一.正常情况下运行etp的脚本
1.连续的etp下发脚本生成
python
#新格式参考 aclnnMatmul|train_performance_aclnn_new.sh --op_name=aclnnMatmul --sheet_name=david_pass --exec_mode=npu_off --version=david121 --build=True --execute_loop_times=1 --timeout=3600 --use_bin=false --determin_method=false --determin_check=false --py_version=py38 --ti=0-0 |1|aclnnMatmul_dv121_0||||||false_true_0_0_false_3_false_false_false_false_false|false|1|aclnn_personal_xrun_aclnn||1982-cloud-121
# 定义基础参数
s0 = 'network|scripts|need_dev_num|desc|node_ip|device_id|node_type|os_type|run_mode|option|culster|ip_num|frames|chip_type|team'
s1 = '|train_performance_aclnn_new.sh --op_name='
s2 = ' --sheet_name='
# s3 = ' --exec_mode=npu_off --version=david121 --cann_path=/home/j60080661/run --build=True --execute_loop_times=1 --timeout=21600 --use_bin=false --determin_method=false --determin_check=false --py_version=py38 --ti='
s3 = ' --exec_mode=npu_off --version=david121 --build=True --execute_loop_times=1 --timeout=21600 --use_bin=false --determin_method=false --determin_check=false --py_version=py38 --ti='
s4 = '||||||false_true_0_0_false_3_false_false_false_false_false|false|1|aclnn_personal_xrun_aclnn||'
#设置变量
'''
int8_KC ->用组1
fp8_KC ->用组2
fp8_TC_dyn ->用组5
'''
sz = 'aclnnQuantMatmulV5'
ym = 'int8_KC'
# ym = 'error_V4_fp32_PrecisionFail'
qz = '1982-cloud-cube-iter1'
# 指定输出文件路径
output_path = "D:\BAO\etp\etp_ALLJP_0416_int8_KC.txt" # 自定义你的保存路径
print("----------------------------开始生成----------------------------------------------------------")
# 写入文件
with open(output_path, 'w') as f:
f.write(s0 + "\n")
print(s0 + "\n")
for i in range(0,1052): #1051
ti_param = f"{sz}{s1}{sz}{s2}{ym}{s3}{i}-{i} |1|{sz}_dv121_{i}{s4}{qz}\n"
f.write(ti_param) # 写入文件
print(ti_param) # 同时输出到控制台
print(f"----------------------------生成完成，结果已保存至: {output_path}----------------------------")
'''
bash run.sh --op_name=aclnnGroupedMatmulV4 --case_file=excel/aclnn/aclnnGroupedMatmulV4.xlsx
--sheet_name=aclnnGroupedMatmulV4_GB --case_name= --exec_mode=npu_off --compare=compare
--bm=bm --framework=aclnn --graphy_path=0 --task_prof=false --py_version=py38
--precision_method=0 --use_bin=false --ti=32-32 --continuous=false
--version=david121 --cann_path=/home/j60080661/run
bash run.sh --op_name=aclnnGroupedMatmulV4 --case_file=excel/aclnn/aclnnGroupedMatmulV4.xlsx
--sheet_name=aclnnGroupedMatmulV4_GB --case_name= --exec_mode=npu_off --compare=compare
--bm=bm --framework=aclnn --graphy_path=0 --task_prof=false --py_version=py38
--precision_method=0 --use_bin=false --ti=1-1 --continuous=false
--golden_mode= --version=david121 --genetic= --SR=False
'''2.离散的etp下发脚本生成
python
#新格式参考 aclnnMatmul|train_performance_aclnn_new.sh --op_name=aclnnMatmul --sheet_name=david_pass --exec_mode=npu_off --version=david121 --build=True --execute_loop_times=1 --timeout=3600 --use_bin=false --determin_method=false --determin_check=false --py_version=py38 --ti=0-0 |1|aclnnMatmul_dv121_0||||||false_true_0_0_false_3_false_false_false_false_false|false|1|aclnn_personal_xrun_aclnn||1982-cloud-121
# 定义基础参数
s0 = 'network|scripts|need_dev_num|desc|node_ip|device_id|node_type|os_type|run_mode|option|culster|ip_num|frames|chip_type|team'
s1 = '|train_performance_aclnn_new.sh --op_name='
s2 = ' --sheet_name='
# s3 = ' --exec_mode=npu_off --version=david121 --cann_path=/home/j60080661/run --build=True --execute_loop_times=1 --timeout=21600 --use_bin=false --determin_method=false --determin_check=false --py_version=py38 --ti='
s3 = ' --exec_mode=npu_off --version=david121 --build=True --execute_loop_times=1 --timeout=21600 --use_bin=false --determin_method=false --determin_check=false --py_version=py38 --ti='
s4 = '||||||false_true_0_0_false_3_false_false_false_false_false|false|1|aclnn_personal_xrun_aclnn||'
#设置变量
'''
int8_KC ->用组1
fp8_KC ->用组2
fp8_TC_dyn ->用组5
'''
sz = 'aclnnQuantMatmulV5'
ym = 'int8_KC'
# ym = 'error_V4_fp32_PrecisionFail'
qz = '1982-cloud-cube-iter1'
# 指定输出文件路径
output_path = "D:\BAO\etp\etp_ALLJP_0416_int8_KC.txt" # 自定义你的保存路径
print("----------------------------开始生成----------------------------------------------------------")
# 定义数字列表
numbers1 = [78, 79, 80, 81, 82, 83, 85, 86, 87, 88, 92, 93, 98, 99, 100, 101, 104, 105, 106, 107, 108, 109, 110, 111, 123, 124, 125, 126, 130, 131, 135, 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]
numbers2 = [69, 70, 71, 72, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 85, 86, 87, 88, 92, 93, 96, 98, 99, 100, 101, 103, 104, 105, 106, 107, 108, 109, 110, 111, 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]
with open(output_path, 'w') as f:
f.write(s0 + "\n")
print(s0 + "\n")
for i in numbers1: # 遍历你提供的列表
ti_param = f"{sz}{s1}{sz}{s2}{ym}{s3}{i}-{i} |1|{sz}_dv121_{i}{s4}{qz}\n"
f.write(ti_param) # 写入文件
print(ti_param) # 同时输出到控制台
# # 写入文件
# with open(output_path, 'w') as f:
# f.write(s0 + "\n")
# print(s0 + "\n")
# for i in range(0,1052): #1051
# ti_param = f"{sz}{s1}{sz}{s2}{ym}{s3}{i}-{i} |1|{sz}_dv121_{i}{s4}{qz}\n"
# f.write(ti_param) # 写入文件
# print(ti_param) # 同时输出到控制台
print(f"----------------------------生成完成，结果已保存至: {output_path}----------------------------")
'''
bash run.sh --op_name=aclnnGroupedMatmulV4 --case_file=excel/aclnn/aclnnGroupedMatmulV4.xlsx
--sheet_name=aclnnGroupedMatmulV4_GB --case_name= --exec_mode=npu_off --compare=compare
--bm=bm --framework=aclnn --graphy_path=0 --task_prof=false --py_version=py38
--precision_method=0 --use_bin=false --ti=32-32 --continuous=false
--version=david121 --cann_path=/home/j60080661/run
bash run.sh --op_name=aclnnGroupedMatmulV4 --case_file=excel/aclnn/aclnnGroupedMatmulV4.xlsx
--sheet_name=aclnnGroupedMatmulV4_GB --case_name= --exec_mode=npu_off --compare=compare
--bm=bm --framework=aclnn --graphy_path=0 --task_prof=false --py_version=py38
--precision_method=0 --use_bin=false --ti=1-1 --continuous=false
--golden_mode= --version=david121 --genetic= --SR=False
'''二.未运行用例定位
结果相关用例清洗,就得到上面这脚本的离散类型的值,我将范围内没有的值,循环下打印出来
python
#coding:utf-8
a = int(input("请输入要查询范围整数的下限值:"))
b = int(input("请输入要查询范围整数的上限值:"))
print("请直接粘贴一大段数字（每行一个，支持前导0）：")
input_text = ""
try:
while True:
line = input()
if line.strip() == "":
break
input_text += line + "\n"
except EOFError:
pass
# 处理逻辑:输入的一大段逐行遍历,且将其转换为整型，如果不在范围内则打印输出
results = []
for line in input_text.strip().splitlines():
num = int(line.strip())
if a < num < b:
results.append(num)
# 输出结果
print(f"\n范围是({a}, {b})，符合条件的数字为：")
if results:
print(",".join(map(str, sorted(results))))
else:
print("没有符合条件的数字。")但是感觉不好使,还是有些费操作,要自己复制结果,还要多操作一下,继续优化用集合去重等如下
python
#coding:utf-8
a = int(input("请输入要查询范围整数的下限值:"))
b = int(input("请输入要查询范围整数的上限值:"))
print("请直接粘贴一大段数字（每行一个，支持前导0）：")
input_text = ""
try:
while True:
line = input()
if line.strip() == "":
break
input_text += line + "\n"
except EOFError:
pass
input_numbers = set()
for line in input_text.strip().splitlines():
if line.strip(): # 防止空行
try:
input_numbers.add(int(line.strip()))
except ValueError:
continue # 忽略非数字行
missing_numbers = []
# 遍历范围 (a, b) 内的整数，即 a+1 到 b-1
n1 = b - a + 1
n2 = 0
for num in range(a, b+1):#闭区间[a,b]
if num not in input_numbers:
n2 += 1
missing_numbers.append(num)
print(f"总共有:{n1}个,匹配到{n1-n2}个,缺失了{n2}个")
# 输出结果
print(f"\n范围是({a}, {b})，范围内缺失的{n2}个数字为：\n{missing_numbers}")
if missing_numbers:#改变量存储
# print(",".join(map(str, sorted(missing_numbers))))
S = ",".join(map(str, sorted(missing_numbers)))
else:
print("范围内没有缺失的数字（所有数字都齐了）。")
print(f"{n2}个缺失值为:\n",S)可以进一步优化,加上输出等
python
#coding:utf-8
a = int(input("请输入要查询范围整数的下限值:"))
b = int(input("请输入要查询范围整数的上限值:"))
print("请直接粘贴一大段数字（每行一个，支持前导0）：")
input_text = ""
try:
while True:
line = input()
if line.strip() == "":
break
input_text += line + "\n"
except EOFError:
pass
# 处理逻辑：
# 1. 先把输入的所有数字转成集合（方便快速查找，且自动去重）
# 2. 遍历从 a+1 到 b-1 的所有整数，看它们是否在输入集合里
# 3. 如果不在，就加入结果列表
input_numbers = set()
for line in input_text.strip().splitlines():
if line.strip(): # 防止空行
try:
input_numbers.add(int(line.strip()))
except ValueError:
continue # 忽略非数字行
missing_numbers = []
matched_numbers = []
# 遍历范围 (a, b) 内的整数，即 a+1 到 b-1
n1 = b - a + 1
n2 = 0
for num in range(a, b+1):#闭区间[a,b]
if num not in input_numbers:
n2 += 1
missing_numbers.append(num)
else: # 匹配的数字加入列表
matched_numbers.append(num)
print(f"总共有:{n1}个,匹配到{n1-n2}个,缺失了{n2}个")
# 输出结果
print(f"\n范围是({a}, {b})，范围内匹配的{n1-n2}个数字为：\n{matched_numbers}")
print(f"\n范围是({a}, {b})，范围内缺失的{n2}个数字为：\n{missing_numbers}")
if missing_numbers:#改变量存储
# print(",".join(map(str, sorted(missing_numbers))))
S = ",".join(map(str, sorted(missing_numbers)))
else:
print("范围内没有缺失的数字（所有数字都齐了）。")三.关于结果表中对应列的整理
对的这才是本章更新整理的原因,前面相关的整理对杂乱的离散型结果用例整理有帮助,但是过于基础,没记录整理的意义
当同样的用例有时候命名也不是规律的,当已运行的用例名列也杂乱无章,这时候清洗应该找规律来泛化.
1.基础版之前规律的用例名整理
直接在表格中新增列用right()函数,生成后用第二章代码生成对应列表,给第一章的离散的脚本用.没有什么操作空间
2.复杂版运行后同样杂乱无章的未运行用例名整理
经过对比发现,在竞品对比中 ,虽然不能直接用后4位数字来开窗排序,但是有规律可言
已运行的用例编号 选在_case 和 _test 之间的数字即可
当然了简单点的方法是后面多余一样的全部去了然后和之前一样取,
但是子页多的话太费操作了,没必要浪费一半时间手动操作,这也是为什么想用公式或者脚本自动化来整理的原因
a.将其用df二维表操作
相关表格准备
python
#coding:utf-8
import pandas as pd
from matplotlib import pyplot as plt
plt.rcParams['font.sans-serif'] = ['SimHei', 'Arial Unicode MS', 'DejaVu Sans'] # 支持中文的字体
plt.rcParams['axes.unicode_minus'] = False # 解决负号 '-' 显示为方块的问题
file_path = r"D:\结果.xlsx"
sheet_name = '整合对比'
# 读取Excel文件中的指定工作表
df = pd.read_excel(file_path, sheet_name=sheet_name) #整合对比
print(df.columns)#打印各列
print(f"形状:{df.shape} 行数:{df.shape[0]}, 列数:{df.shape[1]}\n"
f"列名:{list(df.columns)}")
# print(df.head(2))生成满足条件的新df
python
#excel中的 中间数字
import re
# 提取 C 列中 case 后和 _test 之间的数字
df['extracted_num'] = df['C'].str.extract(r'case(\d+)_test')
df['extracted_num'] = df['extracted_num'].astype(int)
#或者 提取 _case 和 _test 之间的字符串
import re
df['extracted_str'] = df['C'].str.extract(r'_case(.*?)_test')
df['extracted_str'] = df['extracted_str'].str.strip() #去除首尾空格b.可以直接用excel 自带的公式
因为换软件的话 相当于而言操作量也不少,对此我尝试了很多
后来我就拆分,如果不能直接写长的公式命令,那我拆解开来不就好了?
#1.拆分找到对应位置后截取
=IFERROR(
MID(
C2,
FIND("case", C2) + LEN("case"),
FIND("_test", C2, FIND("case", C2)+LEN("case")) - (FIND("case", C2) + LEN("case"))
),
""
)
#2.简化公式
=IFERROR(
MID(C2, FIND("case", C2) + 4,FIND("_test", C2, FIND("case", C2) + 4) - (FIND("case", C2) + 4)),
"")
#3.数据类型转换
=IFERROR((MID(C2,FIND("case",C2)+4,FIND("_test",C2,FIND("case",C2)+4)-(FIND("case",C2)+4)))+0), "")
#4.添加异常打印
=IFERROR((MID(C2,FIND("case",C2)+4,FIND("_test",C2,FIND("case",C2)+4)-(FIND("case",C2)+4)))+0,"未找到case")对此本次竞品相关复杂用例名的已运行序列整理自动化操作已完成,重复之前操作,即可快捷完成etp中未运行用例顺序的定位及脚本自动化生成