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training-diff-ksizes
Raw
estimated_probabilities.k11.txt
kmer size= 11
seq count= 12933823 dropped seqs= 315235
base counts= {'A': 205503171, 'C': 192321024, 'T': 202910059, 'G': 194236790}
state counts= {'Ig_u': 8251, 'Ig_t': 227463, 'Ir_t': 1919898, 'Ir_u': 92814, 'M_u': 12518640, 'M_t': 780203978}
trans counts= {'Ir_t-Ir_t': 1085201, 'Ir_t-Ir_u': 4, 'M_u-Ir_t': 468, 'Ir_t-M_u': 3, 'M_t-Ig_t': 178959, 'Ig_t-M_u': 1, 'Ig_t-Ig_t': 48504, 'M_u-M_u': 63, 'M_u-M_t': 12518088, 'Ig_u-M_t': 8251, 'M_t-M_t': 766669253, 'M_t-Ir_t': 834188, 'Ig_t-M_t': 168373, 'Ir_t-M_t': 747240, 'M_t-Ir_u': 1, 'Ir_u-M_t': 92773, 'Ir_u-Ir_t': 41, 'M_t-M_u': 1045}
Ig_t-Ig_t 0.2132391
Ig_t-M_t 0.7402215
Ig_t-M_u 0.0000044
Ig_u-M_t 1.0000000
Ir_t-Ir_t 0.5652389
Ir_t-Ir_u 0.0000021
Ir_t-M_t 0.3892082
Ir_t-M_u 0.0000016
Ir_u-Ir_t 0.0004417
Ir_u-M_t 0.9995583
M_t-Ig_t 0.0002294
M_t-Ir_t 0.0010692
M_t-Ir_u 0.0000000
M_t-M_t 0.9826523
M_t-M_u 0.0000013
M_u-Ir_t 0.0000374
M_u-M_t 0.9999559
M_u-M_u 0.0000050
Raw
estimated_probabilities.k12.txt
kmer size= 12
seq count= 12933823 dropped seqs= 315235
base counts= {'A': 202313923, 'C': 189196843, 'T': 199563310, 'G': 191278380}
state counts= {'Ig_u': 8312, 'Ig_t': 216441, 'Ir_t': 1827608, 'Ir_u': 84189, 'M_u': 13229059, 'M_t': 766986847}
trans counts= {'Ig_t-M_t': 160190, 'M_u-Ig_u': 38, 'M_u-Ig_t': 507, 'Ir_t-M_t': 718450, 'Ir_t-M_u': 1257, 'Ig_t-M_u': 238, 'Ir_u-M_t': 82330, 'Ir_u-M_u': 241, 'Ir_t-Ir_t': 1030305, 'Ir_t-Ir_u': 2765, 'Ig_t-Ig_t': 46112, 'Ig_t-Ig_u': 68, 'Ig_u-M_u': 38, 'Ig_u-M_t': 8162, 'Ir_u-Ir_u': 309, 'Ir_u-Ir_t': 1305, 'M_u-M_u': 127126, 'Ig_u-Ig_t': 97, 'M_t-Ir_u': 1527, 'M_t-Ir_t': 794574, 'Ig_u-Ig_u': 11, 'M_u-Ir_t': 1424, 'M_u-Ir_u': 244, 'M_u-M_t': 13086075, 'M_t-Ig_t': 169725, 'M_t-Ig_u': 320, 'M_t-M_t': 752931640, 'M_t-M_u': 568790}
Ig_t-Ig_t 0.2130465
Ig_t-Ig_u 0.0003142
Ig_t-M_t 0.7401093
Ig_t-M_u 0.0010996
Ig_u-Ig_t 0.0116699
Ig_u-Ig_u 0.0013234
Ig_u-M_t 0.9819538
Ig_u-M_u 0.0045717
Ir_t-Ir_t 0.5637451
Ir_t-Ir_u 0.0015129
Ir_t-M_t 0.3931095
Ir_t-M_u 0.0006878
Ir_u-Ir_t 0.0155008
Ir_u-Ir_u 0.0036703
Ir_u-M_t 0.9779187
Ir_u-M_u 0.0028626
M_t-Ig_t 0.0002213
M_t-Ig_u 0.0000004
M_t-Ir_t 0.0010360
M_t-Ir_u 0.0000020
M_t-M_t 0.9816748
M_t-M_u 0.0007416
M_u-Ig_t 0.0000383
M_u-Ig_u 0.0000029
M_u-Ir_t 0.0001076
M_u-Ir_u 0.0000184
M_u-M_t 0.9891917
M_u-M_u 0.0096096
Raw
estimated_probabilities.k13.txt
kmer size= 13
seq count= 12933823 dropped seqs= 315235
base counts= {'A': 199185798, 'C': 186012405, 'T': 196233401, 'G': 188302264}
state counts= {'Ig_u': 15562, 'Ig_t': 198701, 'Ir_t': 1654505, 'Ir_u': 169264, 'M_u': 26727498, 'M_t': 740968338}
trans counts= {'Ig_t-M_t': 144823, 'M_u-Ig_u': 2641, 'M_u-Ig_t': 8444, 'Ig_t-M_u': 2957, 'Ir_t-M_u': 17967, 'Ir_t-M_t': 622226, 'Ir_u-M_t': 115454, 'Ir_u-M_u': 12205, 'Ir_t-Ir_t': 909602, 'Ir_t-Ir_u': 42299, 'Ig_t-Ig_t': 41344, 'Ig_t-Ig_u': 960, 'Ig_u-M_u': 1990, 'Ig_u-M_t': 11471, 'Ir_u-Ir_u': 21086, 'Ir_u-Ir_t': 20406, 'M_u-M_u': 6075252, 'M_u-M_t': 20336827, 'M_t-Ir_u': 26264, 'M_t-Ir_t': 709816, 'Ig_u-Ig_u': 604, 'M_u-Ir_t': 14681, 'M_u-Ir_u': 13464, 'Ig_u-Ig_t': 1440, 'M_t-Ig_t': 147473, 'M_t-Ig_u': 4135, 'M_t-M_t': 719737537, 'M_t-M_u': 8071912}
Ig_t-Ig_t 0.2080714
Ig_t-Ig_u 0.0048314
Ig_t-M_t 0.7288489
Ig_t-M_u 0.0148817
Ig_u-Ig_t 0.0925331
Ig_u-Ig_u 0.0388125
Ig_u-M_t 0.7371161
Ig_u-M_u 0.1278756
Ir_t-Ir_t 0.5497729
Ir_t-Ir_u 0.0255660
Ir_t-M_t 0.3760799
Ir_t-M_u 0.0108594
Ir_u-Ir_t 0.1205572
Ir_u-Ir_u 0.1245746
Ir_u-M_t 0.6820942
Ir_u-M_u 0.0721063
M_t-Ig_t 0.0001990
M_t-Ig_u 0.0000056
M_t-Ir_t 0.0009580
M_t-Ir_u 0.0000354
M_t-M_t 0.9713472
M_t-M_u 0.0108937
M_u-Ig_t 0.0003159
M_u-Ig_u 0.0000988
M_u-Ir_t 0.0005493
M_u-Ir_u 0.0005038
M_u-M_t 0.7608953
M_u-M_u 0.2273034
Raw
estimated_probabilities.k14.txt
kmer size= 14
seq count= 12933823 dropped seqs= 315235
base counts= {'A': 196008982, 'C': 182882986, 'T': 192951983, 'G': 185271329}
state counts= {'Ig_u': 35938, 'Ig_t': 169235, 'Ir_t': 1312457, 'Ir_u': 437086, 'M_u': 63734755, 'M_t': 691425809}
trans counts= {'Ig_t-M_t': 119542, 'M_u-Ig_u': 15230, 'M_u-Ig_t': 23175, 'Ig_t-M_u': 6308, 'Ir_t-M_u': 41197, 'Ir_t-M_t': 437925, 'Ir_u-M_t': 189727, 'Ir_u-M_u': 69273, 'Ir_t-Ir_t': 687526, 'Ir_t-Ir_u': 95461, 'Ig_t-Ig_t': 33549, 'Ig_t-Ig_u': 1991, 'Ig_u-M_u': 11264, 'Ig_u-M_t': 17459, 'Ir_u-Ir_u': 134381, 'Ir_u-Ir_t': 43285, 'M_u-M_u': 33881904, 'M_u-M_t': 28685803, 'M_t-Ir_u': 66509, 'M_t-Ir_t': 551804, 'Ig_u-Ig_u': 3559, 'M_u-Ir_t': 29842, 'M_u-Ir_u': 86286, 'Ig_u-Ig_t': 3476, 'M_t-Ig_t': 109035, 'M_t-Ig_u': 8352, 'M_t-M_t': 661975353, 'M_t-M_u': 17167476}
Ig_t-Ig_t 0.1982391
Ig_t-Ig_u 0.0117647
Ig_t-M_t 0.7063669
Ig_t-M_u 0.0372736
Ig_u-Ig_t 0.0967221
Ig_u-Ig_u 0.0990317
Ig_u-M_t 0.4858089
Ig_u-M_u 0.3134287
Ir_t-Ir_t 0.5238465
Ir_t-Ir_u 0.0727346
Ir_t-M_t 0.3336681
Ir_t-M_u 0.0313892
Ir_u-Ir_t 0.0990309
Ir_u-Ir_u 0.3074475
Ir_u-M_t 0.4340725
Ir_u-M_u 0.1584883
M_t-Ig_t 0.0001577
M_t-Ig_u 0.0000121
M_t-Ir_t 0.0007981
M_t-Ir_u 0.0000962
M_t-M_t 0.9574062
M_t-M_u 0.0248291
M_u-Ig_t 0.0003636
M_u-Ig_u 0.0002390
M_u-Ir_t 0.0004682
M_u-Ir_u 0.0013538
M_u-M_t 0.4500810
M_u-M_u 0.5316080
Raw
estimated_probabilities.k15.txt
kmer size= 15
seq count= 12933823 dropped seqs= 315235
base counts= {'A': 192803878, 'C': 179790372, 'T': 189612678, 'G': 182289764}
state counts= {'Ig_u': 56108, 'Ig_t': 140546, 'Ir_t': 955612, 'Ir_u': 732648, 'M_u': 101854446, 'M_t': 640757332}
trans counts= {'Ig_t-M_t': 98409, 'M_u-Ig_u': 32387, 'M_u-Ig_t': 32124, 'Ig_t-M_u': 6426, 'Ir_t-M_u': 45087, 'Ir_t-M_t': 279037, 'Ir_u-M_t': 234550, 'Ir_u-M_u': 153471, 'Ir_t-Ir_t': 482709, 'Ir_t-Ir_u': 108830, 'Ig_t-Ig_t': 27134, 'Ig_t-Ig_u': 1966, 'Ig_u-M_u': 24283, 'Ig_u-M_t': 19590, 'Ir_u-Ir_u': 306452, 'Ir_u-Ir_t': 37441, 'M_u-M_u': 72330408, 'M_u-M_t': 27433000, 'M_t-Ir_u': 68635, 'M_t-Ir_t': 408472, 'Ig_u-Ig_u': 7566, 'M_u-Ir_t': 26990, 'M_u-Ir_u': 204797, 'Ig_u-Ig_t': 4319, 'M_t-Ig_t': 76969, 'M_t-Ig_u': 7877, 'M_t-M_t': 612692746, 'M_t-M_u': 16726429}
Ig_t-Ig_t 0.1930613
Ig_t-Ig_u 0.0139883
Ig_t-M_t 0.7001907
Ig_t-M_u 0.0457217
Ig_u-Ig_t 0.0769765
Ig_u-Ig_u 0.1348471
Ig_u-M_t 0.3491481
Ig_u-M_u 0.4327903
Ir_t-Ir_t 0.5051307
Ir_t-Ir_u 0.1138851
Ir_t-M_t 0.2919982
Ir_t-M_u 0.0471813
Ir_u-Ir_t 0.0511037
Ir_u-Ir_u 0.4182800
Ir_u-M_t 0.3201401
Ir_u-M_u 0.2094744
M_t-Ig_t 0.0001201
M_t-Ig_u 0.0000123
M_t-Ir_t 0.0006375
M_t-Ir_u 0.0001071
M_t-M_t 0.9562009
M_t-M_u 0.0261042
M_u-Ig_t 0.0003154
M_u-Ig_u 0.0003180
M_u-Ir_t 0.0002650
M_u-Ir_u 0.0020107
M_u-M_t 0.2693353
M_u-M_u 0.7101350
Raw
estimated_probabilities.k16.txt
kmer size= 16
seq count= 12933823 dropped seqs= 315235
base counts= {'A': 189654700, 'C': 176634939, 'T': 186302033, 'G': 179286432}
state counts= {'Ig_u': 69022, 'Ig_t': 119864, 'Ir_t': 682619, 'Ir_u': 955564, 'M_u': 127034144, 'M_t': 603016891}
trans counts= {'Ig_t-M_t': 84716, 'M_u-Ig_u': 45360, 'M_u-Ig_t': 34990, 'Ir_t-M_t': 185605, 'Ir_t-M_u': 34078, 'Ig_t-M_u': 4842, 'Ir_u-M_t': 244310, 'Ir_u-M_u': 225922, 'Ir_t-Ir_t': 333069, 'Ir_t-Ir_u': 99083, 'Ig_t-Ig_t': 23169, 'Ig_t-Ig_u': 1401, 'Ig_u-M_u': 34728, 'Ig_u-M_t': 18861, 'Ir_u-Ir_u': 462117, 'Ir_u-Ir_t': 22259, 'M_u-M_u': 101442087, 'M_u-M_t': 22839778, 'M_t-Ir_u': 55440, 'M_t-Ir_t': 309619, 'Ig_u-Ig_u': 10733, 'M_u-Ir_t': 17672, 'M_u-Ir_u': 304384, 'Ig_u-Ig_t': 4332, 'M_t-Ig_t': 57373, 'M_t-Ig_u': 6023, 'M_t-M_t': 579643621, 'M_t-M_u': 12713944}
Ig_t-Ig_t 0.1932941
Ig_t-Ig_u 0.0116882
Ig_t-M_t 0.7067677
Ig_t-M_u 0.0403958
Ig_u-Ig_t 0.0627626
Ig_u-Ig_u 0.1555011
Ig_u-M_t 0.2732607
Ig_u-M_u 0.5031439
Ir_t-Ir_t 0.4879281
Ir_t-Ir_u 0.1451512
Ir_t-M_t 0.2719013
Ir_t-M_u 0.0499224
Ir_u-Ir_t 0.0232941
Ir_u-Ir_u 0.4836065
Ir_u-M_t 0.2556710
Ir_u-M_u 0.2364279
M_t-Ig_t 0.0000951
M_t-Ig_u 0.0000100
M_t-Ir_t 0.0005134
M_t-Ir_u 0.0000919
M_t-M_t 0.9612394
M_t-M_u 0.0210839
M_u-Ig_t 0.0002754
M_u-Ig_u 0.0003571
M_u-Ir_t 0.0001391
M_u-Ir_u 0.0023961
M_u-M_t 0.1797924
M_u-M_u 0.7985419
Raw
estimated_probabilities.k17.txt
kmer size= 17
seq count= 12933823 dropped seqs= 315235
base counts= {'A': 186461193, 'C': 173541994, 'T': 183028530, 'G': 176227799}
state counts= {'Ig_u': 76288, 'Ig_t': 105915, 'Ir_t': 489750, 'Ir_u': 1109254, 'M_u': 140902062, 'M_t': 576576247}
trans counts= {'Ig_t-M_t': 76224, 'M_u-Ig_u': 53762, 'M_u-Ig_t': 35322, 'Ig_t-M_u': 3131, 'Ir_t-M_u': 20020, 'Ir_t-M_t': 138832, 'Ir_u-M_t': 240378, 'Ir_u-M_u': 272751, 'Ir_t-Ir_t': 231618, 'Ir_t-Ir_u': 75922, 'Ig_t-Ig_t': 20866, 'Ig_t-Ig_u': 830, 'Ig_u-M_u': 41276, 'Ig_u-M_t': 17721, 'Ir_u-Ir_u': 582353, 'Ir_u-Ir_t': 12662, 'M_u-M_u': 118540418, 'M_u-M_t': 19190506, 'M_t-Ir_u': 49630, 'M_t-Ir_t': 234654, 'Ig_u-Ig_u': 12631, 'M_u-Ir_t': 10816, 'M_u-Ir_u': 374248, 'Ig_u-Ig_t': 4176, 'M_t-Ig_t': 45551, 'M_t-Ig_u': 4139, 'M_t-M_t': 556912586, 'M_t-M_u': 9437905}
Ig_t-Ig_t 0.1970070
Ig_t-Ig_u 0.0078365
Ig_t-M_t 0.7196714
Ig_t-M_u 0.0295614
Ig_u-Ig_t 0.0547399
Ig_u-Ig_u 0.1655699
Ig_u-M_t 0.2322908
Ig_u-M_u 0.5410549
Ir_t-Ir_t 0.4729311
Ir_t-Ir_u 0.1550219
Ir_t-M_t 0.2834752
Ir_t-M_u 0.0408780
Ir_u-Ir_t 0.0114149
Ir_u-Ir_u 0.5249952
Ir_u-M_t 0.2167024
Ir_u-M_u 0.2458869
M_t-Ig_t 0.0000790
M_t-Ig_u 0.0000072
M_t-Ir_t 0.0004070
M_t-Ir_u 0.0000861
M_t-M_t 0.9658958
M_t-M_u 0.0163689
M_u-Ig_t 0.0002507
M_u-Ig_u 0.0003816
M_u-Ir_t 0.0000768
M_u-Ir_u 0.0026561
M_u-M_t 0.1361975
M_u-M_u 0.8412965
Raw
estimated_probabilities.k18.txt
kmer size= 18
seq count= 12933823 dropped seqs= 315235
base counts= {'A': 183233044, 'C': 170466102, 'T': 179707322, 'G': 173234460}
state counts= {'Ig_u': 79942, 'Ig_t': 96334, 'Ir_t': 359599, 'Ir_u': 1208940, 'M_u': 148652052, 'M_t': 556244061}
trans counts= {'Ig_t-M_t': 69961, 'M_u-Ig_u': 58755, 'M_u-Ig_t': 35161, 'Ig_t-M_u': 2471, 'Ir_t-M_u': 12647, 'Ir_t-M_t': 112054, 'Ir_u-M_t': 233945, 'Ir_u-M_u': 298396, 'Ir_t-Ir_t': 165523, 'Ir_t-Ir_u': 50974, 'Ig_t-Ig_t': 19310, 'Ig_t-Ig_u': 521, 'Ig_u-M_u': 44878, 'Ig_u-M_t': 16856, 'Ir_u-Ir_u': 667197, 'Ir_u-Ir_t': 8214, 'M_u-M_u': 127972346, 'M_u-M_t': 17217247, 'M_t-Ir_u': 38960, 'M_t-Ir_t': 178228, 'Ig_u-Ig_u': 13589, 'M_u-Ir_t': 7634, 'M_u-Ir_u': 430705, 'Ig_u-Ig_t': 4115, 'M_t-Ig_t': 37748, 'M_t-Ig_u': 2730, 'M_t-M_t': 538593998, 'M_t-M_u': 7728177}
Ig_t-Ig_t 0.2004484
Ig_t-Ig_u 0.0054083
Ig_t-M_t 0.7262337
Ig_t-M_u 0.0256503
Ig_u-Ig_t 0.0514748
Ig_u-Ig_u 0.1699857
Ig_u-M_t 0.2108529
Ig_u-M_u 0.5613820
Ir_t-Ir_t 0.4602988
Ir_t-Ir_u 0.1417523
Ir_t-M_t 0.3116082
Ir_t-M_u 0.0351697
Ir_u-Ir_t 0.0067944
Ir_u-Ir_u 0.5518859
Ir_u-M_t 0.1935125
Ir_u-M_u 0.2468245
M_t-Ig_t 0.0000679
M_t-Ig_u 0.0000049
M_t-Ir_t 0.0003204
M_t-Ir_u 0.0000700
M_t-M_t 0.9682692
M_t-M_u 0.0138935
M_u-Ig_t 0.0002365
M_u-Ig_u 0.0003953
M_u-Ir_t 0.0000514
M_u-Ir_u 0.0028974
M_u-M_t 0.1158225
M_u-M_u 0.8608852
Raw
estimated_probabilities.k19.txt
kmer size= 19
seq count= 12933823 dropped seqs= 315235
base counts= {'A': 180060887, 'C': 167322781, 'T': 176424298, 'G': 170214374}
state counts= {'Ig_u': 82172, 'Ig_t': 88923, 'Ir_t': 272785, 'Ir_u': 1271981, 'M_u': 153590634, 'M_t': 538715845}
trans counts= {'Ig_t-M_t': 64750, 'M_u-Ig_u': 61829, 'M_u-Ig_t': 34861, 'Ig_t-M_u': 2089, 'Ir_t-M_u': 10442, 'Ir_t-M_t': 92425, 'Ir_u-M_t': 228702, 'Ir_u-M_u': 313730, 'Ir_t-Ir_t': 120599, 'Ir_t-Ir_u': 34337, 'Ig_t-Ig_t': 18017, 'Ig_t-Ig_u': 413, 'Ig_u-M_u': 47356, 'Ig_u-M_t': 16058, 'Ir_u-Ir_u': 721796, 'Ir_u-Ir_t': 6538, 'M_u-M_u': 133625975, 'M_u-M_t': 16284732, 'M_t-Ir_u': 26071, 'M_t-Ir_t': 139398, 'Ig_u-Ig_u': 14151, 'M_u-Ir_t': 6250, 'M_u-Ir_u': 472857, 'Ig_u-Ig_t': 4045, 'M_t-Ig_t': 32000, 'M_t-Ig_u': 2038, 'M_t-M_t': 522029178, 'M_t-M_u': 6993115}
Ig_t-Ig_t 0.2026135
Ig_t-Ig_u 0.0046445
Ig_t-M_t 0.7281581
Ig_t-M_u 0.0234922
Ig_u-Ig_t 0.0492260
Ig_u-Ig_u 0.1722119
Ig_u-M_t 0.1954194
Ig_u-M_u 0.5763034
Ir_t-Ir_t 0.4421028
Ir_t-Ir_u 0.1258757
Ir_t-M_t 0.3388199
Ir_t-M_u 0.0382792
Ir_u-Ir_t 0.0051400
Ir_u-Ir_u 0.5674582
Ir_u-M_t 0.1797999
Ir_u-M_u 0.2466468
M_t-Ig_t 0.0000594
M_t-Ig_u 0.0000038
M_t-Ir_t 0.0002588
M_t-Ir_u 0.0000484
M_t-M_t 0.9690251
M_t-M_u 0.0129811
M_u-Ig_t 0.0002270
M_u-Ig_u 0.0004026
M_u-Ir_t 0.0000407
M_u-Ir_u 0.0030787
M_u-M_t 0.1060269
M_u-M_u 0.8700138
Raw
estimated_probabilities.k20.txt
kmer size= 20
seq count= 12933823 dropped seqs= 315235
base counts= {'A': 176844469, 'C': 164244874, 'T': 173181507, 'G': 167132902}
state counts= {'Ig_u': 83750, 'Ig_t': 82596, 'Ir_t': 210307, 'Ir_u': 1315533, 'M_u': 157316340, 'M_t': 522395226}
trans counts= {'Ig_t-M_t': 60516, 'M_u-Ig_u': 63873, 'M_u-Ig_t': 34608, 'Ig_t-M_u': 1733, 'Ir_t-M_u': 9102, 'Ir_t-M_t': 76359, 'Ir_u-M_t': 224654, 'Ir_u-M_u': 325895, 'Ir_t-Ir_t': 89163, 'Ir_t-Ir_u': 23403, 'Ig_t-Ig_t': 16922, 'Ig_t-Ig_u': 316, 'Ig_u-M_u': 49382, 'Ig_u-M_t': 15294, 'Ir_u-Ir_u': 757741, 'Ir_u-Ir_t': 5961, 'M_u-M_u': 137689311, 'M_u-M_t': 15742696, 'M_t-Ir_u': 19970, 'M_t-Ir_t': 109487, 'Ig_u-Ig_u': 14555, 'M_u-Ir_t': 5696, 'M_u-Ir_u': 500228, 'Ig_u-Ig_t': 4000, 'M_t-Ig_t': 27066, 'M_t-Ig_u': 1798, 'M_t-M_t': 506275707, 'M_t-M_u': 6639728}
Ig_t-Ig_t 0.2048767
Ig_t-Ig_u 0.0038259
Ig_t-M_t 0.7326747
Ig_t-M_u 0.0209816
Ig_u-Ig_t 0.0477612
Ig_u-Ig_u 0.1737910
Ig_u-M_t 0.1826149
Ig_u-M_u 0.5896358
Ir_t-Ir_t 0.4239659
Ir_t-Ir_u 0.1112802
Ir_t-M_t 0.3630835
Ir_t-M_u 0.0432796
Ir_u-Ir_t 0.0045312
Ir_u-Ir_u 0.5759954
Ir_u-M_t 0.1707703
Ir_u-M_u 0.2477285
M_t-Ig_t 0.0000518
M_t-Ig_u 0.0000034
M_t-Ir_t 0.0002096
M_t-Ir_u 0.0000382
M_t-M_t 0.9691431
M_t-M_u 0.0127102
M_u-Ig_t 0.0002200
M_u-Ig_u 0.0004060
M_u-Ir_t 0.0000362
M_u-Ir_u 0.0031798
M_u-M_t 0.1000703
M_u-M_u 0.8752385
Raw
estimated_probabilities.k21.txt
kmer size= 21
seq count= 12933823 dropped seqs= 315235
base counts= {'A': 173595055, 'C': 161183014, 'T': 169885267, 'G': 164121828}
state counts= {'Ig_u': 84943, 'Ig_t': 77340, 'Ir_t': 163927, 'Ir_u': 1345897, 'M_u': 160392782, 'M_t': 506720275}
trans counts= {'Ir_t-M_u': 6999, 'M_u-Ig_u': 65666, 'M_u-Ig_t': 34246, 'Ig_t-M_u': 1312, 'Ig_t-M_t': 57015, 'Ir_t-M_t': 63752, 'Ir_u-M_t': 220835, 'Ir_u-M_u': 336278, 'Ir_t-Ir_t': 66870, 'Ir_t-Ir_u': 15999, 'Ig_t-Ig_t': 16030, 'Ig_t-Ig_u': 218, 'Ig_u-M_u': 50913, 'Ig_u-M_t': 14744, 'Ir_u-Ir_u': 781860, 'Ir_u-Ir_t': 5609, 'M_u-M_u': 140981290, 'M_u-M_t': 15364986, 'M_t-Ir_u': 15593, 'M_t-Ir_t': 86032, 'Ig_u-Ig_u': 14817, 'M_u-Ir_t': 5416, 'M_u-Ir_u': 520708, 'Ig_u-Ig_t': 3973, 'M_t-Ig_t': 23091, 'M_t-Ig_u': 1469, 'M_t-M_t': 490998943, 'M_t-M_u': 6411912}
Ig_t-Ig_t 0.2072666
Ig_t-Ig_u 0.0028187
Ig_t-M_t 0.7371994
Ig_t-M_u 0.0169641
Ig_u-Ig_t 0.0467725
Ig_u-Ig_u 0.1744346
Ig_u-M_t 0.1735752
Ig_u-M_u 0.5993784
Ir_t-Ir_t 0.4079255
Ir_t-Ir_u 0.0975983
Ir_t-M_t 0.3889048
Ir_t-M_u 0.0426958
Ir_u-Ir_t 0.0041675
Ir_u-Ir_u 0.5809211
Ir_u-M_t 0.1640802
Ir_u-M_u 0.2498542
M_t-Ig_t 0.0000456
M_t-Ig_u 0.0000029
M_t-Ir_t 0.0001698
M_t-Ir_u 0.0000308
M_t-M_t 0.9689743
M_t-M_u 0.0126538
M_u-Ig_t 0.0002135
M_u-Ig_u 0.0004094
M_u-Ir_t 0.0000338
M_u-Ir_u 0.0032465
M_u-M_t 0.0957960
M_u-M_u 0.8789753
Raw
estimated_probabilities.k22.txt
kmer size= 22
seq count= 12933823 dropped seqs= 315235
base counts= {'A': 170402613, 'C': 158060929, 'T': 166618517, 'G': 161084517}
state counts= {'Ig_u': 85668, 'Ig_t': 73015, 'Ir_t': 129753, 'Ir_u': 1366380, 'M_u': 163023048, 'M_t': 491488712}
trans counts= {'Ir_t-M_u': 4680, 'M_u-Ig_u': 67096, 'M_u-Ig_t': 33909, 'Ig_t-M_u': 1005, 'Ig_t-M_t': 54243, 'Ir_t-M_t': 54630, 'Ir_u-M_t': 217538, 'Ir_u-M_u': 344200, 'Ir_t-Ir_t': 51752, 'Ir_t-Ir_u': 10291, 'Ig_t-Ig_t': 15286, 'Ig_t-Ig_u': 180, 'Ig_u-M_u': 51952, 'Ig_u-M_t': 14308, 'Ir_u-Ir_u': 797870, 'Ir_u-Ir_t': 5548, 'M_u-M_u': 143749639, 'M_u-M_t': 15079781, 'M_t-Ir_u': 12127, 'M_t-Ir_t': 67255, 'Ig_u-Ig_u': 15001, 'M_u-Ir_t': 5198, 'M_u-Ir_u': 536424, 'Ig_u-Ig_t': 3937, 'M_t-Ig_t': 19883, 'M_t-Ig_u': 957, 'M_t-M_t': 476068212, 'M_t-M_u': 6265086}
Ig_t-Ig_t 0.2093542
Ig_t-Ig_u 0.0024652
Ig_t-M_t 0.7429021
Ig_t-M_u 0.0137643
Ig_u-Ig_t 0.0459565
Ig_u-Ig_u 0.1751062
Ig_u-M_t 0.1670169
Ig_u-M_u 0.6064341
Ir_t-Ir_t 0.3988501
Ir_t-Ir_u 0.0793122
Ir_t-M_t 0.4210307
Ir_t-M_u 0.0360685
Ir_u-Ir_t 0.0040604
Ir_u-Ir_u 0.5839298
Ir_u-M_t 0.1592075
Ir_u-M_u 0.2519065
M_t-Ig_t 0.0000405
M_t-Ig_u 0.0000019
M_t-Ir_t 0.0001368
M_t-Ir_u 0.0000247
M_t-M_t 0.9686249
M_t-M_u 0.0127472
M_u-Ig_t 0.0002080
M_u-Ig_u 0.0004116
M_u-Ir_t 0.0000319
M_u-Ir_u 0.0032905
M_u-M_t 0.0925009
M_u-M_u 0.8817749
Raw
estimated_probabilities.k23.txt
kmer size= 23
seq count= 12933823 dropped seqs= 315235
base counts= {'A': 167170973, 'C': 154995473, 'T': 163404457, 'G': 157977085}
state counts= {'Ig_u': 86151, 'Ig_t': 69526, 'Ir_t': 106014, 'Ir_u': 1378462, 'M_u': 165327463, 'M_t': 476580372}
trans counts= {'Ig_t-M_t': 51933, 'M_u-Ig_u': 67970, 'M_u-Ig_t': 33579, 'Ig_t-M_u': 884, 'Ir_t-M_u': 3380, 'Ir_t-M_t': 47911, 'Ir_u-M_t': 214295, 'Ir_u-M_u': 349625, 'Ir_t-Ir_t': 42722, 'Ir_t-Ir_u': 5584, 'Ig_t-Ig_t': 14669, 'Ig_t-Ig_u': 183, 'Ig_u-M_u': 52698, 'Ig_u-M_t': 13956, 'Ir_u-Ir_u': 807962, 'Ir_u-Ir_t': 5424, 'Ig_u-Ig_u': 15133, 'M_u-M_t': 14815838, 'M_t-Ir_u': 8835, 'M_t-Ir_t': 52870, 'M_u-M_u': 146167888, 'M_u-Ir_t': 4998, 'M_u-Ir_u': 548446, 'Ig_u-Ig_t': 3901, 'M_t-Ig_t': 17377, 'M_t-Ig_u': 666, 'M_t-M_t': 461436439, 'M_t-M_u': 6144234}
Ig_t-Ig_t 0.2109858
Ig_t-Ig_u 0.0026321
Ig_t-M_t 0.7469580
Ig_t-M_u 0.0127147
Ig_u-Ig_t 0.0452810
Ig_u-Ig_u 0.1756567
Ig_u-M_t 0.1619946
Ig_u-M_u 0.6116934
Ir_t-Ir_t 0.4029845
Ir_t-Ir_u 0.0526723
Ir_t-M_t 0.4519309
Ir_t-M_u 0.0318826
Ir_u-Ir_t 0.0039348
Ir_u-Ir_u 0.5861330
Ir_u-M_t 0.1554595
Ir_u-M_u 0.2536341
M_t-Ig_t 0.0000365
M_t-Ig_u 0.0000014
M_t-Ir_t 0.0001109
M_t-Ir_u 0.0000185
M_t-M_t 0.9682238
M_t-M_u 0.0128923
M_u-Ig_t 0.0002031
M_u-Ig_u 0.0004111
M_u-Ir_t 0.0000302
M_u-Ir_u 0.0033173
M_u-M_t 0.0896151
M_u-M_u 0.8841114
Raw
estimated_probabilities.k24.txt
kmer size= 24
seq count= 12933823 dropped seqs= 315235
base counts= {'A': 163904240, 'C': 151949777, 'T': 160126108, 'G': 154949275}
state counts= {'Ig_u': 86441, 'Ig_t': 66624, 'Ir_t': 90567, 'Ir_u': 1384414, 'M_u': 167344351, 'M_t': 461957003}
trans counts= {'Ir_t-M_u': 2484, 'M_u-Ig_u': 68601, 'M_u-Ig_t': 33215, 'Ig_t-M_u': 699, 'Ig_t-M_t': 50108, 'Ir_t-M_t': 42888, 'Ir_u-M_t': 211087, 'Ir_u-M_u': 353906, 'Ir_t-Ir_t': 37327, 'Ir_t-Ir_u': 3114, 'Ig_t-Ig_t': 14146, 'Ig_t-Ig_u': 131, 'Ig_u-M_u': 53312, 'Ig_u-M_t': 13572, 'Ir_u-Ir_u': 813042, 'Ir_u-Ir_t': 5390, 'Ig_u-Ig_u': 15251, 'M_u-M_t': 14586173, 'M_t-Ir_u': 5657, 'M_t-Ir_t': 43032, 'M_u-M_u': 148286329, 'M_u-Ir_t': 4818, 'M_u-Ir_u': 556888, 'Ig_u-Ig_t': 3882, 'M_t-Ig_t': 15381, 'M_t-Ig_u': 551, 'M_t-M_t': 447053175, 'M_t-M_u': 6036653}
Ig_t-Ig_t 0.2123259
Ig_t-Ig_u 0.0019663
Ig_t-M_t 0.7521013
Ig_t-M_u 0.0104917
Ig_u-Ig_t 0.0449092
Ig_u-Ig_u 0.1764325
Ig_u-M_t 0.1570088
Ig_u-M_u 0.6167444
Ir_t-Ir_t 0.4121479
Ir_t-Ir_u 0.0343834
Ir_t-M_t 0.4735500
Ir_t-M_u 0.0274272
Ir_u-Ir_t 0.0038933
Ir_u-Ir_u 0.5872824
Ir_u-M_t 0.1524739
Ir_u-M_u 0.2556360
M_t-Ig_t 0.0000333
M_t-Ig_u 0.0000012
M_t-Ir_t 0.0000932
M_t-Ir_u 0.0000122
M_t-M_t 0.9677376
M_t-M_u 0.0130676
M_u-Ig_t 0.0001985
M_u-Ig_u 0.0004099
M_u-Ir_t 0.0000288
M_u-Ir_u 0.0033278
M_u-M_t 0.0871626
M_u-M_u 0.8861149
Raw
estimated_probabilities.k25.txt
kmer size= 25
seq count= 12933823 dropped seqs= 315235
base counts= {'A': 160702949, 'C': 148835916, 'T': 156870576, 'G': 151901371}
state counts= {'Ig_u': 86433, 'Ig_t': 64287, 'Ir_t': 80354, 'Ir_u': 1387443, 'M_u': 169093858, 'M_t': 447598437}
trans counts= {'Ig_t-M_t': 48615, 'M_u-Ig_u': 69088, 'M_u-Ig_t': 32888, 'Ig_t-M_u': 609, 'Ir_t-M_u': 1638, 'Ir_t-M_t': 39334, 'Ir_u-M_t': 208456, 'Ir_u-M_u': 357143, 'Ir_t-Ir_t': 33914, 'Ir_t-Ir_u': 2028, 'Ig_t-Ig_t': 13711, 'Ig_t-Ig_u': 122, 'Ig_u-M_u': 53744, 'Ig_u-M_t': 13141, 'Ir_u-Ir_u': 815657, 'Ir_u-Ir_t': 5300, 'Ig_u-Ig_u': 15327, 'M_u-M_t': 14390195, 'M_t-Ir_u': 3491, 'M_t-Ir_t': 36409, 'M_u-M_u': 150113622, 'M_u-Ir_t': 4731, 'M_u-Ir_u': 561995, 'Ig_u-Ig_t': 3858, 'M_t-Ig_t': 13830, 'M_t-Ig_u': 370, 'M_t-M_t': 432898696, 'M_t-M_u': 5954312}
Ig_t-Ig_t 0.2132780
Ig_t-Ig_u 0.0018977
Ig_t-M_t 0.7562182
Ig_t-M_u 0.0094731
Ig_u-Ig_t 0.0446357
Ig_u-Ig_u 0.1773281
Ig_u-M_t 0.1520368
Ig_u-M_u 0.6217995
Ir_t-Ir_t 0.4220574
Ir_t-Ir_u 0.0252383
Ir_t-M_t 0.4895089
Ir_t-M_u 0.0203848
Ir_u-Ir_t 0.0038200
Ir_u-Ir_u 0.5878851
Ir_u-M_t 0.1502447
Ir_u-M_u 0.2574109
M_t-Ig_t 0.0000309
M_t-Ig_u 0.0000008
M_t-Ir_t 0.0000813
M_t-Ir_u 0.0000078
M_t-M_t 0.9671586
M_t-M_u 0.0133028
M_u-Ig_t 0.0001945
M_u-Ig_u 0.0004086
M_u-Ir_t 0.0000280
M_u-Ir_u 0.0033236
M_u-M_t 0.0851018
M_u-M_u 0.8877533
Raw
estimated_probabilities.k26.txt
kmer size= 26
seq count= 12933823 dropped seqs= 315235
base counts= {'A': 157453235, 'C': 145771089, 'T': 153670787, 'G': 148797113}
state counts= {'Ig_u': 86540, 'Ig_t': 62386, 'Ir_t': 73580, 'Ir_u': 1388752, 'M_u': 170621991, 'M_t': 433458975}
trans counts= {'Ir_t-M_u': 1088, 'M_u-Ig_u': 69422, 'M_u-Ig_t': 32553, 'Ig_t-M_u': 524, 'Ig_t-M_t': 47477, 'Ir_t-M_t': 36969, 'Ir_u-M_t': 206008, 'Ir_u-M_u': 359616, 'Ir_t-Ir_t': 31844, 'Ir_t-Ir_u': 1342, 'Ig_t-Ig_t': 13351, 'Ig_t-Ig_u': 132, 'Ig_u-M_u': 54110, 'Ig_u-M_t': 12885, 'Ir_u-Ir_u': 817110, 'Ir_u-Ir_t': 5207, 'Ig_u-Ig_u': 15405, 'M_u-M_t': 14189853, 'M_t-Ir_u': 2290, 'M_t-Ir_t': 31869, 'M_u-M_u': 151727814, 'M_u-Ir_t': 4660, 'M_u-Ir_u': 564876, 'Ig_u-Ig_t': 3831, 'M_t-Ig_t': 12651, 'M_t-Ig_u': 256, 'M_t-M_t': 418965783, 'M_t-M_u': 5864710}
Ig_t-Ig_t 0.2140063
Ig_t-Ig_u 0.0021159
Ig_t-M_t 0.7610201
Ig_t-M_u 0.0083993
Ig_u-Ig_t 0.0442685
Ig_u-Ig_u 0.1780102
Ig_u-M_t 0.1488907
Ig_u-M_u 0.6252600
Ir_t-Ir_t 0.4327806
Ir_t-Ir_u 0.0182387
Ir_t-M_t 0.5024327
Ir_t-M_u 0.0147866
Ir_u-Ir_t 0.0037494
Ir_u-Ir_u 0.5883772
Ir_u-M_t 0.1483404
Ir_u-M_u 0.2589490
M_t-Ig_t 0.0000292
M_t-Ig_u 0.0000006
M_t-Ir_t 0.0000735
M_t-Ir_u 0.0000053
M_t-M_t 0.9665639
M_t-M_u 0.0135300
M_u-Ig_t 0.0001908
M_u-Ig_u 0.0004069
M_u-Ir_t 0.0000273
M_u-Ir_u 0.0033107
M_u-M_t 0.0831654
M_u-M_u 0.8892629
Raw
estimated_probabilities.k27.txt
kmer size= 27
seq count= 12933823 dropped seqs= 315235
base counts= {'A': 154177851, 'C': 142736071, 'T': 150397751, 'G': 145761963}
state counts= {'Ig_u': 86608, 'Ig_t': 60888, 'Ir_t': 69200, 'Ir_u': 1389076, 'M_u': 171919989, 'M_t': 419547875}
trans counts= {'Ir_t-M_u': 829, 'M_u-Ig_u': 69675, 'M_u-Ig_t': 32229, 'Ig_t-M_u': 500, 'Ig_t-M_t': 46522, 'Ir_t-M_t': 35289, 'Ir_u-M_t': 203727, 'Ir_u-M_u': 361636, 'Ir_t-Ir_t': 30563, 'Ir_t-Ir_u': 934, 'Ig_t-Ig_t': 13068, 'Ig_t-Ig_u': 122, 'Ig_u-M_u': 54391, 'Ig_u-M_t': 12643, 'Ir_u-Ir_u': 817962, 'Ir_u-Ir_t': 5143, 'Ig_u-Ig_u': 15496, 'Ig_u-Ig_t': 3817, 'M_t-Ir_u': 1489, 'M_t-Ir_t': 28929, 'M_u-M_u': 153102894, 'M_u-Ir_t': 4565, 'M_u-Ir_u': 566465, 'M_u-M_t': 14008369, 'M_t-Ig_t': 11774, 'M_t-Ig_u': 209, 'M_t-M_t': 405241325, 'M_t-M_u': 5784483}
Ig_t-Ig_t 0.2146236
Ig_t-Ig_u 0.0020037
Ig_t-M_t 0.7640586
Ig_t-M_u 0.0082118
Ig_u-Ig_t 0.0440721
Ig_u-Ig_u 0.1789211
Ig_u-M_t 0.1459796
Ig_u-M_u 0.6280136
Ir_t-Ir_t 0.4416618
Ir_t-Ir_u 0.0134971
Ir_t-M_t 0.5099566
Ir_t-M_u 0.0119798
Ir_u-Ir_t 0.0037025
Ir_u-Ir_u 0.5888533
Ir_u-M_t 0.1466637
Ir_u-M_u 0.2603428
M_t-Ig_t 0.0000281
M_t-Ig_u 0.0000005
M_t-Ir_t 0.0000690
M_t-Ir_u 0.0000035
M_t-M_t 0.9659001
M_t-M_u 0.0137874
M_u-Ig_t 0.0001875
M_u-Ig_u 0.0004053
M_u-Ir_t 0.0000266
M_u-Ir_u 0.0032949
M_u-M_t 0.0814819
M_u-M_u 0.8905474
Raw
estimated_probabilities.k28.txt
kmer size= 28
seq count= 12933823 dropped seqs= 315235
base counts= {'A': 150972513, 'C': 139625178, 'T': 147148452, 'G': 142708905}
state counts= {'Ig_u': 86637, 'Ig_t': 59671, 'Ir_t': 66295, 'Ir_u': 1389073, 'M_u': 173013979, 'M_t': 405839393}
trans counts= {'Ir_t-M_u': 619, 'M_u-Ig_u': 69922, 'M_u-Ig_t': 31888, 'Ig_t-M_u': 472, 'Ig_t-M_t': 45718, 'Ir_t-M_t': 34039, 'Ir_u-M_t': 201759, 'Ir_u-M_u': 363315, 'Ir_t-Ir_t': 29787, 'Ir_t-Ir_u': 786, 'Ig_t-Ig_t': 12828, 'Ig_t-Ig_u': 117, 'Ig_u-M_u': 54696, 'Ig_u-M_t': 12340, 'Ir_u-Ir_u': 818410, 'Ir_u-Ir_t': 5040, 'Ig_u-Ig_u': 15587, 'M_u-M_t': 13846437, 'M_t-Ir_u': 957, 'M_t-Ir_t': 26996, 'M_u-M_u': 154260926, 'M_u-Ir_t': 4472, 'M_u-Ir_u': 567409, 'Ig_u-Ig_t': 3801, 'M_t-Ig_t': 11154, 'M_t-Ig_u': 156, 'M_t-M_t': 391699100, 'M_t-M_u': 5717729}
Ig_t-Ig_t 0.2149788
Ig_t-Ig_u 0.0019608
Ig_t-M_t 0.7661678
Ig_t-M_u 0.0079100
Ig_u-Ig_t 0.0438727
Ig_u-Ig_u 0.1799116
Ig_u-M_t 0.1424334
Ig_u-M_u 0.6313238
Ir_t-Ir_t 0.4493099
Ir_t-Ir_u 0.0118561
Ir_t-M_t 0.5134475
Ir_t-M_u 0.0093371
Ir_u-Ir_t 0.0036283
Ir_u-Ir_u 0.5891771
Ir_u-M_t 0.1452472
Ir_u-M_u 0.2615521
M_t-Ig_t 0.0000275
M_t-Ig_u 0.0000004
M_t-Ir_t 0.0000665
M_t-Ir_u 0.0000024
M_t-M_t 0.9651579
M_t-M_u 0.0140886
M_u-Ig_t 0.0001843
M_u-Ig_u 0.0004041
M_u-Ir_t 0.0000258
M_u-Ir_u 0.0032796
M_u-M_t 0.0800307
M_u-M_u 0.8916096
Raw
estimated_probabilities.k29.txt
kmer size= 29
seq count= 12933823 dropped seqs= 315235
base counts= {'A': 147724646, 'C': 136567813, 'T': 143945222, 'G': 139598779}
state counts= {'Ig_u': 86758, 'Ig_t': 58623, 'Ir_t': 64314, 'Ir_u': 1389082, 'M_u': 173928749, 'M_t': 392308934}
trans counts= {'Ir_t-M_u': 465, 'M_u-Ig_u': 70153, 'M_u-Ig_t': 31527, 'Ig_t-M_u': 429, 'Ig_t-M_t': 45029, 'Ir_t-M_t': 33161, 'Ir_u-M_t': 200073, 'Ir_u-M_u': 364902, 'Ir_t-Ir_t': 29230, 'Ir_t-Ir_u': 727, 'Ig_t-Ig_t': 12623, 'Ig_t-Ig_u': 105, 'Ig_u-M_u': 54979, 'Ig_u-M_t': 12116, 'Ir_u-Ir_u': 818685, 'Ir_u-Ir_t': 4989, 'Ig_u-Ig_u': 15693, 'M_u-M_t': 13666322, 'M_t-Ir_u': 773, 'M_t-Ir_t': 25665, 'M_u-M_u': 155227348, 'M_u-Ir_t': 4430, 'M_u-Ir_u': 567745, 'Ig_u-Ig_t': 3775, 'M_t-Ig_t': 10698, 'M_t-Ig_u': 130, 'M_t-M_t': 378352233, 'M_t-M_u': 5663867}
Ig_t-Ig_t 0.2153250
Ig_t-Ig_u 0.0017911
Ig_t-M_t 0.7681115
Ig_t-M_u 0.0073179
Ig_u-Ig_t 0.0435118
Ig_u-Ig_u 0.1808825
Ig_u-M_t 0.1396528
Ig_u-M_u 0.6337052
Ir_t-Ir_t 0.4544889
Ir_t-Ir_u 0.0113039
Ir_t-M_t 0.5156109
Ir_t-M_u 0.0072302
Ir_u-Ir_t 0.0035916
Ir_u-Ir_u 0.5893713
Ir_u-M_t 0.1440325
Ir_u-M_u 0.2626929
M_t-Ig_t 0.0000273
M_t-Ig_u 0.0000003
M_t-Ir_t 0.0000654
M_t-Ir_u 0.0000020
M_t-M_t 0.9644242
M_t-M_u 0.0144373
M_u-Ig_t 0.0001813
M_u-Ig_u 0.0004033
M_u-Ir_t 0.0000255
M_u-Ir_u 0.0032642
M_u-M_t 0.0785743
M_u-M_u 0.8924767
Raw
estimated_probabilities.k30.txt
kmer size= 30
seq count= 12933823 dropped seqs= 315235
base counts= {'A': 144454720, 'C': 133529144, 'T': 140678610, 'G': 136555398}
state counts= {'Ig_u': 86973, 'Ig_t': 57725, 'Ir_t': 62885, 'Ir_u': 1389091, 'M_u': 174651791, 'M_t': 378969407}
trans counts= {'Ir_t-M_u': 373, 'M_u-Ig_u': 70341, 'M_u-Ig_t': 31210, 'Ig_t-M_u': 408, 'Ig_t-M_t': 44419, 'Ir_t-M_t': 32511, 'Ir_u-M_t': 198397, 'Ir_u-M_u': 366430, 'Ir_t-Ir_t': 28819, 'Ir_t-Ir_u': 643, 'Ig_t-Ig_t': 12427, 'Ig_t-Ig_u': 121, 'Ig_u-M_u': 55252, 'Ig_u-M_t': 12010, 'Ir_u-Ir_u': 818966, 'Ir_u-Ir_t': 4949, 'Ig_u-Ig_u': 15783, 'M_u-M_t': 13490036, 'M_t-Ir_u': 620, 'M_t-Ir_t': 24770, 'M_u-M_u': 156034825, 'M_u-Ir_t': 4347, 'M_u-Ir_u': 567966, 'Ig_u-Ig_t': 3742, 'M_t-Ig_t': 10346, 'M_t-Ig_u': 102, 'M_t-M_t': 365192034, 'M_t-M_u': 5577437}
Ig_t-Ig_t 0.2152793
Ig_t-Ig_u 0.0020961
Ig_t-M_t 0.7694933
Ig_t-M_u 0.0070680
Ig_u-Ig_t 0.0430248
Ig_u-Ig_u 0.1814701
Ig_u-M_t 0.1380888
Ig_u-M_u 0.6352776
Ir_t-Ir_t 0.4582810
Ir_t-Ir_u 0.0102250
Ir_t-M_t 0.5169913
Ir_t-M_u 0.0059315
Ir_u-Ir_t 0.0035628
Ir_u-Ir_u 0.5895697
Ir_u-M_t 0.1428251
Ir_u-M_u 0.2637912
M_t-Ig_t 0.0000273
M_t-Ig_u 0.0000003
M_t-Ir_t 0.0000654
M_t-Ir_u 0.0000016
M_t-M_t 0.9636452
M_t-M_u 0.0147174
M_u-Ig_t 0.0001787
M_u-Ig_u 0.0004027
M_u-Ir_t 0.0000249
M_u-Ir_u 0.0032520
M_u-M_t 0.0772396
M_u-M_u 0.8934052
Raw
estimated_probabilities.k31.txt
kmer size= 31
seq count= 12933823 dropped seqs= 315235
base counts= {'A': 141249232, 'C': 130419484, 'T': 137439145, 'G': 133491423}
state counts= {'Ig_u': 87152, 'Ig_t': 56924, 'Ir_t': 61869, 'Ir_u': 1389091, 'M_u': 175194696, 'M_t': 365809552}
trans counts= {'Ir_t-M_u': 367, 'M_u-Ig_u': 70538, 'M_u-Ig_t': 30884, 'Ig_t-M_u': 461, 'Ig_t-M_t': 43752, 'Ir_t-M_t': 31855, 'Ir_u-M_t': 196867, 'Ir_u-M_u': 367846, 'Ir_t-Ir_t': 28502, 'Ir_t-Ir_u': 645, 'Ig_t-Ig_t': 12281, 'Ig_t-Ig_u': 100, 'Ig_u-M_u': 55509, 'Ig_u-M_t': 11874, 'Ir_u-Ir_u': 819133, 'Ir_u-Ir_t': 4916, 'Ig_u-Ig_u': 15888, 'Ig_u-Ig_t': 3713, 'M_t-Ir_u': 480, 'M_t-Ir_t': 24135, 'M_u-M_u': 156632865, 'M_u-Ir_t': 4316, 'M_u-Ir_u': 568065, 'M_u-M_t': 13335207, 'M_t-Ig_t': 10046, 'M_t-Ig_u': 86, 'M_t-M_t': 352189997, 'M_t-M_u': 5520368}
Ig_t-Ig_t 0.2157438
Ig_t-Ig_u 0.0017567
Ig_t-M_t 0.7686038
Ig_t-M_u 0.0080985
Ig_u-Ig_t 0.0426037
Ig_u-Ig_u 0.1823022
Ig_u-M_t 0.1362447
Ig_u-M_u 0.6369217
Ir_t-Ir_t 0.4606831
Ir_t-Ir_u 0.0104253
Ir_t-M_t 0.5148782
Ir_t-M_u 0.0059319
Ir_u-Ir_t 0.0035390
Ir_u-Ir_u 0.5896899
Ir_u-M_t 0.1417236
Ir_u-M_u 0.2648106
M_t-Ig_t 0.0000275
M_t-Ig_u 0.0000002
M_t-Ir_t 0.0000660
M_t-Ir_u 0.0000013
M_t-M_t 0.9627687
M_t-M_u 0.0150908
M_u-Ig_t 0.0001763
M_u-Ig_u 0.0004026
M_u-Ir_t 0.0000246
M_u-Ir_u 0.0032425
M_u-M_t 0.0761165
M_u-M_u 0.8940503
Raw
estimated_probabilities.k32.txt
kmer size= 32
seq count= 12933823 dropped seqs= 315235
base counts= {'A': 137999982, 'C': 127353678, 'T': 134245363, 'G': 130381673}
state counts= {'Ig_u': 87418, 'Ig_t': 56115, 'Ir_t': 60842, 'Ir_u': 1389188, 'M_u': 175565128, 'M_t': 352822005}
trans counts= {'Ir_t-M_u': 347, 'M_u-Ig_u': 70716, 'M_u-Ig_t': 30556, 'Ig_t-M_u': 413, 'Ig_t-M_t': 43154, 'Ir_t-M_t': 31264, 'Ir_u-M_t': 195346, 'Ir_u-M_u': 369368, 'Ir_t-Ir_t': 28226, 'Ir_t-Ir_u': 594, 'Ig_t-Ig_t': 12130, 'Ig_t-Ig_u': 103, 'Ig_u-M_u': 55815, 'Ig_u-M_t': 11788, 'Ir_u-Ir_u': 819383, 'Ir_u-Ir_t': 4835, 'Ig_u-Ig_u': 15977, 'M_u-M_t': 13190533, 'M_t-Ir_u': 483, 'M_t-Ir_t': 23544, 'M_u-M_u': 157056312, 'M_u-Ir_t': 4237, 'M_u-Ir_u': 568061, 'Ig_u-Ig_t': 3683, 'M_t-Ig_t': 9746, 'M_t-Ig_u': 127, 'M_t-M_t': 339349920, 'M_t-M_u': 5465447}
Ig_t-Ig_t 0.2161632
Ig_t-Ig_u 0.0018355
Ig_t-M_t 0.7690279
Ig_t-M_u 0.0073599
Ig_u-Ig_t 0.0421309
Ig_u-Ig_u 0.1827656
Ig_u-M_t 0.1348464
Ig_u-M_u 0.6384841
Ir_t-Ir_t 0.4639229
Ir_t-Ir_u 0.0097630
Ir_t-M_t 0.5138556
Ir_t-M_u 0.0057033
Ir_u-Ir_t 0.0034805
Ir_u-Ir_u 0.5898287
Ir_u-M_t 0.1406188
Ir_u-M_u 0.2658877
M_t-Ig_t 0.0000276
M_t-Ig_u 0.0000004
M_t-Ir_t 0.0000667
M_t-Ir_u 0.0000014
M_t-M_t 0.9618162
M_t-M_u 0.0154907
M_u-Ig_t 0.0001740
M_u-Ig_u 0.0004028
M_u-Ir_t 0.0000241
M_u-Ir_u 0.0032356
M_u-M_t 0.0751319
M_u-M_u 0.8945758
Raw
estimated_probabilities.txt
kmer size= 30
seq count= 12933823 dropped seqs= 315235
base counts= {'A': 144454720, 'C': 133529144, 'T': 140678610, 'G': 136555398}
state counts= {'Ig_u': 86973, 'Ig_t': 57725, 'Ir_t': 62885, 'Ir_u': 1389091, 'M_u': 174651791, 'M_t': 378969407}
trans counts= {'Ir_t-M_u': 373, 'M_u-Ig_u': 70341, 'M_u-Ig_t': 31210, 'Ig_t-M_u': 408, 'Ig_t-M_t': 44419, 'Ir_t-M_t': 32511, 'Ir_u-M_t': 198397, 'Ir_u-M_u': 366430, 'Ir_t-Ir_t': 28819, 'Ir_t-Ir_u': 643, 'Ig_t-Ig_t': 12427, 'Ig_t-Ig_u': 121, 'Ig_u-M_u': 55252, 'Ig_u-M_t': 12010, 'Ir_u-Ir_u': 818966, 'Ir_u-Ir_t': 4949, 'Ig_u-Ig_u': 15783, 'M_u-M_t': 13490036, 'M_t-Ir_u': 620, 'M_t-Ir_t': 24770, 'M_u-M_u': 156034825, 'M_u-Ir_t': 4347, 'M_u-Ir_u': 567966, 'Ig_u-Ig_t': 3742, 'M_t-Ig_t': 10346, 'M_t-Ig_u': 102, 'M_t-M_t': 365192034, 'M_t-M_u': 5577437}
Ig_t-Ig_t 0.2152793
Ig_t-Ig_u 0.0020961
Ig_t-M_t 0.7694933
Ig_t-M_u 0.0070680
Ig_u-Ig_t 0.0430248
Ig_u-Ig_u 0.1814701
Ig_u-M_t 0.1380888
Ig_u-M_u 0.6352776
Ir_t-Ir_t 0.4582810
Ir_t-Ir_u 0.0102250
Ir_t-M_t 0.5169913
Ir_t-M_u 0.0059315
Ir_u-Ir_t 0.0035628
Ir_u-Ir_u 0.5895697
Ir_u-M_t 0.1428251
Ir_u-M_u 0.2637912
M_t-Ig_t 0.0000273
M_t-Ig_u 0.0000003
M_t-Ir_t 0.0000654
M_t-Ir_u 0.0000016
M_t-M_t 0.9636452
M_t-M_u 0.0147174
M_u-Ig_t 0.0001787
M_u-Ig_u 0.0004027
M_u-Ir_t 0.0000249
M_u-Ir_u 0.0032520
M_u-M_t 0.0772396
M_u-M_u 0.8934052
Raw
Makefile
KSIZE= 30
HASH_SIZE= 4e8
N_HASHES= 4
all: estimated_probabilities.k$(KSIZE).txt
%.fastq.gz:
wget ftp://ftp.sra.ebi.ac.uk/vol1/fastq/SRR172/SRR172903/SRR172903.fastq.gz
wget ftp://ftp.sra.ebi.ac.uk/vol1/fastq/SRR172/SRR172902/SRR172902.fastq.gz
combined_reads.k$(KSIZE).ht: SRR172902.fastq.gz SRR172903.fastq.gz
load-into-counting.py --ksize $(KSIZE) -x $(HASH_SIZE) -N $(N_HASHES) $@ $^ |& tee combined_reads.k$(KSIZE).log
combined_reads_mapping.bam: SRR172902.fastq.gz SRR172903.fastq.gz
bowtie2 -x mockRefG -U SRR172902.fastq.gz -U SRR172903.fastq.gz | samtools view -S -F4 -b - > combined_reads_mapping.bam
estimated_probabilities.k$(KSIZE).txt: combined_reads.k$(KSIZE).ht combined_reads_mapping.bam
python learn.py combined_reads.k$(KSIZE).ht combined_reads_mapping.bam > estimated_probabilities.k$(KSIZE).txt
Display the source blob
Display the rendered blob
Raw
training-diff-ksizes.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 82,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 98,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import pylab\n",
"import glob\n",
"import itertools\n",
"import numpy\n",
"import matplotlib.pyplot as plt\n",
"\n",
"import pandas as pd\n",
"pd.options.display.max_columns=32\n",
"\n",
"#!for K in `seq 13 30`; do make KSIZE=${K}; done \n",
"\n",
"df = pd.DataFrame()\n",
"datalist = [ ( pd.read_csv(filename, delim_whitespace=True, skiprows=5, index_col=0, names=('transition', label))).T for filename, label in itertools.izip(sorted(glob.glob('estimated_probabilities.*.txt')), range(11,32)) ]\n",
"\n",
"df = pd.concat(datalist)\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Values are basically constant or vary smoothly based upon k-size. Will be interested to see how matching (or not) the k-size for EC vs training goes.\n",
"\n",
"Insert to the graph of a trusted k-mer is most often followed by a trusted Match. This pattern holds for k=11..32\n",
"\n",
"Insert to the graph of an untrusted k-mer is most often followed by a Match with a bias for a trusted Match for small k sizes (k=11..14) and an untrusted Match for larger k-sizes (k=15..32)\n",
"\n",
"Insert to the read of a trusted k-mer is most often followed by another insert to the read of a trusted k-mer for small k sizes (k=11..21) or a trusted Match for larger k sizes (k=22..32)\n",
"\n",
"Inserts to the read of an _untrusted_ k-mer has the reverse pattern: most often followed by a trusted match for small k sizes (k=11..16) or another insert to the read of an untrusted kmer for large k sizes (k=17..32)\n",
"\n",
"Trusted matches are nearly always followed by another trusted match and this holds for k=11..32.\n",
"\n",
"Untrusted matches are nearly always followed by a match with small ksizes (k=11..13) preferring trusted k-mers and larger ksizes (k=14..32) preferring untrusted k-mers."
]
},
{
"cell_type": "code",
"execution_count": 104,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Ig_t-Ig_t</th>\n",
" <th>Ig_t-Ig_u</th>\n",
" <th>Ig_t-M_t</th>\n",
" <th>Ig_t-M_u</th>\n",
" <th>Ig_u-Ig_t</th>\n",
" <th>Ig_u-Ig_u</th>\n",
" <th>Ig_u-M_t</th>\n",
" <th>Ig_u-M_u</th>\n",
" <th>Ir_t-Ir_t</th>\n",
" <th>Ir_t-Ir_u</th>\n",
" <th>Ir_t-M_t</th>\n",
" <th>Ir_t-M_u</th>\n",
" <th>Ir_u-Ir_t</th>\n",
" <th>Ir_u-Ir_u</th>\n",
" <th>Ir_u-M_t</th>\n",
" <th>Ir_u-M_u</th>\n",
" <th>M_t-Ig_t</th>\n",
" <th>M_t-Ig_u</th>\n",
" <th>M_t-Ir_t</th>\n",
" <th>M_t-Ir_u</th>\n",
" <th>M_t-M_t</th>\n",
" <th>M_t-M_u</th>\n",
" <th>M_u-Ig_t</th>\n",
" <th>M_u-Ig_u</th>\n",
" <th>M_u-Ir_t</th>\n",
" <th>M_u-Ir_u</th>\n",
" <th>M_u-M_t</th>\n",
" <th>M_u-M_u</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>11</th>\n",
" <td> 0.213239</td>\n",
" <td> NaN</td>\n",
" <td> 0.740221</td>\n",
" <td> 0.000004</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> 1.000000</td>\n",
" <td> NaN</td>\n",
" <td> 0.565239</td>\n",
" <td> 0.000002</td>\n",
" <td> 0.389208</td>\n",
" <td> 0.000002</td>\n",
" <td> 0.000442</td>\n",
" <td> NaN</td>\n",
" <td> 0.999558</td>\n",
" <td> NaN</td>\n",
" <td> 0.000229</td>\n",
" <td> NaN</td>\n",
" <td> 0.001069</td>\n",
" <td> 0.000000</td>\n",
" <td> 0.982652</td>\n",
" <td> 0.000001</td>\n",
" <td> NaN</td>\n",
" <td> NaN</td>\n",
" <td> 0.000037</td>\n",
" <td> NaN</td>\n",
" <td> 0.999956</td>\n",
" <td> 0.000005</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td> 0.213047</td>\n",
" <td> 0.000314</td>\n",
" <td> 0.740109</td>\n",
" <td> 0.001100</td>\n",
" <td> 0.011670</td>\n",
" <td> 0.001323</td>\n",
" <td> 0.981954</td>\n",
" <td> 0.004572</td>\n",
" <td> 0.563745</td>\n",
" <td> 0.001513</td>\n",
" <td> 0.393110</td>\n",
" <td> 0.000688</td>\n",
" <td> 0.015501</td>\n",
" <td> 0.003670</td>\n",
" <td> 0.977919</td>\n",
" <td> 0.002863</td>\n",
" <td> 0.000221</td>\n",
" <td> 0.000000</td>\n",
" <td> 0.001036</td>\n",
" <td> 0.000002</td>\n",
" <td> 0.981675</td>\n",
" <td> 0.000742</td>\n",
" <td> 0.000038</td>\n",
" <td> 0.000003</td>\n",
" <td> 0.000108</td>\n",
" <td> 0.000018</td>\n",
" <td> 0.989192</td>\n",
" <td> 0.009610</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td> 0.208071</td>\n",
" <td> 0.004831</td>\n",
" <td> 0.728849</td>\n",
" <td> 0.014882</td>\n",
" <td> 0.092533</td>\n",
" <td> 0.038812</td>\n",
" <td> 0.737116</td>\n",
" <td> 0.127876</td>\n",
" <td> 0.549773</td>\n",
" <td> 0.025566</td>\n",
" <td> 0.376080</td>\n",
" <td> 0.010859</td>\n",
" <td> 0.120557</td>\n",
" <td> 0.124575</td>\n",
" <td> 0.682094</td>\n",
" <td> 0.072106</td>\n",
" <td> 0.000199</td>\n",
" <td> 0.000006</td>\n",
" <td> 0.000958</td>\n",
" <td> 0.000035</td>\n",
" <td> 0.971347</td>\n",
" <td> 0.010894</td>\n",
" <td> 0.000316</td>\n",
" <td> 0.000099</td>\n",
" <td> 0.000549</td>\n",
" <td> 0.000504</td>\n",
" <td> 0.760895</td>\n",
" <td> 0.227303</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td> 0.198239</td>\n",
" <td> 0.011765</td>\n",
" <td> 0.706367</td>\n",
" <td> 0.037274</td>\n",
" <td> 0.096722</td>\n",
" <td> 0.099032</td>\n",
" <td> 0.485809</td>\n",
" <td> 0.313429</td>\n",
" <td> 0.523846</td>\n",
" <td> 0.072735</td>\n",
" <td> 0.333668</td>\n",
" <td> 0.031389</td>\n",
" <td> 0.099031</td>\n",
" <td> 0.307447</td>\n",
" <td> 0.434073</td>\n",
" <td> 0.158488</td>\n",
" <td> 0.000158</td>\n",
" <td> 0.000012</td>\n",
" <td> 0.000798</td>\n",
" <td> 0.000096</td>\n",
" <td> 0.957406</td>\n",
" <td> 0.024829</td>\n",
" <td> 0.000364</td>\n",
" <td> 0.000239</td>\n",
" <td> 0.000468</td>\n",
" <td> 0.001354</td>\n",
" <td> 0.450081</td>\n",
" <td> 0.531608</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td> 0.193061</td>\n",
" <td> 0.013988</td>\n",
" <td> 0.700191</td>\n",
" <td> 0.045722</td>\n",
" <td> 0.076977</td>\n",
" <td> 0.134847</td>\n",
" <td> 0.349148</td>\n",
" <td> 0.432790</td>\n",
" <td> 0.505131</td>\n",
" <td> 0.113885</td>\n",
" <td> 0.291998</td>\n",
" <td> 0.047181</td>\n",
" <td> 0.051104</td>\n",
" <td> 0.418280</td>\n",
" <td> 0.320140</td>\n",
" <td> 0.209474</td>\n",
" <td> 0.000120</td>\n",
" <td> 0.000012</td>\n",
" <td> 0.000638</td>\n",
" <td> 0.000107</td>\n",
" <td> 0.956201</td>\n",
" <td> 0.026104</td>\n",
" <td> 0.000315</td>\n",
" <td> 0.000318</td>\n",
" <td> 0.000265</td>\n",
" <td> 0.002011</td>\n",
" <td> 0.269335</td>\n",
" <td> 0.710135</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td> 0.193294</td>\n",
" <td> 0.011688</td>\n",
" <td> 0.706768</td>\n",
" <td> 0.040396</td>\n",
" <td> 0.062763</td>\n",
" <td> 0.155501</td>\n",
" <td> 0.273261</td>\n",
" <td> 0.503144</td>\n",
" <td> 0.487928</td>\n",
" <td> 0.145151</td>\n",
" <td> 0.271901</td>\n",
" <td> 0.049922</td>\n",
" <td> 0.023294</td>\n",
" <td> 0.483606</td>\n",
" <td> 0.255671</td>\n",
" <td> 0.236428</td>\n",
" <td> 0.000095</td>\n",
" <td> 0.000010</td>\n",
" <td> 0.000513</td>\n",
" <td> 0.000092</td>\n",
" <td> 0.961239</td>\n",
" <td> 0.021084</td>\n",
" <td> 0.000275</td>\n",
" <td> 0.000357</td>\n",
" <td> 0.000139</td>\n",
" <td> 0.002396</td>\n",
" <td> 0.179792</td>\n",
" <td> 0.798542</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td> 0.197007</td>\n",
" <td> 0.007836</td>\n",
" <td> 0.719671</td>\n",
" <td> 0.029561</td>\n",
" <td> 0.054740</td>\n",
" <td> 0.165570</td>\n",
" <td> 0.232291</td>\n",
" <td> 0.541055</td>\n",
" <td> 0.472931</td>\n",
" <td> 0.155022</td>\n",
" <td> 0.283475</td>\n",
" <td> 0.040878</td>\n",
" <td> 0.011415</td>\n",
" <td> 0.524995</td>\n",
" <td> 0.216702</td>\n",
" <td> 0.245887</td>\n",
" <td> 0.000079</td>\n",
" <td> 0.000007</td>\n",
" <td> 0.000407</td>\n",
" <td> 0.000086</td>\n",
" <td> 0.965896</td>\n",
" <td> 0.016369</td>\n",
" <td> 0.000251</td>\n",
" <td> 0.000382</td>\n",
" <td> 0.000077</td>\n",
" <td> 0.002656</td>\n",
" <td> 0.136197</td>\n",
" <td> 0.841297</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td> 0.200448</td>\n",
" <td> 0.005408</td>\n",
" <td> 0.726234</td>\n",
" <td> 0.025650</td>\n",
" <td> 0.051475</td>\n",
" <td> 0.169986</td>\n",
" <td> 0.210853</td>\n",
" <td> 0.561382</td>\n",
" <td> 0.460299</td>\n",
" <td> 0.141752</td>\n",
" <td> 0.311608</td>\n",
" <td> 0.035170</td>\n",
" <td> 0.006794</td>\n",
" <td> 0.551886</td>\n",
" <td> 0.193513</td>\n",
" <td> 0.246825</td>\n",
" <td> 0.000068</td>\n",
" <td> 0.000005</td>\n",
" <td> 0.000320</td>\n",
" <td> 0.000070</td>\n",
" <td> 0.968269</td>\n",
" <td> 0.013893</td>\n",
" <td> 0.000237</td>\n",
" <td> 0.000395</td>\n",
" <td> 0.000051</td>\n",
" <td> 0.002897</td>\n",
" <td> 0.115822</td>\n",
" <td> 0.860885</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td> 0.202614</td>\n",
" <td> 0.004645</td>\n",
" <td> 0.728158</td>\n",
" <td> 0.023492</td>\n",
" <td> 0.049226</td>\n",
" <td> 0.172212</td>\n",
" <td> 0.195419</td>\n",
" <td> 0.576303</td>\n",
" <td> 0.442103</td>\n",
" <td> 0.125876</td>\n",
" <td> 0.338820</td>\n",
" <td> 0.038279</td>\n",
" <td> 0.005140</td>\n",
" <td> 0.567458</td>\n",
" <td> 0.179800</td>\n",
" <td> 0.246647</td>\n",
" <td> 0.000059</td>\n",
" <td> 0.000004</td>\n",
" <td> 0.000259</td>\n",
" <td> 0.000048</td>\n",
" <td> 0.969025</td>\n",
" <td> 0.012981</td>\n",
" <td> 0.000227</td>\n",
" <td> 0.000403</td>\n",
" <td> 0.000041</td>\n",
" <td> 0.003079</td>\n",
" <td> 0.106027</td>\n",
" <td> 0.870014</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td> 0.204877</td>\n",
" <td> 0.003826</td>\n",
" <td> 0.732675</td>\n",
" <td> 0.020982</td>\n",
" <td> 0.047761</td>\n",
" <td> 0.173791</td>\n",
" <td> 0.182615</td>\n",
" <td> 0.589636</td>\n",
" <td> 0.423966</td>\n",
" <td> 0.111280</td>\n",
" <td> 0.363084</td>\n",
" <td> 0.043280</td>\n",
" <td> 0.004531</td>\n",
" <td> 0.575995</td>\n",
" <td> 0.170770</td>\n",
" <td> 0.247728</td>\n",
" <td> 0.000052</td>\n",
" <td> 0.000003</td>\n",
" <td> 0.000210</td>\n",
" <td> 0.000038</td>\n",
" <td> 0.969143</td>\n",
" <td> 0.012710</td>\n",
" <td> 0.000220</td>\n",
" <td> 0.000406</td>\n",
" <td> 0.000036</td>\n",
" <td> 0.003180</td>\n",
" <td> 0.100070</td>\n",
" <td> 0.875239</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td> 0.207267</td>\n",
" <td> 0.002819</td>\n",
" <td> 0.737199</td>\n",
" <td> 0.016964</td>\n",
" <td> 0.046773</td>\n",
" <td> 0.174435</td>\n",
" <td> 0.173575</td>\n",
" <td> 0.599378</td>\n",
" <td> 0.407925</td>\n",
" <td> 0.097598</td>\n",
" <td> 0.388905</td>\n",
" <td> 0.042696</td>\n",
" <td> 0.004167</td>\n",
" <td> 0.580921</td>\n",
" <td> 0.164080</td>\n",
" <td> 0.249854</td>\n",
" <td> 0.000046</td>\n",
" <td> 0.000003</td>\n",
" <td> 0.000170</td>\n",
" <td> 0.000031</td>\n",
" <td> 0.968974</td>\n",
" <td> 0.012654</td>\n",
" <td> 0.000213</td>\n",
" <td> 0.000409</td>\n",
" <td> 0.000034</td>\n",
" <td> 0.003247</td>\n",
" <td> 0.095796</td>\n",
" <td> 0.878975</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td> 0.209354</td>\n",
" <td> 0.002465</td>\n",
" <td> 0.742902</td>\n",
" <td> 0.013764</td>\n",
" <td> 0.045956</td>\n",
" <td> 0.175106</td>\n",
" <td> 0.167017</td>\n",
" <td> 0.606434</td>\n",
" <td> 0.398850</td>\n",
" <td> 0.079312</td>\n",
" <td> 0.421031</td>\n",
" <td> 0.036069</td>\n",
" <td> 0.004060</td>\n",
" <td> 0.583930</td>\n",
" <td> 0.159208</td>\n",
" <td> 0.251907</td>\n",
" <td> 0.000041</td>\n",
" <td> 0.000002</td>\n",
" <td> 0.000137</td>\n",
" <td> 0.000025</td>\n",
" <td> 0.968625</td>\n",
" <td> 0.012747</td>\n",
" <td> 0.000208</td>\n",
" <td> 0.000412</td>\n",
" <td> 0.000032</td>\n",
" <td> 0.003291</td>\n",
" <td> 0.092501</td>\n",
" <td> 0.881775</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td> 0.210986</td>\n",
" <td> 0.002632</td>\n",
" <td> 0.746958</td>\n",
" <td> 0.012715</td>\n",
" <td> 0.045281</td>\n",
" <td> 0.175657</td>\n",
" <td> 0.161995</td>\n",
" <td> 0.611693</td>\n",
" <td> 0.402984</td>\n",
" <td> 0.052672</td>\n",
" <td> 0.451931</td>\n",
" <td> 0.031883</td>\n",
" <td> 0.003935</td>\n",
" <td> 0.586133</td>\n",
" <td> 0.155460</td>\n",
" <td> 0.253634</td>\n",
" <td> 0.000036</td>\n",
" <td> 0.000001</td>\n",
" <td> 0.000111</td>\n",
" <td> 0.000018</td>\n",
" <td> 0.968224</td>\n",
" <td> 0.012892</td>\n",
" <td> 0.000203</td>\n",
" <td> 0.000411</td>\n",
" <td> 0.000030</td>\n",
" <td> 0.003317</td>\n",
" <td> 0.089615</td>\n",
" <td> 0.884111</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td> 0.212326</td>\n",
" <td> 0.001966</td>\n",
" <td> 0.752101</td>\n",
" <td> 0.010492</td>\n",
" <td> 0.044909</td>\n",
" <td> 0.176432</td>\n",
" <td> 0.157009</td>\n",
" <td> 0.616744</td>\n",
" <td> 0.412148</td>\n",
" <td> 0.034383</td>\n",
" <td> 0.473550</td>\n",
" <td> 0.027427</td>\n",
" <td> 0.003893</td>\n",
" <td> 0.587282</td>\n",
" <td> 0.152474</td>\n",
" <td> 0.255636</td>\n",
" <td> 0.000033</td>\n",
" <td> 0.000001</td>\n",
" <td> 0.000093</td>\n",
" <td> 0.000012</td>\n",
" <td> 0.967738</td>\n",
" <td> 0.013068</td>\n",
" <td> 0.000199</td>\n",
" <td> 0.000410</td>\n",
" <td> 0.000029</td>\n",
" <td> 0.003328</td>\n",
" <td> 0.087163</td>\n",
" <td> 0.886115</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td> 0.213278</td>\n",
" <td> 0.001898</td>\n",
" <td> 0.756218</td>\n",
" <td> 0.009473</td>\n",
" <td> 0.044636</td>\n",
" <td> 0.177328</td>\n",
" <td> 0.152037</td>\n",
" <td> 0.621799</td>\n",
" <td> 0.422057</td>\n",
" <td> 0.025238</td>\n",
" <td> 0.489509</td>\n",
" <td> 0.020385</td>\n",
" <td> 0.003820</td>\n",
" <td> 0.587885</td>\n",
" <td> 0.150245</td>\n",
" <td> 0.257411</td>\n",
" <td> 0.000031</td>\n",
" <td> 0.000001</td>\n",
" <td> 0.000081</td>\n",
" <td> 0.000008</td>\n",
" <td> 0.967159</td>\n",
" <td> 0.013303</td>\n",
" <td> 0.000195</td>\n",
" <td> 0.000409</td>\n",
" <td> 0.000028</td>\n",
" <td> 0.003324</td>\n",
" <td> 0.085102</td>\n",
" <td> 0.887753</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td> 0.214006</td>\n",
" <td> 0.002116</td>\n",
" <td> 0.761020</td>\n",
" <td> 0.008399</td>\n",
" <td> 0.044269</td>\n",
" <td> 0.178010</td>\n",
" <td> 0.148891</td>\n",
" <td> 0.625260</td>\n",
" <td> 0.432781</td>\n",
" <td> 0.018239</td>\n",
" <td> 0.502433</td>\n",
" <td> 0.014787</td>\n",
" <td> 0.003749</td>\n",
" <td> 0.588377</td>\n",
" <td> 0.148340</td>\n",
" <td> 0.258949</td>\n",
" <td> 0.000029</td>\n",
" <td> 0.000001</td>\n",
" <td> 0.000073</td>\n",
" <td> 0.000005</td>\n",
" <td> 0.966564</td>\n",
" <td> 0.013530</td>\n",
" <td> 0.000191</td>\n",
" <td> 0.000407</td>\n",
" <td> 0.000027</td>\n",
" <td> 0.003311</td>\n",
" <td> 0.083165</td>\n",
" <td> 0.889263</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td> 0.214624</td>\n",
" <td> 0.002004</td>\n",
" <td> 0.764059</td>\n",
" <td> 0.008212</td>\n",
" <td> 0.044072</td>\n",
" <td> 0.178921</td>\n",
" <td> 0.145980</td>\n",
" <td> 0.628014</td>\n",
" <td> 0.441662</td>\n",
" <td> 0.013497</td>\n",
" <td> 0.509957</td>\n",
" <td> 0.011980</td>\n",
" <td> 0.003703</td>\n",
" <td> 0.588853</td>\n",
" <td> 0.146664</td>\n",
" <td> 0.260343</td>\n",
" <td> 0.000028</td>\n",
" <td> 0.000000</td>\n",
" <td> 0.000069</td>\n",
" <td> 0.000004</td>\n",
" <td> 0.965900</td>\n",
" <td> 0.013787</td>\n",
" <td> 0.000188</td>\n",
" <td> 0.000405</td>\n",
" <td> 0.000027</td>\n",
" <td> 0.003295</td>\n",
" <td> 0.081482</td>\n",
" <td> 0.890547</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td> 0.214979</td>\n",
" <td> 0.001961</td>\n",
" <td> 0.766168</td>\n",
" <td> 0.007910</td>\n",
" <td> 0.043873</td>\n",
" <td> 0.179912</td>\n",
" <td> 0.142433</td>\n",
" <td> 0.631324</td>\n",
" <td> 0.449310</td>\n",
" <td> 0.011856</td>\n",
" <td> 0.513448</td>\n",
" <td> 0.009337</td>\n",
" <td> 0.003628</td>\n",
" <td> 0.589177</td>\n",
" <td> 0.145247</td>\n",
" <td> 0.261552</td>\n",
" <td> 0.000028</td>\n",
" <td> 0.000000</td>\n",
" <td> 0.000067</td>\n",
" <td> 0.000002</td>\n",
" <td> 0.965158</td>\n",
" <td> 0.014089</td>\n",
" <td> 0.000184</td>\n",
" <td> 0.000404</td>\n",
" <td> 0.000026</td>\n",
" <td> 0.003280</td>\n",
" <td> 0.080031</td>\n",
" <td> 0.891610</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td> 0.215325</td>\n",
" <td> 0.001791</td>\n",
" <td> 0.768111</td>\n",
" <td> 0.007318</td>\n",
" <td> 0.043512</td>\n",
" <td> 0.180883</td>\n",
" <td> 0.139653</td>\n",
" <td> 0.633705</td>\n",
" <td> 0.454489</td>\n",
" <td> 0.011304</td>\n",
" <td> 0.515611</td>\n",
" <td> 0.007230</td>\n",
" <td> 0.003592</td>\n",
" <td> 0.589371</td>\n",
" <td> 0.144033</td>\n",
" <td> 0.262693</td>\n",
" <td> 0.000027</td>\n",
" <td> 0.000000</td>\n",
" <td> 0.000065</td>\n",
" <td> 0.000002</td>\n",
" <td> 0.964424</td>\n",
" <td> 0.014437</td>\n",
" <td> 0.000181</td>\n",
" <td> 0.000403</td>\n",
" <td> 0.000025</td>\n",
" <td> 0.003264</td>\n",
" <td> 0.078574</td>\n",
" <td> 0.892477</td>\n",
" </tr>\n",
" <tr>\n",
" <th>30</th>\n",
" <td> 0.215279</td>\n",
" <td> 0.002096</td>\n",
" <td> 0.769493</td>\n",
" <td> 0.007068</td>\n",
" <td> 0.043025</td>\n",
" <td> 0.181470</td>\n",
" <td> 0.138089</td>\n",
" <td> 0.635278</td>\n",
" <td> 0.458281</td>\n",
" <td> 0.010225</td>\n",
" <td> 0.516991</td>\n",
" <td> 0.005932</td>\n",
" <td> 0.003563</td>\n",
" <td> 0.589570</td>\n",
" <td> 0.142825</td>\n",
" <td> 0.263791</td>\n",
" <td> 0.000027</td>\n",
" <td> 0.000000</td>\n",
" <td> 0.000065</td>\n",
" <td> 0.000002</td>\n",
" <td> 0.963645</td>\n",
" <td> 0.014717</td>\n",
" <td> 0.000179</td>\n",
" <td> 0.000403</td>\n",
" <td> 0.000025</td>\n",
" <td> 0.003252</td>\n",
" <td> 0.077240</td>\n",
" <td> 0.893405</td>\n",
" </tr>\n",
" <tr>\n",
" <th>31</th>\n",
" <td> 0.215744</td>\n",
" <td> 0.001757</td>\n",
" <td> 0.768604</td>\n",
" <td> 0.008098</td>\n",
" <td> 0.042604</td>\n",
" <td> 0.182302</td>\n",
" <td> 0.136245</td>\n",
" <td> 0.636922</td>\n",
" <td> 0.460683</td>\n",
" <td> 0.010425</td>\n",
" <td> 0.514878</td>\n",
" <td> 0.005932</td>\n",
" <td> 0.003539</td>\n",
" <td> 0.589690</td>\n",
" <td> 0.141724</td>\n",
" <td> 0.264811</td>\n",
" <td> 0.000028</td>\n",
" <td> 0.000000</td>\n",
" <td> 0.000066</td>\n",
" <td> 0.000001</td>\n",
" <td> 0.962769</td>\n",
" <td> 0.015091</td>\n",
" <td> 0.000176</td>\n",
" <td> 0.000403</td>\n",
" <td> 0.000025</td>\n",
" <td> 0.003242</td>\n",
" <td> 0.076116</td>\n",
" <td> 0.894050</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Ig_t-Ig_t Ig_t-Ig_u Ig_t-M_t Ig_t-M_u Ig_u-Ig_t Ig_u-Ig_u Ig_u-M_t \\\n",
"11 0.213239 NaN 0.740221 0.000004 NaN NaN 1.000000 \n",
"12 0.213047 0.000314 0.740109 0.001100 0.011670 0.001323 0.981954 \n",
"13 0.208071 0.004831 0.728849 0.014882 0.092533 0.038812 0.737116 \n",
"14 0.198239 0.011765 0.706367 0.037274 0.096722 0.099032 0.485809 \n",
"15 0.193061 0.013988 0.700191 0.045722 0.076977 0.134847 0.349148 \n",
"16 0.193294 0.011688 0.706768 0.040396 0.062763 0.155501 0.273261 \n",
"17 0.197007 0.007836 0.719671 0.029561 0.054740 0.165570 0.232291 \n",
"18 0.200448 0.005408 0.726234 0.025650 0.051475 0.169986 0.210853 \n",
"19 0.202614 0.004645 0.728158 0.023492 0.049226 0.172212 0.195419 \n",
"20 0.204877 0.003826 0.732675 0.020982 0.047761 0.173791 0.182615 \n",
"21 0.207267 0.002819 0.737199 0.016964 0.046773 0.174435 0.173575 \n",
"22 0.209354 0.002465 0.742902 0.013764 0.045956 0.175106 0.167017 \n",
"23 0.210986 0.002632 0.746958 0.012715 0.045281 0.175657 0.161995 \n",
"24 0.212326 0.001966 0.752101 0.010492 0.044909 0.176432 0.157009 \n",
"25 0.213278 0.001898 0.756218 0.009473 0.044636 0.177328 0.152037 \n",
"26 0.214006 0.002116 0.761020 0.008399 0.044269 0.178010 0.148891 \n",
"27 0.214624 0.002004 0.764059 0.008212 0.044072 0.178921 0.145980 \n",
"28 0.214979 0.001961 0.766168 0.007910 0.043873 0.179912 0.142433 \n",
"29 0.215325 0.001791 0.768111 0.007318 0.043512 0.180883 0.139653 \n",
"30 0.215279 0.002096 0.769493 0.007068 0.043025 0.181470 0.138089 \n",
"31 0.215744 0.001757 0.768604 0.008098 0.042604 0.182302 0.136245 \n",
"\n",
" Ig_u-M_u Ir_t-Ir_t Ir_t-Ir_u Ir_t-M_t Ir_t-M_u Ir_u-Ir_t Ir_u-Ir_u \\\n",
"11 NaN 0.565239 0.000002 0.389208 0.000002 0.000442 NaN \n",
"12 0.004572 0.563745 0.001513 0.393110 0.000688 0.015501 0.003670 \n",
"13 0.127876 0.549773 0.025566 0.376080 0.010859 0.120557 0.124575 \n",
"14 0.313429 0.523846 0.072735 0.333668 0.031389 0.099031 0.307447 \n",
"15 0.432790 0.505131 0.113885 0.291998 0.047181 0.051104 0.418280 \n",
"16 0.503144 0.487928 0.145151 0.271901 0.049922 0.023294 0.483606 \n",
"17 0.541055 0.472931 0.155022 0.283475 0.040878 0.011415 0.524995 \n",
"18 0.561382 0.460299 0.141752 0.311608 0.035170 0.006794 0.551886 \n",
"19 0.576303 0.442103 0.125876 0.338820 0.038279 0.005140 0.567458 \n",
"20 0.589636 0.423966 0.111280 0.363084 0.043280 0.004531 0.575995 \n",
"21 0.599378 0.407925 0.097598 0.388905 0.042696 0.004167 0.580921 \n",
"22 0.606434 0.398850 0.079312 0.421031 0.036069 0.004060 0.583930 \n",
"23 0.611693 0.402984 0.052672 0.451931 0.031883 0.003935 0.586133 \n",
"24 0.616744 0.412148 0.034383 0.473550 0.027427 0.003893 0.587282 \n",
"25 0.621799 0.422057 0.025238 0.489509 0.020385 0.003820 0.587885 \n",
"26 0.625260 0.432781 0.018239 0.502433 0.014787 0.003749 0.588377 \n",
"27 0.628014 0.441662 0.013497 0.509957 0.011980 0.003703 0.588853 \n",
"28 0.631324 0.449310 0.011856 0.513448 0.009337 0.003628 0.589177 \n",
"29 0.633705 0.454489 0.011304 0.515611 0.007230 0.003592 0.589371 \n",
"30 0.635278 0.458281 0.010225 0.516991 0.005932 0.003563 0.589570 \n",
"31 0.636922 0.460683 0.010425 0.514878 0.005932 0.003539 0.589690 \n",
"\n",
" Ir_u-M_t Ir_u-M_u M_t-Ig_t M_t-Ig_u M_t-Ir_t M_t-Ir_u M_t-M_t \\\n",
"11 0.999558 NaN 0.000229 NaN 0.001069 0.000000 0.982652 \n",
"12 0.977919 0.002863 0.000221 0.000000 0.001036 0.000002 0.981675 \n",
"13 0.682094 0.072106 0.000199 0.000006 0.000958 0.000035 0.971347 \n",
"14 0.434073 0.158488 0.000158 0.000012 0.000798 0.000096 0.957406 \n",
"15 0.320140 0.209474 0.000120 0.000012 0.000638 0.000107 0.956201 \n",
"16 0.255671 0.236428 0.000095 0.000010 0.000513 0.000092 0.961239 \n",
"17 0.216702 0.245887 0.000079 0.000007 0.000407 0.000086 0.965896 \n",
"18 0.193513 0.246825 0.000068 0.000005 0.000320 0.000070 0.968269 \n",
"19 0.179800 0.246647 0.000059 0.000004 0.000259 0.000048 0.969025 \n",
"20 0.170770 0.247728 0.000052 0.000003 0.000210 0.000038 0.969143 \n",
"21 0.164080 0.249854 0.000046 0.000003 0.000170 0.000031 0.968974 \n",
"22 0.159208 0.251907 0.000041 0.000002 0.000137 0.000025 0.968625 \n",
"23 0.155460 0.253634 0.000036 0.000001 0.000111 0.000018 0.968224 \n",
"24 0.152474 0.255636 0.000033 0.000001 0.000093 0.000012 0.967738 \n",
"25 0.150245 0.257411 0.000031 0.000001 0.000081 0.000008 0.967159 \n",
"26 0.148340 0.258949 0.000029 0.000001 0.000073 0.000005 0.966564 \n",
"27 0.146664 0.260343 0.000028 0.000000 0.000069 0.000004 0.965900 \n",
"28 0.145247 0.261552 0.000028 0.000000 0.000067 0.000002 0.965158 \n",
"29 0.144033 0.262693 0.000027 0.000000 0.000065 0.000002 0.964424 \n",
"30 0.142825 0.263791 0.000027 0.000000 0.000065 0.000002 0.963645 \n",
"31 0.141724 0.264811 0.000028 0.000000 0.000066 0.000001 0.962769 \n",
"\n",
" M_t-M_u M_u-Ig_t M_u-Ig_u M_u-Ir_t M_u-Ir_u M_u-M_t M_u-M_u \n",
"11 0.000001 NaN NaN 0.000037 NaN 0.999956 0.000005 \n",
"12 0.000742 0.000038 0.000003 0.000108 0.000018 0.989192 0.009610 \n",
"13 0.010894 0.000316 0.000099 0.000549 0.000504 0.760895 0.227303 \n",
"14 0.024829 0.000364 0.000239 0.000468 0.001354 0.450081 0.531608 \n",
"15 0.026104 0.000315 0.000318 0.000265 0.002011 0.269335 0.710135 \n",
"16 0.021084 0.000275 0.000357 0.000139 0.002396 0.179792 0.798542 \n",
"17 0.016369 0.000251 0.000382 0.000077 0.002656 0.136197 0.841297 \n",
"18 0.013893 0.000237 0.000395 0.000051 0.002897 0.115822 0.860885 \n",
"19 0.012981 0.000227 0.000403 0.000041 0.003079 0.106027 0.870014 \n",
"20 0.012710 0.000220 0.000406 0.000036 0.003180 0.100070 0.875239 \n",
"21 0.012654 0.000213 0.000409 0.000034 0.003247 0.095796 0.878975 \n",
"22 0.012747 0.000208 0.000412 0.000032 0.003291 0.092501 0.881775 \n",
"23 0.012892 0.000203 0.000411 0.000030 0.003317 0.089615 0.884111 \n",
"24 0.013068 0.000199 0.000410 0.000029 0.003328 0.087163 0.886115 \n",
"25 0.013303 0.000195 0.000409 0.000028 0.003324 0.085102 0.887753 \n",
"26 0.013530 0.000191 0.000407 0.000027 0.003311 0.083165 0.889263 \n",
"27 0.013787 0.000188 0.000405 0.000027 0.003295 0.081482 0.890547 \n",
"28 0.014089 0.000184 0.000404 0.000026 0.003280 0.080031 0.891610 \n",
"29 0.014437 0.000181 0.000403 0.000025 0.003264 0.078574 0.892477 \n",
"30 0.014717 0.000179 0.000403 0.000025 0.003252 0.077240 0.893405 \n",
"31 0.015091 0.000176 0.000403 0.000025 0.003242 0.076116 0.894050 "
]
},
"execution_count": 104,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
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"file_extension": ".py",
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"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
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