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- numpy._core.numerictypes._PreprocessDTypeError
- numpy._core._ufunc_config._unspecified
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- numpy.distutils.system_info.boost_python_info
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- numpy.ma.core._convert2ma
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- numpy.matrixlib.defmatrix.matrix
- numpy.lib._index_tricks_impl.nd_grid
- numpy.lib._index_tricks_impl.MGridClass
- numpy.lib._index_tricks_impl.OGridClass
- numpy.lib._index_tricks_impl.AxisConcatenator
- numpy.lib._index_tricks_impl.RClass
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- numpy._core.memmap.memmap
- numpy._core.records.format_parser
- numpy._core.records.record
- numpy._core.records.recarray
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- numpy._core._internal._missing_ctypes
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- numpy.__config__.DisplayModes
- numpy._array_api_info.__array_namespace_info__
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- numpy.distutils.fcompiler.arm.ArmFlangCompiler
- numpy.distutils.fcompiler.absoft.AbsoftFCompiler
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- numpy.distutils.fcompiler.mips.MIPSFCompiler
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- numpy._core._exceptions._ArrayMemoryError
- numpy._utils._pep440.Infinity
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  Function list name: All functions
Function coverage:
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  - 65
  - 19
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  - 41
  - 42
  - 43
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  - 665
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  - 101
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  - 30
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  - 24
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  - 20
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  - 150
  - 119
  - 155
  - 156
  - 157
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  - 782
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  - 716
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  - 725
  - 728
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  - 743
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  - 758
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  - 764
  - 765
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  - 108
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  - 174
  - 180
  - 181
  - 182
  - 183
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  - 322
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  - 256
  - 257
  - 258
  - 259
  - 260
  - 262
  - 264
  - 266
  - 267
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  - 269
  - 271
  - 273
  - 274
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  - 276
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  - 310
  - 311
  - 312
  - 314
  - 187
  - 316
  - 317
  - 241
  - 246
  - 248
  - 249
  - 250
  - 251
  - 252
  - 253
  - 254
  - 255
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  - 39
  numpy._configtool.main:
  - 32
  - 33
  - 34
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  - 82
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  - 88
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  - 44
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  numpy.f2py.crackfortran.analyzevars.compute_deps:
  - 2792
  - 2789
  - 2790
  - 2791
  numpy.f2py.crackfortran.appenddecl:
  - 898
  - 900
  - 902
  - 904
  - 905
  - 906
  - 908
  - 909
  - 910
  - 911
  - 912
  - 913
  - 914
  - 915
  - 916
  - 918
  - 920
  - 922
  numpy.f2py.crackfortran.appendmultiline:
  - 1561
  - 1564
  - 1566
  numpy.f2py.crackfortran.buildimplicitrules:
  - 2251
  - 2254
  - 2255
  - 2257
  - 2258
  - 2259
  - 2261
  - 2262
  numpy.f2py.crackfortran.character_backward_compatibility_hook:
  - 3616
  - 3623
  - 3591
  - 3625
  - 3626
  - 3627
  - 3628
  - 3631
  - 3632
  - 3635
  - 3638
  - 3639
  - 3644
  - 3613
  numpy.f2py.crackfortran.character_backward_compatibility_hook.fix_usage:
  - 3608
  - 3609
  numpy.f2py.crackfortran.common2fortran:
  - 3312
  - 3308
  - 3309
  - 3310
  numpy.f2py.crackfortran.crack2fortran:
  - 3505
  numpy.f2py.crackfortran.crack2fortrangen:
  - 3235
  - 3237
  - 3238
  - 3239
  - 3240
  - 3242
  - 3244
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  numpy.f2py.crackfortran.crackfortran:
  - 3488
  - 3489
  - 3490
  - 3492
  - 3493
  - 3494
  - 3495
  - 3496
  - 3497
  - 3498
  numpy.f2py.crackfortran.crackline:
  - 774
  - 775
  - 778
  - 780
  - 782
  - 783
  - 784
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  - 721
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  - 738
  - 739
  - 740
  - 742
  - 746
  - 747
  - 751
  - 757
  - 760
  numpy.f2py.crackfortran.cracktypespec:
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  - 1836
  - 1837
  - 1838
  - 1839
  - 1840
  - 1842
  - 1845
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  - 1848
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  - 1859
  - 1860
  - 1861
  - 1862
  - 1863
  - 1864
  - 1868
  - 1872
  - 1873
  - 1875
  - 1876
  - 1877
  - 1878
  - 1879
  - 1880
  - 1882
  - 1883
  - 1885
  numpy.f2py.crackfortran.cracktypespec0:
  - 1572
  - 1574
  - 1577
  - 1578
  - 1579
  - 1580
  - 1583
  - 1584
  - 1585
  - 1586
  - 1589
  - 1590
  - 1591
  numpy.f2py.crackfortran.determineexprtype:
  - 3200
  - 3201
  - 3202
  - 3203
  - 3205
  - 3206
  - 3207
  - 3208
  - 3210
  - 3211
  - 3213
  - 3214
  - 3215
  - 3216
  - 3217
  - 3219
  - 3220
  - 3221
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  - 3224
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  - 3226
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  - 3189
  - 3190
  - 3191
  - 3193
  - 3194
  - 3195
  - 3196
  - 3197
  - 3199
  numpy.f2py.crackfortran.expr2name:
  - 3136
  - 3108
  - 3109
  - 3110
  - 3111
  - 3113
  - 3114
  - 3115
  - 3118
  - 3125
  - 3127
  - 3129
  - 3130
  - 3133
  - 3134
  - 3135
  numpy.f2py.crackfortran.get_parameters:
  - 2445
  - 2446
  - 2447
  - 2450
  - 2453
  - 2454
  - 2455
  - 2456
  - 2457
  - 2459
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  - 2462
  - 2464
  - 2465
  - 2466
  - 2471
  - 2473
  - 2474
  - 2482
  - 2485
  - 2486
  - 2487
  - 2490
  - 2492
  - 2493
  - 2494
  - 2496
  - 2497
  - 2507
  - 2508
  - 2509
  - 2510
  - 2511
  - 2512
  - 2514
  - 2515
  - 2518
  - 2520
  - 2524
  - 2526
  - 2529
  - 2532
  - 2534
  - 2535
  - 2536
  - 2537
  - 2540
  - 2541
  numpy.f2py.crackfortran.get_sorted_names:
  - 2382
  - 2384
  - 2385
  - 2386
  - 2389
  - 2391
  - 2392
  numpy.f2py.crackfortran.get_usedict:
  - 1968
  - 1965
  - 1966
  - 1967
  numpy.f2py.crackfortran.get_useparameters:
  - 1984
  - 1987
  - 1988
  - 1991
  - 1992
  - 1993
  - 1994
  - 1995
  - 1996
  - 1975
  - 1977
  - 1978
  - 1980
  - 1981
  - 1982
  - 1983
  numpy.f2py.crackfortran.getblockname:
  - 1947
  numpy.f2py.crackfortran.getlincoef:
  - 2306
  - 2308
  - 2309
  - 2310
  - 2312
  - 2315
  - 2316
  - 2317
  - 2319
  - 2321
  - 2322
  - 2323
  - 2324
  - 2326
  - 2327
  - 2328
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  - 2331
  - 2332
  - 2333
  - 2335
  - 2337
  - 2338
  - 2339
  - 2340
  - 2281
  - 2302
  numpy.f2py.crackfortran.is_free_format:
  - 331
  - 335
  - 337
  - 338
  - 340
  - 342
  - 346
  - 348
  - 351
  numpy.f2py.crackfortran.markinnerspaces:
  - 1645
  - 1646
  - 1650
  - 1652
  - 1654
  - 1626
  numpy.f2py.crackfortran.markoutercomma:
  - 876
  - 879
  - 883
  - 885
  - 887
  - 889
  numpy.f2py.crackfortran.markouterparen:
  - 864
  - 866
  - 858
  - 859
  - 861
  numpy.f2py.crackfortran.myeval:
  - 2272
  - 2270
  - 2271
  numpy.f2py.crackfortran.openhook:
  - 320
  - 322
  - 327
  - 300
  - 310
  - 311
  - 315
  - 316
  - 317
  - 318
  numpy.f2py.crackfortran.outmess:
  - 238
  - 240
  - 241
  - 242
  - 243
  numpy.f2py.crackfortran.param_eval:
  - 2965
  - 2983
  - 2985
  - 2988
  - 2993
  - 2995
  - 2996
  - 2998
  - 3000
  - 3001
  - 3002
  - 3004
  - 3005
  - 3006
  - 3012
  - 3014
  - 3016
  - 3018
  - 3019
  - 3021
  numpy.f2py.crackfortran.param_parse:
  - 3083
  - 3085
  - 3086
  - 3089
  - 3090
  - 3027
  - 3091
  - 3092
  - 3094
  - 3095
  - 3098
  - 3100
  - 3101
  - 3102
  numpy.f2py.crackfortran.parse_name_for_bind:
  - 967
  - 968
  - 970
  - 971
  - 973
  numpy.f2py.crackfortran.postcrack:
  - 2049
  - 2050
  - 2052
  - 2054
  - 2055
  - 2058
  - 2059
  - 2060
  - 2061
  - 2062
  - 2063
  - 2064
  - 2066
  - 2069
  - 2071
  - 2072
  - 2073
  - 2077
  - 2080
  - 2082
  - 2084
  - 2085
  - 2089
  - 2096
  - 2097
  - 2100
  - 2102
  - 2104
  - 2106
  - 2107
  - 2110
  - 2111
  - 2113
  - 2115
  - 2116
  - 2118
  - 2123
  - 2124
  - 2035
  - 2042
  - 2045
  - 2046
  - 2047
  numpy.f2py.crackfortran.postcrack2:
  - 2017
  - 2019
  - 2021
  - 2023
  - 2025
  - 2027
  - 2005
  - 2007
  - 2008
  - 2011
  - 2012
  - 2014
  - 2015
  numpy.f2py.crackfortran.readfortrancode:
  - 514
  - 524
  - 525
  - 526
  - 527
  - 528
  - 529
  - 532
  - 534
  - 535
  - 536
  - 538
  - 540
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  - 500
  - 502
  - 503
  - 504
  - 505
  - 506
  - 507
  - 511
  numpy.f2py.crackfortran.removespaces:
  - 1616
  - 1611
  - 1612
  - 1615
  numpy.f2py.crackfortran.rmbadname:
  - 275
  numpy.f2py.crackfortran.rmbadname1:
  - 268
  - 269
  numpy.f2py.crackfortran.setattrspec:
  - 1891
  - 1893
  - 1895
  - 1898
  - 1899
  - 1900
  - 1902
  - 1903
  - 1904
  - 1905
  - 1906
  - 1907
  - 1908
  - 1909
  - 1910
  - 1911
  - 1913
  numpy.f2py.crackfortran.setcharselector:
  - 1932
  - 1934
  - 1936
  - 1940
  - 1941
  numpy.f2py.crackfortran.setkindselector:
  - 1920
  - 1922
  - 1925
  - 1926
  - 1918
  numpy.f2py.crackfortran.sortvarnames:
  - 2146
  - 2149
  - 2150
  - 2152
  - 2156
  - 2158
  - 2132
  - 2133
  - 2134
  - 2136
  - 2137
  - 2142
  - 2143
  numpy.f2py.crackfortran.split_by_unquoted:
  - 672
  - 674
  - 675
  - 678
  - 679
  - 680
  - 665
  - 670
  - 671
  numpy.f2py.crackfortran.traverse:
  - 3552
  - 3584
  - 3555
  - 3556
  - 3524
  - 3557
  - 3565
  - 3567
  - 3568
  - 3571
  - 3572
  - 3573
  - 3575
  - 3576
  - 3579
  - 3551
  numpy.f2py.crackfortran.true_intent_list:
  - 3345
  - 3349
  - 3350
  - 3343
  numpy.f2py.crackfortran.undo_rmbadname:
  - 286
  numpy.f2py.crackfortran.undo_rmbadname1:
  - 280
  - 279
  numpy.f2py.crackfortran.unmarkouterparen:
  - 893
  numpy.f2py.crackfortran.updatevars:
  - 1663
  - 1673
  - 1675
  - 1676
  - 1678
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  - 1680
  - 1682
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  - 1684
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  - 1798
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  - 1800
  - 1805
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  - 1810
  - 1811
  - 1812
  - 1814
  - 1819
  - 1820
  - 1821
  - 1822
  - 1825
  - 1826
  numpy.f2py.crackfortran.use2fortran:
  - 3328
  - 3329
  - 3335
  - 3318
  - 3320
  - 3321
  - 3324
  - 3326
  numpy.f2py.crackfortran.vars2fortran:
  - 3456
  - 3458
  - 3459
  - 3461
  - 3462
  - 3464
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  - 3468
  - 3355
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  - 3437
  - 3440
  - 3446
  - 3447
  - 3448
  - 3450
  - 3451
  - 3453
  - 3454
  - 3455
  numpy.f2py.diagnose:
  - 148
  - 149
  numpy.f2py.diagnose.run:
  - 8
  - 9
  - 10
  - 11
  - 12
  - 13
  - 14
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  - 18
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  - 79
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  - 84
  - 86
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  - 103
  - 105
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  - 132
  - 134
  - 135
  - 136
  - 137
  - 138
  - 139
  - 140
  - 141
  - 143
  - 144
  - 145
  numpy.f2py.f2py2e:
  - 1
  numpy.f2py.f2py2e.CombineIncludePaths.__call__:
  - 544
  - 545
  - 546
  - 547
  - 548
  - 550
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  numpy.f2py.f2py2e.buildmodules:
  - 387
  - 388
  - 390
  - 391
  - 392
  - 394
  - 395
  - 396
  - 398
  - 399
  - 400
  - 402
  - 403
  - 404
  - 405
  - 408
  - 409
  - 410
  - 411
  - 413
  - 417
  numpy.f2py.f2py2e.callcrackfortran:
  - 347
  - 349
  - 351
  - 355
  - 356
  - 357
  - 358
  - 359
  - 360
  - 362
  - 363
  - 364
  - 365
  - 368
  - 370
  - 371
  - 374
  - 376
  - 377
  numpy.f2py.f2py2e.dict_append:
  - 425
  - 428
  - 422
  - 423
  numpy.f2py.f2py2e.f2py_parser:
  - 554
  - 555
  - 556
  - 557
  - 558
  numpy.f2py.f2py2e.filter_files:
  - 520
  - 524
  - 525
  - 526
  - 529
  - 530
  - 531
  - 533
  numpy.f2py.f2py2e.get_newer_options:
  - 562
  - 563
  - 564
  - 566
  numpy.f2py.f2py2e.get_prefix:
  - 538
  numpy.f2py.f2py2e.main:
  - 776
  - 777
  - 778
  - 779
  - 782
  - 785
  - 786
  - 788
  numpy.f2py.f2py2e.make_f2py_compile_parser:
  - 571
  - 572
  - 573
  - 574
  numpy.f2py.f2py2e.preparse_sysargv:
  - 586
  - 587
  - 580
  - 582
  numpy.f2py.f2py2e.run_compile:
  - 640
  - 641
  - 643
  - 645
  - 646
  - 647
  - 648
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  - 651
  - 653
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  - 665
  - 667
  - 668
  - 669
  - 675
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  - 677
  - 678
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  - 715
  - 716
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  - 720
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  - 725
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  - 728
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  - 731
  - 604
  - 732
  - 606
  - 734
  - 737
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  - 612
  - 617
  - 620
  - 626
  - 628
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  - 631
  - 634
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  numpy.f2py.f2py2e.run_main:
  - 512
  - 514
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  - 454
  - 455
  - 456
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  - 461
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  - 495
  - 496
  - 497
  - 499
  - 501
  - 502
  - 503
  - 504
  numpy.f2py.f2py2e.scaninputline:
  - 256
  - 258
  - 260
  - 262
  - 263
  - 264
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  numpy.lib.recfunctions._get_fields_and_offsets:
  - 871
  - 874
  - 876
  - 877
  - 879
  - 882
  - 883
  - 885
  - 854
  - 887
  numpy.lib.recfunctions._get_fields_and_offsets.count_elem:
  - 865
  numpy.lib.recfunctions._get_fieldspec:
  - 72
  - 98
  - 91
  numpy.lib.recfunctions._izip_fields:
  - 288
  - 292
  - 293
  - 294
  - 295
  - 296
  - 298
  - 300
  numpy.lib.recfunctions._izip_fields_flat:
  - 275
  - 280
  - 281
  - 282
  - 284
  numpy.lib.recfunctions._izip_records:
  - 304
  - 323
  - 324
  - 318
  numpy.lib.recfunctions._keep_fields:
  - 586
  - 587
  - 588
  - 567
  numpy.lib.recfunctions._rec_append_fields_dispatcher:
  - 726
  - 727
  numpy.lib.recfunctions._zip_descr:
  - 213
  - 223
  numpy.lib.recfunctions._zip_dtype:
  - 198
  - 199
  - 200
  - 202
  - 204
  - 206
  - 208
  - 209
  numpy.lib.recfunctions.append_fields:
  - 657
  - 686
  - 687
  - 690
  - 694
  - 695
  - 696
  - 698
  - 700
  - 701
  - 702
  - 706
  - 707
  - 709
  - 710
  - 711
  - 714
  - 716
  - 717
  - 718
  - 719
  - 720
  - 722
  numpy.lib.recfunctions.apply_along_fields:
  - 1224
  - 1225
  - 1187
  - 1221
  numpy.lib.recfunctions.assign_fields_by_name:
  - 1258
  - 1262
  - 1263
  - 1264
  - 1234
  - 1267
  numpy.lib.recfunctions.drop_fields:
  - 559
  - 561
  - 562
  - 563
  - 506
  - 539
  - 542
  numpy.lib.recfunctions.drop_fields._drop_descr:
  - 547
  - 549
  - 551
  - 552
  - 553
  - 554
  - 556
  numpy.lib.recfunctions.find_duplicates:
  - 1446
  - 1448
  - 1418
  - 1451
  - 1452
  - 1456
  - 1458
  - 1462
  - 1465
  - 1469
  numpy.lib.recfunctions.flatten_descr:
  - 192
  - 193
  - 170
  - 183
  - 187
  - 189
  - 190
  numpy.lib.recfunctions.get_fieldstructure:
  - 256
  - 258
  - 259
  - 227
  - 263
  - 265
  - 266
  - 267
  - 268
  - 253
  numpy.lib.recfunctions.get_names:
  - 129
  - 130
  - 132
  - 133
  - 104
  - 127
  numpy.lib.recfunctions.get_names_flat:
  - 161
  - 162
  - 164
  - 165
  - 166
  - 137
  numpy.lib.recfunctions.join_by:
  - 1539
  - 1543
  - 1544
  - 1546
  - 1547
  - 1549
  - 1553
  - 1554
  - 1555
  - 1559
  - 1560
  - 1568
  - 1569
  - 1572
  - 1573
  - 1577
  - 1582
  - 1583
  - 1585
  - 1587
  - 1588
  - 1589
  - 1590
  - 1592
  - 1593
  - 1599
  - 1602
  - 1603
  - 1604
  - 1607
  - 1484
  - 1612
  - 1615
  - 1619
  - 1621
  - 1630
  - 1633
  - 1635
  - 1637
  - 1639
  - 1643
  - 1645
  - 1647
  - 1651
  - 1654
  - 1656
  - 1533
  numpy.lib.recfunctions.merge_arrays:
  - 413
  - 414
  - 416
  - 419
  - 420
  - 421
  - 423
  - 425
  - 426
  - 430
  - 434
  - 439
  - 441
  - 442
  - 444
  - 449
  - 450
  - 453
  - 454
  - 456
  - 457
  - 458
  - 459
  - 460
  - 463
  - 464
  - 469
  - 470
  - 472
  - 473
  - 474
  - 475
  - 476
  - 479
  - 481
  - 482
  - 483
  - 484
  - 485
  - 486
  - 488
  - 491
  - 492
  - 366
  - 494
  - 495
  numpy.lib.recfunctions.rec_append_fields:
  - 761
  - 732
  numpy.lib.recfunctions.rec_drop_fields:
  - 600
  - 597
  numpy.lib.recfunctions.rec_join:
  - 1668
  - 1678
  numpy.lib.recfunctions.recursive_fill_fields:
  - 64
  - 33
  - 65
  - 67
  - 59
  numpy.lib.recfunctions.rename_fields:
  - 609
  - 644
  - 645
  numpy.lib.recfunctions.rename_fields._recursive_rename_fields:
  - 642
  - 634
  - 635
  - 637
  - 638
  - 639
  numpy.lib.recfunctions.repack_fields:
  - 832
  - 834
  - 771
  - 838
  - 840
  - 841
  - 845
  - 848
  - 850
  - 851
  - 830
  - 831
  numpy.lib.recfunctions.require_fields:
  - 1313
  - 1314
  - 1275
  numpy.lib.recfunctions.stack_arrays:
  - 1326
  - 1365
  - 1367
  - 1369
  - 1370
  - 1375
  - 1377
  - 1378
  - 1379
  - 1380
  - 1381
  - 1383
  - 1385
  - 1386
  - 1387
  - 1390
  - 1391
  - 1394
  - 1395
  - 1397
  - 1399
  - 1400
  - 1402
  - 1404
  - 1405
  - 1407
  numpy.lib.recfunctions.structured_to_unstructured:
  - 1026
  - 1031
  - 1032
  - 1035
  - 1036
  - 1039
  - 1040
  - 1042
  - 1043
  - 1044
  - 1050
  - 1052
  - 1054
  - 1058
  - 1062
  - 1064
  - 1067
  - 941
  - 999
  - 1002
  - 1003
  - 1004
  - 1006
  - 1010
  - 1011
  - 1013
  - 1014
  - 1016
  - 1022
  numpy.lib.recfunctions.unstructured_to_structured:
  - 1154
  - 1156
  - 1160
  - 1163
  - 1168
  - 1170
  - 1173
  - 1177
  - 1180
  - 1077
  - 1131
  - 1134
  - 1138
  - 1139
  - 1140
  - 1141
  - 1142
  - 1143
  - 1145
  - 1148
  - 1150
  - 1151
  numpy.linalg:
  - 1
  - 93
  - 97
  numpy.linalg._linalg:
  - 1
  - 3329
  - 1668
  - 778
  - 656
  - 1170
  - 536
  - 281
  - 3615
  - 2602
  - 3250
  - 2868
  - 2359
  - 3513
  - 1860
  - 965
  - 461
  - 3450
  - 1362
  - 3419
  - 3165
  - 2275
  - 3432
  - 106
  - 363
  - 1515
  - 2157
  - 887
  - 1265
  - 2421
  - 2039
  - 1914
  - 3069
  numpy.linalg._linalg.LinAlgError:
  - 116
  numpy.linalg._linalg._assert_2d:
  - 227
  - 228
  numpy.linalg._linalg._assert_finite:
  - 249
  - 250
  numpy.linalg._linalg._assert_stacked_2d:
  - 233
  - 234
  numpy.linalg._linalg._assert_stacked_square:
  - 245
  - 239
  numpy.linalg._linalg._commonType:
  - 193
  - 195
  - 196
  - 198
  - 199
  - 201
  - 206
  numpy.linalg._linalg._complexType:
  - 187
  numpy.linalg._linalg._is_empty_2d:
  - 255
  numpy.linalg._linalg._makearray:
  - 165
  - 166
  numpy.linalg._linalg._multi_dot:
  - 3058
  - 3051
  - 3052
  - 3059
  numpy.linalg._linalg._multi_dot_matrix_chain_order:
  - 3041
  - 3010
  - 3043
  - 3026
  - 3032
  - 3035
  - 3037
  - 3038
  numpy.linalg._linalg._multi_dot_three:
  - 3003
  - 3004
  - 2989
  - 3006
  numpy.linalg._linalg._multi_svd_norm:
  - 2593
  - 2594
  - 2571
  numpy.linalg._linalg._multidot_dispatcher:
  - 2864
  - 2865
  numpy.linalg._linalg._realType:
  - 184
  numpy.linalg._linalg._to_native_byte_order:
  - 215
  - 216
  - 217
  - 219
  - 220
  numpy.linalg._linalg.cholesky:
  - 870
  - 871
  - 872
  - 873
  - 874
  - 780
  - 876
  - 877
  numpy.linalg._linalg.cond:
  - 1996
  - 1997
  - 1999
  - 2000
  - 2001
  - 2002
  - 2009
  - 2010
  - 2011
  - 2012
  - 2013
  - 2014
  - 2015
  - 2016
  - 2019
  - 2020
  - 2021
  - 2022
  - 2023
  - 2025
  - 2029
  - 1916
  numpy.linalg._linalg.cross:
  - 3310
  - 3311
  - 3313
  - 3252
  - 3320
  numpy.linalg._linalg.det:
  - 2406
  - 2407
  - 2408
  - 2409
  - 2410
  - 2411
  - 2361
  numpy.linalg._linalg.diagonal:
  - 3156
  - 3071
  numpy.linalg._linalg.eig:
  - 1504
  - 1507
  - 1509
  - 1511
  - 1512
  - 1364
  - 1493
  - 1494
  - 1495
  - 1496
  - 1498
  - 1499
  - 1502
  numpy.linalg._linalg.eigh:
  - 1639
  - 1640
  - 1643
  - 1644
  - 1517
  - 1645
  - 1647
  - 1652
  - 1653
  - 1656
  - 1657
  - 1658
  - 1659
  numpy.linalg._linalg.eigvals:
  - 1249
  - 1251
  - 1252
  - 1254
  - 1256
  - 1258
  - 1172
  - 1240
  - 1241
  - 1242
  - 1243
  - 1245
  - 1246
  numpy.linalg._linalg.eigvalsh:
  - 1348
  - 1349
  - 1350
  - 1351
  - 1352
  - 1355
  - 1356
  - 1267
  - 1339
  - 1340
  - 1343
  numpy.linalg._linalg.inv:
  - 641
  - 642
  - 643
  - 645
  - 646
  - 648
  - 649
  - 538
  numpy.linalg._linalg.isComplexType:
  - 170
  numpy.linalg._linalg.lstsq:
  - 2560
  - 2563
  - 2564
  - 2566
  - 2567
  - 2518
  - 2519
  - 2521
  - 2523
  - 2526
  - 2529
  - 2530
  - 2532
  - 2533
  - 2535
  - 2536
  - 2539
  - 2541
  - 2543
  - 2545
  - 2547
  - 2423
  - 2553
  - 2554
  - 2559
  numpy.linalg._linalg.matmul:
  - 3410
  - 3331
  numpy.linalg._linalg.matrix_norm:
  - 3504
  - 3505
  - 3452
  numpy.linalg._linalg.matrix_power:
  - 658
  - 722
  - 723
  - 726
  - 732
  - 734
  - 740
  - 741
  - 742
  - 745
  - 746
  - 747
  - 750
  - 753
  - 754
  - 756
  - 757
  - 764
  - 765
  - 766
  - 767
  numpy.linalg._linalg.matrix_rank:
  - 2137
  - 2144
  - 2146
  - 2148
  - 2131
  - 2134
  - 2135
  - 2136
  - 2041
  - 2139
  - 2140
  - 2141
  - 2143
  numpy.linalg._linalg.matrix_transpose:
  - 3434
  numpy.linalg._linalg.multi_dot:
  - 2976
  - 2977
  - 2980
  - 2982
  - 2983
  - 2953
  - 2955
  - 2957
  - 2958
  - 2960
  - 2967
  - 2870
  - 2966
  - 2968
  - 2969
  - 2970
  - 2973
  - 2974
  numpy.linalg._linalg.norm:
  - 2816
  - 2817
  - 2819
  - 2821
  - 2822
  - 2823
  - 2825
  - 2826
  - 2827
  - 2828
  - 2829
  - 2830
  - 2832
  - 2833
  - 2834
  - 2836
  - 2837
  - 2838
  - 2840
  - 2841
  - 2842
  - 2844
  - 2845
  - 2846
  - 2847
  - 2848
  - 2851
  - 2852
  - 2855
  - 2604
  - 2752
  - 2754
  - 2755
  - 2758
  - 2760
  - 2765
  - 2766
  - 2769
  - 2771
  - 2772
  - 2773
  - 2774
  - 2779
  - 2780
  - 2781
  - 2783
  - 2790
  - 2791
  - 2792
  - 2793
  - 2794
  - 2795
  - 2798
  - 2802
  - 2804
  - 2805
  - 2807
  - 2808
  - 2811
  - 2814
  numpy.linalg._linalg.outer:
  - 948
  - 949
  - 950
  - 889
  - 955
  numpy.linalg._linalg.pinv:
  - 2240
  - 2241
  - 2242
  - 2244
  - 2245
  - 2248
  - 2254
  - 2159
  - 2255
  - 2257
  - 2258
  - 2259
  - 2260
  - 2263
  - 2265
  - 2268
  - 2269
  numpy.linalg._linalg.qr:
  - 1156
  - 1157
  - 1159
  - 1160
  - 1162
  - 1163
  - 1165
  - 967
  - 1099
  - 1100
  - 1106
  - 1108
  - 1111
  - 1116
  - 1117
  - 1119
  - 1120
  - 1121
  - 1122
  - 1124
  - 1125
  - 1127
  - 1130
  - 1131
  - 1132
  - 1133
  - 1135
  - 1136
  - 1137
  - 1138
  - 1139
  - 1141
  - 1142
  - 1143
  - 1149
  numpy.linalg._linalg.slogdet:
  - 2277
  - 2348
  - 2349
  - 2350
  - 2351
  - 2352
  - 2353
  - 2354
  - 2355
  - 2356
  numpy.linalg._linalg.solve:
  - 449
  - 450
  - 452
  - 454
  - 365
  - 437
  - 438
  - 439
  - 440
  - 444
  numpy.linalg._linalg.svd:
  - 1670
  - 1805
  - 1807
  - 1811
  - 1812
  - 1813
  - 1814
  - 1815
  - 1816
  - 1817
  - 1818
  - 1820
  - 1821
  - 1823
  - 1824
  - 1825
  - 1827
  - 1828
  - 1831
  - 1832
  - 1837
  - 1838
  - 1841
  - 1842
  - 1843
  - 1844
  - 1845
  - 1847
  - 1848
  - 1851
  - 1852
  numpy.linalg._linalg.svdvals:
  - 1907
  - 1862
  numpy.linalg._linalg.tensordot:
  - 3421
  numpy.linalg._linalg.tensorinv:
  - 516
  - 519
  - 521
  - 525
  - 526
  - 463
  - 527
  numpy.linalg._linalg.tensorsolve:
  - 352
  - 353
  - 354
  - 330
  - 331
  - 334
  - 335
  - 336
  - 337
  - 338
  - 339
  - 343
  - 346
  - 283
  numpy.linalg._linalg.trace:
  - 3241
  - 3167
  numpy.linalg._linalg.transpose:
  - 273
  - 259
  numpy.linalg._linalg.vecdot:
  - 3617
  - 3661
  numpy.linalg._linalg.vector_norm:
  - 3585
  - 3587
  - 3595
  - 3597
  - 3600
  - 3601
  - 3603
  - 3579
  - 3573
  - 3574
  - 3575
  - 3605
  - 3577
  - 3515
  - 3582
  - 3583
  numpy.linalg.linalg.__getattr__:
  - 9
  - 5
  - 6
  numpy.ma:
  - 1
  - 52
  numpy.ma.core:
  - 1
  - 7208
  - 7209
  - 8270
  - 88
  - 8290
  - 8818
  - 8824
  - 8830
  - 8836
  - 8841
  - 8846
  - 8851
  - 8857
  - 8863
  - 8869
  - 8874
  - 8880
  - 8886
  - 189
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  - 6852
  - 200
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  - 221
  - 222
  - 225
  - 226
  - 1253
  - 1254
  - 1255
  - 1256
  - 1257
  - 1258
  - 1259
  - 1260
  - 1261
  - 1262
  - 1263
  - 1264
  - 1265
  - 1266
  - 1267
  - 1268
  - 1269
  - 1272
  - 1273
  - 1274
  - 1275
  - 1276
  - 1277
  - 1278
  - 1279
  - 1280
  - 1281
  - 1282
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  - 1292
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  - 1300
  - 1302
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  - 1306
  - 1308
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  - 1319
  - 1321
  - 1322
  - 1323
  - 1324
  - 1325
  - 7092
  - 7093
  - 7094
  - 7095
  - 7096
  - 7097
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  - 7099
  - 2492
  - 7100
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  - 7112
  - 7113
  - 7114
  - 7116
  - 7117
  - 7119
  - 2514
  - 2521
  - 2529
  - 2534
  numpy.ma.core.MAError:
  - 155
  numpy.ma.core.MaskError:
  - 163
  numpy.ma.core.MaskedArray:
  - 3424
  - 6272
  - 6273
  - 6274
  - 6275
  - 6276
  - 3791
  - 3792
  - 2774
  - 6269
  - 6270
  - 6271
  numpy.ma.core.MaskedArray.<lambda1>:
  - 6275
  numpy.ma.core.MaskedArray.__add__:
  - 4323
  - 4325
  - 4319
  numpy.ma.core.MaskedArray.__array_finalize__:
  - 3140
  - 3109
  - 3141
  - 3142
  - 3112
  - 3144
  - 3114
  - 3116
  - 3054
  - 3087
  - 3121
  - 3090
  - 3091
  - 3059
  - 3093
  - 3124
  - 3130
  - 3102
  numpy.ma.core.MaskedArray.__array_wrap__:
  - 3169
  - 3173
  - 3174
  - 3176
  - 3147
  - 3153
  - 3156
  - 3157
  - 3188
  - 3159
  - 3160
  - 3191
  - 3198
  - 3164
  - 3166
  - 3167
  numpy.ma.core.MaskedArray.__deepcopy__:
  - 6516
  - 6517
  - 6519
  - 6520
  - 6521
  - 6526
  - 6527
  numpy.ma.core.MaskedArray.__eq__:
  - 4280
  - 4290
  numpy.ma.core.MaskedArray.__float__:
  - 4526
  - 4530
  - 4533
  - 4534
  - 4536
  numpy.ma.core.MaskedArray.__floordiv__:
  - 4384
  - 4388
  - 4390
  numpy.ma.core.MaskedArray.__ge__:
  - 4313
  numpy.ma.core.MaskedArray.__getitem__:
  - 3336
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  - 3359
  - 3362
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  - 3367
  - 3372
  - 3378
  - 3380
  - 3382
  - 3383
  - 3388
  - 3398
  - 3399
  - 3401
  - 3411
  - 3414
  - 3416
  - 3297
  numpy.ma.core.MaskedArray.__getitem__._is_scalar:
  - 3311
  numpy.ma.core.MaskedArray.__getitem__._scalar_heuristic:
  - 3331
  - 3314
  - 3319
  - 3323
  - 3324
  numpy.ma.core.MaskedArray.__getstate__:
  - 6481
  - 6486
  - 6487
  numpy.ma.core.MaskedArray.__gt__:
  - 4316
  numpy.ma.core.MaskedArray.__iadd__:
  - 4416
  - 4420
  - 4421
  - 4422
  - 4423
  - 4425
  - 4427
  - 4428
  - 4429
  numpy.ma.core.MaskedArray.__ifloordiv__:
  - 4481
  - 4482
  - 4467
  - 4471
  - 4472
  - 4473
  - 4474
  - 4476
  - 4478
  - 4479
  numpy.ma.core.MaskedArray.__imul__:
  - 4450
  - 4454
  - 4455
  - 4456
  - 4457
  - 4459
  - 4461
  - 4462
  - 4463
  numpy.ma.core.MaskedArray.__int__:
  - 4546
  - 4539
  - 4548
  - 4543
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  - 879
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  - 1030
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  - 948
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  - 1019
  - 1022
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  - 1067
  - 1038
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  - 1076
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  - 875
  - 876
  - 846
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  - 747
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  - 771
  - 773
  - 777
  - 751
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  - 464
  - 467
  - 470
  - 473
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  - 1921
  - 1953
  - 1954
  - 1955
  - 1956
  - 1957
  - 1958
  - 2001
  - 2002
  - 1947
  - 1948
  - 1949
  - 1950
  - 1951
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  - 1962
  - 1996
  - 1997
  - 1998
  numpy.polynomial.chebyshev.Chebyshev.interpolate.<lambda1>:
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  numpy.polynomial.chebyshev._cseries_to_zseries:
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  - 134
  numpy.polynomial.chebyshev._zseries_der:
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  - 303
  - 304
  - 305
  - 278
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  - 273
  - 211
  - 244
  - 245
  - 246
  - 243
  - 250
  - 247
  numpy.polynomial.chebyshev._zseries_int:
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  - 334
  - 335
  - 336
  - 310
  numpy.polynomial.chebyshev._zseries_mul:
  - 184
  - 207
  numpy.polynomial.chebyshev._zseries_to_cseries:
  - 178
  - 159
  numpy.polynomial.chebyshev.cheb2poly:
  - 451
  - 453
  - 454
  - 455
  - 398
  - 443
  - 444
  - 445
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  - 608
  - 570
  numpy.polynomial.chebyshev.chebcompanion:
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  - 1651
  - 1653
  - 1654
  - 1656
  - 1657
  - 1658
  - 1659
  - 1660
  - 1661
  - 1630
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  - 932
  - 933
  - 934
  - 935
  - 936
  - 937
  - 875
  - 939
  - 941
  - 944
  - 945
  - 946
  - 949
  - 952
  - 953
  - 956
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  - 802
  - 803
  - 805
  - 808
  - 809
  - 810
  - 811
  - 812
  - 750
  - 796
  - 797
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  - 1505
  - 1626
  numpy.polynomial.chebyshev.chebfromroots:
  - 515
  - 566
  numpy.polynomial.chebyshev.chebgauss:
  - 1825
  - 1794
  - 1826
  - 1829
  - 1830
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  - 1258
  - 1215
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  - 1353
  - 1307
  numpy.polynomial.chebyshev.chebint:
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  - 1048
  - 1049
  - 1050
  - 1052
  - 1053
  - 1054
  - 1056
  - 1058
  - 1060
  - 1062
  - 1064
  - 1067
  - 1068
  - 1069
  - 1070
  - 1072
  - 1075
  - 1078
  - 1080
  - 1083
  - 1085
  - 965
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  - 1774
  - 1777
  - 1779
  - 1783
  - 1784
  - 1785
  - 1786
  numpy.polynomial.chebyshev.chebline:
  - 509
  - 508
  - 477
  - 511
  numpy.polynomial.chebyshev.chebmul:
  - 741
  - 742
  - 743
  - 744
  - 745
  - 746
  - 701
  numpy.polynomial.chebyshev.chebmulx:
  - 685
  - 687
  - 656
  - 690
  - 693
  numpy.polynomial.chebyshev.chebpow:
  - 867
  - 869
  - 870
  - 871
  - 817
  - 854
  - 855
  - 856
  - 858
  - 860
  - 861
  - 862
  numpy.polynomial.chebyshev.chebpts1:
  - 1858
  - 1878
  - 1879
  - 1881
  - 1884
  - 1885
  numpy.polynomial.chebyshev.chebpts2:
  - 1889
  - 1906
  - 1907
  - 1909
  - 1912
  - 1913
  numpy.polynomial.chebyshev.chebroots:
  - 1669
  - 1716
  - 1717
  - 1718
  - 1719
  - 1720
  - 1723
  - 1724
  - 1725
  numpy.polynomial.chebyshev.chebsub:
  - 652
  - 612
  numpy.polynomial.chebyshev.chebval:
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  - 1090
  - 1154
  - 1157
  - 1164
  - 1146
  - 1147
  - 1148
  - 1149
  - 1150
  - 1151
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  - 1172
  numpy.polynomial.chebyshev.chebval3d:
  - 1262
  - 1303
  numpy.polynomial.chebyshev.chebvander:
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  - 1391
  - 1392
  - 1395
  - 1398
  - 1401
  - 1404
  - 1406
  numpy.polynomial.chebyshev.chebvander2d:
  - 1410
  - 1453
  numpy.polynomial.chebyshev.chebvander3d:
  - 1457
  - 1501
  numpy.polynomial.chebyshev.chebweight:
  - 1836
  - 1853
  numpy.polynomial.chebyshev.poly2cheb:
  - 389
  - 390
  - 392
  - 393
  - 348
  numpy.polynomial.hermite:
  - 1
  - 206
  - 209
  - 212
  - 215
  numpy.polynomial.hermite.Hermite:
  - 1728
  - 1729
  - 1698
  - 1730
  - 1731
  - 1732
  - 1733
  - 1734
  - 1735
  - 1738
  - 1739
  - 1724
  - 1725
  - 1726
  - 1727
  numpy.polynomial.hermite._normed_hermite_n:
  - 1578
  - 1579
  - 1582
  - 1583
  - 1552
  - 1584
  - 1586
  - 1587
  - 1589
  numpy.polynomial.hermite.herm2poly:
  - 193
  - 195
  - 196
  - 197
  - 143
  - 182
  - 183
  - 184
  - 186
  numpy.polynomial.hermite.hermadd:
  - 315
  - 351
  numpy.polynomial.hermite.hermcompanion:
  - 1472
  - 1441
  - 1473
  - 1475
  - 1476
  - 1477
  - 1478
  - 1479
  - 1480
  - 1481
  - 1469
  - 1470
  numpy.polynomial.hermite.hermder:
  - 672
  - 675
  - 663
  - 651
  - 652
  - 653
  - 654
  - 655
  - 656
  - 658
  - 660
  - 599
  - 664
  - 665
  - 668
  - 671
  numpy.polynomial.hermite.hermdiv:
  - 514
  - 558
  numpy.polynomial.hermite.hermfit:
  - 1307
  - 1437
  numpy.polynomial.hermite.hermfromroots:
  - 259
  - 311
  numpy.polynomial.hermite.hermgauss:
  - 1632
  - 1637
  - 1638
  - 1639
  - 1642
  - 1643
  - 1648
  - 1649
  - 1657
  - 1593
  - 1631
  numpy.polynomial.hermite.hermgrid2d:
  - 1002
  - 947
  numpy.polynomial.hermite.hermgrid3d:
  - 1122
  - 1062
  numpy.polynomial.hermite.hermint:
  - 769
  - 771
  - 773
  - 775
  - 777
  - 780
  - 781
  - 782
  - 783
  - 785
  - 788
  - 791
  - 793
  - 795
  - 680
  - 760
  - 761
  - 762
  - 763
  - 765
  - 766
  - 767
  numpy.polynomial.hermite.hermline:
  - 219
  - 252
  - 253
  - 255
  numpy.polynomial.hermite.hermmul:
  - 448
  - 486
  - 488
  - 495
  - 498
  - 502
  - 505
  - 508
  - 509
  - 510
  numpy.polynomial.hermite.hermmulx:
  - 395
  - 433
  - 435
  - 438
  - 441
  numpy.polynomial.hermite.hermpow:
  - 562
  - 595
  numpy.polynomial.hermite.hermroots:
  - 1538
  - 1539
  - 1540
  - 1541
  - 1542
  - 1545
  - 1546
  - 1547
  - 1488
  numpy.polynomial.hermite.hermsub:
  - 355
  - 391
  numpy.polynomial.hermite.hermval:
  - 800
  - 866
  - 867
  - 868
  - 869
  - 870
  - 871
  - 872
  - 875
  - 878
  - 882
  - 885
  numpy.polynomial.hermite.hermval2d:
  - 894
  - 943
  numpy.polynomial.hermite.hermval3d:
  - 1058
  - 1006
  numpy.polynomial.hermite.hermvander:
  - 1184
  - 1126
  - 1170
  - 1171
  - 1174
  - 1177
  - 1179
  - 1182
  numpy.polynomial.hermite.hermvander2d:
  - 1243
  - 1188
  numpy.polynomial.hermite.hermvander3d:
  - 1303
  - 1247
  numpy.polynomial.hermite.hermweight:
  - 1689
  - 1663
  numpy.polynomial.hermite.poly2herm:
  - 97
  - 134
  - 135
  - 137
  - 138
  numpy.polynomial.hermite_e:
  - 1
  - 207
  - 210
  - 213
  - 216
  numpy.polynomial.hermite_e.HermiteE:
  - 1600
  - 1632
  - 1633
  - 1634
  - 1635
  - 1636
  - 1637
  - 1640
  - 1641
  - 1626
  - 1627
  - 1628
  - 1629
  - 1630
  - 1631
  numpy.polynomial.hermite_e._normed_hermite_e_n:
  - 1504
  - 1505
  - 1502
  - 1496
  - 1497
  - 1500
  - 1501
  - 1470
  numpy.polynomial.hermite_e.herme2poly:
  - 194
  - 196
  - 197
  - 198
  - 145
  - 184
  - 185
  - 186
  - 188
  numpy.polynomial.hermite_e.hermeadd:
  - 315
  - 351
  numpy.polynomial.hermite_e.hermecompanion:
  - 1387
  - 1388
  - 1390
  - 1391
  - 1393
  - 1394
  - 1395
  - 1396
  - 1397
  - 1366
  - 1398
  - 1399
  numpy.polynomial.hermite_e.hermeder:
  - 673
  - 663
  - 649
  - 650
  - 651
  - 652
  - 653
  - 654
  - 656
  - 658
  - 597
  - 661
  - 662
  - 666
  - 669
  - 670
  numpy.polynomial.hermite_e.hermediv:
  - 514
  - 556
  numpy.polynomial.hermite_e.hermefit:
  - 1232
  - 1362
  numpy.polynomial.hermite_e.hermefromroots:
  - 259
  - 311
  numpy.polynomial.hermite_e.hermegauss:
  - 1569
  - 1543
  - 1544
  - 1511
  - 1549
  - 1550
  - 1551
  - 1554
  - 1555
  - 1560
  - 1561
  numpy.polynomial.hermite_e.hermegrid2d:
  - 977
  - 934
  numpy.polynomial.hermite_e.hermegrid3d:
  - 1072
  - 1026
  numpy.polynomial.hermite_e.hermeint:
  - 769
  - 771
  - 773
  - 775
  - 778
  - 779
  - 780
  - 781
  - 783
  - 786
  - 789
  - 791
  - 793
  - 678
  - 758
  - 759
  - 760
  - 761
  - 763
  - 764
  - 765
  - 767
  numpy.polynomial.hermite_e.hermeline:
  - 253
  - 220
  - 252
  - 255
  numpy.polynomial.hermite_e.hermemul:
  - 448
  - 486
  - 488
  - 495
  - 498
  - 502
  - 505
  - 508
  - 509
  - 510
  numpy.polynomial.hermite_e.hermemulx:
  - 395
  - 433
  - 435
  - 438
  - 441
  numpy.polynomial.hermite_e.hermepow:
  - 560
  - 593
  numpy.polynomial.hermite_e.hermeroots:
  - 1456
  - 1457
  - 1458
  - 1459
  - 1460
  - 1463
  - 1464
  - 1465
  - 1406
  numpy.polynomial.hermite_e.hermesub:
  - 355
  - 391
  numpy.polynomial.hermite_e.hermeval:
  - 864
  - 865
  - 866
  - 867
  - 868
  - 869
  - 870
  - 872
  - 875
  - 879
  - 882
  - 798
  numpy.polynomial.hermite_e.hermeval2d:
  - 930
  - 891
  numpy.polynomial.hermite_e.hermeval3d:
  - 981
  - 1022
  numpy.polynomial.hermite_e.hermevander:
  - 1120
  - 1121
  - 1124
  - 1127
  - 1129
  - 1131
  - 1133
  - 1076
  numpy.polynomial.hermite_e.hermevander2d:
  - 1137
  - 1180
  numpy.polynomial.hermite_e.hermevander3d:
  - 1184
  - 1228
  numpy.polynomial.hermite_e.hermeweight:
  - 1591
  - 1575
  numpy.polynomial.hermite_e.poly2herme:
  - 98
  - 136
  - 137
  - 139
  - 140
  numpy.polynomial.laguerre:
  - 1
  - 203
  - 206
  - 209
  - 212
  numpy.polynomial.laguerre.Laguerre:
  - 1664
  - 1633
  - 1665
  - 1666
  - 1667
  - 1668
  - 1669
  - 1670
  - 1673
  - 1674
  - 1659
  - 1660
  - 1661
  - 1662
  - 1663
  numpy.polynomial.laguerre.lag2poly:
  - 192
  - 193
  - 194
  - 143
  - 182
  - 183
  - 184
  - 190
  numpy.polynomial.laguerre.lagadd:
  - 346
  - 310
  numpy.polynomial.laguerre.lagcompanion:
  - 1451
  - 1452
  - 1454
  - 1455
  - 1424
  - 1457
  - 1458
  - 1459
  - 1460
  - 1461
  - 1462
  - 1463
  numpy.polynomial.laguerre.lagder:
  - 672
  - 645
  - 646
  - 647
  - 649
  - 650
  - 651
  - 653
  - 655
  - 593
  - 658
  - 659
  - 660
  - 663
  - 666
  - 667
  numpy.polynomial.laguerre.lagdiv:
  - 552
  - 510
  numpy.polynomial.laguerre.lagfit:
  - 1290
  - 1420
  numpy.polynomial.laguerre.lagfromroots:
  - 306
  - 254
  numpy.polynomial.laguerre.laggauss:
  - 1572
  - 1573
  - 1578
  - 1579
  - 1580
  - 1583
  - 1584
  - 1589
  - 1590
  - 1591
  - 1595
  - 1534
  numpy.polynomial.laguerre.laggrid2d:
  - 994
  - 942
  numpy.polynomial.laguerre.laggrid3d:
  - 1051
  - 1108
  numpy.polynomial.laguerre.lagint:
  - 769
  - 771
  - 773
  - 775
  - 778
  - 779
  - 780
  - 781
  - 783
  - 786
  - 789
  - 792
  - 794
  - 677
  - 758
  - 759
  - 760
  - 761
  - 763
  - 764
  - 765
  - 767
  numpy.polynomial.laguerre.lagline:
  - 216
  - 248
  - 250
  - 247
  numpy.polynomial.laguerre.lagmul:
  - 482
  - 484
  - 491
  - 494
  - 498
  - 501
  - 504
  - 505
  - 506
  - 444
  numpy.polynomial.laguerre.lagmulx:
  - 390
  - 428
  - 430
  - 433
  - 436
  numpy.polynomial.laguerre.lagpow:
  - 556
  - 589
  numpy.polynomial.laguerre.lagroots:
  - 1520
  - 1521
  - 1522
  - 1523
  - 1524
  - 1527
  - 1528
  - 1529
  - 1470
  numpy.polynomial.laguerre.lagsub:
  - 386
  - 350
  numpy.polynomial.laguerre.lagval:
  - 865
  - 866
  - 867
  - 868
  - 869
  - 870
  - 871
  - 873
  - 876
  - 880
  - 883
  - 799
  numpy.polynomial.laguerre.lagval2d:
  - 938
  - 892
  numpy.polynomial.laguerre.lagval3d:
  - 998
  - 1047
  numpy.polynomial.laguerre.lagvander:
  - 1156
  - 1157
  - 1160
  - 1163
  - 1165
  - 1167
  - 1169
  - 1112
  numpy.polynomial.laguerre.lagvander2d:
  - 1226
  - 1173
  numpy.polynomial.laguerre.lagvander3d:
  - 1230
  - 1286
  numpy.polynomial.laguerre.lagweight:
  - 1625
  - 1601
  numpy.polynomial.laguerre.poly2lag:
  - 97
  - 137
  - 138
  - 135
  numpy.polynomial.legendre:
  - 1
  - 226
  - 217
  - 220
  - 223
  numpy.polynomial.legendre.Legendre:
  - 1600
  - 1603
  - 1604
  - 1599
  - 1589
  - 1590
  - 1591
  - 1592
  - 1593
  - 1594
  - 1563
  - 1596
  - 1597
  - 1598
  - 1595
  numpy.polynomial.legendre.leg2poly:
  - 196
  - 197
  - 198
  - 204
  - 206
  - 207
  - 208
  - 150
  numpy.polynomial.legendre.legadd:
  - 363
  - 325
  numpy.polynomial.legendre.legcompanion:
  - 1394
  - 1395
  - 1397
  - 1398
  - 1400
  - 1401
  - 1402
  - 1403
  - 1404
  - 1405
  - 1374
  numpy.polynomial.legendre.legder:
  - 672
  - 673
  - 674
  - 675
  - 676
  - 677
  - 615
  - 679
  - 681
  - 684
  - 685
  - 686
  - 689
  - 692
  - 693
  - 696
  - 700
  numpy.polynomial.legendre.legdiv:
  - 580
  - 535
  numpy.polynomial.legendre.legfit:
  - 1370
  - 1247
  numpy.polynomial.legendre.legfromroots:
  - 321
  - 270
  numpy.polynomial.legendre.leggauss:
  - 1473
  - 1506
  - 1505
  - 1511
  - 1512
  - 1513
  - 1516
  - 1517
  - 1522
  - 1523
  - 1524
  - 1532
  numpy.polynomial.legendre.leggrid2d:
  - 1000
  - 957
  numpy.polynomial.legendre.leggrid3d:
  - 1049
  - 1095
  numpy.polynomial.legendre.legint:
  - 787
  - 788
  - 789
  - 790
  - 792
  - 793
  - 794
  - 796
  - 798
  - 800
  - 802
  - 804
  - 807
  - 808
  - 809
  - 810
  - 812
  - 815
  - 818
  - 820
  - 824
  - 826
  - 705
  numpy.polynomial.legendre.legline:
  - 264
  - 266
  - 230
  - 263
  numpy.polynomial.legendre.legmul:
  - 516
  - 519
  - 523
  - 526
  - 529
  - 530
  - 467
  - 531
  - 507
  - 509
  numpy.polynomial.legendre.legmulx:
  - 449
  - 451
  - 454
  - 457
  - 411
  numpy.polynomial.legendre.legpow:
  - 584
  - 611
  numpy.polynomial.legendre.legroots:
  - 1412
  - 1459
  - 1460
  - 1461
  - 1462
  - 1463
  - 1466
  - 1467
  - 1468
  numpy.polynomial.legendre.legsub:
  - 407
  - 367
  numpy.polynomial.legendre.legval:
  - 898
  - 902
  - 905
  - 895
  - 887
  - 888
  - 889
  - 890
  - 891
  - 892
  - 893
  - 831
  numpy.polynomial.legendre.legval2d:
  - 953
  - 914
  numpy.polynomial.legendre.legval3d:
  - 1004
  - 1045
  numpy.polynomial.legendre.legvander:
  - 1099
  - 1133
  - 1134
  - 1137
  - 1140
  - 1144
  - 1146
  - 1148
  numpy.polynomial.legendre.legvander2d:
  - 1152
  - 1195
  numpy.polynomial.legendre.legvander3d:
  - 1243
  - 1199
  numpy.polynomial.legendre.legweight:
  - 1538
  numpy.polynomial.legendre.poly2leg:
  - 101
  - 141
  - 142
  - 144
  - 145
  numpy.polynomial.polynomial:
  - 1
  - 99
  - 102
  - 903
  - 105
  - 108
  - 851
  numpy.polynomial.polynomial.Polynomial:
  - 1591
  - 1582
  - 1583
  - 1584
  - 1585
  - 1586
  - 1587
  - 1556
  - 1588
  - 1589
  - 1590
  - 1592
  - 1593
  - 1596
  - 1597
  numpy.polynomial.polynomial.Polynomial._repr_latex_term:
  - 1616
  - 1618
  - 1620
  numpy.polynomial.polynomial.Polynomial._str_term_ascii:
  - 1609
  numpy.polynomial.polynomial.Polynomial._str_term_unicode:
  - 1602
  - 1605
  numpy.polynomial.polynomial.polyadd:
  - 218
  - 250
  numpy.polynomial.polynomial.polycompanion:
  - 1475
  - 1476
  - 1478
  - 1479
  - 1447
  - 1481
  - 1482
  - 1483
  numpy.polynomial.polynomial.polyder:
  - 517
  - 518
  - 522
  - 523
  - 524
  - 526
  - 528
  - 531
  - 468
  - 532
  - 533
  - 536
  - 539
  - 540
  - 543
  numpy.polynomial.polynomial.polydiv:
  - 425
  - 371
  - 404
  - 405
  - 409
  - 410
  - 411
  - 413
  numpy.polynomial.polynomial.polyfit:
  - 1443
  - 1293
  numpy.polynomial.polynomial.polyfromroots:
  - 154
  - 214
  numpy.polynomial.polynomial.polygrid2d:
  - 905
  - 957
  numpy.polynomial.polynomial.polygrid3d:
  - 1069
  - 1014
  numpy.polynomial.polynomial.polyint:
  - 640
  - 642
  - 645
  - 646
  - 647
  - 648
  - 650
  - 653
  - 656
  - 658
  - 660
  - 548
  - 623
  - 624
  - 628
  - 630
  - 631
  - 632
  - 634
  - 636
  - 638
  numpy.polynomial.polynomial.polyline:
  - 148
  - 147
  - 116
  - 150
  numpy.polynomial.polynomial.polymul:
  - 365
  - 333
  - 366
  - 367
  numpy.polynomial.polynomial.polymulx:
  - 321
  - 323
  - 291
  - 326
  numpy.polynomial.polynomial.polypow:
  - 464
  - 429
  numpy.polynomial.polynomial.polyroots:
  - 1539
  - 1540
  - 1541
  - 1542
  - 1543
  - 1545
  - 1546
  - 1547
  - 1490
  numpy.polynomial.polynomial.polysub:
  - 254
  - 287
  numpy.polynomial.polynomial.polyval:
  - 744
  - 745
  - 748
  - 749
  - 750
  - 751
  - 754
  - 665
  numpy.polynomial.polynomial.polyval2d:
  - 853
  - 901
  numpy.polynomial.polynomial.polyval3d:
  - 961
  - 1010
  numpy.polynomial.polynomial.polyvalfromroots:
  - 833
  - 834
  - 835
  - 836
  - 837
  - 838
  - 839
  - 840
  - 841
  - 843
  - 760
  numpy.polynomial.polynomial.polyvander:
  - 1123
  - 1124
  - 1127
  - 1130
  - 1132
  - 1134
  - 1136
  - 1073
  numpy.polynomial.polynomial.polyvander2d:
  - 1140
  - 1215
  numpy.polynomial.polynomial.polyvander3d:
  - 1289
  - 1219
  numpy.polynomial.polyutils:
  - 1
  numpy.polynomial.polyutils._add:
  - 560
  - 561
  - 558
  - 567
  numpy.polynomial.polyutils._as_int:
  - 722
  - 706
  numpy.polynomial.polyutils._div:
  - 544
  - 547
  - 549
  - 550
  - 522
  - 554
  - 536
  - 537
  - 540
  - 541
  - 542
  numpy.polynomial.polyutils._fit:
  - 642
  - 643
  - 645
  - 649
  - 653
  - 654
  - 655
  - 657
  - 662
  - 664
  - 666
  - 585
  - 595
  - 596
  - 597
  - 600
  - 602
  - 604
  - 606
  - 608
  - 610
  - 613
  - 616
  - 618
  - 620
  - 621
  - 626
  - 627
  - 628
  - 630
  - 638
  - 639
  numpy.polynomial.polyutils._fromroots:
  - 458
  - 459
  - 461
  - 462
  - 463
  - 464
  - 466
  - 467
  - 468
  - 469
  - 446
  numpy.polynomial.polyutils._gridnd:
  - 506
  - 516
  - 517
  numpy.polynomial.polyutils._nth_slice:
  - 362
  - 363
  numpy.polynomial.polyutils._pow:
  - 673
  - 686
  - 687
  - 688
  - 690
  - 692
  - 693
  - 694
  - 700
  - 701
  numpy.polynomial.polyutils._sub:
  - 571
  - 581
  - 573
  - 574
  numpy.polynomial.polyutils._valnd:
  - 486
  - 488
  - 489
  - 491
  - 495
  - 496
  - 499
  - 500
  - 501
  - 476
  numpy.polynomial.polyutils._vander_nd:
  - 418
  - 422
  - 427
  - 428
  - 367
  - 432
  - 411
  - 412
  - 415
  numpy.polynomial.polyutils._vander_nd_flat:
  - 441
  - 442
  - 436
  numpy.polynomial.polyutils.as_series:
  - 128
  - 66
  - 131
  - 132
  - 133
  - 135
  - 138
  - 139
  - 142
  - 116
  - 117
  - 118
  - 120
  - 122
  - 123
  - 126
  numpy.polynomial.polyutils.format_float:
  - 739
  - 740
  - 741
  - 745
  - 748
  - 749
  - 752
  - 755
  - 728
  - 729
  - 731
  - 733
  - 735
  numpy.polynomial.polyutils.getdomain:
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ep:
  func_name: TestOneInput
  module: '...fuzz_loader'
