Source: python-mne Version: 0.7.1-1 Severity: serious Tags: jessie sid User: [email protected] Usertags: qa-ftbfs-20140114 qa-ftbfs Justification: FTBFS on amd64
Hi, During a rebuild of all packages in sid, your package failed to build on amd64. Relevant part (hopefully): > ====================================================================== > ERROR: Test mne-python config file support > ---------------------------------------------------------------------- > Traceback (most recent call last): > File "/usr/lib/python2.7/dist-packages/nose/case.py", line 197, in runTest > self.test(*self.arg) > File "/«PKGBUILDDIR»/mne/tests/test_utils.py", line 125, in test_config > set_config(key, None) > File "/«PKGBUILDDIR»/mne/utils.py", line 880, in set_config > os.mkdir(directory) > OSError: [Errno 2] No such file or directory: '/sbuild-nonexistent/.mne' > > Name Stmts Miss Cover Missing > ----------------------------------------------------------------- > mne 41 1 98% 80 > mne.baseline 37 9 76% 52, 70, 74-75, > 79-81, 83-84 > mne.beamformer 2 0 100% > mne.beamformer._dics 179 158 12% 66-139, 188-194, > 247-261, 310-403, 484-587 > mne.beamformer._lcmv 213 187 12% 67-182, 192-235, > 288-295, 351-365, 425-437, 490-544, 613-712 > mne.commands 0 0 100% > mne.commands.utils 18 13 28% 18-39 > mne.connectivity 3 0 100% > mne.connectivity.effective 35 0 100% > mne.connectivity.spectral 472 29 94% 31, 34, 37, 40, > 307, 328-330, 382-384, 403, 410, 469, 476, 482, 492, 498, 733, 743, 750-751, > 811, 816, 830, 892-894, 917, 929, 1017, 1021 > mne.connectivity.utils 14 2 86% 11, 14 > mne.coreg 482 269 44% 89-156, 301-303, > 346-348, 359, 366, 370-372, 403-405, 531-534, 572-574, 581, 585-587, 610-624, > 644-699, 717-728, 746-768, 785-802, 831-862, 887-954, 981-1046 > mne.cov 318 34 89% 28, 31, 79-81, > 104-114, 117-119, 173, 237, 267, 351, 363, 371, 373, 444, 476-479, 502, 531, > 619, 632-633, 684 > mne.cuda 163 112 31% 9-10, 43-102, > 154-188, 215-222, 278-314, 359-384 > mne.data 0 0 100% > mne.datasets 3 0 100% > mne.datasets.megsim 1 0 100% > mne.datasets.megsim.megsim 64 56 13% 64-126, 178-189 > mne.datasets.megsim.urls 25 9 64% 150-160 > mne.datasets.sample 1 0 100% > mne.datasets.sample.sample 10 0 100% > mne.datasets.spm_face 1 0 100% > mne.datasets.spm_face.spm_data 12 1 92% 16 > mne.datasets.utils 93 57 39% 51-61, 79, 82, 97, > 105-176, 184 > mne.decoding 4 0 100% > mne.decoding.classifier 115 5 96% 349, 351, 385, 394, > 416 > mne.decoding.csp 87 11 87% 89, 95-96, 99, > 110-111, 119-120, 126-129, 195 > mne.decoding.mixin 5 1 80% 27 > mne.dipole 17 13 24% 34-46 > mne.epochs 823 147 82% 61-63, 71, 73, 76, > 78, 103, 107, 145-169, 175, 179, 183-186, 199, 273, 278, 280, 298, 308, 359, > 396, 636, 663, 706, 718, 725, 903, 912, 917, 949, 970, 975-987, 1019, 1047, > 1052, 1054-1056, 1059, 1061-1063, 1108, 1216-1274, 1303-1329, 1382, 1384, > 1436, 1439, 1441, 1444, 1491, 1507, 1633-1634, 1638-1639, 1658-1659, 1684, > 1687-1688, 1743 > mne.event 281 31 89% 48, 57, 101, > 116-118, 130, 141-142, 154, 216, 226, 355, 358-360, 379-381, 410-411, 425, > 537, 599-602, 635, 671, 674, 696, 699, 710 > mne.fiff 10 0 100% > mne.fiff.brainvision 1 0 100% > mne.fiff.brainvision.brainvision 270 25 91% 137, 159, 174-175, > 228, 338, 344-349, 363-366, 389, 392, 396-399, 403, 406, 410-415 > mne.fiff.bti 1 0 100% > mne.fiff.bti.constants 74 0 100% > mne.fiff.bti.raw 599 92 85% 78, 260, 319, 340, > 363-369, 405-412, 441-459, 634-642, 647-648, 663-669, 675-691, 696-704, > 710-755, 773, 850, 913, 969, 1014, 1017-1018, 1112-1127 > mne.fiff.bti.read 59 4 93% 49, 54, 59, 84 > mne.fiff.bti.transforms 40 0 100% > mne.fiff.channels 12 0 100% > mne.fiff.compensator 57 7 88% 17, 45, 48, 57, 94, > 99, 113 > mne.fiff.constants 545 0 100% > mne.fiff.cov 88 24 73% 46, 58, 63, 69, 73, > 78-85, 101-103, 110-111, 128-131, 158, 168-169, 177-179 > mne.fiff.ctf 131 17 87% 18, 48-53, 59, 67, > 73, 80, 86, 93, 98, 137, 160, 180, 183, 193, 196 > mne.fiff.diff 26 22 15% 15-39 > mne.fiff.edf 1 0 100% > mne.fiff.edf.edf 318 54 83% 82, 155, 174, > 196-198, 217-230, 238, 245-247, 354, 367, 374-377, 381, 388-389, 393-395, > 485-486, 501, 504, 533-552 > mne.fiff.evoked 323 85 74% 94, 103-104, > 108-109, 116-117, 144-145, 152, 172-180, 183, 187-202, 220-221, 237-238, > 247-250, 254-255, 298-302, 316, 445, 464-472, 502-544, 634-635 > mne.fiff.kit 5 0 100% > mne.fiff.kit.constants 62 0 100% > mne.fiff.kit.coreg 88 17 81% 58-64, 69-71, > 90-92, 100-101, 122-124, 126-128, 166-167 > mne.fiff.kit.kit 327 26 92% 116-119, 220, 301, > 312, 347, 388-395, 433, 485-486, 495-498, 533, 537, 552, 560, 578-580, 614-616 > mne.fiff.matrix 61 40 34% 16-21, 45-87, > 105-108, 120, 126 > mne.fiff.meas_info 316 25 92% 103-105, 161-164, > 190, 192, 197, 199, 262, 265, 268, 271, 296, 320-322, 339, 348-351, 355-357, > 478 > mne.fiff.open 101 8 92% 65, 68, 71, 76, > 132, 168-169, 191 > mne.fiff.pick 188 73 61% 39, 46-65, 184, > 188, 194, 206, 208, 219, 221, 227, 229, 231, 233, 243-248, 279, 293-296, 319, > 325, 391-396, 422-459, 489-495 > mne.fiff.proj 235 16 93% 99-101, 224, 230, > 243, 249, 255, 261, 271, 274, 366, 372, 382, 392, 548, 604 > mne.fiff.raw 800 116 86% 143-144, 185-192, > 223-225, 237-238, 244, 253-257, 263, 272-273, 283-287, 344, 363, 368, 374, > 442, 446, 586, 593, 599, 603, 720, 724, 728, 782, 855, 857, 859, 954, 960, > 996-999, 1224, 1232, 1428-1465, 1492-1507, 1545, 1562, 1601, 1603, 1605, > 1654-1661, 1689, 1728, 1732, 1927, 1930, 1944-1947, 1960, 1979, 1991, 1993, > 1995, 2000, 2002-2005, 2072, 2076, 2087 > mne.fiff.tag 250 63 75% 42-47, 50-59, 118, > 143, 152, 154, 156, 159, 230, 247, 253, 256, 264-316, 323, 326, 332, 359-363, > 446-449, 456, 481 > mne.fiff.tree 93 2 98% 20-21 > mne.fiff.write 206 31 85% 69-71, 130-145, > 319, 351-368 > mne.filter 442 39 91% 76, 106, 109, 113, > 477, 481, 494, 500, 527, 529, 616, 711, 720, 732, 735-739, 897, 1015-1022, > 1096, 1169, 1233, 1236, 1240, 1266-1268, 1300, 1306, 1316, 1343-1344, 1359 > mne.fixes 202 48 76% 38, 43-44, 51-52, > 63-68, 98, 113, 123, 144, 149, 162, 175, 185, 195-196, 200, 204, 216-220, > 225-226, 232-236, 243, 249, 350, 353, 358, 361, 364, 367, 377-378, 397, 408, > 492, 494, 501, 508, 526 > mne.forward 2 0 100% > mne.forward._compute_forward 170 147 14% 26-39, 44-51, > 56-62, 67-73, 78-95, 102-124, 129-148, 157-175, 189-196, 211-221, 233-254, > 259-263, 274-280, 286-346 > mne.forward._make_forward 271 254 6% 26-70, 77-111, > 116-142, 147-156, 223-476, 481-494 > mne.forward.forward 797 737 8% 62-65, 96-114, > 137-159, 165-226, 244-314, 319, 325-355, 388-525, 552-641, 660-783, 788-796, > 802-806, 822-846, 867-890, 896-916, 923-972, 978-987, 993-1012, 1019-1049, > 1094-1116, 1159-1183, 1202-1226, 1245-1294, 1366-1509, 1532-1583 > mne.gui 21 17 19% 15-18, 52-56, > 77-80, 86-89 > mne.inverse_sparse 2 0 100% > mne.inverse_sparse._gamma_map 118 106 10% 56-164, 235-301 > mne.inverse_sparse.mxne_debiasing 45 1 98% 128 > mne.inverse_sparse.mxne_inverse 177 159 10% 20-60, 66-87, > 159-256, 261-275, 364-432 > mne.inverse_sparse.mxne_optim 307 11 96% 24, 26, 33, 64, > 198, 317-318, 328-331, 385, 623 > mne.label 538 370 31% 72, 75, 79, 81, > 85-87, 140, 151, 154, 165-167, 262-263, 315-351, 378, 386-389, 400-406, 438, > 448-451, 486, 527-540, 558-582, 615-711, 733-759, 765-777, 817-858, 881-939, > 944-966, 1006-1068, 1087-1119, 1151-1228 > mne.layouts 1 0 100% > mne.layouts.layout 247 64 74% 67, 160, 205, 208, > 263, 274, 363, 366, 378, 414-423, 439-466, 494-518, 534-544, 560-562 > mne.minimum_norm 2 0 100% > mne.minimum_norm.inverse 562 515 8% 41-51, 73-287, > 306-405, 425-438, 444-457, 483-599, 611-671, 675-678, 682-698, 703, 737-776, > 833-892, 899-950, 994-1003, 1031-1047, 1057-1101, 1174-1366, 1383-1389 > mne.minimum_norm.time_frequency 238 213 11% 83-118, 125-185, > 196-252, 316-331, 386-464, 476-591, 656-672 > mne.misc 61 18 70% 28-29, 42, 66-67, > 85-100 > mne.mixed_norm 5 0 100% > mne.parallel 67 27 60% 16, 60-68, 79-83, > 93-96, 121-122, 125-128, 141-144 > mne.preprocessing 5 0 100% > mne.preprocessing.ecg 78 4 95% 64, 162, 168, 172 > mne.preprocessing.eog 45 12 73% 48-68 > mne.preprocessing.ica 570 46 92% 185, 197, 227, > 292-293, 297, 318, 330, 378, 385, 389-390, 394, 501-503, 666-668, 717-730, > 885, 944, 951, 1147-1148, 1178, 1186, 1236, 1337, 1340, 1351, 1364, > 1482-1486, 1490-1491, 1584 > mne.preprocessing.maxfilter 104 70 33% 46, 72, 88, 195-292 > mne.preprocessing.peak_finder 82 18 78% 49, 89-91, 96-98, > 117, 138-140, 151-157, 166 > mne.preprocessing.ssp 83 18 78% 24-25, 109, 140, > 144-145, 155, 158, 161, 164, 166-177 > mne.preprocessing.stim 26 1 96% 40 > mne.proj 152 64 58% 62-63, 65-66, > 68-69, 127, 131, 142, 276-360 > mne.realtime 4 0 100% > mne.realtime.client 164 132 20% 41-60, 65-71, > 99-130, 146-168, 181-197, 207-230, 240, 250-260, 264-265, 269, 282-287, > 297-302, 313-314, 319-320, 324-325, 340-350, 365-370 > mne.realtime.epochs 144 22 85% 158, 220-225, 242, > 251, 315, 323, 328, 359, 363, 367-369, 377, 387, 390-395 > mne.realtime.mockclient 50 3 94% 159, 171, 175 > mne.realtime.stim_server_client 111 7 94% 81, 253-254, > 278-279, 288-289 > mne.selection 38 5 87% 47, 57, 77-79, 95 > mne.simulation 2 0 100% > mne.simulation.evoked 34 25 26% 49-52, 77-86, > 113-125 > mne.simulation.source 78 72 8% 31-45, 74-110, > 151-196 > mne.source_estimate 948 718 24% 29-64, 83-102, > 108-112, 134-156, 163-169, 188-206, 241-327, 334-345, 350-358, 404, 408, 414, > 418, 424, 428, 432, 450-454, 466-479, 509-516, 523, 537-539, 542-548, > 558-565, 568-570, 573-579, 582-584, 587-593, 596-598, 601-607, 610-612, > 615-617, 620, 623, 626, 629, 632-635, 638, 642, 675-691, 737, 896-934, 977, > 1002-1024, 1027-1038, 1042, 1046, 1058-1075, 1091-1117, 1135-1156, 1202-1207, > 1255-1300, 1368-1375, 1421-1422, 1446-1447, 1492, 1515-1532, 1557, 1581, > 1585-1596, 1644-1652, 1686-1746, 1755-1773, 1777-1780, 1825-1875, 1907-1933, > 1968-2000, 2025-2043, 2072-2107, 2127-2129, 2185-2195, 2218, 2237, 2260, > 2283-2292, 2321-2325, 2354-2402, 2408-2420, 2428-2520, 2572-2593 > mne.source_space 891 832 7% 33, 57-61, 64-76, > 86-87, 97, 113-137, 161-178, 199-216, 223-417, 425-447, 463-470, 490-515, > 519, 538-544, 560-580, 585-666, 706-729, 739-804, 840-952, 1018-1132, > 1137-1146, 1163-1182, 1189-1351, 1355, 1361-1449, 1456-1511, 1516-1519, > 1525-1539, 1580-1620, 1625-1636 > mne.stats 4 0 100% > mne.stats.cluster_level 554 77 86% 106, 180-182, 198, > 212-217, 241, 315, 346, 381, 383, 395, 415-422, 435, 441, 454, 458, 465, 469, > 498, 518, 541, 557-570, 586, 605, 610-614, 629-640, 676, 686, 696, 720, > 796-799, 835, 873, 991, 1121, 1248, 1361, 1402-1407, 1422-1423, 1427-1433 > mne.stats.multi_comp 33 0 100% > mne.stats.parametric 77 1 99% 261 > mne.stats.permutations 48 0 100% > mne.surface 637 550 14% 55-109, 115-191, > 210-285, 325, 359-367, 372-386, 394-433, 438-451, 456-458, 485-488, 519-520, > 525-527, 532-542, 564-604, 611-625, 635-638, 643-650, 654-657, 663-735, > 742-798, 819-829, 847-868, 873-889, 919-920, 951-1017, 1022-1040, 1045, > 1053-1064, 1080-1128, 1152-1184, 1191-1221 > mne.time_frequency 6 0 100% > mne.time_frequency.ar 43 9 79% 54, 63, 65, 75, > 148-152 > mne.time_frequency.csd 108 12 89% 48-51, 116, 125, > 131, 177-179, 202-206 > mne.time_frequency.multitaper 166 21 87% 45, 59, 91, > 157-160, 220, 224, 285, 288, 347, 358, 489, 497, 514-516, 523-527, 531 > mne.time_frequency.psd 53 3 94% 53, 60, 132 > mne.time_frequency.stft 87 12 86% 44, 47, 54, 57, 62, > 66, 135, 139, 142, 146, 149, 153 > mne.time_frequency.tfr 155 22 86% 50, 61, 106, 112, > 120-122, 143, 146-148, 188, 325-327, 332, 387-396 > mne.transforms 181 118 35% 29-46, 50-53, > 212-216, 221-237, 254-280, 293-313, 319-321, 342-356, 389-461 > mne.utils 532 112 79% 48, 51-53, 165, > 192-221, 246, 392, 402-411, 421, 450, 457, 465-466, 478-480, 488-490, > 509-511, 530-532, 574-576, 631, 702-704, 721, 742-745, 758-765, 811, 823-826, > 829-831, 854, 858, 866-867, 875, 881-882, 960, 1009, 1120-1126, 1139, > 1141-1144, 1146-1149, 1153, 1178, 1186, 1199, 1203-1207, 1210, 1217-1221, > 1227-1232 > mne.viz 1646 588 64% 89, 115, 198-223, > 316-319, 323, 332-333, 347-348, 358, 376, 396-418, 466-467, 540, 545, 547, > 549-550, 623, 630-631, 652, 656, 659, 714, 716, 718-719, 782-859, 864-895, > 930-986, 1017-1019, 1021, 1037, 1043, 1124-1128, 1147, 1152, 1155, 1160-1161, > 1168-1169, 1173-1178, 1190, 1213-1214, 1222-1236, 1259-1260, 1267-1268, > 1275-1276, 1327-1433, 1463, 1501, 1597-1684, 1691-1708, 1752, 1756, > 1768-1769, 1854, 1885-1886, 1895-1897, 1901, 1908, 1911-1913, 1969, 1973, > 1991, 1996, 1999, 2063-2102, 2131, 2137, 2214, 2220, 2225, 2228-2229, > 2237-2238, 2241, 2246, 2269, 2374, 2414, 2516, 2519, 2522-2523, 2527, 2547, > 2555, 2557-2564, 2577-2583, 2679-2681, 2688-2692, 2699-2700, 2753, 2785-2787, > 2818, 2841, 2855-2856, 2884, 2887-2889, 2908, 2929-2930, 2945-2946, > 2966-2967, 3017, 3033, 3067, 3124, 3131, 3156-3182, 3191, 3193, 3195-3198, > 3201-3209, 3214-3236, 3277, 3287-3291, 3294, 3394-3460 > ----------------------------------------------------------------- > TOTAL 21020 8192 61% > ---------------------------------------------------------------------- > Ran 302 tests in 468.392s > > FAILED (SKIP=100, errors=1) > make[1]: *** [override_dh_auto_test] Error 1 The full build log is available from: http://aws-logs.debian.net/ftbfs-logs/2014/01/14/python-mne_0.7.1-1_unstable.log A list of current common problems and possible solutions is available at http://wiki.debian.org/qa.debian.org/FTBFS . You're welcome to contribute! About the archive rebuild: The rebuild was done on EC2 VM instances from Amazon Web Services, using a clean, minimal and up-to-date chroot. Every failed build was retried once to eliminate random failures. -- To UNSUBSCRIBE, email to [email protected] with a subject of "unsubscribe". Trouble? Contact [email protected]
