHours_Graph = create_card_indicators("Engineering - Monthly NRE Engineering Hours Consumed per Team", Engineering_hours_graph, card_styles["Monthly Engineering Hours Consumed per Team"])
# 04/02 - Create the card for create_monthly_engineering_hours_graph
card_EngineeringHours_Cumulative_Graph = create_card_indicators("Engineering - Monthly NRE Engineering Cumulative Hours Consumed", Engineering_cumulative_hours_graph, card_styles["Monthly Engineering Cumulative Hours Consumed"])
# 04/02 - Create the card for create_engineering_monthly_ECO_KPI
card_Engineering_monthly_ECO_KPI_Graph = create_card_indicators("Engineering - Monthly ECO KPI", Engineering_monthly_ECO_KPI_Graph, card_styles["Monthly ECO KPI"])
#04/04 - Create the card for create_wip_dataframe_dashboard # 04/08 updated
card_wip_dataframe = create_card_indicators(f"Production - Top-Level assemblies on the floor at IDD in Redmond - [{formatted_date}]", Production_wip_dataframe, card_styles["Production - Assemblies on the floor at IDD in Redmond"])
#04/07 - Create card heatmap
Card_Sciforma_Heatmap = create_card_indicators("Engineering - Heatmap", Sciforma_Heatmap, card_styles["Engineering - Heatmap"])
#04/10 - create card for
card_RMA_Received_per_assembly = create_card_indicators("Quality - RMA Received per Assembly", NC_received_per_assembly, card_styles["Quality - Quality - RMA Received per Assembly"])
#05/16 - Create for new Production Introduction
card_New_FTB = create_card_indicators("Engineering - New FTB per customer", FTB_new_per_customer, card_styles["Engineering - New Product Introduction"])
#05/16
dashboard_layout = pn.Row(
# Left column
pn.Column(
pn.Row(card_monthly_metrics, pn.Spacer(width=10), card_yearly_metrics, pn.Spacer(width=10), card_since_inception),
pn.Row(note_since_inception) if selected_program != '1stTB' else pn.Spacer(height=150),
pn.Spacer(height=20),
pn.Row(card_yearly_sales_shipment, pn.Spacer(width=10), card_yearly_RMA, pn.Spacer(width=10), card_backlog),
pn.Spacer(height=150),
pn.Row(card_yoy_sales, pn.Spacer(width=10), card_yoy_shipments),
pn.Spacer(height=30),
pn.Row(card_NRE_Grah, pn.Spacer(width=10), sales_fees_card), # card_EngineeringHours_Cumulative_Graph to be included
pn.Spacer(height=30+150), # 04/14 - Additionnal space need for the html text
pn.Row(card_EngineeringHours_Cumulative_Graph, pn.Spacer(width=10), Card_Sciforma_Heatmap),
pn.Spacer(height=30),
pn.Row(card_EngineeringHours_Graph),
pn.Spacer(height=30),
# Conditionally display card_monthly_RMA for all program exept '1stTB'
#pn.Row(card_Engineering_monthly_ECO_KPI_Graph if selected_program == '1stTB' else card_monthly_RMA, pn.Spacer(width=10), card_RMA_Received_per_assembly if selected_program != '1stTB' else None), #04/18
# ------ 05/16 ---------------
pn.Row(
card_Engineering_monthly_ECO_KPI_Graph if selected_program == '1stTB' else card_monthly_RMA,
pn.Spacer(width=10),
card_New_FTB if selected_program == '1stTB' else card_RMA_Received_per_assembly,
),
#--------------------------------------------------
pn.Spacer(height=30),
pn.Row(card_customers_shipment, pn.Spacer(width=10), card_customers_sales),
#-------- Only for program '1stTB' ----------------
#pn.Spacer(height=30), # 05/16
#pn.Row(card_New_FTB, , pn.Spacer(width=10), None), # 05/16
#--------------------------------------------------
pn.Spacer(height=30),
pn.Row(card_wip_dataframe),
width=2000,
height=850,
sizing_mode='fixed'
),
# Right column
pn.Column(
pn.Spacer(height=5),
card_critical_assemblies,
width=250,
height=850,
margin=(0, 0, 0, 0),
sizing_mode='fixed'
),
width=2400,
height=850,
sizing_mode='fixed',
margin=(0, 0, 0, 10)
)
return dashboard_layout
# 03/08 updated
# Manually trigger the first update to populate the dashboard initially
dashboard_layout = update_data_dashboard(None, file_date) # Call the function directly to get the initial layout
# Trigger the initial filter application when the dashboard loads
apply_filters(None)
#//////////////////////////////////////////////////////////////
###############################################################
# Creating Saving buttons
###############################################################
#//////////////////////////////////////////////////////////////
#---------------------------------------------------------------------
# Save the full interactive dashboard with server-side saving
#---------------------------------------------------------------------
#04/10WIP
def save_full_dashboard(event):
timestamp = datetime.now().strftime("%m-%d-%Y")
save_dashboard_button.loading = True
# Declare global variables for watchers only
global program_watcher, lightplate_watcher, others_watcher
# Initialize watchers as None if they don't exist
if 'program_watcher' not in globals():
program_watcher = None
if 'lightplate_watcher' not in globals():
lightplate_watcher = None
if 'others_watcher' not in globals():
others_watcher = None
# Define local_file_date explicitly as today’s date, no global check
local_file_date = pd.to_datetime(datetime.now().strftime("%m-%d-%Y"))
print(f"local_file_date set in save_full_dashboard: {local_file_date}")
try:
# Verify required global variables exist
required_globals = [
'program_widget_List',
'toggle_lightplate',
'toggle_others',
'unique_programs_List',
'cover_dashboard'
]
missing_vars = [var for var in required_globals if var not in globals()]
if missing_vars:
raise NameError(f"Required global variables not defined: {', '.join(missing_vars)}")
if not unique_programs_List:
raise ValueError("No programs available in unique_programs_List")
# Setup save directory and timestamp
base_filename = f"Full_dashboard_{timestamp}"
save_dir = os.path.join(os.getcwd(), "dashboards")
os.makedirs(save_dir, exist_ok=True)
toggle_combinations = [
(True, True),
(True, False),
(False, True),
(False, False)
]
saved_files = []
total_combinations = len(unique_programs_List) * len(toggle_combinations)
print(f"Generating {total_combinations} dashboard variations...")
# Temporarily disable existing watchers
for watcher, widget in [
(program_watcher, program_widget_List),
(lightplate_watcher, toggle_lightplate),
(others_watcher, toggle_others)
]:
if watcher is not None:
try:
widget.param.unwatch(watcher)
print(f"Detached watcher for {widget.name}")
except Exception as e:
print(f"Warning: Failed to remove watcher: {str(e)}")
try:
for program in unique_programs_List:
program_widget_List.value = program
print(f"Set program_widget_List.value to: {program}")
for lightplate_state, others_state in toggle_combinations:
toggle_lightplate.value = lightplate_state
toggle_others.value = others_state
print(f"Set toggle_lightplate.value to: {lightplate_state}, toggle_others.value to: {others_state}")
# Manually update the dashboard with local_file_date
new_layout = update_data_dashboard(None, local_file_date)
cover_dashboard[-1] = new_layout
current_layout = cover_dashboard[-1]
# Create static title
toggle_info = f"Lightplate: {'On' if lightplate_state else 'Off'}, Others: {'On' if others_state else 'Off'}"
static_title = pn.pane.HTML(f"""
<div style='background-color: {font_top_color}; width: 100%; padding: 10px;'>
<h1 style='font-size: 24px; color: white; text-align: left; margin: 0;'>
{program} Dashboard - {toggle_info}
</h1>
</div>
""", sizing_mode='stretch_width')
# Define widget section (static copy)
widget_section = pn.Row(
pn.widgets.Select(name="Programs", value=program, options=program_widget_List.options, disabled=True),
pn.Spacer(width=50),
pn.Column(
pn.Spacer(height=15),
pn.Row(
pn.widgets.Toggle(name="Lightplate", value=lightplate_state, disabled=True),
pn.Spacer(width=15),
pn.widgets.Toggle(name="Others", value=others_state, disabled=True),
pn.Spacer(width=10),
pn.pane.Markdown("Click to <span style='color: #226AB0;'><b>Include</b></span> / <span style='color: #D9D9D9;'><b>Exclude</b></span> from the Dashboard")
)
),
sizing_mode='stretch_width'
)
# Build static layout
static_layout = pn.Column(
static_title,
pn.layout.Divider(margin=(-10, 0, 0, 0)),
pn.Spacer(height=15),
widget_section,
pn.Spacer(height=20),
pn.layout.Divider(margin=(0, 0, -10, 0)),
current_layout,
sizing_mode='stretch_width'
)
# Save the static layout
filename = f"{base_filename}_{program}_L{'on' if lightplate_state else 'off'}_O{'on' if others_state else 'off'}.html"
save_path = os.path.join(save_dir, filename)
try:
static_layout.save(
save_path,
title=f"{program} Dashboard",
embed=False
)
if not os.path.exists(save_path):
raise IOError(f"File not created: {save_path}")
saved_files.append(filename)
print(f"Saved: {filename}")
except Exception as e:
print(f"Failed to save {filename}: {str(e)}")
continue
finally:
# Reattach watchers
try:
program_watcher = program_widget_List.param.watch(update_cards, 'value')
lightplate_watcher = toggle_lightplate.param.watch(update_cards, 'value')
others_watcher = toggle_others.param.watch(update_cards, 'value')
print("Watchers reattached successfully")
except Exception as e:
print(f"Warning: Failed to reattach watchers: {str(e)}")
success_msg = f"Saved {len(saved_files)} dashboard variations in {save_dir}"
if hasattr(pn.state, 'notifications') and pn.state.notifications:
pn.state.notifications.success(success_msg)
else:
print(success_msg)
except Exception as e:
error_msg = f"❌ Save failed: {str(e)}"
print(error_msg)
if hasattr(pn.state, 'notifications') and pn.state.notifications:
pn.state.notifications.error(error_msg)
finally:
save_dashboard_button.loading = False
#---------------------------------------------------------------------
# Save the current view as a static snapshot with server-side saving
#---------------------------------------------------------------------
def save_current_view(event):
save_view_button.loading = True
try:
selected_program = program_widget_List.value
if not selected_program:
raise ValueError("No program selected.")
# Get the current dashboard layout
current_layout = cover_dashboard[-1] # Last item is dashboard_layout
# Create the static title
toggle_info = f"Lightplate: {'On' if toggle_lightplate.value else 'Off'}, Others: {'On' if toggle_others.value else 'Off'}"
static_title = pn.pane.HTML(f"""
<div style='background-color: {font_top_color}; width: 100%; padding: 10px; box-sizing: border-box;'>
<h1 style='font-size: 24px; color: white; text-align: left; margin: 0;'>Transfer Project Dashboard - {selected_program}</h1>
</div>
""", sizing_mode='stretch_width')
# Define the widget section to match cover_dashboard
widget_section = pn.Row(
program_widget_List,
pn.Spacer(width=50),
pn.Column(
pn.Spacer(height=15),
pn.Row(
toggle_lightplate,
pn.Spacer(width=15),
toggle_others,
pn.Spacer(width=10),
pn.pane.Markdown("Click to <span style='color: #226AB0;'><b>Include</b></span> / <span style='color: #D9D9D9;'><b>Exclude</b></span> from the Dashboard")
)
),
sizing_mode='stretch_width'
)
# Redefine static_layout to include the widget section
static_layout = pn.Column(
static_title,
pn.layout.Divider(margin=(-10, 0, 0, 0), sizing_mode='stretch_width'),
pn.Spacer(height=15),
widget_section, # Include the widget section
pn.Spacer(height=20),
pn.layout.Divider(margin=(0, 0, -10, 0)),
current_layout,
sizing_mode='stretch_width'
)
# Save the static layout
timestamp = datetime.now().strftime("%m-%d-%Y")
filename = f"{selected_program}_Dashboard_{timestamp}.html"
save_path = os.path.join(os.getcwd(), filename)
static_layout.save(save_path, title=f"{selected_program} Dashboard", embed=False)
if hasattr(pn.state, 'notifications') and pn.state.notifications:
pn.state.notifications.success(f"Current view saved: {filename}")
except Exception as e:
error_msg = f"❌ Save failed: {str(e)}"
print(error_msg)
if hasattr(pn.state, 'notifications') and pn.state.notifications:
pn.state.notifications.error(error_msg)
finally:
save_view_button.loading = False
# Define buttons
save_dashboard_button = pn.widgets.Button(
name="Save Dashboard",
button_type="default",
styles={
'background-color': '#D3D3D3',
'color': 'black',
'border': '1px solid #A9A9A9',
'margin': '5px',
'padding': '5px 10px'
}
)
save_view_button = pn.widgets.Button(
name="Save Current View",
button_type="default",
styles={
'background-color': '#006400',
'color': 'white',
'border': '1px solid #004d00',
'margin': '5px',
'padding': '5px 10px'
}
)
# Attach event handlers
save_dashboard_button.on_click(save_full_dashboard)
save_view_button.on_click(save_current_view)
#//////////////////////////////////////////////////////////////
###############################################################
# Creating the overall layout with proper spacing and alignment
###############################################################
#//////////////////////////////////////////////////////////////
# Create the title section with buttons
title_section = pn.Row(
pn.pane.HTML(f"""
<div style='background-color: {font_top_color}; width: 100%; padding: 10px; box-sizing: border-box;'>
<h1 style='font-size: 24px; color: white; text-align: left; margin: 0;'>Transfer Project Dashboard</h1>
</div>
""", sizing_mode='stretch_width'),
pn.Spacer(sizing_mode='stretch_width'), # This will push the buttons to the right
save_dashboard_button,
pn.Spacer(width=10),
save_view_button,
align='center', # Vertically center the title and buttons
sizing_mode='stretch_width',
styles={'background': font_top_color}
)
# Main dashboard layout
cover_dashboard = pn.Column(
title_section,
pn.layout.Divider(margin=(-10, 0, 0, 0), sizing_mode='stretch_width'),
pn.Spacer(height=15),
pn.Row(
program_widget_List, # Existing widget layout
pn.Spacer(width=50), # Space before the toggles
pn.Column( # Use Column to stack Spacer and Row for toggles
pn.Spacer(height=15), # Empty line above the toggle buttons
pn.Row(
toggle_lightplate, # Toggle buttons in the same row
pn.Spacer(width=15), # Space between the buttons
toggle_others,
pn.Spacer(width=10),
pn.pane.Markdown("""
<span style='color: #000000;'>Click to </span><span style='color: #226AB0;'><b>Include</b></span><span style='color: #000000;'> / </span><span style='color: #D9D9D9;'><b>Exclude</b></span><span style='color: #000000;'> from the Dashboard</span>
""")
)
),
sizing_mode='stretch_width' # Ensure the row stretches to fill the width
),
pn.Spacer(height=20),
pn.layout.Divider(margin=(0, 0, -10, 0)),
dashboard_layout,
sizing_mode='stretch_width'
)
# Add CSS styling
pn.extension(notifications=True, raw_css=["""
.bk-root {
background-color: white !important;
}
button.bk-btn[data-name="Save Dashboard"] {
background-color: #D3D3D3 !important;
color: black !important;
border: 1px solid #A9A9A9 !important;
padding: 5px 10px !important;
min-width: 120px !important;
transition: background-color 0.2s ease !important;
}
button.bk-btn[data-name="Save Dashboard"]:hover {
background-color: #C0C0C0 !important;
}
button.bk-btn[data-name="Save Current View"] {
background-color: #006400 !important;
color: white !important;
border: 1px solid #004d00 !important;
padding: 5px 10px !important;
min-width: 120px !important;
transition: background-color 0.2s ease !important;
}
button.bk-btn[data-name="Save Current View"]:hover {
background-color: #008000 !important;
}
.bk-btn.bk-btn-toggle {
margin: 0 !important;
}
"""])
#-------------------------------------------------------------------------------------------------------------------
#||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
#||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
#*****************************************************************************************************************************
##############################################################################################################################
# Tab |Clear to Build summary|
##############################################################################################################################
#*****************************************************************************************************************************
#||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
# Load the data
df_Summary_Database = pd.read_excel(input_file_formatted, sheet_name='Summary', index_col=False)
# Create 'Program' within df_Summary_Database
df_Summary_Database['Program'] = df_Summary_Database['Pty Indice'].map(program_mapping)
# Clean unnecessary columns (do not drop 'IDD Top Level' and 'SEDA Top Level' yet)
#columns_to_drop = ['BOM_Index', 'Is_Make_Part', 'Parent IDD', 'Child IDDs', 'Top Level sharing Components']
columns_to_drop = ['BOM_Index', 'Is_Make_Part', 'Parent IDD', 'Child IDDs']
df_Summary_Database = df_Summary_Database.drop(columns=[col for col in columns_to_drop if col in df_Summary_Database.columns])
# Define max character length for the column 'Top Level sharing Components'
max_length = 150 # Adjust as needed
# Truncate the 'Top Level sharing Components' column
if 'Top Level sharing Components' in df_Summary_Database.columns:
df_Summary_Database['Top Level sharing Components'] = df_Summary_Database['Top Level sharing Components'].astype(str).apply(
lambda x: (x[:max_length] + '...') if len(x) > max_length else x
)
# Apply Phase 4-5 grouping to df_Summary_Database
if 'Program' in df_Summary_Database.columns:
phase_mask = (
df_Summary_Database['Program'].isin(['Phase 4', 'Phase 5']) &
~df_Summary_Database['Pty Indice'].str.contains('Phase5', na=False)
)
df_Summary_Database.loc[phase_mask, 'Program'] = 'Phase 4-5'
# Handle text formatting
acronyms = ['EDA', 'PCB', 'PWB', 'CPA', 'CPSL', 'ISP', 'TBD']
df_Summary_Database['Supplier'] = df_Summary_Database['Supplier'].astype(str).apply(lambda x: title_with_acronyms(x, acronyms))
df_Summary_Database['Description'] = df_Summary_Database['Description'].astype(str).apply(lambda x: title_with_acronyms(x, acronyms))
# Convert 'Max Qty Top-Level' to integer, coercing errors to NaN
df_Summary_Database['Max Qty Top-Level'] = pd.to_numeric(df_Summary_Database['Max Qty Top-Level'], errors='coerce')
# Convert 'Max Qty (GS)' to numeric and coerce errors to NaN
df_Summary_Database['Max Qty (GS)'] = pd.to_numeric(df_Summary_Database['Max Qty (GS)'], errors='coerce')
df_Summary_Database['Max Qty (GS)'] = df_Summary_Database['Max Qty (GS)'].apply(lambda x: '' if pd.isna(x) else int(x))
# If 'Level' column exists, set 'Qty On Hand' to empty string where Level == 0
if 'Level' in df_Summary_Database.columns and 'Qty On Hand' in df_Summary_Database.columns:
df_Summary_Database.loc[df_Summary_Database['Level'] == 0, 'Qty On Hand'] = ''
# Fill NaN values with an empty string and convert the column to string type
df_Summary_Database['Comment'] = df_Summary_Database['Comment'].fillna('').astype(str)
df_Summary_Database['Supplier'] = df_Summary_Database['Supplier'].fillna('').astype(str)
df_Summary_Database['Description'] = df_Summary_Database['Description'].fillna('').astype(str)
# Replace NaN, 'Nan', and 'nan' with an empty string
df_Summary_Database = df_Summary_Database.replace([np.nan, 'Nan', 'nan'], '')
#---------------------------------
# Styling Function
#---------------------------------
def style_summary_table(df):
if df.empty:
return pd.DataFrame().style # Return empty style if DataFrame is empty
styler = df.style.hide(axis='index') # Hide the index column
# Apply dark blue (#5B9BD5) to entire rows where Level = 0, except 'Max Qty Top-Level'
# Apply light blue (#00B0F0) to 'Max Qty Top-Level' column where Level = 0
if 'Level' in df.columns:
styler = styler.apply(
lambda row: [
f'background-color: #5B9BD5; color: white; font-weight: bold; text-align: center;'
if row['Level'] == 0 and col != 'Max Qty Top-Level'
else f'background-color: #00B0F0; color: white; font-weight: bold; text-align: center;'
if row['Level'] == 0 and col == 'Max Qty Top-Level'
else ''
for col in df.columns
],
axis=1
)
# Apply base styles: Center align all text
styler = styler.set_properties(**{
'text-align': 'center',
'vertical-align': 'middle'
})
# Override alignment for 'Top Level sharing Components' and 'Comment' columns
if 'Top Level sharing Components' in df.columns:
styler = styler.set_properties(subset=['Top Level sharing Components'], **{
'text-align': 'left', # Left-align this column
'vertical-align': 'middle'
})
if 'Comment' in df.columns:
styler = styler.set_properties(subset=['Comment'], **{
'text-align': 'left', # Left-align this column
'vertical-align': 'middle'
})
# Zero quantity styling
if 'Max Qty (GS)' in df.columns:
red_mask = df['Max Qty (GS)'] == 0
styler = styler.apply(
lambda row: ['background-color: #FFC7CE' if red_mask.loc[row.name] else '' for _ in row],
axis=1
)
# Supplier-based formatting
supplier_colors = {
'Make Part (Phantom)': '#D9E1F2',
'Make Part': '#D9E1F2',
'Floor Stock Item': '#E0E0E0',
'Make Part CUU': '#CCCCFF'
}
for supplier_pattern, color in supplier_colors.items():
mask = df['Supplier'].str.strip().str.lower() == supplier_pattern.strip().lower()
styler = styler.apply(
lambda row, mask=mask, color=color:
[f'background-color: {color}' if mask.loc[row.name] else '' for _ in row],
axis=1
)
# Status formatting
status_colors = {
'Clear-to-Build': '#C6EFCE',
'Completed - No Backlog': '#6FAC46',
'Not completed - No Backlog': '#ED7D31',
'Shortage': '#FFC7CE'
}
if 'Top-Level Status' in df.columns:
styler = styler.map(
lambda v: f'background-color: {status_colors.get(v, "")}',
subset=['Top-Level Status']
)
# Highlight 'Max Qty Top-Level' in light blue where 'Critical' is True
if 'Critical' in df.columns:
def highlight_critical(row):
return [
'background-color: #00B0F0; color: white' if col == 'Max Qty Top-Level' and row['Critical'] else ''
for col in df.columns
]
styler = styler.apply(highlight_critical, axis=1)
# Level-based coloring
if 'Level' in df.columns:
level_colors = {
0: '#63BE7B', 1: '#A2C075', 2: '#FFEB84',
3: '#FFD166', 4: '#F88E5B', 5: '#F8696B', 6: '#8B0000'
}
styler = styler.map(
lambda v: f'background-color: {level_colors.get(v, "transparent")}',
subset=['Level']
)
return styler
#---------------------------------
# Default values
#---------------------------------
# Default values
default_summary_program = 'Phase 4-5'
default_summary_priority = 6
default_summary_indice = 'P6'
# Filter functions
def filter_summary_priorities(program):
return sorted(df_Summary_Database[df_Summary_Database['Program'] == program]['Priority'].unique().tolist())
def filter_summary_indices(priority):
return sorted(df_Summary_Database[df_Summary_Database['Priority'] == priority]['Pty Indice'].unique().tolist())
# Initialize program widget
program_widget_summary = pn.widgets.Select(
name='Select Program',
options=sorted(df_Summary_Database['Program'].unique()),
value=default_summary_program
)
# Initialize priority widget with program dependency
priority_widget_summary = pn.widgets.Select(
name='Select Priority',
options=filter_summary_priorities(default_summary_program),
value=default_summary_priority
)
# Initialize indice widget with priority dependency
indice_widget_summary = pn.widgets.Select(
name='Select Pty Indice',
options=filter_summary_indices(default_summary_priority),
value=default_summary_indice
)
# Define the callback to update Priority widget when Program changes
def update_priority_widget(event):
selected_program = program_widget_summary.value
priority_widget_summary.options = filter_summary_priorities(selected_program)
if priority_widget_summary.options:
priority_widget_summary.value = priority_widget_summary.options[0]
# Define the callback to update Indice widget when Priority changes
def update_indice_widget(event):
selected_priority = priority_widget_summary.value
indice_widget_summary.options = filter_summary_indices(selected_priority)
if indice_widget_summary.options:
indice_widget_summary.value = indice_widget_summary.options[0]
# Link the Program widget to the callback
program_widget_summary.param.watch(update_priority_widget, 'value')
# Link the Priority widget to the callback
priority_widget_summary.param.watch(update_indice_widget, 'value')
#-------------------------------------------------------------------
# Define filtering widgets for 'IDD Top Level' and 'SEDA Top Level'
#-------------------------------------------------------------------label_idd_top_level = pn.pane.HTML('<b style="color:#2B70B3;">IDD Top Level Filter</b>')
label_seda_top_level = pn.pane.HTML('<b style="color:#2B70B3;">SEDA Top Level Filter</b>')
# Update 05/16
filters_top_level = {
'IDD Top Level': pn.widgets.TextInput(name='', placeholder='Enter IDD Top Level'),
'SEDA Top Level': pn.widgets.TextInput(name='', placeholder='Enter SEDA Top Level'),
}
# Create buttons for applying filters
filters_top_level_button = pn.widgets.Button(name='Apply Filters', button_type='primary')
reset_button = pn.widgets.Button(name='Reset Filters', button_type='danger')
# Set default value to None for all filter widgets
for widget in filters_top_level.values():
widget.value = ''
# Create the layout with labels, filter widgets, and buttons
filter_widgets_top_level = pn.Row(
pn.Column(label_idd_top_level, filters_top_level['IDD Top Level']),
pn.Column(label_seda_top_level, filters_top_level['SEDA Top Level']),
pn.Column(
pn.Spacer(height=25), # Spacer before the buttons
pn.Row(filters_top_level_button, reset_button)
)
)
# Define callback function for the Apply Filters button
def on_top_level_filter_button_click(event):
idd_top_level_filter = filters_top_level['IDD Top Level'].value
seda_top_level_filter = filters_top_level['SEDA Top Level'].value
# Filter the DataFrame based on the selected 'IDD Top Level' and 'SEDA Top Level'
filtered_df = df_Summary_Database.copy()
if idd_top_level_filter:
filtered_df = filtered_df[filtered_df['IDD Top Level'].str.contains(idd_top_level_filter, case=False, na=False)]
if seda_top_level_filter:
filtered_df = filtered_df[filtered_df['SEDA Top Level'].str.contains(seda_top_level_filter, case=False, na=False)]
# Update the widget values based on the filtered DataFrame
if not filtered_df.empty:
# Update program widget value (but keep original options)
program_widget_summary.value = filtered_df['Program'].iloc[0]
# Update priority widget value and options (but keep original options)
priority_options = sorted(filtered_df['Priority'].unique())
priority_widget_summary.value = filtered_df['Priority'].iloc[0]
# Update indice widget value and options (but keep original options)
indice_options = sorted(filtered_df['Pty Indice'].unique())
indice_widget_summary.value = filtered_df['Pty Indice'].iloc[0]
# Update the table
update_summary_table(event)
# Define the summary_html pane
summary_html = pn.pane.HTML(
styles={
'overflow-x': 'auto', # Enables horizontal scrolling
'overflow-y': 'hidden', # Prevents vertical scrolling
'margin': '15px 0',
'background': 'white',
'padding': '10px',
'border': 'none',
'display': 'block',
'width': '100%', # Ensures full width
'min-width': '1500px', # Sets a minimum width of 1500px
'max-width': '100%',
},
sizing_mode='stretch_width' # Expands width dynamically
)
#----------------------------------------------------------
# Unified update function
#----------------------------------------------------------
def update_summary_table(event):
try:
# Filter the DataFrame based on the selected program, priority, and indice
filtered_df = df_Summary_Database[
(df_Summary_Database.Program == program_widget_summary.value) &
(df_Summary_Database.Priority == priority_widget_summary.value) &
(df_Summary_Database['Pty Indice'] == indice_widget_summary.value)
]
# Drop 'IDD Top Level' and 'SEDA Top Level' columns at the very end
filtered_df = filtered_df.drop(columns=['IDD Top Level', 'SEDA Top Level', 'Program'], errors='ignore')
# Add these columns if they exist in your data
if 'Level' in filtered_df.columns:
filtered_df['Level'] = pd.to_numeric(filtered_df['Level'], errors='coerce').fillna(-1).astype(int)
# Apply styling to the filtered DataFrame
styled_table = style_summary_table(filtered_df)
# Hide the 'Critical' column in the styled table before rendering
if 'Critical' in filtered_df.columns:
styled_table = styled_table.hide(axis="columns", subset=['Critical'])
# Apply styles inside a scrollable div
summary_html.object = f"""
<div style='overflow-x: auto; overflow-y: hidden; width: 100%; min-width: 1500px; white-space: nowrap;'>
{styled_table.to_html(index=False)}
</div>
"""
except Exception as e:
print(f"Error updating table: {str(e)}")
summary_html.object = "<div>Error loading data</div>"
# Define callback function for the Reset Filters button
def on_reset_button_click(event):
# Reset the filter widgets to their default values
filters_top_level['IDD Top Level'].value = ''
filters_top_level['SEDA Top Level'].value = ''
# Reset the program, priority, and indice widgets to their default values and options
program_widget_summary.options = sorted(df_Summary_Database['Program'].unique())
program_widget_summary.value = default_summary_program
priority_widget_summary.options = filter_summary_priorities(default_summary_program)
priority_widget_summary.value = default_summary_priority
indice_widget_summary.options = filter_summary_indices(default_summary_priority)
indice_widget_summary.value = default_summary_indice
# Update the table
update_summary_table(event)
reset_button.on_click(on_reset_button_click)
# Define callback function for the Apply Filters button
def on_top_level_filter_button_click(event):
idd_top_level_filter = filters_top_level['IDD Top Level'].value
seda_top_level_filter = filters_top_level['SEDA Top Level'].value
# Filter the DataFrame based on the selected 'IDD Top Level' and 'SEDA Top Level'
filtered_df = df_Summary_Database.copy()
if idd_top_level_filter:
filtered_df = filtered_df[filtered_df['IDD Top Level'].str.contains(idd_top_level_filter, case=False, na=False)]
if seda_top_level_filter:
filtered_df = filtered_df[filtered_df['SEDA Top Level'].str.contains(seda_top_level_filter, case=False, na=False)]
# Update the program, priority, and indice widgets based on the filtered DataFrame
if not filtered_df.empty:
program_widget_summary.options = sorted(filtered_df['Program'].unique())
program_widget_summary.value = filtered_df['Program'].iloc[0]
priority_widget_summary.options = sorted(filtered_df['Priority'].unique())
priority_widget_summary.value = filtered_df['Priority'].iloc[0]
indice_widget_summary.options = sorted(filtered_df['Pty Indice'].unique())
indice_widget_summary.value = filtered_df['Pty Indice'].iloc[0]
# Update the table
update_summary_table(event)
# Link the buttons to their respective update functions
filters_top_level_button.on_click(on_top_level_filter_button_click)
# Set up final watchers
program_widget_summary.param.watch(update_summary_table, 'value')
priority_widget_summary.param.watch(update_summary_table, 'value')
indice_widget_summary.param.watch(update_summary_table, 'value')
# Initial update to populate the table
update_summary_table(None)
#----------------------------------------------------------
# 03/06 - Text above summary table
#----------------------------------------------------------
text_above_summary_table= (
f"This table is based on the tab |Summary| - <b>{formatted_date}</b>:<br>"
"▷ The PNs with a <b>Remain. crit. Qty</b> set to 0 are excluded from the table to reduce data processing.<br>"
)
#----------------------------------------------------------
# Final Layout
#----------------------------------------------------------
summary_tab = pn.Column(
pn.Row(
pn.pane.HTML(f"""
<div style='background-color:#4472C4; padding:10px'>
<h1 style='color:white; margin:0'>Clear to Build [{formatted_date}]</h1>
</div>
""", sizing_mode='stretch_width'),
sizing_mode='stretch_width'
),
pn.Row(
program_widget_summary,
priority_widget_summary,
indice_widget_summary,
sizing_mode='stretch_width'
),
filter_widgets_top_level, # Add the new filtering widgets
pn.layout.Divider(styles={'margin': '10px 0'}), # Removed border
text_above_summary_table,
summary_html,
sizing_mode='stretch_width',
)
#||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
#*****************************************************************************************************************************
##############################################################################################################################
# 05/19 - Tab |Timeline|
##############################################################################################################################
import plotly.graph_objects as go
from dateutil.relativedelta import relativedelta
pn.extension('plotly') # <- required for UI
#*****************************************************************************************************************************
#||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
# Define the program selector widget with 'Phase 4-5' as default
program_widget_timeline = pn.widgets.Select(
name='Select Program',
options=sorted(df_Timeline['Program'].unique().tolist()),
value='Phase 4-5',
)
#---------------------------------------------------------
# Color fomratting for Gantt Generator
#---------------------------------------------------------
# Define colors for each category
category_colors = {
'Project': '#1f77b4',
'Documentation': '#9dcae6',
'Engineering FTB': '#9dcae6',
'Sourcing': '#ffc673',
'Production': '#9bccb5',
'Prototyping': '#e5b6a5',
'FAI': '#d1bbdf',
'Rototellite': '#a3d5d3',
'CPA': '#cfbcd5',
'ISP': '#b4d7c1',
}
category_background_colors = {
'Documentation': '#add8e6',
'Engineering FTB': '#add8e6',
'Sourcing': '#ffd6d1',
'Production': '#bfeceb',
'Project': '#87ceeb',
'Prototyping': '#ffe5d4',
'FAI': '#e6e6fa',
'Rototellite': 'white',
'CPA': 'white',
'ISP': 'white',
}
Dependency_arrow_colors = {
'Documentation': '#00DFDA',
'Engineering FTB': '#00DFDA',
'Sourcing': '#fa9324',
'Production': '#00ff7f',
'Project': '#87ceeb',
'FAI': '#771cb2',
'Prototyping': '#ff4500',
'Rototellite': '#00bfff',
'CPA': '#ff00ff',
'ISP': '#7fff00',
}
#--------------------------------------------------------
# Gantt Generator function
#--------------------------------------------------------
def create_gantt_chart(project_name: str, project_df: pd.DataFrame) -> go.Figure:
"""Generate a Gantt chart for a single project."""
# Clean and prepare data
project_df['Category'] = project_df['Category'].fillna('').str.strip()
project_df['Start'] = pd.to_datetime(project_df['Start'], errors='coerce')
project_df['End'] = pd.to_datetime(project_df['End'], errors='coerce')
project_df['Y_Label'] = project_df.apply(lambda x: f"{x['Task']}", axis=1)
project_df = project_df.dropna(subset=['Start', 'End']).reset_index()
if project_df.empty:
fig = go.Figure()
fig.update_layout(
title=dict(text=f"No Data for {project_name}", font=dict(size=20, color="#1f77b4"), x=0.04, xanchor='left'),
height=600,
width=2400,
plot_bgcolor='white',
paper_bgcolor='white',
margin=dict(l=150),
annotations=[dict(xref="paper", yref="paper", x=0.5, y=0.5, text=f"No valid data for {project_name}.", showarrow=False, font=dict(size=16, color="black"))]
)
return fig
# Determine category order based on latest end date
category_end_dates = project_df.groupby('Category')['End'].max().sort_values()
ordered_categories = category_end_dates.index.tolist()
gantt_data = []
y_labels = []
y_positions = []
category_ranges = {}
current_index = 0
for category in ordered_categories:
if category in project_df['Category'].unique():
y_labels.append(f"<b><span style='color:{category_colors.get(category, '#1f77b4')}'>{category.upper()}</span></b>")
y_positions.append(current_index)
current_index += 1
category_df = project_df[project_df['Category'] == category].sort_values('Start')
category_start_idx = current_index
for _, row in category_df.iterrows():
y_labels.append(f" {row['Y_Label']}")
y_positions.append(current_index)
percentage = 1.0
if isinstance(row['Description'], str) and any(c.isdigit() for c in row['Description']):
percentage = float(next((float(s.rstrip('%'))/100 for s in row['Description'].split() if s.replace('.','',1).isdigit() or s.endswith('%')), 1.0))
task_dict = dict(
Task=f" {row['Y_Label']}",
Start=row['Start'],
Finish=row['End'],
Resource=row['Category'],
Color=category_colors.get(row['Category'], '#1f77b4'),
Index=current_index,
IsMilestone=(row['End'] - row['Start']).days <= 0,
Description=row['Description'],
Dependencies=row['Dependencies'] if pd.notna(row['Dependencies']) else '',
OriginalIndex=row['index'],
ID=row['ID'],
Percentage=percentage,
Completion=row['% Completion'] if '% Completion' in row and pd.notna(row['% Completion']) else None,
Comment=row['Comment'] if pd.notna(row['Comment']) else ''
)
gantt_data.append(task_dict)
current_index += 1
category_end_idx = current_index - 0.5
category_ranges[category] = (category_start_idx - 0.5, category_end_idx)
y_labels = list(reversed(y_labels))
y_positions = list(reversed(y_positions))
for item in gantt_data:
item['Index'] = len(y_labels) - 1 - item['Index']
fig = go.Figure()
# Create bars for tasks
for task in gantt_data:
if not task['IsMilestone']:
completion = task['Completion'] if task['Completion'] is not None else 0.0
is_completed = isinstance(task['Description'], str) and 'completed' in task['Description'].lower()
total_duration = (task['Finish'] - task['Start']).total_seconds() * 1000
if is_completed or completion <= 0:
bar_color = '#00fa85' if is_completed else task['Color']
fig.add_trace(go.Bar(
x=[total_duration],
y=[task['Index']],
base=[task['Start'].timestamp() * 1000],
orientation='h',
marker=dict(color=bar_color, line=dict(color='black', width=1)),
width=0.6,
showlegend=False
))
else:
completed_duration = total_duration * min(completion, 1.0)
remaining_duration = total_duration - completed_duration
fig.add_trace(go.Bar(
x=[completed_duration],
y=[task['Index']],
base=[task['Start'].timestamp() * 1000],
orientation='h',
marker=dict(color=Dependency_arrow_colors.get(task['Resource'], '#00fa85'), line=dict(color='black', width=1)),
width=0.6,
showlegend=False,
name='Completed'
))
if remaining_duration > 0:
fig.add_trace(go.Bar(
x=[remaining_duration],
y=[task['Index']],
base=[task['Start'].timestamp() * 1000 + completed_duration],
orientation='h',
marker=dict(color=task['Color'], line=dict(color='black', width=1)),
width=0.6,
showlegend=False,
name='Remaining'
))
fig.update_layout(
barmode='stack',
yaxis=dict(ticktext=y_labels, tickvals=list(range(len(y_labels))), title='Tasks', tickfont=dict(size=10), showgrid=False, zeroline=False, range=[-0.5, len(y_labels) - 0.5])
)
if is_completed:
fig.add_annotation(x=task['Start'].timestamp() * 1000 + (total_duration / 2), y=task['Index'], text="Completed", showarrow=False, font=dict(size=9, color='black', family='Arial'), xanchor='center', yanchor='middle')
else:
display_text = f"{int(task['Percentage'] * 100)}%" if task['Percentage'] < 1.0 else task['Description']
fig.add_annotation(x=task['Start'].timestamp() * 1000 + (total_duration / 2), y=task['Index'], text=display_text, showarrow=False, font=dict(size=9, color='black', family='Arial'), xanchor='center', yanchor='middle')
fig.add_annotation(x=task['Finish'].timestamp() * 1000 + 500000, y=task['Index'], text="▶", showarrow=False, font=dict(size=12, color=Dependency_arrow_colors.get(task['Resource'], task['Color'])), xanchor='center', yanchor='middle')
def calculate_dynamic_offset(start, finish, chart_width_ms, percentage=0.005, min_offset_ms=1000000):
duration_ms = (finish - start).total_seconds() * 1000
chart_offset = chart_width_ms * percentage
return max(min_offset_ms, chart_offset)
def estimate_text_width_ms(text, chart_width_ms, chart_width_pixels=2400, avg_char_width_pixels=12):
text_length = len(str(text))
text_width_pixels = text_length * avg_char_width_pixels
ms_per_pixel = chart_width_ms / chart_width_pixels
return text_width_pixels * ms_per_pixel
base_x = task['Finish'].timestamp() * 1000
chart_start_ms = project_df['Start'].min().timestamp() * 1000
chart_end_ms = project_df['End'].max().timestamp() * 1000
chart_width_ms = chart_end_ms - chart_start_ms
completion_offset = calculate_dynamic_offset(task['Start'], task['Finish'], chart_width_ms)
padding_offset = calculate_dynamic_offset(task['Start'], task['Finish'], chart_width_ms, percentage=0.01)
arrow_buffer = 1000000
completion_x = base_x + completion_offset + arrow_buffer
completion_text = ""
has_completion = task['Completion'] is not None and task['Completion'] > 0
if has_completion:
completion_text = f"{int(task['Completion'] * 100)}%"
fig.add_annotation(x=completion_x, y=task['Index'], text=completion_text, showarrow=False, font=dict(size=12, color=Dependency_arrow_colors.get(task['Resource'], '#00fa85'), family='Arial', weight='bold'), xanchor='left', yanchor='middle')
if task['Comment']:
if has_completion:
completion_width_ms = estimate_text_width_ms(completion_text, chart_width_ms)
comment_x = completion_x + completion_width_ms + padding_offset
else:
comment_x = base_x + completion_offset + arrow_buffer
fig.add_annotation(x=comment_x, y=task['Index'], text=f"({task['Comment']})", showarrow=False, font=dict(size=9, color='black', family='Arial'), xanchor='left', yanchor='middle')
# Draw milestones
for task in gantt_data:
if task['IsMilestone']:
fig.add_trace(go.Scatter(
x=[task['Start'].timestamp() * 1000],
y=[task['Index']],
mode='markers',
marker=dict(symbol='star', size=15, color=task['Color'], line=dict(color='black', width=1)),
showlegend=False
))
fig.add_annotation(x=task['Start'].timestamp() * 1000 + 15000000, y=task['Index'] - 0.2, text=f"<b>{task['Task']} - {task['Start'].strftime('%m/%d')}</b>", showarrow=False, font=dict(size=8, color='black'), xanchor='left', yanchor='middle')
# Draw dependency arrows
for task in gantt_data:
if task['Dependencies']:
deps = [d.strip() for d in str(task['Dependencies']).split(',') if d.strip()]
for dep in deps:
try:
dep_id = int(float(dep)) if isinstance(dep, (float, str)) and dep != '' else None
if dep_id is not None:
dep_row = project_df[project_df['ID'] == dep_id].iloc[0] if not project_df[project_df['ID'] == dep_id].empty else None
if dep_row is not None:
dep_task = next((t for t in gantt_data if t['Task'] == f" {dep_row['Y_Label']}"), None)
if dep_task:
if task['IsMilestone']:
continue
triangle_x = task['Start'].timestamp() * 1000
vertical_x = triangle_x
dep_start_x = dep_task['Finish'].timestamp() * 1000
dep_y_index = dep_task['Index']
if dep_task['IsMilestone']:
task_name_parts = dep_task['Task'].lower().split()
corresponding_bar = None
for t in gantt_data:
if not t['IsMilestone'] and t['Resource'] == dep_task['Resource']:
t_name_parts = t['Task'].lower().split()
if any(part in t_name_parts for part in task_name_parts if part not in ['completed', 'shipped']):
if t['Finish'] <= dep_task['Start']:
corresponding_bar = t
break
if corresponding_bar:
dep_start_x = corresponding_bar['Finish'].timestamp() * 1000
dep_y_index = corresponding_bar['Index']
line_color = Dependency_arrow_colors.get(dep_task['Resource'], dep_task['Color'])
dash_pattern = "6,4"
line_width = 3
fig.add_shape(type="line", x0=dep_start_x, y0=dep_y_index, x1=vertical_x, y1=dep_y_index, line=dict(color=line_color, width=line_width, dash=dash_pattern), layer='below')
fig.add_shape(type="line", x0=vertical_x, y0=dep_y_index, x1=vertical_x, y1=task['Index'], line=dict(color=line_color, width=line_width, dash=dash_pattern), layer='below')
fig.add_annotation(x=triangle_x, y=task['Index'], text="▶", showarrow=False, font=dict(size=16, color='black'), xanchor='left', yanchor='middle')
fig.add_annotation(x=triangle_x, y=task['Index'], text="▶", showarrow=False, font=dict(size=14, color=line_color), xanchor='left', yanchor='middle')
except (ValueError, IndexError) as e:
print(f"Dependency error for {task['Task']} in {project_name}: {e}")
# Add category background rectangles
for category, (start_idx, end_idx) in category_ranges.items():
start_idx = len(y_labels) - 1 - start_idx
end_idx = len(y_labels) - 1 - end_idx
fig.add_shape(type="rect", x0=project_df['Start'].min().timestamp() * 1000, x1=project_df['End'].max().timestamp() * 1000, y0=start_idx, y1=end_idx, fillcolor=category_background_colors.get(category, 'white'), opacity=0.3, layer="below", line=dict(width=0))
# Draw today's date line
today = datetime.now()
fig.add_shape(type="line", x0=today.timestamp() * 1000, x1=today.timestamp() * 1000, y0=-0.5, y1=len(y_labels) - 0.5, line=dict(color="#006400", width=1, dash="dot"))
fig.add_annotation(x=today.timestamp() * 1000 + 500000, y=len(y_labels) - 0.5, text=f"<b>{today.strftime('%m/%d')}</b>", showarrow=False, font=dict(size=10, color="#006400"), xanchor='left', yanchor='top')
# Add vertical dashed lines every month
one_month = relativedelta(months=1)
start_date = project_df['Start'].min().replace(day=1)
end_date = project_df['End'].max()
current_date_iter = start_date
while current_date_iter <= end_date:
fig.add_shape(type="line", x0=current_date_iter.timestamp() * 1000, x1=current_date_iter.timestamp() * 1000, y0=-0.5, y1=len(y_labels) - 0.5, line=dict(color="lightgray", width=1, dash="4,6"), layer='below')
current_date_iter += one_month
current_date_iter = current_date_iter.replace(day=1)
fig.update_layout(
title=dict(text=f"<b>{project_name}</b> - Gantt Chart", font=dict(size=20, color="#1f77b4"), x=0.04, xanchor='left'),
xaxis=dict(type='date', tickformat='%b %d %Y', title='Date', tickfont=dict(size=10), showgrid=False, dtick="M1"),
yaxis=dict(ticktext=y_labels, tickvals=list(range(len(y_labels))), title='Tasks', tickfont=dict(size=10), showgrid=False),
height=1200, # Reduced height for better stacking
width=2400,
showlegend=False,
plot_bgcolor='white',
paper_bgcolor='white',
margin=dict(l=150)
)
return fig
#---------------------------------------------------------
# Timeline_chart function
#---------------------------------------------------------
@pn.depends(program=program_widget_timeline.param.value)
def Timeline_chart(program: str) -> pn.Column:
"""Generate Gantt charts for all projects within the selected program."""
if not program:
fig = go.Figure()
fig.update_layout(
title=dict(text="No Program Selected", font=dict(size=20, color="#1f77b4"), x=0.04, xanchor='left'),
xaxis=dict(type='date', tickformat='%b %d %Y', title='Date', tickfont=dict(size=10), showgrid=False, visible=False),
yaxis=dict(title='Tasks', tickfont=dict(size=10), showgrid=False, visible=False),
height=1200,
width=2400,
showlegend=False,
plot_bgcolor='white',
paper_bgcolor='white',
margin=dict(l=150),
annotations=[dict(xref="paper", yref="paper", x=0.5, y=0.5, text="Please select a program to view the timeline.", showarrow=False, font=dict(size=16, color="black"))]
)
return pn.Column(pn.pane.Plotly(fig, sizing_mode='stretch_width'), sizing_mode='stretch_width')
program_df = df_Timeline[df_Timeline['Program'] == program].copy()
if program_df.empty:
fig = go.Figure()
fig.update_layout(
title=dict(text=f"No Data for {program}", font=dict(size=20, color="#1f77b4"), x=0.04, xanchor='left'),
xaxis=dict(type='date', tickformat='%b %d %Y', title='Date', tickfont=dict(size=10), showgrid=False, visible=False),
yaxis=dict(title='Tasks', tickfont=dict(size=10), showgrid=False, visible=False),
height=600,
width=2400,
showlegend=False,
plot_bgcolor='white',
paper_bgcolor='white',
margin=dict(l=150),
annotations=[dict(xref="paper", yref="paper", x=0.5, y=0.5, text=f"No timeline data available for {program}.", showarrow=False, font=dict(size=16, color="black"))]
)
return pn.Column(pn.pane.Plotly(fig, sizing_mode='stretch_width'), sizing_mode='stretch_width')
charts = []
for project in sorted(program_df['Project'].unique()):
project_df = program_df[program_df['Project'] == project]
if not project_df.empty:
fig = create_gantt_chart(project, project_df)
charts.append(pn.pane.Plotly(fig, sizing_mode='stretch_width'))
return pn.Column(*charts, sizing_mode='stretch_width')
#--------------------------------------------------------
# Update the Timeline_tab layout
#--------------------------------------------------------
# formatted_date is define earlier in the code
Timeline_tab = pn.Column(
pn.Row(
pn.pane.HTML(f"""
<div style='background-color:#4472C4; padding:10px'>
<h1 style='color:white; margin:0'>Timeline [{formatted_date}]</h1>
</div>
""", sizing_mode='stretch_width'),
sizing_mode='stretch_width'
),
pn.Row(
program_widget_timeline,
),
pn.layout.Divider(styles={'margin': '10px 0'}),
Timeline_chart,
sizing_mode='stretch_width',
)
#|||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
#*****************************************************************************************************************************
##############################################################################################################################
# Define Tabs and serve
##############################################################################################################################
#*****************************************************************************************************************************
#||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
print('Script successfully completed')
tabs = pn.Tabs(
("Dashboard", cover_dashboard),
("Products Status", cadrans_dashboard),
("Project Progress", historic_tab),
("Clear to Build Summary", summary_tab),
("Priority List", priority_tab),
("Snapshot", Snapshot_tab),
("Timeline - Gantt", Timeline_tab) # 05/19
)
# Inject custom CSS to scale down the dashboard
pn.config.raw_css = ["""
.pn-column {
transform: scale(0.8); /* Scale down to 80% of the original size */
transform-origin: top left; /* Ensure scaling starts from the top-left corner */
width: 125%; /* Compensate for the scaling to avoid empty space */
height: 125%; /* Compensate for the scaling to avoid empty space */
}
"""]
# Inject custom CSS to set the background color to white
pn.config.raw_css = ["""
body, .pn-column, .bk-root {
background-color: white !important;
}
"""]
# Render the Dashboard
tabs.servable()
print('Panel dashboard loaded')