I am training a classifier for an imbalanced dataset. In the logs below one can see that the trainingset-auc increases over 1 after the 78th booster is added. What could AUC-ROC value greater than 1 signify? Since the area under the curve is not expected to be greater than 1 could there be something wrong with my oversampled data which is causing this issue? For this training I am using auc-pr as a metric for evaluation to output the best iteration.
I am using the python XGBoost library for this. The hyperparams being used here were tuned for an older version of the same model. Is hyperparam retuning for this version recommended to resolve this?
param = {
'eta':0.05,
'subsample':0.5,
'colsample_bytree': 0.9387046046168422,
'gamma': 4.533133064606544,
'max_depth': 6.167312302144592,
'min_child_weight': 5.369834798013086
}
param['eval_metric']=['auc','aucpr']
param['objective']='binary:logistic'
param['nthread']=6
param['max_depth']=int(param['max_depth'])
watchlist = [(dtrain,'trainingset'), (dtest,'evalualtionset')]
bst = xgb.train(param, dtrain, num_round_opt, watchlist,early_stopping_rounds=10, maximize=True)
[0] trainingset-auc:0.97164 trainingset-aucpr:0.98648 evalualtionset-auc:0.96008 evalualtionset-aucpr:0.65317
[1] trainingset-auc:0.97540 trainingset-aucpr:0.98780 evalualtionset-auc:0.96368 evalualtionset-aucpr:0.67202
[2] trainingset-auc:0.97850 trainingset-aucpr:0.99092 evalualtionset-auc:0.96786 evalualtionset-aucpr:0.71809
[3] trainingset-auc:0.97884 trainingset-aucpr:0.99107 evalualtionset-auc:0.96856 evalualtionset-aucpr:0.72405
[4] trainingset-auc:0.98322 trainingset-aucpr:0.99278 evalualtionset-auc:0.97693 evalualtionset-aucpr:0.79977
[5] trainingset-auc:0.98384 trainingset-aucpr:0.99299 evalualtionset-auc:0.97684 evalualtionset-aucpr:0.79582
[6] trainingset-auc:0.98400 trainingset-aucpr:0.99327 evalualtionset-auc:0.97822 evalualtionset-aucpr:0.80698
[7] trainingset-auc:0.98405 trainingset-aucpr:0.99329 evalualtionset-auc:0.97830 evalualtionset-aucpr:0.80772
[8] trainingset-auc:0.98406 trainingset-aucpr:0.99329 evalualtionset-auc:0.97806 evalualtionset-aucpr:0.80557
[9] trainingset-auc:0.98392 trainingset-aucpr:0.99328 evalualtionset-auc:0.97838 evalualtionset-aucpr:0.80744
[10] trainingset-auc:0.98417 trainingset-aucpr:0.99335 evalualtionset-auc:0.97845 evalualtionset-aucpr:0.80712
[11] trainingset-auc:0.98423 trainingset-aucpr:0.99337 evalualtionset-auc:0.97835 evalualtionset-aucpr:0.80620
[12] trainingset-auc:0.98497 trainingset-aucpr:0.99364 evalualtionset-auc:0.97885 evalualtionset-aucpr:0.80868
[13] trainingset-auc:0.98496 trainingset-aucpr:0.99364 evalualtionset-auc:0.97885 evalualtionset-aucpr:0.80832
[14] trainingset-auc:0.98585 trainingset-aucpr:0.99380 evalualtionset-auc:0.97940 evalualtionset-aucpr:0.80942
[15] trainingset-auc:0.98620 trainingset-aucpr:0.99380 evalualtionset-auc:0.98022 evalualtionset-aucpr:0.81149
[16] trainingset-auc:0.98633 trainingset-aucpr:0.99385 evalualtionset-auc:0.98035 evalualtionset-aucpr:0.81172
[17] trainingset-auc:0.98660 trainingset-aucpr:0.99393 evalualtionset-auc:0.98026 evalualtionset-aucpr:0.81135
[18] trainingset-auc:0.98666 trainingset-aucpr:0.99395 evalualtionset-auc:0.98014 evalualtionset-aucpr:0.81041
[19] trainingset-auc:0.98720 trainingset-aucpr:0.99414 evalualtionset-auc:0.98070 evalualtionset-aucpr:0.81233
[20] trainingset-auc:0.98726 trainingset-aucpr:0.99418 evalualtionset-auc:0.98115 evalualtionset-aucpr:0.81617
[21] trainingset-auc:0.98748 trainingset-aucpr:0.99434 evalualtionset-auc:0.98128 evalualtionset-aucpr:0.81832
[22] trainingset-auc:0.98778 trainingset-aucpr:0.99448 evalualtionset-auc:0.98151 evalualtionset-aucpr:0.82042
[23] trainingset-auc:0.98787 trainingset-aucpr:0.99451 evalualtionset-auc:0.98158 evalualtionset-aucpr:0.82183
[24] trainingset-auc:0.98803 trainingset-aucpr:0.99456 evalualtionset-auc:0.98171 evalualtionset-aucpr:0.82221
[25] trainingset-auc:0.98821 trainingset-aucpr:0.99464 evalualtionset-auc:0.98194 evalualtionset-aucpr:0.82412
[26] trainingset-auc:0.98836 trainingset-aucpr:0.99459 evalualtionset-auc:0.98231 evalualtionset-aucpr:0.82474
[27] trainingset-auc:0.98872 trainingset-aucpr:0.99467 evalualtionset-auc:0.98255 evalualtionset-aucpr:0.82629
[28] trainingset-auc:0.98948 trainingset-aucpr:0.99503 evalualtionset-auc:0.98272 evalualtionset-aucpr:0.82888
[29] trainingset-auc:0.98964 trainingset-aucpr:0.99510 evalualtionset-auc:0.98270 evalualtionset-aucpr:0.82944
[30] trainingset-auc:0.98982 trainingset-aucpr:0.99513 evalualtionset-auc:0.98285 evalualtionset-aucpr:0.83007
[31] trainingset-auc:0.98996 trainingset-aucpr:0.99516 evalualtionset-auc:0.98299 evalualtionset-aucpr:0.83081
[32] trainingset-auc:0.99007 trainingset-aucpr:0.99518 evalualtionset-auc:0.98307 evalualtionset-aucpr:0.83084
[33] trainingset-auc:0.99016 trainingset-aucpr:0.99520 evalualtionset-auc:0.98322 evalualtionset-aucpr:0.83247
[34] trainingset-auc:0.99044 trainingset-aucpr:0.99526 evalualtionset-auc:0.98339 evalualtionset-aucpr:0.83269
[35] trainingset-auc:0.99055 trainingset-aucpr:0.99530 evalualtionset-auc:0.98356 evalualtionset-aucpr:0.83398
[36] trainingset-auc:0.99069 trainingset-aucpr:0.99534 evalualtionset-auc:0.98371 evalualtionset-aucpr:0.83408
[37] trainingset-auc:0.99082 trainingset-aucpr:0.99542 evalualtionset-auc:0.98383 evalualtionset-aucpr:0.83512
[38] trainingset-auc:0.99143 trainingset-aucpr:0.99562 evalualtionset-auc:0.98401 evalualtionset-aucpr:0.83669
[39] trainingset-auc:0.99149 trainingset-aucpr:0.99565 evalualtionset-auc:0.98410 evalualtionset-aucpr:0.83738
[40] trainingset-auc:0.99178 trainingset-aucpr:0.99572 evalualtionset-auc:0.98423 evalualtionset-aucpr:0.83810
[41] trainingset-auc:0.99228 trainingset-aucpr:0.99579 evalualtionset-auc:0.98449 evalualtionset-aucpr:0.83873
[42] trainingset-auc:0.99235 trainingset-aucpr:0.99582 evalualtionset-auc:0.98461 evalualtionset-aucpr:0.83915
[43] trainingset-auc:0.99250 trainingset-aucpr:0.99585 evalualtionset-auc:0.98470 evalualtionset-aucpr:0.83996
[44] trainingset-auc:0.99277 trainingset-aucpr:0.99593 evalualtionset-auc:0.98486 evalualtionset-aucpr:0.84172
[45] trainingset-auc:0.99284 trainingset-aucpr:0.99595 evalualtionset-auc:0.98501 evalualtionset-aucpr:0.84308
[46] trainingset-auc:0.99306 trainingset-aucpr:0.99602 evalualtionset-auc:0.98517 evalualtionset-aucpr:0.84485
[47] trainingset-auc:0.99330 trainingset-aucpr:0.99608 evalualtionset-auc:0.98522 evalualtionset-aucpr:0.84564
[48] trainingset-auc:0.99348 trainingset-aucpr:0.99611 evalualtionset-auc:0.98520 evalualtionset-aucpr:0.84604
[49] trainingset-auc:0.99359 trainingset-aucpr:0.99612 evalualtionset-auc:0.98533 evalualtionset-aucpr:0.84591
[50] trainingset-auc:0.99370 trainingset-aucpr:0.99616 evalualtionset-auc:0.98534 evalualtionset-aucpr:0.84641
[51] trainingset-auc:0.99389 trainingset-aucpr:0.99621 evalualtionset-auc:0.98532 evalualtionset-aucpr:0.84722
[52] trainingset-auc:0.99406 trainingset-aucpr:0.99625 evalualtionset-auc:0.98531 evalualtionset-aucpr:0.84768
[53] trainingset-auc:0.99418 trainingset-aucpr:0.99629 evalualtionset-auc:0.98536 evalualtionset-aucpr:0.84730
[54] trainingset-auc:0.99431 trainingset-aucpr:0.99631 evalualtionset-auc:0.98540 evalualtionset-aucpr:0.84793
[55] trainingset-auc:0.99447 trainingset-aucpr:0.99635 evalualtionset-auc:0.98537 evalualtionset-aucpr:0.84761
[56] trainingset-auc:0.99468 trainingset-aucpr:0.99639 evalualtionset-auc:0.98535 evalualtionset-aucpr:0.84717
[57] trainingset-auc:0.99501 trainingset-aucpr:0.99644 evalualtionset-auc:0.98553 evalualtionset-aucpr:0.84739
[58] trainingset-auc:0.99513 trainingset-aucpr:0.99646 evalualtionset-auc:0.98559 evalualtionset-aucpr:0.84769
[59] trainingset-auc:0.99541 trainingset-aucpr:0.99648 evalualtionset-auc:0.98569 evalualtionset-aucpr:0.84786
[60] trainingset-auc:0.99556 trainingset-aucpr:0.99650 evalualtionset-auc:0.98570 evalualtionset-aucpr:0.84787
[61] trainingset-auc:0.99587 trainingset-aucpr:0.99655 evalualtionset-auc:0.98580 evalualtionset-aucpr:0.84833
[62] trainingset-auc:0.99604 trainingset-aucpr:0.99657 evalualtionset-auc:0.98585 evalualtionset-aucpr:0.84825
[63] trainingset-auc:0.99636 trainingset-aucpr:0.99659 evalualtionset-auc:0.98586 evalualtionset-aucpr:0.84789
[64] trainingset-auc:0.99656 trainingset-aucpr:0.99662 evalualtionset-auc:0.98599 evalualtionset-aucpr:0.84885
[65] trainingset-auc:0.99668 trainingset-aucpr:0.99664 evalualtionset-auc:0.98601 evalualtionset-aucpr:0.84901
[66] trainingset-auc:0.99678 trainingset-aucpr:0.99665 evalualtionset-auc:0.98604 evalualtionset-aucpr:0.84928
[67] trainingset-auc:0.99696 trainingset-aucpr:0.99666 evalualtionset-auc:0.98606 evalualtionset-aucpr:0.84882
[68] trainingset-auc:0.99711 trainingset-aucpr:0.99668 evalualtionset-auc:0.98611 evalualtionset-aucpr:0.84903
[69] trainingset-auc:0.99740 trainingset-aucpr:0.99672 evalualtionset-auc:0.98619 evalualtionset-aucpr:0.84895
[70] trainingset-auc:0.99751 trainingset-aucpr:0.99674 evalualtionset-auc:0.98615 evalualtionset-aucpr:0.84869
[71] trainingset-auc:0.99783 trainingset-aucpr:0.99679 evalualtionset-auc:0.98622 evalualtionset-aucpr:0.84856
[72] trainingset-auc:0.99815 trainingset-aucpr:0.99682 evalualtionset-auc:0.98628 evalualtionset-aucpr:0.84832
[73] trainingset-auc:0.99838 trainingset-aucpr:0.99687 evalualtionset-auc:0.98628 evalualtionset-aucpr:0.84822
[74] trainingset-auc:0.99875 trainingset-aucpr:0.99690 evalualtionset-auc:0.98640 evalualtionset-aucpr:0.84883
[75] trainingset-auc:0.99884 trainingset-aucpr:0.99691 evalualtionset-auc:0.98646 evalualtionset-aucpr:0.84923
[76] trainingset-auc:0.99900 trainingset-aucpr:0.99692 evalualtionset-auc:0.98646 evalualtionset-aucpr:0.84933
[77] trainingset-auc:0.99951 trainingset-aucpr:0.99695 evalualtionset-auc:0.98674 evalualtionset-aucpr:0.84985
[78] trainingset-auc:1.00024 trainingset-aucpr:0.99699 evalualtionset-auc:0.98697 evalualtionset-aucpr:0.84964
[79] trainingset-auc:1.00058 trainingset-aucpr:0.99700 evalualtionset-auc:0.98719 evalualtionset-aucpr:0.84987
[80] trainingset-auc:1.00090 trainingset-aucpr:0.99702 evalualtionset-auc:0.98727 evalualtionset-aucpr:0.84965
[81] trainingset-auc:1.00111 trainingset-aucpr:0.99706 evalualtionset-auc:0.98733 evalualtionset-aucpr:0.85003
[82] trainingset-auc:1.00129 trainingset-aucpr:0.99710 evalualtionset-auc:0.98741 evalualtionset-aucpr:0.85001
[83] trainingset-auc:1.00143 trainingset-aucpr:0.99711 evalualtionset-auc:0.98744 evalualtionset-aucpr:0.85045
[84] trainingset-auc:1.00161 trainingset-aucpr:0.99716 evalualtionset-auc:0.98743 evalualtionset-aucpr:0.85045
[85] trainingset-auc:1.00235 trainingset-aucpr:0.99721 evalualtionset-auc:0.98778 evalualtionset-aucpr:0.85076
[86] trainingset-auc:1.00266 trainingset-aucpr:0.99724 evalualtionset-auc:0.98788 evalualtionset-aucpr:0.85038
[87] trainingset-auc:1.00302 trainingset-aucpr:0.99725 evalualtionset-auc:0.98803 evalualtionset-aucpr:0.85063
[88] trainingset-auc:1.00341 trainingset-aucpr:0.99729 evalualtionset-auc:0.98837 evalualtionset-aucpr:0.85198
[89] trainingset-auc:1.00393 trainingset-aucpr:0.99731 evalualtionset-auc:0.98868 evalualtionset-aucpr:0.85253
[90] trainingset-auc:1.00445 trainingset-aucpr:0.99732 evalualtionset-auc:0.98889 evalualtionset-aucpr:0.85306
[91] trainingset-auc:1.00450 trainingset-aucpr:0.99735 evalualtionset-auc:0.98897 evalualtionset-aucpr:0.85393
[92] trainingset-auc:1.00561 trainingset-aucpr:0.99737 evalualtionset-auc:0.98945 evalualtionset-aucpr:0.85371
[93] trainingset-auc:1.00588 trainingset-aucpr:0.99738 evalualtionset-auc:0.98951 evalualtionset-aucpr:0.85383
[94] trainingset-auc:1.00602 trainingset-aucpr:0.99739 evalualtionset-auc:0.98959 evalualtionset-aucpr:0.85399
[95] trainingset-auc:1.00644 trainingset-aucpr:0.99741 evalualtionset-auc:0.98996 evalualtionset-aucpr:0.85467
[96] trainingset-auc:1.00653 trainingset-aucpr:0.99742 evalualtionset-auc:0.98993 evalualtionset-aucpr:0.85481
[97] trainingset-auc:1.00665 trainingset-aucpr:0.99744 evalualtionset-auc:0.99003 evalualtionset-aucpr:0.85595
[98] trainingset-auc:1.00708 trainingset-aucpr:0.99747 evalualtionset-auc:0.99050 evalualtionset-aucpr:0.85680
[99] trainingset-auc:1.00771 trainingset-aucpr:0.99747 evalualtionset-auc:0.99068 evalualtionset-aucpr:0.85696
[100] trainingset-auc:1.00809 trainingset-aucpr:0.99749 evalualtionset-auc:0.99089 evalualtionset-aucpr:0.85706
[101] trainingset-auc:1.00842 trainingset-aucpr:0.99751 evalualtionset-auc:0.99103 evalualtionset-aucpr:0.85717
[102] trainingset-auc:1.00890 trainingset-aucpr:0.99753 evalualtionset-auc:0.99124 evalualtionset-aucpr:0.85699
[103] trainingset-auc:1.00901 trainingset-aucpr:0.99753 evalualtionset-auc:0.99127 evalualtionset-aucpr:0.85706
[104] trainingset-auc:1.00912 trainingset-aucpr:0.99755 evalualtionset-auc:0.99134 evalualtionset-aucpr:0.85719
[105] trainingset-auc:1.00919 trainingset-aucpr:0.99757 evalualtionset-auc:0.99130 evalualtionset-aucpr:0.85749
[106] trainingset-auc:1.00960 trainingset-aucpr:0.99759 evalualtionset-auc:0.99147 evalualtionset-aucpr:0.85720
[107] trainingset-auc:1.01100 trainingset-aucpr:0.99760 evalualtionset-auc:0.99188 evalualtionset-aucpr:0.85763
[108] trainingset-auc:1.01279 trainingset-aucpr:0.99762 evalualtionset-auc:0.99284 evalualtionset-aucpr:0.85757
[109] trainingset-auc:1.01298 trainingset-aucpr:0.99763 evalualtionset-auc:0.99288 evalualtionset-aucpr:0.85746
[110] trainingset-auc:1.01380 trainingset-aucpr:0.99765 evalualtionset-auc:0.99343 evalualtionset-aucpr:0.85782
[111] trainingset-auc:1.01421 trainingset-aucpr:0.99765 evalualtionset-auc:0.99352 evalualtionset-aucpr:0.85784
[112] trainingset-auc:1.01439 trainingset-aucpr:0.99767 evalualtionset-auc:0.99359 evalualtionset-aucpr:0.85771
[113] trainingset-auc:1.01446 trainingset-aucpr:0.99768 evalualtionset-auc:0.99367 evalualtionset-aucpr:0.85838
[114] trainingset-auc:1.01457 trainingset-aucpr:0.99769 evalualtionset-auc:0.99372 evalualtionset-aucpr:0.85821
[115] trainingset-auc:1.01500 trainingset-aucpr:0.99770 evalualtionset-auc:0.99406 evalualtionset-aucpr:0.85837
[116] trainingset-auc:1.01590 trainingset-aucpr:0.99771 evalualtionset-auc:0.99432 evalualtionset-aucpr:0.85814
[117] trainingset-auc:1.01639 trainingset-aucpr:0.99772 evalualtionset-auc:0.99467 evalualtionset-aucpr:0.85830
[118] trainingset-auc:1.01688 trainingset-aucpr:0.99773 evalualtionset-auc:0.99487 evalualtionset-aucpr:0.85819
[119] trainingset-auc:1.01817 trainingset-aucpr:0.99775 evalualtionset-auc:0.99528 evalualtionset-aucpr:0.85877
[120] trainingset-auc:1.01843 trainingset-aucpr:0.99776 evalualtionset-auc:0.99534 evalualtionset-aucpr:0.85879
[121] trainingset-auc:1.01848 trainingset-aucpr:0.99777 evalualtionset-auc:0.99536 evalualtionset-aucpr:0.85867
[122] trainingset-auc:1.01858 trainingset-aucpr:0.99777 evalualtionset-auc:0.99538 evalualtionset-aucpr:0.85854
[123] trainingset-auc:1.01896 trainingset-aucpr:0.99778 evalualtionset-auc:0.99546 evalualtionset-aucpr:0.85856
[124] trainingset-auc:1.01913 trainingset-aucpr:0.99779 evalualtionset-auc:0.99560 evalualtionset-aucpr:0.85925
[125] trainingset-auc:1.01920 trainingset-aucpr:0.99779 evalualtionset-auc:0.99561 evalualtionset-aucpr:0.85938
[126] trainingset-auc:1.01927 trainingset-aucpr:0.99781 evalualtionset-auc:0.99572 evalualtionset-aucpr:0.86009
[127] trainingset-auc:1.01976 trainingset-aucpr:0.99782 evalualtionset-auc:0.99592 evalualtionset-aucpr:0.86018
[128] trainingset-auc:1.01990 trainingset-aucpr:0.99782 evalualtionset-auc:0.99596 evalualtionset-aucpr:0.86008
[129] trainingset-auc:1.02059 trainingset-aucpr:0.99784 evalualtionset-auc:0.99628 evalualtionset-aucpr:0.86020
[130] trainingset-auc:1.02077 trainingset-aucpr:0.99784 evalualtionset-auc:0.99634 evalualtionset-aucpr:0.86014
[131] trainingset-auc:1.02081 trainingset-aucpr:0.99785 evalualtionset-auc:0.99641 evalualtionset-aucpr:0.86084
[132] trainingset-auc:1.02129 trainingset-aucpr:0.99787 evalualtionset-auc:0.99677 evalualtionset-aucpr:0.86086
Related
I'm trying to get youtube video by using youtube_dl and everything is working fine except for the fact that I only get the audio.
from youtube_dl import YoutubeDL
link = "SOME_YOUTUBE_VIDEO" # as it was only a video
with YoutubeDL({}) as ydl:
info = ydl.extract_info(link, download=False)
url = info['formats'][0]['url']
title = info["title"]
print(url, title)
from youtube_dl import YoutubeDL
link = "https://www.youtube.com/watch?v=Y9wBC3H4iH4"
with YoutubeDL({}) as ydl:
info = ydl.extract_info(link, download=False)
for i, format in enumerate(info['formats']):
print(f"[{i}] {format['format']}")
output:
[youtube] Y9wBC3H4iH4: Downloading webpage
[0] 249 - audio only (tiny)
[1] 250 - audio only (tiny)
[2] 251 - audio only (tiny)
[3] 140 - audio only (tiny)
[4] 160 - 256x144 (144p)
[5] 278 - 256x144 (144p)
[6] 242 - 426x240 (240p)
[7] 133 - 426x240 (240p)
[8] 243 - 640x360 (360p)
[9] 134 - 640x360 (360p)
[10] 244 - 854x480 (480p)
[11] 135 - 854x480 (480p)
[12] 247 - 1280x720 (720p)
[13] 136 - 1280x720 (720p)
[14] 248 - 1920x1080 (1080p)
[15] 137 - 1920x1080 (1080p)
[16] 18 - 640x360 (360p)
[17] 22 - 1280x720 (720p)
It literally says audio only for some of the formats! Select one of the non-audio-only formats, and you won't get audio only. Note that which formats are available very much depends on which video you're trying to download.
I need to find the trendline based on the local minimum and maximum points in my data set. Then plot a trendline defining those maximum/minimum picks and find both trendlines equations. I have tried to find the maximum points based on daily delta but it is not exactly defined the picks because the time interval (x-axis) is not equal between all picks. can someone help to find a better code in Python or R?
my_data
Here is the code I use
> import matplotlib.pyplot as plt from scipy.misc import
> electrocardiogram from scipy.signal import find_peaks x = df1['Oxygen
> Corrected For Saturation (%)'] peaks, _ = find_peaks(x, height=120)
> plt.plot(x) plt.plot(peaks, x[peaks], "x") plt.plot(np.zeros_like(x),
> "--", color="gray") plt.show()
on this sample of my data set
Time Series:
Start = 0
End = 21599
Frequency = 1
[1] 109.0126 109.0126 109.1412 109.1412 108.7555 108.8841
[7] 108.8841 108.6270 108.7555 108.8841 108.6270 109.0126
[13] 108.8841 108.7555 108.6270 108.8841 109.0763 108.9475
[19] 109.0763 109.2051 109.0763 108.6900 109.2051 109.0763
[25] 109.4626 109.4626 108.9475 108.9475 109.0763 109.0763
[31] 109.7845 109.3975 109.0105 109.3975 109.2685 109.3975
[37] 109.5265 109.5265 109.2685 109.3975 109.1395 109.3975
[43] 109.5265 109.5265 109.2685 109.1395 109.2685 109.6555
[49] 109.2051 108.8187 109.2685 109.3975 109.1395 109.3975
[55] 109.5265 109.2051 109.6555 109.6555 109.5914 109.9778
[61] 110.1065 110.2353 110.7504 110.8792 110.8792 110.6217
[67] 111.0080 111.5231 111.3943 111.7807 112.0382 112.0382
[73] 111.9095 112.5534 111.9095 112.2265 111.8408 112.4836
[79] 112.3550 112.4836 112.7407 112.6121 112.9978 112.9978
[85] 112.9978 112.8692 113.1263 113.5120 112.9978 112.9978
[91] 113.6405 113.5120 113.5120 113.5120 113.7691 113.5682
[97] 113.4399 113.3116 113.6965 113.3116 113.6965 113.9532
[103] 113.4399 113.9532 113.8249 114.4665 114.3382 114.2098
[109] 114.0815 114.3382 114.2098 114.2098 113.9532 114.7231
[115] 114.1359 114.3921 114.3921 114.3921 114.5202 114.3921
[121] 114.5202 114.5202 114.6483 114.5202 114.5202 114.5202
[127] 114.3921 114.3921 114.5202 114.7764 114.5202 114.6483
[133] 114.3173 114.5730 114.4451 114.4451 114.5730 114.3173
[139] 114.4451 114.1894 114.1894 114.0615 114.3173 114.0615
[145] 113.9337 113.9867 113.0931 113.7314 113.8590 113.7314
[151] 113.2208 113.4761 112.9655 113.0931 113.3484 113.0931
[157] 113.0931 112.6371 112.6371 112.3823 112.7646 112.5097
[163] 112.6371 112.5097 112.2549 112.7646 112.5097 111.6733
[169] 111.5461 112.1820 112.3092 111.5461 111.9276 112.0548
[175] 111.6733 111.4189 111.4189 111.8004 111.6008 111.2199
[181] 111.4739 111.2199 110.8390 110.9660 111.2199 110.5851
[187] 110.8390 110.9660 110.1338 110.3873 110.6408 110.6408
[193] 110.5140 110.3873 110.5140 110.2606 110.1338 110.0071
[199] 110.1338 110.0071 110.0629 109.8099 109.4304 109.5569
[205] 109.6834 109.6834 109.5569 109.5569 109.4304 109.4304
[211] 109.3602 109.3602 109.3602 108.8550 109.1076 109.3602
[217] 109.2339 109.2339 109.1076 108.9813 109.0373 108.7852
[223] 108.9113 108.9113 108.9113 109.1634 108.6592 108.7852
[229] 108.7852 109.0373 108.5891 108.7150 108.4633 108.5891
[235] 108.5891 108.4633 108.4633 108.4633 108.7150 108.8408
[241] 108.6442 108.5186 108.2674 108.2674 108.3930 108.5186
[247] 108.5186 107.8906 107.8906 108.0162 108.6442 107.8208
[253] 107.9461 107.8208 108.4476 108.1969 108.4476 108.1969
[259] 108.4476 108.1969 108.1969 107.6253 108.5013 108.1259
[265] 108.1259 108.2510 108.3762 108.2510 108.5013 108.1259
[271] 108.2510 108.1793 107.8045 108.0544 108.5541 107.5547
[277] 107.9295 108.4291 107.9295 108.3042 107.9295 108.3042
[283] 108.4812 108.3565 108.4812 107.8577 108.4812 107.7330
[289] 108.2318 108.1071 108.1071 107.7330 107.8577 107.7855
[295] 108.0344 107.9100 108.2834 107.5366 107.9100 108.1589
[301] 108.4078 108.2834 107.9100 107.9100 107.8371 107.9613
[307] 107.8371 107.9613 107.8371 107.8371 108.2098 108.2098
[313] 108.0855 108.3340 108.3340 108.2098 108.0117 108.0117
[319] 108.1357 108.0117 107.8877 108.3837 108.0117 108.1357
[325] 108.0117 107.5156 108.0117 108.5077 108.0611 107.8136
[331] 108.0611 107.5660 108.0611 108.0611 108.1849 108.0611
[337] 108.0611 108.3087 108.3087 107.8136 108.2332 108.0611
[343] 108.1097 108.3568 108.3568 108.4803 108.3568 108.1097
[349] 108.1097 108.1097 108.4803 108.6039 108.3568 108.1573
[355] 107.7873 108.1573 108.5272 108.4039 107.9106 107.9106
[361] 108.1573 107.9106 107.7873 108.5272 108.5272 108.4039
[367] 107.8346 108.2039 107.8346 107.8346 108.2039 107.7115
[373] 107.9577 108.3270 108.2039 108.5732 108.3270 108.0808
[379] 108.3270 108.0039 108.6183 108.1268 108.2497 108.0039
[ reached getOption("max.print") -- omitted 20600 entries ]
enter code here
I have created a sample time series
sr <- structure(c(0.5310173262842, 3.25572767108437, 6.00767408770848,
8.71720569481067, 8.90061816157005, 11.3458017843761, 11.8780239290408,
11.1096200471215, 10.2181658307111, 7.68106828447771, 6.08254778842631,
3.77331493831468, 2.32460612635739, -0.811806523306027, -2.46962251406902,
-5.18619137803562, -6.52238138877948, -7.23973075138485, -9.13814405994064,
-8.38349420197295, -7.47206814043925, -7.72147447793804, -5.12452856469364,
-4.04683892896908, -1.35807110614903, 1.40646738165309, 3.14711512330267,
6.17118408869602, 9.08529877801627, 9.49523162785118, 10.6822759141775,
11.1978729275889, 10.6255041996972, 9.03268924066647, 8.7816883510026,
6.47370739211879, 4.40580528986734, 0.533166586756382, -0.755683435784947,
-3.75971635798706, -5.0257974169312, -7.03157815870787, -7.88414266256784,
-8.86247513623675, -8.78863836972667, -7.51760749016628, -7.71480223824999,
-4.97461916047327, -2.25199707926287, 0.119538577227733, 2.22116378000857,
5.43904351026787, 6.80527350499474, 8.2510591969805, 9.23767804783509,
10.0470098504673, 10.6010911738849, 10.4872767135332, 9.64970869918311,
7.48135037953984, 6.40801706579295, 2.79031207338565, 0.600852117538769,
-2.15253622003924, -3.83503298159308, -6.61090815235403, -7.70316354129315,
-8.10580024430761, -9.8302474480025, -7.96747302316148, -8.13638775878717,
-5.66703638619944, -4.71304119623848, -2.45278459538985, 0.318463293581571,
3.67690911529415, 6.02662806215273, 7.20785518381208, 9.70040091817327,
11.2627145970869, 10.8077036141603, 11.3232437762389, 10.0235316788413,
8.60832270906468, 7.69576415826105, 4.414689864356, 3.0022564046734,
-0.707176590987069, -2.92922440533753, -5.38398987977956, -7.07823691334916,
-8.84206898836627, -8.50344794508366, -8.23613496644785, -7.99119305955105,
-6.90263664771967, -5.99024120758212, -4.04179919691163, -0.88973938626927,
1.20986658055334), .Tsp = c(1, 100, 1), class = "ts")
The function to locate the local maxima and local minima, as suggested by
Answer
find_peaks <- function (x, m = 3){
shape <- diff(sign(diff(x, na.pad = FALSE)))
pks <- sapply(which(shape < 0), FUN = function(i){
z <- i - m + 1
z <- ifelse(z > 0, z, 1)
w <- i + m + 1
w <- ifelse(w < length(x), w, length(x))
if(all(x[c(z : i, (i + 2) : w)] <= x[i + 1])) return(i + 1) else return(numeric(0))
})
Using the function (just need to add - to the name of the data set to get minima)
pk <- find_peaks(sr) #finding the points at which local maxima occurs
mn <- find_peaks(-sr) #finding the points at which local minima occurs
plot(sr) #Plotting the curve
abline(a = coef(lm(sr[pk] ~ pk))[1], b = coef(lm(sr[pk] ~ pk))[2], col = 'green') #Trendline for local maxima
abline(a = coef(lm(sr[mn] ~ mn))[1], b=coef(lm(sr[mn]~mn))[2], col='red') #Trendline for local minima
The plot looks like this:
I hope you could work around this with your data.
I'm getting the following backtrace in my uWSGI logs, I don't know where to begin looking to figure this out:
Thu Feb 12 17:08:18 2015 - mapping worker 1 to CPUs: 0
Thu Feb 12 17:09:40 2015 - WSGI app 0 (mountpoint='') ready in 82 seconds on interpreter 0x129e3d0 pid: 21930 (default app
Thu Feb 12 17:10:11 2015 - *** HARAKIRI ON WORKER 1 (pid: 21930, try: 1) ***
Thu Feb 12 17:10:11 2015 - *** backtrace of 21930 ***
app(uwsgi_backtrace+0x2e) [0x45121e]
app(what_i_am_doing+0x30) [0x451350]
/lib/x86_64-linux-gnu/libc.so.6(+0x36d40) [0x7feea1becd40]
/lib/x86_64-linux-gnu/libc.so.6(__select+0x33) [0x7feea1ca8933]
/usr/lib/x86_64-linux-gnu/libpython2.7.so.1.0(+0x984ed) [0x7feea092f4ed]
/usr/lib/x86_64-linux-gnu/libpython2.7.so.1.0(PyEval_EvalFrameEx+0x4bd4) [0x7feea09f70d4]
/usr/lib/x86_64-linux-gnu/libpython2.7.so.1.0(PyEval_EvalCodeEx+0x80d) [0x7feea09f917d]
/usr/lib/x86_64-linux-gnu/libpython2.7.so.1.0(+0x162310) [0x7feea09f9310]
/usr/lib/x86_64-linux-gnu/libpython2.7.so.1.0(PyObject_Call+0x43) [0x7feea09c0e23]
/usr/lib/x86_64-linux-gnu/libpython2.7.so.1.0(+0x7d30d) [0x7feea091430d]
/usr/lib/x86_64-linux-gnu/libpython2.7.so.1.0(PyObject_Call+0x43) [0x7feea09c0e23]
/usr/lib/x86_64-linux-gnu/libpython2.7.so.1.0(+0x513c8) [0x7feea08e83c8]
/usr/lib/x86_64-linux-gnu/libpython2.7.so.1.0(PyObject_Call+0x43) [0x7feea09c0e23]
/usr/lib/x86_64-linux-gnu/libpython2.7.so.1.0(PyEval_EvalFrameEx+0x2316) [0x7feea09f4816]
/usr/lib/x86_64-linux-gnu/libpython2.7.so.1.0(PyEval_EvalCodeEx+0x80d) [0x7feea09f917d]
/usr/lib/x86_64-linux-gnu/libpython2.7.so.1.0(+0x1623e5) [0x7feea09f93e5]
/usr/lib/x86_64-linux-gnu/libpython2.7.so.1.0(PyObject_Call+0x43) [0x7feea09c0e23]
/usr/lib/x86_64-linux-gnu/libpython2.7.so.1.0(PyEval_EvalFrameEx+0xeb1) [0x7feea09f33b1]
/usr/lib/x86_64-linux-gnu/libpython2.7.so.1.0(PyEval_EvalCodeEx+0x80d) [0x7feea09f917d]
/usr/lib/x86_64-linux-gnu/libpython2.7.so.1.0(+0x1623e5) [0x7feea09f93e5]
/usr/lib/x86_64-linux-gnu/libpython2.7.so.1.0(PyObject_Call+0x43) [0x7feea09c0e23]
/usr/lib/x86_64-linux-gnu/libpython2.7.so.1.0(PyEval_EvalFrameEx+0xeb1) [0x7feea09f33b1]
/usr/lib/x86_64-linux-gnu/libpython2.7.so.1.0(PyEval_EvalCodeEx+0x80d) [0x7feea09f917d]
/usr/lib/x86_64-linux-gnu/libpython2.7.so.1.0(+0x162310) [0x7feea09f9310]
/usr/lib/x86_64-linux-gnu/libpython2.7.so.1.0(PyObject_Call+0x43) [0x7feea09c0e23]
/usr/lib/x86_64-linux-gnu/libpython2.7.so.1.0(PyObject_CallFunctionObjArgs+0x141) [0x7feea097bb61]
/var/www/app/env/local/lib/python2.7/site-packages/newrelic-2.42.0.35/newrelic/packages/wrapt/_wrappers.so(+0x416c) [0x7fee9e18516c]
/usr/lib/x86_64-linux-gnu/libpython2.7.so.1.0(PyObject_Call+0x43) [0x7feea09c0e23]
/usr/lib/x86_64-linux-gnu/libpython2.7.so.1.0(PyEval_EvalFrameEx+0xeb1) [0x7feea09f33b1]
/usr/lib/x86_64-linux-gnu/libpython2.7.so.1.0(PyEval_EvalCodeEx+0x80d) [0x7feea09f917d]
/usr/lib/x86_64-linux-gnu/libpython2.7.so.1.0(PyEval_EvalFrameEx+0x48d8) [0x7feea09f6dd8]
/usr/lib/x86_64-linux-gnu/libpython2.7.so.1.0(PyEval_EvalCodeEx+0x80d) [0x7feea09f917d]
/usr/lib/x86_64-linux-gnu/libpython2.7.so.1.0(+0x1623e5) [0x7feea09f93e5]
/usr/lib/x86_64-linux-gnu/libpython2.7.so.1.0(PyObject_Call+0x43) [0x7feea09c0e23]
/usr/lib/x86_64-linux-gnu/libpython2.7.so.1.0(PyEval_EvalFrameEx+0xeb1) [0x7feea09f33b1]
/usr/lib/x86_64-linux-gnu/libpython2.7.so.1.0(PyEval_EvalCodeEx+0x80d) [0x7feea09f917d]
/usr/lib/x86_64-linux-gnu/libpython2.7.so.1.0(+0x162310) [0x7feea09f9310]
/usr/lib/x86_64-linux-gnu/libpython2.7.so.1.0(PyObject_Call+0x43) [0x7feea09c0e23]
/usr/lib/x86_64-linux-gnu/libpython2.7.so.1.0(PyObject_CallFunctionObjArgs+0x141) [0x7feea097bb61]
/var/www/app/env/local/lib/python2.7/site-packages/newrelic-2.42.0.35/newrelic/packages/wrapt/_wrappers.so(+0x448a) [0x7fee9e18548a]
/usr/lib/x86_64-linux-gnu/libpython2.7.so.1.0(PyObject_Call+0x43) [0x7feea09c0e23]
/usr/lib/x86_64-linux-gnu/libpython2.7.so.1.0(PyEval_EvalFrameEx+0x2316) [0x7feea09f4816]
/usr/lib/x86_64-linux-gnu/libpython2.7.so.1.0(PyEval_EvalCodeEx+0x80d) [0x7feea09f917d]
/usr/lib/x86_64-linux-gnu/libpython2.7.so.1.0(+0x162310) [0x7feea09f9310]
/usr/lib/x86_64-linux-gnu/libpython2.7.so.1.0(PyObject_Call+0x43) [0x7feea09c0e23]
/usr/lib/x86_64-linux-gnu/libpython2.7.so.1.0(+0x7d30d) [0x7feea091430d]
/usr/lib/x86_64-linux-gnu/libpython2.7.so.1.0(PyObject_Call+0x43) [0x7feea09c0e23]
/usr/lib/x86_64-linux-gnu/libpython2.7.so.1.0(+0x12e5f5) [0x7feea09c55f5]
/usr/lib/x86_64-linux-gnu/libpython2.7.so.1.0(PyObject_Call+0x43) [0x7feea09c0e23]
/usr/lib/x86_64-linux-gnu/libpython2.7.so.1.0(PyEval_CallObjectWithKeywords+0x47) [0x7feea097f837]
/usr/lib/uwsgi/plugins/python_plugin.so(python_call+0x11) [0x7feea0e0e4f1]
/usr/lib/uwsgi/plugins/python_plugin.so(uwsgi_request_wsgi+0x127) [0x7feea0e10847]
app(wsgi_req_recv+0xa1) [0x413f31]
app(simple_loop_run+0xc4) [0x44d5d4]
app(uwsgi_ignition+0x17b) [0x45180b]
app(uwsgi_worker_run+0x26d) [0x4523ad]
app(uwsgi_start+0x15e3) [0x453b23]
app(main+0xfb5) [0x413595]
/lib/x86_64-linux-gnu/libc.so.6(__libc_start_main+0xf5) [0x7feea1bd7ec5]
app() [0x413649]
*** end of backtrace ***
Thu Feb 12 17:10:11 2015 - HARAKIRI: --- uWSGI worker 1 (pid: 21930) WAS managing request /v3/remoco/now_next since Thu Feb 12 17:09:40 2015 ---
Thu Feb 12 17:10:13 2015 - *** HARAKIRI ON WORKER 1 (pid: 21930, try: 2) ***
Thu Feb 12 17:10:14 2015 - DAMN ! worker 1 (pid: 21930) died, killed by signal 9 :( trying respawn ...
Thu Feb 12 17:10:14 2015 - Respawned uWSGI worker 1 (new pid: 21985)
Thu Feb 12 17:10:14 2015 - mapping worker 1 to CPUs: 0
I thought maybe the newrelic lines might have something to do with it so I disabled newrelic on the machines but I get the same backtrace without the newrelic lines.
I'm running uWSGI version 1.9.17.1-debian on Ubuntu 14.04 and all updates have been applied.
This is my uWSGI app config:
[uwsgi]
; define variables to use in this script
; process name for easy identification in top
project = app
base_dir = /var/www/%(project)
chdir = %(base_dir)
plugins = python
virtualenv = %(base_dir)/env
uid = www-data
gid = www-data
procname = %(project)
; This value needs to be tuned
workers = 12
max-request = 40000
harakiri = 30
reload-mercy = 8
cpu-affinity = 2
reload-on-as = 512
reload-on-rss = 192
no-orphans = true
master = true
; Create pid file for easier process management
pidfile2 = %(base_dir)/run/%(project).pid
; Specify your app here
wsgi-file = %(base_dir)/project/%(project)-wsgi.py
log-reopen = true
logto2 = %(base_dir)/logs/uwsgi.log
chmod-socket = 666
vacuum = true
enable-threads = true
single-interpreter = true
lazy-apps = true
buffer-size=32768
; unix socket
socket = /tmp/%(project)-server.sock
; Enable stats
stats = /tmp/%(project)-stats.sock
Let me know if there is anything else I can add to this and I will.
I installed python from Macports and also py-graph-tool. I'm getting this error when I try to import the module:
Fatal Python error: PyThreadState_Get: no current thread
Abort trap: 6
What to do please?
Process: Python [23667]
Path: /opt/local/Library/Frameworks/Python.framework/Versions/2.7/Resources/Python.app/Contents/MacOS/Python
Identifier: Python
Version: 2.7.6 (2.7.6)
Code Type: X86-64 (Native)
Parent Process: bash [23467]
Responsible: Terminal [50142]
User ID: 501
Date/Time: 2014-03-07 08:57:24.172 +0100
OS Version: Mac OS X 10.9.2 (13C64)
Report Version: 11
Anonymous UUID: 1B40C56A-15EC-7EF5-8BC6-31731287FB6A
Crashed Thread: 0 Dispatch queue: com.apple.main-thread
Exception Type: EXC_CRASH (SIGABRT)
Exception Codes: 0x0000000000000000, 0x0000000000000000
Application Specific Information:
abort() called
Thread 0 crashed with X86 Thread State (64-bit):
rax: 0x0000000000000000 rbx: 0x00007fff77b6c310 rcx: 0x00007fff5df95238 rdx: 0x0000000000000000
rdi: 0x0000000000000507 rsi: 0x0000000000000006 rbp: 0x00007fff5df95260 rsp: 0x00007fff5df95238
r8: 0x0000000000000040 r9: 0x00007fff5df95200 r10: 0x0000000008000000 r11: 0x0000000000000206
r12: 0x0000000000000000 r13: 0x0000000104803a50 r14: 0x0000000000000006 r15: 0x00000000000003f5
rip: 0x00007fff857e1866 rfl: 0x0000000000000206 cr2: 0x0000000109638d5c
Logical CPU: 0
Error Code: 0x02000148
Trap Number: 133
Binary Images:
0x101c68000 - 0x101c69fff +org.python.python (2.7.6 - 2.7.6) <9EAA7EA7-0461-3548-A192-07D38564031C> /opt/local/Library/Frameworks/Python.framework/Versions/2.7/Resources/Python.app/Contents/MacOS/Python
0x101c72000 - 0x101d88ff7 +org.python.python (2.7.6, [c] 2004-2013 Python Software Foundation. - 2.7.6) <980B2D89-E75B-3026-B8F3-CFFA635C4792> /opt/local/Library/Frameworks/Python.framework/Versions/2.7/Python
0x10205d000 - 0x10205ffff +time.so (0) <7B313687-32FF-34C2-ACD8-C7A1790A3343> /opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/lib-dynload/time.so
0x102064000 - 0x102065fff +cStringIO.so (0) <EF9551C2-2514-3767-9447-9BA101184349> /opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/lib-dynload/cStringIO.so
0x10206a000 - 0x10206dff7 +_collections.so (0) <DDDAB77F-A531-3E13-BB63-240398C3E7C7> /opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/lib-dynload/_collections.so
0x102073000 - 0x102076fff +operator.so (0) <95D19C2D-417E-3A67-A1BB-97F6234BD4A4> /opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/lib-dynload/operator.so
0x10207d000 - 0x102082ff7 +itertools.so (0) <A3DA6CBE-61F7-3D0D-9251-22F54CFA6CB8> /opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/lib-dynload/itertools.so
0x10208d000 - 0x10208efff +_heapq.so (0) <DB5A22ED-B9F3-39EF-A83B-8726F4F8A7E3> /opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/lib-dynload/_heapq.so
0x102092000 - 0x102095fff +strop.so (0) <D4149B0D-D369-37B1-88AB-E54B00955B29> /opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/lib-dynload/strop.so
0x1020da000 - 0x1020dbff7 +_functools.so (0) <B36EA2D6-B7DF-3592-93C3-0001BDFB4FEC> /opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/lib-dynload/_functools.so
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Could it be that you are not using the MacPorts python?
try
port select --list python
This should give you a listing of all you installed pythons. After one of them it says '(active)'.
This must not be 'python27-apple'.
If this is not the case, I have had similar trouble recently. See http://trac.macports.org/ticket/43086
Have you recently updated from an earlier version of OSX?
Have you updated your Xcode to the latest release and also installed the Xcode command line tools? And migrated MacPorts properly? See also http://trac.macports.org/wiki/Migration .
All this is necessary in order to link against the right c++ libraries, which graph-tool and boost are very sensitive to.
Best,
Thomas
I have 2 pythons on my mac (10.8.3): The default, and 2.7 version from homebrew.
So far, I could install modules and use them with my brew python. I installed mapnik with brew install mapnik (mapnik-2.1.0) and it compiled correctly. But, if I open python and enter import mapnik, the following error comes and python exits:
>>> import mapnik
Fatal Python error: PyThreadState_Get: no current thread
Abort trap: 6
Mac os x also shows an error window, with the following content:
Process: Python [60666]
Path: /usr/local/Cellar/python/2.7.3/Frameworks/Python.framework/Versions/2.7/Resources/Python.app/Contents/MacOS/Python
Identifier: Python
Version: 2.7.3 (2.7.3)
Code Type: X86-64 (Native)
Parent Process: bash [60454]
User ID: 501
Date/Time: 2013-03-28 10:21:08.535 +0100
OS Version: Mac OS X 10.8.3 (12D78)
Report Version: 10
Interval Since Last Report: 128837 sec
Crashes Since Last Report: 5
Per-App Crashes Since Last Report: 4
Anonymous UUID: 567121E6-7BAC-335F-E3B0-DD24D1F9E6BA
Crashed Thread: 0 Dispatch queue: com.apple.main-thread
Exception Type: EXC_CRASH (SIGABRT)
Exception Codes: 0x0000000000000000, 0x0000000000000000
Application Specific Information:
abort() called
Thread 0 Crashed:: Dispatch queue: com.apple.main-thread
0 libsystem_kernel.dylib 0x00007fff94500d46 __kill + 10
1 libsystem_c.dylib 0x00007fff92afddf0 abort + 177
2 org.python.python 0x0000000105a77eaa Py_FatalError + 49
3 org.python.python 0x0000000105a76370 PyThreadState_Get + 28
4 org.python.python 0x0000000105a71f16 Py_InitModule4_64 + 58
5 libboost_python-mt.dylib 0x0000000104153288 boost::python::detail::init_module(char const*, void (*)()) + 40
6 org.python.python 0x00000001029fa716 _PyImport_LoadDynamicModule + 150
7 org.python.python 0x00000001029fa428 import_submodule + 296
8 org.python.python 0x00000001029f9ff1 load_next + 268
9 org.python.python 0x00000001029f80fb PyImport_ImportModuleLevel + 794
10 org.python.python 0x00000001029de9d3 builtin___import__ + 132
11 org.python.python 0x000000010296e335 PyObject_Call + 101
12 org.python.python 0x00000001029e8e10 PyEval_CallObjectWithKeywords + 93
13 org.python.python 0x00000001029e61b6 PyEval_EvalFrameEx + 11727
14 org.python.python 0x00000001029e32bd PyEval_EvalCodeEx + 1638
15 org.python.python 0x00000001029e2c51 PyEval_EvalCode + 54
16 org.python.python 0x00000001029f6e58 PyImport_ExecCodeModuleEx + 247
17 org.python.python 0x00000001029f99bf load_source_module + 1053
18 org.python.python 0x00000001029f9c7e load_package + 334
19 org.python.python 0x00000001029fa428 import_submodule + 296
20 org.python.python 0x00000001029f9ff1 load_next + 268
21 org.python.python 0x00000001029f80fb PyImport_ImportModuleLevel + 794
22 org.python.python 0x00000001029de9d3 builtin___import__ + 132
23 org.python.python 0x000000010296e335 PyObject_Call + 101
24 org.python.python 0x00000001029e8e10 PyEval_CallObjectWithKeywords + 93
25 org.python.python 0x00000001029e61b6 PyEval_EvalFrameEx + 11727
26 org.python.python 0x00000001029e32bd PyEval_EvalCodeEx + 1638
27 org.python.python 0x00000001029e2c51 PyEval_EvalCode + 54
28 org.python.python 0x0000000102a01a2b run_mod + 53
29 org.python.python 0x0000000102a0184d PyRun_InteractiveOneFlags + 365
30 org.python.python 0x0000000102a01367 PyRun_InteractiveLoopFlags + 188
31 org.python.python 0x0000000102a01218 PyRun_AnyFileExFlags + 60
32 org.python.python 0x0000000102a120b2 Py_Main + 3210
33 libdyld.dylib 0x00007fff966bf7e1 start + 1
Thread 0 crashed with X86 Thread State (64-bit):
rax: 0x0000000000000000 rbx: 0x00007fff5d29c2b0 rcx: 0x00007fff5d29c298 rdx: 0x0000000000000000
rdi: 0x000000000000ecfa rsi: 0x0000000000000006 rbp: 0x00007fff5d29c2c0 rsp: 0x00007fff5d29c298
r8: 0x00000000000003f5 r9: 0x00007fff5d29c270 r10: 0x00007fff94502342 r11: 0x0000000000000206
r12: 0x00007fff5d29cb00 r13: 0x0000000102e9b656 r14: 0x00000000000003f5 r15: 0x0000000000000000
rip: 0x00007fff94500d46 rfl: 0x0000000000000206 cr2: 0x00007fff7bff7ff0
Logical CPU: 0
Binary Images:
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0x102964000 - 0x102a6cfff +org.python.python (2.7.3, [c] 2004-2012 Python Software Foundation. - 2.7.3) <81E6FB4A-287C-37C3-A26D-D045B604933F> /usr/local/Cellar/python/2.7.3/Frameworks/Python.framework/Versions/2.7/Python
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0x7fff92aa4000 - 0x7fff92b70ff7 libsystem_c.dylib (825.26) <4C9EB006-FE1F-3F8F-8074-DFD94CF2CE7B> /usr/lib/system/libsystem_c.dylib
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0x7fff92ed3000 - 0x7fff92f3cfff libstdc++.6.dylib (56) <EAA2B53E-EADE-39CF-A0EF-FB9D4940672A> /usr/lib/libstdc++.6.dylib
0x7fff93019000 - 0x7fff93065ff7 libauto.dylib (185.1) <73CDC482-16E3-3FC7-9BB4-FBA2DA44DBC2> /usr/lib/libauto.dylib
0x7fff930cf000 - 0x7fff930d5fff libmacho.dylib (829) <BF332AD9-E89F-387E-92A4-6E1AB74BD4D9> /usr/lib/system/libmacho.dylib
0x7fff93177000 - 0x7fff93199ff7 libxpc.dylib (140.42) <BBE558BD-5E55-35E4-89ED-1AA6B056D05A> /usr/lib/system/libxpc.dylib
0x7fff931f6000 - 0x7fff931f7ff7 libremovefile.dylib (23.2) <6763BC8E-18B8-3AD9-8FFA-B43713A7264F> /usr/lib/system/libremovefile.dylib
0x7fff931f8000 - 0x7fff93260ff7 libc++.1.dylib (65.1) <20E31B90-19B9-3C2A-A9EB-474E08F9FE05> /usr/lib/libc++.1.dylib
0x7fff9345d000 - 0x7fff9365dfff libicucore.A.dylib (491.11.2) <FD6282D8-DF3F-3842-8C2E-CF478D2B9669> /usr/lib/libicucore.A.dylib
0x7fff9365e000 - 0x7fff9365efff libkeymgr.dylib (25) <CC9E3394-BE16-397F-926B-E579B60EE429> /usr/lib/system/libkeymgr.dylib
0x7fff936d4000 - 0x7fff936dcfff liblaunch.dylib (442.26.2) <2F71CAF8-6524-329E-AC56-C506658B4C0C> /usr/lib/system/liblaunch.dylib
0x7fff9424d000 - 0x7fff9427bff7 libsystem_m.dylib (3022.6) <B434BE5C-25AB-3EBD-BAA7-5304B34E3441> /usr/lib/system/libsystem_m.dylib
0x7fff94284000 - 0x7fff94286fff libquarantine.dylib (52) <4BE2E642-A14F-340A-B482-5BD2AEFD9C24> /usr/lib/system/libquarantine.dylib
0x7fff94381000 - 0x7fff94382ff7 libdnsinfo.dylib (453.19) <14202FFB-C3CA-3FCC-94B0-14611BF8692D> /usr/lib/system/libdnsinfo.dylib
0x7fff944ef000 - 0x7fff9450aff7 libsystem_kernel.dylib (2050.22.13) <5A961E2A-CFB8-362B-BC43-122704AEB047> /usr/lib/system/libsystem_kernel.dylib
0x7fff94579000 - 0x7fff94587ff7 libsystem_network.dylib (77.10) <0D99F24E-56FE-380F-B81B-4A4C630EE587> /usr/lib/system/libsystem_network.dylib
0x7fff94821000 - 0x7fff9493992f libobjc.A.dylib (532.2) <90D31928-F48D-3E37-874F-220A51FD9E37> /usr/lib/libobjc.A.dylib
0x7fff9493a000 - 0x7fff9494fff7 libdispatch.dylib (228.23) <D26996BF-FC57-39EB-8829-F63585561E09> /usr/lib/system/libdispatch.dylib
0x7fff95c25000 - 0x7fff95d22ff7 libxml2.2.dylib (22.3) <47B09CB2-C636-3024-8B55-6040F7829B4C> /usr/lib/libxml2.2.dylib
0x7fff96350000 - 0x7fff96351ff7 libSystem.B.dylib (169.3) <9089D72D-E714-31E1-80C8-698A8E8B05AD> /usr/lib/libSystem.B.dylib
0x7fff96397000 - 0x7fff9639cfff libcache.dylib (57) <65187C6E-3FBF-3EB8-A1AA-389445E2984D> /usr/lib/system/libcache.dylib
0x7fff96646000 - 0x7fff9664dfff libcopyfile.dylib (89) <876573D0-E907-3566-A108-577EAD1B6182> /usr/lib/system/libcopyfile.dylib
0x7fff966bd000 - 0x7fff966c0ff7 libdyld.dylib (210.2.3) <F59367C9-C110-382B-A695-9035A6DD387E> /usr/lib/system/libdyld.dylib
0x7fff96961000 - 0x7fff96963ff7 libunc.dylib (25) <92805328-CD36-34FF-9436-571AB0485072> /usr/lib/system/libunc.dylib
0x7fff974ba000 - 0x7fff974f0fff libsystem_info.dylib (406.17) <4FFCA242-7F04-365F-87A6-D4EFB89503C1> /usr/lib/system/libsystem_info.dylib
0x7fff97850000 - 0x7fff97888fff libncurses.5.4.dylib (37.3) <68D5B5F5-8252-3F1E-AFF1-C6AFE145DBC1> /usr/lib/libncurses.5.4.dylib
0x7fff978d3000 - 0x7fff97abdff7 com.apple.CoreFoundation (6.8 - 744.18) <A60C3C9B-3764-3291-844C-C487ACF77C2C> /System/Library/Frameworks/CoreFoundation.framework/Versions/A/CoreFoundation
External Modification Summary:
Calls made by other processes targeting this process:
task_for_pid: 0
thread_create: 0
thread_set_state: 0
Calls made by this process:
task_for_pid: 0
thread_create: 0
thread_set_state: 0
Calls made by all processes on this machine:
task_for_pid: 18730
thread_create: 2
thread_set_state: 0
VM Region Summary:
ReadOnly portion of Libraries: Total=109.5M resident=53.3M(49%) swapped_out_or_unallocated=56.2M(51%)
Writable regions: Total=20.6M written=2644K(13%) resident=3256K(15%) swapped_out=0K(0%) unallocated=17.5M(85%)
REGION TYPE VIRTUAL
=========== =======
MALLOC 11.8M
MALLOC guard page 32K
STACK GUARD 56.0M
Stack 8192K
__DATA 2356K
__LINKEDIT 68.3M
__TEXT 41.2M
__UNICODE 544K
shared memory 12K
=========== =======
TOTAL 188.2M
Model: MacBookPro8,2, BootROM MBP81.0047.B27, 4 processors, Intel Core i7, 2 GHz, 8 GB, SMC 1.69f4
Graphics: Intel HD Graphics 3000, Intel HD Graphics 3000, Built-In, 512 MB
Graphics: AMD Radeon HD 6490M, AMD Radeon HD 6490M, PCIe, 256 MB
Memory Module: BANK 0/DIMM0, 4 GB, DDR3, 1333 MHz, 0x80CE, 0x4D34373142353237334348302D4348392020
Memory Module: BANK 1/DIMM0, 4 GB, DDR3, 1333 MHz, 0x80CE, 0x4D34373142353237334348302D4348392020
AirPort: spairport_wireless_card_type_airport_extreme (0x14E4, 0xD6), Broadcom BCM43xx 1.0 (5.106.98.100.16)
Bluetooth: Version 4.1.3f3 11349, 2 service, 11 devices, 1 incoming serial ports
Network Service: Wi-Fi, AirPort, en1
Serial ATA Device: ST9500420AS, 500.11 GB
Serial ATA Device: MATSHITADVD-R UJ-898
USB Device: FaceTime HD Camera (Built-in), apple_vendor_id, 0x8509, 0xfa200000 / 3
USB Device: hub_device, 0x0424 (SMSC), 0x2513, 0xfa100000 / 2
USB Device: Apple Internal Keyboard / Trackpad, apple_vendor_id, 0x0246, 0xfa120000 / 5
USB Device: BRCM2070 Hub, 0x0a5c (Broadcom Corp.), 0x4500, 0xfa110000 / 4
USB Device: Bluetooth USB Host Controller, apple_vendor_id, 0x821a, 0xfa113000 / 8
USB Device: hub_device, 0x0424 (SMSC), 0x2513, 0xfd100000 / 2
USB Device: IR Receiver, apple_vendor_id, 0x8242, 0xfd110000 / 3
Any problems with linking my pythons? how can I fix it? thanks!
It looks like you are running the homebrew python but either boost python or mapnik's python bindings ended up linking against the system python provided by apple. If a full clean and reinstall of boost and Mapnik does not fix this then I recommend stopping by #mapnik irc on freenode for help debugging. Generally to fix you need to call otool -L on boost_python.dylib and or on site_packages/mapnik/_mapnik.so to see which ended up with a linkage to the system python. Then you can fix by using install_name_tool.
I encountered the same problem between python27-apple and python27 (installed by Macport). I tried these steps, and it worked for me.
According to #Evert's comments of different libraries used by different python, I tried to revert the python27-apple to be "active".
sudo port select --list python
For my computer, it showed:
Available versions for python:
none
python25-apple
python26
python26-apple
python27 (active)
python27-apple
in which python27(by Macport) was currently active. Then,
sudo port select python python27-apple
to make python27-apple active as the default lib were called previously. To double check,
sudo port select --list python
It now showed:
Available versions for python:
none
python25-apple
python26
python26-apple
python27
python27-apple (active)
And the python crash had gone when I ran the scripts.
You can use the homebrew python like this:
/usr/bin/python2.7 yourscript.py
I face the similar problem in using androguard. Though it's not quite the same, it can be a reference. My crash also is Fatal Python error: PyThreadState_Get: no current thread
Abort trap: 6. Run otool -L <lib>, suggest the dylib link to the system python.
Try solution:
python-config --ldflags
Copy the output to corresponding MakeFile. For example change LDFLAGS += -lm -lbz2 -lz -llzma -lsnappy -lmuparser -lpython to LDFLAGS += -lm -lbz2 -lz -llzma -lsnappy -lmuparser -L/usr/local/Cellar/python/2.7.10_2/Frameworks/Python.framework/Versions/2.7/lib/python2.7/config -lpython2.7 -ldl -framework CoreFoundation.
make clean, then make again.
Now the dylib is OK, and no error anymore.
Alternatively ...
... when you use otool -L <lib> to find the wrongly linked python dylib you can use install_name_tool to change the symbol like:
install_name_tool -change /System/Library/Frameworks/Python.framework/Versions/2.7/Python /usr/local/Cellar/python/2.7.6_1/Frameworks/Python.framework/Versions/2.7/lib/libpython2.7.dylib <lib>